From 4ed6bd809d782faeb606e2f5bb409dc8e3fa74e3 Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Tue, 9 Apr 2024 20:20:57 +0000 Subject: [PATCH] Suppression des notebooks exploratoires et brouillons --- Descriptive_statistics/debug.ipynb | 148 - Descriptive_statistics/generate_stat_desc.py | 68 - Descriptive_statistics/plot.py | 328 - Spectacle/2_Modelization_spectacle.ipynb | 2075 -- .../2_bis_logit_baseline_statsmodels.ipynb | 2866 --- Spectacle/Exploration_spectacle.ipynb | 2176 -- Spectacle/Stat_desc.ipynb | 9083 -------- .../stat_desc_sport.ipynb | 1608 -- Sport/Modelization/2_Modelization_sport.ipynb | 2821 --- .../3_logit_cross_val_sport.ipynb | 8910 -------- Sport/Modelization/3_model_cv_sport+CA.ipynb | 18751 ---------------- Sport/Modelization/CA_segment_sport.ipynb | 4226 ---- .../segment_analysis_sport_0_6.ipynb | 2972 --- Sport/exploration_sport.ipynb | 2296 -- .../TP_exploratory_analysis-Copy1.ipynb | 7990 ------- .../TP_merge_target_campaigns_links.ipynb | 1768 -- useless/0_Cleaning_and_merge.ipynb | 2850 --- useless/1_Descriptive_Statistics.ipynb | 2101 -- useless/2_Regression_logistique.ipynb | 374 - useless/2_modelisation_pipeline+visu.ipynb | 2770 --- useless/Computes_log_coeff.ipynb | 436 - useless/Exploration_billet_AJ.ipynb | 1964 -- useless/Identification_entreprise.ipynb | 1610 -- useless/Notebook_AR.ipynb | 247 - useless/Notebook_Fanta.ipynb | 825 - useless/TP_access_merge_data.ipynb | 1215 - useless/Temporary_barplot_example_TP.ipynb | 958 - useless/Traitement_Fanta.ipynb | 1833 -- useless/code_base_train_test.ipynb | 460 - useless/code_valeur manquante.ipynb | 2880 --- 30 files changed, 88609 deletions(-) delete mode 100644 Descriptive_statistics/debug.ipynb delete mode 100644 Descriptive_statistics/generate_stat_desc.py delete mode 100644 Descriptive_statistics/plot.py delete mode 100644 Spectacle/2_Modelization_spectacle.ipynb delete mode 100644 Spectacle/2_bis_logit_baseline_statsmodels.ipynb delete mode 100644 Spectacle/Exploration_spectacle.ipynb delete mode 100644 Spectacle/Stat_desc.ipynb delete mode 100644 Sport/Descriptive_statistics/stat_desc_sport.ipynb delete mode 100644 Sport/Modelization/2_Modelization_sport.ipynb delete mode 100644 Sport/Modelization/3_logit_cross_val_sport.ipynb delete mode 100644 Sport/Modelization/3_model_cv_sport+CA.ipynb delete mode 100644 Sport/Modelization/CA_segment_sport.ipynb delete mode 100644 Sport/Modelization/segment_analysis_sport_0_6.ipynb delete mode 100644 Sport/exploration_sport.ipynb delete mode 100644 exploratory_analysis/TP_exploratory_analysis-Copy1.ipynb delete mode 100644 notebooks_merge/TP_merge_target_campaigns_links.ipynb delete mode 100644 useless/0_Cleaning_and_merge.ipynb delete mode 100644 useless/1_Descriptive_Statistics.ipynb delete mode 100644 useless/2_Regression_logistique.ipynb delete mode 100644 useless/2_modelisation_pipeline+visu.ipynb delete mode 100644 useless/Computes_log_coeff.ipynb delete mode 100644 useless/Exploration_billet_AJ.ipynb delete mode 100644 useless/Identification_entreprise.ipynb delete mode 100644 useless/Notebook_AR.ipynb delete mode 100644 useless/Notebook_Fanta.ipynb delete mode 100644 useless/TP_access_merge_data.ipynb delete mode 100644 useless/Temporary_barplot_example_TP.ipynb delete mode 100644 useless/Traitement_Fanta.ipynb delete mode 100644 useless/code_base_train_test.ipynb delete mode 100644 useless/code_valeur manquante.ipynb diff --git a/Descriptive_statistics/debug.ipynb b/Descriptive_statistics/debug.ipynb deleted file mode 100644 index c9b0ad6..0000000 --- a/Descriptive_statistics/debug.ipynb +++ /dev/null @@ -1,148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 83, - "id": "718d4e6d-b90a-4955-90ee-c1518246c07c", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Choisissez le type de compagnie : sport ? musique ? musee ? sport\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_5/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/products_purchased_reduced.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/target_information.csv\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "import warnings\n", - "\n", - "# Ignore warning\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "exec(open('../0_KPI_functions.py').read())\n", - "exec(open('plot.py').read())\n", - "\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "companies = {'musee' : ['1', '2', '3', '4'], # , '101'\n", - " 'sport': ['5'],\n", - " 'musique' : ['10', '11', '12', '13', '14']}\n", - "\n", - "\n", - "type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')\n", - "list_of_comp = companies[type_of_activity] \n", - "\n", - "# Load files\n", - "customer, campaigns_kpi, campaigns_brut, tickets, products = load_files(list_of_comp)\n", - "\n", - "# Identify anonymous customer for each company and remove them from our datasets\n", - "outlier_list = outlier_detection(tickets, list_of_comp)\n", - "\n", - "# Identify valid customer (customer who bought tickets after starting date or received mails after starting date)\n", - "customer_valid_list = valid_customer_detection(products, campaigns_brut)\n", - "\n", - "databases = [customer, campaigns_kpi, campaigns_brut, tickets, products]\n", - "\n", - "for dataset in databases:\n", - " dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier\n", - " dataset = dataset[dataset['customer_id'].isin(customer_valid_list)] # keep only valid customer\n", - " #print(f'shape of {dataset} : ', dataset.shape)\n", - "\n", - "# Identify customer who bought during the period of y\n", - "customer_target_period = identify_purchase_during_target_periode(products)\n", - "customer['has_purchased_target_period'] = np.where(customer['customer_id'].isin(customer_target_period), 1, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "97d1ceba-0ff9-4e36-87ab-7ebca2857798", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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cOHECgOr73q9fP9SoUUPlfXd3d0ejRo3K7FfO91Fpn/k///xTcQpL7vdTYGBgmTW+ThhuqgAvLy9MnToVP/zwA/7++29MmjQJN2/eVBlUbGVlpbKuoaGh0kDapUuXYvTo0WjTpg0iIiIQHx+PX375Bd26dStxwK2dnZ3S9D///AMAmDx5MvT19ZVeY8aMAQCV0PVv9+/fh62trco5bmtra1SvXh33798v491QVfzlmZycLHvdlzE3N0dsbCxatGiB6dOno1mzZrC3t8esWbNKHLPxovv376u8fwBgb2+vmP9vL14NZmBg8NL2Z8+eAXj+OxFCwMbGRuV3Eh8fr/L7KKkmFxcXHDt2DNbW1hg7dixcXFzg4uKCr7/+usz97NSpE+Lj45GTk4Njx46hQ4cOsLKyQqtWrXDs2DEkJycjOTm5xHDz4me2eLB68Wex+D0q7X0s6/NS/Hl7UUltJa377z8Qctb9N23vY2nk9vuyfS1e9p9//sHDhw9hYGCg8llLS0tT+ayp8530MuX9N/H48WO0bdsWZ8+exfz58xETE4NffvkFe/bsAQCtXlxw//59VK9eHXXq1FFqlyQJtra2Ku97Sb+fksj5PnrZZ754+3K/n9St83XBq6WqGH19fcyaNQtfffUVfv/9d9nrb926FX5+fli9erVSe3Z2donLvxhCateuDQAIDQ1F3759S1zH1dW11O1bWVnh7NmzEEIo9Z2eno6CggJF/3K0b98e+vr62LdvH4KDg8tc3sjICAAUA5iLlRTK3NzcsGPHDgghcOnSJWzatAlz586FsbExpk2b9tLtWFlZITU1VaW9eDBpefa1JLVr14YkSTh16lSJV7K92Fba4Mm2bduibdu2KCwsREJCAr755htMnDgRNjY2GDhwYKnb79ixI2bOnImTJ0/i+PHjmDVrlqL9yJEjcHJyUkzLVfzHsbT3saz30MrKqsQBwOoMCrayssK5c+fKta4c/97HF6/aUWcfNdVv8X9c/q14X4v7Kh4UHRUVVeI2TU1Ny1Wrpp04cQJ///03YmJiFEdrAKgM6NUGKysrFBQU4N69e0oBRwiBtLQ0tG7dWml5OYOZ1f0+etlnvvh3Kff76XW/OupFPHJTiUr64AH/d0i1OGHLIUmSyh+7S5cuqRyCLI2rqysaNmyIX3/9FV5eXiW+XvYF17FjRzx+/FjlpndbtmxRzJfL1tYWI0eOxOHDhxX9vOjGjRu4dOkSACjux1A8XWz//v2lbkOSJHh4eOCrr76ChYUFLly4oJhX2v9EO3bsqPiS/bctW7bAxMQEb731llr7V5Z3330XQgjcvXu3xN+Hm5ubrP709PTQpk0brFy5EgCU9rUkb775JszMzLBs2TKkpaWhc+fOAJ4f0UlMTMSuXbvQtGnTcn1evb29YWxsjK1btyq137lzR3FY/WXat2+P48ePK/3hLiwsxM6dO8vcdvv27ZGdna3yudi2bZuMPShb8SnCF/fxl19+QVJSktI+yjnqIadfAKXua7Vq1dCuXTsAzz9r9+/fR2FhYYmftZf9x+ZVKv5D/OJ33Zo1a7S+7eL39cX3PSIiAjk5OeX6jnvRy76PAJT6mXdxcVEEXU18P714FPJ1wiM3lahr165444030KNHDzRu3BhFRUW4ePEilixZgpo1a+Ljjz+W3ee7776LefPmYdasWfD19cXVq1cxd+5cODk5oaCgQK0+1qxZA39/f3Tt2hXDhg1D3bp1kZmZiaSkJFy4cAE//PBDqesGBQVh5cqVGDp0KG7evAk3NzecPn0aX3zxBQICAko8daGOpUuX4q+//sKwYcNw+PBh9OnTBzY2NsjIyMDRo0exceNG7NixA+7u7mjdujVcXV0xefJkFBQUoFatWti7d69ifEixgwcPYtWqVejduzecnZ0hhMCePXvw8OFDxR9w4Pn/pmJiYnDgwAHY2dnB1NQUrq6umDVrFg4ePIj27dvjs88+g6WlJb7//ntERkZi0aJFMDc3L9e+vsjHxwcfffQRhg8fjoSEBLRr1w41atRAamoqTp8+DTc3N4wePfqlfYSHh+PEiRPo3r076tWrh2fPniku2S3rd6KnpwdfX18cOHAATk5OcHFxUdRlaGiI48ePY8KECeXaNwsLC8ycORPTp09HUFAQBg0ahPv372POnDkwMjJSHCUqzX//+1/s378fHTp0wGeffQYTExOsXLlS5XLgkgQFBeGrr75CUFAQPv/8czRs2BCHDh3C4cOHy7UvpXF1dcVHH32Eb775BtWqVYO/vz9u3ryJmTNnwsHBAZMmTVIs6+bmhj179mD16tVo1aoVqlWrBi8vrwr3Czz/n/zo0aORkpKCRo0a4dChQ/j2228xevRoxanfgQMH4vvvv0dAQAA+/vhjvPnmm9DX18edO3cQHR2NXr16oU+fPhp9f8rj7bffRq1atRAcHIxZs2ZBX18f33//PX799Vetb7tz587o2rUrpk6diqysLPj4+ODSpUuYNWsWPD09MWTIkHL1q+73EfD8qEuHDh0wc+ZM1KhRA6tWrcKVK1eULgfXxPdT8+bNAQBr166FqakpjIyM4OTkVOLpyCqn8sYy086dO8X7778vGjZsKGrWrCn09fVFvXr1xJAhQ5Qu/RPi+aj77t27q/Tx4qj/3NxcMXnyZFG3bl1hZGQkWrZsKfbt2yeGDh2qdBVG8dUKX375ZYm1/frrr6J///7C2tpa6OvrC1tbW9GhQwcRHh5e5n7dv39fBAcHCzs7O1G9enXh6OgoQkNDlS6RLK5dnaulihUUFIjNmzeLDh06CEtLS1G9enVRp04d4e/vL7Zt26Z01cW1a9dEly5dhJmZmahTp44YP368iIyMVBr5f+XKFTFo0CDh4uIijI2Nhbm5uXjzzTfFpk2blLZ78eJF4ePjI0xMTAQApff7t99+Ez169BDm5ubCwMBAeHh4qFxlU3w1wr8v0xTi/662+eWXX5Tai6/u+PelvEIIsWHDBtGmTRtRo0YNYWxsLFxcXERQUJBISEgo8z2Ni4sTffr0EY6OjsLQ0FBYWVkJX19fsX///jLfdyGE+PrrrwUAMWrUKKX2zp07CwAq/ZS2z8Wfuxffo3Xr1gl3d3dhYGAgzM3NRa9evRSX3JflzJkz4q233hKGhobC1tZWfPrpp2Lt2rVlXi0lxPOrvwIDA0XNmjWFqampCAwMFD///LOsq6Ve/P29eFWeEM8vsV64cKFo1KiR0NfXF7Vr1xYffPCBuH37ttK6mZmZ4r333hMWFhZCkiSVq3xepG6/xZ+LmJgY4eXlJQwNDYWdnZ2YPn26ylWR+fn5YvHixcLDw0MYGRmJmjVrisaNG4v//Oc/4vr164rl1P1OKklp3z9y/q38/PPPwtvbW5iYmIg6deqIkSNHigsXLqj87jR9tZQQz68Kmzp1qnB0dBT6+vrCzs5OjB49WuW2CqW9RyVR9/sI///y/VWrVgkXFxehr68vGjduLL7//nuVPivy/VRs2bJlwsnJSejp6an176KqkIQQ4hVmKSIiesX8/PyQkZFRrnF8VLVIkoSxY8dixYoVlV1KlcYxN0RERKRTGG6IiIhIp/C0FBEREekUHrkhIiIincJwQ0RERDqF4YaIiIh0yv/cTfyKiorw999/w9TUVOduN01ERKSrhBDIzs6Gvb290gNXS/I/F27+/vtvlaekEhER0evh9u3bKs9Te9H/XLgpfi7S7du3YWZmVsnVEBERkTqysrLg4OCg1gNc/+fCTfGpKDMzM4YbIiKi14w6Q0o4oJiIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIinVK9sgvQNfWnRVZ2CURERJXq5oLulbp9HrkhIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOqdRws3r1ari7u8PMzAxmZmbw9vbGTz/99NJ1YmNj0apVKxgZGcHZ2Rnh4eGvqFoiIiJ6HVRquHnjjTewYMECJCQkICEhAR06dECvXr3wxx9/lLh8cnIyAgIC0LZtWyQmJmL69OmYMGECIiIiXnHlREREVFVVr8yN9+jRQ2n6888/x+rVqxEfH49mzZqpLB8eHo569eph2bJlAIAmTZogISEBixcvRmBg4KsomYiIiKq4KjPmprCwEDt27EBOTg68vb1LXCYuLg5dunRRauvatSsSEhKQn59f4jq5ubnIyspSehEREZHuUuvITa1atSBJklodZmZmyirgt99+g7e3N549e4aaNWti7969aNq0aYnLpqWlwcbGRqnNxsYGBQUFyMjIgJ2dnco6YWFhmDNnjqyaiIiI6PWlVrgpPg2kDa6urrh48SIePnyIiIgIDB06FLGxsaUGnBdDlhCixPZioaGhCAkJUUxnZWXBwcFBQ9UTERFRVaNWuBk6dKjWCjAwMECDBg0AAF5eXvjll1/w9ddfY82aNSrL2traIi0tTaktPT0d1atXh5WVVYn9GxoawtDQUPOFExERUZWkVrjJysqCmZmZ4ueXKV6uvIQQyM3NLXGet7c3Dhw4oNR25MgReHl5QV9fv0LbJSIiIt2g9pib1NRUWFtbw8LCosRTQEIISJKEwsJCtTc+ffp0+Pv7w8HBAdnZ2dixYwdiYmIQFRUF4Pkppbt372LLli0AgODgYKxYsQIhISEYNWoU4uLisH79emzfvl3tbRIREZFuUyvcnDhxApaWlgCA6OhojW38n3/+wZAhQ5Camgpzc3O4u7sjKioKnTt3BgCkpqYiJSVFsbyTkxMOHTqESZMmYeXKlbC3t8fy5ct5GTgREREpSKJ4RO7/iKysLJibm+PRo0cVPoVWkvrTIjXeJxER0evk5oLuGu9Tzt/vct/E78mTJ0hJSUFeXp5Su7u7e3m7JCIiIqow2eHm3r17GD58eKnPgJIz5oaIiIhI02TfoXjixIl48OAB4uPjYWxsjKioKGzevBkNGzbE/v37tVEjERERkdpkH7k5ceIEfvzxR7Ru3RrVqlWDo6MjOnfuDDMzM4SFhaF7d82fZyMiIiJSl+wjNzk5ObC2tgYAWFpa4t69ewAANzc3XLhwQbPVEREREckkO9y4urri6tWrAIAWLVpgzZo1uHv3LsLDw0t8thMRERHRqyT7tNTEiRORmpoKAJg1axa6du2K77//HgYGBti0aZOm6yMiIiKSRXa4GTx4sOJnT09P3Lx5E1euXEG9evVQu3ZtjRZHREREJFe573NTzMTEBC1bttRELUREREQVJjvcCCGwe/duREdHIz09HUVFRUrz9+zZo7HiiIiIiOSSHW4+/vhjrF27Fu3bt4eNjU2JD9EkIiIiqiyyw83WrVuxZ88eBAQEaKMeIiIiogqRfSm4ubk5nJ2dtVELERERUYXJDjezZ8/GnDlz8PTpU23UQ0RERFQhsk9L9evXD9u3b4e1tTXq168PfX19pfm8SzERERFVJtnhZtiwYTh//jw++OADDigmIiKiKkd2uImMjMThw4fxzjvvaKMeIiIiogqRPebGwcEBZmZm2qiFiIiIqMJkh5slS5ZgypQpuHnzphbKISIiIqoY2aelPvjgAzx58gQuLi4wMTFRGVCcmZmpseKIiIiI5JIdbpYtW6aFMoiIiIg0Q3a4GTp0qDbqICIiItII2WNuiIiIiKoyhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTpF9tVROTg4WLFiA48ePIz09HUVFRUrz//rrL40VR0RERCSX7HAzcuRIxMbGYsiQIbCzs+ODM4mIiKhKkR1ufvrpJ0RGRsLHx0cb9RARERFViOwxN7Vq1YKlpaU2aiEiIiKqMNnhZt68efjss8/w5MkTbdRDREREVCGyT0stWbIEN27cgI2NDerXr6/y4MwLFy5orDgiIiIiuWSHm969e2uhDCIiIiLNkB1uZs2apY06iIiIiDSiXDfxe/jwIdatW4fQ0FBkZmYCeH466u7duxotjoiIiEgu2UduLl26hE6dOsHc3Bw3b97EqFGjYGlpib179+LWrVvYsmWLNuokIiIiUovsIzchISEYNmwYrl+/DiMjI0W7v78/Tp48qdHiiIiIiOSSHW5++eUX/Oc//1Fpr1u3LtLS0jRSFBEREVF5yQ43RkZGyMrKUmm/evUq6tSpo5GiiIiIiMpLdrjp1asX5s6di/z8fACAJElISUnBtGnTEBgYqPECiYiIiOSQHW4WL16Me/fuwdraGk+fPoWvry8aNGgAU1NTfP7559qokYiIiEhtsq+WMjMzw+nTp3HixAlcuHABRUVFaNmyJTp16qSN+oiIiIhkkR1uinXo0AEdOnTQZC1EREREFVaum/gdP34c7777LlxcXNCgQQO8++67OHbsmOx+wsLC0Lp1a5iamsLa2hq9e/fG1atXX7pOTEwMJElSeV25cqU8u0JEREQ6Rna4WbFiBbp16wZTU1N8/PHHmDBhAszMzBAQEIAVK1bI6is2NhZjx45FfHw8jh49ioKCAnTp0gU5OTllrnv16lWkpqYqXg0bNpS7K0RERKSDZJ+WCgsLw1dffYVx48Yp2iZMmAAfHx98/vnnSu1liYqKUpreuHEjrK2tcf78ebRr1+6l61pbW8PCwkJW7URERKT7ZB+5ycrKQrdu3VTau3TpUuL9b+R49OgRAMDS0rLMZT09PWFnZ4eOHTsiOjq61OVyc3ORlZWl9CIiIiLdJTvc9OzZE3v37lVp//HHH9GjR49yFyKEQEhICN555x00b9681OXs7Oywdu1aREREYM+ePXB1dUXHjh1LffRDWFgYzM3NFS8HB4dy10hERERVnySEEHJWmD9/PhYvXgwfHx94e3sDAOLj43HmzBl88sknMDMzUyw7YcIEtfsdO3YsIiMjcfr0abzxxhtySkKPHj0gSRL279+vMi83Nxe5ubmK6aysLDg4OODRo0dKtWpK/WmRGu+TiIjodXJzQXeN95mVlQVzc3O1/n7LHnOzfv161KpVC5cvX8bly5cV7RYWFli/fr1iWpIktcPN+PHjsX//fpw8eVJ2sAGAt956C1u3bi1xnqGhIQwNDWX3SURERK8n2eEmOTlZYxsXQmD8+PHYu3cvYmJi4OTkVK5+EhMTYWdnp7G6iIiI6PVV7pv4Ac/DCfD8KE15jB07Ftu2bcOPP/4IU1NTxVPFzc3NYWxsDAAIDQ3F3bt3sWXLFgDAsmXLUL9+fTRr1gx5eXnYunUrIiIiEBERUZFdISIiIh1Rrpv4bdmyBW5ubjA2NoaxsTHc3d3x3Xffye5n9erVePToEfz8/GBnZ6d47dy5U7FMamoqUlJSFNN5eXmYPHky3N3d0bZtW5w+fRqRkZHo27dveXaFiIiIdIzsIzdLly7FzJkzMW7cOPj4+EAIgTNnziA4OBgZGRmYNGmS2n2pM5Z506ZNStNTpkzBlClT5JZNRERE/yNkh5tvvvkGq1evRlBQkKKtV69eaNasGWbPni0r3BARERFpmuzTUqmpqXj77bdV2t9++22kpqZqpCgiIiKi8pIdbho0aIBdu3aptO/cuZPPdyIiIqJKJ/u01Jw5czBgwACcPHkSPj4+kCQJp0+fxvHjx0sMPURERESvkuwjN4GBgTh79ixq166Nffv2Yc+ePahduzbOnTuHPn36aKNGIiIiIrWV6z43rVq1KvWOwERERESVqVz3uSEiIiKqqhhuiIiISKcw3BAREZFOYbghIiIinSI73IwYMQLZ2dkq7Tk5ORgxYoRGiiIiIiIqL9nhZvPmzXj69KlK+9OnTxVP7iYiIiKqLGpfCp6VlQUhBIQQyM7OhpGRkWJeYWEhDh06BGtra60USURERKQutcONhYUFJEmCJElo1KiRynxJkjBnzhyNFkdEREQkl9rhJjo6GkIIdOjQAREREbC0tFTMMzAwgKOjI+zt7bVSJBEREZG61A43vr6+AIDk5GQ4ODigWjVeaEVERERVj+zHLzg6OuLhw4dYv349kpKSIEkSmjZtihEjRsDc3FwbNRIRERGpTfbhl4SEBLi4uOCrr75CZmYmMjIysHTpUri4uODChQvaqJGIiIhIbbKP3EyaNAk9e/bEt99+i+rVn69eUFCAkSNHYuLEiTh58qTGiyQiIiJSl+xwk5CQoBRsAKB69eqYMmUKvLy8NFocERERkVyyT0uZmZkhJSVFpf327dswNTXVSFFERERE5SU73AwYMAAffvghdu7cidu3b+POnTvYsWMHRo4ciUGDBmmjRiIiIiK1yT4ttXjxYkiShKCgIBQUFAAA9PX1MXr0aCxYsEDjBRIRERHJITvcGBgY4Ouvv0ZYWBhu3LgBIQQaNGgAExMTbdRHREREJIvscFPMxMQEbm5umqyFiIiIqMJkh5ucnBwsWLAAx48fR3p6OoqKipTm//XXXxorjoiIiEgu2eFm5MiRiI2NxZAhQ2BnZwdJkrRRFxEREVG5yA43P/30EyIjI+Hj46ONeoiIiIgqRPal4LVq1VJ6IjgRERFRVSI73MybNw+fffYZnjx5oo16iIiIiCpE9mmpJUuW4MaNG7CxsUH9+vWhr6+vNJ8PzyQiIqLKJDvc9O7dWwtlEBEREWmG7HAza9YsbdRBREREpBGyx9wQERERVWUMN0RERKRTGG6IiIhIpzDcEBERkU5huCEiIiKdotbVUiEhIWp3uHTp0nIXQ0RERFRRaoWbxMREpenz58+jsLAQrq6uAIBr165BT08PrVq10nyFRERERDKoFW6io6MVPy9duhSmpqbYvHkzatWqBQB48OABhg8fjrZt22qnSiIiIiI1yR5zs2TJEoSFhSmCDfD8YZrz58/HkiVLNFocERERkVyyw01WVhb++ecflfb09HRkZ2drpCgiIiKi8pIdbvr06YPhw4dj9+7duHPnDu7cuYPdu3fjww8/RN++fbVRIxEREZHaZIeb8PBwdO/eHR988AEcHR3h6OiIwYMHw9/fH6tWrZLVV1hYGFq3bg1TU1NYW1ujd+/euHr1apnrxcbGolWrVjAyMoKzszPCw8Pl7gYRERHpKNnhxsTEBKtWrcL9+/eRmJiICxcuIDMzE6tWrUKNGjVk9RUbG4uxY8ciPj4eR48eRUFBAbp06YKcnJxS10lOTkZAQADatm2LxMRETJ8+HRMmTEBERITcXSEiIiIdJAkhRGUXUezevXuwtrZGbGws2rVrV+IyU6dOxf79+5GUlKRoCw4Oxq+//oq4uLgyt5GVlQVzc3M8evQIZmZmGqu9WP1pkRrvk4iI6HVyc0F3jfcp5++3WpeC9+3bF5s2bYKZmVmZ42r27NmjfqUvePToEQDA0tKy1GXi4uLQpUsXpbauXbti/fr1yM/Ph76+vtK83Nxc5ObmKqazsrLKXR8RERFVfWqFG3Nzc0iSpPhZG4QQCAkJwTvvvIPmzZuXulxaWhpsbGyU2mxsbFBQUICMjAzY2dkpzQsLC8OcOXO0UjMRERFVPWqFm40bN5b4syaNGzcOly5dwunTp8tctjhoFSs+s/ZiOwCEhoYqPT4iKysLDg4OFayWiIiIqiq1wo22jR8/Hvv378fJkyfxxhtvvHRZW1tbpKWlKbWlp6ejevXqsLKyUlne0NAQhoaGGq2XiIiIqi61wo2np2eJR0VKcuHCBbU3LoTA+PHjsXfvXsTExMDJyanMdby9vXHgwAGltiNHjsDLy0tlvA0RERH971Er3PTu3Vvx87Nnz7Bq1So0bdoU3t7eAID4+Hj88ccfGDNmjKyNjx07Ftu2bcOPP/4IU1NTxREZc3NzGBsbA3h+Wunu3bvYsmULgOdXRq1YsQIhISEYNWoU4uLisH79emzfvl3WtomIiEg3qRVuZs2apfh55MiRmDBhAubNm6eyzO3bt2VtfPXq1QAAPz8/pfaNGzdi2LBhAIDU1FSkpKQo5jk5OeHQoUOYNGkSVq5cCXt7eyxfvhyBgYGytk1ERES6SfZ9bszNzZGQkICGDRsqtV+/fh1eXl6Ky7mrKt7nhoiISLsq+z43su9QbGxsXOIVTadPn4aRkZHc7oiIiIg0SvbVUhMnTsTo0aNx/vx5vPXWWwCej7nZsGEDPvvsM40XSERERCSH7HAzbdo0ODs74+uvv8a2bdsAAE2aNMGmTZvQv39/jRdIREREJEe57nPTv39/BhkiIiKqkmSPuSEiIiKqyhhuiIiISKcw3BAREZFOYbghIiIinVKhcCOEgMx7ABIRERFpVbnCzZYtW+Dm5gZjY2MYGxvD3d0d3333naZrIyIiIpJN9qXgS5cuxcyZMzFu3Dj4+PhACIEzZ84gODgYGRkZmDRpkjbqJCIiIlKL7HDzzTffYPXq1QgKClK09erVC82aNcPs2bMZboiIiKhSyT4tlZqairffflul/e2330ZqaqpGiiIiIiIqL9nhpkGDBti1a5dK+86dO1WeFE5ERET0qsk+LTVnzhwMGDAAJ0+ehI+PDyRJwunTp3H8+PESQw8RERHRqyT7yE1gYCDOnj2L2rVrY9++fdizZw9q166Nc+fOoU+fPtqokYiIiEht5XpwZqtWrbB161ZN10JERERUYeUKN0VFRfjzzz+Rnp6OoqIipXnt2rXTSGFERERE5SE73MTHx+P999/HrVu3VO5OLEkSCgsLNVYcERERkVyyw01wcDC8vLwQGRkJOzs7SJKkjbqIiIiIykV2uLl+/Tp2796NBg0aaKMeIiIiogqRfbVUmzZt8Oeff2qjFiIiIqIKU+vIzaVLlxQ/jx8/Hp988gnS0tLg5uYGfX19pWXd3d01WyERERGRDGqFmxYtWkCSJKUBxCNGjFD8XDyPA4qJiIiosqkVbpKTk7VdBxEREZFGqBVuHB0dtV0HERERkUbIvlrK3t4efn5+8PPzg6+vL1xdXbVRFxEREVG5yL5aasmSJTAzM8PSpUvRpEkT2NnZYeDAgQgPD0dSUpI2aiQiIiJSm+wjN4MGDcKgQYMAAP/88w+io6Nx8OBBjB8/HkVFRRxQTERERJWqXM+Wevz4MU6fPo3Y2FjExMQgMTERbm5u8PX11XR9RERERLLIDjdt2rTBpUuX0Lx5c/j5+WH69Olo27YtLCwstFAeERERkTyyx9xcv34dJiYmcHZ2hrOzMxo0aMBgQ0RERFWG7HCTmZmJ6Oho+Pj44NixY/D19YWtrS0GDBiA8PBwbdRIREREpDZJ/Pu2w+Vw/vx5rFixAlu3bn0tBhRnZWXB3Nwcjx49gpmZmcb7rz8tUuN9EhERvU5uLuiu8T7l/P2WPeYmMTERMTExiImJwalTp5CdnQ0PDw98/PHHaN++fbmLJiIiItIE2eGmdevW8PT0hK+vL0aNGoV27dpp5QgIERERUXnIDjeZmZkMM0RERFRlyR5QzGBDREREVZnscENERERUlTHcEBERkU5huCEiIiKdUuFwU1hYiIsXL+LBgweaqIeIiIioQmSHm4kTJ2L9+vUAngcbX19ftGzZEg4ODoiJidF0fURERESyyA43u3fvhoeHBwDgwIEDSE5OxpUrVzBx4kTMmDFD4wUSERERySE73GRkZMDW1hYAcOjQIfTr1w+NGjXChx9+iN9++01WXydPnkSPHj1gb28PSZKwb9++ly4fExMDSZJUXleuXJG7G0RERKSjZIcbGxsbXL58GYWFhYiKikKnTp0AAE+ePIGenp6svnJycuDh4YEVK1bIWu/q1atITU1VvBo2bChrfSIiItJdsu9QPHz4cPTv3x92dnaQJAmdO3cGAJw9exaNGzeW1Ze/vz/8/f3llgBra2tYWFjIXo+IiIh0n+xwM3v2bLi5uSElJQX9+vWDoaEhAEBPTw/Tpk3TeIEl8fT0xLNnz9C0aVP897//fekDO3Nzc5Gbm6uYzsrKehUlEhERUSWRFW7y8/PRpUsXrFmzBoGBgUrzhg4dqtHCSmJnZ4e1a9eiVatWyM3NxXfffYeOHTsiJiYG7dq1K3GdsLAwzJkzR+u1ERERUdUgK9zo6+vj999/hyRJ2qrnpVxdXeHq6qqY9vb2xu3bt7F48eJSw01oaChCQkIU01lZWXBwcNB6rURERFQ5ZA8oDgoKUtznpip46623cP369VLnGxoawszMTOlFREREukv2mJu8vDysW7cOR48ehZeXF2rUqKE0f+nSpRorTh2JiYmws7N7pdskIiKiqkt2uPn999/RsmVLAMC1a9eU5sk9XfX48WP8+eefiunk5GRcvHgRlpaWqFevHkJDQ3H37l1s2bIFALBs2TLUr18fzZo1Q15eHrZu3YqIiAhERETI3Q0iIiLSUbLDTXR0tMY2npCQoHSlU/HYmKFDh2LTpk1ITU1FSkqKYn5eXh4mT56Mu3fvwtjYGM2aNUNkZCQCAgI0VhMRERG93iQhhCjPin/++Sdu3LiBdu3awdjYGEKIShtoLEdWVhbMzc3x6NEjrYy/qT8tUuN9EhERvU5uLuiu8T7l/P2WPaD4/v376NixIxo1aoSAgACkpqYCAEaOHIlPPvmkfBUTERERaYjscDNp0iTo6+sjJSUFJiYmivYBAwYgKipKo8URERERySV7zM2RI0dw+PBhvPHGG0rtDRs2xK1btzRWGBEREVF5yD5yk5OTo3TEplhGRobiUQxERERElUV2uGnXrp3i0mzg+eXfRUVF+PLLL1/6jCciIiKiV0H2aakvv/wSfn5+SEhIQF5eHqZMmYI//vgDmZmZOHPmjDZqJCIiIlKb7CM3TZs2xaVLl/Dmm2+ic+fOyMnJQd++fZGYmAgXFxdt1EhERESkNtlHbgDA1taWT9omIiKiKqlc4ebZs2e4dOkS0tPTUVRUpDSvZ8+eGimMiIiIqDxkh5uoqCgEBQUhIyNDZZ4kSSgsLNRIYURERETlIXvMzbhx49CvXz+kpqaiqKhI6cVgQ0RERJVNdrhJT09HSEgIbGxstFEPERERUYXIDjfvvfceYmJitFAKERERUcXJHnOzYsUK9OvXD6dOnYKbmxv09fWV5k+YMEFjxRERERHJJTvcbNu2DYcPH4axsTFiYmIgSZJiniRJDDdERERUqWSHm//+97+YO3cupk2bhmrVZJ/VIiIiItIq2ekkLy8PAwYMYLAhIiKiKkl2Qhk6dCh27typjVqIiIiIKkz2aanCwkIsWrQIhw8fhru7u8qA4qVLl2qsOCIiIiK5ZIeb3377DZ6engCA33//XWnevwcXExEREVUG2eEmOjpaG3UQERERaUSFRgXfuXMHd+/e1VQtRERERBUmO9wUFRVh7ty5MDc3h6OjI+rVqwcLCwvMmzdP5QnhRERERK+a7NNSM2bMwPr167FgwQL4+PhACIEzZ85g9uzZePbsGT7//HNt1ElERESkFtnhZvPmzVi3bh169uypaPPw8EDdunUxZswYhhsiIiKqVLJPS2VmZqJx48Yq7Y0bN0ZmZqZGiiIiIiIqL9nhxsPDAytWrFBpX7FiBTw8PDRSFBEREVF5yT4ttWjRInTv3h3Hjh2Dt7c3JEnCzz//jNu3b+PQoUPaqJGIiIhIbbKP3Pj6+uLatWvo06cPHj58iMzMTPTt2xdXr15F27ZttVEjERERkdpkH7lJSUmBg4NDiQOHU1JSUK9ePY0URkRERFQeso/cODk54d69eyrt9+/fh5OTk0aKIiIiIiov2eFGCFHiM6QeP34MIyMjjRRFREREVF5qn5YKCQkB8PzhmDNnzoSJiYliXmFhIc6ePYsWLVpovEAiIiIiOdQON4mJiQCeH7n57bffYGBgoJhnYGAADw8PTJ48WfMVEhEREcmgdrgpfhr48OHD8fXXX8PMzExrRRERERGVl+wxNxs3blQKNllZWdi3bx+uXLmi0cKIiIiIykN2uOnfv7/iDsVPnz6Fl5cX+vfvDzc3N0RERGi8QCIiIiI5ZIebkydPKm7Wt3fvXggh8PDhQyxfvhzz58/XeIFEREREcsgON48ePYKlpSUAICoqCoGBgTAxMUH37t1x/fp1jRdIREREJIfscOPg4IC4uDjk5OQgKioKXbp0AQA8ePCA97khIiKiSif78QsTJ07E4MGDUbNmTTg6OsLPzw/A89NVbm5umq6PiIiISBbZ4WbMmDF48803cfv2bXTu3BnVqj0/+OPs7MwxN0RERFTpZIcbAPDy8oKXl5dSW/fu3TVSEBEREVFFyA43I0aMeOn8DRs2lLsYIiIiooqSPaD4wYMHSq/09HScOHECe/bswcOHD2X1dfLkSfTo0QP29vaQJAn79u0rc53Y2Fi0atUKRkZGcHZ2Rnh4uNxdICIiIh0m+8jN3r17VdqKioowZswYODs7y+orJycHHh4eGD58OAIDA8tcPjk5GQEBARg1ahS2bt2KM2fOYMyYMahTp45a6xMREZHuK9eYmxdVq1YNkyZNgp+fH6ZMmaL2ev7+/vD391d7+fDwcNSrVw/Lli0DADRp0gQJCQlYvHgxww0REREBKMdpqdLcuHEDBQUFmuquRHFxcYr76hTr2rUrEhISkJ+fr9VtExER0etB9pGbkJAQpWkhBFJTUxEZGYmhQ4dqrLCSpKWlwcbGRqnNxsYGBQUFyMjIgJ2dnco6ubm5yM3NVUxnZWVptUYiIiKqXLLDTWJiotJ0tWrVUKdOHSxZsqTMK6k0QZIkpWkhRIntxcLCwjBnzhyt10VERERVg+xwEx0drY061GJra4u0tDSltvT0dFSvXh1WVlYlrhMaGqp0tCkrKwsODg5arZOIiIgqj9pjbp4+fYr9+/cjOztbZV5WVhb279+vdPpHG7y9vXH06FGltiNHjsDLywv6+volrmNoaAgzMzOlFxEREekutcPN2rVr8fXXX8PU1FRlnpmZGZYvX45169bJ2vjjx49x8eJFXLx4EcDzS70vXryIlJQUAM+PugQFBSmWDw4Oxq1btxASEoKkpCRs2LAB69evx+TJk2Vtl4iIiHSX2uHm+++/x8SJE0udP3HiRGzevFnWxhMSEuDp6QlPT08Azwcre3p64rPPPgMApKamKoIOADg5OeHQoUOIiYlBixYtMG/ePCxfvpyXgRMREZGC2mNurl+/Dg8Pj1Lnu7u74/r167I27ufnpxgQXJJNmzaptPn6+uLChQuytkNERET/O9Q+clNQUIB79+6VOv/evXtav88NERERUVnUDjfNmjXDsWPHSp1/9OhRNGvWTCNFEREREZWX2uFmxIgRmDdvHg4ePKgy78CBA5g/f/4ruc8NERER0cuoPebmo48+wsmTJ9GzZ080btwYrq6ukCQJSUlJuHbtGvr374+PPvpIm7USERERlUnWs6W2bt2KHTt2oFGjRrh27RquXLkCV1dXbN++Hdu3b9dWjURERERqk32H4v79+6N///7aqIWIiIiowjT2VHAiIiKiqoDhhoiIiHQKww0RERHpFLXCzaVLl1BUVKTtWoiIiIgqTK1w4+npiYyMDACAs7Mz7t+/r9WiiIiIiMpLrXBjYWGB5ORkAMDNmzd5FIeIiIiqLLUuBQ8MDISvry/s7OwgSRK8vLygp6dX4rJ//fWXRgskIiIikkOtcLN27Vr07dsXf/75JyZMmIBRo0bB1NRU27URERERyab2Tfy6desGADh//jw+/vhjhhsiIiKqkmTfoXjjxo2Kn+/cuQNJklC3bl2NFkVERERUXrLvc1NUVIS5c+fC3Nwcjo6OqFevHiwsLDBv3jwONCYiIqJKJ/vIzYwZM7B+/XosWLAAPj4+EELgzJkzmD17Np49e4bPP/9cG3USERERqUV2uNm8eTPWrVuHnj17Kto8PDxQt25djBkzhuGGiIiIKpXs01KZmZlo3LixSnvjxo2RmZmpkaKIiIiIykt2uPHw8MCKFStU2lesWAEPDw+NFEVERERUXrJPSy1atAjdu3fHsWPH4O3tDUmS8PPPP+P27ds4dOiQNmokIiIiUpvsIze+vr64du0a+vTpg4cPHyIzMxN9+/bF1atX0bZtW23USERERKQ22UduAMDe3p4Dh4mIiKhKkn3khoiIiKgqY7ghIiIincJwQ0RERDqF4YaIiIh0SrnCTUFBAY4dO4Y1a9YgOzsbAPD333/j8ePHGi2OiIiISC7ZV0vdunUL3bp1Q0pKCnJzc9G5c2eYmppi0aJFePbsGcLDw7VRJxEREZFaZB+5+fjjj+Hl5YUHDx7A2NhY0d6nTx8cP35co8URERERySX7yM3p06dx5swZGBgYKLU7Ojri7t27GiuMiIiIqDxkH7kpKipCYWGhSvudO3dgamqqkaKIiIiIykt2uOncuTOWLVummJYkCY8fP8asWbMQEBCgydqIiIiIZJN9Wmrp0qXo0KEDmjZtimfPnuH999/H9evXUbt2bWzfvl0bNRIRERGpTXa4qVu3Li5evIgdO3bg/PnzKCoqwocffojBgwcrDTAmIiIiqgyywk1+fj5cXV1x8OBBDB8+HMOHD9dWXURERETlImvMjb6+PnJzcyFJkrbqISIiIqoQ2QOKx48fj4ULF6KgoEAb9RARERFViOwxN2fPnsXx48dx5MgRuLm5oUaNGkrz9+zZo7HiiIiIiOSSHW4sLCwQGBiojVqIiIiIKkx2uNm4caM26iAiIiLSiHI9FZyIiIioqpJ95AYAdu/ejV27diElJQV5eXlK8y5cuKCRwoiIiIjKQ/aRm+XLl2P48OGwtrZGYmIi3nzzTVhZWeGvv/6Cv7+/7AJWrVoFJycnGBkZoVWrVjh16lSpy8bExECSJJXXlStXZG+XiIiIdJPscLNq1SqsXbsWK1asgIGBAaZMmYKjR49iwoQJePTokay+du7ciYkTJ2LGjBlITExE27Zt4e/vj5SUlJeud/XqVaSmpipeDRs2lLsbREREpKNkh5uUlBS8/fbbAABjY2NkZ2cDAIYMGSL72VJLly7Fhx9+iJEjR6JJkyZYtmwZHBwcsHr16peuZ21tDVtbW8VLT09P7m4QERGRjpIdbmxtbXH//n0AgKOjI+Lj4wEAycnJEEKo3U9eXh7Onz+PLl26KLV36dIFP//880vX9fT0hJ2dHTp27Ijo6GiZe0BERES6THa46dChAw4cOAAA+PDDDzFp0iR07twZAwYMQJ8+fdTuJyMjA4WFhbCxsVFqt7GxQVpaWonr2NnZYe3atYiIiMCePXvg6uqKjh074uTJk6VuJzc3F1lZWUovIiIi0l2yr5Zau3YtioqKAADBwcGwtLTE6dOn0aNHDwQHB8su4MXnVAkhSn12laurK1xdXRXT3t7euH37NhYvXox27dqVuE5YWBjmzJkjuy4iIiJ6PckON9WqVUO1av93wKd///7o37+/7A3Xrl0benp6Kkdp0tPTVY7mvMxbb72FrVu3ljo/NDQUISEhiumsrCw4ODjIrpeIiIheD+W6z83Dhw9x7tw5pKenK47iFAsKClKrDwMDA7Rq1QpHjx5VOp119OhR9OrVS+1aEhMTYWdnV+p8Q0NDGBoaqt0fERERvd5kh5sDBw5g8ODByMnJgampqdIpJEmS1A43ABASEoIhQ4bAy8sL3t7eWLt2LVJSUhSnt0JDQ3H37l1s2bIFALBs2TLUr18fzZo1Q15eHrZu3YqIiAhERETI3Q0iIiLSUbLDzSeffIIRI0bgiy++gImJSYU2PmDAANy/fx9z585FamoqmjdvjkOHDsHR0REAkJqaqnTPm7y8PEyePBl3796FsbExmjVrhsjISAQEBFSoDiIiItIdkpBz/TaAGjVq4LfffoOzs7O2atKqrKwsmJub49GjRzAzM9N4//WnRWq8TyIiotfJzQXdNd6nnL/fsi8F79q1KxISEspdHBEREZE2qXVaav/+/Yqfu3fvjk8//RSXL1+Gm5sb9PX1lZbt2bOnZiskIiIikkGtcNO7d2+Vtrlz56q0SZKEwsLCChdFREREVF5qhZsXL/cmIiIiqqpkj7khIiIiqsrUDjdnz57FTz/9pNS2ZcsWODk5wdraGh999BFyc3M1XiARERGRHGqHm9mzZ+PSpUuK6d9++w0ffvghOnXqhGnTpuHAgQMICwvTSpFERERE6lI73Fy8eBEdO3ZUTO/YsQNt2rTBt99+i5CQECxfvhy7du3SSpFERERE6lI73Dx48EDpgZaxsbHo1q2bYrp169a4ffu2ZqsjIiIikkntcGNjY4Pk5GQAzx+DcOHCBXh7eyvmZ2dnq9zzhoiIiOhVUzvcdOvWDdOmTcOpU6cQGhoKExMTtG3bVjH/0qVLcHFx0UqRREREROpS+8GZ8+fPR9++feHr64uaNWti8+bNMDAwUMzfsGEDunTpopUiiYiIiNSldripU6cOTp06hUePHqFmzZrQ09NTmv/DDz+gZs2aGi+QiIiISA61w00xc3PzEtstLS0rXAwRERFRRfEOxURERKRTGG6IiIhIpzDcEBERkU5huCEiIiKdwnBDREREOoXhhoiIiHQKww0RERHpFIYbIiIi0ikMN0RERKRTGG6IiIhIpzDcEBERkU5huCEiIiKdwnBDREREOoXhhoiIiHQKww0RERHpFIYbIiIi0ikMN0RERKRTGG6IiIhIpzDcEBERkU5huCEiIiKdwnBDREREOoXhhoiIiHQKww0RERHpFIYbIiIi0ikMN0RERKRTGG6IiIhIpzDcEBERkU5huCEiIiKdwnBDREREOoXhhoiIiHRKpYebVatWwcnJCUZGRmjVqhVOnTr10uVjY2PRqlUrGBkZwdnZGeHh4a+oUiIiInodVGq42blzJyZOnIgZM2YgMTERbdu2hb+/P1JSUkpcPjk5GQEBAWjbti0SExMxffp0TJgwAREREa+4ciIiIqqqJCGEqKyNt2nTBi1btsTq1asVbU2aNEHv3r0RFhamsvzUqVOxf/9+JCUlKdqCg4Px66+/Ii4uTq1tZmVlwdzcHI8ePYKZmVnFd+IF9adFarxPIiKi18nNBd013qecv9+VduQmLy8P58+fR5cuXZTau3Tpgp9//rnEdeLi4lSW79q1KxISEpCfn6+1WomIiOj1Ub2yNpyRkYHCwkLY2NgotdvY2CAtLa3EddLS0kpcvqCgABkZGbCzs1NZJzc3F7m5uYrpR48eAXieALWhKPeJVvolIiJ6XWjjb2xxn+qccKq0cFNMkiSlaSGESltZy5fUXiwsLAxz5sxRaXdwcJBbKhEREanBfJn2+s7Ozoa5uflLl6m0cFO7dm3o6empHKVJT09XOTpTzNbWtsTlq1evDisrqxLXCQ0NRUhIiGK6qKgImZmZsLKyemmIIqLXT1ZWFhwcHHD79m2tjKkjosojhEB2djbs7e3LXLbSwo2BgQFatWqFo0ePok+fPor2o0ePolevXiWu4+3tjQMHDii1HTlyBF5eXtDX1y9xHUNDQxgaGiq1WVhYVKx4IqrSzMzMGG6IdFBZR2yKVeql4CEhIVi3bh02bNiApKQkTJo0CSkpKQgODgbw/KhLUFCQYvng4GDcunULISEhSEpKwoYNG7B+/XpMnjy5snaBiIiIqphKHXMzYMAA3L9/H3PnzkVqaiqaN2+OQ4cOwdHREQCQmpqqdM8bJycnHDp0CJMmTcLKlSthb2+P5cuXIzAwsLJ2gYiIiKqYSr3PDRGRJuXm5iIsLAyhoaEqp6OJ6H8Hww0RERHplEp/thQRERGRJjHcEBERkU5huCEiIiKdwnBDREREOoXhhohee7Nnz4YkSUovW1vbyi6LiCpJpT9biohIE5o1a4Zjx44ppvX09CqxGiKqTAw3RKQTqlevzqM1RASAp6WISEdcv34d9vb2cHJywsCBA/HXX39VdklEVEl4Ez8ieu399NNPePLkCRo1aoR//vkH8+fPx5UrV/DHH3/AysqqsssjoleM4YaIdE5OTg5cXFwwZcoUhISEVHY5RPSK8bQUEemcGjVqwM3NDdevX6/sUoioEjDcEJHOyc3NRVJSEuzs7Cq7FCKqBAw3RPTamzx5MmJjY5GcnIyzZ8/ivffeQ1ZWFoYOHVrZpRFRJeCl4ET02rtz5w4GDRqEjIwM1KlTB2+99Rbi4+Ph6OhY2aURUSXggGIiIiLSKTwtRURERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RakZaWhvHjx8PZ2RmGhoZwcHBAjx49cPz48coujYh0HB+/QEQad/PmTfj4+MDCwgKLFi2Cu7s78vPzcfjwYYwdOxZXrlyp7BKJSIfxyA0RadyYMWMgSRLOnTuH9957D40aNUKzZs0QEhKC+Ph4AEBKSgp69eqFmjVrwszMDP3798c///yj6GP27Nlo0aIFNmzYgHr16qFmzZoYPXo0CgsLsWjRItja2sLa2hqff/650rYlScLq1avh7+8PY2NjODk54YcfflBaZurUqWjUqBFMTEzg7OyMmTNnIj8/X2Xb3333HerXrw9zc3MMHDgQ2dnZAIAtW7bAysoKubm5Sv0GBgYiKChIo+8lEcnHcENEGpWZmYmoqCiMHTsWNWrUUJlvYWEBIQR69+6NzMxMxMbG4ujRo7hx4wYGDBigtOyNGzfw008/ISoqCtu3b8eGDRvQvXt33LlzB7GxsVi4cCH++9//KgJTsZkzZyIwMBC//vorPvjgAwwaNAhJSUmK+aampti0aRMuX76Mr7/+Gt9++y2++uorlW3v27cPBw8exMGDBxEbG4sFCxYAAPr164fCwkLs379fsXxGRgYOHjyI4cOHV/g9JKIKEkREGnT27FkBQOzZs6fUZY4cOSL09PRESkqKou2PP/4QAMS5c+eEEELMmjVLmJiYiKysLMUyXbt2FfXr1xeFhYWKNldXVxEWFqaYBiCCg4OVttemTRsxevToUutZtGiRaNWqlWK6pG1/+umnok2bNorp0aNHC39/f8X0smXLhLOzsygqKip1O0T0anDMDRFplBACwPPTQ6VJSkqCg4MDHBwcFG1NmzaFhYUFkpKS0Lp1awBA/fr1YWpqqljGxsYGenp6qFatmlJbenq6Uv/e3t4q0xcvXlRM7969G8uWLcOff/6Jx48fo6CgAGZmZkrrvLhtOzs7pe2MGjUKrVu3xt27d1G3bl1s3LgRw4YNe+l+E9GrwdNSRKRRDRs2hCRJSqeBXiSEKDEEvNiur6+vNF+SpBLbioqKyqyruN/4+HgMHDgQ/v7+OHjwIBITEzFjxgzk5eUpLV/Wdjw9PeHh4YEtW7bgwoUL+O233zBs2LAy6yAi7WO4ISKNsrS0RNeuXbFy5Urk5OSozH/48CGaNm2KlJQU3L59W9F++fJlPHr0CE2aNKlwDS+OwYmPj0fjxo0BAGfOnIGjoyNmzJgBLy8vNGzYELdu3SrXdkaOHImNGzdiw4YN6NSpk9KRKCKqPAw3RKRxq1atQmFhId58801ERETg+vXrSEpKwvLly+Ht7Y1OnTrB3d0dgwcPxoULF3Du3DkEBQXB19cXXl5eFd7+Dz/8gA0bNuDatWuYNWsWzp07h3HjxgEAGjRogJSUFOzYsQM3btzA8uXLsXfv3nJtZ/Dgwbh79y6+/fZbjBgxosJ1E5FmMNwQkcY5OTnhwoULaN++PT755BM0b94cnTt3xvHjx7F69WpIkoR9+/ahVq1aaNeuHTp16gRnZ2fs3LlTI9ufM2cOduzYAXd3d2zevBnff/89mjZtCgDo1asXJk2ahHHjxqFFixb4+eefMXPmzHJtx8zMDIGBgahZsyZ69+6tkdqJqOIkUTz6j4hIB0iShL17976ysNG5c2c0adIEy5cvfyXbI6Ky8WopIqJyyMzMxJEjR3DixAmsWLGissshon9huCEiKoeWLVviwYMHWLhwIVxdXSu7HCL6F56WIiIiIp3CAcVERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkU/4fI9Zyp2lGygUAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sale_dynamics(products, campaigns_brut, type_of_activity)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Descriptive_statistics/generate_stat_desc.py b/Descriptive_statistics/generate_stat_desc.py deleted file mode 100644 index dc83609..0000000 --- a/Descriptive_statistics/generate_stat_desc.py +++ /dev/null @@ -1,68 +0,0 @@ -import pandas as pd -import numpy as np -import os -import io -import s3fs -import re -import warnings - -# Ignore warning -warnings.filterwarnings('ignore') - -exec(open('../0_KPI_functions.py').read()) -exec(open('plot.py').read()) - -# Create filesystem object -S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"] -fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL}) - -companies = {'musee' : ['1', '2', '3', '4'], # , '101' - 'sport': ['5'], - 'musique' : ['10', '11', '12', '13', '14']} - - -type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?') -list_of_comp = companies[type_of_activity] - -# Load files -customer, campaigns_kpi, campaigns_brut, tickets, products = load_files(list_of_comp) - -# Identify anonymous customer for each company and remove them from our datasets -outlier_list = outlier_detection(tickets, list_of_comp) - -# Identify valid customer (customer who bought tickets after starting date or received mails after starting date) -customer_valid_list = valid_customer_detection(products, campaigns_brut) - -databases = [customer, campaigns_kpi, campaigns_brut, tickets, products] - -for dataset in databases: - dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier - dataset = dataset[dataset['customer_id'].isin(customer_valid_list)] # keep only valid customer - #print(f'shape of {dataset} : ', dataset.shape) - -# Identify customer who bought during the period of y -customer_target_period = identify_purchase_during_target_periode(products) -customer['has_purchased_target_period'] = np.where(customer['customer_id'].isin(customer_target_period), 1, 0) - -# Generate graph and automatically saved them in the bucket -compute_nb_clients(customer, type_of_activity) - -maximum_price_paid(customer, type_of_activity) - -mailing_consent(customer, type_of_activity) - -mailing_consent_by_target(customer) - -gender_bar(customer, type_of_activity) - -country_bar(customer, type_of_activity) - -lazy_customer_plot(campaigns_kpi, type_of_activity) - -#campaigns_effectiveness(customer, type_of_activity) - -sale_dynamics(products, campaigns_brut, type_of_activity) - -tickets_internet(tickets, type_of_activity) - -box_plot_price_tickets(tickets, type_of_activity) diff --git a/Descriptive_statistics/plot.py b/Descriptive_statistics/plot.py deleted file mode 100644 index 754bc06..0000000 --- a/Descriptive_statistics/plot.py +++ /dev/null @@ -1,328 +0,0 @@ -import pandas as pd -import os -import s3fs -import io -import warnings -from datetime import date, timedelta, datetime -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.dates as mdates -import seaborn as sns - - -def load_files(nb_compagnie): - customer = pd.DataFrame() - campaigns_brut = pd.DataFrame() - campaigns_kpi = pd.DataFrame() - products = pd.DataFrame() - tickets = pd.DataFrame() - - # début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle - for directory_path in nb_compagnie: - df_customerplus_clean_0 = display_databases(directory_path, file_name = "customerplus_cleaned") - df_campaigns_brut = display_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at']) - df_products_purchased_reduced = display_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date']) - df_target_information = display_databases(directory_path, file_name = "target_information") - - df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_brut) - df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced) - df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0) - - - # creation de la colonne Number compagnie, qui permettra d'agréger les résultats - df_tickets_kpi["number_company"]=int(directory_path) - df_campaigns_brut["number_company"]=int(directory_path) - df_campaigns_kpi["number_company"]=int(directory_path) - df_customerplus_clean["number_company"]=int(directory_path) - df_target_information["number_company"]=int(directory_path) - - # Traitement des index - df_tickets_kpi["customer_id"]= directory_path + '_' + df_tickets_kpi['customer_id'].astype('str') - df_campaigns_brut["customer_id"]= directory_path + '_' + df_campaigns_brut['customer_id'].astype('str') - df_campaigns_kpi["customer_id"]= directory_path + '_' + df_campaigns_kpi['customer_id'].astype('str') - df_customerplus_clean["customer_id"]= directory_path + '_' + df_customerplus_clean['customer_id'].astype('str') - df_products_purchased_reduced["customer_id"]= directory_path + '_' + df_products_purchased_reduced['customer_id'].astype('str') - - # Concaténation - customer = pd.concat([customer, df_customerplus_clean], ignore_index=True) - campaigns_kpi = pd.concat([campaigns_kpi, df_campaigns_kpi], ignore_index=True) - campaigns_brut = pd.concat([campaigns_brut, df_campaigns_brut], ignore_index=True) - tickets = pd.concat([tickets, df_tickets_kpi], ignore_index=True) - products = pd.concat([products, df_products_purchased_reduced], ignore_index=True) - - return customer, campaigns_kpi, campaigns_brut, tickets, products - - -def save_file_s3(File_name, type_of_activity): - image_buffer = io.BytesIO() - plt.savefig(image_buffer, format='png') - image_buffer.seek(0) - FILE_PATH = f"projet-bdc2324-team1/stat_desc/{type_of_activity}/" - FILE_PATH_OUT_S3 = FILE_PATH + File_name + type_of_activity + '.png' - with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file: - s3_file.write(image_buffer.read()) - plt.close() - - -def outlier_detection(tickets, company_list, show_diagram=False): - - outlier_list = list() - - for company in company_list: - total_amount_share = tickets[tickets['number_company']==int(company)].groupby('customer_id')['total_amount'].sum().reset_index() - total_amount_share['CA'] = total_amount_share['total_amount'].sum() - total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['CA'] - - total_amount_share_index = total_amount_share.set_index('customer_id') - df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False) - #print('df circulaire : ', df_circulaire.head()) - top = df_circulaire[:1] - #print('top : ', top) - outlier_list.append(top.index[0]) - rest = df_circulaire[1:] - - rest_sum = rest.sum() - - new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])]) - - if show_diagram: - plt.figure(figsize=(3, 3)) - plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5) - plt.axis('equal') - plt.title(f'Répartition des montants totaux pour la compagnie {company}') - plt.show() - return outlier_list - - -def valid_customer_detection(products, campaigns_brut): - products_valid = products[products['purchase_date']>="2021-05-01"] - consumer_valid_product = products_valid['customer_id'].to_list() - - campaigns_valid = campaigns_brut[campaigns_brut["sent_at"]>="2021-05-01"] - consumer_valid_campaigns = campaigns_valid['customer_id'].to_list() - - consumer_valid = consumer_valid_product + consumer_valid_campaigns - return consumer_valid - - -def identify_purchase_during_target_periode(products): - products_target_period = products[(products['purchase_date']>="2022-11-01") - & (products['purchase_date']<="2023-11-01")] - customer_target_period = products_target_period['customer_id'].to_list() - return customer_target_period - - -def remove_elements(lst, elements_to_remove): - return ''.join([x for x in lst if x not in elements_to_remove]) - - -def compute_nb_clients(customer, type_of_activity): - company_nb_clients = customer[customer["purchase_count"]>0].groupby("number_company")["customer_id"].count().reset_index() - plt.bar(company_nb_clients["number_company"], company_nb_clients["customer_id"]/1000) - - plt.xlabel('Company') - plt.ylabel("Number of clients (thousands)") - plt.title(f"Number of clients for {type_of_activity}") - plt.xticks(company_nb_clients["number_company"], ["{}".format(i) for i in company_nb_clients["number_company"]]) - plt.show() - save_file_s3("nb_clients_", type_of_activity) - - -def maximum_price_paid(customer, type_of_activity): - company_max_price = customer.groupby("number_company")["max_price"].max().reset_index() - plt.bar(company_max_price["number_company"], company_max_price["max_price"]) - - plt.xlabel('Company') - plt.ylabel("Maximal price of a ticket Prix") - plt.title(f"Maximal price of a ticket for {type_of_activity}") - plt.xticks(company_max_price["number_company"], ["{}".format(i) for i in company_max_price["number_company"]]) - plt.show() - save_file_s3("Maximal_price_", type_of_activity) - - -def mailing_consent(customer, type_of_activity): - mailing_consent = customer.groupby("number_company")["opt_in"].mean().reset_index() - - plt.bar(mailing_consent["number_company"], mailing_consent["opt_in"]) - - plt.xlabel('Company') - plt.ylabel('Consent') - plt.title(f'Consent of mailing for {type_of_activity}') - plt.xticks(mailing_consent["number_company"], ["{}".format(i) for i in mailing_consent["number_company"]]) - plt.show() - save_file_s3("mailing_consent_", type_of_activity) - - -def mailing_consent_by_target(customer): - df_graph = customer.groupby(["number_company", "has_purchased_target_period"])["opt_in"].mean().reset_index() - # Création du barplot groupé - fig, ax = plt.subplots(figsize=(10, 6)) - - categories = df_graph["number_company"].unique() - bar_width = 0.35 - bar_positions = np.arange(len(categories)) - - # Grouper les données par label et créer les barres groupées - for label in df_graph["has_purchased_target_period"].unique(): - label_data = df_graph[df_graph['has_purchased_target_period'] == label] - values = [label_data[label_data['number_company'] == category]['opt_in'].values[0]*100 for category in categories] - - label_printed = "purchased" if label else "no purchase" - ax.bar(bar_positions, values, bar_width, label=label_printed) - - # Mise à jour des positions des barres pour le prochain groupe - bar_positions = [pos + bar_width for pos in bar_positions] - - # Ajout des étiquettes, de la légende, etc. - ax.set_xlabel('Company') - ax.set_ylabel('Consent') - ax.set_title(f'Consent of mailing according to target for {type_of_activity}') - ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))]) - ax.set_xticklabels(categories) - ax.legend() - - # Affichage du plot - plt.show() - save_file_s3("mailing_consent_target_", type_of_activity) - - -def gender_bar(customer, type_of_activity): - company_genders = customer.groupby("number_company")[["gender_male", "gender_female", "gender_other"]].mean().reset_index() - - # Création du barplot - plt.bar(company_genders["number_company"], company_genders["gender_male"], label = "Homme") - plt.bar(company_genders["number_company"], company_genders["gender_female"], - bottom = company_genders["gender_male"], label = "Femme") - plt.bar(company_genders["number_company"], company_genders["gender_other"], - bottom = company_genders["gender_male"] + company_genders["gender_female"], label = "Inconnu") - - plt.xlabel('Company') - plt.ylabel("Gender") - plt.title(f"Gender of Customer for {type_of_activity}") - plt.legend() - plt.xticks(company_genders["number_company"], ["{}".format(i) for i in company_genders["number_company"]]) - plt.show() - save_file_s3("gender_bar_", type_of_activity) - - -def country_bar(customer, type_of_activity): - company_country_fr = customer.groupby("number_company")["country_fr"].mean().reset_index() - plt.bar(company_country_fr["number_company"], company_country_fr["country_fr"]) - - plt.xlabel('Company') - plt.ylabel("Share of French Customer") - plt.title(f"Share of French Customer for {type_of_activity}") - plt.xticks(company_country_fr["number_company"], ["{}".format(i) for i in company_country_fr["number_company"]]) - plt.show() - save_file_s3("country_bar_", type_of_activity) - - -def lazy_customer_plot(campaigns_kpi, type_of_activity): - company_lazy_customers = campaigns_kpi.groupby("number_company")["nb_campaigns_opened"].mean().reset_index() - plt.bar(company_lazy_customers["number_company"], company_lazy_customers["nb_campaigns_opened"]) - - plt.xlabel('Company') - plt.ylabel("Share of Customers who did not open mail") - plt.title(f"Share of Customers who did not open mail for {type_of_activity}") - plt.xticks(company_lazy_customers["number_company"], ["{}".format(i) for i in company_lazy_customers["number_company"]]) - plt.show() - save_file_s3("lazy_customer_", type_of_activity) - - -def campaigns_effectiveness(customer, type_of_activity): - - campaigns_effectiveness = customer.groupby("number_company")["opt_in"].mean().reset_index() - - plt.bar(campaigns_effectiveness["number_company"], campaigns_effectiveness["opt_in"]) - - plt.xlabel('Company') - plt.ylabel("Number of Customers (thousands)") - plt.title(f"Number of Customers of have bought or have received mails for {type_of_activity}") - plt.legend() - plt.xticks(campaigns_effectiveness["number_company"], ["{}".format(i) for i in campaigns_effectiveness["number_company"]]) - plt.show() - save_file_s3("campaigns_effectiveness_", type_of_activity) - - -def sale_dynamics(products, campaigns_brut, type_of_activity): - purchase_min = products.groupby(['customer_id'])['purchase_date'].min().reset_index() - purchase_min.rename(columns = {'purchase_date' : 'first_purchase_event'}, inplace = True) - purchase_min['first_purchase_event'] = pd.to_datetime(purchase_min['first_purchase_event']) - purchase_min['first_purchase_month'] = pd.to_datetime(purchase_min['first_purchase_event'].dt.strftime('%Y-%m')) - - # Mois du premier mails - first_mail_received = campaigns_brut.groupby('customer_id')['sent_at'].min().reset_index() - first_mail_received.rename(columns = {'sent_at' : 'first_email_reception'}, inplace = True) - first_mail_received['first_email_reception'] = pd.to_datetime(first_mail_received['first_email_reception']) - first_mail_received['first_email_month'] = pd.to_datetime(first_mail_received['first_email_reception'].dt.strftime('%Y-%m')) - - # Fusion - known_customer = pd.merge(purchase_min[['customer_id', 'first_purchase_month']], - first_mail_received[['customer_id', 'first_email_month']], on = 'customer_id', how = 'outer') - - # Mois à partir duquel le client est considere comme connu - - known_customer['known_date'] = pd.to_datetime(known_customer[['first_email_month', 'first_purchase_month']].min(axis = 1), utc = True, format = 'ISO8601') - - # Nombre de commande par mois - purchases_count = pd.merge(products[['customer_id', 'purchase_id', 'purchase_date']].drop_duplicates(), known_customer[['customer_id', 'known_date']], on = ['customer_id'], how = 'inner') - purchases_count['is_customer_known'] = purchases_count['purchase_date'] > purchases_count['known_date'] + pd.DateOffset(months=1) - purchases_count['purchase_date_month'] = pd.to_datetime(purchases_count['purchase_date'].dt.strftime('%Y-%m')) - purchases_count = purchases_count[purchases_count['customer_id'] != 1] - - # Nombre de commande par mois par type de client - nb_purchases_graph = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['purchase_id'].count().reset_index() - nb_purchases_graph.rename(columns = {'purchase_id' : 'nb_purchases'}, inplace = True) - - nb_purchases_graph_2 = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['customer_id'].nunique().reset_index() - nb_purchases_graph_2.rename(columns = {'customer_id' : 'nb_new_customer'}, inplace = True) - - # Graphique en nombre de commande - purchases_graph = nb_purchases_graph - - purchases_graph_used = purchases_graph[purchases_graph["purchase_date_month"] >= datetime(2021,3,1)] - purchases_graph_used_0 = purchases_graph_used[purchases_graph_used["is_customer_known"]==False] - purchases_graph_used_1 = purchases_graph_used[purchases_graph_used["is_customer_known"]==True] - - - merged_data = pd.merge(purchases_graph_used_0, purchases_graph_used_1, on="purchase_date_month", suffixes=("_new", "_old")) - - plt.bar(merged_data["purchase_date_month"], merged_data["nb_purchases_new"], width=12, label="Nouveau client") - plt.bar(merged_data["purchase_date_month"], merged_data["nb_purchases_old"], - bottom=merged_data["nb_purchases_new"], width=12, label="Ancien client") - - - # commande pr afficher slt - plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y')) - - plt.xlabel('Month') - plt.ylabel("Number of Sales") - plt.title(f"Number of Sales for {type_of_activity}") - plt.legend() - plt.show() - save_file_s3("sale_dynamics_", type_of_activity) - - -def tickets_internet(tickets, type_of_activity): - nb_tickets_internet = tickets.groupby("number_company")[["nb_tickets", "nb_tickets_internet"]].sum().reset_index() - nb_tickets_internet["Share_ticket_internet"] = nb_tickets_internet["nb_tickets_internet"]*100 / nb_tickets_internet["nb_tickets"] - - plt.bar(nb_tickets_internet["number_company"], nb_tickets_internet["Share_ticket_internet"]) - - plt.xlabel('Company') - plt.ylabel("Share of Tickets Bought Online") - plt.title(f"Share of Tickets Bought Online for {type_of_activity}") - plt.xticks(nb_tickets_internet["number_company"], ["{}".format(i) for i in nb_tickets_internet["number_company"]]) - plt.show() - save_file_s3("tickets_internet_", type_of_activity) - - -def box_plot_price_tickets(tickets, type_of_activity): - price_tickets = tickets[(tickets['total_amount'] > 0)] - sns.boxplot(data=price_tickets, y="total_amount", x="number_company", showfliers=False, showmeans=True) - plt.title(f"Box plot of price tickets for {type_of_activity}") - plt.xticks(price_tickets["number_company"], ["{}".format(i) for i in price_tickets["number_company"]]) - plt.show() - save_file_s3("box_plot_price_tickets_", type_of_activity) - - diff --git a/Spectacle/2_Modelization_spectacle.ipynb b/Spectacle/2_Modelization_spectacle.ipynb deleted file mode 100644 index 61d85fd..0000000 --- a/Spectacle/2_Modelization_spectacle.ipynb +++ /dev/null @@ -1,2075 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3415114e-9577-4487-89eb-4931620ad9f0", - "metadata": {}, - "source": [ - "# Predict Sales" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f271eb45-1470-4764-8c2e-31374efa1fe5", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "\n", - "import pickle\n", - "import warnings\n", - "#import scikitplot as skplt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3fecb606-22e5-4dee-8efa-f8dff0832299", - "metadata": {}, - "outputs": [], - "source": [ - "warnings.filterwarnings('ignore')\n", - "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", - "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" - ] - }, - { - "cell_type": "markdown", - "id": "ae591854-3003-4c75-a0c7-5abf04246e81", - "metadata": {}, - "source": [ - "### Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "59dd4694-a812-4923-b995-a2ee86c74f85", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "017f7e9a-3ba0-40fa-bdc8-51b98cc1fdb3", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/Generalization/musique\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c479b230-b4bd-4cfb-b76b-d9faf6d95772", - "metadata": {}, - "outputs": [], - "source": [ - "dataset_train, dataset_test = load_train_test()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c24c446d-4e1c-4ac1-a048-f0b8d8559f36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "street_id 0\n", - "structure_id 327067\n", - "mcp_contact_id 135224\n", - "fidelity 0\n", - "tenant_id 0\n", - "is_partner 0\n", - "deleted_at 354365\n", - "gender 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "last_buying_date 119201\n", - "max_price 119201\n", - "ticket_sum 0\n", - "average_price 115193\n", - "average_purchase_delay 119203\n", - "average_price_basket 119203\n", - "average_ticket_basket 119203\n", - "total_price 4008\n", - "purchase_count 0\n", - "first_buying_date 119201\n", - "country 56856\n", - "gender_label 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "country_fr 56856\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 224310\n", - "y_has_purchased 0\n", - "dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "825d14a3-6967-4733-bfd4-64bf61c2bd43", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69eaec12-b30f-4d30-a461-ea520d5cbf77", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d039f31d-0093-46c6-9743-ddec1381f758", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape train : (354365, 17)\n", - "Shape test : (151874, 17)\n" - ] - } - ], - "source": [ - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "a1d6de94-4e11-481a-a0ce-412bf29f692c", - "metadata": {}, - "source": [ - "### Prepare preprocessing and Hyperparameters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b808da43-c444-4e94-995a-7ec6ccd01e2d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0.0: 0.5481283836040216, 1.0: 5.694439980716696}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute Weights\n", - "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", - " y = y_train['y_has_purchased'])\n", - "\n", - "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n", - "weight_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b32a79ea-907f-4dfc-9832-6c74bef3200c", - "metadata": {}, - "outputs": [], - "source": [ - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - "numeric_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n", - " (\"scaler\", StandardScaler()) \n", - "])\n", - "\n", - "categorical_features = ['opt_in'] \n", - "\n", - "# Transformer for the categorical features\n", - "categorical_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", - "])\n", - "\n", - "preproc = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features)\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9809a688-bfbc-4685-a77f-17a8b2b79ab3", - "metadata": {}, - "outputs": [], - "source": [ - "# Set loss\n", - "balanced_scorer = make_scorer(balanced_accuracy_score)\n", - "recall_scorer = make_scorer(recall_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4f9b2bbf-5f8a-4ac1-8e6c-51bd0dd8ac85", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_confusion_matrix(y_test, y_pred):\n", - " conf_matrix = confusion_matrix(y_test, y_pred)\n", - " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - " plt.xlabel('Predicted')\n", - " plt.ylabel('Actual')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", - "\n", - "\n", - "def draw_roc_curve(X_test, y_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - " # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - " fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - " \n", - " # Calcul de l'aire sous la courbe ROC (AUC)\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " plt.figure(figsize = (14, 8))\n", - " plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - " plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - " plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - " plt.xlabel('Taux de faux positifs (FPR)')\n", - " plt.ylabel('Taux de vrais positifs (TPR)')\n", - " plt.title('Courbe ROC : modèle logistique')\n", - " plt.legend(loc=\"lower right\")\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "cf400c70-0192-42cc-9919-f61bae8382b0", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_features_importance(pipeline, model):\n", - " coefficients = pipeline.named_steps['logreg'].coef_[0]\n", - " feature_names = pipeline.named_steps['logreg'].feature_names_in_\n", - " \n", - " # Tracer l'importance des caractéristiques\n", - " plt.figure(figsize=(10, 6))\n", - " plt.barh(feature_names, coefficients, color='skyblue')\n", - " plt.xlabel('Importance des caractéristiques')\n", - " plt.ylabel('Caractéristiques')\n", - " plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n", - " plt.grid(True)\n", - " plt.show()\n", - "\n", - "def draw_prob_distribution(X_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - " plt.figure(figsize=(8, 6))\n", - " plt.hist(y_pred_prob, bins=10, range=(0, 1), color='blue', alpha=0.7)\n", - " \n", - " plt.xlim(0, 1)\n", - " plt.ylim(0, None)\n", - " \n", - " plt.title('Histogramme des probabilités pour la classe 1')\n", - " plt.xlabel('Probabilité')\n", - " plt.ylabel('Fréquence')\n", - " plt.grid(True)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "206d9a95-7c37-4506-949b-e77d225e42c5", - "metadata": {}, - "outputs": [], - "source": [ - "# Hyperparameter\n", - "param_grid = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n", - " 'logreg__penalty': ['l1', 'l2'],\n", - " 'logreg__class_weight': ['balanced', weight_dict]} " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7ff2f7bd-efc1-4f7c-a3c9-caa916aa2f2b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'time_between_purchase',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'fidelity', 'is_email_true',\n",
-       "                                                   'opt_in', 'gender_female',\n",
-       "                                                   'gender_male',\n",
-       "                                                   'gender_other',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
-       "                                                  1.0: 5.694439980716696},\n",
-       "                                    max_iter=5000, solver='saga'))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'time_between_purchase',\n", - " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", - " 'gender_male',\n", - " 'gender_other',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5481283836040216,\n", - " 1.0: 5.694439980716696},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pipeline\n", - "pipeline = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n", - " max_iter=5000)) \n", - "])\n", - "\n", - "pipeline.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "markdown", - "id": "ed415f60-9663-4179-877b-233faf6e1645", - "metadata": {}, - "source": [ - "## Baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2b467511-2ae5-4a16-a502-397c3460471d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'time_between_purchase',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'fidelity', 'is_email_true',\n",
-       "                                                   'opt_in', 'gender_female',\n",
-       "                                                   'gender_male',\n",
-       "                                                   'gender_other',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
-       "                                                  1.0: 5.694439980716696},\n",
-       "                                    max_iter=5000, solver='saga'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'time_between_purchase',\n", - " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", - " 'gender_male',\n", - " 'gender_other',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5481283836040216,\n", - " 1.0: 5.694439980716696},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6356e870-0dfc-4e60-9e48-e2de5e7f9f87", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.8489010627230468\n", - "F1 Score: 0.4775997086140958\n", - "Recall Score: 0.7887218045112782\n" - ] - } - ], - "source": [ - "y_pred = pipeline.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "09387a09-0d53-4c54-baac-f3c2a57a629a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qFbt27SIiIoLU1FTq168PQGpqKlFRUfz000+Eh4ezbNkyoqOjOXz4MGFhYQAkJycTFxdHRkYGfn5+zJgxgyFDhnD8+HHMZjMAo0ePZsqUKRw5cuSGEztVbERERBzNZJ9PTk4OZ86csfnk5OTcUpf2799Peno6LVu2tG4zm800adKEtWvXArBp0yby8vJsYsLCwqhevbo1Zt26dVgsFmtSA9CgQQMsFotNTPXq1a1JDUCrVq3Iyclh06ZN1pgmTZpYk5orMUePHuXAgQM3fF1KbERERBzMXkNRiYmJ1nksVz6JiYm31Kf09HQAQkJCbLaHhIRY96Wnp+Pl5YW/v/9fxgQHBxdoPzg42Cbm6vP4+/vj5eX1lzFXfr4ScyO0KkpEROQuMWTIEPr162ez7c8Vjltx9RCPYRjXHfa5OqaweHvEXJktczPzi1SxERERcTB7VWzMZjN+fn42n1tNbEJDQ4GC1ZCMjAxrpSQ0NJTc3FwyMzP/Mub48eMF2j9x4oRNzNXnyczMJC8v7y9jMjIygIJVpb+ixEZERMTBXLUq6q+UL1+e0NBQVqxYYd2Wm5vL6tWradiwIQCRkZF4enraxBw7dozt27dbY6KiosjKymLDhg3WmPXr15OVlWUTs337do4dO2aNWb58OWazmcjISGvMt99+a7MEfPny5YSFhXHffffd8HUpsREREXFT586dIy0tjbS0NODyhOG0tDQOHTqEyWQiPj6ehIQEFi1axPbt24mLi8PHx4eYmBgALBYL3bp1o3///qxcuZItW7bw1FNPUaNGDZo3bw5A1apVad26Nd27dyc1NZXU1FS6d+9OdHQ04eHhALRs2ZKIiAhiY2PZsmULK1euZMCAAXTv3h0/Pz/g8pJxs9lMXFwc27dvZ9GiRSQkJNCvX7+bSuo0x0ZERMTBXPXk4R9++IFHHnnE+vOV+TnPPPMMSUlJDBo0iOzsbHr16kVmZib169dn+fLllChRwnrMxIkTKVq0KJ06dSI7O5tmzZqRlJSEh4eHNWbevHn07dvXunqqQ4cONs/O8fDw4Msvv6RXr140atQIb29vYmJiGD9+vDXGYrGwYsUKevfuTd26dfH396dfv34F5hRdj55jI/I3oufYiBTkjOfYBD7zkV3aOfV+V7u04840FCUiIiJuQ0NRIiIiDqaXYDqPEhsREREHU2LjPEpsREREHEyJjfO4NLE5f/488+fPZ+3ataSnp2MymQgJCaFRo0Z07doVX19fV3ZPRERE7jIumzy8c+dOqlSpwqBBg8jMzKRcuXKUKVOGzMxMBg4cSHh4ODt37nRV90REROzHTi/BlOtzWcWmd+/eNG7cmPfffx8vLy+bfbm5ucTFxdG7d2++/vprF/VQRETEPjQU5TwuS2zWr1/PDz/8UCCpAfDy8uLVV1+lXr16LuiZiIiI3K1cNhTl7+/Pzz//fM39v/zyS4HXpIuIiNyN7sR3Rbkrl1VsunfvzjPPPMNrr71GixYtCAkJwWQykZ6ezooVK0hISCA+Pt5V3RMREbEbJSXO47LEZuTIkXh7ezNhwgQGDRpk/Us3DIPQ0FBeeeUVBg0a5KruiYiIyF3Ipcu9Bw8ezODBg9m/fz/p6ekAhIaGUr58eVd2S0RExK5UsXGeO+IBfeXLl1cyIyIi7kt5jdPoJZgiIiLiNu6Iio2IiIg701CU8yixERERcTAlNs6jxEZERMTBlNg4j8vn2KSkpLBmzRrrz9OmTaNWrVrExMSQmZnpwp6JiIjI3cblic3AgQM5c+YMANu2baN///60bduWffv20a9fPxf3TkRExA70EkyncflQ1P79+4mIiABg4cKFREdHk5CQwObNm2nbtq2LeyciInL7NBTlPC6v2Hh5eXHhwgUAvvrqK1q2bAlAQECAtZIjIiIiciNcXrF56KGH6NevH40aNWLDhg18/PHHAOzZs4cyZcq4uHfurVGdirz8dHPqRJSjVEkLnV5+my++2Wrd/9ijNen2+EPUrlqWIP/i1O+cyNY9v9q0ERJYgoT4f/Bog/sp4Wtmz4EMxs3+H4u+SitwPi/Ponw7dwA1w8vYtBVg8WXOW89Qo0ppAiw+nDh9jiXfbGX41C84e/6iTRvxsc147vFGlCvlz4nT53j70+8YN3u5/W+OyJ98+uF7rP12Fb8ePICX2cz91WsS9++XKFPuPmuMYRh8NGcW//tiIefOnqVKRHX+/fIQ7i1f0RqTsnghq79axt49P5F94TwfffktxUuUsDnXG6+8xL5f9pD122mKF/ejZt36xP27L4FBwdaYPbt28P6syezdsxMwUfn+ajzbM54KlcMdfSvkFqli4zwur9hMnTqVokWLsmDBAmbMmEHp0qUBWLZsGa1bt3Zx79ybr7eZbXt+5eXRnxS638fbi3U/7mXYlP9es4333nyGKvcF86/4WdT9VwL/XZXG3NHPUTO8YFKaEP8Yx05kFdh+6dIllqzeyhPxs3ig4yi6j5jLI/XDmTK0i03c/xv0BHH/iGLIxEXU/MebPP7STH7YcfAmr1rk5m1P20y7f3Rm3MwPeGPCDPLz8xnevycXs7OtMQvnJ/H5Jx/SI/4VJrz9If4BgQzv928uXDhvjcm5eJE69Rryr6eeu+a5atR5kMGvj2Hmh4sY8sY40n89zOhhA637L1w4z4gBvSgZEsr4mXMZM20OPr6+jBjQi99/z3PMDZDbprd7O4/LKzblypVjyZIlBbZPnDjRBb35e1n+/U6Wf7/zmvs/+nIjAOVKBVwzpv4D5embkGxNMMa8+z/6PPkotaqW5cfdR6xxLRtF0KxBVboOfJfWD1WzaeO3s9m88+kfK+MOHcvk7U+/4+Wnm1u3hZcPofsTDxP5r7f4+WDGzV2oyG16ffw0m5/jh4zkqQ7N+GX3TqrXisQwDBZ/Op9Osd1o2KQZAC+/+gaxHZuxesUy2jz2BACPdXoSgG1bfrjmuTp2esr65+DQMJ548lneGtqP33/Po2hRT349dIBzZ8/w5HM9KRkSCkDXuB70ebYTJ46nU6p0Wbteu8jdxuUVm82bN7Nt2zbrz//973/p2LEjr776Krm5uS7smdyItVv28kTLSPz9fDCZTPyrVSRmr6J8+8PP1pjggBJMH9aVbsM+4EL29f9OS5W08Nijtfhu0x9ttGtcg/2/nqRt4+rsWjKSn758nenDY/D383HIdYn8lfPnzgFQws8CwPFjv5J5+iS1H4yyxnh6eVG9ZiQ/bf/xls9z9kwW36xYxv3Va1K0qCcApcvdh5/lHlZ8+Tl5eXnk5FxkxZefU658RYJDSt3GVYkjqWLjPC5PbHr06MGePXsA2LdvH126dMHHx4dPP/2UQYMGubh3cj2xr8ymqEcRjq4eS9b6SUwZ2oXO/d5h/5GT1pi3Rz3FOwvWsHnnob9s6/3EOE6tncC+5W9x5vxFeo6ab913X5kgypUK4J/Na/P8sLl0H/4htauWZf64bg67NpHCGIbBe1P/HxEP1ObeCpUAyDx1+b/3ewJsq5v3BASSefrUTZ8jacZ/eKJlFDHRTTmRcYzXEv6oYPv4+JIw+V2+WbGUJ1o0oFOrRmzesI4RY6fgUdTlRXi5Fi33dhqXJzZ79uyhVq1aAHz66ac0btyY+fPnk5SUxMKFC697fE5ODmfOnLH5GJfyHdxruWJk7/b4+/nQpsdkGj01lskfrmLeuOeoVikMgF5dm+DnW+yGJvgOGr+QqJgx/OvlWVQoE8SY/v+07itiMlHM7Em3YXP5fstevtv0Mz1fn0fTeuFUvjf4L1oVsa+ZE0dzYN/PDByeWGCf6ap/eQzDuKXfsv/R9Wn+814yo/7fDIoU8WDiW8MwDAOAnJyLTB49kqrVazJuxgeMmTaHcuUr8PqgPuTkXLxOyyLuz+XpvWEYXLp0Cbi83Ds6OhqAsmXLcvLkyb86FIDExERef/11m20eIQ/iWaqe/TsrNsqXCaJnlybUefxNdu1LB2Dbnl9pVKciPTo3pu9byTR9sAr1apQna/0km2O/nzeI5GU/0H34XOu246fOcvzUWfYcOM7p386zck4/Rr+TQvrJM6SfzCIvL59fDv0xv+an/ccBKBsaoHk34hSzJo1mw/erSZzyHkHBIdbt/oFBAGSePkVAUEnr9qzM09zjf+05atdiuccfyz3+lC57L2XvLc+zT7Rm946t3F+9JqtXLCMj/SjjZrxPkSKXfzcdMDyRru0as37NNzRupkUXdyINIzmPyys2devW5c0332Tu3LmsXr2adu3aAZcf3BcSEnKdo2HIkCFkZWXZfIqGRDq62wL4FPMC4NL//SZ5RX6+QZH/+xL3H7uAep0Tqd9lNPW7jKZjnxkAxL4yh5FTv7hm21f+T8DL83LuvS5tH56eHpQvE2SNuVKpOXTstJ2uSKRwhmEwc+Jo1n67ircmzSI0rLTN/pBSpfEPCCLth1Trtry8PLb/uIn7q9e87XNfaQ8uV2xMpiI2/1AW+b/5F5cuGYW2Ia6nOTbO4/KKzaRJk3jyySf5/PPPGTp0KJUqXR6zXrBgAQ0bNrzu8WazGbPZbLPNVMTDIX11N77eXlQs+8dvl/eVDuSBKqXJPHOBw+mZ+Pv5UDbUn1LBlydIVrnvcqJ5/NQZjp86y+4D6fxyKIOpr3VlyIRFnMo6T4dHHqBZg3D++dJMAA6n277v69yFHAD2HT7Brxm/AdDqoQiCA/zYtOMg5y7kULViKG+91JG1W/Zak5ZV63ezeechZo18koHjFlKkiIlJr3Tiq3W7bKo4Io4wY2Ii3361jKEJE/H28bXOqfEpXhyzuRgmk4kO/4rh0w/fI6xMOcLKlOOTD9/DbC5GkxZtrO1knjpJ5ulTHP318nyzg/t+xtvHl5IhoZTws7Bn53b27NpOxAO1KV6iBOlHf2Xe7BmUKl2W+6s9AECtug2YM2MSMyYm0v6fXbhkGCyYNwcPDw8eqF3X+TdHbohyEucxGYZxR6b4Fy9exMPDA09Pz5s+1rv2iw7okft5OLIyy999qcD2uYtTeWHEhzzVvj7vjIotsP/NmUt5a9ZSACqWK8mbfR8jqlYFivuY2Xv4BJM+WGldKn61cqUC2L10lM0D+hrXrczrL7bn/gqhmD2LcuT4b/x3VRrjZ68g69wfzwkpVdLChMH/olmD+zmfncvy73fyyoTPyDxzwR6342/hx5Sxru7CXal949qFbn9pyOs0b9MB+OMBfSmLF3Lu3BmqVK1Oz5eHWCcYA8yfPZOPkmZds50De3/mncnj2L93DxcvZuMfEERk/YZ0fro7gSX/mEu2ZWMqHyXN4tD+XzCZilCh8v3Edu9tTX7k5lQJcfzqykoDltmlnV/Gt7l+0N/cHZvY3A4lNiKFU2IjUpAzEpvKA1Ps0s7P4zSH6npcPhSVn5/PxIkT+eSTTzh06FCBZ9ecPq35EyIicnfTUJTzuHzy8Ouvv86ECRPo1KkTWVlZ9OvXj3/+858UKVKEkSNHurp7IiIichdxeWIzb9483nnnHQYMGEDRokXp2rUr7777LsOHDyc1NfX6DYiIiNzhtCrKeVye2KSnp1OjRg0AihcvTlbW5ZckRkdH8+WXX7qyayIiInZhMtnnI9fn8sSmTJkyHDt2DIBKlSqxfPnlJ9Ru3LixwDJuERERkb/i8sTmH//4BytXrgTgpZdeYtiwYVSuXJmnn36a5557zsW9ExERuX1Fipjs8pHrc/mqqNGjR1v//MQTT1CmTBnWrl1LpUqV6NChgwt7JiIiYh8aRnIelyc2V2vQoAENGjRwdTdERETkLuSSxGbx4sU3HKuqjYiI3O20osl5XJLYdOzY8YbiTCYT+fn5ju2MiIiIgymvcR6XJDaXLl1yxWlFRERcQhUb53H5qigRERERe3FZYrNq1SoiIiI4c+ZMgX1ZWVlUq1aNb7/91gU9ExERsS89edh5XJbYTJo0ie7du+Pn51dgn8VioUePHkycONEFPRMREbEvPXnYeVyW2Pz444+0bn3t16+3bNmSTZs2ObFHIiIicrdz2XNsjh8/jqen5zX3Fy1alBMnTjixRyIiIo6hYSTncVnFpnTp0mzbtu2a+7du3UqpUqWc2CMRERHH0FCU87gssWnbti3Dhw/n4sWLBfZlZ2czYsQIoqOjXdAzERERuVu5bCjqtdde47PPPqNKlSq8+OKLhIeHYzKZ2LVrF9OmTSM/P5+hQ4e6qnsiIiJ2o6Eo53FZYhMSEsLatWvp2bMnQ4YMwTAM4PJffqtWrZg+fTohISGu6p6IiIjdKK9xHpe+BPPee+9l6dKlZGZm8ssvv2AYBpUrV8bf39+V3RIREZG71B3x5GF/f38efPBB6tWrp6RGRETcjise0Pf777/z2muvUb58eby9valQoQKjRo2yea2RYRiMHDmSsLAwvL29adq0KTt27LBpJycnhz59+hAUFISvry8dOnTgyJEjNjGZmZnExsZisViwWCzExsby22+/2cQcOnSI9u3b4+vrS1BQEH379iU3N/fmbuQNuCMSGxEREXfmilVRY8aMYebMmUydOpVdu3YxduxYxo0bx5QpU6wxY8eOZcKECUydOpWNGzcSGhpKixYtOHv2rDUmPj6eRYsWkZyczJo1azh37hzR0dE2L6mOiYkhLS2NlJQUUlJSSEtLIzY21ro/Pz+fdu3acf78edasWUNycjILFy6kf//+t35Tr8FkXJnc4ka8a7/o6i6I3JF+TBnr6i6I3HGqhPg4/Bz1E1fbpZ31Q5rccGx0dDQhISG899571m2PP/44Pj4+zJ07F8MwCAsLIz4+nsGDBwOXqzMhISGMGTOGHj16kJWVRcmSJZk7dy6dO3cG4OjRo5QtW5alS5fSqlUrdu3aRUREBKmpqdSvXx+A1NRUoqKi+OmnnwgPD2fZsmVER0dz+PBhwsLCAEhOTiYuLo6MjIxC30Jwq1SxERERuUvk5ORw5swZm09OTk6hsQ899BArV65kz549wOUn/q9Zs4a2bdsCsH//ftLT02nZsqX1GLPZTJMmTVi7di0AmzZtIi8vzyYmLCyM6tWrW2PWrVuHxWKxJjUADRo0wGKx2MRUr17dmtQAtGrVipycHLu/ZUCJjYiIiIPZaygqMTHROo/lyicxMbHQcw4ePJiuXbty//334+npSe3atYmPj6dr164ApKenAxRYgRwSEmLdl56ejpeXV4H5r1fHBAcHFzh/cHCwTczV5/H398fLy8saYy8uXRUlIiLyd2Cv59gMGTKEfv362Wwzm82Fxn788cd8+OGHzJ8/n2rVqpGWlkZ8fDxhYWE888wz1+ybYRjX7e/VMYXF30qMPSixERERuUuYzeZrJjJXGzhwIK+88gpdunQBoEaNGhw8eJDExESeeeYZQkNDgcvVlD+/wigjI8NaXQkNDSU3N5fMzEybqk1GRgYNGza0xhw/frzA+U+cOGHTzvr16232Z2ZmkpeXZ/dn1mkoSkRExMFcsSrqwoULFCli+8+8h4eHdbl3+fLlCQ0NZcWKFdb9ubm5rF692pq0REZG4unpaRNz7Ngxtm/fbo2JiooiKyuLDRs2WGPWr19PVlaWTcz27ds5duyYNWb58uWYzWYiIyNv7sKuQxUbERERB3PFKxXat2/PW2+9Rbly5ahWrRpbtmxhwoQJPPfcc9Y+xcfHk5CQQOXKlalcuTIJCQn4+PgQExMDgMVioVu3bvTv35/AwEACAgIYMGAANWrUoHnz5gBUrVqV1q1b0717d2bNmgXACy+8QHR0NOHh4QC0bNmSiIgIYmNjGTduHKdPn2bAgAF0797driuiQImNiIiIW5oyZQrDhg2jV69eZGRkEBYWRo8ePRg+fLg1ZtCgQWRnZ9OrVy8yMzOpX78+y5cvp0SJEtaYiRMnUrRoUTp16kR2djbNmjUjKSkJDw8Pa8y8efPo27evdfVUhw4dmDp1qnW/h4cHX375Jb169aJRo0Z4e3sTExPD+PHj7X7deo6NyN+InmMjUpAznmPz0Pjv7NLOmgEP26Udd6aKjYiIiIPp7d7Oo8nDIiIi4jZUsREREXEwVWycR4mNiIiIgymvcR4lNiIiIg6mio3zaI6NiIiIuA1VbERERBxMBRvnUWIjIiLiYBqKch4NRYmIiIjbUMVGRETEwVSwcR4lNiIiIg5WRJmN02goSkRERNyGKjYiIiIOpoKN8yixERERcTCtinIeJTYiIiIOVkR5jdNojo2IiIi4DVVsREREHExDUc6jxEZERMTBlNc4j4aiRERExG2oYiMiIuJgJlSycRYlNiIiIg6mVVHOo6EoERERcRuq2IiIiDiYVkU5jxIbERERB1Ne4zwaihIRERG3oYqNiIiIgxVRycZplNiIiIg4mPIa51FiIyIi4mCaPOw8mmMjIiIibkMVGxEREQdTwcZ5lNiIiIg4mCYPO4+GokRERMRtqGIjIiLiYKrXOI8SGxEREQfTqijn0VCUiIiIuA1VbERERBysiAo2TnNDic3ixYtvuMEOHTrccmdERETckYainOeGEpuOHTveUGMmk4n8/Pzb6Y+IiIjILbuhxObSpUuO7oeIiIjbUsHGeTTHRkRExME0FOU8t5TYnD9/ntWrV3Po0CFyc3Nt9vXt29cuHRMREXEXmjzsPDed2GzZsoW2bdty4cIFzp8/T0BAACdPnsTHx4fg4GAlNiIiIuIyN/0cm5dffpn27dtz+vRpvL29SU1N5eDBg0RGRjJ+/HhH9FFEROSuZjKZ7PKR67vpxCYtLY3+/fvj4eGBh4cHOTk5lC1blrFjx/Lqq686oo8iIiJ3NZOdPnJ9N53YeHp6WrPGkJAQDh06BIDFYrH+WURERMQVbnqOTe3atfnhhx+oUqUKjzzyCMOHD+fkyZPMnTuXGjVqOKKPIiIid7UiGkZympuu2CQkJFCqVCkA3njjDQIDA+nZsycZGRm8/fbbdu+giIjI3c5kss9Hru+mKzZ169a1/rlkyZIsXbrUrh0SERERuVV6QJ+IiIiDaUWT89x0YlO+fPm//Avat2/fbXVIRETE3SivcZ6bTmzi4+Ntfs7Ly2PLli2kpKQwcOBAe/VLRERE5KbddGLz0ksvFbp92rRp/PDDD7fdIREREXejVVHOc9Oroq6lTZs2LFy40F7NiYiIuA2tinIeuyU2CxYsICAgwF7NiYiIuA1XvVLh119/5amnniIwMBAfHx9q1arFpk2brPsNw2DkyJGEhYXh7e1N06ZN2bFjh00bOTk59OnTh6CgIHx9fenQoQNHjhyxicnMzCQ2NhaLxYLFYiE2NpbffvvNJubQoUO0b98eX19fgoKC6Nu3b4EXadvDLT2g78831zAM0tPTOXHiBNOnT7dr50REROTWZGZm0qhRIx555BGWLVtGcHAwe/fu5Z577rHGjB07lgkTJpCUlESVKlV48803adGiBbt376ZEiRLA5bm1X3zxBcnJyQQGBtK/f3+io6PZtGkTHh4eAMTExHDkyBFSUlIAeOGFF4iNjeWLL74AID8/n3bt2lGyZEnWrFnDqVOneOaZZzAMgylTptj1uk2GYRg3c8DIkSNtEpsiRYpQsmRJmjZtyv3332/Xzt2qi7+7ugcid6az2fpyiFytZAnHP/mkz6Jddmlnyj+q3nDsK6+8wvfff893331X6H7DMAgLCyM+Pp7BgwcDl6szISEhjBkzhh49epCVlUXJkiWZO3cunTt3BuDo0aOULVuWpUuX0qpVK3bt2kVERASpqanUr18fgNTUVKKiovjpp58IDw9n2bJlREdHc/jwYcLCwgBITk4mLi6OjIwM/Pz8bue22Ljpv82RI0fa7eQiIiJ/B/Z6jk1OTg45OTk228xmM2azuUDs4sWLadWqFf/6179YvXo1pUuXplevXnTv3h2A/fv3k56eTsuWLW3aatKkCWvXrqVHjx5s2rSJvLw8m5iwsDCqV6/O2rVradWqFevWrcNisViTGoAGDRpgsVhYu3Yt4eHhrFu3jurVq1uTGoBWrVqRk5PDpk2beOSRR+xyf+AW5th4eHiQkZFRYPupU6esJSkRERGxv8TEROs8liufxMTEQmP37dvHjBkzqFy5Mv/73//497//Td++ffnggw8ASE9PBy6/0PrPQkJCrPvS09Px8vLC39//L2OCg4MLnD84ONgm5urz+Pv74+XlZY2xl5uu2Fxr5ConJwcvL6/b7pCIiIi7KWKnFU1DhgyhX79+NtsKq9YAXLp0ibp165KQkABcniO7Y8cOZsyYwdNPP22Nu7qaZBjGdStMV8cUFn8rMfZww4nN5MmTgcsde/fddylevLh1X35+Pt9+++0dM8dGRETkTmKvxOZaw06FKVWqFBERETbbqlatan00S2hoKHC5mnLl5dYAGRkZ1upKaGgoubm5ZGZm2lRtMjIyaNiwoTXm+PHjBc5/4sQJm3bWr19vsz8zM5O8vLwClZzbdcOJzcSJE4HL2dXMmTNthp28vLy47777mDlzpl07JyIiIremUaNG7N6922bbnj17uPfee4HLr0gKDQ1lxYoV1K5dG4Dc3FxWr17NmDFjAIiMjMTT05MVK1bQqVMnAI4dO8b27dsZO3YsAFFRUWRlZbFhwwbq1asHwPr168nKyrImP1FRUbz11lscO3bMmkQtX74cs9lMZGSkXa/7hhOb/fv3A/DII4/w2WefFRhvExERkcK54iWYL7/8Mg0bNiQhIYFOnTqxYcMG3n77bd5++21rn+Lj40lISKBy5cpUrlyZhIQEfHx8iImJAcBisdCtWzf69+9PYGAgAQEBDBgwgBo1atC8eXPgchWodevWdO/enVmzZgGXl3tHR0cTHh4OQMuWLYmIiCA2NpZx48Zx+vRpBgwYQPfu3e26IgpuYY7N119/bdcOiIiIuDt7DUXdjAcffJBFixYxZMgQRo0aRfny5Zk0aRJPPvmkNWbQoEFkZ2fTq1cvMjMzqV+/PsuXL7c+wwYuj9gULVqUTp06kZ2dTbNmzUhKSrIZuZk3bx59+/a1rp7q0KEDU6dOte738PDgyy+/pFevXjRq1Ahvb29iYmIYP3683a/7pp9j88QTT1C3bl1eeeUVm+3jxo1jw4YNfPrpp3bt4K3Qc2xECqfn2IgU5Izn2Axcsvv6QTdgXHS4XdpxZze93Hv16tW0a9euwPbWrVvz7bff2qVTIiIi7kTvinKem05Tz507V+iybk9PT86cOWOXTomIiLgTvd3beW66YlO9enU+/vjjAtuTk5MLLCsTERGRy//Y2uMj13fTFZthw4bx+OOPs3fvXh599FEAVq5cyfz581mwYIHdOygiIiJyo246senQoQOff/45CQkJLFiwAG9vb2rWrMmqVavsvmRLRETEHWgkynluaSp4u3btrBOIf/vtN+bNm0d8fDw//vgj+fn5du2giIjI3U5zbJznlofsVq1axVNPPUVYWBhTp06lbdu2/PDDD/bsm4iIiMhNuamKzZEjR0hKSmL27NmcP3+eTp06kZeXx8KFCzVxWERE5BpUsHGeG67YtG3bloiICHbu3MmUKVM4evQoU6ZMcWTfRERE3EIRk30+cn03XLFZvnw5ffv2pWfPnlSuXNmRfRIRERG5JTdcsfnuu+84e/YsdevWpX79+kydOpUTJ044sm8iIiJuoYjJZJePXN8NJzZRUVG88847HDt2jB49epCcnEzp0qW5dOkSK1as4OzZs47sp4iIyF1Lr1RwnpteFeXj48Nzzz3HmjVr2LZtG/3792f06NEEBwfToUMHR/RRRERE5Ibc1hOaw8PDGTt2LEeOHOGjjz6yV59ERETciiYPO49d3tXu4eFBx44d6dixoz2aExERcSsmlJU4i10SGxEREbk2VVucRy8LFREREbehio2IiIiDqWLjPEpsREREHMyktdpOo6EoERERcRuq2IiIiDiYhqKcR4mNiIiIg2kkynk0FCUiIiJuQxUbERERB9MLLJ1HiY2IiIiDaY6N82goSkRERNyGKjYiIiIOppEo51FiIyIi4mBF9BJMp1FiIyIi4mCq2DiP5tiIiIiI21DFRkRExMG0Ksp5lNiIiIg4mJ5j4zwaihIRERG3oYqNiIiIg6lg4zxKbERERBxMQ1HOo6EoERERcRuq2IiIiDiYCjbOo8RGRETEwTQ84jy61yIiIuI2VLERERFxMJPGopxGiY2IiIiDKa1xHiU2IiIiDqbl3s6jOTYiIiLiNlSxERERcTDVa5xHiY2IiIiDaSTKeTQUJSIiIm5DFRsREREH03Jv51FiIyIi4mAaHnEe3WsRERFxG6rYiIiIOJiGopxHiY2IiIiDKa1xHg1FiYiIiNtQxUZERMTBNBTlPKrYiIiIOFgRO31uR2JiIiaTifj4eOs2wzAYOXIkYWFheHt707RpU3bs2GFzXE5ODn369CEoKAhfX186dOjAkSNHbGIyMzOJjY3FYrFgsViIjY3lt99+s4k5dOgQ7du3x9fXl6CgIPr27Utubu5tXlVBSmxEREQczGQy2eVzqzZu3Mjbb7/NAw88YLN97NixTJgwgalTp7Jx40ZCQ0Np0aIFZ8+etcbEx8ezaNEikpOTWbNmDefOnSM6Opr8/HxrTExMDGlpaaSkpJCSkkJaWhqxsbHW/fn5+bRr147z58+zZs0akpOTWbhwIf3797/la7oWk2EYht1bdbGLv7u6ByJ3prPZ+nKIXK1kCcfPyli0Nd0u7fzjgdCbPubcuXPUqVOH6dOn8+abb1KrVi0mTZqEYRiEhYURHx/P4MGDgcvVmZCQEMaMGUOPHj3IysqiZMmSzJ07l86dOwNw9OhRypYty9KlS2nVqhW7du0iIiKC1NRU6tevD0BqaipRUVH89NNPhIeHs2zZMqKjozl8+DBhYWEAJCcnExcXR0ZGBn5+fna5P6CKjYiIiMOZ7PTJycnhzJkzNp+cnJy/PHfv3r1p164dzZs3t9m+f/9+0tPTadmypXWb2WymSZMmrF27FoBNmzaRl5dnExMWFkb16tWtMevWrcNisViTGoAGDRpgsVhsYqpXr25NagBatWpFTk4OmzZtuqF7eKOU2IiIiDiYyWSfT2JionUey5VPYmLiNc+bnJzM5s2bC41JT79cRQoJCbHZHhISYt2Xnp6Ol5cX/v7+fxkTHBxcoP3g4GCbmKvP4+/vj5eXlzXGXrQqSkRE5C4xZMgQ+vXrZ7PNbDYXGnv48GFeeuklli9fTrFixa7Z5tVzdwzDuO58nqtjCou/lRh7UMVGRETEwYpgssvHbDbj5+dn87lWYrNp0yYyMjKIjIykaNGiFC1alNWrVzN58mSKFi1qraBcXTHJyMiw7gsNDSU3N5fMzMy/jDl+/HiB8584ccIm5urzZGZmkpeXV6CSc7vu2MTm+PHjjBo1ytXdEBERuW32Goq6Gc2aNWPbtm2kpaVZP3Xr1uXJJ58kLS2NChUqEBoayooVK6zH5Obmsnr1aho2bAhAZGQknp6eNjHHjh1j+/bt1pioqCiysrLYsGGDNWb9+vVkZWXZxGzfvp1jx45ZY5YvX47ZbCYyMvKm7+dfuWNXRf3444/UqVPHZjnZjdKqKJHCaVWUSEHOWBW1ZHvBisatiK5+e9WNpk2bWldFAYwZM4bExETmzJlD5cqVSUhI4JtvvmH37t2UKFECgJ49e7JkyRKSkpIICAhgwIABnDp1ik2bNuHh4QFAmzZtOHr0KLNmzQLghRde4N577+WLL74ALi/3rlWrFiEhIYwbN47Tp08TFxdHx44dmTJlym1d09VcNsdm69atf7l/9+7dTuqJiIiIY5nu0LdFDRo0iOzsbHr16kVmZib169dn+fLl1qQGYOLEiRQtWpROnTqRnZ1Ns2bNSEpKsiY1APPmzaNv377W1VMdOnRg6tSp1v0eHh58+eWX9OrVi0aNGuHt7U1MTAzjx4+3+zW5rGJTpEgRTCYThZ3+ynaTyaSKjYgdqWIjUpAzKjZLd2TYpZ221QquPhJbLqvYBAYGMmbMGJo1a1bo/h07dtC+fXsn90pERETuZi5LbCIjIzl69Cj33ntvoft/++23Qqs5IiIid5sid+hQlDtyWWLTo0cPzp8/f8395cqVY86cOU7skYiIiGPo5d7Oc8euirodmmMjUjjNsREpyBlzbJbvOmGXdlpWLWmXdtzZHfscGxEREZGbpVcqiIiIONidutzbHSmxERERcbAiymucRkNRIiIi4jZUsREREXEwDUU5j8srNikpKaxZs8b687Rp06hVqxYxMTEF3iYqIiJyN3LFSzD/rlye2AwcOJAzZ84AsG3bNvr370/btm3Zt28f/fr1c3HvRERE5G7i8qGo/fv3ExERAcDChQuJjo4mISGBzZs307ZtWxf3TkRE5PZpKMp5XF6x8fLy4sKFCwB89dVX1jeDBgQEWCs5IiIid7MiJvt85PpcXrF56KGH6NevH40aNWLDhg18/PHHAOzZs4cyZcq4uHciIiJyN3F5xWbq1KkULVqUBQsWMGPGDEqXLg3AsmXLaN26tYt7J++9M4uYTo8T9WBtmj4cRXyfXhzYv88m5sL58yS8OYoWjzamXp0H6Ni+DZ8kz7eJWfDJx3SLi6VhvTrUrBZeaDXuTFYWr74ykEb1I2lUP5JXXxmoqp3cEdI2/8Cgl3vxWOumPFS3Gt9+s9Jmv2EYvDdrGo+1bsqjjerw4gtx7Nv7S6FtGYZB/749Cm1n9087ie/1PK2bNqBts4aMeWsEFy7YvlMvPf0og17uRfOH6tKuWSMmjUsgLy/Xvhcsdmey0//k+lye2JQrV44lS5bw448/0q1bN+v2iRMnMnnyZBf2TAB+2LiBzl2fZO5HnzDrnTn8np/Pv7t3sw4fAowbk8jaNd+RMHoci75YylOxcYxOeJOvV31ljbl4MZuGjR6mW/d/X/Ncrwzqz+6ffmL6rHeZPutddv/0E0NfGeTQ6xO5EdnZ2VSqHE6/QUML3T/v/ff4eP779Bs0lHff/5jAwCBe7v08Fwp50e8n8z8o9B+okycyiO/VjTJly/F20kf8v8mzOLD3FxJG/nHO/Px8Br3Ui4vZ2Ux/dy4jE8bzzaoVTJ04zn4XKw6hVVHO4/LEZvPmzWzbts3683//+186duzIq6++Sm6ufgtxtRlvv8dj//gnlSpVJvz++xn1ZiLHjh1l184d1pgff0yj/WMdebBefUqXLsMTnTpTJfx+dmzfbo156uk4unV/gQdq1iz0PPv27uX7Nd8xYtSb1KxVm5q1ajPi9Tf4dvXXBSpEIs4W1ehhXuj1Ek0ebVFgn2EYfPrRXJ5+9gWaPNqCCpUqM/T1BHIuXmR5ypc2sT/v+YmP53/AkOFvFGjn++++oWhRT/oNfo1y95WnarUa9Bv8Gt+sWsGRwwcB2JC6lgP79zL8jTFUub8qD9aP4sX4gXzx+QLOnzvnkGsX+zDZ6SPX5/LEpkePHuzZsweAffv20aVLF3x8fPj0008ZNEi/rd9pzp09C4CfxWLdVrtOHVZ/vYrjx49jGAYb1qdy8MB+GjZ66Ibb/fHHLZQoUYIHHvgj8XmgZi1KlChBWtoW+12AiJ0d/fUIp06dpF6DRtZtXl5e1KpTl+1b//hv9+LFbF4fOpCXBw4lMKjgG5rzcvPw9PSkSJE//m/ZbC4GwNa0zQDs2JZG+YqVCCoZbI2pF9WI3Nxcdv+0AxG5AxKbPXv2UKtWLQA+/fRTGjduzPz580lKSmLhwoXXPT4nJ4czZ87YfHJychzc678nwzAYPzaR2nUiqVy5inX7K0Neo0LFSrR8tDF1a1WnV4/neXXYCOpE1r3htk+dPIl/QGCB7f4BgZw6edIu/RdxhNOnLv/3GRBo+9+vf2CgdR/A5P83huoP1Obhpo8W2k6dB+tz6uRJ5n8wm7y8XM6cyWLWtEkA1u/AqVMnCQgIsjnOz8+Cp6envid3uCImk10+cn0uT2wMw+DSpUvA5eXeV55dU7ZsWU7ewBc1MTERi8Vi8xk3JtGhff67SnxzFD/v2cOYcRNsts+fN5etW9P4z9QZfPTJQvoPfIWEN14ndd3am2q/0O+sYaACrNwVrv4P2DCs29asXsXmH9bTt//gax5eoWIlhr7+Fsnzkmj+UF0ea9WEsDJlCQgMtKniFPY9MQwDk/7Ru6NpKMp5XL7cu27durz55ps0b96c1atXM2PGDODyg/tCQkKue/yQIUMKPKHY8DA7pK9/Z4lvvcE336xi9vsfEhIaat1+8eJFJk+ayMTJU2ncpCkAVcLvZ/fuXbw/5z0aRDW8ofYDg4I4fepUge2ZmacJDCpYyRG5UwQEXq6gnD55kqA/DTFlnj5NwP9VITf9sJ5fjxymzSNRNse+NiieB2pFMvXtJABato6mZetoTp86STFvb0wmEx/Pe59SpS8/+iIwMIid27fatHHmTBa///57gYqRyN+VyxObSZMm8eSTT/L5558zdOhQKlWqBMCCBQto2PD6/yiazWbMZttE5uLvDunq35JhGCS+9QarVq7gvaS5lClT1mb/77//zu+/51HkqidHFSniwSXDuOHz1KxZm7Nnz7Jt61ZqPPAAAFu3/sjZs2epVav27V+IiIOElS5DYGAQG9evpcr9VQHIy8slbfMP/LvP5V+6nnrmedo/9oTNcU936UiffoNp9HDTAm1eSZaW/PczvLzMPFj/ckJUrUYtPpj9NidPnrAmURtT1+Ll5UX4/dUcdYliDyq3OI3LE5sHHnjAZlXUFePGjcPDw8MFPZI/S3jjdZYtXcKkKdPx9fHl5IkTABQvUYJixYpRvHhx6j5Yjwnjx2E2F6NUWBibNm5kyeLPGTDoFWs7J0+c4OTJkxw+dAiAX37eg4+PL6VKlcJyzz1UqFiRRg89zKgRrzFs5CgARo0cRuMmj3Bf+QrOv3CRP7lw4Ty/Hj5k/fnYr0f4efcuSlgshIaG8a+uscyd8w5lyt1L2bL38sGctzEXK0bL1u0ACAwqWeiE4ZDQUoSV/uNBpAs/nkf1mrXx9vZh4/q1TP/P/+PffV6mRAk/AOo1aMh95SvyxvBX6N13AGfOZDHtP+Np3/EJfIsXd/BdkNuhZ9A4j8kwbuLX6ruEKjb2U7NaeKHbR72ZyGP/+CdwOWn5z6QJrFu7hjNZWZQKC+PxJzoT+0ycddx/xrQpzJw+9S/byfrtN0Ynvsnqr1cB0OSRRxkydDh+fn6OuLS/pbPZ+nLcis0/bKDvv58tsL1N9GMMHZmAYRjMfns6iz/7hLNnzxBR/QH6DXqNCpUqX7PNh+pWI2H8ZBo3bWbd9sbwIaz7fjXZFy5Q7r7ydH3qWVq362BzXHr6USaMfpNNG9djLmamRat29I4fiJeXl/0u+G+mZAnH/46/fm+WXdqpX9Fy/aC/OZcnNvn5+UycOJFPPvmEQ4cOFXh2zenTp2+6TSU2IoVTYiNSkDMSmw377JPY1KugxOZ6XL4q6vXXX2fChAl06tSJrKws+vXrxz//+U+KFCnCyJEjXd09ERGR26ZVUc7j8opNxYoVmTx5Mu3atfu/h7GlWbelpqYyf/786zdyFVVsRAqnio1IQc6o2Gy0U8XmQVVsrsvlFZv09HRq1KgBQPHixcnKuvyXHx0dzZdffvlXh4qIiNwdVLJxGpcnNmXKlOHYsWMAVKpUieXLlwOwcePGAsu4RURE7kZ6u7fzuDyx+cc//sHKlSsBeOmllxg2bBiVK1fm6aef5rnnnnNx70RERG6f3u7tPC6fY3O11NRU1q5dS6VKlejQocP1DyiE5tiIFE5zbEQKcsYcm00Hztilncj79PiL67njEht7UGIjUjglNiIFOSOx2WynxKaOEpvrcsmThxcvXnzDsbdatREREbljaBjJaVyS2HTs2PGG4kwmE/n5+Y7tjIiIiLgNlyQ2ly5dcsVpRUREXEIrmpzH5S/BFBERcXda0eQ8LlvuvWrVKiIiIjhzpuCEqqysLKpVq8a3337rgp6JiIjI3cplic2kSZPo3r17oW9utlgs9OjRg4kTJ7qgZyIiIvalBw87j8sSmx9//JHWrVtfc3/Lli3ZtGmTE3skIiLiIMpsnMZlic3x48fx9PS85v6iRYty4sQJJ/ZIRERE7nYuS2xKly7Ntm3brrl/69atlCpVyok9EhERcQy9K8p5XJbYtG3bluHDh3Px4sUC+7KzsxkxYgTR0dEu6JmIiIh96V1RzuOyVyocP36cOnXq4OHhwYsvvkh4eDgmk4ldu3Yxbdo08vPz2bx5MyEhITfdtl6pIFI4vVJBpCBnvFJh+5FzdmmnepnidmnHnbn0XVEHDx6kZ8+e/O9//+NKN0wmE61atWL69Oncd999t9SuEhuRwimxESlIiY17uSNegpmZmckvv/yCYRhUrlwZf3//22pPiY1I4ZTYiBTklMTmVzslNqWV2FzPHfHkYX9/fx588EFXd0NERMQhNPHXeVw2eVhERETE3u6Iio2IiIg704om51FiIyIi4mDKa5xHQ1EiIiLiNlSxERERcTSVbJxGiY2IiIiDaVWU82goSkRExA0lJiby4IMPUqJECYKDg+nYsSO7d++2iTEMg5EjRxIWFoa3tzdNmzZlx44dNjE5OTn06dOHoKAgfH196dChA0eOHLGJyczMJDY2FovFgsViITY2lt9++80m5tChQ7Rv3x5fX1+CgoLo27cvubm5dr9uJTYiIiIO5op3Ra1evZrevXuTmprKihUr+P3332nZsiXnz5+3xowdO5YJEyYwdepUNm7cSGhoKC1atODs2bPWmPj4eBYtWkRycjJr1qzh3LlzREdHk5+fb42JiYkhLS2NlJQUUlJSSEtLIzY21ro/Pz+fdu3acf78edasWUNycjILFy6kf//+t35Tr+GOePKwvenJwyKF05OHRQpyxpOH96RfsEs7VUJ9bvnYEydOEBwczOrVq2ncuDGGYRAWFkZ8fDyDBw8GLldnQkJCGDNmDD169CArK4uSJUsyd+5cOnfuDMDRo0cpW7YsS5cupVWrVuzatYuIiAhSU1OpX78+AKmpqURFRfHTTz8RHh7OsmXLiI6O5vDhw4SFhQGQnJxMXFwcGRkZ+Pn53ead+YMqNiIiIo5mstPnNmRlZQEQEBAAwP79+0lPT6dly5bWGLPZTJMmTVi7di0AmzZtIi8vzyYmLCyM6tWrW2PWrVuHxWKxJjUADRo0wGKx2MRUr17dmtQAtGrVipycHDZt2nR7F3YVTR4WERG5S+Tk5JCTk2OzzWw2Yzab//I4wzDo168fDz30ENWrVwcgPT0dgJCQEJvYkJAQDh48aI3x8vIq8A7HkJAQ6/Hp6ekEBwcXOGdwcLBNzNXn8ff3x8vLyxpjL6rYiIiIOJjJTv9LTEy0TtC98klMTLzu+V988UW2bt3KRx99VLBvV03eMQyjwLarXR1TWPytxNiDEhsREREHs9fk4SFDhpCVlWXzGTJkyF+eu0+fPixevJivv/6aMmXKWLeHhoYCFKiYZGRkWKsroaGh5ObmkpmZ+Zcxx48fL3DeEydO2MRcfZ7MzEzy8vIKVHJulxIbERGRu4TZbMbPz8/mc61hKMMwePHFF/nss89YtWoV5cuXt9lfvnx5QkNDWbFihXVbbm4uq1evpmHDhgBERkbi6elpE3Ps2DG2b99ujYmKiiIrK4sNGzZYY9avX09WVpZNzPbt2zl27Jg1Zvny5ZjNZiIjI2/zrtjSqiiRvxGtihIpyBmrovZmZNulnYrB3jcc26tXL+bPn89///tfwsPDrdstFgve3pfbGTNmDImJicyZM4fKlSuTkJDAN998w+7duylRogQAPXv2ZMmSJSQlJREQEMCAAQM4deoUmzZtwsPDA4A2bdpw9OhRZs2aBcALL7zAvffeyxdffAFcXu5dq1YtQkJCGDduHKdPnyYuLo6OHTsyZcoUu9ybK5TYiPyNKLERKcgpic0JOyU2JW88sbnW3JU5c+YQFxcHXK7qvP7668yaNYvMzEzq16/PtGnTrBOMAS5evMjAgQOZP38+2dnZNGvWjOnTp1O2bFlrzOnTp+nbty+LFy8GoEOHDkydOpV77rnHGnPo0CF69erFqlWr8Pb2JiYmhvHjx1934vPNUmIj8jeixEakIHdNbP6utNxbRETEwfSuKOdRYiMiIuJgdl7RLH9Bq6JERETEbahiIyIi4mAq2DiPEhsRERFHU2bjNEpsREREHEyTh51Hc2xERETEbahiIyIi4mBaFeU8SmxEREQcTHmN82goSkRERNyGKjYiIiIOpqEo51FiIyIi4nDKbJxFQ1EiIiLiNlSxERERcTANRTmPEhsREREHU17jPBqKEhEREbehio2IiIiDaSjKeZTYiIiIOJjeFeU8SmxEREQcTXmN02iOjYiIiLgNVWxEREQcTAUb51FiIyIi4mCaPOw8GooSERERt6GKjYiIiINpVZTzKLERERFxNOU1TqOhKBEREXEbqtiIiIg4mAo2zqPERkRExMG0Ksp5NBQlIiIibkMVGxEREQfTqijnUWIjIiLiYBqKch4NRYmIiIjbUGIjIiIibkNDUSIiIg6moSjnUWIjIiLiYJo87DwaihIRERG3oYqNiIiIg2koynmU2IiIiDiY8hrn0VCUiIiIuA1VbERERBxNJRunUWIjIiLiYFoV5TwaihIRERG3oYqNiIiIg2lVlPMosREREXEw5TXOo8RGRETE0ZTZOI3m2IiIiIjbUMVGRETEwbQqynmU2IiIiDiYJg87j4aiRERExG2YDMMwXN0JcU85OTkkJiYyZMgQzGazq7sjcsfQd0PEcZTYiMOcOXMGi8VCVlYWfn5+ru6OyB1D3w0Rx9FQlIiIiLgNJTYiIiLiNpTYiIiIiNtQYiMOYzabGTFihCZHilxF3w0Rx9HkYREREXEbqtiIiIiI21BiIyIiIm5DiY2IiIi4DSU2csNMJhOff/65q7shckfR90LkzqLERgBIT0+nT58+VKhQAbPZTNmyZWnfvj0rV650ddcAMAyDkSNHEhYWhre3N02bNmXHjh2u7pa4uTv9e/HZZ5/RqlUrgoKCMJlMpKWlubpLIi6nxEY4cOAAkZGRrFq1irFjx7Jt2zZSUlJ45JFH6N27t6u7B8DYsWOZMGECU6dOZePGjYSGhtKiRQvOnj3r6q6Jm7obvhfnz5+nUaNGjB492tVdEblzGPK316ZNG6N06dLGuXPnCuzLzMy0/hkwFi1aZP150KBBRuXKlQ1vb2+jfPnyxmuvvWbk5uZa96elpRlNmzY1ihcvbpQoUcKoU6eOsXHjRsMwDOPAgQNGdHS0cc899xg+Pj5GRESE8eWXXxbav0uXLhmhoaHG6NGjrdsuXrxoWCwWY+bMmbd59SKFu9O/F3+2f/9+AzC2bNlyy9cr4i6KujivEhc7ffo0KSkpvPXWW/j6+hbYf88991zz2BIlSpCUlERYWBjbtm2je/fulChRgkGDBgHw5JNPUrt2bWbMmIGHhwdpaWl4enoC0Lt3b3Jzc/n222/x9fVl586dFC9evNDz7N+/n/T0dFq2bGndZjabadKkCWvXrqVHjx63cQdECrobvhciUjglNn9zv/zyC4ZhcP/999/0sa+99pr1z/fddx/9+/fn448/tv4f+KFDhxg4cKC17cqVK1vjDx06xOOPP06NGjUAqFChwjXPk56eDkBISIjN9pCQEA4ePHjT/Ra5nrvheyEihdMcm7854/8ePG0ymW762AULFvDQQw8RGhpK8eLFGTZsGIcOHbLu79evH88//zzNmzdn9OjR7N2717qvb9++vPnmmzRq1IgRI0awdevW657v6j4ahnFL/Ra5nrvpeyEitpTY/M1VrlwZk8nErl27buq41NRUunTpQps2bViyZAlbtmxh6NCh5ObmWmNGjhzJjh07aNeuHatWrSIiIoJFixYB8Pzzz7Nv3z5iY2PZtm0bdevWZcqUKYWeKzQ0FPijcnNFRkZGgSqOiD3cDd8LEbkGl87wkTtC69atb3qS5Pjx440KFSrYxHbr1s2wWCzXPE+XLl2M9u3bF7rvlVdeMWrUqFHoviuTh8eMGWPdlpOTo8nD4lB3+vfizzR5WOQPqtgI06dPJz8/n3r16rFw4UJ+/vlndu3axeTJk4mKiir0mEqVKnHo0CGSk5PZu3cvkydPtv7WCZCdnc2LL77IN998w8GDB/n+++/ZuHEjVatWBSA+Pp7//e9/7N+/n82bN7Nq1SrrvquZTCbi4+NJSEhg0aJFbN++nbi4OHx8fIiJibH/DRHhzv9ewOVJzmlpaezcuROA3bt3k5aWVqC6KfK34urMSu4MR48eNXr37m3ce++9hpeXl1G6dGmjQ4cOxtdff22N4aplrQMHDjQCAwON4sWLG507dzYmTpxo/c00JyfH6NKli1G2bFnDy8vLCAsLM1588UUjOzvbMAzDePHFF42KFSsaZrPZKFmypBEbG2ucPHnymv27dOmSMWLECCM0NNQwm81G48aNjW3btjniVohY3enfizlz5hhAgc+IESMccDdE7g4mw/i/WXIiIiIidzkNRYmIiIjbUGIjIiIibkOJjYiIiLgNJTYiIiLiNpTYiIiIiNtQYiMiIiJuQ4mNiIiIuA0lNiJuaOTIkdSqVcv6c1xcHB07dnR6Pw4cOIDJZCItLc3p5xaRvyclNiJOFBcXh8lkwmQy4enpSYUKFRgwYADnz5936Hn/85//kJSUdEOxSkZE5G5W1NUdEPm7ad26NXPmzCEvL4/vvvuO559/nvPnzzNjxgybuLy8PDw9Pe1yTovFYpd2RETudKrYiDiZ2WwmNDSUsmXLEhMTw5NPPsnnn39uHT6aPXs2FSpUwGw2YxgGWVlZvPDCCwQHB+Pn58ejjz7Kjz/+aNPm6NGjCQkJoUSJEnTr1o2LFy/a7L96KOrSpUuMGTOGSpUqYTabKVeuHG+99RYA5cuXB6B27dqYTCaaNm1qPW7OnDlUrVqVYsWKcf/99zN9+nSb82zYsIHatWtTrFgx6taty5YtW+x450RErk8VGxEX8/b2Ji8vD4BffvmFTz75hIULF+Lh4QFAu3btCAgIYOnSpVgsFmbNmkWzZs3Ys2cPAQEBfPLJJ4wYMYJp06bx8MMPM3fuXCZPnkyFChWuec4hQ4bwzjvvMHHiRB566CGOHTvGTz/9BFxOTurVq8dXX31FtWrV8PLyAuCdd95hxIgRTJ06ldq1a7Nlyxa6d++Or68vzzzzDOfPnyc6OppHH32UDz/8kP379/PSSy85+O6JiFzFxS/hFPlbeeaZZ4zHHnvM+vP69euNwMBAo1OnTsaIESMMT09PIyMjw7p/5cqVhp+fn3Hx4kWbdipWrGjMmjXLMAzDiIqKMv7973/b7K9fv75Rs2bNQs975swZw2w2G++8806hfdy/f78BGFu2bLHZXrZsWWP+/Pk229544w0jKirKMAzDmDVrlhEQEGCcP3/eun/GjBmFtiUi4igaihJxsiVLllC8eHGKFStGVFQUjRs3ZsqUKQDce++9lCxZ0hq7adMmzp07R2BgIMWLF7d+9u/fz969ewHYtWsXUVFRNue4+uc/27VrFzk5OTRr1uyG+3zixAkOHz5Mt27dbPrx5ptv2vSjZs2a+Pj43FA/REQcQUNRIk72yCOPMGPGDDw9PQkLC7OZIOzr62sTe+nSJUqVKsU333xToJ177rnnls7v7e1908dcunQJuDwcVb9+fZt9V4bMDMO4pf6IiNiTEhsRJ/P19aVSpUo3FFunTh3S09MpWrQo9913X6ExVatWJTU1laefftq6LTU19ZptVq5cGW9vb1auXMnzzz9fYP+VOTX5+fnWbSEhIZQuXZp9+/bx5JNPFtpuREQEc+fOJTs725o8/VU/REQcQUNRInew5s2bExUVRceOHfnf//7HgQMHWLt2La+99ho//PADAC+99BKzZ89m9uzZ7NmzhxEjRrBjx45rtlmsWDEGDx7MoEGD+OCDD9i7dy+pqam89957AAQHB+Pt7U1KSgrHjx8nKysLuPzQv8TERP7zn/+wZ88etm3bxpw5c5gwYQIAMTExFClShG7durFz506WLl3K+PHjHXyHRERsKbERuYOZTCaWLl1K48aNee6556hSpQpdunThwIEDhISEANC5c2eGDx/O4MGDiYyM5ODBg/Ts2fMv2x02bBj9+/dn+PDhVK1alc6dO5ORkQFA0aJFmTx5MrNmzSIsLIzHHnsMgOeff553332XpKQkatSoQZMmTUhKSrIuDy9evDhffPEFO3fupHbt2gwdOpQxY8Y48O6IiBRkMjQwLiIiIm5CFRsRERFxG0psRERExG0osRERERG3ocRGRERE3IYSGxEREXEbSmxERETEbSixEREREbehxEZERETchhIbERERcRtKbERERMRtKLERERERt6HERkRERNzG/wdQrUOWXhHMIwAAAABJRU5ErkJggg==", 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", 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", 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P7r77bv3vf//Ta6+9plWrVmnDhg164403JMm5XbvdrpUrV+r222/X6NGjddtttykiIkJjxoxx/qfnnnvu0cKFC3X69Gn169dP9erVU0xMjMtnFfbv369vv/22zPeAv7+/DMO45M8CoKJwTTVwDXh6emr48OEaPny4jh49qs8//1yjR49WQkKC9u7de8k7NwQFBUk6ex3t+f+w/fLLL85rNEvrSkPGufLy8i54tvpC1xjOmjVLHTt21NSpU13GS69vrC5K93vUqFHq3bv3BWvOv+b3XEFBQVq/fn2Z8fMD1tVsx8/PT+np6UpPT9f+/fudZ62TkpL03//+97L7dKFwl5eXp1tuucVl7ELzFhQUpNzc3DLjv/zyi8t+XG5bXl5eqlWrlqT/f63yuHHjXOoOHTrkco335dZZ+r1ZGUr3a8qUKRe9Y0jpf56Cg4M1efJkTZ48WXv27NEnn3yiZ599VgcOHFBGRsZlt5WXl+fyrsrp06d1+PBh5/5d6bEqSd7e3ioqKiqzjUOHDl3wumsz1wObOaZvvPFGSWfv/x0ZGXnF21y4cKEKCwv18ccfKyoqyjmek5NTprZZs2aaO3euDMPQt99+qxkzZuhvf/ubfHx89Oyzz0qS7rvvPt13330qKirS2rVrNX78eCUnJ+umm25SbGysgoOD5ePj47xW+3zlvZYduBqcqQausdq1a+u3v/2tnnjiCR05csR5312bzSZJZc4clb7NP2vWLJfxDRs2aPv27c4PYbVp00Y2m03z5s1zqVu7du1lP1B0LovF4uyl1LfffntF9xa+lqKjo9WoUSNt3rz5gu8gtGrVSv7+/hd9fqdOnXT8+PEyH5ycM2dOhWwnNDRU/fv310MPPaQdO3bo5MmTl92n2bNnuzzOysrS7t27L/tBMknq0qWLtm3bpm+++cZl/P3335fFYlGnTp1cxj/++GOXdw2OHz+uTz/9VHfffbc8PDwkXfh7YfHixRd8+12SPvjgA5cP7u7evVtZWVlX1P/lXOz4aN++vWrXrq1t27ZddH5K3+U4V/369fXkk08qLi6uzGt2MefPz7///W+dPn3auX9XeqxKZ+/+8e2337rUff/995e8ZKm8zBzT8fHx8vDwKBPIr2Sbkly2axiG3n777Us+p0WLFnr11VdVu3btC86LzWZThw4d9NJLL0mS8+4yiYmJ+umnnxQUFHTB74Ga/ItpUHNwphq4BpKSkhQTE6NWrVrpxhtv1O7duzV58mRFRUWpUaNGks6erZGk1157TampqbJarYqOjlZ0dLQGDx6sKVOm6IYbblD37t2ddxSIjIzU008/Lens5RLDhw/X+PHjVadOHd1///3at2+f0tPTFR4efsW3jEpMTNQLL7ygMWPGqEOHDtqxY4f+9re/qUGDBhe8+0lVmjZtmrp3766EhAT1799fdevW1ZEjR7R9+3Z98803mj9//kWf269fP7366qvq16+fxo4dq0aNGmnJkiVaunRpubfTpk0bJSYmqnnz5qpTp462b9+umTNnKjY29oruI71x40YNHDhQDz74oPbu3avnnntOdevWvaJfZPL000/r/fffV48ePfS3v/1NUVFRWrx4sd5880394Q9/KHP3Fw8PD8XFxWn48OE6c+aMXnrpJR07dkzp6enOmsTERM2YMUO33nqrmjdvruzsbE2cOPGilwIcOHBA999/vwYNGqSCggKNGTNG3t7eGjVq1GX7v5yYmBhJ0j//+U/5+/vL29tbDRo0UFBQkKZMmaLU1FQdOXJEv/3tb513sdm8ebMOHjyoqVOnqqCgQJ06dVJycrJuvfVW+fv7a8OGDcrIyLjoOxDn+/jjj+Xp6am4uDjn3T9atGjh/BzDlR6r0tk7kTzyyCMaMmSIHnjgAe3evVsTJkxwnhmuSGaO6ZtuukmjR4/WCy+8oFOnTumhhx6S3W7Xtm3bdOjQIZfvl3PFxcXJy8tLDz30kJ555hn9+uuvmjp1qvLz813qFi1apDfffFO9evVSw4YNZRiGPv74Yx09elRxcXGSzt59ZN++ferSpYvq1auno0eP6rXXXpPValWHDh0kScOGDdNHH32ke+65R08//bSaN2+uM2fOaM+ePVq2bJlGjBihNm3aXHQ/T548qSVLlkg6eyJCklauXKlDhw7Jz89P3bt3v7IXG+6tCj8kCdQIpXdmOP9T5aV69Ohx2bt/TJo0yWjXrp0RHBxseHl5GfXr1zcGDBhg7Nq1y+V5o0aNMiIiIowbbrjB5dPnJSUlxksvvWQ0btzYsFqtRnBwsPHII48Ye/fudXn+mTNnjBdffNGoV6+e4eXlZTRv3txYtGiR0aJFC5dP+ZfeUWD+/Pll9qeoqMgYOXKkUbduXcPb29u48847jYULF5b5dH/pXSQmTpzo8vyLrftCr2NUVJTRo0ePMj1IMp544gmXsYttb/PmzUafPn2MkJAQw2q1GmFhYUbnzp2Nt956q8x6z7dv3z7jgQceMGrVqmX4+/sbDzzwgJGVlVXm7h9Xup1nn33WaNWqlVGnTh3DZrMZDRs2NJ5++mnj0KFDl+yj9LVZtmyZkZKSYtSuXdvw8fEx7r33XuOHH35wqe3QoYNx2223XXA9u3fvNpKTk42goCDDarUa0dHRxsSJE13uglL6Or700ktGenq683vljjvuMJYuXeqyvvz8fGPAgAFGSEiI4evra/zmN78xVq1addE7VMycOdMYOnSoceONNxo2m824++67jY0bN7qss7x3/zCMs3eWadCggeHh4VFmjlauXGn06NHDCAwMNKxWq1G3bl2jR48ezu/DX3/91Xj88ceN5s2bGwEBAYaPj48RHR1tjBkzxuUuMxdS2nN2draRlJTk/H556KGHjP3797vUXs2xOmHCBKNhw4aGt7e30apVK+PLL7+86Gt7oWP1Ys5/7a70mL6U999/37jrrrsMb29vo1atWsYdd9zh8vpfaF2ffvqp0aJFC8Pb29uoW7eu8ac//cn47LPPXH62/fe//zUeeugh4+abbzZ8fHwMu91utG7d2pgxY4ZzPYsWLTK6d+9u1K1b1/Dy8jJCQkKMe++911i1apXL9k6cOGH85S9/MaKjow0vLy/DbrcbzZo1M55++mkjLy/vkvtXelxc6OtKXyPAYhhXcJNdADXWzp07deutt2rMmDEaPXp0VbeDC5gxY4YeffRRbdiwQa1atarqdnCetLQ0paen6+DBg1ybC+CiuPwDuI5s3rxZH3zwgdq1a6eAgADt2LFDEyZMUEBAgAYMGFDV7QEAcN0iVAPXET8/P23cuFHTp0/X0aNHZbfb1bFjR40dO/ait9UDAADmcfkHAAAAYBK31AMAAABMIlQDAAAAJhGqAQAAAJP4oGIFOnPmjH755Rf5+/ub+pWyAAAAqByGYej48eOKiIi44l+MdiUI1RXol19+UWRkZFW3AQAAgMvYu3fvRX9DbHkQqiuQv7+/pLO/bCMwMLCKu0FlczgcWrZsmeLj42W1Wqu6HVQy5tu9MN/uhfl2L0eOHFGDBg2cua2iEKorUOklH/7+/goICKjiblDZHA6HfH19FRAQwA9hN8B8uxfm270w3+7F4XBIUoVfqssHFQEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJM+qbgDXh6Skqu7g2rNapdTUqu4CAABUB5ypBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASVUaqqdOnarmzZsrICBAAQEBio2N1WeffeZc3r9/f1ksFpevtm3buqyjqKhITz31lIKDg+Xn56eePXtq3759LjX5+flKSUmR3W6X3W5XSkqKjh496lKzZ88eJSUlyc/PT8HBwRo6dKiKi4srbd8BAABw/ajSUF2vXj39/e9/18aNG7Vx40Z17txZ9913n7Zu3eqs6datm3Jzc51fS5YscVnHsGHDtGDBAs2dO1erV6/WiRMnlJiYqJKSEmdNcnKycnJylJGRoYyMDOXk5CglJcW5vKSkRD169FBhYaFWr16tuXPn6qOPPtKIESMq/0UAAABAjedZlRtPSkpyeTx27FhNnTpVa9eu1W233SZJstlsCgsLu+DzCwoKNH36dM2cOVNdu3aVJM2aNUuRkZH6/PPPlZCQoO3btysjI0Nr165VmzZtJElvv/22YmNjtWPHDkVHR2vZsmXatm2b9u7dq4iICEnSpEmT1L9/f40dO1YBAQGV9RIAAADgOlClofpcJSUlmj9/vgoLCxUbG+scX7FihUJCQlS7dm116NBBY8eOVUhIiCQpOztbDodD8fHxzvqIiAjFxMQoKytLCQkJWrNmjex2uzNQS1Lbtm1lt9uVlZWl6OhorVmzRjExMc5ALUkJCQkqKipSdna2OnXqdMGei4qKVFRU5Hx87NgxSZLD4ZDD4aiYF6aGsFqruoNrz2o9O8fuNtfuqnSemW/3wHy7F+bbvVTWPFd5qP7uu+8UGxurX3/9VbVq1dKCBQvUtGlTSVL37t314IMPKioqSjt37tTzzz+vzp07Kzs7WzabTXl5efLy8lKdOnVc1hkaGqq8vDxJUl5enjOEnyskJMSlJjQ01GV5nTp15OXl5ay5kPHjxys9Pb3M+PLly+Xr63t1L0QNl5pa1R1UnczMzKpuAdcQ8+1emG/3wny7h5MnT1bKeqs8VEdHRysnJ0dHjx7VRx99pNTUVK1cuVJNmzZV3759nXUxMTFq1aqVoqKitHjxYvXu3fui6zQMQxaLxfn43L+bqTnfqFGjNHz4cOfjY8eOKTIyUp06dVJQUNDFd/o6dM5UuQ2r1aHk5EzFxcXJ6o6n6t2Mw+FQZibz7S6Yb/fCfLuXw4cPV8p6qzxUe3l56ZZbbpEktWrVShs2bNBrr72madOmlakNDw9XVFSUfvjhB0lSWFiYiouLlZ+f73K2+sCBA2rXrp2zZv/+/WXWdfDgQefZ6bCwMK1bt85leX5+vhwOR5kz2Oey2Wyy2Wxlxq1Wq9sdlO78jpk7zrc7Y77dC/PtXphv91BZc1zt7lNtGIbLdcrnOnz4sPbu3avw8HBJUsuWLWW1Wl3ersnNzdWWLVucoTo2NlYFBQVav369s2bdunUqKChwqdmyZYtyc3OdNcuWLZPNZlPLli0rfB8BAABwfanSM9WjR49W9+7dFRkZqePHj2vu3LlasWKFMjIydOLECaWlpemBBx5QeHi4du3apdGjRys4OFj333+/JMlut2vAgAEaMWKEgoKCFBgYqJEjR6pZs2bOu4E0adJE3bp106BBg5xnvwcPHqzExERFR0dLkuLj49W0aVOlpKRo4sSJOnLkiEaOHKlBgwZx5w8AAABcVpWG6v379yslJUW5ubmy2+1q3ry5MjIyFBcXp1OnTum7777T+++/r6NHjyo8PFydOnXSvHnz5O/v71zHq6++Kk9PT/Xp00enTp1Sly5dNGPGDHl4eDhrZs+eraFDhzrvEtKzZ0+9/vrrzuUeHh5avHixhgwZovbt28vHx0fJycl6+eWXr92LAQAAgBqrSkP19OnTL7rMx8dHS5cuvew6vL29NWXKFE2ZMuWiNYGBgZo1a9Yl11O/fn0tWrTostsDAAAAzlftrqkGAAAAahpCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACZ5VnUDAFCT9O0rORxV3cW19emnVd0BAFR/nKkGAAAATKrSUD116lQ1b95cAQEBCggIUGxsrD777DPncsMwlJaWpoiICPn4+Khjx47aunWryzqKior01FNPKTg4WH5+furZs6f27dvnUpOfn6+UlBTZ7XbZ7XalpKTo6NGjLjV79uxRUlKS/Pz8FBwcrKFDh6q4uLjS9h0AAADXjyoN1fXq1dPf//53bdy4URs3blTnzp113333OYPzhAkT9Morr+j111/Xhg0bFBYWpri4OB0/fty5jmHDhmnBggWaO3euVq9erRMnTigxMVElJSXOmuTkZOXk5CgjI0MZGRnKyclRSkqKc3lJSYl69OihwsJCrV69WnPnztVHH32kESNGXLsXAwAAADVWlV5TnZSU5PJ47Nixmjp1qtauXaumTZtq8uTJeu6559S7d29J0nvvvafQ0FDNmTNHjz32mAoKCjR9+nTNnDlTXbt2lSTNmjVLkZGR+vzzz5WQkKDt27crIyNDa9euVZs2bSRJb7/9tmJjY7Vjxw5FR0dr2bJl2rZtm/bu3auIiAhJ0qRJk9S/f3+NHTtWAQEB1/BVAQAAQE1TbT6oWFJSovnz56uwsFCxsbHauXOn8vLyFB8f76yx2Wzq0KGDsrKy9Nhjjyk7O1sOh8OlJiIiQjExMcrKylJCQoLWrFkju93uDNSS1LZtW9ntdmVlZSk6Olpr1qxRTEyMM1BLUkJCgoqKipSdna1OnTpdsOeioiIVFRU5Hx87dkyS5HA45HCzTzJZrVXdwbVntZ6dY3eba3dVOs+l8+5O3PFbvHS+Ob7dA/PtXiprnqs8VH/33XeKjY3Vr7/+qlq1amnBggVq2rSpsrKyJEmhoaEu9aGhodq9e7ckKS8vT15eXqpTp06Zmry8PGdNSEhIme2GhIS41Jy/nTp16sjLy8tZcyHjx49Xenp6mfHly5fL19f3crt+XUlNreoOqk5mZmZVt4BrKDnZ/eZ7yZKq7qDqcHy7F+bbPZw8ebJS1lvloTo6Olo5OTk6evSoPvroI6WmpmrlypXO5RaLxaXeMIwyY+c7v+ZC9eWpOd+oUaM0fPhw5+Njx44pMjJSnTp1UlBQ0CV7vN707VvVHVx7VqtDycmZiouLk9UdT9W7GYfDoczMTM2ZEyeHw73me968qu7g2iudb45v98B8u5fDhw9XynqrPFR7eXnplltukSS1atVKGzZs0GuvvaY///nPks6eRQ4PD3fWHzhwwHlWOSwsTMXFxcrPz3c5W33gwAG1a9fOWbN///4y2z148KDLetatW+eyPD8/Xw6Ho8wZ7HPZbDbZbLYy41ar1e0OSnd+x8wd59udORxWtwvV7vztzfHtXphv91BZc1zt7lNtGIaKiorUoEEDhYWFubwVU1xcrJUrVzoDc8uWLWW1Wl1qcnNztWXLFmdNbGysCgoKtH79emfNunXrVFBQ4FKzZcsW5ebmOmuWLVsmm82mli1bVur+AgAAoOar0jPVo0ePVvfu3RUZGanjx49r7ty5WrFihTIyMmSxWDRs2DCNGzdOjRo1UqNGjTRu3Dj5+voqOTlZkmS32zVgwACNGDFCQUFBCgwM1MiRI9WsWTPn3UCaNGmibt26adCgQZo2bZokafDgwUpMTFR0dLQkKT4+Xk2bNlVKSoomTpyoI0eOaOTIkRo0aBB3/gAAAMBlVWmo3r9/v1JSUpSbmyu73a7mzZsrIyNDcXFxkqRnnnlGp06d0pAhQ5Sfn682bdpo2bJl8vf3d67j1Vdflaenp/r06aNTp06pS5cumjFjhjw8PJw1s2fP1tChQ513CenZs6def/1153IPDw8tXrxYQ4YMUfv27eXj46Pk5GS9/PLL1+iVAAAAQE1WpaF6+vTpl1xusViUlpamtLS0i9Z4e3trypQpmjJlykVrAgMDNWvWrEtuq379+lq0aNElawAAAIALqXbXVAMAAAA1DaEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMCkKg3V48eP11133SV/f3+FhISoV69e2rFjh0tN//79ZbFYXL7atm3rUlNUVKSnnnpKwcHB8vPzU8+ePbVv3z6Xmvz8fKWkpMhut8tutyslJUVHjx51qdmzZ4+SkpLk5+en4OBgDR06VMXFxZWy7wAAALh+VGmoXrlypZ544gmtXbtWmZmZOn36tOLj41VYWOhS161bN+Xm5jq/lixZ4rJ82LBhWrBggebOnavVq1frxIkTSkxMVElJibMmOTlZOTk5ysjIUEZGhnJycpSSkuJcXlJSoh49eqiwsFCrV6/W3Llz9dFHH2nEiBGV+yIAAACgxvOsyo1nZGS4PH733XcVEhKi7Oxs3XPPPc5xm82msLCwC66joKBA06dP18yZM9W1a1dJ0qxZsxQZGanPP/9cCQkJ2r59uzIyMrR27Vq1adNGkvT2228rNjZWO3bsUHR0tJYtW6Zt27Zp7969ioiIkCRNmjRJ/fv319ixYxUQEFAZLwEAAACuA1Uaqs9XUFAgSQoMDHQZX7FihUJCQlS7dm116NBBY8eOVUhIiCQpOztbDodD8fHxzvqIiAjFxMQoKytLCQkJWrNmjex2uzNQS1Lbtm1lt9uVlZWl6OhorVmzRjExMc5ALUkJCQkqKipSdna2OnXqVKbfoqIiFRUVOR8fO3ZMkuRwOORwOCrgFak5rNaq7uDas1rPzrG7zbW7Kp3n0nl3J+74LV463xzf7oH5di+VNc/VJlQbhqHhw4frN7/5jWJiYpzj3bt314MPPqioqCjt3LlTzz//vDp37qzs7GzZbDbl5eXJy8tLderUcVlfaGio8vLyJEl5eXnOEH6ukJAQl5rQ0FCX5XXq1JGXl5ez5nzjx49Xenp6mfHly5fL19f36l6AGi41tao7qDqZmZlV3QKuoeRk95vv8664cysc3+6F+XYPJ0+erJT1VptQ/eSTT+rbb7/V6tWrXcb79u3r/HtMTIxatWqlqKgoLV68WL17977o+gzDkMVicT4+9+9mas41atQoDR8+3Pn42LFjioyMVKdOnRQUFHTR3q5H50yT27BaHUpOzlRcXJys7niq3s04HA5lZmZqzpw4ORzuNd/z5lV1B9de6XxzfLsH5tu9HD58uFLWWy1C9VNPPaVPPvlEX331lerVq3fJ2vDwcEVFRemHH36QJIWFham4uFj5+fkuZ6sPHDigdu3aOWv2799fZl0HDx50np0OCwvTunXrXJbn5+fL4XCUOYNdymazyWazlRm3Wq1ud1C68ztm7jjf7szhsLpdqHbnb2+Ob/fCfLuHyprjKr37h2EYevLJJ/Xxxx/ryy+/VIMGDS77nMOHD2vv3r0KDw+XJLVs2VJWq9XlLZvc3Fxt2bLFGapjY2NVUFCg9evXO2vWrVungoICl5otW7YoNzfXWbNs2TLZbDa1bNmyQvYXAAAA16cqPVP9xBNPaM6cOfrPf/4jf39/57XLdrtdPj4+OnHihNLS0vTAAw8oPDxcu3bt0ujRoxUcHKz777/fWTtgwACNGDFCQUFBCgwM1MiRI9WsWTPn3UCaNGmibt26adCgQZo2bZokafDgwUpMTFR0dLQkKT4+Xk2bNlVKSoomTpyoI0eOaOTIkRo0aBB3/gAAAMAlVemZ6qlTp6qgoEAdO3ZUeHi482ve/13A5+Hhoe+++0733XefGjdurNTUVDVu3Fhr1qyRv7+/cz2vvvqqevXqpT59+qh9+/by9fXVp59+Kg8PD2fN7Nmz1axZM8XHxys+Pl7NmzfXzJkzncs9PDy0ePFieXt7q3379urTp4969eqll19++dq9IAAAAKiRqvRMtWEYl1zu4+OjpUuXXnY93t7emjJliqZMmXLRmsDAQM2aNeuS66lfv74WLVp02e0BAAAA56rSM9UAAADA9YBQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhkOlT/+OOPWrp0qU6dOiVJMgzDdFMAAABATVLuUH348GF17dpVjRs31r333qvc3FxJ0sCBAzVixIgKaxAAAACo7sodqp9++ml5enpqz5498vX1dY737dtXGRkZFdIcAAAAUBN4lveJy5Yt09KlS1WvXj2X8UaNGmn37t2mGwMAAABqinKfqS4sLHQ5Q13q0KFDstlsppoCAAAAapJyh+p77rlH77//vvOxxWLRmTNnNHHiRHXq1KlCmgMAAABqgnJf/jFx4kR17NhRGzduVHFxsZ555hlt3bpVR44c0ddff12RPQLVWt++ksNR1V1cW59+WtUdAABQvZT7THXTpk317bffqnXr1oqLi1NhYaF69+6tTZs26eabb67IHgEAAIBqrdxnqiUpLCxM6enpFdULAAAAUCOV+0z1u+++q/nz55cZnz9/vt577z1TTQEAAAA1SblD9d///ncFBweXGQ8JCdG4ceNMNQUAAADUJOW+/GP37t1q0KBBmfGoqCjt2bPHVFMAqrekpKru4NqzWqXU1KruAgBQXZX7THVISIi+/fbbMuObN29WUFCQqaYAAACAmqTcofp3v/udhg4dquXLl6ukpEQlJSX68ssv9cc//lG/+93vKrJHAAAAoFor9+UfL774onbv3q0uXbrI0/Psas6cOaN+/fpxTTUAAADcSrlDtZeXl+bNm6cXXnhBmzdvlo+Pj5o1a6aoqKiK7A8AAACo9kzdp1qSGjdurMaNG1dELwAAAECNVO5QXVJSohkzZuiLL77QgQMHdObMGZflX375penmAAAAgJqg3KH6j3/8o2bMmKEePXooJiZGFoulIvsCAAAAaoxyh+q5c+fq3//+t+69996K7AcAAACoccp9Sz0vLy/dcsstFdkLAAAAUCOVO1SPGDFCr732mgzDqMh+AAAAgBqn3Jd/rF69WsuXL9dnn32m2267TVar1WX5xx9/bLo5AAAAoCYo95nq2rVr6/7771eHDh0UHBwsu93u8nUlxo8fr7vuukv+/v4KCQlRr169tGPHDpcawzCUlpamiIgI+fj4qGPHjtq6datLTVFRkZ566ikFBwfLz89PPXv21L59+1xq8vPzlZKS4uwvJSVFR48edanZs2ePkpKS5Ofnp+DgYA0dOlTFxcVX/+IAAADArZT7TPW7775reuMrV67UE088obvuukunT5/Wc889p/j4eG3btk1+fn6SpAkTJuiVV17RjBkz1LhxY7344ouKi4vTjh075O/vL0kaNmyYPv30U82dO1dBQUEaMWKEEhMTlZ2dLQ8PD0lScnKy9u3bp4yMDEnS4MGDlZKSok8//VTS2VsE9ujRQzfeeKNWr16tw4cPKzU1VYZhaMqUKab3FQAAANcvU7/85fTp01qxYoV++uknJScny9/fX7/88osCAgJUq1atyz6/NOCWevfddxUSEqLs7Gzdc889MgxDkydP1nPPPafevXtLkt577z2FhoZqzpw5euyxx1RQUKDp06dr5syZ6tq1qyRp1qxZioyM1Oeff66EhARt375dGRkZWrt2rdq0aSNJevvttxUbG6sdO3YoOjpay5Yt07Zt27R3715FRERIkiZNmqT+/ftr7NixCggIMPNSAQAA4DpW7lC9e/dudevWTXv27FFRUZHi4uLk7++vCRMm6Ndff9Vbb7111essKCiQJAUGBkqSdu7cqby8PMXHxztrbDabOnTooKysLD322GPKzs6Ww+FwqYmIiFBMTIyysrKUkJCgNWvWyG63OwO1JLVt21Z2u11ZWVmKjo7WmjVrFBMT4wzUkpSQkKCioiJlZ2erU6dOZfotKipSUVGR8/GxY8ckSQ6HQw6H46r3vyY775J6t2C1Olz+xPXNnefbzX6cSZLzZ7i7/Sx3V8y3e6mseTb1y19atWqlzZs3KygoyDl+//33a+DAgVe9PsMwNHz4cP3mN79RTEyMJCkvL0+SFBoa6lIbGhqq3bt3O2u8vLxUp06dMjWlz8/Ly1NISEiZbYaEhLjUnL+dOnXqyMvLy1lzvvHjxys9Pb3M+PLly+Xr63vZfb6epKZWdQdVJzk5s6pbwDXkjvO9ZElVd1B1MjPdb77dGfPtHk6ePFkp6zV194+vv/5aXl5eLuNRUVH63//+d9Xre/LJJ/Xtt99q9erVZZad/9saDcO47G9wPL/mQvXlqTnXqFGjNHz4cOfjY8eOKTIyUp06dXL5j4Y76Nu3qju49qxWh5KTMzVnTpwcDjc8Ve9m3Hm+582r6g6uPYfDoczMTMXFxZW5uxWuP8y3ezl8+HClrLfcofrMmTMqKSkpM75v3z7nBwiv1FNPPaVPPvlEX331lerVq+ccDwsLk3T2LHJ4eLhz/MCBA86zymFhYSouLlZ+fr7L2eoDBw6oXbt2zpr9+/eX2e7Bgwdd1rNu3TqX5fn5+XI4HGXOYJey2Wyy2Wxlxq1Wq9sdlO78jpnDYXW7kOXO3HG+3ezHmQt3/Hnuzphv91BZc1zuW+rFxcVp8uTJzscWi0UnTpzQmDFjrvhXlxuGoSeffFIff/yxvvzySzVo0MBleYMGDRQWFubydkxxcbFWrlzpDMwtW7aU1Wp1qcnNzdWWLVucNbGxsSooKND69eudNevWrVNBQYFLzZYtW5Sbm+usWbZsmWw2m1q2bHmFrwoAAADcUbnPVL/66qvq1KmTmjZtql9//VXJycn64YcfFBwcrA8++OCK1vHEE09ozpw5+s9//iN/f3/ntct2u10+Pj6yWCwaNmyYxo0bp0aNGqlRo0YaN26cfH19lZyc7KwdMGCARowYoaCgIAUGBmrkyJFq1qyZ824gTZo0Ubdu3TRo0CBNmzZN0tlb6iUmJio6OlqSFB8fr6ZNmyolJUUTJ07UkSNHNHLkSA0aNIg7fwAAAOCSyh2qIyIilJOTow8++EDffPONzpw5owEDBujhhx+Wj4/PFa1j6tSpkqSOHTu6jL/77rvq37+/JOmZZ57RqVOnNGTIEOXn56tNmzZatmyZyyUmr776qjw9PdWnTx+dOnVKXbp00YwZM5z3qJak2bNna+jQoc67hPTs2VOvv/66c7mHh4cWL16sIUOGqH379vLx8VFycrJefvnl8rw8AAAAcCOm7lPt4+Oj3//+9/r9739frucbhnHZGovForS0NKWlpV20xtvbW1OmTLnkL2kJDAzUrFmzLrmt+vXra9GiRZftCQAAADhXuUP1+++/f8nl/fr1K++qAQAAgBrF1H2qz+VwOHTy5El5eXnJ19eXUA0AAAC3Ue67f+Tn57t8nThxQjt27NBvfvObK/6gIgAAAHA9KHeovpBGjRrp73//e5mz2AAAAMD1rEJDtXT2Lhq//PJLRa8WAAAAqLbKfU31J5984vLYMAzl5ubq9ddfV/v27U03BgAAANQU5Q7VvXr1cnlssVh04403qnPnzpo0aZLZvgAAAIAao9yh+syZMxXZBwAAAFBjVfg11QAAAIC7KfeZ6uHDh19x7SuvvFLezQAAAADVXrlD9aZNm/TNN9/o9OnTio6OliR9//338vDw0J133umss1gs5rsEAAAAqrFyh+qkpCT5+/vrvffeU506dSSd/YUwjz76qO6++26NGDGiwpoEAAAAqrNyX1M9adIkjR8/3hmoJalOnTp68cUXufsHAAAA3Eq5Q/WxY8e0f//+MuMHDhzQ8ePHTTUFAAAA1CTlDtX333+/Hn30UX344Yfat2+f9u3bpw8//FADBgxQ7969K7JHAAAAoFor9zXVb731lkaOHKlHHnlEDofj7Mo8PTVgwABNnDixwhoEAAAAqrtyh2pfX1+9+eabmjhxon766ScZhqFbbrlFfn5+FdkfAAAAUO2Z/uUvubm5ys3NVePGjeXn5yfDMCqiLwAAAKDGuOJQff6vJT98+LC6dOmixo0b695771Vubq4kaeDAgdxODwAAAG7likP1K6+8oiVLljgfP/3007JardqzZ498fX2d43379lVGRkbFdgkAAABUY1d8TXVcXJx++9vfKjc3VwMGDNCyZcu0dOlS1atXz6WuUaNG2r17d4U3CgAAAFRXV3ymukWLFlq/fr0+/fRTSVJhYaHLGepShw4dks1mq7gOAQAAgGruqj6oWKdOHS1cuFCSdM899+j99993LrNYLDpz5owmTpyoTp06VWiTAAAAQHVW7lvqTZw4UR07dtTGjRtVXFysZ555Rlu3btWRI0f09ddfV2SPAAAAQLVW7lvqNW3aVN9++61at26tuLg4FRYWqnfv3tq0aZNuvvnmiuwRAAAAqNbKdaba4XAoPj5e06ZNU3p6ekX3BAAAANQo5TpTbbVatWXLFlksloruBwAAAKhxyn35R79+/TR9+vSK7AUAAACokcr9QcXi4mL961//UmZmplq1aiU/Pz+X5a+88orp5gAAAICa4KpD9c8//6ybbrpJW7Zs0Z133ilJ+v77711quCwEAAAA7uSqQ3WjRo2Um5ur5cuXSzr7a8n/8Y9/KDQ0tMKbAwAAAGqCq76m2jAMl8efffaZCgsLK6whAAAAoKYp9wcVS50fsgEAAAB3c9Wh2mKxlLlmmmuoAQAA4M6u+ppqwzDUv39/2Ww2SdKvv/6qxx9/vMzdPz7++OOK6RAAAACo5q46VKempro8fuSRRyqsGQAAAKAmuupQ/e6771ZGHwAAAECNZfqDigAAAIC7I1QDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTqjxUf/XVV0pKSlJERIQsFosWLlzosrx///6yWCwuX23btnWpKSoq0lNPPaXg4GD5+fmpZ8+e2rdvn0tNfn6+UlJSZLfbZbfblZKSoqNHj7rU7NmzR0lJSfLz81NwcLCGDh2q4uLiythtAAAAXEeqPFQXFhaqRYsWev311y9a061bN+Xm5jq/lixZ4rJ82LBhWrBggebOnavVq1frxIkTSkxMVElJibMmOTlZOTk5ysjIUEZGhnJycpSSkuJcXlJSoh49eqiwsFCrV6/W3Llz9dFHH2nEiBEVv9MAAAC4rnhWdQPdu3dX9+7dL1ljs9kUFhZ2wWUFBQWaPn26Zs6cqa5du0qSZs2apcjISH3++edKSEjQ9u3blZGRobVr16pNmzaSpLfffluxsbHasWOHoqOjtWzZMm3btk179+5VRESEJGnSpEnq37+/xo4dq4CAgArcawAAAFxPqjxUX4kVK1YoJCREtWvXVocOHTR27FiFhIRIkrKzs+VwOBQfH++sj4iIUExMjLKyspSQkKA1a9bIbrc7A7UktW3bVna7XVlZWYqOjtaaNWsUExPjDNSSlJCQoKKiImVnZ6tTp05l+ioqKlJRUZHz8bFjxyRJDodDDoejwl+H6sxqreoOrj2r1eHyJ65v7jzfbvbjTJKcP8Pd7We5u2K+3UtlzXO1D9Xdu3fXgw8+qKioKO3cuVPPP/+8OnfurOzsbNlsNuXl5cnLy0t16tRxeV5oaKjy8vIkSXl5ec4Qfq6QkBCXmtDQUJflderUkZeXl7PmfOPHj1d6enqZ8eXLl8vX17dc+1tTpaZWdQdVJzk5s6pbwDXkjvN93hV3biUz0/3m250x3+7h5MmTlbLeah+q+/bt6/x7TEyMWrVqpaioKC1evFi9e/e+6PMMw5DFYnE+PvfvZmrONWrUKA0fPtz5+NixY4qMjFSnTp0UFBR06R27zpwzTW7DanUoOTlTc+bEyeFww1P1bsad53vevKru4NpzOBzKzMxUXFycrO74VpybYb7dy+HDhytlvdU+VJ8vPDxcUVFR+uGHHyRJYWFhKi4uVn5+vsvZ6gMHDqhdu3bOmv3795dZ18GDB51np8PCwrRu3TqX5fn5+XI4HGXOYJey2Wyy2Wxlxq1Wq9sdlO78jpnDYXW7kOXO3HG+3ezHmQt3/Hnuzphv91BZc1zld/+4WocPH9bevXsVHh4uSWrZsqWsVqvLWza5ubnasmWLM1THxsaqoKBA69evd9asW7dOBQUFLjVbtmxRbm6us2bZsmWy2Wxq2bLltdg1AAAA1FBVfqb6xIkT+vHHH52Pd+7cqZycHAUGBiowMFBpaWl64IEHFB4erl27dmn06NEKDg7W/fffL0my2+0aMGCARowYoaCgIAUGBmrkyJFq1qyZ824gTZo0Ubdu3TRo0CBNmzZNkjR48GAlJiYqOjpakhQfH6+mTZsqJSVFEydO1JEjRzRy5EgNGjSIO38AAADgkqo8VG/cuNHlzhql1yinpqZq6tSp+u677/T+++/r6NGjCg8PV6dOnTRv3jz5+/s7n/Pqq6/K09NTffr00alTp9SlSxfNmDFDHh4ezprZs2dr6NChzruE9OzZ0+Xe2B4eHlq8eLGGDBmi9u3by8fHR8nJyXr55Zcr+yUAAABADVflobpjx44yDOOiy5cuXXrZdXh7e2vKlCmaMmXKRWsCAwM1a9asS66nfv36WrRo0WW3BwAAAJyrxl1TDQAAAFQ3hGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJjkWdUNAACqt6Skqu7g2rNapdTUqu4CQE1CqK4E/ftXdQcAAAC4lrj8AwAAADCpykP1V199paSkJEVERMhisWjhwoUuyw3DUFpamiIiIuTj46OOHTtq69atLjVFRUV66qmnFBwcLD8/P/Xs2VP79u1zqcnPz1dKSorsdrvsdrtSUlJ09OhRl5o9e/YoKSlJfn5+Cg4O1tChQ1VcXFwZuw0AAIDrSJWH6sLCQrVo0UKvv/76BZdPmDBBr7zyil5//XVt2LBBYWFhiouL0/Hjx501w4YN04IFCzR37lytXr1aJ06cUGJiokpKSpw1ycnJysnJUUZGhjIyMpSTk6OUlBTn8pKSEvXo0UOFhYVavXq15s6dq48++kgjRoyovJ0HAADAdaHKr6nu3r27unfvfsFlhmFo8uTJeu6559S7d29J0nvvvafQ0FDNmTNHjz32mAoKCjR9+nTNnDlTXbt2lSTNmjVLkZGR+vzzz5WQkKDt27crIyNDa9euVZs2bSRJb7/9tmJjY7Vjxw5FR0dr2bJl2rZtm/bu3auIiAhJ0qRJk9S/f3+NHTtWAQEB1+DVAAAAQE1U5aH6Unbu3Km8vDzFx8c7x2w2mzp06KCsrCw99thjys7OlsPhcKmJiIhQTEyMsrKylJCQoDVr1shutzsDtSS1bdtWdrtdWVlZio6O1po1axQTE+MM1JKUkJCgoqIiZWdnq1OnTmX6KyoqUlFRkfPxsWPHJElWq0OSoyJfClRDZ+f5//+J6xvz7V5K59nhYL7dQek8M9/uobLmuVqH6ry8PElSaGioy3hoaKh2797trPHy8lKdOnXK1JQ+Py8vTyEhIWXWHxIS4lJz/nbq1KkjLy8vZ835xo8fr/T09DLjDz64XL6+vleyi7gOJCdnVnULuIaYb/eSmcl8uxPm2z2cPHmyUtZbrUN1KYvF4vLYMIwyY+c7v+ZC9eWpOdeoUaM0fPhw5+Njx44pMjJS8+d3khR0yf5Q81mtDiUnZ2rOnDg5HNaqbgeVjPl2L6XzHRcXJ6uV+b7eORwOZWYy3+7i8OHDlbLeah2qw8LCJJ09ixweHu4cP3DggPOsclhYmIqLi5Wfn+9ytvrAgQNq166ds2b//v1l1n/w4EGX9axbt85leX5+vhwOR5kz2KVsNptsNluZ8bP/4HJQuguHw0rIciPMt3uxWq2ELDfCfLuHyprjKr/7x6U0aNBAYWFhLm/HFBcXa+XKlc7A3LJlS1mtVpea3NxcbdmyxVkTGxurgoICrV+/3lmzbt06FRQUuNRs2bJFubm5zpply5bJZrOpZcuWlbqfAAAAqNmq/Ez1iRMn9OOPPzof79y5Uzk5OQoMDFT9+vU1bNgwjRs3To0aNVKjRo00btw4+fr6Kjk5WZJkt9s1YMAAjRgxQkFBQQoMDNTIkSPVrFkz591AmjRpom7dumnQoEGaNm2aJGnw4MFKTExUdHS0JCk+Pl5NmzZVSkqKJk6cqCNHjmjkyJEaNGgQd/4AAADAJVV5qN64caPLnTVKr1FOTU3VjBkz9Mwzz+jUqVMaMmSI8vPz1aZNGy1btkz+/v7O57z66qvy9PRUnz59dOrUKXXp0kUzZsyQh4eHs2b27NkaOnSo8y4hPXv2dLk3toeHhxYvXqwhQ4aoffv28vHxUXJysl5++eXKfgkAAABQw1V5qO7YsaMMw7jocovForS0NKWlpV20xtvbW1OmTNGUKVMuWhMYGKhZs2Zdspf69etr0aJFl+0ZAAAAOFe1vqYaAAAAqAkI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJM+qbgAAgOqqb1/J4ajqLq6tTz+t6g6Amokz1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhU7UN1WlqaLBaLy1dYWJhzuWEYSktLU0REhHx8fNSxY0dt3brVZR1FRUV66qmnFBwcLD8/P/Xs2VP79u1zqcnPz1dKSorsdrvsdrtSUlJ09OjRa7GLAAAAqOGqfaiWpNtuu025ubnOr++++865bMKECXrllVf0+uuva8OGDQoLC1NcXJyOHz/urBk2bJgWLFiguXPnavXq1Tpx4oQSExNVUlLirElOTlZOTo4yMjKUkZGhnJwcpaSkXNP9BAAAQM3kWdUNXAlPT0+Xs9OlDMPQ5MmT9dxzz6l3796SpPfee0+hoaGaM2eOHnvsMRUUFGj69OmaOXOmunbtKkmaNWuWIiMj9fnnnyshIUHbt29XRkaG1q5dqzZt2kiS3n77bcXGxmrHjh2Kjo6+YF9FRUUqKipyPj527JgkyWp1SHJU5EuAaujsPP//P3F9Y77dizvPt8P9dlmO/9tphzvuvBuqrHmuEaH6hx9+UEREhGw2m9q0aaNx48apYcOG2rlzp/Ly8hQfH++stdls6tChg7KysvTYY48pOztbDofDpSYiIkIxMTHKyspSQkKC1qxZI7vd7gzUktS2bVvZ7XZlZWVdNFSPHz9e6enpZcYffHC5fH19K/AVQHWWnJxZ1S3gGmK+3Ys7zveSJVXdQdXJzHS/+XZHJ0+erJT1VvtQ3aZNG73//vtq3Lix9u/frxdffFHt2rXT1q1blZeXJ0kKDQ11eU5oaKh2794tScrLy5OXl5fq1KlTpqb0+Xl5eQoJCSmz7ZCQEGfNhYwaNUrDhw93Pj527JgiIyM1f34nSUHl2l/UHFarQ8nJmZozJ04Oh7Wq20ElY77dizvP97x5Vd3BtedwOJSZmam4uDhZre413+7o8OHDlbLeah+qu3fv7vx7s2bNFBsbq5tvvlnvvfee2rZtK0myWCwuzzEMo8zY+c6vuVD95dZjs9lks9nKjJ/9AcxB6S4cDqvb/aPrzphv9+KO8+3OmdJqtRKq3UBlzXG1D9Xn8/PzU7NmzfTDDz+oV69eks6eaQ4PD3fWHDhwwHn2OiwsTMXFxcrPz3c5W33gwAG1a9fOWbN///4y2zp48GCZs+AAAFzPkpKquoNrz2qVUlOrugvUdDXi7h/nKioq0vbt2xUeHq4GDRooLCzM5Rqo4uJirVy50hmYW7ZsKavV6lKTm5urLVu2OGtiY2NVUFCg9evXO2vWrVungoICZw0AAABwMdX+TPXIkSOVlJSk+vXr68CBA3rxxRd17NgxpaamymKxaNiwYRo3bpwaNWqkRo0aady4cfL19VVycrIkyW63a8CAARoxYoSCgoIUGBiokSNHqlmzZs67gTRp0kTdunXToEGDNG3aNEnS4MGDlZiYeNEPKQIAgOtL377ud/eTTz+t6g6uH9U+VO/bt08PPfSQDh06pBtvvFFt27bV2rVrFRUVJUl65plndOrUKQ0ZMkT5+flq06aNli1bJn9/f+c6Xn31VXl6eqpPnz46deqUunTpohkzZsjDw8NZM3v2bA0dOtR5l5CePXvq9ddfv7Y7CwAAgBqp2ofquXPnXnK5xWJRWlqa0tLSLlrj7e2tKVOmaMqUKRetCQwM1KxZs8rbJgAAANxYjbumGgAAAKhuCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEnV/teUAwAAoHIkJVV1B9cPzlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASofo8b775pho0aCBvb2+1bNlSq1atquqWAAAAUM0Rqs8xb948DRs2TM8995w2bdqku+++W927d9eePXuqujUAAABUY4Tqc7zyyisaMGCABg4cqCZNmmjy5MmKjIzU1KlTq7o1AAAAVGOeVd1AdVFcXKzs7Gw9++yzLuPx8fHKysq64HOKiopUVFTkfFxQUPB/fztSWW2iWnHo5MmTkg5LslZ1M6h0zLd7Yb7dC/PtXs7mNMMwKnSthOr/c+jQIZWUlCg0NNRlPDQ0VHl5eRd8zvjx45Wenl5mfNGixpXSI6qfBQuqugNcS8y3e2G+3Qvz7X4OHz4su91eYesjVJ/HYrG4PDYMo8xYqVGjRmn48OHOx0ePHlVUVJT27NlToZOE6unYsWOKjIzU3r17FRAQUNXtoJIx3+6F+XYvzLd7KSgoUP369RUYGFih6yVU/5/g4GB5eHiUOSt94MCBMmevS9lsNtlstjLjdrudg9KNBAQEMN9uhPl2L8y3e2G+3csNN1TsRwv5oOL/8fLyUsuWLZWZmekynpmZqXbt2lVRVwAAAKgJOFN9juHDhyslJUWtWrVSbGys/vnPf2rPnj16/PHHq7o1AAAAVGOE6nP07dtXhw8f1t/+9jfl5uYqJiZGS5YsUVRU1BU932azacyYMRe8JATXH+bbvTDf7oX5di/Mt3uprPm2GBV9PxEAAADAzXBNNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVF+lN998Uw0aNJC3t7datmypVatWXbJ+5cqVatmypby9vdWwYUO99dZb16hTVISrme+PP/5YcXFxuvHGGxUQEKDY2FgtXbr0GnYLs672+C719ddfy9PTU7fffnvlNogKdbXzXVRUpOeee05RUVGy2Wy6+eab9c4771yjbmHW1c737Nmz1aJFC/n6+io8PFyPPvqoDh8+fI26hRlfffWVkpKSFBERIYvFooULF172ORWS1wxcsblz5xpWq9V4++23jW3bthl//OMfDT8/P2P37t0XrP/5558NX19f449//KOxbds24+233zasVqvx4YcfXuPOUR5XO99//OMfjZdeeslYv3698f333xujRo0yrFar8c0331zjzlEeVzvfpY4ePWo0bNjQiI+PN1q0aHFtmoVp5Znvnj17Gm3atDEyMzONnTt3GuvWrTO+/vrra9g1yutq53vVqlXGDTfcYLz22mvGzz//bKxatcq47bbbjF69el3jzlEeS5YsMZ577jnjo48+MiQZCxYsuGR9ReU1QvVVaN26tfH444+7jN16663Gs88+e8H6Z555xrj11ltdxh577DGjbdu2ldYjKs7VzveFNG3a1EhPT6/o1lAJyjvfffv2Nf7yl78YY8aMIVTXIFc735999plht9uNw4cPX4v2UMGudr4nTpxoNGzY0GXsH//4h1GvXr1K6xGV40pCdUXlNS7/uELFxcXKzs5WfHy8y3h8fLyysrIu+Jw1a9aUqU9ISNDGjRvlcDgqrVeYV575Pt+ZM2d0/PhxBQYGVkaLqEDlne93331XP/30k8aMGVPZLaIClWe+P/nkE7Vq1UoTJkxQ3bp11bhxY40cOVKnTp26Fi3DhPLMd7t27bRv3z4tWbJEhmFo//79+vDDD9WjR49r0TKusYrKa/xGxSt06NAhlZSUKDQ01GU8NDRUeXl5F3xOXl7eBetPnz6tQ4cOKTw8vNL6hTnlme/zTZo0SYWFherTp09ltIgKVJ75/uGHH/Tss89q1apV8vTkR2lNUp75/vnnn7V69Wp5e3trwYIFOnTokIYMGaIjR45wXXU1V575bteunWbPnq2+ffvq119/1enTp9WzZ09NmTLlWrSMa6yi8hpnqq+SxWJxeWwYRpmxy9VfaBzV09XOd6kPPvhAaWlpmjdvnkJCQiqrPVSwK53vkpISJScnKz09XY0bN75W7aGCXc3xfebMGVksFs2ePVutW7fWvffeq1deeUUzZszgbHUNcTXzvW3bNg0dOlR//etflZ2drYyMDO3cuVOPP/74tWgVVaAi8hqnV65QcHCwPDw8yvyv9sCBA2X+d1MqLCzsgvWenp4KCgqqtF5hXnnmu9S8efM0YMAAzZ8/X127dq3MNlFBrna+jx8/ro0bN2rTpk168sknJZ0NXYZhyNPTU8uWLVPnzp2vSe+4euU5vsPDw1W3bl3Z7XbnWJMmTWQYhvbt26dGjRpVas8ov/LM9/jx49W+fXv96U9/kiQ1b95cfn5+uvvuu/Xiiy/yTvN1pqLyGmeqr5CXl5datmypzMxMl/HMzEy1a9fugs+JjY0tU79s2TK1atVKVqu10nqFeeWZb+nsGer+/ftrzpw5XHtXg1ztfAcEBOi7775TTk6O8+vxxx9XdHS0cnJy1KZNm2vVOsqhPMd3+/bt9csvv+jEiRPOse+//1433HCD6tWrV6n9wpzyzPfJkyd1ww2uEcnDw0PS/z+DietHheW1q/pYo5srvSXP9OnTjW3bthnDhg0z/Pz8jF27dhmGYRjPPvuskZKS4qwvvUXL008/bWzbts2YPn06t9SrQa52vufMmWN4enoab7zxhpGbm+v8Onr0aFXtAq7C1c73+bj7R81ytfN9/Phxo169esZvf/tbY+vWrcbKlSuNRo0aGQMHDqyqXcBVuNr5fvfddw1PT0/jzTffNH766Sdj9erVRqtWrYzWrVtX1S7gKhw/ftzYtGmTsWnTJkOS8corrxibNm1y3kKxsvIaofoqvfHGG0ZUVJTh5eVl3HnnncbKlSudy1JTU40OHTq41K9YscK44447DC8vL+Omm24ypk6deo07hhlXM98dOnQwJJX5Sk1NvfaNo1yu9vg+F6G65rna+d6+fbvRtWtXw8fHx6hXr54xfPhw4+TJk9e4a5TX1c73P/7xD6Np06aGj4+PER4ebjz88MPGvn37rnHXKI/ly5df8t/jysprFsPgfQwAAADADK6pBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQCo1o4ePar09HTl5uZWdSsAcFGEagBwA2lpabr99ttNr8disWjhwoUXXb5r1y5ZLBbl5ORIklasWCGLxaKjR49KkmbMmKHatWtf1Tb79++vU6dOKTw8vHxNA8A1QKgGgGqmf//+slgsslgsslqtatiwoUaOHKnCwsKqbu2yIiMjlZubq5iYmAsu79u3r77//nvn48uF/UmTJqlWrVoaP358RbcKABXKs6obAACU1a1bN7377rtyOBxatWqVBg4cqMLCQk2dOtWlzuFwyGq1VlGXZXl4eCgsLOyiy318fOTj43PF6xsxYkRFtAUAlY4z1QBQDdlsNoWFhSkyMlLJycl6+OGHtXDhQueZ3XfeeUcNGzaUzWaTYRjas2eP7rvvPtWqVUsBAQHq06eP9u/fX2a906ZNU2RkpHx9ffXggw86L8uQpA0bNiguLk7BwcGy2+3q0KGDvvnmmzLryM3NVffu3eXj46MGDRpo/vz5zmXnX/5xvnMv/5gxY4bS09O1efNm55n5GTNmSJIKCgo0ePBghYSEKCAgQJ07d9bmzZvL/XoCQGUjVANADeDj4yOHwyFJ+vHHH/Xvf/9bH330kTO89urVS0eOHNHKlSuVmZmpn376SX379nVZR+nzPv30U2VkZCgnJ0dPPPGEc/nx48eVmpqqVatWae3atWrUqJHuvfdeHT9+3GU9zz//vB544AFt3rxZjzzyiB566CFt3779qvepb9++GjFihG677Tbl5uYqNzdXffv2lWEY6tGjh/Ly8rRkyRJlZ2frzjvvVJcuXXTkyJGr3g4AXAtc/gEA1dz69es1Z84cdenSRZJUXFysmTNn6sYbb5QkZWZm6ttvv9XOnTsVGRkpSZo5c6Zuu+02bdiwQXfddZck6ddff9V7772nevXqSZKmTJmiHj16aNKkSQoLC1Pnzp1dtjtt2jTVqVNHK1euVGJionP8wQcf1MCBAyVJL7zwgjIzMzVlyhS9+eabV7VfPj4+qlWrljw9PV0uGfnyyy/13Xff6cCBA7LZbJKkl19+WQsXLtSHH36owYMHX9V2AOBa4Ew1AFRDixYtUq1ateTt7a3Y2Fjdc889mjJliiQpKirKGaglafv27YqMjHQGaklq2rSpateu7XIGuX79+s5ALUmxsbE6c+aMduzYIUk6cOCAHn/8cTVu3Fh2u112u10nTpzQnj17XHqLjY0t87g8Z6ovJjs7WydOnFBQUJBq1arl/Nq5c6d++umnCtsOAFQkzlQDQDXUqVMnTZ06VVarVRERES4fRvTz83OpNQxDFoulzDouNl6qdFnpn/3799fBgwc1efJkRUVFyWazKTY2VsXFxZft91LbuVpnzpxReHi4VqxYUWbZ1d6ODwCuFUI1AFRDfn5+uuWWW66otmnTptqzZ4/27t3rPFu9bds2FRQUqEmTJs66PXv26JdfflFERIQkac2aNbrhhhvUuHFjSdKqVav05ptv6t5775Uk7d27V4cOHSqzvbVr16pfv34uj++4445y7aeXl5dKSkpcxu68807l5eXJ09NTN910U7nWCwDXGpd/AEAN17VrVzVv3lwPP/ywvvnmG61fv179+vVThw4d1KpVK2edt7e3UlNTtXnzZq1atUpDhw5Vnz59nNcz33LLLZo5c6a2b9+udevW6eGHH77g7e/mz5+vd955R99//73GjBmj9evX68knnyxX7zfddJN27typnJwcHTp0SEVFReratatiY2PVq1cvLV26VLt27VJWVpb+8pe/aOPGjeV7kQCgkhGqAaCGK/0th3Xq1NE999yjrl27qmHDhpo3b55L3S233KLevXvr3nvvVXx8vGJiYlw+XPjOO+8oPz9fd9xxh1JSUjR06FCFhISU2V56errmzp2r5s2b67333tPs2bPVtGnTcvX+wAMPqFu3burUqZNuvPFGffDBB7JYLFqyZInuuece/f73v1fjxo31u9/9Trt27VJoaGi5tgMAlc1iGIZR1U0AAAAANRlnqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMCk/wfKxHLdiW5ZHgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_prob_distribution(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "## Cross Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7f0535de-34f1-4e97-b993-b429ecf0a554", - "metadata": {}, - "outputs": [], - "source": [ - "y_train = y_train['y_has_purchased']" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "f7fca463-d7d6-493b-8329-fdfa92457f78", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'logreg__C': 0.0009765625, 'logreg__class_weight': 'balanced', 'logreg__penalty': 'l1'}\n", - "Best cross-validation score: 0.65\n", - "Test set score: 0.64\n" - ] - } - ], - "source": [ - "# Cross validation\n", - "\n", - "grid_search = GridSearchCV(pipeline, param_grid, cv=3, scoring=recall_scorer, error_score='raise',\n", - " n_jobs=-1)\n", - "\n", - "grid_search.fit(X_train, y_train)\n", - "\n", - "# Print the best parameters and the best score\n", - "print(\"Best parameters found: \", grid_search.best_params_)\n", - "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n", - "\n", - "# Evaluate the best model on the test set\n", - "test_score = grid_search.score(X_test, y_test)\n", - "print(\"Test set score: {:.2f}\".format(test_score))" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "56bd7828-4de1-4166-bea0-5d5e152b9d38", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = grid_search.predict(X_test)\n", - "\n", - "draw_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "319fe0eb-4d4a-492c-bd50-3f08ab483021", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_roc_curve(X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ab122f66-1591-43ea-a364-2564f09b2bb3", - "metadata": {}, - "source": [ - "# Segmentation du score de prédiction" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "210b931c-6d46-4ebf-a9c7-d1ee05c3fadf", - "metadata": {}, - "outputs": [], - "source": [ - "# Création d'un dataframe avec le score\n", - "dataset_for_segmentation = dataset_test[['customer_id'] + numeric_features + categorical_features]\n", - "\n", - "y_predict_proba = pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - "dataset_for_segmentation['prediction_probability'] = y_predict_proba\n", - "\n", - "# Arrondir les valeurs de la colonne 'prediction_probability' et les multiplier par 10\n", - "dataset_for_segmentation['category'] = dataset_for_segmentation['prediction_probability'].apply(lambda x: int(x * 10))\n", - "\n", - "dataset_for_segmentation['prediction'] = y_pred\n", - "\n", - "def premiere_partie(chaine):\n", - " if chaine:\n", - " return chaine.split('_')[0]\n", - " else:\n", - " return None\n", - "\n", - "dataset_for_segmentation['company_number'] = dataset_for_segmentation['customer_id'].apply(lambda x: premiere_partie(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "969f1f92-d715-4d74-85a7-437e72838cb5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - " mean mean mean mean \n", - "category \n", - "0 0.113637 0.006274 1.586366 0.005821 \n", - "1 0.810841 0.128432 9.611292 0.125295 \n", - "2 1.159419 0.339253 15.182143 0.337577 \n", - "3 2.153080 0.744161 27.820044 0.734881 \n", - "4 2.044749 0.777640 27.353145 0.754549 \n", - "5 3.237988 0.958520 46.637380 0.807655 \n", - "6 3.592233 1.102881 49.989226 0.878014 \n", - "7 3.747016 1.391266 40.710335 0.914702 \n", - "8 5.698276 1.567006 63.033699 0.907915 \n", - "9 14.505956 3.211571 107.288514 1.011628 \n", - "10 2262.859155 45.619718 11051.732394 1.464789 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - " mean mean mean \n", - "category \n", - "0 0.000647 548.790455 548.773103 \n", - "1 0.018186 525.437516 525.275222 \n", - "2 0.323824 501.529129 501.415505 \n", - "3 0.600982 287.051054 286.675385 \n", - "4 0.079213 297.179255 295.019902 \n", - "5 0.484785 387.464785 380.145068 \n", - "6 0.599906 268.627019 250.949344 \n", - "7 0.160990 309.716173 274.795570 \n", - "8 0.334248 326.485952 257.940194 \n", - "9 0.157119 369.696066 209.280306 \n", - "10 0.154930 467.111875 31.146796 \n", - "\n", - " time_between_purchase nb_tickets_internet fidelity gender_female \\\n", - " mean mean mean mean \n", - "category \n", - "0 -0.977118 0.001585 0.000776 0.000000 \n", - "1 -0.729328 0.054312 0.111832 0.245480 \n", - "2 -0.554439 0.969939 0.304757 0.392570 \n", - "3 0.105360 1.776035 0.659878 0.288813 \n", - "4 1.898178 0.293760 0.894877 0.666980 \n", - "5 7.111357 2.080397 1.164958 0.497758 \n", - "6 17.539247 2.525994 1.420921 0.534607 \n", - "7 34.796876 0.844250 1.963028 0.650364 \n", - "8 68.425460 2.794279 2.413009 0.606583 \n", - "9 160.348544 3.514464 5.394498 0.669314 \n", - "10 435.950994 54.295775 64.704225 0.507042 \n", - "\n", - " gender_male gender_other nb_campaigns nb_campaigns_opened \n", - " mean mean mean mean \n", - "category \n", - "0 0.000032 0.999968 13.984219 1.302720 \n", - "1 0.495929 0.258591 18.413562 3.718711 \n", - "2 0.297258 0.310173 17.395042 2.608084 \n", - "3 0.253244 0.457943 16.790421 4.173954 \n", - "4 0.301424 0.031596 16.954707 6.060621 \n", - "5 0.259769 0.242473 27.006406 12.457719 \n", - "6 0.304259 0.161134 14.073285 4.604134 \n", - "7 0.263464 0.086172 26.186317 8.891703 \n", - "8 0.251567 0.141850 30.987461 11.676332 \n", - "9 0.223766 0.106920 45.928247 18.241634 \n", - "10 0.295775 0.197183 53.352113 26.070423 " - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Grouper le DataFrame par la colonne 'category' et calculer la moyenne pour chaque groupe\n", - "summary_stats = dataset_for_segmentation.groupby('category')[numeric_features].describe()\n", - "\n", - "# Sélectionner uniquement la colonne 'mean' pour chaque variable numérique\n", - "mean_stats = summary_stats.loc[:, (slice(None), 'mean')]\n", - "\n", - "# Afficher le DataFrame résultant\n", - "mean_stats" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Spectacle/2_bis_logit_baseline_statsmodels.ipynb b/Spectacle/2_bis_logit_baseline_statsmodels.ipynb deleted file mode 100644 index b7d337e..0000000 --- a/Spectacle/2_bis_logit_baseline_statsmodels.ipynb +++ /dev/null @@ -1,2866 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "56949d8f-4eaf-4685-9989-ba0b4b1945b7", - "metadata": {}, - "source": [ - "# Baseline logit on spectacle companies with statmodels" - ] - }, - { - "cell_type": "markdown", - "id": "eae443dc-6c28-401a-a30e-e02f5f4da2df", - "metadata": {}, - "source": [ - "## Importation des packages et des données" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "72480e84-2ccc-481a-9353-1199e4358d62", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "\n", - "import statsmodels.api as sm\n", - "\n", - "import pickle\n", - "import warnings" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7090dc21-7889-4776-a0a4-f7c6a5416d53", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2f0d08c9-5b26-4eff-9c89-4a46f427dbf7", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/Generalization/musique\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "438d0138-a254-464c-9e94-f7436576c1d5", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ebe9a887-61a4-4a5e-ac64-231307dd7647", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_426/3642896088.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "/tmp/ipykernel_426/3642896088.py:11: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dataset_train, dataset_test = load_train_test()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b21fdea2-02c4-4222-b4e0-635e423f91c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "street_id 0\n", - "structure_id 327067\n", - "mcp_contact_id 135224\n", - "fidelity 0\n", - "tenant_id 0\n", - "is_partner 0\n", - "deleted_at 354365\n", - "gender 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "last_buying_date 119201\n", - "max_price 119201\n", - "ticket_sum 0\n", - "average_price 115193\n", - "average_purchase_delay 119203\n", - "average_price_basket 119203\n", - "average_ticket_basket 119203\n", - "total_price 4008\n", - "purchase_count 0\n", - "first_buying_date 119201\n", - "country 56856\n", - "gender_label 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "country_fr 56856\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 224310\n", - "y_has_purchased 0\n", - "dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "42c4d034-8bc1-4ebb-a1ff-60c0a86f8f7c", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "94b4498d-6ae8-4c96-adbc-7ba1b8348160", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape train : (354365, 17)\n", - "Shape test : (151874, 17)\n" - ] - } - ], - "source": [ - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "29206597-bce8-41e0-9b68-9b9a2843787a", - "metadata": {}, - "source": [ - "## optionnel : calcul des poids\n", - "On pourrait utiliser les poids pour gérer le déséquilibre de classe, mais dans une optique exploratoire, c'est pas indispensable et ça a pas été utilisé ici !" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6224fd31-c190-4168-b395-e0bf5806d79d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0.0: 0.5481283836040216, 1.0: 5.694439980716696}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute Weights\n", - "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", - " y = y_train['y_has_purchased'])\n", - "\n", - "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n", - "weight_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4680f202-979e-483f-89b8-9df877203bcf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.54812838, 0.54812838, 0.54812838, ..., 5.69443998, 0.54812838,\n", - " 0.54812838])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Calcul des poids inverses à la fréquence des classes\n", - "class_counts = np.bincount(y_train['y_has_purchased'])\n", - "class_weights = len(y_train['y_has_purchased']) / (2 * class_counts)\n", - "\n", - "# Sélection des poids correspondants à chaque observation\n", - "weights = class_weights[y_train['y_has_purchased'].values.astype(int)]\n", - "weights" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5f747be4-e70b-491c-8f0a-46cb278a2dee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[354365. 354365. 354365. ... 354365. 354365. 354365.]\n", - "354365\n" - ] - } - ], - "source": [ - "# verif\n", - "print(2 * weights * class_counts[y_train['y_has_purchased'].values.astype(int)])\n", - "print(len(y_train['y_has_purchased']))" - ] - }, - { - "cell_type": "markdown", - "id": "bd1f7d9d-1aff-49e4-81ca-038f732b1595", - "metadata": {}, - "source": [ - "## définition des variables X et y" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ab25a901-28da-4504-a7d1-bf41fa5068bc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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354365 rows × 17 columns

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constnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
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\n", - "
" - ], - "text/plain": [ - " const nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "1 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "2 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "3 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "4 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "... ... ... ... ... ... \n", - "354360 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "354361 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "354362 1.0 -0.000838 0.092966 -0.009150 1.219633 \n", - "354363 1.0 -0.012631 0.021122 -0.005227 1.219633 \n", - "354364 1.0 -0.024425 -0.050722 -0.048383 -0.768294 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 -0.599511 0.755994 0.783940 \n", - "1 -0.599511 0.755994 0.783940 \n", - "2 -0.599511 0.755994 0.783940 \n", - "3 -0.599511 0.755994 0.783940 \n", - "4 -0.599511 0.755994 0.783940 \n", - "... ... ... ... \n", - "354360 -0.599511 0.755994 0.783940 \n", - "354361 -0.599511 0.755994 0.783940 \n", - "354362 -0.599511 -1.665887 -1.557073 \n", - "354363 -0.599511 -1.871668 -1.755983 \n", - "354364 -0.599511 0.755994 0.783940 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 -0.264693 1 1 1 \n", - "1 -0.264693 1 1 0 \n", - "2 -0.264693 1 1 0 \n", - "3 -0.264693 1 0 0 \n", - "4 -0.264693 1 0 0 \n", - "... ... ... ... ... \n", - "354360 -0.264693 1 0 0 \n", - "354361 -0.264693 1 1 0 \n", - "354362 -0.264693 1 0 1 \n", - "354363 -0.264693 1 1 0 \n", - "354364 -0.264693 1 0 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "0 0 0.607945 0.522567 \n", - "1 0 0.306155 1.701843 \n", - "2 1 0.708542 -0.420854 \n", - "3 0 0.205558 -0.420854 \n", - "4 0 -0.297426 -0.420854 \n", - "... ... ... ... \n", - "354360 0 0.004365 -0.420854 \n", - "354361 1 0.406752 0.050856 \n", - "354362 0 -0.096232 0.994277 \n", - "354363 1 -0.398023 -0.420854 \n", - "354364 1 0.004365 -0.420854 \n", - "\n", - "[354365 rows x 15 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "54421677-640f-4f37-9a0d-d9a2cc3572b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.234602\n", - " Iterations 8\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y No. Observations: 354365\n", - "Model: Logit Df Residuals: 354350\n", - "Method: MLE Df Model: 14\n", - "Date: Thu, 21 Mar 2024 Pseudo R-squ.: 0.2112\n", - "Time: 07:58:13 Log-Likelihood: -83135.\n", - "converged: True LL-Null: -1.0540e+05\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "=======================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "---------------------------------------------------------------------------------------\n", - "const -3.6025 0.091 -39.755 0.000 -3.780 -3.425\n", - "nb_tickets -0.0230 0.010 -2.191 0.028 -0.044 -0.002\n", - "nb_purchases -0.0519 0.014 -3.609 0.000 -0.080 -0.024\n", - "total_amount 0.0799 0.021 3.841 0.000 0.039 0.121\n", - "nb_suppliers 0.1694 0.010 17.662 0.000 0.151 0.188\n", - "vente_internet_max -0.8764 0.011 -82.965 0.000 -0.897 -0.856\n", - "purchase_date_min 0.5881 0.015 39.936 0.000 0.559 0.617\n", - "purchase_date_max -1.4197 0.016 -89.592 0.000 -1.451 -1.389\n", - "nb_tickets_internet 0.2895 0.013 22.652 0.000 0.264 0.315\n", - "is_email_true 0.8651 0.088 9.797 0.000 0.692 1.038\n", - "opt_in -1.9976 0.019 -107.305 0.000 -2.034 -1.961\n", - "gender_female 0.7032 0.024 29.395 0.000 0.656 0.750\n", - "gender_male 0.8071 0.024 33.201 0.000 0.759 0.855\n", - "nb_campaigns 0.2850 0.009 30.633 0.000 0.267 0.303\n", - "nb_campaigns_opened 0.2061 0.007 28.245 0.000 0.192 0.220\n", - "=======================================================================================\n" - ] - } - ], - "source": [ - "# 2. modele avec var standardisées (permet de mieux jauger l'importance réelle de chaque variable)\n", - "\n", - "model_logit = sm.Logit(y, X)\n", - "# model_logit = sm.Logit(y, X)\n", - "\n", - "# Ajustement du modèle aux données\n", - "result = model_logit.fit()\n", - "\n", - "# Affichage des résultats\n", - "print(result.summary())" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "13cc3362-7bb2-46fa-8bd8-e5a8e53260b8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully (Exit mode 0)\n", - " Current function value: 0.23562928627877766\n", - " Iterations: 240\n", - " Function evaluations: 243\n", - " Gradient evaluations: 240\n", - "const 0.000000e+00\n", - "nb_tickets 2.477006e-01\n", - "nb_purchases 1.636902e-03\n", - "total_amount 8.839088e-04\n", - "nb_suppliers 1.906550e-65\n", - "vente_internet_max 0.000000e+00\n", - "purchase_date_min 0.000000e+00\n", - "purchase_date_max 0.000000e+00\n", - "nb_tickets_internet 7.232680e-112\n", - "is_email_true 8.202187e-08\n", - "opt_in 0.000000e+00\n", - "gender_female 1.624424e-170\n", - "gender_male 4.961315e-220\n", - "nb_campaigns 6.276733e-205\n", - "nb_campaigns_opened 2.228531e-176\n", - "dtype: float64\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y No. Observations: 354365\n", - "Model: Logit Df Residuals: 354350\n", - "Method: MLE Df Model: 14\n", - "Date: Thu, 21 Mar 2024 Pseudo R-squ.: 0.2111\n", - "Time: 10:45:37 Log-Likelihood: -83152.\n", - "converged: True LL-Null: -1.0540e+05\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "=======================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "---------------------------------------------------------------------------------------\n", - "const -3.1162 0.081 -38.383 0.000 -3.275 -2.957\n", - "nb_tickets -0.0136 0.012 -1.156 0.248 -0.037 0.009\n", - "nb_purchases -0.0385 0.012 -3.149 0.002 -0.063 -0.015\n", - "total_amount 0.0588 0.018 3.325 0.001 0.024 0.094\n", - "nb_suppliers 0.1638 0.010 17.085 0.000 0.145 0.183\n", - "vente_internet_max -0.8651 0.011 -82.182 0.000 -0.886 -0.844\n", - "purchase_date_min 0.5790 0.015 39.391 0.000 0.550 0.608\n", - "purchase_date_max -1.4088 0.016 -89.101 0.000 -1.440 -1.378\n", - "nb_tickets_internet 0.2857 0.013 22.475 0.000 0.261 0.311\n", - "is_email_true 0.4224 0.079 5.363 0.000 0.268 0.577\n", - "opt_in -1.9818 0.019 -106.856 0.000 -2.018 -1.945\n", - "gender_female 0.6553 0.024 27.835 0.000 0.609 0.701\n", - "gender_male 0.7578 0.024 31.663 0.000 0.711 0.805\n", - "nb_campaigns 0.2835 0.009 30.547 0.000 0.265 0.302\n", - "nb_campaigns_opened 0.2061 0.007 28.315 0.000 0.192 0.220\n", - "=======================================================================================\n" - ] - } - ], - "source": [ - "# 2.bis on fait de même pour un modèle logit avec pénalité \n", - "# pas besoin de redefinir le modèle, il faut faire un fit_regularized\n", - "\n", - "# sans spécification, le alpha optimal est déterminé par cross validation\n", - "# remplacer alpha=32 par la valeur optimale trouvée par cross validation dans la pipeline avec .best_params\n", - "# attention, dans scikit learn, l'hyperparamètre est C = 1/alpha, pas oublier de prendre l'inverse de ce C optimal\n", - "\n", - "result = model_logit.fit_regularized(method='l1', alpha = 32)\n", - "\n", - "print(result.pvalues)\n", - "print(result.summary())" - ] - }, - { - "cell_type": "markdown", - "id": "8c3dec50-7b9d-40f6-83b6-6cae26962cf8", - "metadata": {}, - "source": [ - "### Other method : take into account the weigths ! Pb : with this method, no penalty allowed" - ] - }, - { - "cell_type": "code", - "execution_count": 247, - "id": "2e3ca381-54e3-445b-bb37-d7ce953cb856", - "metadata": {}, - "outputs": [], - "source": [ - "# define a function to generate summaries of logit model\n", - "\n", - "def model_logit(X, y, weight_dict, add_constant=False) :\n", - " # Generate sample weights based on class weights computed earlier\n", - " sample_weights = np.array([weight_dict[class_] for class_ in y])\n", - "\n", - " if add_constant :\n", - " X_const = sm.add_constant(X)\n", - " else :\n", - " X_const = X\n", - " \n", - " # Use GLM from statsmodels with Binomial family for logistic regression\n", - " model = sm.GLM(y, X_const, family=sm.families.Binomial(), freq_weights=sample_weights)\n", - " \n", - " # fit without penalty\n", - " result = model.fit()\n", - "\n", - " result_summary = result.summary()\n", - " \n", - " return result_summary" - ] - }, - { - "cell_type": "code", - "execution_count": 248, - "id": "4cd424a0-7c55-47ff-840e-1354e8dcf863", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Generalized Linear Model Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y No. Observations: 354365\n", - "Model: GLM Df Residuals: 354350\n", - "Model Family: Binomial Df Model: 14\n", - "Link Function: Logit Scale: 1.0000\n", - "Method: IRLS Log-Likelihood: -1.8693e+05\n", - "Date: Thu, 21 Mar 2024 Deviance: 3.7387e+05\n", - "Time: 13:19:33 Pearson chi2: 1.97e+16\n", - "No. Iterations: 100 Pseudo R-squ. (CS): 0.2820\n", - "Covariance Type: nonrobust \n", - "=======================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "---------------------------------------------------------------------------------------\n", - "const -1.3943 0.062 -22.456 0.000 -1.516 -1.273\n", - "nb_tickets -0.3312 0.016 -20.967 0.000 -0.362 -0.300\n", - "nb_purchases 0.9258 0.098 9.491 0.000 0.735 1.117\n", - "total_amount 0.8922 0.042 21.393 0.000 0.810 0.974\n", - "nb_suppliers 0.2238 0.007 32.137 0.000 0.210 0.237\n", - "vente_internet_max -0.7453 0.007 -100.473 0.000 -0.760 -0.731\n", - "purchase_date_min 0.7123 0.015 46.063 0.000 0.682 0.743\n", - "purchase_date_max -1.3328 0.017 -79.297 0.000 -1.366 -1.300\n", - "nb_tickets_internet 0.1784 0.011 16.366 0.000 0.157 0.200\n", - "is_email_true 0.8635 0.061 14.086 0.000 0.743 0.984\n", - "opt_in -1.7487 0.010 -174.737 0.000 -1.768 -1.729\n", - "gender_female 0.8084 0.013 60.803 0.000 0.782 0.835\n", - "gender_male 0.8731 0.014 64.332 0.000 0.846 0.900\n", - "nb_campaigns 0.1751 0.006 31.101 0.000 0.164 0.186\n", - "nb_campaigns_opened 0.2962 0.005 54.145 0.000 0.285 0.307\n", - "=======================================================================================\n" - ] - } - ], - "source": [ - "# with the function\n", - "\n", - "# 1. logit with weights\n", - "results_logit_weight = model_logit(X,y,weight_dict=weight_dict)\n", - "print(results_logit_weight)" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "id": "84dd6242-a9c3-4dee-a58b-abc5f1c6f8fa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Generalized Linear Model Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y No. Observations: 354365\n", - "Model: GLM Df Residuals: 354350\n", - "Model Family: Binomial Df Model: 14\n", - "Link Function: Logit Scale: 1.0000\n", - "Method: IRLS Log-Likelihood: -83141.\n", - "Date: Thu, 21 Mar 2024 Deviance: 1.6628e+05\n", - "Time: 13:20:06 Pearson chi2: 4.52e+15\n", - "No. Iterations: 8 Pseudo R-squ. (CS): 0.1180\n", - "Covariance Type: nonrobust \n", - "=======================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "---------------------------------------------------------------------------------------\n", - "const -3.6025 0.091 -39.755 0.000 -3.780 -3.425\n", - "nb_tickets -0.0230 0.010 -2.191 0.028 -0.044 -0.002\n", - "nb_purchases -0.0519 0.014 -3.609 0.000 -0.080 -0.024\n", - "total_amount 0.0799 0.021 3.841 0.000 0.039 0.121\n", - "nb_suppliers 0.1694 0.010 17.662 0.000 0.151 0.188\n", - "vente_internet_max -0.8764 0.011 -82.965 0.000 -0.897 -0.856\n", - "purchase_date_min 0.5881 0.015 39.936 0.000 0.559 0.617\n", - "purchase_date_max -1.4197 0.016 -89.592 0.000 -1.451 -1.389\n", - "nb_tickets_internet 0.2895 0.013 22.652 0.000 0.264 0.315\n", - "is_email_true 0.8651 0.088 9.797 0.000 0.692 1.038\n", - "opt_in -1.9976 0.019 -107.305 0.000 -2.034 -1.961\n", - "gender_female 0.7032 0.024 29.395 0.000 0.656 0.750\n", - "gender_male 0.8071 0.024 33.201 0.000 0.759 0.855\n", - "nb_campaigns 0.2850 0.009 30.633 0.000 0.267 0.303\n", - "nb_campaigns_opened 0.2061 0.007 28.245 0.000 0.192 0.220\n", - "=======================================================================================\n" - ] - } - ], - "source": [ - "# 2. logit without weights\n", - "\n", - "results_logit = model_logit(X.drop(\"const\", axis=1),y,weight_dict={0:1, 1:1}, add_constant=True)\n", - "print(results_logit)" - ] - }, - { - "cell_type": "markdown", - "id": "36c5e770-72b3-4482-ad61-45b511a11f06", - "metadata": {}, - "source": [ - "## graphique LASSO - quelles variables sont importantes dans le modèle ? " - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "id": "af208fdf-b4c2-4acd-b29e-c5b67bec3a4d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "results for solver lbfgs\n", - "intercept : -3.617357317895187\n", - "coefficients : [[-0.03114285 -0.06607353 0.10099873 0.16977395 -0.87625108 0.58870838\n", - " -1.42022841 0.28837776 0.87461022 -2.00037064 0.70874574 0.8136523\n", - " 0.2850802 0.20640785]]\n", - "\n", - "\n", - "results for solver newton-cg\n", - "intercept : -3.5774790840156467\n", - "coefficients : [[-0.0224498 -0.05092757 0.07842438 0.16941048 -0.87645255 0.58801191\n", - " -1.41953483 0.28961165 0.84037075 -1.99757163 0.70302619 0.8068438\n", - " 0.2849652 0.20613618]]\n", - "\n", - "\n", - "results for solver newton-cholesky\n", - "intercept : -3.602198310216717\n", - "coefficients : [[-0.02297134 -0.05187501 0.07986323 0.1693883 -0.87639043 0.58815512\n", - " -1.41963236 0.28949836 0.86505556 -1.99695897 0.70307973 0.80688729\n", - " 0.2849131 0.20610117]]\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "results for solver sag\n", - "intercept : -1.251116606796448\n", - "coefficients : [[-0.02952178 -0.05691972 0.08940743 0.18616406 -0.85908081 0.46577384\n", - " -1.26014292 0.32512459 -1.00339802 -1.84528471 0.15832219 0.24753693\n", - " 0.26318328 0.21288782]]\n", - "\n", - "\n", - "results for solver saga\n", - "intercept : -1.112341737293756\n", - "coefficients : [[-0.03349226 -0.02298918 0.09611619 0.23784438 -0.80928967 0.28520739\n", - " -1.01029862 0.30172469 -0.99503611 -1.53140972 -0.04449765 0.02363137\n", - " 0.20352875 0.22580284]]\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# difference entre les solveurs (les resultats de statsmodel s'approchent de newtown cholesky)\n", - "\n", - "for solver in [\"lbfgs\", \"newton-cg\", \"newton-cholesky\", \"sag\", \"saga\"] :\n", - " modele_logit = LogisticRegression(penalty=None, solver=solver)\n", - " modele_logit.fit(X.drop(\"const\", axis=1), y)\n", - " print(f\"results for solver {solver}\")\n", - " print(f\"intercept : {modele_logit.intercept_[0]}\")\n", - " print(f\"coefficients : {modele_logit.coef_}\")\n", - " print(\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "e65ab8d9-54e5-4092-ad75-ac1909cb1f60", - "metadata": {}, - "source": [ - "on passe au graphique\n" - ] - }, - { - "cell_type": "code", - "execution_count": 449, - "id": "f0006351-9b43-449e-81a7-b4510dd55366", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])" - ] - }, - "execution_count": 449, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# il faut environ alpha = 25k pour annuler tous les coeffs\n", - "# on utilise pas de balance pour les classes pour le moment car les résultats de statsmodels n equilibrent \n", - "# pas les classes - on utilisera cette option pr la validation croisee\n", - "\n", - "modele_logit = LogisticRegression(penalty=\"l1\", C=1/25000, # class_weight=\"balanced\", \n", - " solver=\"liblinear\" )\n", - "modele_logit.fit(X.drop(\"const\", axis=1),y)\n", - "modele_logit.coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 370, - "id": "24083a2f-e520-4229-a510-09e352b25cbd", - "metadata": {}, - "outputs": [], - "source": [ - "params = np.logspace(-5, 5, 11, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 371, - "id": "9c1c8efe-27e9-4307-82bd-ea356f219ebf", - "metadata": {}, - "outputs": [], - "source": [ - "results=[]\n", - "for param in params :\n", - " modele_logit = LogisticRegression(penalty=\"l1\", C=param, # class_weight=\"balanced\", \n", - " solver=\"liblinear\" )\n", - " modele_logit.fit(X.drop(\"const\", axis=1),y)\n", - " results.append(modele_logit.coef_)" - ] - }, - { - "cell_type": "code", - "execution_count": 383, - "id": "ceaec969-e72e-4520-afaf-7bcf5dad8365", - "metadata": {}, - "outputs": [], - "source": [ - "results.reverse()" - ] - }, - { - "cell_type": "code", - "execution_count": 384, - "id": "5b7c8d26-d1f8-441f-ab1d-89845e3e1ea3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([[-0.02299412, -0.05192013, 0.0799274 , 0.16931227, -0.87633381,\n", - " 0.58813399, -1.41967385, 0.28951886, 0.85509191, -1.99754475,\n", - " 0.70287087, 0.80669243, 0.28498239, 0.2061286 ]]),\n", - " array([[-0.02299201, -0.05191491, 0.07992075, 0.16931139, -0.87634243,\n", - " 0.58813708, -1.41968623, 0.28952223, 0.85577021, -1.99756453,\n", - " 0.70288563, 0.80669012, 0.28498258, 0.20612949]]),\n", - " array([[-0.02299764, -0.05192605, 0.07993569, 0.16930528, -0.87632586,\n", - " 0.58811345, -1.41964512, 0.28952983, 0.85374762, -1.99754811,\n", - " 0.70282334, 0.80664228, 0.28498228, 0.20613025]]),\n", - " array([[-0.02298949, -0.05191449, 0.07991828, 0.16931317, -0.87634417,\n", - " 0.58812319, -1.4196808 , 0.2895181 , 0.85546622, -1.99754003,\n", - " 0.70302758, 0.80684757, 0.28498265, 0.20613162]]),\n", - " array([[-0.02296458, -0.05187503, 0.07985942, 0.16928133, -0.87628414,\n", - " 0.5880753 , -1.41959837, 0.28951824, 0.85207105, -1.99743532,\n", - " 0.70275613, 0.80657079, 0.28497271, 0.20612744]]),\n", - " array([[-0.02266765, -0.05140588, 0.07913905, 0.16914597, -0.8759943 ,\n", - " 0.58782322, -1.41931263, 0.28941107, 0.84058764, -1.99706383,\n", - " 0.70135753, 0.805146 , 0.2849354 , 0.20613043]]),\n", - " array([[-0.01986108, -0.04710671, 0.07249967, 0.16755623, -0.8727931 ,\n", - " 0.58521605, -1.41621509, 0.28835319, 0.7063547 , -1.99262169,\n", - " 0.68764121, 0.79104559, 0.28452484, 0.20613349]]),\n", - " array([[ 0. , -0.02274081, 0.03249772, 0.15656967, -0.84560728,\n", - " 0.5601391 , -1.38630664, 0.27683263, 0. , -1.95240872,\n", - " 0.55820164, 0.65806397, 0.27970382, 0.20620792]]),\n", - " array([[ 0.00000000e+00, 0.00000000e+00, 1.55329481e-03,\n", - " 1.30027639e-01, -6.87367967e-01, 3.13022684e-01,\n", - " -1.08971896e+00, 1.74908692e-01, 0.00000000e+00,\n", - " -1.67160475e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 2.21231437e-01, 2.08973175e-01]]),\n", - " array([[ 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , -0.2624159 , 0. , -0.01813001, -0.22665172,\n", - " 0. , 0. , 0. , 0.01487092]]),\n", - " array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])]" - ] - }, - "execution_count": 384, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 392, - "id": "9f6e6532-c593-4f3a-a718-5f4593749eb4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n", - " 1.e+03, 1.e+04, 1.e+05])" - ] - }, - "execution_count": 392, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# le paramètre C est l'inverse de alpha. On préfère donc afficher les valeurs de alpha qui sont plus parlantes\n", - "# un alpha grand correspond à une plus grande pénalité \n", - "# et on utilise flip pour inverser le vecteur, et classer les alphas par ordre croissant\n", - "# par souci de coherence et de lisibilité, on inverse donc aussi l'ordre des resultats\n", - "\n", - "alphas_sorted = np.flip(1/params)\n", - "alphas_sorted" - ] - }, - { - "cell_type": "code", - "execution_count": 447, - "id": "1de056b5-e37c-4272-9acb-a197bdb5ea3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", - " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'],\n", - " dtype='object')" - ] - }, - "execution_count": 447, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_colnames = X.drop(\"const\", axis=1).columns\n", - "X_colnames" - ] - }, - { - "cell_type": "code", - "execution_count": 448, - "id": "4436abe2-ac0f-480d-aa12-491c059f906a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# graphique\n", - "\n", - "plt.figure(figsize=[12,8], dpi=110)\n", - "\n", - "for i in range(len(X_colnames)) :\n", - " var_name = X_colnames[i]\n", - " plt.plot(alphas_sorted, [results[p][0][i] for p in range(len(results))], label = var_name)\n", - "\n", - "plt.legend()\n", - "plt.title(\"Evolution de la valeur des coefficents du logit LASSO en fonction du paramètre de pénalité alpha\")\n", - "plt.xlabel(\"alpha\")\n", - "plt.ylabel(\"valeur du coefficient\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 444, - "id": "4771b91f-baff-493b-a6f7-ddce02164333", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# hide right part of the graphic\n", - "# some coefficients are still strictly positive even for alpha =10k, which makes the graphic quite confusing\n", - "# alternative syntax\n", - "\n", - "endpoint = 9\n", - "\n", - "fig, ax = plt.subplots(figsize=[12,8], dpi=110)\n", - "\n", - "for i in range(len(X_colnames)) :\n", - " var_name = X_colnames[i]\n", - " ax.plot(alphas_sorted[:endpoint], [results[p][0][i] for p in range(len(results[:endpoint]))], label=var_name)\n", - " \n", - "ax.set(xlabel=\"alpha\",\n", - " ylabel=\"valeur du coefficient\",\n", - " title = \"Evolution de la valeur des coefficents du logit LASSO en fonction du paramètre de pénalité alpha\")\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c3c9bb8c-5d8b-47a6-b0b5-273217ff2664", - "metadata": {}, - "source": [ - "A retenir : \\\n", - "D'après le premier tableau de résultats, toutes les variables sont significatives au seuil de 5%, et à l'exception de nb tickets, elles sont même significatives à 0.1%. \\\n", - "Le graphique ci-dessus confirme que opt in, purchase date max, ventes internet max sont très importantes dans le modèle (on l'avait déjà remarqué car les valeurs des coefficients étaient élevées). \\\n", - "Au contraire, des variables qui avaient un fort coefficient comme is email true (0.87) se trouvent finalement fortement pénalisées et tombent plus vite à 0 que les autres. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Spectacle/Exploration_spectacle.ipynb b/Spectacle/Exploration_spectacle.ipynb deleted file mode 100644 index c8d6a0f..0000000 --- a/Spectacle/Exploration_spectacle.ipynb +++ /dev/null @@ -1,2176 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0eefb67b-5399-44fa-9c1c-7724ec1c7cd2", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os\n", - "import s3fs\n", - "import warnings\n", - "from datetime import date, timedelta, datetime\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "37977b4e-42e7-4d8e-8b9a-6843292fd128", - "metadata": {}, - "outputs": [], - "source": [ - "# Import KPI construction functions\n", - "#exec(open('0_KPI_functions.py').read())\n", - "exec(open('../0_KPI_functions.py').read())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cca62d72-f809-41a9-bb06-1be7d6b09307", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv',\n", - " 'projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv',\n", - " 'projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv',\n", - " 'projet-bdc2324-team1/0_Input/Company_10/target_information.csv']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "BUCKET = \"projet-bdc2324-team1/0_Input/Company_10\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0e1ce56c-2e50-456c-ba97-ed4a699cc8d4", - "metadata": {}, - "outputs": [], - "source": [ - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Input/Company_10/customerplus_cleaned.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " df_customerplus_cleaned = pd.read_csv(file_in, sep=\",\")\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bcdba447-90f7-450c-b4a3-6da656e38493", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_491/3710670046.py:6: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " purchases = pd.read_csv(file_in, sep=\",\", parse_dates = ['purchase_date'], date_parser=custom_date_parser)\n" - ] - } - ], - "source": [ - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Input/Company_10/products_purchased_reduced.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " purchases = pd.read_csv(file_in, sep=\",\", parse_dates = ['purchase_date'], date_parser=custom_date_parser)\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "637aa400-f49a-4d8d-802a-868b241f8a9d", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "dic_base=['campaigns_information','customerplus_cleaned','products_purchased_reduced','target_information']\n", - "for nom_base in dic_base:\n", - " FILE_PATH_S3_fanta = 'projet-bdc2324-team1/0_Input/Company_10/' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e60529b5-986f-4685-91e1-782c2b022e09", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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21165101618561Newsletter mensuelleFalsemanual_static_filter
31165102618560Newsletter mensuelleFalsemanual_static_filter
41165103618558Newsletter mensuelleFalsemanual_static_filter
..................
69253169815818580Newsletter mensuelleFalsemanual_static_filter
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69258 rows × 5 columns

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" - ], - "text/plain": [ - " id customer_id target_name target_type_is_import \\\n", - "0 1165098 618562 Newsletter mensuelle False \n", - "1 1165100 618559 Newsletter mensuelle False \n", - "2 1165101 618561 Newsletter mensuelle False \n", - "3 1165102 618560 Newsletter mensuelle False \n", - "4 1165103 618558 Newsletter mensuelle False \n", - "... ... ... ... ... \n", - "69253 1698158 18580 Newsletter mensuelle False \n", - "69254 1698159 18569 Newsletter mensuelle False \n", - "69255 1698160 2962 Newsletter mensuelle False \n", - "69256 1698161 3825 Newsletter mensuelle False \n", - "69257 1698162 5731 Newsletter mensuelle False \n", - "\n", - " target_type_name \n", - "0 manual_static_filter \n", - "1 manual_static_filter \n", - "2 manual_static_filter \n", - "3 manual_static_filter \n", - "4 manual_static_filter \n", - "... ... \n", - "69253 manual_static_filter \n", - "69254 manual_static_filter \n", - "69255 manual_static_filter \n", - "69256 manual_static_filter \n", - "69257 manual_static_filter \n", - "\n", - "[69258 rows x 5 columns]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "targets" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "27a3c2bf-0541-43b4-b62d-4621692f6c66", - "metadata": {}, - "outputs": [], - "source": [ - "pd.reset_option('display.max_rows',70000)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "51e57220-021f-4b0f-a2c9-360d612c9f75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Newsletter mensuelle\n", - "1 Newsletter mensuelle\n", - "2 Newsletter mensuelle\n", - "3 Newsletter mensuelle\n", - "4 Newsletter mensuelle\n", - " ... \n", - "9995 Newsletter mensuelle\n", - "9996 Newsletter mensuelle\n", - "9997 Newsletter mensuelle\n", - "9998 Newsletter mensuelle\n", - "9999 Newsletter mensuelle\n", - "Name: target_name, Length: 10000, dtype: object" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "targets[\"target_name\"].head(10000)" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "db3748e6-795e-459c-86dd-3389455af217", - "metadata": {}, - "outputs": [], - "source": [ - "companies = {'musee' : ['1', '2', '3', '4', '101'],\n", - " 'sport': ['5', '6', '7', '8', '9'],\n", - " 'musique' : ['10', '11', '12', '13', '14']}" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "d6767ba6-94ef-43f9-8f67-15ecdb41a70b", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Choisissez le type de compagnie : sport ? musique ? musee ? musique\n" - ] - } - ], - "source": [ - "type_of_comp = input('Choisissez le type de compagnie : sport ? musique ? musee ?')\n", - "list_of_comp = companies[type_of_comp] \n" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "050963aa-5cdc-4ff2-a380-16efec89adf0", - "metadata": {}, - "outputs": [], - "source": [ - "# Dossier d'exportation\n", - "BUCKET_OUT = f'projet-bdc2324-team1/Generalization/{type_of_comp}'" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "21a32b69-de53-45ce-9e31-22c45c223924", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Generalization/musique'" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BUCKET_OUT" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "177c4742-5ec6-4326-b984-09e673791801", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Generalization/musique'" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'projet-bdc2324-team1/Generalization/musique'" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "80c6d397-117e-493d-ab0f-7698dbfa8cc4", - "metadata": {}, - "outputs": [], - "source": [ - "def display_covering_time(df, company, datecover):\n", - " \"\"\"\n", - " This function draws the time coverage of each company\n", - " \"\"\"\n", - " min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n", - " max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n", - " datecover[company] = [datetime.strptime(min_date, \"%Y-%m-%d\") + timedelta(days=x) for x in range((datetime.strptime(max_date, \"%Y-%m-%d\") - datetime.strptime(min_date, \"%Y-%m-%d\")).days)]\n", - " print(f'Couverture Company {company} : {min_date} - {max_date}')\n", - " return datecover\n", - "\n", - "\n", - "def compute_time_intersection(datecover):\n", - " \"\"\"\n", - " This function returns the time coverage for all companies\n", - " \"\"\"\n", - " timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n", - " intersection = set.intersection(*timestamps_sets)\n", - " intersection_list = list(intersection)\n", - " formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n", - " return sorted(formated_dates)\n", - "\n", - "\n", - "def df_coverage_modelization(sport, coverage_train = 0.7):\n", - " \"\"\"\n", - " This function returns start_date, end_of_features and final dates\n", - " that help to construct train and test datasets\n", - " \"\"\"\n", - " datecover = {}\n", - " for company in sport:\n", - " df_products_purchased_reduced = display_databases(company, file_name = \"products_purchased_reduced\",\n", - " datetime_col = ['purchase_date'])\n", - " datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n", - " #print(datecover.keys())\n", - " dt_coverage = compute_time_intersection(datecover)\n", - " start_date = dt_coverage[0]\n", - " end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n", - " final_date = dt_coverage[-1]\n", - " return start_date, end_of_features, final_date\n", - " \n", - "\n", - "def dataset_construction(min_date, end_features_date, max_date, directory_path):\n", - " \n", - " # Import customerplus\n", - " df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - " df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", - " df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n", - " \n", - " # Filtre de cohérence pour la mise en pratique de notre méthode\n", - " max_date = pd.to_datetime(max_date, utc = True, format = 'ISO8601') \n", - " end_features_date = pd.to_datetime(end_features_date, utc = True, format = 'ISO8601')\n", - " min_date = pd.to_datetime(min_date, utc = True, format = 'ISO8601')\n", - "\n", - " #Filtre de la base df_campaigns_information\n", - " df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= end_features_date) & (df_campaigns_information['sent_at'] >= min_date)]\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - " \n", - " #Filtre de la base df_products_purchased_reduced\n", - " df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]\n", - "\n", - " print(\"Data filtering : SUCCESS\")\n", - " \n", - " # Fusion de l'ensemble et creation des KPI\n", - "\n", - " # KPI sur les campagnes publicitaires\n", - " df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n", - "\n", - " # KPI sur le comportement d'achat\n", - " df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n", - "\n", - " # KPI sur les données socio-démographiques\n", - " df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)\n", - " \n", - " print(\"KPIs construction : SUCCESS\")\n", - " \n", - " # Fusion avec KPI liés au customer\n", - " df_customer = pd.merge(df_customerplus_clean, df_campaigns_kpi, on = 'customer_id', how = 'left')\n", - " \n", - " # Fill NaN values\n", - " df_customer[['nb_campaigns', 'nb_campaigns_opened']] = df_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)\n", - " \n", - " # Fusion avec KPI liés au comportement d'achat\n", - " df_customer_product = pd.merge(df_tickets_kpi, df_customer, on = 'customer_id', how = 'outer')\n", - " \n", - " # Fill NaN values\n", - " df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0)\n", - "\n", - " print(\"Explanatory variable construction : SUCCESS\")\n", - "\n", - " # 2. Construction of the explained variable \n", - " df_products_purchased_to_predict = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > end_features_date)]\n", - "\n", - " # Indicatrice d'achat\n", - " df_products_purchased_to_predict['y_has_purchased'] = 1\n", - "\n", - " y = df_products_purchased_to_predict[['customer_id', 'y_has_purchased']].drop_duplicates()\n", - "\n", - " print(\"Explained variable construction : SUCCESS\")\n", - " \n", - " # 3. Merge between explained and explanatory variables\n", - " dataset = pd.merge(df_customer_product, y, on = ['customer_id'], how = 'left')\n", - "\n", - " # 0 if there is no purchase\n", - " dataset[['y_has_purchased']].fillna(0) \n", - " \n", - " return dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "2a746097-0cbf-4bd6-b13b-6ee3e5c36fad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Couverture Company 10 : 2016-03-07 - 2023-09-25\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Couverture Company 11 : 2015-06-26 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - ":13: DtypeWarning: Columns (4,8,10) have mixed types. Specify dtype option on import or set low_memory=False.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Couverture Company 12 : 2016-06-14 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Couverture Company 13 : 2010-07-31 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - ":13: DtypeWarning: Columns (8,9) have mixed types. Specify dtype option on import or set low_memory=False.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Couverture Company 14 : 1901-01-01 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", - "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", - "A typical example is when you are setting values in a column of a DataFrame, like:\n", - "\n", - "df[\"col\"][row_indexer] = value\n", - "\n", - "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'NaT' has dtype incompatible with datetime64[ns, UTC], please explicitly cast to a compatible dtype first.\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data filtering : SUCCESS\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":27: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_11/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", - "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", - "A typical example is when you are setting values in a column of a DataFrame, like:\n", - "\n", - "df[\"col\"][row_indexer] = value\n", - "\n", - "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'NaT' has dtype incompatible with datetime64[ns, UTC], please explicitly cast to a compatible dtype first.\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data filtering : SUCCESS\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":27: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_12/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - ":13: DtypeWarning: Columns (4,8,10) have mixed types. Specify dtype option on import or set low_memory=False.\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", - "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", - "A typical example is when you are setting values in a column of a DataFrame, like:\n", - "\n", - "df[\"col\"][row_indexer] = value\n", - "\n", - "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - ":27: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_13/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", - "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", - "A typical example is when you are setting values in a column of a DataFrame, like:\n", - "\n", - "df[\"col\"][row_indexer] = value\n", - "\n", - "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'NaT' has dtype incompatible with datetime64[ns, UTC], please explicitly cast to a compatible dtype first.\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data filtering : SUCCESS\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":27: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_14/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - ":13: DtypeWarning: Columns (8,9) have mixed types. Specify dtype option on import or set low_memory=False.\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", - "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", - "A typical example is when you are setting values in a column of a DataFrame, like:\n", - "\n", - "df[\"col\"][row_indexer] = value\n", - "\n", - "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - "/tmp/ipykernel_438/573049956.py:55: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'NaT' has dtype incompatible with datetime64[ns, UTC], please explicitly cast to a compatible dtype first.\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data filtering : SUCCESS\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":27: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n" - ] - } - ], - "source": [ - "# Create test dataset and train dataset for sport companies\n", - "\n", - "start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7)\n", - "\n", - "for company in list_of_comp:\n", - " dataset_test = dataset_construction(min_date = start_date, end_features_date = end_of_features,\n", - " max_date = final_date, directory_path = company) " - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "01900e04-61e7-4a1b-8c9c-b72e42ba9507", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exportation dataset test : SUCCESS\n" - ] - } - ], - "source": [ - " # Exportation\n", - "FILE_KEY_OUT_S3 = \"dataset_test\" + company + \".csv\"\n", - "FILE_PATH_OUT_S3 = BUCKET_OUT + \"/\" + FILE_KEY_OUT_S3\n", - " \n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " dataset_test.to_csv(file_out, index = False)\n", - " \n", - "print(\"Exportation dataset test : SUCCESS\")" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "b0de2e18-edff-416c-b623-e3e23016029d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Generalization/musique/dataset_test14.csv'" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FILE_PATH_OUT_S3" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "8f56d6ee-82c9-43e2-813d-33d6aaa458dd", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dataset_test14' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[105], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdataset_test14\u001b[49m\n", - "\u001b[0;31mNameError\u001b[0m: name 'dataset_test14' is not defined" - ] - } - ], - "source": [ - "dataset_test14" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9232a8df-c51a-4f10-9fc8-ce4f8ad8aab4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Spectacle/Stat_desc.ipynb b/Spectacle/Stat_desc.ipynb deleted file mode 100644 index d5d4a08..0000000 --- a/Spectacle/Stat_desc.ipynb +++ /dev/null @@ -1,9083 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "be628bfc-0bca-48b0-97c9-29063289127e", - "metadata": {}, - "source": [ - "# Statistiques descriptives : compagnies offrant des spectacles" - ] - }, - { - "cell_type": "markdown", - "id": "0bf5450b-f44d-430a-aed7-d875dc365048", - "metadata": {}, - "source": [ - "## Importations et chargement des données" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "aa915888-cede-4eb0-8a26-7df573d29a3e", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os\n", - "import s3fs\n", - "import warnings\n", - "from datetime import date, timedelta, datetime\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "import re\n", - "import io" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "17949e81-c30b-4fdf-9872-d7dc2b22ba9e", - "metadata": {}, - "outputs": [], - "source": [ - "# Import KPI construction functions\n", - "#exec(open('0_KPI_functions.py').read())\n", - "exec(open('../0_KPI_functions.py').read())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9c1737a2-bad8-4266-8dec-452085d8cfe7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv',\n", - " 'projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv',\n", - " 'projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv',\n", - " 'projet-bdc2324-team1/0_Input/Company_10/target_information.csv']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "BUCKET = \"projet-bdc2324-team1/0_Input/Company_10\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a35dc2f6-2017-4b21-abd2-2c4c112c96b2", - "metadata": {}, - "outputs": [], - "source": [ - "# test avec company 10\n", - "\n", - "dic_base=['campaigns_information','customerplus_cleaned','products_purchased_reduced','target_information']\n", - "for nom_base in dic_base:\n", - " FILE_PATH_S3_fanta = 'projet-bdc2324-team1/0_Input/Company_10/' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "40b705eb-fd18-436b-b150-61611a3c6a84", - "metadata": {}, - "outputs": [], - "source": [ - "# fonction permettant d'extraire une table à partir du numéro de la compagnie (directory_path)\n", - "\n", - "def display_databases(directory_path, file_name, datetime_col = None):\n", - " \"\"\"\n", - " This function returns the file from s3 storage \n", - " \"\"\"\n", - " file_path = \"projet-bdc2324-team1\" + \"/0_Input/Company_\" + directory_path + \"/\" + file_name + \".csv\"\n", - " print(\"File path : \", file_path)\n", - " with fs.open(file_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser) \n", - " return df \n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c56decc3-de19-4786-82a4-1386c72a6bfb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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11799178369844096133guichet2016-04-28 17:58:26+02:009.0Falsecirquele grand tabo t gourmand jeune5èmes hurlantstest 2016/20172016-11-18 00:00:00+01:001901-01-01 00:09:21+00:09True
21799179369844096131guichet2016-04-28 17:58:26+02:009.0Falsethéâtrele grand tabo t gourmand jeunedom juantest 2016/20172016-12-07 00:00:00+01:001901-01-01 00:09:21+00:09True
31799180369844096131guichet2016-04-28 17:58:26+02:009.0Falsethéâtrele grand tabo t gourmand jeunevanishing pointtest 2016/20172017-01-04 00:00:00+01:001901-01-01 00:09:21+00:09True
41799181369844096133guichet2016-04-28 17:58:26+02:0012.0Falsecirquela cite des congresabo t gourmand jeunea o lang photest 2016/20172017-01-03 00:00:00+01:001901-01-01 00:09:21+00:09True
...................................................
49230932522326217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231032522336217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231132522346217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231232522356217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231332522366217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
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492314 rows × 16 columns

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" - ], - "text/plain": [ - " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", - "0 1799177 36984 409613 2 guichet \n", - "1 1799178 36984 409613 3 guichet \n", - "2 1799179 36984 409613 1 guichet \n", - "3 1799180 36984 409613 1 guichet \n", - "4 1799181 36984 409613 3 guichet \n", - "... ... ... ... ... ... \n", - "492309 3252232 621716 710062 1 guichet \n", - "492310 3252233 621716 710062 1 guichet \n", - "492311 3252234 621716 710062 1 guichet \n", - "492312 3252235 621716 710062 1 guichet \n", - "492313 3252236 621716 710062 1 guichet \n", - "\n", - " purchase_date amount is_full_price name_event_types \\\n", - "0 2016-04-28 17:58:26+02:00 9.0 False danse \n", - "1 2016-04-28 17:58:26+02:00 9.0 False cirque \n", - "2 2016-04-28 17:58:26+02:00 9.0 False théâtre \n", - "3 2016-04-28 17:58:26+02:00 9.0 False théâtre \n", - "4 2016-04-28 17:58:26+02:00 12.0 False cirque \n", - "... ... ... ... ... \n", - "492309 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", - "492310 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", - "492311 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", - "492312 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", - "492313 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", - "\n", - " name_facilities name_categories \\\n", - "0 le grand t abo t gourmand jeune \n", - "1 le grand t abo t gourmand jeune \n", - "2 le grand t abo t gourmand jeune \n", - "3 le grand t abo t gourmand jeune \n", - "4 la cite des congres abo t gourmand jeune \n", - "... ... ... \n", - "492309 cap nort tarif sco co 1 seance scolaire \n", - "492310 cap nort tarif sco co 1 seance scolaire \n", - "492311 cap nort tarif sco co 1 seance scolaire \n", - "492312 cap nort tarif sco co 1 seance scolaire \n", - "492313 cap nort tarif sco co 1 seance scolaire \n", - "\n", - " name_events name_seasons start_date_time \\\n", - "0 aringa rossa test 2016/2017 2016-09-27 00:00:00+02:00 \n", - "1 5èmes hurlants test 2016/2017 2016-11-18 00:00:00+01:00 \n", - "2 dom juan test 2016/2017 2016-12-07 00:00:00+01:00 \n", - "3 vanishing point test 2016/2017 2017-01-04 00:00:00+01:00 \n", - "4 a o lang pho test 2016/2017 2017-01-03 00:00:00+01:00 \n", - "... ... ... ... \n", - "492309 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", - "492310 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", - "492311 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", - "492312 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", - "492313 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", - "\n", - " end_date_time open \n", - "0 1901-01-01 00:09:21+00:09 True \n", - "1 1901-01-01 00:09:21+00:09 True \n", - "2 1901-01-01 00:09:21+00:09 True \n", - "3 1901-01-01 00:09:21+00:09 True \n", - "4 1901-01-01 00:09:21+00:09 True \n", - "... ... ... \n", - "492309 1901-01-01 00:09:21+00:09 True \n", - "492310 1901-01-01 00:09:21+00:09 True \n", - "492311 1901-01-01 00:09:21+00:09 True \n", - "492312 1901-01-01 00:09:21+00:09 True \n", - "492313 1901-01-01 00:09:21+00:09 True \n", - "\n", - "[492314 rows x 16 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "products_purchased_reduced" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "afd044b8-ac83-4a35-b959-700cae0b3b41", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/target_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - ":28: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables imported for tenant 10\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_11/target_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - ":28: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables imported for tenant 11\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_12/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - "/tmp/ipykernel_465/3170175140.py:10: DtypeWarning: Columns (4,8,10) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_12/target_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - ":28: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables imported for tenant 12\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_13/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_13/target_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - ":28: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables imported for tenant 13\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_14/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - "/tmp/ipykernel_465/3170175140.py:10: DtypeWarning: Columns (8,9) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_14/target_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - ":28: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables imported for tenant 14\n" - ] - } - ], - "source": [ - "# création des bases contenant les KPI pour les 5 compagnies de spectacle\n", - "\n", - "# liste des compagnies de spectacle\n", - "nb_compagnie=['10','11','12','13','14']\n", - "\n", - "# début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle\n", - "for directory_path in nb_compagnie:\n", - " df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - " df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", - " df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n", - " df_target_information = display_databases(directory_path, file_name = \"target_information\")\n", - " \n", - " df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n", - " df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n", - " df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)\n", - "\n", - " \n", - "# creation de la colonne Number compagnie, qui permettra d'agréger les résultats\n", - " df_tickets_kpi[\"number_compagny\"]=int(directory_path)\n", - " df_campaigns_kpi[\"number_compagny\"]=int(directory_path)\n", - " df_customerplus_clean[\"number_compagny\"]=int(directory_path)\n", - " df_target_information[\"number_compagny\"]=int(directory_path)\n", - "\n", - " if nb_compagnie.index(directory_path)>=1:\n", - " customerplus_clean_spectacle=pd.concat([customerplus_clean_spectacle,df_customerplus_clean],axis=0)\n", - " campaigns_information_spectacle=pd.concat([campaigns_information_spectacle,df_campaigns_kpi],axis=0)\n", - " products_purchased_reduced_spectacle=pd.concat([products_purchased_reduced_spectacle,df_tickets_kpi],axis=0)\n", - " target_information_spectacle=pd.concat([target_information_spectacle,df_target_information],axis=0)\n", - " else:\n", - " customerplus_clean_spectacle=df_customerplus_clean\n", - " campaigns_information_spectacle=df_campaigns_kpi\n", - " products_purchased_reduced_spectacle=df_tickets_kpi\n", - " target_information_spectacle=df_target_information\n", - "\n", - " print(f\"Tables imported for tenant {directory_path}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "b5a4a031-9533-4a50-8569-5f4246691a7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...purchase_countfirst_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagny
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180312319517NaNNaN01556FalseNaN0True...22020-01-01 14:06:52+00:00frfemale1001.0011
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3 rows × 29 columns

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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity \\\n", - "17 2 139 NaN NaN 0 \n", - "18031 2 319517 NaN NaN 0 \n", - "291642 2 757541 303.0 5.0 1 \n", - "\n", - " tenant_id is_partner deleted_at gender is_email_true ... \\\n", - "17 875 False NaN 2 False ... \n", - "18031 1556 False NaN 0 True ... \n", - "291642 862 False NaN 1 True ... \n", - "\n", - " purchase_count first_buying_date country gender_label \\\n", - "17 3 NaN NaN other \n", - "18031 2 2020-01-01 14:06:52+00:00 fr female \n", - "291642 3 2016-09-08 14:50:00+00:00 fr male \n", - "\n", - " gender_female gender_male gender_other country_fr has_tags \\\n", - "17 0 0 1 NaN 0 \n", - "18031 1 0 0 1.0 0 \n", - "291642 0 1 0 1.0 1 \n", - "\n", - " number_compagny \n", - "17 10 \n", - "18031 11 \n", - "291642 14 \n", - "\n", - "[3 rows x 29 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customerplus_clean_spectacle[customerplus_clean_spectacle[\"customer_id\"]==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b9b6ec1f-36fb-4ee9-a1ed-09ff41878005", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'customerplus_clean_spectacle' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcustomerplus_clean_spectacle\u001b[49m[customerplus_clean_spectacle[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcustomer_id\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", - "\u001b[0;31mNameError\u001b[0m: name 'customerplus_clean_spectacle' is not defined" - ] - } - ], - "source": [ - "customerplus_clean_spectacle[customerplus_clean_spectacle[\"customer_id\"]==1]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "a12c1b7d-6f6f-483e-b215-6336d7a51057", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'street_id', 'structure_id', 'mcp_contact_id',\n", - " 'fidelity', 'tenant_id', 'is_partner', 'deleted_at', 'gender',\n", - " 'is_email_true', 'opt_in', 'last_buying_date', 'max_price',\n", - " 'ticket_sum', 'average_price', 'average_purchase_delay',\n", - " 'average_price_basket', 'average_ticket_basket', 'total_price',\n", - " 'purchase_count', 'first_buying_date', 'country', 'gender_label',\n", - " 'gender_female', 'gender_male', 'gender_other', 'country_fr',\n", - " 'has_tags', 'number_compagny'],\n", - " dtype='object')" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customerplus_clean_spectacle.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "05b9a396-dcd7-4d3d-8b39-5ca48beba4b0", - "metadata": {}, - "outputs": [], - "source": [ - "#customerplus_clean_spectacle.isna().sum()\n", - "#campaigns_information_spectacle.isna().sum()\n", - "#products_purchased_reduced_spectacle.isna().sum()\n", - "#target_information_spectacle.isna().sum()" - ] - }, - { - "cell_type": "markdown", - "id": "81e15508-32ca-46f1-a03d-1febddbbf5b4", - "metadata": {}, - "source": [ - "### Ajout : importation de la table train_set pour faire les stats desc dessus" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3a1fdd6b-ac43-4e90-9a31-4f522bcc44bb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3450421856.py:9: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " train_set_spectacle = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "# importation de la table train_set pour les compagnies de spectacle (ou musique)\n", - "\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "path_train_set_spectacle = \"projet-bdc2324-team1/Generalization/musique/Train_set.csv\"\n", - "\n", - "with fs.open(path_train_set_spectacle, mode=\"rb\") as file_in:\n", - " train_set_spectacle = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3a4c1ff4-2861-4e86-99df-26eea0370dc3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internet...countrygender_labelgender_femalegender_malegender_othercountry_frnb_campaignsnb_campaigns_openedtime_to_openy_has_purchased
010_4927790.00.00.00.00.0550.0550.0-1.00.0...frfemale1001.013.04.08 days 04:08:270.0
110_5634240.00.00.00.00.0550.0550.0-1.00.0...frother0011.010.09.00 days 01:39:58.5555555550.0
210_443690.00.00.00.00.0550.0550.0-1.00.0...frmale0101.014.00.0NaN0.0
310_6202710.00.00.00.00.0550.0550.0-1.00.0...NaNother001NaN9.00.0NaN0.0
410_6876440.00.00.00.00.0550.0550.0-1.00.0...NaNother001NaN4.00.0NaN0.0
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5 rows × 40 columns

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" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 10_492779 0.0 0.0 0.0 0.0 \n", - "1 10_563424 0.0 0.0 0.0 0.0 \n", - "2 10_44369 0.0 0.0 0.0 0.0 \n", - "3 10_620271 0.0 0.0 0.0 0.0 \n", - "4 10_687644 0.0 0.0 0.0 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 550.0 550.0 \n", - "1 0.0 550.0 550.0 \n", - "2 0.0 550.0 550.0 \n", - "3 0.0 550.0 550.0 \n", - "4 0.0 550.0 550.0 \n", - "\n", - " time_between_purchase nb_tickets_internet ... country gender_label \\\n", - "0 -1.0 0.0 ... fr female \n", - "1 -1.0 0.0 ... fr other \n", - "2 -1.0 0.0 ... fr male \n", - "3 -1.0 0.0 ... NaN other \n", - "4 -1.0 0.0 ... NaN other \n", - "\n", - " gender_female gender_male gender_other country_fr nb_campaigns \\\n", - "0 1 0 0 1.0 13.0 \n", - "1 0 0 1 1.0 10.0 \n", - "2 0 1 0 1.0 14.0 \n", - "3 0 0 1 NaN 9.0 \n", - "4 0 0 1 NaN 4.0 \n", - "\n", - " nb_campaigns_opened time_to_open y_has_purchased \n", - "0 4.0 8 days 04:08:27 0.0 \n", - "1 9.0 0 days 01:39:58.555555555 0.0 \n", - "2 0.0 NaN 0.0 \n", - "3 0.0 NaN 0.0 \n", - "4 0.0 NaN 0.0 \n", - "\n", - "[5 rows x 40 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_set_spectacle.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4632384d-2a06-445d-9fdb-b0c91b37ebaf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 1.])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on remplace les valeurs has purchased = NaN par des 0\n", - "train_set_spectacle[\"y_has_purchased\"] = train_set_spectacle[\"y_has_purchased\"].fillna(0)\n", - "train_set_spectacle[\"y_has_purchased\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5fd56696-b479-46c7-8a59-fb8137db5fb5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10, 11, 12, 13, 14])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on reproduit une colonne avec le numéro de la compagnie \n", - "\n", - "train_set_spectacle[\"number_company\"] = train_set_spectacle[\"customer_id\"].apply(lambda x : int(re.split(\"_\", str(x))[0]))\n", - "train_set_spectacle[\"number_company\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "91c6e047-43d2-456c-81f1-087026eef4f0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internet...gender_labelgender_femalegender_malegender_othercountry_frnb_campaignsnb_campaigns_openedtime_to_openy_has_purchasednumber_company
010_4927790.00.00.00.00.0550.0550.0-1.00.0...female1001.013.04.08 days 04:08:270.010
110_5634240.00.00.00.00.0550.0550.0-1.00.0...other0011.010.09.00 days 01:39:58.5555555550.010
210_443690.00.00.00.00.0550.0550.0-1.00.0...male0101.014.00.0NaN0.010
310_6202710.00.00.00.00.0550.0550.0-1.00.0...other001NaN9.00.0NaN0.010
410_6876440.00.00.00.00.0550.0550.0-1.00.0...other001NaN4.00.0NaN0.010
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5 rows × 41 columns

\n", - "
" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 10_492779 0.0 0.0 0.0 0.0 \n", - "1 10_563424 0.0 0.0 0.0 0.0 \n", - "2 10_44369 0.0 0.0 0.0 0.0 \n", - "3 10_620271 0.0 0.0 0.0 0.0 \n", - "4 10_687644 0.0 0.0 0.0 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 550.0 550.0 \n", - "1 0.0 550.0 550.0 \n", - "2 0.0 550.0 550.0 \n", - "3 0.0 550.0 550.0 \n", - "4 0.0 550.0 550.0 \n", - "\n", - " time_between_purchase nb_tickets_internet ... gender_label \\\n", - "0 -1.0 0.0 ... female \n", - "1 -1.0 0.0 ... other \n", - "2 -1.0 0.0 ... male \n", - "3 -1.0 0.0 ... other \n", - "4 -1.0 0.0 ... other \n", - "\n", - " gender_female gender_male gender_other country_fr nb_campaigns \\\n", - "0 1 0 0 1.0 13.0 \n", - "1 0 0 1 1.0 10.0 \n", - "2 0 1 0 1.0 14.0 \n", - "3 0 0 1 NaN 9.0 \n", - "4 0 0 1 NaN 4.0 \n", - "\n", - " nb_campaigns_opened time_to_open y_has_purchased \\\n", - "0 4.0 8 days 04:08:27 0.0 \n", - "1 9.0 0 days 01:39:58.555555555 0.0 \n", - "2 0.0 NaN 0.0 \n", - "3 0.0 NaN 0.0 \n", - "4 0.0 NaN 0.0 \n", - "\n", - " number_company \n", - "0 10 \n", - "1 10 \n", - "2 10 \n", - "3 10 \n", - "4 10 \n", - "\n", - "[5 rows x 41 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_set_spectacle.head()" - ] - }, - { - "cell_type": "markdown", - "id": "fff306c2-1d41-4ef6-867b-ba9a7cf4ee68", - "metadata": {}, - "source": [ - "## Statistiques descriptives" - ] - }, - { - "cell_type": "markdown", - "id": "0549bdc4-edd7-4511-916e-26e94b5a30f5", - "metadata": {}, - "source": [ - "### 0. Détection du client anonyme (outlier) - utile pour la section 3" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5b460061-f8b5-4a6b-ba59-539446d8487f", - "metadata": {}, - "outputs": [], - "source": [ - "def outlier_detection(directory_path = \"1\", coupure = 1):\n", - " df_tickets = display_databases(directory_path, file_name = 'products_purchased_reduced' , datetime_col = ['purchase_date'])\n", - " df_tickets_kpi = tickets_kpi_function(df_tickets)\n", - "\n", - " if directory_path == \"101\" :\n", - " df_tickets_1 = display_databases(directory_path, file_name = 'products_purchased_reduced_1' , datetime_col = ['purchase_date'])\n", - " df_tickets_kpi_1 = tickets_kpi_function(df_tickets_1)\n", - "\n", - " df_tickets_kpi = pd.concat([df_tickets_kpi, df_tickets_kpi_1])\n", - " # Part du CA par customer\n", - " total_amount_share = df_tickets_kpi.groupby('customer_id')['total_amount'].sum().reset_index()\n", - " total_amount_share['total_amount_entreprise'] = total_amount_share['total_amount'].sum()\n", - " total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['total_amount_entreprise']\n", - " \n", - " total_amount_share_index = total_amount_share.set_index('customer_id')\n", - " df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False)\n", - " \n", - " top = df_circulaire[:coupure]\n", - " rest = df_circulaire[coupure:]\n", - " \n", - " # Calculez la somme du reste\n", - " rest_sum = rest.sum()\n", - " \n", - " # Créez une nouvelle série avec les cinq plus grandes parts et 'Autre'\n", - " new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])])\n", - " \n", - " # Créez le graphique circulaire\n", - " plt.figure(figsize=(3, 3))\n", - " plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5)\n", - " plt.axis('equal') # Assurez-vous que le graphique est un cercle\n", - " plt.title('Répartition des montants totaux')\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cccee90c-67d1-4e14-8410-1210a5ef97d9", - "metadata": {}, - "outputs": [], - "source": [ - "# def d'une fonction permettant de générer un barplot à plusieurs barres selon une modalité \n", - "\n", - "def multiple_barplot(data, x, y, var_labels, bar_width=0.35,\n", - " figsize=(10, 6), xlabel=None, ylabel=None, title=None, dico_labels = None) :\n", - "\n", - " # si on donne aucun nom pour la legende, le graphique reprend les noms des variables x et y \n", - " xlabel = x if xlabel==None else xlabel\n", - " ylabel = y if ylabel==None else ylabel\n", - " \n", - " fig, ax = plt.subplots(figsize=figsize)\n", - " \n", - " categories = data[x].unique()\n", - " bar_width = bar_width\n", - " bar_positions = np.arange(len(categories))\n", - " \n", - " # Grouper les données par label et créer les barres groupées\n", - " for label in data[var_labels].unique():\n", - " label_data = data[data[var_labels] == label]\n", - " values = [label_data[label_data[x] == category][y].values[0] for category in categories]\n", - " \n", - " # label_printed = \"achat durant la période\" if label else \"aucun achat\"\n", - " label_printed = f\"{var_labels}={label}\" if dico_labels==None else dico_labels[label]\n", - " \n", - " ax.bar(bar_positions, values, bar_width, label=label_printed)\n", - " \n", - " # Mise à jour des positions des barres pour le prochain groupe\n", - " bar_positions = [pos + bar_width for pos in bar_positions]\n", - "\n", - " # Ajout des étiquettes, de la légende, etc.\n", - " ax.set_xlabel(xlabel)\n", - " ax.set_ylabel(ylabel)\n", - " ax.set_title(title)\n", - " ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))])\n", - " ax.set_xticklabels(categories)\n", - " ax.legend()\n", - " \n", - " # Affichage du plot - la proportion de français est la même selon qu'il y ait achat sur la période ou non\n", - " # sauf compagnie 12, et peut-être 13\n", - " # plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "id": "b6417f09-a6c7-4319-95b3-98c95ec5a3b7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_436/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# outlier à enlever (dépend des stats desc !)\n", - "outlier_detection(directory_path=\"10\") # mettre 2 si on veut le 1er client non anonyme" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "f08c082e-f76f-41f3-9530-3e6700eb74d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outlier for tenant 10\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_436/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "data": { - "image/png": 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YMYNWrVoxcuTIMs+zWbNmAHz00Uf4+vpiMpmoU6dOuS0/V2vevDnr1q1j6dKlhIWF4evrS2xsLLfccgv9+/enRYsWBAQEsG/fPj7//HPat29/zTX2kvIHBQUxbdo0nnvuOUaNGsXw4cNJTk5m1qxZmEwmZsyYYZcJii7jPXr0wMPDg+HDhzNlyhRyc3N5//33SU1NLZJj5MiRjBgxgokTJzJkyBCOHz/Oa6+9VmT3ya5du3jssccYPXq0rSTnzZvH0KFDmTNnDk888cQNfb5lpsmhphKUNKxIKesRy6ioKNWgQQNVWFiolFJqx44d6h//+IeqWbOmcnd3V6Ghoap79+7qgw8+KDLPFStWqJEjRyp/f3/b0cBDhw7ZvcbevXtVjx49lK+vrwoICFDDhg1TJ06cKHJ0+NJRzqvH1ymlVF5enho/frwKCQlROp2uxHGYlyxatEg1atRIubu7OzwOs2HDhsrd3V0FBwerESNGlDgO82qjR49W0dHRRe6/Wlpamho3bpzy9/dXXl5eqkePHmr//v3FjsM8evSoGjdunAoPD1fu7u4qJCREdejQwW70wX/+8x/VoUMHFRwcrDw8PFRUVJS6//771bFjx66Z49JR8m+++cbu/pKWk+J+Lzk5OeqZZ55R0dHRyt3dXYWFhamHH364xHGYVyvuqO2cOXNUnTp1lMFgsBvlcKPLz6X3dWl5UUqpuLg41bFjR+Xl5WU3DnPq1Kmqbdu2tjHIdevWVU8++aQ6f/78NT7Ra+dXSqlPPvlEtWjRQnl4eCg/Pz81cOBA21CmS661jC9dulTddNNNymQyqfDwcPXPf/7TNvLiyiPzFotFvfbaa6pu3brKZDKptm3bqjVr1th93pmZmapRo0aqSZMmKisryy7DpEmTlLu7e5GRIBVNp1T1vszu/PnzGTt2LFu3brXtFxVCiOtRJfZhCiGEM5DCFEIIB1X7TXIhhCgvsoYphBAOksIUQggHSWEKIYSDpDCFEMJBUphCCOEgKUwhhHCQFKYQQjhIClMIIRwkhSmEEA6SwhRCCAdJYQohhIOkMIUQwkFSmEII4aAqcYkK4UIK8yE3DXIvgKUQ9G6gN1z8rxvoDJfvczOBu0nrxMKFSGGKypGbDsnxkHLE+t/U45CTerkcc9Ks/19Q8gW8iuXuDd5B4BUM3iHgGwo1wqFGbfALh6AG4B9ZAW9IuCI5H6YoX9kpcHIbnN1jLcbkw9afrLPaZTL5Qc0mUKvpxZ9mULMxGH1Lf64QV5DCFNdPKTi7F47/AQl/wsmtkHpU61QO0oF/FIS3hjqdoU4XCKqndSjh5KQwRdmkJcCBZXD0N2tR5qRonaj8+EVB3c5QpyvU7QI+NbVOJJyMFKYo3dn9sH8p7PsJEuO0TlN5ajaBet2h6WCIaKN1GuEEpDBFUUrBqe2XSzL5kNaJtBdYF5oNhebDIKSh1mmERqQwxWUZSbB9AWz/HC6c0DqN8wptYS3O5kOtR+OFy5DCFHBkPWybB/t/to59FI7R6SHmNrh5PDTqbx0bKqo1KUxXlZMKcV/Ctk9lk7s8+EVC23HQZgx4BWqdRlQQKUxXk3oMNvwHdn4DhTlap6l+3L2g5X3Q4REIiNE6jShnUpiuIu0E/PZ/ELcILAVap6n+dAZoMhA6PQWhzbROI8qJFGZ1d+EUbHgd/v4CzPlap3E9Oj00/wd0f16+olkNSGFWV+mJ1k3v7QvAnKd1GmEwwi0ToNPT4OmvdRpxnaQwq5v8bOsa5aZ3oTBX6zTiaiZ/62b6LQ+Cm1HrNKKMpDCrk93fw4ppkH5S6ySiNH5RcPs0aPEPrZOIMpDCrA6SD8NPT8LR9VonEWVVpwvc+bYcUa8ipDCrMnMB/D7Hugkum99Vl7s33D4d2k0AvVwEwZlJYVZVJ7fBkkfg3D6tk4jyEnkrDJwLwQ20TiJKIIVZ1Vgs8PsbsG62fI2xOnIzQdep0OEx+aqlE5LCrEoykuD7B6znohTVW+1WMGSenNTYyUhhVhUHV8DihyH7vNZJRGUx1oBB70Pj/lonERdJYTq7wnxYNRM2vwfIr8r16KDj49aDQrKJrjkpTGeWchS+GQ2JO7ROIrRWpzMM+S/4hGidxKVJYTqrE5vhf/dCdrLWSYSz8K0N/1gAkTdrncRlSWE6o93fwQ8Py3fARVF6d+g9G9o9oHUSlySF6Wx+ex3WvIjsrxTX1P4R6Pki6HRaJ3EpUpjOwlwIPz0Bf3+udRJRVTQbAoM+ADcPrZO4DClMZ5B7Ab4eBUfWaZ1EVDUxnWD4IjD6ap3EJUhhai3rPHx2J5zdo3USUVXVbg0jvpNrCVUCKUwtZafAZwPgzG6tk4iqLqQxjPwBaoRpnaRak8LUSnaKdc3yzC6tk4jqIiAGxvwMfhFaJ6m25FxSWshJhQUDpSxF+Uo9Bp/fBVkydreiSGFWtpw0WDAIknZqnURUR+cPwheDIS9D6yTVkhRmZcq9YF0DSIzTOomozhLjYNFwKJCTSpc3KczKUpALC4fB6e1aJxGu4NgG+HasdXyvKDdSmJVBKfhhAiRs0TqJcCUHlsGPj1iXP1EupDArw6oZsHeJ1imEK9qxCJY/q3WKakMKs4IdifsNNr6ldQzhyra8D9sXaJ2iWpDCrEBbjiTT65tMFtV+FmWQ7/sKDf38NJz6S+sUVZ4MXK8gCSnZDHx3IylZ+QCMrH2KWTmvoM+RMXJCIzUi4MH14B2sdZIqS9YwK0BugZkJn/9lK0uAz0+Hc496ibyAhhomEy4t/aT1yLnFrHWSKksKswK89PM+9iWmF7n/z7QadEl5juSwzhqkEgLrFUdXzdQ6RZUlhVnOVuxJ4vPNx0t8PCnPg1uPP8jeyOGVmEqIK/zxNuxZrHWKKkkKsxwlXshhynelf+WxwKKj76EBLAl/GqV3q4RkQlxlySRIPqx1iipHCrOcWCyKJ/4XR1p2gcPPefxwa14JfBFl9KvAZEIUIz8TFk8Ei0XrJFWKFGY5eXdtPFuOppT5eR+ejGKM28sU+NWpgFRCXEPCZusYTeEwGVZUDnYkpDHk/T8otFz/RxnlmcvSmh/hd2ZzOSYTohRunvDwRgiqp3WSKkHWMG9QodnC1O933VBZApzIMXHryUnERw4pp2RCOKAwx7o/UzbNHSKFeYP+u/FosUOIrkeO2cAdh4bwa8TjKJ38akQlObEJtnygdYoqQf4qb8DJ1GzmrDpU7vN9MP4W5oT8G+XhU+7zFqJYq/8tR80dIIV5A2Ys2UN2fsV8a+KtE3V50OMVCmtEVsj8hbBTmANLHtE6hdOTwrxOv+xKZPX+sxX6GivOB9IrcyaZNdtU6OsIAcCJP2DXt1qncGpSmNchK6+QWUv3VsprHc725JbTT3AiYkClvJ5wcStnQEGO1imclhTmdfjotyMkpVfe9VKyCg10jh/Ob5EPo9BV2usKF5R+Eja+rXUKpyWFWUbJmXnM+/2oJq896lAnPqw1A+XupcnrCxex8S3ISNI6hVOSwiyjuWvjyczT7sJSrxxvyBNeL2P2CdMsg6jmCrJg/atap3BKUphlcDI1m4VbTmgdgyVnanJn3r/JDm6hdRRRXW1fIMOMiiGFWQZvrjxEfqFzfCNiT4Y37c88TWJ4L62jiOrIUmgdmynsSGE66OCZDH74+6TWMexcKHCjw5FR/Bl5v9ZRRHW0dwmc3ad1Cqcihemgt1cf4ga/Ll4hlNLxj0O3syDseZSbSes4olpR8MdcrUM4FSlMB5xMzeaX3c591HD60SZM9X0Zi1eI1lFEdbLra8g4o3UKpyGF6YD5G49hdsbVy6t8lRjKUPNL5AY21jqKqC7M+XJijitIYZYiM6+Qr7YmaB3DYdsv+NDp/FTO1e6udRRRXWz7L+RnaZ3CKUhhluJ/f54gQ8Nxl9fjXL47tx4dx46okVpHEdVBbhps/1zrFE5BCvMazBbF/D+OaR3jupiVnoEH+/B1+DMovbvWcURVt/k9uZ45UpjX9OueJE6mVu0TEUw5fBOz/F/C4hmodRRRlaUdh30/ap1Cc1KY17DoT+2/1VMe5p+O4D5eIt+/vtZRRFUmm+VSmCU5k57LxvjzWscoN5tS/eiW9i9SQztqHUVUVUfWQWbFngPW2UlhlmBJ3CmnHKh+I07lGrn1xMPsj7xb6yiiKlJmlz/BsBRmCb7ffkrrCBUiz6Kn96GBLA2fjNK7aR1HVDU7v9I6gaakMIuxLzGd/UkZWseoUI8ebstrQS+gjDW0jiKqksQ4OHdQ6xSakcIsxg9/V8+1y6u9nxDNWMNsCvxitI4iqhIXXsuUwryKxaJYEucahQmwLiWAHunTSa91i9ZRRFWx6xtQ1WwHv4OkMK/yd0IaZ9LztI5RqY7lmLjl5CMcibhL6yiiKkg7DglbtE6hCSnMq6w/4JrDJnLMBrrHD2N15KMonSwWohQHl2udQBPyl3GVtQfOaR1BU/cfas/cmrNQHt5aRxHO7PAarRNoQgrzCucy8th9+oLWMTT3n+P1mGicTaFvuNZRhLNK3AlZyVqnqHRSmFdYf/Ccq+7LLuKXc8H0zf43mSGttI4inJKCo+u0DlHppDCvsM5F91+W5GCWJ+0TnyQhop/WUYQzcsHNcinMi8wWxYZD1ee74+Ulo9CNTvH3sTHyQRQ6reMIZ3J4ndYJKp0U5kX7k9K5kFOgdQyndd+hLswLnY5y89Q6inAW6Sfh/CGtU1QqKcyLdiTIwZ7SvHgslqe8Z2P2DtU6inAWLrZZLoV5UVxCqtYRqoTvz9RkUP4L5AQ30zqKcAYnNmmdoFJJYV4ka5iO25XhTYczU0gK76F1FKG1039rnaBSlbkwf/vtNwYMGEDt2rXR6XQsXrzY7vEzZ84wZswYateujZeXF7179+bQIfv9HF27dkWn09n93HPPPbbHjx07xv3330+dOnXw9PSkXr16zJgxg/z8fLv5PP7447Rp0waj0UjLli3L+lZssvIKOXS2ep+dqLylFrjR/sgYtkWN0zqK0FLqMchxna2zMhdmVlYWN910E3Pnzi3ymFKKQYMGceTIEZYsWcLff/9NdHQ0d9xxB1lZ9pfpfOCBB0hMTLT9fPjhh7bH9u/fj8Vi4cMPP2TPnj28+eabfPDBBzz33HNFXm/cuHHcffeNnRB358kL1e5kwZVBKR1DD97BwtrPoQxGreMIrSTu0DpBpSnzGWT79OlDnz59in3s0KFDbN68md27d9O0aVMA3nvvPWrWrMmiRYsYP368bVovLy9CQ4s/eNC7d2969+5tu123bl0OHDjA+++/z+uvv267/+233wbg3Llz7Ny5s6xvxSYuIe26nyvgX0easTfsJV7IfQV9jgzNcjmJO6FuV61TVIpy3YeZl2c9y4/JZLLdZzAY8PDw4Pfff7ebduHChQQHB9O0aVOefvppMjKuvUl84cIFAgMr5sqHexPTK2S+rmRhYm3+YXmJvMBYraOIynZ2n9YJKk25FmajRo2Ijo7m2WefJTU1lfz8fF555RWSkpJITEy0TXffffexaNEi1q1bx7Rp0/juu+8YPHhwifM9fPgw77zzDg899FB5xrU5ci6zQubrarZd8KVT8nMkh3XROoqoTGf3ap2g0pTrRV3c3d357rvvuP/++wkMDMRgMHDHHXcU2YR/4IEHbP/frFkzGjRoQNu2bdm+fTutW7e2m/b06dP07t2bYcOG2W3SlxelFEfPZ5U+oXDI2Tx32h17gMX1I2iesFDrOKIynD8IFgvoq/+gm3J/h23atCEuLo60tDQSExNZvnw5ycnJ1KlTp8TntG7dGnd39yJH00+fPk23bt1o3749H330UXlHBSApPZfsfHOFzNtVmZWeAYf68X34P1F6d63jiIpWkA0XTmidolJU2D8Jfn5+hISEcOjQIbZt28bAgQNLnHbPnj0UFBQQFhZmu+/UqVN07dqV1q1b8+mnn6KvoH+9jidnV8h8BUw+3IoXA17EYgrQOoqoaOmntU5QKcq8SZ6ZmUl8fLzt9tGjR4mLiyMwMJCoqCi++eYbQkJCiIqKYteuXTz++OMMGjSInj17Atb9kQsXLqRv374EBwezd+9ennrqKVq1akXHjh0B65pl165diYqK4vXXX+fcucsn9b3yyHp8fDyZmZkkJSWRk5NDXFwcAE2aNMHDw8Oh95OQon1h5ibsJn3Ld+SfOYw5M4WQu/6FV8P2tsePv9q/2Of5dx2L3y1DSpxv+tYlZMQtw5x+Dr1nDbxiOxLQZTQ6N+tnk7lnLWnrP0MV5OLToicB3S6PqSy8cIYzX00jbPQc9Eav635v805FciDwZf7r/zoeaYevez7CyWUklj5NNVDmwty2bRvdunWz3Z48eTIAo0ePZv78+SQmJjJ58mTOnDlDWFgYo0aNYtq0abbpPTw8WL16NW+99RaZmZlERkbSr18/ZsyYgcFgAGDFihXEx8cTHx9PRESE3eurK05YOX78eNavX2+73aqV9dyNR48eJSYmxqH3k5CaU7YPoAKo/Fzca9bFp3kPzi1+ucjjEZM+t7udc2Qbyb+8jVdsxxLnmblnLanr5xPc93GM4Y0pSDlF8rI5AATe/gDm7AukLH+HoL5P4OYfytlvZ2GMao5XvZsBSP71PQK6jLmhsrzk9xQ/upme5+fQj/FP+uOG5yecULoUZrG6du1qV1pXe+yxx3jsscdKfDwyMtKu5IozZswYxowZU2qWdevWlTpNaU45QWF61muLZ722JT5u8LHfpM2O34Ipujnu/iWfBCP/9H5MEY3xbtIVADe/Wng17kx+ovWa0oVpSeiMXng37gyAKaoFBedPQL2bydq7Dp3BDa/YDjf4zi47lWvklhMTWVovnIYJ35TbfIWTcJE1zOp/WKsUqdn5pU/kRMxZqeQc3opPi57XnM4Y3oS8pMPknT4AQEFaEjmHt+F5cQ3SLTAcVZBn3Q2Qk0F+4kE8QmIw52SQtmEhgT3KfwhXnkVPz0N38UvEEyidodznLzSUkaR1gkpRrsOKqqK0KlaYmbtXo/fwxKvhtdf+vJt0wZyTTtLCZwAFFjM+rfrid+swAAwmH4L7Pcn5n95AFebj3aw7nnXbcH7ZHHzb9KfwwhnOfvcCWArx63gv3o1uK7f38HB8Ox6JfIGn0mejy5Pv8FcLLrKG6fKFWdVOGpy5cxXeTbraDtyUJPfETi5s+orAng9jrB1LYeppUlZ9TJr3Ivw7DgfAq2EHu+LNPbGTgnPHCezxEKc/mkDwgH9i8A4gccFkTJHNMHj7l9v7mJsQw76g2XxofA23dNcYklKtuUhhuvwmeVUqzNyE3RSmnMTnpmtvjgOkbfgCn6bd8b2pFx4hMXg17IB/l1Gkb/4WpSxFpleFBaSseJ/AXpMoTE1EWcyYoprjHhSBe2A4eYkHyv39rE4OpGfmTDJqlrz/VlQRLnLQRwqzChVm5s6VeITWx6Nm3VKnVQV5oLO/Bo9OpwcUxV0aM+2P/2Gq2wZjaH1QFrBcHsyvLIXWb3JUgCPZJm499QTHIkoepyuqgMIcMBdqnaLCuXRhZuYVUmDW/rxulvwc8s8cIf/MEcA6BjL/zBEK0y9fxdKSl032gd9LPNhz/qf/kLp+vu22Z/12ZPy9jKy9660HfI7+TdqGL/Csfws6vf0Bl/xzx8ne/xv+t40AwC0wAnR6MnasIPvwVgqST+IR1qCc3/VlWWY9XePvZm3kRJTOpRfJqs1SdVY+rpdL78N0lrXL/KRDnFl0+VyfqWs+AcC72e0E93sSgKx9v4GyHswpTmH6ObiibPw63APoSNvwBebMZPSefnjWb0dA55F2z1NKkfLrXAK6P4Dew3qWKb27kaC+T5Cy8n2UuYDAHg/h5htcnm+5WGMP3cbT0aFMSnkVXYF8v7/KMeeDe/W+SJ5OXWtQZTV39HwW3V5fp3UMcZX+IeeZw6u4ZZzSOoooi38eBu+K/4dVS7L9I5zOT+eC6Z8zi6yQllpHEWVhdo4ttookhSmc0v5ML25NnMyp8OLP7i+ckLlqjWm+Hi5dmLrSJxEayih0Y2zSUPL962sdRTjCUv2Pkrv0QR/hvHoEp/Cs/2rqJC5Dl5andRzhCBdYw5TCFE7l4chjPGD4hcCkDeAkVw4ptChmrstj4a4CkjIVYT46xrT04PnOHuh1OgrMiufX5LEsvpAjqRb8jDruqOvGK3cYqe1b8kbcx3/ls2BnAbvPWse8tgkz8PLtJtqFXx72tXBnAVNX55KVr7i/lQf/1/Py9bKOpVno+Xk22yZ4U8PoBNtLxXwhorqRwhSa83YzMyNqNwNzF2M8V/7fKLpRr/6ezwfbCvhskImmNQ1sO21m7JIc/Izw+K1Gsgtge5KZaZ2N3FRLT2qu4onledy5KJttE3xKnO+644UMb+ZOh0gTJjd4bWM+PT/PYs9EH8Jr6DmfbWH80hzmD/SkboCefl9m0zXGQL+G1rPYP/xzDq/cYXSOsgTwKPm9VhcuXZjubi69C1dzdb1yeSF8C+2Tv0d/+lzpT9DIppNmBsa62Yoqxl/Pot0FbEu0rlH5mXSsHOlt95x3+uho90kWJy5YiPIrfjlbONj+XKMfDzDx7d4CVh8tZNRNHhxJVfgZddzdzPq63eoY2HvOQr+G8OWuAjwMOgY3dqJLgJhqaJ2gwrl0Y/h5OtHC5kK6BqayssH3rNZPomPCh+iznbcsAW6LMrD6aCEHk62bzjuSzPx+wkzf+iWvb1zIU+gAf5Pja3/ZBVBggUBP63MaBOrJLlD8nWgmJUex9ZSZFrUMpOQopq/NZW4fUylzrGTG6l+YLr2G6WN0w92gc4qvR7qC8REJPOS+jKDE39BlV53P/JmOHlzIVTSam4VBD2YLvNTdyPDmxf+Dm1uomLoql3ubu5dpc3nqqlzCfa37PwECPHV8NsiTUYtzyClQjLrJnV713Ri3JIdH23lwNM3Cnf/LpsAMM7saGdpEwxUADx/QV/9znLp0YYJ1LfN8ZvU/uqcVT4OZaVF7uSv/RzzP79E6znX5ak8hX+wq4MshnjQN0ROXZOaJX/Oo7atjdEv70+wVmBX3fJuDRcF7/RxfA3xtYx6Ldhewbow3JrfLJXtXY3fuumKze92xQnadNTO3r4n6b2eyaIgnoT7Wzf/O0QZqemu00egCa5cghSmFWUGiPHN5MWIrHVN+wJBYtc/G/c+VuUztaOSei/sSm9cycPyCYvbv+XaFWWBW/OPbHI6mWVgzysvhtcvX/8jj5Q15rBrlTYtaJa+l5RUqJv6cyxeDPYlPsVBogS4x1j/hhkF6tpw0MyBWo8J0gf2XIIWJv5cHICd6KC+3BV5gWtA6GiYtRZeg/RU5y0N2Aeiv6j6DDixX7FW4VJaHki2sHe1FkJdjxfV/G/N4cUMev47wom3ta2/SvvBbHn3qu9E6zMDfiWYKrwhQYAZN9yzJGqZr8JcDP+VidO2TTDItJyRxHbqE6jUeb0BDN17akEeUn46mNa1l9cbmfMa1tC47hRbF0G9y2J5o5qfhXpgVJGVaP4NATx0eBmvbjvohh3BfHbPvsG6qv7Yxj2lr8/hysCcx/nrbc3w8dPh42Df0nrNmvtpTSNyD1qPxjYL16HU65m3PJ9RHx/7zFm4upXArlKxhuoZAb8euXy6KMuot/Ct6H0MLfsTr/C6t41SYd/qYmLY2j4nLcjmbpajtq+PBNu5M72IE4GS64scD1q8FtvzQfmtl7Wgvul7cbD5xwYL+ilPwvbc1n3wzDP3G/sqlM7p4MLPr5f2fSikm/JTLm72MeF8sUk93HfMHmZi0LJe8Qpjb10R4DQ0HvfiUfAXTa/njjz/o1KkTPXr0YPny5WV67syZM1m8eDFxcXHX9drXw6VP7wbw1qpDvLnqoNYxqpRwUx4vRG6jS+oPGDJPax1HOIOuz0HXZ8r8tPHjx+Pj48Mnn3zC3r17iYqKcvi5jhZmQUEB7u7lsyXp0uMwAWKCvUqfSABwi386yxos5XePR+ie8K6UpbjM3/GiuyQrK4uvv/6ahx9+mP79+zN//nzbY/Pnz8ff399u+sWLF6O7eNmV+fPnM2vWLHbs2IFOp0On09mer9Pp+OCDDxg4cCDe3t68+OKLACxdupQ2bdpgMpmoW7cus2bNorCwbCcMcflN8ugg79IncnH3hp3mMa9fqXV6dbXbPynKSUB0mZ/y1VdfERsbS2xsLCNGjODRRx9l2rRptlK8lrvvvpvdu3ezfPlyVq1aBYCfn5/t8RkzZjB79mzefPNNDAYDv/76KyNGjODtt9+mU6dOHD58mAkTJtimdZTLF2ZMkKxhFsddr5gavZ97CpfifS4OUrVOJJxaQJ0yP2XevHmMGGG9jlTv3r3JzMxk9erV3HHHHaU+19PTEx8fH9zc3AgNLbr/9N5772XcuHG22yNHjmTq1KmMHj0agLp16/LCCy8wZcoUKcyy8PfywM/T3Wmu76O1UGM+L0T+RbcLP+CWeFLrOKIq8PCFGmFlesqBAwf4888/+f777wFwc3Pj7rvv5r///a9DhVmatm3tL938119/sXXrVl566SXbfWazmdzcXLKzs/HycmzFyeULE6xrmTtOXtA6hqZa+2Uwq+ZvNDv7I7qTGVrHEVVJUL0yP2XevHkUFhYSHh5uu08phbu7O6mpqej1eq4+Hl1Q4PhKjbe3/a42i8XCrFmzGDx4cJFpTSbHv5ElhQnEBHu7bGEOC03iSe8VhJ1eiS7BXPoThLhacMMyTV5YWMiCBQv4z3/+Q8+e9peNHjJkCAsXLqRevXpkZGSQlZVlK7+rj4Z7eHhgNju2zLZu3ZoDBw5Qv/6Nnb1fChNoHFaDJXGuc8TXoLPwTHQ895p/xOfcdkjTOpGo0kJiyzT5Tz/9RGpqKvfff7/dgRqAoUOHMm/ePFavXo2XlxfPPfccjz76KH/++afdUXSAmJgYjh49SlxcHBEREfj6+mI0Got9zenTp9O/f38iIyMZNmwYer2enTt3smvXLttRdEe4/LAigJsi/LWOUClCPAp4v/4W9of8iwlJM61lKcSNiri5TJPPmzePO+64o0hZgnUNMy4ujmPHjvHFF1+wbNkymjdvzqJFi5g5c2aRaXv37k23bt0ICQlh0aJFJb5mr169+Omnn1i5ciU333wzt956K2+88QbR0WU7uu/yA9cBsvIKaT7zV7vvBlcnLWtkMqvWBlqcXYIuL13rOKI60Rlg6gkwVv+zrYNskgPgbXSjQU1fDpypXgc7BtU6y9O+Kwk//Su6hOp/RT+hgVpNXKYsQQrT5qZIv2pRmAadhclRhxmpfqLG2a3gmseyRGWJaKd1gkolhXlRy8gAvt5WdccdBrgX8kJ0HL0yfsD9zFGt4whXESmF6ZJuiiy6A7oqaOqbxb9DN9Lq3GL0J9O0jiNcjRSma2oUWqNKfeOnf8h5pvitJPL0cnQJVSOzqGa8giGwrtYpKpUU5kUGvY5ODYL5aWei1lFKpNMpnog8yhjdT/id2QxVf5erqMoib9E6QaWTwrxC19iaTlmYfu6FzIraSd+sH/A4e1jrOEJYNeihdYJKJ4V5ha6xIeh04CwjUxv5ZPNC2B+0Ob8Y/akUreMIcQUdxPbVOkSlk8K8QrCPkWa1/dh1StuxOL1DknnGbzUxib+gS8jTNIsQxYpoC761tE5R6aQwr9ItNkSTwtTpFI9EHGOcYRkBSRtl/6Rwbi64dglSmEV0ia3J22viK+31fN0KmRG1mwE5izGek2sLiSqiUT+tE2hCCvMqrSL9qelr5GxGxW4K1/PK4cXwzdyS/AP60+cr9LWEKFdB9ct8hqLqQgrzKnq9joEta/Pxhor5tsztQSk8F7CGuknL0CXkVshrCFGhXHRzHKQwizW4dUS5F+ZDEcd5wP0XAhM3oMtyksPwQlwPF90cBynMYjUOq0GjUF/2J93YkRdvg4Xp0bsZmLsY0/n95ZROCA0F1nXJAeuXSGGWYHDrcF5edn0lF+OZy4sRW2if8gOG02fLOZkQGmo9Ghy4DG51JScQLsHZ9Fzav7IGcxnOKtwlKJV/Ba6jQeJP6ApzKjCdEBoweMDkfeAdrHUSzcgaZglq1jDRoV4QGw6VfgR7fHgCD3n8QlDietk/KaqvRv1duixBCvOa7rk5qsTC9DSYeT56H4PzfsQzeXclJxNCA23Hap1Ac1KY19CraS3C/EwkXrg8/CfClMdLkX9yW8oPGE4naZhOiEoUWA/qdNY6hebkqpHX4GbQM+JW61XlOgZcYHmDJWxwf4QuCe9jyJKyFC6kzRitEzgFOehTitSsfHL/N5bQk8vQKYvWcYSofAbjxYM9QVon0ZysYZYiwNuDsNAwKUvhulreK2V5kRSmIzo8Yr3+shCuRu8Gtz2pdQqnIYXpiIAYaDpI6xRCVL7mwyAgWusUTkMK01G3PQm47jcchAvS6eG2yVqncCpSmI4KbW7911YIV9FsKIQ01DqFU5HCLIvbp1mPGApR3endoOtUrVM4HSnMsvCPgnYPaJ1CiIp303AIqqd1CqcjhVlWnZ8Gk7/WKYSoOG6e0OUZrVM4JSnMsvIMgE5PaZ1CiIrT+Snwj9Q6hVOSwrwetzwIflFapxCi/AXWgw6Pa53CaUlhXg83o/UAkBDVTd//AzcPrVM4LSnM69V8GMR00jqFEOWn8Z1Q/3atUzg1KczrpdPBne+Au5fWSYS4ce7e0Hu21imcnhTmjQisA7dP1zqFEDeuyz/BL0LrFE5PCvNGtXsQIm/VOoUQ1y84Fto/onWKKkEK80bp9TBwLriZtE4iRNkZPGDIx2Bw1zpJlSCFWR6CG8jXyETVdPt0CLtJ6xRVhhRmeenwGNRupXUKIRxXr7tsipeRFGZ50Rtg8CdgrKF1EiFK5xUMgz6wjvYQDpPCLE/B9WHQe1qnEKJ0g94D31pap6hypDDLW+MB0FG+WiacWLsJ0LCX1imqJLlqZEWwmGHBQDi2QeskQtir1RzGrwJ3GdVxPWQNsyLoDTD0U/CtrXUSIS7zqQX3/k/K8gZIYVYUnxD4x2egl/Ftwgm4ecLwRfJtnhskhVmRItvJ93OFE9DB4A8hvI3WQao8KcyK1u4BuHWi1imEK7t9GjQZqHWKakEKszL0ehmaDtY6hXBFLe+TKwSUIzlKXlkK8+GLwXLkXFSemE4w8gf5nng5kjXMyuLmYd3pLl+fFJUhtDnc/bmUZTmTwqxMRl8Y8T2ENNY6iajOajaBkUusF+wT5UoKs7J5BcKoxRAQo3USUR0FNYBRS8A7SOsk1ZIUphZ8Q2HMz9aFW4jyEtQARi8Fn5paJ6m25KCPlrLOWw8EJe7QOomo6kIaw+gfpSwrmBSm1nLTYdFwOP671klEVRXa3LrPUjbDK5xskmvNVANGfAcN+2idRFRFMZ2sm+FSlpVCCtMZuJvg7i+gxT1aJxFVScsR1nGWcjS80sgmuTNRCpY/C1ve1zqJcGo667V4Ok3WOojLkcJ0Rn/Nh2VTwJyndRLhbNw84a4PoOkgrZO4JClMZ3XyL/h6JKSf0jqJcBY+teCeRRAhZx3SihSmM8s6D9+Mke+fC+uZ0ocvAv9IrZO4NClMZ2cxw8rpsGmu1kmEVto9CD1fADej1klcnhRmVbH7e/jxUcjP1DqJqCxewdarO8oFy5yGFGZVcv4QLJ4IJ//UOomoaPW6W68bLpfCdSpSmFWNxQKb34U1L0FhjtZpRHkzeFiHDLV/BHQ6rdOIq0hhVlXn42HJJEjYrHUSUV6CY2HwR1C7pdZJRAmkMKsyi8U6yH31C7K2WZW5e0OXKdB+kpzw18lJYVYHyYdhySNw4g+tk4iyanyn9cqicvnbKkEKszrZ+Q2smgnpJ7VOIkoTWA/6vgb179A6iSgDKczqpiAHNr4NG+dAQbbWacTV3DytV3Hs+JiMq6yCpDCrq4wkWPcK/P05WAq1TiP0btDyXug8Rb6tU4VJYVZ35+NhzQuwdwkgv+pKp9NDs6HQdSoE1dM6jbhBUpiu4uw++GMu7PoazPlap6n+9G7Wouz0FIQ01DqNKCdSmK4mIwm2fAjb/gu5aVqnqX4MRmg5HG57Uq4MWg1JYbqq/CzY/jlsfg/SjmudpuoLqg+tR0PL++RyEdWYFKars5hh34+wfQEcWQ/KrHWiqsPgAY36Q9ux1mvryFcZqz0pTHFZxhnY/S3s/BoS47RO47wC61rXJluNAO9grdOISiSFKYp37iDs/Mp6kCjthNZptBdUHxr1g9h+ENlO1iZdlBSmuDalIGELHFgGh9dC0i5cY3iSDsLbWEuyUT8IidU6kHACUpiibLLOw5F1cGQtHF5Xvb6G6R0CEe2gwR0Q2xd8Q7VOJJyMFKa4MecPWdc8j2+07vdMPaZ1IsfoDFCribUgIy/+BNbVOpVwclKYonzlpELiDkjcaR0sf3YvnD+o7ffaPXys+yCDG1o3rSNutm5uG320yySqJClMUfEsFrhwAtJPW38ykiAj8eJ/kyDj4n1lLVV3bzD5gakGGGtY/+tdEwKiwT8a/KMgsA7UqF0x70u4HClM4TzMhdavbVoKwHzpJ9968hBzvnXMqNHHWo7GGmBw0zqxcDFSmEII4SC91gGEEKKqkMIUQggHSWEKIYSDpDCFEMJBUphCCOEgKUwhhHCQFKYQQjhIClMIIRwkhSmEEA6SwhRCCAdJYQohhIOkMIUQwkFSmEII4SApTCGEcJAUphBCOEgKUwghHCSFKYQQDpLCFEIIB/0/tjE/E8JiR/kAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outlier for tenant 11\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_436/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outlier for tenant 12\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_436/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - "/tmp/ipykernel_436/3170175140.py:10: DtypeWarning: Columns (4,8,10) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outlier for tenant 13\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_436/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outlier for tenant 14\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_436/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", - "/tmp/ipykernel_436/3170175140.py:10: DtypeWarning: Columns (8,9) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# boucle pour identifier les outliers de chaque compagnie (et le client principal non anonyme)\n", - "\n", - "# nb_compagnie=['10','11','12','13','14']\n", - "for company_number in nb_compagnie :\n", - " print(f\"outlier for tenant {company_number}\")\n", - " outlier_detection(directory_path=company_number, coupure = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbe1af6a-79e9-45c7-a810-c6df3bf647f7", - "metadata": {}, - "outputs": [], - "source": [ - "# print(products_purchased_reduced_spectacle.loc[products_purchased_reduced_spectacle[\"number_compagny\"]==10][\"total_amount\"].describe())\n", - "\n", - "products_purchased_reduced_spectacle.loc[(products_purchased_reduced_spectacle[\"number_compagny\"]==10) & \n", - "(products_purchased_reduced_spectacle[\"customer_id\"]==19521)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20e2b8a2-f31c-42a4-8ea5-7ad67ab66915", - "metadata": {}, - "outputs": [], - "source": [ - "# company 11 \n", - "# etrange, pas de vente sur internet, et un seul supplier. Plus de 9k achats\n", - "products_purchased_reduced_spectacle.loc[(products_purchased_reduced_spectacle[\"number_compagny\"]==11) & \n", - "(products_purchased_reduced_spectacle[\"customer_id\"]==36)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5dbce57c-d091-4ce2-92f9-1201deb2462e", - "metadata": {}, - "outputs": [], - "source": [ - "# company 12\n", - "products_purchased_reduced_spectacle.loc[(products_purchased_reduced_spectacle[\"number_compagny\"]==12) & \n", - "(products_purchased_reduced_spectacle[\"customer_id\"]==1706757)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a243b57-19da-4e29-a53d-bb8d03e2ab77", - "metadata": {}, - "outputs": [], - "source": [ - "# company 13\n", - "products_purchased_reduced_spectacle.loc[(products_purchased_reduced_spectacle[\"number_compagny\"]==13) & \n", - "(products_purchased_reduced_spectacle[\"customer_id\"]==8422)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d9b01bc-9584-4882-bd06-7de8acb8a88f", - "metadata": {}, - "outputs": [], - "source": [ - "# company 14\n", - "# a-t-on vrmt un outlier ? A acheté quasi 3k tickets, pr 96 achats\n", - "products_purchased_reduced_spectacle.loc[(products_purchased_reduced_spectacle[\"number_compagny\"]==14) & \n", - "(products_purchased_reduced_spectacle[\"customer_id\"]==6354)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "033c1e00-52bd-4651-b893-57bda531760e", - "metadata": {}, - "outputs": [], - "source": [ - "# verifs dans les tables customerplus (outlier incertain pr 11 et 14)\n", - "\n", - "customerplus_clean_spectacle.loc[(customerplus_clean_spectacle[\"customer_id\"]==36) &\n", - "(customerplus_clean_spectacle[\"number_compagny\"]==11)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28ac8cda-32fa-4fb7-a75b-e1cc24871c39", - "metadata": {}, - "outputs": [], - "source": [ - "customerplus_clean_spectacle.loc[(customerplus_clean_spectacle[\"customer_id\"]==6354) &\n", - "(customerplus_clean_spectacle[\"number_compagny\"]==14)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3faea297-2cc5-4704-af85-77d95f600cc1", - "metadata": {}, - "outputs": [], - "source": [ - "customerplus_clean_spectacle.loc[(customerplus_clean_spectacle[\"customer_id\"]==8422) &\n", - "(customerplus_clean_spectacle[\"number_compagny\"]==13)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b165ea79-347b-46fb-8217-635d9e888c65", - "metadata": {}, - "outputs": [], - "source": [ - "customerplus_clean_spectacle.loc[(customerplus_clean_spectacle[\"customer_id\"]==19521) &\n", - "(customerplus_clean_spectacle[\"number_compagny\"]==10)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "282b0a96-5e78-48aa-9c2c-7d00d3907add", - "metadata": {}, - "outputs": [], - "source": [ - "customerplus_clean_spectacle.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "ad47a812-a744-49c5-8079-0919b49ef24c", - "metadata": {}, - "outputs": [], - "source": [ - "# on enlève les outliers des tables\n", - "\n", - "outliers_musique_dico = {10 : 19521, 11 : 36, 12 : 1706757, 13 : 8422}\n", - "\n", - "# outlier_music_list = list(outliers_musique_dico.values())\n" - ] - }, - { - "cell_type": "markdown", - "id": "41cbc46d-5649-46a2-884c-dd291fb0f217", - "metadata": {}, - "source": [ - "for tenant_number, customer_id in outliers_musique_dico.items() :\n", - "\n", - " print(tenant_number, customer_id)\n", - " \n", - " customerplus_clean_spectacle = customerplus_clean_spectacle[(customerplus_clean_spectacle['number_compagny']!= tenant_number) |\n", - " (customerplus_clean_spectacle['customer_id']!= customer_id) ]\n", - "\n", - " campaigns_information_spectacle = campaigns_information_spectacle[(campaigns_information_spectacle['number_compagny']!= tenant_number) |\n", - " (campaigns_information_spectacle['customer_id']!= customer_id) ]\n", - "\n", - " products_purchased_reduced_spectacle = products_purchased_reduced_spectacle[(products_purchased_reduced_spectacle['number_compagny']!= tenant_number) |\n", - " (products_purchased_reduced_spectacle['customer_id']!= customer_id) ]\n", - "\n", - " target_information_spectacle = target_information_spectacle[(target_information_spectacle['number_compagny']!= tenant_number) |\n", - " (target_information_spectacle['customer_id']!= customer_id) ]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb7f4c95-817b-4145-9319-11d2f62b24d9", - "metadata": {}, - "outputs": [], - "source": [ - "# on vérifie que les outliers sont pas dans le train set " - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "b50e1de8-28fe-42bd-bd81-dde7e36b64fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['10_19521', '11_36', '12_1706757', '13_8422']" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "outliers_train_set_musique = [str(tenant_id) + \"_\" + str(customer_id) for tenant_id, customer_id in outliers_musique_dico.items()]\n", - "outliers_train_set_musique" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "1753d45d-beac-48a4-9bc4-f84925320a89", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internet...gender_labelgender_femalegender_malegender_othercountry_frnb_campaignsnb_campaigns_openedtime_to_openy_has_purchasednumber_company
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0 rows × 41 columns

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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [customer_id, nb_tickets, nb_purchases, total_amount, nb_suppliers, vente_internet_max, purchase_date_min, purchase_date_max, time_between_purchase, nb_tickets_internet, street_id, structure_id, mcp_contact_id, fidelity, tenant_id, is_partner, deleted_at, gender, is_email_true, opt_in, last_buying_date, max_price, ticket_sum, average_price, average_purchase_delay, average_price_basket, average_ticket_basket, total_price, purchase_count, first_buying_date, country, gender_label, gender_female, gender_male, gender_other, country_fr, nb_campaigns, nb_campaigns_opened, time_to_open, y_has_purchased, number_company]\n", - "Index: []\n", - "\n", - "[0 rows x 41 columns]" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_set_spectacle[train_set_spectacle[\"customer_id\"].isin(outliers_train_set_musique)] # OK" - ] - }, - { - "cell_type": "markdown", - "id": "0884e326-c87c-4ac1-8525-68a63411dfb0", - "metadata": {}, - "source": [ - "### 0.1 Evolution des commandes" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c5c713ab-a1a6-478a-b707-4da68be0d63a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_13/campaigns_information.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_465/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" - ] - } - ], - "source": [ - "# Importation - Chargement des données temporaires - on prend compagnie 13 car c'est elle qui a le + de données\n", - "company_number = \"13\"\n", - "nom_dataframe = 'df'+ company_number +'_tickets'\n", - "\n", - "purchases = display_databases(company_number, file_name = 'products_purchased_reduced' , datetime_col = ['purchase_date'])\n", - "campaigns = display_databases(company_number,'campaigns_information', ['sent_at'])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9940f219-cee8-4ac3-8691-dedf6fb927e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_dateamountis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasonsstart_date_timeend_date_timeopen
03194287708131112internet2016-05-29 11:04:08.767000+00:00-110.0Falseles nuits de l'orangerie 2016jardins de l'orangeriecarré orblanche neige - ballet preljocajsaison 2015-20162016-06-17 21:00:00+02:001901-01-01 00:09:21+00:09True
13506812673570132internet2016-08-08 08:00:41.723000+00:0085.0Falseopéra royal 2016-2017opéra royalcatégorie 3cecilia bartoli : la cenerentolasaison 2016-20172017-02-24 20:00:00+01:001901-01-01 00:09:21+00:09True
23506912673570132internet2016-08-08 08:00:41.723000+00:0085.0Falseopéra royal 2016-2017opéra royalcatégorie 3cecilia bartoli : la cenerentolasaison 2016-20172017-02-24 20:00:00+01:001901-01-01 00:09:21+00:09True
33507012673570132internet2016-08-08 08:00:41.723000+00:00-85.0Falseopéra royal 2016-2017opéra royalcatégorie 3cecilia bartoli : la cenerentolasaison 2016-20172017-02-24 20:00:00+01:001901-01-01 00:09:21+00:09True
43507128486070142internet2016-11-29 10:39:12.600000+00:00100.0Falseopéra royal 2016-2017opéra royalcatégorie 3cecilia bartoli : la cenerentolasaison 2016-20172017-02-24 20:00:00+01:001901-01-01 00:09:21+00:09True
...................................................
70242223932999310021603108353867305internet2023-08-25 19:28:38.553000+00:0034.0Falseles grandes eaux de versailles 2023jardinsentrée simplenocturnes electro 23/09/2023saison 2023-20242023-09-23 20:30:00+02:001901-01-01 00:09:21+00:09True
70242233932999410021603108353867305internet2023-08-25 19:28:38.553000+00:0034.0Falseles grandes eaux de versailles 2023jardinsentrée simplenocturnes electro 23/09/2023saison 2023-20242023-09-23 20:30:00+02:001901-01-01 00:09:21+00:09True
7024224394338808422108637947305guérites jardins2023-08-29 08:46:23.107000+00:009.0Falseles grandes eaux de versailles 2023jardinsentrée simpleles jardins musicaux 2023saison 2023-20242023-08-29 09:00:00+02:001901-01-01 00:09:21+00:09True
7024225394338798422108637937305guérites jardins2023-08-29 08:09:54.207000+00:0010.0Falseles grandes eaux de versailles 2023jardinsentrée simpleles jardins musicaux 2023saison 2023-20242023-08-29 09:00:00+02:001901-01-01 00:09:21+00:09True
7024226394338788422108637937305guérites jardins2023-08-29 08:09:54.207000+00:009.0Falseles grandes eaux de versailles 2023jardinsentrée simpleles jardins musicaux 2023saison 2023-20242023-08-29 09:00:00+02:001901-01-01 00:09:21+00:09True
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7024227 rows × 16 columns

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" - ], - "text/plain": [ - " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", - "0 3194 287708 1311 12 internet \n", - "1 35068 126735 7013 2 internet \n", - "2 35069 126735 7013 2 internet \n", - "3 35070 126735 7013 2 internet \n", - "4 35071 284860 7014 2 internet \n", - "... ... ... ... ... ... \n", - "7024222 39329993 10021603 10835386 7305 internet \n", - "7024223 39329994 10021603 10835386 7305 internet \n", - "7024224 39433880 8422 10863794 7305 guérites jardins \n", - "7024225 39433879 8422 10863793 7305 guérites jardins \n", - "7024226 39433878 8422 10863793 7305 guérites jardins \n", - "\n", - " purchase_date amount is_full_price \\\n", - "0 2016-05-29 11:04:08.767000+00:00 -110.0 False \n", - "1 2016-08-08 08:00:41.723000+00:00 85.0 False \n", - "2 2016-08-08 08:00:41.723000+00:00 85.0 False \n", - "3 2016-08-08 08:00:41.723000+00:00 -85.0 False \n", - "4 2016-11-29 10:39:12.600000+00:00 100.0 False \n", - "... ... ... ... \n", - "7024222 2023-08-25 19:28:38.553000+00:00 34.0 False \n", - "7024223 2023-08-25 19:28:38.553000+00:00 34.0 False \n", - "7024224 2023-08-29 08:46:23.107000+00:00 9.0 False \n", - "7024225 2023-08-29 08:09:54.207000+00:00 10.0 False \n", - "7024226 2023-08-29 08:09:54.207000+00:00 9.0 False \n", - "\n", - " name_event_types name_facilities \\\n", - "0 les nuits de l'orangerie 2016 jardins de l'orangerie \n", - "1 opéra royal 2016-2017 opéra royal \n", - "2 opéra royal 2016-2017 opéra royal \n", - "3 opéra royal 2016-2017 opéra royal \n", - "4 opéra royal 2016-2017 opéra royal \n", - "... ... ... \n", - "7024222 les grandes eaux de versailles 2023 jardins \n", - "7024223 les grandes eaux de versailles 2023 jardins \n", - "7024224 les grandes eaux de versailles 2023 jardins \n", - "7024225 les grandes eaux de versailles 2023 jardins \n", - "7024226 les grandes eaux de versailles 2023 jardins \n", - "\n", - " name_categories name_events name_seasons \\\n", - "0 carré or blanche neige - ballet preljocaj saison 2015-2016 \n", - "1 catégorie 3 cecilia bartoli : la cenerentola saison 2016-2017 \n", - "2 catégorie 3 cecilia bartoli : la cenerentola saison 2016-2017 \n", - "3 catégorie 3 cecilia bartoli : la cenerentola saison 2016-2017 \n", - "4 catégorie 3 cecilia bartoli : la cenerentola saison 2016-2017 \n", - "... ... ... ... \n", - "7024222 entrée simple nocturnes electro 23/09/2023 saison 2023-2024 \n", - "7024223 entrée simple nocturnes electro 23/09/2023 saison 2023-2024 \n", - "7024224 entrée simple les jardins musicaux 2023 saison 2023-2024 \n", - "7024225 entrée simple les jardins musicaux 2023 saison 2023-2024 \n", - "7024226 entrée simple les jardins musicaux 2023 saison 2023-2024 \n", - "\n", - " start_date_time end_date_time open \n", - "0 2016-06-17 21:00:00+02:00 1901-01-01 00:09:21+00:09 True \n", - "1 2017-02-24 20:00:00+01:00 1901-01-01 00:09:21+00:09 True \n", - "2 2017-02-24 20:00:00+01:00 1901-01-01 00:09:21+00:09 True \n", - "3 2017-02-24 20:00:00+01:00 1901-01-01 00:09:21+00:09 True \n", - "4 2017-02-24 20:00:00+01:00 1901-01-01 00:09:21+00:09 True \n", - "... ... ... ... \n", - "7024222 2023-09-23 20:30:00+02:00 1901-01-01 00:09:21+00:09 True \n", - "7024223 2023-09-23 20:30:00+02:00 1901-01-01 00:09:21+00:09 True \n", - "7024224 2023-08-29 09:00:00+02:00 1901-01-01 00:09:21+00:09 True \n", - "7024225 2023-08-29 09:00:00+02:00 1901-01-01 00:09:21+00:09 True \n", - "7024226 2023-08-29 09:00:00+02:00 1901-01-01 00:09:21+00:09 True \n", - "\n", - "[7024227 rows x 16 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d634c10c-b8f2-4f70-854d-d1e00e1f2ddc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
011245682021-05-17 13:52:32+02:00NaTNaNFLASH129 - CD Cadmus et Hermione5212021-05-12 00:00:00+02:00
121611442021-05-17 13:54:25+02:00NaTNaNIND154 - Reouverture OR5172021-04-30 00:00:00+02:00
232660972021-05-17 13:54:28+02:00NaTNaNIND155 - MEV Bicentenaire de Napoléon5202021-05-07 00:00:00+02:00
342257492021-05-17 13:58:12+02:00NaTNaNIND157 - reprise des spectacles5292021-05-14 00:00:00+02:00
45586682021-05-17 13:59:34+02:00NaTNaNIND157 - reprise des spectacles5292021-05-14 00:00:00+02:00
...........................
321856413614761377752022-04-02 15:35:54+02:002022-03-31 05:08:18+00:002022-03-31 07:08:23+02:00IND187 - GEM/JM8652022-03-30 00:00:00+02:00
3218565129948721532252022-04-02 15:20:25+02:002022-03-30 17:38:14+00:002022-03-30 19:38:19+02:00IND187 - GEM/JM8652022-03-30 00:00:00+02:00
3218566129912621420622022-04-02 15:15:37+02:002022-03-30 17:36:58+00:002022-03-30 19:37:01+02:00IND187 - GEM/JM8652022-03-30 00:00:00+02:00
32185676110191859492022-04-02 15:40:42+02:002021-11-09 17:57:13+00:002021-11-09 18:57:14+01:00IND173 - tout public automne6712021-11-09 00:00:00+01:00
32185681262689669952022-04-02 15:26:10+02:002022-03-10 20:50:44+00:002022-03-10 21:50:48+01:00FLASH172 - Campagne Concert Chefs d'état8282022-03-10 00:00:00+01:00
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"3218568 2022-03-10 20:50:44+00:00 2022-03-10 21:50:48+01:00 \n", - "\n", - " campaign_name campaign_service_id \\\n", - "0 FLASH129 - CD Cadmus et Hermione 521 \n", - "1 IND154 - Reouverture OR 517 \n", - "2 IND155 - MEV Bicentenaire de Napoléon 520 \n", - "3 IND157 - reprise des spectacles 529 \n", - "4 IND157 - reprise des spectacles 529 \n", - "... ... ... \n", - "3218564 IND187 - GEM/JM 865 \n", - "3218565 IND187 - GEM/JM 865 \n", - "3218566 IND187 - GEM/JM 865 \n", - "3218567 IND173 - tout public automne 671 \n", - "3218568 FLASH172 - Campagne Concert Chefs d'état 828 \n", - "\n", - " campaign_sent_at \n", - "0 2021-05-12 00:00:00+02:00 \n", - "1 2021-04-30 00:00:00+02:00 \n", - "2 2021-05-07 00:00:00+02:00 \n", - "3 2021-05-14 00:00:00+02:00 \n", - "4 2021-05-14 00:00:00+02:00 \n", - "... ... \n", - "3218564 2022-03-30 00:00:00+02:00 \n", - "3218565 2022-03-30 00:00:00+02:00 \n", - "3218566 2022-03-30 00:00:00+02:00 \n", - "3218567 2021-11-09 00:00:00+01:00 \n", - "3218568 2022-03-10 00:00:00+01:00 \n", - "\n", - "[3218569 rows x 8 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "campaigns" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "a8654575-d6c2-4d7e-baee-fa573c8c8e1e", - "metadata": {}, - "outputs": [], - "source": [ - "# Mois du premier achat\n", - "purchase_min = purchases.groupby(['customer_id'])['purchase_date'].min().reset_index()\n", - "purchase_min.rename(columns = {'purchase_date' : 'first_purchase_event'}, inplace = True)\n", - "purchase_min['first_purchase_event'] = pd.to_datetime(purchase_min['first_purchase_event'])\n", - "purchase_min['first_purchase_month'] = pd.to_datetime(purchase_min['first_purchase_event'].dt.strftime('%Y-%m'))\n", - "\n", - "# Mois du premier mails\n", - "first_mail_received = campaigns.groupby('customer_id')['sent_at'].min().reset_index()\n", - "first_mail_received.rename(columns = {'sent_at' : 'first_email_reception'}, inplace = True)\n", - "first_mail_received['first_email_reception'] = pd.to_datetime(first_mail_received['first_email_reception'])\n", - "first_mail_received['first_email_month'] = pd.to_datetime(first_mail_received['first_email_reception'].dt.strftime('%Y-%m'))\n", - "\n", - "# Fusion \n", - "known_customer = pd.merge(purchase_min[['customer_id', 'first_purchase_month']], \n", - " first_mail_received[['customer_id', 'first_email_month']], on = 'customer_id', how = 'outer')\n", - "\n", - "# Mois à partir duquel le client est considere comme connu\n", - "known_customer['known_date'] = pd.to_datetime(known_customer[['first_email_month', 'first_purchase_month']].min(axis = 1), utc = True, format = 'ISO8601')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "39e265f9-aa7c-4fc8-bda2-fb40774b92b7", - "metadata": {}, - "outputs": [], - "source": [ - "# Nombre de commande par mois\n", - "purchases_count = pd.merge(purchases[['customer_id', 'purchase_id', 'purchase_date']].drop_duplicates(), known_customer[['customer_id', 'known_date']], on = ['customer_id'], how = 'inner')\n", - "purchases_count['is_customer_known'] = purchases_count['purchase_date'] > purchases_count['known_date'] + pd.DateOffset(months=1)\n", - "purchases_count['purchase_date_month'] = pd.to_datetime(purchases_count['purchase_date'].dt.strftime('%Y-%m'))\n", - "purchases_count = purchases_count[purchases_count['customer_id'] != 1]\n", - "\n", - "# Nombre de commande par mois par type de client\n", - "nb_purchases_graph = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['purchase_id'].count().reset_index()\n", - "nb_purchases_graph.rename(columns = {'purchase_id' : 'nb_purchases'}, inplace = True)\n", - "\n", - "nb_purchases_graph_2 = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['customer_id'].nunique().reset_index()\n", - "nb_purchases_graph_2.rename(columns = {'customer_id' : 'nb_new_customer'}, inplace = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f4931879-826c-4a12-8d7a-37386df5f98f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " purchase_date_month is_customer_known nb_purchases\n", - "0 2010-07-01 False 1\n", - "1 2010-08-01 False 17\n", - "2 2010-09-01 False 34\n", - "3 2010-10-01 False 18\n", - "4 2010-11-01 False 26\n", - ".. ... ... ...\n", - "231 2023-09-01 True 37251\n", - "232 2023-10-01 False 2903\n", - "233 2023-10-01 True 30905\n", - "234 2023-11-01 False 372\n", - "235 2023-11-01 True 549\n", - "\n", - "[236 rows x 3 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases_graph" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a4aec3a1-2dbe-477c-9364-dd19a498cdce", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Graphique en nombre de commande\n", - "purchases_graph = nb_purchases_graph\n", - "\n", - "purchases_graph_used = purchases_graph[purchases_graph[\"purchase_date_month\"] >= datetime(2021,3,1)]\n", - "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==False]\n", - "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==True]\n", - "\n", - "\n", - "# Création du barplot\n", - "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Nouveau client\")\n", - "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_1[\"nb_purchases\"], \n", - " bottom = purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Ancien client\")\n", - "\n", - "\n", - "# commande pr afficher slt\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y'))\n", - "\n", - "# date_form = DateFormatter(\"%m-%d\")\n", - "# plt.xaxis.set_major_formatter(date_form)\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Mois')\n", - "plt.ylabel(\"Nombre d'achats\")\n", - "plt.title(\"Nombre d'achats - compagnie 13\")\n", - "plt.legend()\n", - "\n", - "# save graphic - export to S3 bucket\n", - "\"\"\"\n", - "FILE_PATH = \"projet-bdc2324-team1/graphics/music/\"\n", - "FILE_NAME = \"sales_trend_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)\n", - "\"\"\"\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "42f8171c-e80d-4faa-b278-21fcbe3b242c", - "metadata": {}, - "source": [ - "### 1. customerplus_clean" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "47f98721-53dd-4f8f-85ac-88043ee8d967", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...total_pricepurchase_countfirst_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frnumber_compagny
0821538139NaNNaN0875FalseNaN2True...0.00NaNNaNother001NaN10
18091261063NaNNaN0875FalseNaN2True...0.00NaNfrother0011.010
2110051063NaNNaN0875FalseNaN2False...NaN14NaNfrother0011.010
31766312731NaNNaN0875FalseNaN0False...NaN1NaNfrfemale1001.010
43810012395NaNNaN0875FalseNaN0True...NaN1NaNfrfemale1001.010
5307036139NaNNaN0875FalseNaN2True...NaN1NaNNaNother001NaN10
629461063NaNNaN0875FalseNaN2False...NaN8NaNfrother0011.010
71844111139NaNNaN0875FalseNaN2False...NaN3NaNfrother0011.010
89231139NaNNaN0875FalseNaN0True...NaN1NaNNaNfemale100NaN10
99870139NaNNaN0875FalseNaN2True...NaN1NaNNaNother001NaN10
\n", - "

10 rows × 28 columns

\n", - "
" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity tenant_id \\\n", - "0 821538 139 NaN NaN 0 875 \n", - "1 809126 1063 NaN NaN 0 875 \n", - "2 11005 1063 NaN NaN 0 875 \n", - "3 17663 12731 NaN NaN 0 875 \n", - "4 38100 12395 NaN NaN 0 875 \n", - "5 307036 139 NaN NaN 0 875 \n", - "6 2946 1063 NaN NaN 0 875 \n", - "7 18441 11139 NaN NaN 0 875 \n", - "8 9231 139 NaN NaN 0 875 \n", - "9 9870 139 NaN NaN 0 875 \n", - "\n", - " is_partner deleted_at gender is_email_true ... total_price \\\n", - "0 False NaN 2 True ... 0.0 \n", - "1 False NaN 2 True ... 0.0 \n", - "2 False NaN 2 False ... NaN \n", - "3 False NaN 0 False ... NaN \n", - "4 False NaN 0 True ... NaN \n", - "5 False NaN 2 True ... NaN \n", - "6 False NaN 2 False ... NaN \n", - "7 False NaN 2 False ... NaN \n", - "8 False NaN 0 True ... NaN \n", - "9 False NaN 2 True ... NaN \n", - "\n", - " purchase_count first_buying_date country gender_label gender_female \\\n", - "0 0 NaN NaN other 0 \n", - "1 0 NaN fr other 0 \n", - "2 14 NaN fr other 0 \n", - "3 1 NaN fr female 1 \n", - "4 1 NaN fr female 1 \n", - "5 1 NaN NaN other 0 \n", - "6 8 NaN fr other 0 \n", - "7 3 NaN fr other 0 \n", - "8 1 NaN NaN female 1 \n", - "9 1 NaN NaN other 0 \n", - "\n", - " gender_male gender_other country_fr number_compagny \n", - "0 0 1 NaN 10 \n", - "1 0 1 1.0 10 \n", - "2 0 1 1.0 10 \n", - "3 0 0 1.0 10 \n", - "4 0 0 1.0 10 \n", - "5 0 1 NaN 10 \n", - "6 0 1 1.0 10 \n", - "7 0 1 1.0 10 \n", - "8 0 0 NaN 10 \n", - "9 0 1 NaN 10 \n", - "\n", - "[10 rows x 28 columns]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# visu de la table\n", - "customerplus_clean_spectacle.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "738e063b-f84e-4a00-b35d-6d1d657e3c09", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre de lignes de la table : 1523684\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "street_id 0\n", - "structure_id 1460622\n", - "mcp_contact_id 729163\n", - "fidelity 0\n", - "tenant_id 0\n", - "is_partner 0\n", - "deleted_at 1523684\n", - "gender 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "last_buying_date 762879\n", - "max_price 762879\n", - "ticket_sum 0\n", - "average_price 667328\n", - "average_purchase_delay 762915\n", - "average_price_basket 762915\n", - "average_ticket_basket 762915\n", - "total_price 95551\n", - "purchase_count 0\n", - "first_buying_date 762879\n", - "country 429485\n", - "gender_label 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "country_fr 429485\n", - "number_compagny 0\n", - "dtype: int64" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# nombre de NaN\n", - "print(\"Nombre de lignes de la table : \",customerplus_clean_spectacle.shape[0])\n", - "customerplus_clean_spectacle.isna().sum()" - ] - }, - { - "cell_type": "markdown", - "id": "b44054b3-d850-4bc9-bc73-feb9979908bc", - "metadata": {}, - "source": [ - "#### Nombre de clients de la compagnie" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "884a33d0-c275-4ab4-ab1f-8b53e563fb95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " number_compagny already_purchased customer_id\n", - "0 10 True 45263\n", - "1 11 True 35312\n", - "2 12 True 216104\n", - "3 13 True 388730\n", - "4 14 True 101642\n", - " number_compagny already_purchased customer_id\n", - "0 10 False 53530\n", - "1 11 False 35994\n", - "2 12 False 26620\n", - "3 13 False 379005\n", - "4 14 False 241484\n" - ] - } - ], - "source": [ - "# nouveau barplot pr les clients : on regarde la taille totale de la base et on distingue clients ayant acheté / pas acheté\n", - "\n", - "# variable relative à l'achat\n", - "customerplus_clean_spectacle[\"already_purchased\"] = customerplus_clean_spectacle[\"purchase_count\"]>0\n", - "\n", - "nb_customers_purchasing_spectacle = customerplus_clean_spectacle[customerplus_clean_spectacle[\"already_purchased\"]].groupby([\"number_compagny\",\"already_purchased\"])[\"customer_id\"].count().reset_index()\n", - "nb_customers_no_purchase_spectacle = customerplus_clean_spectacle[~customerplus_clean_spectacle[\"already_purchased\"]].groupby([\"number_compagny\",\"already_purchased\"])[\"customer_id\"].count().reset_index()\n", - "\n", - "print(nb_customers_purchasing_spectacle)\n", - "print(nb_customers_no_purchase_spectacle)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "41c9fb5a-708b-4f85-9918-00337151f155", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(nb_customers_purchasing_spectacle[\"number_compagny\"], nb_customers_purchasing_spectacle[\"customer_id\"]/1000, label = \"clients ayant acheté\")\n", - "plt.bar(nb_customers_no_purchase_spectacle[\"number_compagny\"], nb_customers_no_purchase_spectacle[\"customer_id\"]/1000, \n", - " bottom = nb_customers_purchasing_spectacle[\"customer_id\"]/1000, label = \"clients ciblés par un mail\")\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Compagnie')\n", - "plt.ylabel(\"Nombre de clients (en milliers)\")\n", - "plt.title(\"Nombre de clients identifiés pour les compagnies de spectacle\")\n", - "plt.legend()\n", - "\n", - "# Affichage du barplot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "a41dfb3e-12b6-4a7b-9282-698d9476b17b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# syntaxe à retenir pr exporter des images !!\n", - "\n", - "\n", - "FILE_PATH = \"projet-bdc2324-team1/graphics/music/\"\n", - "FILE_NAME = \"number_customers_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "# Création du barplot\n", - "plt.bar(nb_customers_purchasing_spectacle[\"number_compagny\"], nb_customers_purchasing_spectacle[\"customer_id\"]/1000, label = \"clients ayant acheté\")\n", - "plt.bar(nb_customers_no_purchase_spectacle[\"number_compagny\"], nb_customers_no_purchase_spectacle[\"customer_id\"]/1000, \n", - " bottom = nb_customers_purchasing_spectacle[\"customer_id\"]/1000, label = \"clients ciblés par un mail\")\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Compagnie')\n", - "plt.ylabel(\"Nombre de clients (en milliers)\")\n", - "plt.title(\"Nombre de clients identifiés pour les compagnies de spectacle\")\n", - "plt.legend()\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "markdown", - "id": "85b6c7a9-d970-4071-8633-45bc1f50e157", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "#### Prix maximal payé par un client (utile ??)" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "id": "fd11c547-7128-4ef6-ad7b-4b7c2a30cd9e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnymax_price
01013823.0
111108.0
2125000.0
3133180.0
414456.0
\n", - "
" - ], - "text/plain": [ - " number_compagny max_price\n", - "0 10 13823.0\n", - "1 11 108.0\n", - "2 12 5000.0\n", - "3 13 3180.0\n", - "4 14 456.0" - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# prix maximal payé par un client pour chaque compagnie - très variable : de 108 à 13823\n", - "\n", - "company_max_price = customerplus_clean_spectacle.groupby(\"number_compagny\")[\"max_price\"].max().reset_index()\n", - "company_max_price" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "b8f8f162-4153-4cfe-bfaa-d981d414510d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(company_max_price[\"number_compagny\"], company_max_price[\"max_price\"])\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Company')\n", - "plt.ylabel(\"Prix maximal d'un billet vendu\")\n", - "plt.title(\"Prix maximal de vente observé par compagnie de spectacle\")\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "bff23e5d-d7ed-4092-ae3c-5df503e54a6d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 762879.000000\n", - "mean 0.079068\n", - "std 3.969729\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 3334.000000\n", - "Name: purchase_count, dtype: float64" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customerplus_clean_spectacle[customerplus_clean_spectacle[\"first_buying_date\"].isna()][\"purchase_count\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "89466dbd-14d2-4ede-9ca0-b9c32b764e25", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 7.608090e+05\n", - "mean 3.863940e+00\n", - "std 1.685825e+03\n", - "min 1.000000e+00\n", - "25% 1.000000e+00\n", - "50% 1.000000e+00\n", - "75% 2.000000e+00\n", - "max 1.469325e+06\n", - "Name: purchase_count, dtype: float64" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customerplus_clean_spectacle[~customerplus_clean_spectacle[\"first_buying_date\"].isna()][\"purchase_count\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "5f9feae4-35f4-43b6-adeb-f75773900a2d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1523688 rows × 30 columns

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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity \\\n", - "0 821538 139 NaN NaN 0 \n", - "1 809126 1063 NaN NaN 0 \n", - "2 11005 1063 NaN NaN 0 \n", - "3 17663 12731 NaN NaN 0 \n", - "4 38100 12395 NaN NaN 0 \n", - "... ... ... ... ... ... \n", - "343121 4667645 122 NaN 1534181.0 0 \n", - "343122 4667649 122 NaN 1534177.0 0 \n", - "343123 4667660 122 NaN 1534165.0 0 \n", - "343124 4667679 122 NaN 1534132.0 0 \n", - "343125 4667686 122 NaN 1567949.0 0 \n", - "\n", - " tenant_id is_partner deleted_at gender is_email_true ... \\\n", - "0 875 False NaN 2 True ... \n", - "1 875 False NaN 2 True ... \n", - "2 875 False NaN 2 False ... \n", - "3 875 False NaN 0 False ... \n", - "4 875 False NaN 0 True ... \n", - "... ... ... ... ... ... ... \n", - "343121 862 False NaN 2 True ... \n", - "343122 862 False NaN 2 True ... \n", - "343123 862 False NaN 0 True ... \n", - "343124 862 False NaN 2 True ... \n", - "343125 862 False NaN 0 True ... \n", - "\n", - " first_buying_date country gender_label gender_female gender_male \\\n", - "0 NaN NaN other 0 0 \n", - "1 NaN fr other 0 0 \n", - "2 NaN fr other 0 0 \n", - "3 NaN fr female 1 0 \n", - "4 NaN fr female 1 0 \n", - "... ... ... ... ... ... \n", - "343121 NaN NaN other 0 0 \n", - "343122 NaN NaN other 0 0 \n", - "343123 NaN NaN female 1 0 \n", - "343124 NaN NaN other 0 0 \n", - "343125 NaN NaN female 1 0 \n", - "\n", - " gender_other country_fr has_tags number_compagny already_purchased \n", - "0 1 NaN 0 10 False \n", - "1 1 1.0 0 10 False \n", - "2 1 1.0 0 10 False \n", - "3 0 1.0 0 10 False \n", - "4 0 1.0 0 10 False \n", - "... ... ... ... ... ... \n", - "343121 1 NaN 0 14 False \n", - "343122 1 NaN 0 14 False \n", - "343123 0 NaN 0 14 False \n", - "343124 1 NaN 0 14 False \n", - "343125 0 NaN 0 14 False \n", - "\n", - "[1523688 rows x 30 columns]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customerplus_clean_spectacle[\"already_purchased\"] = customerplus_clean_spectacle[\"first_buying_date\"].isna()==False\n", - "customerplus_clean_spectacle" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "cec4f1eb-cec8-409d-8b2c-1e01f1bf81ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...first_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagnyalready_purchased
2110051063NaNNaN0875FalseNaN2False...NaNfrother0011.0010False
31766312731NaNNaN0875FalseNaN0False...NaNfrfemale1001.0010False
43810012395NaNNaN0875FalseNaN0True...NaNfrfemale1001.0010False
5307036139NaNNaN0875FalseNaN2True...NaNNaNother001NaN010False
629461063NaNNaN0875FalseNaN2False...NaNfrother0011.0010False
..................................................................
3389333625705648752NaN1253864.00862FalseNaN0True...NaNfrfemale1001.0014False
3389543627626636890NaN1253887.00862FalseNaN0True...NaNfrfemale1001.0014False
3389593628124653042NaN1253899.00862FalseNaN0True...NaNfrfemale1001.0014False
3389863631189648423NaN1253928.00862FalseNaN0True...NaNfrfemale1001.0014False
3390393635380659417NaN1253975.00862FalseNaN1True...NaNfrmale0101.0014False
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26246 rows × 30 columns

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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity \\\n", - "2 11005 1063 NaN NaN 0 \n", - "3 17663 12731 NaN NaN 0 \n", - "4 38100 12395 NaN NaN 0 \n", - "5 307036 139 NaN NaN 0 \n", - "6 2946 1063 NaN NaN 0 \n", - "... ... ... ... ... ... \n", - "338933 3625705 648752 NaN 1253864.0 0 \n", - "338954 3627626 636890 NaN 1253887.0 0 \n", - "338959 3628124 653042 NaN 1253899.0 0 \n", - "338986 3631189 648423 NaN 1253928.0 0 \n", - "339039 3635380 659417 NaN 1253975.0 0 \n", - "\n", - " tenant_id is_partner deleted_at gender is_email_true ... \\\n", - "2 875 False NaN 2 False ... \n", - "3 875 False NaN 0 False ... \n", - "4 875 False NaN 0 True ... \n", - "5 875 False NaN 2 True ... \n", - "6 875 False NaN 2 False ... \n", - "... ... ... ... ... ... ... \n", - "338933 862 False NaN 0 True ... \n", - "338954 862 False NaN 0 True ... \n", - "338959 862 False NaN 0 True ... \n", - "338986 862 False NaN 0 True ... \n", - "339039 862 False NaN 1 True ... \n", - "\n", - " first_buying_date country gender_label gender_female gender_male \\\n", - "2 NaN fr other 0 0 \n", - "3 NaN fr female 1 0 \n", - "4 NaN fr female 1 0 \n", - "5 NaN NaN other 0 0 \n", - "6 NaN fr other 0 0 \n", - "... ... ... ... ... ... \n", - "338933 NaN fr female 1 0 \n", - "338954 NaN fr female 1 0 \n", - "338959 NaN fr female 1 0 \n", - "338986 NaN fr female 1 0 \n", - "339039 NaN fr male 0 1 \n", - "\n", - " gender_other country_fr has_tags number_compagny already_purchased \n", - "2 1 1.0 0 10 False \n", - "3 0 1.0 0 10 False \n", - "4 0 1.0 0 10 False \n", - "5 1 NaN 0 10 False \n", - "6 1 1.0 0 10 False \n", - "... ... ... ... ... ... \n", - "338933 0 1.0 0 14 False \n", - "338954 0 1.0 0 14 False \n", - "338959 0 1.0 0 14 False \n", - "338986 0 1.0 0 14 False \n", - "339039 0 1.0 0 14 False \n", - "\n", - "[26246 rows x 30 columns]" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# attention, on a des cas où le client a pas de première date d'achat alors qu'il compte plusieurs achats\n", - "# on peut donc avoir une date de première achat valant NaN non pas parce que l'individu n'a jamais acheté \n", - "# mais simplement car elle n'est pas renseignée\n", - "\n", - "customerplus_clean_spectacle[(customerplus_clean_spectacle[\"already_purchased\"]==False) &\n", - "(customerplus_clean_spectacle[\"purchase_count\"]>0)]" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "b5904039-a967-47d5-ba13-1b805bcd76ca", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...first_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagnyalready_purchased
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0 rows × 30 columns

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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [customer_id, street_id, structure_id, mcp_contact_id, fidelity, tenant_id, is_partner, deleted_at, gender, is_email_true, opt_in, last_buying_date, max_price, ticket_sum, average_price, average_purchase_delay, average_price_basket, average_ticket_basket, total_price, purchase_count, first_buying_date, country, gender_label, gender_female, gender_male, gender_other, country_fr, has_tags, number_compagny, already_purchased]\n", - "Index: []\n", - "\n", - "[0 rows x 30 columns]" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# cpdt, si un client a un nombre d'achats nul, il a bien une date de premier achat valant NaN, OK\n", - "customerplus_clean_spectacle[(customerplus_clean_spectacle[\"already_purchased\"]) &\n", - "(customerplus_clean_spectacle[\"purchase_count\"]==0)]" - ] - }, - { - "cell_type": "markdown", - "id": "703d9986-4497-404f-881a-45ca44b25beb", - "metadata": {}, - "source": [ - "#### différence de consentement aux campagnes de mails (opt in)" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "e940bfcf-29cc-4d4c-ae5e-e2a8cecf28af", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "number_compagny already_purchased\n", - "10 False 0.234840\n", - " True 0.236242\n", - "11 False 0.141746\n", - " True 0.002804\n", - "12 False 0.485950\n", - " True 0.244780\n", - "13 False 0.084057\n", - " True 0.177213\n", - "14 False 0.885553\n", - " True 0.308859\n", - "Name: opt_in, dtype: float64" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# différence de consentement aux campagnes de mails (opt in)\n", - "\n", - "# en se restreignant au personnes n'ayant pas acheté, on a quand même des individus acceptant d'être ciblés\n", - "customerplus_clean_spectacle[customerplus_clean_spectacle[\"first_buying_date\"].isna()][\"opt_in\"].unique()\n", - "\n", - "# taux de consentement variés\n", - "customerplus_clean_spectacle[\"already_purchased\"] = customerplus_clean_spectacle[\"purchase_count\"] > 0\n", - "customerplus_clean_spectacle.groupby([\"number_compagny\", \"already_purchased\"])[\"opt_in\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "id": "a5e79beb-9ba0-4c89-b084-e27ff0d65dcc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnyalready_purchasedopt_in
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\n", - "
" - ], - "text/plain": [ - " number_compagny already_purchased opt_in\n", - "0 10 False 0.234840\n", - "1 10 True 0.236242\n", - "2 11 False 0.141746\n", - "3 11 True 0.002804\n", - "4 12 False 0.485950\n", - "5 12 True 0.244780\n", - "6 13 False 0.084057\n", - "7 13 True 0.177213\n", - "8 14 False 0.885553\n", - "9 14 True 0.308859" - ] - }, - "execution_count": 209, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_graph = customerplus_clean_spectacle.groupby([\"number_compagny\", \"already_purchased\"])[\"opt_in\"].mean().reset_index()\n", - "df_graph" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "id": "5be56c41-7697-481a-84ea-f77a2041484b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot groupé\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "\n", - "categories = df_graph[\"number_compagny\"].unique()\n", - "bar_width = 0.35\n", - "bar_positions = np.arange(len(categories))\n", - "\n", - "# Grouper les données par label et créer les barres groupées\n", - "for label in df_graph[\"already_purchased\"].unique():\n", - " label_data = df_graph[df_graph['already_purchased'] == label]\n", - " values = [label_data[label_data['number_compagny'] == category]['opt_in'].values[0]*100 for category in categories]\n", - "\n", - " label_printed = \"client ayant déjà acheté\" if label else \"client n'ayant jamais acheté\"\n", - " ax.bar(bar_positions, values, bar_width, label=label_printed)\n", - "\n", - " # Mise à jour des positions des barres pour le prochain groupe\n", - " bar_positions = [pos + bar_width for pos in bar_positions]\n", - "\n", - "# Ajout des étiquettes, de la légende, etc.\n", - "ax.set_xlabel('Compagnie')\n", - "ax.set_ylabel('Part de consentement (%)')\n", - "ax.set_title('Part de consentement au mailing selon les compagnies')\n", - "ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))])\n", - "ax.set_xticklabels(categories)\n", - "ax.legend()\n", - "\n", - "# sauvegarde dans le MinIO\n", - "\n", - "FILE_NAME = \"consent_customers_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "id": "91b743c4-5473-41e1-b97e-cf06904f0fa8", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasedopt_in
0100.055.896356
1101.050.795672
2110.04.856590
3111.00.046125
4120.037.098498
5121.00.021608
6130.032.457022
7131.019.461217
8140.069.470107
9141.026.682793
\n", - "
" - ], - "text/plain": [ - " number_company y_has_purchased opt_in\n", - "0 10 0.0 55.896356\n", - "1 10 1.0 50.795672\n", - "2 11 0.0 4.856590\n", - "3 11 1.0 0.046125\n", - "4 12 0.0 37.098498\n", - "5 12 1.0 0.021608\n", - "6 13 0.0 32.457022\n", - "7 13 1.0 19.461217\n", - "8 14 0.0 69.470107\n", - "9 14 1.0 26.682793" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on refait le graphique sur train set \n", - "\n", - "df_graph = train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[\"opt_in\"].mean().reset_index()\n", - "df_graph[\"opt_in\"] = 100 * df_graph[\"opt_in\"]\n", - "df_graph" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "728e0021-4f95-4601-bb01-032db2cf6571", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.43006504592722195\n", - "0.2889608343987336\n" - ] - } - ], - "source": [ - "# pourquoi une telle différence sur la variable opt in ??\n", - "print(train_set_spectacle[\"opt_in\"].mean())\n", - "print(customerplus_clean_spectacle[\"opt_in\"].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "274b4bc5-277f-476a-8bc1-c1764b1df2de", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8473746548562269\n", - "0.7573747808905485\n" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "e1d837e1-c445-424b-867a-48b1e790f703", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "genre = homme : \n", - "0.3754292890099192\n", - "0.3103924435775397\n", - "email vérifié : \n", - "0.9966249488521722\n", - "0.936015604285403\n", - "nationalité française : \n", - "0.7882316165225254\n", - "0.7573741156773128\n", - "nbre d'achats : \n", - "1.7069010765735895\n", - "0.9938799646120849\n" - ] - } - ], - "source": [ - "# pour les autres variables, la distribution semble similaire\n", - "\n", - "print(\"genre = homme : \")\n", - "print(train_set_spectacle[\"gender_male\"].mean())\n", - "print(customerplus_clean_spectacle[\"gender_male\"].mean())\n", - "\n", - "print(\"email vérifié : \")\n", - "print(train_set_spectacle[\"is_email_true\"].mean())\n", - "print(customerplus_clean_spectacle[\"is_email_true\"].mean())\n", - "\n", - "print(\"nationalité française : \")\n", - "print(train_set_spectacle[\"country_fr\"].mean())\n", - "print(customerplus_clean_spectacle[\"country_fr\"].mean())\n", - "\n", - "# sauf pr nbre d'achats - à verif\n", - "print(\"nbre d'achats : \")\n", - "print(train_set_spectacle[\"purchase_count\"].mean())\n", - "print(customerplus_clean_spectacle[\"purchase_count\"].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "id": "43deeeb5-8092-42fc-b80b-59d2c58093de", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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VK1dW9erVLfbVrFmzTJdKSH/fx+bl5WV+XrFiRUlSixYtLEaeMtozati4caPOnz+vbt26WewnPT1dzZs317Zt2zJdVpbVcd24cSNTv7OHPf35XrVs2dLif5gzzkmrVq0s1stoP3bsmLntjz/+UKdOnRQcHGyuMyoqStLf90PklDv1nXXr1kmSOnToYLFe+/bt5ex85wsoateurb/++kvPPPOMvvvuO/3555+Z1lmyZIliYmIUGhpq0TdatGhhUUNWVq9ercaNGyssLMyivXv37rp27VqmiS7u5Wfln+zpz7Vr11Z8fLxGjRqlzZs36+bNm3fc/rVr17Ru3Tp16NDBPLqfFXuPv3Xr1hbPb9cnz58/n+nyu8aNG5t/N0iSk5OTnn76aR06dEgnTpwwt0+fPl01a9aUu7u7nJ2d5eLiolWrVln03XXr1snHx0fNmze32MczzzyT5bGaTKZMIw9Vq1a943tna/+ypa9mJeNS1cDAQJvW/6fHHntMLi4umdoXLFig+vXry9vb23z+Zs6cafPP/t328zv11bv5HM+Oi4uL/P39c3yWXeR/BCUUKJ9//rm2bdumnTt36tSpU9q9e7fq168vSdq6dauaNm0qSfrkk0+0YcMGbdu2TcOGDZOkTH/kZwyv34tz585Z/CLOEBwcbPH8zJkzkv6eacjFxcXi0bt3b0m67S+6s2fPKjQ01OIP2axqcXZ2zvSHislkUnBwcKZLZwICAjJtw83NzeI8TZ48WYMHD9aiRYsUExOjYsWKqW3btjp48OBdH5f1fjMuq7N+f7Jy5swZ7d69O9O+fHx8ZBhGpn0VK1bM4rmrq+tt22/cuGFxXO3bt8+0r3HjxskwDPMlOzlxXFmxtz/fq7s9V1euXFGDBg20ZcsWjRo1SmvXrtW2bdu0cOHCHK/zTuc4o49b/0w6Oztn2d+tdenSxXyJ4ZNPPqnAwEDVqVNHK1asMK9z5swZ/e9//8vULx566CFJt/85PnfuXJafO6GhoRb123q8trKnP8+fP1/dunXTp59+qrp166pYsWLq2rVrtvddStKFCxeUlpZ2x4k67D3+u+2TGaw/h//ZlrGvCRMm6OWXX1adOnX0zTffaPPmzdq2bZuaN29ucZ6z+6zPqk2SPD095e7ubtHm5uaWqUZrtvYvW/pqVjKOybo2W2T13i1cuFAdOnRQyZIl9eWXX2rTpk3atm2bnnvuuTsea4a77ed36qt38zl+O+7u7jn+uYv8j3uUUKBUrFjRPOudtXnz5snFxUVLliyx+CWQ3XTMttxUficBAQHaunVrpnbrPyqKFy8uSRo6dKjatWuX5bbKly+f7X5KlCih9evXKz09PduwFBAQoFu3buns2bMWYckwDCUlJelf//rXHY/HmpeXl0aMGKERI0bozJkz5tGlNm3a6Lfffrvn47JX8eLF5eHhkWnSiX8uz6n9SNKHH36Y7axU2f2BlFPs6c9ubm6ZbhKXMv/hmRtWr16tU6dOae3ateZRJElZTrOb2zL+4Dpz5oxKlixpbr9165bN56JHjx7q0aOHrl69qp9++knDhw9X69at9fvvv6t06dIqXry4qlatqtGjR2f5+ow/+rOr7/Tp05naM/6XP6f6rzV7+nPx4sU1adIkTZo0SceOHdPixYs1ZMgQJScnZztLWrFixeTk5GQxSpOVvD7+rMJdRltGX/nyyy8VHR2tadOmWaxnfdO+rZ/198qe/nWnvprd9iXZFRAyZPU788svv1RERITmz59vsTyrz6Ocdqe+mtOf4xcuXMi1n1HkXwQlFBoZX0T7z5s/r1+/ri+++MKu7ViPqtxOTEyMvv76ay1evNji8oE5c+ZYrFe+fHlFRkZq165diouLs6se6e9LxebOnav4+PhsL79r3Lixxo8fry+//FKvv/66uf2bb77R1atXM00+YK+goCB1795du3bt0qRJk3Tt2rV7Pq7sZPcetG7dWnFxcQoICFBERESO7c9a/fr15e/vr3379qlv3745tl17+pY9/Tk8PFy7d++2aFu9enWmS5FyQ8YfR9bTBH/88ce5vm9rGZOQzJ8/XzVr1jS3//e//7VpRrp/8vLyUosWLZSamqq2bdtq7969Kl26tFq3bq2lS5fqwQcfVNGiRe3aZuPGjfXtt9/q1KlTFn/wfv755/L09My1qaLvtj+XKlVKffv21apVq7Rhw4Zs1/Pw8FBUVJQWLFig0aNHZ/vHZF4f/6pVq3TmzBnzH8NpaWmaP3++HnzwQfPol8lkytR3d+/erU2bNllcIhgVFaWvv/5ay5YtM18GJynTLHr36m76V3Z9NSulS5eWh4eHDh8+nGmZPZ9PGUwmk1xdXS1CUlJSUpaz3uWmrPqqPf3+TqNYp06d0o0bNywmeMD9gaCEQqNVq1aaMGGCOnXqpJdeeknnzp3Te++9Z9f3PEhSlSpVtHbtWv3vf/9TSEiIfHx8sh0V6dq1qyZOnKiuXbtq9OjRioyM1NKlS/XDDz9kWvfjjz9WixYt1KxZM3Xv3l0lS5bU+fPntX//fu3YsUMLFizItqZnnnlGs2bNUq9evXTgwAHFxMQoPT1dW7ZsUcWKFdWxY0c1adJEzZo10+DBg3Xp0iXVr1/fPOtdjRo11KVLF7vOgyTVqVNHrVu3VtWqVVW0aFHt379fX3zxherWrStPT897Pq7sVKlSRfPmzdP8+fNVpkwZubu7q0qVKurXr5+++eYbNWzYUK+//rqqVq2q9PR0HTt2TD/++KMGDBigOnXq2L0/a97e3vrwww/VrVs3nT9/Xu3bt1dgYKDOnj2rXbt26ezZs5n+B9rW47K1b9nTn7t06aK33npLb7/9tqKiorRv3z5NmTJFfn5+dtdor3r16qlo0aLq1auXhg8fLhcXF3311VfatWtXru/b2kMPPaRnnnlG77//vpycnNSoUSPt3btX77//vvz8/G576aokvfjii/Lw8FD9+vUVEhKipKQkjRkzRn5+fuYR2XfeeUcrVqxQvXr19Oqrr6p8+fK6ceOGjhw5oqVLl2r69OnZXoI2fPhw8z0ob7/9tooVK6avvvpK33//vcaPH59r75et/fnixYuKiYlRp06dVKFCBfn4+Gjbtm1avnx5tiPGGSZMmKB///vfqlOnjoYMGaKyZcvqzJkzWrx4sT7++GP5+Pjk+fEXL15cjRo10ltvvWWe9e63336zCDetW7fWyJEjNXz4cEVFRenAgQN65513FBERYRGuu3XrpokTJ+rZZ5/VqFGjVLZsWS1btsz8WX+nvmUrW/uXLX01K66urqpbt642b96caVmVKlW0cOFCTZs2TQ8//LCKFCmS7RUcGTK+ZqN3795q3769jh8/rpEjRyokJMR8iXZusKWv2vM5XqVKFUnSBx98oG7dusnFxUXly5eXj4+PJJnPV0xMTK4dE/Iph04lAdgoYxYn69nOrH322WdG+fLlDTc3N6NMmTLGmDFjjJkzZ2aazaZ06dJGq1atstxGQkKCUb9+fcPT09OQlOXsa/904sQJ48knnzS8vb0NHx8f48knnzQ2btyYaSYkwzCMXbt2GR06dDACAwMNFxcXIzg42GjUqJExffr0O56D69evG2+//bYRGRlpuLq6GgEBAUajRo2MjRs3WqwzePBgo3Tp0oaLi4sREhJivPzyy8aFCxcstpXd8VvPQDZkyBCjVq1aRtGiRc3n9PXXXzf+/PNPu48ru/cwY+ajNWvWmNuOHDliNG3a1PDx8TEkWczEdOXKFePNN980ypcvb7i6uhp+fn5GlSpVjNdff91ihitJRp8+fSz2lTF707vvvptlDQsWLLBoX7dundGqVSujWLFihouLi1GyZEmjVatWFutlzOR29uxZi9dazzxmGPb3LVv7c0pKijFo0CAjLCzM8PDwMKKiooyEhAS7Zr2z9Zxk9T5u3LjRqFu3ruHp6WmUKFHCeOGFF4wdO3Zk+hm411nvbOk7N27cMPr3728EBgYa7u7uxiOPPGJs2rTJ8PPzM15//fXbnovZs2cbMTExRlBQkOHq6mqEhoYaHTp0MM+GmOHs2bPGq6++akRERBguLi5GsWLFjIcfftgYNmyYxSyL1sdhGIaxZ88eo02bNoafn5/h6upqVKtWLdPnRHbnPuO9sl7fWlZ9zzDu3J9v3Lhh9OrVy6hatarh6+treHh4GOXLlzeGDx9uXL169bb7NAzD2Ldvn/HUU08ZAQEBhqurq1GqVCmje/fuxo0bN3Lk+LPrB1n9DGb8/E+dOtV48MEHDRcXF6NChQrGV199ZfHalJQUY+DAgUbJkiUNd3d3o2bNmsaiRYuMbt26ZZoB7tixY0a7du0sPuuXLl2aaebTbt26GV5eXpnOT1b9P6s+Ykv/srWvZmXmzJmGk5OTcerUKYv28+fPG+3btzf8/f0Nk8lkrjW7z4gMY8eONcLDww03NzejYsWKxieffJLlsWY3693d9HN7+qotn+OGYRhDhw41QkNDjSJFimT6XOnSpYtRpUqVbOtB4WUyDKtvLgQAoBDZuHGj6tevr6+++irTjJQonEwmk/r06aMpU6bk6n7i4uL05ptv6tixY3eczCK/uHHjhkqVKqUBAwZo8ODBji4n37t06ZJCQ0M1ceJEvfjii44uB3mMS+8AAIXGihUrtGnTJj388MPy8PDQrl27NHbsWEVGRt7x8jHgdjJCV4UKFXTz5k2tXr1akydP1rPPPltgQpL09+xtI0aMUGxsrPr27WvxFQrIbOLEiSpVqpR69Ojh6FLgAAQlAECh4evrqx9//FGTJk3S5cuXVbx4cbVo0UJjxoy5qymRgQyenp6aOHGijhw5opSUFJUqVUqDBw/Wm2++6ejS7PbSSy/pr7/+0h9//GG+PwdZ8/X1VXx8vE3fxYbCh0vvAAAAAMAKXzgLAAAAAFYISgAAAABghaAEAAAAAFYK/Z1p6enpOnXqlHx8fCy+ORoAAADA/cUwDF2+fFmhoaF3/LLoQh+UTp06pbCwMEeXAQAAACCfOH78+B2n9i/0QcnHx0fS3yfD19fXwdUAAAAAcJRLly4pLCzMnBFup9AHpYzL7Xx9fQlKAAAAAGy6JYfJHAAAAADACkEJAAAAAKwQlAAAAADASqG/R8lWaWlpunnzpqPLAAo0FxcXOTk5OboMAACAe3bfByXDMJSUlKS//vrL0aUAhYK/v7+Cg4P53jIAAFCg3fdBKSMkBQYGytPTkz/ugLtkGIauXbum5ORkSVJISIiDKwIAALh793VQSktLM4ekgIAAR5cDFHgeHh6SpOTkZAUGBnIZHgAAKLDu68kcMu5J8vT0dHAlQOGR8fPEPX8AAKAgu6+DUgYutwNyDj9PAACgMCAoAQAAAIAVghIKlO7du6tt27aOLgMAAACFnEMncwgPD9fRo0cztffu3VsfffSRDMPQiBEjNGPGDF24cEF16tTRRx99pIceeij3axvyfa7vI8ORsa3ybF+Q1q5dq5iYGF24cEH+/v6OLgcAAAD5kENHlLZt26bTp0+bHytWrJAkPfXUU5Kk8ePHa8KECZoyZYq2bdum4OBgNWnSRJcvX3Zk2QAAAAAKOYcGpRIlSig4ONj8WLJkiR588EFFRUXJMAxNmjRJw4YNU7t27VS5cmXNnj1b165d05w5cxxZtsMtX75c//73v+Xv76+AgAC1bt1ahw8fNi9fu3atTCaTxZfoJiQkyGQy6ciRI+a2DRs2KCoqSp6enipatKiaNWumCxcuSPp7tG/SpEkW+61evbpiY2PNz00mkz799FM98cQT8vT0VGRkpBYvXnzb2r/88kvVqlVLPj4+Cg4OVqdOnczfu5Nh7969atWqlXx9feXj46MGDRpYHJ8kvffeewoJCVFAQID69OljMcPa7fZx5MgRxcTESJKKFi0qk8mk7t2737ZmAAAA3H/yzT1Kqamp+vLLL/Xcc8/JZDIpMTFRSUlJatq0qXkdNzc3RUVFaePGjQ6s1PGuXr2q/v37a9u2bVq1apWKFCmiJ554Qunp6TZvIyEhQY0bN9ZDDz2kTZs2af369WrTpo3S0tLsqmXEiBHq0KGDdu/erZYtW6pz5846f/58tuunpqZq5MiR2rVrlxYtWqTExESLoHLy5Ek1bNhQ7u7uWr16tbZv367nnntOt27dMq+zZs0aHT58WGvWrNHs2bMVHx+v+Ph4m/YRFhamb775RpJ04MABnT59Wh988IFdxwwAAIDCL9984eyiRYv0119/mf+gTUpKkiQFBQVZrBcUFJTlfU0ZUlJSlJKSYn5+6dKlnC/WwZ588kmL5zNnzlRgYKD27dunypUr27SN8ePHq1atWpo6daq57W7u/erevbueeeYZSVJcXJw+/PBDbd26Vc2bN89y/eeee8787zJlymjy5MmqXbu2rly5Im9vb3300Ufy8/PTvHnz5OLiIkkqV66cxTaKFi2qKVOmyMnJSRUqVFCrVq20atUqvfjiizbto1ixYpKkwMBA7lECAABAlvLNiNLMmTPVokULhYaGWrRbfyeLYRi3/Z6WMWPGyM/Pz/wICwvLlXod6fDhw+rUqZPKlCkjX19fRURESJKOHTtm8zYyRpTuVdWqVc3/9vLyko+PT6ZL6f5p586devzxx1W6dGn5+PgoOjpa0v/VnpCQoAYNGphDUlYeeughOTk5mZ+HhIRY7PNO+wAAAADuJF8EpaNHj2rlypV64YUXzG3BwcGS/m9kKUNycnKmUaZ/Gjp0qC5evGh+HD9+PHeKdqA2bdro3Llz+uSTT7RlyxZt2bJF0t+XnElSkSJ/v62GYZhf8897eCTJw8PjtvsoUqSIxeuz2oakTIHGZDJlewng1atX1bRpU3l7e+vLL7/Utm3b9O2331rUfqe67rRPW/YBAAAA3Em+CEqzZs1SYGCgWrX6v2myIyIiFBwcbJ4JT/r7D91169apXr162W7Lzc1Nvr6+Fo/C5Ny5c9q/f7/efPNNNW7cWBUrVjRPwJChRIkSkqTTp0+b2xISEizWqVq1qlatWpXtfkqUKGHx+kuXLikxMfGeav/tt9/0559/auzYsWrQoIEqVKiQafSpatWq+vnnn7MMZTm1D1dXV0my+34sAAAA3D8cfo9Senq6Zs2apW7dusnZ+f/KMZlM6tevn+Li4hQZGanIyEjFxcXJ09NTnTp1cmDFjlW0aFEFBARoxowZCgkJ0bFjxzRkyBCLdcqWLauwsDDFxsZq1KhROnjwoN5//32LdYYOHaoqVaqod+/e6tWrl1xdXbVmzRo99dRTKl68uBo1aqT4+Hi1adNGRYsW1VtvvWVxudvdKFWqlFxdXfXhhx+qV69e+vXXXzVy5EiLdfr27asPP/xQHTt21NChQ+Xn56fNmzerdu3aKl++fI7so3Tp0jKZTFqyZIlatmwpDw8PeXt739OxAQAAx8nL77/MC3zHZv7g8BGllStX6tixYxY34GcYNGiQ+vXrp969e6tWrVo6efKkfvzxR/n4+Dig0vyhSJEimjdvnrZv367KlSvr9ddf17vvvmuxjouLi+bOnavffvtN1apV07hx4zRq1CiLdcqVK6cff/xRu3btUu3atVW3bl1999135rA6dOhQNWzYUK1bt1bLli3Vtm1bPfjgg/dUe4kSJRQfH68FCxaoUqVKGjt2rN577z2LdQICArR69WpduXJFUVFRevjhh/XJJ5/c9p4le/dRsmRJjRgxQkOGDFFQUJD69u17T8cFAACAwsdkWN+IUshcunRJfn5+unjxYqbL8G7cuKHExERFRETI3d3dQRUChQs/VwCAvMaIEmx1u2xgzeEjSgAAAACQ3xCUAAAAAMAKQQkAAAAArBCUAAAAAMAKQQkAAAAArBCUAAAAAMAKQQkAAAAArBCUAAAAAMAKQQnIx1JTUxUXF6f9+/c7uhQAAID7CkHpPhQbG6vq1as7bP9r166VyWTSX3/95bAa7kb37t3Vtm3bPN3nwIEDtWfPHlWoUOGO6+ZEfQX1vQEAAMhpzo4uIN+K9cvDfV3Mu33lkCNHjigiIkI7d+50aOjKCbGxsVq0aJESEhIcXYqFb775Rr/++quWL18uk8l0x/U/+OADGYaRB5UBAAAUfowooUBKTU11dAm57sknn9Tq1avl6up62/XS0tKUnp4uPz8/+fv7501xAAAAhRxBqQBavny5/v3vf8vf318BAQFq3bq1Dh8+bLHOiRMn1LFjRxUrVkxeXl6qVauWtmzZYrHOF198ofDwcPn5+aljx466fPmyzfuIiIiQJNWoUUMmk0nR0dHZ1rt06VKVK1dOHh4eiomJ0ZEjRyyWZ3Up4KRJkxQeHm5+nnFZ2ZgxYxQaGqpy5cpJkr788kvVqlVLPj4+Cg4OVqdOnZScnGx+XcalZKtWrVKtWrXk6empevXq6cCBA5Kk+Ph4jRgxQrt27ZLJZJLJZFJ8fHy2x/JPtrwP1qKjo9W3b1/17dvX/Lo333zTYiQoNTVVgwYNUsmSJeXl5aU6depo7dq15uXx8fHy9/fXkiVLVKlSJbm5ueno0aOZLr1LSUnRq6++qsDAQLm7u+vf//63tm3bZlHPnd4bSdq4caMaNmwoDw8PhYWF6dVXX9XVq1dtOkcAAAAFFUGpALp69ar69++vbdu2adWqVSpSpIieeOIJpaenS5KuXLmiqKgonTp1SosXL9auXbs0aNAg83JJOnz4sBYtWqQlS5ZoyZIlWrduncaOHWvzPrZu3SpJWrlypU6fPq2FCxdmWevx48fVrl07tWzZUgkJCXrhhRc0ZMiQuzruVatWaf/+/VqxYoWWLFki6e9QMXLkSO3atUuLFi1SYmKiunfvnum1w4YN0/vvv69ffvlFzs7Oeu655yRJTz/9tAYMGKCHHnpIp0+f1unTp/X000/bVM+dzlF2Zs+eLWdnZ23ZskWTJ0/WxIkT9emnn5qX9+jRQxs2bNC8efO0e/duPfXUU2revLkOHjxoXufatWsaM2aMPv30U+3du1eBgYGZ9jNo0CB98803mj17tnbs2KGyZcuqWbNmOn/+vCTb3ps9e/aoWbNmateunXbv3q358+dr/fr16tu3r03nCAAAoKDiHqUC6Mknn7R4PnPmTAUGBmrfvn2qXLmy5syZo7Nnz2rbtm0qVqyYJKls2bIWr0lPT1d8fLx8fHwkSV26dNGqVas0evRom/ZRokQJSVJAQICCg4OzrXXatGkqU6aMJk6cKJPJpPLly2vPnj0aN26c3cft5eWlTz/91OJStIzAI0llypTR5MmTVbt2bV25ckXe3t7mZaNHj1ZUVJQkaciQIWrVqpVu3LghDw8PeXt7y9nZ+bbHkZU7naPshIWFZTofEydO1IsvvqjDhw9r7ty5OnHihEJDQyX9PaHD8uXLNWvWLMXFxUmSbt68qalTp6patWpZ7uPq1auaNm2a4uPj1aJFC0nSJ598ohUrVmjmzJl64403bHpv3n33XXXq1En9+vWTJEVGRmry5MmKiorStGnT5O7ubtc5AwAAKCgYUSqADh8+rE6dOqlMmTLy9fU1XwZ37NgxSVJCQoJq1KhhDklZCQ8PN4ckSQoJCbG4ZO1O+7DV/v379cgjj1hMRlC3bl27tpGhSpUqme7X2blzpx5//HGVLl1aPj4+5ksAreusWrWq+d8hISGSZHG8d+Nuz1FW5+PgwYNKS0vTjh07ZBiGypUrJ29vb/Nj3bp1Fpf1ubq6WhxTVrXdvHlT9evXN7e5uLiodu3a5qnGbXlvtm/frvj4eItamjVrpvT0dCUmJtpwlgAAAAomRpQKoDZt2igsLEyffPKJQkNDlZ6ersqVK5snOPDw8LjjNlxcXCyem0wmi0vG7rQPW9kyC1uRIkUyrXfz5s1M63l5eVk8v3r1qpo2baqmTZvqyy+/VIkSJXTs2DE1a9YsU53/PN6MYHCnS+TuJKfO0T+lp6fLyclJ27dvl5OTk8Wyf46QeXh43HYmvIzzab2OYRjmNlvem/T0dPXs2VOvvvpqpmWlSpW64+sBAAAKKoJSAXPu3Dnt379fH3/8sRo0aCBJWr9+vcU6VatW1aeffqrz58/fdlTpXvaRMbKTlpZ2221VqlRJixYtsmjbvHmzxfMSJUooKSnJ4o94W6bq/u233/Tnn39q7NixCgsLkyT98ssvd3ydNVdX1zsehzVbzlF2rI9/8+bNioyMlJOTk2rUqKG0tDQlJyebt3s3ypYtK1dXV61fv16dOnWS9Hf4/OWXX8yX0dny3tSsWVN79+7NdOkmAABAYceldwVM0aJFFRAQoBkzZujQoUNavXq1+vfvb7HOM888o+DgYLVt21YbNmzQH3/8oW+++UabNm3KsX0EBgbKw8NDy5cv15kzZ3TxYtbfBdWrVy8dPnxY/fv314EDBzRnzpxMs8pFR0fr7NmzGj9+vA4fPqyPPvpIy5Ytu2OdpUqVkqurqz788EP98ccfWrx4sUaOHGnTMf5TeHi4EhMTlZCQoD///FMpKSl3fI0t5yg7x48fN5+PuXPn6sMPP9Rrr70mSSpXrpw6d+6srl27auHChUpMTNS2bds0btw4LV261OZj8vLy0ssvv6w33nhDy5cv1759+/Tiiy/q2rVrev755yXZ9t4MHjxYmzZtUp8+fZSQkKCDBw9q8eLFeuWVV2yuBQAAoCAiKBUwRYoU0bx587R9+3ZVrlxZr7/+ut59912LdVxdXfXjjz8qMDBQLVu2VJUqVTR27NhMl3Ldyz6cnZ01efJkffzxxwoNDdXjjz+e5bZKlSqlb775Rv/73/9UrVo1TZ8+3TwhQYaKFStq6tSp+uijj1StWjVt3bpVAwcOvGOdJUqUUHx8vBYsWKBKlSpp7Nixeu+992w6xn968skn1bx5c8XExKhEiRKaO3fuHV9jyznKTteuXXX9+nXVrl1bffr00SuvvKKXXnrJvHzWrFnq2rWrBgwYoPLly+uxxx7Tli1bzKNmtho7dqyefPJJdenSRTVr1tShQ4f0ww8/qGjRopJse2+qVq2qdevW6eDBg2rQoIFq1Kiht956y3yfFwAAQGFlMmy5UaEAu3Tpkvz8/HTx4kX5+vpaLLtx44YSExMVERHB7F3IE9HR0apevbomTZrk6FJyDT9XAIC8Fj7ke0eXkKOOjG3l6BIKrdtlA2uMKAEAAACAFYISAAAAAFhh1jsgD61du9bRJQAAAMAGjCgBAAAAgBWCEgAAAABYIShJKuQT/wF5ip8nAABQGNzXQcnFxUWSdO3aNQdXAhQeGT9PGT9fAAAABdF9PZmDk5OT/P39lZycLEny9PSUyWRycFVAwWQYhq5du6bk5GT5+/vb/AXHAAAA+dF9HZQkKTg4WJLMYQnAvfH39zf/XAEAABRU931QMplMCgkJUWBgoG7evOnocoACzcXFhZEkAABQKNz3QSmDk5MTf+ABAAAAkHSfT+YAAAAAAFkhKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFghKAEAAACAFYISAAAAAFhxeFA6efKknn32WQUEBMjT01PVq1fX9u3bzcsNw1BsbKxCQ0Pl4eGh6Oho7d2714EVAwAAACjsHBqULly4oPr168vFxUXLli3Tvn379P7778vf39+8zvjx4zVhwgRNmTJF27ZtU3BwsJo0aaLLly87rnAAAAAAhZqzI3c+btw4hYWFadasWea28PBw878Nw9CkSZM0bNgwtWvXTpI0e/ZsBQUFac6cOerZs2delwwAAADgPuDQEaXFixerVq1aeuqppxQYGKgaNWrok08+MS9PTExUUlKSmjZtam5zc3NTVFSUNm7cmOU2U1JSdOnSJYsHAAAAANjDoUHpjz/+0LRp0xQZGakffvhBvXr10quvvqrPP/9ckpSUlCRJCgoKsnhdUFCQeZm1MWPGyM/Pz/wICwvL3YMAAAAAUOg4NCilp6erZs2aiouLU40aNdSzZ0+9+OKLmjZtmsV6JpPJ4rlhGJnaMgwdOlQXL140P44fP55r9QMAAAAonBwalEJCQlSpUiWLtooVK+rYsWOSpODgYEnKNHqUnJycaZQpg5ubm3x9fS0eAAAAAGAPhwal+vXr68CBAxZtv//+u0qXLi1JioiIUHBwsFasWGFenpqaqnXr1qlevXp5WisAAACA+4dDZ717/fXXVa9ePcXFxalDhw7aunWrZsyYoRkzZkj6+5K7fv36KS4uTpGRkYqMjFRcXJw8PT3VqVMnR5YOAAAAoBBzaFD617/+pW+//VZDhw7VO++8o4iICE2aNEmdO3c2rzNo0CBdv35dvXv31oULF1SnTh39+OOP8vHxcWDlAAAAAAozk2EYhqOLyE2XLl2Sn5+fLl68yP1KAAAAhVD4kO8dXUKOOjK2laNLKLTsyQYOvUcJAAAAAPIjghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVZ0cXcL8JH/K9o0vIcUfGtnJ0CQAAAECOYkQJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKwQlAAAAADACkEJAAAAAKw4O7oAFAKxfo6uIGfFXnR0BQAAAHAwh44oxcbGymQyWTyCg4PNyw3DUGxsrEJDQ+Xh4aHo6Gjt3bvXgRUDAAAAuB/YPaJ05MgR/fzzzzpy5IiuXbumEiVKqEaNGqpbt67c3d3tLuChhx7SypUrzc+dnJzM/x4/frwmTJig+Ph4lStXTqNGjVKTJk104MAB+fj42L0vAAAAALCFzUFpzpw5mjx5srZu3arAwECVLFlSHh4eOn/+vA4fPix3d3d17txZgwcPVunSpW0vwNnZYhQpg2EYmjRpkoYNG6Z27dpJkmbPnq2goCDNmTNHPXv2tHkfAAAAAGAPmy69q1mzpiZMmKBnn31WR44cUVJSkrZv367169dr3759unTpkr777julp6erVq1aWrBggc0FHDx4UKGhoYqIiFDHjh31xx9/SJISExOVlJSkpk2bmtd1c3NTVFSUNm7caOdhAgAAAIDtbBpRGjlypFq1apXtcjc3N0VHRys6OlqjRo1SYmKiTTuvU6eOPv/8c5UrV05nzpzRqFGjVK9ePe3du1dJSUmSpKCgIIvXBAUF6ejRo9luMyUlRSkpKebnly5dsqkWAAAAAMhgU1C6XUiyVrx4cRUvXtymdVu0aGH+d5UqVVS3bl09+OCDmj17th555BFJkslksniNYRiZ2v5pzJgxGjFihM31AgAAAIC1e5r17vvvv9cbb7yh/v3765tvvrnnYry8vFSlShUdPHjQfN9SxshShuTk5EyjTP80dOhQXbx40fw4fvz4PdcFAAAA4P5y10Hprbfe0qBBg2QymWQYhl5//XX17dv3nopJSUnR/v37FRISooiICAUHB2vFihXm5ampqVq3bp3q1auX7Tbc3Nzk6+tr8QAAAAAAe9g869327dv18MMPm5/Pnz9fu3btkoeHhySpe/fuio6O1pQpU2ze+cCBA9WmTRuVKlVKycnJGjVqlC5duqRu3brJZDKpX79+iouLU2RkpCIjIxUXFydPT0916tTJjkMEAAAAAPvYHJReeuklNWjQwBxWypQpowkTJqh9+/ZKTU3VtGnTVK5cObt2fuLECT3zzDP6888/VaJECT3yyCPavHmzeXrxQYMG6fr16+rdu7cuXLigOnXq6Mcff+Q7lAAAAADkKpsvvdu6dauCg4NVs2ZN/e9//9Nnn32mHTt2qF69emrQoIFOnDihOXPm2LXzefPm6dSpU0pNTdXJkyf1zTffqFKlSublJpNJsbGxOn36tG7cuKF169apcuXKdu0DAAAAAOxl84iSk5OThgwZog4dOujll1+Wl5eXpkyZotDQ0NysDwAAAADynN2TOZQpU0Y//PCD2rZtq4YNG+qjjz7KjboAAAAAwGFsDkoXL17U4MGD1aZNG7355ptq166dtmzZoq1bt+qRRx7Rnj17crNOAAAAAMgzNgelbt26afPmzWrVqpUOHDigl19+WQEBAZo9e7ZGjx6tDh06aPDgwblZKwAAAADkCZvvUVq1apV27typsmXL6sUXX1TZsmXNyxo3bqwdO3Zo5MiRuVIkAAAAAOQlm0eUIiMjNWPGDP3++++aPn26eQrvDB4eHoqLi8vxAgEAAAAgr9kclD777DOtXr1aNWrU0Jw5czRt2rTcrAsAAAAAHMbmS++qV6+uX375JTdrAQAAAIB8we7pwW1hGEZubBYAAAAA8oRNQalixYqaM2eOUlNTb7vewYMH9fLLL2vcuHE5UhwAAAAAOIJNl9599NFHGjx4sPr06aOmTZuqVq1aCg0Nlbu7uy5cuKB9+/Zp/fr12rdvn/r27avevXvndt0AAAAAkGtsCkqNGjXStm3btHHjRs2fP19z5szRkSNHdP36dRUvXlw1atRQ165d9eyzz8rf3z+XSwYAAACA3GXzZA6SVK9ePdWrVy+3agEAAACAfCFXJnMAAAAAgIKMoAQAAAAAVghKAAAAAGCFoAQAAAAAVghKAAAAAGDF7qDk5OSk5OTkTO3nzp2Tk5NTjhQFAAAAAI5kd1AyDCPL9pSUFLm6ut5zQQAAAADgaDZ/j9LkyZMlSSaTSZ9++qm8vb3Ny9LS0vTTTz+pQoUKOV8hAAAAAOQxm4PSxIkTJf09ojR9+nSLy+xcXV0VHh6u6dOn53yFAAAAAJDHbA5KiYmJkqSYmBgtXLhQRYsWzbWiAAAAAMCRbA5KGdasWZMbdQAAAABAvmF3UEpLS1N8fLxWrVql5ORkpaenWyxfvXp1jhUHAAAAAI5gd1B67bXXFB8fr1atWqly5coymUy5URcAAAAAOIzdQWnevHn6+uuv1bJly9yoBwAAAAAczu7vUXJ1dVXZsmVzoxYAAAAAyBfsDkoDBgzQBx98kO0XzwIAAABAQWf3pXfr16/XmjVrtGzZMj300ENycXGxWL5w4cIcKw4AAAAAHMHuoOTv768nnngiN2oBAAAAgHzB7qA0a9as3KgDAAAAAPINu+9RkqRbt25p5cqV+vjjj3X58mVJ0qlTp3TlypUcLQ4AAAAAHMHuEaWjR4+qefPmOnbsmFJSUtSkSRP5+Pho/PjxunHjhqZPn54bdQIAAABAnrF7ROm1115TrVq1dOHCBXl4eJjbn3jiCa1atSpHiwMAAAAAR7irWe82bNggV1dXi/bSpUvr5MmTOVYYAAAAADiK3SNK6enpSktLy9R+4sQJ+fj45EhRAAAAAOBIdgelJk2aaNKkSebnJpNJV65c0fDhw9WyZcucrA0AAAAAHMLuS+8mTpyomJgYVapUSTdu3FCnTp108OBBFS9eXHPnzs2NGgEAAAAgT9kdlEJDQ5WQkKC5c+dqx44dSk9P1/PPP6/OnTtbTO4AAAAAAAWV3UFJkjw8PPTcc8/pueeey+l6AAAAAMDh7ioonTx5Uhs2bFBycrLS09Mtlr366qs5UhgAAAAAOIrdQWnWrFnq1auXXF1dFRAQIJPJZF5mMpkISgAAAAAKPLuD0ttvv623335bQ4cOVZEidk+aBwAAAAD5nt1J59q1a+rYsSMhCQAAAEChZXfaef7557VgwYLcqAUAAAAA8gW7L70bM2aMWrdureXLl6tKlSpycXGxWD5hwoQcKw4AAAAAHMHuoBQXF6cffvhB5cuXl6RMkzkAAAAAQEFnd1CaMGGCPvvsM3Xv3j0XygEAAAAAx7P7HiU3NzfVr18/N2oBAAAAgHzB7qD02muv6cMPP8yNWgAAAAAgX7D70rutW7dq9erVWrJkiR566KFMkzksXLgwx4oDAAAAAEewe0TJ399f7dq1U1RUlIoXLy4/Pz+Lx90aM2aMTCaT+vXrZ24zDEOxsbEKDQ2Vh4eHoqOjtXfv3rveBwAAAADYwu4RpVmzZuV4Edu2bdOMGTNUtWpVi/bx48drwoQJio+PV7ly5TRq1Cg1adJEBw4ckI+PT47XAQAAAADSXYwoSdKtW7e0cuVKffzxx7p8+bIk6dSpU7py5Yrd27py5Yo6d+6sTz75REWLFjW3G4ahSZMmadiwYWrXrp0qV66s2bNn69q1a5ozZ87dlA0AAAAANrE7KB09elRVqlTR448/rj59+ujs2bOS/h79GThwoN0F9OnTR61atdKjjz5q0Z6YmKikpCQ1bdrU3Obm5qaoqCht3LjR7v0AAAAAgK3svvTutddeU61atbRr1y4FBASY25944gm98MILdm1r3rx52rFjh7Zt25ZpWVJSkiQpKCjIoj0oKEhHjx7NdpspKSlKSUkxP7906ZJdNQEAAACA3UFp/fr12rBhg1xdXS3aS5curZMnT9q8nePHj+u1117Tjz/+KHd392zXM5lMFs8Nw8jU9k9jxozRiBEjbK4DAAAAAKzZfeldenq60tLSMrWfOHHCrgkWtm/fruTkZD388MNydnaWs7Oz1q1bp8mTJ8vZ2dk8kpQxspQhOTk50yjTPw0dOlQXL140P44fP25zTQAAAAAg3UVQatKkiSZNmmR+bjKZdOXKFQ0fPlwtW7a0eTuNGzfWnj17lJCQYH7UqlVLnTt3VkJCgsqUKaPg4GCtWLHC/JrU1FStW7dO9erVy3a7bm5u8vX1tXgAAAAAgD3svvRu4sSJiomJUaVKlXTjxg116tRJBw8eVPHixTV37lybt+Pj46PKlStbtHl5eSkgIMDc3q9fP8XFxSkyMlKRkZGKi4uTp6enOnXqZG/ZAAAAAGAzu4NSaGioEhISNG/ePG3fvl3p6el6/vnn1blzZ3l4eORocYMGDdL169fVu3dvXbhwQXXq1NGPP/7IdygBAAAAyFUmwzAMe17w008/qV69enJ2tsxYt27d0saNG9WwYcMcLfBeXbp0SX5+frp48WK+uAwvfMj3ji4hxx1xL2QjfLEXHV0BAACwQ2H7++rI2FaOLqHQsicb2H2PUkxMjM6fP5+p/eLFi4qJibF3cwAAAACQ79gdlLKbnvvcuXPy8vLKkaIAAAAAwJFsvkepXbt2kv6e5a579+5yc3MzL0tLS9Pu3btvOxsdAAAAABQUNgclPz8/SX+PKPn4+FhM3ODq6qpHHnlEL774Ys5XCAAAAAB5zOagNGvWLElSeHi4Bg4cyGV2AAAAAAotu6cHHz58eG7UAQAAAAD5ht2TOZw5c0ZdunRRaGionJ2d5eTkZPEAAAAAgILO7hGl7t2769ixY3rrrbcUEhKS5Qx4AAAAAFCQ2R2U1q9fr59//lnVq1fPhXIAAAAAwPHsvvQuLCxMhmHkRi0AAAAAkC/YHZQmTZqkIUOG6MiRI7lQDgAAAAA4nt2X3j399NO6du2aHnzwQXl6esrFxcVi+fnz53OsOAAAAABwBLuD0qRJk3KhDABAfhM+5HtHl5Djjoxt5egSAAAFhN1BqVu3brlRBwAAAADkG3bfoyRJhw8f1ptvvqlnnnlGycnJkqTly5dr7969OVocAAAAADiC3UFp3bp1qlKlirZs2aKFCxfqypUrkqTdu3dr+PDhOV4gAAAAAOQ1u4PSkCFDNGrUKK1YsUKurq7m9piYGG3atClHiwMAAAAAR7A7KO3Zs0dPPPFEpvYSJUro3LlzOVIUAAAAADiS3UHJ399fp0+fztS+c+dOlSxZMkeKAgAAAABHsjsoderUSYMHD1ZSUpJMJpPS09O1YcMGDRw4UF27ds2NGgEAAAAgT9kdlEaPHq1SpUqpZMmSunLliipVqqSGDRuqXr16evPNN3OjRgAAAADIU3Z/j5KLi4u++uorjRw5Ujt27FB6erpq1KihyMjI3KgPAAAAAPKc3UEpQ5kyZVSmTBmlpaVpz549unDhgooWLZqTtQEAAACAQ9h96V2/fv00c+ZMSVJaWpqioqJUs2ZNhYWFae3atTldHwAAAADkObuD0n//+19Vq1ZNkvS///1Pf/zxh3777Tf169dPw4YNy/ECAQAAACCv2R2U/vzzTwUHB0uSli5dqg4dOqhcuXJ6/vnntWfPnhwvEAAAAADymt1BKSgoSPv27VNaWpqWL1+uRx99VJJ07do1OTk55XiBAAAAAJDX7J7MoUePHurQoYNCQkJkMpnUpEkTSdKWLVtUoUKFHC8QAAAAAPKa3UEpNjZWlStX1vHjx/XUU0/Jzc1NkuTk5KQhQ4bkeIEAAAAAkNfuanrw9u3bZ2rr1q3bPRcDAAAAAPnBXQWlVatWadWqVUpOTlZ6errFss8++yxHCgMAAAAAR7E7KI0YMULvvPOOatWqZb5PCQAAAAAKE7uD0vTp0xUfH68uXbrkRj0AAAAA4HB2Tw+empqqevXq5UYtAAAAAJAv2B2UXnjhBc2ZMyc3agEAAACAfMHuS+9u3LihGTNmaOXKlapatapcXFwslk+YMCHHigMAAAAAR7A7KO3evVvVq1eXJP36668Wy5jYAQAAAEBhYHdQWrNmTW7UAQAAAAD5ht33KP3TiRMndPLkyZyqBQAAAADyBbuDUnp6ut555x35+fmpdOnSKlWqlPz9/TVy5MhMXz4LAAAAAAWR3ZfeDRs2TDNnztTYsWNVv359GYahDRs2KDY2Vjdu3NDo0aNzo04AAAAAyDN2B6XZs2fr008/1WOPPWZuq1atmkqWLKnevXsTlAAAAAAUeHYHpfPnz6tChQqZ2itUqKDz58/nSFEAAKBwCh/yvaNLyFFHxrZydAkAcond9yhVq1ZNU6ZMydQ+ZcoUVatWLUeKAgAAAABHsntEafz48WrVqpVWrlypunXrymQyaePGjTp+/LiWLl2aGzUCAAAAQJ6ye0QpKipKBw4c0BNPPKG//vpL58+fV7t27XTgwAE1aNAgN2oEAAAAgDxl94iSJJUsWZJJGwAAAAAUWnaPKM2aNUsLFizI1L5gwQLNnj07R4oCAAAAAEeyOyiNHTtWxYsXz9QeGBiouLi4HCkKAAAAABzJ7qB09OhRRUREZGovXbq0jh07liNFAQAAAIAj2R2UAgMDtXv37kztu3btUkBAQI4UBQAAAACOZHdQ6tixo1599VWtWbNGaWlpSktL0+rVq/Xaa6+pY8eOuVEjAAAAAOQpu2e9GzVqlI4eParGjRvL2fnvl6enp6tr167cowQAAACgULB7RMnV1VXz58/XgQMH9NVXX2nhwoU6fPiwPvvsM7m6utq1rWnTpqlq1ary9fWVr6+v6tatq2XLlpmXG4ah2NhYhYaGysPDQ9HR0dq7d6+9JQMAAACAXe7qe5QkKTIyUpGRkfe08wceeEBjx45V2bJlJUmzZ8/W448/rp07d+qhhx7S+PHjNWHCBMXHx6tcuXIaNWqUmjRpogMHDsjHx+ee9g0AAAAA2bF7RCkntWnTRi1btlS5cuVUrlw5jR49Wt7e3tq8ebMMw9CkSZM0bNgwtWvXTpUrV9bs2bN17do1zZkzx5FlAwAAACjkHBqU/iktLU3z5s3T1atXVbduXSUmJiopKUlNmzY1r+Pm5qaoqCht3Lgx2+2kpKTo0qVLFg8AAAAAsIfDg9KePXvk7e0tNzc39erVS99++60qVaqkpKQkSVJQUJDF+kFBQeZlWRkzZoz8/PzMj7CwsFytHwAAAEDh4/CgVL58eSUkJGjz5s16+eWX1a1bN+3bt8+83GQyWaxvGEamtn8aOnSoLl68aH4cP34812oHAAAAUDjdVVD6+eef9eyzz6pu3bo6efKkJOmLL77Q+vXr7d6Wq6urypYtq1q1amnMmDGqVq2aPvjgAwUHB0tSptGj5OTkTKNM/+Tm5maeRS/jAQAAAAD2sDsoffPNN2rWrJk8PDy0c+dOpaSkSJIuX76cI9+jZBiGUlJSFBERoeDgYK1YscK8LDU1VevWrVO9evXueT8AAAAAkB27g9KoUaM0ffp0ffLJJ3JxcTG316tXTzt27LBrW//5z3/0888/68iRI9qzZ4+GDRumtWvXqnPnzjKZTOrXr5/i4uL07bff6tdff1X37t3l6empTp062Vs2AAAAANjM7u9ROnDggBo2bJip3dfXV3/99Zdd2zpz5oy6dOmi06dPy8/PT1WrVtXy5cvVpEkTSdKgQYN0/fp19e7dWxcuXFCdOnX0448/8h1KAAAAAHKV3UEpJCREhw4dUnh4uEX7+vXrVaZMGbu2NXPmzNsuN5lMio2NVWxsrJ1VAgAAAMDds/vSu549e+q1117Tli1bZDKZdOrUKX311VcaOHCgevfunRs1AgAAAECesntEadCgQbp48aJiYmJ048YNNWzYUG5ubho4cKD69u2bGzUCAAAAQJ6yOyhJ0ujRozVs2DDt27dP6enpqlSpkry9vXO6NgAAAABwiLsKSpLk6empWrVq5WQtAAAAAJAv2BSU2rVrZ/MGFy5ceNfFAAAAAEB+YNNkDn5+fuaHr6+vVq1apV9++cW8fPv27Vq1apX8/PxyrVAAAAAAyCs2jSjNmjXL/O/BgwerQ4cOmj59upycnCRJaWlp6t27t3x9fXOnSgAAAADIQ3ZPD/7ZZ59p4MCB5pAkSU5OTurfv78+++yzHC0OAAAAABzB7qB069Yt7d+/P1P7/v37lZ6eniNFAQAAAIAj2T3rXY8ePfTcc8/p0KFDeuSRRyRJmzdv1tixY9WjR48cLxAAAAAA8prdQem9995TcHCwJk6cqNOnT0uSQkJCNGjQIA0YMCDHCwQAAACAvGZ3UCpSpIgGDRqkQYMG6dKlS5LEJA4AAAAACpW7/sJZiYAEAAAAoHCyezIHAAAAACjs7mlECQAAAEAOi/VzdAU5L/aioyuwGyNKAAAAAGDF7qD0+eefKyUlJVN7amqqPv/88xwpCgAAAAAcye6g1KNHD128mHno7PLly3yPEgAAAIBCwe6gZBiGTCZTpvYTJ07Iz68QXk8JAAAA4L5j82QONWrUkMlkkslkUuPGjeXs/H8vTUtLU2Jiopo3b54rRQIAAABAXrI5KLVt21aSlJCQoGbNmsnb29u8zNXVVeHh4XryySdzvEAAAAAAyGs2B6Xhw4crLS1NpUuXVrNmzRQSEpKbdQEAAACAw9h1j5KTk5N69eqlGzdu5FY9AAAAAOBwdk/mUKVKFf3xxx+5UQsAAAAA5At2B6XRo0dr4MCBWrJkiU6fPq1Lly5ZPAAAAACgoLP5HqUMGTPbPfbYYxbThGdMG56WlpZz1QEAAACAA9gdlNasWZMbdQAAAABAvmF3UIqKisqNOgAAAAAg37A7KGW4du2ajh07ptTUVIv2qlWr3nNRAAAAAOBIdgels2fPqkePHlq2bFmWy7lHCQAAAEBBZ/esd/369dOFCxe0efNmeXh4aPny5Zo9e7YiIyO1ePHi3KgRAAAAAPKU3SNKq1ev1nfffad//etfKlKkiEqXLq0mTZrI19dXY8aMUatWrXKjTgAAAADIM3aPKF29elWBgYGSpGLFiuns2bOS/v4i2h07duRsdQAAAADgAHYHpfLly+vAgQOSpOrVq+vjjz/WyZMnNX36dIWEhOR4gQAAAACQ1+y+9K5fv346deqUJGn48OFq1qyZvvrqK7m6uio+Pj6n6wMAAACAPGd3UOrcubP53zVq1NCRI0f022+/qVSpUipevHiOFgcAAAAAjmDzpXfXrl1Tnz59VLJkSQUGBqpTp076888/5enpqZo1axKSAAAAABQaNgel4cOHKz4+Xq1atVLHjh21YsUKvfzyy7lZGwAAAAA4hM2X3i1cuFAzZ85Ux44dJUnPPvus6tevr7S0NDk5OeVagQAAAACQ12weUTp+/LgaNGhgfl67dm05OzubJ3YAAAAAgMLC5qCUlpYmV1dXizZnZ2fdunUrx4sCAAAAAEey+dI7wzDUvXt3ubm5mdtu3LihXr16ycvLy9y2cOHCnK0QAAAAAPKYzUGpW7dumdqeffbZHC0GAAAAAPIDm4PSrFmzcrMOAAAAAMg3bL5HCQAAAADuFwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALDi0KA0ZswY/etf/5KPj48CAwPVtm1bHThwwGIdwzAUGxur0NBQeXh4KDo6Wnv37nVQxQAAAADuBw4NSuvWrVOfPn20efNmrVixQrdu3VLTpk119epV8zrjx4/XhAkTNGXKFG3btk3BwcFq0qSJLl++7MDKAQAAABRmzo7c+fLlyy2ez5o1S4GBgdq+fbsaNmwowzA0adIkDRs2TO3atZMkzZ49W0FBQZozZ4569uzpiLIBAAAAFHL56h6lixcvSpKKFSsmSUpMTFRSUpKaNm1qXsfNzU1RUVHauHGjQ2oEAAAAUPg5dETpnwzDUP/+/fXvf/9blStXliQlJSVJkoKCgizWDQoK0tGjR7PcTkpKilJSUszPL126lEsVAwAAACis8s2IUt++fbV7927NnTs30zKTyWTx3DCMTG0ZxowZIz8/P/MjLCwsV+oFAAAAUHjli6D0yiuvaPHixVqzZo0eeOABc3twcLCk/xtZypCcnJxplCnD0KFDdfHiRfPj+PHjuVc4AAAAgELJoUHJMAz17dtXCxcu1OrVqxUREWGxPCIiQsHBwVqxYoW5LTU1VevWrVO9evWy3Kabm5t8fX0tHgAAAABgD4feo9SnTx/NmTNH3333nXx8fMwjR35+fvLw8JDJZFK/fv0UFxenyMhIRUZGKi4uTp6enurUqZMjSwcAAJBi/RxdQc6LvejoCoB8waFBadq0aZKk6Ohoi/ZZs2ape/fukqRBgwbp+vXr6t27ty5cuKA6deroxx9/lI+PTx5XCwAAAOB+4dCgZBjGHdcxmUyKjY1VbGxs7hcEAAAAAMonkzkAAAAAQH5CUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKwQlAAAAALBCUAIAAAAAKw4NSj/99JPatGmj0NBQmUwmLVq0yGK5YRiKjY1VaGioPDw8FB0drb179zqmWAAAAAD3DYcGpatXr6patWqaMmVKlsvHjx+vCRMmaMqUKdq2bZuCg4PVpEkTXb58OY8rBQAAAHA/cXbkzlu0aKEWLVpkucwwDE2aNEnDhg1Tu3btJEmzZ89WUFCQ5syZo549e+ZlqQAAAADuI/n2HqXExEQlJSWpadOm5jY3NzdFRUVp48aNDqwMAAAAQGHn0BGl20lKSpIkBQUFWbQHBQXp6NGj2b4uJSVFKSkp5ueXLl3KnQIBAAAAFFr5dkQpg8lksnhuGEamtn8aM2aM/Pz8zI+wsLDcLhEAAABAIZNvg1JwcLCk/xtZypCcnJxplOmfhg4dqosXL5ofx48fz9U6AQAAABQ++TYoRUREKDg4WCtWrDC3paamat26dapXr162r3Nzc5Ovr6/FAwAAAADs4dB7lK5cuaJDhw6ZnycmJiohIUHFihVTqVKl1K9fP8XFxSkyMlKRkZGKi4uTp6enOnXq5MCqAQAAABR2Dg1Kv/zyi2JiYszP+/fvL0nq1q2b4uPjNWjQIF2/fl29e/fWhQsXVKdOHf3444/y8fFxVMkAAAAA7gMODUrR0dEyDCPb5SaTSbGxsYqNjc27ogAAAADc9/LtPUoAAAAA4CgEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACwQlACAAAAACsEJQAAAACw4uzoAgDcu/Ah3zu6hBx3ZGwrR5cAAADuY4woAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCEoAQAAAIAVghIAAAAAWCkQQWnq1KmKiIiQu7u7Hn74Yf3888+OLgkAAABAIZbvg9L8+fPVr18/DRs2TDt37lSDBg3UokULHTt2zNGlAQAAACik8n1QmjBhgp5//nm98MILqlixoiZNmqSwsDBNmzbN0aUBAAAAKKScHV3A7aSmpmr79u0aMmSIRXvTpk21cePGLF+TkpKilJQU8/OLFy9Kki5dupR7hdohPeWao0vIcZdMhqNLyFn5pK/Yo1D2qwL4PhQ29CvkhsLWrwrd70CJ34P5AP0q92T8HjCMO5/jfB2U/vzzT6WlpSkoKMiiPSgoSElJSVm+ZsyYMRoxYkSm9rCwsFypEZKfowvIaWML3REVSH6THF0BCiP6FXJaofyNwe9BhyuU70A+61eXL1+Wn9/ta8rXQSmDyWSyeG4YRqa2DEOHDlX//v3Nz9PT03X+/HkFBARk+xrcvUuXLiksLEzHjx+Xr6+vo8tBIUG/Qm6gXyGn0aeQG+hXucswDF2+fFmhoaF3XDdfB6XixYvLyckp0+hRcnJyplGmDG5ubnJzc7No8/f3z60S8f/5+vryw4wcR79CbqBfIafRp5Ab6Fe5504jSRny9WQOrq6uevjhh7VixQqL9hUrVqhevXoOqgoAAABAYZevR5QkqX///urSpYtq1aqlunXrasaMGTp27Jh69erl6NIAAAAAFFL5Pig9/fTTOnfunN555x2dPn1alStX1tKlS1W6dGlHlwb9fanj8OHDM13uCNwL+hVyA/0KOY0+hdxAv8o/TIYtc+MBAAAAwH0kX9+jBAAAAACOQFACAAAAACsEJQAAAACwQlACAAAAACsEJdzRTz/9pDZt2ig0NFQmk0mLFi2yWG4YhmJjYxUaGioPDw9FR0dr7969jikWBcad+tXChQvVrFkzFS9eXCaTSQkJCQ6pEwXL7frVzZs3NXjwYFWpUkVeXl4KDQ1V165dderUKccVjALhTp9XsbGxqlChgry8vFS0aFE9+uij2rJli2OKRYFxp371Tz179pTJZNKkSZPyrD4QlGCDq1evqlq1apoyZUqWy8ePH68JEyZoypQp2rZtm4KDg9WkSRNdvnw5jytFQXKnfnX16lXVr19fY8eOzePKUJDdrl9du3ZNO3bs0FtvvaUdO3Zo4cKF+v333/XYY485oFIUJHf6vCpXrpymTJmiPXv2aP369QoPD1fTpk119uzZPK4UBcmd+lWGRYsWacuWLQoNDc2jymBmAHaQZHz77bfm5+np6UZwcLAxduxYc9uNGzcMPz8/Y/r06Q6oEAWRdb/6p8TEREOSsXPnzjytCQXf7fpVhq1btxqSjKNHj+ZNUSjwbOlXFy9eNCQZK1euzJuiUOBl169OnDhhlCxZ0vj111+N0qVLGxMnTszz2u5njCjhniQmJiopKUlNmzY1t7m5uSkqKkobN250YGUAcGcXL16UyWSSv7+/o0tBIZGamqoZM2bIz89P1apVc3Q5KMDS09PVpUsXvfHGG3rooYccXc59ydnRBaBgS0pKkiQFBQVZtAcFBeno0aOOKAkAbHLjxg0NGTJEnTp1kq+vr6PLQQG3ZMkSdezYUdeuXVNISIhWrFih4sWLO7osFGDjxo2Ts7OzXn31VUeXct9iRAk5wmQyWTw3DCNTGwDkFzdv3lTHjh2Vnp6uqVOnOrocFAIxMTFKSEjQxo0b1bx5c3Xo0EHJycmOLgsF1Pbt2/XBBx8oPj6ev6cciKCEexIcHCzp/0aWMiQnJ2caZQKA/ODmzZvq0KGDEhMTtWLFCkaTkCO8vLxUtmxZPfLII5o5c6acnZ01c+ZMR5eFAurnn39WcnKySpUqJWdnZzk7O+vo0aMaMGCAwsPDHV3efYOghHsSERGh4OBgrVixwtyWmpqqdevWqV69eg6sDAAyywhJBw8e1MqVKxUQEODoklBIGYahlJQUR5eBAqpLly7avXu3EhISzI/Q0FC98cYb+uGHHxxd3n2De5RwR1euXNGhQ4fMzxMTE5WQkKBixYqpVKlS6tevn+Li4hQZGanIyEjFxcXJ09NTnTp1cmDVyO/u1K/Onz+vY8eOmb/j5sCBA5L+HsXMGMkErN2uX4WGhqp9+/basWOHlixZorS0NPNoeLFixeTq6uqospHP3a5fBQQEaPTo0XrssccUEhKic+fOaerUqTpx4oSeeuopB1aN/O5Ovwet/yPHxcVFwcHBKl++fF6Xev9y9LR7yP/WrFljSMr06Natm2EYf08RPnz4cCM4ONhwc3MzGjZsaOzZs8exRSPfu1O/mjVrVpbLhw8f7tC6kb/drl9lTDWf1WPNmjWOLh352O361fXr140nnnjCCA0NNVxdXY2QkBDjscceM7Zu3erospHP3en3oDWmB897JsMwjNyNYgAAAABQsHCPEgAAAABYISgBAAAAgBWCEgAAAABYISgBAAAAgBWCEgAAAABYISgBAAAAgBWCEgAAAABYISgBAO7JkSNHNGrUKF25csXRpQAAkGMISgCAu5aamqoOHTooICBA3t7eebLPtWvXymQy6a+//sqT/RVW0dHR6tevn6PLAIB8i6AEAAVM9+7dZTKZNHbsWIv2RYsWyWQy5WktAwYMUJMmTfTyyy/n6X5x7xYuXKiRI0c6ugwAyLecHV0AAMB+7u7uGjdunHr27KmiRYs6rI4PP/zQpvVSU1Pl6uqay9XAHsWKFXN0CQCQrzGiBAAF0KOPPqrg4GCNGTMm23ViY2NVvXp1i7ZJkyYpPDzc/Lx79+5q27at4uLiFBQUJH9/f40YMUK3bt3SG2+8oWLFiumBBx7QZ599ZrGdkydP6umnn1bRokUVEBCgxx9/XEeOHMm03TFjxig0NFTlypWTJO3Zs0eNGjWSh4eHAgIC9NJLL93x3qalS5eqXLly8vDwUExMjMV+MmzcuFENGzaUh4eHwsLC9Oqrr+rq1au33e7ixYtVq1Ytubu7q3jx4mrXrp152YULF9S1a1cVLVpUnp6eatGihQ4ePGheHh8fL39/fy1ZskTly5eXp6en2rdvr6tXr2r27NkKDw9X0aJF9corrygtLc38uvDwcI0cOVKdOnWSt7e3QkNDM4XNCRMmqEqVKvLy8lJYWJh69+6d6Rx98sknCgsLk6enp5544glNmDBB/v7+5uUZ7/0XX3yh8PBw+fn5qWPHjrp8+bJ5HetL71JTUzVo0CCVLFlSXl5eqlOnjtauXXvbcwgAhRlBCQAKICcnJ8XFxenDDz/UiRMn7mlbq1ev1qlTp/TTTz9pwoQJio2NVevWrVW0aFFt2bJFvXr1Uq9evXT8+HFJ0rVr1xQTEyNvb2/99NNPWr9+vby9vdW8eXOlpqaat7tq1Srt379fK1as0JIlS3Tt2jU1b95cRYsW1bZt27RgwQKtXLlSffv2zba248ePq127dmrZsqUSEhL0wgsvaMiQIRbr7NmzR82aNVO7du20e/duzZ8/X+vXr7/tdr///nu1a9dOrVq10s6dO7Vq1SrVqlXLvLx79+765ZdftHjxYm3atEmGYahly5a6efOmeZ1r165p8uTJmjdvnpYvX661a9eqXbt2Wrp0qZYuXaovvvhCM2bM0H//+1+Lfb/77ruqWrWqduzYoaFDh+r111/XihUrzMuLFCmiyZMn69dff9Xs2bO1evVqDRo0yLx8w4YN6tWrl1577TUlJCSoSZMmGj16dKZjPHz4sBYtWqQlS5ZoyZIlWrduXabLNf+pR48e2rBhg+bNm6fdu3frqaeeUvPmzS0CIgDcVwwAQIHSrVs34/HHHzcMwzAeeeQR47nnnjMMwzC+/fZb458f68OHDzeqVatm8dqJEycapUuXtthW6dKljbS0NHNb+fLljQYNGpif37p1y/Dy8jLmzp1rGIZhzJw50yhfvryRnp5uXiclJcXw8PAwfvjhB/N2g4KCjJSUFPM6M2bMMIoWLWpcuXLF3Pb9998bRYoUMZKSkrI81qFDhxoVK1a02NfgwYMNScaFCxcMwzCMLl26GC+99JLF637++WejSJEixvXr17Pcbt26dY3OnTtnuez33383JBkbNmwwt/3555+Gh4eH8fXXXxuGYRizZs0yJBmHDh0yr9OzZ0/D09PTuHz5srmtWbNmRs+ePc3PS5cubTRv3txif08//bTRokWLLGsxDMP4+uuvjYCAAIv1W7VqZbFO586dDT8/P/Pz4cOHG56ensalS5fMbW+88YZRp04d8/OoqCjjtddeMwzDMA4dOmSYTCbj5MmTFttt3LixMXTo0GxrA4DCjBElACjAxo0bp9mzZ2vfvn13vY2HHnpIRYr836+DoKAgValSxfzcyclJAQEBSk5OliRt375dhw4dko+Pj7y9veXt7a1ixYrpxo0bOnz4sPl1VapUsbgvaf/+/apWrZq8vLzMbfXr11d6eroOHDiQZW379+/XI488YjFJRd26dS3W2b59u+Lj4821eHt7q1mzZkpPT1diYmKW201ISFDjxo2z3aezs7Pq1KljbgsICFD58uW1f/9+c5unp6cefPBBi/MWHh5uMftfUFCQ+bxlV3/dunUttrtmzRo1adJEJUuWlI+Pj7p27apz586ZLyU8cOCAateubbEN6+fS35f5+fj4mJ+HhIRkqiXDjh07ZBiGypUrZ3Ee161bZ/GeAsD9hMkcAKAAa9iwoZo1a6b//Oc/6t69u8WyIkWKyDAMi7Z/XjqWwcXFxeK5yWTKsi09PV2SlJ6erocfflhfffVVpm2VKFHC/O9/BiJJMgwj21n5smu3rj8r6enp6tmzp1599dVMy0qVKpXlazw8PLLdXnb7tK7f3vN2OxnbPXr0qFq2bKlevXpp5MiRKlasmNavX6/nn3/e/N5ldR6zqtmeWtLT0+Xk5KTt27fLycnJYlleTfsOAPkNQQkACrixY8eqevXq5gkTMpQoUUJJSUk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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# with the generic function\n", - "multiple_barplot(df_graph, x=\"number_company\", y=\"opt_in\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"aucun achat\", 1 : \"achat durant la période\"},\n", - " xlabel = \"Numéro de compagnie\", ylabel = \"Part de consentement (%)\", \n", - " title = \"Part de consentement au mailing selon les compagnies (train set)\")\n", - "\n", - "# save in the s3\n", - "\n", - "FILE_NAME = \"consent_customers_train_set_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "id": "360047fc-70a4-4876-b0f1-c0af5cc93e17", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [] - }, - { - "cell_type": "markdown", - "id": "5fcff5cb-923b-44d7-b345-0bee89d30ea2", - "metadata": {}, - "source": [ - "#### Etude du genre" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "id": "32960530-cb46-4eeb-a6d2-1dcf5fb640d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnygender_malegender_femalegender_other
0100.1815820.3438400.474578
1110.1795220.3144480.506030
2120.3463810.4540380.199581
3130.3181080.5030930.178799
4140.3319540.3161810.351865
\n", - "
" - ], - "text/plain": [ - " number_compagny gender_male gender_female gender_other\n", - "0 10 0.181582 0.343840 0.474578\n", - "1 11 0.179522 0.314448 0.506030\n", - "2 12 0.346381 0.454038 0.199581\n", - "3 13 0.318108 0.503093 0.178799\n", - "4 14 0.331954 0.316181 0.351865" - ] - }, - "execution_count": 216, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# genre \n", - "\n", - "company_genders = customerplus_clean_spectacle.groupby(\"number_compagny\")[[\"gender_male\", \"gender_female\", \"gender_other\"]].mean().reset_index()\n", - "company_genders" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "id": "1b4a49d7-7bfe-4e80-aa7e-c9c6d4bc46e2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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A/Pzzz6r/fb1MQkICVqxYgZYtW2LLli3YtGkTevfuDQCoXLkyjI2NMXTo0GL/R1rUD0mhqlWroqCgANnZ2Wo/dJmZmWr9pCzHxMQEISEhCAkJwf379/HHH3/gf//7H4KCgpCeng5LS8sip69WrRquX79e/EBoqXB8Q0ND0atXryL71KtXr9TLKVTS9a1atarGOAOaY1+4Pl9++WWxZ4UV98cL0N24AkBOTg62b9+OsLAwzJw5U9Wel5eHu3fvqvXVdv2A539Q8/LyNNoL/7gUsre3R5MmTbBw4cIi66tevXqxtRee8XP9+nW4uLgU2afwO5CRkaHx2s2bN1XvhS4VN0a1a9cGAPzzzz84efIkYmJiMGzYMFWfixcvqk1TuXJlyGQy1R+wVy2jpEr7eSyvz72U72VmZiZq1Kihev7ib6K2v9n//oyVxJ49e3Dz5k3s27dPtbUGgMYp4yVdTmnHtKJguClnGRkZRf6Pr3BTYuEPb1BQEExMTHDp0iVVSHnZPIcMGYJ27dohISEBvXr1wqhRo9C8eXN4eHjA0tISAQEBSElJQZMmTdQ2R2sjICAAS5YsQWxsLCZOnKhq/+GHH9T6lXQ5dnZ26NOnD27cuIHJkyfj6tWraNiwYZF9O3fujDlz5mDPnj149913Ja3Hv9WrVw916tTByZMnsWjRohLP50Wv+h8tIG19C8f+5MmTapvoXxz71q1bw87ODqmpqZgwYYLkunU1rsDz3TBCCNUWnULffvstFAqFWltAQAB+/fVX3Lp1S/WDqVAoirzmk7u7O/7++2+1tj179uDhw4dqbe+99x7i4+NRq1YtVK5cWVLtgYGBMDY2xooVK+Dn51dkHz8/P1hYWOD7779H3759Ve3Xr1/Hnj17Xvo/3pKKjY1V+x1ISkrCtWvXMHr0aABQneX04pivWrVK7bmVlRV8fX2xdetWfPbZZ6rv6MOHDzXOgCqN0n4ey+tz/2+v+l7GxsbCx8dH9fynn35CQUEB3nnnHQDa/2bXrVsXtWrVQnR0NEJCQjTes0LFbRnW9r3Wdjkv0uWY6hPDTTkLCgpCzZo10a1bN9SvXx9KpRInTpzA559/Dmtra0yaNAnA8x/yefPmYdasWbh8+TI6deqEypUr49atWzh69CisrKwwd+5cKBQKDBw4EDKZDD/88AOMjY0RExODZs2aoX///jhw4ADMzMywfPlyvP3222jTpg3GjRsHd3d3PHjwABcvXsS2bduwZ8+eYmsODAxE27ZtMX36dDx69Ai+vr44ePAgvvvuO42+2i6nW7duaNy4MXx9fVGtWjVcu3YNkZGRcHNzQ506dYqtZfLkyYiLi0OPHj0wc+ZMtGzZEk+ePEFiYiLee+89BAQEaP1erFq1Cp07d0ZQUBCGDx+OGjVq4O7duzhz5gyOHz+OjRs3aj2vQl5eXvjxxx8RFxcHT09PmJubw8vLq1TrGx0dja5du2LBggVwdHREbGysxunZ1tbW+PLLLzFs2DDcvXsXffr0gYODA27fvo2TJ0/i9u3bWLFixUuXo6txtbGxQdu2bfHpp5/C3t4e7u7uSExMxJo1a2BnZ6fW9+OPP8avv/6Kd999F3PmzIGlpSW+/vrrIvfpDx06FLNnz8acOXPQrl07pKam4quvvoKtra1av3nz5iEhIQH+/v6YOHEi6tWrh6dPn+Lq1auIj4/HypUri91U7+7ujv/973+YP38+njx5goEDB8LW1hapqam4c+cO5s6dCzs7O8yePRv/+9//EBwcjIEDByI7Oxtz586Fubk5wsLCtB4rbR07dgyjR49G3759kZ6ejlmzZqFGjRoYP348AKB+/fqoVasWZs6cCSEEqlSpgm3btmnsIiscn65duyIoKAiTJk2CQqHAp59+Cmtra40tayWli89jeXzupXwvN2/eDBMTE3Ts2BGnT5/G7Nmz0bRpU/Tr1w+A9r/ZAPD111+jW7duaNWqFaZMmQJXV1ekpaVh586diI2NBQDVoQXLly/HsGHDYGpqinr16sHf3x+VK1fG2LFjERYWBlNTU8TGxuLkyZMa66fNcnT93lUYej6g+Y0TFxcnBg0aJOrUqSOsra2FqampcHV1FUOHDhWpqaka/bdu3SoCAgKEjY2NkMvlws3NTfTp00d1Ot6sWbOEkZGR2L17t9p0SUlJwsTEREyaNEnVduXKFTFy5EhRo0YNYWpqKqpVqyb8/f2LPePi3+7fvy9Gjhwp7OzshKWlpejYsaM4e/ZskWewaLOczz//XPj7+wt7e3thZmYmXF1dxahRo8TVq1dfWcu9e/fEpEmThKurqzA1NRUODg6ia9eu4uzZs6o+L9ZV1NlSQghx8uRJ0a9fP+Hg4CBMTU2Fk5OTePfdd8XKlStVfQrPJvjrr7/Upi1qnlevXhWBgYGiUqVKqtNHS7u+qampomPHjsLc3FxUqVJFjBo1Svzyyy9Frk9iYqLo2rWrqFKlijA1NRU1atQQXbt2FRs3bnzlcl41roVnS3366aca07443tevXxe9e/cWlStXFpUqVRKdOnUS//zzj8aZTUI8P/OnVatWQi6XCycnJ/HRRx+J1atXa5wpkpeXJ6ZPny5cXFyEhYWFaNeunThx4kSR87x9+7aYOHGi8PDwEKampqJKlSrCx8dHzJo1S+0swuKsX79etGjRQpibmwtra2vh7e2tdnkAIYT49ttvRZMmTYSZmZmwtbUVPXr00DgTa9iwYcLKykpj/u3atRONGjXSaHdzcxNdu3ZVPS/87O3atUsMHTpU2NnZqc7UunDhgtq0hZ+TSpUqicqVK4u+ffuKtLS0Ir+jW7ZsEV5eXqrP4uLFi8XEiRNF5cqV1foBEB988EGRdf57zIs6nViI0n0ey+Nzr833svBsqeTkZNGtWzdhbW0tKlWqJAYOHChu3bqlMc9X/WYXOnTokOjcubOwtbUVcrlc1KpVS+OMrNDQUFG9enVhZGSktt5JSUnCz89PWFpaimrVqonRo0eL48ePa1zGQpvllMV7VxHIhBCinPMUEdFLxcTEYMSIEbhy5YrqQohvosJx+Ouvv1564HlpPXv2DM2aNUONGjWwa9euMlvO6yg8PBxz587F7du3y+R4Kiob3C1FRPSGGTVqFDp27AhnZ2dkZmZi5cqVOHPmjFZXzCZ6HTDcEBG9YR48eIBp06bh9u3bMDU1RfPmzREfH1/q60cRVRTcLUVEREQGhVcoJiIiIoPCcENEREQGheGGiIiIDMobd0CxUqnEzZs3UalSJdWVHomIiKhiE0LgwYMHqF69usb9FV/0xoWbmzdvFnvPGCIiIqrY0tPTX3lD0Dcu3FSqVAnA88GxsbHRczVERESkjdzcXLi4uKj+jr/MGxduCndF2djYMNwQERG9ZrQ5pIQHFBMREZFBYbghIiIig8JwQ0RERAbljTvmhoiICAAUCgWePXum7zLoX8zMzF55mrc2GG6IiOiNIoRAZmYm7t+/r+9S6AVGRkbw8PCAmZlZqebDcENERG+UwmDj4OAAS0tLXtC1gii8yG5GRgZcXV1L9b4w3BAR0RtDoVCogk3VqlX1XQ69oFq1arh58yYKCgpgampa4vnwgGIiInpjFB5jY2lpqedKqCiFu6MUCkWp5sNwQ0REbxzuiqqYdPW+MNwQERGRQWG4ISIiIoPCA4qJiIgAuM/8rdyWdXVxV8nTDB8+HPfv38fWrVvV2vft24eAgADcu3cPdnZ2uinwNcctN0RERGRQGG6IiIgMyKZNm9CoUSPI5XK4u7vj888/V3vd3d0dCxYsQHBwMKytreHm5oZffvkFt2/fRo8ePWBtbQ0vLy8cO3ZMNU1MTAzs7Oywfft21KtXD5aWlujTpw8ePXqEdevWwd3dHZUrV8aHH36odqZTfn4+pk+fjho1asDKygpvvfUW9u3bV+ZjwHBDRERkIJKTk9GvXz8MGDAAp06dQnh4OGbPno2YmBi1fsuWLUPr1q2RkpKCrl27YujQoQgODsaQIUNw/Phx1K5dG8HBwRBCqKZ5/PgxvvjiC/z444/YsWMH9u3bh169eiE+Ph7x8fH47rvvsHr1avz888+qaUaMGIGDBw/ixx9/xN9//42+ffuiU6dOuHDhQpmOA4+5ISJ63YTb6ruC10d4jr4r0Knt27fD2tpare3fW0qWLl2K9u3bY/bs2QCAunXrIjU1FZ9++imGDx+u6telSxeMGTMGADBnzhysWLECLVq0QN++fQEAM2bMgJ+fH27dugUnJycAz68RtGLFCtSqVQsA0KdPH3z33Xe4desWrK2t0bBhQwQEBGDv3r3o378/Ll26hA0bNuD69euoXr06AGDatGnYsWMH1q5di0WLFpXNIIHhhoiI6LUREBCAFStWqLUdOXIEQ4YMAQCcOXMGPXr0UHu9devWiIyMhEKhgLGxMQCgSZMmqtcdHR0BAF5eXhptWVlZqnBjaWmpCjaFfdzd3dXClqOjI7KysgAAx48fhxACdevWVasnLy+vzK8OzXBDRET0mrCyskLt2rXV2q5fv676txBC40J4/961VOjftzYo7F9Um1KpLHKawj5FtRVOo1QqYWxsjOTkZFWoKvTi1iddY7ghIiIyEA0bNsSBAwfU2pKSklC3bl2NgFHWvL29oVAokJWVhTZt2pTrshluiIiIDMTUqVPRokULzJ8/H/3798ehQ4fw1VdfISoqqtxrqVu3LgYPHozg4GB8/vnn8Pb2xp07d7Bnzx54eXmhS5cuZbZsvZ8tFRUVBQ8PD5ibm8PHxwf79+9/af/Y2Fg0bdoUlpaWcHZ2xogRI5CdnV1O1RIREVVczZs3x08//YQff/wRjRs3xpw5czBv3jy1g4nL09q1axEcHIypU6eiXr166N69O44cOQIXF5cyXa5MFLUzrpzExcVh6NChiIqKQuvWrbFq1Sp8++23SE1Nhaurq0b/AwcOoF27dli2bBm6deuGGzduYOzYsahTpw62bNmi1TJzc3Nha2uLnJwc2NjY6HqViIjKHs+W0t4LZ0s9ffoUV65cUf2nmiqWl70/Uv5+63XLzdKlSzFq1CiMHj0aDRo0QGRkJFxcXDSOBC90+PBhuLu7Y+LEifDw8MDbb7+NMWPGqF1oiIiIiN5segs3+fn5SE5ORmBgoFp7YGAgkpKSipzG398f169fR3x8PIQQuHXrFn7++Wd07Vr8PTry8vKQm5ur9iAiIiLDpbcDiu/cuQOFQqE6l76Qo6MjMjMzi5zG398fsbGx6N+/P54+fYqCggJ0794dX375ZbHLiYiIwNy5c3VaOxH9f9w9oj0Du5gcUUWm9wOKizof/8W2QqmpqZg4cSLmzJmD5ORk7NixA1euXMHYsWOLnX9oaChycnJUj/T0dJ3WT0RERBWL3rbc2Nvbw9jYWGMrTVZWlsbWnEIRERFo3bo1PvroIwDPr7BoZWWFNm3aYMGCBXB2dtaYRi6XQy6X634FiIiIqELS25YbMzMz+Pj4ICEhQa09ISEB/v7+RU7z+PFjGBmpl1x4USI9nvRFREREFYhed0uFhITg22+/RXR0NM6cOYMpU6YgLS1NtZspNDQUwcHBqv7dunXD5s2bsWLFCly+fBkHDx7ExIkT0bJlS9VNuYiIiOjNptcrFPfv3x/Z2dmYN28eMjIy0LhxY8THx8PNzQ0AkJGRgbS0NFX/4cOH48GDB/jqq68wdepU2NnZ4d1338Unn3yir1UgIiKiCkavF/HTB17Ej0iHeLaU9nR5thTHXXu8iN9rxSAu4kdERESka7xxJhEREVC+W8RKsCVv+PDhWLdunUb7hQsXULt2bV1UZTAYboiIiF4TnTp1wtq1a9XaqlWrpqdqKi7uliIiInpNyOVyODk5qT2MjY2xbds2+Pj4wNzcHJ6enpg7dy4KCgpU08lkMqxatQrvvfceLC0t0aBBAxw6dAgXL17EO++8AysrK/j5+eHSpUuqacLDw9GsWTNER0fD1dUV1tbWGDduHBQKBZYsWQInJyc4ODhg4cKFajXm5OTgv//9LxwcHGBjY4N3330XJ0+eLLcxAhhuiIiIXms7d+7EkCFDMHHiRKSmpmLVqlWIiYnRCB3z589HcHAwTpw4gfr162PQoEEYM2YMQkNDVTegnjBhgto0ly5dwu+//44dO3Zgw4YNiI6ORteuXXH9+nUkJibik08+wccff4zDhw8DeH7Nua5duyIzMxPx8fFITk5G8+bN0b59e9y9e7d8BgTcLUVERPTa2L59O6ytrVXPO3fujFu3bmHmzJkYNmwYAMDT0xPz58/H9OnTERYWpuo7YsQI9OvXDwAwY8YM+Pn5Yfbs2QgKCgIATJo0CSNGjFBbnlKpRHR0NCpVqoSGDRsiICAA586dQ3x8PIyMjFCvXj188skn2LdvH1q1aoW9e/fi1KlTyMrKUt0d4LPPPsPWrVvx888/47///W+Zjk8hhhsiIqLXREBAAFasWKF6bmVlhdq1a+Ovv/5S21KjUCjw9OlTPH78GJaWlgCe37KoUOFtjry8vNTanj59itzcXNWp1u7u7qhUqZJaH2NjY7W7BTg6OiIrKwsAkJycjIcPH6Jq1apqdT958kRtl1dZY7ghIiJ6TRSGmX9TKpWYO3cuevXqpdH/39eKMTU1Vf278AbVRbUplcoipynsU1Rb4TRKpRLOzs7Yt2+fRi12dnYvWzWdYrghIiJ6jTVv3hznzp2rEKeDN2/eHJmZmTAxMYG7u7ve6mC4ISIieo3NmTMH7733HlxcXNC3b18YGRnh77//xqlTp7BgwYJyraVDhw7w8/PD+++/j08++QT16tXDzZs3ER8fj/fffx++vr7lUgfPliIiInqNBQUFYfv27UhISECLFi3QqlUrLF26VHWfxvIkk8kQHx+Ptm3bYuTIkahbty4GDBiAq1evqo7zKZc6eG8pIiox3uNIe7y3lH7w3lKvFd5bioiIiKgIDDdERERkUBhuiIiIyKAw3BAREZFBYbghIqI3zht2Ls1rQ1fvC8MNERG9MQqvrvv48WM9V0JFyc/PBwAYGxuXaj68iB8REb0xjI2NYWdnp7oXkqWlpeq2A6RfSqUSt2/fhqWlJUxMShdPGG6IiOiN4uTkBACqgEMVh5GREVxdXUsdOBluiIjojSKTyeDs7AwHBwc8e/ZM3+XQv5iZmandcbykGG6IiOiNZGxsXOpjO6hi4gHFREREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFD0Hm6ioqLg4eEBc3Nz+Pj4YP/+/cX2HT58OGQymcajUaNG5VgxERERVWR6DTdxcXGYPHkyZs2ahZSUFLRp0wadO3dGWlpakf2XL1+OjIwM1SM9PR1VqlRB3759y7lyIiIiqqj0Gm6WLl2KUaNGYfTo0WjQoAEiIyPh4uKCFStWFNnf1tYWTk5OqsexY8dw7949jBgxopwrJyIioopKb+EmPz8fycnJCAwMVGsPDAxEUlKSVvNYs2YNOnToADc3t7IokYiIiF5Dersr+J07d6BQKODo6KjW7ujoiMzMzFdOn5GRgd9//x0//PDDS/vl5eUhLy9P9Tw3N7dkBRMREdFrQe8HFMtkMrXnQgiNtqLExMTAzs4O77///kv7RUREwNbWVvVwcXEpTblERERUwekt3Njb28PY2FhjK01WVpbG1pwXCSEQHR2NoUOHwszM7KV9Q0NDkZOTo3qkp6eXunYiIiKquPQWbszMzODj44OEhAS19oSEBPj7+7902sTERFy8eBGjRo165XLkcjlsbGzUHkRERGS49HbMDQCEhIRg6NCh8PX1hZ+fH1avXo20tDSMHTsWwPOtLjdu3MD69evVpluzZg3eeustNG7cWB9lExERUQWm13DTv39/ZGdnY968ecjIyEDjxo0RHx+vOvspIyND45o3OTk52LRpE5YvX66PkomIiKiCkwkhhL6LKE+5ubmwtbVFTk4Od1ERlVa4rb4reH2E5+hwXhx3rely3EmvpPz91vvZUkRERES6xHBDREREBoXhhoiIiAwKww0REREZlBKFm/3792PIkCHw8/PDjRs3AADfffcdDhw4oNPiiIiIiKSSHG42bdqEoKAgWFhYICUlRXXfpgcPHmDRokU6L5CIiIhICsnhZsGCBVi5ciW++eYbmJqaqtr9/f1x/PhxnRZHREREJJXkcHPu3Dm0bdtWo93Gxgb379/XRU1EREREJSY53Dg7O+PixYsa7QcOHICnp6dOiiIiIiIqKcnhZsyYMZg0aRKOHDkCmUyGmzdvIjY2FtOmTcP48ePLokYiIiIirUm+t9T06dORk5ODgIAAPH36FG3btoVcLse0adMwYcKEsqiRiIiISGslunHmwoULMWvWLKSmpkKpVKJhw4awtrbWdW1EREREkkneLbVmzRoAgKWlJXx9fdGyZUtYW1ujoKAAoaGhOi+QiIiISArJ4Wbq1Kno3bs37t69q2o7e/YsWrZsiZ9++kmnxRERERFJJTncpKSk4NatW/Dy8kJCQgK+/vprNG/eHI0bN8aJEyfKoEQiIiIi7Uk+5sbDwwN//vknpkyZgk6dOsHY2Bjr16/HgAEDyqI+IiIiIklKdEDx9u3bsWHDBvj7++PcuXP45ptv0LZtW1SvXl3X9REREVUM4bb6ruD1EZ6j18WX6Do3/fr1w/Tp0/Hnn3/i77//hlwuh5eXF4+5ISIiIr2TvOXm4MGDOHLkCJo2bQoAcHJyQnx8PL7++muMHDkS/fr103mRRERERNqSHG6Sk5Mhl8s12j/44AN06NBBJ0URERERlZTk3VJyuRyXLl3Cxx9/jIEDByIrKwsAsGPHDhQUFOi8QCIiIiIpJIebxMREeHl54ciRI9i8eTMePnwIAPj7778RFham8wKJiIiIpJAcbmbOnIkFCxYgISEBZmZmqvaAgAAcOnRIp8URERERSSU53Jw6dQo9e/bUaK9WrRqys7N1UhQRERFRSUkON3Z2dsjIyNBoT0lJQY0aNXRSFBEREVFJSQ43gwYNwowZM5CZmQmZTAalUomDBw9i2rRpCA4OLosaiYiIiLQmOdwsXLgQrq6uqFGjBh4+fIiGDRuibdu28Pf3x8cff1wWNRIRERFpTfJ1bkxNTREbG4v58+fj+PHjUCqV8Pb2Rp06dcqiPiIiIiJJSnRvKQDw9PSEp6cnFAoFTp06hXv37qFy5cq6rI2IiIhIMsm7pSZPnow1a9YAABQKBdq1a4fmzZvDxcUF+/bt03V9RERERJJIDjc///yz6r5S27Ztw+XLl3H27FlMnjwZs2bN0nmBRERERFJIDjd37tyBk5MTACA+Ph79+vVD3bp1MWrUKJw6dUrnBRIRERFJITncODo6IjU1FQqFAjt27FDdLPPx48cwNjaWXEBUVBQ8PDxgbm4OHx8f7N+//6X98/LyMGvWLLi5uUEul6NWrVqIjo6WvFwiIiIyTJIPKB4xYgT69esHZ2dnyGQydOzYEQBw5MgR1K9fX9K84uLiMHnyZERFRaF169ZYtWoVOnfujNTUVLi6uhY5Tb9+/XDr1i2sWbMGtWvXRlZWFm/YSURERCqSw014eDgaN26M9PR09O3bF3K5HABgbGyMmTNnSprX0qVLMWrUKIwePRoAEBkZiZ07d2LFihWIiIjQ6L9jxw4kJibi8uXLqFKlCgDA3d1d6ioQERGRASvRqeB9+vTRaBs2bJikeeTn5yM5OVkjEAUGBiIpKanIaX799Vf4+vpiyZIl+O6772BlZYXu3btj/vz5sLCwkLR8IiIiMkwlvs5Nad25cwcKhQKOjo5q7Y6OjsjMzCxymsuXL+PAgQMwNzfHli1bcOfOHYwfPx53794t9ribvLw85OXlqZ7n5ubqbiWIiIiowpF8QLGuyWQytedCCI22QkqlEjKZDLGxsWjZsiW6dOmCpUuXIiYmBk+ePClymoiICNja2qoeLi4uOl8HIiIiqjj0Fm7s7e1hbGyssZUmKytLY2tOIWdnZ9SoUQO2traqtgYNGkAIgevXrxc5TWhoKHJyclSP9PR03a0EERERVTh6CzdmZmbw8fFBQkKCWntCQgL8/f2LnKZ169a4efMmHj58qGo7f/48jIyMULNmzSKnkcvlsLGxUXsQERGR4SpRuLl06RI+/vhjDBw4EFlZWQCen8l0+vRpSfMJCQnBt99+i+joaJw5cwZTpkxBWloaxo4dC+D5Vpfg4GBV/0GDBqFq1aoYMWIEUlNT8eeff+Kjjz7CyJEjeUAxERERAShBuElMTISXlxeOHDmCzZs3q7ai/P333wgLC5M0r/79+yMyMhLz5s1Ds2bN8OeffyI+Ph5ubm4AgIyMDKSlpan6W1tbIyEhAffv34evry8GDx6Mbt264YsvvpC6GkRERGSgZEIIIWUCPz8/9O3bFyEhIahUqRJOnjwJT09P/PXXX3j//fdx48aNsqpVJ3Jzc2Fra4ucnBzuoiIqrXDbV/eh58JzdDgvjrvWOO76octx//+k/P2WvOXm1KlT6Nmzp0Z7tWrVkJ2dLXV2RERERDolOdzY2dkhIyNDoz0lJQU1atTQSVFEREREJSU53AwaNAgzZsxAZmYmZDIZlEolDh48iGnTpqkd/EtERESkD5LDzcKFC+Hq6ooaNWrg4cOHaNiwIdq2bQt/f398/PHHZVEjERERkdYk337B1NQUsbGxmDdvHlJSUqBUKuHt7Y06deqURX1EREREkpT43lK1atVCrVq1dFkLERERUalJDjcjR4586evF3cCSiIiIqDxIDjf37t1Te/7s2TP8888/uH//Pt59912dFUZERERUEpLDzZYtWzTalEolxo8fD09PT50URURERFRSOrlxppGREaZMmYJly5bpYnZEREREJaazu4JfunQJBQUFupodERERUYlI3i0VEhKi9lwIgYyMDPz2228YNmyYzgojIiIiKgnJ4SYlJUXtuZGREapVq4bPP//8lWdSEREREZU1yeFm7969ZVEHERERkU7o7JgbIiIioopA8pYbb29vyGQyrfoeP35cckFEREREpSE53HTq1AlRUVFo2LAh/Pz8AACHDx/G6dOnMW7cOFhYWOi8SCIiIiJtSQ43t2/fxsSJEzF//ny19rCwMKSnp/P2C0RERKRXko+52bhxI4KDgzXahwwZgk2bNumkKCIiIqKSkhxuLCwscODAAY32AwcOwNzcXCdFEREREZWU5N1SkydPxrhx45CcnIxWrVoBeH7MTXR0NObMmaPzAomIiIikkBxuZs6cCU9PTyxfvhw//PADAKBBgwaIiYlBv379dF4gERERkRSSww0A9OvXj0GGiIiIKiRexI+IiIgMiuQtNwqFAsuWLcNPP/2EtLQ05Ofnq71+9+5dnRVHREREJJXkLTdz587F0qVL0a9fP+Tk5CAkJAS9evWCkZERwsPDy6BEIiIiIu1J3nITGxuLb775Bl27dsXcuXMxcOBA1KpVC02aNMHhw4cxceLEsqjz9RFuq+8KXh/hOfqugIiIDJDkLTeZmZnw8vICAFhbWyMn5/kfqPfeew+//fabbqsjIiIikkhyuKlZsyYyMjIAALVr18auXbsAAH/99RfkcrluqyMiIiKSSHK46dmzJ3bv3g0AmDRpEmbPno06deogODgYI0eO1HmBRERERFJIPuZm8eLFqn/36dMHNWvWRFJSEmrXro3u3bvrtDgiIiIiqUp0Eb9/a9Wqleo2DERERET6VqJwc/78eezbtw9ZWVlQKpVqr0m9v1RUVBQ+/fRTZGRkoFGjRoiMjESbNm2K7Ltv3z4EBARotJ85cwb169eXtFwiIiIyTJLDzTfffINx48bB3t4eTk5OkMlkqtdkMpmkcBMXF4fJkycjKioKrVu3xqpVq9C5c2ekpqbC1dW12OnOnTsHGxsb1fNq1apJXQ0iIiIyUJLDzYIFC7Bw4ULMmDGj1AtfunQpRo0ahdGjRwMAIiMjsXPnTqxYsQIRERHFTufg4AA7O7tSL5+IiIgMj+Szpe7du4e+ffuWesH5+flITk5GYGCgWntgYCCSkpJeOq23tzecnZ3Rvn177N2796V98/LykJubq/YgIiIiwyU53PTt21d1bZvSuHPnDhQKBRwdHdXaHR0dkZmZWeQ0zs7OWL16NTZt2oTNmzejXr16aN++Pf78889ilxMREQFbW1vVw8XFpdS1ExERUcWl1W6pL774QvXv2rVrY/bs2Th8+DC8vLxgamqq1lfq7Rf+fcwOAAghNNoK1atXD/Xq1VM99/PzQ3p6Oj777DO0bdu2yGlCQ0MREhKiep6bm8uAQ0REZMC0CjfLli1Te25tbY3ExEQkJiaqtctkMq3Djb29PYyNjTW20mRlZWlszXmZVq1a4fvvvy/2dblczisnExERvUG0CjdXrlzR+YLNzMzg4+ODhIQE9OzZU9WekJCAHj16aD2flJQUODs767w+es3whqXa4w1LicjAlfoifqUREhKCoUOHwtfXF35+fli9ejXS0tIwduxYAM93Kd24cQPr168H8PxsKnd3dzRq1Aj5+fn4/vvvsWnTJmzatEmfq0FEREQViORw06dPH/j6+mLmzJlq7Z9++imOHj2KjRs3aj2v/v37Izs7G/PmzUNGRgYaN26M+Ph4uLm5AQAyMjKQlpam6p+fn49p06bhxo0bsLCwQKNGjfDbb7+hS5cuUleDiIiIDJRMCCGkTFCtWjXs2bMHXl5eau2nTp1Chw4dcOvWLZ0WqGu5ubmwtbVFTk6O2oUAdYa7R7Sny90jHHftcdz1g+OuHxx3/SiD3d9S/n5LPhX84cOHMDMz02g3NTXlNWSIiIhI7ySHm8aNGyMuLk6j/ccff0TDhg11UhQRERFRSUk+5mb27Nno3bs3Ll26hHfffRcAsHv3bmzYsEHS8TZEREREZUFyuOnevTu2bt2KRYsW4eeff4aFhQWaNGmCP/74A+3atSuLGomIiIi0VqJTwbt27YquXbvquhYiIiKiUpN8zA0RERFRRcZwQ0RERAaF4YaIiIgMCsMNERERGZQSh5v8/HycO3cOBQUFuqyHiIiIqFQkh5vHjx9j1KhRsLS0RKNGjVT3fpo4cSIWL16s8wKJiIiIpJAcbkJDQ3Hy5Ens27cP5ubmqvYOHToUeeViIiIiovIk+To3W7duRVxcHFq1agWZTKZqb9iwIS5duqTT4oiIiIikkrzl5vbt23BwcNBof/TokVrYISIiItIHyeGmRYsW+O2331TPCwPNN998Az8/P91VRkRERFQCkndLRUREoFOnTkhNTUVBQQGWL1+O06dP49ChQ0hMTCyLGomIiIi0JnnLjb+/Pw4ePIjHjx+jVq1a2LVrFxwdHXHo0CH4+PiURY1EREREWivRjTO9vLywbt06XddCREREVGpahZvc3FytZ2hjY1PiYoiIiIhKS6twY2dnp/WZUAqFolQFEREREZWGVuFm7969qn9fvXoVM2fOxPDhw1VnRx06dAjr1q1DRERE2VRJREREpCWtwk27du1U/543bx6WLl2KgQMHqtq6d+8OLy8vrF69GsOGDdN9lURERERakny21KFDh+Dr66vR7uvri6NHj+qkKCIiIqKSkhxuXFxcsHLlSo32VatWwcXFRSdFEREREZWU5FPBly1bht69e2Pnzp1o1aoVAODw4cO4dOkSNm3apPMCiYiIiKSQvOWmS5cuuHDhAnr06IG7d+8iOzsbPXr0wPnz59GlS5eyqJGIiIhIayW6iF/NmjWxcOFCXddCREREVGqSt9wQERERVWQMN0RERGRQGG6IiIjIoDDcEBERkUGRHG6ePHmCx48fq55fu3YNkZGR2LVrl04LIyIiIioJyeGmR48eWL9+PQDg/v37eOutt/D555+jR48eWLFiheQCoqKi4OHhAXNzc/j4+GD//v1aTXfw4EGYmJigWbNmkpdJREREhktyuDl+/DjatGkDAPj555/h6OiIa9euYf369fjiiy8kzSsuLg6TJ0/GrFmzkJKSgjZt2qBz585IS0t76XQ5OTkIDg5G+/btpZZPREREBk5yuHn8+DEqVaoEANi1axd69eoFIyMjtGrVCteuXZM0r6VLl2LUqFEYPXo0GjRogMjISLi4uLxyC9CYMWMwaNAg1V3JiYiIiApJDje1a9fG1q1bkZ6ejp07dyIwMBAAkJWVBRsbG63nk5+fj+TkZNX0hQIDA5GUlFTsdGvXrsWlS5cQFham1XLy8vKQm5ur9iAiIiLDJTnczJkzB9OmTYO7uzveeust1daTXbt2wdvbW+v53LlzBwqFAo6Ojmrtjo6OyMzMLHKaCxcuYObMmYiNjYWJiXYXV46IiICtra3qwZt7EhERGTbJ4aZPnz5IS0vDsWPHsGPHDlV7+/btERkZKbkAmUym9lwIodEGAAqFAoMGDcLcuXNRt25drecfGhqKnJwc1SM9PV1yjURERPT6kBxuRo4cCSsrK3h7e8PI6P8mb9SoET755BOt52Nvbw9jY2ONrTRZWVkaW3MA4MGDBzh27BgmTJgAExMTmJiYYN68eTh58iRMTEywZ8+eIpcjl8thY2Oj9iAiIiLDJTncrFu3Dk+ePNFof/LkieoUcW2YmZnBx8cHCQkJau0JCQnw9/fX6G9jY4NTp07hxIkTqsfYsWNRr149nDhxAm+99ZbUVSEiIiIDpPVdwXNzcyGEgBACDx48gLm5ueo1hUKB+Ph4ODg4SFp4SEgIhg4dCl9fX/j5+WH16tVIS0vD2LFjATzfpXTjxg2sX78eRkZGaNy4sdr0Dg4OMDc312gnIiKiN5fW4cbOzg4ymQwymazIY15kMhnmzp0raeH9+/dHdnY25s2bh4yMDDRu3Bjx8fFwc3MDAGRkZLzymjdERERE/6Z1uNm7dy+EEHj33XexadMmVKlSRfWamZkZ3NzcUL16dckFjB8/HuPHjy/ytZiYmJdOGx4ejvDwcMnLJCIiIsOldbhp164dAODKlStwcXFRO5iYiIiIqKLQOtwUcnNzw/3793H06FFkZWVBqVSqvR4cHKyz4oiIiIikkhxutm3bhsGDB+PRo0eoVKmS2jVpZDIZww0RERHpleR9S1OnTsXIkSPx4MED3L9/H/fu3VM97t69WxY1EhEREWlNcri5ceMGJk6cCEtLy7Koh4iIiKhUJIeboKAgHDt2rCxqISIiIio1ycfcdO3aFR999BFSU1Ph5eUFU1NTtde7d++us+KIiIiIpJIcbv7zn/8AAObNm6fxmkwmg0KhKH1VRERERCUkOdy8eOo3ERERUUVSqivxPX36VFd1EBEREemE5HCjUCgwf/581KhRA9bW1rh8+TIAYPbs2VizZo3OCyQiIiKSQnK4WbhwIWJiYrBkyRKYmZmp2r28vPDtt9/qtDgiIiIiqSSHm/Xr12P16tUYPHgwjI2NVe1NmjTB2bNndVocERERkVQluohf7dq1NdqVSiWePXumk6KIiIiISkpyuGnUqBH279+v0b5x40Z4e3vrpCgiIiKikpJ8KnhYWBiGDh2KGzduQKlUYvPmzTh37hzWr1+P7du3l0WNRERERFqTHG66deuGuLg4LFq0CDKZDHPmzEHz5s2xbds2dOzYsSxqJKIKyv3pD/ou4bVxVd8FEL1BJIcb4Pn9pYKCgnRdCxEREVGpleoifkREREQVjVZbbqpUqYLz58/D3t4elStXhkwmK7bv3bt3dVYcERERkVRahZtly5ahUqVKAIDIyMiyrIeIiIioVLQKN8OGDSvy30REREQVjVbhJjc3V+sZ2tjYlLgYIiKiiopnB2rvqp6Xr1W4sbOze+lxNgAghIBMJoNCodBJYUREREQloVW42bt3b1nXQURERKQTWoWbdu3alXUdRERERDoh+To3a9euxcaNGzXaN27ciHXr1umkKCIiIqKSkhxuFi9eDHt7e412BwcHLFq0SCdFEREREZWU5NsvXLt2DR4eHhrtbm5uSEtL00lRRERUPJ61o72r+i6A9ELylhsHBwf8/fffGu0nT55E1apVdVIUERERUUlJDjcDBgzAxIkTsXfvXigUCigUCuzZsweTJk3CgAEDyqJGIiIiIq1J3i21YMECXLt2De3bt4eJyfPJlUolgoODecwNERER6Z3kLTdmZmaIi4vDuXPnEBsbi82bN+PSpUuIjo6GmZmZ5AKioqLg4eEBc3Nz+Pj4YP/+/cX2PXDgAFq3bo2qVavCwsIC9evXx7JlyyQvk4iIiAyX5C03herUqYM6deqUauFxcXGYPHkyoqKi0Lp1a6xatQqdO3dGamoqXF1dNfpbWVlhwoQJaNKkCaysrHDgwAGMGTMGVlZW+O9//1uqWoiIiMgwSN5yo0tLly7FqFGjMHr0aDRo0ACRkZFwcXHBihUriuzv7e2NgQMHolGjRnB3d8eQIUMQFBT00q09RERE9GbRW7jJz89HcnIyAgMD1doDAwORlJSk1TxSUlKQlJT00iso5+XlITc3V+1BREREhktv4ebOnTtQKBRwdHRUa3d0dERmZuZLp61Zsybkcjl8fX3xwQcfYPTo0cX2jYiIgK2trerh4uKik/qJiIioYtLrbikAGncbL7y7+Mvs378fx44dw8qVKxEZGYkNGzYU2zc0NBQ5OTmqR3p6uk7qJiIiooqpRAcU79+/H6tWrcKlS5fw888/o0aNGvjuu+/g4eGBt99+W6t52Nvbw9jYWGMrTVZWlsbWnBcVXiHZy8sLt27dQnh4OAYOHFhkX7lcDrlcrlVNRERE9PqTvOVm06ZNCAoKgoWFBVJSUpCXlwcAePDggaTr3JiZmcHHxwcJCQlq7QkJCfD399d6PkIIVQ1EREREksPNggULsHLlSnzzzTcwNTVVtfv7++P48eOS5hUSEoJvv/0W0dHROHPmDKZMmYK0tDSMHTsWwPNdSsHBwar+X3/9NbZt24YLFy7gwoULWLt2LT777DMMGTJE6moQERGRgZK8W+rcuXNo27atRruNjQ3u378vaV79+/dHdnY25s2bh4yMDDRu3Bjx8fFwc3MDAGRkZKjdjFOpVCI0NBRXrlyBiYkJatWqhcWLF2PMmDFSV4OIiIgMlORw4+zsjIsXL8Ld3V2t/cCBA/D09JRcwPjx4zF+/PgiX4uJiVF7/uGHH+LDDz+UvAwiIiJ6c0jeLTVmzBhMmjQJR44cgUwmw82bNxEbG4tp06YVG1KIiIiIyovkLTfTp09HTk4OAgIC8PTpU7Rt2xZyuRzTpk3DhAkTyqJGIiIiIq2V6FTwhQsXYtasWUhNTYVSqUTDhg1hbW2t69qIiIiIJCvxjTMtLS3h6+ury1qIiIiISk2rcNOrVy+tZ7h58+YSF0NERERUWlodUPzvezPZ2Nhg9+7dOHbsmOr15ORk7N69G7a2tmVWKBEREZE2tNpys3btWtW/Z8yYgX79+mHlypUwNjYGACgUCowfPx42NjZlUyURERGRliSfCh4dHY1p06apgg0AGBsbIyQkBNHR0TotjoiIiEgqyeGmoKAAZ86c0Wg/c+YMlEqlTooiIiIiKinJZ0uNGDECI0eOxMWLF9GqVSsAwOHDh7F48WKMGDFC5wUSERERSSE53Hz22WdwcnLCsmXLkJGRAeD5LRmmT5+OqVOn6rxAIiIiIikkhxsjIyNMnz4d06dPR25uLgDwQGIiIiKqMEp8ET+AoYaIiIgqHskHFBMRERFVZAw3REREZFAYboiIiMigSA4369evR15enkZ7fn4+1q9fr5OiiIiIiEpKcrgZMWIEcnJyNNofPHjA69wQERGR3kkON0IIyGQyjfbr16/zxplERESkd1qfCu7t7Q2ZTAaZTIb27dvDxOT/JlUoFLhy5Qo6depUJkUSERERaUvrcPP+++8DAE6cOIGgoCBYW1urXjMzM4O7uzt69+6t8wKJiIiIpNA63ISFhUGhUMDNzQ1BQUFwdnYuy7qIiIiISkTSMTfGxsYYO3Ysnj59Wlb1EBEREZWK5AOKvby8cPny5bKohYiIiKjUJIebhQsXYtq0adi+fTsyMjKQm5ur9iAiIiLSJ8k3ziw8I6p79+5qp4QXniKuUCh0Vx0RERGRRJLDzd69e8uiDiIiIiKdkBxu2rVrVxZ1EBEREemE5HBT6PHjx0hLS0N+fr5ae5MmTUpdFBEREVFJSQ43t2/fxogRI/D7778X+TqPuSEiIiJ9kny21OTJk3Hv3j0cPnwYFhYW2LFjB9atW4c6derg119/LYsaiYiIiLQmecvNnj178Msvv6BFixYwMjKCm5sbOnbsCBsbG0RERKBr165lUScRERGRViRvuXn06BEcHBwAAFWqVMHt27cBPL+43/HjxyUXEBUVBQ8PD5ibm8PHxwf79+8vtu/mzZvRsWNHVKtWDTY2NvDz88POnTslL5OIiIgMl+RwU69ePZw7dw4A0KxZM6xatQo3btzAypUrJd9vKi4uDpMnT8asWbOQkpKCNm3aoHPnzkhLSyuy/59//omOHTsiPj4eycnJCAgIQLdu3ZCSkiJ1NYiIiMhASd4tNXnyZNy8eRPA85tpBgUFITY2FmZmZoiJiZE0r6VLl2LUqFEYPXo0ACAyMhI7d+7EihUrEBERodE/MjJS7fmiRYvwyy+/YNu2bfD29pa6KkRERGSAJIebwYMHq/7t7e2Nq1ev4uzZs3B1dYW9vb3W88nPz0dycjJmzpyp1h4YGIikpCSt5qFUKvHgwQNUqVKl2D55eXnIy8tTPectIoiIiAyb1rulHj9+jA8++AA1atSAg4MDBg0ahDt37sDS0hLNmzeXFGwA4M6dO1AoFHB0dFRrd3R0RGZmplbz+Pzzz/Ho0SP069ev2D4RERGwtbVVPVxcXCTVSURERK8XrcNNWFgYYmJi0LVrVwwYMAAJCQkYN25cqQv49/2pgP+7R9WrbNiwAeHh4YiLi1Md4FyU0NBQ5OTkqB7p6emlrpmIiIgqLq13S23evBlr1qzBgAEDAABDhgxB69atoVAoYGxsLHnB9vb2MDY21thKk5WVpbE150VxcXEYNWoUNm7ciA4dOry0r1wuh1wul1wfERERvZ603nKTnp6ONm3aqJ63bNkSJiYmqoOLpTIzM4OPjw8SEhLU2hMSEuDv71/sdBs2bMDw4cPxww8/8Jo6REREpEHrLTcKhQJmZmbqE5uYoKCgoMQLDwkJwdChQ+Hr6ws/Pz+sXr0aaWlpGDt2LIDnu5Ru3LiB9evXA3gebIKDg7F8+XK0atVKtdXHwsICtra2Ja6DiIiIDIfW4UYIgeHDh6vt4nn69CnGjh0LKysrVdvmzZu1Xnj//v2RnZ2NefPmISMjA40bN0Z8fDzc3NwAABkZGWrXvFm1ahUKCgrwwQcf4IMPPlC1Dxs2TPJp6ERERGSYtA43w4YN02gbMmRIqQsYP348xo8fX+RrLwaWffv2lXp5REREZNi0Djdr164tyzqIiIiIdELyRfzo5dyf/qDvEl4bV/VdABERGSSGGzIIDJXau6rvAoiIypjkG2cSERERVWQMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBoXhhoiIiAwKww0REREZFIYbIiIiMigMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBoXhhoiIiAwKww0REREZFIYbIiIiMigMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBoXhhoiIiAwKww0REREZFIYbIiIiMigMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDovdwExUVBQ8PD5ibm8PHxwf79+8vtm9GRgYGDRqEevXqwcjICJMnTy6/QomIiOi1oNdwExcXh8mTJ2PWrFlISUlBmzZt0LlzZ6SlpRXZPy8vD9WqVcOsWbPQtGnTcq6WiIiIXgd6DTdLly7FqFGjMHr0aDRo0ACRkZFwcXHBihUriuzv7u6O5cuXIzg4GLa2tuVcLREREb0O9BZu8vPzkZycjMDAQLX2wMBAJCUl6Ww5eXl5yM3NVXsQERGR4dJbuLlz5w4UCgUcHR3V2h0dHZGZmamz5URERMDW1lb1cHFx0dm8iYiIqOLR+wHFMplM7bkQQqOtNEJDQ5GTk6N6pKen62zeREREVPGY6GvB9vb2MDY21thKk5WVpbE1pzTkcjnkcrnO5kdEREQVm9623JiZmcHHxwcJCQlq7QkJCfD399dTVURERPS609uWGwAICQnB0KFD4evrCz8/P6xevRppaWkYO3YsgOe7lG7cuIH169erpjlx4gQA4OHDh7h9+zZOnDgBMzMzNGzYUB+rQERERBWMXsNN//79kZ2djXnz5iEjIwONGzdGfHw83NzcADy/aN+L17zx9vZW/Ts5ORk//PAD3NzccPXq1fIsnYiIiCoovYYbABg/fjzGjx9f5GsxMTEabUKIMq6IiIiIXmd6P1uKiIiISJcYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUvYebqKgoeHh4wNzcHD4+Pti/f/9L+ycmJsLHxwfm5ubw9PTEypUry6lSIiIieh3oNdzExcVh8uTJmDVrFlJSUtCmTRt07twZaWlpRfa/cuUKunTpgjZt2iAlJQX/+9//MHHiRGzatKmcKyciIqKKSq/hZunSpRg1ahRGjx6NBg0aIDIyEi4uLlixYkWR/VeuXAlXV1dERkaiQYMGGD16NEaOHInPPvusnCsnIiKiikpv4SY/Px/JyckIDAxUaw8MDERSUlKR0xw6dEijf1BQEI4dO4Znz56VWa1ERET0+jDR14Lv3LkDhUIBR0dHtXZHR0dkZmYWOU1mZmaR/QsKCnDnzh04OztrTJOXl4e8vDzV85ycHABAbm5uaVehSMq8x2UyX0Oky/eA4649jrt+cNz1g+OuH2XxN7ZwnkKIV/bVW7gpJJPJ1J4LITTaXtW/qPZCERERmDt3rka7i4uL1FJJx2wj9V3Bm4njrh8cd/3guOtHWY77gwcPYGtr+9I+egs39vb2MDY21thKk5WVpbF1ppCTk1OR/U1MTFC1atUipwkNDUVISIjquVKpxN27d1G1atWXhihDkpubCxcXF6Snp8PGxkbf5bwROOb6wXHXD467frxp4y6EwIMHD1C9evVX9tVbuDEzM4OPjw8SEhLQs2dPVXtCQgJ69OhR5DR+fn7Ytm2bWtuuXbvg6+sLU1PTIqeRy+WQy+VqbXZ2dqUr/jVlY2PzRnwBKhKOuX5w3PWD464fb9K4v2qLTSG9ni0VEhKCb7/9FtHR0Thz5gymTJmCtLQ0jB07FsDzrS7BwcGq/mPHjsW1a9cQEhKCM2fOIDo6GmvWrMG0adP0tQpERERUwej1mJv+/fsjOzsb8+bNQ0ZGBho3boz4+Hi4ubkBADIyMtSueePh4YH4+HhMmTIFX3/9NapXr44vvvgCvXv31tcqEBERUQWj9wOKx48fj/Hjxxf5WkxMjEZbu3btcPz48TKuyrDI5XKEhYVp7J6jssMx1w+Ou35w3PWD4148mdDmnCoiIiKi14Te7y1FREREpEsMN0RERGRQGG6IiIjIoDDcEBERkUFhuDEQf/75J7p164bq1atDJpNh69ataq8LIRAeHo7q1avDwsIC77zzDk6fPq2fYg3Iq8Z98+bNCAoKgr29PWQyGU6cOKGXOg3Ny8b92bNnmDFjBry8vGBlZYXq1asjODgYN2/e1F/BBuJVn/fw8HDUr18fVlZWqFy5Mjp06IAjR47op1gD8qpx/7cxY8ZAJpMhMjKy3OqriBhuDMSjR4/QtGlTfPXVV0W+vmTJEixduhRfffUV/vrrLzg5OaFjx4548OBBOVdqWF417o8ePULr1q2xePHicq7MsL1s3B8/fozjx49j9uzZOH78ODZv3ozz58+je/fueqjUsLzq8163bl189dVXOHXqFA4cOAB3d3cEBgbi9u3b5VypYXnVuBfaunUrjhw5otXtCQyeIIMDQGzZskX1XKlUCicnJ7F48WJV29OnT4Wtra1YuXKlHio0TC+O+79duXJFABApKSnlWtOb4GXjXujo0aMCgLh27Vr5FPUG0Gbcc3JyBADxxx9/lE9Rb4Dixv369euiRo0a4p9//hFubm5i2bJl5V5bRcItN2+AK1euIDMzE4GBgao2uVyOdu3aISkpSY+VEZWPnJwcyGSyN/a+cvqQn5+P1atXw9bWFk2bNtV3OQZNqVRi6NCh+Oijj9CoUSN9l1Mh6P0KxVT2Cu+k/uLd1h0dHXHt2jV9lERUbp4+fYqZM2di0KBBb8zNBfVp+/btGDBgAB4/fgxnZ2ckJCTA3t5e32UZtE8++QQmJiaYOHGivkupMLjl5g0ik8nUngshNNqIDMmzZ88wYMAAKJVKREVF6bucN0JAQABOnDiBpKQkdOrUCf369UNWVpa+yzJYycnJWL58OWJiYvh7/i8MN28AJycnAP+3BadQVlaWxtYcIkPx7Nkz9OvXD1euXEFCQgK32pQTKysr1K5dG61atcKaNWtgYmKCNWvW6Lssg7V//35kZWXB1dUVJiYmMDExwbVr1zB16lS4u7vruzy9Ybh5A3h4eMDJyQkJCQmqtvz8fCQmJsLf31+PlRGVjcJgc+HCBfzxxx+oWrWqvkt6YwkhkJeXp+8yDNbQoUPx999/48SJE6pH9erV8dFHH2Hnzp36Lk9veMyNgXj48CEuXryoen7lyhWcOHECVapUgaurKyZPnoxFixahTp06qFOnDhYtWgRLS0sMGjRIj1W//l417nfv3kVaWprqGivnzp0D8HxrWuEWNZLuZeNevXp19OnTB8ePH8f27duhUChUWy2rVKkCMzMzfZX92nvZuFetWhULFy5E9+7d4ezsjOzsbERFReH69evo27evHqt+/b3qd+bF8G5qagonJyfUq1evvEutOPR9uhbpxt69ewUAjcewYcOEEM9PBw8LCxNOTk5CLpeLtm3bilOnTum3aAPwqnFfu3Ztka+HhYXpte7X3cvGvfC0+6Iee/fu1Xfpr7WXjfuTJ09Ez549RfXq1YWZmZlwdnYW3bt3F0ePHtV32a+9V/3OvIinggshE0KIso1PREREROWHx9wQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQUZnIzMzEhx9+CE9PT8jlcri4uKBbt27YvXu3vksjIgPHe0sRkc5dvXoVrVu3hp2dHZYsWYImTZrg2bNn2LlzJz744AOcPXtW3yUSkQHjlhsi0rnx48dDJpPh6NGj6NOnD+rWrYtGjRohJCQEhw8fBgCkpaWhR48esLa2ho2NDfr164dbt26p5hEeHo5mzZohOjoarq6usLa2xrhx46BQKLBkyRI4OTnBwcEBCxcuVFu2TCbDihUr0LlzZ1hYWMDDwwMbN25U6zNjxgzUrVsXlpaW8PT0xOzZs/Hs2TONZX/33Xdwd3eHra0tBgwYgAcPHgAA1q9fj6pVq2rc7bp3794IDg7W6VgSkXQMN0SkU3fv3sWOHTvwwQcfwMrKSuN1Ozs7CCHw/vvv4+7du0hMTERCQgIuXbqE/v37q/W9dOkSfv/9d+zYsQMbNmxAdHQ0unbtiuvXryMxMRGffPIJPv74Y1VgKjR79mz07t0bJ0+exJAhQzBw4ECcOXNG9XqlSpUQExOD1NRULF++HN988w2WLVumseytW7di+/bt2L59OxITE7F48WIAQN++faFQKPDrr7+q+t+5cwfbt2/HiBEjSj2GRFRKer5xJxEZmCNHjggAYvPmzcX22bVrlzA2NhZpaWmqttOnTwsAqrtIh4WFCUtLS5Gbm6vqExQUJNzd3YVCoVC11atXT0RERKieAxBjx45VW95bb70lxo0bV2w9S5YsET4+PqrnRS37o48+Em+99Zbq+bhx40Tnzp1VzyMjI4Wnp6dQKpXFLoeIygePuSEinRJCAHi+e6g4Z86cgYuLC1xcXFRtDRs2hJ2dHc6cOYMWLVoAANzd3VGpUiVVH0dHRxgbG8PIyEitLSsrS23+fn5+Gs9PnDihev7zzz8jMjISFy9exMOHD1FQUAAbGxu1aV5ctrOzs9py/vOf/6BFixa4ceMGatSogbVr12L48OEvXW8iKh/cLUVEOlWnTh3IZDK13UAvEkIUGQJebDc1NVV7XSaTFdmmVCpfWVfhfA8fPowBAwagc+fO2L59O1JSUjBr1izk5+er9X/Vcry9vdG0aVOsX78ex48fx6lTpzB8+PBX1kFEZY/hhoh0qkqVKggKCsLXX3+NR48eabx+//59NGzYEGlpaUhPT1e1p6amIicnBw0aNCh1DS8eg3P48GHUr18fAHDw4EG4ublh1qxZ8PX1RZ06dXDt2rUSLWf06NFYu3YtoqOj0aFDB7UtUUSkPww3RKRzUVFRUCgUaNmyJTZt2oQLFy7gzJkz+OKLL+Dn54cOHTqgSZMmGDx4MI4fP46jR48iODgY7dq1g6+vb6mXv3HjRkRHR+P8+fMICwvD0aNHMWHCBABA7dq1kZaWhh9//BGXLl3CF198gS1btpRoOYMHD8aNGzfwzTffYOTIkaWum4h0g+GGiHTOw8MDx48fR0BAAKZOnYrGjRujY8eO2L17N1asWAGZTIatW7eicuXKaNu2LTp06ABPT0/ExcXpZPlz587Fjz/+iCZNmmDdunWIjY1Fw4YNAQA9evTAlClTMGHCBDRr1gxJSUmYPXt2iZZjY2OD3r17w9raGu+//75Oaiei0pOJwqP/iIgMgEwmw5YtW8otbHTs2BENGjTAF198US7LI6JX49lSREQlcPfuXezatQt79uzBV199pe9yiOhfGG6IiEqgefPmuHfvHj755BPUq1dP3+UQ0b9wtxQREREZFB5QTERERAaF4YaIiIgMCsMNERERGRSGGyIiIjIoDDdERERkUBhuiIiIyKAw3BAREZFBYbghIiIig8JwQ0RERAbl/wGOyLwlO/mwSwAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(company_genders[\"number_compagny\"], company_genders[\"gender_male\"], label = \"Homme\")\n", - "plt.bar(company_genders[\"number_compagny\"], company_genders[\"gender_female\"], \n", - " bottom = company_genders[\"gender_male\"], label = \"Femme\")\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Company')\n", - "plt.ylabel(\"Part de clients de chaque sexe\")\n", - "plt.title(\"Sexe des clients de chaque compagnie de spectacle\")\n", - "plt.legend()\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "c7348c95-e506-4002-90d9-d3b6768af985", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasedgender_malegender_femalegender_othershare_of_women
0100.00.1408620.2887750.57036367.213639
1101.00.2845320.7148310.00063771.528662
2110.00.2899000.5126690.19743163.878535
3111.00.3210330.6097790.06918865.510406
4120.00.3575460.4706540.17179956.828519
5121.00.3968240.4940580.10911855.457191
6130.00.3631980.4929560.14384657.577983
7131.00.3797030.5166050.10369357.637000
8140.00.4476760.4436460.10867849.773906
9141.00.4876950.4714980.04080849.155702
\n", - "
" - ], - "text/plain": [ - " number_company y_has_purchased gender_male gender_female gender_other \\\n", - "0 10 0.0 0.140862 0.288775 0.570363 \n", - "1 10 1.0 0.284532 0.714831 0.000637 \n", - "2 11 0.0 0.289900 0.512669 0.197431 \n", - "3 11 1.0 0.321033 0.609779 0.069188 \n", - "4 12 0.0 0.357546 0.470654 0.171799 \n", - "5 12 1.0 0.396824 0.494058 0.109118 \n", - "6 13 0.0 0.363198 0.492956 0.143846 \n", - "7 13 1.0 0.379703 0.516605 0.103693 \n", - "8 14 0.0 0.447676 0.443646 0.108678 \n", - "9 14 1.0 0.487695 0.471498 0.040808 \n", - "\n", - " share_of_women \n", - "0 67.213639 \n", - "1 71.528662 \n", - "2 63.878535 \n", - "3 65.510406 \n", - "4 56.828519 \n", - "5 55.457191 \n", - "6 57.577983 \n", - "7 57.637000 \n", - "8 49.773906 \n", - "9 49.155702 " - ] - }, - "execution_count": 218, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# sur le train set \n", - "company_genders = train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[[\"gender_male\", \"gender_female\", \"gender_other\"]].mean().reset_index()\n", - "company_genders[\"share_of_women\"] = 100 * (company_genders[\"gender_female\"]/(1-company_genders[\"gender_other\"]))\n", - "company_genders" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "id": "b36e5a8f-45dc-4b74-8137-80b7e916aa84", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# création barplot avec la fonction générique\n", - "\n", - "multiple_barplot(company_genders, x=\"number_company\", y=\"share_of_women\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"aucun achat\", 1 : \"achat durant la période\"},\n", - " xlabel = \"Numéro de compagnie\", ylabel = \"Part de femmes (%)\", \n", - " title = \"Part de femmes selon les compagnies de spectacle (train set)\")\n", - "\n", - "# save in the s3\n", - "\n", - "FILE_NAME = \"gender_train_set_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "markdown", - "id": "9504e6b6-d97c-4aa9-a56a-f9f97264be05", - "metadata": {}, - "source": [ - "#### Etude du pays d'origine" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "id": "ed6374e5-f36c-4f8e-9dba-602715b726f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
number_compagnycountry_fr
0100.996136
1110.994838
2120.002119
3130.831794
4140.993978
\n", - "
" - ], - "text/plain": [ - " number_compagny country_fr\n", - "0 10 0.996136\n", - "1 11 0.994838\n", - "2 12 0.002119\n", - "3 13 0.831794\n", - "4 14 0.993978" - ] - }, - "execution_count": 220, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# pays d'origine (France VS reste du monde)\n", - "\n", - "company_country_fr = customerplus_clean_spectacle.groupby(\"number_compagny\")[\"country_fr\"].mean().reset_index()\n", - "company_country_fr" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "id": "8d95cdd9-2ab3-4c9a-8442-bb9b98e0dd18", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(company_country_fr[\"number_compagny\"], company_country_fr[\"country_fr\"])\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Company')\n", - "plt.ylabel(\"Part de clients français\")\n", - "plt.title(\"Nationalité des clients de chaque compagnie de spectacle\")\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "id": "b459f81f-6d30-44fa-ad65-e85acbf12fd2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasedcountry_fr
0100.099.833259
1101.099.935317
2110.099.486493
3111.099.808521
4120.00.155933
5121.00.079799
6130.082.894264
7131.094.744832
8140.099.238475
9141.099.032154
\n", - "
" - ], - "text/plain": [ - " number_company y_has_purchased country_fr\n", - "0 10 0.0 99.833259\n", - "1 10 1.0 99.935317\n", - "2 11 0.0 99.486493\n", - "3 11 1.0 99.808521\n", - "4 12 0.0 0.155933\n", - "5 12 1.0 0.079799\n", - "6 13 0.0 82.894264\n", - "7 13 1.0 94.744832\n", - "8 14 0.0 99.238475\n", - "9 14 1.0 99.032154" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# graphique sur le train set\n", - "\n", - "company_country_fr = train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[[\"country_fr\"]].mean().reset_index()\n", - "company_country_fr[\"country_fr\"] = 100 * company_country_fr[\"country_fr\"]\n", - "company_country_fr" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "id": "4a037b48-1d65-4ed3-a012-7d6f5a312533", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generic function to generate the barplot ON THE TRAIN SET - nationality\n", - "\n", - "multiple_barplot(company_country_fr, x=\"number_company\", y=\"country_fr\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"aucun achat\", 1 : \"achat durant la période\"},\n", - " xlabel = \"Numéro de compagnie\", ylabel = \"Part de clients français (%)\", \n", - " title = \"Part de clients français des compagnies de spectacle (train set)\")\n", - "\n", - "# save in the s3\n", - "\n", - "FILE_NAME = \"nationality_fr_train_set_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "markdown", - "id": "ecfd112e-270a-4223-b80f-7e95e57d199d", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "### 2. campaigns_information" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "b37e7ddf-321a-4ebe-9742-9e760a541d29", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre de lignes de la table : 688953\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 301495\n", - "number_compagny 0\n", - "dtype: int64" - ] - }, - "execution_count": 189, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# nombre de nan\n", - "print(\"Nombre de lignes de la table : \",campaigns_information_spectacle.shape[0])\n", - "campaigns_information_spectacle.isna().sum()" - ] - }, - { - "cell_type": "markdown", - "id": "47c15a1d-bef8-4105-87f3-607958667569", - "metadata": {}, - "source": [ - "#### Part de clients n'ouvrant jamais les mails" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "id": "de1ecaac-25bb-4853-b8ab-3ef2ca6917ed", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idnb_campaignsnb_campaigns_openedtime_to_opennumber_compagnyno_campaign_opened
02940.0NaT10True
13730.0NaT10True
23941.00 days 05:16:3810False
34141.00 days 01:12:2910False
44440.0NaT10True
.....................
254699683776911.00 days 23:42:1514False
254700687503810.0NaT14True
254701687506610.0NaT14True
254702687509910.0NaT14True
254703687514311.00 days 01:17:0114False
\n", - "

688953 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " customer_id nb_campaigns nb_campaigns_opened time_to_open \\\n", - "0 29 4 0.0 NaT \n", - "1 37 3 0.0 NaT \n", - "2 39 4 1.0 0 days 05:16:38 \n", - "3 41 4 1.0 0 days 01:12:29 \n", - "4 44 4 0.0 NaT \n", - "... ... ... ... ... \n", - "254699 6837769 1 1.0 0 days 23:42:15 \n", - "254700 6875038 1 0.0 NaT \n", - "254701 6875066 1 0.0 NaT \n", - "254702 6875099 1 0.0 NaT \n", - "254703 6875143 1 1.0 0 days 01:17:01 \n", - "\n", - " number_compagny no_campaign_opened \n", - "0 10 True \n", - "1 10 True \n", - "2 10 False \n", - "3 10 False \n", - "4 10 True \n", - "... ... ... \n", - "254699 14 False \n", - "254700 14 True \n", - "254701 14 True \n", - "254702 14 True \n", - "254703 14 False \n", - "\n", - "[688953 rows x 6 columns]" - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# part de clients n'ouvrant jamais les mails par compagnie\n", - "\n", - "campaigns_information_spectacle[\"no_campaign_opened\"] = pd.isna(campaigns_information_spectacle[\"time_to_open\"])\n", - "campaigns_information_spectacle" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "id": "b5a0060f-a9dd-435b-844f-b24674b8bc27", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnyno_campaign_opened
0100.605656
1110.294001
2120.475719
3130.353820
4140.428148
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" - ], - "text/plain": [ - " number_compagny no_campaign_opened\n", - "0 10 0.605656\n", - "1 11 0.294001\n", - "2 12 0.475719\n", - "3 13 0.353820\n", - "4 14 0.428148" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "company_lazy_customers = campaigns_information_spectacle.groupby(\"number_compagny\")[\"no_campaign_opened\"].mean().reset_index()\n", - "company_lazy_customers" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "id": "788c90e0-f13a-4804-ace7-e5159fddd7fd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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xY8cqtbVhwwYBQKxfv15tjIUdz+RyuXBwcBDNmjVTmj8pKUkYGhqq/bx0OV4X/G4mJycLAwMDMXLkSKV2Hj16JBwcHESvXr2EEELcu3dPABDR0dFq16MwcrlcODk5iUaNGinlADdu3FBZj02bNql8L4UQ4uTJkwKAWLp0aaHt6Bqfi4uLkMlkIjExUam8Y8eOwsrKSjx58kQIofv+q+uxrV69emq/ewXlH0sGDx4sGjZsqCjXtZ2C3/FX2aYFFev0dPPmzWFoaAhLS0u89957cHBwwK+//gp7e3sAwL59+9ChQwdYW1tDX18fhoaGmDFjBjIyMpCenq5Ul4+PD9zd3YsThsL+/fthaWmJoKAgpfKQkBCl91euXMG///6ruO7yxYsXilfnzp2RmpqqdMq3oF9//RUmJiY6nTLR5Pnz59i7dy+6desGMzMzlTieP3+ucjqh4Lr5+PgA+N8lAZq8/fbbePDgAT744AP89NNPKqcXtQkMDIS+vn6R205ISEBQUBBsbW0V+0FoaCjkcjkuXbqkmG/06NFIT0/H5s2bAbz8X9GyZcsQGBiodPrl2rVrCAkJgYODg6I+Pz8/AFA5BSeTydClSxelMh8fH522V2H27duHunXr4u2331YqHzhwIIQQOvWKFBQWFoabN29iz549irI1a9bAwcEBAQEBAIAjR44gMzMTAwYMUNpX8vLy8O677+LkyZMqp0p69OhRjDVUlp6ejvDwcDg7O8PAwACGhoZwcXEBoLq9dfHrr78CAD7++ONXju2/XuW7IZPJ0LlzZ8V7AwMD1KpVC46OjmjYsKGi3MbGBnZ2dip1Ll++HI0aNYKJiYliG+3du7dY20eTgteKt2jRAi4uLopTxUePHsWzZ89UzpQ4OzujXbt2KpcxFEVQUFCxepUBYNeuXfDy8kKDBg2U9l1/f3+lSziaNm0KAOjVqxd+/PFHne/Gocs+lb+NCm6bt99+G56enirbxtHRUdGbDPzvs2/QoIFSj6KnpycA9ftZwc+rV69eMDAwUDq1r8vx7OLFi0hLS0OvXr2U6qtRowZatmypdn2Lc7z+/fff8eLFC4SGhip9TiYmJvDz81N8TjY2Nnjrrbfw+eefY9GiRUhISFC6NKcwFy9exO3btxESEqJ0WtzFxUXlDN2uXbtQqVIldOnSRSmWBg0awMHBQePAxaLEV69ePdSvX1+pLCQkBFlZWYiPj1fEosv+WxLHts2bN6Nly5awsLBQHEtWrVqldCwpbjuvsk0LKlbSuG7dOpw8eRIJCQm4ffs2zp49q9iBT5w4gU6dOgF4eR3GX3/9hZMnT2Lq1KkAXp5K/i9Np8d0lZGRoUhY/8vBwUHp/Z07dwAAEyZMgKGhodJr+PDhAKAxobp79y6cnJyUutKLG++LFy/w1VdfqcSR/wNWMA5bW1ul9/nd2gW3pzr9+/fH6tWrkZSUhB49esDOzg7NmjVDXFycTvEWp+3k5GS0atUKt27dwpdffonDhw/j5MmTiuvq/rtsw4YN0apVK8W0Xbt24caNGxgxYoRinsePH6NVq1Y4fvw45syZgwMHDuDkyZPYtm2b2ljMzMwUp7X/G3f+9ZHFkZGRoXZ/zf8hycjIKHKdAQEBcHR0VFwzdf/+fezcuROhoaGKA3/+ftuzZ0+V/WX+/PkQQihOleZ71e9VXl4eOnXqhG3btmHixInYu3cvTpw4ofjPjC77XUF3796Fvr6+yvfyVb3Kd0PdfmJkZAQbGxuVeY2MjJT2n0WLFmHYsGFo1qwZtm7dimPHjuHkyZN49913i7V9NFG3zRwcHBT7XP6/he2fxdk3873KvnTnzh2cPXtWZb+1tLSEEEJxnGvdujV27NihSFyqV68OLy8vbNq0SWP9uuxTRd02hX32BcuNjIwAQO0xpWA8BgYGsLW1VbSl6/Esf351v2/qyoDifR/yjzFNmzZV+axiY2MVn5NMJsPevXvh7++PBQsWoFGjRqhatSpGjRqFR48eFVp//noUth8XjOXBgwcwMjJSiSUtLU3jb3RR4tMUS368uu6/r3ps27ZtG3r16oVq1aph/fr1OHr0KE6ePIlBgwYp7V/FbedVtmlBxbqm0dPTUzF6uqAffvgBhoaG2LVrl9LBuLDb55TEvaJsbW1x4sQJlfKCA2Hyr1uZPHkyunfvrrYuDw+PQtupWrUq/vzzT+Tl5b1S4li5cmXo6+ujf//+hf6Pwc3Nrdj1qxMWFoawsDA8efIEhw4dwsyZM/Hee+/h0qVLit6jkrRjxw48efIE27ZtU6q/sIulR40ahffffx/x8fH4+uuv4e7ujo4dOyqm79u3D7dv38aBAwcU/xsHoPU6mpJka2uL1NRUlfL8QRf5+1f+fl/wmiN1X8z8/WDJkiV48OABNm7ciOzsbISFhSnmya/3q6++KnQUbsEfkFf9Xv399984c+YMYmJiMGDAAEW5ukE7JiYmaq+vKri+VatWhVwuR1pamsZExNjYWG19r5L4vA7r169HmzZtsGzZMqVyTT+exaVuUF9aWhpq1aoF4H+JQmH753+v2TMxMcHDhw9V5ivsh+NV9qUqVarA1NRUcQ2zuun5goODERwcjOzsbBw7dgxRUVEICQmBq6srfH191S6vyz71321TcMRwwW1TUtLS0lCtWjXF+xcvXiAjI0MRi67Hs/z585O6gm2UlPxtsGXLFq2/By4uLli1ahUA4NKlS/jxxx8xa9Ys5OTkYPny5WqXyV+PwvbjgrHY2tqqDDLKZ2lpWSLxaYolP15d919dj22FWb9+Pdzc3BAbG6v0fSt4HCxuO6+6Tf+rxJ8Ik3/T7/92jz979gzff/99keoxNjbW+X/rbdu2xaNHj1RGD23cuFHpvYeHB2rXro0zZ86gSZMmal+aNl5AQACeP3/+yvdZMjMzQ9u2bZGQkAAfHx+1cRT836IudNlm5ubmCAgIwNSpU5GTk4N//vmnuKuhUf6O/99bcwgh8O2336qdv1u3bqhRowbGjx+PPXv2YPjw4UpfHnX1AcCKFSuKHWNReqQAoH379jh//rzi1EW+devWQSaToW3btgCgOKV+9uxZpfkKG90WFhaG58+fY9OmTYiJiYGvry/q1KmjmN6yZUtUqlQJ58+fL3S/ze/1KKn1Lcr2dnV1RXp6utIPW05ODn7//Xel+fJPtxdMstTVV3Db7du3D48fP9Yad2mSyWQq2+fs2bOv5f53GzZsUHp/5MgRJCUlKQbb+Pr6wtTUFOvXr1ea7+bNm9i3b5/SQClXV1dcunRJ6QcpIyND4x0giuu9997D1atXYWtrq3a/VTfq3tjYGH5+fpg/fz4AqIze/S9d9ql27doBgMq2OXnyJC5cuKAyiKwkFPy8fvzxR7x48ULxeen6/fLw8ICDg4PK3T2Sk5NL9PPy9/eHgYEBrl69WugxRh13d3dMmzYN3t7eKsfFguvh6OiITZs2Kd1FJSkpSWU93nvvPWRkZEAul6uNQ1PHTlHi++eff3DmzBmlso0bN8LS0hKNGjVSxKLL/qvrsa2w32iZTAYjIyOl37y0tDSV0dO6tlNQSW7TEhk9/V+BgYFYtGgRQkJC8NFHHyEjIwMLFy4s8n29vL29ceDAAfz8889wdHSEpaVloSsWGhqKxYsXIzQ0FHPnzkXt2rWxe/dulR8t4OWXMiAgAP7+/hg4cCCqVauGzMxMXLhwAfHx8Yrr6tT54IMPsGbNGoSHh+PixYto27Yt8vLycPz4cXh6eqJPnz46r9+XX36Jd955B61atcKwYcPg6uqKR48e4cqVK/j555+LdX2ct7c3tm3bhmXLlqFx48bQ09NDkyZN8OGHH8LU1BQtW7aEo6Mj0tLSEBUVBWtra8W1RCWtY8eOMDIywgcffICJEyfi+fPnWLZsGe7fv692fn19fXz88cf49NNPYW5urnL9UYsWLVC5cmWEh4dj5syZMDQ0xIYNG1S+9EVhaWkJFxcX/PTTT2jfvj1sbGxQpUqVQp9OMHbsWKxbtw6BgYGIjIyEi4sLfvnlFyxduhTDhg1TXJvr4OCADh06ICoqCpUrV4aLiwv27t2rOPVUUJ06deDr64uoqCikpKRg5cqVStMtLCzw1VdfYcCAAcjMzETPnj1hZ2eHu3fv4syZM7h7965OBxFvb28AwPz58xEQEAB9fX34+PioTTjr1KmDt956C5MmTYIQAjY2Nvj555/VXtLQu3dvzJgxA3369MEnn3yC58+fY8mSJZDL5UrztWrVCv3798ecOXNw584dvPfeezA2NkZCQgLMzMwwcuRIAC8vp5g+fTpmzJgBPz8/nD9/Hl9//bXGkZ9SeO+99zB79mzMnDkTfn5+uHjxIiIjI+Hm5oYXL16UaFunTp3CkCFD8P777yMlJQVTp05FtWrVFJfVVKpUCdOnT8eUKVMQGhqKDz74ABkZGYiIiICJiYnS3Rj69++PFStWoF+/fvjwww+RkZGBBQsWaLxDRXGNGTMGW7duRevWrTF27Fj4+PggLy8PycnJ+OOPPzB+/Hg0a9YMM2bMwM2bN9G+fXtUr14dDx48wJdffql0nZ86uuxTHh4e+Oijj/DVV19BT08PAQEBitHTzs7OGDt2bImv97Zt22BgYICOHTsqRk/Xr19fcW2irsczPT09REREYOjQoejZsycGDRqEBw8eICIiAo6Ojq98qVQ+V1dXREZGYurUqbh27RreffddVK5cGXfu3MGJEydgbm6OiIgInD17FiNGjMD777+P2rVrw8jICPv27cPZs2cxadKkQuvX09PD7NmzMWTIEHTr1g0ffvghHjx4oBi5/F99+vTBhg0b0LlzZ4wePRpvv/02DA0NcfPmTezfvx/BwcHo1q2b2naKEp+TkxOCgoIwa9YsODo6Yv369YiLi8P8+fMV97XVdf/V9djm7e2NH374AbGxsahZsyZMTEzg7e2tuPXg8OHD0bNnT6SkpGD27NlwdHTE5cuXFTHr2k5Br7JNVeg8ZEb8byRfwZFEBa1evVp4eHgIY2NjUbNmTREVFSVWrVolACiNhnVxcRGBgYFq60hMTBQtW7YUZmZmAoDWEUc3b94UPXr0EBYWFsLS0lL06NFDHDlyRGW0qhBCnDlzRvTq1UvY2dkJQ0ND4eDgINq1ayeWL1+udRs8e/ZMzJgxQ9SuXVsYGRkJW1tb0a5dO3HkyBGl9dI2ejq/fNCgQaJatWrC0NBQVK1aVbRo0UIxIlKI/42y3Lx5s8qyBevMzMwUPXv2FJUqVRIymUwxOm7t2rWibdu2wt7eXhgZGQknJyfRq1cvcfbsWY3rmt/G559/rjINOow6/vnnn0X9+vWFiYmJqFatmvjkk0/Er7/+Wuio0Rs3bggAIjw8XG19R44cEb6+vsLMzExUrVpVDBkyRMTHx6tshwEDBghzc3OV5QuOGBRCiD179oiGDRsKY2NjAUDriP2kpCQREhIibG1thaGhofDw8BCff/65yqj71NRU0bNnT2FjYyOsra1Fv379xKlTp9TuB0IIsXLlSgFAmJqaqoyoz3fw4EERGBgobGxshKGhoahWrZoIDAxU2jfy1/Hu3bsqy2dnZ4shQ4aIqlWrKvaPwkanCyHE+fPnRceOHYWlpaWoXLmyeP/990VycrLaz3737t2iQYMGwtTUVNSsWVN8/fXXare3XC4XixcvFl5eXsLIyEhYW1sLX19fxUjx/DgnTpwonJ2dhampqfDz8xOJiYmFjp4ueDzSdWRyYfuJn5+fqFevnkp5weNVdna2mDBhgqhWrZowMTERjRo1Ejt27FA7mrzgNivq6Ok//vhD9O/fX1SqVEkxSvry5csq83/33XfCx8dHsW2Dg4PFP//8ozLf2rVrhaenpzAxMRF169YVsbGxhY6eVvf9L0zBz0gIIR4/fiymTZsmPDw8FHF5e3uLsWPHKu64sWvXLhEQECCqVasmjIyMhJ2dnejcubM4fPiw1jZ12afkcrmYP3++cHd3F4aGhqJKlSqiX79+IiUlRakuXT/7fADExx9/rHifv8+fPn1adOnSRfF79MEHH4g7d+4oLavr8UyIl8eHWrVqCSMjI+Hu7i5Wr14tgoODlUbWFuV4re67KYQQO3bsEG3bthVWVlbC2NhYuLi4iJ49e4o9e/YIIYS4c+eOGDhwoKhTp44wNzcXFhYWwsfHRyxevFhpxHZhvvvuO8VvZ/56qPu+5ObmioULFyp+PywsLESdOnXE0KFD1e73+XSNL//z3LJli6hXr54wMjISrq6uYtGiRSp16rL/CqHbfnjjxg3RqVMnYWlpKQAorfe8efOEq6urMDY2Fp6enuLbb78t9jFU3R0SirtNC5IJUeCO20QS+eqrrzBq1Cj8/fffqFevntThEEku/952J0+eLPQUIZUds2bNQkREBO7evftarpXM9+DBA7i7u6Nr164qZyZIO1dXV3h5eWHXrl1Sh1LulPjpaaKiSkhIwPXr1xEZGYng4GAmjERE/y8tLQ1z585F27ZtYWtri6SkJCxevBiPHj3C6NGjpQ6P3jBMGkly3bp1Q1paGlq1alXo6DsiojeRsbExbty4geHDhyMzMxNmZmZo3rw5li9fzv9gU6nj6WkiIiIi0qrEb7lDRERERBUPk0YiIiIi0opJIxERERFpxYEwxZSXl4fbt2/D0tKyRB6FSERERK+fEAKPHj2Ck5NTid0g/U3BpLGYbt++DWdnZ6nDICIiomJISUlReR45acaksZjyn1GdkpLyWh6/RURERCUvKysLzs7Oit9x0h2TxmLKPyVtZWXFpJGIiKic4aVlRceT+URERESkFZNGIiIiItKKSSMRERERacWkkYiIiIi0YtJIRERERFoxaSQiIiIirZg0EhEREZFWTBqJiIiISCsmjURERESkFZNGIiIiItKKSSMRERERacWkkYiIiIi0YtJIRERERFoxaSQiIiIirSpM0rh06VK4ubnBxMQEjRs3xuHDhzXOn52djalTp8LFxQXGxsZ46623sHr16lKKloiIiKh8MZA6gJIQGxuLMWPGYOnSpWjZsiVWrFiBgIAAnD9/HjVq1FC7TK9evXDnzh2sWrUKtWrVQnp6Ol68eFHKkavnOukXqUMoV27MC5Q6BCIiogpPJoQQUgfxqpo1a4ZGjRph2bJlijJPT0907doVUVFRKvP/9ttv6NOnD65duwYbG5titZmVlQVra2s8fPgQVlZWxY5dHSaNRcOkkYiIdPU6f78runJ/ejonJwenT59Gp06dlMo7deqEI0eOqF1m586daNKkCRYsWIBq1arB3d0dEyZMwLNnzwptJzs7G1lZWUovIiIiojdFuT89fe/ePcjlctjb2yuV29vbIy0tTe0y165dw59//gkTExNs374d9+7dw/Dhw5GZmVnodY1RUVGIiIgo8fiJiIiIyoNy39OYTyaTKb0XQqiU5cvLy4NMJsOGDRvw9ttvo3Pnzli0aBFiYmIK7W2cPHkyHj58qHilpKSU+DoQERERlVXlvqexSpUq0NfXV+lVTE9PV+l9zOfo6Ihq1arB2tpaUebp6QkhBG7evInatWurLGNsbAxjY+OSDZ6IiIionCj3PY1GRkZo3Lgx4uLilMrj4uLQokULtcu0bNkSt2/fxuPHjxVlly5dgp6eHqpXr/5a4yUiIiIqj8p90ggA48aNw3fffYfVq1fjwoULGDt2LJKTkxEeHg7g5anl0NBQxfwhISGwtbVFWFgYzp8/j0OHDuGTTz7BoEGDYGpqKtVqEBEREZVZ5f70NAD07t0bGRkZiIyMRGpqKry8vLB79264uLgAAFJTU5GcnKyY38LCAnFxcRg5ciSaNGkCW1tb9OrVC3PmzJFqFYiIiIjKtApxn0Yp8D6NZQfv00hERLrifRqLr0KcniYiIiKi14tJIxERERFpxaSRiIiIiLRi0khEREREWjFpJCIiIiKtmDQSERERkVZMGomIiIhIKyaNRERERKQVk0YiIiIi0opJIxERERFpxaSRiIiIiLRi0khEREREWjFpJCIiIiKtmDQSERERkVZMGomIiIhIK4PSamjnzp06zxsUFPQaIyEiIiKioiq1pLFr1646zSeTySCXy19vMERERERUJKWWNObl5ZVWU0RERERUwnhNIxERERFpVWo9jUuWLMFHH30EExMTLFmyROO8o0aNKqWoiIiIiEgXpZY0Ll68GH379oWJiQkWL15c6HwymYxJIxEREVEZU2pJ4/Xr19X+TURERERlH69pJCIiIiKtSq2nsaCbN29i586dSE5ORk5OjtK0RYsWSRQVEREREakjSdK4d+9eBAUFwc3NDRcvXoSXlxdu3LgBIQQaNWokRUhEREREpIEkp6cnT56M8ePH4++//4aJiQm2bt2KlJQU+Pn54f3335ciJCIiIiLSQJKk8cKFCxgwYAAAwMDAAM+ePYOFhQUiIyMxf/58KUIiIiIiIg0kSRrNzc2RnZ0NAHBycsLVq1cV0+7duydFSERERESkgSTXNDZv3hx//fUX6tati8DAQIwfPx7nzp3Dtm3b0Lx5cylCIiIiIiINJEkaFy1ahMePHwMAZs2ahcePHyM2Nha1atXSeONvIiIiIpKGJEljzZo1FX+bmZlh6dKlUoRBRERERDqS7D6N+R4/foy8vDylMisrK4miISIiIiJ1JBkIc/36dQQGBsLc3BzW1taoXLkyKleujEqVKqFy5cpShEREREREGkjS09i3b18AwOrVq2Fvbw+ZTCZFGERERESkI0mSxrNnz+L06dPw8PCQonkiIiIiKiJJTk83bdoUKSkpUjRNRERERMUgSU/jd999h/DwcNy6dQteXl4wNDRUmu7j4yNFWERERERUCEmSxrt37+Lq1asICwtTlMlkMgghIJPJIJfLpQiLiIiIiAohSdI4aNAgNGzYEJs2beJAGCIiIqJyQJKkMSkpCTt37kStWrWkaJ6IiIiIikiSgTDt2rXDmTNnpGiaiIiIiIpBkp7GLl26YOzYsTh37hy8vb1VBsIEBQVJERYRERERFUKSpDE8PBwAEBkZqTKNA2GIiIiIyh5JksaCz5omIiIiorJNkmsaiYiIiKh8YdJIRERERFoxaSQiIiIirSpM0rh06VK4ubnBxMQEjRs3xuHDhwud98CBA5DJZCqvf//9txQjJiIiIio/KkTSGBsbizFjxmDq1KlISEhAq1atEBAQgOTkZI3LXbx4EampqYpX7dq1SyliIiIiovJFktHTwMsR1FeuXEF6errKaOrWrVsXqa5FixZh8ODBGDJkCAAgOjoav//+O5YtW4aoqKhCl7Ozs0OlSpWKHDsRERHRm0aSpPHYsWMICQlBUlIShBBK04p6n8acnBycPn0akyZNUirv1KkTjhw5onHZhg0b4vnz56hbty6mTZuGtm3bFjpvdnY2srOzFe+zsrJ0jpGIiIiovJPk9HR4eDiaNGmCv//+G5mZmbh//77ilZmZWaS67t27B7lcDnt7e6Vye3t7pKWlqV3G0dERK1euxNatW7Ft2zZ4eHigffv2OHToUKHtREVFwdraWvFydnYuUpxERERE5ZkkPY2XL1/Gli1bUKtWrRKrUyaTKb0XQqiU5fPw8ICHh4fiva+vL1JSUrBw4cJCT41PnjwZ48aNU7zPyspi4khERERvDEl6Gps1a4YrV66USF1VqlSBvr6+Sq9ienq6Su+jJs2bN8fly5cLnW5sbAwrKyulFxEREdGbQpKexpEjR2L8+PFIS0uDt7c3DA0Nlab7+PjoXJeRkREaN26MuLg4dOvWTVEeFxeH4OBgnetJSEiAo6OjzvMTERERvUkkSRp79OgBABg0aJCiTCaTKU4pF2UgDACMGzcO/fv3R5MmTeDr64uVK1ciOTkZ4eHhAF6eWr516xbWrVsH4OXoaldXV9SrVw85OTlYv349tm7diq1bt5bQGhIRERFVLJIkjdevXy/R+nr37o2MjAxERkYiNTUVXl5e2L17N1xcXAAAqampSvdszMnJwYQJE3Dr1i2YmpqiXr16+OWXX9C5c+cSjYuIiIioopCJgve8IZ1kZWXB2toaDx8+LPHrG10n/VKi9VV0N+YFSh0CERGVE6/z97uik6SnMf80cWFCQ0NLKRIiIiIi0oUkSePo0aOV3ufm5uLp06cwMjKCmZkZk0YiIiKiMkaSW+7892be9+/fx+PHj3Hx4kW888472LRpkxQhEREREZEGkiSN6tSuXRvz5s1T6YUkIiIiIumVmaQRAPT19XH79m2pwyAiIiKiAiS5pnHnzp1K74UQSE1Nxddff42WLVtKERIRERERaSBJ0ti1a1el9zKZDFWrVkW7du3wxRdfSBESEREREWkgSdKYl5cnRbNEVAbxvqS64z1JiUhKZeqaRiIiIiIqmyRJGnv27Il58+aplH/++ed4//33JYiIiIiIiDSRJGk8ePAgAgNVT7O8++67OHTokAQREREREZEmkiSNjx8/hpGRkUq5oaEhsrKyJIiIiIiIiDSRJGn08vJCbGysSvkPP/yAunXrShAREREREWkiyejp6dOno0ePHrh69SratWsHANi7dy82bdqEzZs3SxESEREREWkgSdIYFBSEHTt24LPPPsOWLVtgamoKHx8f7NmzB35+flKEREREREQaSJI0AkBgYKDawTBEREREVPbwPo1EREREpJUkPY16enqQyWSFTpfL5aUYDRERERFpI0nSuH37dqX3ubm5SEhIwNq1axERESFFSERERESkgSRJY3BwsEpZz549Ua9ePcTGxmLw4MESREVEREREhSlT1zQ2a9YMe/bskToMIiIiIiqgzCSNz549w1dffYXq1atLHQoRERERFSDJ6enKlSsrDYQRQuDRo0cwMzPD+vXrpQiJiIiIiDSQJGmMjo5Weq+np4eqVauiWbNmqFy5shQhEREREZEGkiSNAwYMkKJZIiIiIiomyZ4IAwBPnz5FcnIycnJylMp9fHwkioiIiIiI1JEkabx79y7CwsLw66+/qp3Om3sTERERlS2SjJ4eM2YM7t+/j2PHjsHU1BS//fYb1q5di9q1a2Pnzp1ShEREREREGkjS07hv3z789NNPaNq0KfT09ODi4oKOHTvCysoKUVFRCAwMlCIsIiIiIiqEJD2NT548gZ2dHQDAxsYGd+/eBQB4e3sjPj5eipCIiIiISANJkkYPDw9cvHgRANCgQQOsWLECt27dwvLly+Ho6ChFSERERESkgSSnp8eMGYPU1FQAwMyZM+Hv748NGzbAyMgIMTExUoRERERERBpIkjT27dtX8XfDhg1x48YN/Pvvv6hRowaqVKkiRUhEREREpIGk92nMZ2ZmhkaNGkkdBhEREREVQpJrGomIiIiofCkTPY1ERERvAtdJv0gdQrlxYx5vv1fWsKeRiIiIiLSSJGlMTk6GEEKlXAiB5ORkCSIiIiIiIk0kSRrd3NwUN/T+r8zMTLi5uUkQERERERFpIknSKISATCZTKX/8+DFMTEwkiIiIiIiINCnVgTDjxo0DAMhkMkyfPh1mZmaKaXK5HMePH0eDBg1KMyQiIiIi0kGpJo0JCQkAXvY0njt3DkZGRoppRkZGqF+/PiZMmFCaIRERERGRDko1ady/fz8AYODAgfjqq69gaWlZms0TERERUTGV+jWNL168wPr165GUlFTaTRMRERFRMZV60mhgYAAXFxfI5fLSbpqIiIiIikmS0dPTpk3D5MmTkZmZKUXzRERERFREkjxGcMmSJbhy5QqcnJzg4uICc3Nzpenx8fFShEVEREREhZAkaezatWuJ17l06VJ8/vnnSE1NRb169RAdHY1WrVppXe6vv/6Cn58fvLy8kJiYWOJxEREREVUEkiSNM2fOLNH6YmNjMWbMGCxduhQtW7bEihUrEBAQgPPnz6NGjRqFLvfw4UOEhoaiffv2uHPnTonGRERERFSRSHJNIwA8ePAA3333ndK1jfHx8bh161aR61q0aBEGDx6MIUOGwNPTE9HR0XB2dsayZcs0Ljd06FCEhITA19e3WOtARERE9KaQJGk8e/Ys3N3dMX/+fCxcuBAPHjwAAGzfvh2TJ08uUl05OTk4ffo0OnXqpFTeqVMnHDlypNDl1qxZg6tXr+rc65mdnY2srCylFxEREdGbQpKkcdy4cRg4cCAuX76s9KzpgIAAHDp0qEh13bt3D3K5HPb29krl9vb2SEtLU7vM5cuXMWnSJGzYsAEGBrqdoY+KioK1tbXi5ezsXKQ4iYiIiMozSZLGkydPYujQoSrl1apVKzTR00Ymkym9F0KolAEvn3EdEhKCiIgIuLu761z/5MmT8fDhQ8UrJSWlWHESERERlUeSDIQxMTFRe3r34sWLqFq1apHqqlKlCvT19VWSzfT0dJXeRwB49OgRTp06hYSEBIwYMQIAkJeXByEEDAwM8Mcff6Bdu3YqyxkbG8PY2LhIsRERERFVFJL0NAYHByMyMhK5ubkAXvYSJicnY9KkSejRo0eR6jIyMkLjxo0RFxenVB4XF4cWLVqozG9lZYVz584hMTFR8QoPD4eHhwcSExPRrFmz4q8YERERUQUlSU/jwoUL0blzZ9jZ2eHZs2fw8/NDWloafH19MXfu3CLXN27cOPTv3x9NmjSBr68vVq5cieTkZISHhwN4eWr51q1bWLduHfT09ODl5aW0vJ2dHUxMTFTKiYiIiOglSZJGKysr/Pnnn9i3bx/i4+ORl5eHRo0aoUOHDsWqr3fv3sjIyEBkZCRSU1Ph5eWF3bt3w8XFBQCQmpqK5OTkklwFIiIiojeKTAghSrvRGzduwNXVtbSbLVFZWVmwtrbGw4cPYWVlVaJ1u076pUTrq+huzAuUOgR6Bdzfdcd9vfzj/q6717W/v87f74pOkmsaa9asiXfeeQcrVqxQ3NibiIiIiMouSZLGU6dOwdfXF3PmzIGTkxOCg4OxefNmZGdnSxEOEREREWkhSdLYqFEjfP7550hOTsavv/4KOzs7DB06FHZ2dhg0aJAUIRERERGRBpI9exp4eaudtm3b4ttvv8WePXtQs2ZNrF27VsqQiIiIiEgNSZPGlJQULFiwAA0aNEDTpk1hbm6Or7/+WsqQiIiIiEgNSW65s3LlSmzYsAF//fUXPDw80LdvX+zYsaPcj6gmIiIiqqgkSRpnz56NPn364Msvv0SDBg2kCIGIiIiIikCSpDE5ORkymUyKpomIiIioGCRJGg8fPqxxeuvWrUspEiIiIiLShSRJY5s2bVTK/tvzKJfLSzEaIiIiItJGktHT9+/fV3qlp6fjt99+Q9OmTfHHH39IERIRERERaSBJT6O1tbVKWceOHWFsbIyxY8fi9OnTEkRFRERERIWR9D6NBVWtWhUXL16UOgwiIiIiKkCSnsazZ88qvRdCIDU1FfPmzUP9+vWlCImIiIiINJAkaWzQoAFkMhmEEErlzZs3x+rVq6UIiYiIiIg0kCRpvH79utJ7PT09VK1aFSYmJlKEQ0RERERaSJI0uri4SNEsERERERWTZANhDh48iC5duqBWrVqoXbs2goKCtN70m4iIiIikIUnSuH79enTo0AFmZmYYNWoURowYAVNTU7Rv3x4bN26UIiQiIiIi0kCS09Nz587FggULMHbsWEXZ6NGjsWjRIsyePRshISFShEVEREREhZAkabx27Rq6dOmiUh4UFIQpU6ZIEBER0ZvFddIvUodQbtyYFyh1CERlgiSnp52dnbF3716V8r1798LZ2VmCiIiIiIhIE0l6GsePH49Ro0YhMTERLVq0gEwmw59//omYmBh8+eWXUoRERERERBpIkjQOGzYMDg4O+OKLL/Djjz8CADw9PREbG4vg4GApQiIiIiIiDSRJGgGgW7du6Natm1TNExEREVERSHafRiIiIiIqP5g0EhEREZFWTBqJiIiISCsmjURERESklSRJY2RkJJ4+fapS/uzZM0RGRkoQERERERFpIknSGBERgcePH6uUP336FBERERJERERERESaSJI0CiEgk8lUys+cOQMbGxsJIiIiIiIiTUr1Po2VK1eGTCaDTCaDu7u7UuIol8vx+PFjhIeHl2ZIRERERKSDUk0ao6OjIYTAoEGDEBERAWtra8U0IyMjuLq6wtfXtzRDIiIiIiIdlGrSOGDAAACAm5sbWrRoAUNDw9JsnoiIiIiKSZLHCPr5+SEvLw+XLl1Ceno68vLylKa3bt1airCIiIiIqBCSJI3Hjh1DSEgIkpKSIIRQmiaTySCXy6UIi4iIiIgKIUnSGB4ejiZNmuCXX36Bo6Oj2pHURERERFR2SJI0Xr58GVu2bEGtWrWkaJ6IiIiIikiS+zQ2a9YMV65ckaJpIiIiIioGSXoaR44cifHjxyMtLQ3e3t4qo6h9fHykCIuIiIiICiFJ0tijRw8AwKBBgxRlMplM8aQYDoQhIiIiKlskSRqvX78uRbNEREREVEySJI0uLi5SNEtERERExSRJ0pjv/PnzSE5ORk5OjlJ5UFCQRBERERERkTqSJI3Xrl1Dt27dcO7cOcW1jAAU92vkNY1EREREZYskt9wZPXo03NzccOfOHZiZmeGff/7BoUOH0KRJExw4cECKkIiIiIhIA0mSxqNHjyIyMhJVq1aFnp4e9PT08M477yAqKgqjRo0qVp1Lly6Fm5sbTExM0LhxYxw+fLjQef/880+0bNkStra2MDU1RZ06dbB48eLirg4RERFRhSfJ6Wm5XA4LCwsAQJUqVXD79m14eHjAxcUFFy9eLHJ9sbGxGDNmDJYuXYqWLVtixYoVCAgIwPnz51GjRg2V+c3NzTFixAj4+PjA3Nwcf/75J4YOHQpzc3N89NFHr7x+RERERBWNJD2NXl5eOHv2LICXT4dZsGAB/vrrL0RGRqJmzZpFrm/RokUYPHgwhgwZAk9PT0RHR8PZ2RnLli1TO3/Dhg3xwQcfoF69enB1dUW/fv3g7++vsXeSiIiI6E0mSdI4bdo05OXlAQDmzJmDpKQktGrVCrt378aSJUuKVFdOTg5Onz6NTp06KZV36tQJR44c0amOhIQEHDlyBH5+foXOk52djaysLKUXERER0ZtCktPT/v7+ir9r1qyJ8+fPIzMzE5UrV1aMoNbVvXv3IJfLYW9vr1Rub2+PtLQ0jctWr14dd+/exYsXLzBr1iwMGTKk0HmjoqIQERFRpNiIiIiIKgpJehpjYmLw7NkzpTIbG5siJ4z/VXDZ/EcSanL48GGcOnUKy5cvR3R0NDZt2lTovJMnT8bDhw8Vr5SUlGLHSkRERFTeSJI0Tp48Gfb29hg8eLDOp5ALU6VKFejr66v0Kqanp6v0Phbk5uYGb29vfPjhhxg7dixmzZpV6LzGxsawsrJSehERERG9KSRJGm/evIn169fj/v37aNu2LerUqYP58+drPZ2sjpGRERo3boy4uDil8ri4OLRo0ULneoQQyM7OLnL7RERERG8CSa5p1NfXR1BQEIKCgpCeno7169cjJiYG06dPx7vvvovBgwejS5cu0NPTLacdN24c+vfvjyZNmsDX1xcrV65EcnIywsPDAbzs2bx16xbWrVsHAPjmm29Qo0YN1KlTB8DL+zYuXLgQI0eOfD0rTOWC66RfpA6h3LgxL1DqEIiIqJRJ+uxpALCzs0PLli1x8eJFXLp0CefOncPAgQNRqVIlrFmzBm3atNFaR+/evZGRkYHIyEikpqbCy8sLu3fvhouLCwAgNTUVycnJivnz8vIwefJkXL9+HQYGBnjrrbcwb948DB069HWtJhEREVG5JlnSeOfOHXz//fdYs2YNrl27hq5du2LXrl3o0KEDnj17hmnTpmHAgAFISkrSqb7hw4dj+PDhaqfFxMQovR85ciR7FYmIiIiKQJKksUuXLvj999/h7u6ODz/8EKGhobCxsVFMNzU1xfjx4/loPyIiIqIyQpKk0c7ODgcPHoSvr2+h8zg6OuL69eulGBURERERFUaSpHHVqlVa55HJZIprEomIiIhIWpJd0/jkyRMcPHgQycnJyMnJUZo2atQoiaIiIiIiInUkSRoTEhLQuXNnPH36FE+ePIGNjQ3u3bsHMzMz2NnZMWkkIiIiKmMkubn32LFj0aVLF2RmZsLU1BTHjh1DUlISGjdujIULF0oREhERERFpIEnSmJiYiPHjx0NfXx/6+vrIzs6Gs7MzFixYgClTpkgREhERERFpIEnSaGhoCJlMBgCwt7dX3Hjb2tpa6SbcRERERFQ2SHJNY8OGDXHq1Cm4u7ujbdu2mDFjBu7du4fvv/8e3t7eUoRERERERBpI0tP42WefwdHREQAwe/Zs2NraYtiwYUhPT8fKlSulCImIiIiINJCkp7FJkyaKv6tWrYrdu3dLEQYRERER6UiSnkYiIiIiKl9Ktaexbdu2igEwALBv377SbJ6IiIiIiqlUk8aBAweWZnNEREREVEJKNWkcMGBAaTZHRERERCVEsmdPA0BOTg7S09ORl5enVF6jRg2JIiIiIiIidSRJGi9duoTBgwfjyJEjSuVCCMhkMsjlcinCIiIiIqJCSJI0hoWFwcDAALt27YKjo6PS4BgiIiIiKnskSRoTExNx+vRp1KlTR4rmiYiIiKiIJLlPY926dXHv3j0pmiYiIiKiYpAkaZw/fz4mTpyIAwcOICMjA1lZWUovIiIiIipbJDk93aFDBwBA+/btlco5EIaIiIiobJIkady/f78UzRIRERFRMUmSNPr5+UnRLBEREREVkyTXNBIRERFR+cKkkYiIiIi0YtJIRERERFoxaSQiIiIirSRJGp89e4anT58q3iclJSE6Ohp//PGHFOEQERERkRaSJI3BwcFYt24dAODBgwdo1qwZvvjiCwQHB2PZsmVShEREREREGkiSNMbHx6NVq1YAgC1btsDe3h5JSUlYt24dlixZIkVIRERERKSBJEnj06dPYWlpCQD4448/0L17d+jp6aF58+ZISkqSIiQiIiIi0kCSpLFWrVrYsWMHUlJS8Pvvv6NTp04AgPT0dFhZWUkREhERERFpIEnSOGPGDEyYMAGurq5o1qwZfH19AbzsdWzYsKEUIRERERGRBpI8RrBnz5545513kJqaivr16yvK27dvj+7du0sREhERERFpIElP46BBg2Bubo6GDRtCT+9/IdSrVw/z58+XIiQiIiIi0kCSpHHt2rV49uyZSvmzZ88Ut+IhIiIiorKjVE9PZ2VlQQgBIQQePXoEExMTxTS5XI7du3fDzs6uNEMiIiIiIh2UatJYqVIlyGQyyGQyuLu7q0yXyWSIiIgozZCIiIiISAelmjTu378fQgi0a9cOW7duhY2NjWKakZERXFxc4OTkVJohEREREZEOSjVp9PPzAwBcv34dzs7OSoNgiIiIiKjskuSWOy4uLnjw4AFOnDiB9PR05OXlKU0PDQ2VIiwiIiIiKoQkSePPP/+Mvn374smTJ7C0tIRMJlNMk8lkTBqJiIiIyhhJzg+PHz8egwYNwqNHj/DgwQPcv39f8crMzJQiJCIiIiLSQJKk8datWxg1ahTMzMykaJ6IiIiIikiSpNHf3x+nTp2SomkiIiIiKgZJrmkMDAzEJ598gvPnz8Pb2xuGhoZK04OCgqQIi4iIiIgKIUnS+OGHHwIAIiMjVabJZDLI5fIi17l06VJ8/vnnSE1NRb169RAdHY1WrVqpnXfbtm1YtmwZEhMTkZ2djXr16mHWrFnw9/cvcrtEREREbwJJTk/n5eUV+ipOwhgbG4sxY8Zg6tSpSEhIQKtWrRAQEIDk5GS18x86dAgdO3bE7t27cfr0abRt2xZdunRBQkLCq64aERERUYUk+d21nz9//sp1LFq0CIMHD8aQIUPg6emJ6OhoODs7Y9myZWrnj46OxsSJE9G0aVPUrl0bn332GWrXro2ff/75lWMhIiIiqogkSRrlcjlmz56NatWqwcLCAteuXQMATJ8+HatWrSpSXTk5OTh9+jQ6deqkVN6pUyccOXJEpzry8vLw6NEjpccaFpSdnY2srCylFxEREdGbQpKkce7cuYiJicGCBQtgZGSkKPf29sZ3331XpLru3bsHuVwOe3t7pXJ7e3ukpaXpVMcXX3yBJ0+eoFevXoXOExUVBWtra8XL2dm5SHESERERlWeSJI3r1q3DypUr0bdvX+jr6yvKfXx88O+//xarzv8+VQYAhBAqZeps2rQJs2bNQmxsLOzs7Aqdb/LkyXj48KHilZKSUqw4iYiIiMojSUZP37p1C7Vq1VIpz8vLQ25ubpHqqlKlCvT19VV6FdPT01V6HwuKjY3F4MGDsXnzZnTo0EHjvMbGxjA2Ni5SbEREREQVhSQ9jfXq1cPhw4dVyjdv3oyGDRsWqS4jIyM0btwYcXFxSuVxcXFo0aJFoctt2rQJAwcOxMaNGxEYGFikNomIiIjeNJL0NM6cORP9+/fHrVu3kJeXh23btuHixYtYt24ddu3aVeT6xo0bh/79+6NJkybw9fXFypUrkZycjPDwcAAvTy3funUL69atA/AyYQwNDcWXX36J5s2bK3opTU1NYW1tXXIrSkRERFRBSNLT2KVLF8TGxmL37t2QyWSYMWMGLly4gJ9//hkdO3Yscn29e/dGdHQ0IiMj0aBBAxw6dAi7d++Gi4sLACA1NVXpno0rVqzAixcv8PHHH8PR0VHxGj16dImtIxEREVFFIklPI/Dy+dMl+QSW4cOHY/jw4WqnxcTEKL0/cOBAibVLRERE9CaQ/ObeRERERFT2lVpPo42NDS5duoQqVaqgcuXKGm+Hk5mZWVphEREREZEOSi1pXLx4MSwtLQG8fIwfEREREZUfpZY0DhgwQO3fRERERFT2lVrSWJRnNVtZWb3GSIiIiIioqEotaaxUqZLWx/rlP/pPLpeXUlREREREpItSSxr3799fWk0RERERUQkrtaTRz8+vtJoiIiIiohImyX0a16xZg82bN6uUb968GWvXrpUgIiIiIiLSRJKkcd68eahSpYpKuZ2dHT777DMJIiIiIiIiTSRJGpOSkuDm5qZS7uLiovSMaCIiIiIqGyRJGu3s7HD27FmV8jNnzsDW1laCiIiIiIhIE0mSxj59+mDUqFHYv38/5HI55HI59u3bh9GjR6NPnz5ShEREREREGpTa6On/mjNnDpKSktC+fXsYGLwMIS8vD6GhobymkYiIiKgMkiRpNDIyQmxsLObMmYPExESYmprC29sbLi4uUoRDRERERFpIkjTmq127NmrXri1lCERERESkA0muaSQiIiKi8oVJIxERERFpxaSRiIiIiLRi0khEREREWkmWNB4+fBj9+vWDr68vbt26BQD4/vvv8eeff0oVEhEREREVQpKkcevWrfD394epqSkSEhKQnZ0NAHj06BHv00hERERUBkmSNM6ZMwfLly/Ht99+C0NDQ0V5ixYtEB8fL0VIRERERKSBJEnjxYsX0bp1a5VyKysrPHjwoPQDIiIiIiKNJEkaHR0dceXKFZXyP//8EzVr1pQgIiIiIiLSRJKkcejQoRg9ejSOHz8OmUyG27dvY8OGDZgwYQKGDx8uRUhEREREpIEkjxGcOHEiHj58iLZt2+L58+do3bo1jI2NMWHCBIwYMUKKkIiIiIhIA8mePT137lxMnToV58+fR15eHurWrQsLCwupwiEiIiIiDSRLGgHAzMwMTZo0kTIEIiIiItJBqSWN3bt313nebdu2vcZIiIiIiKioSm0gjLW1teJlZWWFvXv34tSpU4rpp0+fxt69e2FtbV1aIRERERGRjkqtp3HNmjWKvz/99FP06tULy5cvh76+PgBALpdj+PDhsLKyKq2QiIiIiEhHktxyZ/Xq1ZgwYYIiYQQAfX19jBs3DqtXr5YiJCIiIiLSQJKk8cWLF7hw4YJK+YULF5CXlydBRERERESkiSSjp8PCwjBo0CBcuXIFzZs3BwAcO3YM8+bNQ1hYmBQhEREREZEGkiSNCxcuhIODAxYvXozU1FQALx8tOHHiRIwfP16KkIiIiIhIA0mSRj09PUycOBETJ05EVlYWAHAADBEREVEZJunNvQEmi0RERETlgSQDYYiIiIiofGHSSERERERaMWkkIiIiIq0kSRrXrVuH7OxslfKcnBysW7dOgoiIiIiISBNJksawsDA8fPhQpfzRo0e8TyMRERFRGSRJ0iiEgEwmUym/efMmrK2tJYiIiIiIiDQp1VvuNGzYEDKZDDKZDO3bt4eBwf+al8vluH79Ot59993SDImIiIiIdFCqSWPXrl0BAImJifD394eFhYVimpGREVxdXdGjR4/SDImIiIiIdFCqSePMmTMhl8vh4uICf39/ODo6lmbzRERERFRMpX5No76+PsLDw/H8+fMSrXfp0qVwc3ODiYkJGjdujMOHDxc6b2pqKkJCQuDh4QE9PT2MGTOmRGMhIiIiqmgkGQjj7e2Na9eulVh9sbGxGDNmDKZOnYqEhAS0atUKAQEBSE5OVjt/dnY2qlatiqlTp6J+/folFgcRERFRRSVJ0jh37lxMmDABu3btQmpqKrKyspReRbVo0SIMHjwYQ4YMgaenJ6Kjo+Hs7Ixly5apnd/V1RVffvklQkNDOVqbiIiISAelek1jvvwR0kFBQUq33sm/FY9cLte5rpycHJw+fRqTJk1SKu/UqROOHDlSMgETERERveEkSRr3799fYnXdu3cPcrkc9vb2SuX29vZIS0srsXays7OVnmJTnB5RIiIiovJKkqTRz8+vxOsseLPwwm4gXlxRUVGIiIgosfqIiIiIyhNJksZ8T58+RXJyMnJycpTKfXx8dK6jSpUq0NfXV+lVTE9PV+l9fBWTJ0/GuHHjFO+zsrLg7OxcYvUTERERlWWSJI13795FWFgYfv31V7XTi3JNo5GRERo3boy4uDh069ZNUR4XF4fg4OBXjjWfsbExjI2NS6w+IiIiovJEktHTY8aMwf3793Hs2DGYmprit99+w9q1a1G7dm3s3LmzyPWNGzcO3333HVavXo0LFy5g7NixSE5ORnh4OICXvYShoaFKyyQmJiIxMRGPHz/G3bt3kZiYiPPnz5fI+hERERFVNJL0NO7btw8//fQTmjZtCj09Pbi4uKBjx46wsrJCVFQUAgMDi1Rf7969kZGRgcjISKSmpsLLywu7d++Gi4sLgJc38y54z8aGDRsq/j59+jQ2btwIFxcX3Lhx45XXj4iIiKiikSRpfPLkCezs7AAANjY2uHv3Ltzd3eHt7Y34+Phi1Tl8+HAMHz5c7bSYmBiVMiFEsdohIiIiehNJcnraw8MDFy9eBAA0aNAAK1aswK1bt7B8+XI+j5qIiIioDJKkp3HMmDG4ffs2AGDmzJnw9/fHhg0bYGRkpLZXkIiIiIikJUnS2LdvX8XfDRs2xI0bN/Dvv/+iRo0aqFKlihQhEREREZEGpXp6+unTp/j4449RrVo12NnZISQkBPfu3YOZmRkaNWrEhJGIiIiojCrVpHHmzJmIiYlBYGAg+vTpg7i4OAwbNqw0QyAiIiKiYijV09Pbtm3DqlWr0KdPHwBAv3790LJlS8jlcujr65dmKERERERUBKXa05iSkoJWrVop3r/99tswMDBQDIohIiIiorKpVJNGuVwOIyMjpTIDAwO8ePGiNMMgIiIioiIq1dPTQggMHDhQ6RnOz58/R3h4OMzNzRVl27ZtK82wiIiIiEiLUk0aBwwYoFLWr1+/0gyBiIiIiIqhVJPGNWvWlGZzRERERFRCJHmMIBERERGVL0waiYiIiEgrJo1EREREpBWTRiIiIiLSikkjEREREWnFpJGIiIiItGLSSERERERaMWkkIiIiIq2YNBIRERGRVkwaiYiIiEgrJo1EREREpBWTRiIiIiLSikkjEREREWnFpJGIiIiItGLSSERERERaMWkkIiIiIq2YNBIRERGRVkwaiYiIiEgrJo1EREREpBWTRiIiIiLSikkjEREREWnFpJGIiIiItGLSSERERERaMWkkIiIiIq2YNBIRERGRVkwaiYiIiEgrJo1EREREpBWTRiIiIiLSikkjEREREWnFpJGIiIiItGLSSERERERaMWkkIiIiIq2YNBIRERGRVkwaiYiIiEgrJo1EREREpBWTRiIiIiLSqsIkjUuXLoWbmxtMTEzQuHFjHD58WOP8Bw8eROPGjWFiYoKaNWti+fLlpRQpERERUflTIZLG2NhYjBkzBlOnTkVCQgJatWqFgIAAJCcnq53/+vXr6Ny5M1q1aoWEhARMmTIFo0aNwtatW0s5ciIiIqLyoUIkjYsWLcLgwYMxZMgQeHp6Ijo6Gs7Ozli2bJna+ZcvX44aNWogOjoanp6eGDJkCAYNGoSFCxeWcuRERERE5UO5TxpzcnJw+vRpdOrUSam8U6dOOHLkiNpljh49qjK/v78/Tp06hdzc3NcWKxEREVF5ZSB1AK/q3r17kMvlsLe3Vyq3t7dHWlqa2mXS0tLUzv/ixQvcu3cPjo6OKstkZ2cjOztb8f7hw4cAgKysrFddBRV52U9LvM6KrKQ+A2533ZXkfs/trjtud2lwu0vjdfy+/rdeIcRrqb8iK/dJYz6ZTKb0XgihUqZtfnXl+aKiohAREaFS7uzsXNRQqYRZR0sdwZuH21wa3O7S4HaXxuve7o8ePYK1tfXrbaSCKfdJY5UqVaCvr6/Sq5ienq7Sm5jPwcFB7fwGBgawtbVVu8zkyZMxbtw4xfu8vDxkZmbC1tZWY3JaUWRlZcHZ2RkpKSmwsrKSOpw3Bre7NLjdpcHtLo03bbsLIfDo0SM4OTlJHUq5U+6TRiMjIzRu3BhxcXHo1q2bojwuLg7BwcFql/H19cXPP/+sVPbHH3+gSZMmMDQ0VLuMsbExjI2NlcoqVar0asGXQ1ZWVm/EQaWs4XaXBre7NLjdpfEmbXf2MBZPuR8IAwDjxo3Dd999h9WrV+PChQsYO3YskpOTER4eDuBlL2FoaKhi/vDwcCQlJWHcuHG4cOECVq9ejVWrVmHChAlSrQIRERFRmVbuexoBoHfv3sjIyEBkZCRSU1Ph5eWF3bt3w8XFBQCQmpqqdM9GNzc37N69G2PHjsU333wDJycnLFmyBD169JBqFYiIiIjKtAqRNALA8OHDMXz4cLXTYmJiVMr8/PwQHx//mqOqOIyNjTFz5kyVU/T0enG7S4PbXRrc7tLgdiddyQTHnBMRERGRFhXimkYiIiIier2YNBIRERGRVkwaiYiIiEgrJo1EREREpBWTRlJy6NAhdOnSBU5OTpDJZNixY4fSdCEEZs2aBScnJ5iamqJNmzb4559/pAm2AtG23bdt2wZ/f39UqVIFMpkMiYmJksRZ0Wja7rm5ufj000/h7e0Nc3NzODk5ITQ0FLdv35Yu4ApA274+a9Ys1KlTB+bm5qhcuTI6dOiA48ePSxNsBaJtu//X0KFDIZPJEB0dXWrxUfnApJGUPHnyBPXr18fXX3+tdvqCBQuwaNEifP311zh58iQcHBzQsWNHPHr0qJQjrVi0bfcnT56gZcuWmDdvXilHVrFp2u5Pnz5FfHw8pk+fjvj4eGzbtg2XLl1CUFCQBJFWHNr2dXd3d3z99dc4d+4c/vzzT7i6uqJTp064e/duKUdasWjb7vl27NiB48eP8xF7pJ4gKgQAsX37dsX7vLw84eDgIObNm6coe/78ubC2thbLly+XIMKKqeB2/6/r168LACIhIaFUY3oTaNru+U6cOCEAiKSkpNIJqoLTZZs/fPhQABB79uwpnaDeAIVt95s3b4pq1aqJv//+W7i4uIjFixeXemxUtrGnkXR2/fp1pKWloVOnTooyY2Nj+Pn54ciRIxJGRlQ6Hj58CJlM9kY+d14KOTk5WLlyJaytrVG/fn2pw6nQ8vLy0L9/f3zyySeoV6+e1OFQGVVhnghDr19aWhoAwN7eXqnc3t4eSUlJUoREVGqeP3+OSZMmISQkBFZWVlKHU6Ht2rULffr0wdOnT+Ho6Ii4uDhUqVJF6rAqtPnz58PAwACjRo2SOhQqw9jTSEUmk8mU3gshVMqIKpLc3Fz06dMHeXl5WLp0qdThVHht27ZFYmIijhw5gnfffRe9evVCenq61GFVWKdPn8aXX36JmJgYHstJIyaNpDMHBwcA/+txzJeenq7S+0hUUeTm5qJXr164fv064uLi2MtYCszNzVGrVi00b94cq1atgoGBAVatWiV1WBXW4cOHkZ6ejho1asDAwAAGBgZISkrC+PHj4erqKnV4VIYwaSSdubm5wcHBAXFxcYqynJwcHDx4EC1atJAwMqLXIz9hvHz5Mvbs2QNbW1upQ3ojCSGQnZ0tdRgVVv/+/XH27FkkJiYqXk5OTvjkk0/w+++/Sx0elSG8ppGUPH78GFeuXFG8v379OhITE2FjY4MaNWpgzJgx+Oyzz1C7dm3Url0bn332GczMzBASEiJh1OWftu2emZmJ5ORkxT0CL168COBl729+DzAVnabt7uTkhJ49eyI+Ph67du2CXC5X9LLb2NjAyMhIqrDLNU3b3NbWFnPnzkVQUBAcHR2RkZGBpUuX4ubNm3j//fcljLr803aMKfgfIkNDQzg4OMDDw6O0Q6WyTOrh21S27N+/XwBQeQ0YMEAI8fK2OzNnzhQODg7C2NhYtG7dWpw7d07aoCsAbdt9zZo1aqfPnDlT0rjLO03bPf/2Rupe+/fvlzr0ckvTNn/27Jno1q2bcHJyEkZGRsLR0VEEBQWJEydOSB12uaftGFMQb7lD6siEEOL1pqVEREREVN7xmkYiIiIi0opJIxERERFpxaSRiIiIiLRi0khEREREWjFpJCIiIiKtmDQSERERkVZMGomIiIhIKyaNRERERKQVk0YiKrPS0tIwcuRI1KxZE8bGxnB2dkaXLl2wd+9eqUMjInrj8NnTRFQm3bhxAy1btkSlSpWwYMEC+Pj4IDc3F7///js+/vhj/Pvvv1KHSET0RmFPIxGVScOHD4dMJsOJEyfQs2dPuLu7o169ehg3bhyOHTsGAEhOTkZwcDAsLCxgZWWFXr164c6dO4o6Zs2ahQYNGmD16tWoUaMGLCwsMGzYMMjlcixYsAAODg6ws7PD3LlzldqWyWRYtmwZAgICYGpqCjc3N2zevFlpnk8//RTu7u4wMzNDzZo1MX36dOTm5qq0/f3338PV1RXW1tbo06cPHj16BABYt24dbG1tkZ2drVRvjx49EBoaWqLbkoioJDBpJKIyJzMzE7/99hs+/vhjmJubq0yvVKkShBDo2rUrMjMzcfDgQcTFxeHq1avo3bu30rxXr17Fr7/+it9++w2bNm3C6tWrERgYiJs3b+LgwYOYP38+pk2bpkhE802fPh09evTAmTNn0K9fP3zwwQe4cOGCYrqlpSViYmJw/vx5fPnll/j222+xePFilbZ37NiBXbt2YdeuXTh48CDmzZsHAHj//fchl8uxc+dOxfz37t3Drl27EBYW9srbkIioxAkiojLm+PHjAoDYtm1bofP88ccfQl9fXyQnJyvK/vnnHwFAnDhxQgghxMyZM4WZmZnIyspSzOPv7y9cXV2FXC5XlHl4eIioqCjFewAiPDxcqb1mzZqJYcOGFRrPggULROPGjRXv1bX9ySefiGbNmineDxs2TAQEBCjeR0dHi5o1a4q8vLxC2yEikgqvaSSiMkcIAeDlaeLCXLhwAc7OznB2dlaU1a1bF5UqVcKFCxfQtGlTAICrqyssLS0V89jb20NfXx96enpKZenp6Ur1+/r6qrxPTExUvN+yZQuio6Nx5coVPH78GC9evICVlZXSMgXbdnR0VGrnww8/RNOmTXHr1i1Uq1YNa9aswcCBAzWuNxGRVHh6mojKnNq1a0MmkymdDi5ICKE2uSpYbmhoqDRdJpOpLcvLy9MaV369x44dQ58+fRAQEIBdu3YhISEBU6dORU5OjtL82tpp2LAh6tevj3Xr1iE+Ph7nzp3DwIEDtcZBRCQFJo1EVObY2NjA398f33zzDZ48eaIy/cGDB6hbty6Sk5ORkpKiKD9//jwePnwIT0/PV46h4DWOx44dQ506dQAAf/31F1xcXDB16lQ0adIEtWvXRlJSUrHaGTJkCNasWYPVq1ejQ4cOSj2nRERlCZNGIiqTli5dCrlcjrfffhtbt27F5cuXceHCBSxZsgS+vr7o0KEDfHx80LdvX8THx+PEiRMIDQ2Fn58fmjRp8srtb968GatXr8alS5cwc+ZMnDhxAiNGjAAA1KpVC8nJyfjhhx9w9epVLFmyBNu3by9WO3379sWtW7fw7bffYtCgQa8cNxHR68KkkYjKJDc3N8THx6Nt27YYP348vLy80LFjR+zduxfLli2DTCbDjh07ULlyZbRu3RodOnRAzZo1ERsbWyLtR0RE4IcffoCPjw/Wrl2LDRs2oG7dugCA4OBgjB07FiNGjECDBg1w5MgRTJ8+vVjtWFlZoUePHrCwsEDXrl1LJHYiotdBJvKvOCciIgAvrz3cvn17qSVxHTt2hKenJ5YsWVIq7RERFQdHTxMRSSQzMxN//PEH9u3bh6+//lrqcIiINGLSSEQkkUaNGuH+/fuYP38+PDw8pA6HiEgjnp4mIiIiIq04EIaIiIiItGLSSERERERaMWkkIiIiIq2YNBIRERGRVkwaiYiIiEgrJo1EREREpBWTRiIiIiLSikkjEREREWnFpJGIiIiItPo/RtMfWh5Sd9cAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(company_lazy_customers[\"number_compagny\"], company_lazy_customers[\"no_campaign_opened\"])\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Company')\n", - "plt.ylabel(\"Part de clients n'ayant ouvert aucun mail\")\n", - "plt.title(\"Part de clients n'ayant ouvert aucun mail pour les compagnies de spectacle\")\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "33233fb9-707d-44c0-80e2-a131756110a1", - "metadata": {}, - "source": [ - "#### Taux d'ouverture des campagnes de mails" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "id": "c48015c2-6451-4089-93b7-6d55d3b2e553", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnynb_campaignsnb_campaigns_openedratio_campaigns_opened
010734772126151.00.171687
111342396129833.00.379190
2123168123810722.00.255900
3133218569793581.00.246563
4142427043723846.00.298242
\n", - "
" - ], - "text/plain": [ - " number_compagny nb_campaigns nb_campaigns_opened ratio_campaigns_opened\n", - "0 10 734772 126151.0 0.171687\n", - "1 11 342396 129833.0 0.379190\n", - "2 12 3168123 810722.0 0.255900\n", - "3 13 3218569 793581.0 0.246563\n", - "4 14 2427043 723846.0 0.298242" - ] - }, - "execution_count": 227, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# taux d'ouverture des campaigns\n", - "\n", - "company_campaigns_stats = campaigns_information_spectacle.groupby(\"number_compagny\")[[\"nb_campaigns\", \"nb_campaigns_opened\"]].sum().reset_index()\n", - "company_campaigns_stats[\"ratio_campaigns_opened\"] = company_campaigns_stats[\"nb_campaigns_opened\"] / company_campaigns_stats[\"nb_campaigns\"]\n", - "company_campaigns_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "id": "d06ab865-4832-4fe9-918b-e5ff72bebee4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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VoUIFp/bUn8vtLOfxxx/X7NmzNWLECG3evDnNrRfpuXLlitavX682bdqocOHCmU47bdo0VapUSV5eXsqTJ488PDy0evVq7du3L820zz77rHLl+v+71SOPPCJJjr8+U7cfPXrUqT2jvomPj9f27dsdbfPnz1eNGjWUP39+R00zZsxwqmn9+vWSpDZt2jjNr127dhmua+r9JSIiQteuXXNczlu8eLFsNptefPFFp88lODhYFSpUcDzt+9BDD6lgwYIaMGCApk2bpr1792a4zJQ2bdqka9eu6YUXXnBqr169usLCwpzaFi9erHLlyqlixYpOtdSvXz/LTx6HhITosccecwz7+/srMDBQFStWdJyZlP7/55XystPNmzc1atQolSlTRp6ensqTJ488PT21f//+dLeNzNxpfyXL6FiXlJSk77//XtJfx7V8+fKpdevWTtMlH+fu5LiW7E6PQwcOHNCvv/7q+LxTH/NOnjyp3377TdJf+/nSpUs1cOBArVu3TlevXs3SMpYuXaratWs7PsP0rFmzRmXKlNHjjz/u1N6pUycZY9Kczbrf9/P0ZHV/S76c26ZNG/3nP//R8ePHM5xnSmvXrpWkNPt+6u+GO/meTOl26mvfvr3TVY+wsDBVr17dUevtbL9Z2Q4zc+nSJQ0YMEAPPfSQ8uTJozx58ih//vy6fPmy0/ZwJ8v5u32ammXB8ujRo3rqqad0/PhxTZw4UT/88IOio6Md1+ezehBIT6NGjRQUFKRr166pT58+yp079y3fc/bs2XRP+QYHB2fpvXny5EkTOGw2m4KDg2956SU9/v7+atq0qebOneu472n27Nl6/PHHVbZsWcdyb968qX//+9/y8PBweiVfKj9z5ozTfDO7HcAKqeeffI9G69at09Q4duxYGWMcl4/Tc/bs2XQ/g9Rtt7OcL7/8Uh07dtT06dNVrVo1+fv7q0OHDpneT3v+/HklJiY6LjNkZPz48erevbuqVq2qr7/+Wps3b1Z0dLQaNGiQ7jbt7+/vNOzp6Zlp+7Vr1zLth5RtydvdggUL1KZNGxUpUkSffvqpNm3apOjoaHXp0sVpfsnbceplZ3QpRJICAgKchpNv+Uhe11OnTskYo6CgoDSfy+bNmx3bp5+fn9avX6+KFStq8ODBKlu2rEJDQzV06NBMg3/yOmZ1G9m9e3eaOnx8fGSMSbOvpCd130h/fTZZ+bz69OmjIUOGqHnz5vrvf/+rLVu2KDo6WhUqVLjt492d9leyzI51yX2avO+lvj0gMDBQefLkuaPjWrI7PQ4l7+f9+vVL8zn26NFD0v8/5k2aNEkDBgzQokWLVLt2bfn7+6t58+bav39/psv4888/b7mfnz17Nt11SP7jInXf3O/7eXqyur/VrFlTixYt0s2bN9WhQwc98MADKleu3C3vhU1ej9S1pe6rO/meTOl26svoc0r+jG5n+83KdpiZ9u3ba/LkyXr55Ze1fPlybd26VdHR0SpcuLDT53Yny/m7fZqaZfdYLlq0SJcvX9aCBQucziyk93tMXl5eTjeLJztz5ozjPoGUkh/uKFu2rN544w099dRTKliwYKb1BAQEaOvWrWnas/LwTkBAgG7evKk///zTKVwaYxQbG+v4i0f6a4dMb13SO0h37txZ8+fP18qVK1WsWDFFR0dr6tSpjvEFCxZU7ty59dJLL6lnz57p1la8eHGn4dt9Cvx2+z71/JOn+fe//53hk663OqCl9xmkbrud5RQqVEgTJkzQhAkTdPToUX377bcaOHCgTp8+neaBi2T+/v7KnTv3LR+M+PTTT1WrVi2nz0lSlu57uxOZ9U3yAffTTz9V8eLF9eWXXzp9Pqk/1+Tt+Ny5c05fOll9gC09hQoVks1m0w8//JDufcYp28qXL6958+bJGKPdu3dr9uzZGj58uLy9vTVw4MB055+8jhn1Q8qfzypUqJC8vb0zvC8sve3ZSsn3AI8aNcqp/cyZMypQoMBtz+9O+itZejflp95uAgICtGXLFhljnLab06dP6+bNm47+8vLykpR2e8oseN7pr1EkL3PQoEFq2bJlutMk36uaL18+x/23p06dcpy9bNKkiX799dcMl1G4cOFb7ucBAQHpPgx64sQJpzqt4u77eXpuZ39r1qyZmjVrpoSEBG3evFmjR49W+/btFR4ermrVqqX7/uT1OHv2rFO4TL0ed/I9mVpW68voc0qu73a236xshxmJi4vT4sWLNXToUKdjQfJ9uCndyXKs6NOULDtjmbzhp/xiMcbo448/TjNteHi4du/e7dT2+++/O04ZpzR9+nR9+umnmjx5sr799ltduHDBcek4M7Vr19bFixfT3DT9+eef3/K9yQ+FfPrpp07tX3/9tS5fvuz0cEp667JmzRpdunQpzXzr1aunIkWKaNasWZo1a5a8vLycLlfkzZtXtWvX1o4dOxQREaHKlSuneaX+ay49mf31eTt9n54aNWqoQIEC2rt3b7r1Va5c2fFXenpq166tX375Rbt27XJqT/253OlyihUrptdee01169Z1uqSUWvITfvPnz8/0LzGbzZYmQO3evTvN77paJaO+8fHxcTyIZbPZ5Onp6fRlExsbm+Zp0cjISEl/ndFNad68eXdcX+PGjWWM0fHjx9P9TMqXL5/mPTabTRUqVND777+vAgUKZPq5PPHEE/Ly8tJnn33m1L5x48Y0Tz82btxYBw8eVEBAQLq13O3fcE1v21iyZEmWL/1lNt+s9leyjI51uXLlUs2aNSX9dVy7dOmSFi1a5DTd3LlzHeOlv/5g8/LySnOcSO9p5L+rdOnSKlmypHbt2pXhfu7j45PmfUFBQerUqZPatWun3377TVeuXMlwGQ0bNtTatWszPcY9/fTT2rt3b5q+njt3rmw2m2rXrn3nK5kOd9/P03Mn+5vdbldkZKTGjh0rSek+UZ0suY9T7/upvxus+p7MSn1ffPGFjDGO4SNHjmjjxo2OhzZvZ/vNynaY0Xe3zWaTMSbN8Wb69OlpfvkhK8tJzco+lSw8Y1m3bl15enqqXbt26t+/v65du6apU6fq/PnzaaZ96aWX9OKLL6pHjx5q1aqVjhw5onHjxqW59Lxnzx698cYb6tixoyNMzpgxQ61bt9aECRPUq1evDOvp0KGD3n//fXXo0EEjR45UyZIl9d1332n58uVZWpf69etrwIABio+PV40aNRxPhT/66KN66aWXnNZlyJAhevvttxUZGam9e/dq8uTJ8vPzSzPf3Llzq0OHDho/frx8fX3VsmXLNNNNnDhRTz75pJ566il1795d4eHhunjxog4cOKD//ve/t3yyTpLKlSsnSfroo4/k4+MjLy8vFS9eXAEBAVnu+4zkz59f//73v9WxY0edO3dOrVu3VmBgoP7880/t2rVLf/75Z5qzeyn16tVLM2fOVKNGjTRixAjHU+GpzzhkdTlxcXGqXbu22rdvr4cfflg+Pj6Kjo7WsmXLMvwLMtn48eP15JNPqmrVqho4cKAeeughnTp1St9++60+/PBD+fj4qHHjxnrnnXc0dOhQRUZG6rffftPw4cNVvHhx3bx5M0t9djtCQ0PVtGlTRUVFKSQkRJ9++qlWrlypsWPHOn4bsHHjxlqwYIF69OjheGr6nXfeUUhIiNNlwQYNGqhGjRrq27ev4uPj9dhjj2nTpk2OIJHyHrGsqlGjhv7xj3+oc+fO2rZtm2rWrKl8+fLp5MmT2rBhg8qXL6/u3btr8eLF+uCDD9S8eXOVKFFCxhgtWLBAFy5cUN26dTOcf8GCBdWvXz+NGDFCL7/8sp577jnFxMQ4np5NqVevXvr6669Vs2ZN9e7dWxEREUpKStLRo0e1YsUK9e3bV1WrVr3tdcyqxo0ba/bs2Xr44YcVERGhn376Se++++4dXe660/5KFhAQoO7du+vo0aMqVaqUvvvuO3388cfq3r27ihUrJumvY+KUKVPUsWNHHT58WOXLl9eGDRs0atQoPfvss3rmmWckyXEP7cyZM/Xggw+qQoUK2rp1a5b+KL8TH374oRo2bKj69eurU6dOKlKkiM6dO6d9+/Zp+/btmj9/viSpatWqaty4sSIiIlSwYEHt27dPn3zyiapVq5bp72YOHz5cS5cuVc2aNTV48GCVL19eFy5c0LJly9SnTx89/PDD6t27t+bOnatGjRpp+PDhCgsL05IlS/TBBx+oe/fuKlWqlKXr7O77eXqyur+9/fbbOnbsmJ5++mk98MADunDhgiZOnCgPDw9HCE5PvXr1VLNmTfXv31+XL19W5cqV9eOPP+qTTz5JM+3f+Z68nfpOnz6tFi1a6JVXXlFcXJyGDh0qLy8vx1P0Uta336xshw8++KC8vb312Wef6ZFHHlH+/PkVGhqq0NBQ1axZU++++64KFSqk8PBwrV+/XjNmzEhzdSQry0mPFdnDIcuP+aSS3lPh//3vf02FChWMl5eXKVKkiPnnP//peNIr5VOASUlJZty4caZEiRLGy8vLVK5c2axZs8bpyeRLly6Zhx9+2JQpUybNU3I9e/Y0Hh4eaZ7gS+3YsWOmVatWJn/+/MbHx8e0atXKbNy4MUtPXV29etUMGDDAhIWFGQ8PDxMSEmK6d+9uzp8/7zRdQkKC6d+/vylatKjx9vY2kZGRZufOnRk+ff37778bSUaSWblyZbrLPnTokOnSpYspUqSI8fDwMIULFzbVq1c3I0aMcEyT/NTa/Pnz053HhAkTTPHixU3u3Lmd1jcrfZ+V+a9fv940atTI+Pv7Gw8PD1OkSBHTqFGjDKdPae/evaZu3brGy8vL+Pv7m65du5pvvvkm3SfZb7Wca9eumW7dupmIiAjj6+trvL29TenSpc3QoUMzfLoydS3PPfecCQgIMJ6enqZYsWKmU6dO5tq1a8aYvz7ffv36mSJFihgvLy9TqVIls2jRItOxY0cTFhbmmE/yU3bvvvuu0/wz6sfkp4JTPmEZFhZmGjVqZL766itTtmxZ4+npacLDw8348ePT1D1mzBgTHh5u7Ha7eeSRR8zHH3/seOIzpXPnzpnOnTubAgUKmLx585q6deuazZs3p3n6Mvm9f/75Z7p1Jj+9nGzmzJmmatWqJl++fMbb29s8+OCDpkOHDo4nrH/99VfTrl078+CDDxpvb2/j5+dnHn/8cTN79uxbfCJ/baOjR482RYsWNZ6eniYiIsL897//TfdXIy5dumT+9a9/mdKlSxtPT0/j5+dnypcvb3r37m1iY2MzXU5kZKQpW7ZsmvbkzyE1SaZnz56O4fPnz5uuXbuawMBAkzdvXvPkk0+aH374IU2dWXkq/O/0V/J6rFu3zlSuXNnY7XYTEhJiBg8enOZJ1bNnz5pu3bqZkJAQkydPHhMWFmYGDRrk2N6TxcXFmZdfftkEBQWZfPnymSZNmpjDhw9n+JRx6u0mI+k9FW6MMbt27TJt2rQxgYGBxsPDwwQHB5s6deqYadOmOaYZOHCgqVy5silYsKCx2+2mRIkSpnfv3ubMmTO3XG5MTIzp0qWLCQ4ONh4eHiY0NNS0adPGnDp1yjHNkSNHTPv27U1AQIDx8PAwpUuXNu+++67T0985aT9P7zssK/vb4sWLTcOGDU2RIkWMp6enCQwMNM8++6z54Ycf0vtonFy4cMF06dLFaT1+/fXXdH99JSvfk+nJSn3Jn+cnn3xi3njjDVO4cGFjt9vNU0895fQrEsmysv0ak7Xt8IsvvjAPP/yw8fDwcFrv5DxTsGBB4+PjYxo0aGB+/vnndD+nWy0no19+uNM+Tc1mTIrzvABcJjw8XOXKldPixYvv6nI+//xzvfDCC/rxxx9VvXr1u7os3H21atXSmTNn9PPPP7u6FGQB+7n7W7dunWrXrq358+en+RUF3Br/Nx9wH/viiy90/PhxlS9fXrly5dLmzZv17rvvqmbNmnzZAPcJ9nO4E4IlcB/z8fHRvHnzNGLECF2+fFkhISHq1KmTRowY4erSAFiE/RzuhEvhAAAAsISl//MOAAAAci6CJQAAACxBsAQAAIAleHjHIklJSTpx4oR8fHzu+L83AwAA95YxRhcvXlRoaKhlPyifkxEsLXLixAkVLVrU1WUAAIA7EBMTc0f/cxacESwtkvz/gcbExMjX19fF1QAAgKyIj49X0aJF0/1/6XH7CJYWSb787evrS7AEACCb4TY2a3AzAQAAACxBsAQAAIAlCJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEvkcXUBgDsLH7jE1SVkG4fHNHJ1CQAAF+OMJQAAACxBsAQAAIAlCJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBEsAAABYIkcEy6lTpyoiIkK+vr7y9fVVtWrVtHTpUsf4Tp06yWazOb2eeOIJF1YMAACQ/eRxdQH3wgMPPKAxY8booYcekiTNmTNHzZo1044dO1S2bFlJUoMGDTRr1izHezw9PV1SKwAAQHaVI4JlkyZNnIZHjhypqVOnavPmzY5gabfbFRwc7IryAAAA7gs54lJ4SomJiZo3b54uX76satWqOdrXrVunwMBAlSpVSq+88opOnz6d6XwSEhIUHx/v9AIAAMjJckyw3LNnj/Lnzy+73a5u3bpp4cKFKlOmjCSpYcOG+uyzz7RmzRq99957io6OVp06dZSQkJDh/EaPHi0/Pz/Hq2jRovdqVQAAANySzRhjXF3EvXD9+nUdPXpUFy5c0Ndff63p06dr/fr1jnCZ0smTJxUWFqZ58+apZcuW6c4vISHBKXjGx8eraNGiiouLk6+v711bD9xb4QOXuLqEbOPwmEauLgEAblt8fLz8/Pz4/rZIjrjHUvrrYZzkh3cqV66s6OhoTZw4UR9++GGaaUNCQhQWFqb9+/dnOD+73S673X7X6gUAAMhucsyl8NSMMRle6j579qxiYmIUEhJyj6sCAADIvnLEGcvBgwerYcOGKlq0qC5evKh58+Zp3bp1WrZsmS5duqSoqCi1atVKISEhOnz4sAYPHqxChQqpRYsWri4dAAAg28gRwfLUqVN66aWXdPLkSfn5+SkiIkLLli1T3bp1dfXqVe3Zs0dz587VhQsXFBISotq1a+vLL7+Uj4+Pq0sHAADINnJEsJwxY0aG47y9vbV8+fJ7WA0AAMD9KcfeYwkAAABrESwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBEsAAABYgmAJAAAASxAsAQAAYAmCJQAAACxBsAQAAIAlCJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAskSOC5dSpUxURESFfX1/5+vqqWrVqWrp0qWO8MUZRUVEKDQ2Vt7e3atWqpV9++cWFFQMAAGQ/OSJYPvDAAxozZoy2bdumbdu2qU6dOmrWrJkjPI4bN07jx4/X5MmTFR0dreDgYNWtW1cXL150ceUAAADZR44Ilk2aNNGzzz6rUqVKqVSpUho5cqTy58+vzZs3yxijCRMm6K233lLLli1Vrlw5zZkzR1euXNHnn3/u6tIBAACyjRwRLFNKTEzUvHnzdPnyZVWrVk2HDh1SbGys6tWr55jGbrcrMjJSGzduzHA+CQkJio+Pd3oBAADkZDkmWO7Zs0f58+eX3W5Xt27dtHDhQpUpU0axsbGSpKCgIKfpg4KCHOPSM3r0aPn5+TleRYsWvav1AwAAuLscEyxLly6tnTt3avPmzerevbs6duyovXv3OsbbbDan6Y0xadpSGjRokOLi4hyvmJiYu1Y7AABAdpDH1QXcK56ennrooYckSZUrV1Z0dLQmTpyoAQMGSJJiY2MVEhLimP706dNpzmKmZLfbZbfb727RAID7WvjAJa4uIVs5PKaRq0vALeSYM5apGWOUkJCg4sWLKzg4WCtXrnSMu379utavX6/q1au7sEIAAIDsJUecsRw8eLAaNmyookWL6uLFi5o3b57WrVunZcuWyWazqVevXho1apRKliypkiVLatSoUcqbN6/at2/v6tIBAACyjRwRLE+dOqWXXnpJJ0+elJ+fnyIiIrRs2TLVrVtXktS/f39dvXpVPXr00Pnz51W1alWtWLFCPj4+Lq4cAAAg+8gRwXLGjBmZjrfZbIqKilJUVNS9KQgAAOA+lGPvsQQAAIC1CJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAskcfVBdxKTEyMDh8+rCtXrqhw4cIqW7as7Ha7q8sCAABAKm4ZLI8cOaJp06bpiy++UExMjIwxjnGenp566qmn9I9//EOtWrVSrlycdAUAAHAHbpfK3nzzTZUvX1779+/X8OHD9csvvyguLk7Xr19XbGysvvvuOz355JMaMmSIIiIiFB0d7eqSAQAAIDc8Y+np6amDBw+qcOHCacYFBgaqTp06qlOnjoYOHarvvvtOR44cUZUqVVxQKQAAAFJyu2D57rvvZnnaZ5999i5WAgAAgNvhdsEyI2fOnNGWLVuUmJioKlWqKCQkxNUlAQAAIIVsESy//vprde3aVaVKldKNGzf022+/acqUKercubOrSwMAAMD/uN3DO5J06dIlp+Fhw4Zp69at2rp1q3bs2KH58+frrbfeclF1AAAASI9bBsvHHntM33zzjWM4T548On36tGP41KlT8vT0dEVpAAAAyIBbXgpfvny5evToodmzZ2vKlCmaOHGi2rZtq8TERN28eVO5cuXS7NmzXV0mAAAAUnDLYBkeHq7vvvtOn3/+uSIjI/Xmm2/qwIEDOnDggBITE/Xwww/Ly8vL1WUCAAAgBbe8FJ6sffv2jvsqa9WqpaSkJFWsWJFQCQAA4Ibc8oylJC1dulR79+5VhQoVNGPGDK1bt07t27fXs88+q+HDh8vb29vVJQIAACAFtzxj2b9/f3Xq1EnR0dF69dVX9c4776hWrVrasWOH7Ha7KlasqKVLl7q6TAAAAKTglsFy5syZ+u677zRv3jxFR0frk08+kfTXf/c4YsQILViwQCNHjnRxlQAAAEjJLYNl3rx5dejQIUlSTExMmnsqy5Ytqw0bNriiNAAAAGTALYPl6NGj1aFDB4WGhioyMlLvvPOOq0sCAADALbjlwzsvvPCCGjRooD/++EMlS5ZUgQIFXF0SAAAAbsEtg6UkBQQEKCAgwNVlAHCB8IFLXF1CtnF4TCNXlwAADm53Kbxbt26KiYnJ0rRffvmlPvvss1tON3r0aFWpUkU+Pj4KDAxU8+bN9dtvvzlN06lTJ9lsNqfXE088cUfrAAAAkBO53RnLwoULq1y5cqpevbqaNm2qypUrKzQ0VF5eXjp//rz27t2rDRs2aN68eSpSpIg++uijW85z/fr16tmzp6pUqaKbN2/qrbfeUr169bR3717ly5fPMV2DBg00a9YsxzD/HzkAAEDWuV2wfOedd/T6669rxowZmjZtmn7++Wen8T4+PnrmmWc0ffp01atXL0vzXLZsmdPwrFmzFBgYqJ9++kk1a9Z0tNvtdgUHB//9lQAAAMiB3C5YSlJgYKAGDRqkQYMG6cKFCzpy5IiuXr2qQoUK6cEHH5TNZvtb84+Li5Mk+fv7O7WvW7dOgYGBKlCggCIjIzVy5EgFBgb+rWUBAADkFG4ZLFMqUKCApU+FG2PUp08fPfnkkypXrpyjvWHDhnruuecUFhamQ4cOaciQIapTp45++ukn2e32NPNJSEhQQkKCYzg+Pt6yGgEAALIjtw+WVnvttde0e/fuND+w3rZtW8e/y5Urp8qVKyssLExLlixRy5Yt08xn9OjRGjZs2F2vFwAAILtwu6fC76bXX39d3377rdauXasHHngg02lDQkIUFham/fv3pzt+0KBBiouLc7yy+iQ7AADA/SpHnLE0xuj111/XwoULtW7dOhUvXvyW7zl79qxiYmIUEhKS7ni73Z7uJXIAAICcKkcEy549e+rzzz/XN998Ix8fH8XGxkqS/Pz85O3trUuXLikqKkqtWrVSSEiIDh8+rMGDB6tQoUJq0aKFi6sHgLuPH6W/PfwwPZA+t78UfvPmTa1atUoffvihLl68KEk6ceKELl26lOV5TJ06VXFxcapVq5ZCQkIcry+//FKSlDt3bu3Zs0fNmjVTqVKl1LFjR5UqVUqbNm2Sj4/PXVkvAACA+41bn7E8cuSIGjRooKNHjyohIUF169aVj4+Pxo0bp2vXrmnatGlZmo8xJtPx3t7eWr58uRUlAwAA5FhufcbyzTffVOXKlXX+/Hl5e3s72lu0aKHVq1e7sDIAAACk5tZnLDds2KAff/wxzX+tGBYWpuPHj7uoKgAAAKTHrc9YJiUlKTExMU37sWPHuPcRAADAzbh1sKxbt64mTJjgGLbZbLp06ZKGDh2qZ5991nWFAQAAIA23vhQ+fvx41alTR2XKlNG1a9fUvn177d+/X4UKFdIXX3zh6vIAAACQglsHyyJFimjnzp2aN2+efvrpJyUlJalr16564YUXnB7mAQAAgOu5bbC8ceOGSpcurcWLF6tz587q3Lmzq0sCAABAJtz2HksPDw8lJCTIZrO5uhQAAABkgdsGS0l6/fXXNXbsWN28edPVpQAAAOAW3PZSuCRt2bJFq1ev1ooVK1S+fHnly5fPafyCBQtcVBkAAABSc+tgWaBAAbVq1crVZQAAACAL3DpYzpo1y9UlAAAAIIvc+h5LAAAAZB9ufcayePHimT4V/scff9zDagAAAJAZtw6WvXr1chq+ceOGduzYoWXLlumf//yna4oCAABAutw6WL755pvptk+ZMkXbtm27x9UAAAAgM9nyHsuGDRvq66+/dnUZAAAASCFbBsuvvvpK/v7+ri4DAAAAKbj1pfBHH33U6eEdY4xiY2P1559/6oMPPnBhZQAAAEjNrYNls2bNnIJlrly5VLhwYdWqVUsPP/ywCysDAABAam4dLKOiolxdAgAAALLIre+xzJ07t06fPp2m/ezZs8qdO7cLKgIAAEBG3DpYGmPSbU9ISJCnp+c9rgYAAACZcctL4ZMmTZIk2Ww2TZ8+Xfnz53eMS0xM1Pfff889lgAAAG7GLYPl+++/L+mvM5bTpk1zuuzt6emp8PBwTZs2zVXlAQAAIB1uGSwPHTokSapdu7YWLlyoAgUKuLYgAAAA3JLb3mN548YNHTlyRCdOnHB1KQAAAMgCtw2WHh4eSkhIcPodSwAAALgvtw2WkvT6669r7NixunnzpqtLAQAAwC245T2WybZs2aLVq1drxYoVKl++vPLly+c0fsGCBS6qDAAAAKm5dbAsUKCAWrVq5eoyAAAAkAVuHSxnzZrl6hIAAACQRW59j6Uk3bx5U6tWrdKHH36oixcvSpJOnDihS5cuubgyAAAApOTWZyyPHDmiBg0a6OjRo0pISFDdunXl4+OjcePG6dq1a/xIOgAAgBtx6zOWb775pipXrqzz58/L29vb0d6iRQutXr3ahZUBAAAgNbc+Y7lhwwb9+OOP8vT0dGoPCwvT8ePHXVQVAAAA0uPWZyyTkpKUmJiYpv3YsWPy8fHJ8nxGjx6tKlWqyMfHR4GBgWrevLl+++03p2mMMYqKilJoaKi8vb1Vq1Yt/fLLL397HQAAAHIKtw6WdevW1YQJExzDNptNly5d0tChQ/Xss89meT7r169Xz549tXnzZq1cuVI3b95UvXr1dPnyZcc048aN0/jx4zV58mRFR0crODhYdevWdTwwBAAAgMy59aXw999/X7Vr11aZMmV07do1tW/fXvv371ehQoX0xRdfZHk+y5YtcxqeNWuWAgMD9dNPP6lmzZoyxmjChAl666231LJlS0nSnDlzFBQUpM8//1yvvvqqpesFAABwP3LrYBkaGqqdO3fqiy++0Pbt25WUlKSuXbvqhRdecHqY53bFxcVJkvz9/SVJhw4dUmxsrOrVq+eYxm63KzIyUhs3bkw3WCYkJCghIcExHB8ff8f1AAAA3A/cOlheuXJFefPmVZcuXdSlSxdL5mmMUZ8+ffTkk0+qXLlykqTY2FhJUlBQkNO0QUFBOnLkSLrzGT16tIYNG2ZJTQAAAPcDt77HMjAwUC+++KKWL1+upKQkS+b52muvaffu3eleSrfZbE7Dxpg0bckGDRqkuLg4xysmJsaS+gAAALIrtw6Wc+fOVUJCglq0aKHQ0FC9+eabio6OvuP5vf766/r222+1du1aPfDAA4724OBgSf//zGWy06dPpzmLmcxut8vX19fpBQAAkJO5dbBs2bKl5s+fr1OnTmn06NHat2+fqlevrlKlSmn48OFZno8xRq+99poWLFigNWvWqHjx4k7jixcvruDgYK1cudLRdv36da1fv17Vq1e3bH0AAADuZ24dLJP5+Pioc+fOWrFihXbt2qV8+fLd1v2NPXv21KeffqrPP/9cPj4+io2NVWxsrK5evSrpr0vgvXr10qhRo7Rw4UL9/PPP6tSpk/Lmzav27dvfrdUCAAC4r7j1wzvJrl27pm+//Vaff/65li1bpsDAQPXr1y/L7586daokqVatWk7ts2bNUqdOnSRJ/fv319WrV9WjRw+dP39eVatW1YoVK27rh9gBAAByMrcOlitWrNBnn32mRYsWKXfu3GrdurWWL1+uyMjI25qPMeaW09hsNkVFRSkqKuoOqwUAAMjZ3DpYNm/eXI0aNdKcOXPUqFEjeXh4uLokAAAAZMCtg2VsbCxPWwMAAGQTbh0sL1y4oAsXLmQ4vlixYveuGAAAAGTKrYNleHh4hj9QLkmJiYn3sBoAAABkxq2D5Y4dO5yGb9y4oR07dmj8+PEaOXKki6oCAABAetw6WFaoUCFNW+XKlRUaGqp3331XLVu2dEFVAAAASE+2+IH01EqVKvW3/mtHAAAAWM+tz1jGx8c7DRtjdPLkSUVFRalkyZIuqgoAAADpcetgWaBAgTQP7xhjVLRoUc2bN89FVQEAACA9bh0s165d6zScK1cuFS5cWA899JDy5HHr0gEAAHIct05nt/tfNwIAAMB13DpYStLBgwc1YcIE7du3TzabTY888ojefPNNPfjgg64uDQAAACm49VPhy5cvV5kyZbR161ZFRESoXLly2rJli8qWLauVK1e6ujwAAACk4NZnLAcOHKjevXtrzJgxadoHDBigunXruqgyAAAApObWZyz37dunrl27pmnv0qWL9u7d64KKAAAAkBG3DpaFCxfWzp0707Tv3LlTgYGB974gAAAAZMitL4W/8sor+sc//qE//vhD1atXl81m04YNGzR27Fj17dvX1eUBAAAgBbcOlkOGDJGPj4/ee+89DRo0SJIUGhqqqKgovfHGGy6uDgAAACm5dbC02Wzq3bu3evfurYsXL0qSfHx8XFwVAAAA0uPWwTKlnB4owwcucXUJ2cbhMY1cXQIAADmSWwbL2rVrO/0f4WvWrHFhNQAAAMgKtwyWnTp1cnUJAAAAuE1uGSw7duzo6hIAAABwm9z6dywBAACQfbjdGcuCBQs63V+ZmXPnzt3lagAAAJBVbhcsJ0yY4Pj32bNnNWLECNWvX1/VqlWTJG3atEnLly/XkCFDXFQhAAAA0uN2wTLl/ZWtWrXS8OHD9dprrzna3njjDU2ePFmrVq1S7969XVEiAAAA0uHW91guX75cDRo0SNNev359rVq1ygUVAQAAICNuHSwDAgK0cOHCNO2LFi1SQECACyoCAABARtzuUnhKw4YNU9euXbVu3TrHPZabN2/WsmXLNH36dBdXBwAAgJTcOlh26tRJjzzyiCZNmqQFCxbIGKMyZcroxx9/VNWqVV1dHgAAAFJw62ApSVWrVtVnn33m6jIAAABwC259jyUAAACyD4IlAAAALEGwBAAAgCVyRLD8/vvv1aRJE4WGhspms2nRokVO4zt16iSbzeb0euKJJ1xTLAAAQDbl1sHy1KlTGY7bvXt3ludz+fJlVahQQZMnT85wmgYNGujkyZOO13fffXdbtQIAAOR0bv1UePny5TV9+nQ1bdrUqf3//u//NGTIEF29ejVL82nYsKEaNmyY6TR2u13BwcF3XCsAAEBO59ZnLAcMGKC2bduqW7duunr1qo4fP646dero3Xff1ZdffmnpstatW6fAwECVKlVKr7zyik6fPp3p9AkJCYqPj3d6AQAA5GRuHSz79u2rzZs368cff1RERIQiIiLk7e2t3bt3pzmL+Xc0bNhQn332mdasWaP33ntP0dHRqlOnjhISEjJ8z+jRo+Xn5+d4FS1a1LJ6AAAAsiO3DpaSVKJECZUtW1aHDx9WfHy82rRpo6CgIEuX0bZtWzVq1EjlypVTkyZNtHTpUv3+++9asmRJhu8ZNGiQ4uLiHK+YmBhLawIAAMhu3DpYJp+pPHDggHbv3q2pU6fq9ddfV5s2bXT+/Pm7ttyQkBCFhYVp//79GU5jt9vl6+vr9AIAAMjJ3DpY1qlTR23bttWmTZv0yCOP6OWXX9aOHTt07NgxlS9f/q4t9+zZs4qJiVFISMhdWwYAAMD9xq2fCl+xYoUiIyOd2h588EFt2LBBI0eOzPJ8Ll26pAMHDjiGDx06pJ07d8rf31/+/v6KiopSq1atFBISosOHD2vw4MEqVKiQWrRoYdm6AAAA3O/cOlimDpXJcuXKpSFDhmR5Ptu2bVPt2rUdw3369JEkdezYUVOnTtWePXs0d+5cXbhwQSEhIapdu7a+/PJL+fj4/L0VAAAAyEHcOlgOHz480/Fvv/12luZTq1YtGWMyHL98+fLbqgsAAABpuXWwXLhwodPwjRs3dOjQIeXJk0cPPvhgloMlAAAA7j63DpY7duxI0xYfH69OnTpx/yMAAICbceunwtPj6+ur4cOH39Y9lgAAALj7sl2wlKQLFy4oLi7O1WUAAAAgBbe+FD5p0iSnYWOMTp48qU8++UQNGjRwUVUAAABIj1sHy/fff99pOFeuXCpcuLA6duyoQYMGuagqAAAApMetg+WhQ4dcXQIAAACyKFveYwkAAAD349ZnLCUpOjpa8+fP19GjR3X9+nWncQsWLHBRVQAAAEjNrc9Yzps3TzVq1NDevXu1cOFC3bhxQ3v37tWaNWvk5+fn6vIAAACQglsHy1GjRun999/X4sWL5enpqYkTJ2rfvn1q06aNihUr5uryAAAAkIJbB8uDBw+qUaNGkiS73a7Lly/LZrOpd+/e+uijj1xcHQAAAFJy62Dp7++vixcvSpKKFCmin3/+WdJfP5B+5coVV5YGAACAVNwyWHbp0kUXL17UU089pZUrV0qS2rRpozfffFOvvPKK2rVrp6efftrFVQIAACAlt3wqfM6cORozZowmT56sa9euSZIGDRokDw8PbdiwQS1btuT/CgcAAHAzbhksjTGS/roUnixXrlzq37+/+vfv76qyAAAAkAm3vBQuSTabzdUlAAAA4Da45RlLSSpVqtQtw+W5c+fuUTUAAAC4FbcNlsOGDeNH0AEAALIRtw2Wzz//vAIDA11dBgAAALLILe+x5P5KAACA7Mctg2XyU+EAAADIPtzyUnhSUpKrSwAAAMBtcsszlgAAAMh+CJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWCJHBMvvv/9eTZo0UWhoqGw2mxYtWuQ03hijqKgohYaGytvbW7Vq1dIvv/zimmIBAACyqRwRLC9fvqwKFSpo8uTJ6Y4fN26cxo8fr8mTJys6OlrBwcGqW7euLl68eI8rBQAAyL7yuLqAe6Fhw4Zq2LBhuuOMMZowYYLeeusttWzZUpI0Z84cBQUF6fPPP9err756L0sFAADItnLEGcvMHDp0SLGxsapXr56jzW63KzIyUhs3bszwfQkJCYqPj3d6AQAA5GQ5PljGxsZKkoKCgpzag4KCHOPSM3r0aPn5+TleRYsWvat1AgAAuLscHyyT2Ww2p2FjTJq2lAYNGqS4uDjHKyYm5m6XCAAA4NZyxD2WmQkODpb015nLkJAQR/vp06fTnMVMyW63y2633/X6AAAAsoscf8ayePHiCg4O1sqVKx1t169f1/r161W9enUXVgYAAJC95IgzlpcuXdKBAwccw4cOHdLOnTvl7++vYsWKqVevXho1apRKliypkiVLatSoUcqbN6/at2/vwqoBAACylxwRLLdt26batWs7hvv06SNJ6tixo2bPnq3+/fvr6tWr6tGjh86fP6+qVatqxYoV8vHxcVXJAAAA2U6OCJa1atWSMSbD8TabTVFRUYqKirp3RQEAANxncvw9lgAAALAGwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBEsAAABYgmAJAAAASxAsAQAAYAmCJQAAACxBsAQAAIAlCJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBMv/iYqKks1mc3oFBwe7uiwAAIBsI4+rC3AnZcuW1apVqxzDuXPndmE1AAAA2QvBMoU8efJwlhIAAOAOcSk8hf379ys0NFTFixfX888/rz/++CPDaRMSEhQfH+/0AgAAyMkIlv9TtWpVzZ07V8uXL9fHH3+s2NhYVa9eXWfPnk13+tGjR8vPz8/xKlq06D2uGAAAwL0QLP+nYcOGatWqlcqXL69nnnlGS5YskSTNmTMn3ekHDRqkuLg4xysmJuZelgsAAOB2uMcyA/ny5VP58uW1f//+dMfb7XbZ7fZ7XBUAAID74oxlBhISErRv3z6FhIS4uhQAAIBsgWD5P/369dP69et16NAhbdmyRa1bt1Z8fLw6duzo6tIAAACyBS6F/8+xY8fUrl07nTlzRoULF9YTTzyhzZs3KywszNWlAQAAZAsEy/+ZN2+eq0sAAADI1rgUDgAAAEsQLAEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBEsAAABYgmAJAAAASxAsAQAAYAmCJQAAACxBsAQAAIAlCJYAAACwBMESAAAAliBYAgAAwBIESwAAAFiCYAkAAABLECwBAABgCYIlAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBEsAAABYgmAJAAAASxAsAQAAYAmCJQAAACxBsEzhgw8+UPHixeXl5aXHHntMP/zwg6tLAgAAyDYIlv/z5ZdfqlevXnrrrbe0Y8cOPfXUU2rYsKGOHj3q6tIAAACyBYLl/4wfP15du3bVyy+/rEceeUQTJkxQ0aJFNXXqVFeXBgAAkC0QLCVdv35dP/30k+rVq+fUXq9ePW3cuNFFVQEAAGQveVxdgDs4c+aMEhMTFRQU5NQeFBSk2NjYdN+TkJCghIQEx3BcXJwkKT4+/q7UmJRw5a7M935k5WdAv2cd/e4aVvU7fX576HfXuBvfscnzNMZYPu+ciGCZgs1mcxo2xqRpSzZ69GgNGzYsTXvRokXvSm3IOr8Jrq4gZ6LfXYN+dw363TXuZr9fvHhRfn5+d28BOQTBUlKhQoWUO3fuNGcnT58+neYsZrJBgwapT58+juGkpCSdO3dOAQEBGYbR+0l8fLyKFi2qmJgY+fr6urqcHIN+dw363TXod9fIaf1ujNHFixcVGhrq6lLuCwRLSZ6ennrssce0cuVKtWjRwtG+cuVKNWvWLN332O122e12p7YCBQrczTLdkq+vb4448Lgb+t016HfXoN9dIyf1O2cqrUOw/J8+ffropZdeUuXKlVWtWjV99NFHOnr0qLp16+bq0gAAALIFguX/tG3bVmfPntXw4cN18uRJlStXTt99953CwsJcXRoAAEC2QLBMoUePHurRo4ery8gW7Ha7hg4dmuZ2ANxd9Ltr0O+uQb+7Bv2Ov8NmeL4eAAAAFuAH0gEAAGAJgiUAAAAsQbAEAACAJQiWAAAAsATBEpn6/vvv1aRJE4WGhspms2nRokVO440xioqKUmhoqLy9vVWrVi398ssvrin2PnKrfl+wYIHq16+vQoUKyWazaefOnS6p836TWb/fuHFDAwYMUPny5ZUvXz6FhoaqQ4cOOnHihOsKvk/canuPiorSww8/rHz58qlgwYJ65plntGXLFtcUex+5Vb+n9Oqrr8pms2nChAn3rD5kTwRLZOry5cuqUKGCJk+enO74cePGafz48Zo8ebKio6MVHBysunXr6uLFi/e40vvLrfr98uXLqlGjhsaMGXOPK7u/ZdbvV65c0fbt2zVkyBBt375dCxYs0O+//66mTZu6oNL7y62291KlSmny5Mnas2ePNmzYoPDwcNWrV09//vnnPa70/nKrfk+2aNEibdmyhf/yEFljgCySZBYuXOgYTkpKMsHBwWbMmDGOtmvXrhk/Pz8zbdo0F1R4f0rd7ykdOnTISDI7duy4pzXlBJn1e7KtW7caSebIkSP3pqgcICv9HhcXZySZVatW3ZuicoCM+v3YsWOmSJEi5ueffzZhYWHm/fffv+e1IXvhjCXu2KFDhxQbG6t69eo52ux2uyIjI7Vx40YXVgbcG3FxcbLZbCpQoICrS8kxrl+/ro8++kh+fn6qUKGCq8u5ryUlJemll17SP//5T5UtW9bV5SCb4H/ewR2LjY2VJAUFBTm1BwUF6ciRI64oCbhnrl27poEDB6p9+/by9fV1dTn3vcWLF+v555/XlStXFBISopUrV6pQoUKuLuu+NnbsWOXJk0dvvPGGq0tBNsIZS/xtNpvNadgYk6YNuJ/cuHFDzz//vJKSkvTBBx+4upwcoXbt2tq5c6c2btyoBg0aqE2bNjp9+rSry7pv/fTTT5o4caJmz57N8Ry3hWCJOxYcHCzp/5+5THb69Ok0ZzGB+8WNGzfUpk0bHTp0SCtXruRs5T2SL18+PfTQQ3riiSc0Y8YM5cmTRzNmzHB1WfetH374QadPn1axYsWUJ08e5cmTR0eOHFHfvn0VHh7u6vLgxgiWuGPFixdXcHCwVq5c6Wi7fv261q9fr+rVq7uwMuDuSA6V+/fv16pVqxQQEODqknIsY4wSEhJcXcZ966WXXtLu3bu1c+dOxys0NFT//Oc/tXz5cleXBzfGPZbI1KVLl3TgwAHH8KFDh7Rz5075+/urWLFi6tWrl0aNGqWSJUuqZMmSGjVqlPLmzav27du7sOrs71b9fu7cOR09etTxG4q//fabpL/OIiefScbty6zfQ0ND1bp1a23fvl2LFy9WYmKi42y9v7+/PD09XVV2tpdZvwcEBGjkyJFq2rSpQkJCdPbsWX3wwQc6duyYnnvuORdWnf3d6jiT+g8nDw8PBQcHq3Tp0ve6VGQnrn4sHe5t7dq1RlKaV8eOHY0xf/3k0NChQ01wcLCx2+2mZs2aZs+ePa4t+j5wq36fNWtWuuOHDh3q0rqzu8z6PfmnndJ7rV271tWlZ2uZ9fvVq1dNixYtTGhoqPH09DQhISGmadOmZuvWra4uO9u71XEmNX5uCFlhM8aYuxtdAQAAkBNwjyUAAAAsQbAEAACAJQiWAAAAsATBEgAAAJYgWAIAAMASBEsAAABYgmAJAAAASxAsAQAAYAmCJYBsJTY2Vq+//rpKlCghu92uokWLqkmTJlq9erWrSwOAHI//KxxAtnH48GHVqFFDBQoU0Lhx4xQREaEbN25o+fLl6tmzp3799VdXlwgAORpnLAFkGz169JDNZtPWrVvVunVrlSpVSmXLllWfPn20efNmSdLRo0fVrFkz5c+fX76+vmrTpo1OnTrlmEdUVJQqVqyomTNnqlixYsqfP7+6d++uxMREjRs3TsHBwQoMDNTIkSOdlm2z2TR16lQ1bNhQ3t7eKl68uObPn+80zYABA1SqVCnlzZtXJUqU0JAhQ3Tjxo00y/7kk08UHh4uPz8/Pf/887p48aIkae7cuQoICFBCQoLTfFu1aqUOHTpY2pcAcDcQLAFkC+fOndOyZcvUs2dP5cuXL834AgUKyBij5s2b69y5c1q/fr1WrlypgwcPqm3btk7THjx4UEuXLtWyZcv0xRdfaObMmWrUqJGOHTum9evXa+zYsfrXv/7lCKvJhgwZolatWmnXrl168cUX1a5dO+3bt88x3sfHR7Nnz9bevXs1ceJEffzxx3r//ffTLHvRokVavHixFi9erPXr12vMmDGSpOeee06JiYn69ttvHdOfOXNGixcvVufOnf92HwLAXWcAIBvYsmWLkWQWLFiQ4TQrVqwwuXPnNkePHnW0/fLLL0aS2bp1qzHGmKFDh5q8efOa+Ph4xzT169c34eHhJjEx0dFWunRpM3r0aMewJNOtWzen5VWtWtV07949w3rGjRtnHnvsMcdwesv+5z//aapWreoY7t69u2nYsKFjeMKECaZEiRImKSkpw+UAgLvgHksA2YIxRtJfl6Qzsm/fPhUtWlRFixZ1tJUpU0YFChTQvn37VKVKFUlSeHi4fHx8HNMEBQUpd+7cypUrl1Pb6dOnneZfrVq1NMM7d+50DH/11VeaMGGCDhw4oEuXLunmzZvy9fV1ek/qZYeEhDgt55VXXlGVKlV0/PhxFSlSRLNmzVKnTp0yXW8AcBdcCgeQLZQsWVI2m83p0nNqxph0A1jqdg8PD6fxNpst3bakpKRb1pU8382bN+v5559Xw4YNtXjxYu3YsUNvvfWWrl+/7jT9rZbz6KOPqkKFCpo7d662b9+uPXv2qFOnTresAwDcAcESQLbg7++v+vXra8qUKbp8+XKa8RcuXFCZMmV09OhRxcTEONr37t2ruLg4PfLII3+7htT3XG7evFkPP/ywJOnHH39UWFiY3nrrLVWuXFklS5bUkSNH7mg5L7/8smbNmqWZM2fqmWeecToDCwDujGAJINv44IMPlJiYqMcff1xff/219u/fr3379mnSpEmqVq2annnmGUVEROiFF17Q9u3btXXrVnXo0EGRkZGqXLny317+/PnzNXPmTP3+++8aOnSotm7dqtdee02S9NBDD+no0aOaN2+eDh48qEmTJmnhwoV3tJwXXnhBx48f18cff6wuXbr87boB4F4hWALINooXL67t27erdu3a6tu3r8qVK6e6detq9erVmjp1qmw2mxYtWqSCBQuqZs2aeuaZZ1SiRAl9+eWXlix/2LBhmjdvniIiIjRnzhx99tlnKlOmjCSpWbNm6t27t1577TVVrFhRGzdu1JAhQ+5oOb6+vmrVqpXy58+v5s2bW1I7ANwLNpN8RzwAIEM2m00LFy68Z0Gvbt26euSRRzRp0qR7sjwAsAJPhQOAGzl37pxWrFihNWvWaPLkya4uBwBuC8ESANxIpUqVdP78eY0dO1alS5d2dTkAcFu4FA4AAABL8PAOAAAALEGwBAAAgCUIlgAAALAEwRIAAACWIFgCAADAEgRLAAAAWIJgCQAAAEsQLAEAAGAJgiUAAAAs8f8AB+iZCxGA9GcAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(company_campaigns_stats[\"number_compagny\"], 100 * company_campaigns_stats[\"ratio_campaigns_opened\"])\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Company')\n", - "plt.ylabel(\"Taux d'ouverture (%)\")\n", - "plt.title(\"Taux d'ouverture des campagnes de mails pour les compagnies de spectacle\")\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 230, - "id": "5c37e063-a717-4a8c-828e-b386b87e8409", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# création d'un barplot permettant de visualiser les 2 indicateurs sur le même graphique\n", - "\n", - "# Création du premier barplot\n", - "plt.bar(company_campaigns_stats[\"number_compagny\"], 100 * company_campaigns_stats[\"ratio_campaigns_opened\"],\n", - " label = \"taux d'ouverture\", alpha = 0.7)\n", - "\n", - "# Création du deuxième barplot à côté du premier\n", - "bar_width = 0.4 # Largeur des barres\n", - "indices2 = company_campaigns_stats[\"number_compagny\"] + bar_width\n", - "plt.bar(indices2, 100 * (1 - company_lazy_customers[\"no_campaign_opened\"]), \n", - " label='Part de clients ouvrant des mails', alpha=0.7, width=bar_width)\n", - "\n", - "# Ajout des étiquettes et de la légende\n", - "plt.xlabel('Compagnie')\n", - "plt.ylabel('Taux (%)')\n", - "plt.title('Lien entre taux d ouverture des mails et nombre de clients actifs')\n", - "plt.legend()\n", - "\n", - "# save in the s3\n", - "\n", - "FILE_NAME = \"stats_mail_opening_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "markdown", - "id": "638ab84b-15a5-4e70-b140-f121c68c82f5", - "metadata": {}, - "source": [ - "#### on refait les mêmes stats sur le train set" - ] - }, - { - "cell_type": "code", - "execution_count": 231, - "id": "4fdf4134-d32c-42c3-ab4f-36ad4783332c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internet...gender_femalegender_malegender_othercountry_frnb_campaignsnb_campaigns_openedtime_to_openy_has_purchasednumber_companyno_campaign_opened
010_4927790.00.00.00.00.0550.0550.0-1.00.0...1001.013.04.08 days 04:08:270.010False
110_5634240.00.00.00.00.0550.0550.0-1.00.0...0011.010.09.00 days 01:39:58.5555555550.010False
210_443690.00.00.00.00.0550.0550.0-1.00.0...0101.014.00.0NaN0.010True
310_6202710.00.00.00.00.0550.0550.0-1.00.0...001NaN9.00.0NaN0.010True
410_6876440.00.00.00.00.0550.0550.0-1.00.0...001NaN4.00.0NaN0.010True
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5 rows × 42 columns

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" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 10_492779 0.0 0.0 0.0 0.0 \n", - "1 10_563424 0.0 0.0 0.0 0.0 \n", - "2 10_44369 0.0 0.0 0.0 0.0 \n", - "3 10_620271 0.0 0.0 0.0 0.0 \n", - "4 10_687644 0.0 0.0 0.0 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 550.0 550.0 \n", - "1 0.0 550.0 550.0 \n", - "2 0.0 550.0 550.0 \n", - "3 0.0 550.0 550.0 \n", - "4 0.0 550.0 550.0 \n", - "\n", - " time_between_purchase nb_tickets_internet ... gender_female \\\n", - "0 -1.0 0.0 ... 1 \n", - "1 -1.0 0.0 ... 0 \n", - "2 -1.0 0.0 ... 0 \n", - "3 -1.0 0.0 ... 0 \n", - "4 -1.0 0.0 ... 0 \n", - "\n", - " gender_male gender_other country_fr nb_campaigns nb_campaigns_opened \\\n", - "0 0 0 1.0 13.0 4.0 \n", - "1 0 1 1.0 10.0 9.0 \n", - "2 1 0 1.0 14.0 0.0 \n", - "3 0 1 NaN 9.0 0.0 \n", - "4 0 1 NaN 4.0 0.0 \n", - "\n", - " time_to_open y_has_purchased number_company \\\n", - "0 8 days 04:08:27 0.0 10 \n", - "1 0 days 01:39:58.555555555 0.0 10 \n", - "2 NaN 0.0 10 \n", - "3 NaN 0.0 10 \n", - "4 NaN 0.0 10 \n", - "\n", - " no_campaign_opened \n", - "0 False \n", - "1 False \n", - "2 True \n", - "3 True \n", - "4 True \n", - "\n", - "[5 rows x 42 columns]" - ] - }, - "execution_count": 231, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# same statistics on the train set\n", - "\n", - "train_set_spectacle.head()" - ] - }, - { - "cell_type": "markdown", - "id": "924300e5-d6a9-4686-a938-f5f99afda70c", - "metadata": {}, - "source": [ - "#### Part de clients n'ouvrant aucun mail" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "id": "14ff9886-742c-4a60-8824-5d31f7c76aea", - "metadata": {}, - "outputs": [], - "source": [ - "train_set_spectacle[\"no_campaign_opened\"] = train_set_spectacle[\"nb_campaigns_opened\"]==0" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "id": "16285593-a0fa-461c-aeb8-c64ffdf9a0d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasedno_campaign_opened
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" - ], - "text/plain": [ - " number_company y_has_purchased no_campaign_opened\n", - "0 10 0.0 73.553379\n", - "1 10 1.0 35.582432\n", - "2 11 0.0 42.609537\n", - "3 11 1.0 32.887454\n", - "4 12 0.0 100.000000\n", - "5 12 1.0 100.000000\n", - "6 13 0.0 68.335897\n", - "7 13 1.0 52.833256\n", - "8 14 0.0 44.334881\n", - "9 14 1.0 28.807320" - ] - }, - "execution_count": 235, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "company_lazy_customers = train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[\"no_campaign_opened\"].mean().reset_index()\n", - "company_lazy_customers[\"no_campaign_opened\"] = 100 * company_lazy_customers[\"no_campaign_opened\"] \n", - "company_lazy_customers" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "id": "d35f00e3-b9b0-42b3-9dce-785c1ad5506c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# graphic for non opening mails customers for music companies (train set)\n", - "\n", - "multiple_barplot(company_lazy_customers, x=\"number_company\", y=\"no_campaign_opened\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"aucun achat\", 1 : \"achat durant la période\"},\n", - " xlabel = \"Compagnie\", ylabel = \"Part de clients n'ayant ouvert aucun mail (%)\", \n", - " title = \"Part de clients des compagnies de spectacle n'ouvrant aucun mail (train set)\")\n", - "\n", - "# save in the s3\n", - "\n", - "FILE_NAME = \"no_mail_opened_train_set_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "markdown", - "id": "f3407307-7cc1-4f57-a3ae-7c83773b4b81", - "metadata": {}, - "source": [ - "#### Part globale de mails ouverts pour chaque compagnie" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "id": "b391f5b2-2424-4758-8ae5-f0fdacdfae66", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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354365 rows × 42 columns

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" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 10_492779 0.0 0.0 0.0 0.0 \n", - "1 10_563424 0.0 0.0 0.0 0.0 \n", - "2 10_44369 0.0 0.0 0.0 0.0 \n", - "3 10_620271 0.0 0.0 0.0 0.0 \n", - "4 10_687644 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... ... \n", - "354360 14_4685578 0.0 0.0 0.0 0.0 \n", - "354361 14_4652175 0.0 0.0 0.0 0.0 \n", - "354362 14_4736169 2.0 2.0 50.0 1.0 \n", - "354363 14_4957203 1.0 1.0 55.0 1.0 \n", - "354364 14_4690653 0.0 0.0 0.0 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 550.000000 550.000000 \n", - "1 0.0 550.000000 550.000000 \n", - "2 0.0 550.000000 550.000000 \n", - "3 0.0 550.000000 550.000000 \n", - "4 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "354360 0.0 550.000000 550.000000 \n", - "354361 0.0 550.000000 550.000000 \n", - "354362 0.0 91.030556 91.020139 \n", - "354363 0.0 52.284028 52.284028 \n", - "354364 0.0 550.000000 550.000000 \n", - "\n", - " time_between_purchase nb_tickets_internet ... gender_female \\\n", - "0 -1.000000 0.0 ... 1 \n", - "1 -1.000000 0.0 ... 0 \n", - "2 -1.000000 0.0 ... 0 \n", - "3 -1.000000 0.0 ... 0 \n", - "4 -1.000000 0.0 ... 0 \n", - "... ... ... ... ... \n", - "354360 -1.000000 0.0 ... 0 \n", - "354361 -1.000000 0.0 ... 0 \n", - "354362 0.010417 0.0 ... 1 \n", - "354363 0.000000 0.0 ... 0 \n", - "354364 -1.000000 0.0 ... 0 \n", - "\n", - " gender_male gender_other country_fr nb_campaigns \\\n", - "0 0 0 1.0 13.0 \n", - "1 0 1 1.0 10.0 \n", - "2 1 0 1.0 14.0 \n", - "3 0 1 NaN 9.0 \n", - "4 0 1 NaN 4.0 \n", - "... ... ... ... ... \n", - "354360 0 1 NaN 7.0 \n", - "354361 1 0 1.0 11.0 \n", - "354362 0 0 1.0 6.0 \n", - "354363 1 0 1.0 3.0 \n", - "354364 1 0 NaN 7.0 \n", - "\n", - " nb_campaigns_opened time_to_open y_has_purchased \\\n", - "0 4.0 8 days 04:08:27 0.0 \n", - "1 9.0 0 days 01:39:58.555555555 0.0 \n", - "2 0.0 NaN 0.0 \n", - "3 0.0 NaN 0.0 \n", - "4 0.0 NaN 0.0 \n", - "... ... ... ... \n", - "354360 0.0 NaN 0.0 \n", - "354361 2.0 3 days 06:21:17 0.0 \n", - "354362 6.0 0 days 17:30:10.166666666 1.0 \n", - "354363 0.0 NaN 0.0 \n", - "354364 0.0 NaN 0.0 \n", - "\n", - " number_company no_campaign_opened \n", - "0 10 False \n", - "1 10 False \n", - "2 10 True \n", - "3 10 True \n", - "4 10 True \n", - "... ... ... \n", - "354360 14 True \n", - "354361 14 False \n", - "354362 14 False \n", - "354363 14 True \n", - "354364 14 True \n", - "\n", - "[354365 rows x 42 columns]" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# part de mails ouverts de chaque compagnie\n", - "\n", - "train_set_spectacle" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "dc8cfd36-0eb2-4ef3-877d-626fd0a9ced4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnynb_campaignsnb_campaigns_openedratio_campaigns_opened
010734772126151.00.171687
111342396129833.00.379190
2123168123810722.00.255900
3133218569793581.00.246563
4142427043723846.00.298242
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" - ], - "text/plain": [ - " number_compagny nb_campaigns nb_campaigns_opened ratio_campaigns_opened\n", - "0 10 734772 126151.0 0.171687\n", - "1 11 342396 129833.0 0.379190\n", - "2 12 3168123 810722.0 0.255900\n", - "3 13 3218569 793581.0 0.246563\n", - "4 14 2427043 723846.0 0.298242" - ] - }, - "execution_count": 238, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# taux d'ouverture des campaigns\n", - "\n", - "company_campaigns_stats = campaigns_information_spectacle.groupby(\"number_compagny\")[[\"nb_campaigns\", \"nb_campaigns_opened\"]].sum().reset_index()\n", - "company_campaigns_stats[\"ratio_campaigns_opened\"] = company_campaigns_stats[\"nb_campaigns_opened\"] / company_campaigns_stats[\"nb_campaigns\"]\n", - "company_campaigns_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "id": "30b28426-088a-4153-b2aa-c20f11b2b771", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasednb_campaignsnb_campaigns_openedperc_campaigns_opened
0100.0143960.018472.012.831342
1101.010609.05177.048.798190
2110.084676.027658.032.663328
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6130.01182992.0275366.023.277080
7131.0107160.041244.038.488242
8140.0822836.0219220.026.642004
9141.092099.034256.037.194758
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" - ], - "text/plain": [ - " number_company y_has_purchased nb_campaigns nb_campaigns_opened \\\n", - "0 10 0.0 143960.0 18472.0 \n", - "1 10 1.0 10609.0 5177.0 \n", - "2 11 0.0 84676.0 27658.0 \n", - "3 11 1.0 20848.0 10927.0 \n", - "4 12 0.0 0.0 0.0 \n", - "5 12 1.0 0.0 0.0 \n", - "6 13 0.0 1182992.0 275366.0 \n", - "7 13 1.0 107160.0 41244.0 \n", - "8 14 0.0 822836.0 219220.0 \n", - "9 14 1.0 92099.0 34256.0 \n", - "\n", - " perc_campaigns_opened \n", - "0 12.831342 \n", - "1 48.798190 \n", - "2 32.663328 \n", - "3 52.412701 \n", - "4 NaN \n", - "5 NaN \n", - "6 23.277080 \n", - "7 38.488242 \n", - "8 26.642004 \n", - "9 37.194758 " - ] - }, - "execution_count": 239, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "company_campaigns_stats = train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[[\"nb_campaigns\", \"nb_campaigns_opened\"]].sum().reset_index()\n", - "company_campaigns_stats[\"perc_campaigns_opened\"] = 100* (company_campaigns_stats[\"nb_campaigns_opened\"] / company_campaigns_stats[\"nb_campaigns\"])\n", - "company_campaigns_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "id": "9cebe912-fce1-4f4f-9d87-9649605296c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasednb_campaignsnb_campaigns_openedperc_campaigns_opened
0100.0143960.018472.012.831342
1101.010609.05177.048.798190
2110.084676.027658.032.663328
3111.020848.010927.052.412701
6130.01182992.0275366.023.277080
7131.0107160.041244.038.488242
8140.0822836.0219220.026.642004
9141.092099.034256.037.194758
\n", - "
" - ], - "text/plain": [ - " number_company y_has_purchased nb_campaigns nb_campaigns_opened \\\n", - "0 10 0.0 143960.0 18472.0 \n", - "1 10 1.0 10609.0 5177.0 \n", - "2 11 0.0 84676.0 27658.0 \n", - "3 11 1.0 20848.0 10927.0 \n", - "6 13 0.0 1182992.0 275366.0 \n", - "7 13 1.0 107160.0 41244.0 \n", - "8 14 0.0 822836.0 219220.0 \n", - "9 14 1.0 92099.0 34256.0 \n", - "\n", - " perc_campaigns_opened \n", - "0 12.831342 \n", - "1 48.798190 \n", - "2 32.663328 \n", - "3 52.412701 \n", - "6 23.277080 \n", - "7 38.488242 \n", - "8 26.642004 \n", - "9 37.194758 " - ] - }, - "execution_count": 240, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "company_campaigns_stats = company_campaigns_stats[company_campaigns_stats[\"number_company\"]!=12]\n", - "company_campaigns_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "id": "1c32cd86-e08d-4b8a-90f1-27ad0df0ffeb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# graphic - overall rate of opened mails (train set for music companies)\n", - "\n", - "FILE_NAME = \"overall_mail_opening_train_set_music.png\"\n", - "FILE_PATH_OUT_S3 = FILE_PATH + FILE_NAME\n", - "\n", - "multiple_barplot(company_campaigns_stats, x=\"number_company\", y=\"perc_campaigns_opened\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"clients n'ayant pas acheté\", 1 : \"clients ayant acheté sur la période\"},\n", - " xlabel = \"Compagnie\", ylabel = \"Part de mails ouverts (%)\", \n", - " title = \"Taux d'ouverture global des mails envoyés par les compagnies de spectacle (train set)\")\n", - "\n", - "# save in the s3\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as file_out:\n", - " plt.savefig(file_out)" - ] - }, - { - "cell_type": "markdown", - "id": "783f6fb2-5f26-42a9-a22d-f4ece44bfaf2", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "### 3. products_purchased_reduced" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "74534ded-8121-43fb-8cf8-af353bed2c77", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre de lignes de la table : 764880\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "number_compagny 0\n", - "dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# nombre de nan\n", - "print(\"Nombre de lignes de la table : \",products_purchased_reduced_spectacle.shape[0])\n", - "products_purchased_reduced_spectacle.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6db089d5-5517-4aee-a5fd-53f20ae3f0d7", - "metadata": {}, - "outputs": [], - "source": [ - "#importation librairies\n", - "import warnings\n", - "warnings.simplefilter(\"ignore\")\n", - "import pandas as pd\n", - "import numpy as np\n", - "import statsmodels\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from scipy.stats import shapiro\n", - "from numpy.random import randn\n", - "import scipy.stats as st\n", - "%matplotlib inline\n", - "\n", - "#col_purchase=[\"nb_tickets\",\"nb_purchases\",\"total_amount\",\"nb_suppliers\",\"time_between_purchase\",\"nb_tickets_internet\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "943b8088-9ca2-40a4-b658-2cfae1589fac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "30.0\n", - "62.0\n", - "120.0\n", - "90.0\n", - "Moustache inferieure -105.0\n", - "Moustache superieure 255.0\n" - ] - } - ], - "source": [ - "#identification des valeur manquantes\n", - "#calcule des quartile de la variable valeur(taille de la population)\n", - "Q1=np.percentile(products_purchased_reduced_spectacle[\"total_amount\"], 25) # Q1\n", - "Q2=np.percentile(products_purchased_reduced_spectacle[\"total_amount\"], 50) # Q2\n", - "Q3=np.percentile(products_purchased_reduced_spectacle[\"total_amount\"], 75) # Q3\n", - "print(Q1)\n", - "print(Q2)\n", - "print(Q3)\n", - "\n", - "#intervale interquartile de la variable Valeur\n", - "\n", - "IQ=Q3-Q1\n", - "print(IQ)\n", - "\n", - "#la valeur minimale des moustache de la variable Valeur\n", - "\n", - "M_inf=Q1-1.5*IQ\n", - "M_sup=Q3+1.5*IQ\n", - "\n", - "print(\"Moustache inferieure\",M_inf)#moustache inferieur\n", - "print(\"Moustache superieure\",M_sup)#moustache sup\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "c3adb0cd-8292-4c6f-9d4e-8352a6967022", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id int64\n", - "nb_tickets int64\n", - "nb_purchases int64\n", - "total_amount float64\n", - "nb_suppliers int64\n", - "vente_internet_max int64\n", - "purchase_date_min float64\n", - "purchase_date_max float64\n", - "time_between_purchase float64\n", - "nb_tickets_internet float64\n", - "number_compagny int64\n", - "dtype: object" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "products_purchased_reduced_spectacle.dtypes" - ] - }, - { - "cell_type": "markdown", - "id": "a63e6d13-429b-4b01-ad11-27e5eea68cbd", - "metadata": {}, - "source": [ - "#histogrames des variable quantitatives\n", - "col_purchase=[\"nb_tickets\",\"nb_purchases\",\"total_amount\",\"nb_suppliers\",\"time_between_purchase\",\"nb_tickets_internet\"]\n", - "for col in col_purchase:\n", - " plt.figure()\n", - " sns.histplot(products_purchased_reduced_spectacle[col], kde=True, color='red')" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "5a08b5a5-7d56-4543-945a-38f6219d831d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Filtrer les données pour inclure uniquement les valeurs positives de total_amount et exclusion des valeur aberrantes\n", - "filtered_products_purchased_reduced_spectacle = products_purchased_reduced_spectacle[(products_purchased_reduced_spectacle['total_amount'] > 0) & (products_purchased_reduced_spectacle['total_amount'] <= 255)]\n", - "\n", - "# Créer le graphique en utilisant les données filtrées\n", - "sns.boxplot(data=filtered_data, y=\"total_amount\", x=\"number_compagny\", showfliers=False, showmeans=True)\n", - "\n", - "# Titre du graphique\n", - "plt.title(\"Boite à moustache du chiffre d'affaire selon les compagnies de spectacles\")\n", - "\n", - "# Afficher le graphique\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "76e08ece-0b58-4b3a-abca-53e30ccc907b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistique F : 317.1792172580724\n", - "Valeur de p : 3.665389608154993e-273\n", - "Nombre de degrés de liberté entre les groupes : 4\n", - "Nombre de degrés de liberté à l'intérieur des groupes : 670581\n", - "Il y a des différences significatives entre au moins une des entrepries .\n" - ] - } - ], - "source": [ - "#test d'anova pour voir si la difference de chiffre d'affaire est statistiquement significative\n", - "\n", - "from scipy.stats import f_oneway\n", - "\n", - "# Créez une liste pour stocker les données de chaque groupe\n", - "groupes = []\n", - "\n", - "# Parcourez chaque modalité de la variable catégorielle et divisez les données en groupes\n", - "for modalite in filtered_products_purchased_reduced_spectacle['number_compagny'].unique():\n", - " groupe = filtered_products_purchased_reduced_spectacle[filtered_products_purchased_reduced_spectacle['number_compagny'] == modalite]['total_amount']\n", - " groupes.append(groupe)\n", - "\n", - "# Effectuez le test ANOVA\n", - "f_statistic, p_value = f_oneway(*groupes)\n", - "\n", - "# Nombre total d'observations\n", - "N = sum(len(groupe) for groupe in groupes)\n", - "\n", - "# Nombre de groupes ou de catégories\n", - "k = len(groupes)\n", - "\n", - "# Degrés de liberté entre les groupes\n", - "df_between = k - 1\n", - "\n", - "# Degrés de liberté à l'intérieur des groupes\n", - "df_within = N - k\n", - "\n", - "# Affichez les résultats\n", - "print(\"Statistique F :\", f_statistic)\n", - "print(\"Valeur de p :\", p_value)\n", - "\n", - "print(\"Nombre de degrés de liberté entre les groupes :\", df_between)\n", - "print(\"Nombre de degrés de liberté à l'intérieur des groupes :\", df_within)\n", - "\n", - "if p_value < 0.05:\n", - " print(\"Il y a des différences significatives entre au moins une des entrepries .\")\n", - "else:\n", - " print(\"Il n'y a pas de différences significatives entre les entreprises .\")" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "9ec6e1c5-f3bc-4041-b32e-b62762246eb7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#repartition Chiffre d'affaire selon y_has_purchased\n", - "\n", - "# Filtrer les données pour inclure uniquement les valeurs positives de total_amount et exclusion des valeur aberrantes\n", - "train_set_spectacle_filtered = train_set_spectacle[(train_set_spectacle['total_amount'] > 0) & (train_set_spectacle['total_amount'] <= 255)]\n", - "\n", - "# Créer le graphique en utilisant les données filtrées\n", - "sns.boxplot(data=train_set_spectacle_filtered, y=\"total_amount\", x=\"y_has_purchased\", showfliers=False, showmeans=True)\n", - "\n", - "# Titre du graphique\n", - "plt.title(\"Boite à moustache du chiffre d'affaire selon le statut d'achat du client\")\n", - "\n", - "# Afficher le graphique\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b55de4b-913e-4bc1-b4f2-cc0b1824d0e2", - "metadata": {}, - "outputs": [], - "source": [ - "#graphe sur le taux de ticket acheté" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "aacf2c34-f7ea-4d6e-935b-c5db01f03bbe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_compagnynb_ticketsnb_tickets_internetTaux_ticket_internet
010492314126262.025.646640
11131896916348.05.125263
21259102842045.07.113876
31370242271247482.017.759705
414335741125638.037.421107
\n", - "
" - ], - "text/plain": [ - " number_compagny nb_tickets nb_tickets_internet Taux_ticket_internet\n", - "0 10 492314 126262.0 25.646640\n", - "1 11 318969 16348.0 5.125263\n", - "2 12 591028 42045.0 7.113876\n", - "3 13 7024227 1247482.0 17.759705\n", - "4 14 335741 125638.0 37.421107" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Taux de ticket payé par internet selon les compagnies\n", - "\n", - "purchase_spectacle = products_purchased_reduced_spectacle.groupby(\"number_compagny\")[[\"nb_tickets\", \"nb_tickets_internet\"]].sum().reset_index()\n", - "purchase_spectacle[\"Taux_ticket_internet\"] = purchase_spectacle[\"nb_tickets_internet\"]*100 / purchase_spectacle[\"nb_tickets\"]\n", - "purchase_spectacle" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "f71bb53d-724b-454d-8743-305d20eec2b0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Création du barplot\n", - "plt.bar(purchase_spectacle[\"number_compagny\"], purchase_spectacle[\"Taux_ticket_internet\"])\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Company')\n", - "plt.ylabel(\"Taux d'achat de tickets en ligne (%)\")\n", - "plt.title(\"Taux d'achat des tickets en ligne selon les compagnies de spectacle\")\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "id": "86fa4d7f-9b5f-4487-beb8-eb23771f724c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasednb_ticketsnb_tickets_internetTaux_ticket_internet
0100.09957.05450.054.735362
1101.07941.03424.043.117995
2110.010361.05.00.048258
3111.09638.00.00.000000
4120.035600.00.00.000000
5121.011520.00.00.000000
6130.0131759.0105406.079.999089
7131.01004076.013902.01.384557
8140.044596.00.00.000000
9141.016694.00.00.000000
\n", - "
" - ], - "text/plain": [ - " number_company y_has_purchased nb_tickets nb_tickets_internet \\\n", - "0 10 0.0 9957.0 5450.0 \n", - "1 10 1.0 7941.0 3424.0 \n", - "2 11 0.0 10361.0 5.0 \n", - "3 11 1.0 9638.0 0.0 \n", - "4 12 0.0 35600.0 0.0 \n", - "5 12 1.0 11520.0 0.0 \n", - "6 13 0.0 131759.0 105406.0 \n", - "7 13 1.0 1004076.0 13902.0 \n", - "8 14 0.0 44596.0 0.0 \n", - "9 14 1.0 16694.0 0.0 \n", - "\n", - " Taux_ticket_internet \n", - "0 54.735362 \n", - "1 43.117995 \n", - "2 0.048258 \n", - "3 0.000000 \n", - "4 0.000000 \n", - "5 0.000000 \n", - "6 79.999089 \n", - "7 1.384557 \n", - "8 0.000000 \n", - "9 0.000000 " - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Taux de ticket payé en ligne selon y_has_purchase par compagnies avec la base de train\n", - "\n", - "purchase_spectacle_train = train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[[\"nb_tickets\", \"nb_tickets_internet\"]].sum().reset_index()\n", - "purchase_spectacle_train[\"Taux_ticket_internet\"] = purchase_spectacle_train[\"nb_tickets_internet\"]*100 / purchase_spectacle_train[\"nb_tickets\"]\n", - "purchase_spectacle_train" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "d11335b7-e35a-44c7-8ce4-661216978151", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multiple_barplot(purchase_spectacle_train, x=\"number_company\", y=\"Taux_ticket_internet\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"clients n'ayant pas acheté\", 1 : \"clients ayant acheté sur la période\"},\n", - " xlabel = \"Numéro de compagnie\", ylabel = \"Taux de ticket acheté par internet (%)\", \n", - " title = \"Taux de ticket achété en ligne selon y_has_purchased par compagnies de spectacle (train set)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "f8444cab-d4c5-4afd-b472-476e702c09cc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", - "\n", - "# Créer le graphique à barres\n", - "sns.barplot(data=purchase_spectacle_train, x=\"y_has_purchased\", y=\"Taux_ticket_internet\",ci=None)\n", - "\n", - "\n", - "# Titre du graphique\n", - "plt.title(\"Taux moyen de tickets achetés selon le statut d'achat du client\")\n", - "\n", - "# Ajouter une étiquette à l'axe des abscisses\n", - "plt.xlabel(\"Statut d'achat du client\")\n", - "\n", - "# Ajouter une étiquette à l'axe des ordonnées\n", - "plt.ylabel(\"Taux de tickets internet\")\n", - "\n", - "# Afficher le graphique\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "9ba02de7-3087-4b0c-884a-dc4a6ca92c3b", - "metadata": {}, - "outputs": [], - "source": [ - "#stat sur la variable temps ecoulé entre le premier et le dernier achat" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "59a95248-0261-4970-9e91-e43d50cf4d69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Boite à moustache du temps ecoulés entre le premier et le dernier achat selon les compagnies de spectacles')" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#repartition des client selon le temps ecoulés entre le premier et le denier achat par compagnie\n", - "\n", - "sns.boxplot(data=products_purchased_reduced_spectacle, y=\"time_between_purchase\",x=\"number_compagny\",showfliers=False,showmeans=True)\n", - "plt.title(\"Boite à moustache du temps ecoulés entre le premier et le dernier achat selon les compagnies de spectacles\")" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "e2c51e28-6197-48f0-ab6d-9fc7b3b0de74", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistique F : 7956.05932109542\n", - "Valeur de p : 0.0\n", - "Nombre de degrés de liberté entre les groupes : 4\n", - "Nombre de degrés de liberté à l'intérieur des groupes : 764875\n", - "Il y a des différences significatives entre au moins une des entrepries .\n" - ] - } - ], - "source": [ - "#test d'anova pour voir si la difference de temps entre le premier et le dernier achat est statistiquement significative\n", - "\n", - "from scipy.stats import f_oneway\n", - "\n", - "# Créez une liste pour stocker les données de chaque groupe\n", - "groupes = []\n", - "\n", - "# Parcourez chaque modalité de la variable catégorielle et divisez les données en groupes\n", - "for modalite in products_purchased_reduced_spectacle['number_compagny'].unique():\n", - " groupe = products_purchased_reduced_spectacle[products_purchased_reduced_spectacle['number_compagny'] == modalite]['time_between_purchase']\n", - " groupes.append(groupe)\n", - "\n", - "# Effectuez le test ANOVA\n", - "f_statistic, p_value = f_oneway(*groupes)\n", - "\n", - "# Nombre total d'observations\n", - "N = sum(len(groupe) for groupe in groupes)\n", - "\n", - "# Nombre de groupes ou de catégories\n", - "k = len(groupes)\n", - "\n", - "# Degrés de liberté entre les groupes\n", - "df_between = k - 1\n", - "\n", - "# Degrés de liberté à l'intérieur des groupes\n", - "df_within = N - k\n", - "\n", - "# Affichez les résultats\n", - "print(\"Statistique F :\", f_statistic)\n", - "print(\"Valeur de p :\", p_value)\n", - "\n", - "print(\"Nombre de degrés de liberté entre les groupes :\", df_between)\n", - "print(\"Nombre de degrés de liberté à l'intérieur des groupes :\", df_within)\n", - "\n", - "if p_value < 0.05:\n", - " print(\"Il y a des différences significatives entre au moins une des entrepries .\")\n", - "else:\n", - " print(\"Il n'y a pas de différences significatives entre les entreprises .\")" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "75a003ab-f42a-4b2d-a0a8-284e673e71f7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasedtime_between_purchase
0100.045.791114
1101.0193.080793
2110.027.640469
3111.0129.853892
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5121.058.548598
6130.010.012525
7131.093.545373
8140.03.879196
9141.010.745213
\n", - "
" - ], - "text/plain": [ - " number_company y_has_purchased time_between_purchase\n", - "0 10 0.0 45.791114\n", - "1 10 1.0 193.080793\n", - "2 11 0.0 27.640469\n", - "3 11 1.0 129.853892\n", - "4 12 0.0 16.418446\n", - "5 12 1.0 58.548598\n", - "6 13 0.0 10.012525\n", - "7 13 1.0 93.545373\n", - "8 14 0.0 3.879196\n", - "9 14 1.0 10.745213" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#repartition des client selon le temps ecoulés entre le premier et le denier achat par compagnie\n", - "purchase_train_time= train_set_spectacle.groupby([\"number_company\", \"y_has_purchased\"])[\"time_between_purchase\"].mean().reset_index()\n", - "purchase_train_time" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "f27921a9-1253-4c02-9bff-8cd3c4a9a5d9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multiple_barplot(purchase_train_time, x=\"number_company\", y=\"time_between_purchase\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"clients n'ayant pas acheté\", 1 : \"clients ayant acheté sur la période\"},\n", - " xlabel = \"Numéro de compagnie\", ylabel = \"Taux de ticket acheté par internet (%)\", \n", - " title = \"temps moyen entre le premier et le dernier achat selon y_has_purchased par compagnies de spectacle (train set)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "74f06e96-3c25-4eca-8190-25b0a4ab0d75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id int64\n", - "nb_tickets int64\n", - "nb_purchases int64\n", - "total_amount float64\n", - "nb_suppliers int64\n", - "vente_internet_max int64\n", - "purchase_date_min float64\n", - "purchase_date_max float64\n", - "time_between_purchase float64\n", - "nb_tickets_internet float64\n", - "number_compagny int64\n", - "dtype: object" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "products_purchased_reduced_spectacle.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "aa6655c0-c602-4485-8b38-3117227464e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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764880 rows × 11 columns

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" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 19482 88 29 872.0 2 \n", - "1 19484 3 2 62.0 1 \n", - "2 19485 131 21 1878.0 2 \n", - "3 19486 10 4 96.0 1 \n", - "4 19487 2 1 33.0 1 \n", - "... ... ... ... ... ... \n", - "99580 6884747 2 1 40.0 1 \n", - "99581 6884748 2 1 40.0 1 \n", - "99582 6884750 4 1 80.0 1 \n", - "99583 6884751 2 1 40.0 1 \n", - "99584 6884753 2 1 40.0 1 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 1 2643.092500 718.149398 \n", - "1 0 1745.021736 1743.045035 \n", - "2 1 2649.044745 85.240845 \n", - "3 0 1944.077604 1742.794225 \n", - "4 0 1742.877766 1742.877766 \n", - "... ... ... ... \n", - "99580 0 0.193750 0.193750 \n", - "99581 0 0.186806 0.186806 \n", - "99582 0 0.136111 0.136111 \n", - "99583 0 0.122917 0.122917 \n", - "99584 0 0.047222 0.047222 \n", - "\n", - " time_between_purchase nb_tickets_internet number_compagny \n", - "0 1924.943102 8.0 10 \n", - "1 1.976701 0.0 10 \n", - "2 2563.803900 84.0 10 \n", - "3 201.283380 0.0 10 \n", - "4 0.000000 0.0 10 \n", - "... ... ... ... \n", - "99580 0.000000 0.0 14 \n", - "99581 0.000000 0.0 14 \n", - "99582 0.000000 0.0 14 \n", - "99583 0.000000 0.0 14 \n", - "99584 0.000000 0.0 14 \n", - "\n", - "[764880 rows x 11 columns]" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "products_purchased_reduced_spectacle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be04e2f9-60b9-4b44-ab36-06a365b21e32", - "metadata": {}, - "outputs": [], - "source": [ - "#Stat sur les canaux de vente" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "20a70ec0-38f6-470e-a442-7884a150613a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Repartition du nombre de canaux de vente selon les entreprise\n", - "\n", - "# Filtrer les données pour inclure uniquement les valeurs positives de total_amount et exclusion des valeur aberrantes\n", - "purchase_canaux = products_purchased_reduced_spectacle[(products_purchased_reduced_spectacle['nb_tickets'] > 0) ]\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "sns.barplot(x='number_compagny', y='nb_suppliers', data=purchase_canaux, ci=None) # ci=None pour ne pas afficher les intervalles de confiance\n", - "plt.title('Nombre moyen de canaux de vente par entreprise')\n", - "plt.xlabel('number_compagny')\n", - "plt.ylabel('Nombre moyen de caneaux ')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "ee901539-37d1-4dfa-8e78-38e4947c3d35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 697297.000000\n", - "mean 0.110917\n", - "std 0.319561\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 8.000000\n", - "Name: nb_suppliers, dtype: float64" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_set_spectacle[\"nb_suppliers\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "7389053e-54ae-4167-9afd-aa5d194822ef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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number_companyy_has_purchasednb_suppliers
0100.01.118250
1101.01.340136
2110.01.033992
3111.01.155239
4120.00.153296
5121.00.220174
6130.01.007711
7131.01.083750
8140.01.000000
9141.01.000000
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" - ], - "text/plain": [ - " number_company y_has_purchased nb_suppliers\n", - "0 10 0.0 1.118250\n", - "1 10 1.0 1.340136\n", - "2 11 0.0 1.033992\n", - "3 11 1.0 1.155239\n", - "4 12 0.0 0.153296\n", - "5 12 1.0 0.220174\n", - "6 13 0.0 1.007711\n", - "7 13 1.0 1.083750\n", - "8 14 0.0 1.000000\n", - "9 14 1.0 1.000000" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#repartition des client selon le nombre moyen de canaux utilisé pour l'achat de ticket par compagnie sur base de train\n", - "\n", - "#purchase_train_canaux = train_set_spectacle[(train_set_spectacle['nb_tickets'] > 0) ]\n", - "\n", - "purchase_train_canaux_filtered= purchase_train_canaux.groupby([\"number_company\", \"y_has_purchased\"])[\"nb_suppliers\"].mean().reset_index()\n", - "purchase_train_canaux_filtered" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "e4079e46-db8b-4a25-9da6-37b1405c57d9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multiple_barplot(purchase_train_canaux_filtered, x=\"number_company\", y=\"nb_suppliers\", var_labels=\"y_has_purchased\",\n", - " dico_labels = {0 : \"clients n'ayant pas acheté\", 1 : \"clients ayant acheté sur la période\"},\n", - " xlabel = \"Numéro de compagnie\", ylabel = \"Nombre moyen de canaux d'achat\", \n", - " title = \"Nombre moyen de canaux d'acht selon y_has_purchased par compagnies de spectacle (train set)\")" - ] - }, - { - "cell_type": "markdown", - "id": "b9e84af4-a02b-4f83-81ae-b7a73475d060", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "### 4. target_information" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2867eceb-1f72-406c-adc2-adfedcaf60e6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre de lignes de la table : 6240166\n" - ] - }, - { - "data": { - "text/plain": [ - "id 0\n", - "customer_id 0\n", - "target_name 0\n", - "target_type_is_import 0\n", - "target_type_name 0\n", - "number_compagny 0\n", - "dtype: int64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# nombre de nan\n", - "print(\"Nombre de lignes de la table : \",target_information_spectacle.shape[0])\n", - "target_information_spectacle.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "561f361d-7d39-430a-9e27-a32f6c2f7b50", - "metadata": {}, - "outputs": [], - "source": [ - "# pas exploitable" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "904cbf32-77b6-49dd-a96c-9e7e5a0175c3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/Descriptive_statistics/stat_desc_sport.ipynb b/Sport/Descriptive_statistics/stat_desc_sport.ipynb deleted file mode 100644 index e2b0c7e..0000000 --- a/Sport/Descriptive_statistics/stat_desc_sport.ipynb +++ /dev/null @@ -1,1608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 50, - "id": "dd143b00-1989-44cf-8558-a30087d17f70", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os\n", - "import s3fs\n", - "import warnings\n", - "from datetime import date, timedelta, datetime\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "08c63120-1b56-4145-9014-18a637b22876", - "metadata": {}, - "outputs": [], - "source": [ - "exec(open('../../0_KPI_functions.py').read())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f8bd679d-fa76-49d4-9ec1-9f15516f16d3", - "metadata": {}, - "outputs": [], - "source": [ - "# Ignore warning\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "ec9e996d-3eae-4836-8cf5-268e5dc0d672", - "metadata": {}, - "source": [ - "# Statistiques descriptives : compagnies sport" - ] - }, - { - "cell_type": "markdown", - "id": "43f81515-fbd0-49c0-b3f8-0e0fb663e2c1", - "metadata": {}, - "source": [ - "## Importations et chargement des données" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "945c59bb-05b4-4f21-82f0-0db40d7957b3", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "41a67995-0a08-45c0-bbad-6e6cee5474c8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_5/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/products_purchased_reduced.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/products_purchased_reduced.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_7/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_7/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_7/products_purchased_reduced.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_7/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_8/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_8/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_8/products_purchased_reduced.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_8/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_9/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_9/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_9/products_purchased_reduced.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_9/target_information.csv\n" - ] - } - ], - "source": [ - "# création des bases contenant les KPI pour les 5 compagnies de spectacle\n", - "\n", - "# liste des compagnies de spectacle\n", - "nb_compagnie=['5','6','7','8','9']\n", - "\n", - "customer_sport = pd.DataFrame()\n", - "campaigns_sport_brut = pd.DataFrame()\n", - "campaigns_sport_kpi = pd.DataFrame()\n", - "products_sport = pd.DataFrame()\n", - "tickets_sport = pd.DataFrame()\n", - "\n", - "# début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle\n", - "for directory_path in nb_compagnie:\n", - " df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - " df_campaigns_brut = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", - " df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n", - " df_target_information = display_databases(directory_path, file_name = \"target_information\")\n", - " \n", - " df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n", - " df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n", - " df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)\n", - "\n", - " \n", - "# creation de la colonne Number compagnie, qui permettra d'agréger les résultats\n", - " df_tickets_kpi[\"number_company\"]=int(directory_path)\n", - " df_campaigns_brut[\"number_company\"]=int(directory_path)\n", - " df_campaigns_kpi[\"number_company\"]=int(directory_path)\n", - " df_customerplus_clean[\"number_company\"]=int(directory_path)\n", - " df_target_information[\"number_company\"]=int(directory_path)\n", - "\n", - "# Traitement des index\n", - " df_tickets_kpi[\"customer_id\"]= directory_path + '_' + df_tickets_kpi['customer_id'].astype('str')\n", - " df_campaigns_brut[\"customer_id\"]= directory_path + '_' + df_campaigns_brut['customer_id'].astype('str')\n", - " df_campaigns_kpi[\"customer_id\"]= directory_path + '_' + df_campaigns_kpi['customer_id'].astype('str') \n", - " df_customerplus_clean[\"customer_id\"]= directory_path + '_' + df_customerplus_clean['customer_id'].astype('str') \n", - " df_products_purchased_reduced[\"customer_id\"]= directory_path + '_' + df_products_purchased_reduced['customer_id'].astype('str') \n", - "\n", - "# Concaténation\n", - " customer_sport = pd.concat([customer_sport, df_customerplus_clean], ignore_index=True)\n", - " campaigns_sport_kpi = pd.concat([campaigns_sport_kpi, df_campaigns_kpi], ignore_index=True)\n", - " campaigns_sport_brut = pd.concat([campaigns_sport_brut, df_campaigns_brut], ignore_index=True) \n", - " tickets_sport = pd.concat([tickets_sport, df_tickets_kpi], ignore_index=True)\n", - " products_sport = pd.concat([products_sport, df_products_purchased_reduced], ignore_index=True)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "866a137c-7385-4f12-9349-b0202c71dff3", - "metadata": {}, - "outputs": [], - "source": [ - "# Construct dataset concerning only customer after start date\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "62922029-8071-402e-8115-c145a2874a2f", - "metadata": {}, - "source": [ - "## Statistiques descriptives" - ] - }, - { - "cell_type": "markdown", - "id": "d347bca9-3041-4414-b18e-19b626998a3e", - "metadata": {}, - "source": [ - "### 0. Détection du client anonyme (outlier) - utile pour la section 3" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c4d4b2ad-8a3c-477b-bc52-dd4860527bfe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([5, 6, 7, 8, 9])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sport_comp = tickets_sport['number_company'].unique()\n", - "sport_comp" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "97a9e235-1c04-46bf-9f3c-5496e141cc40", - "metadata": {}, - "outputs": [], - "source": [ - "def outlier_detection(company_list, show_diagram=False):\n", - "\n", - " outlier_list = list()\n", - " \n", - " for company in company_list:\n", - " total_amount_share = tickets_sport[tickets_sport['number_company']==company].groupby('customer_id')['total_amount'].sum().reset_index()\n", - " total_amount_share['CA'] = total_amount_share['total_amount'].sum()\n", - " total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['CA']\n", - " \n", - " total_amount_share_index = total_amount_share.set_index('customer_id')\n", - " df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False)\n", - " top = df_circulaire[:1]\n", - " outlier_list.append(top.index[0])\n", - " rest = df_circulaire[1:]\n", - " \n", - " # Calculez la somme du reste\n", - " rest_sum = rest.sum()\n", - " \n", - " # Créez une nouvelle série avec les cinq plus grandes parts et 'Autre'\n", - " new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])])\n", - " \n", - " # Créez le graphique circulaire\n", - " if show_diagram:\n", - " plt.figure(figsize=(3, 3))\n", - " plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5)\n", - " plt.axis('equal') # Assurez-vous que le graphique est un cercle\n", - " plt.title(f'Répartition des montants totaux pour la compagnie {company}')\n", - " plt.show()\n", - " return outlier_list\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "770cd3fc-bfe2-4a69-89bc-0eb946311130", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['5_191835', '6_591412', '7_49632', '8_1942', '9_19683']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "outlier_list = outlier_detection(sport_comp)\n", - "outlier_list" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "70b6e961-c303-465e-93f4-609721d38454", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Suppression Réussie\n" - ] - } - ], - "source": [ - "# On filtre les outliers\n", - "\n", - "def remove_elements(lst, elements_to_remove):\n", - " return ''.join([x for x in lst if x not in elements_to_remove])\n", - " \n", - "databases = [customer_sport, campaigns_sport, tickets_sport, products_sport]\n", - "\n", - "for dataset in databases:\n", - " dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))\n", - "\n", - "# On test\n", - "\n", - "bool = '5_191835' in customer_sport['customer_id']\n", - "if not bool:\n", - " print(\"Suppression Réussie\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b54b920a-7b46-490f-ba7e-d1859055a4e3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...total_pricepurchase_countfirst_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frnumber_company
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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity tenant_id \\\n", - "0 5_6009745 1372685 NaN NaN 0 1771 \n", - "1 5_6011228 1372685 NaN NaN 0 1771 \n", - "2 5_6058950 1372685 NaN NaN 0 1771 \n", - "3 5_6062404 1372685 NaN NaN 0 1771 \n", - "4 5_250217 78785 NaN 11035.0 0 1771 \n", - "\n", - " is_partner deleted_at gender is_email_true ... total_price \\\n", - "0 False NaN 2 True ... 0.0 \n", - "1 False NaN 2 True ... 0.0 \n", - "2 False NaN 2 True ... 0.0 \n", - "3 False NaN 2 True ... 0.0 \n", - "4 False NaN 0 True ... NaN \n", - "\n", - " purchase_count first_buying_date country gender_label gender_female \\\n", - "0 0 NaN af other 0 \n", - "1 0 NaN af other 0 \n", - "2 0 NaN af other 0 \n", - "3 0 NaN af other 0 \n", - "4 0 NaN fr female 1 \n", - "\n", - " gender_male gender_other country_fr number_company \n", - "0 0 1 0.0 5 \n", - "1 0 1 0.0 5 \n", - "2 0 1 0.0 5 \n", - "3 0 1 0.0 5 \n", - "4 0 0 1.0 5 \n", - "\n", - "[5 rows x 28 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customer_sport.head()" - ] - }, - { - "cell_type": "markdown", - "id": "d40fe668-e1d7-4544-9db8-02498afe65fe", - "metadata": {}, - "source": [ - "### 1. customerplus_clean" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "eec1ac0b-2502-452b-97e6-69ffb77156d6", - "metadata": {}, - "outputs": [], - "source": [ - "def compute_nb_clients(customer_sport):\n", - " company_nb_clients = customer_sport[customer_sport[\"purchase_count\"]>0].groupby(\"number_company\")[\"customer_id\"].count().reset_index()\n", - " plt.bar(company_nb_clients[\"number_company\"], company_nb_clients[\"customer_id\"]/1000)\n", - "\n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Company')\n", - " plt.ylabel(\"Nombre de clients (milliers)\")\n", - " plt.title(\"Nombre de clients de chaque compagnie de sport\")\n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "db4494e7-6f65-4f7e-bf8c-8ec321d0b02d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "compute_nb_clients(customer_sport)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a12a59a0-edfe-4e52-8037-9b875f823b33", - "metadata": {}, - "outputs": [], - "source": [ - "def maximum_price_paid(customer_sport):\n", - " company_max_price = customer_sport.groupby(\"number_company\")[\"max_price\"].max().reset_index()\n", - " # Création du barplot\n", - " plt.bar(company_max_price[\"number_company\"], company_max_price[\"max_price\"])\n", - " \n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Company')\n", - " plt.ylabel(\"Prix maximal d'un billet vendu\")\n", - " plt.title(\"Prix maximal de vente observé par compagnie de sport\")\n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2c7c2d26-4e35-4163-b771-fa4d3e8ca83e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "maximum_price_paid(customer_sport)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90e050a5-2561-49d9-8ad8-877bdb368ed1", - "metadata": {}, - "outputs": [], - "source": [ - "#def sale_canal(customer_sport)\n", - " # avg_supp_event = customer_sport['nb_suppliers'].mean()\n", - " # avg_supp_event.plot(kind='bar')\n", - " # plt.xlabel(\"Type d'évènement\")\n", - " #plt.ylabel('Nombre de Canaux de Ventes Moyen')\n", - " #plt.title(\"Nombre de Canaux de Ventes Moyen utilisé par les Consommateurs par type d'évènement\")\n", - " #plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa4974b5-637e-43e6-86c4-ee7a3adb89d0", - "metadata": {}, - "outputs": [], - "source": [ - "# Nombre Total de tickets achetés sur Internet par compagnie" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "597d4361-8beb-43f4-9224-8f7dc34b187c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistiques Descriptives company 5\n", - " average_price average_price_basket average_ticket_basket \\\n", - "count 145390.000000 68869.000000 68869.000000 \n", - "mean 11.070309 65.969693 3.655202 \n", - "std 16.353610 195.462869 13.119612 \n", - "min 0.000000 0.000000 1.000000 \n", - "25% 0.000000 20.000000 1.000000 \n", - "50% 0.000000 45.000000 2.000000 \n", - "75% 20.000000 79.500000 3.000000 \n", - "max 500.000000 24159.405000 2139.833333 \n", - "\n", - " purchase_count total_price \n", - "count 471598.00000 3.950770e+05 \n", - "mean 0.29900 2.608544e+01 \n", - "std 7.22753 2.089636e+03 \n", - "min 0.00000 0.000000e+00 \n", - "25% 0.00000 0.000000e+00 \n", - "50% 0.00000 0.000000e+00 \n", - "75% 0.00000 0.000000e+00 \n", - "max 3532.00000 1.262516e+06 \n", - "Statistiques Descriptives company 6\n", - " average_price average_price_basket average_ticket_basket \\\n", - "count 33779.000000 33779.000000 33779.000000 \n", - "mean 24.033859 56.711279 2.413530 \n", - "std 21.217031 72.841926 3.763809 \n", - "min -52.740000 -1046.666667 1.000000 \n", - "25% 10.000000 19.000000 1.080000 \n", - "50% 19.333333 39.000000 2.000000 \n", - "75% 30.000000 72.990000 3.000000 \n", - "max 199.990000 3922.845361 309.047619 \n", - "\n", - " purchase_count total_price \n", - "count 79938.000000 79938.000000 \n", - "mean 2.842090 102.251041 \n", - "std 74.949889 4290.159858 \n", - "min 0.000000 -3140.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 1.000000 54.980000 \n", - "max 14750.000000 762695.290000 \n", - "Statistiques Descriptives company 7\n", - " average_price average_price_basket average_ticket_basket \\\n", - "count 39524.000000 39524.000000 39524.000000 \n", - "mean 33.110568 155.618778 3.365885 \n", - "std 85.221328 1085.613137 6.283143 \n", - "min 0.000000 0.000000 1.000000 \n", - "25% 17.250000 25.000000 1.800000 \n", - "50% 25.000000 57.676364 2.000000 \n", - "75% 43.054691 115.837500 3.555556 \n", - "max 10770.000000 86160.000000 400.000000 \n", - "\n", - " purchase_count total_price \n", - "count 68800.000000 68800.000000 \n", - "mean 3.290029 944.593729 \n", - "std 88.071870 12118.394731 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 1.000000 9.000000 \n", - "75% 2.000000 132.000000 \n", - "max 22934.000000 940874.200000 \n", - "Statistiques Descriptives company 8\n", - " average_price average_price_basket average_ticket_basket \\\n", - "count 129198.000000 129198.000000 129198.000000 \n", - "mean 18.409977 38.492520 2.258036 \n", - "std 19.159059 71.136628 5.270858 \n", - "min -20.000000 -1545.000000 1.000000 \n", - "25% 0.000000 0.000000 1.000000 \n", - "50% 15.000000 20.000000 2.000000 \n", - "75% 28.461538 52.500000 2.000000 \n", - "max 390.000000 7618.227273 750.000000 \n", - "\n", - " purchase_count total_price \n", - "count 197376.000000 197376.000000 \n", - "mean 4.637448 130.336075 \n", - "std 96.228665 2791.899946 \n", - "min 0.000000 -36124.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 1.000000 0.000000 \n", - "75% 2.000000 75.000000 \n", - "max 40272.000000 702080.290000 \n", - "Statistiques Descriptives company 9\n", - " average_price average_price_basket average_ticket_basket \\\n", - "count 102710.000000 102710.000000 102710.000000 \n", - "mean 60.312171 62.384177 1.042402 \n", - "std 50.018101 52.009984 0.268064 \n", - "min -291.670000 -291.670000 1.000000 \n", - "25% 41.500000 42.350000 1.000000 \n", - "50% 59.000000 61.070000 1.000000 \n", - "75% 74.550000 77.710000 1.000000 \n", - "max 1116.500000 1216.950000 23.000000 \n", - "\n", - " purchase_count total_price \n", - "count 181134.000000 181134.000000 \n", - "mean 1.021354 63.476966 \n", - "std 1.805412 129.781944 \n", - "min 0.000000 -291.670000 \n", - "25% 0.000000 0.000000 \n", - "50% 1.000000 0.000000 \n", - "75% 1.000000 80.000000 \n", - "max 273.000000 14343.950000 \n" - ] - } - ], - "source": [ - "for company in sport_comp:\n", - " print(f'Statistiques Descriptives company {company}')\n", - " company_data = customer_sport[customer_sport['number_company'] == company][['average_price', 'average_price_basket',\n", - " 'average_ticket_basket', 'purchase_count', 'total_price']]\n", - " print(company_data.describe())" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5058d3c9-73a0-4e01-881e-4d2423f0d291", - "metadata": {}, - "outputs": [], - "source": [ - "customer_sport[\"already_purchased\"] = customer_sport[\"purchase_count\"] > 0" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "848963c9-6129-4106-80b5-76bf814b70d1", - "metadata": {}, - "outputs": [], - "source": [ - "def mailing_consent(customer_sport):\n", - " df_graph = customer_sport.groupby([\"number_company\", \"already_purchased\"])[\"opt_in\"].mean().reset_index()\n", - " # Création du barplot groupé\n", - " fig, ax = plt.subplots(figsize=(10, 6))\n", - " \n", - " categories = df_graph[\"number_company\"].unique()\n", - " bar_width = 0.35\n", - " bar_positions = np.arange(len(categories))\n", - " \n", - " # Grouper les données par label et créer les barres groupées\n", - " for label in df_graph[\"already_purchased\"].unique():\n", - " label_data = df_graph[df_graph['already_purchased'] == label]\n", - " values = [label_data[label_data['number_company'] == category]['opt_in'].values[0]*100 for category in categories]\n", - " \n", - " label_printed = \"purchased\" if label else \"no purchase\"\n", - " ax.bar(bar_positions, values, bar_width, label=label_printed)\n", - " \n", - " # Mise à jour des positions des barres pour le prochain groupe\n", - " bar_positions = [pos + bar_width for pos in bar_positions]\n", - " \n", - " # Ajout des étiquettes, de la légende, etc.\n", - " ax.set_xlabel('Numero de compagnie')\n", - " ax.set_ylabel('Part de consentement (%)')\n", - " ax.set_title('Part de consentement au mailing selon les compagnies')\n", - " ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))])\n", - " ax.set_xticklabels(categories)\n", - " ax.legend()\n", - " \n", - " # Affichage du plot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b78ef715-c645-4625-a128-4f5b49e5339d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mailing_consent(customer_sport)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d8071891-e6f5-4d93-b039-9e99c20ec4b0", - "metadata": {}, - "outputs": [], - "source": [ - "def gender_bar(customer_sport):\n", - " company_genders = customer_sport.groupby(\"number_company\")[[\"gender_male\", \"gender_female\", \"gender_other\"]].mean().reset_index()\n", - " \n", - " # Création du barplot\n", - " plt.bar(company_genders[\"number_company\"], company_genders[\"gender_male\"], label = \"Homme\")\n", - " plt.bar(company_genders[\"number_company\"], company_genders[\"gender_female\"], \n", - " bottom = company_genders[\"gender_male\"], label = \"Femme\")\n", - " plt.bar(company_genders[\"number_company\"], company_genders[\"gender_other\"], \n", - " bottom = company_genders[\"gender_male\"] + company_genders[\"gender_female\"], label = \"Inconnu\")\n", - " \n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Company')\n", - " plt.ylabel(\"Part de clients de chaque sexe\")\n", - " plt.title(\"Sexe des clients de chaque compagnie de musee\")\n", - " plt.legend()\n", - "\n", - " # Définir les étiquettes de l'axe x\n", - " plt.xticks(company_genders[\"number_company\"], [\"{}\".format(i) for i in company_genders[\"number_company\"]])\n", - " \n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2fc30f1d-cf64-4efb-9442-4d97bb50b29f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gender_bar(customer_sport)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4b3bb641-814b-4679-9a67-4eca87a920a6", - "metadata": {}, - "outputs": [], - "source": [ - "def country_bar(customer_sport):\n", - " company_country_fr = customer_sport.groupby(\"number_company\")[\"country_fr\"].mean().reset_index()\n", - " # Création du barplot\n", - " plt.bar(company_country_fr[\"number_company\"], company_country_fr[\"country_fr\"])\n", - " \n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Company')\n", - " plt.ylabel(\"Part de clients français\")\n", - " plt.title(\"Nationalité des clients de chaque compagnie de sport\")\n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "01258674-6b98-49e4-93f4-f4185964999f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "country_bar(customer_sport)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "1336c230-2e02-4559-90ac-a43bbb65b1c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'street_id', 'structure_id', 'mcp_contact_id',\n", - " 'fidelity', 'tenant_id', 'is_partner', 'deleted_at', 'gender',\n", - " 'is_email_true', 'opt_in', 'last_buying_date', 'max_price',\n", - " 'ticket_sum', 'average_price', 'average_purchase_delay',\n", - " 'average_price_basket', 'average_ticket_basket', 'total_price',\n", - " 'purchase_count', 'first_buying_date', 'country', 'gender_label',\n", - " 'gender_female', 'gender_male', 'gender_other', 'country_fr',\n", - " 'number_company', 'already_purchased'],\n", - " dtype='object')" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customer_sport.columns" - ] - }, - { - "cell_type": "markdown", - "id": "43d63ea3-75f4-4356-a7e9-35905d86baa5", - "metadata": {}, - "source": [ - "### 2. campaigns_information" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "8d116e34-cdd6-4ef9-8622-474da79f79ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre de lignes de la table : 463098\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 178826\n", - "number_company 0\n", - "dtype: int64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Nombre de lignes de la table : \",campaigns_sport.shape[0])\n", - "campaigns_sport.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "724d3c33-c219-4212-b8b6-dd78481674cb", - "metadata": {}, - "outputs": [], - "source": [ - "def lazy_customer_plot(campaigns_sport_kpi):\n", - " company_lazy_customers = campaigns_sport_kpi.groupby(\"number_company\")[\"no_campaign_opened\"].mean().reset_index()\n", - " # Création du barplot\n", - " plt.bar(company_lazy_customers[\"number_company\"], company_lazy_customers[\"no_campaign_opened\"])\n", - " \n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Company')\n", - " plt.ylabel(\"Part de clients n'ayant ouvert aucun mail\")\n", - " plt.title(\"Part de clients n'ayant ouvert aucun mail pour les compagnies de sport\")\n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e513f308-3a9c-40ed-99d5-ed420bd67384", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lazy_customer_plot(campaigns_sport_kpi)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "1b7ac0f0-903e-45ae-8f44-dc37ed36eafc", - "metadata": {}, - "outputs": [], - "source": [ - "def campaigns_effectiveness(customer_sport, Train=False):\n", - " if not Train:\n", - " customer_sport[\"already_purchased\"] = customer_sport[\"purchase_count\"]>0\n", - "\n", - " nb_customers_purchasing = customer_sport[customer_sport[\"already_purchased\"]].groupby([\"number_company\",\"already_purchased\"])[\"customer_id\"].count().reset_index()\n", - " nb_customers_no_purchase = customer_sport[~customer_sport[\"already_purchased\"]].groupby([\"number_company\",\"already_purchased\"])[\"customer_id\"].count().reset_index()\n", - "\n", - " plt.bar(nb_customers_purchasing[\"number_company\"], nb_customers_purchasing[\"customer_id\"]/1000, label = \"has purchased\")\n", - " plt.bar(nb_customers_no_purchase[\"number_company\"], nb_customers_no_purchase[\"customer_id\"]/1000, \n", - " bottom = nb_customers_purchasing[\"customer_id\"]/1000, label = \"has not purchased\")\n", - " \n", - " \n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Company')\n", - " plt.ylabel(\"Nombre de clients (en milliers)\")\n", - " plt.title(\"Nombre de clients ayant acheté ou été ciblés par des mails pour les compagnies de sport\")\n", - " plt.legend()\n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "3e05edab-fb8a-423b-b0ae-94e36eeeb3cd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "campaigns_effectiveness(customer_sport)" - ] - }, - { - "cell_type": "markdown", - "id": "5d08698b-e3ab-4038-ad26-990297520d43", - "metadata": {}, - "source": [ - "## Evolution des Commandes" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "93fd7b09-690d-490f-8a59-01be25da7445", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['ticket_id', 'customer_id', 'purchase_id', 'event_type_id',\n", - " 'supplier_name', 'purchase_date', 'amount', 'is_full_price',\n", - " 'name_event_types', 'name_facilities', 'name_categories', 'name_events',\n", - " 'name_seasons', 'start_date_time', 'end_date_time', 'open'],\n", - " dtype='object')" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "products_sport.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "2f5e32e1-224f-4cc4-a5c3-c4d5857df83c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idnb_campaignsnb_campaigns_openedtime_to_opennumber_companyno_campaign_opened
05_160516262.00 days 01:30:275False
15_1605177349.02 days 01:30:16.9090909095False
25_160518250.0NaT5True
35_160519465.00 days 09:31:47.2500005False
45_160520359.01 days 14:34:51.5714285715False
.....................
4630939_172034010.0NaT9True
4630949_172035211.00 days 08:30:329False
4630959_172035310.0NaT9True
4630969_172035411.00 days 00:00:059False
4630979_172035511.00 days 00:19:399False
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463098 rows × 6 columns

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" - ], - "text/plain": [ - " customer_id nb_campaigns nb_campaigns_opened \\\n", - "0 5_160516 26 2.0 \n", - "1 5_160517 73 49.0 \n", - "2 5_160518 25 0.0 \n", - "3 5_160519 46 5.0 \n", - "4 5_160520 35 9.0 \n", - "... ... ... ... \n", - "463093 9_1720340 1 0.0 \n", - "463094 9_1720352 1 1.0 \n", - "463095 9_1720353 1 0.0 \n", - "463096 9_1720354 1 1.0 \n", - "463097 9_1720355 1 1.0 \n", - "\n", - " time_to_open number_company no_campaign_opened \n", - "0 0 days 01:30:27 5 False \n", - "1 2 days 01:30:16.909090909 5 False \n", - "2 NaT 5 True \n", - "3 0 days 09:31:47.250000 5 False \n", - "4 1 days 14:34:51.571428571 5 False \n", - "... ... ... ... \n", - "463093 NaT 9 True \n", - "463094 0 days 08:30:32 9 False \n", - "463095 NaT 9 True \n", - "463096 0 days 00:00:05 9 False \n", - "463097 0 days 00:19:39 9 False \n", - "\n", - "[463098 rows x 6 columns]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "campaigns_sport" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "b917f58e-fb8c-485b-808c-a53c04745833", - "metadata": {}, - "outputs": [], - "source": [ - "def sale_dynamics(products_sport, campaigns_sport_brut):\n", - " # Mois du premier achat\n", - " purchase_min = products_sport.groupby(['customer_id'])['purchase_date'].min().reset_index()\n", - " purchase_min.rename(columns = {'purchase_date' : 'first_purchase_event'}, inplace = True)\n", - " purchase_min['first_purchase_event'] = pd.to_datetime(purchase_min['first_purchase_event'])\n", - " purchase_min['first_purchase_month'] = pd.to_datetime(purchase_min['first_purchase_event'].dt.strftime('%Y-%m'))\n", - "\n", - " # Mois du premier mails\n", - " first_mail_received = campaigns_sport_brut.groupby('customer_id')['sent_at'].min().reset_index()\n", - " first_mail_received.rename(columns = {'sent_at' : 'first_email_reception'}, inplace = True)\n", - " first_mail_received['first_email_reception'] = pd.to_datetime(first_mail_received['first_email_reception'])\n", - " first_mail_received['first_email_month'] = pd.to_datetime(first_mail_received['first_email_reception'].dt.strftime('%Y-%m'))\n", - "\n", - " # Fusion \n", - " known_customer = pd.merge(purchase_min[['customer_id', 'first_purchase_month']], \n", - " first_mail_received[['customer_id', 'first_email_month']], on = 'customer_id', how = 'outer')\n", - "\n", - " # Mois à partir duquel le client est considere comme connu\n", - "\n", - " known_customer['known_date'] = pd.to_datetime(known_customer[['first_email_month', 'first_purchase_month']].min(axis = 1), utc = True, format = 'ISO8601')\n", - "\n", - " # Nombre de commande par mois\n", - " purchases_count = pd.merge(products_sport[['customer_id', 'purchase_id', 'purchase_date']].drop_duplicates(), known_customer[['customer_id', 'known_date']], on = ['customer_id'], how = 'inner')\n", - " purchases_count['is_customer_known'] = purchases_count['purchase_date'] > purchases_count['known_date'] + pd.DateOffset(months=1)\n", - " purchases_count['purchase_date_month'] = pd.to_datetime(purchases_count['purchase_date'].dt.strftime('%Y-%m'))\n", - " purchases_count = purchases_count[purchases_count['customer_id'] != 1]\n", - " \n", - " # Nombre de commande par mois par type de client\n", - " nb_purchases_graph = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['purchase_id'].count().reset_index()\n", - " nb_purchases_graph.rename(columns = {'purchase_id' : 'nb_purchases'}, inplace = True)\n", - " \n", - " nb_purchases_graph_2 = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['customer_id'].nunique().reset_index()\n", - " nb_purchases_graph_2.rename(columns = {'customer_id' : 'nb_new_customer'}, inplace = True)\n", - "\n", - " # Graphique en nombre de commande\n", - " purchases_graph = nb_purchases_graph\n", - " \n", - " purchases_graph_used = purchases_graph[purchases_graph[\"purchase_date_month\"] >= datetime(2021,3,1)]\n", - " purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==False]\n", - " purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==True]\n", - " \n", - " \n", - " # Création du barplot\n", - " plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Nouveau client\")\n", - " plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_1[\"nb_purchases\"], \n", - " bottom = purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Ancien client\")\n", - " \n", - " \n", - " # commande pr afficher slt\n", - " plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y'))\n", - " \n", - " \n", - " # Ajout de titres et d'étiquettes\n", - " plt.xlabel('Mois')\n", - " plt.ylabel(\"Nombre d'achats\")\n", - " plt.title(\"Nombre d'achats - Sport\")\n", - " plt.legend()\n", - " \n", - " # Affichage du barplot\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "7f0275ec-5cc5-436c-8d50-5263fd8a6945", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sale_dynamics(products_sport, campaigns_sport_brut)" - ] - }, - { - "cell_type": "markdown", - "id": "23b35899-728c-4674-bbbc-157643c16abe", - "metadata": {}, - "source": [ - "# 3 - Caractéristiques Démographiques" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "b1bb86c5-3f40-4d5c-bef0-d6e8693c6b5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bdc2324-data/5/5customersplus.csv\n" - ] - }, - { - "data": { - "text/plain": [ - "Index(['id', 'lastname', 'firstname', 'birthdate', 'email', 'street_id',\n", - " 'created_at', 'updated_at', 'civility', 'is_partner', 'extra',\n", - " 'deleted_at', 'reference', 'gender', 'is_email_true', 'extra_field',\n", - " 'identifier', 'opt_in', 'structure_id', 'note', 'profession',\n", - " 'language', 'mcp_contact_id', 'need_reload', 'last_buying_date',\n", - " 'max_price', 'ticket_sum', 'average_price', 'fidelity',\n", - " 'average_purchase_delay', 'average_price_basket',\n", - " 'average_ticket_basket', 'total_price', 'preferred_category',\n", - " 'preferred_supplier', 'preferred_formula', 'purchase_count',\n", - " 'first_buying_date', 'last_visiting_date', 'zipcode', 'country', 'age',\n", - " 'tenant_id'],\n", - " dtype='object')" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "directory_path = '5'\n", - "file_name = \"5customersplus.csv\"\n", - "file_path = \"bdc2324-data\" + \"/\" + directory_path + \"/\" + file_name\n", - "print(file_path)\n", - "with fs.open(file_path, mode=\"rb\") as file_in:\n", - " customersplus = pd.read_csv(file_in, sep=\",\")\n", - " \n", - "customersplus.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "8adba9cc-e257-4c57-8149-c9af48c12b6f", - "metadata": {}, - "outputs": [], - "source": [ - "def load_customer_brut_dataset(directory_path):\n", - " file_name = str(directory_path) + \"customersplus.csv\"\n", - " print(file_name)\n", - " file_path = \"bdc2324-data\" + \"/\" + str(directory_path) + \"/\" + file_name\n", - " print(file_path)\n", - " with fs.open(file_path, mode=\"rb\") as file_in:\n", - " customersplus = pd.read_csv(file_in, sep=\",\")\n", - " return customersplus" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "4c8f511b-2740-4b8d-bd99-d0ecf7d64e74", - "metadata": {}, - "outputs": [], - "source": [ - "def percent_of_na(company, column):\n", - " df = load_customer_brut_dataset(company)\n", - " if column in df.columns:\n", - " na_percentage = df[column].isna().mean() * 100\n", - " non_na_percentage = 100 - na_percentage\n", - " \n", - " labels = ['Valeurs Manquantes', 'Non-Valeurs Manquantes']\n", - " sizes = [na_percentage, non_na_percentage]\n", - " colors = ['#ff9999','#66b3ff']\n", - " explode = (0.1, 0)\n", - " \n", - " plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)\n", - " plt.axis('equal') \n", - " plt.title('Pourcentage de Valeurs Manquantes : {}'.format(column))\n", - " #plt.show()\n", - " else:\n", - " print(f\"The column {column} doesn't exist for the company {company}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "1ca50118-a32d-4dda-8fdf-92443f0f5196", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n", - "5customersplus.csv\n", - "bdc2324-data/5/5customersplus.csv\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[100], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m company \u001b[38;5;129;01min\u001b[39;00m customer_sport[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumber_company\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m----> 2\u001b[0m \u001b[43mpercent_of_na\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompany\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mprofession\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[99], line 2\u001b[0m, in \u001b[0;36mpercent_of_na\u001b[0;34m(company, column)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpercent_of_na\u001b[39m(company, column):\n\u001b[0;32m----> 2\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mload_customer_brut_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompany\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m column \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m 4\u001b[0m na_percentage \u001b[38;5;241m=\u001b[39m df[column]\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39mmean() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\n", - "Cell \u001b[0;32mIn[95], line 7\u001b[0m, in \u001b[0;36mload_customer_brut_dataset\u001b[0;34m(directory_path)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(file_path)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m fs\u001b[38;5;241m.\u001b[39mopen(file_path, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m file_in:\n\u001b[0;32m----> 7\u001b[0m customersplus \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m customersplus\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1024\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1011\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1012\u001b[0m dialect,\n\u001b[1;32m 1013\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1020\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1021\u001b[0m )\n\u001b[1;32m 1022\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1024\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/pandas/io/parsers/readers.py:624\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[0;32m--> 624\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1921\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1914\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m 1915\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1916\u001b[0m \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[1;32m 1917\u001b[0m (\n\u001b[1;32m 1918\u001b[0m index,\n\u001b[1;32m 1919\u001b[0m columns,\n\u001b[1;32m 1920\u001b[0m col_dict,\n\u001b[0;32m-> 1921\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[1;32m 1922\u001b[0m \u001b[43m \u001b[49m\u001b[43mnrows\u001b[49m\n\u001b[1;32m 1923\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1924\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m 1925\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:234\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[0;32m--> 234\u001b[0m chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 235\u001b[0m \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[1;32m 236\u001b[0m data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n", - "File \u001b[0;32mparsers.pyx:838\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsers.pyx:921\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsers.pyx:1083\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsers.pyx:1456\u001b[0m, in \u001b[0;36mpandas._libs.parsers._maybe_upcast\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/numpy/core/multiarray.py:1131\u001b[0m, in \u001b[0;36mputmask\u001b[0;34m(a, mask, values)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;124;03m copyto(dst, src, casting='same_kind', where=True)\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1126\u001b[0m \n\u001b[1;32m 1127\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (dst, src, where)\n\u001b[0;32m-> 1131\u001b[0m \u001b[38;5;129m@array_function_from_c_func_and_dispatcher\u001b[39m(_multiarray_umath\u001b[38;5;241m.\u001b[39mputmask)\n\u001b[1;32m 1132\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mputmask\u001b[39m(a, \u001b[38;5;241m/\u001b[39m, mask, values):\n\u001b[1;32m 1133\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1134\u001b[0m \u001b[38;5;124;03m putmask(a, mask, values)\u001b[39;00m\n\u001b[1;32m 1135\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1171\u001b[0m \n\u001b[1;32m 1172\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 1173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (a, mask, values)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "data": { - "image/png": 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cKOnn5+/WrVt16623auXKlTp58mSp7XXu3FmvvPKKZs6cqY0bN/p12UNF3+Pbt2+vxo0bux7HxcWpZcuWfv3+UXUEOpTptddeU1ZWlrZs2aIDBw5o27Ztrgu8jx49qqioqFIXvzscDtWvX9/v7ipvnF0xTkeOHJEk17Um3hw6dEiSNGzYMEVHR3v8e+yxx2SMcXWTOtWrV8/jsbP7wJ8P9CeffFK33HKLunTpon/961/auHGjsrKydNlll3msf/ToUa/dgyWnOeufPHlyqfpvvfVWSVJ2drbPenztp379+tW6H0lKSEjQddddpw8++EAHDx5UYWGhFi5cqF69eql58+aSpO+//149e/bU/v379cwzz2jdunXKyspyXf9T1jk+dOiQjDHKyMgoVePGjRvLra8sJZ9bkvS3v/1N99xzj5YsWaI+ffooJSVFgwcPLjXIoTwTJ07U0qVL9eSTTyo+Pl7Dhg3zulxFz03J56n083O15POs5O9aKv37r4hFixZp+PDhyszM1MKFC7VhwwZlZWVpwoQJysvLK7W8P3X64nxedurUqdTv/O2333b9zh0Oh1atWqUBAwZo1qxZuvjii5WWlqY77rhDp06dqvSxehMVFVXqmJzns+T7W8nn1U8//SRjjNfnW8OGDb1uw93Ro0dVWFioZ599ttT5uOKKKyT9+jqdOnWqnnjiCW3cuFGXX3656tWrp379+umzzz5zbe/tt9/W2LFj9fLLL6tbt25KSUnRmDFjfF5j66yhIu/xVfn9o+q4hg5luuCCC9SxY0ev8+rVq6fCwkIdOXLE4wVvjNHBgwfVqVMn17TY2FjXwAZ3vt7QHA6Hx2Pn9vft26dzzjnH6zqpqamSpGeffdY1eKOkyl535c3ChQvVu3dvPf/88x7TS36o1KtXT5s3by61fsk3Umf9U6dO1dChQ73us1WrVj7rCdR+nCZOnKiXXnpJr732mlq2bKnDhw9r9uzZrvlLlixRTk6OFi1apCZNmrim+3PhempqqhwOh9atW+f1Okz3aXFxcV6fW9nZ2a5jdVfyuSVJiYmJeuCBB/TAAw/o0KFDrta6K6+8Ut9880259ToNHTpUt912mx599FHddNNNio+P97pcVc6NL/Xq1fP64extmr/nbOHChWrWrJnefvttj/Pmbd2qcu73n//8p8c58aZJkyauVradO3fqnXfe0f3336/8/Pwy75FYUYWFhTp69KhHUHGez5LhpeTzqm7duoqIiNCPP/5YarsHDhyQJK/PT/f1na15vnommjVrJunn4HnnnXfqzjvv1PHjx/Xxxx/rL3/5iwYMGKAffvjBNUjm6aef1tNPP63vv/9e77//vqZMmaLDhw/rgw8+8Lr9irzHw3q00KHSnAMMFi5c6DH9X//6l3JycjwGIDRt2lTbtm3zWG716tU6ffq0X/v67W9/q8jIyFLhyV337t1Vp04dffXVV+rYsaPXfzExMf4enouvb5gOh6NU2Ni2bZs2bNjgMa1Xr146deqUVqxY4TH9rbfe8njcqlUrnXfeedq6davP+t27s0rq06ePTp06pffff99j+htvvFGt+3Hq0qWL2rRpo/nz52v+/PmqXbu2R3em8wPO/RwZY/TSSy+Vu+1BgwbJGKP9+/d7re+iiy5yLevtubVz585yu419ycjI0Lhx4zRy5Ejt2LGj1IjGssTHx+u+++7TlVdeqVtuucXnclU5N7706dNHX375pbZu3eoxveTvX/L/nDkcDsXExHiElYMHD3od5eovX63gAwYMUFRUlL799lufz0tvWrZsqWnTpumiiy7S559/Xum6fHn99dc9HjvPp/toYG8SExPVpUsXLVq0yONYi4uLtXDhQjVq1EgtW7b0uX5CQoL69OmjLVu2qG3btl7Ph7cWsTp16mjYsGG67bbbdOzYMe3Zs6fUMo0bN9btt9+u/v37l3nOKvIeD+vRQodK69+/vwYMGKB77rlHJ0+eVPfu3V0joDp06OBxY9nRo0dr+vTpuu+++9SrVy999dVX+vvf/67atWv7ta+mTZvqL3/5ix566CHl5ua6bjXy1VdfKTs7Ww888ICSkpL07LPPauzYsTp27JiGDRum9PR0HTlyRFu3btWRI0fKDIS+XHTRRVq7dq2WLl2qBg0aKDk5Wa1atdKgQYP00EMPacaMGerVq5d27NihBx98UM2aNfO4t9nYsWP11FNP6YYbbtDMmTPVokULrVixQitXrpQkj1tavPDCC7r88ss1YMAAjRs3TpmZmTp27Ji+/vprff7553r33Xd91jlmzBg99dRTGjNmjB5++GGdd955+ve//+3aj7uq7MfdhAkTdOedd2rHjh2aNGmSR4tU//79FRMTo5EjR+ruu+9WXl6enn/+ef3000/lbrd79+66+eabNX78eH322We69NJLlZiYqB9//FGffvqpLrroIldgGj16tG644Qbdeuutuuaaa7R3717NmjWrQvfB69KliwYNGqS2bduqbt26+vrrr7VgwQJ169ZNCQkJfm9HkqulpCxVOTe+/PGPf9S8efM0cOBAzZw50zXK1VsLo7/nbNCgQVq0aJFuvfVWDRs2TD/88IMeeughNWjQoMLd0U7OMP7MM89o7Nixio6OVqtWrdS0aVM9+OCDuvfee/Xdd9/psssuU926dXXo0CFt3rzZ1Yq6bds23X777br22mt13nnnKSYmRqtXr9a2bds0ZcqUMve9d+9eNW/eXGPHjvXrOrqYmBjNnj1bp0+fVqdOnVyjXC+//HL16NGj3PUfeeQR9e/fX3369NHkyZMVExOj5557Ttu3b9ebb77ptbXY3TPPPKMePXqoZ8+euuWWW9S0aVOdOnVKu3bt0tKlS13XuF155ZVq06aNOnbsqLS0NO3du1dPP/20mjRpovPOO08nTpxQnz59NGrUKJ1//vlKTk5WVlaWPvjgA5+t9FLF3uMRBKwbj4Fg5hzFmJWVVeZyubm55p577jFNmjQx0dHRpkGDBuaWW24xP/30k8dyZ8+eNXfffbc555xzTHx8vOnVq5f54osvfI5y9bXf1157zXTq1MnExcWZpKQk06FDB4/Rm8YY88knn5iBAwealJQUEx0dbTIzM83AgQPNu+++61rGOcr1yJEjXo/bfQTeF198Ybp3724SEhKMJNcowLNnz5rJkyebzMxMExcXZy6++GKzZMmSUiPFjPl5BN/QoUNNUlKSSU5ONtdcc43597//7XVU6tatW83w4cNNenq6iY6ONvXr1zd9+/Y1c+bM8f2L+MW+ffvMNddc47Gf9evXlxrlWtX9OB05csTExMQYSWbz5s2l5i9dutS0a9fOxMXFmczMTPPnP//ZrFixotSoS2/nzBhj5s2bZ7p06WISExNNfHy8ad68uRkzZoz57LPPXMsUFxebWbNmmXPPPdfExcWZjh07mtWrV/sc5er+PHCaMmWK6dixo6lbt66JjY015557rvnTn/5ksrOzyzz+srbpztsoV3/PTa9evcyFF15YapveztlXX31l+vfvb+Li4kxKSoqZOHGiee+990pt099zZowxjz76qGnatKmJjY01F1xwgXnppZdcrx93ksxtt91Wqk5vI2qnTp1qGjZsaCIiIkrVtmTJEtOnTx9Tq1YtExsba5o0aWKGDRtmPv74Y2OMMYcOHTLjxo0z559/vklMTDRJSUmmbdu25qmnnjKFhYWl9u/OOZrUn1HrY8eONYmJiWbbtm2md+/eJj4+3qSkpJhbbrnFnD592q9jN8aYdevWmb59+7qew127djVLly71WlfJUa7OeRMmTDCZmZkmOjrapKWlmUsuucTMnDnTtczs2bPNJZdcYlJTU01MTIxp3LixmThxotmzZ48xxpi8vDzzu9/9zrRt29bUqlXLxMfHm1atWpkZM2aYnJwcj2Mu+Zzy9z2+SZMmZuDAgaXqL2/kNKqPwxhuwQ9YwXnPre+//77MwR5AVaxdu1Z9+vTRmjVryu0mxK/GjRunf/7zn35fFgJYjS5XIAD+/ve/S5LOP/98FRQUaPXq1frb3/6mG264gTAHAKgyAh0QAAkJCXrqqae0Z88enT17Vo0bN9Y999yjadOmWV0aACAE0OUKAABgc9y2BAAAwOYIdAAAADZHoAMAALA5Ah0AAIDNEegAAABsjkAHAABgcwQ6AAAAmyPQAQAA2ByBDgAAwOYIdAAAADZHoAMAALA5Ah0AAIDNEegAAABsjkAHAABgcwQ6AAAAmyPQAQAA2ByBDgAAwOYIdAAAADZHoAMAALA5Ah0AAIDNRVldAABY5q23pJMnrdt/69ZSjx7W7R9AyHAYY4zVRQBAjXj5Zam42OoqKq9OHWn4cKurAGADBDoA9vfii1ZXEFipqdLQoVZXASCIEOgA2Eu4hTd/padLgwdbXQUAixDoAASvjz6Sdu+2ugr7GjhQysy0ugoAAUCgAxA8srKkLVusriJ03Xyz1RUAqCEEOgDWogvVGlyHB4QUAh2AwNq1S1q92uoqUBKtd4CtEegA1DxCnL0Q7gDbIdABqBn790vLl1tdBaqKcAfYAoEOQPXimrjQdM450uWXW10FAB8IdACqbskS6fBhq6tAoNBqBwQdAh2AyqM1LrzFxUljxlhdBQAR6ABU1Nq10s6dVleBYEOrHWApAh0A/7zyipSfb3UVCHb8dQrAEgQ6AGWjWxWVwd+WBQKKQAfAO4IcqkPdutK111pdBRDyCHQAPBHkUBMIdkCNItAB+BlBDoFQp440fLjVVQAhh0AHhDuCHKzANXZAtSLQAeGKIIdgwKhYoFoQ6IBw8+670k8/WV0F4In72AFVQqADwsX+/dLy5VZXAZSNYAdUCoEOCAd0r8JO4uOl0aOtrgKwFQIdEMpeekniJQ67orUO8BuBDghFdK8ilBDsgHIR6IBQQ/cqQlHr1lKPHlZXAQQtAh0QKrKypC1brK4CqFm01gFeEeiAUECrHMJJs2ZS//5WVwEEFQIdYGdbt0qbNlldBWANWusAFwIdYFe0ygFS375SixZWVwFYjkAH2BFhDvBEax3CXFgFuqioKBUVFUmSwuiwEUpef13KybG6CiA4EeoQxioU6BwOh+tn99Wc02sqJNWpU0cnTpzwuQ9/9x/Mgc7XuS1vXqio6edQSKBVDijfOedIl19udRVAwEVUdsUWAbxm4fjx4z7njRw5MmB1+Gv27NlVWt/9mG688caqloNQQJgD/PPDD7xeEJYq3UIn/dqa4q11peSy/swvuYy3fUdGRqqwsLDU9AYNGujHH3/0ul5xcbEk7y103upIT0/XoUOHyj22srbRvHlzffvttxU+vpLL+TvdOc3bsZZVQ3m/h8oef0VqKauG6dOna+bMmT7ri46O9ng+lJxve0uWSIcPW10FYE90wSKMVLqFTpLi4uK8Tvf1Ae3PdF/LLFq0SJJ8BoMDBw54nW6M0ebNm73OS0pK8jr9cBU+QCMiIhQVFeUR5iIjIyu9vYoo61jdzZ8/X5Lvcx3oWqZNm+Z6nJycrOTkZPXs2VOSPMKct+eJe5iLiKjS0zn4vPgiYQ6oihdflHbtsroKICAq1ULn3qpS8ueSy3lb19vj/fv3q1GjRqXW87b/ffv2KTMzs9xr4tLS0pSdne2aX3J5b3XGxcXp7NmzZS5TmeMtj3P59PR0HT58WJGRkXI4HCosLHRNK2t75R2r+z5q+tiqq5aIiAgZYxQfH68zZ8543X/IXntHlxFQfSIjpYkTra4CqFFVbtKoaCvP4sWLvU7PzMwstV33f+6cwc8ZEjb9cmPViIgIj3WcocKfY3D+c4a56uLrGHxxdvUWFRW5Wp+c09xV5libN29egcr9V1O1OENabm5uueexouc5aO3fT5gDqltREa8rhLxKB7qabhGJi4tzfUBHRUWV2qd7d17nzp2rVFPJ8OhwOPTb3/62CtX/XEtqamqp/VSXyhxrTXX9BqKWkr+f5ORk175jYmJKLWtLH30kLV9udRVA6CLUIYQF/KKjIUOG+LVcbm6uiouLVVxcrIKCglLzu3TpIunXYNCwYUPXPGNMhULG3LlzXfty/lu5cqXXZTt06OD3do8cOVLhWqRfrxcs+bNTVY61ugWqlpK/n5MnT7rmnT171vLzUGWvvSbt3m11FUDoI9QhRFUp0JX3AVoT3WBXXXWVx2Nnt+SSJUtc05xdgOWJj4+XJE2YMEEOh8N13ZrD4SjVNeyc/sUXX/hVp3N5f2txN2TIELVv317t27f3GoArc6z+1FrWdnwtUxO1ZGRk6Oqrry513V5UVFSpOtxbcW3bMvfii1JentVVAOGDwRIIQZUeFOHkftuIyty2pDIDCcq7wbE3/lyc78458KJDhw4+Q1xFb9tS8pYr7so7dn9uFeJel7djbdWqlXbu3OnXdipza5PqrsUYo9mzZ2vy5Mk+t+1t3w0aNPA56jno0FoAWCc9XRo82OoqgGoRVn/6C+UL2VGjwYgwB1gvPl4aPdrqKoAqI9DBA4EuQAhzQPCIiJD4qzywuRC7EytgA4Q5ILgUF/O6hO3RQgcEEh8aQHDjz4XBpmihAwKFMAcEP16nsCla6IBfTFr28/8vDKrmDe/fzw2DAbuhpQ42QwsdIOmva3/9edKynzNYtSDMAfZESx1shhY64Bd/+rd0pvjXxxenSZO6VHGjfCgA9kZLHWyCQAe4eeML6ZN9vz6OkfRsZbtgCXNAaCDUwQYIdIAXzuvpnCp8XR1hDggthDoEOa6hA7woGeBKBrwyEeaA0MPrGkGOQAf4UKlQx5s+ELp4fSOIEeiAMrwwSIpyezxpmfSNrxGwvNkDoW/RIqsrALziGjrADy9skj4/8uvjNvWk33dzW4AwB4SPLl2kdu2srgLwQKAD/LR/v/Tgll8fu0bAEuaA8MMgCQQZAh1QQe7X0rU884PuOr7CumIAWIdQhyDCNXRABbkPltgZ30ifxre0rhgA1qF1HkGEFjqgknbP/ZceTx+sIkekLj39pa4/+X9WlwTACrTUIQgQ6IDK+OWbea4jStPSR+p0ZLya5h/W3dlLFGlxaQAsQKiDxehyBSrKrZsl3hRq1qEFapZ/SHti0jUl4waddRDpgLDz0UdWV4AwR6ADKsLLNTORkqZkv6d+p7bqZES87swYq2MRCYGvDYB1du+2ugKEObpcAX8tWSIdPlzmIp/FNtNLKb+RQ9Lk7PfVouBQQEoDECToeoVFaKED/FVOmJOkjmd3677D78oho8dTr9K6+FYBKAxA0GDkKyxCCx3gjwq+SZ92xGhaxkjlRsSqx+mvNfrkuhoqDEDQiYiQbrzR6ioQZmihA8pTiW/cSSZfTxx8TY3ys/Vp0gX6a+pgFdVAaQCCUHHxz39aBgggWuiAsrz1lnTyZJU28Vrtnvq/xAuUXHRGMw+/pThTWE3FAQhqXE+HAKKFDihLFcOcJI05sU5jf1qrUxHxuqv+GEbAAuGC6+kQQLTQAb5U85vxnuh6ejR1iCQHI2CBcJGaKg0danUVCAO00AHe1MA366YFRzXr4ELFmAJGwALhIjvb6goQJgh0QEk1eDFzLZOn2QdfU3rhCS2s20uv1e5ZY/sCECToekUA0OUKlBSgN98X6/TT/0torsb5RzQlezF/AxYIdQySQA2ihQ5wF8Bv0jcfX6URxz/V99GpuifjBuU5ogK2bwBAaCHQAU5btwZ8l33PfKU/Z7+v07+MgM2OSAp4DQAChK5X1CACHeC0aZMlu21RcEiPHHpdDmM0LWOk/hedYUkdAALglVesrgAhikAHSJZ/c65bfEZPHnxNdYtO64nUq/RJ/AWW1gOghuTnW10BQhSBDggSMSrSI4ffVJu87/VG3Z56pfalVpcEoCbQ9YoawChXIAjfXFckttOSWp11TkG2pjICFgg93HAY1YwWOoS3N96wugKvLs/ZqjuOLtcP0am6O2M0I2CBUMMNh1HNCHQIb6dPW12BTxfmH9DDh95UXkQ0I2CBUBSEvQOwLwIdwpcN3kxTi0/ryYOvKqE4X/dmjNTO6PpWlwQACEIEOoSnGvzzXtUt1hTpsUMLdV7+j5qdeqXWJjACFggZNvhiCXtgUATCk03fRBcnd9IHSe3V9cxOjT/xidXlAKgOzZpJ/ftbXQVsjhY6hJ9ly6yuoNKGnMrSLcc+1MaElpqZOkRFclhdEoCq2r3b6goQAgh0CD8HDlhdQZW0P7tXDxx+WweiU3R3xg3KZQQsYH827TVA8KDLFeFl3jypsNDqKvzmmDSp1DTzwguSpFxHtO5Nv05nI6L1wKF3lHbT9V634Vze2/amDRigh365F1abGTP05cGDinY4lD9nTnUdAgB/3Xyz1RXAxmihQ3ixeZiTpJjf/U6SFG8K9PihhcosOKZ7M0aWu53N33xTat7MlStdP3958KAkEeYAq9BKhyqgrwbhw6ZvliVb2ArcGtUjZfSX7CV6s9Yl0vvFGnHi/9T3zFceyztd/vzzkqSJXbvq5fHjPeY5f140cWKNHQcAoObQQgcEoemLFlVo+ZEn12viT6v0du3uml+7l6TSLXLb7r5bkjR340aNdAuJ7vsa0rlzZUsGUB1s+sUT1qOFDuHBZm+Ss1etqvA6nfO+U4PDP+mv6ddoX3Q93fdUmiQpJS5OkpSZmela9q3PP5ckPTF0qCa7BTr3Vjv3lkEAQHAj0AEh5JyinzTr4GuqdfN417Sjzzzj+rlkSHMGuJ7nnqt1330n6ecAeCwvT45Jkwh1gBVefJEBEqgwulwR+mzWOidJd/XrV+l13cPcLe8X6oiPvwG73+2vZZzKy5MkXdWmjUcABADYA4EOCELOW4lUVMku005ndml6xkjtiG5QatlGDz7oWs4Z6L7Nzq7UfgFUMxt+EYW16HJFaAuBN8WSty+JKjHd2S3qvlxyTIxq/f73kqSU1AYyc3Zr+In/U79fRsDW+8MfJElxkZGSpF2PPCLHpEn68uBBn7dLAQAEL1rogCDl6/q1Aj+uazuVn+/6t/fAXk3NXqx3a1+iebV7S5KO/dIil/vccxXeP4AACYEvpAgc/lIEQhdvhh5ORsRpWvpIpRae0L3ZixUpXvpA0GNwBPxECx0QJmoV52n2wVeV74jWnzNuUK4j2uqSAJSHL6bwE4EOoen1162uIChFq1gzj7ytlmd/1F31x+hIRLLVJQEAqgGBDqEpJ8fqCoLa745/rKEnNml6xgh97WUELIAg8tZbVlcAGyDQIfS43V8Nvv3mzHbdmb1Mf0sdqFUJF1pdDgBfTp60ugLYAIEOoWf5cqsrsI2WBQf18KE3tLhWF71cp4/V5QDwJSvL6goQ5Ah0QJhLKT6j2Qdf1a6Y+nog7RoVyWF1SQBK2rLF6goQ5Ah0CC2MCKuUWBXpr4ffVO2iM5qcMZoRsABgMwQ6AJJ+fjP447EV6pfzX03OGM0IWCDY8IUVZSDQIXQsW2Z1BSFh0OktuuWnlZqRMZwRsABgEwQ6hI4DB6yuIGS0ObtfDxx6R/+od5k+TmhjdTkAnBjFDx8IdAC8Sis+pScOLdDK5PZ6qU5fq8sBIDGKHz4R6BAauLakRsSZQj16aKF+ikzUA2nXqJARsAAQlAh0AMoUKenuo0t1Yd4PuifjBuUwAhaw1rx5VleAIESgA+CXYac26/rj/9HUjOt1KJIRsIBlCgutrgBBiEAH+6O7NWAuPrtXU44s0cNp1+jLmIZWlwMA+AWBDkCFNCw6rkcOva75dfvqw8SLrC4HCE8vvWR1BQgyBDrYG0P4LZFoCvTYodeVFd9CL9bpZ3U5QPgxxuoKEGQIdLA3hvBbJlJG92YvVkLxWT2YyghYALASgQ5Aldxw8lP1z9mqv6SPYgQsEEhcPww3BDoAVdYtd5duO7pC96Vfp0ORtawuBwDCDoEO9sW306DSpOiY7j/8rp6sdyUjYAEgwAh0AKpNssnTw4ff0KJaXbWSEbBAzXv9dasrQJAg0AGoVlEymp69SPuj6ull/gYsULNycqyuAEGCQAd7ors16E04sVYtzv6ov6YOZgQsANQwAh2AGtM792uNOLFeD6RfywhYoKZwP06IQAeghjUvOKy7spfqkbQhOsgIWKD6cT9OiEAHIADqFOfqvsP/1Ny6ffVlbKbV5QBAyCHQwX64fs6WYlSse7OXaH18K0bAAkA1I9ABCKibjq+Ww0jz6/S2uhQgdCxaZHUFsBiBDkDA/fbMf9U1Z4dm1xvICFigOmRnW10BLEagg73s2mV1BagmFxT8qLE/faIn6l3JCFgAqCICHexl9WqrK0A1Si0+rT8dXa45KQMYAQsAVUCgA2CpWBXpT0eX6d/JF2s7I2CByqMHI6wR6ABYLkLShONrtS8qVasTLrS6HMCe6MEIawQ6AEHjspytSi86qbdqdbO6FACwFQId7OP1162uAAHQ5uwP6pOzXS/V6csIWADwE4EO9pGTY3UFCJCMolO64cR/NK9OH512xFhdDgAEPQIdgKAUbwo18fhqvZfcUT8yAhbwzzvvWF0BLEKgAxC0IiVdf3K9tsc11tfRDawuBwh+x49bXQEsQqADEPT652xXQUSU1sefZ3UpABCUCHSwhwULrK4AFmt79gc1yT+i5YkdrC4FAIIOgQ72kJtrdQUIAplFx9XrzJdanNSJEbAA4IZAB8BWkky+rjz9mT5Ias8IWMCb/futrgAWINABsJ0oGQ06vUVbY5vqcESy1eUAwWX5cqsrgAUIdABsq3veTh2NStbu6DSrSwEASxHoANjaBfkHFFecr//GNLK6FACwDIEOwW/ZMqsrQJBrUHRCTQuytTnuXKtLAQBLEOgQ/A4csLoC2ECyyVOHvD3aGNecEbAAwg6BDkDIiFaxuuZ9q69jGinHEW11OQAQMAQ6ACHnovwfdCSytn6KiLe6FAAICAIdgJDUtDBbhYrUIW5rgnD08stWV4AAI9ABCFlpxaeVZPK0L6qu1aUAgVVcbHUFCDACHYCQlmgKlF54Qnuj6lldCgDUGAIdgJAXo2I1KTyqA5F1GAELICQR6BDc+JuEqEYNi47rZES88hVpdSkAUK0IdAhuH31kdQUIMSnFZ1TgiFQeoQ5ACCHQIbjl51tdAUJQoslXhAyhDkDIINABCEsxKla0inWWt0EAIYB3MgBhK1JGsSpmoAQA2yPQAQh7UTJWlwAAVUKgAwAgFO3aZXUFCCACHQAAoWjLFqsrQAAR6AAACEU5OVZXgAAi0AEAEIq47VNYIdABAADYHIEOAADA5gh0AAAANkegAwAAsDkCHQAAgM0R6AAAAGyOQAcAAGBzBDoAAACbI9ABAADYHIEOAADA5gh0AAAANkegAwAAsDkCHQAAgM0R6AAAAGyOQAcAQCiqVcvqChBABDoAAEJRz55WV4AAItABABCKMjOtrgABRKADAACwOQIdAACAzRHoAAAAbI5ABwAAYHMEOgS31q2trgAAgKBHoENw69HD6goAAAh6BDoAAACbI9ABAADYHIEOAADA5gh0AACEGgaUhR0CHQAAoYYBZWGHQAcAAGBzBDoEv8REqysAACCoEegQ/K6/3uoKAAAIagQ6AAAAmyPQAQAA2ByBDgCAUMItS8ISgQ4AgFDCLUvCEoEOAADA5gh0sIebb7a6AgAAghaBDgAAwOYIdAAAADZHoAMAIFRweUrYItABAADYHIEO9tGli9UVAAAQlAh0sI927ayuAACAoESgAwAgFCQlWV0BLESgAwAgFIwaZXUFsBCBDvaSnm51BQAABB0CHexl8GCrKwAAIOgQ6AAAAGyOQAcAgN1xQ+GwR6CD/TCSCwAADwQ62A8juQAA8ECgAwDAzlJTra4AQYBABwCAnQ0danUFCAIEOtgTFwADAOBCoAMAALA5Ah0AAHZFbwV+QaCDfXEhMAAAkgh0sDMuBAYAQBKBDgAAe+rb1+oKEEQIdLA3ul0BhKsWLayuAEGEQAd7o9sVAAACHQAAtkN3K0og0MH+mjWzugIACCy6W1ECgQ7217+/1RUAAGApAh0AAHbCzYThBYEOoYHrSQAAYSzK6gKAatGihbR6tdVVACGn61//qk1795aabl54QZJU5447dOLsWZ/zfXFMmlRq2sSuXfXy+PFe57tvL2rSJBVJat+wobbMmFHuMYSUuDirK0CQooUOAOCTe5hzuE2Pv/VW7d+/32uYk6QBTz7pc5vuYS3SbfrcjRslSRl33lnmOkW//B92YU6SxoyxugIEKVroEDpuvll68UWrqwBCxki3VjH3FjLHpEnKKypSowcfLDV/8ebNGjp3rj7csaPc7ZfcptPhnByP+e7znD+X1wIIhBta6AAAXu0+etSv5dxb7oZ07uz39p3hLLJE92rzlBTX/MZ33+2a7vw5xuFQWOJaYZTBYYwxVhcBVJtdu7iWDqhGztAVHxWl18eO1dC5c13znhg6VJMXLZL0cwgb2amTZq5c6ZpfViuat2vofLXYOed5W6e8/YQURreiDAQ6hB66XYFqEzFpknx9SJQVspzzvanKOle1aaP3t2/3mJcSF6ejzzzjc5shIT5eGj3a6ioQxOhyBQD4VPzCCzIvvKDftmqlxOjoUoHL/DL/wvr1lZqQUKHWMue67uts/uabUsv94Y03XD87w5z7esfy8ip0TLZEmEM5GBSB0MPgCKDarfxl5GmLqVO9zt/+wAOSym5988f+kydLTfvbJ59IKr9FEAhnBDoAgE+ugQv69XYh0s/Xz7nPj5BU7Dbf2/VwJVvvHJMmldpuyUEVznV/26qVJOm6iy/WW59/7hHs4iIjFdK4dg5+oMsVoYk3QKBauYeuC+vX110DBnjMdw9z00rMK2neqFFet9syLc1juf3797t+drYQvumlhS73uefK3B8QDhgUgdBFtysAuxs4UMrMtLoK2AAtdAhdtNIBsDvCHPxEoAMAIBh16GB1BbARAh1CW5cuVlcAAJXTqZPVFcBGCHQIbe3aWV0BAFRc69ZWVwCbIdAh9NFtAcBuevSwugLYDIEOoY9uCwB20rKl1RXAhgh0CA9cSwfALnr3troC2BCBDuGBa+kA2ME551hdAWyKQIfwMXCg1RUAQNkuv9zqCmBTBDqED27QCSCY8aUTVUCgQ3jhr0cACFZ86UQVEOgAALAaXzZRRQQ6hB/eOAEAIYZAh/AUH291BQDwM75kohoQ6BCeRo+2ugIAkKKirK4AIYJAh/DVt6/VFQAIdxMmWF0BQgSBDuGrRQurKwAQzrhNCaoRgQ7hjWtXAFiF25SgGhHogJgYqysAEG74MolqRqADxo2zugIA4aRhQ6srQAgi0AES35YBBM6gQVZXgBBEoAMAIFD48ogaQqADnHijBVCTuKE5ahCBDnDHbQQA1BRuaI4aRKAD3HEbAQA1gR4A1DACHVASb7wAqlN6utUVIAwQ6ABvCHUAqsvgwVZXgDBAoAN8qVvX6goA2B1fDhEgBDrAl2uvtboCAHbWoYPVFSCMEOiAsvDtGkBldepkdQUIIwQ6oDzcygRARfFlEAFGoAPKk5kpRUVZXQUAuyDMwQIEOsAfEyZYXQEAO6BFHxYh0AH+4ls3gLLExXFzcliGQAdUBKEOgC9jxlhdAcIYgQ6oKG5FAKAkvuzBYgQ6oKI6dZIieOkA+AVhDkGATyWgMm680eoKAAQDBkEgSBDogMriWzkQ3tLTGQSBoEGgA6qCUAeEr8GDra4AcCHQAVVFqAPCD697BBkCHVAdeHMHwgevdwQhAh1QXbidCRD6CHMIUgQ6oLp06iTVqWN1FQBqShCFOYfDIYfDYXUZCCIEOqA6DR8uxcdbXQWA6lZNYa6sIOacZ+eg5n4MN5a4vVMoHJ8/rDpGAh1Q3UaPlmJirK4CQHWpgZa56Ohon/OMMdW+v8qYPn16ldafO3duNVUCfxDogJowbhx/TQIIBdUc5ubNmydJKiws9Jh+9dVXu35u2LChR2uWPy0+06dPL3Mdb9vwtYzz38yZM71Or0zr08iRI71O9+dYvc33dxnncs6fW7VqVWqdqKioaqulrHPubb358+eXOb8iHCZYvgoAoejFF62uAEBl1dA1c84P6n379inzlxsTO6c1b95c+/fvV15eniQpIiJCxcXFrnWdH9nO5Us+dv7s/tFujCm1fHnb8CYqKsoVRH1Fh5LbiI6OVn5+fqnpzvXj4+P9PlZvjDHavHmzunTpUuYyzm20bNlSO3bs8NhuZGSkCgsLq6WWtLQ0ZWdnS5KSk5MVGxurI0eOeKznbdvu86Ojo1VQUOCxX3/QhADUpCC6iBpABQTgtduoUaNS03bt2qXc3FwZY2SMUVFRUbkf6hG/9AbEx8fLGKPi4uIqddtOmzbNtX+nLl26qKCgoNR0X1q2bClJrmDiS0WO1blcyWWcYS42Ntbv+mqqliNHjrh+PnnypMdj53plbfuJJ55Qfn5+pY6DQAfUNEIdYC81/Jot+UEdUeLyjMWLF1eo6825vdzc3GoZePDQQw+5fnaGpU2bNrm269497I/IyEhJUoMGDUrNq+ixlsXZuiZJF154YYXXr85afPG27auvvloxv1x3PXnyZK/dsf4g0AGBQKgD7CHAr9UOHTq4Atm+ffskSUOHDvVYpiLBomQgSU5OrlJ9GzdudF335/T+++97XIvmy6JFiyTJ1b144MCBUstU5VjL4mvQSVFRkc91aqqWktss+e/ee+/V2bNndd1113ksO2HCBM2ePdv/bXMNHRBACxZIublWVwHAmwCGuUsvvVTr1q3zmObr+jj3a8S8LeMePMq7ts3bdXu+9uvNgAED9OGHH/pcruS1aiVrK2+f5R1ryf24bzM+Pl5nzpzxmF9yGW/bdV5DVx21+LtMefz5nZZECx0QSKNHS6mpVlcBoKQAt8z95z//8XjsrUXJ2YJT1gX/UungEBUV5bPbsFGjRoqIiPC79cl9O9HR0a4w569p06YpPT3d1fpY3n7KO1ZvNm3aJMmzy7mkJ554otS+aqIWd7Vq1VKLFi28btvXKFyHw+Hqopa8d1P7rJsWOsACa9dKO3daXQUAybJLIny1wjRu3Fg//PCD13V8tQLNnj1bkydP9msdf7fpzzoleRtN6m2+c/3KHKu3aeWNPi1rGWcLXU3V4s/5r+h59oZAB1hl1y5p9WqrqwDCG9e3hqyKdnPaHYEOsBr3qgOsQZgLaQQ6AIFHqAMCJybm57/mgpBGoANgDUIdUPMaNpQGDbK6CqDaEeiAYEKoA2rOwIHSL7fsAEINgQ4INu+8Ix0/bnUVQGjhejmEOAIdEIz275eWL7e6CiA0EOYQBgh0QDCjCxaovKQkadQoq6sAAoJABwQ7Qh1QcVwvhzBDoAPs4NNPpa++sroKwB7oYkUYItABdkJrHeCbwyHddJPVVQCWINABdkOoA0qjixVhjkAH2NHWrdKmTVZXAQQHulgBAh1ga7TWIZzxVx8AFwIdYHcffSTt3m11FUBg0SoHeCDQAaGC1jqEgzp1pOHDra4CCDoEOiCULFokZWdbXQVQM2iVA3wi0AGhiNY6hJJmzaT+/a2uAghqBDogVPH3YBEKaJUD/EKgA0LdvHlSYaHVVQAVw33lgAoh0AHhgm5Y2EFqqjR0qNVVALZDoAPCCd2wCGZ0rwKVRqADwhH3rkMwIcgBVUagA8LZK69I+flWV4FwxXVyQLUh0AHg+joEVpcuUrt2VlcBhBQCHYBfEexQk1q3lnr0sLoKICQR6ACURrBDdWrZUurd2+oqgJBGoAPgG8EOVcE1ckDAEOgAlO/116WcHKurgF0wahUIOAIdAP/t2iWtXm11FQhWBDnAMgQ6AJVDdywkKSlJGjXK6iqAsEegA1A1CxZIublWV4FAozUOCCoEOgDVh1a70Fa3rnTttVZXAcALAh2A6se1dqGF1jgg6BHoANSsd96Rjh+3ugpUFCEOsBUCHYDAWbRIys62ugr4QogDbItAB8AaWVnSli1WVwFCHBASCHQAgsNLL0m8HdW8Dh2kTp2srgJANSPQAQhOjJitHtwnDggLBDoA9kALnn9at5Z69LC6CgABRqADYE/790vLl1tdhbVq1ZKuu87qKgAEAQIdgNCydq20c6fVVVS/vn2lFi2srgJAkCLQAQgfwXzD48hIaeJEq6sAYFMEOgDwZdcuacMGKS/P/+v3YmJ+7gpt25YWNQABQ6ADAACwuQirCwAAAEDVEOgAAABsjkAHAABgcwQ6AAAAmyPQAQAA2ByBDgAAwOYIdAAAADZHoAMAALA5Ah0AAIDNEegAAABsjkAHAABgcwQ6AAAAmyPQAQAA2ByBDgAAwOYIdAAAADZHoAMAALA5Ah0AAIDNEegAAABsjkAHAABgcwQ6AAAAmyPQAQAA2ByBDgAAwOYIdAAAADZHoAMAALA5Ah0AAIDN/X9M5rITgvJ5mgAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for company in customer_sport['number_company']:\n", - " percent_of_na(company, 'profession')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97326d89-d6f9-4e8f-9395-5c81def3831a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/Modelization/2_Modelization_sport.ipynb b/Sport/Modelization/2_Modelization_sport.ipynb deleted file mode 100644 index f653877..0000000 --- a/Sport/Modelization/2_Modelization_sport.ipynb +++ /dev/null @@ -1,2821 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3415114e-9577-4487-89eb-4931620ad9f0", - "metadata": {}, - "source": [ - "# Predict Sales" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f271eb45-1470-4764-8c2e-31374efa1fe5", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.calibration import calibration_curve\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "\n", - "import pickle\n", - "import warnings\n", - "#import scikitplot as skplt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3fecb606-22e5-4dee-8efa-f8dff0832299", - "metadata": {}, - "outputs": [], - "source": [ - "warnings.filterwarnings('ignore')\n", - "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", - "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" - ] - }, - { - "cell_type": "markdown", - "id": "ae591854-3003-4c75-a0c7-5abf04246e81", - "metadata": {}, - "source": [ - "### Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "59dd4694-a812-4923-b995-a2ee86c74f85", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "017f7e9a-3ba0-40fa-bdc8-51b98cc1fdb3", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c479b230-b4bd-4cfb-b76b-d9faf6d95772", - "metadata": {}, - "outputs": [], - "source": [ - "dataset_train, dataset_test = load_train_test()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c24c446d-4e1c-4ac1-a048-f0b8d8559f36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "street_id 0\n", - "structure_id 222825\n", - "mcp_contact_id 70874\n", - "fidelity 0\n", - "tenant_id 0\n", - "is_partner 0\n", - "deleted_at 224213\n", - "gender 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "last_buying_date 66139\n", - "max_price 66139\n", - "ticket_sum 0\n", - "average_price 66023\n", - "average_purchase_delay 66139\n", - "average_price_basket 66139\n", - "average_ticket_basket 66139\n", - "total_price 116\n", - "purchase_count 0\n", - "first_buying_date 66139\n", - "country 23159\n", - "gender_label 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "country_fr 23159\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 123159\n", - "y_has_purchased 0\n", - "dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "825d14a3-6967-4733-bfd4-64bf61c2bd43", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69eaec12-b30f-4d30-a461-ea520d5cbf77", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d039f31d-0093-46c6-9743-ddec1381f758", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape train : (224213, 17)\n", - "Shape test : (96096, 17)\n" - ] - } - ], - "source": [ - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "a1d6de94-4e11-481a-a0ce-412bf29f692c", - "metadata": {}, - "source": [ - "### Prepare preprocessing and Hyperparameters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b808da43-c444-4e94-995a-7ec6ccd01e2d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute Weights\n", - "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", - " y = y_train['y_has_purchased'])\n", - "\n", - "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n", - "weight_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b32a79ea-907f-4dfc-9832-6c74bef3200c", - "metadata": {}, - "outputs": [], - "source": [ - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - "numeric_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n", - " (\"scaler\", StandardScaler()) \n", - "])\n", - "\n", - "categorical_features = ['opt_in'] \n", - "\n", - "# Transformer for the categorical features\n", - "categorical_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", - "])\n", - "\n", - "preproc = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features)\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9809a688-bfbc-4685-a77f-17a8b2b79ab3", - "metadata": {}, - "outputs": [], - "source": [ - "# Set loss\n", - "balanced_scorer = make_scorer(balanced_accuracy_score)\n", - "recall_scorer = make_scorer(recall_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "4f9b2bbf-5f8a-4ac1-8e6c-51bd0dd8ac85", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_confusion_matrix(y_test, y_pred):\n", - " conf_matrix = confusion_matrix(y_test, y_pred)\n", - " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - " plt.xlabel('Predicted')\n", - " plt.ylabel('Actual')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", - "\n", - "\n", - "def draw_roc_curve(X_test, y_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - " # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - " fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - " \n", - " # Calcul de l'aire sous la courbe ROC (AUC)\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " plt.figure(figsize = (14, 8))\n", - " plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - " plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - " plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - " plt.xlabel(\"False Positive Rate\")\n", - " plt.ylabel(\"True Positive Rate\")\n", - " plt.title(\"ROC Curve\", size=18)\n", - " plt.legend(loc=\"lower right\")\n", - " plt.show()\n", - "\n", - "\n", - "def draw_calibration_curve(X_test, y_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - " frac_pos, mean_pred = calibration_curve(y_test, y_pred_prob, n_bins=10)\n", - "\n", - " # Plot the calibration curve\n", - " plt.plot(mean_pred, frac_pos, 's-', label='Logistic Regression')\n", - " plt.plot([0, 1], [0, 1], 'k--', label='Perfectly calibrated')\n", - " plt.xlabel('Mean predicted value')\n", - " plt.ylabel('Fraction of positive predictions')\n", - " plt.title(\"Calibration Curve\")\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "cf400c70-0192-42cc-9919-f61bae8382b0", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_features_importance(pipeline, model, randomF = False):\n", - " if randomF:\n", - " coefficients = pipeline.named_steps[model].feature_importances_\n", - " else: \n", - " coefficients = pipeline.named_steps[model].coef_[0]\n", - " \n", - " feature_names = pipeline.named_steps[model].feature_names_in_\n", - " \n", - " # Tracer l'importance des caractéristiques\n", - " plt.figure(figsize=(10, 6))\n", - " plt.barh(feature_names, coefficients, color='skyblue')\n", - " plt.xlabel(\"Features' Importance\")\n", - " plt.ylabel('Caractéristiques')\n", - " plt.title(\"Features' Importance\")\n", - " plt.grid(True)\n", - " plt.show()\n", - "\n", - "def draw_prob_distribution(X_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - " plt.figure(figsize=(8, 6))\n", - " plt.hist(y_pred_prob, bins=10, range=(0, 1), color='blue', alpha=0.7)\n", - " \n", - " plt.xlim(0, 1)\n", - " plt.ylim(0, None)\n", - " \n", - " plt.title('Histogramme des probabilités pour la classe 1')\n", - " plt.xlabel('Probabilité')\n", - " plt.ylabel('Fréquence')\n", - " plt.grid(True)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "206d9a95-7c37-4506-949b-e77d225e42c5", - "metadata": {}, - "outputs": [], - "source": [ - "# Hyperparameter\n", - "param_grid = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n", - " 'logreg__penalty': ['l1', 'l2'],\n", - " 'logreg__class_weight': ['balanced', weight_dict]} " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7ff2f7bd-efc1-4f7c-a3c9-caa916aa2f2b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'time_between_purchase',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'is_email_true', 'opt_in',\n",
-       "                                                   'gender_female',\n",
-       "                                                   'gender_male',\n",
-       "                                                   'gender_other',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver='saga'))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'time_between_purchase',\n", - " 'nb_tickets_internet',\n", - " 'is_email_true', 'opt_in',\n", - " 'gender_female',\n", - " 'gender_male',\n", - " 'gender_other',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pipeline\n", - "pipeline = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n", - " max_iter=5000, n_jobs=-1)) \n", - "])\n", - "\n", - "pipeline.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "markdown", - "id": "ed415f60-9663-4179-877b-233faf6e1645", - "metadata": {}, - "source": [ - "## Baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2b467511-2ae5-4a16-a502-397c3460471d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'time_between_purchase',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'is_email_true', 'opt_in',\n",
-       "                                                   'gender_female',\n",
-       "                                                   'gender_male',\n",
-       "                                                   'gender_other',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver='saga'))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'time_between_purchase',\n", - " 'nb_tickets_internet',\n", - " 'is_email_true', 'opt_in',\n", - " 'gender_female',\n", - " 'gender_male',\n", - " 'gender_other',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6356e870-0dfc-4e60-9e48-e2de5e7f9f87", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.764547952047952\n", - "F1 Score: 0.4741074748977315\n", - "Recall Score: 0.7449963476990504\n" - ] - } - ], - "source": [ - "y_pred = pipeline.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "09387a09-0d53-4c54-baac-f3c2a57a629a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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Em3IhYqVMiFj5eyaK0/PQotJ+bNy4cXaXIOJTlAkRK2VCxJtyIWKlTIhYOSkTagiJiIiIiIiIiDiMGkJ+rFmzZnaXIOJTlAkRK2VCxJtyIWKlTIhYOSkTagj5sdjYWLtLEPEpyoSIlTIh4k25ELFSJkSsnJQJNYT82KJFi+wuQcSnKBMiVsqEiDflQsRKmRCxclIm1BASEREREREREXEYbTvvx1JSUkhISLC7DBGfoUyIWCkTIt6UCxErZULEyt8zoW3nHWLdunV2lyDiU5QJEStlQsSbciFipUyIWDkpE2oI+bE9e/bYXYKIT1EmRKyUCRFvyoWIlTIhYuWkTKgh5MciIiLsLkHEpygTIlbKhIg35ULESpkQsXJSJrSGkIiIiIiIiIhIOaA1hBxizJgxdpcg4lOUCRErZULEm3IhYqVMiFg5KRNqCImIiIiIiIiIOIwaQn6sUaNGdpcg4lOUCRErZULEm3IhYqVMiFg5KRNqCPmxypUr212CiE9RJkSslAkRb8qFiJUyIWLlpEyoIeTH5s+fb3cJIj5FmRCxUiZEvCkXIlbKhIiVkzKhhpCIiIiIiIiIiMNo23k/duDAAapUqWJ3GSI+Q5kQsVImRLwpFyJWyoSIlb9nQtvOO8TWrVvtLkHEpygTIlbKhIg35ULESpkQsXJSJtQQ8mM7d+60uwQRn6JMiFgpEyLelAsRK2VCxMpJmVBDyI+FhITYXYKIT1EmRKyUCRFvyoWIlTIhYuWkTNi6htDcuXN57bXXWLZsGfv27WPSpEn079//rM/57bffGDZsGGvXrqV69eo8+uijDBkypMjvWZ7WEBIREREREREROc5v1hDKzMykVatWvPvuu0V6/Pbt2+nbty8XX3wxK1as4Mknn+TBBx9kwoQJpVypbxo3bpzdJYj4FGVCxEqZEPGmXIhYKRMiVk7KRJCdb96nTx/69OlT5Md/8MEH1KpVi7feeguApk2bsnTpUl5//XWuu+66UqrSdxUUFNhdgohPUSZErJQJEW/KhYiVMiHF4XYb5BW6OX6dkYFx0u1jfxrGSbex3FnUxxsYJz3nxH3HH3nyaxy/6Onka58sr+N57on3O/HcY9+XYbA/NYes3Hz+TIYr8wqICLG1XVIm/Oo7XLRoEb1797aMXX755Xz66afk5+cTHBzs9Zzc3Fxyc3M9x2lpaaVeZ1mpV6+e3SWI+BRlQsRKmRDxplyIWJWnTBS6DY5k5bFubxrpOQW4T2o0wIlGAJaxk25ztsaC9+tYXu2UBsSZn1u0x3Ga98grcHuaMYZheJochgH5hW4OpecSFOg6qdFh4HafaMIYmA2dPUeyyS0o9Pq7AEjJzCMztwC3YT7WbRgUGubruA2DArdtK86UqgpZqQyd/y2hBXl80/dfDMnIIyLer9ol58SvvsP9+/dTpUoVy1iVKlUoKCggOTmZatWqeT1n9OjRjBo1ymt83LhxREREMGDAAH755RdSU1OpXLkyHTt2ZOrUqQC0bdsWt9vNn3/+CUC/fv2YP38+KSkpxMfH061bNyZPngxAy5YtCQ4OZtmyZQBceeWVLF26lAMHDhATE0Pv3r0ZP348AM2bNycqKoo//vgDMJtaa9asISkpicjISK666irGjh0LQOPGjalYsSILFiwA4LLLLmPTpk3s2rULt9tNp06dGDt2LG63m/r165OYmMjcuXMB6N69O7t27WLbtm0EBQUxcOBAJkyYQF5eHrVr16Z+/frMnj0bgK5du3Lw4EE2bdoEwODBg5kyZQpZWVnUqFGDZs2aMXPmTAA6d+5Mamoq69atA2DgwIH89NNPpKenU7VqVdq2bcv06dMB6NChAzk5OaxevRqAa6+9ljlz5nDkyBEqVqxI586d+eGHHwBo06YNACtWrADg6quvZtGiRSQnJ1OhQgW6d+/OpEmTALjgggsICwtjyZIlAPTt25fly5ezf/9+oqOjueKKKzxT/Zo1a0ZsbCyLFi0CoHfv3qxbt449e/YQERFBv379GDNmDACNGjWicuXKzJ8/H4BLL72UrVu3snPnTkJCQrjuuusYN24cBQUF1KtXj1q1ajFnzhwAunXrRlJSElu3biUgIIBBgwYxceJEcnNzqVWrFo0aNeLnn38GoEuXLiQnJ7Nx40YABg0axNSpU8nMzCQxMZEWLVowY8YMADp16kRGRgZr164F4Prrr2fmzJmkpaVRpUoV2rdvz7Rp0wBo164d+fn5rFq1CoD+/fszd+5cDh8+TEJCAl27dmXKlCkAtG7dmoCAAJYvXw7AVVddxeLFizl48CCxsbH07NmTiRMnAtCiRQsiIiJYvHgxYM7uW7lyJXv37iUqKoq+ffvy/fffA9CkSRPi4+NZuHAhAL169WLDhg3s3r2b8PBw+vfvz3fffYdhGDRs2JCqVasyb948AHr06MGOHTvYvn07wcHBXH/99YwfP578/Hzq1q1LnTp1+PXXXwG4+OKL2b9/P5s3b8blcnHjjTeydetWtm3bRs2aNWnSpAmzZs0C4KKLLuLw4cNs2LABgBtuuIHp06eTkZFB9erVadWqFT/++CMAHTt2JCsrizVr1gD49WdEaGgoAwYM0GcEzv2MiIyMJC0tTZ8Rxz4jJk+eTHZ2tj4jHP4ZkZOTQ2RkpD4j9HNEuf2MaNy0GUcLQ1ixchUFbhct23Vg67btJCcnExYWRoeOHZk7dx4GUCMxEXAx9YNxx96nFXv3JnEoOZmgoGA6derE/AULMAyDKlWqUiG+AuvXrcc4lo3k5BQOHDxAQEAgbdp34I8lyygoKKBCfDwVK1Ziw8ZNGEDdunVJT8/g4KFDGECrVq1Zu24tuXn5hETEEFMhnm07dpGe78IVHkNBQSGZ2dlgQFyFChxNTaWw0E1QUBCh4eGkp6djGBAWFk5GXiEZuQUUuMGNC/F/J/4rGrgAV0AAhtsNQECACxcu3Ib5XzsgIMBs0BkGuCAoMIiCwgLPfS6XC3eh2fwKCgrEcBu43W5cLswJJTlZ3LR0GvfN/46Y3EzcLheTuvcn5dABDmxL8cvPiKysrKL/Xdu5qPTJXC7XXy4q3ahRI+644w6eeOIJz9iCBQvo2rUr+/bto2rVql7POd0MoZo1a5aLRaXHjBnD4MGD7S5DxGcoEyJWyoSIN+VCyhvDMNiXmsOqPams2nOUMYt3cSQr3+6ybJUYF05ihXACXSdaC8dvnjSE61jrwXWaPpLr5Oee8hrWsbM/jtO8x+lf7zSPc1nvCwxwERoUgMsFAS7Xsftdx44h0OUiKiyIkMBAXC7zfVyuEzWaYy4qRARTKTrU+5vGfN1K0aGEBQcSGOAi8Nj7BAa4CAwwb0eEBHle+3h9p/4dHn8v6/dx4u/s1L8D1+n+I5Qmw4D/+z8YPhy2bDHHWreGN95gzP79fn2eKM6i0n41Q6hq1ars37/fMnbw4EGCgoJISEg47XNCQ0MJDT39/+wiIiIiIiK+wjAMcgvcFLgNCgsN8t1uCt0G+YXH/zQocLvZmZLF2r1prN5zlNVJqSRn5Hm9VuXoUIIDAwgJCjj2j3tz/Pg/3s1egouUlBQqJiSc1EA49o94TvpH/Un/oD8+dnKD5fhxaFAgQQEuAgJOjB1vXAQcawK4XC4CXHjGIkODCAkKICQwgAoRwVSNDSfg2H0nv47neRx//onvIyEqlKjQIEKDAggNDiA8OLDsGwziP3btgjvugGOzXKlSBV58EW6/HQID4disTyfwq4bQydOCj5s5cybt27c/7fpB5V23bt3sLkHEpygTIlbKhIg35ULsYhgGR7LyMQyD7PxCth7KZMWuI6zak0pWXgF7j+ZwNCuPtJziL/IcGOCicZVoWtaIpVXNOPq2qEZsRNH+fZSUlERiYmKx31PEb8XFwerVEBoKDz8Mjz8O0dGeu510nrC1IZSRkcGW49OzMLeV//PPP4mPj6dWrVo88cQTJCUl8dVXXwEwZMgQ3n33XYYNG8bdd9/NokWL+PTTTz3XbTuNPrxFrJQJEStlQsSbciHFVeg2Z+UYhrmortuAgkI3R7LyKSh0k19oUOg2OJyVx47kTA5n5nE4M4+M3AJ2Hc5i79FsjmTlkVvg5lwW6wg6dqlOcGDAsT9dhAYF0rpmHB3rxtOyRixNq8UQFhx4Tt+fMiHlXk4OfPcd3HabOTUtJga+/RYaNoTatb0e7qRM2NoQWrp0KT169PAcDxs2DIDbbruNL774gn379rFr1y7P/XXr1mX69OkMHTqU//73v1SvXp23337bkVvOA2zdupWOHTvaXYaIz1AmRKyUCRFvyoVvMIwTlz/lFxqkZOSSk+8mv9DN4aw883KpQjc7UrKIDA2k4Nhj8wrcbDqQQbW4MLNB4zYbNG7DwDBO3HYf24Upr8BNTkEhBYWGZ4ekgkKDjNyCY40eg4JCN5sPZlAxKsTT7HEbZiOo8NgW2yXN5YL6laKICQuia8NK1KsYSYXIEKrHhlE5JozQoABPI6i0L31SJqTcMgwYNw4eewx27ICICLjhBvO+yy4749OclAlbG0Ldu3c/7dZ/x33xxRdeY5dccolnNwOnCwgIsLsEEZ+iTIhYKRMi3pyYC7fbIDOvgPScAjJyC0jPyScn39rkMAzIyS8kNTuf1Ox80nLycbvNjbHzCw1y8gvJLSgkN99ssOQVmA2agmONm+ONleNNnoJj69+Y93s/ttAHt64+3To8ZxMaFEBkaJA5ayfARWCgi1rxEdRJiCQ+MoTosCDCQ4JoXj2GSlGhuFxQMSr0nGfylBYnZkIcYMkSGDoUju2ySWIihIQU6alOyoTP7DJWVoqz4raIiIiIiK/KPHZJUkZuAQfTctlzJIvMvEIKCt3sT8the3Ime49mk5yR55MNmNNJjAsnONDFjpQsWteMIzI0kM0HMuhUL4HgABdBgS7cBiRn5NK4SrRnoeEA14mFho/fDjg2wyYixNwtyZxxY868CQsOJDT4xCycoABz56bY8GACXC7PeGCAi5CgAEKDAo697vEFjiEkMEALF4v4mj174Mkn4euvzeOICHj0UXM3schIe2srI+V2lzGxmjhxIgMGDLC7DBGfoUyIWCkTIt7sykVegZvcgkKy8ws5mJbL3qPZ5q5RboPCY5dN7TmcRVpOAbkF5qVT+YXmrJq0nHxy8gs9M3XScwpIzc4nI7d4iw8HBbiIDgsiOiyYsOAATmz8bAoJCiAuIpiY8GBiwoIJDnQde14AYcHmTlVhwQGEBgUSHBhAUKC5nk1QQMCJP4MCjjVujt0fYH2cedtsxAQFnnieuTOVmit20LlCypUbboBFi8zbt9wCL70ENWoU6yWclAk1hPxYbm6u3SWI+BRlQsRKmRDxdq65yC0oZH9qDou3H2bLoQwwzEupth7K4EhWHi6Xi/wCt2edm+MzdQoKDdKL2bgpjtjwYOIigkmIDKFmfATRYUEEBQRQMSqEepWiqFkhgkrRocRFBBMapBkt4k3nCvFrbjcUFsLxXcdfeAGeeQbefBM6dDinl3RSJtQQ8mO1atWyuwQRn6JMiFgpEyLeipqLnSmZTF21j1nrDvDn7qMl9v4uF8SFB1MrPoLQoGOXMgWalyZVjg6lUnQoIYGBhAQdnz3jIiosmMiQQIICzUuXosOCqBARQlxEMLHhwWryyHnRuUL81sKF8NBD0K8fjBhhjl16KfToYX7YniMnZUINIT/WqFEju0sQ8SnKhIiVMiHi7Wy5OJqVxzd/7OK1GRvP+JiIEHPHqw51K9CwcjQVIkKICAmkelw4UWFBnsulokKDzIbOscujwkMCiQ4L0roz4nN0rhC/s3OnuXPY2LHm8Z495hpBoaHm8Xl+xjopE2oI+bGff/6ZwYMH212GiM9QJkSslAkRb6fmIiUjl5nrDjB99T4WbU2h4KTFlxPjwqlRIZzEuHAGtq9Jp7rxWudGyh2dK8RvpKfD6NHwxhuQm2s2fu68E55//kQzqAQ4KRNqCImIiIhIuZKTX8juw1kcysglM7eQjNx8MnILycwt4I99oWz8v7Uczcpj95FsVuw6wskbcDWpGk2zajH0a5PIJY0q2fdNiIjICT//DH/7Gxw4YB736GE2hlq3trUsf6eGkB/r0qWL3SWI+BRlQsRKmZDyLK/Aze4jWexIzmR7ciY7UjLZkZxlbrWemo1xxl3WQ+HgDsvIBYmx9LmgKn1aVKNuRWdsSyxynM4V4hfq1IHDh6FBA3j9dbjmmvO+NOxMnJQJNYT8WHJysqMWvBL5K8qEiJUyIeVFanY+25Mz2XYog5W7j7Js1xE27k8nv/CMXR+iQoOoGhtGZGgQ0aFBRIYGEhkaRHbaEerWqEZseDBVY8NoW6sCNeMjyvC7EfEtOleIT9qyBWbOhPvuM48bNIBZs6BzZwgJKdW3dlIm1BDyYxs3bqRt27Z2lyHiM5QJEStlQvxFZm4BGw+ksysli/TcAtKy8z0zf7YnZ5KSmXfa50WEBFInIZI6FSOO/RlJ3YqR1EmIpGJUyGkXbx4zZgyDr+hR2t+SiN/QuUJ8ytGj5tbxb78NBQVw4YVw/P/PSy4pkxKclAk1hERERESkTB3OzGPJjsMs2X6YxTsOs3ZvGoXuM8/2AagSE0rdipHUrRhF1ZgwOtaN58J68dqxS0SkPCgogI8+gpEjITnZHLviCoiKsreucs5lGGe+wro8SktLIzY2ltTUVGJiYuwu57y43W4CAgLsLkPEZygTIlbKhPiC5Ixclu08QtKRbLYcymDt3jRW7j7q9bhK0aHUrxRJbHgwkaFB1I6PpG6lSOpVNGf+RIWWzO8xlQsRK2VCbDdjBgwbBuvWmcdNm8K//w19+thSjr9nojg9D80Q8mNTp07lmmuusbsMEZ+hTIhYKRNih3V705iwfA8rdh1h04EMMnILzvjYwR1rcmG9BDrUiad6XHiZ1KdciFgpE2KrzEy45RY4dAgSEmDUKLjnHggOtq0kJ2VCDSE/lpmZaXcJIj5FmRCxUiakrCQdzeanNfv5YuF2dh/O9rq/UnQoIYEBXNO6OnUrRnJJo0pUiQmzoVLlQuRUyoSUuaNHITbW3CUsMhJGj4a1a+Hpp6FCBburc1Qm1BDyY4mJiXaXIOJTlAkRK2VCStv6fWm8MG0dC7akeMYCXBAaFMgjlzemVnwEHerGExtu3296T6VciFgpE1Jm8vLgvffguefgww9h4EBz/M477a3rFE7KhBpCfqxFixZ2lyDiU5QJEStlQkpaodtg4dZkFm5N4dcNB9mwPx0wm0Cta8ZxbZtELm9RlcrR9sz+KQrlQsRKmZBSZxgwdSo8/DBs3myO/e9/JxpCPsZJmfDflZKEGTNm2F2CiE9RJkSslAkpKTn5hfzn5830fvM3bvl0Me/P2cqG/ekEB7q4vHkVfh3enYn3deGWznV8uhkEyoXIqZQJKVWrVkGvXnDNNWYzqHJl+PhjmDjR7srOyEmZ0AwhERERETmtg2k5TPlzL2//spn0Y4tDhwUH0KdFNbo2qEjPppWJiwixuUoREfFJL78MI0aA2w0hIeZOYk88AX6+23d5ooaQH+vUqZPdJYj4FGVCxEqZkHOVlVfA//25l2emrCWv0O0Zv75dDZ65uhkxYb6zJlBxKRciVsqElJp27cxm0MCB8MorULeu3RUViZMyoYaQH8vIyLC7BBGfokyIWCkTUlw5+YWM+mEtY5fsxm2YY4lx4VzXNpEh3esTEeL/PzoqFyJWyoSUCMOA8eMhNRXuussc69ULVq8GP1uTx0mZ0BpCfmzt2rV2lyDiU5QJEStlQorjy4U76Pnv3xiz2GwGJcaF06NxJX57pDvDejcuF80gUC5ETqVMyHlbuhS6dYMbboChQ2H//hP3+VkzCJyVifJxZhcRERGRYssvdPPR3G28NmOjZfz9m9tyRYuquFwumyoTERGfl5QETz4JX31lHoeHm+sERUfbW5cUmcswDMPuIspSWloasbGxpKamEuPni1nl5+cTHOy/1/CLlDRlQsRKmZAzMQyDMYt3887szexLzfGM168Uyff3diYhKtTG6kqXciFipUxIsWVlweuvm+sCZWWZY3/7G7z0EtSsaW9tJcDfM1GcnocuGfNjM2fOtLsEEZ+iTIhYKRNyqkK3wbRV+2g+cgZPTlrNvtQcKkWH8s8eDZh8fxd+ebh7uW4GgXIhciplQoptzx54/nmzGdS5M/zxB3z9dbloBoGzMqFLxvxYWlqa3SWI+BRlQsRKmZDjdqVk8fy0daxNSmXvsRlBYcEB3Nq5Dg9d1rDcrA9UFMqFiJUyIUWyffuJXcIaNYIXXoA6dcx1g8rZ5cVOyoRzzv7lUJUqVewuQcSnKBMiVsqE7D2azcj/W8usdQc8Y9GhQVzUIIEn+zaldkKkjdXZQ7kQsVIm5Kx27oTHH4fvvzcXj27Txhx/7DF76ypFTsqEGkJ+rH379naXIOJTlAkRK2XCuQ6m5/Dx3G18uXAneYVuz/i/ejbkH93rExYcaGN19lIuRKyUCTmtjAx4+WX4978hJ8ecBfTrrycaQuWYkzKhNYT82LRp0+wuQcSnKBMiVsqEc7jdBmv3pvLZ/O1c8dZcOr74Cx/P205eoZsL68Uzfkhndrx8JUN7NXJ0MwiUC5FTKRNi4XbD559Dw4bw4otmM+iSS2DZMnMHMQdwUiY0Q0hERETEzxS6DdbtTeOP7Sn8vi2FxdsPk5ZTYHlMTFgQr17fisubV9H28SIiUjR9+sDxRZXr14fXXoP+/cvdOkFiUkPIj7Vr187uEkR8ijIhYqVMlC+7D2excGsyczcnM3fjIdJzrQ2gyJBA2tWJ58J68XSqm0C72hVsqtS3KRciVsqEWPTrB7//Dk8/DQ88AKHle+fJ03FSJtQQ8mP5+fl2lyDiU5QJEStlwr8dycxj4dYUFmxNZsGWZHamZFnujw4NokPdeDrVjadTvQRaVI8hKFCrAfwV5ULESplwsNRUc7ewLl3MWUAA99wDAwdCpUq2lmYnJ2VCDSE/tmrVKpo3b253GSI+Q5kQsVImfI9hGOQWuMnKKyQ7v5DsvAKy8gpJzylgw/509h3NJjkjl72pOSzdcRi3ceK5gQEuWteMo0v9BC5pXInWNSsQGKAp/MWlXIhYKRMOVFAAn3wCzzwDhw7BxInQty+EhEBQkKObQeCsTKghJCIiIlJMBYVuZq47wLZDGWTlFZoNnrxCsk5q8mTlFZKTX3jS/QVk5xdamjx/pXZCBD2bVKFLgwQ61o0nOiy49L4pEREp/2bNgqFDYe1a87hJE3MnsWCdX5zIZRhGMX4s8X9paWnExsaSmppKTEyM3eWcl+zsbMLDw+0uQ8RnKBMiVspEyXK7DbYeymDRthQ+mbedXYez/vpJZxESFEBESCDhwYGEhwRSMTKUVjVjqRQdSsWoUGpUiKBDnQpaELqEKRciVsqEQ2zebO4SNnWqeRwfD6NGwb33qhl0Cn/PRHF6Hpoh5Mfmzp3L5ZdfbncZIj5DmRCxUibOnWEY7DmSzao9qazcc5SVu4+yJimVzLxCz2PiI0O4rGllIkODiAgJJCIkyNPcOd7oiQgJOuXYvD88OFDr/dhEuRCxUiYcYvt2sxkUFAT//Kd5uVgFbT5wOk7KhBpCfuzw4cN2lyDiU5QJEStlougOpeeyas9RVu5JZdWeo6zak8rhzDyvx4UHB9IiMYY+LapxY8eaRIToRyl/o1yIWCkT5VR+PqxaBcd3zOrd21xAeuBAaNTI3tp8nJMyoZ9i/FhCQoLdJYj4FGVCxEqZOL20nHxWH5v5s2q32QDam5rj9bjgQBdNqsbQskYsrWrE0bJmLA0qRWlmj59TLkSslIlyxjBg2jQYPhz27jUvFatSxbxvxAh7a/MTTsqE1hDyY1lZWURERNhdhojPUCZErJSJE/alZjNxeRJT/kxi04EMr/tdLmhQKYqWNeJoVTOWljXiaFI1mrDgQBuqldKkXIhYKRPlyJo15jpBs2aZx5Uqwfjx0K2bvXX5GX/PhNYQcogpU6YwePBgu8sQ8RnKhIiVUzORmp3PmqRUVu1JZXXSUX7dcIjs/ELLY2rGh5vNnxpm86dFYixRofqxyAmcmguRM1EmyoGDB801gT7+GNxuc/v4hx6CJ5+E2Fi7q/M7TsqEfvIRERERv5WVV8DavWms3H2U1ceaQNuTM8/4+Fevb8llTasQHxlShlWKiIiUkowMaNYMUlLM4+uvh1degXr17K1L/IIaQn6sdevWdpcg4lOUCRGr8piJQ+m5zFp3gOW7jrB6TyqbD6bjPs3F78dnALVMPD4DKIboMG2rK+UzFyLnQ5nwc1FRcPPNMH8+vPmmLg8rAU7KhBpCfiwgQItaipxMmRCx8udM5BW4WbcvjS0HMzxfWw9lnHb2T9WYMFrWiKVljVguONYEqqAZQHIG/pwLkdKgTPiZ5cvNBaPfeAOONy5efhlCQ0H/LUuEkzKhhpAfW758OY0bN7a7DBGfoUyIWPl6JnLyC1m7N5UVu45yKD2X5Iw8DmfmsjMli21nuewL4JpW1bmmVXVa1oilckxYGVUs5YGv50KkrCkTfmLfPnOXsC++MHcSe/JJmD7dvC883NbSyhsnZUINIRERESkzWw5mMHlFEvM2H2LdvjTyC8++2emF9eJpUDmKBpWiaFA5mgaVo6gSE4rL5SqjikVERGyUnW3OBho9GjKP/bLk5pvNY5HzpG3n/Vh6ejrR0dF2lyHiM5QJEStfyUTS0Wx+WrOfySuSWJ2UarmvYlQIjatG06RqDPGRIVSMCiE+MpSKUSG0SIwlONA507albPhKLkR8hTLhwyZNMncL27XLPL7wQnjrLejUyc6qyj1/z4S2nXeIxYsX07NnT7vLEPEZyoSIlV2ZyC0oZMn2I8zZeJA5mw6x5WCG576gABeXNKrElS2r0aFOPDUqhGu2j5QpnStErJQJH5aUZDaDatY0dw678UbQObPUOSkTagj5sYMHD9pdgohPUSZErMoyE3uOZPHrhoP8tukQC7emkJVX6LkvwAXtalfg6lbVufKCaiREhZZZXSKn0rlCxEqZ8CG7d8PevSdmAN17r7le0F13aZ2gMuSkTKgh5MdiY2PtLkHEpygTIlalkQnDMNiRksXeo9nsPZrNloMZzFi7nx0pWZbHVYoO5ZJGlejeuBIXN6hEbIS2fBffoHOFiJUy4QMyMuDVV+G11yAxEdauNXcNCw6GBx6wuzrHcVImtIaQH8vNzSU0VL9lFTlOmRCxKslM7DmSxZQ/9zJh2Z7T7gB2fBZQ98aV6d64Es2qxehSMPFJOleIWCkTNnK74auvzB3D9u0zx7p1gzFjoHp1e2tzMH/PRHF6Hlqp0Y9NnDjR7hJEfIoyIWJ1vpk4kJbD17/v5IYPFtH1lV95bcZGTzOoYeUoLm5YkZs61eLtwW1YObI344ZcxP09GtC8eqyaQeKzdK4QsVImbDJvHnTsCHfcYTaD6tWDCRNgzhw1g2zmpEzokjEREREBzMvB/th+mMkrkli684hlMWiXCzrXS6B/60SuuKAqMWG6BExEROScrFhhzgQCiImBp56CBx80LxMTKUNqCPmxFi1a2F2CiE9RJkSs/ioThW6DWev289umQ6zcncq6fWlej6lXKZIrL6jGoA41qVEhorRKFSkzOleIWCkTZcTthoBjF+i0aQNXXQU1asCoUVC5sr21iYWTMqGGkB+LiNAP5iInUyZErE6XiZz8QpbuOMKynUd48+dNp33ejR1q0rNpFS5IjKVqbFhplylSpnSuELFSJkpZYSF8+im8/jrMn3+i+TN5MgQG2lqanJ6TMqGGkB9bvHgx9evXt7sMEZ+hTIhYnZyJNUmpTFqRxMTleziSle95TGx4MC1rxHJ586pUjAqlc70E7Qgm5ZrOFSJWykQp+uUXGDoUVq82j995B55/3rytZpDPclIm1BASEREpp/ZmB/DR3K28MWsTOfluz3iVmFAuSIzlypbV6NOiGmHB+qFURESkxGzaBMOHww8/mMcVKsDIkXDfffbWJXIKbTvvx44ePUpcXJzdZYj4DGVCBA6m5fDDqn0s2JLM7A0HLfc1qhLF8N6N6dm0CoEB2gVMnEnnChErZaIEGQY89hi8+SYUFEBQkNkEGjkS4uPtrk6KyN8zUZyeh2YI+bGVK1dyySWX2F2GiM9QJsTJVuw6wvtztjJz3QHLeO2ECC6sm0CHuvFc366GTdWJ+A6dK0SslIkS5HJBTo7ZDLrySnPdoCZN7K5KislJmVBDyI/t3bvX7hJEfIoyIU6TkVvAL+sP8On87azak+oZb1A5iosbViQueQ3/uuNKGysU8T06V4hYKRPnwTBg+nSoUweaNzfHRo40dxDr3dvW0uTcOSkTagj5saioKLtLEPEpyoQ4xcG0HB6bsIolO46QkVvgGb+8eRXu6VaPtrUq4HK5+OGHbTZWKeKbdK4QsVImztGaNfDwwzBzJvTsCbNmmTOEEhLUDPJzTsqE1hDyY4WFhQRqdXoRD2VCyru1e1N5b85Wftt4yNIIuveSevRpUY3WNeMsj1cmRLwpFyJWykQxHTpkzgL68ENwuyE4GP71L3jpJfO2+D1/z0Rxeh4BZVSTlILvv//e7hJEfIoyIeVRckYu3/yxk2vfW8CVb89n2qp9ZOQWUCs+gleuu4BtL/XliT5NvZpBoEyInI5yIWKlTBRRbq65JlDDhvD++2YzaMAAWL8eXntNzaByxEmZ0CVjIiIiPmZfajaLtx9mzsZDTFqRZLmvQeUonrmqGRfVTyAoUL/XERERKRNffw2PPGLebtPG3EnMIQsPS/mlhpAfa6IV60UslAnxd5m5Bbz18yY+nrfdMh4WHMDgjrW4tXMd6laMLPLrKRMi3pQLEStl4iyysyE83Lx9223w7bdwyy1w663gx5cUydk5KRNqCPmx+Ph4u0sQ8SnKhPij7LxCJizfw/tztrIvNRv3sZX9QgID+NuFtbm6VTXa1KpwTq+tTIh4Uy5ErJSJ09i3D556ChYsgFWrICTEvCRs9my7K5My4KRMqCHkxxYuXEjt2rXtLkPEZygT4k/cboPxy/bw9JQ15Ba4PeOJceEMbF+D+3s0IPg8LwlTJkS8KRciVsrESbKzzUvBXnoJMjPNsZkzzW3kxTGclAk1hERERMrYmqRUnpmyhuW7jnrGRvRtyiWNK9GoSrR9hYmIiDiRYcDYsfDYY7BrlznWqZPZHOrc2d7aREqRtp33Y8nJyVSsWNHuMkR8hjIhvi4jt4DXZ2zkq0U7cBsQERLIQ5c15PaL6hISVPILRCsTIt6UCxErx2ciLQ369IGFC83jGjXg5Zdh8GAI0OYNTuTvmdC28w6xYcMGu0sQ8SnKhPiqP3cf5aXp62n3/Cy+WGg2g7o1qsTsh7tzT7f6pdIMAmVC5HSUCxErx2ciOtr8ioiA556DjRvh5pvVDHIwJ2VCl4z5sd27d9tdgohPUSbE12zcn843f+zkq0U7PWOVokMZelkjbuxQk4AAV6m+vzIh4k25ELFyXCYyM+GNN2DIEKhUCVwueP99c+HoxES7qxMf4KRMqCHkx8KPb4EoIoAyIfYzDINFW1MYt2wPk1YkWe6rVzGSId3rc22bxPNeLLqolAkRb8qFiJVjMuF2w9dfw5NPwt695k5i771n3le3rr21iU9xTCbQGkJ2lyMiIuVAbkEhczYe4pN521iy44jlvi4NEhhySX0ubljJpupEREQcbv58GDoUli41j+vWhddfhwED7K1LpBRoDSGH+O677+wuQcSnKBNS1tbvS+PGjxbR+KmfuPfrZSzZcYSw4AAGd6zJK9ddwMyh3fjmrgttawYpEyLelAsRq3Kdie3b4YYb4OKLzWZQdLS5YPS6dWoGyRmV60ycQpeM+TGHTe4S+UvKhJSFg+k5vDB1PSv3HGVnSpblvqbVYvjktvYkxvnGVGNlQsSbciFiVa4z8eabMG6cuUD0nXfC889DlSp2VyU+rlxn4hRqCPmxhg0b2l2CiE9RJqS0GIbBgi0p3PrZH7hP+RnhkkaVGNi+Bn1aVCOwlBeJLi5lQsSbciFiVa4yUVgIR47A8S3Dn3kGdu+GZ5+FVq1sLU38R7nKxF9QQ8iPVa1a1e4SRHyKMiEl7XBmHqN+WMvs9QdJzy2w3HdHlzoMbFeTZtV9dz06ZULEm3IhYlVuMjF7trlOUJUqMGOGuXtYxYowaZLdlYmfKTeZKAKtIeTH5s2bZ3cJIj5FmZCSsiM5kzs+X0zb52cx5c+9nmZQj8aVeOaqZmx7qS8jr27u080gUCZETke5ELHy+0xs3gz9+kHPnrBqFSxZYs4KEjlHfp+JYtAMIRERESArr4BP521n3uZkFu84bLmvV7MqvHhtCypHh9lUnYiIiFgcOWKuCfTuu5CfD4GBcN99MHIkJCTYXZ2IX1BDyI/16NHD7hJEfIoyIcXldhtMWZnE279sYXtypuW+kKAA/nFJff7RvT5hwYE2VXh+lAkRb8qFiJVfZmLlSnNGUEqKedy3r7mNfNOm9tYl5YJfZuIcqSHkx3bs2OGo6xtF/ooyIUW1IzmTq9+Z77UuEMB1bWtwQ/sadKrn/79dVCZEvCkXIlZ+mYmmTSE+3lwv6I034PLL7a5IyhG/zMQ50hpCfmz79u12lyDiU5QJ+SuGYfDJvG10f32OpRnUsU48/7mxNVtf6su/b2hVLppBoEyInI5yIWLlF5lYtw7uvRfy8szjkBBz4eiVK9UMkhLnF5koIZoh5MeCg4PtLkHEpygTcia7D2fx31+38N0S6yKTr1x3Ade2qUFIUPn8/YgyIeJNuRCx8ulMJCebW8Z/8IG5pXzTpvDQQ+Z9devaWZmUYz6diRLmMgzDsLuIspSWlkZsbCypqanExPj27jAiInLuDMNg5Z5UFmxJ5rUZGy33ta0Vxxd/70hMmHNO+CIiIn4jL89cLPq55yA11Ry79lp49VVo0MDe2kR8XHF6HuXzV6IOMX78eLtLEPEpyoQAJGfk8vYvm6n7xHT6/3eBpRl0efMqzH2kBxPv6+KIZpAyIeJNuRCx8qlMGAZMmQLNm8PDD5vNoNat4ddfYeJENYOkTPhUJkqZLhnzY/n5+XaXIOJTlAlnyytwc+eXS5i3OdkyHhjg4ob2NRlyST1qJ0TaVJ09lAkRb8qFiJXPZeLdd2HLFnPB6BdfhNtvN7eUFykjPpeJUqSGkB+rq+tmRSyUCWfKyivg9Rmb+GyBdQHARy5vzN861SY2ovzPBDoTZULEm3IhYmV7JvbvNxeJjo8Hl8vcNWzMGHjiCYiOtrc2cSTbM1GG1BDyY3Xq1LG7BBGfokw4x7ZDGXz7xy7W709jwZYUy31tasXx3T0XEhqk3yYqEyLelAsRK9sykZMDb74JL70Et91mzgwCuOAC80vEJk46T2gNIT/266+/2l2CiE9RJsq/75fu5tJ/z+HSf//GJ/O3W5pBrWrE8tEt7Zh0Xxc1g45RJkS8KRciVmWeCcOA7783dwx78knIyIAVK6CgoGzrEDkDJ50nNENIRER8Wn6hmwnL9vD4xNVe93VtUJGhvRrStlYFXC6XDdWJiIhIkS1ZAkOHwoIF5nFiIrz8Mtx0EwRoroJIWVNDyI9dfPHFdpcg4lOUifJly8F03puzlYnLkyzjFaNC+OS2DrSuGWdPYX5EmRDxplyIWJVZJr76yrw0DCAiAh57DIYPN2+L+BAnnSfUEPJj+/fvp0aNGnaXIeIzlAn/ZhgGWw9l8tmC7czfnMyuw1mW+ytFh/LRLe1oU6uCTRX6H2VCxJtyIWJVZpno0wfi4uCaa8x1gxITS/89Rc6Bk84TmpfnxzZv3mx3CSI+RZnwT4Zh8MpPG6j7xHQue+M3vv1jl6UZdFOnWqx/7gqWjLhMzaBiUiZEvCkXIlalkgm3G/73P7jzzhNjlSqZ28l/+aWaQeLTnHSe0AwhP6b1MkSslAn/kpVXwOQVe3lykvfaQJ3rJfDApQ3oXD9B/13Pg/7uRLwpFyJWJZ6JhQvhoYfM9YIABg2C3r3N2wkJJfteIqXASecJl2EYht1FlKW0tDRiY2NJTU0lJibG7nJERBxn3uZDPD15DTtSrJeE1a8UyZR/diUqVL+rEBER8Ts7d5rrAo0dax5HRZm7iA0dCmFh9tYm4iDF6XnokjE/NnnyZLtLEPEpyoRv+3zBduo8Po1bPl3saQZVjArhxg41+eBvbfnl4e5qBpUwZULEm3IhYnXemcjOhhEjoHFjsxnkcsFdd8HmzfDEE2oGid9x0nlCP3n7sezsbLtLEPEpyoRv2pGcyY0f/c7+tBzP2AWJsQzqUJObO9Vy1LTcsqZMiHhTLkSszjsTgYHw/feQmws9esAbb0Dr1iVSm4gdnHSeUEPIj9WsWdPuEkR8ijLhW5KOZnPXl0tZvy/NM1Y5OpSPb21PK20ZXyaUCRFvyoWI1TllYv586NQJgoMhJATefx8yM80dxPSLHvFzTjpPqCHkx5o0aWJ3CSI+RZnwDftSs/nXd3+yePthy/i4IZ3pUCfepqqcSZkQ8aZciFgVKxNbtsAjj8DkyfCf/8CDD5rjl11WKrWJ2MFJ5wmtIeTHZs2aZXcJIj5FmbBXdl4hny/YTufRsy3NoKeubMr20X3VDLKBMiHiTbkQsSpSJo4eheHDoVkzsxkUGAgHD5Z2aSK2cNJ5QjOERETkvM1cu597vl7mOXa5YNhljfjnpQ20RpCIiIi/KiiAjz+GZ56B5GRz7Ior4N//NptDIuLX1BDyYxdddJHdJYj4FGWibGXkFvDMlDWsSUpl04EMz/g1rarzfP8WxIYH21idgDIhcjrKhYjVWTNx//3w0Ufm7aZNzQWjr7iibAoTsYmTzhO6ZMyPHT58+K8fJOIgykTZyCtw889vl9Ni5AwmLk/yNIPa1opjw/NX8PbgNmoG+QhlQsSbciFi5ZUJwzhx+/77oXJlePddWLVKzSBxBCedJ2xvCL333nvUrVuXsLAw2rVrx7x58876+G+++YZWrVoRERFBtWrVuOOOO0hJSSmjan3Lhg0b7C5BxKcoE6VrTVIq//puBY2e+pGpq/Z5xlvViOWjW9oxfshFhAUH2lihnEqZEPGmXIhYeTKRkgIPPGCuFXRcy5awa5fZGArSxSXiDE46T9ia6rFjx/LQQw/x3nvv0aVLFz788EP69OnDunXrqFWrltfj58+fz6233sqbb77J1VdfTVJSEkOGDOGuu+5i0qRJNnwHIiLl33eLd/H4xNVe4wPb1eDV61tqjSARERE/FlBQAG++Cc89Zy4eHRQEDz0Ex7feDg21szwRKUUuwzh5TmDZ6tSpE23btuX999/3jDVt2pT+/fszevRor8e//vrrvP/++2zdutUz9s477/Dqq6+ye/fuIr1nWloasbGxpKamEhMTc/7fhI0KCwsJDNRv40WOUyZKTk5+Id8t3sU3f+xi88EMy30P92rE/T0aEBCgRpCvUyZEvCkXIscYBvzwA8bw4bg2bzbHWrY01wnq2dPe2kRs5O/nieL0PGy7ZCwvL49ly5bRu3dvy3jv3r1ZuHDhaZ9z0UUXsWfPHqZPn45hGBw4cIDx48dz5ZVXnvF9cnNzSUtLs3yVF9OnT7e7BBGfokyUjPfnbKXJ0z/x7A/r2Hwwg6AAF9e3q8GCxy9lx8tX8kDPhmoG+QllQsSbciECbN8OvXpBv35mM6hyZXPx6OXL1QwSx3PSecK2S8aSk5MpLCykSpUqlvEqVaqwf//+0z7noosu4ptvvmHQoEHk5ORQUFDANddcwzvvvHPG9xk9ejSjRo3yGh83bhwREREMGDCAX375hdTUVCpXrkzHjh2ZOnUqAG3btsXtdvPnn38C0K9fP+bPn09KSgrx8fF069aNyZMnA9CyZUuCg4NZtszcdvnKK69k6dKlHDhwgJiYGHr37s348eMBaN68OVFRUfzxxx8AXH755axZs4akpCQiIyO56qqrGDt2LACNGzemYsWKLFiwAIDLLruMTZs2sWvXLvbtM9fwGDt2LG63m/r165OYmMjcuXMB6N69O7t27WLbtm0EBQUxcOBAJkyYQF5eHrVr16Z+/frMnj0bgK5du3Lw4EE2bdoEwODBg5kyZQpZWVnUqFGDZs2aMXPmTAA6d+5Mamoq69atA2DgwIH89NNPpKenU7VqVdq2besJUYcOHcjJyWH1avNyk2uvvZY5c+Zw5MgRKlasSOfOnfnhhx8AaNOmDQArVqwA4Oqrr2bRokUkJydToUIFunfv7rk08IILLiAsLIwlS5YA0LdvX5YvX87+/fuJjo7miiuuYNy4cQA0a9aM2NhYFi1aBJhNx3Xr1rFnzx4iIiLo168fY8aMAaBRo0ZUrlyZ+fPnA3DppZeydetWdu7cSUhICNdddx3jxo2joKCAevXqUatWLebMmQNAt27dSEpKYuvWrQQEBDBo0CAmTpxIbm4utWrVolGjRvz8888AdOnSheTkZDZu3AjAoEGDmDp1KpmZmSQmJtKiRQtmzJgBmDPpMjIyWLt2LQDXX389M2fOJC0tjSpVqtC+fXumTZsGQLt27cjPz2fVqlUA9O/fn7lz53L48GESEhLo2rUrU6ZMAaB169YEBASwfPlyAK666ioWL17MwYMHiY2NpWfPnkycOBGAFi1aEBERweLFiwHo06cPK1euZO/evURFRdG3b1++//57AJo0aUJ8fLynsdurVy82bNjA7t27CQ8Pp3///nz33XcYhkHDhg2pWrWqZ+2wHj16sGPHDrZv305wcDDXX38948ePJz8/n7p161KnTh1+/fVXAC6++GL279/P5s2bcblc3HjjjWzZsoUxY8ZQs2ZNmjRpwqxZswDzs+Pw4cOe64FvuOEGpk+fTkZGBtWrV6dVq1b8+OOPAHTs2JGsrCzWrFkD4NefEaGhoQwYMKBInxFHC4NZkFOT+VuObSl7TMf4PHpUyeWqCy9g/9Z1zNVnhF99RiQnJ5OWlqbPiGOfEZMnTyY7O1ufEefwGVGefo5ISkryrF/p9M8I/Rzh3M+IyTNmcNXChQQFB7PmssvYeN11FEREcNnhw47/jAD9HOH0z4ikpCTatGnjt58RWVlZFJVtl4zt3buXxMREFi5cSOfOnT3jL774Il9//fVpF3Jat24dl112GUOHDuXyyy9n3759PPLII3To0IFPP/30tO+Tm5tLbm6u5zgtLY2aNWuWi0vGfvvtNy655BK7yxDxGcpE8e1MyeTqd+aTllNgGb+4YUXevamtdgvzc8qEiDflQhwpNxcmT4ZBg06M/fADtGjBb7t2KRMiJ/H380RxLhmzbYZQxYoVCQwM9JoNdPDgQa9ZQ8eNHj2aLl268MgjjwBmFywyMpKLL76YF154gWrVqnk9JzQ0lNByuhBaq1at7C5BxKcoE0W392g2o35Yy4y1ByzjfS+oytDLGtGwSrRNlUlJUiZEvCkX4iiGARMmwKOPmpeJxcae2Dr+6qsBaFWhgo0FivgeJ50nbFtDKCQkhHbt2nmmWR03a9YsLrrootM+Jysri4AAa8nHF3uycW1s2xyfdiYiJmXir6Vm5TPqh7Vc9PJsSzPo9YGt2PHylbx3czs1g8oRZULEm3IhjrFsGVxyCQwcaDaDqleH/HyvhykTIlZOyoSt284PGzaMW265hfbt29O5c2c++ugjdu3axZAhQwB44oknSEpK4quvvgLM6zzvvvtu3n//fc8lYw899BAdO3akevXqdn4rIiI+ze02+HbxLp6avMYzFh0axD8vbcDfu9YlONC23w+IiIhISdq7F558Er76ypwhFB4OjzxizhKKjLS7OhHxIbY2hAYNGkRKSgrPPfcc+/bto0WLFkyfPp3atWsDsG/fPnbt2uV5/O233056ejrvvvsuDz/8MHFxcVx66aW88sordn0LturYsaPdJYj4FGXCm2EYLNqawk2f/GEZH9yxJi9dewEul3YLK8+UCRFvyoWUa4YBffrAsUV/+dvf4KWXoGbNMz5FmRCxclImbG0IAdx3333cd999p73viy++8Bp74IEHeOCBB0q5Kv9QnNXDRZxAmTih0G3w4HcrmLZqn2W8Vc04vrqjI7ERWizaCZQJEW/KhZQ7brfZCAoMBJcLnn0WXnsN3noLivAPW2VCxMpJmdA1An7s+FZ1ImJSJkzTVu2j/pPTvZpBX/69I1Pu76JmkIMoEyLelAspVxYtgs6d4YMPToz17w8LFhSpGQTKhMipnJQJ22cIiYjI+TMMg7d/2cKbP2+yjEeHBTH/sUu1fbyIiEh5snMnPP44fPedeXzgANx7LwQFmbOERESKwGU4bHuutLQ0YmNjSU1NJSYmxu5yzktubi6hoaF2lyHiM5yaiQVbkrn5lDWCWtWM48s7OhAXEWJTVeILnJoJkbNRLsSvpafDyy/DG29ATo7Z/LnjDnjhBahW7ZxeUpkQsfL3TBSn56FLxvzYL7/8YncJIj7FSZkwZwRtps7j0yzNoJY1YpkzvDtT7u+iZpA4KhMiRaVciN/66Sdo1MhcJDonB7p3N7eW//TTc24GgTIhcionZUKXjPmx1NRUu0sQ8SlOycTXi3bw6oyNpOcUWMYn39+F1jXj7ClKfJJTMiFSHMqF+K2qVc1Lw+rXh9dfh379SuTyMGVCxMpJmVBDyI9VrlzZ7hJEfEp5zsSqPUd5fMJq1u1Ls4y3qhnHywMuoGk1/74EVkpHec6EyLlSLsRvbN0K8+fDbbeZx61bw48/mjODSvByFmVCxMpJmdAaQn4sPT2d6Ohou8sQ8RnlMRMH0nJ44NsVLN5x2DLeu1kVHu/ThHqVomyqTPxBecyEyPlSLsTnpaaaawK9/ba5pfyaNdC4cam9nTIhYuXvmdAaQg4xdepUu0sQ8SnlLRMPjFlBp5d+8TSDKkWH8o/u9Vk8oicf3dpezSD5S+UtEyIlQbkQn1VQYG4f37CheUlYXh5ceikElO4/2ZQJESsnZUKXjImI+Jg1Salc9c58y9gL/Vvwtwtr21SRiIiIlKqZM2HYMFi71jxu3NjcSaxPH20jLyKlRg0hP9a2bVu7SxDxKeUhE5NXJPHQ2D89x6FBAax6tjehQYH2FSV+qzxkQqSkKRfic44eheuvN7eUj4+HZ5+FIUMgOLhM3l6ZELFyUibUEPJjbrfb7hJEfIo/Z2LpjsMMH7eSHSlZAFSMCuXNQa24uGElmysTf+bPmRApLcqF+IT0dDi+RklcHIwcCbt3wzPPmE2hMqRMiFg5KRNaQ8iP/fnnn3aXIOJT/DETWXkF3Pv1Uq7/YJGnGdS/dXVmD79EzSA5b/6YCZHSplyIrfLzzcWia9c2LxM77uGH4a23yrwZBMqEyKmclAnNEBIRscH8zcl8u3gn01fvt4y/PbgN17SqblNVIiIiUioMA6ZNg+HDYeNGc+zTT6F3b3vrEhFH07bzfiwrK4uIiAi7yxDxGf6Qiamr9vLitPXsS83xjMVFBDOofU3+eWkDosPKZr0AcQZ/yIRIWVMupMytWWMuGD1rlnlcqZK5rfydd0Kg/WsEKhMiVv6eCW077xDz58//6weJOIgvZ8IwDF6bsYF/frvC0wyKDgvio1vasfypXjzRt6maQVLifDkTInZRLqRMPf88tGplNoNCQuDRR2HzZrjnHp9oBoEyIXIqJ2VCl4z5sZSUFLtLEPEpvpiJnPxCXpy2nq9/32kZ/+auTnRpUNGmqsQpfDETInZTLqRMNWsGbre5i9grr0C9enZX5EWZELFyUibUEPJj8TYsOifiy3wtExOW7eHhcSstY9e1rcHrA1vicrlsqkqcxNcyIeILlAspNYYBkyZBXh7ceKM5NmAALFsGPryNtTIhYuWkTGgNIT+WnZ1NeHi43WWI+AxfysSSHYcZ+MEiz3HjKtGMuedC4iNDbKxKnMaXMiHiK5QLKRXLl8PQoTB3LiQkwJYt5nbyfkCZELHy90xoDSGHmDx5st0liPgUX8lETn6hpRn0+xM9mTG0m5pBUuZ8JRMivkS5kBK1dy/ccQe0b282g8LC4B//gGD/WRdQmRCxclImdMmYiEgJu/XTxZ7bfzzZkyoxYTZWIyIiIiUuOxv+/W94+WXIzDTHbroJRo+GWrXsrU1EpIjUEPJjLVu2tLsEEZ/iC5l4dPxKFu84DMAr112gZpDYyhcyIeJrlAspERs3wjPPmOsGXXghvPmm+acfUiZErJyUCTWE/FiwH01FFSkLdmdi2Ng/mbgiCYDLm1dhUAf9hlDsZXcmRHyRciHnbM8eqFHDvN26NTz5JDRvbi4g7cebRSgTIlZOyoTWEPJjy5Yts7sEEZ9iVybWJKXScMR0TzNoYLsafPC3drbUInIynSdEvCkXUmy7d8PNN5tbxm/adGL8hRdg8GC/bgaBMiFyKidlQjOERETO0YG0HK58ex7JGXmesds61+bZa5prW3kRERF/l5EBr74Kr70GOTlm42fWLGjUyO7KRERKhLad92NpaWl+/z2IlKSyykROfiGjfljHmMW7LOOvXteSGzrULPX3FykqnSdEvCkX8pfcbvj6a3jiCdi3zxzr1s1cJ6htW3trKwXKhIiVv2dC2847xNKlS+0uQcSnlHYmft+WQv//LqDJ0z9ZmkFvD27DjpevVDNIfI7OEyLelAs5K8OAnj3h9tvNZlDdujBhAsyZUy6bQaBMiJzKSZnQJWN+7MCBA3aXIOJTSisTKRm53PzJH2zYn24Z79mkMu//rR0hQeqti2/SeULEm3IhZ+VyQe/esGwZPP00PPgghIbaXVWpUiZErJyUCTWE/Jg/T2MTKQ0lnQnDMHhk/CrGL9vjGQtwwah+LbipYy0CA7ROkPg2nSdEvCkXYpGWBi++aDaBevY0x4YOhTvvhMqV7a2tjCgTIlZOyoTWEPJj+fn5jtoST+SvlGQmMnMLuOOLJSzeftgz1q91dd4a1FoLRovf0HlCxJtyIQAUFsKnn8JTT8GhQ9CiBfz5JwQG2l1ZmVMmRKz8PRNaQ8ghxo8fb3cJIj6lpDIxacUemo+c4WkGXVgvno0vXMF/bmyjZpD4FZ0nRLwpF8LPP0ObNnDvvWYzqFEjGD0aApz5TyNlQsTKSZnQJWMiIses2nOU8cv28NWinZ6xZ65qxt+71rWxKhERESkRmzbB8OHwww/mcYUKMHIk3Hcf+PFsABGRc6WGkB9r3ry53SWI+JRzyURWXgHDxq5k88F0th7K9IwfXyvolgtrl2SJImVK5wkRb8qFg61caTaDgoLMJtDIkRAfb3dVtlMmRKyclAk1hPxYVFSU3SWI+JSiZiI1K59n/m8Nczcd4khWvuW+sOAAnrumBQPb19DlYeL3dJ4Q8aZcOEh+PmzcaK4PBHD99fD443DbbdCkib21+RBlQsTKSZlw5oWy5cQff/xhdwkiPuWvMpFbUMjwcStp9dxMpvy519IMqlEhnLH3XMiG5/twQ4eaagZJuaDzhIg35cIBDAOmT4eWLaFHD0hNNcddLnOtIDWDLJQJESsnZUIzhESk3DMMg0/nb+eFaest45c0qsQDlzagWfUYIkL0cSgiIuL31q6FYcNg5kzzuGJFWLcOOne2ty4RER+kbef92OHDh4nXdc8iHqfLRE5+IX3fnse2k9YHuqdbPe7rXp+4iJCyLlGkTOk8IeJNuSinDh0y1wT68ENwu81Foh96CEaMgNhYu6vzacqEiJW/Z0LbzjvEmjVr7C5BxKecmom9R7O54NkZnmZQu9oV+OXhS3iyb1M1g8QRdJ4Q8aZclEOHD0PjxvD++2YzaMAAWL8eXn1VzaAiUCZErJyUCV0j4ceSkpLsLkHEpxzPhGEYvPzTBj78bZvnvju61GHk1c7ZMUAEdJ4QOR3lohyKj4d+/cxdxN54A7p3t7siv6JMiFg5KRNqCPmxyMhIu0sQ8SmRkZEcSs+lw4s/W8bfu7ktfS+oZlNVIvbReULEm3JRDqxYAY89Bu+9Bw0amGNvvw0RERAYaG9tfkiZELFyUia0hpAfc7vdBAToqj+R435Zv587v1zmOa4QEczCx3sSHqIfDsWZdJ4Q8aZc+LF9+8w1gb74wtxJbOBA+P57u6vye8qEiJW/Z0JrCDnE2LFj7S5BxCcs3JpMncenWZpBz/drzopneqsZJI6m84SIN+XCD2Vnw4svQsOG8PnnZjPoxhvhtdfsrqxcUCZErJyUCV0yJiJ+bdnOI9z08R+e46AAF3882ZOEqFAbqxIREZESMXEiDB0Ku3aZx506wZtvaht5EZESoIaQH2vcuLHdJYjYZn9qDgM/XMjuw9mesce7VGDI1RfZWJWIb9F5QsSbcuFn1q83m0E1asDLL8PgweDHl3L4ImVCxMpJmVBDyI9VrFjR7hJEypzbbfDWL5t5+5fNnrGo0CB+/NfFGBnJNlYm4nt0nhDxplz4uD17zG3kW7Y0j4cNg7Aw+Mc/zEWjpcQpEyJWTsqE2ut+bMGCBXaXIFKmdiRn0vGlny3NoLu61mX1s72pGR+hTIicQpkQ8aZc+KjMTBg5Eho1gltugcJCczw8HB5+WM2gUqRMiFg5KROaISQiPi85I5d/fbeCBVtSPGMta8Ty9Z2diA0PtrEyEREROS9uN/zvf/DEE7B3rzkWEwMpKVC5sr21iYiUc9p23o8dOnSISpUq2V2GSKnJzivkzZ838dHcbZbxj29tT69mVbwer0yIWCkTIt6UCx8yf765YPTSpeZxnTrmzmHXXQcul62lOYkyIWLl75koTs9DM4T82KZNm/z6f1SRs/ljWwqDPvrdMja4Y02G9WpMpejT7yCmTIhYKRMi3pQLH7FgAVx8sXk7OhpGjIB//ctcL0jKlDIhYuWkTGgNIT+26/j2myLlzKo9Ry3NoObVY1j+dC9GD2h5xmYQKBMip1ImRLwpFzY6+cKEiy4yG0L33AObN8Njj6kZZBNlQsTKSZnQDCE/Fhp65n8Yi/iro1l5XPOuuZBbeHAgM4d2o2Z80RaSVCZErJQJEW/KhQ0KC+Gzz+Cdd8zLxGJizEvCfvkFgrUWoN2UCRErJ2VCawiJiM/IyS/k5k/+YNnOIwDMe7RHkZtBIiIi4oNmzzbXCVq1yjwePRoef9zemkREyrHi9Dx0yZgfGzt2rN0liJSYn9cd4OJXf2XZziOEBAbw7V2dit0MUiZErJQJEW/KRRnZvBn694eePc1mUFwcvPkmDBtmd2VyCmVCxMpJmdAlY37M7XbbXYLIeTuYnsONH/7OtuRMAEKDAnjv5rZc1KBisV9LmRCxUiZEvCkXpcww4JFH4O23IT8fAgPhvvtg5EhISLC7OjkNZULEykmZUEPIj9WvX9/uEkTOWU5+IY+OX8X/rdzrGetUN553b2p71oWjz0aZELFSJkS8KRelzOWCQ4fMZlDfvvD669C0qd1VyVkoEyJWTsqEGkJ+LDEx0e4SRM7JmqRUrnpnvmVsWK9GPNiz4Xm9rjIhYqVMiHhTLkrBTz9Bo0ZQr555/NJLcNNNcPnl9tYlRaJMiFg5KRNaQ8iPzZ071+4SRIrtz91H6fffBZ7jzvUSWPF0r/NuBoEyIXIqZULEm3JRgtatgz59zK9HHjkxnpioZpAfUSZErJyUCc0QEpEykZNfyKgf1jFm8S4AYsKC+OLvHWlbq4LNlYmIiEixJCfDs8/CBx+YW8oHB0PduuB2Q4B+3ywi4i/UEPJj3bt3t7sEkSLZfCCdvm/PI7/QACA2PJjp/7qYxLjwEn0fZULESpkQ8aZcnIe8PHj3XXjuOUhNNcf694fXXoMGDWwtTc6dMiFi5aRMqIXvx3bt2mV3CSJ/6cPfttLrzbmeZlDPJpVZPKJniTeDQJkQOZUyIeJNuTgP770HDz9sNoNat4bZs2HSJDWD/JwyIWLlpEyoIeTHtm3bZncJImc16oe1jP5xg+f48zs68OntHQgNCiyV91MmRKyUCRFvykUx5eWduH3PPdChA3zyCSxdCj162FeXlBhlQsTKSZnQJWN+LChI//nENxmGwfBxq5iwfA8AdStGMuOhboQElW4PWpkQsVImRLwpF0V04AA89RSsWAF//AGBgRARYd52ueyuTkqQMiFi5aRMuAzDMOwuoiylpaURGxtLamoqMTExdpcjUu5sOZjObZ8tIeloNgC14iP47ZHuuPTDo4iIiO/LyYG33jK3jk9PN8d+/hl69rS1LBERKZri9Dx0yZgfmzBhgt0liFgs3XGYy96Y62kGXd+uBrMfvqTMmkHKhIiVMiHiTbk4A8OAceOgaVN44gmzGdSxIyxYoGZQOadMiFg5KRPOmQtVDuWdfE23iM1+WX+AO79cCpi7iD10WUPu6FK3TGtQJkSslAkRb8rFaaSkmLuFzZ9vHicmwssvw003aRt5B1AmRKyclAk1hPxY7dq17S5BBMMw+GLhDp6bug4wlxWY/fAlJESFlnktyoSIlTIh4k25OI34eCgoMNcIevRRGD4cIiPtrkrKiDIhYuWkTKgh5Mfq169vdwnicGuSUrn988UkZ5hd9Coxofzwz662NINAmRA5lTIh4k25ALKy4O234b77ICbG/G3OZ59BdDTUqGF3dVLGlAkRKydlQnNA/djs2bPtLkEcbOqqvVz1znxPM2hwx5r89kgPKseE2VaTMiFipUyIeHN0Ltxu+N//oFEjc52g0aNP3Ne0qZpBDuXoTIichpMyoRlCIlJsP67exz+/XeE5fvX6ltzQvqaNFYmIiMhZLVwIDz0ES5aYx7VrQ4cOtpYkIiL2UkPIj3Xt2tXuEsSB/vvrFl6bsRGADnUq8OXfOxIR4hsfJcqEiJUyIeLNcbnYuRMeewzGjjWPo6JgxAizORRm36xe8R2Oy4TIX3BSJnTJmB87ePCg3SWIw/y0Zr+nGQTw8a3tfaYZBMqEyKmUCRFvjsvFs8+azSCXC+66CzZvhscfVzNIPByXCZG/4KRMqCHkxzZt2mR3CeIgczYeZMj/lnmOlz11GXERITZW5E2ZELFSJkS8lftcFBZCauqJ4+efh759Yfly+PhjqFrVvtrEJ5X7TIgUk5MyoYaQiPylxdsPW9YM+vOZXrbtJCYiIiJnMGcOtG9vzgQ6rkYNmDYNWre2qyoREfFRLsMwDLuLKEtpaWnExsaSmppKTEyM3eWI+Lz352zllZ82ABAY4GLRE5dSOVrTzEVERHzGli3wyCMwebJ5HBcHGzZAlSp2ViUiIjYoTs9DM4T82JQpU+wuQcqxrLwCrn1vgacZVK9iJMuf6uXTzSBlQsRKmRDxVq5ycfQoDB8OzZqZzaDAQLj/fnOdIDWDpIjKVSZESoCTMuE7q8FKsWVlZdldgpRTR7PyaP3cLM/xFc2r8u5NbQgK9O0esjIhYqVMiHgrN7lYssRcGyg52Ty+4gr497/N5pBIMZSbTIiUECdlQg0hP1ajRg27S5ByaFdKFtd9sNBz/PcudXnmav/44VKZELFSJkS8lZtcNGsGoaHQtKnZCOrTx+6KxE+Vm0yIlBAnZUINIT/WTL8BkhJ2JDOP279YzKH0XCJCArm/RwPu79HA7rKKTJkQsVImRLz5bS7Wr4cPP4Q33oCAAIiMhF9+gXr1IDjY7urEj/ltJkRKiZMy4dvXf8hZzZw50+4SpBxZk5RKm+dnse1QJgBT7u/iV80gUCZETqVMiHjzu1ykpMADD8AFF8B//gNffXXivsaN1QyS8+Z3mRApZU7KhGYIiQgb96dz/UmXiX17VycaVom2sSIRERGHy8uD996DUaPMxaMBrrkGLrrI1rJERKT8UEPIj3Xu3NnuEqQcWLAlmb9/sYTcAjcRIYFMub+L3zaDlAkRK2VCxJvP58IwYOpUePhhc7cwgJYt4c034dJL7a1NyiWfz4RIGXNSJtQQ8mOpqal2lyB+buSUNXy5aCcA0aFBTLr/IhpU9s9mECgTIqdSJkS8+UUuXnzRbAZVrmzevuMOc0t5kVLgF5kQKUNOyoTWEPJj69ats7sE8WMvTV/vaQYBzH/sUr9uBoEyIXIqZULEm0/m4sAByMgwb7tc8NZb8PjjZlPorrvUDJJS5ZOZELGRkzKhhpCIw6Rk5HLR6F/4aO42AFrWiGXHy1cSG6FFKUVERMpUTg688go0bAgvv3xi/MILYfRoiImxrzYRESn3XIZhGHYXUZbS0tKIjY0lNTWVGD8/yRYUFBAUpKv+pOh+WLmXB8as8Bw3qRrN//2zKyFB5aM3rEyIWCkTIt58IheGARMmwKOPwvbt5ljXrvDbb+aW8iJlyCcyIeJD/D0Txel56Izjx3766Se7SxA/8se2FEszaHDHmvz0ULdy0wwCZULkVMqEiDfbc7FsGVxyCQwcaDaDqleHL79UM0hsY3smRHyMkzLhv20vIT093e4SxE+s35fGoI9+B8ylCX4edgn1K0XZXFXJUyZErJQJEW+25uKjj+Dee83b4eHwyCPmLKHISPtqEsfTuULEykmZUEPIj1WtWtXuEsQPfLVoB89MWes5/m14D2olRNhYUelRJkSslAkRb7bmok8fiIiAAQPgpZegZk37ahE5RucKESsnZUINIT/Wtm1bu0sQH/fOL5v596xNJ44Htym3zSBQJkROpUyIeCuzXLjdMGYMLF0Kb75pjtWsCVu3goP+sSG+T+cKESsnZUIXKvux6dOn212C+KhCt8FV78zzNIMCA1ysHXU5V7eqbnNlpUuZELFSJkS8lUkuFi2Czp3hb38zt5BfuPDEfWoGiY/RuULEykmZ0AwhkXJmwZZkbv7kD89xYlw4vw7vXq4WjxYREfFJO3fC44/Dd9+Zx1FR8OST0KaNvXWJiIichhpCfqxDhw52lyA+ZtnOw5Zm0IvXtuDmTrVtrKhsKRMiVsqEiLdSyUVWlrkm0L//DTk55g4Od9wBL7wA1aqV/PuJlCCdK0SsnJQJNYT8WE5Ojt0liA/5dP52np+6znP85d87ckmjSjZWVPaUCRErZULEW6nkwjDg88/NZlD37vDGG5oVJH5D5woRKydlQteQ+LHVq1fbXYL4iLu/WuppBlWJCeWPJ3s6rhkEyoTIqZQJEW8llos//jAXjgZz2/j//hcmTYLZs9UMEr+ic4WIlZMyoYaQiB9Ly8nnmnfnM2vdAQA61oln0eM9qRITZnNlIiIi5dTWrXDddXDhhfC//50Y79/f/HK57KpMRESkWFyGYRh2F1GW0tLSiI2NJTU1lZiYGLvLOS85OTmEhekf/k6VX+im4YgfPcdVYkL5/YmeuBz8g6gyIWKlTIh4O+dcpKaaawK9/Tbk5UFAADz9NDz7bInXKFKWdK4QsfL3TBSn56EZQn5szpw5dpcgNur7n3me28N7N3J8MwiUCZFTKRMi3oqdi4IC+OADaNgQXn/dbAb17g2rVqkZJOWCzhUiVk7KhBaV9mNHjhyxuwSxySfztrH5YAYAN3eqxT8vbWhzRb5BmRCxUiZEvBU7F3//O3z9tXm7SRNzJ7E+fXRpmJQbOleIWDkpE5oh5McqVqxodwlig1d+2sAL09YDEBoUwPP9Wthcke9QJkSslAkRb8XOxb33QkICvPOOOSuob181g6Rc0blCxMpJmdAaQn4sIyODqKgou8uQMjRx+R6Gfb/Sc7z+uSsIDwm0sSLfokyIWCkTIt7OmovDh2HUKLMB9MwzJ8YzM82dxETKIZ0rRKz8PRNaQ8ghfvjhB7tLkDJiGAYPfbfC0wzqUKcCm1/so2bQKZQJEStlQsTbaXORn28uFt2ggfnn6NFw6NCJ+9UMknJM5woRKydl4pwaQgUFBfz88898+OGHpKenA7B3714yMjJKtDgRgdTsfG786Hcm/7kXgCZVo/n6zk4EB6qfKyIicl4MA6ZOhQsugH/9C44cMW9PnQqVKtldnYiISKkq9r8od+7cyQUXXEC/fv24//77OXTstyevvvoqw4cPL3YB7733HnXr1iUsLIx27doxb968sz4+NzeXESNGULt2bUJDQ6lfvz6fffZZsd+3PGjTpo3dJUgpSzqaTYcXf+aP7YcB6HtBVX7818WEBWtm0OkoEyJWyoSIN08utm6Fyy+Hq6+GjRvNBtCHH8KKFdCzp71FipQhnStErJyUiWLvMvavf/2L9u3bs3LlShISEjzj1157LXfddVexXmvs2LE89NBDvPfee3Tp0oUPP/yQPn36sG7dOmrVqnXa59xwww0cOHCATz/9lAYNGnDw4EEKCgqK+22I+Lx5mw9xy6eLPcfv3dyWvhdUs7EiERGRciQoCObNg5AQeOghePJJiI21uyoREZEyU+yG0Pz581mwYAEhISGW8dq1a5OUlFSs13rjjTe48847PY2kt956ixkzZvD+++8zevRor8f/9NNP/Pbbb2zbto34+HgA6tSpU9xvodxYsWIFTZo0sbsMKQVr96ZamkET/tGZdrXjbazIPygTIlbKhMhJcnNhxgxWZGaauahdG774Ajp0gHr17K5OxDY6V4hYOSkTxb5kzO12U1hY6DW+Z88eoqOji/w6eXl5LFu2jN69e1vGe/fuzcKFC0/7nP/7v/+jffv2vPrqqyQmJtKoUSOGDx9Odnb2Gd8nNzeXtLQ0y5eIL8vILeDKt+cDEBMWxOIne6oZJCIicq4MAyZMgGbNoF8/Km7adOK+QYPUDBIREccq9gyhXr168dZbb/HRRx8B4HK5yMjIYOTIkfTt27fIr5OcnExhYSFVqlSxjFepUoX9+/ef9jnbtm1j/vz5hIWFMWnSJJKTk7nvvvs4fPjwGdcRGj16NKNGjfIaHzduHBEREQwYMIBffvmF1NRUKleuTMeOHZk6dSoAbdu2xe128+effwLQr18/5s+fT0pKCvHx8XTr1o3JkycD0LJlS4KDg1m2bBkAV155JUuXLuXAgQPExMTQu3dvxo8fD0Dz5s2Jiorijz/+AODyyy9nzZo1JCUlERkZyVVXXcXYsWMBaNy4MRUrVmTBggUAXHbZZWzatIldu3YRGGiuIzN27Fjcbjf169cnMTGRuXPnAtC9e3d27drFtm3bCAoKYuDAgUyYMIG8vDxq165N/fr1mT17NgBdu3bl4MGDbDr2Q9LgwYOZMmUKWVlZ1KhRg2bNmjFz5kwAOnfuTGpqKuvWrQNg4MCB/PTTT6Snp1O1alXatm3L9OnTAejQoQM5OTmsXr0aMC8tnDNnDkeOHKFixYp07tzZs4r78Ws1V6xYAcDVV1/NokWLSE5OpkKFCnTv3p1JkyYBcMEFFxAWFsaSJUsA6Nu3L8uXL2f//v1ER0dzxRVXMG7cOACaNWtGbGwsixYtAsym47p169izZw8RERH069ePMWPGANCoUSMqV67M/PlmQ+bSSy9l69at7Ny5k5CQEK677jrGjRtHQUEB9erVo1atWsyZMweAbt26kZSUxNatWwkICGDQoEFMnDiR3NxcatWqRaNGjfj5558B6NKlC8nJyWzcuBGAQYMGMXXqVA6nZfLxjjjP/6e31zpKRvJe9u/IYO3atQBcf/31zJw5k7S0NKpUqUL79u2ZNm0aAO3atSM/P59Vq1YB0L9/f+bOncvhw4dJSEiga9euTJkyBYDWrVsTEBDA8uXLAbjqqqtYvHgxBw8eJDY2lp49ezJx4kQAWrRoQUREBIsXm7OW+vTpw8qVK9m7dy9RUVH07duX77//HoAmTZoQHx/vaez26tWLDRs2sHv3bsLDw+nfvz/fffcdhmHQsGFDqlat6lk7rEePHuzYsYPt27cTHBzM9ddfz/jx48nPz6du3brUqVOHX3/9FYCLL76Y/fv3s3nzZlwuFzfeeCNBQUGMGTOGmjVr0qRJE2bNmgXARRddxOHDh9mwYQNgXno6ffp0MjIyqF69Oq1ateLHH38EoGPHjmRlZbFmzRoAv/6MCA0NZcCAAfqMoPx8RmRmZpKYmEiLFi2YMWMGAJ06dSIj4/SfEfHx8aSlpekz4thnxOTJk8nOztZnhIM+I9Z89RX13nmHysf+22bFxRGQns6aNWv0GaGfI/QZgfkZ0aZNG8//w077jNDPEfqMON1nREFBAXv27PHbz4isrCyKymUYhlHkR2PuJtajRw8CAwPZvHkz7du3Z/PmzVSsWJG5c+dSuXLlIr9OYmIiCxcupHPnzp7xF198ka+//trzl3my3r17M2/ePPbv30/ssWu8J06cyPXXX09mZibh4eFez8nNzSU3N9dznJaWRs2aNUlNTSUmJqY437rPmTVrFr169bK7DCkh6/am0fftE4uq39GlDiOvbm5jRf5HmRCxUibEsfbuhREj4MsvzRlCYWHwyCPw6KPMWrRIuRA5ic4VIlb+nom0tDRiY2OL1PMo9gyh6tWr8+eff/Ldd9+xbNky3G43d955JzfffPNpGzJnUrFiRQIDA71mAx08eNBr1tBx1apVIzEx0dMMAmjatCmGYbBnzx4aNmzo9ZzQ0FBCQ0OLXJc/SU5OtrsEKSHDx61k/LI9nuP/3tSWK1tqAeniUiZErJQJcSS3G3r0gOOXht18M4weDTVrAsqFyKmUCRErJ2Wi2A2huXPnctFFF3HHHXdwxx13eMYLCgqYO3cu3bp1K9LrhISE0K5dO2bNmsW1117rGZ81axb9+vU77XO6dOnCuHHjyMjIICoqCoBNmzYREBBAjRo1ivut+L0KFSrYXYKco9TsfMYt3U1aTgFT/kxiZ8qJaX3/u7MTXRtWtLE6/6VMiFgpE+IYhmF+BQSYX088YW4h/9Zb0KmT5aHKhYiVMiFi5aRMFPuSscDAQPbt2+d1aVhKSgqVK1c+7YLTZzJ27FhuueUWPvjgAzp37sxHH33Exx9/zNq1a6lduzZPPPEESUlJfPXVVwBkZGTQtGlTLrzwQkaNGkVycjJ33XUXl1xyCR9//HGR3rM406d8XU5ODmFhYXaXIcU0ffU+7vtmudd4Ylw48x7tQUCAy4aqygdlQsRKmRBH+P13GDoU7rsPbrnFHHO7weUyv06hXIhYKRMiVv6eieL0PIq9y5hhGLhOc3JNSUkhMjKyWK81aNAg3nrrLZ577jlat27N3LlzmT59OrVr1wZg37597Nq1y/P4qKgoZs2axdGjR2nfvj0333wzV199NW+//XZxv41y4fiiZ+If9hzJov9/F3iaQYEBLipGhXD7RXV48NIGzB5+iZpB50mZELFSJqRc273bvBysc2ezKfTii2YjCMxZQqf5eRWUC5FTKRMiVk7KRJEvGRswYABg7ip2++23W9blKSwsZNWqVVx00UXFLuC+++7jvvvuO+19X3zxhdfYyat5i/iLPUey6P3mXLLyTsygW/xkTxKiyuf6ViIiIqUmIwNefRVeew1ycszGz223mQ2hgGL/rlNERMSxitwQOr6Qs2EYREdHWxaQDgkJ4cILL+Tuu+8u+QrljC644AK7S5AiuOXTP5i32VyYLCo0iMf7NOH6djUICw60ubLyR5kQsVImpNyZNg3uvhv27TOPu3WDN9+Etm2L/BLKhYiVMiFi5aRMFLkh9PnnnwNQp04dhg8fXuzLw6Tk+fN1jU7x6fztnmYQwCe3tefCegk2VlS+KRMiVsqElDsxMWYzqG5dc4bQgAFnvDTsTJQLEStlQsTKSZko9rzakSNHqhnkI5YsWWJ3CXIWy3Ye4YVp6wC4IDGWTS/0UTOolCkTIlbKhPi9bdtg3LgTxxdfDJMmwfr1cN11xW4GgXIhciplQsTKSZko9rbzAOPHj+f7779n165d5OXlWe5bvtx79yQRp0k6ms09Xy3FMKBjnXjG3nvhaRdjFxERkdNISzPXBHrrLQgMhAsvhJo1zfv697ezMhERkXKj2DOE3n77be644w4qV67MihUr6NixIwkJCWzbto0+ffqURo1yBn379rW7BDmN//y8mW6v/kpKZh51EiL44JZ2agaVEWVCxEqZEL9TWAgffQQNGpgLR+flQdeu5p8lRLkQsVImRKyclIliN4Tee+89PvroI959911CQkJ49NFHmTVrFg8++CCpqamlUaOcgWZj+Z77v1nOmz9votBtUDEqhA9uaUd8ZIjdZTmGMiFipUyIX/nlF2jTBu69Fw4dgsaNYepUmDED6tcvsbdRLkSslAkRKydlotgNoV27dnm2lw8PDyc9PR2AW265hTFjxpRsdXJW+/fvt7sEOcm0VfuYttrc9aRuxUgWPH4pTarG2FyVsygTIlbKhPiNAwfgyith9WqoUAH+8x/z9pVXntM6QWejXIhYKRMiVk7KRLEbQlWrViUlJQWA2rVr8/vvvwOwfft2DMMo2erkrKKjo+0uQY5ZtzeN+781O8nRYUH8+K+LCQ3StvJlTZkQsVImxKdlZZ24XaUKPPYYPPggbNli/hkcXCpvq1yIWCkTIlZOyoTLKGYX56677qJmzZqMHDmSDz74gGHDhtGlSxeWLl3KgAED+PTTT0ur1hKRlpZGbGwsqampxMT49+yNgoICgoLOaV1wKUETlu3h4XErPceLn+xJ5RjnbFXoS5QJEStlQnxSfj588AGMGmVeEnbhhWX69sqFiJUyIWLl75koTs+j2DOEPvroI0aMGAHAkCFD+OKLL2jatCmjRo3i/fffP7eK5ZyMO3kbVrHF17/vtDSDpj7QVc0gGykTIlbKhPgUw4Bp06BlS3MGUEoK2PCzo3IhYqVMiFg5KRPFbnsFBAQQEHCij3TDDTdwww03AJCUlERiYmLJVSfiw37deJCnJ68BoEpMKJPv70K12HCbqxIREfFBa9bAww/DzJnmccWK8PzzcNdd9tYlIiLiYMWeIXQ6+/fv54EHHqBBgwYl8XJSRM2aNbO7BMdKzcrnjs+XABAaFMBP/+qmZpAPUCZErJQJ8QnPPAOtWpnNoOBgGD4cNm+GIUPAhin5yoWIlTIhYuWkTBS5IXT06FFuvvlmKlWqRPXq1Xn77bdxu90888wz1KtXj99//53PPvusNGuVU8TGxtpdgiMVFLq54j9zPce/PHwJFbS1vE9QJkSslAnxCXXqgNsN114L69bBa69BXJxt5SgXIlbKhIiVkzJR5IbQk08+ydy5c7ntttuIj49n6NChXHXVVcyfP58ff/yRJUuWMHjw4NKsVU6xaNEiu0twnC0HM7j037+xLzUHgNEDLqBGhQibq5LjlAkRK2VCypxhwOTJ8MMPJ8Zuuw0WLoSJE8EHZpMrFyJWyoSIlZMyUeR5utOmTePzzz/nsssu47777qNBgwY0atSIt956qxTLE/Edu1Ky6Pv2PPIK3AC8ct0FDOpQy+aqREREfMSff8LQoTBnDtSoAT17QkQEBAZC5852VyciIiKnKPK288HBwezcuZPq1asDEBERweLFi2nRokWpFljSytO28ykpKSQkJNhdhiOs3ZvKtf9dSF6h2Qz6+s6OXNywks1VyamUCRErZULKxP798NRT8Nln5gyhsDBzAeknnzQbQj5GuRCxUiZErPw9E6Wy7bzb7SY4ONhzHBgYSGRk5LlXKedt3bp1dpfgCIfSc7n98yWeZtCEf3RWM8hHKRMiVsqElKrsbHjpJWjYED791GwG3XgjbNgAL7zgk80gUC5ETqVMiFg5KRNFvmTMMAxuv/12QkNDAcjJyWHIkCFeTaGJEyeWbIVyRnv27LG7hHKv0G1w5dvzOJSeC8D4IZ1pVzve5qrkTJQJEStlQkrVsmUwYoR5u2NHePNNuOgie2sqAuVCxEqZELFyUiaK3BC67bbbLMd/+9vfSrwYKZ4IH/3NW3lhGAbP/bCWg+m5hAQG8NrAlrSvo2aQL1MmRKyUCSlxBw5AlSrm7a5d4Z//hAsvhMGDIaDIE89tpVyIWCkTIlZOykSR1xAqL8rTGkJSur5cuIOR/7cWgBevbcHNnWrbXJGIiIhNdu+GJ54wdxDbuBESE+2uSERERE6jVNYQEt8zZswYu0sot9bvS/M0g+7qWlfNID+hTIhYKRNy3jIzYeRIaNwYvvnGPP7xR7urOi/KhYiVMiFi5aRMFPmSMRGnyC908/D3KwFoWi2Gx/o0sbkiERGRMuZ2w9dfmzuF7d1rjnXtaq4T1L69vbWJiIhIiVBDyI81atTI7hLKpccnrGbdvjQCXPDmoFYEB2oinb9QJkSslAk5J243XHIJzJ9vHtetC6++CtddBy6XvbWVAOVCxEqZELFyUib0L10/VrlyZbtLKHfScvKZsNxcVf6aVtVpUlXrTPkTZULESpmQcxIQAF26QHQ0vPIKrFsH119fLppBoFyInEqZELFyUibUEPJj84//5k5KzCdzt3luvzawlY2VyLlQJkSslAkpkrQ0c8HoxYtPjI0YAZs3w6OPQliYfbWVAuVCxEqZELFyUibOqSH09ddf06VLF6pXr87OnTsBeOutt5gyZUqJFidSlnalZPH27C0A3H5RHV0qJiIi5VthIXz8MTRsCC+/DA89BMc3n42OPrG9vIiIiJRLxf4X7/vvv8+wYcPo27cvR48epbCwEIC4uDjeeuutkq5PzuLSSy+1u4Ryo6DQzbXvLQCgRoVwHtdC0n5JmRCxUibkjGbPhrZt4Z574OBBaNTIXEDaAZQLEStlQsTKSZkodkPonXfe4eOPP2bEiBEEBgZ6xtu3b8/q1atLtDg5u61bt9pdQrkxe8NBUjLzAPjs9g6EBQf+xTPEFykTIlbKhHjZvBn694eePWHVKoiLM3cOW70arrqq3KwTdDbKhYiVMiFi5aRMFLshtH37dtq0aeM1HhoaSmZmZokUJUVz/HI9OX8fHVs76No2iTSqEm1zNXKulAkRK2VCvPz2G0yZAoGB8MADsGWLealYSIjdlZUZ5ULESpkQsXJSJoq97XzdunX5888/qV27tmX8xx9/pFmzZiVWmPy1EAf98Faaflqzj6U7jxDggrsurmt3OXIelAkRK2VCKCiAbdvMS8IA7rjDnBn0j39A06b21mYT5ULESpkQsXJSJlyGcXz1wKL5/PPPefrpp/n3v//NnXfeySeffMLWrVsZPXo0n3zyCTfeeGNp1Voi0tLSiI2NJTU1lZgYbSnudAWFbto+P4u0nAJu7FCTl69raXdJIiIiJeOnn2DYMMjIgI0bITzc7opERESklBWn51HsS8buuOMORo4cyaOPPkpWVhY33XQTH3zwAf/5z398vhlU3owbN87uEvzex/O2k5ZTAMAjlze2uRo5X8qEiJUy4VDr1kGfPubX+vWQlQVr19pdlc9QLkSslAkRKydlotiXjAHcfffd3H333SQnJ+N2u6lcuXJJ1yVFUFBQYHcJfi0nv5BXftoAwAOXNiAhKtTmiuR8KRMiVsqEwyQnw7PPwgcfmFvKBwfDgw/CU0+Zi0cLoFyInEqZELFyUiaK3RAaNWoUf/vb36hfvz4VK1YsjZqkiOrVq2d3CX5t1A/rAAgOdDHkkvo2VyMlQZkQsVImHGT/fnNNoKNHzeP+/eG116BBAzur8knKhYiVMiFi5aRMFPuSsQkTJtCoUSMuvPBC3n33XQ4dOlQadUkR1KpVy+4S/NbRrDzGLN4FwN0X1yMy9Jwmy4mPUSZErJQJB6laFXr0gNatYfZsmDRJzaAzUC5ErJQJESsnZaLYDaFVq1axatUqLr30Ut544w0SExPp27cv3377LVlZWaVRo5zBnDlz7C7Bbz03dZ3nttYOKj+UCRErZaIcW7kSrroKkpJOjH32GSxdajaG5IyUCxErZULEykmZKHZDCKB58+a89NJLbNu2jV9//ZW6devy0EMPUbVq1ZKuT6TEzVy7n4nLzR+gB3esicvlsrkiERGRItq/H+6+G9q0gWnT4JlnTtwXFweBgbaVJiIiIv7lnBpCJ4uMjCQ8PJyQkBDy8/NLoiYpom7dutldgt8pKHTz8LiVAESHBfFC/wtsrkhKkjIhYqVMlCM5OTB6NDRsCJ98AoYBgwbB00/bXZnfUS5ErJQJESsnZeKcGkLbt2/nxRdfpFmzZrRv357ly5fz7LPPsn///pKuT84i6eRp4lIk//llM+nHtpmf92gPAgM0O6g8USZErJSJcmLiRHPB6CefhIwM6NAB5s+H776DOnXsrs7vKBciVsqEiJWTMlHshlDnzp1p0KAB48aN44477mDnzp3Mnj2bu+66i9jY2NKoUc5g69atdpfgVzJyC3hn9hYABrRJJC4ixOaKpKQpEyJWykQ58fvvsGMHJCbC11+bx1262F2V31IuRKyUCRErJ2Wi2Fsr9ejRg08++YTmzZuXRj1SDAEB533Fn6O8/ON6z+2RV+v/3/JImRCxUib81J49kJkJjY9tejBiBFSoAA8+CJGR9tZWDigXIlbKhIiVkzLhMgzDsLuIspSWlkZsbCypqanExMTYXY6UkZ0pmfR6cy55BW4euqwhD13WyO6SRERErLKy4LXX4JVXoG1bmDcPtPGBiIiIFENxeh5FmiE0bNgwnn/+eSIjIxk2bNhZH/vGG28UvVI5LxMnTmTAgAF2l+HzcvIL+cf/lpNX4KZSdCj/7NHA7pKklCgTIlbKhJ9wu+Hbb+Hxx63byB85AvHx9tVVTikXIlbKhIiVkzJRpIbQihUrPDuIrVixolQLkqLLzc21uwS/cP83y1m3L42gABff3NWJoEDnTAF0GmVCxEqZ8AMLF8JDD8GSJeZx7drw6qswcKBmB5US5ULESpkQsXJSJorUEPr1119Pe1vsVatWLbtL8Hk/rNzLLxsOAvDuTW1oVCXa5oqkNCkTIlbKhI/7+Wfo1cu8HRVlrhX00EMQFmZrWeWdciFipUyIWDkpE8WeKvH3v/+d9PR0r/HMzEz+/ve/l0hRUjSNGmkdnLM5lJ7Lw+NWAlArPoIrWlSzuSIpbcqEiJUy4YNOXrqxRw9o3Rruugs2bzYvGVMzqNQpFyJWyoSIlZMyUeyG0Jdffkl2drbXeHZ2Nl999VWJFCVF8/PPP9tdgs8pKHTz6k8buPGjRXR48WfyCtwAjLnnQpsrk7KgTIhYKRM+pLAQPvkEOneGnBxzLDDQ3EL+44+halV763MQ5ULESpkQsXJSJoq87XxaWhqGYWAYBunp6YSd9BuswsJCpk+fTuXKlUulSJGi2Lg/nWvenU/usSYQQHCgi7cGtSExLtzGykRExNF+/RWGDoWV5qxVPvrI3EIeIDTUvrpERETE0YrcEIqLi8PlcuFyuU47hcrlcjFq1KgSLU7OrkuXLnaXYIu8AjebDqRzKD2XP3cfJTU7H7dh8NWinZ7HdKhTgft6NKB59RgqR2v6vVM4NRMiZ6JM2GzzZnjkEZgyxTyOjYVnnoEhQ+yty+GUCxErZULEykmZKHJD6Ndff8UwDC699FImTJhA/EnboIaEhFC7dm2qV69eKkXK6SUnJztqwSvDMHjlp4188NvWsz5uwj860662tul1IqdlQuSvKBM2KSyERx+Fd96B/Hzz0rB774VRo6BiRburczzlQsRKmRCxclImitwQuuSSSwDYvn07tWrVwqWtUG23ceNG2rZta3cZZWLPkSweGLOCFbuOWsbb1Ipj9+FsOtSpQNNqMfRqVoWm1WLsKVJs56RMiBSFMmGTwEDYutVsBl1xBfz739Csmd1VyTHKhYiVMiFi5aRMFKkhtGrVKlq0aEFAQACpqamsXr36jI9t2bJliRUnAvDTmn0M+d9yz/FlTSvz0S3tCQhQU1JERHzEjBnQsiVUO7aj5euvm7OC+vSxty4RERGRM3AZxsn7n55eQEAA+/fvp3LlygQEBOByuTjd01wuF4WFhaVSaElJS0sjNjaW1NRUYmL8eyaJ2+0mIKDYG8X5lQNpOVz1znwOpecC0LRaDOOHdCYytMiT28RBnJAJkeJQJsrA+vUwfDhMnw533AGffWZ3RfIXlAsRK2VCxMrfM1GcnkeRvsvt27dTqVIlz+1t27axfft2r69t27adf/VSZFOnTrW7hFKz7VAG7V/4mU4v/eJpBv087BJ+/NfFagbJGZXnTIicC2WiFKWkwAMPwAUXmM2goCCIj4e//j2b2Ey5ELFSJkSsnJSJIv3Lunbt2qe9LfbKzMy0u4RSc9kbv+E+9jN1dGgQj17RmAaVo+wtSnxeec6EyLlQJkpBXh689565QPTRo+ZYv37w2mvQsKGtpUnRKBciVsqEiJWTMlHseVBffvkl06ZN8xw/+uijxMXFcdFFF7Fz586zPFNKWmJiot0llIo1SameZtA7g9uwetTl3NK5jq01iX8or5kQOVfKRCl47TUYOtRsBrVsCb/8ApMnqxnkR5QLEStlQsTKSZkodkPopZdeIjw8HIBFixbx7rvv8uqrr1KxYkWGDh1a4gXKmbVo0cLuEkrFbZ8tBqBJ1WiublXd5mrEn5TXTIicK2WihBQUnLh9//3QvDl89BEsXw6XXmpfXXJOlAsRK2VCxMpJmSh2Q2j37t00aNAAgMmTJ3P99ddzzz33MHr0aObNm1fiBcqZzZgxw+4SStyWg+mkZOYBcF+PBjZXI/6mPGZC5HwoE+fpwAG45x7o1evE2kBxcbB6Ndx9t7m9vPgd5ULESpkQsXJSJordEIqKiiIlJQWAmTNnctlllwEQFhZGdnZ2yVYnjnPnl0sBaJEYw9Utq9lcjYiIOFJODrzyinkZ2Mcfw5w5sGjRiftdLttKExERESkpxd6uqVevXtx11120adOGTZs2ceWVVwKwdu1a6tSpU9L1yVl06tTJ7hJK1Nq9qexMyQLg+X4tcOkHbimm8pYJkfOlTBSTYcD48fDoo7BjhznWrh28+SZcdJGtpUnJUS5ErJQJESsnZaLYM4T++9//0rlzZw4dOsSECRNISEgAYNmyZQwePLjEC5Qzy8jIsLuEElNQ6GbwR78DEBMWRJtaFWyuSPxRecqESElQJorhwAHo1g1uuMFsBlWvDl9+CYsXw8UX212dlCDlQsRKmRCxclImij1DKC4ujnfffddrfNSoUSVSkBTd2rVradmypd1llIgvF+0kLcdctHPy/V1srkb8VXnKhEhJUCaKISHB3DksPBweecScJRQZaXdVUgqUCxErZULEykmZKHZDCODo0aN8+umnrF+/HpfLRdOmTbnzzjuJjY0t6frEAZIzcnl+6joA+reuTr1KUTZXJCIi5V5WFnzwAdx3H4SFQVAQfP212RiqWdPu6kRERERKncswjm+bUTRLly7l8ssvJzw8nI4dO2IYBkuXLiU7O5uZM2fStm3b0qq1RKSlpREbG0tqaioxMTF2l3Ne8vPzCQ4OtruM8/bgmBX838q9AKwddTmRoefUpxQpN5kQKSnKxGm43TBmDDz+OOzZAy+/DI89ZndVUoaUCxErZULEyt8zUZyeR7HXEBo6dCjX/H979x0eRbm+cfybXknohBIglNCRJkgHRZogiBSVI+IBPYiKYjuWnwI2bCCWA3pERT1ILzakiHRQupTQpLeAtAQS0uf3x8LCuAESSHZ2M/fnunKxOzs78+yGe0Me3nnfO+9k3759zJw5k1mzZrF37166dOnCk08+eb01y3WYP3++1SXcsPlb453NoMdvraJmkNyQgpAJkbykTPzNqlWOyaH/8Q9HM6h8ecdKYmIryoWImTIhYmanTOT6t++1a9fy2Wef4e9/6an+/v4899xzNGrUKE+Lk6tLTEy0uoQbkpll8MTkjQAUDg3gidv0j3K5Md6eCZG8pkxcsH+/Y0TQ5MmO++Hh8MILMHSoY84gsRXlQsRMmRAxs1Mmcj1CKCIiggMHDrhsP3jwIIUKFcqToiRnSpUqZXUJN2TwxHWcT88EYNq/muLvl+u/jiIm3p4JkbymTFzw9NOOZpCPD/zzn7BzJ7z4oppBNqVciJgpEyJmdspErkcI9enThwEDBvDee+/RrFkzfHx8WL58Oc8++6yWnXczbx6RFXckkXlbjwGOS8WqllIzUW6cN2dCJD/YNhOZmXD+vGMkEMCbb0JiIrz9NtSvb21tYjnb5kLkCpQJETM7ZSLXQzLee+89evToQb9+/ahYsSIVKlSgf//+9OzZk7fffjs/apQr+Omnn6wu4bq9OGuz8/aT7WItrEQKEm/OhEh+sGUmliyBm2+Gy+c1jI2F+fPVDBLAprkQuQplQsTMTpnI9QihwMBAPvjgA0aOHMnu3bsxDIMqVaoQGhqaH/VJAbRm3yk2HjwDwC9PtcbP18fagkRExPvt3g3PPQczZzru79sH774LRYpYWpaIiIiIp8rxCKHk5GQeffRRypYtS8mSJRk4cCClS5embt26agZZpGHDhlaXcF3+s+hPAG6tXpIqJcMtrkYKEm/NhEh+sUUmEhIcjaCaNR3NIF9feOQR2LFDzSDJli1yIZILyoSImZ0ykeMRQsOGDWPChAn07duX4OBgJk2axCOPPMK0adPysz65ivT0dKtLyLV35m5n8Y6/ALSqmOQ5b8yESH4q8JlYuRK6d4e/HD9XaN8eRo+GWrUsLUs8W4HPhUguKRMiZnbKRI5HCM2cOZPPP/+c//73v3z44Yf89NNPzJ49m8zMzPysT65i06ZNVpeQK0cTzjN28W4AetQvy03Rha0tSAocb8uESH4r8JmoUQOysqB6dfjpJ5g7V80guaYCnwuRXFImRMzslIkcN4QOHjxIy5YtnfcbN26Mv78/R44cyZfCpGDZeewsTUf+6rw/7E79g11ERHJp+3Z46SUwDMf9IkVg0SLYtAk6d3YsKy8iIiIiOeJjGBf/VXV1fn5+xMfHU6JECee2QoUKsWnTJmJiYvKtwLyWmJhIZGQkCQkJREREWF3ODTl//jwhISFWl5Ejt763mD0nkgCYObgZDcprXgfJe96UCRF3KDCZOHUKRoyAsWMhI8MxV9Bdd1ldlXipApMLkTyiTIiYeXsmctPzyPEIIcMw6N+/Pz169HB+paSkMGjQINM2cZ+lS5daXUKOfPzrLmcz6KN766sZJPnGWzIh4i5en4n0dPjwQ6hSxfFnRgZ07arLwuSGeH0uRPKYMiFiZqdM5HhS6QceeMBl2z/+8Y88LUZy59SpU1aXcE0ZmVnMXH8YgJvKRdL1pjIWVyQFmTdkQsSdvDYThuGYE+iZZxyrhQHUqeOYMLpdO2trE6/ntbkQySfKhIiZnTKR44bQl19+mZ91yHUoVqyY1SVc00+bjzpHB314b32Lq5GCzhsyIeJOXpuJrCx4/nlHM6hECXj9dRgwAPz8rK5MCgCvzYVIPlEmRMzslIkczyFUUBSkOYSSk5MJDQ21uowrSkrN4PbRSziSkMK9jcszskcdq0uSAs7TMyHibl6Vib/+gkKFIDjYcX/BAvjlF3jxRYiMtLY2KVC8KhcibqBMiJh5eybyZQ4h8Tzfffed1SVc1WfL9nAkIYVCwf4837G61eWIDXh6JkTczSsykZoK7757aZ6gi26/Hd5+W80gyXNekQsRN1ImRMzslAk1hCRfGIbBp0v2APBwy0pEhgZYXJGIiHgUw3CsFlazJjz3HCQmwty5l5aUFxEREZF8pYaQF6tXr57VJVzR73tPcT49E4C7G5azuBqxC0/OhIgVPDYT69dD27Zw992wZw+ULg0TJjguEfPxsbo6KeA8NhciFlEmRMzslIkcTyotnsfX13P7ec9O/wOA7vXKUKZwiMXViF14ciZErOCRmfj4YxgyxDESKDgYnn3WMUIoPNzqysQmPDIXIhZSJkTM7JSJ63ql33zzDc2bN6dMmTLs378fgDFjxtjqWjtPsH79eqtLyNYvccc4eOo8AL1vjra4GrETT82EiFU8MhPt2oG/P9x3n2MVsVdfVTNI3MojcyFiIWVCxMxOmch1Q2jcuHE89dRTdO7cmTNnzpCZ6bgsqHDhwowZMyav6xMv9NWqfQA0q1yMZpWLW1uMiIhYxzBg0iR45ZVL26pXhz//hIkToXx562oTERERsblcLztfs2ZN3nzzTbp3706hQoX4448/qFSpElu2bKFNmzacOHEiv2rNEwVp2fmzZ89SqFAhq8sw2XciiTbvLQZgzpCW1Czj3e+xeBdPzISIlSzNxG+/wdChjj99fGDjRqhb15paRC6jnxUiZsqEiJm3ZyJfl53fu3cv9evXd9keFBREUlJSbg8nN2D16tVWl+Di40V/AhAR7K9mkLidJ2ZCxEqWZOLgQejbF5o2dTSDwsIcl4VVrer+WkSyoZ8VImbKhIiZnTKR64ZQTEwMGzdudNn+888/U7NmzbyoSXLo+PHjVpdgcvBUMtPXHQLg/T71rC1GbMnTMiFiNbdmIinJcWlYbCx8+61jVNCDD8LOnfB//wchWmBAPIN+VoiYKRMiZnbKRK5XGXv22Wd59NFHSUlJwTAMVq9ezaRJkxg5ciTjx4/PjxrlCiIjI60uwWT8sj0A3FQuklurl7S4GrEjT8uEiNXcmom0NPjPfyAlBVq1gvffhwYN3Hd+kRzSzwoRM2VCxMxOmcj1HEIAn332Ga+//joHDx4EoGzZsgwfPpwBAwbkeYF5rSDNIZSamkpQUJDVZQBgGAYxL8wB4KN769P1pjIWVyR25EmZEPEE+Z6JDRugXj3HaCBwTBQdEgJ33XVpm4iH0c8KETNlQsTM2zORr3MIATz00EPs37+f48ePEx8fz8GDB72iGVTQzJw50+oSnL5dfcB5+7YaGh0k1vCkTIh4gnzLxJ490LOnYwTQ999f2t63L/TooWaQeDT9rBAxUyZEzOyUietqCF1UvHhxSpbUL/8Cv+05BUC96MKEBub6SkQREfEGiYnw739DjRowYwb4+sLmzVZXJSIiIiLXIde/ucfExOBzlf/527Nnzw0VJDlXu3Ztq0sAICMziwVx8QA817GaxdWInXlKJkQ8RZ5lIjMTPv/cMTn0X385tt1+O4waBXXq5M05RNxEPytEzJQJETM7ZSLXDaEnn3zSdD89PZ0NGzYwd+5cnn322byqS3IgNDTU6hIAWBB3jJT0LAL9fGlQvojV5YiNeUomRDxFnmXi3nth2jTH7dhYGD0aOnfWpWHilfSzQsRMmRAxs1Mmct0QeuKJJ7Ld/p///Ie1a9fecEGSc6tXr6Zy5cpWl8Hny/cC0LlOFMEBfhZXI3bmKZkQ8RR5lokHH4RffoFhw2DwYAgIuPFjilhEPytEzJQJETM7ZeKG5hC6XKdOnZgxY0ZeHU68RML5dNbuPw1AWy01LyLi/U6dgiefhA8+uLStUyfYtw+eeELNIBEREZECIs9m/50+fTpFixbNq8NJDnTq1MnqEli68y/n7Tu11LxYzBMyIeJJcpWJ9HT45BMYPtzRFIqIgAcegMKFHY9fY9lSEW+hnxUiZsqEiJmdMpHrEUL169enQYMGzq/69etTunRpXnzxRV588cX8qFGu4I8//rC6BH7bcxKATrWjrjrZuIg7eEImRDxJjjJhGDBnDtStC0OGOJpBtWvD9OmXmkEiBYh+VoiYKRMiZnbKRK5HCHXv3t1039fXlxIlStCmTRuqV6+eV3VJDhw5csTqEpi7xbG6WKc6pS2uRMQzMiHiSa6ZiV274PHHYd48x/3ixeG112DgQPDPs0HEIh5FPytEzJQJETM7ZSJX/9rLyMigYsWKdOjQgaioqPyqSXIoPDzc0vOv+PMEJ5PS8PP1oVXV4pbWIgLWZ0LE01wzExkZjsmiAwIc8wa99BJERrqlNhGr6GeFiJkyIWJmp0z4GIZh5OYJoaGhbNu2jQoVKuRXTfkqMTGRyMhIEhISiPDy+RAyMzPx87NuVa+BX63ll23H6FK3NB/f18CyOkQusjoTIp7GJROpqbB0Kdx++6VtX34JrVqBTVbTENHPChEzZULEzNszkZueR67nEGrSpAkbNmy47uL+buzYscTExBAcHEzDhg1ZtmxZjp63YsUK/P39qVevXp7V4m2mTp1q2bnPpqTzy7ZjAAxqrV8ixDNYmQkRT+TMhGHArFlQqxZ07AibN1/a6cEH1QwSW9HPChEzZULEzE6ZyPUEAYMHD+bpp5/m0KFDNGzYkLCwMNPjdevWzfGxpkyZwpNPPsnYsWNp3rw5n376KZ06dSIuLo7y5ctf8XkJCQn069eP2267jWPHjuX2JUgemLXhMAC+PlCrjHePtBIRKdA2bICnnoLFix33o6LgyBGoU8fSskRERETEWjluCP3zn/9kzJgx9OnTB4AhQ4Y4H/Px8cEwDHx8fMjMzMzxyUePHs2AAQMYOHAgAGPGjGHevHmMGzeOkSNHXvF5//rXv7jvvvvw8/Nj9uzZOT5fQWPlJN7ztzoacX1uLq/VxcRjaGJ7kcscPUr7KVPg++8dI4SCg+Hpp+Hf/4ZChayuTsQy+lkhYqZMiJjZKRM5bgh99dVXvPXWW+zduzdPTpyWlsa6det4/vnnTdvbt2/PypUrr/i8L7/8kt27d/O///2P119//ZrnSU1NJTU11Xk/MTHx+ov2MEWLFrXkvMlpGSz/8wQAd9Uva0kNItmxKhMiHicjA265hWIHDjju33MPvPUWeOn8fyJ5ST8rRMyUCREzO2Uixw2hi3NP59Vk0idOnCAzM5NSpUqZtpcqVYr4+Phsn7Nr1y6ef/55li1bhn8Ol8MdOXIkI0aMcNk+bdo0QkND6dGjBwsXLiQhIYGSJUvSuHFjfvzxRwAaNGhAVlYWGzduBKBbt24sX76ckydPUrRoUVq1auUcoVS3bl0CAgJYt24dAHfccQdr167l2LFjRERE0L59e6ZPnw5ArVq1CA8P5/fffwegQ4cObNmyhcOHDxMWFkaXLl2YMmUKANWqVaN48eKsWLECgHbt2rFz504OHDjA0aNHeeqpp5gyZQpZWVlUrlyZsmXLsnTpUgDatGnDgQMH2LNnD/7+/vTq1YsZM2aQlpZGhQoVqFy5Mr/++isALVq04Pjx4+zcuROAe++9l++++47k5GTKlStHzZo1mT9/PgBZpWsBEO6fxZ+r5tEguhdz587l7NmzREVF0aBBA+bMmQPAzTffTEpKCpsvzFdx1113sXjxYk6fPk3x4sVp2rQpP/zwAwD169cHcM5R1bVrV1atWsWJEycoUqQIbdq0YdasWQDUqVOH4OBg1qxZA0Dnzp1Zv3498fHxFCpUiI4dOzJt2jQAatasSWRkJKtWrQIcTce4uDgOHTpEaGgo3bp1Y9KkSQDExsZSsmRJli9fDsCtt97K7t272b9/P4GBgdx9991MmzaNjIwMKlWqRPny5Vl84TKMVq1acfjwYXbv3o2vry99+vRh5syZpKamUr58eWJjY/nll18AaN68OSdOnGDHjh0A9OnThx9//JGkpCTKli1L7dq1mXdhGegmTZpw7tw5tm7dCkDPnj2ZP38+iYmJlCpVikaNGvHTTz8B0LBhQ9LT09m0aRMA3bt3Z+nSpZw6dYpixYrRokULvvvuOwDq1auHr68v69evB6BLly6sXr2a48ePExkZyW233cbMmTMBqF27NqGhoaxevRqATp068ccff3DkyBHCw8Pp3Lmz81rb6tWrU7RoUWdj9/bbb2f79u0cPHiQkJAQunfvzuTJkzEMg6pVqxIVFeWcO6xt27bs27ePvXv3EhAQQM+ePZk+fTrp6enExMRQsWJFFi1aBEDLli2Jj49n165d+Pj4cM899zBz5kyioqKIjo6mevXqLFiwAIBmzZpx6tQptm/fDkDv3r2ZM2cO586do0yZMtx00038/PPPADRu3Jjk5GS2bNkC4NWfEUFBQfTo0cPtnxFNmzYlISGBuLg4AHr10mcEuOEz4sLf2Z69ejF//nxKt25NmaVLKTR+PN//9ResXEnD1FRbf0bMnj2b8+fP6zPC5p8Rhw8fpmPHjvb7jNC/I/QZcYXPiI0bNxISEgLoM8LW/47QZ4TzM+Lw4cPcc889XvsZkZycTE7leJUxX19fjh07RokSJXJ88Ks5cuQIZcuWZeXKlTRt2tS5/Y033uCbb75xvpkXZWZmcssttzBgwAAGDRoEwPDhw5k9e7bzDcpOdiOEoqOjC8QqY5MmTeLee+91+3mfnfYH09YdolaZCH4a0tLt5xe5EqsyIWK51ath6FDH5WB33unYlpnJpMmTubdvX2trE/Ew+lkhYqZMiJh5eyZys8pYriaVjo2NveZ8MadOncrRsYoXL46fn5/LaKDjx4+7jBoCOHv2LGvXrmXDhg089thjAGRlZWEYBv7+/syfP59bb73V5XlBQUEEBQXlqCZvc/vlywa7SXpmFgsurC7Wq2E5t59f5GqsyISIpQ4dghdegP/9z3F/+HDo2hV8fMDPj9s7dLC0PBFPpJ8VImbKhIiZnTKRq4bQiBEjiIyMzJMTBwYG0rBhQxYsWMBdd93l3L5gwQK6devmsn9ERIRzKOBFY8eO5ddff2X69OnExMTkSV3eZPv27bRo0cKt51yz9xRnktPx9/Wh7y2ai0I8ixWZELFEUhK88w68+y6cP+/Y1r8/vPGGoxl0gTIh4kq5EDFTJkTM7JSJXDWE7rnnHkqWLJlnJ3/qqae4//77adSoEU2bNuW///0vBw4ccF4S9sILL3D48GG+/vprfH19qV27tun5JUuWJDg42GW7XRw8eNDt5/x40Z8AtKlWggA/X7efX+RqrMiEiNv98AMMGuRYOh6gZUt4/31o2NBlV2VCxJVyIWKmTIiY2SkTOW4I5cfS4n369OHkyZO8+uqrHD16lNq1azNnzhznxNVHjx7lwMUVUsTFxcnf3OV4Ygord58E4N7G5d16bpGccHcmRCzh6+toBsXEOEYI9ehhGhV0OWVCxJVyIWKmTIiY2SkTuZpUOj4+Pk9HCFkhNxMsidkHv+zi/V92UqVkOAuGtsqXJqGIiPzN3r0QFwd33OG4bxgweTLcdRcEB1tbm4iIiIh4lNz0PHJ8zU9WVpbXN4MKmsmTJ7v1fN9tPAxAw/JF1AwSj+TuTIjkq8REx4TRNWpA375w4oRju48P3HtvjppByoSIK+VCxEyZEDGzUyY0CYwXy+HgrjxxPi2TPSeSAOh6Uxm3nVckN9yZCZF8k5kJn30GVavCW29Baio0agRnz+b6UMqEiCvlQsRMmRAxs1MmcjWptHiWqlWruu1cc7ceBaBQsD+NY4q67bwiueHOTIjki19/haFDYdMmx/3YWHjvPejS5YrzBF2NMiHiSrkQMVMmRMzslAk1hLxYVFSU2861aPtfAPSoX5ZAfw0sE8/kzkyI5LkDB6B9e8cIocKFYdgwGDwYAgOv+5DKhIgr5ULETJkQMbNTJvSbvRdbtmyZW86TlWUwZ7NjhFC7mqXcck6R6+GuTIjkmdTUS7fLl4fHHoPHH4c//4Qnn7yhZhAoEyLZUS5EzJQJETM7ZUINIbmmuVvjycgyCA304+aKulxMROSGZWTAf/4DFSrAli2Xtr//Pnz4IRQrZl1tIiIiImILagh5sbZt27rlPNuPJgJQs3QEwQF+bjmnyPVwVyZEbsjcuVC3rmM00LFj8PHHlx7L4xUclQkRV8qFiJkyIWJmp0yoIeTF9u3b55bzrNh9EoAOtexzLaV4J3dlQuS6xMVBp06Or23bHKOAxo41N4TymDIh4kq5EDFTJkTM7JQJNYS82N69e/P9HKeT0li3/zSAVhcTj+eOTIhcl5decowKmjsXAgLg6acd8wQ98gj459/6DsqEiCvlQsRMmRAxs1MmtMqYFwsICMj3c3z0658A+Pn6ULdcZL6fT+RGuCMTIteleHHH6mHdu8O770KVKm45rTIh4kq5EDFTJkTM7JQJH8MwDKuLcKfExEQiIyNJSEggIiLC6nI83n2f/cbK3Se5qVwk3z3WwupyREQ8n2HA999DoUJw662ObWlp8Pvv0LKltbWJiIiISIGWm56HLhnzYtOnT8/X46dlZPHbHsf8QS93qZmv5xLJC/mdCZFr+uMPuO02x0igwYMhPd2xPTDQkmaQMiHiSrkQMVMmRMzslAk1hLxY+sVfNPLJL9uOkWVARLA/DcoXyddzieSF/M6EyBXFx8NDD0H9+rBoEQQFQY8ejuXlLaRMiLhSLkTMlAkRMztlQnMIebGYmJh8Pf73G48A0K5mKXx983YpZJH8kN+ZEHGRkgLvvw9vvgnnzjm29ekDb70FFStaWhooEyLZUS5EzJQJETM7ZUINIS9WMR9/2UhJz2TFnycAuOfm8vl2HpG8lJ+ZEMnW4sXw4ouO240bO5pDzZpZWtLllAkRV8qFiJkyIWJmp0zokjEvtmjRonw79uIdf3E2NYPCoQE0KF84384jkpfyMxMiTqdOXbrdoQM88AB88w2sWuVRzSBQJkSyo1yImCkTImZ2yoQaQpKtib/vB+CmcoXx99NfExERDh92NH+qVoWTjgn38fGBCRPgH/8AX31WioiIiIj30L9evVjLfFyxZs9fSQDcFF04384hktfyMxNiY8nJMGIExMbC1187Rgj99JPVVeWIMiHiSrkQMVMmRMzslAk1hLxYfHx8vhz38JnzHD5zHoABze0zoZZ4v/zKhNhUVhb873+ORtDw4Y7GULNm8Pvv0K+f1dXliDIh4kq5EDFTJkTM7JQJNYS82K5du/LluCt2OSaTrlQ8jMjQgHw5h0h+yK9MiA2lp0OLFnD//Y5LxSpUgClTYPlyx+TRXkKZEHGlXIiYKRMiZnbKhBpCXszHJ3+Wgt91/CwARr4cXST/5FcmxIYCAuCmmyA83LGk/Pbt0Lu3Y84gL6JMiLhSLkTMlAkRMztlwscwDFv93p+YmEhkZCQJCQlERERYXY5HenTien7afJS+Tcrzxl11rC5HRCT/nT0LI0c6JoeuWdOx7eRJx0ihqChraxMRERERyaHc9Dw0QsiLzZ49O1+Ou+nwGQBax5bIl+OL5Jf8yoQUYJmZ8PnnjpXDRo6Ep5++9FixYl7fDFImRFwpFyJmyoSImZ0y4W91AXL9zp8/n+fHzMwyOHjKcdwqJcPz/Pgi+Sk/MiEF2KJF8NRTsHGj436VKjBoEBiG110adiXKhIgr5ULETJkQMbNTJjRCyItFR0fn+TF/3X7cebtCsbA8P75IfsqPTEgB9OefcNddcOutjmZQZCSMHg1bt0K3bgWmGQTKhEh2lAsRM2VCxMxOmdAIIS9WvXr1PD/mrA2HAMfvQ36+BeeXIrGH/MiEFEDffQezZ4Ofn2NE0PDhULy41VXlC2VCxJVyIWKmTIiY2SkTGiHkxRYsWJDnx5yzOR6AZztUy/Nji+S3/MiEFAAZGbBv36X7jz8OAwfCpk3w8ccFthkEyoRIdpQLETNlQsTMTplQQ0icziSnOW93rOXdE6mKiAAwb55j+fhOnRwrhgEEBsJnn11aTUxERERExIbUEPJizZo1y9PjbY8/67xdqYQmlBbvk9eZEC+2bRt07gwdO0JcHBw/7thmM8qEiCvlQsRMmRAxs1Mm1BDyYqdOncrT4205nABAy6oF9/IJKdjyOhPihU6edFwSVqcO/Pwz+PvD0KGOiaTr1rW6OrdTJkRcKRciZsqEiJmdMqGGkBfbvn17nh7vhz+OAFA/unCeHlfEXfI6E+Jl9u1zLB3/8ceQmQl33ulYOWz0aChSxOrqLKFMiLhSLkTMlAkRMztlQquMidOJc445hMoVDbW4EhGR61ChAjRoACdOOJpAt91mdUUiIiIiIh7LxzAMw+oi3CkxMZHIyEgSEhKIiIiwupwbkpmZiZ+fX54c69DpZFq8vQiAP4a1JzIkIE+OK+JOeZkJ8QKbNjmWjB8/HooWdWz76y/Hbf09AJQJkewoFyJmyoSImbdnIjc9D10y5sXmzJmTZ8datfskANVKFVIzSLxWXmZCPNixY/Dww1C/PsyaBa++eumxEiXUDLqMMiHiSrkQMVMmRMzslAldMubFzp07l2fHurjCWIMKhfPsmCLulpeZEA+UkgIffABvvAFnL6yK2KsXPPGEtXV5MGVCxJVyIWKmTIiY2SkTagh5sTJlyuTZsRbtOA5AZS03L14sLzMhHmbmTHjmGdi713G/USN4/31o0cLaujycMiHiSrkQMVMmRMzslAldMubFbrrppjw71uHT5wEoWzgkz44p4m55mQnxMPPmOZpBZcrAV1/B77+rGZQDyoSIK+VCxEyZEDGzUybUEPJiP//8c54c5+CpZFIzsgBoXrV4nhxTxAp5lQnxAEeOOJaRv+jVV2HECNi5E/r1A1/9+MoJZULElXIhYqZMiJjZKRP6F7Ww72SS83ZEsCaUFhELJSc7mj9Vq8Kjj17aXqoUvPIKhIVZV5uIiIiISAGiOYS8WOPGjfPkOFsOJwJQv3zhPDmeiFXyKhNigawsmDQJnn8eDh1ybDt92jF5dKFC1tbmxZQJEVfKhYiZMiFiZqdMaISQF0tOTs6T48za4PjlK6aY/uddvFteZULcbNUqaNoU/vEPRzOofHmYPBlWrFAz6AYpEyKulAsRM2VCxMxOmVBDyItt2bIlT46z85hjWb1WsSXy5HgiVsmrTIgb/fADNGsGq1dDeLhjSfnt26FPH/Dxsbo6r6dMiLhSLkTMlAkRMztlQpeM2VzahcmkARpWKGJhJSJiS+3bQ5Uq0KoVvP46lC5tdUUiIiIiIrbgYxiGYXUR7pSYmEhkZCQJCQlERERYXc4NSU1NJSgo6IaO8cfBM3T7zwoA9o7sjI/+R168WF5kQvJRVpZjyfiJE2HuXPC/8H8SyckQGmptbQWUMiHiSrkQMVMmRMy8PRO56XnokjEvtnDhwhs+xter9jtvqxkk3i4vMiH5ZOlSuPlm+Oc/YeFC+PrrS4+pGZRvlAkRV8qFiJkyIWJmp0yoIeTFEhISbvgYS3YeB+DuBuVu+FgiVsuLTEge270b7r4bWreG9eshMhLeew/69rW6MltQJkRcKRciZsqEiJmdMqE5hLxYyZIlb/gYJ86lAdAkpugNH0vEanmRCckj6enw0kvwwQeQlga+vvCvf8GIEVBCE9i7izIh4kq5EDFTJkTM7JQJNYS8WOPGjW/o+Ykp6c7bt9awz196KbhuNBOSh/z9Yc0aRzOofXsYNQpq17a6KttRJkRcKRciZsqEiJmdMqFLxrzYjz/+eEPPP3Ay2Xm7eLj3TpolctGNZkJu0IIFcOqU47aPD3z4Ifz0k2MCaTWDLKFMiLhSLkTMlAkRMztlQg0hG/ttz0kAgvz110BEbsD27dCli2Mk0GuvXdpepw507uxoDomIiIiIiEdRJ8CLNWjQ4Iae/9sex//kN6tcLC/KEbHcjWZCcunUKXjiCUfj56efHJeJBQRYXZVcRpkQcaVciJgpEyJmdsqE5hDyYllZWTf0/P0nkwBoV7NUXpQjYrkbzYTkUHo6jBsHw4fD6dOObV27wrvvQrVqlpYmZsqEiCvlQsRMmRAxs1MmNELIi23cuPG6n2sYBruOnwOgUvHwPKpIxFo3kgnJheHDHSODTp92jA5asAC+/17NIA+kTIi4Ui5EzJQJETM7ZUINIZvaf9mE0vXLF7auEBHxDpf/T8mQIVClCnz6KWzYAO3aWVeXiIiIiIhcFx/DMAyri3CnxMREIiMjSUhIICIiwupybkhycjKhoaHX9dz//baf/5u9hULB/mwe3iGPKxOxxo1kQq7g+HF45RWIj4fZsy9tz8oCX/2fgqdTJkRcKRciZsqEiJm3ZyI3PQ/9a96LLV++/Lqf+/OWowD0a1ohr8oRsdyNZEL+JjXVMSdQ1aqOkUDffQeXD59VM8grKBMirpQLETNlQsTMTpnQv+i92MmTJ6/7uSv+dDy3RmnvHiUlcrkbyYRcYBgwYwbUrAnPPQeJidCgASxZAvXqWV2d5JIyIeJKuRAxUyZEzOyUCa0y5sWKFi16Xc+7/CrBisXC8qocEctdbybkgsOH4b77YOlSx/3SpeHNN6FfP40I8lLKhIgr5ULETJkQMbNTJtQQ8mKtWrW6ruftOZHkvF21lFYYk4LjejMhFxQvDgcPQnAwPPusY4RQuD4jvJkyIeJKuRAxUyZEzOyUCf2XrxebffkEr7nw0ybH/EElCwUR5O+XhxWJWOt6M2Fb58/Dxx9DRobjflAQfPst7NgBr76qZlABoEyIuFIuRMyUCREzO2VCI4RsaO3+0wDcXNE+Q+FE5DKGAZMnw7//7RgR5OcHjzzieOyWW6ytTURERERE3EINIS9Wt27d63rewVPJABQNC8zLckQsd72ZsJXffoOhQx1/AkRHQ1SUtTVJvlEmRFwpFyJmyoSImZ0yoUvGvFhAQMB1Pc/nwp/1yxfOs1pEPMH1ZsIWDh6Evn2haVNHMygsDF5/3XF52F13WV2d5BNlQsSVciFipkyImNkpE2oIebF169bl+jlJqRnOSaV1yZgUNNeTCdt46CHH/EA+PvDgg7BrF7z0EoSEWF2Z5CNlQsSVciFipkyImNkpE7pkzGZW7T7pvB1dNNTCSkQkX2VlQVqaY8UwcCwfn5oKo0ZBgwbW1iYiIiIiIpbzMQzDsLoId0pMTCQyMpKEhAQiIiKsLueGJCYm5vo1jF38J+/M3QHAvrfuyI+yRCxzPZkokJYudcwT1KaNowEktqVMiLhSLkTMlAkRM2/PRG56HrpkzIutXbs218/ZfvQsAPc1KZ/X5YhY7noyUaDs2QM9e0Lr1rB+PXz9NZw7Z3VVYiHbZ0IkG8qFiJkyIWJmp0yoIeTFjh07luvnfP/HEQBqRBXK63JELHc9mSgQEhLgueegRg2YMQN8feFf/4KtWyE83OrqxEK2zYTIVSgXImbKhIiZnTKhOYS8WG6HsZ1OSnPebl9Ly0xLwePNQzuv25Il0KsX/PWX4367djB6NNSpY21d4hFsmQmRa1AuRMyUCREzO2VCcwh5sfT09Fwtibfp0Bnu/HgFURHB/PbibflYmYg1cpuJAuHoUYiNhTJlHPMF3XGHYyUxEWyaCZFrUC5EzJQJETNvz4TmELKJ6dOn52r/LYcTAShRKCg/yhGxXG4z4ZV27oSRIy/dL10afv0VtmyBLl3UDBITW2RCJJeUCxEzZULEzE6ZUEPIRuITzgNgYKtBYSIFw+nTjpXDatWCF1+EX3659NjNN4MX/y+GiIiIiIi4n+YQ8mK1atXK1f5/HEoAoGvdMvlRjojlcpsJr5CeDp98AsOHw6lTjm133AHltVKgXFuBzITIDVIuRMyUCREzO2VCDSEvFp7L1YN2HnMsOV+7bGR+lCNiudxmwqMZBvz8Mzz9NGzf7thWu7Zjwujbb7e2NvEaBSoTInlEuRAxUyZEzOyUCV0y5sV+//33HO+bkJzO0YQUAGJLacl5KZhykwmPl54Ojz7qaAYVLw7jxsGGDWoGSa4UqEyI5BHlQsRMmRAxs1MmNELIJjYcPO28rUmlRTzUiRNQuDD4+0NgoGPVsFWr4KWXHNtFRERERETyiEYIebEOHTrkeN/Vex1zj3Srp/mDpODKTSY8SmoqvPceVK4Mn39+aXuPHvDuu2oGyXXz2kyI5CPlQsRMmRAxs1Mm1BDyYlu2bMnxvnM2HwV0uZgUbLnJhEcwDJg1y7Fy2LPPQmKi475IHvG6TIi4gXIhYqZMiJjZKRNqCHmxw4cP53jfrAsrzZcpHJxP1YhYLzeZsNyGDdC2rWMU0O7dEBUFX3wBP/1kdWVSgHhVJkTcRLkQMVMmRMzslAnNIeTFwsLCcrRfakYmh04nA3BLpWL5WZKIpXKaCcuNGuUYEWQYEBzsWEns+efBRisaiHt4TSZE3Ei5EDFTJkTM7JQJH8MwDKuLcKfExEQiIyNJSEggIiLC6nJuSFZWFr6+1x7kdeh0Mi3eXoSfrw9/vtEJHx8fN1Qn4n45zYTl1q6Fxo3hnnvgrbegfHmrK5ICymsyIeJGyoWImTIhYubtmchNz8N7X6UwZcqUHO33x8EEADKzDDWDpEDLaSbcyjBgyhTHpNEXNWoEu3bBt9+qGST5yiMzIWIx5ULETJkQMbNTJnTJmA2cS023ugQRe1q9GoYOhZUrISAA7rrLsZIYXPpTRERERETEAhoh5MWqVauWo/3+OpsKQI8GZfOzHBHL5TQT+e7QIbj/fmjSxNEMCg2Fl1+G0qWtrkxsxmMyIeJBlAsRM2VCxMxOmdAIIS9WvHjxHO138NR5AMoVCc3PckQsl9NM5JukJHj3XXjnHTjvyB0PPABvvAFl1ZAV97M8EyIeSLkQMVMmRMzslAmNEPJiK1asyNF++04mAVC5hH1mSxd7ymkm8k1CgmOuoPPnoUULWLMGJkxQM0gsY3kmRDyQciFipkyImNkpExohZAO/7z0FQNGwQIsrESmA4uKgZk3H7TJlHEvKFysGd98NmsRdREREREQ8lEYIebF27drlav9CwQH5VImIZ8htJm7I3r3QuzfUqgWLFl3a/q9/Qc+eagaJR3BrJkS8hHIhYqZMiJjZKRNqCHmxnTt3XnOfvSeSnLerlAzPz3JELJeTTNywxER44QWoUQOmTQNfX8dqYiIeyC2ZEPEyyoWImTIhYmanTKgh5MUOHDhwzX1W7z0JQPHwQMKDdIWgFGw5ycR1y8yE8eOhalV46y1ITYXbboMNG+Df/86/84rcgHzNhIiXUi5EzJQJETM7ZUIdAi8WFBR0zX02HjwDQFmtMCY2kJNMXLcePeD77x23Y2Mdk0d36aJLw8Sj5WsmRLyUciFipkyImNkpEz6GYRhWF+FOiYmJREZGkpCQQEREhNXl5LtGry/gxLk02lQrwYQHG1tdjoj3mjwZHnkEhg2DwYMhUJO0i4iIiIiIZ8lNz0OXjHmxKVOmXHOfk0lpALSoUjy/yxGxXE4ykSOnT8NTT8GXX17a1qcP7NkDTz6pZpB4jTzLhEgBolyImCkTImZ2yoQuGfNiWVlZ19zn4vivxjFF87kaEevlJBNXlZEBn37qGAV08iSULOlYSSwszHFpWJEieVOoiJvccCZECiDlQsRMmRAxs1MmNELIi1WuXPmqj6ekZzpvVygalt/liFjuWpm4qrlzoW5deOwxRzOoZk34+mtHM0jES91QJkQKKOVCxEyZEDGzUybUEPJiZcuWverjR86cd96OCNFgMCn4rpWJbO3cCZ06Ob62bYNixWDsWPjjD+jQIe+LFHGj68qESAGnXIiYKRMiZnbKhBpCXmzp0qVXfXzX8XMAVCoRho9WQhIbuFYmspWQ4BgdFBAATz8Nf/7pmDzaX01U8X7XlQmRAk65EDFTJkTM7JQJ/cZTgJ04lwpAarp9roEUuaa0NPj9d2jZ0nH/5pvho4+gY0eoUsXa2kRERERERNxEI4S8WJs2ba76+PJdJwBoW72EG6oRsd5VM2EY8N13UKsWtGvnWDHsosceUzNICqRr/ZwQsSPlQsRMmRAxs1Mm1BDyYgcOHLjq4xcnlQ4PCnBHOSKWu2Im/vgDbrsNund3XBJWpAjs3evW2kSscK2fEyJ2pFyImCkTImZ2yoQaQl5sz+UjHLKxPf4sAK1ii7ujHBHLuWTi2DF46CGoXx8WLYKgIHjxRdi1y9EgEingrvVzQsSOlAsRM2VCxMxOmbC8ITR27FhiYmIIDg6mYcOGLFu27Ir7zpw5k9tvv50SJUoQERFB06ZNmTdvnhur9Sz+V5n0du+JJI4mpABQPSrCXSWJWMqUidRUqFcPxo93XC7Wpw9s3w5vvAGFCllWo4g7Xe3nhIhdKRciZsqEiJmdMuFjGIZh1cmnTJnC/fffz9ixY2nevDmffvop48ePJy4ujvLly7vs/+STT1KmTBnatm1L4cKF+fLLL3nvvff4/fffqV+/fo7OmZiYSGRkJAkJCUREFNxGyY+bjvDYtxuILhrCsudutbocEfcwDLh8Rb1XX4Uff4T334fmza2rS0RERERExA1y0/OwdITQ6NGjGTBgAAMHDqRGjRqMGTOG6Ohoxo0bl+3+Y8aM4bnnnuPmm2+matWqvPnmm1StWpUffvjBzZV7hhkzZlzxsXX7TwNQt1xhN1UjYrE1azhRsyYsXnxp2/PPw2+/qRkktnW1nxMidqVciJgpEyJmdsqEZQ2htLQ01q1bR/v27U3b27dvz8qVK3N0jKysLM6ePUvRokWvuE9qaiqJiYmmr4IiLS3tio/5XRglcTYlw13liFjj0CHo1w8aN6b49u3wf/936bHAQPC1/MpYEctc7eeEiF0pFyJmyoSImZ0yYdnFcSdOnCAzM5NSpUqZtpcqVYr4+PgcHWPUqFEkJSXRu3fvK+4zcuRIRowY4bJ92rRphIaG0qNHDxYuXEhCQgIlS5akcePG/PjjjwA0aNCArKwsNm7cCEC3bt1Yvnw5J0+epGjRorRq1YrZs2cDULduXQICAli3bh0Ad9xxB2vXruXYsWNERETQvn17pk+fDkCtWrUIDw/n999/B6BDhw5s2bKFw4cPExYWRpcuXZgyZQoA1apVo3jx4qxYsQKAdu3asXPnTg4cOOBsbk2ZMoWsrCwqV65M2bJlWbp0KXN3hQF+RAckMWnSJPz9/enVqxczZswgLS2NChUqULlyZX799VcAWrRowfHjx9m5cycA9957L9999x3JycmUK1eOmjVrMn/+fACaNm1KQkICcXFxAPTq1Yu5c+dy9uxZoqKiaNCgAXPmzAHg5ptvJiUlhc2bNwNw1113sXjxYk6fPk3x4sVp2rSpc4TXxcv+NmzYAEDXrl1ZtWoVJ06coEiRIrRp04ZZs2YBUKdOHYKDg1mzZg0AnTt3Zv369cTHx1OoUCE6duzItGnTAKhZsyaRkZGsWrUKcDQd4+LiOHToEKGhoXTr1o1JkyYBEBsbS8mSJVm+fDkAt956K7t372b//v0EBgZy9913M23aNDIyMqhUqRLly5dn8YURKa1ateLw4cPs3r0bX19f+vTpw8yZM0lNTaV8+fLExsbyyy+/ANC8eXNOnDjBjh07AOjTpw8//vgjSUlJlC1bltq1azvnx2rSpAnnzp1j69atAPTs2ZP58+eTmJhIqVKlaNSoET/99BMADRs2JD09nU2bNgHQvXt3li5dyqlTpyhWrBgtWrTgu+++A6BevXr4+vqyfv16ALp06cLq1as5fvw4kZGR3HbbbcycOROA2rVrExoayurVqwHo1KkTf/zxB0eOHCE8PJzOnTszdepUAKpXr07RokWdjd3bb7+d7du3c/DgQUJCQujevTuTJ0/GMAyqVq1KVFSUc+6wtm3bsm/fPvbu3UtAQAA9e/Zk+vTppKenExMTQ8WKFVm0aJHj/W7YEEaNotSECfhf+NDeecstxN1zD8WXL6d69eosWLAAgGbNmnHq1Cm2b98OQO/evZkzZw7nzp2jTJky3HTTTfz8888ANG7cmOTkZLZs2QLg1Z8RQUFB9OjRI9vPCHAsqXngwAH27Nmjz4gC+hmRnp5OYmKiLT8jWrZsSXx8PLt27cLHx4d77rmH2bNnc/78eaKjo/UZYePPiFOnTrFlyxZ9Rtj83xH6jLj0GREREeH8O6zPCP07Qp8Rizh16hSHDh3y2s+I5ORkcsqyOYSOHDlC2bJlWblyJU2bNnVuf+ONN/jmm2+cb+aVTJo0iYEDB/Ldd9/Rrl27K+6XmppKamqq835iYiLR0dEFYg6hY8eOuTTULrppxHwSzqfzRf9G3Fo9+31EvNbs2fDYY3D4sON+8+bw/vscK1/+ipkQsaOr/ZwQsSvlQsRMmRAx8/ZMeMUcQsWLF8fPz89lNNDx48ev+eZPmTKFAQMGMHXq1Ks2gwCCgoKIiIgwfRUUFzvuf5eZZXA2JR2AKiW0mpIUQMnJjmZQhQowZQosWwY333zFTIjYlTIh4kq5EDFTJkTM7JQJyxpCgYGBNGzY0DnM6qIFCxbQrFmzKz5v0qRJ9O/fn2+//ZY77rgjv8v0StuOJpJlQEiAH+WKhFhdjsiN278fLgzxBuDee+Hzzx3LyPfubV5ZTERERERERK7JsjmEAJ566inuv/9+GjVqRNOmTfnvf//LgQMHGDRoEAAvvPAChw8f5uuvvwYczaB+/frxwQcfcMsttzhHF4WEhBAZGWnZ67BKixYtst2+cNtxAGJLhePrq1+UxYudPQsjR8Lo0VC4MOzaBYUKORpA//yny+5XyoSIXSkTIq6UCxEzZULEzE6ZsHT5nT59+jBmzBheffVV6tWrx9KlS5kzZw4VKlQA4OjRoxw4cMC5/6effkpGRgaPPvoopUuXdn498cQTVr0ESx0/fjzb7efTMx03NGpCvFVmpmMEUNWqjoZQairUrAmnT1/1aVfKhIhdKRMirpQLETNlQsTMTpmwfD3mwYMHs2/fPlJTU1m3bh2tWrVyPjZhwgTnrOoAixcvxjAMl68JEya4v3APcHGW/r9bvfckALdVL+nOckTyxqJF0KgRDBwIx45BlSqOSaQXLoTy5a/61CtlQsSulAkRV8qFiJkyIWJmp0xYesmY5I/MLMfCcZEhARZXIpJLO3fCrbc6bkdGwiuvOFYTCwy0ti4REREREZECxrJl562SmyXYvFXF538CYOq/mtI4pqjF1YhcQ3o6BFzWvOzfH8LCYMQIKF7csrJERERERES8jVcsOy837rvvvnPZlpyW4bwdWyrcneWI5E5GBowdCzExsHfvpe1ffgn/+c91NYOyy4SInSkTIq6UCxEzZULEzE6ZUEPIiyUnJ7ts2xF/1nlbl4yJx5o3D266CR59FA4fhg8/vPTYDUyGnl0mROxMmRBxpVyImCkTImZ2yoQaQl6sXLlyLtu2HE5w3vbRKmPiabZtgzvugI4dIS4OihWDjz+Gd97Jk8NnlwkRO1MmRFwpFyJmyoSImZ0yoUmlvVjNmjVdtq3a41hhrEvd0u4uR+Tqnn8e3nvPsaS8vz88/ji8/DIUKZJnp8guEyJ2pkyIuFIuRMyUCREzO2VCI4S82Pz581227Tx2DoAAP31rxcOEhDiaQd26OUYHjR6dp80gyD4TInamTIi4Ui5EzJQJETM7ZUIjhAqY0EA/ABpWyNtftEVyxTDgxx+hZElo0sSx7dlnoVUraNvW2tpEREREREREI4S8WdOmTV22HT59HoBaZa6+vJxIvtm0CW6/He680zFpdFaWY3toaL43g7LLhIidKRMirpQLETNlQsTMTplQQ8iLJSQkmO5nZhmcTEoDoEShICtKEjs7dgwefhjq14eFCyEoyNEYSk93Wwl/z4SI3SkTIq6UCxEzZULEzE6ZUEPIi8XFxZnuXxwdBFA6MsTd5YhdpaTA229D1arw2WeOEUG9ejlWFBs50tEYcpO/Z0LE7pQJEVfKhYiZMiFiZqdMaA6hAmT3iXPO236+WnJe3OT77x0riAE0agTvvw8tWlhbk4iIiIiIiFyVj2EYhtVFuFNiYiKRkZEkJCQQEeHd8+xkZGTg73+ppzd1zUGem7GJqIhgfnvxNgsrkwIvMREu5icrC3r2hO7d4R//AF/rBh7+PRMidqdMiLhSLkTMlAkRM2/PRG56HrpkzIvNnTvXdH/DwTMANKtSzIJqxBaOHIH+/aFGDTh71rHN1xdmzoR+/SxtBoFrJkTsTpkQcaVciJgpEyJmdsqEGkJe7OzFX8gvOH1hQunE8xlWlCMFWXIyvPaaY56gr75yNIY88IPy75kQsTtlQsSVciFipkyImNkpE947DkqIiooy3d9zYQ6h6lGFrChHCqKsLJg0yTFH0KFDjm3NmjnmCWrc2NrasvH3TIjYnTIh4kq5EDFTJkTM7JQJNYS8WIMGDUz30zMd00GVitCS85IHUlKgbVv47TfH/QoVHKuJ9e4NPp45afnfMyFid8qEiCvlQsRMmRAxs1MmdMmYF5szZ47p/t4TSQBUKakRQpIHgoMhJgbCw+HNNx3LyPfp47HNIHDNhIjdKRMirpQLETNlQsTMTplQQ6gAKh4eaHUJ4o3OnYOXX4Z9+y5tGzUKdu2CF16AkBDLShMREREREZG8pUvGvNjNN9/svH02Jd15u0xh/eIuuZCV5Zgo+sUXIT7e0QCaPNnxWOnS1taWS5dnQkSUCZHsKBciZsqEiJmdMqGGkBdLSUlx3t557NJM6GFB+rZKDi1ZAkOHwoYNjvuVKzsuC/NSl2dCRJQJkewoFyJmyoSImZ0yoUvGvNjmzZsv3T6UAECAn+fO7yIeZPduuPtuaNPG0QyKjIT33oOtW+Guu6yu7rpdngkRUSZEsqNciJgpEyJmdsqEhpIUEIdOnwegdtlIiysRr/D11zBzJvj6wr/+BSNGQIkSVlclIiIiIiIibuJjGIZhdRHulJiYSGRkJAkJCURERFhdzg1JSUkhODgYgKembmTm+sPcUac0/+lrn2XyJIcyMuD4cShTxnH/3DkYNAiefx5q17a2tjx0eSZERJkQyY5yIWKmTIiYeXsmctPz0CVjXmzx4sXO2wnJjkmlKxYPtaga8VgLFkD9+tCtm2MCaXAsJf+//xWoZhCYMyEiyoRIdpQLETNlQsTMTplQQ8iLnT592nl765FEABpWKGJVOeJptm+HLl2gfXvYsgX27HGsIFaAXZ4JEVEmRLKjXIiYKRMiZnbKhBpCXqx48eIAGIZBfKJjJnQtOS+cOgVPPAF16sBPP4G/Pzz5JPz5J1SrZnV1+epiJkTEQZkQcaVciJgpEyJmdsqEJpX2Yk2bNgUgMSXDuS26iC4Zs7UdO6BpU7jY1e7aFd59t8A3gi66mAkRcVAmRFwpFyJmyoSImZ0yoRFCXuyHH34AYEf8WQCKhAYQFqQen61VrQqVKzvmBlqwAL7/3jbNILiUCRFxUCZEXCkXImbKhIiZnTKhhlABsO2oY/6g5LRMiysRt9uyBe67z7FqGDiWkf/uO9iwAdq1s7Y2ERERERER8VhqCHmx+vXrA3D4zHkAYoqHWVmOuNPx445l42+6CSZNgnfeufRYmTKOeYNs6GImRMRBmRBxpVyImCkTImZ2yoQ9f2ssoKKLav6gAi81FT78EF5/HRIdI8Po2RP697e0LBEREREREfEuGiHkxTZs2ADA8QsrjDUoryXnC7SZM6FmTXjuOUczqEEDWLIEpk2DSpWsrs4jXMyEiDgoEyKulAsRM2VCxMxOmVBDqABYteckAMXCAi2uRPLVtGmwZw+ULg0TJsCaNdCqldVViYiIiIiIiBfSJWNerGvXrsBlk0n7WFiM5L2jRx1/li7t+POttyA2Fp59FsLDravLg13MhIg4KBMirpQLETNlQsTMTpnQCCEvtmrVKgB8fRydoPKaQ6hgOH/eMUdQ1arw9NOXtleoACNGqBl0FRczISIOyoSIK+VCxEyZEDGzUyY0QsiLnThxgvNpmSScTwegUgmtMubVDAMmT4Z//xsOHnRs27cPUlIgONjS0rzFiRMnrC5BxKMoEyKulAsRM2VCxMxOmdAIIS9WpEgRDp5Odt4vER5kYTVyQ377DZo1g/vuczSDoqNh4kRYsULNoFwoUkQTq4tcTpkQcaVciJgpEyJmdsqEj2EYhtVFuFNiYiKRkZEkJCQQERFhdTk3JCUlhd8PnOWBL1YTXTSEZc/danVJcj2mToU+fRy3w8Lg+efhqacgVJcA5lZKSgrBaqCJOCkTIq6UCxEzZULEzNszkZueh0YIebFZs2ax/2QSAKUjQyyuRq5b585Qtiz07w87d8L//Z+aQddp1qxZVpcg4lGUCRFXyoWImTIhYmanTGgOIS8X4Ofo6V1sDImHy8qCr7+G2bNh5kzw9XVMEh0XB14+Yk1ERERERES8h0YIebE6deqQku5Ycr5RxaIWVyPXtHQp3HwzPPggfPcdTJ9+6TE1g/JEnTp1rC5BxKMoEyKulAsRM2VCxMxOmVBDyIsFBweTlJrhuO3vZ3E1ckV79kDPntC6Naxf72j+vPMOdOtmdWUFjjdf6yuSH5QJEVfKhYiZMiFiZqdMqCHkxdasWcPmwwkARIYEWFyNuEhJcSwhX6MGzJjhuDxs0CDYtQuefRaCtCpcXluzZo3VJYh4FGVCxJVyIWKmTIiY2SkTmkPIy51PzwIgPFjfSo8TGAi//AJpaXD77TBqFNho+KGIiIiIiIh4Lo0Q8mKdO3cmJc0xh1CJQhpt4hF+/RWSLkzw7esLY8fCjz/CvHlqBrlB586drS5BxKMoEyKulAsRM2VCxMxOmVBDyIutX7+e7fGJAMQUC7O4GpvbsQO6doXbboN33720vUkTuOMO8PGxrjYbWb9+vdUliHgUZULElXIhYqZMiJjZKRNqCHmx+Ph4ElMck0oXDQu0uBqbOnUKnnwSatd2jATy83PMHSSWiI+Pt7oEEY+iTIi4Ui5EzJQJETM7ZUITz3ixsPBCztslI3TJmFulp8Mnn8Dw4Y6mEDhGAr33HlSvbmlpdlaoUKFr7yRiI8qEiCvlQsRMmRAxs1MmfAzDMKwuwp0SExOJjIwkISGBiIgIq8u5IccTkmk8chEAu97oRICfBny5zdChMGaM43atWjB6NLRvb2lJAhkZGfj7q88tcpEyIeJKuRAxUyZEzLw9E7npeaiD4MWmzvreeVvNIDe4vHc6ZAiUKwfjxsHGjWoGeYhp06ZZXYKIR1EmRFwpFyJmyoSImZ0y4b1tLyE5UxMVu8Vff8Err0ByMnz1lWNbTAzs3Qte3DkWERERERER+9KwEi9WrnwMAJEhARZXUkClpjrmBKpSxTFf0Ndfw86dlx5XM8jj1KxZ0+oSRDyKMiHiSrkQMVMmRMzslAk1hLxYQHAoAGUKh1hcSQFjGDBrlmNuoGefhcREqF8fFi+G2Firq5OriIyMtLoEEY+iTIi4Ui5EzJQJETM7ZUINIS+2Ycs2AMKD/CyupAA5cABuvRV69IDduyEqCr74Atasgdatra5OrmHVqlVWlyDiUZQJEVfKhYiZMiFiZqdM6JoXL3YsxdEI0iVjeahIEdi2DYKD4emn4fnnITzc6qpERERERERE8pRGCHmx2KqVATiVlGZxJV7s/HkYPx6yshz3CxWCb7+F7dvh9dfVDPIy7bXam4iJMiHiSrkQMVMmRMzslAk1hLzY77viAahd1j7XOOYZw4DJk6F6dXjoIcfti269FSpUsK42uW5xcXFWlyDiUZQJEVfKhYiZMiFiZqdM6JIxL+aXngwEkpyWaXUp3mX1ahg6FFaudNwvVw7CwqytSfLEoUOHrC5BxKMoEyKulAsRM2VCxMxOmdAIIS9m+Dr6edVKFbK4Ei9x8CD84x/QpImjGRQaCq++Cjt2QLduVlcneSA0NNTqEkQ8ijIh4kq5EDFTJkTM7JQJH8MwDKuLcKfExEQiIyNJSEggIiLC6nJuyMCv1vDLtuO81q0W9zetaHU5nq9lS1i+3HH7gQfgjTegbFlraxIRERERERHJI7npeWiEkBf788ARAIqEBVpciYfKyoK0yybcfv11R1NozRqYMEHNoAJo0qRJVpcg4lGUCRFXyoWImTIhYmanTKgh5MXSsnwACA/SVFAuVqxwXBr21luXtrVuDUuWQKNG1tUlIiIiIiIi4gHUEPJi8Sl+AESEBFhciQfZtw/69IEWLWDtWhg3DlJTLz3u42NZaZL/YmNjrS5BxKMoEyKulAsRM2VCxMxOmVBDyEsZhkHWhdmfiobqkjESE+GFFxzLyE+dCr6+8PDDsHEjBAVZXZ24ScmSJa0uQcSjKBMirpQLETNlQsTMTplQQ8hLpWZkOW8XC7d5Q+iXX6BqVcflYampcNttsGEDfPoplCpldXXiRssvThouIoAyIZId5ULETJkQMbNTJjT5jJdKOJ/uvB0WaPNvY6VKcOaMoyk0ahR06aJLw0RERERERESuQiOEvFR8Qorztq+vzZofu3bBhx9eul+pEixcCFu2QNeuagbZ2K233mp1CSIeRZkQcaVciJgpEyJmdsqEGkJe6mjCeatLcL/Tp+Gpp6BWLXjiCVi9+tJjLVpAoM0vnRN2795tdQkiHkWZEHGlXIiYKRMiZnbKhBpCXiopNROAsoVDLK7EDdLT4eOPHZeEvf++436nTlC4sNWViYfZv3+/1SWIeBRlQsSVciFipkyImNkpEzaffMZ7ZWQ5JpUuGVHAV9D6+Wd4+mnYts1xv2ZNGD0aOnSwti7xSIEaJSZiokyIuFIuRMyUCREzO2XCxzAMw+oi3CkxMZHIyEgSEhKIiIiwupzrNnr+Dj789U8614libN+GVpeTP5KTISYGjh+HYsXgtdfgoYfAX31MERERERERkb/LTc9Dl4x5KZ8LEyefSU6/xp5e5tQpuNijDA2Ft992jBD680945BE1g+Sqpk2bZnUJIh5FmRBxpVyImCkTImZ2yoQaQl7q4rCuEoUKyCVjaWmOS8EqV4YpUy5t798f3ntP8wVJjmRkZFhdgohHUSZEXCkXImbKhIiZnTKhhpCX2n38HAAlvb0hZBjw3XeOlcOefhrOnIFvv7W6KvFSlSpVsroEEY+iTIi4Ui5EzJQJETM7ZUINIS8VFuQHePklY3/8AbfdBt27Oy4JK1UKxo+HWbOsrky8VPny5a0uQcSjKBMirpQLETNlQsTMTplQQ8hLXZxmp4y3Ljv/9ttQvz4sWgRBQfDCC7BrFwwYAH5+VlcnXmrx4sVWlyDiUZQJEVfKhYiZMiFiZqdMaIZeL5WS4Vh2PiIkwOJKrlOTJo6uVp8+8NZbULGi1RWJiIiIiIiI2IYaQl5q48HTAAT6+VhcSQ4YBkydCqdPw6BBjm1t2sDWrVCzpqWlScHSqlUrq0sQ8SjKhIgr5ULETJkQMbNTJtQQ8lIxxcM5eOo8Z1M9fAb0NWtg6FBYscKxjPydd0KZMo7H1AySPHb48GHKli1rdRkiHkOZEHGlXIiY3UgmMjMzSU/34jlNRbJx+PBhihUrZnUZVxUYGIiv743PAKSGkJdKv3DJWLkioRZXcgWHDsGLL8I33zjuh4bCc89BZKS1dUmBtnv3bho3bmx1GSIeQ5kQcaVciJhdTyYMwyA+Pp4zZ87kT1EiFvL19WXv3r1Wl3FVvr6+xMTEEBgYeEPHUUPIS6VnOhpCgX4eNi94cjK8+65j0ujz5x3b7r8f3nwTypWztjYp8PKiSy5SkCgTIq6UCxGz68nExWZQyZIlCQ0NxcfHC6axEMmhM2fOULhwYavLuKKsrCyOHDnC0aNHKV++/A3lz8cwLq5XZQ+JiYlERkaSkJBARESE1eVct4rP/wTAF/0bcWv1UhZXc5ndux2XgqWlQfPm8P77cPPNVlclIiIiIiJ5IDMzk507d1KyZEmPv6xGpKBKSEjgyJEjVKlShYAA80JTuel56L9IvFTVkuEApF24dMxSf/556Xblyo7RQFOnwrJlagaJW82cOdPqEkQ8ijIh4kq5EDHLbSYuzhkUGuqhU1eI3KDTp09bXcI1XbxULDMz84aOo4aQl8q8MLCrSOiNXTN4Q/btcywbHxsLa9de2v7009CrF2joqLhZamqq1SWIeBRlQsSVciFidr2Z0GViUlBlZXnAoItryKv8qSHkpTKzHA0hfyvmEDp71jFhdPXqjpFAAEuXur8Okb8pX7681SWIeBRlQsSVciFipkyImAUFBVldgtuoIeSlMjIvNIR83diZz8yE8eOhalUYORJSU6FtW1i/Hp56yn11iFxBbGys1SWIeBRlQsSVciFipkxIdj7//HPat29vdRmWsLohlJqaSvny5Vm3bl2+n0sNIS+VcWEYm7+fGxtCnTvDQw/BsWNQpQrMng0LF0K9eu6rQeQqfvnlF6tLEPEoyoSIK+VCxMxOmejfvz8+Pj74+Pjg7+9P+fLleeSRR7KdM2blypV07tyZIkWKEBwcTJ06dRg1alS2c7YsWrSIzp07U6xYMUJDQ6lZsyZPP/00hw8fdsfLynOpqam88sorvPzyy1aXkm8Mw2D48OGUKVOGkJAQ2rRpw9atWwHHpMxXMmbMGKpVq0ZISAjR0dEMHTqUlJQU5+PDhw93/h27+BUVFWU6xsyZM+nQoQPFixfHx8eHjRs3mh4PCgrimWee4d///nfeveArUEPISx1LdFzr6+/OpVPvvhsiI2HUKNi6Fbp10zxBIiIiIiLiNTp27MjRo0fZt28f48eP54cffmDw4MGmfWbNmkXr1q0pV64cixYtYvv27TzxxBO88cYb3HPPPVy+UPenn35Ku3btiIqKYsaMGcTFxfHJJ5+QkJDAqFGj3Pa60tLS8uxYM2bMIDw8nJYtW97QcS5OQO6J3nnnHUaPHs3HH3/MmjVriIqK4vbbb+fs2bNXfM7EiRN5/vnnGTZsGNu2bePzzz9nypQpvPDCC6b9atWqxdGjR51fmzdvNj2elJRE8+bNeeutt654rr59+7Js2TK2bdt2Yy/0GtQQ8lJ+Fy4Vy7crxs6cgWeegRkzLm0bMMCxothTT0GghZNZi1xB8+bNrS5BxKMoEyKulAsRs7zIhGEYJKdlWPJ1eXMmJ4KCgoiKiqJcuXK0b9+ePn36MH/+fOfjSUlJPPTQQ9x5553897//pV69elSsWJGBAwfy1VdfMX36dKZemEf10KFDDBkyhCFDhvDFF1/Qpk0bKlasSKtWrRg/fjyvvPLKFes4c+YMDz/8MKVKlSI4OJjatWvz448/Ao5RJvX+dhXGmDFjqFixovN+//796d69OyNHjqRMmTLExsbywgsvcMstt7icq27dugwbNsx5/8svv6RGjRoEBwdTvXp1xo4da9p/8uTJ3HnnnaZta9as4fbbb6d48eJERkbSunVr1q9fb9rHx8eHTz75hG7duhEWFsbrr78OwA8//EDDhg0JDg6mUqVKjBgxgoyMDOfzRo8eTZ06dQgLCyM6OprBgwdz7ty5K753N8owDMaMGcNLL71Ejx49qF27Nl999RXJycl8++23hIeHZ/u8VatW0bx5c+677z4qVqxI+/btuffee1l7+QJLgL+/P1FRUc6vEiVKmB6///77eeWVV2jXrt0VayxWrBjNmjVj0qRJN/6Cr8I/X48u+cbPx4dMDMKC8vhbmJEBn30Gr7wCJ05AhQrQpQsEBYGfHxQvnrfnE8lDJ06c0MSIIpdRJkRcKRciZnmRifPpmdR8ZV4eVZQ7ca92IDTw+n4n2rNnD3PnziUgIMC5bf78+Zw8eZJnnnnGZf+uXbsSGxvLpEmT6NOnD9OmTSMtLY3nnnsu2+MXLlw42+1ZWVl06tSJs2fP8r///Y/KlSsTFxeHn59frupfuHAhERERLFiwwNkYe+utt9i9ezeVK1cGYOvWrWzevJnp06cD8NlnnzFs2DA+/vhj6tevz4YNG3jooYcICwvjgQceAGDZsmX07dvXdK6zZ8/ywAMP8OGHHwIwatQoOnfuzK5duyhUqJBzv2HDhjFy5Ejef/99/Pz8mDdvHv/4xz/48MMPadmyJbt37+bhhx927gvg6+vLhx9+SMWKFdm7dy+DBw/mueeec2lUXa5Tp04sW7bsqu/PlZpKe/fuJT4+3jRHUlBQEK1bt2blypX84x//yHYeoRYtWvC///2P1atX07hxY/bs2cOcOXOc79tFu3btokyZMgQFBdGkSRPefPNNKlWqdNVas9O4ceNrvsYbpYaQFzIMg7RMxxxCgf55OMhr3jzHkvEXrp2kRg3H5WE2mmVdvNuOHTto0KCB1WWIeAxlQsSVciFiZrdM/Pjjj4SHh5OZmemc+2X06NHOx3fu3AlAjRo1sn1+9erVnfvs2rWLiIgISpcunasafvnlF1avXs22bduck3pfT8MgLCyM8ePHE3jZ1Rt169bl22+/dc7/M3HiRG6++WbneV577TVGjRpFjx49AIiJiSEuLo5PP/2UBx54gDNnznDmzBnKlCljOtett95quv/pp59SpEgRlixZQpcuXZzb77vvPv75z386799///08//zzzqZJpUqVeO2113juueecDaEnn3zSuX9MTAyvvfYajzzyyFUbQuPHj+f8+fM5fq8uFx8fD0CpUqVM20uVKsX+/ftJSUkhLCzM5Xn33HMPf/31Fy1atMAwDDIyMnjkkUd4/vnnnfs0adKEr7/+mtjYWI4dO8brr79Os2bN2Lp1K8WKFctVnWXLlmXfvn25f4G5oIaQF0rNyHLeDsiLZed37oShQ2HOHMf9YsVgxAh4+GG4rFsuIiIiIiLydyEBfsS92sGyc+dG27ZtGTduHMnJyYwfP56dO3fy+OOPu+x3pUvRDMPA58I8qpffzo2NGzdSrly5G17hrU6dOqZmEDjmnvniiy94+eWXMQyDSZMmORsuf/31FwcPHmTAgAE89NBDzudkZGQQGRkJ4GyyBAcHm457/PhxXnnlFX799VeOHTtGZmYmycnJHDhwwLRfo0aNTPfXrVvHmjVreOONN5zbLjbjkpOTCQ0NZdGiRbz55pvExcWRmJhIRkYGKSkpJCUlZduYAUez5Eb9/Xt3re/n4sWLeeONNxg7dixNmjThzz//5IknnqB06dLOBlynTp2c+9epU4emTZtSuXJlvvrqK57K5crcISEhJCcn5+o5uaWGkBdKTb/UEArKixFCBw86mkH+/vD44/Dyy1CkyI0fV8TN+vTpY3UJIh5FmRBxpVyImOVFJnx8fK77si13CwsLo0qVKgB8+OGHtG3blhEjRvDaa68BOJs027Zto1mzZi7P3759OzVr1nTum5CQwNGjR3M1SigkJOSqj/v6+ro0pLKboDm7Zsl9993H888/z/r16zl//jwHDx7knnvuARyXqoHjsrEmTZqYnnfxcrVixYrh4+PjsvJa//79+euvvxgzZgwVKlQgKCiIpk2bukxm/feasrKyGDFihHNE0uWCg4PZv38/nTt3ZtCgQbz22msULVqU5cuXM2DAgKtOSn0jl4xdXPUrPj7e9H07fvw4pUqVomjRotk+7+WXX+b+++9n4MCBgKPhk5SUxMMPP8xLL72EbzYLPoWFhVGnTh127dp11Vqzc+rUKZf5h/KaJpX2QulZN9gQSkuD1asv3b/tNnjzTcelYqNHqxkkXuviRHwi4qBMiLhSLkTM7J6JYcOG8d5773HkyBEA2rdvT9GiRbNdIez7779n165d3HvvvQD07NmTwMBA3nnnnWyPfebMmWy3161bl0OHDjkvPfu7EiVKEB8fb2oK/X1p8ispV64crVq1YuLEiUycOJF27do5L40qVaoUZcuWZc+ePVSpUsX0FRMTA0BgYCA1a9YkLi7OdNxly5YxZMgQOnfuTK1atQgKCuLEiRPXrKdBgwbs2LHD5XxVqlTB19eXtWvXkpGRwahRo7jllluIjY11fi+uZvz48WzcuPGqX1cSExNDVFQUCxYscG5LS0tjyZIlNGvWjISEhGyfl5yc7NL08fPzwzCMK44oS01NZdu2bbm+rBBgy5Yt1K9fP9fPyw3vaOOKSUam4y+bL7kcomgY8MMPjtXDjhyBXbvg4l/Mvy2VJ+KNkpKSrC5BxKMoEyKulAsRM7tnok2bNtSqVYs333yTjz/+mLCwMD799FPuueceHn74YR577DEiIiJYuHAhzz77LD179qR3794AREdH8/777/PYY4+RmJhIv379qFixIocOHeLrr78mPDw828ZS69atadWqFXfffTejR4+mSpUqbN++HR8fHzp27EibNm3466+/eOedd+jZsydz587l559/JiIiIkevqW/fvgwfPpy0tDTef/9902PDhw9nyJAhRERE0KlTJ1JTU1m7di2nT592XtLUoUMHli9fbprbp0qVKnzzzTc0atSIxMREnn322WuOdAJ45ZVX6NKlC9HR0fTq1QtfX182bdrE5s2bef3116lcuTIZGRl89NFHdO3alRUrVvDJJ59c87g3csmYj48PTz75JG+++SZVq1alatWqvPnmm4SGhnLfffc5Rz3169ePsmXLMnLkSMAxqfjo0aOpX7++85Kxl19+mTvvvNM5wuqZZ56ha9eulC9fnuPHj/P666+TmJhomnj61KlTHDhwwNn42rFjB4BzVbKLli1b5hy5lm8Mm0lISDAAIyEhwepSrtuBk0lGhX//aFR54cecP+mPPwzj1lsNw9EWMoySJQ1j8eL8K1LEAkuWLLG6BBGPokyIuFIuRMxym4nz588bcXFxxvnz5/OpovzzwAMPGN26dXPZPnHiRCMwMNA4cOCAc9vSpUuNjh07GpGRkUZgYKBRs2ZN47333jMyMjJcnr9gwQKjQ4cORpEiRYzg4GCjevXqxjPPPGMcOXLkirWcPHnSePDBB41ixYoZwcHBRu3atY0ff7z0+924ceOM6OhoIywszOjXr5/xxhtvGBUqVLjmazEMwzh9+rQRFBRkhIaGGmfPns329darV88IDAw0ihQpYrRq1cqYOXOm8/Ft27YZISEhxpkzZ5zb1q9fbzRq1MgICgoyqlatakybNs2oUKGC8f777zv3AYxZs2a5nG/u3LlGs2bNjJCQECMiIsJo3Lix8d///tf5+OjRo43SpUsbISEhRocOHYyvv/7aAIzTp09f8f27UVlZWcawYcOMqKgoIygoyGjVqpWxefNmwzAMIzEx0TAMw2jdurXxwAMPOJ+Tnp5uDB8+3KhcubIRHBxsREdHG4MHDzbV2adPH6N06dJGQECAUaZMGaNHjx7G1q1bTef+8ssvDcDla9iwYc59Vq5caRQuXNhITk7Otv6r5TA3PQ8fw7jC2KYCKjExkcjISBISEnLcYfU0+04k0ea9xYQF+rH11Y5X3/nYMcecQJ9/DllZjhXDhg51jAjy0tcvciWnTp264jW/InakTIi4Ui5EzHKbiZSUFPbu3UtMTIzLxMNScPTu3Zv69evzgg2vJMnIyMDf39qLqXr16kX9+vV58cUXs338ajnMTc9Dcwh5oeS0TACyMjOuvmNSEtSqBZ995mgG9eoF27bByJFqBkmBNG/ePKtLEPEoyoSIK+VCxEyZkOy8++67hIeHW12GJa40h5C7pKamctNNNzF06NB8P5fmEPJCWRcGdZ3PvMb8QWFh8MADsHQpvP8+tGjhhupERERERETEm1WoUIHHH3/c6jJsKSgoiP/7v/9zy7k0QsgLpWY4VhkrXSjA/MC6ddCmDaxff2nbG2/A77+rGSS28PflM0XsTpkQcaVciJgpEyJmdhoZZXlDaOzYsc7r3ho2bMiyZcuuuv+SJUto2LAhwcHBVKpUKUczkBc0aRcaQv4+F6Z/OnIE+veHm2+GJUvg8usMg4PB1/Jvs4hbnDt3zuoSRDyKMiHiSrkQMVMmRMwyMzOtLsFtLO0UTJkyhSeffJKXXnqJDRs20LJlSzp16sSBAwey3X/v3r107tyZli1bsmHDBl588UWGDBnCjBkz3Fy5tdIyHQ0hv6REeO01qFoVvvrKsX7YP/7hmDNIxIa2bt1qdQkiHkWZEHGlXIiYKRMiZufPn7e6BLexdA6h0aNHM2DAAAYOHAjAmDFjmDdvHuPGjWPkyJEu+3/yySeUL1+eMWPGAFCjRg3Wrl3Le++9x9133+3O0i2VnpFFh50reXXhp5B40rGxaVMYMwYaN7a0NhERERERERHxfJaNEEpLS2PdunW0b9/etL19+/asXLky2+esWrXKZf8OHTqwdu1a0tPTs31OamoqiYmJpi9vl5GVRYmkM5RKPAnly8PkybBihZpBYns9e/a0ugQRj6JMiLhSLkTMlAkRsyJFilhdgttYNkLoxIkTZGZmUqpUKdP2UqVKER8fn+1z4uPjs90/IyODEydOULp0aZfnjBw5khEjRrhsnzZtGqGhofTo0YOFCxeSkJBAyZIlady4MT/++CMADRo0ICsri40bNwLQrVs3li9fzsmTJylatCitWrVi9uzZANStW5eAgADWrVsHwB133MHatWs5duwYERERtG/fnunTpwNQq1YtwsPD+f333wFHU2vLli0cPnyYsLAwunTpwpQpUwCoVq0axYsXZ8WKFQC0a9eOg7t3sKrpbXwSmMygqWOZ8v33ZE2eTOXKlSlbtixLly4FoE2bNhw4cIA9e/bg7+9Pr169mDFjBmlpaVSoUIHKlSvz66+/AtCiRQuOHz/Ozp07Abj33nv57rvvSE5Oply5ctSsWZP58+cD0LRpUxISEoiLiwOgV69ezJ07l7NnzxIVFUWDBg2YM2cOADfffDMpKSls3rwZgLvuuovFixdz+vRpihcvTtOmTfnhhx8AqF+/PgAbNmwAoGvXrqxatYoTJ05QpEgR2rRpw6xZswCoU6cOwcHBrFmzBoDOnTuzfv164uPjKVSoEB07dmTatGkA1KxZk8jISFatWgU4mo5xcXEcOnSI0NBQunXrxqRJkwCIjY2lZMmSLF++HIBbb72V3bt3s3//fgIDA7n77ruZNm0aGRkZVKpUifLly7N48WIAWrVqxeHDh9m9eze+vr706dOHmTNnkpqaSvny5YmNjeWXX34BoHnz5pw4cYIdO3YA0KdPH3788UeSkpIoW7YstWvXdi4B2qRJE86dO+ccztuzZ0/mz59PYmIipUqVolGjRvz0008ANGzYkPT0dDZt2gRA9+7dWbp0KadOnaJYsWK0aNGC7777DoB69erh6+vL+guTkHfp0oXVq1dz/PhxIiMjue2225g5cyYAtWvXJjQ0lNWrVwPQqVMn/vjjD44cOUJ4eDidO3dm6tSpAFSvXp2iRYs6G7u3334727dv5+DBg4SEhNC9e3cmT56MYRhUrVqVqKgo59xhbdu2Zd++fezdu5eAgAB69uzJ9OnTSU9PJyYmhooVK7Jo0SIAWrZsSXx8PLt27cLHx4d77rmHTz/9lGLFihEdHU316tVZsGABAM2aNePUqVNs374dgN69ezNnzhzOnTtHmTJluOmmm/j5558BaNy4McnJyWzZsgXAKz8jdu7cyYEDBwgKCqJHjx5MmTKFrKwsfUbY8DPi7Nmz3HPPPfqMuPAZMXv2bM6fP6/PCJt/Rhw7dox27drpM0L/jtBnBI7PiJ07d2JcWMU4J58Rq1atomLFiqSlpZGRkUFqaio+Pj4ULVqU06dPk5WVRVBQEEFBQc7/jC9UqBDp6emkpKQAUKxYMc6cOUNmZiaBgYGEhIQ4l/oODw8nMzPTedlO0aJFSUhIIDMzk4CAAEJDQ537hoWFYRgGycnJgOMX+bNnz5KRkUFAQABhYWGcOXMGgNDQUADnvoULFyYpKYn09HT8/f0pVKgQp0+fdu7r4+NDUlISAJGRkSQnJ5Oeno6fnx+RkZGcOnUKgJCQEPz8/JxzMUVGRnL+/HnS0tLw8/OjcOHCnDzpuKojODiYgIAAzp49C0BERASpqamkpqbi6+tLkSJFOHXqFIZhEBQURGBgoHPfy9/Da73f4eHhZGRkON/vy9/Da73fRYoUITEx0fl+X/4eXu399vf3Jzw83PR+X/4eXu39DgkJwdfX1/R+X/4eXu39joiIICUlhbS0NJf38Grv98X38PL3++J7mJGRQZEiRa74fgcGBhIcHGx6v6/0d/bv73dYWBhZWVmm9/tKf2f//n4XLlyYc+fOkZGRQVZWFoZh8OOPP5Kenm76jLi4f074GBfT72ZHjhyhbNmyrFy5kqZNmzq3v/HGG3zzzTfOD9zLxcbG8uCDD/LCCy84t61YsYIWLVpw9OhRoqKiXJ5z8Rt+UWJiItHR0SQkJBAREZHHr8q9Jk2axL333mt1GSIeQ5kQMVMmRFwpFyJmuc1ESkoKe/fudS4MJFLQnDx5kmLFilldxlVdLYeJiYlERkbmqOdh2SVjxYsXx8/Pz2U00PHjx11GAV0UFRWV7f7+/v5X/IYFBQURERFh+ioorvQ+idiVMiFipkyIuFIuRMyUCfepWLGicz5cO2rTpg1PPvmk876nvh8BAQFWl+A2ljWEAgMDadiwoXMo5kULFiygWbNm2T6nadOmLvvPnz+fRo0a2eqbdlGjRo2sLkHEoygTImbKhIgr5ULEzE6Z6N+/Pz4+Pvj4+ODv70/58uV55JFHnJcuFVTDhw93vm4fHx8iIyNp2bIlS5YssbSuNWvW8PDDD1taQ3bCwsKsLsFtLF12/qmnnmL8+PF88cUXbNu2jaFDh3LgwAEGDRoEwAsvvEC/fv2c+w8aNIj9+/fz1FNPsW3bNr744gs+//xznnnmGategqUuXu8tIg7KhIiZMiHiSrkQMbNbJjp27MjRo0fZt28f48eP54cffmDw4MFWl5XvatWqxdGjRzl69CirVq2iatWqdOnSxTm3jRVKlCjhnNvJk1ycw8cOLG0I9enThzFjxvDqq69Sr149li5dypw5c6hQoQIAR48e5cCBA879Y2JimDNnDosXL6ZevXq89tprfPjhh7Zacl5ERERERMQjJSVd+evCBMc52vfChLvX3Pc6BAUFERUVRbly5Wjfvj19+vRxTnoPkJmZyYABA4iJiSEkJIRq1arxwQcfmI7Rv39/unfvznvvvUfp0qUpVqwYjz76qGnl6+PHj9O1a1dCQkKIiYlh4sSJLrUcOHCAbt26ER4eTkREBL179+bYsWPOx4cPH069evX44osvKF++POHh4TzyyCNkZmbyzjvvEBUVRcmSJXnjjTeu+br9/f2JiooiKiqKmjVrMmLECM6dO+dcDABg9OjR1KlTh7CwMKKjoxk8eLBz8maA/fv307VrV4oUKUJYWBi1atVyLgIAEBcXR+fOnQkPD6dUqVLcf//9nDhx4oo1/f2SMR8fH8aPH89dd91FaGgoVatW5fvvvzc9J7fnkKuztCEEMHjwYPbt20dqairr1q2jVatWzscmTJjgXHnhotatW7N+/XpSU1PZu3evczSRHTVs2NDqEkQ8ijIhYqZMiLhSLkTM8jQT4eFX/vr7f+KXLHnlfTt1Mu9bsWL2+92gPXv2MHfuXNP0I1lZWZQrV46pU6cSFxfHK6+8wosvvuhcCe+iRYsWsXv3bhYtWsRXX33FhAkTmDBhgvPx/v37s2/fPn799VemT5/O2LFjOX78uPNxwzDo3r07p06dYsmSJSxYsIDdu3fTp08f03l2797Nzz//zNy5c5k0aRJffPEFd9xxB4cOHWLJkiW8/fbb/N///R+//fZbjl93amoqEyZMoHDhwlSrVs253dfXlw8//JAtW7bw1Vdf8euvv/Lcc885H3/00UdJTU1l6dKlbN68mbfffpvwC9+Ho0eP0rp1a+rVq8fatWuZO3cux44do3fv3jmuC2DEiBH07t2bTZs20blzZ/r27etcYSyvznEtdrpkzLJl5+XGXd6BFhFlQuTvlAkRV8qFiJndMvHjjz86lwm/uCz76NGjnY8HBAQwYsQI5/2YmBhWrlzJ1KlTTY2HIkWK8PHHH+Pn50f16tW54447WLhwIQ899BA7d+7k559/5rfffqNJkyYAfP7559SoUcP5/F9++YVNmzaxd+9eoqOjAfjmm2+oVasWa9as4eabbwYcDaovvviCQoUKUbNmTdq2bcuOHTuYM2cOvr6+VKtWjbfffpvFixdzyy23XPF1b9682dm8SU5OplChQkyZMsW06NLlEz7HxMTw2muv8cgjjzB27FjAMaLp7rvvpk6dOgBUqlTJuf+4ceNo0KABb775pnPbF198QXR0NDt37iQ2Nvaq35eL+vfv71z17s033+Sjjz5i9erVdOzYMc/OcS0WLcRuCTWEvNimTZuoVauW1WWIeAxlQsRMmRBxpVyImOVpJi67vMiFn5/5/mWjZVz4/u1Cln37rrukv2vbti3jxo0jOTmZ8ePHs3PnTh5//HHTPp988gnjx49n//79nD9/nrS0NOrVq2fap1atWvhd9ppKly7N5s2bAdi2bRv+/v6mCburV69O4cKFnfe3bdtGdHS0sxkEULNmTQoXLsy2bducDaGKFStSqFAh5z6lSpXCz88P38veo1KlSplGH2WnWrVqzsuvzp49y5QpU+jVqxeLFi1y1rlo0SLefPNN4uLiSExMJCMjg5SUFJKSkggLC2PIkCE88sgjzJ8/n3bt2nH33XdTt25dANatW8eiRYucTafL7d69O8fNmovHA8dInUKFCjlfW16d41qSk5MJCQnJk2N5OssvGRMREREREZECICzsyl/BwTnf9++/jF9pv+sqMYwqVapQt25dPvzwQ1JTU00jgqZOncrQoUP55z//yfz589m4cSMPPvggaWlppuP8fZVrHx8fsrKygEsjTHx8fK5Yh2EY2T7+9+3Znedq576SwMBAqlSpQpUqVahfvz5vvfUWZcuWdc7hs3//fjp37kzt2rWZMWMG69at4z//+Q9waRTZwIED2bNnD/fffz+bN2+mUaNGfPTRR4BjJFPXrl3ZuHGj6WvXrl2maWGu5WqvLa/OIZdohJAX6969u9UliHgUZULETJkQcaVciJjZPRPDhg2jU6dOPPLII5QpU4Zly5bRrFkz08pju3fvztUxa9SoQUZGBmvXrqVx48YA7Nixw7R6Vc2aNTlw4AAHDx50jhKKi4sjISHBdGlZfvLz8+P8hQm8165dS0ZGBqNGjXKOPvr7vEkA0dHRDBo0iEGDBvHCCy/w2Wef8fjjj9OgQQNmzJhBxYoV8ffPnzaDO84BjssB7UIjhLzY0qVLrS5BxKMoEyJmyoSIK+VCxMzumWjTpg21atVyzktTpUoV1q5dy7x589i5cycvv/wya9asydUxq1WrRseOHXnooYf4/fffWbduHQMHDjRdhtSuXTvq1q1L3759Wb9+PatXr6Zfv360bt3adKlZXsnIyCA+Pp74+Hh27drF66+/TlxcHN26dQOgcuXKZGRk8NFHH7Fnzx6++eYbPvnkE9MxnnzySebNm8fevXtZv349v/76q7N59eijj3Lq1CnuvfdeVq9ezZ49e5g/fz7//Oc/yczMzJPX4I5zgOOSOrtQQ8iLXZxtXUQclAkRM2VCxJVyIWKmTMBTTz3FZ599xsGDBxk0aBA9evSgT58+NGnShJMnT5pGC+XUl19+SXR0NK1bt6ZHjx48/PDDlCxZ0vm4j48Ps2fPpkiRIrRq1Yp27dpRqVIlpkyZkpcvzWnr1q2ULl2a0qVLU69ePaZOncq4cePo168fAPXq1WP06NG8/fbb1K5dm4kTJzJy5EjTMTIzM3n00UepUaMGHTt2pFq1as4Jp8uUKcOKFSvIzMykQ4cO1K5dmyeeeILIyEjTfEc3wh3nAEfzzC58DDtNoQ0kJiYSGRlJQkKCaUZ1bzR//nzat29vdRkiHkOZEDFTJkRcKRciZrnNREpKCnv37iUmJobgv88LJFIAJCQkEBkZaXUZV3W1HOam56ERQl6sRYsWVpcg4lGUCREzZULElXIhYqZMiJhlt4pZQaWGkBf77rvvrC5BxKMoEyJmyoSIK+VCxEyZEDG7fPLvgk4NIRERERERERERm1FDyIvVq1fP6hJEPIoyIWKmTIi4Ui5EzJQJEbPQ0FCrS3AbNYS8WF7OpC5SECgTImbKhIgr5ULE7HozYbO1icRGfHx8rC7hmvIqf/qJ6MXWr19vdQkiHkWZEDFTJkRcKRciZrnNREBAAADJycn5UY6I5ZKSkqwu4ZrS0tIA8PPzu6Hj+OdFMSIiIiIiIlLw+fn5UbhwYY4fPw44Lq/xhhEVIjmVlpZGSkqK1WVcUVZWFn/99RehoaH4+99YS8fHsNlYv8TERCIjI0lISCAiIsLqcm7I2bNnKVSokNVliHgMZULETJkQcaVciJhdTyYMwyA+Pt5WqzGJfWRlZXn85cW+vr7ExMQQGBjo8lhueh4aIeTFVq9ezW233WZ1GSIeQ5kQMVMmRFwpFyJm15MJHx8fSpcuTcmSJUlPT8+nykSssWrVKpo2bWp1GVcVGBiYJ00rNYS82MVhmiLioEyImCkTIq6UCxGzG8mEn5/fDc9hIuJp4uPjCQ4OtroMt/DscVByVZGRkVaXIOJRlAkRM2VCxJVyIWKmTIiY2SkTmkPIi6WmphIUFGR1GSIeQ5kQMVMmRFwpFyJmyoSImbdnIjc9D40Q8mIzZ860ugQRj6JMiJgpEyKulAsRM2VCxMxOmbDdHEIXB0QlJiZaXMmNS05OLhCvQySvKBMiZsqEiCvlQsRMmRAx8/ZMXKw9JxeD2e6SsUOHDhEdHW11GSIiIiIiIiIi+eLgwYOUK1fuqvvYriGUlZXFkSNHKFSoED4+PlaXc90SExOJjo7m4MGDXj8XkkheUCZEzJQJEVfKhYiZMiFiVhAyYRgGZ8+epUyZMtdcmt52l4z5+vpes0vmTSIiIrz2L6pIflAmRMyUCRFXyoWImTIhYubtmcjpSmmaVFpERERERERExGbUEBIRERERERERsRk1hLxUUFAQw4YNIygoyOpSRDyCMiFipkyIuFIuRMyUCREzu2XCdpNKi4iIiIiIiIjYnUYIiYiIiIiIiIjYjBpCIiIiIiIiIiI2o4aQiIiIiIiIiIjNqCEkIiIiIiIiImIzagh5sLFjxxITE0NwcDANGzZk2bJlV91/yZIlNGzYkODgYCpVqsQnn3zipkpF3CM3mZg5cya33347JUqUICIigqZNmzJv3jw3ViuS/3L7c+KiFStW4O/vT7169fK3QBE3y20mUlNTeemll6hQoQJBQUFUrlyZL774wk3VirhHbnMxceJEbrrpJkJDQyldujQPPvggJ0+edFO1Ivlr6dKldO3alTJlyuDj48Ps2bOv+ZyC/Hu2GkIeasqUKTz55JO89NJLbNiwgZYtW9KpUycOHDiQ7f579+6lc+fOtGzZkg0bNvDiiy8yZMgQZsyY4ebKRfJHbjOxdOlSbr/9dubMmcO6deto27YtXbt2ZcOGDW6uXCR/5DYTFyUkJNCvXz9uu+02N1Uq4h7Xk4nevXuzcOFCPv/8c3bs2MGkSZOoXr26G6sWyV+5zcXy5cvp168fAwYMYOvWrUybNo01a9YwcOBAN1cukj+SkpK46aab+Pjjj3O0f0H/PVvLznuoJk2a0KBBA8aNG+fcVqNGDbp3787IkSNd9v/3v//N999/z7Zt25zbBg0axB9//MGqVavcUrNIfsptJrJTq1Yt+vTpwyuvvJJfZYq4zfVm4p577qFq1ar4+fkxe/ZsNm7c6IZqRfJfbjMxd+5c7rnnHvbs2UPRokXdWaqI2+Q2F++99x7jxo1j9+7dzm0fffQR77zzDgcPHnRLzSLu4uPjw6xZs+jevfsV9ynov2drhJAHSktLY926dbRv3960vX379qxcuTLb56xatcpl/w4dOrB27VrS09PzrVYRd7ieTPxdVlYWZ8+e1T/6pUC43kx8+eWX7N69m2HDhuV3iSJudT2Z+P7772nUqBHvvPMOZcuWJTY2lmeeeYbz58+7o2SRfHc9uWjWrBmHDh1izpw5GIbBsWPHmD59OnfccYc7ShbxOAX992x/qwsQVydOnCAzM5NSpUqZtpcqVYr4+PhsnxMfH5/t/hkZGZw4cYLSpUvnW70i+e16MvF3o0aNIikpid69e+dHiSJudT2Z2LVrF88//zzLli3D318//qVguZ5M7Nmzh+XLlxMcHMysWbM4ceIEgwcP5tSpU5pHSAqE68lFs2bNmDhxIn369CElJYWMjAzuvPNOPvroI3eULOJxCvrv2Roh5MF8fHxM9w3DcNl2rf2z2y7irXKbiYsmTZrE8OHDmTJlCiVLlsyv8kTcLqeZyMzM5L777mPEiBHExsa6qzwRt8vNz4msrCx8fHyYOHEijRs3pnPnzowePZoJEyZolJAUKLnJRVxcHEOGDOGVV15h3bp1zJ07l7179zJo0CB3lCrikQry79n6L0IPVLx4cfz8/Fw698ePH3fpTl4UFRWV7f7+/v4UK1Ys32oVcYfrycRFU6ZMYcCAAUybNo127drlZ5kibpPbTJw9e5a1a9eyYcMGHnvsMcDxy7BhGPj7+zN//nxuvfVWt9Qukh+u5+dE6dKlKVu2LJGRkc5tNWrUwDAMDh06RNWqVfO1ZpH8dj25GDlyJM2bN+fZZ58FoG7duoSFhdGyZUtef/11rx8NIZJbBf33bI0Q8kCBgYE0bNiQBQsWmLYvWLCAZs2aZfucpk2buuw/f/58GjVqREBAQL7VKuIO15MJcIwM6t+/P99++62ufZcCJbeZiIiIYPPmzWzcuNH5NWjQIKpVq8bGjRtp0qSJu0oXyRfX83OiefPmHDlyhHPnzjm37dy5E19fX8qVK5ev9Yq4w/XkIjk5GV9f86+Ifn5+wKVRESJ2UuB/zzbEI02ePNkICAgwPv/8cyMuLs548sknjbCwMGPfvn2GYRjG888/b9x///3O/ffs2WOEhoYaQ4cONeLi4ozPP//cCAgIMKZPn27VSxDJU7nNxLfffmv4+/sb//nPf4yjR486v86cOWPVSxDJU7nNxN8NGzbMuOmmm9xUrUj+y20mzp49a5QrV87o2bOnsXXrVmPJkiVG1apVjYEDB1r1EkTyXG5z8eWXXxr+/v7G2LFjjd27dxvLly83GjVqZDRu3NiqlyCSp86ePWts2LDB2LBhgwEYo0ePNjZs2GDs37/fMAz7/Z6thpAH+89//mNUqFDBCAwMNBo0aGAsWbLE+dgDDzxgtG7d2rT/4sWLjfr16xuBgYFGxYoVjXHjxrm5YpH8lZtMtG7d2gBcvh544AH3Fy6ST3L7c+JyaghJQZTbTGzbts1o166dERISYpQrV8546qmnjOTkZDdXLZK/cpuLDz/80KhZs6YREhJilC5d2ujbt69x6NAhN1ctkj8WLVp01d8R7PZ7to9haOyfiIiIiIiIiIidaA4hERERERERERGbUUNIRERERERERMRmpIRa9QAACS9JREFU1BASEREREREREbEZNYRERERERERERGxGDSEREREREREREZtRQ0hERERERERExGbUEBIRERERERERsRk1hEREREREREREbEYNIREREfFoEyZMoHDhwlaXcd0qVqzImDFjrrrP8OHDqVevnlvqEREREQE1hERERMQN+vfvj4+Pj8vXn3/+aXVpTJgwwVRT6dKl6d27N3v37s2T469Zs4aHH37Yed/Hx4fZs2eb9nnmmWdYuHBhnpzvSv7+OkuVKkXXrl3ZunVrro/jzQ06ERERcVBDSERERNyiY8eOHD161PQVExNjdVkAREREcPToUY4cOcK3337Lxo0bufPOO8nMzLzhY5coUYLQ0NCr7hMeHk6xYsVu+FzXcvnr/Omnn0hKSuKOO+4gLS0t388tIiIinkUNIREREXGLoKAgoqKiTF9+fn6MHj2aOnXqEBYWRnR0NIMHD+bcuXNXPM4ff/xB27ZtKVSoEBERETRs2JC1a9c6H1+5ciWtWrUiJCSE6OhohgwZQlJS0lVr8/HxISoqitKlS9O2bVuGDRvGli1bnCOYxo0bR+XKlQkMDKRatWp88803pucPHz6c8uXLExQURJkyZRgyZIjzscsvGatYsSIAd911Fz4+Ps77l18yNm/ePIKDgzlz5ozpHEOGDKF169Z59jobNWrE0KFD2b9/Pzt27HDuc7Xvx+LFi3nwwQdJSEhwjjQaPnw4AGlpaTz33HOULVuWsLAwmjRpwuLFi69aj4iIiFhHDSERERGxlK+vLx9++CFbtmzhq6++4tdff+W555674v59+/alXLlyrFmzhnXr1vH8888TEBAAwObNm+nQoQM9evRg06ZNTJkyheXLl/PYY4/lqqaQkBAA0tPTmTVrFk888QRPP/00W7Zs4V//+hcPPvggixYtAmD69Om8//77fPrpp+zatYvZs2dTp06dbI+7Zs0aAL788kuOHj3qvH+5du3aUbhwYWbMmOHclpmZydSpU+nbt2+evc4zZ87w7bffAjjfP7j696NZs2aMGTPGOdLo6NGjPPPMMwA8+OCDrFixgsmTJ7Np0yZ69epFx44d2bVrV45rEhERETcyRERERPLZAw88YPj5+RlhYWHOr549e2a779SpU41ixYo573/55ZdGZGSk836hQoWMCRMmZPvc+++/33j44YdN25YtW2b4+voa58+fz/Y5fz/+wYMHjVtuucUoV66ckZqaajRr1sx46KGHTM/p1auX0blzZ8MwDGPUqFFGbGyskZaWlu3xK1SoYLz//vvO+4Axa9Ys0z7Dhg0zbrrpJuf9IUOGGLfeeqvz/rx584zAwEDj1KlTN/Q6ASMsLMwIDQ01AAMw7rzzzmz3v+ha3w/DMIw///zT8PHxMQ4fPmzafttttxkvvPDCVY8vIiIi1vC3th0lIiIidtG2bVvGjRvnvB8WFgbAokWLePPNN4mLiyMxMZGMjAxSUlJISkpy7nO5p556ioEDB/LNN9/Qrl07evXqReXKlQFYt24df/75JxMnTnTubxgGWVlZ7N27lxo1amRbW0JCAuHh4RiGQXJyMg0aNGDmzJkEBgaybds206TQAM2bN+eDDz4AoFevXowZM4ZKlSrRsWNHOnfuTNeuXfH3v/5/ZvXt25emTZty5MgRypQpw8SJE+ncuTNFihS5oddZqFAh1q9fT0ZGBkuWLOHdd9/lk08+Me2T2+8HwPr16zEMg9jYWNP21NRUt8yNJCIiIrmnhpCIiIi4RVhYGFWqVDFt279/P507d2bQoEG89tprFC1alOXLlzNgwADS09OzPc7w4cO57777+Omnn/j5558ZNmwYkydP5q677iIrK4t//etfpjl8LipfvvwVa7vYKPH19aVUqVIujQ8fHx/TfcMwnNuio6PZsWMHCxYs4JdffmHw4MG8++67LFmyxHQpVm40btyYypUrM3nyZB555BFmzZrFl19+6Xz8el+nr6+v83tQvXp14uPj6dOnD0uXLgWu7/txsR4/Pz/WrVuHn5+f6bHw8PBcvXYRERFxDzWERERExDJr164lIyODUaNG4evrmNpw6tSp13xebGwssbGxDB06lHvvvZcvv/ySu+66iwYNGrB161aXxtO1XN4o+bsaNWqwfPly+vXr59y2cuVK0yickJAQ7rzzTu68804effRRqlevzubNm2nQoIHL8QICAnK0etl9993HxIkTKVeuHL6+vtxxxx3Ox673df7d0KFDGT16NLNmzeKuu+7K0fcjMDDQpf769euTmZnJ8ePHadmy5Q3VJCIiIu6hSaVFRETEMpUrVyYjI4OPPvqIPXv28M0337hcwnS58+fP89hjj7F48WL279/PihUrWLNmjbM58+9//5tVq1bx6KOPsnHjRnbt2sX333/P448/ft01Pvvss0yYMIFPPvmEXbt2MXr0aGbOnOmcTHnChAl8/vnnbNmyxfkaQkJCqFChQrbHq1ixIgsXLiQ+Pp7Tp09f8bx9+/Zl/fr1vPHGG/Ts2ZPg4GDnY3n1OiMiIhg4cCDDhg3DMIwcfT8qVqzIuXPnWLhwISdOnCA5OZnY2Fj69u1Lv379mDlzJnv37mXNmjW8/fbbzJkzJ1c1iYiIiHuoISQiIiKWqVevHqNHj+btt9+mdu3aTJw4kZEjR15xfz8/P06ePEm/fv2IjY2ld+/edOrUiREjRgBQt25dlixZwq5du2jZsiX169fn5ZdfpnTp0tddY/fu3fnggw949913qVWrFp9++ilffvklbdq0AaBw4cJ89tlnNG/enLp167Jw4UJ++OGHK86dM2rUKBYsWEB0dDT169e/4nmrVq3KzTffzKZNm5yri12Ul6/ziSeeYNu2bUybNi1H349mzZoxaNAg+vTpQ4kSJXjnnXcAx8pp/fr14+mnn6ZatWrceeed/P7770RHR+e6JhEREcl/PoZhGFYXISIiIiIiIiIi7qMRQiIiIiIiIiIiNqOGkIiIiIiIiIiIzaghJCIiIiIiIiJiM2oIiYiIiIiIiIjYjBpCIiIiIiIiIiI2o4aQiIiIiIiIiIjNqCEkIiIiIiIiImIzagiJiIiIiIiIiNiMGkIiIiIiIiIiIjajhpCIiIiIiIiIiM2oISQiIiIiIiIiYjP/D5v3BVHc1bkeAAAAAElFTkSuQmCC", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_calibration_curve(X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb", - "metadata": {}, - "source": [ - "## Cross Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7f0535de-34f1-4e97-b993-b429ecf0a554", - "metadata": {}, - "outputs": [], - "source": [ - "y_train = y_train['y_has_purchased']" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "f7fca463-d7d6-493b-8329-fdfa92457f78", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'logreg__C': 0.0009765625, 'logreg__class_weight': 'balanced', 'logreg__penalty': 'l1'}\n", - "Best cross-validation score: 0.65\n", - "Test set score: 0.64\n" - ] - } - ], - "source": [ - "# Cross validation\n", - "\n", - "grid_search = GridSearchCV(pipeline, param_grid, cv=3, scoring=recall_scorer, error_score='raise',\n", - " n_jobs=-1)\n", - "\n", - "grid_search.fit(X_train, y_train)\n", - "\n", - "# Print the best parameters and the best score\n", - "print(\"Best parameters found: \", grid_search.best_params_)\n", - "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n", - "\n", - "# Evaluate the best model on the test set\n", - "test_score = grid_search.score(X_test, y_test)\n", - "print(\"Test set score: {:.2f}\".format(test_score))" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "56bd7828-4de1-4166-bea0-5d5e152b9d38", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = grid_search.predict(X_test)\n", - "\n", - "draw_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "319fe0eb-4d4a-492c-bd50-3f08ab483021", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_roc_curve(X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ab122f66-1591-43ea-a364-2564f09b2bb3", - "metadata": {}, - "source": [ - "# Segmentation du score de prédiction" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "279e18c7-29d8-4328-963a-18babd13c2c8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ebe4pgiAUu6cMHjxYMDIyEjIzM1Xq9ezZU5DJZOLvAOX94eXr7McffxQACMePHy9xv+ruWdpetw4ODkKrVq1U6t24caPYNaHu+Jo0aSIEBweXGNu4ceM03m/q1q2rksvp06cLEomk2HkODAws9jvk5W2VXv6Zqcjvf6o6fFSBiKgKSSQSBAUFicv6+vpwd3eHo6OjynOCNjY2qF27Nm7cuFGsjWHDhqkst2nTBnXr1sWhQ4cAPJ/46MmTJ8WG/zk7O6Nz587FhlADwIABA8p0HAcPHkTXrl1haWkJPT09GBgYYM6cObh//z6ys7NV6jZv3lwcmQAAxsbGaNCggcqx7dmzB506dYKXl5fGfe7atQtNmjRB8+bNUVhYKP7r3r272uHjLzp+/DiePn2q8dyVZz8tW7YEAISEhODHH3/E//73vxLP2YvHCgDjxo0rsd7ff/+NoUOHwsHBQTzHHTt2BAC1w1TV5TA7Oxvh4eFwdnaGvr4+DAwMxONVtpGfn4/Dhw8jJCSkXHMCHDt2DA8ePMDw4cNVzpdCoUCPHj1w+vTpYqMKtLnemjdvDkNDQ3zwwQfYsGGD2sdxyqIs14C2ynLsfn5+SEhIQGxsLE6cOKHV6/seP36M06dPo3///jA2NhbLzc3N0adPn2L1z507h3feeQe2trbiNRMaGoqioiJcvXpVpa6DgwP8/PxUyry9vVV+Lv38/HD+/HmMHTsWSUlJGr8lLs3Tp09x4MAB9OvXDzKZTOVcBQUF4enTpzhx4kSp7XTq1AmmpqbisvJ+0bNnT5VvG5XlymPR9p545coV3L59G0OHDlVpr27dumjTpo3Ktrt27YKlpSX69++vUt6qVSvY29vj8OHDGo9j165dsLKyQp8+fVTORfPmzeHg4FDivaysMZZ3Py96+edV23aV5yAkJERl+4EDB0JfX/0g55f3lZSUhMLCQoSGhqrsy9jYGB07dhT3ZWNjg/r162Px4sVYunQpzp07p/LYDVD+e8rBgwfRpUsXODs7q5SHhYUhPz+/2KSu77zzjsqyt7c3AKj9fV6Ssly3d+7cKXaeXVxc0LZt21L34+fnhz179mDGjBlISUnBkydPyhTnyw4dOoTGjRujWbNmKuVDhw4td5sV+f1PVYcdB0REVUgmk6l8AAAAQ0ND2NjYFKtraGiIp0+fFit3cHBQW6YcVqj8r7phhE5OTsWGRstkMlhYWGh9DKdOnUK3bt0APH+m8ujRozh9+jRmzpwJAMX+6LC1tS3WhpGRkUq9f/75p9SJ8e7evYsLFy7AwMBA5Z+5uTkEQSjx/eHKY9Z07sqznw4dOmD79u3iH7VvvfUWmjRpIj4Xqsk///wDPT09tbEoPXr0CO3bt8fJkycRGxuLlJQUnD59Glu3bgVQ/Byry6FCoUC3bt2wdetWREZG4sCBAzh16pT4AU3Zxr///ouioqJyT0yoHDI/cODAYuds4cKFEAQBDx48UNlGmzcd1K9fH/v370ft2rUxbtw41K9fH/Xr1y/38/VluQa0VZZj/+GHHzB8+HB888038Pf3h42NDUJDQ3Hnzh2N7f/7779QKBRaxZyZmYn27dvjf//7H1asWIEjR47g9OnT4rPJ5fm5jIqKwpIlS3DixAn07NkTtra26NKlS5lfM3v//n0UFhZi5cqVxc6TsiO1pJ9fpZfvk4aGhiWWK++f2t4Ty3qfyMvLE+/pL/7Lzs4u8Xju3r2LnJwcGBoaFjsfd+7cqdR7WXn3o6Tu3qJtu8pY7e3tVbbX19dXe/0BxXOk/Blr2bJlsX398MMP4r4kEgkOHDiA7t27Y9GiRWjRogXs7OwwYcIEPHz4EED57yn379/XeO28eJxKLx+bkZERgOI/g6Up63X78nnWVPayzz//HNOnT8f27dvRqVMn2NjYIDg4GH/99VeZ4n0x7sq8zwIV+/1PVYdzHBARvebUfdC4c+cO3N3dAfzfHy1ZWVnF6t2+fbvYe7bL+lzgli1bYGBggF27dql0gmzfvr1M7bzIzs4Ot27dKrFOrVq1YGJionZiReV6TZTnRNO5e3HyqLLsp2/fvujbty8KCgpw4sQJLFiwAEOHDoWrqyv8/f3Vbm9nZ4eioiLcuXNH4wfogwcP4vbt20hJSRFHGQDQ+Gy5uhz+97//xfnz55GQkIDhw4eL5S9PGmdjYwM9Pb1Sz78myvOxcuVKjbN1v/zHq7bXXPv27dG+fXsUFRXhjz/+wMqVKzFp0iTY29tj8ODBZYqzLNeA8rp++fngl/84Lcux16pVC8uXL8fy5cuRmZmJHTt2YMaMGcjOzsbevXvVbmttbQ2JRKIx5hdt374djx8/xtatW1VGULw4MVxZ6evrIyIiAhEREcjJycH+/fvxySefoHv37rh586bWb+6wtraGnp4e3n//fY0jbaryVX7a3hNLu0ZeVKtWLdja2uLIkSNq91nSuVFuqynv5ubmGrctT4zl2Y+Sup9VbdtVxnr37l3UqVNHXF9YWKhxbo+X96fMzc8//1zqyKC6deti7dq1AICrV6/ixx9/RHR0NJ49e4Yvv/wSQPnuKba2thqvnRdjrGxlvW7VzXtSUsekkqmpqTj/y927d8XRB3369MHly5fLFbc21yfw/F6rbh6Ge/fuqZzXivz+p6rDEQdERK+5TZs2qSwfO3YMN27cEGcg9vf3h4mJCb777juVerdu3RKHXGrj5W8flSQSCfT19VUmeXry5Ak2btxYxiP5Pz179sShQ4dKnHm6d+/eSE9Ph62tLXx9fYv9e3nm6Be1bt0axsbGGs9dRfdjZGSEjh07YuHChQBQbLbvl48VAFavXq2xjvKPZ+U3VUpr1qzRuE152zAxMUHHjh3x008/lfitjaZvzdq2bQsrKyukpaWpPV++vr7iN8Dlpaenh1atWonfnp89e7bEmNQpyzWgzPGFCxdUyl986wVQ/mN3cXHB+PHjERgYKB6LOqampvDz88PWrVtVRh89fPgQO3fuVKmrLt+CIODrr7/W2H5ZWFlZYeDAgRg3bhwePHhQ7I0TJZHJZOjUqRPOnTsHb29vtedJ0zfQlUHbe6KnpyccHR3x/fffq7wh5saNGzh27JjKtr1798a9e/fw4MEDNGzYsNi/Fx/Pelnv3r1x//59FBUVqT0XJU3UWdYYy7ufkmjbrnLy2x9++EFl+59//lmcwLU03bt3h76+PtLT0zX+jKnToEEDzJo1C02bNlX7M6bpnqJOly5dxM7cF3377beQyWRV9vrGsly3Dg4O+PHHH1XqZWZmFrsmSmNvb4+wsDAMGTIEV65cEd82UZZ7badOnfDnn3/i/PnzKuWbN28uVtfV1bXYffbq1avF/haoyO9/qjoccUBE9Jr7448/MHr0aLz77ru4efMmZs6ciTp16mDs2LEAnv+BP3v2bHzyyScIDQ3FkCFDcP/+fcTExMDY2Fir2csBoGnTpti6dStWr16Nt99+G1KpFL6+vujVqxeWLl2KoUOH4oMPPsD9+/exZMmSYh9Qy2LevHnYs2cPOnTogE8++QRNmzZFTk4O9u7di4iICDRs2BCTJk3CL7/8gg4dOmDy5Mnw9vaGQqFAZmYm9u3bhylTpqBVq1Zq27e2tsbUqVMRGxurcu6io6OLDZ/Udj9z5szBrVu30KVLF7z11lvIycnBihUrVOYiUKd9+/Z4//33ERsbi7t376J3794wMjLCuXPnIJPJ8PHHH6NNmzawtrZGeHg45s6dCwMDA2zatKnYH2IladiwIerXr48ZM2ZAEATY2Nhg586dSE5OLlZX+aaFVq1aYcaMGXB3d8fdu3exY8cOrFmzBubm5mjSpAmA57N3m5ubw9jYGPXq1YOtrS1WrlyJ4cOH48GDBxg4cCBq166Nf/75B+fPn8c///xTYieJJl9++SUOHjyIXr16wcXFBU+fPhW/beratSuA599s1q1bF7/++iu6dOkCGxsb1KpVS+0fkWW5Blq2bAlPT09MnToVhYWFsLa2xrZt2/D777+r1DMzM9Pq2HNzc9GpUycMHToUDRs2hLm5OU6fPo29e/cWe0b+ZZ9++il69OiBwMBATJkyBUVFRVi4cCFMTU1VHgEJDAyEoaEhhgwZgsjISDx9+hSrV6/Gv//+W+Zzr9SnTx80adIEvr6+sLOzw40bN7B8+XLUrVtX5c0s2lixYgXatWuH9u3b46OPPoKrqysePnyIa9euYefOnVq9jaS8tL0nSqVSfPrppxg9ejT69euHMWPGICcnR+01MnjwYGzatAm9e/fGxIkT4efnB0NDQ9y6dQsHDhxAcHCwxtwqtw0KChK3NTAwwK1bt3Do0CH07dsX/fr1U7tteWIsz35Kom27jRs3xpAhQxAfHw89PT107twZf/75J+Lj42FpaanyBghNXF1dMW/ePMycORN///03evToAWtra9y9exenTp0Svy2/cOECxo8fj3fffRceHh4wNDTEwYMHceHCBcyYMQOAdvcUdebOnYtdu3ahU6dOmDNnDmxsbLBp0ybs3r0bixYtgqWlZZnPoTbKct3GxMTgww8/xMCBAzFy5Ejk5OQgJiYGjo6OpZ7nVq1aoXfv3vD29oa1tTUuXbqEjRs3wt/fXxw507RpUwDAwoUL0bNnT+jp6cHb21ttx+ikSZOwbt069OrVC7GxseJbFdSNXnj//ffx3nvvYezYsRgwYABu3LiBRYsWFZtvpyK//6kKVdu0jERENYimtyqYmpoWq9uxY0ehcePGxcrr1q0r9OrVq1ib+/btE95//33ByspKnHH5r7/+Krb9N998I3h7ewuGhoaCpaWl0LdvX+HPP/9UqaMpJkEQhAcPHggDBw4UrKysBIlEojKj8rp16wRPT0/ByMhIcHNzExYsWCCsXbu22Ez0Lx/Di8f84ozJgiAIN2/eFEaOHCk4ODgIBgYGgpOTkxASEiLcvXtXrPPo0SNh1qxZgqenp3hcTZs2FSZPnizcuXNH7XEoKRQKYcGCBYKzs7NgaGgoeHt7Czt37lQbizb72bVrl9CzZ0+hTp06gqGhoVC7dm0hKChIOHLkSIlxCMLzWbCXLVsmNGnSRGzf399f2Llzp1jn2LFjgr+/vyCTyQQ7Ozth9OjRwtmzZ4vNVl9SDtPS0oTAwEDB3NxcsLa2Ft59910hMzNT7ZsI0tLShHfffVewtbUVDA0NBRcXFyEsLEx4+vSpWGf58uVCvXr1BD09vWJxHD58WOjVq5dgY2MjGBgYCHXq1BF69eqlMuu88s0J//zzT7FYX36rwvHjx4V+/foJdevWFYyMjARbW1uhY8eOwo4dO1S2279/v+Dj4yMYGRmpzNSu7s0IZbkGrl69KnTr1k2wsLAQ7OzshI8//ljYvXu32jdLlHbsT58+FcLDwwVvb2/BwsJCMDExETw9PYW5c+cKjx8/Vpu7F+3YsUP8WXZxcRHi4uKKnS9BEISdO3cKzZo1E4yNjYU6deoI06ZNE/bs2VMsZk33nOHDh6vMwB4fHy+0adNGqFWrlrjvUaNGCRkZGSXGq+6tCsrykSNHCnXq1BEMDAwEOzs7oU2bNuIbYUoCQBg3bpza/SxevFilXDm7/ctvPNDmnqis5+HhIRgaGgoNGjQQ1q1bV+zcCMLzt+IsWbJEPOdmZmZCw4YNhQ8//FDlnqzu+tJ2W00qO0Z1Srq3aNvu06dPhYiICKF27dqCsbGx0Lp1a+H48eOCpaWlMHnyZLGeut+ZL9q+fbvQqVMnwcLCQjAyMhLq1q0rDBw4UNi/f78gCIJw9+5dISwsTGjYsKFgamoqmJmZCd7e3sKyZcuEwsJCQRC0v6eouz9evHhR6NOnj2BpaSkYGhoKzZo1K3Z9a7ruNP08vEzT21y0vW6/+uorwd3dXeWa6Nu3r+Dj41Pi8c2YMUPw9fUVrK2txd/pkydPFu7duyfWKSgoEEaPHi3Y2dmJfw8o41T3ZgTl7x5jY2PBxsZGGDVqlPDrr78WuxcpFAph0aJFgpubm2BsbCz4+voKBw8eLPfvZXq1JILwwrgnIiJ6bSjf3X369GmNwzOJqOyUj/lwZm6iqnfs2DG0bdsWmzZtqtBM+1SynJwcNGjQAMHBwfjqq6+qOxykpKSgU6dOOHTokHjPpTcbH1UgIiIiIqIKS05OxvHjx/H222/DxMQE58+fR1xcHDw8PEp9TIe0d+fOHXz22Wfo1KkTbG1tcePGDSxbtgwPHz7ExIkTqzs8qqHYcUBERERERBVmYWGBffv2Yfny5Xj48CFq1aqFnj17YsGCBcVeTUzlZ2RkhIyMDIwdOxYPHjwQJ2388ssv0bhx4+oOj2ooPqpARERERERERBrxdYxEREREREREpBE7DoiIiIiIiIhII3YcEBEREREREZFGnByRSMcoFArcvn0b5ubmkEgk1R0OERERERFVE0EQ8PDhQzg5OUEq1TyugB0HRDrm9u3bcHZ2ru4wiIiIiIjoNXHz5k289dZbGtez44BIx5ibmwN4fnOwsLCo5mi0I5fLsW/fPnTr1g0GBgbVHQ5VIeZaNzDPuoO51g3Ms25gnmumvLw8ODs7i58RNGHHAZGOUT6eYGFh8UZ1HMhkMlhYWPAXVQ3HXOsG5ll3MNe6gXnWDcxzzVbaI8ycHJGIiIiIiIiINGLHARERERERERFpxI4DIiIiIiIiItKIHQdEREREREREpBE7DoiIiIiIiIhII3YcEBEREREREZFG7DggIiIiIiIiIo3YcUBEREREREREGrHjgIiIiIiIiIg0YscBEREREREREWnEjgMiIiIiIiIi0ogdB0RERERERESkETsOiIiIiIiIiEgjdhwQERERERERkUbsOCAiIiIiIiIijdhxQEREREREREQaseOAiIiIiIiIiDRixwERERERERERaaRf3QEQESnFnbuntlyqKITnK46FiIiIiIie44gDIiIiIiIiItKIHQdEREREREREpBE7DoiIiIiIiIhII3YcEP1/GRkZkEgkSE1Nre5QiIiIiIiIXhvsOKBKER0djebNm1d3GCrCwsIQHBysdX1nZ2dkZWWhSZMmFd53QEAAJBKJxn+urq4V3gcREREREdGrwLcqEP1/enp6cHBwqJS2tm7dimfPngEAbt68CT8/P+zfvx+NGzcW9/WiZ8+ewdDQsFL2TUREREREVJk44oBECoUCCxcuhLu7O4yMjODi4oLPPvsMADB9+nQ0aNAAMpkMbm5umD17NuRyOQAgISEBMTExOH/+vPiNekJCQoViyczMRN++fWFmZgYLCwuEhITg7t274nrlCIc1a9bA2dkZMpkM7777LnJycsT1GzZswK+//irGlJKSUuI+X35UISUlBRKJBAcOHICvry9kMhnatGmDK1eulBq/jY0NHBwc4ODgADs7OwCAra2tWNayZUvExsYiLCwMlpaWGDNmjLg/5TEAQGpqKiQSCTIyMsSyY8eOoUOHDjAxMYGzszMmTJiAx48fa3VeiYiIiIiIyoojDkgUFRWFr7/+GsuWLUO7du2QlZWFy5cvAwDMzc2RkJAAJycnXLx4EWPGjIG5uTkiIyMxaNAg/Pe//8XevXuxf/9+AIClpWW54xAEAcHBwTA1NcXhw4dRWFiIsWPHYtCgQSof/q9du4Yff/wRO3fuRF5eHkaNGoVx48Zh06ZNmDp1Ki5duoS8vDysX78ewPMP8+Uxc+ZMxMfHw87ODuHh4Rg5ciSOHj1a7uNTWrx4MWbPno1Zs2YBAG7dulXqNhcvXkT37t3x6aefYu3atfjnn38wfvx4jB8/XjzOlxUUFKCgoEBczsvLAwDI5XKx8+d1IVUUllj+usVLlU+ZY+a6ZmOedQdzrRuYZ93APNdM2uaTHQcEAHj48CFWrFiBVatWYfjw4QCA+vXro127dgAgfrgFAFdXV0yZMgU//PADIiMjYWJiAjMzM+jr61fKUP/9+/fjwoULuH79OpydnQEAGzduROPGjXH69Gm0bNkSAPD06VNs2LABb731FgBg5cqV6NWrF+Lj4+Hg4AATExMUFBRUOKbPPvsMHTt2BADMmDEDvXr1wtOnT2FsbFyhdjt37oypU6eKy9p0HCxevBhDhw7FpEmTAAAeHh74/PPP0bFjR6xevVptTAsWLEBMTEyx8n379kEmk5X/AKqAZynrk5OTX0kcVP2Ya93APOsO5lo3MM+6gXmuWfLz87Wqx44DAgBcunQJBQUF6NKli9r1P//8M5YvX45r167h0aNHKCwshIWFRZXF4uzsLHYaAECjRo1gZWWFS5cuiR0HLi4uYqcBAPj7+0OhUODKlSuVNlcBAHh7e4v/7+joCADIzs6Gi4tLhdr19fUt8zZnzpzBtWvXsGnTJrFMEAQoFApcv34dXl5exbaJiopCRESEuJyXlwdnZ2d069atynJYXssu3FdbLlUUwuP2GQQGBsLAwOAVR0WvklwuR3JyMnNdwzHPuoO51g3Ms25gnmsm5Wjk0rDjgAAAJiYmGtedOHECgwcPRkxMDLp37w5LS0ts2bIF8fHxVRKLIAiQSCRalysp15VUpzxevDEq21YoFBVu19TUVGVZKn0+5YggCGLZy0OHFAoFPvzwQ0yYMKFYe5o6MoyMjGBkZFSs3MDA4LW76SukJd+SXseYqWow17qBedYdzLVuYJ51A/Ncs2ibS3YcEIDnQ95NTExw4MABjB49WmXd0aNHUbduXcycOVMsu3HjhkodQ0NDFBUVVUosjRo1QmZmJm7evCmOOkhLS0Nubq7KN+qZmZm4ffs2nJycAADHjx+HVCpFgwYNKj2mV0E5iWJWVhasra0BQJyoUalFixb4888/4e7u/qrDIyIiIiIiHcW3KhAAwNjYGNOnT0dkZCS+/fZbpKen48SJE1i7di3c3d2RmZmJLVu2ID09HZ9//jm2bdumsr2rqyuuX7+O1NRU3Lt3T2UyvrLq2rUrvL29MWzYMJw9exanTp1CaGgoOnbsqDK839jYGMOHD8f58+dx5MgRTJgwASEhIeJjCq6urrhw4QKuXLmCe/fuvfYTubi7u8PZ2RnR0dG4evUqdu/eXWxUx/Tp03H8+HGMGzcOqamp+Ouvv7Bjxw58/PHH1RQ1ERERERHVdOw4INHs2bMxZcoUzJkzB15eXhg0aBCys7PRt29fTJ48GePHj0fz5s1x7NgxzJ49W2XbAQMGoEePHujUqRPs7Ozw/ffflzsOiUSC7du3w9raGh06dEDXrl3h5uaGH374QaWeu7s7+vfvj6CgIHTr1g1NmjTBF198Ia4fM2YMPD094evrCzs7u0p5E0JVMjAwwPfff4/Lly+jWbNmWLhwIWJjY1XqeHt74/Dhw/jrr7/Qvn17+Pj4YPbs2eLcC0RERERERJVNIrz4QDXRGyI6Ohrbt28vNpSfSpeXlwdLS0vk5ua+dpMjxp27p7ZcqiiE562TCAoK4jN1NZxcLkdiYiJzXcMxz7qDudYNzLNuYJ5rJm0/G3DEARERERERERFpxI4DqhKbNm2CmZmZ2n/16tXTuK5x48ZVFtP8+fM17rdnz55lbq9x48Ya23vxdYlERERERERvMr5VgarEO++8g1atWqldZ2BgoHGiQm2HPUVHRyM6OrpMMYWHhyMkJETtupJeR6lJYmKixuOwt7cvc3sEzPCppbZcLpcj8dYrDoaIiIiIiACw44CqiLm5OczNzas7DBU2NjawsbGptPbq1q1baW0RERERERG9rvioAhERERERERFpxI4DIiIiIiIiItKIHQdEREREREREpBHnOCCi10rcuXvFyqSKQnhWQyxERERERMQRB0RERERERERUAnYcEBEREREREZFG7DggnZSRkQGJRILU1NTqDuW1ioWIiIiIiOhl7DigMouOjkbz5s2rOwwVYWFhCA4O1rq+s7MzsrKy0KRJk0rZf0JCAiQSSbF/33zzTaW0T0REREREVF04OSLpJD09PTg4OFRqmxYWFrhy5YpKmaWlZaXug4iIiIiI6FXjiAMdpVAosHDhQri7u8PIyAguLi747LPPAADTp09HgwYNIJPJ4ObmhtmzZ0MulwN4/s16TEwMzp8/L36rnpCQUKFYMjMz0bdvX5iZmcHCwgIhISG4e/euuF45wmHNmjVwdnaGTCbDu+++i5ycHHH9hg0b8Ouvv4oxpaSklLjPlx8PSElJgUQiwYEDB+Dr6wuZTIY2bdoU6wgoiUQigYODg8o/ExMT7N27F+3atYOVlRVsbW3Ru3dvpKena2zn33//xbBhw2BnZwcTExN4eHhg/fr14vr//e9/GDRoEKytrWFra4u+ffsiIyND6ziJiIiIiIjKgiMOdFRUVBS+/vprLFu2DO3atUNWVhYuX74MADA3N0dCQgKcnJxw8eJFjBkzBubm5oiMjMSgQYPw3//+F3v37sX+/fsBVOxbdUEQEBwcDFNTUxw+fBiFhYUYO3YsBg0apPLh/9q1a/jxxx+xc+dO5OXlYdSoURg3bhw2bdqEqVOn4tKlS8jLyxM/YNvY2JQrnpkzZyI+Ph52dnYIDw/HyJEjcfTo0XIfHwA8fvwYERERaNq0KR4/fow5c+agX79+SE1NhVRavO9u9uzZSEtLw549e1CrVi1cu3YNT548AQDk5+ejU6dOaN++PX777Tfo6+sjNjYWPXr0wIULF2BoaFisvYKCAhQUFIjLeXl5AAC5XC52CL1OpIpCjWWvY7xUuZQ5Zq5rNuZZdzDXuoF51g3Mc82kbT7ZcaCDHj58iBUrVmDVqlUYPnw4AKB+/fpo164dAGDWrFliXVdXV0yZMgU//PADIiMjYWJiAjMzM+jr61fKUP/9+/fjwoULuH79OpydnQEAGzduROPGjXH69Gm0bNkSAPD06VNs2LABb731FgBg5cqV6NWrF+Lj48Vv9gsKCioc02effYaOHTsCAGbMmIFevXrh6dOnMDY2LnXb3NxcmJmZictmZma4c+cOBgwYoFJv7dq1qF27NtLS0tTOsZCZmQkfHx/4+voCeJ4DpS1btkAqleKbb76BRCIBAKxfvx5WVlZISUlBt27dirW3YMECxMTEFCvft28fZDJZqcf1qnmWsC45OfmVxUHVi7nWDcyz7mCudQPzrBuY55olPz9fq3rsONBBly5dQkFBAbp06aJ2/c8//4zly5fj2rVrePToEQoLC2FhYVFlsTg7O4udBgDQqFEjWFlZ4dKlS2LHgYuLi9hpAAD+/v5QKBS4cuVKpc5V4O3tLf6/o6MjACA7OxsuLi6lbmtubo6zZ8+Ky8rRBOnp6Zg9ezZOnDiBe/fuQaFQAHjeQaCu4+Cjjz7CgAEDcPbsWXTr1g3BwcFo06YNAODMmTO4du0azM3NVbZ5+vSpxscfoqKiEBERIS7n5eXB2dkZ3bp1q7K8VsSyC/eLlUkVhfC4fQaBgYEwMDCohqjoVZHL5UhOTmauazjmWXcw17qBedYNzHPNpByNXBp2HOggExMTjetOnDiBwYMHIyYmBt27d4elpSW2bNmC+Pj4KolFEATxm3NtypWU60qqUx4v3gSVbSs/6JdGKpXC3d29WHmfPn3g7OyMr7/+Gk5OTlAoFGjSpAmePXumtp2ePXvixo0b2L17N/bv348uXbpg3LhxWLJkCRQKBd5++21s2rSp2HZ2dnZq2zMyMoKRkZHaY30db/oKqebb0usaM1U+5lo3MM+6g7nWDcyzbmCeaxZtc8nJEXWQh4cHTExMcODAgWLrjh49irp162LmzJnw9fWFh4cHbty4oVLH0NAQRUVFlRJLo0aNkJmZiZs3b4plaWlpyM3NhZeXl1iWmZmJ27dvi8vHjx+HVCpFgwYNKj2mynT//n1cunQJs2bNQpcuXeDl5YV///231O3s7OwQFhaG7777DsuXL8dXX30FAGjRogX++usv1K5dG+7u7ir/+AYHIiIiIiKqCuw40EHGxsaYPn06IiMj8e233yI9PR0nTpzA2rVr4e7ujszMTGzZsgXp6en4/PPPsW3bNpXtXV1dcf36daSmpuLevXsqE++VVdeuXeHt7Y1hw4bh7NmzOHXqFEJDQ9GxY0fxGX9lzMOHD8f58+dx5MgRTJgwASEhIeJjCq6urrhw4QKuXLmCe/fuvTaTtijffPDVV1/h2rVrOHjwoMpjA+rMmTMHv/76K65du4Y///wTu3btEjtRhg0bhlq1aqFv3744cuQIrl+/jsOHD2PixIm4devWqzgkIiIiIiLSMew40FGzZ8/GlClTMGfOHHh5eWHQoEHIzs5G3759MXnyZIwfPx7NmzfHsWPHMHv2bJVtBwwYgB49eqBTp06ws7PD999/X+44JBIJtm/fDmtra3To0AFdu3aFm5sbfvjhB5V67u7u6N+/P4KCgtCtWzc0adIEX3zxhbh+zJgx8PT0hK+vL+zs7Cr8JoTKIpVKsWXLFpw5cwZNmjTB5MmTsXjx4hK3MTQ0RFRUFLy9vdGhQwfo6elhy5YtAACZTIbffvsNLi4u6N+/P7y8vDBy5Eg8efLktZyvgIiIiIiI3nwSQRCE6g6CqCTR0dHYvn07UlNTqzuUGiEvLw+WlpbIzc19LTsb4s7dK1YmVRTC89ZJBAUF8Zm6Gk4ulyMxMZG5ruGYZ93BXOsG5lk3MM81k7afDTjigIiIiIiIiIg0YscBVdimTZtgZmam9l+9evU0rmvcuHGVxTR//nyN++3Zs2eZ22vcuLHG9tS94YCIiIiIiKim4OsYqcLeeecdtGrVSu06AwMDjRMVajvEKTo6GtHR0WWKKTw8HCEhIWrXlfQ6Sk0SExM1Hoe9vX2Z2yPNZvjUKlYml8uRyLkfiYiIiIiqBTsOqMLMzc1hbm5e3WGosLGxgY2NTaW1V7du3Upri4iIiIiI6E3CRxWIiIiIiIiISCN2HBARERERERGRRuw4ICIiIiIiIiKNOMcBEb0xll24D4X0+W1L3SSKRERERERU+TjigIiIiIiIiIg0YscBEREREREREWnEjgMiIiIiIiIi0ogdB9VEIpFg+/bt1R1GtXB1dcXy5curO4waISMjAxKJBKmpqdUdChERERER1VCcHJFeudOnT8PU1LS6wyAiIiIiIiItsOOAXjk7O7vqDoGIiIiIiIi09EY8qhAQEIAJEyYgMjISNjY2cHBwQHR0NAD1Q7VzcnIgkUiQkpICAEhJSYFEIkFSUhJ8fHxgYmKCzp07Izs7G3v27IGXlxcsLCwwZMgQ5OfnV2m8L8rKykLPnj1hYmKCevXq4aefftJ6H7du3cLgwYNhY2MDU1NT+Pr64uTJkwCA9PR09O3bF/b29jAzM0PLli2xf/9+le1dXV0RGxuL0NBQmJmZoW7duvj111/xzz//oG/fvjAzM0PTpk3xxx9/iNskJCTAysoK27dvR4MGDWBsbIzAwEDcvHlTrKPtvl98VOHy5cto164djI2N0ahRI+zfv1/lUQ5ljrdu3YpOnTpBJpOhWbNmOH78uNjGjRs30KdPH1hbW8PU1BSNGzdGYmKiVufy8OHD8PPzg5GRERwdHTFjxgwUFhaK6wMCAjB+/HiMHz8eVlZWsLW1xaxZsyAIgljn2bNniIyMRJ06dWBqaopWrVqJ19+L5y4pKQleXl4wMzNDjx49kJWVpRLL+vXr4eXlBWNjYzRs2BBffPGFyvpTp07Bx8cHxsbG8PX1xblz57Q6RiIiIiIiovJ6Y0YcbNiwARERETh58iSOHz+OsLAwtG3bFh4eHlq3ER0djVWrVkEmkyEkJAQhISEwMjLC5s2b8ejRI/Tr1w8rV67E9OnTqyzewMBAsc7s2bMRFxeHFStWYOPGjRgyZAiaNGkCLy+vEtt+9OgROnbsiDp16mDHjh1wcHDA2bNnoVAoxPVBQUGIjY2FsbExNmzYgD59+uDKlStwcXER21m2bBnmz5+P2bNnY9myZXj//ffRtm1bjBw5EosXL8b06dMRGhqKP//8ExKJBACQn5+Pzz77DBs2bIChoSHGjh2LwYMH4+jRo2Xat5JCoUBwcDBcXFxw8uRJPHz4EFOmTFF73DNnzsSSJUvg4eGBmTNnYsiQIbh27Rr09fUxbtw4PHv2DL/99htMTU2RlpYGMzOzUvP0v//9D0FBQQgLC8O3336Ly5cvY8yYMTA2Nlbp7NmwYQNGjRqFkydP4o8//sAHH3yAunXrYsyYMQCAESNGICMjA1u2bIGTkxO2bduGHj164OLFi+I1mp+fjyVLlmDjxo2QSqV47733MHXqVGzatAkA8PXXX2Pu3LlYtWoVfHx8cO7cOYwZMwampqYYPnw4Hj9+jN69e6Nz58747rvvcP36dUycOLHUYywoKEBBQYG4nJeXBwCQy+WQy+Wlbv86UMYpVRQWK6OaRZlX5rdmY551B3OtG5hn3cA810za5lMivPi16WsqICAARUVFOHLkiFjm5+eHzp07Izw8HPXq1cO5c+fQvHlzAM9HHFhbW+PQoUMICAhASkoKOnXqhP3796NLly4AgLi4OERFRSE9PR1ubm4AgPDwcGRkZGDv3r1VFm9cXByA55MjhoeHY/Xq1WKd1q1bo0WLFsW+ZX7ZV199halTpyIjIwM2NjZaxdS4cWN89NFHGD9+PIDn3/q3b98eGzduBADcuXMHjo6OmD17NubNmwcAOHHiBPz9/ZGVlQUHBwckJCRgxIgROHHiBFq1agXg+WgBLy8vnDx5En5+flrve9KkSZg0aRL27t2LPn364ObNm3BwcAAA7N+/H4GBgdi2bRuCg4ORkZGBevXq4ZtvvsGoUaMAAGlpaWjcuDEuXbqEhg0bwtvbGwMGDMDcuXO1Oh9KM2fOxC+//IJLly6JnSNffPEFpk+fjtzcXEilUgQEBCA7O1ulA2XGjBnYsWMH0tLSkJ6eDg8PD9y6dQtOTk5i2127doWfnx/mz58vnrtr166hfv364n7mzZuHO3fuAABcXFywcOFCDBkyRGwjNjYWiYmJOHbsGL766itERUXh5s2bkMlkAIAvv/wSH330kcr1/7Lo6GjExMQUK9+8ebPYDhERERER6Z78/HwMHToUubm5sLCw0FjvjRlx4O3trbLs6OiI7Ozscrdhb28PmUwmdhooy06dOlWxQNXsC1Afr7+/f7FlbWbHT01NhY+Pj8ZOg8ePHyMmJga7du3C7du3UVhYiCdPniAzM1NjjPb29gCApk2bFivLzs4WP9Tr6+vD19dXrNOwYUNYWVnh0qVL8PPz03rfSleuXIGzs7PYPgCNHRAvxuvo6CjG1rBhQ0yYMAEfffQR9u3bh65du2LAgAHFcqDOpUuX4O/vL3YIAEDbtm3x6NEj3Lp1Sxwl0bp1a5U6/v7+iI+PR1FREc6ePQtBENCgQQOVtgsKCmBraysuy2QysdNAeQzKa+Kff/7BzZs3MWrUKHEUAwAUFhbC0tJSjLVZs2YqH/ZfvobUiYqKQkREhLicl5cHZ2dndOvWrcSbw+tELpcjOTkZfzm9DYX0+W1rsrdtKVvRm0iZ68DAQBgYGFR3OFRFmGfdwVzrBuZZNzDPNZNyNHJp3piOg5cvTolEAoVCAan0+TQNLw6c0DTc4sU2JBKJxjarMt7SvPjhVBMTE5MS10+bNg1JSUlYsmQJ3N3dYWJigoEDB+LZs2caY1TuV13Zy3Gri1FZpu2+lQRB0OqYS4tt9OjR6N69O3bv3o19+/ZhwYIFiI+Px8cff1xim+r2r7yWtI1LoVBAT08PZ86cgZ6ensq6Fx+XUHdNKPelPI6vv/5aHM2hpGyzvIODjIyMYGRkVKzcwMDgjbvpK6T6YsfBmxY7lc2beH1S2THPuoO51g3Ms25gnmsWbXP5RkyOWBLlDP0vTjL3przT/sSJE8WWGzZsWOp23t7eSE1NxYMHD9SuP3LkCMLCwtCvXz80bdoUDg4OyMjIqIyQUVhYqDJh4pUrV5CTkyPGXdZ9N2zYEJmZmbh7965Ydvr06XLF5uzsjPDwcGzduhVTpkzB119/Xeo2jRo1wrFjx1Q+lB87dgzm5uaoU6eOWKYuVx4eHtDT04OPjw+KioqQnZ0Nd3d3lX8vjqQoib29PerUqYO///67WBv16tUTYz1//jyePHmiMS4iIiIiIqLK9sZ3HJiYmKB169aIi4tDWloafvvtN8yaNau6w9LKTz/9hHXr1uHq1auYO3cuTp06Jc4DUJIhQ4bAwcEBwcHBOHr0KP7++2/88ssv4lsG3N3dsXXrVqSmpuL8+fMYOnRopY6k+Pjjj3Hy5EmcPXsWI0aMQOvWrcXHC8q678DAQNSvXx/Dhw/HhQsXcPToUcycOROA9t/4A8CkSZOQlJSE69ev4+zZszh48GCpk0wCwNixY3Hz5k18/PHHuHz5Mn799VfMnTsXERER4mgWALh58yYiIiJw5coVfP/991i5cqU4MWGDBg0wbNgwhIaGYuvWrbh+/TpOnz6NhQsXav1mB+D5XAQLFizAihUrcPXqVVy8eBHr16/H0qVLAQBDhw6FVCrFqFGjkJaWhsTERCxZskTr9omIiIiIiMrjje84AIB169ZBLpfD19cXEydORGxsbHWHpJWYmBhs2bIF3t7e2LBhAzZt2oRGjRqVup2hoSH27duH2rVrIygoCE2bNkVcXJw4pH3ZsmWwtrZGmzZt0KdPH3Tv3h0tWrSolJhlMhmmT5+OoUOHwt/fHyYmJtiyZYu4vqz71tPTw/bt2/Ho0SO0bNkSo0ePFjt+jI2NtY6rqKgI48aNg5eXF3r06AFPT89SJ5kEgDp16iAxMRGnTp1Cs2bNEB4ejlGjRhXrfAoNDcWTJ0/g5+eHcePG4eOPP8YHH3wgrl+/fj1CQ0MxZcoUeHp64p133sHJkyfh7Oys9TGMHj0a33zzDRISEtC0aVN07NgRCQkJ4ogDMzMz7Ny5E2lpafDx8cHMmTOxcOFCrdsnIiIiIiIqjzfirQr0ekhISMCkSZOQk5NTpfs5evQo2rVrp/IGguoUEBCA5s2bY/ny5dUdSqXIy8uDpaVlqTOnvk7kcjkSExNx5a1W4hwHM3xqVXNUVBWUuQ4KCuLzkzUY86w7mGvdwDzrBua5ZtL2s8EbMzki1Vzbtm2DmZkZPDw8cO3aNUycOBFt27Z9LToNiIiIiIiIdF2NeFShsmVmZsLMzEztP6lUCqlUqnG9ptcOlsX8+fM1tt+zZ89KOMLXy8OHDzF27Fg0bNgQYWFhaNmyJX799ddKaTs8PFzjuQwPD6+UfRAREREREdVkHHGghpOTU7nfzODk5FTh/YeHhyMkJETtutJexViVwsLCEBYWVunthoaGIjQ0tNLbBYB58+Zh6tSpatdpO0w/JSWlEiOiipjsbcuhcURERERErxg7DtTQ19eHu7t7te3fxsYGNjY21bb/mqR27dqoXbt2dYdBRERERET0xuKjCkRERERERESkETsOiIiIiIiIiEgjPqpARG+MZRfui69jBPhKRiIiIiKiV4EjDoiIiIiIiIhII3YcEBEREREREZFG7DggIiIiIiIiIo3YcUBVTiKRYPv27dUdRrVwdXXF8uXLqzsMIiIiIiKicuPkiERV6PTp0zA1Na3uMIiIiIiIiMqNHQdEVcjOzq66QyAiIiIiIqoQPqrwmgkICMCECRMQGRkJGxsbODg4IDo6GgCQkZEBiUSC1NRUsX5OTg4kEglSUlIAACkpKZBIJEhKSoKPjw9MTEzQuXNnZGdnY8+ePfDy8oKFhQWGDBmC/Pz8Ko33RVlZWejZsydMTExQr149/PTTT1rv49atWxg8eDBsbGxgamoKX19fnDx5EgCQnp6Ovn37wt7eHmZmZmjZsiX279+vsr2rqytiY2MRGhoKMzMz1K1bF7/++iv++ecf9O3bF2ZmZmjatCn++OMPcZuEhARYWVlh+/btaNCgAYyNjREYGIibN2+KdbTd94uPKly+fBnt2rWDsbExGjVqhP3796s8yqHM8datW9GpUyfIZDI0a9YMx48fF9u4ceMG+vTpA2tra5iamqJx48ZITEzU+nwSERERERGVBUccvIY2bNiAiIgInDx5EsePH0dYWBjatm0LDw8PrduIjo7GqlWrIJPJEBISgpCQEBgZGWHz5s149OgR+vXrh5UrV2L69OlVFm9gYKBYZ/bs2YiLi8OKFSuwceNGDBkyBE2aNIGXl1eJbT969AgdO3ZEnTp1sGPHDjg4OODs2bNQKBTi+qCgIMTGxsLY2BgbNmxAnz59cOXKFbi4uIjtLFu2DPPnz8fs2bOxbNkyvP/++2jbti1GjhyJxYsXY/r06QgNDcWff/4JiUQCAMjPz8dnn32GDRs2wNDQEGPHjsXgwYNx9OjRMu1bSaFQIDg4GC4uLjh58iQePnyIKVOmqD3umTNnYsmSJfDw8MDMmTMxZMgQXLt2Dfr6+hg3bhyePXuG3377DaampkhLS4OZmZnGc1hQUICCggJxOS8vDwAgl8shl8tLPP+vC2WcUkWh2nKqOZQ5ZW5rNuZZdzDXuoF51g3Mc82kbT4lgiAIVRwLlUFAQACKiopw5MgRsczPzw+dO3dGeHg46tWrh3PnzqF58+YAno84sLa2xqFDhxAQEICUlBR06tQJ+/fvR5cuXQAAcXFxiIqKQnp6Otzc3AAA4eHhyMjIwN69e6ss3ri4OADPJ0cMDw/H6tWrxTqtW7dGixYt8MUXX5TY/ldffYWpU6ciIyMDNjY2WsXUuHFjfPTRRxg/fjyA59/6t2/fHhs3bgQA3LlzB46Ojpg9ezbmzZsHADhx4gT8/f2RlZUFBwcHJCQkYMSIEThx4gRatWoF4PloAS8vL5w8eRJ+fn5a73vSpEmYNGkS9u7diz59+uDmzZtwcHAAAOzfvx+BgYHYtm0bgoODkZGRgXr16uGbb77BqFGjAABpaWlo3LgxLl26hIYNG8Lb2xsDBgzA3LlztTof0dHRiImJKVa+efNmyGQyrdogIiIiIqKaJz8/H0OHDkVubi4sLCw01uOIg9eQt7e3yrKjoyOys7PL3Ya9vT1kMpnYaaAsO3XqVMUCVbMvQH28/v7+xZZffORCk9TUVPj4+GjsNHj8+DFiYmKwa9cu3L59G4WFhXjy5AkyMzM1xmhvbw8AaNq0abGy7Oxs8UO9vr4+fH19xToNGzaElZUVLl26BD8/P633rXTlyhU4OzuL7QPQ2AHxYryOjo5ibA0bNsSECRPw0UcfYd++fejatSsGDBhQLAcvioqKQkREhLicl5cHZ2dndOvWrcSbw+tELpcjOTkZfzm9DYX0/25bk71tqzEqqgrKXAcGBsLAwKC6w6EqwjzrDuZaNzDPuoF5rpmUo5FLw46D19DLP4gSiQQKhQJS6fMpKV4cJKJpaMmLbUgkEo1tVmW8pVE+ElASExOTEtdPmzYNSUlJWLJkCdzd3WFiYoKBAwfi2bNnGmNU7ldd2ctxq4tRWabtvpUEQdDqmEuLbfTo0ejevTt2796Nffv2YcGCBYiPj8fHH3+sti0jIyMYGRmp3cebdtNXSPVVOg7etPhJe2/i9UllxzzrDuZaNzDPuoF5rlm0zSUnR3yDKGfoz8rKEsu0+db+dXDixIliyw0bNix1O29vb6SmpuLBgwdq1x85cgRhYWHo168fmjZtCgcHB2RkZFRGyCgsLFSZMPHKlSvIyckR4y7rvhs2bIjMzEzcvXtXLDt9+nS5YnN2dkZ4eDi2bt2KKVOm4Ouvvy5XO0RERERERKVhx8EbxMTEBK1bt0ZcXBzS0tLw22+/YdasWdUdllZ++uknrFu3DlevXsXcuXNx6tQpcR6AkgwZMgQODg4IDg7G0aNH8ffff+OXX34R3zLg7u6OrVu3IjU1FefPn8fQoUMrdSTFxx9/jJMnT+Ls2bMYMWIEWrduLT5eUNZ9BwYGon79+hg+fDguXLiAo0ePYubMmQC0G32hNGnSJCQlJeH69es4e/YsDh48WOokk0REREREROXFjoM3zLp16yCXy+Hr64uJEyciNja2ukPSSkxMDLZs2QJvb29s2LABmzZtQqNGjUrdztDQEPv27UPt2rURFBSEpk2bIi4uDnp6egCevy3B2toabdq0QZ8+fdC9e3e0aNGiUmKWyWSYPn06hg4dCn9/f5iYmGDLli3i+rLuW09PD9u3b8ejR4/QsmVLjB49Wuz4MTY21jquoqIijBs3Dl5eXujRowc8PT1LnWSSiIiIiIiovPhWBSI1EhISMGnSJOTk5FTpfo4ePYp27drh2rVrqF+/fpXuSykvLw+Wlpalzpz6OpHL5UhMTMSVt1qpzHEww6dWNUZFVUGZ66CgID4/WYMxz7qDudYNzLNuYJ5rJm0/G3ByRKJXaNu2bTAzM4OHhweuXbuGiRMnom3btq+s04CIiIiIiKis+KiCjsvMzISZmZnaf1KpFFKpVON6Ta8dLIv58+drbL9nz56VcISvl4cPH2Ls2LFo2LAhwsLC0LJlS/z666/VHRYREREREZFGHHGg45ycnMr9ZgYnJ6cK7z88PBwhISFq15X2KsaqFBYWhrCwsEpvNzQ0FKGhoZXerq6Y7G3LoXFERERERK8YOw50nL6+Ptzd3att/zY2NrCxsam2/RMREREREVHJ+KgCEREREREREWnEjgMiIiIiIiIi0ogdB0RERERERESkEec4IKJqFXfuXql1pIpCeL6CWIiIiIiIqDiOOCAiIiIiIiIijdhxQEREREREREQaseOAaqSwsDAEBwdXdxhqRUdHo3nz5tUdBhERERERkVbYcUBUhSQSCbZv317dYRAREREREZUbOw6I1BAEAYWFhdUdhtbetHiJiIiIiOjNwY6DN0hAQAAmTJiAyMhI2NjYwMHBAdHR0QCAjIwMSCQSpKamivVzcnIgkUiQkpICAEhJSYFEIkFSUhJ8fHxgYmKCzp07Izs7G3v27IGXlxcsLCwwZMgQ5OfnVzjehw8fYtiwYTA1NYWjoyOWLVuGgIAATJo0Sazz7NkzREZGok6dOjA1NUWrVq3EeAEgISEBVlZWSEpKgpeXF8zMzNCjRw9kZWWJdYqKihAREQErKyvY2toiMjISgiCoxCIIAhYtWgQ3NzeYmJigWbNm+Pnnn8X1L54bX19fGBkZ4ciRI6Ue4+rVq1G/fn0YGhrC09MTGzduFNe5uroCAPr16weJRCIuK23cuBGurq6wtLTE4MGD8fDhwyqPl4iIiIiIqKzYcfCG2bBhA0xNTXHy5EksWrQI8+bNQ3JycpnaiI6OxqpVq3Ds2DHcvHkTISEhWL58OTZv3ozdu3cjOTkZK1eurHCsEREROHr0KHbs2IHk5GQcOXIEZ8+eVakzYsQIHD16FFu2bMGFCxfw7rvvokePHvjrr7/EOvn5+ViyZAk2btyI3377DZmZmZg6daq4Pj4+HuvWrcPatWvx+++/48GDB9i2bZvKfmbNmoX169dj9erV+PPPPzF58mS89957OHz4sEq9yMhILFiwAJcuXYK3t3eJx7dt2zZMnDgRU6ZMwX//+198+OGHGDFiBA4dOgQAOH36NABg/fr1yMrKEpcBID09Hdu3b8euXbuwa9cuHD58GHFxcVUaLxERERERUXnoV3cAVDbe3t6YO3cuAMDDwwOrVq3CgQMH4OHhoXUbsbGxaNu2LQBg1KhRiIqKQnp6Otzc3AAAAwcOxKFDhzB9+vRyx/nw4UNs2LABmzdvRpcuXQA8/wDt5OQk1klPT8f333+PW7duieVTp07F3r17sX79esyfPx8AIJfL8eWXX6J+/foAgPHjx2PevHliO8uXL0dUVBQGDBgAAPjyyy+RlJQkrn/8+DGWLl2KgwcPwt/fHwDg5uaG33//HWvWrEHHjh3FuvPmzUNgYKBWx7hkyRKEhYVh7NixAJ53lJw4cQJLlixBp06dYGdnBwCwsrKCg4ODyrYKhQIJCQkwNzcHALz//vs4cOAAPvvss0qPt6CgAAUFBeJyXl4egOfnVS6Xa3WsVUmqKP0RC2Wd1yFeqlrKHDPXNRvzrDuYa93APOsG5rlm0jaf7Dh4w7z8rbKjoyOys7PL3Ya9vT1kMpnYaaAsO3XqVIXi/PvvvyGXy+Hn5yeWWVpawtPTU1w+e/YsBEFAgwYNVLYtKCiAra2tuCyTycROA0D1mHNzc5GVlSV+wAYAfX19+Pr6io8rpKWl4enTp8U+YD979gw+Pj4qZb6+vlof46VLl/DBBx+olLVt2xYrVqwodVtXV1ex0+DlY6rseBcsWICYmJhi5fv27YNMJit1+6rmWXoVUVlH19Cbi7nWDcyz7mCudQPzrBuY55pF20fU2XHwhjEwMFBZlkgkUCgUkEqfP3Xy4rP9mnqPXmxDIpFobLMilHFIJBK15cDzb9319PRw5swZ6OnpqdQzMzNTG6+yzZfnMCiJ8lh2796NOnXqqKwzMjJSWTY1NdW6XWUsLxIEoViZOiWd88qONyoqChEREeJyXl4enJ2d0a1bN1hYWJS6fVVbduF+qXWkikJ43D6DwMDAYueOaha5XI7k5GTmuoZjnnUHc60bmGfdwDzXTMrRyKVhx0ENoRwWn5WVJX4r/eJEia9a/fr1YWBggFOnTsHZ2RnA84vyr7/+Eofa+/j4oKioCNnZ2Wjfvn259mNpaQlHR0ecOHECHTp0AAAUFhbizJkzaNGiBQCgUaNGMDIyQmZmpsow/4ry8vLC77//jtDQULHs2LFj8PLyEpcNDAxQVFRUpnYrO14jI6NiHQ7K2F6Hm75Cqv1t6HWJmaoec60bmGfdwVzrBuZZNzDPNYu2uWTHQQ1hYmKC1q1bIy4uDq6urrh37x5mzZpVbfGYm5tj+PDhmDZtGmxsbFC7dm3MnTsXUqlU/Ea+QYMGGDZsGEJDQxEfHw8fHx/cu3cPBw8eRNOmTREUFKTVviZOnIi4uDh4eHjAy8sLS5cuRU5OjkosU6dOxeTJk6FQKNCuXTvk5eXh2LFjMDMzw/Dhw8t1jNOmTUNISAhatGiBLl26YOfOndi6dSv2798v1nF1dcWBAwfQtm1bGBkZwdrautR2qypeIiIiIiKi8uBbFWqQdevWQS6Xw9fXFxMnTkRsbGy1xrN06VL4+/ujd+/e6Nq1K9q2bQsvLy8YGxuLddavX4/Q0FBMmTIFnp6eeOedd3Dy5ElxlII2pkyZgtDQUISFhcHf3x/m5ubo16+fSp1PP/0Uc+bMwYIFC+Dl5YXu3btj586dqFevXrmPLzg4GCtWrMDixYvRuHFjrFmzBuvXr0dAQIBYJz4+HsnJyXB2di42P0FJqiJeIiIiIiKi8pAIZXlYnKgCHj9+jDp16iA+Ph6jRo2q7nB0Vl5eHiwtLZGbm/tazHEQd+5eqXWkikJ43jqJoKAgDo2r4eRyORITE5nrGo551h3MtW5gnnUD81wzafvZgI8qUJU5d+4cLl++DD8/P+Tm5oqvUOzbt281R0ZERERERETaYscBaZSZmYlGjRqpXad8bYem1/mlpaUBAJYsWYIrV67A0NAQb7/9No4cOYJatWpVTcCVrHHjxrhx44badWvWrMGwYcNecURERERERESvHjsOSCMnJ6dyv5nByckJLi4uOHPmTOUG9QolJiZqfKWlvb39K46m5prhU3pHklwuR+KtVxAMEREREREVw44D0khfXx/u7u7VHUa1qVu3bnWHQEREREREVO34VgUiIiIiIiIi0ogdB0RERERERESkETsOiIiIiIiIiEgjznFARG+MZRfuQyH9v9uWNhMrEhERERFRxXDEARERERERERFpxI4DIiIiIiIiItKIHQdEREREREREpBE7DqjGCwsLQ3BwcHWHUS4JCQmwsrKq7jCIiIiIiEiHseOAiIiIiIiIiDRixwFRKQRBQGFhYXWHQUREREREVC3YcfCGCggIwIQJExAZGQkbGxs4ODggOjoaAJCRkQGJRILU1FSxfk5ODiQSCVJSUgAAKSkpkEgkSEpKgo+PD0xMTNC5c2dkZ2djz5498PLygoWFBYYMGYL8/PwKx/vw4UMMGzYMpqamcHR0xLJlyxAQEIBJkyaJdZ49e4bIyEjUqVMHpqamaNWqlRgv8H/D9pOSkuDl5QUzMzP06NEDWVlZYp2ioiJERETAysoKtra2iIyMhCAIKrEIgoBFixbBzc0NJiYmaNasGX7++Wdx/YvnxtfXF0ZGRjhy5EiJxxcdHY3mzZtj3bp1cHFxgZmZGT766CMUFRVh0aJFcHBwQO3atfHZZ5+pbLd06VI0bdoUpqamcHZ2xtixY/Ho0aMS97Vz5068/fbbMDY2hpubG2JiYtixQUREREREVUa/9Cr0utqwYQMiIiJw8uRJHD9+HGFhYWjbti08PDy0biM6OhqrVq2CTCZDSEgIQkJCYGRkhM2bN+PRo0fo168fVq5cienTp1co1oiICBw9ehQ7duyAvb095syZg7Nnz6J58+ZinREjRiAjIwNbtmyBk5MTtm3bhh49euDixYviMeXn52PJkiXYuHEjpFIp3nvvPUydOhWbNm0CAMTHx2PdunVYu3YtGjVqhPj4eGzbtg2dO3cW9zNr1ixs3boVq1evhoeHB3777Te89957sLOzQ8eOHcV6kZGRWLJkCdzc3LSaZyA9PR179uzB3r17kZ6ejoEDB+L69eto0KABDh8+jGPHjmHkyJHo0qULWrduDQCQSqX4/PPP4erqiuvXr2Ps2LGIjIzEF198oXYfSUlJeO+99/D555+jffv2SE9PxwcffAAAmDt3rtptCgoKUFBQIC7n5eUBAORyOeRyeanH9TpQxilVFKotp5pDmVPmtmZjnnUHc60bmGfdwDzXTNrmUyK8/HUsvRECAgJQVFSk8k24n58fOnfujPDwcNSrVw/nzp0TP5jn5OTA2toahw4dQkBAAFJSUtCpUyfs378fXbp0AQDExcUhKioK6enpcHNzAwCEh4cjIyMDe/fuLXesDx8+hK2tLTZv3oyBAwcCAHJzc+Hk5IQxY8Zg+fLlSE9Ph4eHB27dugUnJydx265du8LPzw/z589HQkICRowYgWvXrqF+/foAgC+++ALz5s3DnTt3AABOTk6YOHGi2NFRWFiIevXq4e2338b27dvx+PFj1KpVCwcPHoS/v7+4n9GjRyM/Px+bN28Wz8327dvRt29frY4xOjoaixcvxp07d2Bubg4A6NGjB65cuYL09HRIpc8H9zRs2BBhYWGYMWOG2nZ++uknfPTRR7h37x6A56MsJk2ahJycHABAhw4d0LNnT0RFRYnbfPfdd4iMjMTt27c1xhYTE1OsfPPmzZDJZFodHxERERER1Tz5+fkYOnQocnNzYWFhobEeRxy8wby9vVWWHR0dkZ2dXe427O3tIZPJxE4DZdmpU6cqFOfff/8NuVwOPz8/sczS0hKenp7i8tmzZyEIAho0aKCybUFBAWxtbcVlmUwmdhoAqsecm5uLrKwslQ4BfX19+Pr6io8rpKWl4enTpwgMDFTZz7Nnz+Dj46NS5uvrW6bjdHV1FTsNgOfnTk9PT+w0UJa9mKNDhw5h/vz5SEtLQ15eHgoLC/H06VM8fvwYpqamxfZx5swZnD59WuWRh6KiIjx9+hT5+flqOwKioqIQEREhLufl5cHZ2RndunUr8ebwOpHL5UhOTsZfTm9DIf2/29Zkb9sStqI3kTLXgYGBMDAwqO5wqIowz7qDudYNzLNuYJ5rJuVo5NKw4+AN9vIPrEQigUKhED+ovjiYRNMQlBfbkEgkGtusCGUcEolEbTkAKBQK6Onp4cyZM9DT01OpZ2ZmpjZeZZtlGTSjPJbdu3ejTp06KuuMjIxUltV9cC+JuthKOp83btxAUFAQwsPD8emnn8LGxga///47Ro0apTFfCoUCMTEx6N+/f7F1xsbGarcxMjIqdmzKeN+0m75Cqq/ScfCmxU/aexOvTyo75ll3MNe6gXnWDcxzzaJtLtlxUAPZ2dkBALKyssRv0V+cKPFVq1+/PgwMDHDq1Ck4OzsDeN6z9ddff4lzCvj4+KCoqAjZ2dlo3759ufZjaWkJR0dHnDhxAh06dADw/FGFM2fOoEWLFgCARo0awcjICJmZmSrzGVSHP/74A4WFhYiPjxc7e3788ccSt2nRogWuXLkCd3f3VxEiEREREREROw5qIhMTE7Ru3RpxcXFwdXXFvXv3MGvWrGqLx9zcHMOHD8e0adNgY2OD2rVrY+7cuZBKpeIohAYNGmDYsGEIDQ1FfHw8fHx8cO/ePRw8eBBNmzZFUFCQVvuaOHEi4uLi4OHhAS8vLyxdulScH0AZy9SpUzF58mQoFAq0a9cOeXl5OHbsGMzMzDB8+PCqOAVq1a9fH4WFhVi5ciX69OmDo0eP4ssvvyxxmzlz5qB3795wdnbGu+++C6lUigsXLuDixYuIjY19RZETEREREZEu4esYa6h169ZBLpfD19cXEydOrPYPlUuXLoW/vz969+6Nrl27om3btvDy8lIZXr9+/XqEhoZiypQp8PT0xDvvvIOTJ0+KoxS0MWXKFISGhiIsLAz+/v4wNzdHv379VOp8+umnmDNnDhYsWAAvLy90794dO3fuRL169SrteLXRvHlzLF26FAsXLkSTJk2wadMmLFiwoMRtunfvjl27diE5ORktW7ZE69atsXTpUtStW/cVRU1ERERERLqGb1WgavH48WPUqVMH8fHxGDVqVHWHo1Py8vJgaWlZ6syprxO5XI7ExERceauVyhwHM3xqVWNUVBWUuQ4KCuLzkzUY86w7mGvdwDzrBua5ZtL2swEfVaBX4ty5c7h8+TL8/PyQm5uLefPmAYDWrzskIiIiIiKi6sGOA9JKZmYmGjVqpHZdfn4+AKh9FSDw/BWIALBkyRJcuXIFhoaGePvtt3HkyBHUqvVmfGPcuHFj3LhxQ+26NWvWYNiwYa84IiIiIiIioleDHQekFScnp3K/mcHJyQkuLi44c+ZM5Qb1CiUmJmp8RaK9vf0rjkZ3Tfa25dA4IiIiIqJXjB0HpBV9fX2dfgUgJx8kIiIiIiJdxbcqEBEREREREZFG7DggIiIiIiIiIo34qAIRvTGWXbiv8jpGgK9kJCIiIiKqahxxQEREREREREQaseOAiIiIiIiIiDRixwERERERERERacSOA3pjhIWFITg4uMr389VXX8HZ2RlSqRTLly+v8v2VxNXVtdpjICIiIiIi3cbJEYlekJeXh/Hjx2Pp0qUYMGAALC0tqzskIiIiIiKiasWOA9IZgiCgqKgI+vqaL/vMzEzI5XL06tULjo6OrzA6IiIiIiKi1xMfVahmAQEBmDBhAiIjI2FjYwMHBwdER0cDADIyMiCRSJCamirWz8nJgUQiQUpKCgAgJSUFEokESUlJ8PHxgYmJCTp37ozs7Gzs2bMHXl5esLCwwJAhQ5Cfn1/heB8+fIhhw4bB1NQUjo6OWLZsGQICAjBp0iSxzrNnzxAZGYk6derA1NQUrVq1EuMFgISEBFhZWSEpKQleXl4wMzNDjx49kJWVJdYpKipCREQErKysYGtri8jISAiCoBKLIAhYtGgR3NzcYGJigmbNmuHnn38W1794bnx9fWFkZIQjR45oPLaEhAQ0bdoUAODm5gaJRIKMjAwAwM6dO/H222/D2NgYbm5uiImJQWFhobitRCLBmjVr0Lt3b8hkMnh5eeH48eO4du0aAgICYGpqCn9/f6Snp4vbpKeno2/fvrC3t4eZmRlatmyJ/fv3l3j+c3Nz8cEHH6B27dqwsLBA586dcf78+RK3ISIiIiIiqgiOOHgNbNiwARERETh58iSOHz+OsLAwtG3bFh4eHlq3ER0djVWrVkEmkyEkJAQhISEwMjLC5s2b8ejRI/Tr1w8rV67E9OnTKxRrREQEjh49ih07dsDe3h5z5szB2bNn0bx5c7HOiBEjkJGRgS1btsDJyQnbtm1Djx49cPHiRfGY8vPzsWTJEmzcuBFSqRTvvfcepk6dik2bNgEA4uPjsW7dOqxduxaNGjVCfHw8tm3bhs6dO4v7mTVrFrZu3YrVq1fDw8MDv/32G9577z3Y2dmhY8eOYr3IyEgsWbIEbm5usLKy0nhsgwYNgrOzM7p27YpTp07B2dkZdnZ2SEpKwnvvvYfPP/8c7du3R3p6Oj744AMAwNy5c8XtP/30UyxduhRLly7F9OnTMXToULi5uSEqKgouLi4YOXIkxo8fjz179gAAHj16hKCgIMTGxsLY2BgbNmxAnz59cOXKFbi4uBSLTxAE9OrVCzY2NkhMTISlpSXWrFmDLl264OrVq7CxsVF7XAUFBSgoKBCX8/LyAAByuRxyuVzj+XidKOOUKgo1rqOaQZlP5rVmY551B3OtG5hn3cA810za5lMivPw1Lr1SAQEBKCoqUvkm3M/PD507d0Z4eDjq1auHc+fOiR/Mc3JyYG1tjUOHDiEgIAApKSno1KkT9u/fjy5dugAA4uLiEBUVhfT0dLi5uQEAwsPDkZGRgb1795Y71ocPH8LW1habN2/GwIEDATz/BtzJyQljxozB8uXLkZ6eDg8PD9y6dQtOTk7itl27doWfnx/mz5+PhIQEjBgxAteuXUP9+vUBAF988QXmzZuHO3fuAACcnJwwceJEsaOjsLAQ9erVw9tvv43t27fj8ePHqFWrFg4ePAh/f39xP6NHj0Z+fj42b94snpvt27ejb9++Wh1jamoqfHx8cP36dbi6ugIAOnTogJ49eyIqKkqs99133yEyMhK3b98G8HzEwaxZs/Dpp58CAE6cOAF/f3+sXbsWI0eOBABs2bIFI0aMwJMnTzTuv3Hjxvjoo48wfvx4AM8nR5w0aRImTZqEgwcPol+/fsjOzoaRkZG4jbu7OyIjI8XOjJdFR0cjJiamWPnmzZshk8m0Oi9ERERERFTz5OfnY+jQocjNzYWFhYXGehxx8Brw9vZWWXZ0dER2dna527C3t4dMJhM7DZRlp06dqlCcf//9N+RyOfz8/MQyS0tLeHp6istnz56FIAho0KCByrYFBQWwtbUVl2UymdhpAKgec25uLrKyslQ6BPT19eHr6ys+rpCWloanT58iMDBQZT/Pnj2Dj4+PSpmvr295DxkAcObMGZw+fRqfffaZWFZUVISnT58iPz9f/PD9cg4AiI8+KMuePn2KvLw8WFhY4PHjx4iJicGuXbtw+/ZtFBYW4smTJ8jMzNQYx6NHj1TOIwA8efJE5RGIl0VFRSEiIkJczsvLg7OzM7p161bizeF1IpfLkZycjL+c3oZCqnrbmuxtq2ErehMpcx0YGAgDA4PqDoeqCPOsO5hr3cA86wbmuWZSjkYuDTsOXgMv/+BJJBIoFApIpc+noHhxUIimoSQvtiGRSDS2WRHKOCQSidpyAFAoFNDT08OZM2egp6enUs/MzExtvMo2yzL4RXksu3fvRp06dVTWvfhtPACYmppq3a6mfcXExKB///7F1hkbG4v//3IONJUpY582bRqSkpKwZMkSuLu7w8TEBAMHDsSzZ880xuHo6KgyX4RSSY9gGBkZFTsnytjetJu+QqpfrOPgTTsG0s6beH1S2THPuoO51g3Ms25gnmsWbXPJjoPXmJ2dHQAgKytL/Bb9xYkSX7X69evDwMBAfP4feN5D9ddff4lzCvj4+KCoqAjZ2dlo3759ufZjaWkJR0dHnDhxAh06dADw/FGFM2fOoEWLFgCARo0awcjICJmZmSrzGVSFFi1a4MqVK3B3d6/Udo8cOYKwsDD069cPwPM5D5STMWqK486dO9DX1xcfoyAiIiIiIqpq7Dh4jZmYmKB169aIi4uDq6sr7t27h1mzZlVbPObm5hg+fDimTZsGGxsb1K5dG3PnzoVUKhW/TW/QoAGGDRuG0NBQxMfHw8fHB/fu3cPBgwfRtGlTBAUFabWviRMnIi4uDh4eHvDy8sLSpUuRk5OjEsvUqVMxefJkKBQKtGvXDnl5eTh27BjMzMwwfPjwSjvuOXPmoHfv3nB2dsa7774LqVSKCxcu4OLFi4iNjS13u+7u7ti6dSv69OkDiUSC2bNnlzgqpGvXrvD390dwcDAWLlwIT09P3L59G4mJiQgODq7wIxlERERERETq8HWMr7l169ZBLpfD19cXEydOrNAH1cqwdOlS+Pv7o3fv3ujatSvatm0LLy8vlSH769evR2hoKKZMmQJPT0+88847OHnypDhKQRtTpkxBaGgowsLC4O/vD3Nzc/GbeaVPP/0Uc+bMwYIFC+Dl5YXu3btj586dqFevXqUdLwB0794du3btQnJyMlq2bInWrVtj6dKlqFu3boXaXbZsGaytrdGmTRv06dMH3bt3F0dUqCORSJCYmIgOHTpg5MiRaNCgAQYPHoyMjAxxTgUiIiIiIqLKxrcqUIU8fvwYderUQXx8PEaNGlXd4ZAW8vLyYGlpWerMqa8TuVyOxMREXHmrVbE5Dmb41KqmqKgqKHMdFBTE5ydrMOZZdzDXuoF51g3Mc82k7WcDPqpAZXLu3DlcvnwZfn5+yM3Nxbx58wBA69cdEhERERER0ZuFHQc6JjMzE40aNVK7Lj8/HwDE1wu+LC0tDQCwZMkSXLlyBYaGhnj77bdx5MgR1Kr1Znzr27hxY9y4cUPtujVr1mDYsGGvOCIiIiIiIqLXGzsOdIyTk1O538zg5OQEFxcXnDlzpnKDeoUSExM1vtKS8wS8/iZ723JoHBERERHRK8aOAx2jr69f6a8VfJNUdEJDIiIiIiIiXcO3KhARERERERGRRpXWcZCTk1NZTRERERERERHRa6JcHQcLFy7EDz/8IC6HhITA1tYWderUwfnz5ystOCIiIiIiIiKqXuWa42DNmjX47rvvAADJyclITk7Gnj178OOPP2LatGnYt29fpQZJRLon7tw98f+likJ4VmMsRERERES6rFwdB1lZWXB2dgYA7Nq1CyEhIejWrRtcXV3RqlWrSg2QiIiIiIiIiKpPuR5VsLa2xs2bNwEAe/fuRdeuXQEAgiCgqKio8qIjIiIiIiIiompVro6D/v37Y+jQoQgMDMT9+/fRs2dPAEBqaqpOv+qPqLK5urpi+fLl1R0GERERERHpsHI9qrBs2TK4urri5s2bWLRoEczMzAA8f4Rh7NixlRogUU2RkJCASZMmlekNJKdPn4apqWnVBUVERERERFSKcnUcGBgYYOrUqcXKJ02aVNF4iOgFdnZ21R0CERERERHpuHI9qgAAGzduRLt27eDk5IQbN24AAJYvX45ff/210oKjmisgIAATJkxAZGQkbGxs4ODggOjoaABARkYGJBIJUlNTxfo5OTmQSCRISUkBAKSkpEAikSApKQk+Pj4wMTFB586dkZ2djT179sDLywsWFhYYMmQI8vPzKxxvQUEBJkyYgNq1a8PY2Bjt2rXD6dOnxfXKeHbv3o1mzZrB2NgYrVq1wsWLF8X1I0aMQG5uLiQSCSQSiXi8JXn5UQWJRIJvvvkG/fr1g0wmg4eHB3bs2FHh4yMiIiIiItKkXB0Hq1evRkREBHr27ImcnBxxQkQrKys+j01a27BhA0xNTXHy5EksWrQI8+bNQ3JycpnaiI6OxqpVq3Ds2DHcvHkTISEhWL58OTZv3ozdu3cjOTkZK1eurHCskZGR+OWXX7BhwwacPXsW7u7u6N69Ox48eKBSb9q0aViyZAlOnz6N2rVr45133oFcLkebNm2wfPlyWFhYICsrC1lZWWpH7WgjJiYGISEhuHDhAoKCgjBs2LBicRAREREREVWWcj2qsHLlSnz99dcIDg5GXFycWO7r61vuD0Oke7y9vTF37lwAgIeHB1atWoUDBw7Aw8ND6zZiY2PRtm1bAMCoUaMQFRWF9PR0uLm5AQAGDhyIQ4cOYfr06eWO8/Hjx1i9ejUSEhLEiUC//vprJCcnY+3atZg2bZpYd+7cuQgMDATwvGPkrbfewrZt2xASEgJLS0tIJBI4ODiUOxYACAsLw5AhQwAA8+fPx8qVK3Hq1Cn06NFDbf2CggIUFBSIy3l5eQAAuVwOuVxeoViqklRRWOz/X+d4qXIoc8xc12zMs+5grnUD86wbmOeaSdt8lqvj4Pr16/Dx8SlWbmRkhMePH5enSdJB3t7eKsuOjo7Izs4udxv29vaQyWRip4Gy7NSpUxWKMz09HXK5XOygAJ7P8+Hn54dLly6p1PX39xf/38bGBp6ensXqVNSLx2xqagpzc/MSz9uCBQsQExNTrHzfvn2QyWSVGltl8lRTVtYRKfTmYq51A/OsO5hr3cA86wbmuWbR9rHucnUc1KtXD6mpqahbt65K+Z49e9CoUaPyNEk6yMDAQGVZIpFAoVBAKn3+BI0gCOI6TT1hL7YhkUg0tlkRyjgkEkmx8pfL1NGmTlmU9RijoqIQEREhLufl5cHZ2RndunWDhYVFpcZWmZZduC/+v1RRCI/bZxAYGFjs+KlmkcvlSE5OZq5rOOZZdzDXuoF51g3Mc82kHI1cmnJ1HEybNg3jxo3D06dPIQgCTp06he+//x4LFizAN998U54miUTKNwlkZWWJI1tenCjxVXN3d4ehoSF+//13DB06FMDzG+cff/xR7E0iJ06cgIuLCwDg33//xdWrV9GwYUMAgKGhoTgfyKtkZGQEIyOjYuUGBgav9U1fIS1+e3rdY6bKw1zrBuZZdzDXuoF51g3Mc82ibS7L1XEwYsQIFBYWIjIyEvn5+Rg6dCjq1KmDFStWYPDgweVpkkhkYmKC1q1bIy4uDq6urrh37x5mzZpVbfGYmprio48+wrRp02BjYwMXFxcsWrQI+fn5GDVqlErdefPmwdbWFvb29pg5cyZq1aqF4OBgAM/fkPDo0SMcOHAAzZo1g0wme60fFSAiIiIiIgIq8DrGMWPG4MaNG8jOzsadO3dw8+bNYh+iiMpr3bp1kMvl8PX1xcSJExEbG1ut8cTFxWHAgAF4//330aJFC1y7dg1JSUmwtrYuVm/ixIl4++23kZWVhR07dsDQ0BAA0KZNG4SHh2PQoEGws7PDokWLquNQiIiIiIiIykQivPggORGVS0pKCjp16oR///0XVlZW1R1OifLy8mBpaYnc3NzXeo6DuHP3xP+XKgrheeskgoKCODSuhpPL5UhMTGSuazjmWXcw17qBedYNzHPNpO1ng3JPjljShG9///13eZolIiIiIiIiotdMuToOXp4QTi6X49y5c9i7d6/KO+2JXheZmZka3/ihfAWJpvkG0tLSxAkPK9ORI0fQs2dPjesfPXpU6fskIiIiIiIqq3J1HEycOFFt+X/+8x/88ccfFQqIqCo4OTmV+80MTk5OpdYJCAhAWZ/68fX1rda3RbzuZvjUEv9fLpcj8VY1BkNEREREpMPK1XGgSc+ePREVFYX169dXZrNEFaavrw93d/fqDkOFiYnJaxcTERERERHRy8r9VgV1fv75Z9jY2FRmk0RERERERERUjco14sDHx0dlckRBEHDnzh38888/+OKLLyotOCIiIiIiIiKqXuXqOAgODlZZlkqlsLOzQ0BAABo2bFgZcRERERERERHRa6BcHQdz586t7DiIiNSKO3cPUkUhPKs7ECIiIiIiHVWujoO8vDyt61pYWJRnF0RERERERET0GihXx4GVlZXKHAfqCIIAiUSCoqKicgVGRERERERERNWvXB0H69evx4wZMxAWFgZ/f38AwPHjx7FhwwYsWLAArq6ulRkjEREREREREVWTcnUcfPvtt1i6dCmGDBkilr3zzjto2rQpvvrqK6SkpFRWfFQDBAQEoHnz5li+fHl1h1IuGRkZqFevHs6dO4fmzZsjJSUFnTp1wr///gsrK6vqDo+IiIiIiKhKScuz0fHjx+Hr61us3NfXF6dOnapwUFSzbN26FZ9++ml1h1Fuzs7OyMrKQpMmTcq8bUJCAjsXiIiIiIjojVaujgNnZ2d8+eWXxcrXrFkDZ2fnCgdFNYuNjQ3Mzc2rO4xy09PTg4ODA/T1yzVARyvPnj2rsraJiIiIiIgqolwdB8uWLcMXX3yBJk2aYPTo0Rg9ejSaNGmCL774AsuWLavsGOklAQEBmDBhAiIjI2FjYwMHBwdER0cDeD6sXiKRIDU1Vayfk5MDiUQiPkKSkpICiUSCpKQk+Pj4wMTEBJ07d0Z2djb27NkDLy8vWFhYYMiQIcjPz6+UeCdNmiQuf/HFF/Dw8ICxsTHs7e0xcOBArdoRBAGLFi2Cm5sbTExM0KxZM/z888/i+vIe1969e9GuXTtYWVnB1tYWvXv3Rnp6urhe3TnVRkpKCkaMGIHc3FxIJBJIJBIxT66uroiNjUVYWBgsLS0xZswYMf6cnByxjdTUVEgkEmRkZIhlx44dQ4cOHWBiYgJnZ2dMmDABjx8/LlNsRERERERE2irXV6hBQUG4evUqVq9ejcuXL0MQBPTt2xfh4eEccfCKbNiwARERETh58iSOHz+OsLAwtG3bFh4eHlq3ER0djVWrVkEmkyEkJAQhISEwMjLC5s2b8ejRI/Tr1w8rV67E9OnTKy3uP/74AxMmTMDGjRvRpk0bPHjwAEeOHNFq21mzZmHr1q1YvXo1PDw88Ntvv+G9996DnZ0dOnbsWO7jevz4MSIiItC0aVM8fvwYc+bMQb9+/ZCamgqptFx9awCANm3aYPny5ZgzZw6uXLkCADAzMxPXL168GLNnz8asWbMAALdu3Sq1zYsXL6J79+749NNPsXbtWvzzzz8YP348xo8fj/Xr16vdpqCgAAUFBeKy8nWqcrkccrm83Mf3qkgVhZAqCgHgjYiXKkaZY+a6ZmOedQdzrRuYZ93APNdM2uaz3GOvnZ2dMX/+/PJuThXk7e2NuXPnAgA8PDywatUqHDhwoEwdB7GxsWjbti0AYNSoUYiKikJ6ejrc3NwAAAMHDsShQ4cqteMgMzMTpqam6N27N8zNzVG3bl34+PiUut3jx4+xdOlSHDx4UHyTh5ubG37//XesWbNGpeOgrMc1YMAAlX2tXbsWtWvXRlpaWrnmNVAyNDSEpaUlJBIJHBwciq3v3Lkzpk6dKi5r03GwePFiDB06VBzB4eHhgc8//xwdO3bE6tWrYWxsXGybBQsWICYmplj5vn37IJPJynBE1cPzhf9PTk6utjjo1WKudQPzrDuYa93APOsG5rlm0XaEudYdBxcuXECTJk0glUpx4cKFEut6e3tr2yyV08vn2NHREdnZ2eVuw97eHjKZTPxwrSyr7MkuAwMDUbduXbi5uaFHjx7o0aMH+vXrV+oH2LS0NDx9+hSBgYEq5c+ePSvW8VDW40pPT8fs2bNx4sQJ3Lt3DwqFAsDzTo6KdByURt0Eo6U5c+YMrl27hk2bNollgiBAoVDg+vXr8PLyKrZNVFQUIiIixOW8vDw4OzujW7dusLCwKF/wr9CyC/chVRTC4/YZBAYGwsDAoLpDoiokl8uRnJzMXNdwzLPuYK51A/OsG5jnmkk5Grk0WnccNG/eHHfu3EHt2rXRvHlzSCQSCIJQrJ5EIkFRUZH2kVK5vPzDKpFIoFAoxKH1L+ZG0/CTF9uQSCQa26xM5ubmOHv2LFJSUrBv3z7MmTMH0dHROH36dIlvH1DGsXv3btSpU0dlnZGRkcpyWY+rT58+cHZ2xtdffw0nJycoFAo0adKkyicsNDU1VVnWJncKhQIffvghJkyYUKw9FxcXtfsxMjIqdo6A5+fpTbjpK6T/d5t6U2KmimOudQPzrDuYa93APOsG5rlm0TaXWnccXL9+HXZ2duL/0+tJmaOsrCzxm/iyTupX1fT19dG1a1d07doVc+fOhZWVFQ4ePIj+/ftr3KZRo0YwMjJCZmamymMJFXX//n1cunQJa9asQfv27QEAv//+e6W1b2hoqHVH2ou5s7a2BlA8dy1atMCff/4Jd3f3SouRiIiIiIioJFp3HNStW1f8/xs3bqBNmzbFXk9XWFiIY8eOqdSlV8vExAStW7dGXFwcXF1dce/ePXHyvdfBrl278Pfff6NDhw6wtrZGYmIiFAoFPD09S9zO3NwcU6dOxeTJk6FQKNCuXTvk5eXh2LFjMDMzw/Dhw8sVj7W1NWxtbfHVV1/B0dERmZmZmDFjRrnaUsfV1RWPHj3CgQMH0KxZM8hkMo2PZbi7u8PZ2RnR0dGIjY3FX3/9hfj4eJU606dPR+vWrTFu3DiMGTMGpqamuHTpEpKTk7Fy5cpKi5uIiIiIiEipXFPGd+rUCQ8ePChWnpubi06dOlU4KKqYdevWQS6Xw9fXFxMnTkRsbGx1hySysrLC1q1b0blzZ3h5eeHLL7/E999/j8aNG5e67aeffoo5c+ZgwYIF8PLyQvfu3bFz507Uq1ev3PFIpVJs2bIFZ86cQZMmTTB58mQsXry43O29rE2bNggPD8egQYNgZ2eHRYsWaaxrYGCA77//HpcvX0azZs2wcOHCYrnz9vbG4cOH8ddff6F9+/bw8fHB7Nmz4ejoWGkxExERERERvUgiqJuooBRSqRR3794Vh1YrXb16Fb6+vlpPsEBEr15eXh4sLS2Rm5v7RkyOGHfuHqSKQnjeOomgoCA+U1fDyeVyJCYmMtc1HPOsO5hr3cA86wbmuWbS9rNBmV7HqHwGXSKRICwsTGXCtaKiIly4cAFt2rQpZ8hERERERERE9LopU8eBpaUlgOezvpubm8PExERcZ2hoiNatW2PMmDGVGyFVu8zMTDRq1EjtOuV7PzU9t5+WlqZxtn9t91GWdl61nj174siRI2rXffLJJ/jkk09ecURERERERESVq0wdB+vXrwfwfMK3qVOnFnudHNVMTk5O5X4zg5OTU6XsQ9t2XrVvvvkGT548UbvOxsbmFUdTM83wqfV8aNyt6o6EiIiIiEg3lanjQCkyMlLlXfM3btzAtm3b0KhRI3Tr1q3SgqPXg76+fpW//u9V7KMq1KlTp7pDICIiIiIiqlLleqtC37598e233wIAcnJy4Ofnh/j4ePTt2xerV6+u1ACJiIiIiIiIqPqUq+Pg7NmzaN++PQDg559/hoODA27cuIFvv/0Wn3/+eaUGSERERERERETVp1yPKuTn58Pc3BwAsG/fPvTv3x9SqRStW7fGjRs3KjVAIiKlZRfuQyEt122L3hBSRSE8wVzXdMyz7mCudQPzrBuY58o1w6dWdYdQJuUaceDu7o7t27fj5s2bSEpKEuc1yM7OfiPeC09ERERERERE2ilXx8GcOXMwdepUuLq6olWrVvD39wfwfPSBj49PpQZIRERERERERNWnXGNMBg4ciHbt2iErKwvNmjUTy7t06YJ+/fpVWnBEREREREREVL3KNeIAABwcHODj4wOp9P+a8PPzQ8OGDSslMKLqIggCPvjgA9jY2EAikSA1NRUBAQGYNGlSidu5urpi+fLlWu8nOjoazZs3F5fDwsIQHBxcrpiJiIiIiIiqitYjDvr374+EhARYWFigf//+JdbdunVrhQMjqi579+5FQkICUlJS4Obmhlq1amHr1q0wMDCo0v2uWLECgiCIywEBAWjevHmZOiOIiIiIiIgqm9YdB5aWlpBIJOL/E9VU6enpcHR0RJs2bcQyGxubKt8vf66IiIiIiOh1pPWjCuvXr4e5uTkEQUB0dDT+85//YP369Wr/Eb0oICAAEyZMQGRkJGxsbODg4IDo6GgAQEZGhvg4gFJOTg4kEglSUlIAACkpKZBIJEhKSoKPjw9MTEzQuXNnZGdnY8+ePfDy8oKFhQWGDBmC/Pz8CsUaFhaGjz/+GJmZmZBIJHB1dRWP4cVHFbKzs9GnTx+YmJigXr162LRpU7G2cnNz8cEHH6B27dqwsLBA586dcf78+RL3rXxUISwsDIcPH8aKFSsgkUggkUhw/fp1uLu7Y8mSJSrb/fe//4VUKkV6enqFjp2IiIiIiEidMk+OKAgCPDw88Oeff8LDw6MqYqIaaMOGDYiIiMDJkydx/PhxhIWFoW3btmW6hqKjo7Fq1SrIZDKEhIQgJCQERkZG2Lx5Mx49eoR+/fph5cqVmD59ernjXLFiBerXr4+vvvoKp0+fhp6entp6YWFhuHnzJg4ePAhDQ0NMmDAB2dnZ4npBENCrVy/Y2NggMTERlpaWWLNmDbp06YKrV6+WOoJhxYoVuHr1Kpo0aYJ58+YBAOzs7DBy5EisX78eU6dOFeuuW7cO7du3R/369dW2VVBQgIKCAnE5Ly8PACCXyyGXy7U7MdVMGadUUVjNkVBVU+aYua7ZmGfdwVzrBuZZNzDPlet1+Ttc2zjK3HEglUrh4eGB+/fvs+OAtObt7Y25c+cCADw8PLBq1SocOHCgTNdQbGws2rZtCwAYNWoUoqKikJ6eDjc3NwDP3/Zx6NChCnUcWFpawtzcHHp6enBwcFBb5+rVq9izZw9OnDiBVq1aAQDWrl0LLy8vsc6hQ4dw8eJFZGdnw8jICACwZMkSbN++HT///DM++OCDUuMwNDSETCZTiWPEiBGYM2cOTp06BT8/P8jlcnz33XdYvHixxrYWLFiAmJiYYuX79u2DTCYrMY7XjcftM9UdAr0izLVuYJ51B3OtG5hn3cA8V47EW9UdwXPajtgu1+sYFy1ahGnTpmH16tVo0qRJeZogHePt7a2y7OjoqPINfVnbsLe3h0wmEzsNlGWnTp2qWKBauHTpEvT19eHr6yuWNWzYEFZWVuLymTNn8OjRI9ja2qps++TJkwo9UuDo6IhevXph3bp18PPzw65du/D06VO8++67GreJiopCRESEuJyXlwdnZ2d069YNFhYW5Y7lVZLL5UhOTsZfTm9DIS3XbYveEFJFITxun2GuazjmWXcw17qBedYNzHPlmuxtW3qlV0A5Grk05cr4e++9h/z8fDRr1gyGhoYwMTFRWf/gwYPyNEs12MtvJJBIJFAoFOLrPF98m4Cm4TIvtiGRSDS2WdWUsSonC1VHoVDA0dFRnKfhRS92MJTH6NGj8f7772PZsmVYv349Bg0aVOLIASMjI3HUw4sMDAyq/E0RlU0h1ecvKh3BXOsG5ll3MNe6gXnWDcxz5Xhd/g7XNo5yZZyvh6PKYmdnBwDIysqCj48PAKhMlPg68vLyQmFhIf744w/4+fkBAK5cuYKcnByxTosWLXDnzh3o6+uLEyyWlaGhIYqKioqVBwUFwdTUFKtXr8aePXvw22+/lat9IiIiIiIibZSr42D48OGVHQfpKBMTE7Ru3RpxcXFwdXXFvXv3MGvWrOoOq0Senp7o0aMHxowZg6+++gr6+vqYNGmSysibrl27wt/fH8HBwVi4cCE8PT1x+/ZtJCYmIjg4WOUxB01cXV1x8uRJZGRkwMzMDDY2NpBKpdDT00NYWBiioqLg7u4Of3//qjxcIiIiIiLScVq/jvFl6enpmDVrFoYMGSI+q7537178+eeflRYc6YZ169ZBLpfD19cXEydORGxsbHWHVKr169fD2dkZHTt2RP/+/cXXLipJJBIkJiaiQ4cOGDlyJBo0aIDBgwcjIyMD9vb2Wu1j6tSp0NPTQ6NGjWBnZ4fMzExx3ahRo/Ds2TOMHDmy0o+NiIiIiIjoRRLhxYfLNbhy5Qo8PT3F5cOHD6Nnz55o27YtfvvtN1y6dAlubm5YtGgRTp06hZ9//rlKgybSdUePHkVAQABu3bqldUeEUl5eHiwtLZGbm/tGTY6YmJiIK2+14jN1NZxUUQjPWyeZ6xqOedYdzLVuYJ51A/NcuWb41KruEABo/9lAqxEHW7duxbBhw8TnrWfMmIHY2FgkJyfD0NBQrNepUyccP368gqETkSYFBQW4du0aZs+ejZCQkDJ3GhAREREREZWVVh0HU6dOha2tLbp37w4AuHjxIvr161esnp2dHe7fv1+5ERKVUWZmJszMzNT+k0qlkEqlGte/+DjA6+j777+Hp6cncnNzsWjRouoOh4iIiIiIdIBWY0wMDAzw+eefY+vWrQCev04uKysL9erVU6l37tw51KlTp/KjJCoDJyencr+ZwcnJqXKDqWRhYWEICwur7jCqzWRv29fm1TVUNeRyORJvMdc1HfOsO5hr3cA86wbmWbeV6eGU/v37AwCGDh2K6dOn46effoJEIoFCocDRo0cxdepUhIaGVkmgRNrS19eHu7t7dYdBRERERERUI5TrrQqfffYZXFxcUKdOHTx69AiNGjVChw4d0KZNm9f+VXpEREREREREpL1yTYdpYGCATZs24dNPP8XZs2ehUCjg4+MDDw+Pyo6PiIiIiIiIiKpRuUYczJs3D/n5+XBzc8PAgQMREhICDw8PPHnyBPPmzavsGImIiIiIiIiompRrxEFMTAzCw8Mhk8lUyvPz8xETE4M5c+ZUSnBEpHvizt0rViZVFMKzGmIhIiIiIqJyjjgQBAESiaRY+fnz52FjY1PhoIiIiIiIiIjo9VCmEQfW1taQSCSQSCRo0KCBSudBUVERHj16hPDw8EoPkoiIiIiIiIiqR5k6DpYvXw5BEDBy5EjExMTA0tJSXGdoaAhXV1f4+/tXepBUMRKJBNu2bUNwcHCltRkWFoacnBxs37691LoZGRmoV68ezp07h+bNm1daDOWVkJCASZMmIScnp7pDISIiIiIieu2VqeNg+PDhAIB69eqhbdu20Ncv1xQJ9AbR9KF/xYoVEAThlcVRmZ0fgwYNQlBQUJm2CQgIQPPmzbF8+fIK77+yVEWHEBERERER0cvKNcfB48ePceDAgWLlSUlJ2LPn/7V37/E91///x2/vbexgJ2PYGJtsM3IYc8pplGMlh5pQLCLJ+TB8ctgiRpTTp5NickjqI5/K0MIQOUZ8IodlllotFcM0s/f794ff3l/v7G0HY+x9v14uu3y8Ts/X4/V87L0+r8f7+Xq+Ntx2UHLv8/DwwNPTs7jDKBRnZ2cqVKhQLOe+evVqsZxXRERERESksApVOJgwYQLZ2dk3rTeZTEyYMOG2gypO4eHhDB8+nKioKLy8vKhUqRLR0dHA9W/fDQYDhw4dMu9//vx5DAYDiYmJACQmJmIwGNi0aROhoaE4OzvTtm1b0tLS2LBhAyEhIbi7u9OrVy8yMjLuaLw3Sk1NpVOnTjg7OxMQEMDHH3+cr/YDAgIACA0NxWAwEB4eDlx/VOHGb7qNRiOzZs2iRo0aODo6UrVqVV599dVc2zQajQwcOJCgoCDOnDkDwOeff07Dhg1xcnKievXqxMTEcO3aNQD8/f0B6NatGwaDwbz83Xff0aZNG9zc3HB3d6dhw4bs378/z2uKi4uzKHpER0dTv359li9fjr+/Px4eHjz99NNcvHjRfK3btm1j/vz55jk+kpOTATh69CidO3fG1dWVihUr8uyzz3Lu3P+9FSA8PJyhQ4cyevRoypcvT7t27cy/I5s3byYsLAwXFxceeughjh8/bhFnYfpERERERESkqBWqcHDy5Elq1ap10/qaNWty6tSp2w6quC1btowyZcqwZ88eZs+ezSuvvEJCQkKB2oiOjmbRokXs2rWLn376iYiICObNm8eqVatYv349CQkJLFy48K7FO3nyZHr06MF3333HM888Q69evTh27Fiebe/duxeAr776itTUVNauXZvrfhMnTmTWrFlMnjyZo0ePsmrVKipWrHjTflevXiUiIoL9+/fz9ddfU61aNTZt2sQzzzzD8OHDOXr0KO+88w5xcXHmwsO+ffsAWLp0KampqeblPn36UKVKFfbt28eBAweYMGECpUqVyn/H3SApKYl169bxxRdf8MUXX7Bt2zZiY2OB649lNGvWjIEDB5Kamkpqaip+fn6kpqbSunVr6tevz/79+9m4cSO//fYbERERFm0vW7YMBwcHdu7cyTvvvGNe//LLLzN37lz279+Pg4MD/fv3N28rbJ+IiIiIiIgUtUJNUuDh4cGPP/5407ecp06dokyZMkURV7GqW7cuU6dOBSAwMJBFixaxefNmAgMD893G9OnTad68OQADBgxg4sSJJCUlUb16dQCefPJJtm7dyvjx4+9YvO3atTPv89RTT/H8888DMG3aNHPh4s0337xl297e3gCUK1eOSpUq5brPxYsXmT9/PosWLTLPg/HAAw/QokULi/0uXbrEo48+ypUrV0hMTDRPrvnqq68yYcIE87HVq1dn2rRpREVFMXXqVHMMnp6eFjGkpKQwbtw4atasab72wjIajcTFxeHm5gbAs88+y+bNm3n11Vfx8PCgdOnSuLi4WJz/rbfeokGDBsyYMcO8bsmSJfj5+XHixAmCgoIAqFGjBrNnzzbv8+uvv5qvu3Xr1sD1UTyPPvoof//9N05OToXuk9xkZmaSmZlpXk5PTwcgKyuLrKyswnXYHWRnvGZ13b0YrxStnBwr1yWb8mw7lGvboDzbBuW5ZMpvPgtVOOjSpQsjR47k008/5YEHHgCuFw3GjBlDly5dCtPkPaVu3boWyz4+PqSlpRW6jYoVK+Li4mIuGuSsy/k2/3blJ95/vu2iWbNmFo9c3I5jx46RmZnJww8/fMv9evXqRZUqVdi8eTMuLi7m9QcOHGDfvn0WjzZkZ2fz999/k5GRYbHvjUaPHs3zzz/P8uXLeeSRR3jqqafMv48F5e/vby4aQP5yfuDAAbZu3Yqrq+tN25KSksyFg7CwsFyPvzFvPj4+AKSlpVG1atVC90luZs6cSUxMzE3rv/zyywK1c7cE32JbQUf+yP1LubYNyrPtUK5tg/JsG5TnkiW/j88XqnDw2muv0bFjR2rWrEmVKlUAOHv2LC1btmTOnDmFafKe8s/h7gaDAaPRiJ3d9Sc7bnybgLUKzY1tGAwGq23eyXjzYjAYiuT8zs7O+dqvc+fOrFixgt27d9O2bVvzeqPRSExMDN27d7/pGCcnJ6vtRUdH07t3b9avX8+GDRuYOnUqq1evplu3bgW+hsL0odFo5PHHH2fWrFk3bcspBABWR+H883ckp82c/y1Mn+Rm4sSJjB492rycnp6On58f7du3x93dvUBt3Q1vHP7jpnV2xmsE/nKAdu3aFfpxFLk/ZGVlkZCQoFyXcMqz7VCubYPybBuU55IpZzRyXgr9qMKuXbtISEjgu+++w9nZmbp169KqVavCNHffyBkenpqaSmhoKECRfWt/p+3evZu+fftaLOdcw62ULl0aINfJMHMEBgbi7OzM5s2bzY9D5ObFF1/kwQcfpEuXLqxfv948TL9BgwYcP36cGjVqWD22VKlSucYQFBREUFAQo0aNolevXixdurRQhYO8lC5d+qbzN2jQgP/85z/4+/sX+atJb6dP/snR0RFHR8dcj78X/+gb7az35b0asxQ95do2KM+2Q7m2DcqzbVCeS5b85rLQdzsGg4H27dvTvn37wjZx33F2dqZp06bExsbi7+/PuXPnmDRpUnGHlS8ff/wxYWFhtGjRgpUrV7J3717ef//9PI+rUKECzs7ObNy4kSpVquDk5GSemyCHk5MT48ePJyoqitKlS9O8eXN+//13vv/+ewYMGGCx77Bhw8jOzuaxxx5jw4YNtGjRgilTpvDYY4/h5+fHU089hZ2dHYcPH+bIkSNMnz4duP4owebNm2nevDmOjo44OTkxbtw4nnzySQICAjh79iz79u2jR48eRddpN/D392fPnj0kJyfj6uqKl5cXL730EosXL6ZXr16MGzeO8uXLc+rUKVavXs3ixYuxt7cv9PkK0ydly5YtqssVERERERExK9RbFQAuX75MfHw8b7/9NgsWLLD4KcmWLFlCVlYWYWFhjBgxwnwTd6+LiYlh9erV1K1bl2XLlrFy5cpc34zxTw4ODixYsIB33nkHX19fnnjiiVz3mzx5MmPGjGHKlCmEhITQs2dPq3MEjBw5kpiYGDp37syuXbvo0KEDX3zxBQkJCTRq1IimTZvy+uuvU61aNfMxc+fOJSEhAT8/P0JDQ7G3t+ePP/6gb9++BAUFERERQadOnXJ9lr8ojB07Fnt7e2rVqoW3tzcpKSn4+vqyc+dOsrOz6dChAw8++CAjRozAw8PD/FhLYRWmT0RERERERO4Eg+nGB/bz6eDBg3Tu3JmMjAwuX76Ml5cX586dw8XFhQoVKvDjjz/eiVhFpAikp6fj4eHBhQsX7sk5DmIPnrtpnZ3xGsFn99C5c2cNjSvhsrKyiI+PV65LOOXZdijXtkF5tg3Kc8mU33uDQn0tOmrUKB5//HH+/PNPnJ2d2b17N2fOnKFhw4YlYnJEEREREREREbmuUIWDQ4cOMWbMGOzt7bG3tyczMxM/Pz9mz57Nv/71r6KOsURLSUnB1dU11x87Ozvs7Oysbk9JSbnt88+YMcNq+506dSqCK7z7OnXqZPWaZsyYUdzhiYiIiIiI3FcKNTliqVKlzK+Pq1ixIikpKYSEhODh4VEkN7O2xNfXt9BvZvD19b3t8w8ePJiIiIhct+X3NYv3mvfee48rV67kus3Ly+suRyMFNSG0/E3rsrKyiD9bDMGIiIiIiEjhCgehoaHs37+foKAg2rRpw5QpUzh37hzLly+nTp06RR1jiebg4HDLV+7daV5eXiXuZrpy5crFHYKIiIiIiEiJUahHFWbMmIGPjw8A06ZNo1y5crz44oukpaXx7rvvFmmAIiIiIiIiIlJ8CjziwGQy4e3tTe3atQHw9vYmPj6+yAMTERERERERkeJX4BEHJpOJwMBAzp7VA8ciIiIiIiIiJV2BCwd2dnYEBgbyxx9/3Il4RESseuOw/u6IiIiIiNxthZrjYPbs2YwbN47//e9/RR2PiIiIiIiIiNxDCvVWhWeeeYaMjAzq1atH6dKlb3pt359//lkkwYmIiIiIiIhI8SpU4WDevHlFHIaIiIiIiIiI3IsKVTjo169fUcdx30pMTKRNmzb89ddfeHp6FksMcXFxjBw5kvPnzxfL+eW6yMhIzp8/z7p164o7FBERERERkSJTqDkObnTlyhXS09Mtfkqq8PBwRo4cabHuoYceIjU1FQ8Pj+IJqggZDAbd9IqIiIiIiIiFQhUOLl++zNChQ6lQoQKurq6ULVvW4seWlC5dmkqVKmEwGIo7FLmDrl69WtwhiIiIiIiIFItCFQ6ioqLYsmULb775Jo6Ojrz33nvExMTg6+vLBx98kO92wsPDGT58OFFRUXh5eVGpUiWio6MBSE5OxmAwcOjQIfP+58+fx2AwkJiYCFx/TMBgMLBp0yZCQ0Nxdnambdu2pKWlsWHDBkJCQnB3d6dXr15kZGQU5lLNIiMj2bZtG/Pnz8dgMGAwGEhOTjbHkPOYQFxcHJ6ennzxxRcEBwfj4uLCk08+yeXLl1m2bBn+/v6ULVuWYcOGkZ2dbW7/6tWrREVFUblyZcqUKUOTJk3M15lf69atIygoCCcnJ9q1a8dPP/1ksf3zzz+nYcOGODk5Ub16dWJiYrh27RoA/v7+AHTr1g2DwYC/vz8XLlzA3t6eAwcOAGAymfDy8qJRo0bmNj/88EN8fHzMyz///DM9e/akbNmylCtXjieeeILk5GSLOJYuXUpISAhOTk7UrFmTN99807wtJ+9r166lTZs2uLi4UK9ePb755pt89UFO/9+qLyIjI+natavFcSNHjiQ8PNy8HB4eztChQxk9ejTly5enXbt2AHz//fc8+uijuLu74+bmRsuWLUlKSrJoa86cOfj4+FCuXDleeuklsrKyzNtWrFhBWFgYbm5uVKpUid69e5OWlmbe/tdff9GnTx+8vb1xdnYmMDCQpUuXFqh/RUREREREilKh5jj4/PPP+eCDDwgPD6d///60bNmSGjVqUK1aNVauXEmfPn3y3dayZcsYPXo0e/bs4ZtvviEyMpLmzZsTGBiY7zaio6NZtGgRLi4uREREEBERgaOjI6tWreLSpUt069aNhQsXMn78+MJcLgDz58/nxIkTPPjgg7zyyisAeHt753rTlpGRwYIFC1i9ejUXL16ke/fudO/eHU9PT+Lj4/nxxx/p0aMHLVq0oGfPngA899xzJCcns3r1anx9ffn000/p2LEjR44cyVdfZGRk8Oqrr7Js2TJKly7NkCFDePrpp9m5cycAmzZt4plnnmHBggXmm91BgwYBMHXqVPbt20eFChVYunQpHTt2xN7eHg8PD+rXr09iYiINGzbk8OHDABw+fJj09HTc3d1JTEykdevW5hjatGlDy5Yt2b59Ow4ODkyfPp2OHTty+PBhSpcuzeLFi5k6dSqLFi0iNDSUgwcPMnDgQMqUKWMxd8bLL7/MnDlzCAwM5OWXX6ZXr16cOnUKB4e8f2Xz6ov8WrZsGS+++CI7d+7EZDLx888/06pVK8LDw9myZQvu7u7s3LnTXHwB2Lp1Kz4+PmzdupVTp07Rs2dP6tevz8CBA4HrBaJp06YRHBxMWloao0aNIjIykvj4eAAmT57M0aNH2bBhA+XLl+fUqVNcuXIl3/2bm8zMTDIzM83LOY8TZWVlWRQ17mU5cdoZr903MUvh5ORXeS7ZlGfboVzbBuXZNijPJVN+81mowsGff/5JQEAAAO7u7ubXL7Zo0YIXX3yxQG3VrVuXqVOnAhAYGMiiRYvYvHlzgQoH06dPp3nz5gAMGDCAiRMnkpSURPXq1QF48skn2bp1620VDjw8PChdujQuLi5UqlTplvtmZWXx1ltv8cADD5jPv3z5cn777TdcXV2pVasWbdq0YevWrfTs2ZOkpCQ+/PBDzp49i6+vLwBjx45l48aNLF26lBkzZuQZX1ZWFosWLaJJkybA9ZvekJAQ9u7dS+PGjXn11VeZMGGC+ea8evXqTJs2jaioKKZOnYq3tzcAnp6eFtcXHh5OYmIiY8aMITExkYcffpgff/yRr7/+ms6dO5OYmMioUaMAWL16NXZ2drz33nvmRzeWLl2Kp6cniYmJtG/fnmnTpjF37ly6d+8OQEBAAEePHuWdd96xKByMHTuWRx99FICYmBhq167NqVOnqFmz5m33RX7VqFGD2bNnm5f/9a9/4eHhwerVqylVqhQAQUFBFseULVuWRYsWYW9vT82aNXn00UfZvHmzuXDQv39/877Vq1dnwYIFNG7cmEuXLuHq6kpKSgqhoaGEhYUB/zcSBPLXv7mZOXMmMTExN63/8ssvcXFxyXd/3AsCfzlA/C/FHYXcDQkJCcUdgtwFyrPtUK5tg/JsG5TnkiW/I/MLVTioXr06ycnJVKtWjVq1arFmzRoaN27M559/XuA3C9StW9di2cfHx2LodkHbqFixIi4uLuaiQc66vXv3FqjN2+Hi4mIuGuSc39/fH1dXV4t1Odf57bffYjKZbroJzczMpFy5cvk6p4ODg/lmE6BmzZp4enpy7NgxGjduzIEDB9i3bx+vvvqqeZ/s7Gz+/vtvMjIyrN5AhoeH8/7772M0Gtm2bRsPP/wwVatWZdu2bTRo0IATJ06YRxwcOHCAU6dO4ebmZtHG33//TVJSEr///js//fQTAwYMMN9IA1y7du2mySVvzGnOoxBpaWn5Khzk1Rf5dWMbAIcOHaJly5bmokFuateujb29vUXsR44cMS8fPHiQ6OhoDh06xJ9//onRaAQgJSWFWrVq8eKLL9KjRw++/fZb2rdvT9euXXnooYeAvPvXmokTJzJ69Gjzcnp6On5+frRv3x53d/d89ETxy8rKIiEhgZO+DRlRv2JxhyN3UE6u27Vrd8vPmtzflGfboVzbBuXZNijPJVN+X25QqMLBc889x3fffUfr1q2ZOHEijz76KAsXLiQrK4s33nijQG3985fOYDBgNBqxs7s+/YLJZDJvszaM4sY2DAaD1TbvltzOf6uYjEajeS6BG286AYtiQ15ym6AxZ53RaCQmJsb8Tf+NnJycrLbZqlUrLl68yLfffsuOHTuYNm0afn5+zJgxg/r161OhQgVCQkLM52jYsCErV668qR1vb2/+/vtvABYvXmweDZDjn9f9z5zmtJ9ft+oLOzs7i98ryP13q0yZMhbLzs7OeZ73Vnm+fPky7du3p3379qxYsQJvb29SUlLo0KGDefLFTp06cebMGdavX89XX33Fww8/zEsvvcScOXPy7F9rHB0dcXR0zDXW++2PvtHO4b6LWQrnfvz9lIJTnm2Hcm0blGfboDyXLPnNZaEKBzlD0wHatGnDDz/8wP79+6lRo8ZNIwgKK+dGKDU1ldDQUACLiRKLQ+nSpS0mNCwqoaGhZGdnk5aWRsuWLQvVxrVr19i/f7/5G/Xjx49z/vx58zf0DRo04Pjx49SoUcNqG6VKlbrp+nLmOVi0aBEGg4FatWrh6+vLwYMH+eKLL8yjDXLO8dFHH1GhQoVcv8n28PCgcuXK/PjjjwWaB6Og8uoLb29v/ve//1kcc+jQoTw/NHXr1mXZsmVkZWUV6o/lDz/8wLlz54iNjcXPzw+A/fv337Sft7c3kZGRREZG0rJlS8aNG8ecOXPy7F8REREREZE7oUBvVdiyZQu1atW6aThD1apVefjhh+nVqxc7duwoksCcnZ1p2rQpsbGxHD16lO3btzNp0qQiabuw/P392bNnD8nJyZw7d67IRjEEBQXRp08f+vbty9q1azl9+jT79u1j1qxZ5knz8lKqVCmGDRvGnj17+Pbbb3nuuedo2rSp+eZ5ypQpfPDBB0RHR/P9999z7NgxPvroI4s+9ff3Z/Pmzfz666/89ddf5vXh4eGsWLGC1q1bYzAYKFu2LLVq1eKjjz6yeBNBnz59KF++PE888QQ7duzg9OnTbNu2jREjRnD27Fng+kSWM2fONE82eeTIEZYuXcrrr79eBD2Zv75o27Yt+/fv54MPPuDkyZNMnTr1pkJCboYOHUp6ejpPP/00+/fv5+TJkyxfvpzjx4/nK66qVatSunRpFi5cyI8//shnn33GtGnTLPaZMmUK//3vfzl16hTff/89X3zxhXlER376V0REREREpKgVqHAwb948Bg4caPXb5BdeeKFIbwCXLFlCVlYWYWFhjBgxgunTpxdZ24UxduxY7O3tqVWrlnmYeVFZunQpffv2ZcyYMQQHB9OlSxf27Nlj/mY6Ly4uLowfP57evXvTrFkznJ2dWb16tXl7hw4d+OKLL0hISKBRo0Y0bdqU119/nWrVqpn3mTt3LgkJCfj5+ZlHecD1USXZ2dkWRYLWrVuTnZ1tMeLAxcWF7du3U7VqVbp3705ISAj9+/fnypUr5t+Z559/nvfee4+4uDjq1KlD69atiYuLM0+2WRTy0xeTJ08mKiqKRo0acfHiRfr27Ztnu+XKlWPLli1cunSJ1q1b07BhQxYvXpzv0Qfe3t7ExcXx8ccfU6tWLWJjY5kzZ47FPqVLl2bixInUrVuXVq1aYW9vb449P/0rIiIiIiJS1Aymfz7sfQvVqlVj48aN5m9A/+mHH36gffv2RXpDLVIQcXFxjBw5kvPnzxd3KPes9PR0PDw8uHDhwn1TcMjKyiI+Pp7jVZoQ1fDWbzWR+1tOrjt37qznJ0sw5dl2KNe2QXm2DcpzyZTfe4MCjTj47bffbvlL4uDgwO+//16QJkVERERERETkHlagwkHlypUtXi33T4cPHza/Ou9elJKSgqura64/dnZ22NnZWd1enKMoOnXqZDWuGTNmFFtcxUF9ISIiIiIicncV6K0KnTt3ZsqUKXTq1OmmV/hduXKFqVOn8thjjxVpgEXJ19e30G9m8PX1LdpgCuC9997jypUruW7z8vK6y9EUr7z6wsvLi8jIyLsblNw1o+qWK+4QRERERERsToEKB5MmTWLt2rUEBQUxdOhQgoODMRgMHDt2jH//+99kZ2fz8ssv36lYb5uDg8MtX0d4r6pcuXJxh3DPUF+IiIiIiIjcXQUqHFSsWJFdu3bx4osvMnHiRHLmVTQYDHTo0IE333yTihUr3pFARUREREREROTuK1DhAK6/WSE+Pp6//vqLU6dOYTKZCAwMpGzZsnciPhEREREREREpRgUuHOQoW7YsjRo1KspYRERuEnvwHHbGawQXdyAiIiIiIjaqQG9VEBERERERERHbosKBiIiIiIiIiFilwoGIiIiIiIiIWKXCgdySwWBg3bp1xR1GgdyPMYuIiIiIiNyrVDgQmxcdHU39+vWLOwwREREREZF7kgoHNiwrK6u4QxAREREREZF7nE0WDsLDwxk+fDhRUVF4eXlRqVIloqOjAUhOTsZgMHDo0CHz/ufPn8dgMJCYmAhAYmIiBoOBTZs2ERoairOzM23btiUtLY0NGzYQEhKCu7s7vXr1IiMjo0jiHTp0KEOHDsXT05Ny5coxadIkTCaTeZ/chud7enoSFxdncV1r1qwhPDwcJycnVqxYAcCSJUuoXbs2jo6O+Pj4MHToUIt2zp07R7du3XBxcSEwMJDPPvvMvC07O5sBAwYQEBCAs7MzwcHBzJ8/3+L4xMREGjduTJkyZfD09KR58+acOXPGvP3zzz+nYcOGODk5Ub16dWJiYrh27Vq++ubkyZO0atUKJycnatWqRUJCwk37jB8/nqCgIFxcXKhevTqTJ082F03i4uKIiYnhu+++w2AwYDAYzH124cIFBg0aRIUKFXB3d6dt27Z89913+YorZxTDkiVLqFq1Kq6urrz44otkZ2cze/ZsKlWqRIUKFXj11Vctjnv99depU6cOZcqUwc/PjyFDhnDp0iXz9v79+1O3bl0yMzOB68Wfhg0b0qdPn3zFJSIiIiIiUlAOxR1AcVm2bBmjR49mz549fPPNN0RGRtK8eXMCAwPz3UZ0dDSLFi3CxcWFiIgIIiIicHR0ZNWqVVy6dIlu3bqxcOFCxo8fXyTxDhgwgD179rB//34GDRpEtWrVGDhwYIHaGT9+PHPnzmXp0qU4Ojry1ltvMXr0aGJjY+nUqRMXLlxg586dFsfExMQwe/ZsXnvtNRYuXEifPn04c+YMXl5eGI1GqlSpwpo1ayhfvjy7du1i0KBB+Pj4EBERwbVr1+jatSsDBw7kww8/5OrVq+zduxeDwQDApk2beOaZZ1iwYAEtW7YkKSmJQYMGATB16tRbXovRaKR79+6UL1+e3bt3k56ezsiRI2/az83Njbi4OHx9fTly5AgDBw7Ezc2NqKgoevbsyf/+9z82btzIV199BYCHhwcmk4lHH30ULy8v4uPj8fDw4J133uHhhx/mxIkTeHl55dnXSUlJbNiwgY0bN5KUlMSTTz7J6dOnCQoKYtu2bezatYv+/fvz8MMP07RpUwDs7OxYsGAB/v7+nD59miFDhhAVFcWbb74JwIIFC6hXrx4TJkzgjTfeYPLkyZw7d868PTeZmZnmQgNAeno6cL3ocD+MOrEzXsPOeL2QdD/EK7cnJ8fKdcmmPNsO5do2KM+2QXkumfKbT4Ppxq+tbUR4eDjZ2dns2LHDvK5x48a0bduWwYMHExAQwMGDB83PvZ8/f56yZcuydetWwsPDSUxMpE2bNnz11Vc8/PDDAMTGxjJx4kSSkpKoXr06AIMHDyY5OZmNGzfedrxpaWl8//335hvuCRMm8Nlnn3H06FHg+oiDTz/9lK5du5qP8/T0ZN68eURGRpKcnExAQADz5s1jxIgR5n0qV67Mc889x/Tp03M9t8FgYNKkSUybNg2Ay5cv4+bmRnx8PB07dsz1mJdeeonffvuNTz75hD///JNy5cqRmJhI69atb9q3VatWdOrUiYkTJ5rXrVixgqioKH755Zdb9suXX35J586dSU5OpkqVKgBs3LiRTp063dQXN3rttdf46KOP2L9/P3C9ALRu3TqLUSZbtmyhW7dupKWl4ejoaF5fo0YNoqKizMUNa6Kjo3nttdf49ddfcXNzA6Bjx44cP36cpKQk7OyuD/apWbMmkZGRTJgwIdd2Pv74Y1588UXOnTtnXvfNN9/QunVrJkyYwMyZM9m8eTOtWrW6ZSwxMTE3rV+1ahUuLi63vA4RERERESm5MjIy6N27NxcuXMDd3d3qfjY74qBu3boWyz4+PqSlpRW6jYoVK5qHwt+4bu/evbcX6P/XtGlTc9EAoFmzZsydO5fs7Gzs7e3z3U5YWJj532lpafzyyy/m4oc1N15nmTJlcHNzs+irt99+m/fee48zZ85w5coVrl69ai66eHl5ERkZSYcOHWjXrh2PPPIIERER+Pj4AHDgwAH27dtnMWQ/Ozubv//+m4yMjFve2B47doyqVauaiwZwvV/+6ZNPPmHevHmcOnWKS5cuce3atVt+KHLiunTpEuXKlbNYf+XKFZKSkm55bA5/f39z0QCu/z7Y29ubiwY5627sy61btzJjxgyOHj1Keno6165d4++//+by5cuUKVPGfI1jx45l2rRpjB8//pZFA4CJEycyevRo83J6ejp+fn60b98+z364F7xx+A/sjNcI/OUA7dq1o1SpUsUdktxBWVlZJCQkKNclnPJsO5Rr26A82wbluWTKGY2cF5stHPzzl91gMGA0Gs03dTcOxLA2fOPGNgwGg9U27waDwcA/B4/kFnfOzSeAs7Nzvtq+1XWtWbOGUaNGMXfuXJo1a4abmxuvvfYae/bsMe+/dOlShg8fzsaNG/noo4+YNGkSCQkJNG3aFKPRSExMDN27d7/pvE5OTreMK7fBMjcWVwB2797N008/TUxMDB06dMDDw4PVq1czd+7cW7ZtNBrx8fExz2txI09Pz1semyO3frtVX545c4bOnTszePBgpk2bhpeXF19//TUDBgywyKXRaGTnzp3Y29tz8uTJPONwdHS0GDVxY3z3wx99o93//Zm6X2KW26dc2wbl2XYo17ZBebYNynPJkt9c2mzhwBpvb28AUlNTCQ0NBbAYwl5cdu/efdNyYGCgebSBt7c3qamp5u0nT57Mc2JGNzc3/P392bx5M23atClUXDt27OChhx5iyJAh5nW5fSMfGhpKaGgoEydOpFmzZqxatYqmTZvSoEEDjh8/To0aNQp87lq1apGSksIvv/yCr68vcH0Y/4127txJtWrVePnll83rbpyYEaB06dJkZ2dbrGvQoAG//vorDg4O+Pv7Fzi2wti/fz/Xrl1j7ty55gLWmjVrbtrvtdde49ixY2zbto0OHTqwdOlSnnvuubsSo4iIiIiI2B4VDv7B2dmZpk2bEhsbi7+/P+fOnWPSpEnFHRY//fQTo0eP5oUXXuDbb79l4cKFFt+at23blkWLFpm/xR8/fny+qkfR0dEMHjyYChUq0KlTJy5evMjOnTsZNmxYvuKqUaMGH3zwAZs2bSIgIIDly5ezb98+AgICADh9+jTvvvsuXbp0wdfXl+PHj3PixAn69u0LwJQpU3jsscfw8/Pjqaeews7OjsOHD3PkyBGr8y7keOSRRwgODqZv377MnTuX9PR0iwJBTnwpKSmsXr2aRo0asX79ej799FOLfXImIjx06BBVqlTBzc2NRx55hGbNmtG1a1dmzZpFcHAwv/zyC/Hx8XTt2tXikY+i8sADD3Dt2jUWLlzI448/zs6dO3n77bct9jl06BBTpkzhk08+oXnz5syfP58RI0bQunVri8dkREREREREiopNvo4xL0uWLCErK4uwsDBGjBiR5w3s3dC3b1+uXLlC48aNeemllxg2bJjFBH1z587Fz8+PVq1a0bt3b8aOHZuvie/69evHvHnzePPNN6lduzaPPfZYvoa/5xg8eDDdu3enZ8+eNGnShD/++MNi9IGLiws//PADPXr0ICgoiEGDBjF06FBeeOEFADp06MAXX3xBQkICjRo1omnTprz++utUq1Ytz3Pb2dnx6aefkpmZSePGjXn++edver3hE088wahRoxg6dCj169dn165dTJ482WKfHj160LFjR9q0aYO3tzcffvghBoOB+Ph4WrVqRf/+/QkKCuLpp58mOTmZihUr5rt/CqJ+/fq8/vrrzJo1iwcffJCVK1cyc+ZM8/a///6bPn36EBkZyeOPPw7AgAEDeOSRR3j22WdvGjUhIiIiIiJSFGzyrQr3m/DwcOrXr8+8efOKOxQpAdLT0/Hw8Mhz5tR7RezBc9gZrxF8dg+dO3fWM3UlXFZWFvHx8cp1Cac82w7l2jYoz7ZBeS6Z8ntvoBEHIiIiIiIiImKVCgd3QUpKCq6urrn+2NnZYWdnZ3V7SkpKcYdfbFauXGm1X2rXrl2ssdWuXdtqbCtXrizW2ERERERERIqSJke8C3x9fQv9ZgZfX99cXwloC7p06UKTJk1y3Vbcw6Pi4+OtvqbzTs2BYKsmhJa/PjTubHFHIiIiIiJim1Q4uAscHBwK9bpBW+fm5oabm1txh5Gr/EzeKCIiIiIiUhLoUQURERERERERsUqFAxERERERERGxSoUDEREREREREbFKcxyIyD0t9uA57IzXCC7uQEREREREbJRGHIiIiIiIiIiIVSociIiIiIiIiIhVKhxIgRgMBtatW1fcYRTI/Rhzjvs5dhERERERKRlUOBD5h+joaOrXr1/cYQCQmppKp06dijsMERERERGxYZocUcyysrIoVapUcYchN6hUqVJxhyAiIiIiIjZOIw6A8PBwhg8fTlRUFF5eXlSqVIno6GgAkpOTMRgMHDp0yLz/+fPnMRgMJCYmApCYmIjBYGDTpk2Ehobi7OxM27ZtSUtLY8OGDYSEhODu7k6vXr3IyMgokniHDh3K0KFD8fT0pFy5ckyaNAmTyWTeJ7ch7p6ensTFxVlc15o1awgPD8fJyYkVK1YAsGTJEmrXro2joyM+Pj4MHTrUop1z587RrVs3XFxcCAwM5LPPPjNvy87OZsCAAQQEBODs7ExwcDDz58+3OD4xMZHGjRtTpkwZPD09ad68OWfOnDFv//zzz2nYsCFOTk5Ur16dmJgYrl27lq++OXnyJK1atcLJyYlatWqRkJBw0z7jx48nKCgIFxcXqlevzuTJk8nKygIgLi6OmJgYvvvuOwwGAwaDwdxnFy5cYNCgQVSoUAF3d3fatm3Ld999l6+4ckYxLFmyhKpVq+Lq6sqLL75IdnY2s2fPplKlSlSoUIFXX33V4rgb85iTs7Vr19KmTRtcXFyoV68e33zzTb5iEBERERERKQyNOPj/li1bxujRo9mzZw/ffPMNkZGRNG/enMDAwHy3ER0dzaJFi3BxcSEiIoKIiAgcHR1ZtWoVly5dolu3bixcuJDx48cXSbwDBgxgz5497N+/n0GDBlGtWjUGDhxYoHbGjx/P3LlzWbp0KY6Ojrz11luMHj2a2NhYOnXqxIULF9i5c6fFMTExMcyePZvXXnuNhQsX0qdPH86cOYOXlxdGo5EqVaqwZs0aypcvz65duxg0aBA+Pj5ERERw7do1unbtysCBA/nwww+5evUqe/fuxWAwALBp0yaeeeYZFixYQMuWLUlKSmLQoEEATJ069ZbXYjQa6d69O+XLl2f37t2kp6czcuTIm/Zzc3MjLi4OX19fjhw5wsCBA3FzcyMqKoqePXvyv//9j40bN/LVV18B4OHhgclk4tFHH8XLy4v4+Hg8PDx45513ePjhhzlx4gReXl559nVSUhIbNmxg48aNJCUl8eSTT3L69GmCgoLYtm0bu3bton///jz88MM0bdrUajsvv/wyc+bMITAwkJdffplevXpx6tQpHBz0cRYRERERkaKnO43/r27duuYb08DAQBYtWsTmzZsLVDiYPn06zZs3B2DAgAFMnDiRpKQkqlevDsCTTz7J1q1bi6Rw4OfnxxtvvIHBYCA4OJgjR47wxhtvFLhwMHLkSLp3725xDWPGjGHEiBHmdY0aNbI4JjIykl69egEwY8YMFi5cyN69e+nYsSOlSpUiJibGvG9AQAC7du1izZo1REREkJ6ezoULF3jsscd44IEHAAgJCTHv/+qrrzJhwgT69esHQPXq1Zk2bRpRUVF5Fg6++uorjh07RnJyMlWqVDHH9885AiZNmmT+t7+/P2PGjOGjjz4iKioKZ2dnXF1dcXBwsHhMYMuWLRw5coS0tDQcHR0BmDNnDuvWreOTTz4xFzduxWg0smTJEtzc3KhVqxZt2rTh+PHjxMfHY2dnR3BwMLNmzSIxMfGWhYOxY8fy6KOPAteLOLVr1+bUqVPUrFkz1/0zMzPJzMw0L6enpwPXH03JGWlxL7MzXsPOeH3Eyf0Qr9yenBwr1yWb8mw7lGvboDzbBuW5ZMpvPlU4+P/q1q1rsezj40NaWlqh26hYsaJ5KPyN6/bu3Xt7gf5/TZs2NX9LD9CsWTPmzp1LdnY29vb2+W4nLCzM/O+0tDR++eUXHn744Vsec+N1lilTBjc3N4u+evvtt3nvvfc4c+YMV65c4erVq+bJBr28vIiMjKRDhw60a9eORx55hIiICHx8fAA4cOAA+/btsxiyn52dzd9//01GRgYuLi5W4zp27BhVq1Y1Fw3ger/80yeffMK8efM4deoUly5d4tq1a7i7u9/ymg8cOMClS5coV66cxforV66QlJR0y2Nz+Pv74+bmZl6uWLEi9vb22NnZWazL6/fuxv7P6be0tDSrhYOZM2daFHNyfPnll7fsz3tF8A3/zu3REymZlGvboDzbDuXaNijPtkF5Llny+yi9Cgf/3z8nBTQYDBiNRvNN3Y3zB1irytzYhsFgsNrm3WAwGCxihtzjLlOmjPnfzs7O+Wr7Vte1Zs0aRo0axdy5c2nWrBlubm689tpr7Nmzx7z/0qVLGT58OBs3buSjjz5i0qRJJCQk0LRpU4xGIzExMRajIHI4OTndMq5/Xm9ObDfavXs3Tz/9NDExMXTo0AEPDw9Wr17N3Llzb9m20WjEx8fHPK/FjTw9PW95bI7c+q0wvyP//D3Lic+aiRMnMnr0aPNyeno6fn5+tG/fPs+Cyb3gjcN/YGe8RuAvB2jXrp0m8CzhsrKySEhIUK5LOOXZdijXtkF5tg3Kc8mUMxo5Lyoc5MHb2xu4/lq80NBQAIuJEovL7t27b1oODAw0jzbw9vYmNTXVvP3kyZN5VpPc3Nzw9/dn8+bNtGnTplBx7dixg4ceeoghQ4aY1+X2jXxoaCihoaFMnDiRZs2asWrVKpo2bUqDBg04fvw4NWrUKPC5a9WqRUpKCr/88gu+vr4AN00cuHPnTqpVq8bLL79sXnfjxIwApUuXJjs722JdgwYN+PXXX3FwcMDf37/AsRUnR0dH8+MVNypVqtR98UffaPd/f6bul5jl9inXtkF5th3KtW1Qnm2D8lyy5DeXKhzkwdnZmaZNmxIbG4u/vz/nzp2zeEa+uPz000+MHj2aF154gW+//ZaFCxdafGvetm1bFi1aZP4Wf/z48fn6pYiOjmbw4MFUqFCBTp06cfHiRXbu3MmwYcPyFVeNGjX44IMP2LRpEwEBASxfvpx9+/YREBAAwOnTp3n33Xfp0qULvr6+HD9+nBMnTtC3b18ApkyZwmOPPYafnx9PPfUUdnZ2HD58mCNHjjB9+vRbnvuRRx4hODiYvn37MnfuXNLT0y0KBDnxpaSksHr1aho1asT69ev59NNPLfbx9/fn9OnTHDp0iCpVquDm5sYjjzxCs2bN6Nq1K7NmzSI4OJhffvmF+Ph4unbtavHIh4iIiIiISEmi1zHmw5IlS8jKyiIsLIwRI0bkeQN7N/Tt25crV67QuHFjXnrpJYYNG2YxQd/cuXPx8/OjVatW9O7dm7Fjx+brefZ+/foxb9483nzzTWrXrs1jjz3GyZMn8x3X4MGD6d69Oz179qRJkyb88ccfFqMPXFxc+OGHH+jRowdBQUEMGjSIoUOH8sILLwDQoUMHvvjiCxISEmjUqBFNmzbl9ddfp1q1anme287Ojk8//ZTMzEwaN27M888/f9PrDZ944glGjRrF0KFDqV+/Prt27WLy5MkW+/To0YOOHTvSpk0bvL29+fDDDzEYDMTHx9OqVSv69+9PUFAQTz/9NMnJyVSsWDHf/SMiIiIiInK/MZhyezBc7mnh4eHUr1+fefPmFXcoch9KT0/Hw8ODCxcu3BdzHMQePIed8RrBZ/fQuXNnDY0r4bKysoiPj1euSzjl2XYo17ZBebYNynPJlN97A404EBERERERERGrVDgoBikpKbi6uub6Y2dnh52dndXtKSkpxR1+sVm5cqXVfqldu3axxla7dm2rsa1cubJYYxMREREREbkdmhyxGPj6+hb6zQy+vr65vhLQFnTp0oUmTZrkuq24h0vFx8dbfU2n5kAQEREREZH7mQoHxcDBwaFQrxu0dW5ubri5uRV3GLnKz+SNUjgTQstff6bubHFHIiIiIiJim/SogoiIiIiIiIhYpcKBiIiIiIiIiFilwoGIiIiIiIiIWKU5DkTkvvHG4T8w2ln/szUhtPxdjEZERERExDZoxIGIiIiIiIiIWKXCgYiIiIiIiIhYpcKBiIiIiIiIiFilwoHcUYmJiRgMBs6fP1/coYiIiIiIiEghqHAgZnfiJv+hhx4iNTUVDw+PfB8TGRlJ165diyyGouDv78+8efOKOwwREREREZG7ToUDuaNKly5NpUqVMBgMd/3cV69evevnFBERERERKWlstnAQHh7O8OHDiYqKwsvLi0qVKhEdHQ1AcnIyBoOBQ4cOmfc/f/48BoOBxMRE4P++nd+0aROhoaE4OzvTtm1b0tLS2LBhAyEhIbi7u9OrVy8yMjJuK9Z33nmHypUrYzQaLdZ36dKFfv36mZc///xzGjZsiJOTE9WrVycmJoZr166ZtxsMBt577z26deuGi4sLgYGBfPbZZ+ZrbtOmDQBly5bFYDAQGRkJgMlkYvbs2VSvXh1nZ2fq1avHJ598kq/Y/zmKIS4uDk9PTzZt2kRISAiurq507NiR1NRUAKKjo1m2bBn//e9/MRgMFn3+888/07NnT8qWLUu5cuV44oknSE5ONp8rZ6TCzJkz8fX1JSgoyJzLtWvX0qZNG1xcXKhXrx7ffPONRZy7du2iVatWODs74+fnx/Dhw7l8+TJw/XflzJkzjBo1yhxTXnKu84svviA4OBgXFxeefPJJLl++zLJly/D396ds2bIMGzaM7Oxs83ErVqwgLCwMNzc3KlWqRO/evUlLSzNvf+WVV/D19eWPP/4wr+vSpQutWrW66fdDRERERESkKFh/IboNWLZsGaNHj2bPnj188803REZG0rx5cwIDA/PdRnR0NIsWLcLFxYWIiAgiIiJwdHRk1apVXLp0iW7durFw4ULGjx9f6Difeuophg8fztatW3n44YcB+Ouvv9i0aROff/45AJs2beKZZ55hwYIFtGzZkqSkJAYNGgTA1KlTzW3FxMQwe/ZsXnvtNRYuXEifPn04c+YMfn5+/Oc//6FHjx4cP34cd3d3nJ2dAZg0aRJr167lrbfeIjAwkO3bt/PMM8/g7e1N69atC3w9GRkZzJkzh+XLl2NnZ8czzzzD2LFjWblyJWPHjuXYsWOkp6ezdOlSALy8vMjIyKBNmza0bNmS7du34+DgwPTp0+nYsSOHDx+mdOnSAGzevBl3d3cSEhIwmUzmc7788svMmTOHwMBAXn75ZXr16sWpU6dwcHDgyJEjdOjQgWnTpvH+++/z+++/M3ToUIYOHcrSpUtZu3Yt9erVY9CgQQwcOLBA17lgwQJWr17NxYsX6d69O927d8fT05P4+Hh+/PFHevToQYsWLejZsydwfZTEtGnTCA4OJi0tjVGjRhEZGUl8fLz5OjZu3Mjzzz/Pp59+yttvv8327dv57rvvsLPLvQ6YmZlJZmameTk9PR2ArKwssrKyCpC54pMTp53xWr72k/tXTg6Vy5JNebYdyrVtUJ5tg/JcMuU3nwbTjXdXNiQ8PJzs7Gx27NhhXte4cWPatm3L4MGDCQgI4ODBg9SvXx+4PuKgbNmybN26lfDwcBITE2nTpg1fffWV+WY+NjaWiRMnkpSURPXq1QEYPHgwycnJbNy48bbifeKJJyhfvjzvv/8+AO+++y5Tp07l7Nmz2Nvb06pVKzp16sTEiRPNx6xYsYKoqCh++eUX4PqIg0mTJjFt2jQALl++jJubG/Hx8XTs2NF8TX/99Reenp7mfcqXL8+WLVto1qyZue3nn3+ejIwMVq1adcu4/9lmXFwczz33HKdOneKBBx4A4M033+SVV17h119/Ba6PHDh//jzr1q0zt7NkyRJmz57NsWPHzN/4X716FU9PT9atW0f79u2JjIxk48aNpKSkmAsJycnJBAQE8N577zFgwAAAjh49Su3atTl27Bg1a9akb9++ODs7884775jP9/XXX9O6dWsuX76Mk5MT/v7+jBw5kpEjR+YrX7ld5+DBg1m+fDm//fYbrq6uAHTs2BF/f3/efvvtXNvZt28fjRs35uLFi+ZjfvzxR+rXr8+QIUNYuHAh7777Ln369LEaS3R0NDExMTetX7VqFS4uLvm6HhERERERKXkyMjLo3bs3Fy5cwN3d3ep+Nj3ioG7duhbLPj4+FsPCC9pGxYoVcXFxMRcNctbt3bv39gIF+vTpw6BBg3jzzTdxdHRk5cqVPP3009jb2wNw4MAB9u3bx6uvvmo+Jjs7m7///puMjAzzDeKN8ZYpUwY3N7dbXvPRo0f5+++/adeuncX6q1evEhoaWqhrcXFxMd9MQ/76/cCBA5w6dQo3NzeL9X///TdJSUnm5Tp16piLBje68bp9fHwASEtLo2bNmua2V65cad7HZDJhNBo5ffo0ISEhBbvA/++f11mxYkX8/f3NBYCcdTde+8GDB4mOjubQoUP8+eef5scPUlJSqFWrFgDVq1dnzpw5vPDCC/Ts2fOWRQOAiRMnMnr0aPNyeno6fn5+tG/f/pZ/HO4lWVlZJCQkcNK3IUY763+2RtUtdxejkjshJ9ft2rWjVKlSxR2O3CHKs+1Qrm2D8mwblOeSKWc0cl5sunDwz194g8GA0Wg0D/m+cTCGtSEcN7ZhMBistnm7Hn/8cYxGI+vXr6dRo0bs2LGD119/3bzdaDQSExND9+7dbzrWyckp13jzE1/OtvXr11O5cmWLbY6OjoW6ltxiyGvgi9FopGHDhhY39zm8vb3N/y5Tpkye58wZsZBzbUajkRdeeIHhw4ffdFzVqlVvGdet5Hadt+r/y5cv0759e9q3b8+KFSvw9vYmJSWFDh063DTR4/bt27G3tyc5OZlr167h4GD9o+zo6JhrrkqVKnXf/dE32jncsnBwv12PWHc//n5KwSnPtkO5tg3Ks21QnkuW/ObSpgsH1uTciKamppq/Vb9xosTi4OzsTPfu3Vm5ciWnTp0iKCiIhg0bmrc3aNCA48ePU6NGjUKfI+eb+hsn66tVqxaOjo6kpKQUaj6DwsZxYwxw/fo++ugjKlSoUOTfkjdo0IDvv//+ln2XW0xF7YcffuDcuXPExsbi5+cHwP79+2/a76OPPmLt2rUkJibSs2dPpk2bluujCCIiIiIiIkXBZt+qcCvOzs40bdqU2NhYjh49yvbt25k0aVJxh0WfPn1Yv349S5Ys4ZlnnrHYNmXKFD744AOio6P5/vvvOXbsGB999FGB4q5WrRoGg4EvvviC33//nUuXLuHm5sbYsWMZNWoUy5YtIykpiYMHD/Lvf/+bZcuWFfUlAuDv78/hw4c5fvw4586dIysriz59+lC+fHmeeOIJduzYwenTp9m2bRsjRozg7Nmzt3W+8ePH88033/DSSy9x6NAhTp48yWeffcawYcMsYtq+fTs///wz586du91LzFXVqlUpXbo0Cxcu5Mcff+Szzz4zz0eR4+zZs7z44ovMmjWLFi1aEBcXx8yZM9m9e/cdiUlERERERESFAyuWLFlCVlYWYWFhjBgxgunTpxd3SLRt2xYvLy+OHz9O7969LbZ16NCBL774goSEBBo1akTTpk15/fXXqVatWr7br1y5MjExMUyYMIGKFSsydOhQAKZNm8aUKVOYOXMmISEhdOjQgc8//5yAgIAivb4cAwcOJDg4mLCwMLy9vdm5cycuLi5s376dqlWr0r17d0JCQujfvz9Xrly57REIdevWZdu2bZw8eZKWLVsSGhrK5MmTzXMhwPXXICYnJ/PAAw9YPBpRlLy9vYmLi+Pjjz+mVq1axMbGMmfOHPN2k8lEZGQkjRs3NuemXbt2DB06lGeeeYZLly7dkbhERERERMS22exbFURsVXp6Oh4eHnnOnHovycrKIj4+nuNVmtxyjoMJoeXvYlRyJ+TkunPnznp+sgRTnm2Hcm0blGfboDyXTPm9N9CIAxERERERERGxSoWDuyQlJQVXV9dcf+zs7LCzs7O6PSUlpbjDt2rw4MFW4x48eHBxh3fHdOrUyep1z5gxo7jDExERERERKTJ6q8Jd4uvrW+g3M/j6+hZtMEXolVdeYezYsbluu1+GwRfGe++9x5UrV3Ld5uXldZejsR2j6pbT0DgRERERkbtMhYO7xMHB4bZelXivqlChAhUqVCjuMO66ypUrF3cIIiIiIiIid4UeVRARERERERERq1Q4EBERERERERGr9KiCiNwTYg+es7rNzniN4LsYi4iIiIiI/B+NOBARERERERERq1Q4EBERERERERGrVDgQEREREREREatUOJB7ksFgYN26dcUdxm2Jjo6mfv365uXIyEi6du1abPGIiIiIiIgUhiZHFLlL5s+fj8lkKu4wRERERERECkSFA5G7xMPD47aON5lMZGdn4+Cgj62IiIiIiNw9elShBAgPD2f48OFERUXh5eVFpUqViI6OBiA5ORmDwcChQ4fM+58/fx6DwUBiYiIAiYmJGAwGNm3aRGhoKM7OzrRt25a0tDQ2bNhASEgI7u7u9OrVi4yMjDsa741SU1Pp1KkTzs7OBAQE8PHHH+er/atXrzJ06FB8fHxwcnLC39+fmTNnAgXrj/Xr11OvXj2cnJxo0qQJR44cMR8TFxeHp6cn69atIygoCCcnJ9q1a8dPP/1kNa5/PqpgMpmYPXs21atXx9nZmXr16vHJJ5+Yt9+Yl7CwMBwdHdmxYwffffcdbdq0wc3NDXd3dxo2bMj+/fvz1TciIiIiIiIFpa8uS4hly5YxevRo9uzZwzfffENkZCTNmzcnMDAw321ER0ezaNEiXFxciIiIICIiAkdHR1atWsWlS5fo1q0bCxcuZPz48Xcs3nbt2pn3mTx5MrGxscyfP5/ly5fTq1cvHnzwQUJCQm7Z9oIFC/jss89Ys2YNVatW5aeffrrlDb0148aNY/78+VSqVIl//etfdOnShRMnTlCqVCkAMjIyePXVV1m2bBmlS5dmyJAhPP300+zcuTNf7U+aNIm1a9fy1ltvERgYyPbt23nmmWfw9vamdevW5v2ioqKYM2cO1atXx9PTk9atWxMaGspbb72Fvb09hw4dMseUm8zMTDIzM83L6enpAGRlZZGVlVXgfrlT7IzX8tx2L8Urd0ZOjpXrkk15th3KtW1Qnm2D8lwy5TefKhyUEHXr1mXq1KkABAYGsmjRIjZv3lygwsH06dNp3rw5AAMGDGDixIkkJSVRvXp1AJ588km2bt1aJIUDa/HeWDh46qmneP755wGYNm0aCQkJLFy4kDfffPOWbaekpBAYGEiLFi0wGAxUq1atUDFOnTrVHM+yZcuoUqUKn376KREREcD1D9miRYto0qSJeZ+QkBD27t1L48aNb9n25cuXef3119myZQvNmjUDoHr16nz99de88847FoWDV155xaJfUlJSGDduHDVr1gTIM8czZ84kJibmpvVffvklLi4ueXXDXROcj30SEhLueBxyb1CubYPybDuUa9ugPNsG5blkye+IchUOSoi6detaLPv4+JCWllboNipWrIiLi4u5aJCzbu/evbcXaC7ngtzjzbmhvnH5xkcMrImMjKRdu3YEBwfTsWNHHnvsMdq3b1/gGG88v5eXF8HBwRw7dsy8zsHBgbCwMPNyzZo18fT05NixY3kWDo4ePcrff/9tURCA649ZhIaGWqy78RwAo0eP5vnnn2f58uU88sgjPPXUUzzwwANWzzVx4kRGjx5tXk5PT8fPz4/27dvj7u5+yzjvpjcO/2F1m53xGoG/HKBdu3a3HF0h97+srCwSEhKU6xJOebYdyrVtUJ5tg/JcMuWMRs6LCgclxD8/vAaDAaPRiJ3d9WksbpzN39pwlBvbMBgMVtu8k/HmxWAw5LlPgwYNOH36NBs2bOCrr74iIiKCRx55hE8++aRA/ZGf8+cWT35izLnW9evXU7lyZYttjo6OFstlypSxWI6OjqZ3796sX7+eDRs2MHXqVFavXk23bt1yPZejo+NNbcL1HNxLf/SNdnn/ObrXYpY7R7m2Dcqz7VCubYPybBuU55Ilv7nU5IglnLe3N3B9osEc+fnW/l6we/fum5Zzhufnxd3dnZ49e7J48WI++ugj/vOf//Dnn38WqD9uPP9ff/3FiRMnLM5/7do1i0kJjx8/zvnz5/MVY61atXB0dCQlJYUaNWpY/Pj5+eV5fFBQEKNGjeLLL7+ke/fuLF26NM9jRERERERECkMjDko4Z2dnmjZtSmxsLP7+/pw7d45JkyYVd1j58vHHHxMWFkaLFi1YuXIle/fu5f3338/zuDfeeAMfHx/q16+PnZ0dH3/8MZUqVcLT0xM7O7t898crr7xCuXLlqFixIi+//DLly5e3eCtCqVKlGDZsGAsWLKBUqVIMHTqUpk2b5vmYAoCbmxtjx45l1KhRGI1GWrRoQXp6Ort27cLV1ZV+/frletyVK1cYN24cTz75JAEBAZw9e5Z9+/bRo0ePPM8pIiIiIiJSGCoc2IAlS5bQv39/wsLCCA4OZvbs2YV65v9ui4mJYfXq1QwZMoRKlSqxcuVKatWqledxrq6uzJo1i5MnT2Jvb0+jRo2Ij483P6aQ3/6IjY1lxIgRnDx5knr16vHZZ59RunRp83YXFxfGjx9P7969OXv2LC1atGDJkiX5vr5p06ZRoUIFZs6cyY8//oinpycNGjTgX//6l9Vj7O3t+eOPP+jbty+//fYb5cuXp3v37rlOfigiIiIiIlIUDKYbH/YWERITE2nTpg1//fUXnp6eue4TFxfHyJEjOX/+/F2NrSikp6fj4eHBhQsX7qnJEWMPnrO6zc54jeCze+jcubOeqSvhsrKyiI+PV65LOOXZdijXtkF5tg3Kc8mU33sDzXEgIiIiIiIiIlapcCAFlpKSgqura64/dnZ22NnZWd2ekpJy2+efMWOG1fY7depUBFcoIiIiIiIiOTTHgRSYr69vod/M4Ovre9vnHzx4MBEREbluc3Z2vu32w8PDyesJnsjISCIjI2/7XPJ/JoSWt7otKyuL+LN3MRgRERERETFT4UAKzMHBgRo1ahTb+b28vPDy8iq284uIiIiIiNgSPaogIiIiIiIiIlapcCAiIiIiIiIiVqlwICIiIiIiIiJWaY4DEbknxB48Z3WbnfEawXcxFhERERER+T8acSAiIiIiIiIiVqlwICIiIiIiIiJWqXAgJUpkZCRdu3Yt7jBERERERERKDBUO5I4KDw9n5MiRd+04W6IiiYiIiIiI3A0qHIiIiIiIiIiIVSoc3GfCw8MZPnw4UVFReHl5UalSJaKjowFITk7GYDBw6NAh8/7nz5/HYDCQmJgIQGJiIgaDgU2bNhEaGoqzszNt27YlLS2NDRs2EBISgru7O7169SIjI+O2Yo2MjGTbtm3Mnz8fg8GAwWAgOTkZgG3bttG4cWMcHR3x8fFhwoQJXLt27ZbHZWdnM2DAAAICAnB2diY4OJj58+cXOr6NGzfSokULPD09KVeuHI899hhJSUnm7Tn9uWbNGlq2bImzszONGjXixIkT7Nu3j7CwMFxdXenYsSO///67+Tij0cgrr7xClSpVcHR0pH79+mzcuNG8PScH58+fN687dOiQRf/ExcXh6enJpk2bCAkJMZ8nNTUVgOjoaJYtW8Z///tfcx/l5FhERERERKQoqXBwH1q2bBllypRhz549zJ49m1deeYWEhIQCtREdHc2iRYvYtWsXP/30ExEREcybN49Vq1axfv16EhISWLhw4W3FOX/+fJo1a8bAgQNJTU0lNTUVPz8/fv75Zzp37kyjRo347rvveOutt3j//feZPn36LY8zGo1UqVKFNWvWcPToUaZMmcK//vUv1qxZU6j4Ll++zOjRo9m3bx+bN2/Gzs6Obt26YTQaLfabOnUqkyZN4ttvv8XBwYFevXoRFRXF/Pnz2bFjB0lJSUyZMsXiuufOncucOXM4fPgwHTp0oEuXLpw8ebJA8WVkZDBnzhyWL1/O9u3bSUlJYezYsQCMHTuWiIgIczEhNTWVhx56qFD9ICIiIiIicisOxR2AFFzdunWZOnUqAIGBgSxatIjNmzcTGBiY7zamT59O8+bNARgwYAATJ04kKSmJ6tWrA/Dkk0+ydetWxo8fX+g4PTw8KF26NC4uLlSqVMm8/s0338TPz49FixZhMBioWbMmv/zyC+PHj2fKlClWj7O3tycmJsa8HBAQwK5du1izZg0REREFjq9Hjx4Wy++//z4VKlTg6NGjPPjgg+b1Y8eOpUOHDgCMGDGCXr16sXnzZov+i4uLM+8/Z84cxo8fz9NPPw3ArFmz2Lp1K/PmzePf//53vuPLysri7bff5oEHHgBg6NChvPLKKwC4urri7OxMZmamRR/lJjMzk8zMTPNyenq6uf2srKx8x3On2Rmv5bntXopX7oycHCvXJZvybDuUa9ugPNsG5blkym8+VTi4D9WtW9di2cfHh7S0tEK3UbFiRVxcXMxFg5x1e/fuvb1ArTh27BjNmjXDYDCY1zVv3pxLly5x9uxZqlatavXYt99+m/fee48zZ85w5coVrl69Sv369QsVR1JSEpMnT2b37t2cO3fOPNIgJSXFonDwz74CqFOnjsW6nP5PT0/nl19+MRcVbry+7777rkDxubi4mIsGULg8A8ycOdOi4JLjyy+/xMXFpcDt3SnB+dinoCNr5P6lXNsG5dl2KNe2QXm2DcpzyZLfx9NVOLgPlSpVymLZYDBgNBqxs7v+5InJZDJvs1ZBurENg8Fgtc07wWQyWRQNctblnNeaNWvWMGrUKObOnUuzZs1wc3PjtddeY8+ePYWK4/HHH8fPz4/Fixfj6+uL0WjkwQcf5OrVqxb7/bOvclv3z77K7fpy1uU3T7nl5MZj8mvixImMHj3avJyeno6fnx/t27fH3d29wO3dKW8c/sPqNjvjNQJ/OUC7du1u6hcpWbKyskhISFCuSzjl2XYo17ZBebYNynPJlDMaOS8qHJQg3t7eAKSmphIaGgpgMVFicShdujTZ2dkW62rVqsV//vMfi5vpXbt24ebmRuXKla0et2PHDh566CGGDBliXnfjZIYF8ccff3Ds2DHeeecdWrZsCcDXX39dqLZu5O7ujq+vL19//TWtWrUyr9+1axeNGzcGLPNUtmxZoHB5yq2PcuPo6Iijo+NN60uVKnVP/dE32uX95+hei1nuHOXaNijPtkO5tg3Ks21QnkuW/OZSkyOWIM7OzjRt2pTY2FiOHj3K9u3bmTRpUrHG5O/vz549e0hOTjY/DjBkyBB++uknhg0bxg8//MB///tfpk6dyujRo83fxud2XI0aNdi/fz+bNm3ixIkTTJ48mX379hUqrrJly1KuXDneffddTp06xZYtWyy+lb8d48aNY9asWXz00UccP36cCRMmcOjQIUaMGAFAjRo18PPzIzo6mhMnTrB+/Xrmzp1b4PP4+/tz+PBhjh8/zrlz5/S8mYiIiIiI3BEqHJQwS5YsISsri7CwMEaMGGF+U0FxGTt2LPb29tSqVQtvb29SUlKoXLky8fHx7N27l3r16jF48GAGDBhgUeTI7bjBgwfTvXt3evbsSZMmTfjjjz8sRh8UhJ2dHatXr+bAgQM8+OCDjBo1itdee61Irnn48OGMGTOGMWPGUKdOHTZu3Mhnn31mnryyVKlSfPjhh/zwww/Uq1ePWbNmFSpPAwcOJDg4mLCwMLy9vdm5c2eRxC8iIiIiInIjg6kwD02LyH0rPT0dDw8PLly4cE/NcRB78JzVbXbGawSf3UPnzp01NK6Ey8rKIj4+Xrku4ZRn26Fc2wbl2TYozyVTfu8NNOJARERERERERKzS5IhySykpKdSqVSvXbTmv7rD2Sr+jR4/e8tWKd9Kt4obijU1EREREROR+osKB3JKvr2+h38zg6+tbtMEU8Ny3irs4YxMREREREbmfqHAgt+Tg4ECNGjWKO4wCu1/jtmUTQstb3ZaVlUX82bsYjIiIiIiImGmOAxERERERERGxSoUDEREREREREbFKhQMRERERERERsUpzHIjIPSf24DmLZTvjNYKLKRYREREREVunEQciIiIiIiIiYpUKByIiIiIiIiJilQoHIiIiIiIiImKVCgdSLAwGA+vWrSvuMAosLi4OT0/P4g5DRERERETkrlHhQERERERERESsUuFA5P/Lysoq7hBERERERETuOSoc3AfCw8MZPnw4UVFReHl5UalSJaKjowFITk7GYDBw6NAh8/7nz5/HYDCQmJgIQGJiIgaDgU2bNhEaGoqzszNt27YlLS2NDRs2EBISgru7O7169SIjI+OOxnuj1NRUOnXqhLOzMwEBAXz88cf5aj/nmlevXs1DDz2Ek5MTtWvXNl8v5P5Iwbp16zAYDObl6Oho6tevz5IlS6hevTqOjo6YTCbOnz/PoEGDqFixIk5OTjz44IN88cUXFm1t2rSJkJAQXF1d6dixI6mpqeZt+/bto127dpQvXx4PDw9at27Nt99+a3F8dHQ0VatWxdHREV9fX4YPH27edvXqVaKioqhcuTJlypShSZMmFtd25swZHn/8ccqWLUuZMmWoXbs28fHx+eo7ERERERGRgnIo7gAkf5YtW8bo0aPZs2cP33zzDZGRkTRv3pzAwMB8txEdHc2iRYtwcXEhIiKCiIgIHB0dWbVqFZcuXaJbt24sXLiQ8ePH37F427VrZ95n8uTJxMbGMn/+fJYvX06vXr148MEHCQkJydc5xo0bx7x586hVqxavv/46Xbp04fTp05QrVy7fcZ46dYo1a9bwn//8B3t7e4xGI506deLixYusWLGCBx54gKNHj2Jvb28+JiMjgzlz5rB8+XLs7Ox45plnGDt2LCtXrgTg4sWL9OvXjwULFgAwd+5cOnfuzMmTJ3Fzc+OTTz7hjTfeYPXq1dSuXZtff/2V7777ztz+c889R3JyMqtXr8bX15dPP/2Ujh07cuTIEQIDA3nppZe4evUq27dvp0yZMhw9ehRXV1er15iZmUlmZqZ5OT09Hbg+wuJeHWVhZ7yW6/K9Gq8UnZwcK9clm/JsO5Rr26A82wbluWTKbz5VOLhP1K1bl6lTpwIQGBjIokWL2Lx5c4EKB9OnT6d58+YADBgwgIkTJ5KUlET16tUBePLJJ9m6dWuRFA6sxXtj4eCpp57i+eefB2DatGkkJCSwcOFC3nzzzXydY+jQofTo0QOAt956i40bN/L+++8TFRWV7zivXr3K8uXL8fb2BuDLL79k7969HDt2jKCgIABz/+TIysri7bff5oEHHjDH8corr5i3t23b1mL/d955h7Jly7Jt2zYee+wxUlJSqFSpEo888gilSpWiatWqNG7cGICkpCQ+/PBDzp49i6+vLwBjx45l48aNLF26lBkzZpCSkkKPHj2oU6dOrvH908yZM4mJiblp/ZdffomLi0u+++puCrayPiEh4a7GIcVHubYNyrPtUK5tg/JsG5TnkiW/I85VOLhP1K1b12LZx8eHtLS0QrdRsWJFXFxcLG46K1asyN69e28v0FzOBbnH26xZs5uWb3zkIi83Hu/g4EBYWBjHjh0rUJzVqlUzFw0ADh06RJUqVcxFg9y4uLiYiwZw87WlpaUxZcoUtmzZwm+//UZ2djYZGRmkpKQA1wsm8+bNo3r16nTs2JHOnTvz+OOP4+DgwLfffovJZLrp/JmZmeaRFMOHD+fFF1/kyy+/5JFHHqFHjx439feNJk6cyOjRo83L6enp+Pn50b59e9zd3fPZU3fXG4f/sFi2M14j8JcDtGvXjlKlShVTVHI3ZGVlkZCQoFyXcMqz7VCubYPybBuU55IpZzRyXlQ4uE/888NpMBgwGo3Y2V2fpsJkMpm3WRtucmMbBoPBapt3Mt683DgHQWHkHG9nZ2fRJ5B7v5QpU8Zi2dnZOc9z5HZtN54rMjKS33//nXnz5lGtWjUcHR1p1qwZV69eBcDPz4/jx4+TkJDAV199xZAhQ3jttdfYtm0bRqMRe3t7Dhw4YPF4BGB+HOH555+nQ4cOrF+/ni+//JKZM2cyd+5chg0blmu8jo6OODo65nod9+offaNd7n+a7uWYpWgp17ZBebYdyrVtUJ5tg/JcsuQ3l5oc8T6X8235jZPzFeRb++K0e/fum5Zr1qxZqOOvXbvGgQMHzMd7e3tz8eJFLl++bN4nP/1St25dzp49y4kTJ/Idxz/t2LGD4cOH07lzZ2rXro2joyPnzp2z2MfZ2ZkuXbqwYMECEhMT+eabbzhy5AihoaFkZ2eTlpZGjRo1LH4qVapkPt7Pz4/Bgwezdu1axowZw+LFiwsdr4iIiIiIyK1oxMF9ztnZmaZNmxIbG4u/vz/nzp1j0qRJxR1Wvnz88ceEhYXRokULVq5cyd69e3n//ffzffy///1vAgMDCQkJ4Y033uCvv/6if//+ADRp0gQXFxf+9a9/MWzYMPbu3UtcXFyebbZu3ZpWrVrRo0cPXn/9dWrUqMEPP/yAwWCgY8eO+YqrRo0aLF++nLCwMNLT0xk3bpzFSIa4uDiys7PNMS5fvhxnZ2eqVatGuXLl6NOnD3379mXu3LmEhoZy7tw5tmzZQp06dejcuTMjR46kU6dOBAUF8ddff7Fly5Z8TygpIiIiIiJSUBpxUAIsWbKErKwswsLCGDFiBNOnTy/ukPIlJiaG1atXU7duXZYtW8bKlSupVatWvo+PjY1l1qxZ1KtXjx07dvDf//6X8uXLA+Dl5cWKFSuIj4+nTp06fPjhh7m+EjI3//nPf2jUqBG9evWiVq1aREVFkZ2dne+4lixZwl9//UVoaCjPPvssw4cPp0KFCubtnp6eLF68mObNm1O3bl02b97M559/bp7DYOnSpfTt25cxY8YQHBxMly5d2LNnD35+fgBkZ2fz0ksvERISQseOHQkODs73hJIiIiIiIiIFZTD980FwkXtccnIyAQEBHDx4kPr16xd3OPed9PR0PDw8uHDhwj07OWLsQctHO+yM1wg+u4fOnTvrmboSLisri/j4eOW6hFOebYdybRuUZ9ugPJdM+b030IgDEREREREREbFKhQO5SUpKCq6urrn+2NnZYWdnZ3V7zisHb8eMGTOstt+pU6ciuEIRERERERHJL02OKDfx9fUt9JsZfH19b/v8gwcPJiIiItdtzs7OVK5c+aZXLUrJMiG0vMVyVlYW8WeLKRgRERERERunwoHcxMHBgRo1ahTb+b28vPDy8iq284uIiIiIiMj/0aMKIiIiIiIiImKVCgciIiIiIiIiYpUeVRCR+8Ybh//AaFd0f7b+OZeCiIiIiIjcTCMORERERERERMQqFQ5ERERERERExCoVDkRERERERETEKhUOpMQyGAysW7euSNuMjIyka9eu+do3OTkZg8HAoUOHijQGERERERGRu0mFA5FcWLvpnz9/PnFxcXctjjtR/BARERERESkIvVVBpAA8PDyKOwQREREREZG7SiMOBIDw8HCGDx9OVFQUXl5eVKpUiejoaCD3b9/Pnz+PwWAgMTERgMTERAwGA5s2bSI0NBRnZ2fatm1LWloaGzZsICQkBHd3d3r16kVGRsYdjfdGqampdOrUCWdnZwICAvj444/z1X5AQAAAoaGhGAwGwsPDgZsfVTAajcyaNYsaNWrg6OhI1apVefXVV3Nt02g0MnDgQIKCgjhz5gwAn3/+OQ0bNsTJyYnq1asTExPDtWvXAPD39wegW7duGAwG8/J3331HmzZtcHNzw93dnYYNG7J///58XZeIiIiIiEhBacSBmC1btozRo0ezZ88evvnmGyIjI2nevDmBgYH5biM6OppFixbh4uJCREQEERERODo6smrVKi5dukS3bt1YuHAh48ePv2PxtmvXzrzP5MmTiY2NZf78+SxfvpxevXrx4IMPEhIScsu29+7dS+PGjfnqq6+oXbs2pUuXznW/iRMnsnjxYt544w1atGhBamoqP/zww037Xb16ld69e5OUlMTXX39NhQoV2LRpE8888wwLFiygZcuWJCUlMWjQIACmTp3Kvn37qFChAkuXLqVjx47Y29sD0KdPH0JDQ3nrrbewt7fn0KFDlCpVyuq1ZGZmkpmZaV5OT08HICsri6ysrFv2w70iJ04747U70q7cO3JyotyUbMqz7VCubYPybBuU55Ipv/k0mEwm0x2ORe4D4eHhZGdns2PHDvO6xo0b07ZtWwYPHkxAQAAHDx6kfv36wPURB2XLlmXr1q2Eh4eTmJhImzZt+Oqrr3j44YcBiI2NZeLEiSQlJVG9enUABg8eTHJyMhs3brxj8cbGxgLX5wcYPHgwb731lnmfpk2b0qBBA958881btp+cnHzTNcP1EQfnz59n3bp1XLx4EW9vbxYtWsTzzz9vtY0dO3YQExPDlStXWL9+vflxh1atWtGpUycmTpxoPmbFihVERUXxyy+/mK/h008/tRjl4O7uzsKFC+nXr1+++io6OpqYmJib1q9atQoXF5d8tSEiIiIiIiVPRkYGvXv35sKFC7i7u1vdTyMOxKxu3boWyz4+PqSlpRW6jYoVK+Li4mIuGuSs27t37+0Fmsu5IPd4mzVrdtNyUb3l4NixY2RmZpoLJdb06tWLKlWqsHnzZosb9QMHDrBv3z6LRxuys7P5+++/ycjIsHpTP3r0aJ5//nmWL1/OI488wlNPPcUDDzxg9fwTJ05k9OjR5uX09HT8/Pxo3779Lf843EuysrJISEjgpG9DjHZF92drVN1yRdaWFI2cXLdr1+6WI2nk/qY82w7l2jYoz7ZBeS6ZckYj50WFAzH75x8Ag8GA0WjEzu76VBg3Dk6xNqTlxjYMBoPVNu9kvHkxGAxFcn5nZ+d87de5c2dWrFjB7t27adu2rXm90WgkJiaG7t2733SMk5OT1faio6Pp3bs369evZ8OGDUydOpXVq1fTrVu3XPd3dHTE0dHxpvWlSpW67/7oG+0cirRwcL9dvy25H38/peCUZ9uhXNsG5dk2KM8lS35zqckRJU/e3t7A9YkGcxTVt/Z32u7du29arlmzZp7H5cxpkJ2dbXWfwMBAnJ2d2bx58y3bevHFF4mNjaVLly5s27bNvL5BgwYcP36cGjVq3PSTU6wpVapUrjEEBQUxatQovvzyS7p3787SpUvzvCYREREREZHC0IgDyZOzszNNmzYlNjYWf39/zp07x6RJk4o7rHz5+OOPCQsLo0WLFqxcuZK9e/fy/vvv53lchQoVcHZ2ZuPGjVSpUgUnJ6ebXsXo5OTE+PHjiYqKonTp0jRv3pzff/+d77//ngEDBljsO2zYMLKzs3nsscfYsGEDLVq0YMqUKTz22GP4+fnx1FNPYWdnx+HDhzly5AjTp08Hrr9ZYfPmzTRv3hxHR0ecnJwYN24cTz75JAEBAZw9e5Z9+/bRo0ePous0ERERERGRG2jEgeTLkiVLyMrKIiwsjBEjRphvbO91MTExrF69mrp167Js2TJWrlxJrVq18jzOwcGBBQsW8M477+Dr68sTTzyR636TJ09mzJgxTJkyhZCQEHr27Gl1XoiRI0cSExND586d2bVrFx06dOCLL74gISGBRo0a0bRpU15//XWqVatmPmbu3LkkJCTg5+dHaGgo9vb2/PHHH/Tt25egoCAiIiLo1KlTrpMfioiIiIiIFAW9VUHExqSnp+Ph4ZHnzKn3kqysLOLj4zlepUmRznEwIbR8kbUlRSMn1507d9bzkyWY8mw7lGvboDzbBuW5ZMrvvYFGHIiIiIiIiIiIVSocSLFISUnB1dU11x87Ozvs7Oysbk9JSbnt88+YMcNq+506dSqCKxQRERERESkZNDmiFAtfX99Cv5nB19f3ts8/ePBgIiIict2W39csyt03qm45DY0TEREREbnLVDiQYuHg4ECNGjWK7fxeXl54eXkV2/lFRERERETuF3pUQURERERERESsUuFARERERERERKxS4UBERERERERErFLhQERERERERESsUuFARERERERERKxS4UBERERERERErFLhQERERERERESsUuFARERERERERKxS4UBERERERERErFLhQERERERERESsUuFARERERERERKxS4UBERERERERErFLhQERERERERESsUuFARERERERERKxS4UBERERERERErFLhQERERERERESsUuFARERERERERKxS4UBERERERERErHIo7gBE5O4ymUwApKenF3Mk+ZeVlUVGRgbp6emUKlWquMORO0i5tg3Ks+1Qrm2D8mwblOeSKeeeIOcewRoVDkRszMWLFwHw8/Mr5khERERERORecPHiRTw8PKxuN5jyKi2ISIliNBr55ZdfcHNzw2AwFHc4+ZKeno6fnx8//fQT7u7uxR2O3EHKtW1Qnm2Hcm0blGfboDyXTCaTiYsXL+Lr64udnfWZDDTiQMTG2NnZUaVKleIOo1Dc3d31HyoboVzbBuXZdijXtkF5tg3Kc8lzq5EGOTQ5ooiIiIiIiIhYpcKBiIiIiIiIiFilwoGI3PMcHR2ZOnUqjo6OxR2K3GHKtW1Qnm2Hcm0blGfboDzbNk2OKCIiIiIiIiJWacSBiIiIiIiIiFilwoGIiIiIiIiIWKXCgYiIiIiIiIhYpcKBiIiIiIiIiFilwoGI3PPefPNNAgICcHJyomHDhuzYsaO4Q5IiFB0djcFgsPipVKlScYclRWD79u08/vjj+Pr6YjAYWLduncV2k8lEdHQ0vr6+ODs7Ex4ezvfff188wUqh5ZXnyMjImz7jTZs2LZ5gpdBmzpxJo0aNcHNzo0KFCnTt2pXjx49b7KPPdMmQn1zrc217VDgQkXvaRx99xMiRI3n55Zc5ePAgLVu2pFOnTqSkpBR3aFKEateuTWpqqvnnyJEjxR2SFIHLly9Tr149Fi1alOv22bNn8/rrr7No0SL27dtHpUqVaNeuHRcvXrzLkcrtyCvPAB07drT4jMfHx9/FCKUobNu2jZdeeondu3eTkJDAtWvXaN++PZcvXzbvo890yZCfXIM+17ZGr2MUkXtakyZNaNCgAW+99ZZ5XUhICF27dmXmzJnFGJkUlejoaNatW8ehQ4eKOxS5gwwGA59++ildu3YFrn8z6evry8iRIxk/fjwAmZmZVKxYkVmzZvHCCy8UY7RSWP/MM1z/ZvL8+fM3jUSQ+9vvv/9OhQoV2LZtG61atdJnugT7Z65Bn2tbpBEHInLPunr1KgcOHKB9+/YW69u3b8+uXbuKKSq5E06ePImvry8BAQE8/fTT/Pjjj8Udktxhp0+f5tdff7X4fDs6OtK6dWt9vkugxMREKlSoQFBQEAMHDiQtLa24Q5LbdOHCBQC8vLwAfaZLsn/mOoc+17ZFhQMRuWedO3eO7OxsKlasaLG+YsWK/Prrr8UUlRS1Jk2a8MEHH7Bp0yYWL17Mr7/+ykMPPcQff/xR3KHJHZTzGdbnu+Tr1KkTK1euZMuWLcydO5d9+/bRtm1bMjMzizs0KSSTycTo0aNp0aIFDz74IKDPdEmVW65Bn2tb5FDcAYiI5MVgMFgsm0ymm9bJ/atTp07mf9epU4dmzZrxwAMPsGzZMkaPHl2MkcndoM93ydezZ0/zvx988EHCwsKoVq0a69evp3v37sUYmRTW0KFDOXz4MF9//fVN2/SZLlms5Vqfa9ujEQcics8qX7489vb2N31TkZaWdtM3GlJylClThjp16nDy5MniDkXuoJw3Z+jzbXt8fHyoVq2aPuP3qWHDhvHZZ5+xdetWqlSpYl6vz3TJYy3XudHnuuRT4UBE7lmlS5emYcOGJCQkWKxPSEjgoYceKqao5E7LzMzk2LFj+Pj4FHcocgcFBARQqVIli8/31atX2bZtmz7fJdwff/zBTz/9pM/4fcZkMjF06FDWrl3Lli1bCAgIsNiuz3TJkVeuc6PPdcmnRxVE5J42evRonn32WcLCwmjWrBnvvvsuKSkpDB48uLhDkyIyduxYHn/8capWrUpaWhrTp08nPT2dfv36FXdocpsuXbrEqVOnzMunT5/m0KFDeHl5UbVqVUaOHMmMGTMIDAwkMDCQGTNm4OLiQu/evYsxaimoW+XZy8uL6OhoevTogY+PD8nJyfzrX/+ifPnydOvWrRijloJ66aWXWLVqFf/9739xc3Mzjyzw8PDA2dkZg8Ggz3QJkVeuL126pM+1LTKJiNzj/v3vf5uqVatmKl26tKlBgwambdu2FXdIUoR69uxp8vHxMZUqVcrk6+tr6t69u+n7778v7rCkCGzdutUE3PTTr18/k8lkMhmNRtPUqVNNlSpVMjk6OppatWplOnLkSPEGLQV2qzxnZGSY2rdvb/L29jaVKlXKVLVqVVO/fv1MKSkpxR22FFBuOQZMS5cuNe+jz3TJkFeu9bm2TQaTyWS6m4UKEREREREREbl/aI4DEREREREREbFKhQMRERERERERsUqFAxERERERERGxSoUDEREREREREbFKhQMRERERERERsUqFAxERERERERGxSoUDEREREREREbFKhQMRERERuS8lJyczffp0Ll26VNyhiIiUaCociIiIiNzDwsPDGTlyZHGHcc+5evUqERERlCtXDldX1zz39/f3Z968eYU+X1xcHJ6enoU+XkTkfqbCgYiIiBRaZGQkXbt2Le4wrEpOTsZgMHDo0KHiDkUKKK/frTFjxtCuXTtefPHFfLW3b98+Bg0alK99cysy9OzZkxMnTuTreBGRksahuAMQERERuROuXr1a3CHYpKtXr1K6dOk7fp6FCxfma7+ceLy9vW/rfM7Ozjg7O99WGyIi9yuNOBAREZEiEx4ezrBhwxg5ciRly5alYsWKvPvuu1y+fJnnnnsONzc3HnjgATZs2GA+JjExEYPBwPr166lXrx5OTk40adKEI0eOWLT9n//8h9q1a+Po6Ii/vz9z58612O7v78/06dOJjIzEw8ODgQMHEhAQAEBoaCgGg4Hw8HDg+rfP7dq1o3z58nh4eNC6dWu+/fZbi/YMBgPvvfce3bp1w8XFhcDAQD777DOLfb7//nseffRR3N3dcXNzo2XLliQlJZm3L126lJCQEJycnKhZsyZvvvnmLfvv8uXL9O3bF1dXV3x8fG66Rrh+IxwVFUXlypUpU6YMTZo0ITEx0bz9zJkzPP7445QtW5YyZcpQu3Zt4uPjrZ4zMzOTqKgo/Pz8cHR0JDAwkPfffx+A7OxsBgwYQEBAAM7OzgQHBzN//nyL43NGBsycORNfX1+CgoIAWLFiBWFhYbi5uVGpUiV69+5NWlpavvovOjqaZcuW8d///heDwYDBYDBf488//0zPnj0pW7Ys5cqV44knniA5OTnPeP45iiA6OpqqVavi6OiIr68vw4cPB67/Dp85c4ZRo0aZzw25P6oQGxtLxYoVcXNzY8CAAUyYMIH69eubt+f2mEnXrl2JjIy8Y/kUEbkTVDgQERGRIrVs2TLKly/P3r17GTZsGC+++CJPPfUUDz30EN9++y0dOnTg2WefJSMjw+K4cePGMWfOHPbt20eFChXo0qULWVlZABw4cICIiAiefvppjhw5QnR0NJMnTyYuLs6ijddee40HH3yQAwcOMHnyZPbu3QvAV199RWpqKmvXrgXg4sWL9OvXjx07drB7924CAwPp3LkzFy9etGgvJiaGiIgIDh8+TOfOnenTpw9//vkncP0GtlWrVjg5ObFlyxYOHDhA//79uXbtGgCLFy/m5Zdf5tVXX+XYsWPMmDGDyZMns2zZMqt9N27cOLZu3cqnn37Kl19+SWJiIgcOHLDY57nnnmPnzp2sXr2aw4cP89RTT9GxY0dOnjwJwEsvvURmZibbt2/nyJEjzJo165ZzAPTt25fVq1ezYMECjh07xttvv23e32g0UqVKFdasWcPRo0eZMmUK//rXv1izZo1FG5s3b+bYsWMkJCTwxRdfANdviKdNm8Z3333HunXrOH36tMUN8636b+zYsURERNCxY0dSU1NJTU3loYceIiMjgzZt2uDq6sr27dv5+uuvcXV1pWPHjhYjTHKL50affPIJb7zxBu+88w4nT55k3bp11KlTB4C1a9dSpUoVXnnlFfO5c7NmzRqmTp3Kq6++yv79+/Hx8cmzMJSbos6niMgdYRIREREppH79+pmeeOIJ83Lr1q1NLVq0MC9fu3bNVKZMGdOzzz5rXpeammoCTN98843JZDKZtm7dagJMq1evNu/zxx9/mJydnU0fffSRyWQymXr37m1q166dxbnHjRtnqlWrlnm5WrVqpq5du1rsc/r0aRNgOnjw4C2v49q1ayY3NzfT559/bl4HmCZNmmRevnTpkslgMJg2bNhgMplMpokTJ5oCAgJMV69ezbVNPz8/06pVqyzWTZs2zdSsWbNc97948aKpdOnSufbDiBEjTCaTyXTq1CmTwWAw/fzzzxbHPvzww6aJEyeaTCaTqU6dOqbo6OhbXm+O48ePmwBTQkJCvvY3mUymIUOGmHr06GFe7tevn6lixYqmzMzMWx63d+9eE2C6ePGiyWTKu//++btlMplM77//vik4ONhkNBrN6zIzM03Ozs6mTZs23TKeatWqmd544w2TyWQyzZ071xQUFGT13Dfum2Pp0qUmDw8P83KzZs1MgwcPttinSZMmpnr16pmXW7dubc5djieeeMLUr18/k8lU9PkUEblTNOJAREREilTdunXN/7a3t6dcuXLmb3MBKlasCHDTsPVmzZqZ/+3l5UVwcDDHjh0D4NixYzRv3txi/+bNm3Py5Emys7PN68LCwvIVY1paGoMHDyYoKAgPDw88PDy4dOkSKSkpVq+lTJkyuLm5meM+dOgQLVu2pFSpUje1//vvv/PTTz8xYMAAXF1dzT/Tp0+3eJThRklJSVy9ejXXfsjx7bffYjKZCAoKsmh327Zt5naHDx/O9OnTad68OVOnTuXw4cNW++HQoUPY29vTunVrq/u8/fbbhIWF4e3tjaurK4sXL76pn+rUqXPTvAYHDx7kiSeeoFq1ari5uZkfE8k59lb9Z82BAwc4deoUbm5u5mv38vLi77//tujX3OK50VNPPcWVK1eoXr06AwcO5NNPPzWPFMmvY8eOWeQKuGk5L0WdTxGRO0WTI4qIiEiR+ueNoMFgsFiX88y40WjMs62cfU0mk/nfOUwm0037lylTJl8xRkZG8vvvvzNv3jyqVauGo6MjzZo1u2lCxdyuJSfuW02Ul7PP4sWLadKkicU2e3v7XI/J7Xpya9fe3p4DBw7c1E7O8PXnn3+eDh06sH79er788ktmzpzJ3LlzGTZs2E3t5TXZ35o1axg1ahRz586lWbNmuLm58dprr7Fnzx6L/f7Z75cvX6Z9+/a0b9+eFStW4O3tTUpKCh06dDD3cWEmGjQajTRs2JCVK1fetO3GyQ/z+j3w8/Pj+PHjJCQk8NVXXzFkyBBee+01tm3bVqBCRl7s7OxuymvO4zdQ9PkUEblTNOJARERE7gm7d+82//uvv/7ixIkT1KxZE4BatWrx9ddfW+y/a9cugoKCrN6IA+ZvnW8clQCwY8cOhg8fTufOnc0TLp47d65A8datW5cdO3ZY3AjmqFixIpUrV+bHH3+kRo0aFj85Ezb+U40aNShVqlSu/ZAjNDSU7Oxs0tLSbmq3UqVK5v38/PwYPHgwa9euZcyYMSxevDjXc9apUwej0ci2bdty3b5jxw4eeughhgwZQmhoKDVq1LA6YuJGP/zwA+fOnSM2NpaWLVtSs2bNm0aY3Kr/4Hru/pm3Bg0acPLkSSpUqHDT9Xt4eOQZ142cnZ3p0qULCxYsIDExkW+++cY8IWdu5/6nkJAQi1wBNy17e3tbzJGQnZ3N//73P/NyUedTROROUeFARERE7gmvvPIKmzdv5n//+x+RkZGUL1+erl27AjBmzBg2b97MtGnTOHHiBMuWLWPRokWMHTv2lm1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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "coefficients = pipeline.named_steps['logreg'].coef_[0]\n", - "feature_names = pipeline.named_steps['logreg'].feature_names_in_\n", - "\n", - "# Tracer l'importance des caractéristiques\n", - "plt.figure(figsize=(10, 6))\n", - "plt.barh(feature_names, coefficients, color='skyblue')\n", - "plt.xlabel('Importance des caractéristiques')\n", - "plt.ylabel('Caractéristiques')\n", - "plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "210b931c-6d46-4ebf-a9c7-d1ee05c3fadf", - "metadata": {}, - "outputs": [], - "source": [ - "# Création d'un dataframe avec le score\n", - "dataset_for_segmentation = dataset_test[['customer_id'] + numeric_features + categorical_features]\n", - "\n", - "y_predict_proba = pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - "dataset_for_segmentation['prediction_probability'] = y_predict_proba\n", - "\n", - "# Arrondir les valeurs de la colonne 'prediction_probability' et les multiplier par 10\n", - "dataset_for_segmentation['category'] = dataset_for_segmentation['prediction_probability'].apply(lambda x: int(x * 10))\n", - "\n", - "dataset_for_segmentation['prediction'] = y_pred\n", - "\n", - "def premiere_partie(chaine):\n", - " if chaine:\n", - " return chaine.split('_')[0]\n", - " else:\n", - " return None\n", - "\n", - "dataset_for_segmentation['company_number'] = dataset_for_segmentation['customer_id'].apply(lambda x: premiere_partie(x))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "055e47dd-9ff3-4853-a46d-d5a5edc1f361", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "969f1f92-d715-4d74-85a7-437e72838cb5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetfidelitygender_femalegender_malegender_othernb_campaignsnb_campaigns_opened
meanmeanmeanmeanmeanmeanmeanmeanmeanmeanmeanmeanmeanmeanmean
category
00.1136370.0062741.5863660.0058210.000647548.790455548.773103-0.9771180.0015850.0007760.0000000.0000320.99996813.9842191.302720
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21.1594190.33925315.1821430.3375770.323824501.529129501.415505-0.5544390.9699390.3047570.3925700.2972580.31017317.3950422.608084
32.1530800.74416127.8200440.7348810.600982287.051054286.6753850.1053601.7760350.6598780.2888130.2532440.45794316.7904214.173954
42.0447490.77764027.3531450.7545490.079213297.179255295.0199021.8981780.2937600.8948770.6669800.3014240.03159616.9547076.060621
53.2379880.95852046.6373800.8076550.484785387.464785380.1450687.1113572.0803971.1649580.4977580.2597690.24247327.00640612.457719
63.5922331.10288149.9892260.8780140.599906268.627019250.94934417.5392472.5259941.4209210.5346070.3042590.16113414.0732854.604134
73.7470161.39126640.7103350.9147020.160990309.716173274.79557034.7968760.8442501.9630280.6503640.2634640.08617226.1863178.891703
85.6982761.56700663.0336990.9079150.334248326.485952257.94019468.4254602.7942792.4130090.6065830.2515670.14185030.98746111.676332
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\n", - "
" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - " mean mean mean mean \n", - "category \n", - "0 0.113637 0.006274 1.586366 0.005821 \n", - "1 0.810841 0.128432 9.611292 0.125295 \n", - "2 1.159419 0.339253 15.182143 0.337577 \n", - "3 2.153080 0.744161 27.820044 0.734881 \n", - "4 2.044749 0.777640 27.353145 0.754549 \n", - "5 3.237988 0.958520 46.637380 0.807655 \n", - "6 3.592233 1.102881 49.989226 0.878014 \n", - "7 3.747016 1.391266 40.710335 0.914702 \n", - "8 5.698276 1.567006 63.033699 0.907915 \n", - "9 14.505956 3.211571 107.288514 1.011628 \n", - "10 2262.859155 45.619718 11051.732394 1.464789 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - " mean mean mean \n", - "category \n", - "0 0.000647 548.790455 548.773103 \n", - "1 0.018186 525.437516 525.275222 \n", - "2 0.323824 501.529129 501.415505 \n", - "3 0.600982 287.051054 286.675385 \n", - "4 0.079213 297.179255 295.019902 \n", - "5 0.484785 387.464785 380.145068 \n", - "6 0.599906 268.627019 250.949344 \n", - "7 0.160990 309.716173 274.795570 \n", - "8 0.334248 326.485952 257.940194 \n", - "9 0.157119 369.696066 209.280306 \n", - "10 0.154930 467.111875 31.146796 \n", - "\n", - " time_between_purchase nb_tickets_internet fidelity gender_female \\\n", - " mean mean mean mean \n", - "category \n", - "0 -0.977118 0.001585 0.000776 0.000000 \n", - "1 -0.729328 0.054312 0.111832 0.245480 \n", - "2 -0.554439 0.969939 0.304757 0.392570 \n", - "3 0.105360 1.776035 0.659878 0.288813 \n", - "4 1.898178 0.293760 0.894877 0.666980 \n", - "5 7.111357 2.080397 1.164958 0.497758 \n", - "6 17.539247 2.525994 1.420921 0.534607 \n", - "7 34.796876 0.844250 1.963028 0.650364 \n", - "8 68.425460 2.794279 2.413009 0.606583 \n", - "9 160.348544 3.514464 5.394498 0.669314 \n", - "10 435.950994 54.295775 64.704225 0.507042 \n", - "\n", - " gender_male gender_other nb_campaigns nb_campaigns_opened \n", - " mean mean mean mean \n", - "category \n", - "0 0.000032 0.999968 13.984219 1.302720 \n", - "1 0.495929 0.258591 18.413562 3.718711 \n", - "2 0.297258 0.310173 17.395042 2.608084 \n", - "3 0.253244 0.457943 16.790421 4.173954 \n", - "4 0.301424 0.031596 16.954707 6.060621 \n", - "5 0.259769 0.242473 27.006406 12.457719 \n", - "6 0.304259 0.161134 14.073285 4.604134 \n", - "7 0.263464 0.086172 26.186317 8.891703 \n", - "8 0.251567 0.141850 30.987461 11.676332 \n", - "9 0.223766 0.106920 45.928247 18.241634 \n", - "10 0.295775 0.197183 53.352113 26.070423 " - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Grouper le DataFrame par la colonne 'category' et calculer la moyenne pour chaque groupe\n", - "summary_stats = dataset_for_segmentation.groupby('category')[numeric_features].describe()\n", - "\n", - "# Sélectionner uniquement la colonne 'mean' pour chaque variable numérique\n", - "mean_stats = summary_stats.loc[:, (slice(None), 'mean')]\n", - "\n", - "# Afficher le DataFrame résultant\n", - "mean_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "14da601e-7b1b-469c-bab1-de8fad4047f2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot histogram\n", - "plt.figure(figsize=(8, 6))\n", - "plt.hist(y_predict_proba, bins=10, range=(0, 1), color='blue', alpha=0.7)\n", - "\n", - "# Réglage des limites des axes x et y\n", - "plt.xlim(0, 1)\n", - "plt.ylim(0, None) # Laissez le maximum sur l'axe y pour s'ajuster automatiquement\n", - "\n", - "plt.title('Histogramme des probabilités pour la classe 1')\n", - "plt.xlabel('Probabilité')\n", - "plt.ylabel('Fréquence')\n", - "plt.grid(True)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "98119520-17ae-4b15-afb2-3e2ba0ceaeb0", - "metadata": {}, - "source": [ - "### Random Forest" - ] - }, - { - "cell_type": "markdown", - "id": "59280d0d-b03e-445c-b9e8-689960275b7d", - "metadata": {}, - "source": [ - "#### Benchmark " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "d585a6b9-6943-45a3-b37b-4fb3c0164a0c", - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_rf = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('randomF', RandomForestClassifier(class_weight = weight_dict,\n", - " n_jobs=-1)) \n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "6f1aacc1-c251-43bd-8681-919ec5efbd87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'time_between_purchase',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'is_email_true', 'opt_in',\n",
-       "                                                   'gender_female',\n",
-       "                                                   'gender_male',\n",
-       "                                                   'gender_other',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in'])])),\n",
-       "                ('randomF',\n",
-       "                 RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                      1.0: 3.486549107420539},\n",
-       "                                        n_jobs=-1))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'time_between_purchase',\n", - " 'nb_tickets_internet',\n", - " 'is_email_true', 'opt_in',\n", - " 'gender_female',\n", - " 'gender_male',\n", - " 'gender_other',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in'])])),\n", - " ('randomF',\n", - " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " n_jobs=-1))])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline_rf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "ad83f5de-3e0d-40d0-bcb0-427530642d22", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.8915667665667666\n", - "F1 Score: 0.5773505313539385\n", - "Recall Score: 0.5198685171658145\n" - ] - } - ], - "source": [ - "y_pred = pipeline_rf.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d48d7b80-1a30-47f4-a179-e7522d2a905a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AcHR01IlzdHQUj6nVapibm8PGxuaxMQ4ODhWu5+DgoBPz6HVsbGxgbm4uxlQXlzwTERHpm0RLnmfMmIEpU6bo7JPL5U88b+3atRg4cKBOteNBt3T7JQhChX2PejSmsviniakOVlqIiIiMhFwuh7W1tc72pKTl0qVLOHjwIMaMGSPuUyqVAFCh0pGfny9WRZRKJUpKSqDRaB4bc+3atQrXvH79uk7Mo9fRaDQoLS2tUIF5EiYtRERE+maA1UMPrV+/Hg4ODvDz8xP3tWrVCkqlUlxRBDyY95KcnIwePXoAADw8PGBmZqYTk5eXh6ysLDHGy8sLhYWFOHbsmBhz9OhRFBYW6sRkZWUhLy9PjElMTIRcLoeHh0eN7oXDQ0RERPpmoCfilpeXY/369Rg5ciRMTf/6lS+TyRAWFoaoqCg4OzvD2dkZUVFRsLS0RFBQEABAoVBg9OjRCA8Ph52dHWxtbREREQF3d3d4e3sDANq3b48BAwYgNDQUq1evBgCMHTsW/v7+cHFxAQD4+PjA1dUVwcHBWLBgAQoKChAREYHQ0NAarRwCmLQQERHVWQcPHkRubi5GjRpV4di0adNQXFyM8ePHQ6PRwNPTE4mJiWjYsKEYs3jxYpiammLo0KEoLi5Gv379sGHDBpiYmIgxW7ZsweTJk8VVRoGBgYiJ+evxCiYmJti7dy/Gjx+Pnj17wsLCAkFBQVi4cGGN74fPaSF6jvA5LUQVPYvntFi+vk6Sdu7FVUw+niestBAREelZTVfJUOU4EZeIiIiMAistRERE+sZCiySYtBAREekZh4ekweEhIiIiMgqstBAREekZKy3SYNJCRESkZ0xapMGkhYiISM+YtEiDc1qIiIjIKLDSQkREpG8stEiCSQsREZGecXhIGhweIiIiIqPASgsREZGesdIiDSYtREREesakRRocHiIiIiKjwEoLERGRnrHSIg0mLURERPrGnEUSHB4iIiIio8BKCxERkZ5xeEgaTFqIiIj0jEmLNJi0EBER6RmTFmkYNGkpKirC1q1bkZqaCrVaDZlMBkdHR/Ts2RNvvfUWrKysDNk9IiIiqkUMNhH3zJkzaNeuHaZNmwaNRoPmzZujWbNm0Gg0mDp1KlxcXHDmzBlDdY+IiEg6Mom255zBKi0TJkxAr169sHHjRpibm+scKykpQUhICCZMmIDDhw8bqIdERETS4PCQNAyWtBw9ehTHjx+vkLAAgLm5OWbOnIlu3boZoGdERERUGxlseMjGxgbnz5+v8vhvv/0GGxubZ9gjIiIi/ZDJZJJszzuDVVpCQ0MxcuRIfPjhh+jfvz8cHR0hk8mgVqtx4MABREVFISwszFDdIyIikgwTDmkYLGmZPXs2LCwssGjRIkybNk38CxUEAUqlEh988AGmTZtmqO4RERFRLWPQJc/Tp0/H9OnTkZOTA7VaDQBQKpVo1aqVIbtFREQkKVZapFErHi7XqlUrJipERFR3MWeRBF+YSEREREahVlRaiIiI6jIOD0mDSQsREZGeMWmRBpMWIiIiPWPSIg2Dz2lJSEhASkqK+PPy5cvRqVMnBAUFQaPRGLBnREREVJsYPGmZOnUqbt++DQA4deoUwsPDMWjQIFy4cAFTpkwxcO+IiIgkwBcmSsLgw0M5OTlwdXUFAMTFxcHf3x9RUVH45ZdfMGjQIAP3joiI6O/j8JA0DF5pMTc3x7179wAABw8ehI+PDwDA1tZWrMAQERERGbzS8vLLL2PKlCno2bMnjh07hm+//RYAcO7cOTRr1szAvXu+/Lp3Dlqo7CrsX/Xtj3h/7jZYWZjj08mvIaBvB9gqrHDpagFWxP6ANd/9NSfp+zXvoVdXZ53zv/s+A+98sP6x11m4PhEfLd0l/tynWztEjvfHi21VuHtPi617jiFy+W6UlZVLdbtET21b7FZs+/YbXP3jDwBAm7bOGPfueLz8Sm8AwL2iIixZ/DkOJx1E4a1bUDVtiqARwRg6PKhCW4IgYML/heLnlJ+weOlyvNrPGwCQfuwoxvzznUqvvyX2O7i5d9DT3ZE+sNIiDYNXWmJiYmBqaort27dj5cqVaNq0KQBg//79GDBggIF793x5+e0FaOk9Q9wG/d8yAMCOAycAAPMjXkf/Hq7456yv0WnIp1i25TAWTXsT/n3cddpZG/ezTjsTP/2mwrXmrNijEzN3TYJ4zM1ZhZ3L3kVi6hl0f2su3pmxHn693fHp5Nf0ePdE1efgqMR770dg67Y4bN0Wh26e3fHexAn47bcHb65fMC8aqSk/IWruAsTv3oe3g0MwN+pTHE46WKGtzV9vrPQXWqdOnXHohxSdbcjrb0LVtCledHOvEE+1m6He8vzHH3/g7bffhp2dHSwtLdGpUydkZGSIxwVBwOzZs6FSqWBhYYE+ffrg9OnTOm1otVpMmjQJ9vb2sLKyQmBgIK5cuaITo9FoEBwcDIVCAYVCgeDgYNy6dUsnJjc3FwEBAbCysoK9vT0mT56MkpKSGt2PwSstzZs3x549eyrsX7x4sQF683y7obmr83PEP93we+51/JTx4D/Enh1aYfOeo+LP63b8jNGv90QX1+bY88Mp8bzi+yW4dvPOY691t+h+lTFv+nog6/xVRH/5IJG5cPkGPl62CxujQ/DZ6n24e0/71PdIJIU+fV/V+XnSe+9jW+w3OPmfTLRt64z//CcTAa8NxkvdPAEAbwwdhu3ffYvTWVno+6q3eN7ZX3/Fpq/XY2vsdvTr87JOm2bm5rBv3Fj8ubS0FD/8kIThb43gv9qpWjQaDXr27Im+ffti//79cHBwwO+//45GjRqJMfPnz8eiRYuwYcMGtGvXDp9++in69++Ps2fPomHDhgCAsLAw7N69G7GxsbCzs0N4eDj8/f2RkZEBExMTAEBQUBCuXLmChIQH/90eO3YsgoODsXv3bgBAWVkZ/Pz80LhxY6SkpODmzZsYOXIkBEHAsmXLqn1PBq+0/PLLLzh16q9feP/+978xePBgzJw5s8YZGEnHzNQEwwe9hI3/PiLuS828AP/e7lA1VgAAenV1hnMLBxxMzdY5d9igrricNBcZ22ch+v1/oIGlvEL7U0L648rheUiL/QDTRvvCzNREPCY3N8V9balOfLG2FBb1zdG5fXMpb5PobysrK8P+fXtRXHwPHTt2BgB07tIFyYeTcO3aNQiCgGNH03DpYg569PwrMSkuLsYHU6dgxqyPdJKTqiQfTsItjQavDR6it3sh/TFEpWXevHlwcnLC+vXr0a1bN7Rs2RL9+vVDmzZtADyosixZsgSzZs3CkCFD4Obmho0bN+LevXvYunUrAKCwsBBr167F559/Dm9vb3Tu3BmbN2/GqVOncPDgg8phdnY2EhIS8NVXX8HLywteXl5Ys2YN9uzZg7NnzwIAEhMTcebMGWzevBmdO3eGt7c3Pv/8c6xZs6ZG81cNnrSMGzcO586dAwBcuHABw4cPh6WlJb777jtMmzbNwL17fgX27YBGDS2wefdRcV/4vO+QfUGN3xM/w+1jX2DX8vF4L/pbpGZeEGNi96Vj5IwN8A39AnPXJGBwv46I/TxUp+3lW3/AOzPWY8DYL7Dq22RMHNEHX8wcJh4/kJqN7h1bY+gAD9SrJ4OqsQIfjPEFADRpbK3nOyeqnvPnzqJ71854qbM7PvtXJBYvXY42bdsCAD6Y8SFat2kLn1d7oWsnN4wfNwYzP4pEF4+u4vkL5kWjY+fOOpWXx4nfsR09er4MZZMmerkf0jOJljxrtVrcvn1bZ9NqK68+79q1C127dsWbb74JBwcHdO7cGWvWrBGP5+TkQK1WiwtgAEAul6N3795ITU0FAGRkZKC0tFQnRqVSwc3NTYw5cuQIFAoFPD09xZju3btDoVDoxLi5uUGlUokxvr6+0Gq1OsNVT2Lw4aFz586hU6dOAIDvvvsOvXr1wtatW/Hzzz9j+PDhWLJkyWPP12q1Ff7ChPIyyOqZVHEGVcfIwT3w/c9nkHe9UNw34a0+6ObeEq+/twq5eQV4uUtbfDFjGNQ3buPw0QfZ9Pr4VDH+zO95+C03H6lbp6PTC82Q+euDMdBlWw6LMVnnr+LW7WJ8s3AMPvzi3ygoLMKhtF8xc8lOLJ05HGs/eQfa0j8xd00CenZpy4m4VGu0bNkK2+J24s6d2zh4IBEfzZyOtRs2o03btti6ZRNOnszEFzEroVKpkHH8OKI+mYPGjR3Q3asHfkg6hPSjafh2e3y1rnVNrUbqzylY8PkS/d4U1XrR0dGYM2eOzr7IyEjMnj27QuyFCxewcuVKTJkyBTNnzsSxY8cwefJkyOVyvPPOO1Cr1QAAR0dHnfMcHR1x6dIlAIBarYa5uTlsbGwqxDw8X61Ww8HBocL1HRwcdGIevY6NjQ3Mzc3FmOoweNIiCALKyx/8Ijp48CD8/f0BAE5OTrhx48YTz6/sL9DE8SWYNekmfWefE82b2OBVTxcMj/grI68vN8OcSQEYNmUNElIeTNLKOn8VHVyaISy4n5i0POpE9mWUlP6Jts0dxKTlUcdO5gAA2jjZo6CwCACwdHMSlm5OQpPGCmhu30MLlS0+mfwaLv5xU8pbJXpqZubmaN6iBQDgRTd3nM46hS2bv8a0D2Zi6ZLFWLw0Br169wEAtHN5AWfPZmPj+rXo7tUDx46m4fLlXLzs9ZJOm+Fhk9DFoyvWbtiks39nfBwUjRqh9yNzach4SDUPacaMGRUevCqXVxyCB4Dy8nJ07doVUVFRAIDOnTvj9OnTWLlyJd5556+VaY/2TRCEJ/b30ZjK4p8m5kkMnrR07doVn376Kby9vZGcnIyVK1cCeFC2ejQrq0xlf4EOr0zXS1+fF8GBXsgvuIP9P/01g9zM1ATmZqYoFwSd2LKyctSrV/X/4VzbNIG5mSnybhRWGdPxBScAgPpGxXHNh5WeoQO64nJeAU78erlG90L0rAiCgNKSEvz555/488/SCt+LevVMxO/PqDFj8Y833tQ5/sbgAERMn4HeffpWaPffO3cgIHAwzMzM9HsTpDdSJS1yubzKJOVRTZo0ER/e+lD79u0RFxcHAFAqlQAeVEGa/M+wY35+vvj7V6lUoqSkBBqNRqfakp+fjx49eogx165dq3D969ev67Rz9OhRneMajQalpaXV+l3/kMGTliVLlmDEiBHYuXMnZs2ahbb/HRPevn27+IE8TmV/gRwaenoymQzvvNYdW/Yc1RmKuVN0Hz8eP4+osMEovl+K3LwCvOLRFiP8u2H6oh0AgFbN7DF8UFd8n3IGNzR30b6NEnPfH4IT2Zdx5L/zXjw7tEI395ZITj+Hwrv30fXF5pgf8Tp2/3ASl9V/vWvq/Xf6ITE1G+Xl5XitXydE/LM/3p62DuXlukkTkSEsXbIIL7/SC45KJe4VFSFh/z4cTz+GFau/QoMGDdD1pW5YtHAB5PL6aKJSISM9HXt27UTEtA8AAPaNG1c6+bZJExWaNXPS2XfsaBr+uHIF/xjyxjO5N9IPQyz46tmzpzgR9qFz586hxX8rhK1atYJSqcSBAwfQufODSeQlJSVITk7GvHnzAAAeHh4wMzPDgQMHMHToUABAXl4esrKyMH/+fACAl5cXCgsLcezYMXTr9mCU4+jRoygsLBR/j3t5eeGzzz5DXl6emCAlJiZCLpfDw8Oj2vdk8KSlQ4cOOquHHlqwYIG4lIqenVc9XdC8iS027kyrcOydD9bhX5New4aokbCxtkRuXgFmL98jPlyutPRP9O3mgglv9UUDS3NcUd9CQkoWPlu9X0w2tCWleMOnC2aOGwi5mSly8wqwbkcqFm08oHMtn56umDbGF3IzU5w69wfefP9LJP58Rv8fAFE13Lx5A7M+mIbr1/PRoGFDtGvnghWrv4JXj54AgHkLFuGLJYswY3oEbhcWoolKhYmT38ebw96q8bXi47ajU6fOaP3fFR9E1fX++++jR48eiIqKwtChQ3Hs2DF8+eWX+PLLLwE8+EdqWFgYoqKi4OzsDGdnZ0RFRcHS0hJBQQ8ehKhQKDB69GiEh4fDzs4Otra2iIiIgLu7O7y9H0wib9++PQYMGIDQ0FCsXr0awIMlz/7+/nBxcQEA+Pj4wNXVFcHBwViwYAEKCgoQERGB0NBQWFtXf4GFTBCEOvdPV4vOEw3dBaJaSZMeY+guENU69Z/BP9+dpyY8Oagazi+o2UNX9+zZgxkzZuD8+fNo1aoVpkyZgtDQv1Z0CoKAOXPmYPXq1dBoNPD09MTy5cvh5uYmxty/fx9Tp07F1q1bUVxcjH79+mHFihVwcvqrKlhQUIDJkydj164HTzYPDAxETEyMzjNhcnNzMX78eCQlJcHCwgJBQUFYuHBhtYe7gFqQtJSVlWHx4sXYtm0bcnNzKzybpaCgoMZtMmkhqhyTFqKKnkXS0m6aNEnLufnP95PiDf6cljlz5mDRokUYOnQoCgsLMWXKFAwZMgT16tWrdAkXERERPZ8MnrRs2bIFa9asQUREBExNTfHWW2/hq6++wscff4y0tIrzKoiIiIyNod49VNcYPGlRq9Vwd3/w8q8GDRqgsPDBEld/f3/s3bvXkF0jIiKShEwmzfa8M3jS0qxZM+Tl5QEA2rZti8TERABAenp6jSbnEBERUd1m8KTlH//4Bw4dOgQAeO+99/DRRx/B2dkZ77zzDkaNGmXg3hEREf199erJJNmedwZ/TsvcuXPFP7/xxhto1qwZUlNT0bZtWwQGBhqwZ0RERNLg0I40DJ60PKp79+7o3r27obtBREREtYxBkpaHD5+pDlZbiIjI2HHljzQMkrQMHjy4WnEymQxlZWX67QwREZGeMWeRhkGSlvLy8icHERER1RGstEjD4KuHiIiIiKrDYElLUlISXF1dcfv27QrHCgsL8eKLL+LHH380QM+IiIikxSfiSsNgScuSJUuqfCW1QqHAuHHjsHjxYgP0jIiISFp8Iq40DJa0/Oc//8GAAVW/rdLHxwcZGRnPsEdERERUmxnsOS3Xrl2DmZlZlcdNTU1x/fr1Z9gjIiIi/eDQjjQMVmlp2rQpTp06VeXxkydPokmTJs+wR0RERPrB4SFpGCxpGTRoED7++GPcv3+/wrHi4mJERkbC39/fAD0jIiKi2shgw0MffvghduzYgXbt2mHixIlwcXGBTCZDdnY2li9fjrKyMsyaNctQ3SMiIpIMh4ekYbCkxdHREampqXj33XcxY8YMCIIA4MFfrK+vL1asWAFHR0dDdY+IiEgyzFmkYdAXJrZo0QL79u2DRqPBb7/9BkEQ4OzsDBsbG0N2i4iIiGqhWvGWZxsbG7z00kuG7gYREZFecHhIGrUiaSEiIqrLmLNIg0kLERGRnrHSIg2+MJGIiIiMAistREREesZCizSYtBAREekZh4ekweEhIiIiMgqstBAREekZCy3SYNJCRESkZxwekgaHh4iIiMgosNJCRESkZyy0SINJCxERkZ5xeEgaHB4iIiIio8BKCxERkZ6x0iINJi1ERER6xpxFGkxaiIiI9IyVFmlwTgsREREZBVZaiIiI9IyFFmmw0kJERKRnMplMkq0mZs+eXeF8pVIpHhcEAbNnz4ZKpYKFhQX69OmD06dP67Sh1WoxadIk2Nvbw8rKCoGBgbhy5YpOjEajQXBwMBQKBRQKBYKDg3Hr1i2dmNzcXAQEBMDKygr29vaYPHkySkpKavYhgkkLERFRnfXiiy8iLy9P3E6dOiUemz9/PhYtWoSYmBikp6dDqVSif//+uHPnjhgTFhaG+Ph4xMbGIiUlBXfv3oW/vz/KysrEmKCgIGRmZiIhIQEJCQnIzMxEcHCweLysrAx+fn4oKipCSkoKYmNjERcXh/Dw8BrfD4eHiIiI9MxQw0OmpqY61ZWHBEHAkiVLMGvWLAwZMgQAsHHjRjg6OmLr1q0YN24cCgsLsXbtWmzatAne3t4AgM2bN8PJyQkHDx6Er68vsrOzkZCQgLS0NHh6egIA1qxZAy8vL5w9exYuLi5ITEzEmTNncPnyZahUKgDA559/jpCQEHz22Wewtrau9v2w0kJERKRn9WQySTatVovbt2/rbFqttsrrnj9/HiqVCq1atcLw4cNx4cIFAEBOTg7UajV8fHzEWLlcjt69eyM1NRUAkJGRgdLSUp0YlUoFNzc3MebIkSNQKBRiwgIA3bt3h0Kh0Ilxc3MTExYA8PX1hVarRUZGRs0+xxpFExERkcFER0eLc0cebtHR0ZXGenp64uuvv8b333+PNWvWQK1Wo0ePHrh58ybUajUAwNHRUeccR0dH8ZharYa5uTlsbGweG+Pg4FDh2g4ODjoxj17HxsYG5ubmYkx1cXiIiIhIz6QaHpoxYwamTJmis08ul1caO3DgQPHP7u7u8PLyQps2bbBx40Z07979v/3S7ZggCE+c8PtoTGXxTxNTHay0EBER6ZlUq4fkcjmsra11tqqSlkdZWVnB3d0d58+fF+e5PFrpyM/PF6siSqUSJSUl0Gg0j425du1ahWtdv35dJ+bR62g0GpSWllaowDwJkxYiIiI9qyeTZvs7tFotsrOz0aRJE7Rq1QpKpRIHDhwQj5eUlCA5ORk9evQAAHh4eMDMzEwnJi8vD1lZWWKMl5cXCgsLcezYMTHm6NGjKCws1InJyspCXl6eGJOYmAi5XA4PD48a3QOHh4iIiOqgiIgIBAQEoHnz5sjPz8enn36K27dvY+TIkZDJZAgLC0NUVBScnZ3h7OyMqKgoWFpaIigoCACgUCgwevRohIeHw87ODra2toiIiIC7u7u4mqh9+/YYMGAAQkNDsXr1agDA2LFj4e/vDxcXFwCAj48PXF1dERwcjAULFqCgoAAREREIDQ2t0cohgEkLERGR3hni3UNXrlzBW2+9hRs3bqBx48bo3r070tLS0KJFCwDAtGnTUFxcjPHjx0Oj0cDT0xOJiYlo2LCh2MbixYthamqKoUOHori4GP369cOGDRtgYmIixmzZsgWTJ08WVxkFBgYiJiZGPG5iYoK9e/di/Pjx6NmzJywsLBAUFISFCxfW+J5kgiAIT/uB1FYWnScaugtEtZImPebJQUTPmfrP4J/vfquPPTmoGvaO6yZJO8aKc1qIiIjIKHB4iIiISM9k4BsTpcCkhYiISM/+7sofeoDDQ0RERGQUWGkhIiLSM0OsHqqLmLQQERHpGXMWaXB4iIiIiIwCKy1ERER6Vo+lFkkwaSEiItIz5izSYNJCRESkZ5yIKw3OaSEiIiKjwEoLERGRnrHQIg0mLURERHrGibjS4PAQERERGQVWWoiIiPSMdRZpMGkhIiLSM64ekgaHh4iIiMgosNJCRESkZ/VYaJFEtZKWXbt2VbvBwMDAp+4MERFRXcThIWlUK2kZPHhwtRqTyWQoKyv7O/0hIiIiqlS1kpby8nJ994OIiKjOYqFFGpzTQkREpGccHpLGUyUtRUVFSE5ORm5uLkpKSnSOTZ48WZKOERER1RWciCuNGictJ06cwKBBg3Dv3j0UFRXB1tYWN27cgKWlJRwcHJi0EBERkV7U+Dkt77//PgICAlBQUAALCwukpaXh0qVL8PDwwMKFC/XRRyIiIqMmk8kk2Z53NU5aMjMzER4eDhMTE5iYmECr1cLJyQnz58/HzJkz9dFHIiIioyaTaHve1ThpMTMzE7M9R0dH5ObmAgAUCoX4ZyIiIiKp1XhOS+fOnXH8+HG0a9cOffv2xccff4wbN25g06ZNcHd310cfiYiIjFo9Du1IosaVlqioKDRp0gQA8Mknn8DOzg7vvvsu8vPz8eWXX0reQSIiImMnk0mzPe9qXGnp2rWr+OfGjRtj3759knaIiIiIqDJ8uBwREZGeceWPNGqctLRq1eqxH/6FCxf+VoeIiIjqGuYs0qhx0hIWFqbzc2lpKU6cOIGEhARMnTpVqn4RERER6ahx0vLee+9Vun/58uU4fvz43+4QERFRXcPVQ9Ko8eqhqgwcOBBxcXFSNUdERFRncPWQNCSbiLt9+3bY2tpK1RwREVGdwYm40niqh8v974cvCALUajWuX7+OFStWSNo5IiIioodqnLS89tprOklLvXr10LhxY/Tp0wcvvPCCpJ17WurUpYbuAlGtVFxSZuguENU69U1N9H4NyeZiPOdqnLTMnj1bD90gIiKquzg8JI0aJ38mJibIz8+vsP/mzZswMdF/tkpEREQ1Fx0dDZlMpvPoEkEQMHv2bKhUKlhYWKBPnz44ffq0znlarRaTJk2Cvb09rKysEBgYiCtXrujEaDQaBAcHQ6FQQKFQIDg4GLdu3dKJyc3NRUBAAKysrGBvb4/JkyejpKSkRvdQ46RFEIRK92u1Wpibm9e0OSIiojqvnkya7Wmlp6fjyy+/RIcOHXT2z58/H4sWLUJMTAzS09OhVCrRv39/3LlzR4wJCwtDfHw8YmNjkZKSgrt378Lf3x9lZX8NNwcFBSEzMxMJCQlISEhAZmYmgoODxeNlZWXw8/NDUVERUlJSEBsbi7i4OISHh9foPqo9PLR06YN5IjKZDF999RUaNGig05kff/yx1sxpISIiqk3+TsLxd929excjRozAmjVr8Omnn4r7BUHAkiVLMGvWLAwZMgQAsHHjRjg6OmLr1q0YN24cCgsLsXbtWmzatAne3t4AgM2bN8PJyQkHDx6Er68vsrOzkZCQgLS0NHh6egIA1qxZAy8vL5w9exYuLi5ITEzEmTNncPnyZahUKgDA559/jpCQEHz22Wewtrau1r1UO2lZvHixeJOrVq3SGQoyNzdHy5YtsWrVquo2R0RERDWk1Wqh1Wp19snlcsjl8irPmTBhAvz8/ODt7a2TtOTk5ECtVsPHx0enrd69eyM1NRXjxo1DRkYGSktLdWJUKhXc3NyQmpoKX19fHDlyBAqFQkxYAKB79+5QKBRITU2Fi4sLjhw5Ajc3NzFhAQBfX19otVpkZGSgb9++1br/aictOTk5AIC+fftix44dsLGxqe6pREREzzWpJuJGR0djzpw5OvsiIyOrXCQTGxuLX375Benp6RWOqdVqAICjo6POfkdHR1y6dEmMMTc3r/A739HRUTxfrVbDwcGhQvsODg46MY9ex8bGBubm5mJMddR49dDhw4dregoREdFzTarhoRkzZmDKlCk6+6qqsly+fBnvvfceEhMTUb9+/SrbfDShEgThiUnWozGVxT9NzJPUeCLuG2+8gblz51bYv2DBArz55ps1bY6IiIiqSS6Xw9raWmerKmnJyMhAfn4+PDw8YGpqClNTUyQnJ2Pp0qUwNTUVKx+PVjry8/PFY0qlEiUlJdBoNI+NuXbtWoXrX79+XSfm0etoNBqUlpZWqMA8To2TluTkZPj5+VXYP2DAAPz44481bY6IiKjOM8S7h/r164dTp04hMzNT3Lp27YoRI0YgMzMTrVu3hlKpxIEDB8RzSkpKkJycjB49egAAPDw8YGZmphOTl5eHrKwsMcbLywuFhYU4duyYGHP06FEUFhbqxGRlZSEvL0+MSUxMhFwuh4eHR7XvqcbDQ3fv3q10abOZmRlu375d0+aIiIjqPEO85blhw4Zwc3PT2WdlZQU7Oztxf1hYGKKiouDs7AxnZ2dERUXB0tISQUFBAACFQoHRo0cjPDwcdnZ2sLW1RUREBNzd3cXVRO3bt8eAAQMQGhqK1atXAwDGjh0Lf39/uLi4AAB8fHzg6uqK4OBgLFiwAAUFBYiIiEBoaGi1Vw4BT1FpcXNzw7ffflthf2xsLFxdXWvaHBERUZ1XT6JNatOmTUNYWBjGjx+Prl274o8//kBiYiIaNmwoxixevBiDBw/G0KFD0bNnT1haWmL37t06q4i3bNkCd3d3+Pj4wMfHBx06dMCmTZvE4yYmJti7dy/q16+Pnj17YujQoRg8eDAWLlxYo/7KhKqeFleFXbt24fXXX0dQUBBeffVVAMChQ4ewdetWbN++HYMHD65RB/ShsLjc0F0gqpXKa/Z1J3ou2Fjq/2nuM/edk6SdqEHtJGnHWNV4eCgwMBA7d+5EVFQUtm/fDgsLC3Ts2BFJSUk1KvEQERE9L/jqIWnUOGkBAD8/P3Ey7q1bt7BlyxaEhYXhP//5j85jfYmIiMgwc1rqoqceIktKSsLbb78NlUqFmJgYDBo0CMePH5eyb0RERESiGlVarly5gg0bNmDdunUoKirC0KFDUVpairi4OE7CJSIiqgILLdKodqVl0KBBcHV1xZkzZ7Bs2TJcvXoVy5Yt02ffiIiI6gRDv+W5rqh2pSUxMRGTJ0/Gu+++C2dnZ332iYiIiKiCaldafvrpJ9y5cwddu3aFp6cnYmJicP36dX32jYiIqE6oJ5NJsj3vqp20eHl5Yc2aNcjLy8O4ceMQGxuLpk2bory8HAcOHMCdO3f02U8iIiKjZYjH+NdFNV49ZGlpiVGjRiElJQWnTp1CeHg45s6dCwcHBwQGBuqjj0RERER/76nALi4umD9/Pq5cuYJvvvlGqj4RERHVKZyIK42nerjco0xMTDB48OBa8Qh/IiKi2kYGZhxSkCRpISIioqqxSiINfbw0koiIiEhyrLQQERHpGSst0mDSQkREpGcyrleWBIeHiIiIyCiw0kJERKRnHB6SBpMWIiIiPePokDQ4PERERERGgZUWIiIiPePLDqXBpIWIiEjPOKdFGhweIiIiIqPASgsREZGecXRIGkxaiIiI9KweX5goCSYtREREesZKizQ4p4WIiIiMAistREREesbVQ9Jg0kJERKRnfE6LNDg8REREREaBlRYiIiI9Y6FFGkxaiIiI9IzDQ9Lg8BAREREZBVZaiIiI9IyFFmkwaSEiItIzDmtIg58jERERGQVWWoiIiPRMxvEhSTBpISIi0jOmLNJg0kJERKRnXPIsDc5pISIiqoNWrlyJDh06wNraGtbW1vDy8sL+/fvF44IgYPbs2VCpVLCwsECfPn1w+vRpnTa0Wi0mTZoEe3t7WFlZITAwEFeuXNGJ0Wg0CA4OhkKhgEKhQHBwMG7duqUTk5ubi4CAAFhZWcHe3h6TJ09GSUlJje+JSQsREZGeySTaaqJZs2aYO3cujh8/juPHj+PVV1/Fa6+9JiYm8+fPx6JFixATE4P09HQolUr0798fd+7cEdsICwtDfHw8YmNjkZKSgrt378Lf3x9lZWViTFBQEDIzM5GQkICEhARkZmYiODhYPF5WVgY/Pz8UFRUhJSUFsbGxiIuLQ3h4eA3vCJAJgiDU+KxarrC43NBdIKqVyuve153ob7OxNNH7Nbb+cuXJQdUQ1KXZ3zrf1tYWCxYswKhRo6BSqRAWFobp06cDeFBVcXR0xLx58zBu3DgUFhaicePG2LRpE4YNGwYAuHr1KpycnLBv3z74+voiOzsbrq6uSEtLg6enJwAgLS0NXl5e+PXXX+Hi4oL9+/fD398fly9fhkqlAgDExsYiJCQE+fn5sLa2rnb/WWkhIiIyElqtFrdv39bZtFrtE88rKytDbGwsioqK4OXlhZycHKjVavj4+IgxcrkcvXv3RmpqKgAgIyMDpaWlOjEqlQpubm5izJEjR6BQKMSEBQC6d+8OhUKhE+Pm5iYmLADg6+sLrVaLjIyMGt0/kxYiIiI9k8lkkmzR0dHi3JGHW3R0dJXXPXXqFBo0aAC5XI7/+7//Q3x8PFxdXaFWqwEAjo6OOvGOjo7iMbVaDXNzc9jY2Dw2xsHBocJ1HRwcdGIevY6NjQ3Mzc3FmOri6iEiIiI9k6pCMGPGDEyZMkVnn1wurzLexcUFmZmZuHXrFuLi4jBy5EgkJyeLxx99fowgCE98psyjMZXFP01MdbDSQkREZCTkcrm4Gujh9rikxdzcHG3btkXXrl0RHR2Njh074osvvoBSqQSACpWO/Px8sSqiVCpRUlICjUbz2Jhr165VuO7169d1Yh69jkajQWlpaYUKzJMwaSEiItIzqYaH/i5BEKDVatGqVSsolUocOHBAPFZSUoLk5GT06NEDAODh4QEzMzOdmLy8PGRlZYkxXl5eKCwsxLFjx8SYo0ePorCwUCcmKysLeXl5YkxiYiLkcjk8PDxq1H8ODxEREemZIR4tN3PmTAwcOBBOTk64c+cOYmNj8cMPPyAhIQEymQxhYWGIioqCs7MznJ2dERUVBUtLSwQFBQEAFAoFRo8ejfDwcNjZ2cHW1hYRERFwd3eHt7c3AKB9+/YYMGAAQkNDsXr1agDA2LFj4e/vDxcXFwCAj48PXF1dERwcjAULFqCgoAAREREIDQ2t0cohgEkLERFRnXTt2jUEBwcjLy8PCoUCHTp0QEJCAvr37w8AmDZtGoqLizF+/HhoNBp4enoiMTERDRs2FNtYvHgxTE1NMXToUBQXF6Nfv37YsGEDTEz+Wia+ZcsWTJ48WVxlFBgYiJiYGPG4iYkJ9u7di/Hjx6Nnz56wsLBAUFAQFi5cWON74nNaiJ4jfE4LUUXP4jkt2/+T9+SganijYxNJ2jFWrLQQERHpGSeQSoNJCxERkZ5JMYmWmPwRERGRkWClhYiISM9YZ5EGkxYiIiI94+iQNDg8REREREaBlRYiIiI9q8cBIknU2krLtWvX8K9//cvQ3SAiIvrbZDJptuddrU1a1Go15syZY+huEBERUS1hsOGhkydPPvb42bNnn1FPiIiI9EvG4SFJGCxp6dSpE2QyGSp7i8DD/XwYDxER1QX8dSYNgyUtdnZ2mDdvHvr161fp8dOnTyMgIOAZ94qIiIhqK4MlLR4eHrh69SpatGhR6fFbt25VWoUhIiIyNlw9JA2DJS3jxo1DUVFRlcebN2+O9evXP8MeERER6QeHh6QhE+pgOaOwuNzQXSCqlcrr3ted6G+zsTTR+zUSs69L0o5P+8aStGOsau2SZyIiIqL/xSfiEhER6RmXPEuDSQsREZGe1WPOIgkODxEREZFRYKWFiIhIzzg8JA2DV1oSEhKQkpIi/rx8+XJ06tQJQUFB0Gg0BuwZERGRNPjCRGkYPGmZOnUqbt++DQA4deoUwsPDMWjQIFy4cAFTpkwxcO+IiIiotjD48FBOTg5cXV0BAHFxcfD390dUVBR++eUXDBo0yMC9IyIi+vs4PCQNg1dazM3Nce/ePQDAwYMH4ePjAwCwtbUVKzBERETGrJ5Mmu15Z/BKy8svv4wpU6agZ8+eOHbsGL799lsAwLlz59CsWTMD946IiIhqC4MnLTExMRg/fjy2b9+OlStXomnTpgCA/fv3Y8CAAQbuHQFA/rVriPnic6T+/CO0Wi2aN2+JD2d/ivauLwIA5nw0A3t379Q5x829A9ZtepCAXv3jDwz286607aj5i+Ht8+DvOfy98Th39ldoCm6iobU1unl6YeJ7EWjs4KC/myN6CoMHeUOdd7XC/teHvoWpMz7C4UMHsDNuG37NPo3CW7fwdWwc2rm014ndGbcN3+/fi7O/nsG9oiIc+DENDRtai8czjh/DhNCQSq+/bvO3cH3RXdJ7Iv3i8JA0+O4heqzbtwsRPGwIPF7yxOtvDoeNrR2uXMmFStUUzZyaA3iQtBQU3MRHcz4TzzMzM4NC0QgAUFZWBo2mQKfdnXHbsGnDOuw/9CMsLa0AAFs3bYB7x06wt2+M6/n5+GLRfADA2q+/eQZ3+nzgu4ekoSkoQHl5mfjz77+dx+R3x2D5mg3w6NoN+/fswtU/rsC+sQOiP/m40qQldsvXKNFqAQArli2ukLSUlpbgdmGhzjmrVyxD+tEj2LEnETIuJZHMs3j3UMp5aVbDvuxsI0k7xsrglZZffvkFZmZmcHd/8K+Gf//731i/fj1cXV0xe/ZsmJubG7iHz7ev138FB2UTfPyvKHGf6r/VsP9lZmYOe/vKX+RlYmJS4dgPSYfg7TtATFgAICg4RPxzE1VTjBwViqnvT8SfpaUwNTP7m3dCJB0bW1udn79e/xWaOTmhi8dLAICB/oEAgKtX/6iyjeEj3gHwoKJSGTMzc9j9z/fmz9JS/JR8GG8OC2LCYoT4NyYNg0/EHTduHM6dOwcAuHDhAoYPHw5LS0t89913mDZtmoF7Rz8lH0Z71xfxQUQYfPv2xNvDhmBn3LYKcb8cPwbfvj3xeuAAfDbnIxQU3Kyyzewzp3HubDZeG/xGlTGFhbeQsG83OnTszISFarXS0hIk7NsN/9eG6DWZ+DH5MApvaeAXOFhv1yCq7QxeaTl37hw6deoEAPjuu+/Qq1cvbN26FT///DOGDx+OJUuWPPZ8rVYL7X9LrOK+cjPI5XI99fj58seVy9jxXSyC3g7BP8eMxemsU/h8fhTMzM3hFzAYANDj5VfQr78vmqhUuPrHH1i1fCnGh4bg62/iKq2U7Yrfjlat26BDp84Vji1bshDfxW7F/fvFcOvQEYuWrtT3LRL9LcmHD+HunTvwC/iHXq+ze2ccPL16wlHZRK/XIf2ox+qYJAxeaREEAeXlD+agHDx4UHw2i5OTE27cuPHE86Ojo6FQKHS2RQvm6rXPz5PycgEuL7hi/OT34fKCK4a8MQyvDXkTcd/FijH9fQfh5V590KZtO7zSuy++WL4auZcu4eeffqjQ3v379/H9/r0IHPx6pdcLHjkam76Nw7KVX8GkngnmfPgB6uC0K6pDdu/cge49X9HrhPH8a2ocPfIzAqr43lDtJ5Noe94ZvNLStWtXfPrpp/D29kZycjJWrnzwL+ucnBw4Ojo+8fwZM2ZUeHLu/XIOJ0jFvrE9WrVpo7OvZavWOHww8THnOKBJkybIzb1U4VjSwe9x//59DPJ/rdJzG9nYoJGNDVq0aIWWrdsgwLcvTp3MRIeOFasyRIaWd/UPpB89grkLv9Drdfb8Ox4KRSP06t1Xr9chqu0MnrQsWbIEI0aMwM6dOzFr1iy0bdsWALB9+3b06NHjiefL5fIKQ0ECVw9JpkPHLrh08aLOvtxLF6FsoqrynFu3NLh2TV3pxNxd8XHo1advhYmMlfpvhaW0pLRGfSZ6VvbsioeNrS16vNJbb9cQBAF7dsVjoH8g53cZM5ZJJGHwpKVDhw44depUhf0LFiyAiYn+l6HR4wW9PRKjQ4Kw/qvV8PYZgNNZp7Az7jvM/GgOAODevSKsWbUcffv1h729A/Ku/oEVyxajUSMb9Hm1v05bl3Mv4cQvx7EkZnWF65w+dRKnT59Cp05d0NDaGn/8cQWrVyxDM6fmcO/Y6VncKlGNlJeXY++/4zHIfzBMTXX/U1pYeAvX1Hm4kZ8PAGLib2dnL64IunnjOm7evIErubkAgN/Pn4OllRUclU3ExwUAwPFjabj6xxUODRk5PqdFGgZPWqpSv359Q3eBALi6uWP+oqVYsXQx1n65AqqmzTBl6gcY4BcAAKhXzwS/nT+Hfbv/jTt37sC+sT08unoiav4iWFlZ6bS1e+cONHZwhKdXzwrXkdevj8OHDuDLlctwv7gYdvaN4dXzZXw293Mue6daKf3oEajVeQgYPKTCsZ+SD+PTyFnizx99EA4AGD1uPEL/byIAYMf2b7F29Qox5v9GP1gC/eGcz+Af+Nek3t07d8C9Y2e0aq07TEv0PDL4w+XKysqwePFibNu2Dbm5uSgpKdE5XlBQUMWZVePD5Ygqx4fLEVX0LB4ud+xC4ZODqqFba4Uk7Rgrg68emjNnDhYtWoShQ4eisLAQU6ZMwZAhQ1CvXj3Mnj3b0N0jIiL627h6SBoGr7S0adMGS5cuhZ+fHxo2bIjMzExxX1paGrZu3VrjNllpIaocKy1EFT2LSku6RJWWl1hpMSy1Wi0+wr9BgwYo/O+7Nvz9/bF3715Ddo2IiEgaLLVIwuBJS7NmzZCXlwcAaNu2LRITHzz/Iz09nU+1JSKiOkEm0f9qIjo6Gi+99BIaNmwIBwcHDB48GGfPntWJEQQBs2fPhkqlgoWFBfr06YPTp0/rxGi1WkyaNAn29vawsrJCYGAgrly5ohOj0WgQHBwsPuQ1ODgYt27d0onJzc1FQEAArKysYG9vj8mTJ1eYx/okBk9a/vGPf+DQoUMAgPfeew8fffQRnJ2d8c4772DUqFEG7h0REdHfJ5NJs9VEcnIyJkyYgLS0NBw4cAB//vknfHx8UFRUJMbMnz8fixYtQkxMDNLT06FUKtG/f3/cuXNHjAkLC0N8fDxiY2ORkpKCu3fvwt/fH2Vlf73pPCgoCJmZmUhISEBCQgIyMzMRHBwsHi8rK4Ofnx+KioqQkpKC2NhYxMXFITw8vGafo6HntDwqLS0NqampaNu2LQIDA5+qDc5pIaoc57QQVfQs5rRkXLwtSTseLa2f+tzr16/DwcEBycnJ6NWrFwRBgEqlQlhYGKZPnw7gQVXF0dER8+bNw7hx41BYWIjGjRtj06ZNGDZsGADg6tWrcHJywr59++Dr64vs7Gy4uroiLS0Nnp6eAB78Lvfy8sKvv/4KFxcX7N+/H/7+/rh8+TJUqgcPJ42NjUVISAjy8/NhbV29+zJ4peVR3bt3x5QpU546YSEiIqptpJrSotVqcfv2bZ3t0ZcGV+XhnFHb/z6RPCcnB2q1Gj4+PmKMXC5H7969kZqaCgDIyMhAaWmpToxKpYKbm5sYc+TIESgUCjFhAR78LlcoFDoxbm5uYsICAL6+vtBqtcjIyKhW/wEDPVxu165d1Y5l8kJEREZPokm00dHRmDNnjs6+yMjIJz4iRBAETJkyBS+//DLc3NwAPFgIA6DCe/4cHR1x6dIlMcbc3Bw2NjYVYh6er1ar4VDJC0MdHBx0Yh69jo2NDczNzcWY6jBI0jJ48OBqxclkMp0xMyIioudZZS8Jrs6ilYkTJ+LkyZNISUmpcEz2yGQZQRAq7HvUozGVxT9NzJMYZHiovLy8WhsTFiIiqgukWj0kl8thbW2tsz0paZk0aRJ27dqFw4cPo1mzZuJ+pVIJABUqHfn5+WJVRKlUoqSkBBqN5rEx165dq3Dd69ev68Q8eh2NRoPS0tIKFZjHqXVzWoiIiOoaQ6weEgQBEydOxI4dO5CUlIRWrVrpHG/VqhWUSiUOHDgg7ispKUFycjJ69OgBAPDw8ICZmZlOTF5eHrKyssQYLy8vFBYW4tixY2LM0aNHUVhYqBOTlZUlPuIEABITEyGXy+Hh4VHtezJY0pKUlARXV1fcvl1xRnVhYSFefPFF/PjjjwboGRERkfGbMGECNm/ejK1bt6Jhw4ZQq9VQq9UoLi4G8GC4JiwsDFFRUYiPj0dWVhZCQkJgaWmJoKAgAIBCocDo0aMRHh6OQ4cO4cSJE3j77bfh7u4Ob29vAED79u0xYMAAhIaGIi0tDWlpaQgNDYW/vz9cXFwAAD4+PnB1dUVwcDBOnDiBQ4cOISIiAqGhodVeOQQYcMlzYGAg+vbti/fff7/S40uXLsXhw4cRHx9f47a55JmoclzyTFTRs1jy/J/cO08OqoaOzRtWO7aquSLr169HSEgIgAfVmDlz5mD16tXQaDTw9PTE8uXLxcm6AHD//n1MnToVW7duRXFxMfr164cVK1bAyclJjCkoKMDkyZPFhTaBgYGIiYlBo0aNxJjc3FyMHz8eSUlJsLCwQFBQEBYuXFijB8kaLGlp0aIFEhIS0L59+0qP//rrr/Dx8UFubm6N22bSQlQ5Ji1EFT2TpOWyREmLU/WTlrrIYMND165dg5mZWZXHTU1Ncf369WfYIyIiIqrNDJa0NG3aFKdOnary+MmTJ9GkSZNn2CMiIiL9MMS7h+oigyUtgwYNwscff4z79+9XOFZcXIzIyEj4+/sboGdERETSMsTqobrIYHNarl27hi5dusDExAQTJ06Ei4sLZDIZsrOzsXz5cpSVleGXX36p0frthzinhahynNNCVNGzmNOSdeWuJO24NWsgSTvGyqAvTLx06RLeffddfP/993jYDZlMBl9fX6xYsQItW7Z8qnaZtBBVjkkLUUVMWoxHrXjLs0ajwW+//QZBEODs7FzhHQc1xaSFqHJMWogqeiZJyx8SJS1Nn++kxSDvHnqUjY0NXnrpJUN3g4iISC84iVYafIw/ERERGYVaUWkhIiKqy7jyRxpMWoiIiPSMOYs0ODxERERERoGVFiIiIn1jqUUSTFqIiIj0jKuHpMHhISIiIjIKrLQQERHpGVcPSYNJCxERkZ4xZ5EGkxYiIiJ9Y9YiCc5pISIiIqPASgsREZGecfWQNJi0EBER6Rkn4kqDw0NERERkFFhpISIi0jMWWqTBpIWIiEjfmLVIgsNDREREZBRYaSEiItIzrh6SBpMWIiIiPePqIWlweIiIiIiMAistREREesZCizSYtBAREekbsxZJMGkhIiLSM07ElQbntBAREZFRYKWFiIhIz7h6SBpMWoiIiPSMOYs0ODxERERERoGVFiIiIj3j8JA0mLQQERHpHbMWKXB4iIiIiIwCKy1ERER6xuEhabDSQkREpGcyibaa+vHHHxEQEACVSgWZTIadO3fqHBcEAbNnz4ZKpYKFhQX69OmD06dP68RotVpMmjQJ9vb2sLKyQmBgIK5cuaITo9FoEBwcDIVCAYVCgeDgYNy6dUsnJjc3FwEBAbCysoK9vT0mT56MkpKSGt0PkxYiIqI6qqioCB07dkRMTEylx+fPn49FixYhJiYG6enpUCqV6N+/P+7cuSPGhIWFIT4+HrGxsUhJScHdu3fh7++PsrIyMSYoKAiZmZlISEhAQkICMjMzERwcLB4vKyuDn58fioqKkJKSgtjYWMTFxSE8PLxG9yMTBEGo4WdQ6xUWlxu6C0S1Unnd+7oT/W02liZ6v0ZeYc0qClVpojB/6nNlMhni4+MxePBgAA+qLCqVCmFhYZg+fTqAB1UVR0dHzJs3D+PGjUNhYSEaN26MTZs2YdiwYQCAq1evwsnJCfv27YOvry+ys7Ph6uqKtLQ0eHp6AgDS0tLg5eWFX3/9FS4uLti/fz/8/f1x+fJlqFQqAEBsbCxCQkKQn58Pa2vrat0DKy1ERER6JpPof1qtFrdv39bZtFrtU/UpJycHarUaPj4+4j65XI7evXsjNTUVAJCRkYHS0lKdGJVKBTc3NzHmyJEjUCgUYsICAN27d4dCodCJcXNzExMWAPD19YVWq0VGRka1+8ykhYiISN8kmtQSHR0tzht5uEVHRz9Vl9RqNQDA0dFRZ7+jo6N4TK1Ww9zcHDY2No+NcXBwqNC+g4ODTsyj17GxsYG5ubkYUx1cPURERGQkZsyYgSlTpujsk8vlf6tN2SNLmwRBqLDvUY/GVBb/NDFPwkoLERGRnkm1ekgul8Pa2lpne9qkRalUAkCFSkd+fr5YFVEqlSgpKYFGo3lszLVr1yq0f/36dZ2YR6+j0WhQWlpaoQLzOExaiIiI9Ewmk2aTUqtWraBUKnHgwAFxX0lJCZKTk9GjRw8AgIeHB8zMzHRi8vLykJWVJcZ4eXmhsLAQx44dE2OOHj2KwsJCnZisrCzk5eWJMYmJiZDL5fDw8Kh2nzk8REREVEfdvXsXv/32m/hzTk4OMjMzYWtri+bNmyMsLAxRUVFwdnaGs7MzoqKiYGlpiaCgIACAQqHA6NGjER4eDjs7O9ja2iIiIgLu7u7w9vYGALRv3x4DBgxAaGgoVq9eDQAYO3Ys/P394eLiAgDw8fGBq6srgoODsWDBAhQUFCAiIgKhoaHVXjkEcMkz0XOFS56JKnoWS56v3/lTknYaN6xZreGHH35A3759K+wfOXIkNmzYAEEQMGfOHKxevRoajQaenp5Yvnw53NzcxNj79+9j6tSp2Lp1K4qLi9GvXz+sWLECTk5OYkxBQQEmT56MXbt2AQACAwMRExODRo0aiTG5ubkYP348kpKSYGFhgaCgICxcuLBGw1tMWoieI0xaiCp6JknLXYmSlgbP9wAJ57QQERGRUXi+UzYiIqJngO9LlAaTFiIiIj3jW56lweEhIiIiMgqstBAREemZjANEkmDSQkREpGccHpIGh4eIiIjIKDBpISIiIqPA4SEiIiI94/CQNJi0EBER6Rkn4kqDw0NERERkFFhpISIi0jMOD0mDSQsREZGeMWeRBoeHiIiIyCiw0kJERKRvLLVIgkkLERGRnnH1kDQ4PERERERGgZUWIiIiPePqIWkwaSEiItIz5izSYNJCRESkb8xaJME5LURERGQUWGkhIiLSM64ekgaTFiIiIj3jRFxpcHiIiIiIjIJMEATB0J2gukmr1SI6OhozZsyAXC43dHeIag1+N4ieDpMW0pvbt29DoVCgsLAQ1tbWhu4OUa3B7wbR0+HwEBERERkFJi1ERERkFJi0EBERkVFg0kJ6I5fLERkZyYmGRI/gd4Po6XAiLhERERkFVlqIiIjIKDBpISIiIqPApIWIiIiMApMWqjaZTIadO3cauhtEtQq/F0TPDpMWAgCo1WpMmjQJrVu3hlwuh5OTEwICAnDo0CFDdw0AIAgCZs+eDZVKBQsLC/Tp0wenT582dLeojqvt34sdO3bA19cX9vb2kMlkyMzMNHSXiPSKSQvh4sWL8PDwQFJSEubPn49Tp04hISEBffv2xYQJEwzdPQDA/PnzsWjRIsTExCA9PR1KpRL9+/fHnTt3DN01qqOM4XtRVFSEnj17Yu7cuYbuCtGzIdBzb+DAgULTpk2Fu3fvVjim0WjEPwMQ4uPjxZ+nTZsmODs7CxYWFkKrVq2EDz/8UCgpKRGPZ2ZmCn369BEaNGggNGzYUOjSpYuQnp4uCIIgXLx4UfD39xcaNWokWFpaCq6ursLevXsr7V95ebmgVCqFuXPnivvu378vKBQKYdWqVX/z7okqV9u/F/8rJydHACCcOHHiqe+XyBiYGjhnIgMrKChAQkICPvvsM1hZWVU43qhRoyrPbdiwITZs2ACVSoVTp04hNDQUDRs2xLRp0wAAI0aMQOfOnbFy5UqYmJggMzMTZmZmAIAJEyagpKQEP/74I6ysrHDmzBk0aNCg0uvk5ORArVbDx8dH3CeXy9G7d2+kpqZi3Lhxf+MTIKrIGL4XRM8jJi3Pud9++w2CIOCFF16o8bkffvih+OeWLVsiPDwc3377rfgf59zcXEydOlVs29nZWYzPzc3F66+/Dnd3dwBA69atq7yOWq0GADg6Oursd3R0xKVLl2rcb6InMYbvBdHziHNannPCfx+ILJPJanzu9u3b8fLLL0OpVKJBgwb46KOPkJubKx6fMmUKxowZA29vb8ydOxe///67eGzy5Mn49NNP0bNnT0RGRuLkyZNPvN6jfRQE4an6TfQkxvS9IHqeMGl5zjk7O0MmkyE7O7tG56WlpWH48OEYOHAg9uzZgxMnTmDWrFkoKSkRY2bPno3Tp0/Dz88PSUlJcHV1RXx8PABgzJgxuHDhAoKDg3Hq1Cl07doVy5Ytq/RaSqUSwF8Vl4fy8/MrVF+IpGAM3wui55JBZ9RQrTBgwIAaTzhcuHCh0Lp1a53Y0aNHCwqFosrrDB8+XAgICKj02AcffCC4u7tXeuzhRNx58+aJ+7RaLSfikl7V9u/F/+JEXHpesNJCWLFiBcrKytCtWzfExcXh/PnzyM7OxtKlS+Hl5VXpOW3btkVubi5iY2Px+++/Y+nSpeK/FgGguLgYEydOxA8//IBLly7h559/Rnp6Otq3bw8ACAsLw/fff4+cnBz88ssvSEpKEo89SiaTISwsDFFRUYiPj0dWVhZCQkJgaWmJoKAg6T8QItT+7wXwYMJwZmYmzpw5AwA4e/YsMjMzK1QlieoMQ2dNVDtcvXpVmDBhgtCiRQvB3NxcaNq0qRAYGCgcPnxYjMEjSzunTp0q2NnZCQ0aNBCGDRsmLF68WPwXpVarFYYPHy44OTkJ5ubmgkqlEiZOnCgUFxcLgiAIEydOFNq0aSPI5XKhcePGQnBwsHDjxo0q+1deXi5ERkYKSqVSkMvlQq9evYRTp07p46MgEtX278X69esFABW2yMhIPXwaRIYnE4T/zjgjIiIiqsU4PERERERGgUkLERERGQUmLURERGQUmLQQERGRUWDSQkREREaBSQsREREZBSYtREREZBSYtBDVQbNnz0anTp3En0NCQjB48OBn3o+LFy9CJpMhMzPzmV+biOoeJi1Ez1BISAhkMhlkMhnMzMzQunVrREREoKioSK/X/eKLL7Bhw4ZqxTLRIKLaytTQHSB63gwYMADr169HaWkpfvrpJ4wZMwZFRUVYuXKlTlxpaSnMzMwkuaZCoZCkHSIiQ2KlhegZk8vlUCqVcHJyQlBQEEaMGIGdO3eKQzrr1q1D69atIZfLIQgCCgsLMXbsWDg4OMDa2hqvvvoq/vOf/+i0OXfuXDg6OqJhw4YYPXo07t+/r3P80eGh8vJyzJs3D23btoVcLkfz5s3x2WefAQBatWoFAOjcuTNkMhn69Okjnrd+/Xq0b98e9evXxwsvvIAVK1boXOfYsWPo3Lkz6tevj65du+LEiRMSfnJE9LxjpYXIwCwsLFBaWgoA+O2337Bt2zbExcXBxMQEAODn5wdbW1vs27cPCoUCq1evRr9+/XDu3DnY2tpi27ZtiIyMxPLly/HKK69g06ZNWLp0KVq3bl3lNWfMmIE1a9Zg8eLFePnll5GXl4dff/0VwIPEo1u3bjh48CBefPFFmJubAwDWrFmDyMhIxMTEoHPnzjhx4gRCQ0NhZWWFkSNHoqioCP7+/nj11VexefNm5OTk4L333tPzp0dEzxUDv7CR6LkycuRI4bXXXhN/Pnr0qGBnZycMHTpUiIyMFMzMzIT8/Hzx+KFDhwRra2vh/v37Ou20adNGWL16tSAIguDl5SX83//9n85xT09PoWPHjpVe9/bt24JcLhfWrFlTaR9zcnIEAMKJEyd09js5OQlbt27V2ffJJ58IXl5egiAIwurVqwVbW1uhqKhIPL5y5cpK2yIiehocHiJ6xvbs2YMGDRqgfv368PLyQq9evbBs2TIAQIsWLdC4cWMxNiMjA3fv3oWdnR0aNGggbjk5Ofj9998BANnZ2fDy8tK5xqM//6/s7GxotVr069ev2n2+fv06Ll++jNGjR+v049NPP9XpR8eOHWFpaVmtfhAR1RSHh4iesb59+2LlypUwMzODSqXSmWxrZWWlE1teXo4mTZrghx9+qNBOo0aNnur6FhYWNT6nvLwcwIMhIk9PT51jD4exBEF4qv4QEVUXkxaiZ8zKygpt27atVmyXLl2gVqthamqKli1bVhrTvn17pKWl4Z133hH3paWlVdmms7MzLCwscOjQIYwZM6bC8YdzWMrKysR9jo6OaNq0KS5cuIARI0ZU2q6rqys2bdqE4uJiMTF6XD+IiGqKw0NEtZi3tze8vLwwePBgfP/997h48SJSU1Px4Ycf4vjx4wCA9957D+vWrcO6detw7tw5REZG4vTp01W2Wb9+fUyfPh3Tpk3D119/jd9//x1paWlYu3YtAMDBwQEWFhZISEjAtWvXUFhYCODBA+uio6PxxRdf4Ny5czh16hTWr1+PRYsWAQCCgoJQr149jB49GmfOnMG+ffuwcOFCPX9CRPQ8YdJCVIvJZDLs27cPvXr1wqhRo9CuXTsMHz4cFy9ehKOjIwBg2LBh+PjjjzF9+nR4eHjg0qVLePfddx/b7kcffYTw8HB8/PHHaN++PYYNG4b8/HwAgKmpKZYuXYrVq1dDpVLhtddeAwCMGTMGX331FTZs2AB3d3f07t0bGzZsEJdIN2jQALt378aZM2fQuXNnzJo1C/PmzdPjp0NEzxuZwIFoIiIiMgKstBAREZFRYNJCRERERoFJCxERERkFJi1ERERkFJi0EBERkVFg0kJERERGgUkLERERGQUmLURERGQUmLQQERGRUWDSQkREREaBSQsREREZBSYtREREZBT+H62JjmjJdsJmAAAAAElFTkSuQmCC", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_confusion_matrix(y_test, y_pred)\n", - "draw_roc_curve(X_test, y_test)\n", - "draw_features_importance(pipeline_rf, 'randomF', randomF =True)\n", - "draw_prob_distribution(X_test)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/Modelization/3_logit_cross_val_sport.ipynb b/Sport/Modelization/3_logit_cross_val_sport.ipynb deleted file mode 100644 index ef23062..0000000 --- a/Sport/Modelization/3_logit_cross_val_sport.ipynb +++ /dev/null @@ -1,8910 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ff8cc602-e733-4a31-bf46-a31087511fe0", - "metadata": {}, - "source": [ - "# Predict sales - sports companies" - ] - }, - { - "cell_type": "markdown", - "id": "415e466a-1a71-4150-bff7-2f8904766df4", - "metadata": {}, - "source": [ - "## Importations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b5aaf421-850a-4a86-8e99-2c1f0723bd6c", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "from sklearn.naive_bayes import GaussianNB\n", - "\n", - "import pickle\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "c2f44070-451e-4109-9a08-3b80011d610f", - "metadata": {}, - "source": [ - "## Load data " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b5f8135f-b6e7-4d6d-b8e1-da185b944aff", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2668a243-4ff8-40c6-9de2-5c9c07bcf714", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "13eba3e1-3ea5-435b-8b05-6d7d5744cbe2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1481/2459610029.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "street_id 0\n", - "structure_id 222825\n", - "mcp_contact_id 70874\n", - "fidelity 0\n", - "tenant_id 0\n", - "is_partner 0\n", - "deleted_at 224213\n", - "gender 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "last_buying_date 66139\n", - "max_price 66139\n", - "ticket_sum 0\n", - "average_price 66023\n", - "average_purchase_delay 66139\n", - "average_price_basket 66139\n", - "average_ticket_basket 66139\n", - "total_price 116\n", - "purchase_count 0\n", - "first_buying_date 66139\n", - "country 23159\n", - "gender_label 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "country_fr 23159\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 123159\n", - "y_has_purchased 0\n", - "dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train, dataset_test = load_train_test()\n", - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e46622e7-0fc1-43f8-a7e7-34a5e90068b2", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " \"\"\"\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - " \"\"\"\n", - "\n", - " # we suppress fidelity, time between purchase, and gender other (colinearity issue)\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", - " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'is_email_true', \n", - " 'opt_in', 'gender_female', 'gender_male', 'nb_campaigns', 'nb_campaigns_opened']\n", - " \n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cec4f386-e643-4bd8-b8cd-8917d2c1b3d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape train : (224213, 14)\n", - "Shape test : (96096, 14)\n" - ] - } - ], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)\n", - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "c9e8edbd-7ff6-42f9-a8eb-10d27ca19c8a", - "metadata": {}, - "source": [ - "## Logistic" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "639b432a-c39c-4bf8-8ee2-e136d156e0dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute Weights\n", - "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", - " y = y_train['y_has_purchased'])\n", - "\n", - "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n", - "weight_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "34644a00-85a5-41c9-98df-41178cb3ac69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 2.0 1.0 60.00 1.0 \n", - "1 8.0 3.0 140.00 1.0 \n", - "2 2.0 1.0 50.00 1.0 \n", - "3 3.0 1.0 90.00 1.0 \n", - "4 2.0 1.0 78.00 1.0 \n", - "... ... ... ... ... \n", - "224208 0.0 0.0 0.00 0.0 \n", - "224209 1.0 1.0 20.00 1.0 \n", - "224210 0.0 0.0 0.00 0.0 \n", - "224211 1.0 1.0 97.11 1.0 \n", - "224212 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 355.268981 355.268981 \n", - "1 0.0 373.540289 219.262269 \n", - "2 0.0 5.202442 5.202442 \n", - "3 0.0 5.178958 5.178958 \n", - "4 0.0 5.174039 5.174039 \n", - "... ... ... ... \n", - "224208 0.0 550.000000 550.000000 \n", - "224209 1.0 392.501030 392.501030 \n", - "224210 0.0 550.000000 550.000000 \n", - "224211 1.0 172.334074 172.334074 \n", - "224212 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 0.0 True False 0 \n", - "1 0.0 True False 0 \n", - "2 0.0 True False 0 \n", - "3 0.0 True False 0 \n", - "4 0.0 True False 1 \n", - "... ... ... ... ... \n", - "224208 0.0 True False 0 \n", - "224209 1.0 True False 0 \n", - "224210 0.0 True True 0 \n", - "224211 1.0 True False 0 \n", - "224212 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "0 1 0.0 0.0 \n", - "1 1 0.0 0.0 \n", - "2 1 0.0 0.0 \n", - "3 1 0.0 0.0 \n", - "4 0 0.0 0.0 \n", - "... ... ... ... \n", - "224208 1 34.0 3.0 \n", - "224209 1 23.0 6.0 \n", - "224210 1 8.0 4.0 \n", - "224211 1 13.0 5.0 \n", - "224212 1 4.0 4.0 \n", - "\n", - "[224213 rows x 14 columns]" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "295676df-36ac-43d8-8b31-49ff08efd6e7", - "metadata": {}, - "outputs": [], - "source": [ - "# preprocess data \n", - "# numeric features - standardize\n", - "# categorical features - encode\n", - "# encoded features - do nothing\n", - "\n", - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", - " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'nb_campaigns', \n", - " 'nb_campaigns_opened' # , 'gender_male', 'gender_female'\n", - " ]\n", - "\n", - "numeric_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n", - " (\"scaler\", StandardScaler()) \n", - "])\n", - "\n", - "categorical_features = ['opt_in', 'is_email_true'] \n", - "\n", - "# Transformer for the categorical features\n", - "categorical_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", - "])\n", - "\n", - "preproc = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features)\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "f46fb56e-c908-40b4-868f-9684d1ae01c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "nb_tickets_internet 0\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "dtype: int64" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[numeric_features].isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "e729781b-4d65-42c5-bdc5-82b4d653aaf0", - "metadata": {}, - "outputs": [], - "source": [ - "# Set loss\n", - "balanced_scorer = make_scorer(balanced_accuracy_score)\n", - "recall_scorer = make_scorer(recall_score)\n", - "f1_scorer = make_scorer(f1_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "a7ebbe6f-70ba-4276-be18-f10e7bfd7423", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_confusion_matrix(y_test, y_pred):\n", - " conf_matrix = confusion_matrix(y_test, y_pred)\n", - " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - " plt.xlabel('Predicted')\n", - " plt.ylabel('Actual')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", - "\n", - "\n", - "def draw_roc_curve(X_test, y_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - " # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - " fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - " \n", - " # Calcul de l'aire sous la courbe ROC (AUC)\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " plt.figure(figsize = (14, 8))\n", - " plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - " plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - " plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - " plt.xlabel('Taux de faux positifs (FPR)')\n", - " plt.ylabel('Taux de vrais positifs (TPR)')\n", - " plt.title('Courbe ROC : modèle logistique')\n", - " plt.legend(loc=\"lower right\")\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "2334eb51-e6ea-4fd0-89ce-f54cd474d332", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_features_importance(pipeline, model):\n", - " coefficients = pipeline.named_steps['logreg'].coef_[0]\n", - " feature_names = pipeline.named_steps['logreg'].feature_names_in_\n", - " \n", - " # Tracer l'importance des caractéristiques\n", - " plt.figure(figsize=(10, 6))\n", - " plt.barh(feature_names, coefficients, color='skyblue')\n", - " plt.xlabel('Importance des caractéristiques')\n", - " plt.ylabel('Caractéristiques')\n", - " plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n", - " plt.grid(True)\n", - " plt.show()\n", - "\n", - "def draw_prob_distribution(X_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - " plt.figure(figsize=(8, 6))\n", - " plt.hist(y_pred_prob, bins=10, range=(0, 1), color='blue', alpha=0.7)\n", - " \n", - " plt.xlim(0, 1)\n", - " plt.ylim(0, None)\n", - " \n", - " plt.title('Histogramme des probabilités pour la classe 1')\n", - " plt.xlabel('Probabilité')\n", - " plt.ylabel('Fréquence')\n", - " plt.grid(True)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "83917b97-4d9b-4e3c-ba27-1e546ce885d3", - "metadata": {}, - "outputs": [], - "source": [ - "# Hyperparameter\n", - "\n", - "param_c = np.logspace(-10, 4, 15, base=2)\n", - "# param_penalty_type = ['l1', 'l2', 'elasticnet']\n", - "param_penalty_type = ['l1']\n", - "param_grid = {'logreg__C': param_c,\n", - " 'logreg__penalty': param_penalty_type} " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "3ae25049-920c-4a6d-a59d-c26e3b45dec6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1024" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2 ** 10" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "ba4cde9f-a614-4a43-81b9-e16e78aa6c4c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in',\n",
-       "                                                   'is_email_true'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver='saga'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pipeline\n", - "pipeline = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n", - " max_iter=5000)) \n", - "])\n", - "\n", - "pipeline.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "1e4c1be5-176d-4222-9b3c-fe27225afe36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
430000.00.00.00.00.0550.000000550.0000000.0TrueTrue0114.012.0
1839230.00.00.00.00.0550.000000550.0000000.0TrueTrue0119.011.0
973730.00.00.00.00.0550.000000550.0000000.0TrueFalse007.02.0
669567.02.0254.01.01.0378.343062370.4539477.0TrueFalse010.00.0
1164870.00.00.00.00.0550.000000550.0000000.0TrueFalse105.00.0
.............................................
1404730.00.00.00.00.0550.000000550.0000000.0TrueTrue1083.011.0
1537680.00.00.00.00.0550.000000550.0000000.0TrueTrue1012.01.0
11088612.06.0430.01.01.0490.688726153.68633012.0TrueFalse1040.012.0
1153902.01.079.91.00.057.49852457.4985240.0TrueFalse0111.06.0
249193.03.0149.01.00.0457.437319457.4371690.0TrueFalse010.00.0
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1000 rows × 14 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "43000 0.0 0.0 0.0 0.0 \n", - "183923 0.0 0.0 0.0 0.0 \n", - "97373 0.0 0.0 0.0 0.0 \n", - "66956 7.0 2.0 254.0 1.0 \n", - "116487 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "140473 0.0 0.0 0.0 0.0 \n", - "153768 0.0 0.0 0.0 0.0 \n", - "110886 12.0 6.0 430.0 1.0 \n", - "115390 2.0 1.0 79.9 1.0 \n", - "24919 3.0 3.0 149.0 1.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "43000 0.0 550.000000 550.000000 \n", - "183923 0.0 550.000000 550.000000 \n", - "97373 0.0 550.000000 550.000000 \n", - "66956 1.0 378.343062 370.453947 \n", - "116487 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "140473 0.0 550.000000 550.000000 \n", - "153768 0.0 550.000000 550.000000 \n", - "110886 1.0 490.688726 153.686330 \n", - "115390 0.0 57.498524 57.498524 \n", - "24919 0.0 457.437319 457.437169 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "43000 0.0 True True 0 \n", - "183923 0.0 True True 0 \n", - "97373 0.0 True False 0 \n", - "66956 7.0 True False 0 \n", - "116487 0.0 True False 1 \n", - "... ... ... ... ... \n", - "140473 0.0 True True 1 \n", - "153768 0.0 True True 1 \n", - "110886 12.0 True False 1 \n", - "115390 0.0 True False 0 \n", - "24919 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "43000 1 14.0 12.0 \n", - "183923 1 19.0 11.0 \n", - "97373 0 7.0 2.0 \n", - "66956 1 0.0 0.0 \n", - "116487 0 5.0 0.0 \n", - "... ... ... ... \n", - "140473 0 83.0 11.0 \n", - "153768 0 12.0 1.0 \n", - "110886 0 40.0 12.0 \n", - "115390 1 11.0 6.0 \n", - "24919 1 0.0 0.0 \n", - "\n", - "[1000 rows x 14 columns]" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# reduce X_train to reduce the training time\n", - "\n", - "X_train_subsample = X_train.sample(n=1000, random_state=42)\n", - "y_train_subsample = y_train.loc[X_train_subsample.index]\n", - "X_train_subsample" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "2b09c2cd-fd5c-49b3-be66-cec6c5ec1351", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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y_has_purchased
430000.0
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" - ], - "text/plain": [ - " y_has_purchased\n", - "43000 0.0\n", - "183923 0.0\n", - "97373 0.0\n", - "66956 1.0\n", - "116487 0.0\n", - "... ...\n", - "140473 0.0\n", - "153768 0.0\n", - "110886 1.0\n", - "115390 0.0\n", - "24919 0.0\n", - "\n", - "[1000 rows x 1 columns]" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_train_subsample" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "6c33fcd8-17d8-4390-b836-faec9ada9acd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in',\n",
-       "                                                   'is_email_true'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver='saga'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "710ccccc-50c9-4aba-8cf1-11483dbbdd1c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']}" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "ab078cf8-0d4c-4b23-9f33-2483cf605b06", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "make_scorer(f1_score, response_method='predict')" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f1_scorer" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "8062169e-8305-42b0-aeff-8f714117da40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
430000.00.00.00.00.0550.000000550.0000000.0TrueTrue0114.012.0
1839230.00.00.00.00.0550.000000550.0000000.0TrueTrue0119.011.0
973730.00.00.00.00.0550.000000550.0000000.0TrueFalse007.02.0
669567.02.0254.01.01.0378.343062370.4539477.0TrueFalse010.00.0
1164870.00.00.00.00.0550.000000550.0000000.0TrueFalse105.00.0
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1404730.00.00.00.00.0550.000000550.0000000.0TrueTrue1083.011.0
1537680.00.00.00.00.0550.000000550.0000000.0TrueTrue1012.01.0
11088612.06.0430.01.01.0490.688726153.68633012.0TrueFalse1040.012.0
1153902.01.079.91.00.057.49852457.4985240.0TrueFalse0111.06.0
249193.03.0149.01.00.0457.437319457.4371690.0TrueFalse010.00.0
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1000 rows × 14 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "43000 0.0 0.0 0.0 0.0 \n", - "183923 0.0 0.0 0.0 0.0 \n", - "97373 0.0 0.0 0.0 0.0 \n", - "66956 7.0 2.0 254.0 1.0 \n", - "116487 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "140473 0.0 0.0 0.0 0.0 \n", - "153768 0.0 0.0 0.0 0.0 \n", - "110886 12.0 6.0 430.0 1.0 \n", - "115390 2.0 1.0 79.9 1.0 \n", - "24919 3.0 3.0 149.0 1.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "43000 0.0 550.000000 550.000000 \n", - "183923 0.0 550.000000 550.000000 \n", - "97373 0.0 550.000000 550.000000 \n", - "66956 1.0 378.343062 370.453947 \n", - "116487 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "140473 0.0 550.000000 550.000000 \n", - "153768 0.0 550.000000 550.000000 \n", - "110886 1.0 490.688726 153.686330 \n", - "115390 0.0 57.498524 57.498524 \n", - "24919 0.0 457.437319 457.437169 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "43000 0.0 True True 0 \n", - "183923 0.0 True True 0 \n", - "97373 0.0 True False 0 \n", - "66956 7.0 True False 0 \n", - "116487 0.0 True False 1 \n", - "... ... ... ... ... \n", - "140473 0.0 True True 1 \n", - "153768 0.0 True True 1 \n", - "110886 12.0 True False 1 \n", - "115390 0.0 True False 0 \n", - "24919 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "43000 1 14.0 12.0 \n", - "183923 1 19.0 11.0 \n", - "97373 0 7.0 2.0 \n", - "66956 1 0.0 0.0 \n", - "116487 0 5.0 0.0 \n", - "... ... ... ... \n", - "140473 0 83.0 11.0 \n", - "153768 0 12.0 1.0 \n", - "110886 0 40.0 12.0 \n", - "115390 1 11.0 6.0 \n", - "24919 1 0.0 0.0 \n", - "\n", - "[1000 rows x 14 columns]" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_subsample" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "0270013a-6523-4cf8-8de0-569c0d1c5db5", - "metadata": {}, - "outputs": [], - "source": [ - "warnings.filterwarnings('ignore')\n", - "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", - "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "7a49d78a-5a9b-44a9-95cf-3fca1b3febfa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 4.0, 'logreg__penalty': 'l1'}\n", - "Best classification accuracy in train is: 0.4972844559251812\n" - ] - } - ], - "source": [ - "# run the pipeline on the subsample\n", - "\n", - "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "logit_grid.fit(X_train_subsample, y_train_subsample)\n", - "\n", - "# print results\n", - "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n", - "print('Best classification F1 score in train is: {}'.format(logit_grid.best_score_))\n", - "# print('Classification accuracy on test is: {}'.format(logit_grid.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "b1d5e71d-1078-4370-86e8-52b1ae378898", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01])" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_c" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "cfe04739-fe9c-4802-9d34-885a8cfce0dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessor',\n",
-       "                                        ColumnTransformer(transformers=[('num',\n",
-       "                                                                         Pipeline(steps=[('scaler',\n",
-       "                                                                                          StandardScaler())]),\n",
-       "                                                                         ['nb_tickets',\n",
-       "                                                                          'nb_purchases',\n",
-       "                                                                          'total_amount',\n",
-       "                                                                          'nb_suppliers',\n",
-       "                                                                          'vente_internet_max',\n",
-       "                                                                          'purchase_date_min',\n",
-       "                                                                          'purchase_date_max',\n",
-       "                                                                          'nb_tickets_internet',\n",
-       "                                                                          'nb_campaigns',\n",
-       "                                                                          'nb_campaigns_opened']),\n",
-       "                                                                        ('cat',\n",
-       "                                                                         Pipeline(steps=[(...\n",
-       "                                                                         1.0: 3.486549107420539},\n",
-       "                                                           max_iter=5000,\n",
-       "                                                           solver='saga'))]),\n",
-       "             param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n",
-       "       1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
-       "       2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
-       "       4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
-       "                         'logreg__penalty': ['l1']},\n",
-       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets',\n", - " 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[(...\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000,\n", - " solver='saga'))]),\n", - " param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']},\n", - " scoring=make_scorer(f1_score, response_method='predict'))" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "logit_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "6debc66c-a56d-41fa-8ef8-ba388e0e14fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']}" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "e394cc04-5d0b-4a64-9aa0-415dc8a3cbbc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", - "Best classification accuracy in train is: 0.42160313383818665\n", - "Classification accuracy on test is: 0.47078982841737305\n" - ] - } - ], - "source": [ - "# run the pipeline on the full sample\n", - "\n", - "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "logit_grid.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "8e6cf558-a4f4-4159-9835-364ee3bb1ed2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", - "Best classification F1 score in train is: 0.42160313383818665\n", - "Classification F1 score on test is: 0.47078982841737305\n" - ] - } - ], - "source": [ - "# print results\n", - "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n", - "print('Best classification F1 score in train is: {}'.format(logit_grid.best_score_))\n", - "print('Classification F1 score on test is: {}'.format(logit_grid.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "e2ff26cb-f137-4a23-9add-bdb61bebdf9c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessor',\n",
-       "                                        ColumnTransformer(transformers=[('num',\n",
-       "                                                                         Pipeline(steps=[('scaler',\n",
-       "                                                                                          StandardScaler())]),\n",
-       "                                                                         ['nb_tickets',\n",
-       "                                                                          'nb_purchases',\n",
-       "                                                                          'total_amount',\n",
-       "                                                                          'nb_suppliers',\n",
-       "                                                                          'vente_internet_max',\n",
-       "                                                                          'purchase_date_min',\n",
-       "                                                                          'purchase_date_max',\n",
-       "                                                                          'nb_tickets_internet',\n",
-       "                                                                          'nb_campaigns',\n",
-       "                                                                          'nb_campaigns_opened']),\n",
-       "                                                                        ('cat',\n",
-       "                                                                         Pipeline(steps=[(...\n",
-       "                                                                         1.0: 3.486549107420539},\n",
-       "                                                           max_iter=5000,\n",
-       "                                                           solver='saga'))]),\n",
-       "             param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n",
-       "       1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
-       "       2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
-       "       4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
-       "                         'logreg__penalty': ['l1']},\n",
-       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets',\n", - " 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[(...\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000,\n", - " solver='saga'))]),\n", - " param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']},\n", - " scoring=make_scorer(f1_score, response_method='predict'))" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "5d553da2-5c2a-491a-b4d2-f31c30c201a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'scoring': make_scorer(f1_score, response_method='predict'),\n", - " 'estimator': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))]),\n", - " 'n_jobs': None,\n", - " 'refit': True,\n", - " 'cv': 3,\n", - " 'verbose': 0,\n", - " 'pre_dispatch': '2*n_jobs',\n", - " 'error_score': nan,\n", - " 'return_train_score': False,\n", - " 'param_grid': {'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']},\n", - " 'multimetric_': False,\n", - " 'best_index_': 5,\n", - " 'best_score_': 0.42160313383818665,\n", - " 'best_params_': {'logreg__C': 0.03125, 'logreg__penalty': 'l1'},\n", - " 'best_estimator_': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(C=0.03125,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, penalty='l1',\n", - " solver='saga'))]),\n", - " 'refit_time_': 305.1356477737427,\n", - " 'feature_names_in_': array(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", - " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'], dtype=object),\n", - " 'scorer_': make_scorer(f1_score, response_method='predict'),\n", - " 'cv_results_': {'mean_fit_time': array([ 11.07076669, 13.15744201, 27.35094929, 40.0343461 ,\n", - " 94.58210254, 140.45846391, 159.83818332, 162.80178094,\n", - " 163.94260454, 171.08749111, 169.26621262, 166.36741408,\n", - " 167.91208776, 173.06720233, 170.93666704]),\n", - " 'std_fit_time': array([ 0.09462032, 1.51362591, 6.70859141, 22.68643753, 28.72690872,\n", - " 70.8434823 , 85.23159321, 79.71538593, 82.70486235, 84.79706797,\n", - " 86.79005212, 84.67956107, 83.94889047, 89.68716252, 89.41361431]),\n", - " 'mean_score_time': array([0.11632609, 0.10857773, 0.18140252, 0.1291213 , 0.11651532,\n", - " 0.07535577, 0.12481014, 0.16039928, 0.15685773, 0.07996233,\n", - " 0.12988146, 0.10067987, 0.1194102 , 0.09737802, 0.09390028]),\n", - " 'std_score_time': array([0.02131792, 0.03620144, 0.05853886, 0.06555575, 0.03228018,\n", - " 0.01433186, 0.03501336, 0.05466042, 0.06882891, 0.01002881,\n", - " 0.00495894, 0.00905774, 0.04075337, 0.03269379, 0.01990173]),\n", - " 'param_logreg__C': masked_array(data=[0.0009765625, 0.001953125, 0.00390625, 0.0078125,\n", - " 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1.0, 2.0,\n", - " 4.0, 8.0, 16.0],\n", - " mask=[False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_logreg__penalty': masked_array(data=['l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1',\n", - " 'l1', 'l1', 'l1', 'l1', 'l1', 'l1'],\n", - " mask=[False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'params': [{'logreg__C': 0.0009765625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.001953125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.00390625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.0078125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.015625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.03125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.0625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.25, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.5, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 1.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 2.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 4.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 8.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 16.0, 'logreg__penalty': 'l1'}],\n", - " 'split0_test_score': array([0.27289073, 0.2738913 , 0.27382853, 0.27409759, 0.27454764,\n", - " 0.27661894, 0.2766145 , 0.27584723, 0.27571682, 0.27576295,\n", - " 0.27580092, 0.27577943, 0.27581248, 0.27581909, 0.27581909]),\n", - " 'split1_test_score': array([0.4714244 , 0.47196015, 0.48362373, 0.48891733, 0.49066854,\n", - " 0.49091122, 0.49086284, 0.49065871, 0.49062783, 0.49049541,\n", - " 0.49048106, 0.49045238, 0.49043804, 0.49043804, 0.4904237 ]),\n", - " 'split2_test_score': array([0.50689906, 0.50092334, 0.4981377 , 0.49759178, 0.49725836,\n", - " 0.49727924, 0.49708801, 0.49738305, 0.49751781, 0.49738248,\n", - " 0.49738248, 0.49738248, 0.49738248, 0.49738248, 0.49738248]),\n", - " 'mean_test_score': array([0.4170714 , 0.4155916 , 0.41852999, 0.42020223, 0.42082484,\n", - " 0.42160313, 0.42152178, 0.42129633, 0.42128749, 0.42121361,\n", - " 0.42122149, 0.42120476, 0.421211 , 0.4212132 , 0.42120842]),\n", - " 'std_test_score': array([0.10297463, 0.1008925 , 0.10249081, 0.10337226, 0.10346859,\n", - " 0.10255226, 0.10249644, 0.10288467, 0.10297243, 0.10288758,\n", - " 0.10286646, 0.10287015, 0.10285136, 0.10284824, 0.10284503]),\n", - " 'rank_test_score': array([14, 15, 13, 12, 11, 1, 2, 3, 4, 6, 5, 10, 8, 7, 9],\n", - " dtype=int32)},\n", - " 'n_splits_': 3}" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid.__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "3573f34e-25d5-4afb-82cc-52323e2f63c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.64495866, -0.23909623, 0.54323933, 0.85687092, -0.04235755,\n", - " 0.87304348, -1.34756336, 0.21177838, 0.051939 , 0.04496588,\n", - " 0.2103007 , -0.59054784, 0. , 0. ]])" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# coefficients trouvés pour le modèle optimal\n", - "logit_grid.best_estimator_.named_steps[\"logreg\"].coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "0332a814-61fb-4b71-836a-e8ace70b1a44", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'preprocessor': ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler', StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases', 'total_amount',\n", - " 'nb_suppliers', 'vente_internet_max',\n", - " 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in', 'is_email_true'])]),\n", - " 'logreg': LogisticRegression(C=4.0,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, penalty='l1', solver='saga')}" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid.best_estimator_.named_steps" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "287615b9-e062-4b84-be61-26b9364b2cf4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.38031755])" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid.best_estimator_.named_steps[\"logreg\"].intercept_" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "4d50899d-cc0b-4a71-9406-f8b0a277c4a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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224213 rows × 14 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 2.0 1.0 60.00 1.0 \n", - "1 8.0 3.0 140.00 1.0 \n", - "2 2.0 1.0 50.00 1.0 \n", - "3 3.0 1.0 90.00 1.0 \n", - "4 2.0 1.0 78.00 1.0 \n", - "... ... ... ... ... \n", - "224208 0.0 0.0 0.00 0.0 \n", - "224209 1.0 1.0 20.00 1.0 \n", - "224210 0.0 0.0 0.00 0.0 \n", - "224211 1.0 1.0 97.11 1.0 \n", - "224212 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 355.268981 355.268981 \n", - "1 0.0 373.540289 219.262269 \n", - "2 0.0 5.202442 5.202442 \n", - "3 0.0 5.178958 5.178958 \n", - "4 0.0 5.174039 5.174039 \n", - "... ... ... ... \n", - "224208 0.0 550.000000 550.000000 \n", - "224209 1.0 392.501030 392.501030 \n", - "224210 0.0 550.000000 550.000000 \n", - "224211 1.0 172.334074 172.334074 \n", - "224212 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 0.0 True False 0 \n", - "1 0.0 True False 0 \n", - "2 0.0 True False 0 \n", - "3 0.0 True False 0 \n", - "4 0.0 True False 1 \n", - "... ... ... ... ... \n", - "224208 0.0 True False 0 \n", - "224209 1.0 True False 0 \n", - "224210 0.0 True True 0 \n", - "224211 1.0 True False 0 \n", - "224212 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "0 1 0.0 0.0 \n", - "1 1 0.0 0.0 \n", - "2 1 0.0 0.0 \n", - "3 1 0.0 0.0 \n", - "4 0 0.0 0.0 \n", - "... ... ... ... \n", - "224208 1 34.0 3.0 \n", - "224209 1 23.0 6.0 \n", - "224210 1 8.0 4.0 \n", - "224211 1 13.0 5.0 \n", - "224212 1 4.0 4.0 \n", - "\n", - "[224213 rows x 14 columns]" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# c'est la 2ème variable nb_purchases qui a été supprimée par le LASSO\n", - "X_train" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "e53b1f79-762d-4f1f-8505-91de1088af42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.25" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# best param : alpha = 32 (alpha =1/4 sur le petit subsample)\n", - "1/logit_grid.best_params_[\"logreg__C\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "41bcaaf6-ab58-4004-a3c5-586d77e872d1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.7187395937395937\n", - "F1 Score: 0.44926236857119567\n", - "Recall Score: 0.8052593133674215\n" - ] - } - ], - "source": [ - "# print results for the best model\n", - "\n", - "y_pred = logit_grid.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "a454bb57-76eb-4a22-9950-0733d39e449f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# confusion matrix \n", - "\n", - "draw_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "25ec1701-ade5-4419-8b46-8a1bb109cf84", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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damnKHjt2jKVLl1qO69+/P8eOHSMnJyfX72vdunXz9XWKiIgUBo0QEhERKQTt2rXj/fffZ/To0bRs2ZK77rqLhg0bkpWVxdq1a/noo49o1KgRV199NXXr1uXf//4377zzDn5+fvTp04fdu3fz1FNPUblyZUsD5KabbuLGG29k9OjRDB48mD179jBhwgTKlCmT7xpXrVrFqFGjGDp0KPv27eOJJ56gYsWKjB49GjDndHnnnXcYMWIEx48fZ8iQIcTGxnL06FHWrVvH0aNHef/99y/p+/PKK6/Qpk0bxo0bx3//+18Ahg8fzpo1a3jttdfYvXs3t912G2XLlmXLli1MnDiRHTt28M0331CjRg3X8wwdOpSnnnqKcePGsXnzZkaOHEnNmjVJTU1l+fLlfPjhhwwbNuyCS8///vvv9OjRg6effjrP8whdqpdffpmePXvSrVs3HnroIYKCgnjvvffYsGEDkyZNco3oevLJJ/nxxx/p3r07Tz/9NGFhYfzf//2f25xN1apV4/nnn+epp55i165d9O3bl5iYGA4fPszy5csJDw/n+eefz7WW0+c+8cQT7Ny5k6uuuoqSJUty+PBhVqxYQXh4OM8991yevzY/Pz8mTJjADTfcQP/+/bnjjjvIyMjg1Vdf5eTJk4wfP9517PPPP0+/fv3o3bs39913Hzk5Obz66qtERERw/PjxC77O1VdfTaNGjWjVqhVlypRhz549vPnmm1StWpXatWsD0LhxYwDeeustRowYQWBgIHXr1s11ZM7NN9/MxIkTufnmm3nxxRepXbs2s2fP5pdffnE79qabbuLDDz/kxhtv5Pbbb+fYsWNMmDCBqKgoy3HXX389//vf/+jbty/33XcfrVu3JjAwkP3797NgwQIGDBjAtddem+fvrYiISKGwbz5rERGR4u+vv/4yRowYYVSpUsUICgoywsPDjebNmxtPP/20ceTIEddxOTk5xiuvvGLUqVPHCAwMNEqXLm3ceOONxr59+yzP53Q6jQkTJhg1atQwQkJCjFatWhnz588/7ypjU6ZMcavp9Cpjc+fONW666SajRIkSRmhoqNG3b19j27Ztbsf//vvvRr9+/YyYmBgjMDDQqFixotGvX79cn/tsp1cZe/XVV3N9fOjQoUZAQICxfft2y/7Zs2cbffv2NUqVKuV6vZtuusnYuHHjeV/r999/N4YMGWKUL1/eCAwMNKKioox27doZr776qpGYmHjBOk9/r5555pkLHmcY5ipTDRs2dNtftWpVo1+/fm77AbcVwP744w+je/fuRnh4uBEaGmq0bdvW+Omnn9zOXbJkidG2bVsjODjYKFeunPHwww8bH330Ua6rZ82YMcPo1q2bERUVZQQHBxtVq1Y1hgwZYvz666+uY85dZSw/5+bm3FXGzn6+Nm3aGCEhIUZ4eLjRo0cPY8mSJW7nT58+3WjcuLERFBRkVKlSxRg/frxx7733GiVLlrQcd+4qY6+//rrRvn17o3Tp0q5zR44caezevdty3tixY40KFSoYfn5+ljpzWyls//79xuDBg42IiAgjMjLSGDx4sLF06VK3VcYMwzC++OILo379+kZISIjRoEEDY/LkyW6rjBmGYWRlZRmvvfaa0bRpUyMkJMSIiIgw6tWrZ9xxxx255kxERKSoOQwjl/HVIiIiIiJFKCsri2bNmlGxYkWPuM1v9+7dVK9enc8++8xtBUAREZHiQLeMiYiIiEiRGzlyJD179qR8+fLExcXxwQcf8M8///DWW2/ZXZqIiIhPUENIRERERIpcUlISDz30EEePHiUwMJAWLVowe/ZsrrzySrtLExER8Qm6ZUxERERERERExMdo2XkRERERERERER+jhpCIiIiIiIiIiI9RQ0hERERERERExMf43KTSTqeTgwcPEhkZicPhsLscEREREREREZECYRgGSUlJVKhQAT+/C48B8rmG0MGDB6lcubLdZYiIiIiIiIiIFIp9+/ZRqVKlCx7jcw2hyMhIwPzmREVF2VzN5ZkzZw5XXXWV3WWIeAxlQsRKmRBxp1yIWCkTIlbenonExEQqV67s6n1ciM8tO5+YmEh0dDQJCQle3xDKzs4mIMDnenoi56VMiFgpEyLulAsRK2VCxMrbM5GfnocmlfZiU6ZMsbsEEY+iTIhYKRMi7pQLEStlQsTKlzKhhpCIiIiIiIiIiI9RQ8iLNWjQwO4SRDyKMiFipUyIuFMuRKyUCRErX8qEGkJeLDo62u4SRDyKMiFipUyIuFMuRKyUCRErX8qEGkJebNmyZXaXIOJRlAkRK2VCxJ1yIWKlTIhY+VIm1BASEREREREREfExWnbeix07doxSpUrZXYaIx1AmRKyUCRF3yoWIlTIhYuXtmdCy8z5i06ZNdpcg4lGUCRErZULEnXIhYqVMiFj5UibUEPJi+/fvt7sEEY+iTIhYKRMi7pQLEStlQsTKlzKhhpAXCwsLs7sEEY+iTIhYKRMi7pQLEStlQsTKlzKhOYRERERERERERIoBzSHkIyZNmmR3CSIeRZkQsVImRNwpFyJWyoSIlS9lQg0hEREREREREREfo4aQF6tTp47dJYh4FGVCxEqZEHGnXIhYKRMiVr6UCTWEvFhsbKzdJYh4FGVCxEqZEHGnXIhYKRMiVr6UCTWEvNjixYvtLkHEoygTIlbKhIg75ULESpkQsfKlTKghJCIiIiIiIiLiY7TsvBc7fPgwZcuWtbsMEY+hTIhYKRMi7pQLEStlQsTK2zOhZed9xI4dO+wuQcSjKBMiVsqEiDvlQsRKmRCx8qVMqCHkxfbs2WN3CSIeRZkQsVImRNwpFyJWyoSIlS9lQg0hLxYUFGR3CSIeRZkQsVImRNwpFyJWyoSIlS9lwtY5hBYtWsSrr77K6tWrOXToENOnT2fgwIEXPOf333/ngQceYOPGjVSoUIFHHnmEO++8M8+vWZzmEBIREREREREROc1r5hBKSUmhadOmvPvuu3k6fteuXfTt25dOnTqxdu1aHn/8ce69916mTp1ayJV6pilTpthdgohHUSZErJQJEXfKhYiVMiFi5UuZCLDzxfv06UOfPn3yfPwHH3xAlSpVePPNNwGoX78+q1at4rXXXmPw4MGFVKXnys7OtrsEEY+iTIhYKRMi7pQLEStlQnxdQmoWR5LSSUzPIjE1i1VH4ZrsHIID/O0urdDZ2hDKr2XLltGrVy/Lvt69e/PJJ5+QlZVFYGCg2zkZGRlkZGS4thMTEwu9zqJSo0YNu0sQ8SjKhIiVMiHiTrkQsVImxJc4nQbZToMcp8GavSf4ctlu5m06THRKAmMWf0NwdiaT+97HfcmZVCgRane5hc6rGkJxcXGULVvWsq9s2bJkZ2cTHx9P+fLl3c55+eWXee6559z2T5kyhbCwMAYNGsRvv/1GQkICsbGxtG7dmpkzZwLQokULnE4nf/31FwADBgxg8eLFHDt2jJiYGDp37syMGTMAaNKkCYGBgaxevRqAfv36sWrVKg4fPkxUVBS9evXi+++/B6Bhw4ZERESwfPlywGxqbdiwgQMHDhAeHk7//v2ZPHkyAHXr1qV06dIsWbIEgCuvvJKtW7eyd+9enE4nbdq0YfLkyTidTmrWrEnFihVZtGgRAF27dmXv3r3s3LmTgIAAhg4dytSpU8nMzKRq1arUrFmT+fPnA9CxY0eOHDnC1q1bARg+fDg//PADqampVKpUiQYNGjB37lwA2rVrR0JCAps2bQJg6NChzJkzh6SkJMqVK0eLFi2YPXs2AFdccQXp6en8/fffAFx77bUsXLiQEydOULp0adq1a8dPP/0EQPPmzQFYu3YtAFdffTXLli0jPj6ekiVL0rVrV6ZPnw5A48aNCQkJYeXKlQD07duXNWvWEBcXR2RkJFdddZVrqF+DBg2Ijo5m2bJlAPTq1YtNmzaxf/9+wsLCGDBgAJMmTQKgTp06xMbGsnjxYgC6d+/Ojh072LNnD0FBQQwePJgpU6aQnZ1NjRo1qFKlCgsXLgSgc+fOHDhwgB07duDn58ewYcOYNm0aGRkZVKlShTp16vDrr78C0KFDB+Lj49myZQsAw4YNY+bMmaSkpFCxYkUaNWrEL7/8AkCbNm1ITk5m48aNAAwZMoS5c+eSmJhI2bJladWqFbNmzQKgZcuWZGVlsX79egAGDhzIokWLOH78OKVKlaJjx4788MMPADRr1gw/Pz/WrFkDQP/+/VmxYgVHjhwhOjqaHj16MG3aNAAaNWpEWFgYK1asAMzRfevWrePgwYNERETQt29fvvvuOwDq1atHTEwMS5cuBaBnz55s3ryZffv2ERoaysCBA/n2228xDIPatWtTrlw5/vjjDwC6devG7t272bVrF4GBgQwZMoTvv/+erKwsqlevTrVq1ViwYAEAnTp1Ii4ujm3btuFwOLj++uvZsWMHO3fupHLlytSrV4958+YB0L59e44fP87mzZsBuO6665g9ezbJyclUqFCBpk2b8vPPPwPQunVrUlNT2bBhA4BXv0cEBwczaNAgvUfgu+8R4eHhJCYm6j3i1HvEjBkzSEtL03uEj79HpKenEx4ervcI/Ryh9wjM94igoCDXv2G9R+jniMt9j5g+4weynFCvYWOyDQdr1/9NttNB2w6d+OvvjRw9foKg0HDqNmjM0j9XkGVAmbLlMfwC2bV3H9lOBxWrVuPQ4aMkJKdi+AVSqkxZ9uw/SLYBgSGhOPwCSEpOxmlAaFg4aRmZZGZlY+AgKCSElNQ0nAbg54+Bg6zsHJwGOHFwrsCcLG5dPZP7ln5LVEYKThz82G0gBw8d4uD24175HpGamur2dZ6PrZNKn83hcFx0Uuk6depw6623MnbsWNe+JUuW0LFjRw4dOkS5cuXczslthFDlypWLxaTSkyZNYvjw4XaXIeIxlAkRK2VCxJ1yIWKlTHg/wzBHvOQYBk4n5BgGOTnmdo7TwHn6ceeZ47JzDHYfSyE5PZuMbCcZ2TmkZ5l/ZmQ7Sc8y/8zIcn/M3J/j+jP9rO1sp0e0Fy7OMLhq53LGLf6CMnH7zH3NmsEbbzApLs6rM5GfSaW9aoRQuXLliIuLs+w7cuQIAQEBlCpVKtdzgoODCQ4OLoryRERERERERC5bdo7ZeMnKcZKZ7eSbFXv5dsU+UjKyLY2ebKeBZwzxcBfo7yAkwJ/gQD+CA/wJDvAjOPDUnwF+hARa94WcfVyA/6nts8/xJ9DfQYC/A38/PwL8HPg5Tm878HeYfwb4O8485ueHv/9Zj/k5CDiwj7A7bsd/oTlikLJl4cUX4ZZbwN8fTo3o8gVe1RA6e8jfaXPnzqVVq1a5zh9U3HXu3NnuEkQ8ijIhYqVMiLhTLkSslInLE5eQzuLt8ed9PDvHydGkDM4dOBOfnMHxlEwMzFE9TsPAwBztcyQpgw0HEtzOuVSnmyV+fhDg54efw9xXOiKY8iVCrc0ZSzPGbOaEnNOUOfsx67lnHgsK8MPfz/0WLY9Qrgxs3ADBwfDgg/DYYxAZ6XrYlzJha0MoOTmZ7du3u7Z37drFX3/9RUxMDFWqVGHs2LEcOHCAL7/8EoA777yTd999lwceeIDbb7+dZcuW8cknn7juyfQ1Bw4coGLFinaXIeIxlAkRK2VCxJ1yIWLlrZnIcRpk5TjJOTVJcFxCOpnZTrKdzlO3SHHqMScpGTmuz7NyDLJznCRnZJOamWOOwDk1CifL9af5fAYGiWnZJKRlkZiWRZbTaanBMCAj23meCgtWqfAgHuhVh3Y1SpkNHj/HWY2eUyNgzhoJY46OMR/zeenp8O23MGIEOBwQFQXffAO1a0PVqm6He2smLoWtDaFVq1bRrVs31/YDDzwAwIgRI/j88885dOgQe/fudT1evXp1Zs+ezZgxY/i///s/KlSowNtvv+2TS84D7Nixg9atW9tdhojHUCZErJQJEXfKRf5kZOcQn5zJkcR04pMzycox//N79i0q5rgGctl/el/uwxzy9ByG9bnOfT7LM+fyfLnVk5fXzsh2kpKRbY7aOLuGUxvGqU/Pfh3jrOcwOLPj9KiP8x13di2ZOU6ysp0Y4HptwzBwnjrPeeoJTj92elRJepbZ8DhfTZy3VoOj8fGUXrXknOMN96/n9Oe5fF2ZOU6yc05/b876nrn+tH7fzj7u7NcBI9fnd32/z/o+JmVk4ylKhQfRqGJ0ro/5OSA2MoQAf2tjJtDfj0olzdE5OBz4OcDP4cABhAcH0KJqSUqFBxFwqvHjcKixk2+GAVOmwKOPwu7dEBYG111nPnbllec9zZeuE7Y2hLp27XreCwTA559/7ravS5curtUMfJ2fn5/dJYh4FGVCxEqZEHGnXJj/qT6ZmsXR5AyOJGZwNDmdo0kZHE3K4MipP09/npCWZXe5UugC2Lf3pN1FXLYgfz9KRwSZo2XOGTkTGmTe0mTOLeNHoL+D8OAAwoICCPJ3EOjvR1CA31l/Ogjy98NpQPUy4ZQIDSQ6NJCgAPf3Dz+Hg/LRIWrYeJqVK2HMGDi1yiYVK0JQUJ5O9aXrhMesMlZU8jPjtoiIiIiIt0jPyjGbOcnuzZ2jSWeaPkeTM8jKyft/AYL8/SgTGUzpiCCCA/1d+8/+7+/Z/xd2nPXI6f0Xe/xcZ//n2pHLsY6LHGs9PvfXu1j9wYF+RAQH4HCc2Wd+bn1d19eI46zPTx17+pizXjDX40496OeAkCB//E6NGDl9rMO1DX5+jlPnmY/5nbpFKPhUs8LhcFi+Z7nXfqagc78my7ln1YylZsdZn3Pq9f0tz3fuMWfXcfb33fL9OHfbcrzDUmugvx8lwgJdt0cF+fvp9iiB/fvh8cfhq6/M7bAweOQReOghCA+3t7YiUmxXGROradOmMWjQILvLEPEYyoSIlTIh4s5bc2EYBgcT0vl7fwJ7jqWcM5LHbPYkpufvFpoSYYHERgZTJjKYMhHBxEaFUCbC3HbtjwwmOjRQox+KMW/NhEiurrsOli0zP7/pJnjpJahUKV9P4UuZUEPIi2VkZNhdgohHUSZErJQJEXfekAvDMNh/Io0NBxL4+9THxoOJHE/JvOi5QQF+p5o7wWc1d0JczZ3TjZ5SEUGuER3i27whEyLn5XRCTg6cXnX8hRfg6adh4kS44opLekpfyoQaQl6sSpUqdpcg4lGUCRErZULEnaflwjAM9h5P5e8DCWw4kMiGAwlsOJjAyVT3uXsC/BzULhtJ3bIRlI060+Q5u+kTFRKg0TySL56WCZE8W7oU7r8fBgyAJ54w93XvDt26nf9+1DzwpUyoIeTF6tSpY3cJIh5FmRCxUiZE3NmZixMpmWw5nMSWuCQ2xyWx9XASW+OScl0tKdDfQd1ykTSuGE3DCtE0rhhN3XKRhARqVI8ULF0rxOvs2WOuHDZ5srm9f785R1BwsLl9mU1xX8qEGkJe7Ndff2X48OF2lyHiMZQJEStlQsRdYeXiZGomS7YfY1d8Monp2SSlZ5GYlk1iehaJ6dkcOpnGkaTcb0MICvCjfrlIGlY0Gz+NK0ZTu2yEbumSIqFrhXiNpCR4+WV44w3IyDAbPyNHwrhxZ5pBBcCXMqGGkIiIiIjIWQzDICPbSWpmDqmZ2af+ND9PO+vz1MwcjqdksmR7PH/tO4kzDwt3VSoZSr1ykdQtF0mdspHUKxdFjTLhBPr7zjLHIiL59uuvcOONcPiwud2tm9kYatbM1rK8nRpCXqxDhw52lyDiUZQJEStlQnxJYnoWe4+lkpyRbWninNvAOZJVjeXfrrU0eVIzc0jLOrWdkU1aVk6emjvnqlM2gmaVSxAdGkhUSCCRIQFEhQYSGRJI6YggapeNJCJYP36LZ9G1QrxCtWpw/DjUqgWvvQbXXHPZt4adjy9lQlckLxYfH+9TE16JXIwyIWKlTEhxdCw5g+1Hktl2JJntpz62HUnicGLhrAoTFOBHeJA/YUEBhAb5ExbkT2ig+WdYcAARQQE0r1KCznXKUKFEaKHUIFKYdK0Qj7R9O8ydC6NHm9u1asG8edCuHQQFFepL+1Im1BDyYlu2bKFFixZ2lyHiMZQJEStlQryVYRgcTsxwNXvObv5caOn10hHBRIcGuJo3bo2cIH92bN5E21bNz2ruBJjNnVPHhp11bGigPwG6lUuKOV0rxKOcPGkuHf/225CdDW3bwul/n126FEkJvpQJNYRERERExBZOp8GBk2lsO5JkNn8OmyN/dhxJznXlLTDvEKhUMpTasZHUio2gVmwEtWMjqBkbQVRI4EVfc9LJvxjeqUZBfykiInI5srPho4/gmWcgPt7cd9VVEBFhb13FnMMwjEu4Q9p7JSYmEh0dTUJCAlFRUXaXc1mcTid+fvqtlchpyoSIlTIhniIrx8meY6mnRvkkuW752nE0mfQsZ67n+Ps5qFoqjNqupo/ZAKpZJoLQoEtffUu5ELFSJsR2v/wCDzwAmzaZ2/Xrw+uvQ58+tpTj7ZnIT89DI4S82MyZM7nmmmvsLkPEYygTIlbKhBQ1wzDYcyyVvw8knLrNy2z+7IpPISsn999BBvn7UaNMuKXpU7tsBNVKhRMUUPA/kCsXIlbKhNgqJQVuugmOHoVSpeC55+Df/4bAi4/4LCy+lAk1hLxYSkqK3SWIeBRlQsRKmZCCluM0SEjL4nhKBsdTzD+PpWRyIiWTAyfTWLw9nn3H03I9NyzI37zFq0wEtcqeaf5ULhlapPP0KBciVsqEFLmTJyE62rwHODwcXn4ZNm6Ep56CkiXtrs6nMqGGkBerWLGi3SWIeBRlQsRKmZBLlZiexda4JP6JS2LzoUS2xCWxKz6FE6mZF12OPdDfQaOK0dSJjaR22QjXPD8VokPx8yucJYLzQ7kQsVImpMhkZsJ778Hzz8OHH8LQoeb+kSPtrescvpQJNYS8WKNGjewuQcSjKBMiVsqEXEx2jpNd8Slsjktic1wimw8lsTkuiQMncx/lc1p0aCAx4UGUDAskJjyYmPBASkcE07JqSdrWKEV4sOf+iKlciFgpE1LoDANmzoQHH4Rt28x9X399piHkYXwpE557tZaL+uWXXxg+fLjdZYh4DGVCxEqZkNMMw+BocgabDyWxJS6Jf+LMUT/bjiSTmZ37pM7lo0OoVy6SeuWjqFfOvL0rNjKEEmGBBHrxUuzKhYiVMiGFav16c8Lo334zt2Nj4cUX4dZb7a3rAnwpE2oIiYiIiBQThmGw93gqB06msf94mmvkz5a4JI6lZOZ6TliQP3XLRVKvnNn4qXfq8+gw+yb0FBGRYmD8eHjiCXA6ISjIbAyNHQtevtp3caKGkBdr06aN3SWIeBRlQsRKmfANyRnZLNkez+9bj/L7lqPnvd3L4YDqpcKpVz6SumWjqFc+kvrloqhU0jPm9ikqyoWIlTIhhaZlS7MZNHQovPIKVK9ud0V54kuZUEPIiyUnJ9tdgohHUSZErJSJ4ic9K4c9x1LZfSyF7UeS+WPbUVbtPkH2WTM9B/n7UTkmlLJRIdQtZzZ96pWPpHZsJKFB/jZW7xmUCxErZUIKhGHA999DQgKMGmXu69kT/v4bvGxOHl/KhBpCXmzjxo00adLE7jJEPIYyIWKlTHif7Bwnh5MyiEtI41BCOvuOp7HnWAq7j6Ww51gqhxLScz2vWqkwutQpQ9e6sbSpEUNYkH7EOx/lQsRKmZDLtmoVjBkDixdDRAT07w/lypmPeVkzCHwrE/ppQURERMQG2TlONsclsWr3cVbuOcFfe09yKCHtosu6R4YEUL10OFVLhdOqakm61ClDtdLhRVO0iIjIaQcOwOOPw5dfmtuhoeY8QZGR9tYleeYwDOMiP3YUL4mJiURHR5OQkECUl09mlZWVRWCgJnwUOU2ZELFSJjxHjtNgx9FkNhxIYMOBRDYcTGDjgQRSMnPcjg30d1A2KoQK0aGULxFCtVLhVCsdZv5ZKpwSYYE4HL4z509BUy5ErJQJybfUVHjtNXNeoNRUc9+NN8JLL0HlyvbWVgC8PRP56XlohJAXmzt3Lv369bO7DBGPoUyIWCkT9jEMg+1Hklm8PZ4l2+P5c+dxkjOy3Y6LDAmgRZWSXFGtJC2rxlArNoJS4UE+NclzUVMuRKyUCcm3/fth3DjIzoZ27eDNN6F1a7urKjC+lAk1hLxYYmKi3SWIeBRlQsRKmShahxLSWLL9GEtONYGOJGVYHg8L8qdhhSgaVYymUYVoGlWMplZsBP5q/hQp5ULESpmQPNm168wqYXXqwAsvQLVqcN115jKWxYgvZUINIS9WtmxZu0sQ8SjKhIiVMlGwDMPgUEI6Gw8msuFAAvtPpJGRnUNmtpPtR5PZeTTFcnxwgB+tq8fQoVZpOtYqTf3yUWr+eADlQsRKmZAL2rMHHnsMvvvOnDy6eXNz/6OP2ltXIfKlTKgh5MVatWpldwkiHkWZELFSJi7f3mOp/L7tKIu2HmX1nhMcT8k877F+DmhcqQQda5WiQ63StKhSkpBALfPuaZQLEStlQnKVnAzjx8Prr0N6ujkKaMGCMw2hYsyXMqGGkBebNWsWw4cPt7sMEY+hTIhYKRMXZxgGu4+lsvNoMseSMzmanMGBk2lsP5LM9iPJbg0gfz8HtWMjaFghmhplwgkN9Cc40I8yEcG0qVGK6FDvnYTSVygXIlbKhFg4nfDFF+bqYXFx5r4uXWDiRJ9oBoFvZUINIREREfEpielZzNt4mPlbjrB+/0n2HU8777EBfg5anFravX3NUtQvH6VRPyIiUnz16QNz55qf16wJr74KAwcWu3mCxKSGkBdr2bKl3SWIeBRlQsRKmYC0zBz+iUtk59EUDp1MY93+kyzaGk9mjtN1TJC/H3XKRVA6IpjSEcGUjQqmVmwEtWMjqVkmgtAgNYCKE+VCxEqZEIsBA+DPP+Gpp+CeeyA42O6KipwvZUINIS+WlZVldwkiHkWZELHy1Uys33+SORvi+HHdQfafyH30T63YCPo2Lk+rqiVpUbUkEcH6kchX+GouRM5HmfBhCQnmamEdOpijgAD+/W8YOhTKlLG1NDv5Uib0048XW79+PQ0bNrS7DBGPoUyIWPlCJnKcBst3HeOndQfZdDCRfSfS3Ob9KR0RTL1ykVQoEULVUuH0bFCWOmUjbapY7OYLuRDJD2XCB2Vnw3//C08/DUePwrRp0LcvBAVBQIBPN4PAtzKhhpCIiIh4lcOJ6fy+9ShzN8axfOdxkjKyLY/7+znoVLs0V9YvS9/G5YkJD7KpUhEREQ8zbx6MGQMbN5rb9eqZK4kFalEEX+QwDMOwu4iilJiYSHR0NAkJCURFRdldzmVJS0sjNDTU7jJEPIYyIWJVXDLhdBos3h7P7L8PsXzXcXbFp1gejw4N5KqG5ehWL5bKMaFUjgkjKkQ/2EruiksuRAqKMuEjtm2DBx6AmTPN7ZgYeO45uOMONYPO4e2ZyE/PQyOEvNiiRYvo3bu33WWIeAxlQsTKmzNxJDGd+ZuPsGjbUZZsP0ZC2pn7+f0c0LBCNFfWL0u3emVoWCEafz+tfiJ54825ECkMyoSP2LXLbAYFBMB//mPeLlaypN1VeSRfyoQaQl7s+PHjdpcg4lGUCRErb8vEseQMvl25j7kb41i3P8HyWGigP1c3Lc9VjcrRqlqMRgDJJfO2XIgUNmWimMrKgvXr4fSKWb16mRNIDx0KderYW5uH86VMqCHkxUqVKmV3CSIeRZkQsfKGTOw/kcrsvw/xw18H2XQokbNvZG9WuQTta5aiXc1StK1RikB/P/sKlWLDG3IhUpSUiWLGMGDWLHjoITh40LxVrGxZ87EnnrC3Ni/hS5nQHEJeLDU1lbCwMLvLEPEYyoSIlSdn4sDJNN6Yu5Xpa/fjPOsnkSaVohneugo96scSGxliX4FSbHlyLkTsoEwUIxs2mPMEzZtnbpcpA99/D50721uXl/H2TOSn56FftXmxH374we4SRDyKMiFi5WmZiE/O4KXZ/9Dv7T/o+Mp8pq4xm0Fta8Tw/ICGrHiiBz/+pyPDW1dRM0gKjaflQsRuykQxcOQI3HknNG1qNoOCguCRR8zRQWoG5ZsvZUK3jImIiEihSMnI5sd1B1m45QjbjiSz86h1dbC2NWJ4uHddWlaNsalCERERL5ecDA0awLFj5vaQIfDKK1Cjhr11iVdQQ8iLNWvWzO4SRDyKMiFiZVcmDiem8/rcLcxaf4iUzBzLY1ViwujXpDw3ta1KhRLeu6SreC9dK0SslAkvFxEBN9wAixfDxIkaEVQAfCkTagh5MT8/3fEncjZlQsSqqDORlePkzV+38vmS3a5GUPXS4QxpWYkmlaKpWzaS2CjdCib20rVCxEqZ8DJr1pgTRr/xBpxuXIwfD8HBoL/LAuFLmfCdr7QYWrNmjd0liHgUZULEqqgyYRgG363aR9dXF/J/C3aQkplD8yol+O6Odsx/sAt3d6tFp9pl1AwSj6BrhYiVMuElDh2C226DVq1gwQJ4/PEzj4WGqhlUgHwpExohJCIiIpfseEom9327lj+2xQMQFuTPQ73qckv7avj5OWyuTkRExMulpZmjgV5+GVJOzcV3ww3mtshl0rLzXiwpKYnIyEi7yxDxGMqEiFVhZsLpNPhk8S6+Xr6HPcdScTjg/h51GNmpOhHB+n2TeC5dK0SslAkPNn063H8/7N1rbrdtC2++CW3a2FlVseftmdCy8z5ixYoVdpcg4lGUCRGrwsrE8p3HuPa9Jbw4+x/2HEulZFgg/xvZhvuurK1mkHg8XStErJQJD3bggNkMqlwZvvkGli5VM6gI+FIm9FObFzty5IjdJYh4FGVCxKqgM5GelcPEX7fyyR+7yHYahAb6M6ZnbYa1qkJ0WGCBvpZIYdG1QsRKmfAg+/bBwYNnmj533AGGAaNGmfMESZHwpUyoIeTFoqOj7S5BxKMoEyJWBZWJPcdSmLn+ENPW7GfHUXP+gt4Ny/LCwMaUiQwukNcQKSq6VohYKRMeIDkZJkyAV1+FihVh40Zz1bDAQLjnHrur8zm+lAnNIeTFMjIyCA7WD+IipykTIlaXm4ld8Sk8NWMDi7fHu/bFhAfxeN/6DGpeUZNGi1fStULESpmwkdMJX35prhh26JC5r3NnmDQJKlSwtzYf5u2Z0BxCPmLatGl2lyDiUZQJEatLzYRhGLz2yxb6vLXI1Qy6olpJXry2EfMf7MKQlpXUDBKvpWuFiJUyYZM//oDWreHWW81mUI0aMHUqLFyoZpDNfCkTumVMREREXPYdT+XZHzfy22bz/vnW1WJ4fmBD6pXz7lG1IiIiHmPtWnMkEEBUFDz5JNx7r3mbmEgRUkPIizVq1MjuEkQ8ijIhYpWfTCRnZPP5kl188PtOkjOy8XPAf7rX5v4etTUaSIoVXStErJSJIuJ0gt+pG3SaN4f+/aFSJXjuOYiNtbc2sfClTKgh5MXCwsLsLkHEoygTIlZ5yUR6Vg7vzN/Gf//YRUa2E4DmVUrw7NUNaVq5RCFXKFL0dK0QsVImCllODnzyCbz2GixefKb5M2MG+PvbWprkzpcyoTmEvNiKFSvsLkHEoygTIlYXyoRhGHz4+w7avPQb/7dgBxnZTkIC/Rg/qDFT7minZpAUW7pWiFgpE4Xot9/M0UB33AHbtsE775x5TM0gj+VLmdAIIRERER9z4GQaz/+0kV82HgagQnQIN7Styr871yDQX78rEhERuSxbt8JDD8FPP5nbJUvCM8/A6NH21iVyDi0778VOnjxJiRIl7C5DxGMoEyJW52Yix2nw7vztvLtgG1k5Bn4OeLJfA0a0r4a/5gkSH6FrhYiVMlGADAMefRQmToTsbAgIMJtAzzwDMTF2Vyd55O2Z0LLzPmLdunV2lyDiUZQJEauzMzFnwyF6vL6Qib9uJSvHoE31GGbf14nbOlZXM0h8iq4VIlbKRAFyOCA93WwG9esHf/8Nb72lZpCX8aVM6JYxL3bw4EG7SxDxKMqEiNXBgwfZfyKVJ2dsYOGWowBEhQTw8FX1uLFNFRwONYLE9+haIWKlTFwGw4DZs6FaNWjY0Nz3zDPmCmK9etlamlw6X8qEGkJeLCIiwu4SRDyKMiFyxpHEdDamRvDau0s4lpIJwIh2VXnkqnqEB+vyL75L1woRK2XiEm3YAA8+CHPnQo8eMG+eOUKoVCk1g7ycL2VCcwh5sZycHPw1O72IizIhAtk5Tt5fuIM3f9tGjtO8xFcsEcrbw5vRsqqGrIvoWiFipUzk09Gj5iigDz8EpxMCA+G+++Cll8zPxet5eyY0h5CP+O677+wuQcSjKBPi65btOEaPN37n9XlbyXEalArK4Z7utZhzfyc1g0RO0bVCxEqZyKOMDHjtNahdG95/32wGDRoE//wDr76qZlAx4kuZ0JhxERGRYuDLZbt5+oeNAAQF+PHCgEZkb1vMv3rVtbkyERGRYuCrr+Dhh83Pmzc3VxLr0sXemkQukxpCXqxevXp2lyDiUZQJ8VVTVu1zNYMAFj3cjXLRIawNUCZEzqVrhYiVMnEBaWkQGmp+PmIEfPMN3HQT3HwzePEtRXJhvpQJNYS8WIyWLxSxUCbE1+Q4DR6bup4pq/cD0LxKCabd1d61epgyIeJOuRCxUiZycegQPPkkLFkC69dDUJB5S9j8+XZXJkXAlzKhOYS82NKlS+0uQcSjKBPiS3KcBj0n/u5qBt3Svhrf39nespS8MiHiTrkQsVImzpKWZk4OXbs2fPopbNliriImPsWXMqGGkIiIiJcxDIM+by1i59EUAKqXDufZaxri7+e4yJkiIiLixjDg22+hXj144glISYE2bWDpUujf3+7qRAqNbhnzYj179rS7BBGPokyIr5jx1wG2Hk4G4M1hzRjYvGKuxykTIu6UCxErn89EYiL06WM2fwAqVYLx42H4cPDT+Alf5EuZ0L9wL7Z582a7SxDxKMqE+II9x1IYM3kdAM0qlzhvMwiUCZHcKBciVj6fichI8yMsDJ5/3rxN7IYb1AzyYb6UCY0Q8mL79u2zuwQRj6JMSHF1JCmdr5ft4ZPFu0jJzHHtHzeg0QXPUyZE3CkXIlY+l4mUFHjjDbjzTihTBhwOeP99c+Loiuf/JYv4Dl/KhBpCXiz09BKIIgIoE1L8rNt3kpFfrCQ+OdPtseGtq9C4UvQFz1cmRNwpFyJWPpMJpxO++goefxwOHjRXEnvvPfOx6tXtrU08is9kAnAYhmHYXURRSkxMJDo6moSEBKKiouwuR0RExM3SHfE89+MmthxOsuyvVy6Sm9pVZWjLygQFaCi7iIhInixeDGPGwKpV5nb16vDaazBokL11iRSC/PQ89NOkF/v222/tLkHEoygT4s0OnEzj1s9WUO2xWfzr4+WWZtDbw5uze3w/5tzfmRvaVM1zM0iZEHGnXIhYFetM7NoF110HnTqZzaDISHPC6E2b1AyS8yrWmTiHbhnzYj42uEvkopQJ8UaLt8Xz8PfrOJSQ7vbY60ObMrhlpUt+bmVCxJ1yIWJVrDMxcSJMmWJOED1yJIwbB2XL2l2VeLhinYlzqCHkxWrXrm13CSIeRZkQb7HzaDJzNsYxYc4Wt8eaVi7BpyNaUSoi+LJfR5kQcadciFgVq0zk5MCJE1C6tLn99NOwbx88+yw0bWpraeI9ilUmLkINIS9Wrlw5u0sQ8SjKhHiq/SdSmbn+EFsPJzFtzQG3xwP8HIztW58b2lQhJNC/wF5XmRBxp1yIWBWbTMyfb84TVLYs/PKLuXpY6dIwfbrdlYmXKTaZyAPNIeTF/vjjD7tLEPEoyoTYLS0zhz3HUpi5/iAPTVlHtcdm0eDpOXR8ZQHjf97s1gy6umkFFjzUle0v9WVkx+oF2gwCZUIkN8qFiJXXZ2LbNhgwAHr0gPXrYeVKc1SQyCXy+kzkg0YIiYiIXILsHCcHT6az8WACGw4m8MNfB9l/Is3tuNTMHADKR4dQo0w4A5pVpGf9spQMDyrqkkVERIqPEyfMOYHefReyssDfH0aPhmeegVKl7K5OxCuoIeTFunXrZncJIh5FmZCi8NO6g9wzae1Fj6taKowrqsVwU9uqVC0VRomwom8AKRMi7pQLESuvzMS6deaIoGPHzO2+fc1l5OvXt7cuKRa8MhOXSA0hL7Z7926fur9R5GKUCSks+0+kMnX1Ad6Zv41sp/vKE+FB/gxqUYnbO9WgUslQ/PwcNlTpTpkQcadciFh5ZSbq14eYGHO+oDfegN697a5IihGvzMQlUkPIi+3atYu2bdvaXYaIx1AmpCDFJ2fwwcId/G/5XtKyciyPVSwRyhP96tOrQVkC/D13Oj5lQsSdciFi5RWZ2LQJ3noL3nkHgoLMj19+gcqVIUD/pZWC5RWZKCBKjxcLDAy0uwQRj6JMyOVKSMti4rytzN0Yx8GEdLfH7+xSk36Ny9O4UrQN1eWfMiHiTrkQsfLoTMTHm0vGf/CBuaR8/fpw//3mY9Wr21mZFGMenYkC5jAMw33sezGWmJhIdHQ0CQkJREVF2V2OiIh4gKwcJ5NW7OXpHza6Pdascgnu7FKDHvXLEujBo4FERESKjcxMc7Lo55+HhARz37XXwoQJUKuWvbWJeLj89Dz0k60X+/777+0uQcSjKBOSX5nZTp79cSO1n/jZ0gyqEB3Ca0Obsnt8P2bc3YGrGpX3ymaQMiHiTrkQsfKoTBgG/PADNGwIDz5oNoOaNYMFC2DaNDWDpEh4VCYKmW4Z82JZWVl2lyDiUZQJyatjyRmMm7mJGX8dtOy/pX017u5WizKRwTZVVrCUCRF3yoWIlcdl4t13Yft2c8LoF1+EW24xl5QXKSIel4lCpIaQF6uu+2ZFLJQJuZh5mw7z4qxN7D6WatkfGxnM/93QgiuqxdhUWeFQJkTcKRciVrZnIi7OnCQ6JgYcDnPVsEmTYOxYiIy0tzbxSbZnogipIeTFqlWrZncJIh5FmZDzWbX7OI9N+5vtR5It+7vVLcNz1zSiSqkwmyorXMqEiDvlQsTKtkykp8PEifDSSzBihDkyCKBxY/NDxCa+dJ3wvgkRxGXBggV2lyDiUZQJOdeS7fG0fek3hnywzNUMqhUbwZvDmrF7fD8+u7V1sW0GgTIhkhvlQsSqyDNhGPDdd+aKYY8/DsnJsHYtZGcXbR0i5+FL1wmNEBIRkWLlcGI609ceYPzPm90e+2pkazrVLmNDVSIiIsLKlTBmDCxZYm5XrAjjx8O//gV+GqsgUtTUEPJinTp1srsEEY+iTPi21XtOMPj9pbk+9lT/Bozs6Dv3g5+mTIi4Uy5ErIosE19+ad4aBhAWBo8+Cg89ZH4u4kF86TqhhpAXi4uLo1KlSnaXIeIxlAnfYhgGJ1Kz+GrZHt5dsI2sHMPyeLVSYTzRrwE9G5S1qUL7KRMi7pQLEasiy0SfPlCiBFxzjTlvUMWKhf+aIpfAl64Tagh5sW3bttGqVSu7yxDxGMqEb4hPzuC7VfuYMGeL22NNKkVzT/faPt0EOpsyIeJOuRCxKpRMOJ3wzTewYAF88om5r0wZczn5UqUK9rVECpgvXSfUEPJiDofD7hJEPIoyUfzsP5HKnA1xbDqYyK//HCYxPfcJJxtWiGJ011r0a1K+iCv0bMqEiDvlQsSqwDOxdCncf785XxDAsGHQq5f5uZpB4gV86TrhMAzDuPhhxUdiYiLR0dEkJCQQFRVldzkiInKW4ymZbDiQwOSV+5j196ELHntt84r8p3stapQO96kLt4iIiEfas8ecF2jyZHM7IsJcRWzMGAgJsbc2ER+Sn56HRgh5sRkzZjBw4EC7yxDxGMqEd9lzLIVvlu9l+toDpGXlkHSe0T8A5aNDuKJaDF3qlOGKajHFeqn4gqRMiLhTLkSsLjsTaWnwwgvw+uuQkQEOB4wcCePGQblyBVanSFHxpeuEGkJeLC0tze4SRDyKMuH5dsenMG7mJlbsPn7BBtAV1UrStkYp+jQqT4MKGs15qZQJEXfKhYjVZWfC3x+++85sBnXrBm+8Ac2aFUhtInbwpeuEGkJerHLlynaXIOJRlAnPtSs+hbv/t4ZNhxLdHmtTPYYOtUrTvV4sFUqEEhMeZEOFxZMyIeJOuRCxuqRMLF4MbdpAYCAEBcH770NKirmCmG7jFi/nS9cJNYS8WL169ewuQcSjKBOe5699Jxn4f0vc9jeuGM3YPvVoV7OU5v8pRMqEiDvlQsQqX5nYvh0efhhmzIC33oJ77zX3X3llodQmYgdfuk742V2AXLp58+bZXYKIR1EmPENyRjaPTV1PtcdmuTWDrr+iMuue7sVP93Skfa3SagYVMmVCxJ1yIWKVp0ycPAkPPQQNGpjNIH9/OHKksEsTsYUvXSc0QkhERArM0u3x3PTpCnKc1gUsxw1sxE1tq9pUlYiIiFyS7Gz4+GN4+mmIjzf3XXWVOYF0gwb21iYil00NIS/Wvn17u0sQ8SjKhH3+3HmMEZ+uICPb6dr37841uKltVSrHaEUwuygTIu6UCxGrC2bi7rvho4/Mz+vXNyeMvuqqoilMxCa+dJ3QLWNe7Pjx43aXIOJRlImit+94Kk2fm8v1H/3pagbVLRvJX0/35PG+9dUMspkyIeJOuRCxcsuEcdYo37vvhthYePddWL9ezSDxCb50nbC9IfTee+9RvXp1QkJCaNmyJX/88ccFj//f//5H06ZNCQsLo3z58tx6660cO3asiKr1LJs3b7a7BBGPokwUHcMweGzqejpNWEBCWhYA0aGBvDCwEXPu70SJMK0U5gmUCRF3yoWIlSsTx47BPfeYcwWd1qQJ7N1rNoYCdHOJ+AZfuk7Y2hCaPHky999/P0888QRr166lU6dO9OnTh7179+Z6/OLFi7n55psZOXIkGzduZMqUKaxcuZJRo0YVceUiIr7rSGI61cfO5tuV+1z7bmhThb+e7smNbatqomgREREv4pedDRMnQq1a5kigt9+GfWeu8QQH21eciBQqh2EYxsUPKxxt2rShRYsWvP/++6599evXZ+DAgbz88stux7/22mu8//777Nixw7XvnXfeYcKECew7+03rAhITE4mOjiYhIYGoqKjL/yJslJOTg7+/v91liHgMZaJwOZ0G4+ds5qNFO137YiOD+f3hboQG6fvuiZQJEXfKhcgphgE//YTx0EM4tm0z9zVpYs4T1KOHvbWJ2MjbrxP56XnYNkIoMzOT1atX06tXL8v+Xr16sXTp0lzPad++Pfv372f27NkYhsHhw4f5/vvv6dev33lfJyMjg8TERMtHcTF79my7SxDxKMpE4Vm//yQ1Hp9taQbd1bUmyx/voWaQB1MmRNwpFyLArl3QsycMGGA2g2Jjzcmj16xRM0h8ni9dJ2y7ETQ+Pp6cnBzKli1r2V+2bFni4uJyPad9+/b873//Y9iwYaSnp5Odnc0111zDO++8c97Xefnll3nuuefc9k+ZMoWwsDAGDRrEb7/9RkJCArGxsbRu3ZqZM2cC0KJFC5xOJ3/99RcAAwYMYPHixRw7doyYmBg6d+7MjBkzAGjSpAmBgYGsXr0agH79+rFq1SoOHz5MVFQUvXr14vvvvwegYcOGREREsHz5cgB69+7Nhg0bOHDgAOHh4fTv35/JkycDULduXUqXLs2SJUsAuPLKK9m6dSt79+7l0KFDgHnrndPppGbNmlSsWJFFixYB0LVrV/bu3cvOnTsJCAhg6NChTJ06lczMTKpWrUrNmjWZP38+AB07duTIkSNs3boVgOHDh/PDDz+QmppKpUqVaNCgAXPnzgWgXbt2JCQksGnTJgCGDh3KnDlzSEpKoly5crRo0cIVoiuuuIL09HT+/vtvAK699loWLlzIiRMnKF26NO3ateOnn34CoHnz5gCsXbsWgKuvvpply5YRHx9PyZIl6dq1K9OnTwegcePGhISEsHLlSgD69u3LmjVriIuLIzIykquuuoopU6YA0KBBA6Kjo1m2bBlgNh03bdrE/v37CQsLY8CAAUyaNAmAOnXqEBsby+LFiwHo3r07O3bsYM+ePQQFBTF48GCmTJlCdnY2NWrUoEqVKixcuBCAzp07c+DAAXbs2IGfnx/Dhg1j2rRpZGRkUKVKFerUqcOvv/4KQIcOHYiPj2fLli0ADBs2jJkzZ5KSkkLFihVp1KgRv/zyC2COpEtOTmbjxo0ADBkyhLlz55KYmEjZsmVp1aoVs2bNAqBly5ZkZWWxfv16AAYOHMiiRYs4fvw4pUqVomPHjvzwww8ANGvWDD8/P9asWQNA//79WbFiBUeOHCE6OpoePXowbdo0ABo1akRYWBgrVqwAoE+fPqxbt46DBw8SERFB3759+e677wCoV68eMTExrsZuz5492bx5M/v27SM0NJSBAwfy7bffYhgGtWvXply5cq65w7p168bu3bvZtWsXgYGBDBkyhO+//56srCyqV69OtWrVWLBgAQCdOnUiLi6Obdu24XA4uP7669m+fTuTJk2icuXK1KtXj3nz5gHme8fx48dd9wNfd911zJ49m+TkZCpUqEDTpk35+eefAWjdujWpqals2LABwKvfI4KDgxk0aNBlv0ek5cAXB8pwWniAkyWP9+b3eT/z7bdr9R7hwe8R8fHxJCYm6j3i1HvEjBkzSEtL03tEAb9HgHf9HHHgwAHX/JW+/h6hnyN89z1ixi+/0H/pUgICA9lw5ZVsGTyY7LAwrjx+3OffI0A/R/j6e8SBAwdo3ry5175HpKamkle23TJ28OBBKlasyNKlS2nXrp1r/4svvshXX32V60ROmzZt4sorr2TMmDH07t2bQ4cO8fDDD3PFFVfwySef5Po6GRkZZGRkuLYTExOpXLlysbhl7Pfff6dLly52lyHiMZSJgmUYBp8t2c2rv2whLSuH8CB/Zt3biWqlw+0uTfJImRBxp1yIT8rIgBkzYNiwM/t++gkaNeL3vXuVCZGzePt1Ij+3jNk2Qqh06dL4+/u7jQY6cuSI26ih015++WU6dOjAww8/DJhdsPDwcDp16sQLL7xA+fLl3c4JDg4muJhOhNa0aVO7SxDxKMpEwTAMg0+X7OatX7eSmJ4NQKC/g8l3tFMzyMsoEyLulAvxKYYBU6fCI4+Yt4lFR59ZOv7qqwFoWrKkjQWKeB5fuk7YNodQUFAQLVu2dA2zOm3evHm0b98+13NSU1Px87OWfHqyJxvnxrbN6WFnImJSJi5fWmYOfd76g3EzN7maQbd2qMbap3vRqGK0zdVJfikTIu6UC/EZq1dDly4wdKjZDKpQAbKy3A5TJkSsfCkTto0QAnjggQe46aabaNWqFe3ateOjjz5i79693HnnnQCMHTuWAwcO8OWXXwLmfZ63334777//vuuWsfvvv5/WrVtToUIFO78UERGvt2LXca77cJll32e3XEG3erE2VSQiIiL5dvAgPP44fPmlOUIoNBQeftgcJRSukb4icoatDaFhw4Zx7Ngxnn/+eQ4dOkSjRo2YPXs2VatWBeDQoUPs3bvXdfwtt9xCUlIS7777Lg8++CAlSpSge/fuvPLKK3Z9CbZq3bq13SWIeBRl4tIs2HyEWz9fadn3cO+63N2tlk0VSUFRJkTcKRdSrBkG9OkDpyb95cYb4aWXoHLl856iTIhY+VImbG0IAYwePZrRo0fn+tjnn3/utu+ee+7hnnvuKeSqvEN+Zg8X8QXKRN4dPJnGh7/v4I9t8eyMT3HtLxsVzP9GtaVWbISN1UlBUSZE3CkXUuw4nWYjyN8fHA549ll49VV4803Iw39slQkRK1/KhG1zCMnlO71UnYiYlIkLy8jO4fMlu6gxdhbtx8/ni2V7LM2gz2+9guWPX6lmUDGiTIi4Uy6kWFm2DNq1gw8+OLNv4EBYsiRPzSBQJkTO5UuZsH2EkIiIFK7NcYm8NHszi7YedXusS50yPHtNQ6pr9TARERHvsWcPPPYYfPutuX34MNxxBwQEmKOERETywGH42PJciYmJREdHk5CQQFRUlN3lXJaMjAyCg4PtLkPEYygTZzidBo9OXc+cDXEkZWRbHutWtwwP9qqrVcN8gDIh4k65EK+WlATjx8Mbb0B6utn8ufVWeOEFKF/+kp5SmRCx8vZM5KfnoVvGvNhvv/1mdwkiHkWZMH34+w5avjCPKav3W5pB93avxbYX+/DZra3VDPIRyoSIO+VCvNacOVCnjjlJdHo6dO1qLi3/ySeX3AwCZULkXL6UCd0y5sUSEhLsLkHEo/h6Jo4kpjPyi1X8feDM96F19Rge7l2XFlVK4u+nIeS+xtczIZIb5UK8Vrly5q1hNWvCa6/BgAEFcnuYMiFi5UuZUEPIi8XGxtpdgohH8eVM/LIxjju+Wu3a/lebKjzZrz5hQXqb92W+nAmR81EuxGvs2AGLF8OIEeZ2s2bw88/myKACvJ1FmRCx8qVMaA4hL5aUlERkZKTdZYh4DF/NxPS1+xkzeZ1re2TH6jzVv4GNFYmn8NVMiFyIciEeLyHBnBPo7bfNJeU3bIC6dQvt5ZQJEStvz4TmEPIRM2fOtLsEEY/ii5mYt+mwpRn0xyPd1AwSF1/MhMjFKBfisbKzzeXja9c2bwnLzITu3cGvcP/LpkyIWPlSJnQvgYiIl/px3UHunbTWtT3zno5UjgmzsSIRERG5JHPnwgMPwMaN5nbduuZKYn36aBl5ESk0agh5sRYtWthdgohH8YVMJKVn8d8/drFwyxHW7TcnvKtRJpxv/92W2MgQm6sTT+MLmRDJL+VCPM7JkzBkiLmkfEwMPPss3HknBAYWycsrEyJWvpQJNYS8mNPptLsEEY9SnDPx66bDvD5vK/8cSrTsv7J+Wd66vhnhwXo7F3fFORMil0q5EI+QlASn5ygpUQKeeQb27YOnnzabQkVImRCx8qVMaA4hL/bXX3/ZXYKIRymOmVi95zjdX1/IqC9XWZpBLaqU4Of7OvHfEa3UDJLzKo6ZELlcyoXYKivLnCy6alXzNrHTHnwQ3nyzyJtBoEyInMuXMqH/RYiIeJg1e0/w4qx/2BWfwvGUTMtjz1zdgOGtqxAS6G9TdSIiIpJvhgGzZsFDD8GWLea+Tz6BXr3srUtEfJqWnfdiqamphIVpAlmR07w9EwmpWVz/8Z9ut4UBPD+gITe3q1b0RYlX8/ZMiBQG5UKK3IYN5oTR8+aZ22XKmMvKjxwJ/vb/gkeZELHy9kxo2XkfsXjxYrtLEPEo3pyJvcdSafr8XEsz6O5uNfn72V7sHt9PzSC5JN6cCZHColxIkRo3Dpo2NZtBQUHwyCOwbRv8+98e0QwCZULkXL6UCd0y5sWOHTtmdwkiHsUbM7H/RCo3f7qCnUdTXPu61i3DZ7dcgUPLzMpl8sZMiBQ25UKKVIMG4HSaq4i98grUqGF3RW6UCRErX8qEGkJeLMaGSedEPJk3ZWLN3hMMem+pZV+5qBAe6FWH61pVtqkqKW68KRMiRUW5kEJjGDB9OmRmwvXXm/sGDYLVq8GDl7FWJkSsfCkTmkPIi6WlpREaGmp3GSIewxsysX7/SYZ9+CdpWTmW/Xd1rckjvetqVJAUKG/IhEhRUy6kUKxZA2PGwKJFUKoUbN9uLifvBZQJEStvz4TmEPIRM2bMsLsEEY/iyZk4nJjOqC9Wcs27SyzNoFvaV2PHS3159Kp6agZJgfPkTIjYRbmQAnXwINx6K7RqZTaDQkLgrrsgMNDuyvJMmRCx8qVM6JYxEZFClJ6Vw8D/W8LmuCTL/i9ua02XOmVsqkpEREQuS1oavP46jB8PKafmAfzXv+Dll6FKFXtrExHJIzWEvFiTJk3sLkHEo3hSJgzD4PW5W3l3wXbL/hcGNuL6KyoT4K8BmlL4PCkTIp5CuZACsWULPP20OW9Q27YwcaL5pxdSJkSsfCkTagh5sUAvGooqUhQ8IRNHktKZsmo/b/26jcwcp2t/x1ql+Wpka90WJkXKEzIh4mmUC7lk+/dDpUrm582aweOPQ8OG5gTSXnx9VyZErHwpE/oVtRdbvXq13SWIeBQ7MzF/82GqPTaL1i/+xqu/bHE1g5pVLsG6p3vx9ag2agZJkdN1QsSdciH5tm8f3HCDuWT81q1n9r/wAgwf7tXNIFAmRM7lS5nQCCERkcswZdU+Hv5+vdv+4a2rMKRlRVpW9Z1lK0VERIqV5GSYMAFefRXS083Gz7x5UKeO3ZWJiBQILTvvxRITE73+axApSEWZiWU7jnHft2s5kpRh2f/itY24oU3VIqlB5GJ0nRBxp1zIRTmd8NVXMHYsHDpk7uvc2ZwnqEULe2srBMqEiJW3Z0LLzvuIVatW2V2CiEcpikzsOJrM9R8tY/jHf7qaQYH+Dn76T0d2j++nZpB4FF0nRNwpF3JBhgE9esAtt5jNoOrVYepUWLiwWDaDQJkQOZcvZUK3jHmxw4cP212CiEcpzEwYhsF7C3fw6i9bXPtCA/35+OZWdKxdutBeV+Ry6Doh4k65kAtyOKBXL1i9Gp56Cu69F4KD7a6qUCkTIla+lAk1hLyYNw9jEykMhZmJ8T9v5sNFOwHwc8C9PWpz/5WaQ0A8m64TIu6UC7FITIQXXzSbQD16mPvGjIGRIyE21t7aiogyIWLlS5nQHEJeLCsry6eWxBO5mMLIRFaOk0e/X8+0tQcAuK1DdZ7qX18rholX0HVCxJ1yIQDk5MAnn8CTT8LRo9CoEfz1F/j7211ZkVMmRKy8PROaQ8hHfP/993aXIOJRCjoTJ1Mzue3zla5m0KAWFdUMEq+i64SIO+VC+PVXaN4c7rjDbAbVqQMvvwx+vvlfI2VCxMqXMqFbxkREcrElLonrPlxGQloWAA/2rMM9PWrbXJWIiIhcsq1b4aGH4KefzO2SJeGZZ2D0aPDi0QAiIpdKDSEv1rBhQ7tLEPEoBZWJjQcT6Pf2Ytf2M1c34NYO1QvkuUWKkq4TIu6UCx+2bp3ZDAoIMJtAzzwDMTF2V2U7ZULEypcyoYaQF4uIiLC7BBGPUhCZ2Hk0mTu+Wu3a/uimlvRqWO6yn1fEDrpOiLhTLnxIVhZs2WLODwQwZAg89hiMGAH16tlbmwdRJkSsfCkTvnmjbDGxfPlyu0sQ8SiXm4kvlu6m++u/s/9EGkH+fsx/sIuaQeLVdJ0Qcadc+ADDgNmzoUkT6NYNEhLM/Q6HOVeQmkEWyoSIlS9lQg0hEfF5yRnZjPpiJc/8uBGAAD8HX49qQ40yvvPbARERkWJh40a46iro1w82bzb3bdpkb00iIh5Ky857sePHjxOj+55FXC4lEx8v2smLs/9xbTerXILv7mhHUID65eL9dJ0QcadcFFNHj5pzAn34ITid5iTR998PTzwB0dF2V+fRlAkRK2/PhJad9xEbNmywuwQRj5KfTBxJSuf2L1dZmkEThzVlxt0d1AySYkPXCRF3ykUxdPw41K0L779vNoMGDYJ//oEJE9QMygNlQsTKlzKhSaW92IEDB+wuQcSj5DUTO48mM+DdJSRlZANQtVQYL1/bmPa1ShdmeSJFTtcJEXfKRTEUEwMDBpiriL3xBnTtandFXkWZELHypUyoIeTFwsPD7S5BxKPkJRPT1uznge/WARAS6McHN7aka93Ywi5NxBa6Toi4Uy6KgbVr4dFH4b33oFYtc9/bb0NYGPj721ubF1ImRKx8KROaQ8iLOZ1O/Px0a4vIaRfKRGpmNo9N/Zsf1x107ZtxdweaVS5RRNWJFD1dJ0TcKRde7NAhc06gzz83VxIbOhS++87uqryeMiFi5e2Z0BxCPmLy5Ml2lyDiUc6XifcX7qDxs3NdzaD65aNY81RPNYOk2NN1QsSdcuGF0tLgxRehdm347DOzGXT99fDqq3ZXViwoEyJWvpQJ3TImIsXWhgMJ9H9nsWXfK4Mbc12ryjgcDpuqEhERkTybNg3GjIG9e83tNm1g4kRo187eukREigE1hLxY3bp17S5BxKOczkR6Vg4jPl3B8l3HXY9FhgSw9LHuRIYE2lWeSJHTdULEnXLhZf75x2wGVaoE48fD8OHgxbdyeCJlQsTKlzKhhpAXK11aKyKJnK106dIYhkHPib+z73gaABVLhPLIVXUZ0KyizdWJFD1dJ0TcKRcebv9+cxn5Jk3M7QcegJAQuOsuc9JoKXDKhIiVL2VC7XUvtmTJErtLEPEoS5Ys4a6v17iaQQ/1qsOSx7qrGSQ+S9cJEXfKhYdKSYFnnoE6deCmmyAnx9wfGgoPPqhmUCFSJkSsfCkTGiEkIsXCkcR0XvsnnGOZcQDULRvJf7rXtrkqERERuSCnE77+GsaOhYOnVgKNioJjxyA21t7aRESKOS0778WOHj1KmTJl7C5DxHYLtxzhls9WurZbV4/hq5GtCQ7wt7EqEfvpOiHiTrnwIIsXmxNGr1plblerZq4cNngwaPGHIqNMiFh5eya07LyP2Lp1q90liNju+Z82WZpB9/WozXd3tFMzSARdJ0Ryo1x4iCVLoFMnsxkUGWlOGP3PPzBkiJpBRUyZELHypUyoIeTF9p5eflPEB+U4DW747598umQXAKUjgri3TjJjetaxuTIRz6HrhIg75cJGZ9+Y0L692RD6979h2zZ49FFz8mgpcsqEiJUvZUINIS8WHBxsdwkitpix9gA1H5/Nku3HAIgKCWDlE1dSrYSWlBc5m64TIu6UCxvk5MDHH0PTppCYaO5zOOC33+DDD6FsWXvr83HKhIiVL2VCcwiJiNfIynHy4qx/+Hzpbte+/3SrxUO969pXlIiIiJzf/PnmPEHr15vbL78Mjz1mb00iIsWY5hDyEZMnT7a7BJEi88e2o7R7+TdXM6h0RDB/PNLN0gxSJkSslAkRd8pFEdm2DQYOhB49zGZQiRIwcSI88IDdlck5lAkRK1/KhJad92JOp9PuEkQKnWEYPPPjRr5ctse17+5uNXmgZ138/ayTTioTIlbKhIg75aKQGQY8/DC8/TZkZYG/P4weDc88A6VK2V2d5EKZELHypUyoIeTFatasaXcJIoVq+c5jjJ3+NzuPprj2zbynI40qRud6vDIhYqVMiLhTLgqZwwFHj5rNoL594bXXoH59u6uSC1AmRKx8KRNqCHmxihUr2l2CSKFwOg06TVjAgZNprn33X1mb+3rUxnGBpWiVCRErZULEnXJRCObMgTp1oEYNc/ull+Bf/4Leve2tS/JEmRCx8qVMaA4hL7Zo0SK7SxApcFvikmj5wjxXMyjAz8Hkf7fl/ivrXLAZBMqEyLmUCRF3ykUB2rQJ+vQxPx5++Mz+ihXVDPIiyoSIlS9lQiOERMRj7DiaTO83z7wB146NYO6YzhdtBImIiEgRio+HZ5+FDz4wl5QPDITq1cHpBD/9vllExFuoIeTFunbtancJIgVmS1ySpRn0zvDmXN20Qr6eQ5kQsVImRNwpF5chMxPefReefx4SEsx9AwfCq69CrVq2liaXTpkQsfKlTKiF78X27t1rdwkily09K4cO4+dbmkFT72qX72YQKBMi51ImRNwpF5fhvffgwQfNZlCzZjB/PkyfrmaQl1MmRKx8KRNqCHmxnTt32l2CyGVJSs+i4yvWyaM/v/UKWlaNuaTnUyZErJQJEXfKRT5lZp75/N//hiuugP/+F1atgm7d7KtLCowyIWLlS5nQLWNeLCBAf33ivfafSKXnG4tIy8oBoFHFKGaM7kCA/6X3qZUJEStlQsSdcpFHhw/Dk0/C2rWwfDn4+0NYmPm55vYrVpQJEStfyoTDMAzD7iKKUmJiItHR0SQkJBAVFWV3OSI+6XBiOm1e+s21/e6/mtO/Sf5vERMREZEClp4Ob75pLh2flGTu+/VX6NHD1rJERCRv8tPz0C1jXmzq1Kl2lyCSbwlpWVz34TLX9oc3tSywZpAyIWKlTIi4Uy7OwzBgyhSoXx/GjjWbQa1bw5IlagYVc8qEiJUvZcJ3xkIVQ5ln39Mt4gX+2neSf3+5iiNJGQB8cVtrutQpU2DPr0yIWCkTIu6Ui1wcO2auFrZ4sbldsSKMHw//+peWkfcByoSIlS9lQg0hL1a1alW7SxDJk7iEdO6fvJY/dx537ftmVBva1ypdoK+jTIhYKRMi7pSLXMTEQHa2OUfQI4/AQw9BeLjdVUkRUSZErHwpE2oIebGaNWvaXYLIRU1asZex0/627Jt5T0caVYwu8NdSJkSslAkRd8oFkJoKb78No0dDVJQ5SfSnn0JkJFSqZHd1UsSUCRErX8qExoB6sfnz59tdgsgFjZ223tIMmjCkCbte7lsozSBQJkTOpUyIuPPpXDid8PXXUKeOOU/Qyy+feax+fTWDfJRPZ0IkF76UCY0QEpECt2zHMe74ahWJ6dkAhAX58/vD3SgTGWxzZSIiIj5q6VK4/35YudLcrloVrrjC1pJERMReagh5sY4dO9pdgohFVo6TTq8sIC4x3bVvYLMKTBzWDIfDUeivr0yIWCkTIu58Lhd79sCjj8LkyeZ2RAQ88YTZHAoJsbU08Qw+lwmRi/ClTOTrlrEtW7bw7LPP0qNHD2rWrEn58uVp0qQJI0aM4JtvviEjI6Ow6pRcHDlyxO4SRCxGfbHK1QyqUzaCH//TgTevb14kzSBQJkTOpUyIuPO5XDz7rNkMcjhg1CjYtg0ee0zNIHHxuUyIXIQvZSJPDaG1a9fSs2dPmjZtyqJFi7jiiiu4//77GTduHDfeeCOGYfDEE09QoUIFXnnlFTWGisjWrVvtLkHE5d352/h961EAbmxbhbljutCkUokirUGZELFSJkTcFftc5ORAQsKZ7XHjoG9fWLMGPv4YypWzrzbxSMU+EyL55EuZyNMtYwMHDuThhx9m8uTJxMTEnPe4ZcuWMXHiRF5//XUef/zxAitSRDxXSkY2N32ynDV7TwLmyKAXBja2tygRERFftHAhjBkDtWrBlCnmvkqVYNYsW8sSERHP5DAMw7jYQZmZmQQFBeX5SfN7fFFKTEwkOjqahIQEoqKi7C5HxKsdT8mk71t/uG4Ta1sjhv+Naou/X9HcIiYiIiLA9u3w8MMwY4a5XaIEbN4MZcvaWZWIiNggPz2PPN0yltfmzoEDB/J1vFyeH374we4SxIdtOJBAi3HzXM2gAc0qMOl2e5tByoSIlTIh4q5Y5eLkSXjoIWjQwGwG+fvD3Xeb8wSpGSR5VKwyIVIAfCkTBbLKWFxcHC+++CL//e9/SUtLK4inlDxITU21uwTxQYZh8Pj0v5m0Yp9r3/s3tKBP4/I2VmVSJkSslAkRd8UmFytXmnMDxceb21ddBa+/bjaHRPKh2GRCpID4UibyvMrYyZMnueGGGyhTpgwVKlTg7bffxul08vTTT1OjRg3+/PNPPv3008KsVc5RqVIlu0sQH5OelUP9p+e4mkGVY0L5ZlQbj2gGgTIhci5lQsRdsclFgwYQHAz168Ps2fDzz2oGySUpNpkQKSC+lIk8jxB6/PHHWbRoESNGjGDOnDmMGTOGOXPmkJ6ezs8//0yXLl0Ks07JRQNd9KWI3fLZCtKznAD0bFCWj29uZXNFVsqEiJUyIeLOa3Pxzz/w4Yfwxhvg5wfh4fDbb1CjBgQG2l2deDGvzYRIIfGlTOR5hNCsWbP47LPPeO211/jxxx8xDIM6deowf/58NYNsMnfuXLtLEB+Rme1k8PtL+XPncQCua1XJ45pBoEyInEuZEHHndbk4dgzuuQcaN4a33oIvvzzzWN26agbJZfO6TIgUMl/KRJ5HCB08eNDVKatRowYhISGMGjWq0AoTEfsZhkH3139nV3yKa98t7avx7DUNbaxKRETEB2RmwnvvwXPPmZNHA1xzDbRvb2tZIiJSfOS5IeR0Ogk86zcQ/v7+hIeHF0pRkjft2rWzuwQpxg4lpHH7l6sszaCXrm3Mv9pUsbGqC1MmRKyUCRF3Hp8Lw4CZM+HBB83VwgCaNIGJE6F7d3trk2LJ4zMhUsR8KRN5bggZhsEtt9xCcHAwAOnp6dx5551uTaFp06YVbIVyXgkJCXaXIMXUxoMJ9Ht7sWu7RplwfvpPR8KDC2RhwkKjTIhYKRMi7rwiFy++aDaDYmPNz2+91VxSXqQQeEUmRIqQL2Uiz3MIjRgxgtjYWKKjo4mOjubGG2+kQoUKru3TH1J0Nm3aZHcJUgxtOJDA0A+WAeDnMEcFzX+wq8c3g0CZEDmXMiHiziNzcfgwJCebnzsc8Oab8NhjZlNo1Cg1g6RQeWQmRGzkS5nI8//wPvvss8KsQ0Q8wJLt8dzy2QqycgxiwoP46Z6OVCwRandZIiIixVN6ujlR9Isvwr33wgsvmPvbtjU/RERECpHDMAwjrwfv2bOHuXPnkpWVRdeuXb1yObbExESio6NJSEggKirK7nIuS3Z2NgEBnj9qQzxffHIGA/9vCftPpLn2/Tm2B+WiQ2ysKv+UCRErZULEnUfkwjBg6lR45BHYtcvc17Ej/P67uaS8SBHyiEyIeBBvz0R+eh55vuIsWrSIhg0bcscdd/Cf//yHZs2aMWnSpMsuVi7dnDlz7C5BvJhhGPy+9Sgdxs+n1Qu/WppBvz3YxeuaQaBMiJxLmRBxZ3suVq+GLl1g6FCzGVShAnzxhZpBYhvbMyHiYXwpE3m+6jz11FN069aN/fv3c+zYMW677TYeeeSRwqxNLiIpKcnuEsRLzd98mBbj5jHi0xUcOHmmEXRrh2rsfKkvNctE2FjdpVMmRKyUCRF3tubio4+gVSv44w8IDYWnn4atW+Hmm9UMEtvoWiFi5UuZyPM4qL///ptFixZRoUIFAF5//XU+/vhjTpw4QcmSJQutQDm/cuXK2V2CeBnDMJg4bytvz99u2f9Qrzrc2aUmAf7e/cOoMiFipUyIuLM1F336QFgYDBoEL70ElSvbV4vIKbpWiFj5Uiby3BA6efIksbGxru3w8HDCwsI4efKkGkI2adGihd0liBf551Aifd76w7UdHuTPzHs7Ub10uI1VFSxlQsRKmRBxV2S5cDph0iRYtQomTjT3Va4MO3aAD/1nQzyfrhUiVr6UiXwNB9i0aRPr1693fRiGwT///GPZJ0Vn9uzZdpcgXuLd+dsszaAb21Zhw3O9i1UzCJQJkXMpEyLuiiQXy5ZBu3Zw443mEvJLl555TM0g8TC6VohY+VIm8jV1do8ePTh3UbL+/fvjcDgwDAOHw0FOTk6BFigil84wDEZ+sYr5m4+49s24uwPNKpewrygREZHias8eeOwx+PZbczsiAh5/HJo3t7cuERGRXOS5IbTr9JKY4jGuuOIKu0sQD5aUnsUD362zNIPWP9uLqJBAG6sqXMqEiJUyIeKuUHKRmmrOCfT665CeDg4H3HorvPAClC9f8K8nUoB0rRCx8qVM5Lkh9MUXX/DQQw8RFhZWmPVIPqSnp9tdgnioL5ft5ukfNrq2h7euwvMDGhLo5ZNGX4wyIWKlTIi4K5RcGAZ89pnZDOraFd54Q6OCxGvoWiFi5UuZyPP/Dp977jmSk5MLsxbJp7///tvuEsTD7IpPodpjsyzNoHf/1ZyXBzUu9s0gUCZEzqVMiLgrsFwsX25OHA0QHg7/938wfTrMn69mkHgVXStErHwpE3n+H+K5cweJiGd5d/42ur220LVdITqEFY/3oH+TCvYVJSIiUtzs2AGDB0PbtvD112f2DxxofjgcdlUmIiKSLw4jj50ePz8/Dh8+TJkyZQq7pkKVmJhIdHQ0CQkJREVF2V3OZUlPTyckJMTuMsQD3P2/Ncz6+5Br+44uNRjbp76NFdlDmRCxUiZE3F1yLhISzDmB3n4bMjPBzw+eegqefbbAaxQpSrpWiFh5eyby0/PI9ypjAQEXPmXNmjX5eUq5DAsXLuSqq66yuwyx2a74FEszaNuLfXzi9rDcKBMiVsqEiLt85yI7G/77X3j6aTh61NzXq5c5T1DDhoVTpEgR0rVCxMqXMpGvhlDv3r2JiIgorFokn06cOGF3CWKzQwlpltvENo+7ymebQaBMiJxLmRBxl+9c3HYbfPWV+Xm9euZKYn366NYwKTZ0rRCx8qVM5Ksh9PDDDxMbG1tYtUg+lS5d2u4SxEZZOU76vb3YtT373k6EBPrbWJH9lAkRK2VCxF2+c3HHHTB7tnlr2B13QGBgodQlYhddK0SsfCkTeZ5DyN/fn0OHDnl9Q6g4zSGUnJysEVs+Kj0rh9u/XMUf2+IB+OimlvRqWM7mquynTIhYKRMi7i6Yi+PH4bnnoFQp8xax01JSzJXERIohXStErLw9E/npeWiVMS/2008/2V2C2OC3fw5T76k5rmbQm8OaqRl0ijIhYqVMiLjLNRdZWeZk0bVqmX++/PKZ+YJAzSAp1nStELHypUzk+ZaxXbt2+dTQKRFPk56Vw9M/bOC7Vftd+94e3pxrmmpZeRERkUtiGDBrFjz0EGzZYu5r3BgmTgQvX1lXRETkYvI0Qmj8+PGUKVMGP7+LH758+XJmzZqV5wLee+89qlevTkhICC1btuSPP/644PEZGRk88cQTVK1aleDgYGrWrMmnn36a59crTpo3b253CVJE9h1Ppd5TcyzNoPkPdlEz6BzKhIiVMiHizpWLHTugd2+4+mqzGVSmDHz4IaxdCz162FukSBHStULEypcykacRQps2baJKlSoMHTqUa665hlatWlHm1G9NsrOz2bRpE4sXL+brr7/m0KFDfPnll3l68cmTJ3P//ffz3nvv0aFDBz788EP69Onjer3cXHfddRw+fJhPPvmEWrVqceTIEbKzs/P45Yp4n51Hk7nqrTON0qaVopl6V3sCfHg1MRERkcsWEAB//AFBQXD//fD44xAdbXdVIiIiRSZP/6P88ssvmT9/Pk6nkxtuuIFy5coRFBREZGQkwcHBNG/enE8//ZRbbrmFzZs306lTpzy9+BtvvMHIkSMZNWoU9evX580336Ry5cq8//77uR4/Z84cfv/9d2bPns2VV15JtWrVaN26Ne3bt8/7V1yMrF271u4SpJBtP5JE99d/JzPbCcCrQ5rww386qhl0HsqEiJUyIXKWjAz48cczuahaFT7/HP75B155Rc0g8Vm6VohY+VIm8jyHUJMmTfjwww/54IMPWL9+Pbt37yYtLY3SpUvTrFmzfM8vlJmZyerVq3nssccs+3v16sXSpUtzPefHH3+kVatWTJgwga+++orw8HCuueYaxo0bR2hoaK7nZGRkkJGR4dpOTEzMV50idolPzqD/O2eWlZ96VztaVo2xsSIREREvZBgwbRo88gjs3EnpZ58989iwYbaVJSIiYrc8N4ROczgcNG3alKZNm17WC8fHx5OTk0PZsmUt+8uWLUtcXFyu5+zcuZPFixcTEhLC9OnTiY+PZ/To0Rw/fvy88wi9/PLLPPfcc277p0yZQlhYGIMGDeK3334jISGB2NhYWrduzcyZMwFo0aIFTqeTv/76C4ABAwawePFijh07RkxMDJ07d2bGjBmA2TALDAxk9erVAPTr149Vq1Zx+PBhoqKi6NWrF99//z0ADRs2JCIiguXLlwPQu3dvNmzYwIEDBwgPD6d///5MnjwZgLp161K6dGmWLFkCwJVXXsnWrVvZu3cv/v7+gHnrndPppGbNmlSsWJFFixYB0LVrV/bu3cvOnTsJCAhg6NChTJ06lczMTKpWrUrNmjWZP38+AB07duTIkSNs3boVgOHDh/PDDz+QmppKpUqVaNCgAXPnzgWgXbt2JCQksGnTJgCGDh3KnDlzSEpKoly5crRo0YLZs2cDcMUVV5Cens7ff/8NwLXXXsvChQs5ceIEpUuXpl27dq5Z3E/fq3m6I3v11VezbNky4uPjKVmyJF27dmX69OkANG7cmJCQEFauXAlA3759WbNmDXFxcURGRnLVVVcxZcoUABo0aEB0dDTLli0DzKbjpk2b2L9/P2FhYQwYMIBJkyYBUKdOHWJjY1m82GzEdO/enR07drBnzx6CgoIYPHgwU6ZMITs7mxo1alClShUWLlwIQOfOnTlw4AA7duzAz8+PYcOGMW3aNDIyMqhSpQp16tTh119/BaBDhw7Ex8ez5dQElsOGDWPmzJmkpKRQsWJFGjVqRP+3F5Oe5Y+fA57uUpqtS39h61IYMmQIc+fOJTExkbJly9KqVSvXvF0tW7YkKyuL9evXAzBw4EAWLVrE8ePHKVWqFB07duSHH34AoFmzZvj5+bFmzRoA+vfvz4oVKzhy5AjR0dH06NGDadOmAdCoUSPCwsJYsWIFAH369GHdunUcPHiQiIgI+vbty3fffQdAvXr1iImJcTV2e/bsyebNm9m3bx+hoaEMHDiQb7/9FsMwqF27NuXKlXPNHdatWzd2797Nrl27CAwMZMiQIXz//fdkZWVRvXp1qlWrxoIFCwDo1KkTcXFxbNu2DYfDwfXXX09AQACTJk2icuXK1KtXj3nz5gHQvn17jh8/zubNmwHz1tPZs2eTnJxMhQoVaNq0KT///DMArVu3JjU1lQ0bNgB49XtEcHAwgwYN0ntEMX2P+OWXXwBo06YNycnJbNy4EbC+R8TExJCYmKj3iFPvETNmzCAtLU3vET70HrHhyy+p8c47xJ76u00tUQK/pCQ2bNig9wj9HKH3CMz3iObNm7v+Dfvae4R+jtB7RG7vEdnZ2ezfv99r3yNSU1PJK4dh03ryBw8epGLFiixdupR27dq59r/44ot89dVXrm/m2Xr16sUff/xBXFwc0aeG9U6bNo0hQ4aQkpKS6yih3EYIVa5cmYSEBKKiogrhKys68+bNo2fPnnaXIQXMMAwm/LKF9xfuAODrkW3oWFsr/OWFMiFipUyIzzp4EJ54Ar74whwhFBICDz8MjzzCvGXLlAuRs+haIWLl7ZlITEwkOjo6Tz2PfI8QKiilS5fG39/fbTTQkSNH3EYNnVa+fHkqVqzoagYB1K9fH8Mw2L9/P7Vr13Y7Jzg4mODg4IIt3kPEx8fbXYIUgqd+2MDXf+4F4D/daqkZlA/KhIiVMiE+yemEbt3g1EgEbrgBXn4ZKlcGlAuRcykTIla+lAnbZqYNCgqiZcuWrmFWp82bN++8k0R36NCBgwcPkpyc7Nq3detW/Pz8qFSpUqHW64lKlixpdwlSwLYdTuJ/y81m0JX1YxnTs47NFXkXZULESpkQn2EYZiMIwM8Pxo6Ftm3hzz/h669dzSBQLkTOpUyIWPlSJmy7ZQzMuW9uuukmPvjgA9q1a8dHH33Exx9/zMaNG6latSpjx47lwIEDrmXsk5OTqV+/Pm3btuW5554jPj6eUaNG0aVLFz7++OM8vWZ+hk95uvT0dEJCQuwuQwpIYnoWLZ6fR7bToGnlEswY3R6Hw2F3WV5FmRCxUibEJ/z5J4wZA6NHw003mfucTnA4zI9zKBciVsqEiJW3ZyI/PY/LHiGUmJjIjBkz+Oeff/J97rBhw3jzzTd5/vnnadasGYsWLWL27NlUrVoVgEOHDrF3717X8REREcybN4+TJ0/SqlUrbrjhBq6++mrefvvty/0yvNLpSc+keHj7121kO83+7PhBjdUMugTKhIiVMiHF2r595u1g7dqZTaEXX7SOEjrPdVS5ELFSJkSsfCkT+Z5D6LrrrqNz58785z//IS0tjVatWrF7924Mw+Dbb79l8ODB+Xq+0aNHM3r06Fwf+/zzz932nT2bt0hx8cT0v123ij3cuy71y3v36DUREZFCk5wMEybAq69CerrZ+BkxwmwI+dk2G4KIiIjXyfdVc9GiRXTq1AkwO2eGYXDy5EnefvttXnjhhQIvUM6vcePGdpcgBWDR1qOuZlDr6jGM7lrT5oq8lzIhYqVMSLEzaxbUqQPjxpnNoM6dYdUq+OwzqFAhT0+hXIhYKRMiVr6UiXw3hBISEoiJiQFgzpw5DB48mLCwMPr168e2bdsKvEA5P2++r1FMqZnZ3PzpCtf2t7e31a1il0GZELFSJqTYiYqCQ4egenX4/ntYuBBatMjXUygXIlbKhIiVL2Ui3w2hypUrs2zZMlJSUpgzZw69evUC4MSJEz71jfMEK1eutLsEuUyPTf3b9fkv93fGz0/NoMuhTIhYKRPi9XbuhClTzmx36gTTp8M//8DgweedJ+hClAsRK2VCxMqXMpHvhtD999/PDTfcQKVKlahQoQJdu3YFzFvJfGlolcjl2ngwgR/XHQRgRLuq1C0XaXNFIiIiHiIxER59FOrXN+cH2rfvzGMDB0JwsG2liYiIFBd5WnY+MTHRslzZ6tWr2bt3Lz179iQiIgKAWbNmUaJECTp06FB41RaA4rTsfEJCAtHR0XaXIZdoyPtLWbXnBOWiQlg2trtuFSsAyoSIlTIhXicnBz75BJ58Eo4eNff17Anvvw81C2aOPeVCxEqZELHy9kwU+LLzJUuW5MiRIwB0796dmjVrcu2117qaQQD9+vXz+GZQcbNmzRq7S5BLNHP9QVbtOQHAxze3UjOogCgTIlbKhHiV336D5s3hjjvMZlDdujBzJvzyS4E1g0C5EDmXMiFi5UuZyFNDKCIigmPHjgGwcOFCsrKyCrUoyZu4uDi7S5BLkJiexdM/bASgUcUoGlfy3u6zp1EmRKyUCfEahw9Dv37w999QsiS89Zb5eb9+lzRP0IUoFyJWyoSIlS9lIiAvB1155ZV069aN+vXrA3DttdcSFBSU67Hz588vuOrkgiIjNeeMNxr/82aOp2QC8MWtrW2upnhRJkSslAnxaKmpEBZmfl62rDln0MmT8MwzcGpF28KgXIhYKRMiVr6UiTzNIZSWlsYXX3zBjh07eP3117n99tsJO30BP8fEiRMLvMiCVJzmEMrOziYgIE89PfEQS3fE86+PlwMwflBjrm9dxeaKihdlQsRKmRCPlJUFH3wAzz1n3hLWtm2RvrxyIWKlTIhYeXsm8tPzyNNXGRoayp133gnAqlWreOWVVyhRosRlFyqXZ8qUKQwfPtzuMiSPDMPg2R/NW8UqRIcwuGUlmysqfpQJEStlQjyKYcDs2fDQQ7B5s7nv/feLvCGkXIhYKRMiVr6UiXy3vRYsWFAYdYgUe9+t2sfWw8kE+DmYNroDgf55msJLRETE+23YAA8+CHPnmtulS8O4cTBqlL11iYiI+LA8NYQeeOABxo0bR3h4OA888MAFj33jjTcKpDC5uAYNGthdguRRRnYOT0zfAECP+rGUiw6xuaLiSZkQsVImxCM8/TS8+CI4nRAYCPfdB088ATaNNlcuRKyUCRErX8pEnhpCa9euda0stmbNGi2R7SGio7U6lbf4bMlusp3mdF339ahjczXFlzIhYqVMiEeoVs1sBl17LUyYALVq2VqOciFipUyIWPlSJvLUEDr7NrGFCxcWVi2ST8uWLaNatWp2lyEXkZ6Vw/sLdwDwUK86NKjg3ZOZezJlQsRKmZAiZxjwww/g7w9XX23uGzEC6teHdu3sre0U5ULESpkQsfKlTOR7EpPbbruNpKQkt/0pKSncdtttBVKUSHHy5bLdJKRlERkcwC0dqttdjoiISOH46y/o3t0cCTR6tLmsPJjNIQ9pBomIiMgZ+W4IffHFF6SlpbntT0tL48svvyyQoiRvevXqZXcJchHbjyTz2tytANzToxYRwd67fKE3UCZErJQJKRJxcebk0C1awMKFEBJijgryUMqFiJUyIWLlS5nIc0MoMTGRhIQEDMMgKSmJxMRE18eJEyeYPXs2sbGxhVmrnGPTpk12lyAX4HQaXPnG72RmOykRFsitGh1U6JQJEStlQgpVWhq89BLUrg2ffGLeLnb99eaS8i+8AGFhdleYK+VCxEqZELHypUzkebhCiRIlcDgcOBwO6tRxnxTX4XDw3HPPFWhxcmH79++3uwS5gA8X7XR9/vyARlpmvggoEyJWyoQUqtWrzdXCAFq3hokToX17e2vKA+VCxEqZELHypUzkuSG0YMECDMOge/fuTJ06lZiYGNdjQUFBVK1alQoVKhRKkZK7MA/9zZvA4cR0XpmzGYAhLStxTVNloygoEyJWyoQUuMOHoWxZ8/OOHeE//4G2bWH4cPDzjl98KBciVsqEiJUvZcJhGIaRnxP27NlDlSpVvHbp+cTERKKjo0lISCAqSqs9SeF47qeNfLZkNwDrnu5FdFigvQWJiIhcjn37YOxYmDEDtmyBihXtrkhERERykZ+eR55+lbN+/XqcTicACQkJ/P3336xfvz7XDyk6kyZNsrsEyYVhGK5m0LiBjdQMKkLKhIiVMiGXLSUFnnkG6taF//3P3P75Z7uruizKhYiVMiFi5UuZyNMtY82aNSMuLo7Y2FiaNWuGw+Egt4FFDoeDnJycAi9SxJuMnfY3AEEBfgxoplvFRETECzmd8NVX8PjjcPCgua9jR3OeoFat7K1NRERECkSeGkK7du2iTJkyrs/FM+Q2ubfYa9mOY3y7ch8A/ZuUJypEo4OKkjIhYqVMyCVxOqFLF1i82NyuXh0mTIDBg8FLpww4m3IhYqVMiFj5Uiby1BCqWrVqrp+LvWJjY+0uQc5iGAaPTjVvm6wdG8HrQ5vaXJHvUSZErJQJuSR+ftChA6xbB08+CffeCyEhdldVYJQLEStlQsTKlzKR7+UgvvjiC2bNmuXafuSRRyhRogTt27dnz549BVqcXNji07+5E4/w1m/b2Hs8FT8H/HdEK6+deN2bKRMiVsqE5Eliojlh9IoVZ/Y98QRs2waPPFKsmkGgXIicS5kQsfKlTOS7IfTSSy8RGhoKwLJly3j33XeZMGECpUuXZsyYMQVeoIg3+PrPPbz56zYA7utRh6qlwm2uSERE5CJycuDjj6F2bRg/Hu6/H07PERkZeWZ5eRERESmW8nTL2Nn27dtHrVq1AJgxYwZDhgzh3//+Nx06dKBr164FXZ9cQPfu3e0uQYCsHCdv/roVgBJhgdzTvZbNFfkuZULESpmQ85o/H8aMgdMrxNapY04g7QOUCxErZULEypcyke8RQhERERw7dgyAuXPncuWVVwIQEhJCWlpawVYnF7Rjxw67SxCg71t/EJ+cCcB3d7TDz0+3itlFmRCxUibEzbZtMHAg9OhhNoNKlDBXDvv7b+jfv1hMGn0xyoWIlTIhYuVLmch3Q6hnz56MGjWKUaNGsXXrVvr16wfAxo0bqVatWkHXJxegOZvs9/Wfe9h2JBmACYObUKdspM0V+TZlQsRKmRA3v/8OP/wA/v5wzz2wfbt5q1hQkN2VFRnlQsRKmRCx8qVM5Lsh9H//93+0a9eOo0ePMnXqVEqVKgXA6tWrGT58eIEXKOcX5EM/vHmihNQsnpyxAYA+jcpx3RWVba5IlAkRK2VCyM6GrVvPbN96q9kI+vtvePttOPVznC9RLkSslAkRK1/KhMMwTs8e6BsSExOJjo4mISGBqKgou8sRL2UYBiM+W8mirUcBWPF4D2KjitcqLCIi4uXmzIEHHoDkZNiyBU4tCiIiIiLFV356HvkeIQRw8uRJXn/9dUaNGsXtt9/OG2+8QUJCwiUVK5duypQpdpfgs/63fK+rGTR+UGM1gzyEMiFipUz4qE2boE8f8+OffyA1FTZutLsqj6FciFgpEyJWvpSJfDeEVq1aRc2aNZk4cSLHjx8nPj6eiRMnUrNmTdasWVMYNcp5ZGdn212CT5qzIc51q9jw1pW5vnUVmyuS05QJEStlwsfEx8N//gNNmpijgwID4cEHzXmCWrWyuzqPoVyIWCkTIla+lIl8Lzs/ZswYrrnmGj7++GMCAszTs7OzGTVqFPfffz+LFi0q8CIldzVq1LC7BJ+zJS6JO79e7dp+rE99G6uRcykTIlbKhA+Ji4P69eHkSXN74EB49VWoVcvOqjySciFipUyIWPlSJvLdEFq1apWlGQQQEBDAI488Qiv99qlIVamikSlFKTkjmxv++ycAJcICWfZYD0KD/G2uSs6mTIhYKRM+pFw56NYNdu2CN94wP5dcKRciVsqEiJUvZSLft4xFRUWxd+9et/379u0jMlJLbhelhQsX2l2CT3lp9j/EJ2cCMOe+zmoGeSBlQsRKmSjG1q2D/v3hwIEz+z79FFatUjPoIpQLEStlQsTKlzKR74bQsGHDGDlyJJMnT2bfvn3s37+fb7/9llGjRmnZeSm2/t6fwDfLzUbooBYVKRetSaRFRMQGcXFw++3QvDnMmgVPP33msRIlwF+/rBAREZG8yfctY6+99hoOh4Obb77ZNdlSYGAgd911F+PHjy/wAuX8OnfubHcJPiEpPYtr/m8xAOWjQ3jp2sY2VyTno0yIWCkTxUh6OkycCC+9ZC4jDzBsGDz1lL11eSHlQsRKmRCx8qVM5HuEUFBQEG+99RYnTpzgr7/+Yu3atRw/fpyJEycSHBxcGDXKeRw4e5i4FJpXf9mCYZiffz2qDSGB+u2rp1ImRKyUiWJi2jRzwujHHzebQVdcAYsXw7ffQrVqdlfndZQLEStlQsTKlzKR74bQaWFhYZQoUYKYmBjCwsIKsibJox07dthdQrG37XASXy7bA8CojtWpWSbC5orkQpQJEStlopj480/YvRsqVoSvvjK3O3SwuyqvpVyIWCkTIla+lIl8N4Sys7N56qmniI6Oplq1alStWpXo6GiefPJJsrKyCqNGOQ8/v0vu50ke/fsrc4n5EmGBPNS7rs3VyMUoEyJWyoSX2r8ftmw5s/3EE+atYlu2wI03gv5eL4tyIWKlTIhY+VImHIZx+maYvLnzzjuZPn06zz//PO3atQNg2bJlPPvsswwYMIAPPvigUAotKImJiURHR5OQkEBUVJTd5YgHW7D5CLd+vhKAFwY24sa2VW2uSEREirXUVHj1VXjlFWjRAv74AxwOu6sSERERL5Kfnke+W1+TJk3i888/54477qBJkyY0adKEO+64g08//ZRJkyZdctGSf9OmTbO7hGLrcGK6qxlUIiyQG9pUsbkiyQtlQsRKmfASTid8/TXUqQPPPgtpaeb+EydsLau4Ui5ErJQJEStfykS+G0IhISFUy2UCw2rVqhEUFFQQNUkeZWRk2F1CsZSV46Tty7+5tn99oAsO/YbWKygTIlbKhBdYuhTatoWbboIDB6BqVZg82RwdFBNjd3XFknIhYqVMiFj5Uiby3RC6++67GTdunOWblJGRwYsvvsh//vOfAi1OLqxKFY1aKWiHE9NpP36+a1Wxt4c3p3SEVs/zFsqEiJUy4eF+/dWcHHrlSoiIgJdfhs2b4brrdKtYIVIuRKyUCRErX8pEQH5PWLt2Lb/99huVKlWiadOmAKxbt47MzEx69OjBoEGDXMf60lArO9SpU8fuEoqVHUeTGfbhn8Qnm83O4a2rcE3TCjZXJfmhTIhYKRMeyDDONHu6dYNmzaBVKxg3DsqVs7U0X6FciFgpEyJWvpSJfI8QKlGiBIMHD6Z///5UrlyZypUr079/fwYNGkR0dLTlQwrXr7/+ancJxYZhGAx4d4mrGfTeDS14eVBjm6uS/FImRKyUCQ+SkwP//S+0awfp6eY+f39zCfmPP1YzqAgpFyJWyoSIlS9lIt8jhD777LPCqEPEVh8t2klyRjYALw9qTN/G5W2uSEREio0FC2DMGFi3ztz+6CO4917z82DdliwiIiL2yPcIIfEcHTp0sLuEYuHnvw/x8s+bARjeujLDW/vOPaPFjTIhYqVM2GzbNhg4EP6fvfsOb6rs/zj+7i6d7Ja9954KspeAoogIKg58REVUVFScKDhwMn48PiCKihMQWYrIUJmKbAQsS/YoIBTaQunO748jgWMKNJDkJM3ndV29SE5Ozvkm5ZPSL/e57w4djGZQbCyMGgUDB1pdmV9TLkTMlAkRM3/KhBpCPuz48eNWl+DzPly2i4e/Wg9A2SKFGH5THYsrkquhTIiYKRMWycmBp56COnVgzhzj0rBBg+Cvv2DIENCqrJZSLkTMlAkRM3/KhBpCPmz79u1Wl+Cz0rNyGPLNRkbOM0YGNS5fmF+eakdYcJDFlcnVUCZEzJQJiwQFwa5dkJUFXbvCpk3wv/9B8eJWVyYoFyL/pkyImPlTJpyeQ0jEl63bl8Src7fyx4FT9m3FIkP5dmBLAgO1xK+IiFyhBQugfn0o9c8cdO+9Bw89BN26WVuXiIiIyEUE2Gw229Ue5NSpUxQuXNgF5bhfSkoKsbGxJCcnExMTY3U5VyU3N5fAQA3yupzsnFzW7jvJ8zM3s+f4GdNjRSNDWfpMO6LDQyyqTlxJmRAxUyY8YOtWePppmDcP7rsPPvnE6orkMpQLETNlQsTM1zPhTM/D6Vf59ttvM23aNPv9Pn36UKxYMcqUKcMf51bPEI+YO3eu1SV4teycXB6fuoGqL/7I7R/+bmoGvdWrHjte78b6YZ3VDCpAlAkRM2XCjU6cgMceg3r1jGZQcDAULQpX//9s4mbKhYiZMiFi5k+ZcPqSsYkTJ/Lll18CsGjRIhYtWsSPP/7IN998wzPPPMPChQtdXqTk7cyZM5ffyU/9lHCUAZ+vddj+6X3NaF+jpAUViScoEyJmyoQbZGbC+PEwYgScOmVsu/lmePddqFbN0tIkf5QLETNlQsTMnzLhdEMoMTGRcuXKAUbnrE+fPnTp0oWKFStyzTXXuLxAubgyZcpYXYLXsNls/Lz1GAdPpvH77iTm/3nE/tgDrSsxtGtNQoJ8d9if5I8yIWKmTLjBu+/CSy8Zt+vXhzFjjGXlxWcoFyJmyoSImT9lwumGUJEiRThw4ADlypVj/vz5vP7664DxC3lOTo7LC5SLq1u3rtUlWM5ms/HcjM1MW3vA4bHCESHMGnQdlYpHWlCZWEGZEDFTJlwkO9u4JAzgkUdgyhR4/HH4z3+MFcXEpygXImbKhIiZP2XC6SETvXr14s4776Rz586cOHGCbv+snrFx40aqVq3q8gLl4hYsWGB1CZZIz8ph6ur9fLJiD5Wen+fQDGpbvQS3NyvHL0+1UzPIz/hrJkQuRpm4SkePwoMPQufO5+cGKlwYNm+GBx5QM8hHKRciZsqEiJk/ZcLpEUJjxoyhYsWKHDhwgHfeeYeoqCjAuJRs0KBBLi9QJD0rh/X7TrLr79N8sHQ3h06dddinU62SvNO7AUUjQy2oUERECpT0dPi//4M33oDUVGPbypXQsqVxOyDAutpEREREXMQly877koK07Pzu3bupXLmy1WW41f/9tJMxP+1w2B4aFEhmTi69Gpfhhnql6FgrzoLqxNv4QyZEnKFMOMlmg2+/haFDYe9eY1uTJsY8Qa1bW1qauI5yIWKmTIiY+XomnOl55GuE0HfffUe3bt0ICQnhu+++u+S+N910U/4rlaty+vRpq0twm6ycXFq9/QtHUzJM23s1KkPNUtHc36oyQYH6H1oxK8iZELkSyoQTjh6F3r1hxQrjfunS8OabcNddEKhFCQoS5ULETJkQMfOnTOSrIdSzZ0+OHDlCyZIl6dmz50X3CwgI0MTSHvTnn39Sv359q8twuZNnMmn02iL7/fY1SvBJ/2YEaIi+XEZBzYTIlVImnFCsmLGMfKFC8MwzxiihSM1DVxApFyJmyoSImT9lIl8Nodzc3Dxvi7jasZR0Oo5ear//aPuqPH19DQsrEhGRAiktDT74AAYNgvBwYxWxL74wGkPlylldnYiIiIjbaQ4hH5aVlUVISIjVZbhUxed+sN9+9eY63NOionXFiM8piJkQuRrKRB5yc41l4597Dg4ehLfegmeftboq8SDlQsRMmRAx8/VMONPzuKKL4s+cOcO8efP44IMPGDdunOlLPGfhwoVWl+Ay36w5QIf3ltjvD+5YTc0gcVpByoSIKygT/3JupbC77jKaQeXLQ7VqVlclHqZciJgpEyJm/pQJp5ed37BhA927dyctLY0zZ85QtGhRjh8/TkREBCVLlmTw4MHuqFPykJKSYnUJLjF302GGzthkv9+wXGGGdK5uYUXiqwpKJkRcRZn4x759xoigqVON+1FR8Pzz8OSTxpxB4leUCxEzZULEzJ8y4fQIoSeffJIePXqQlJREoUKF+P3339m3bx9NmjThvffec0eNchFxcb6/1PqcjYd49OsN9vszB7Vk1qCWFlYkvqwgZELElZSJfzz1lNEMCgiA//wHduyAF15QM8hPKRciZsqEiJk/ZcLpOYQKFy7MqlWrqFGjBoULF2blypXUqlWLVatWce+997Jt2zZ31eoSBWkOoZSUFJ9+DeOX/MU787cDEB8TzpcDrqFqySiLqxJf5uuZEHE1v81ETg6cPWuMBAKjAfToo/D229CokbW1ieX8NhciF6FMiJj5eibcOodQSEiIffnvuLg49u/fD0BsbKz9tnjGDz/8cPmdvNSJ0xn2ZlCRiBCWDW2vZpBcNV/OhIg7+GUmli6FZs3giSfOb6teHRYuVDNIAD/NhcglKBMiZv6UCafnEGrUqBFr166levXqtG/fnpdffpnjx4/zxRdfUK9ePXfUKAXMoVNnue6tXwCjGbTqhU6EBl/R/OYiIiKGXbtg6FCYOdO4v3cvvPsuFCliaVkiIiIi3srp38JHjhxJqVKlAHjttdcoVqwYDz/8MMeOHePDDz90eYFycU2aNLG6hCty+4cr7bef6lJDzSBxGV/NhIi7+EUmkpONRlDt2kYzKDAQHn4Ytm9XM0jy5Be5EHGCMiFi5k+ZcGqEkM1mo0SJEtSpUweAEiVKMG/ePLcUJpeXlZVldQlO+2DpLg4knQXglR61uevaChZXJAWJL2ZCxJ0KfCZ++w169oS//zbud+kCo0fDP/9OEclLgc+FiJOUCREzf8qEU0MzbDYb1apV4+DBg+6qR5ywadOmy+/kRU6eyeStH41JxxuXL8x911WyuCIpaHwtEyLuVuAzUasW5OZCzZrwww8wf76aQXJZBT4XIk5SJkTM/CkTTjWEAgMDqVatGidOnHBXPVKAnWsGAXw14FoLKxEREZ+0bRu8+CKcWyC1SBFYvBg2bYLu3Y1l5UVEREQkX5xedv6HH37grbfeYsKECdStW9dddblNQVp2/uzZsxQqVMjqMvLl4Mk0Wr29GIB3bq1Pn2blLK5ICiJfyoSIJxSYTCQlwYgRMH48ZGcbcwXdcovVVYmPKjC5EHERZULEzNcz4dZl5++66y5Wr15NgwYNKFSoEEWLFjV9iecsW7bM6hLy7c15xuig6nFRagaJ2/hSJkQ8weczkZUF48ZB1arGn9nZ0KOHLguTq+LzuRBxMWVCxMyfMuH0svNjxowhQEOyvUJSUpLVJeRLelYOP2xOBOD2ZuUtrkYKMl/JhIin+GwmbDZjTqCnnzZWCwOoV8+YMLpTJ2trE5/ns7kQcRNlQsTMnzLhdEOof//+bihDrkSxYsWsLiFf3pm/3X77nhZaVUzcx1cyIeIpPpuJ3Fx47jmjGVSiBLz+Otx/PwQFWV2ZFAA+mwsRN1EmRMz8KRNOzyHUvn177rrrLnr37k1sbKy76nKbgjSHUFpaGhEREVaXcUkp6VnUH74QgOe61WRg2yoWVyQFmS9kQsSTfCoTf/8N0dEQHm7cX7QIfvoJXngBfPDfG+K9fCoXIh6gTIiY+Xom3DqHUL169XjppZeIj4/n1ltvZfbs2WRmZl5xsXLl5syZY3UJlzV19X7AWPjlvusqWluMFHi+kAkRT/KJTGRkwLvvnp8n6JzOneHtt9UMEpfziVyIeJAyIWLmT5lwuiE0btw4Dh06xJw5c4iOjubee+8lPj6eBx98kKVLl7qjRvFR6Vk5vLvAuFzslRtrExasof4iIvIPm81YLax2bRg6FFJSYP7880vKi4iIiIhbOd0QAggMDKRLly5MnjyZo0ePMnHiRFavXk2HDh1cXZ9cQsOGDa0u4ZKmrN5PVo7xD/tbm5S1uBrxB96eCRFP89pMrF8P7dvDrbfC7t1QqhRMnmxcIqaFK8TNvDYXIhZRJkTM/CkTTk8qfaEjR44wdepUvvzySzZt2kSzZs1cVZfkQ2DgFfXzPCIrJ5cJS3YBcH+rSkSHh1hckfgDb86EiBW8MhPvvw+DBxsjgcLD4ZlnjBFCUVFWVyZ+witzIWIhZULEzJ8y4fQrTUlJ4dNPP6Vz586UK1eOCRMm0KNHD3bs2MGqVavcUaNcxPr1660u4aJ+/es4x1IzABjSubrF1Yi/8OZMiFjBKzPRqRMEB8OddxqriL36qppB4lFemQsRCykTImb+lAmnRwjFxcVRpEgR+vTpw8iRIzUqSPL06twEAFpXK05k2FUNRBMREV9ls8HUqbB1q9H4AahZE/76C8qXt7Y2ERERET/n9LLzCxcupFOnTj47jKogLTufmppKdHS01WU4WL7zb+7+eDUAXw24huuqFre4IvEX3poJEatYmonff4cnnzT+DAiAjRuhfn1rahG5gH5WiJgpEyJmvp4Jty4736VLF59tBhU0q1evtrqEPE1cuhuA4lGhagaJR3lrJkSsYkkmDhyAfv2gRQujGRQZaYwOqlbN87WI5EE/K0TMlAkRM3/KhK7l8WHHjh2zugQHZzNzWPHXcQAeaV/V4mrE33hjJkSs5NFMnDkDb78N774L6enGqKD+/eH116F0ac/VIXIZ+lkhYqZMiJj5UybUEPJhsbGxVpfgYOXu4/bbd19bwcJKxB95YyZErOTRTGRmwv/+ZzSD2rSBMWOgcWPPnV8kn/SzQsRMmRAx86dMOD2HkK8rSHMIZWRkEBYWZnUZJk9O28isDYfoUjuOD+9panU54me8MRMiVnJ7JjZsgIYNjdFAAF99BYUKwS23nN8m4mX0s0LETJkQMfP1TLh1DqELpaenX83T5SrNnDnT6hJMbDYbszYcAqB7vVIWVyP+yNsyIWI1t2Vi927o3dsYAfTdd+e39+sHvXqpGSReTT8rRMyUCREzf8qE0w2h3NxcXnvtNcqUKUNUVBS7dxsTCA8bNoyPP/7Y5QWK75ix/pD9dodaJS2sRERE3CIlBZ59FmrVghkzIDAQNm+2uioRERERuQJON4Ref/11Jk+ezDvvvENoaKh9e7169Zg0aZJLi5NLq1u3rtUlmIz9aQcAlYpHEhMeYnE14o+8LRMiVnNZJnJy4MMPoWpVeOcdY76gzp2NpeRfesk15xDxEP2sEDFTJkTM/CkTTjeEPv/8cz788EP69etHUFCQfXv9+vXZtm2bS4uTS4uIiLC6BLv9J9I4ePIsAI931NLCYg1vyoSIN3BZJu64Ax56CP7+G6pXh7lzYcECqFfPNccX8SD9rBAxUyZEzPwpE043hA4dOkTVqo7Liefm5pKVleWSoiR/Vq9ebXUJdj9uSQSgTOFC3NxQywuLNbwpEyLewGWZuO8+KFIExo6FLVvghhs0T5D4LP2sEDFTJkTM/CkTTi87X6dOHZYvX06FCuYlxadPn06jRo1cVpj4li9X7QOgf8uKBOiXBBER35WUBK++CpUqweOPG9u6dYO9e8HHV+cUERERkfOcbgi98sor3H333Rw6dIjc3FxmzpzJ9u3b+fzzz5k7d647apSL6Natm9UlAHDidAYHkozLxbrWjbe4GvFn3pIJEW/hVCaysuCDD2D4cKMpFBMD994LhQsbj6sZJAWEflaImCkTImb+lAmnLxnr0aMH06ZNY968eQQEBPDyyy+zdetWvv/+ezp37uyOGuUi/vjjD6tLAGDe5kT77XJF/ed6S/E+3pIJEW+Rr0zYbDBvHtSvD4MHG82gunXh22/PN4NEChD9rBAxUyZEzPwpE06PEAK4/vrruf76611dizjp8OHDVpcAwPKdxwEoER1mcSXi77wlEyLe4rKZ2LkTHnvMmCAaoHhxeO01GDAAgq/onwgiXk8/K0TMlAkRM3/KhP6158OioqKsLoGM7ByWbP8bgFdvqmNxNeLvvCETIt7kspnIzoaffoKQEHjiCXjxRYiN9UhtIlbRzwoRM2VCxMyfMhFgs9lsl9upSJEi+Z4oOCkp6aqLcqeUlBRiY2NJTk4mxsfnQ8jJySEoKMjSGuZtTmTQV+spEhHC+mGdNaG0WMobMiHiTRwykZEBy5bBhZd4f/optGkDVap4vkARC+hnhYiZMiFi5uuZcKbnka85hMaOHcuYMWMYM2YML730EmBcNjZ8+HCGDx9uv3xs2LBhThc7fvx4KlWqRHh4OE2aNGH58uX5et6vv/5KcHAwDRs2dPqcBcU333xjdQl8+useAKqWjFIzSCznDZkQ8Sb2TNhsMGsW1KkDXbvC5s3nd7rvPjWDxK/oZ4WImTIhYuZPmcjXJWP33nuv/fatt97Kq6++yqOPPmrfNnjwYN5//31++uknnnzyyXyffNq0aTzxxBOMHz+e6667jokTJ9KtWzcSEhIoX778RZ+XnJzMPffcQ8eOHTl69Gi+zyeulZGdw5q9JwHoUDPO4mpERCRPGzbAkCGwZIlxPz4eDh+GevUsLUtERERErOX0KmMLFiyga9euDtuvv/56fvrpJ6eONXr0aO6//34GDBhArVq1GDt2LOXKlWPChAmXfN5DDz3EnXfeSYsWLZw6X0FTs2ZNS8//89Zj9tt3XnPxBp6Ip1idCRGvkphIl2nToEkToxkUHm7MEbRjB2hhCPFj+lkhYqZMiJj5UyacbggVK1aMWbNmOWyfPXs2xYoVy/dxMjMzWbduHV26dDFt79KlC7/99ttFn/fpp5+ya9cuXnnllXydJyMjg5SUFNNXQVG0aFFLz//hst0A3HddRWILhVhaiwhYnwkRr5GdDddeS7E5c4zLxW6/HbZtg9dfh+hoq6sTsZR+VoiYKRMiZv6UCadXGRsxYgT3338/S5YssY/Q+f3335k/fz6TJk3K93GOHz9OTk4OcXHmS43i4uI4cuRIns/ZuXMnzz33HMuXLyc4n8vhvvnmm4wYMcJh+/Tp04mIiKBXr178/PPPJCcnU7JkSZo3b87cuXMBaNy4Mbm5uWzcuBGAm2++mRUrVnDixAmKFi1KmzZtmD17NgD169cnJCSEdevWAXDDDTewdu1ajh49SkxMDF26dOHbb78FoE6dOkRFRbFq1SrAGF21ZcsWDh06RGRkJDfeeCPTpk0DoEaNGhQvXpxff/0VgE6dOrFjxw72799PYmIiQ4YMYdq0aeTm5lKlShXKlCnDsmXLAGjXrh379+9n9+7dBAcHc9tttzFjxgwyMzOpUKECVapU4ZdffgGgVatWHDt2jB07dgBwxx13MGfOHNLS0ihbtiy1a9dm4cKFALRo0YI1u/9m44FTAPRrVpa5c+eSmppKfHw8jRs3Zt68eQA0a9aM9PR0Nv8zX8Utt9zCkiVLOHnyJMWLF6dFixZ8//33ADRq1AiADRs2ANCjRw9WrlzJ8ePHKVKkCO3atbM3I+vVq0d4eDhr1qwBoHv37qxfv54jR44QHR1N165dmT59OgC1a9cmNjaWlStXAkbTMSEhgYMHDxIREcHNN9/MlClTAKhevTolS5ZkxYoVAHTo0IFdu3axb98+QkNDufXWW5k+fTrZ2dlUrlyZ8uXLs+SfyzDatGnDoUOH2LVrF4GBgfTt25eZM2eSkZFB+fLlqV69un0U3XXXXcfx48fZvn07AH379mXu3LmcOXOGMmXKULduXRb8swz0Nddcw+nTp/nzzz8B6N27NwsXLiQlJYW4uDiaNm3KDz/8AECTJk3Iyspi06ZNAPTs2ZNly5aRlJREsWLFaNWqFXPmzAGgYcOGBAYGsn79egBuvPFGVq9ezbFjx4iNjaVjx47MnDkTgLp16xIREcHq1asB6NatG3/88QeHDx8mKiqK7t2726+1rVmzJkWLFrU3djt37sy2bds4cOAAhQoVomfPnkydOhWbzUa1atWIj4+3zx3Wvn179u7dy549ewgJCaF37958++23ZGVlUalSJSpWrMjixYsBaN26NUeOHGHnzp0EBARw++23M3PmTOLj4ylXrhw1a9Zk0aJFALRs2ZKkpCS2bdsGQJ8+fZg3bx6nT5+mdOnSNGjQgB9//BGA5s2bk5aWxpYtWwB8+jMiLCyMXr16WfIZkZycTEJCAgC33XYb8+fP12eEuz8j/vk72/u221i4cCGl2ral9LJlRE+axHd//w2//UaTjAy//oyYPXs2Z8+e1WeEn39GHDp0iK5du/rfZ4T+HaHPiIt8RmzcuJFChQoB+ozw639H6DPC/hlx6NAhbr/9dp/9jEhLSyO/8rXK2L+tWrWKcePGsXXrVmw2G7Vr12bw4MFcc801+T7G4cOHKVOmDL/99pvp0q833niDL774wv5mnpOTk8O1117L/fffz8CBAwEYPnw4s2fPtr9BecnIyCAjI8N+PyUlhXLlyhWIVcamTJnCHXfcYcm5H5+6gTkbDxNbKIQ/Xuly+SeIeICVmRCx1OrV8OST8OyzcNNNxracHKZMncod/fpZW5uIl9HPChEzZULEzNcz4cwqY1fUEHKFzMxMIiIimD59Orfccot9++OPP87GjRtZunSpaf9Tp05RpEgR0/Jvubm52Gw2goKCWLhwIR06dLjseQvSsvPHjx+nePHiHj9velYOjV5dxNmsHB5uV4Vnu/rPNZbi3azKhIhlDh6E55+HL7807jdqBOvWwT+rPioTIo6UCxEzZULEzNcz4fJl590hNDSUJk2a2IdZnbNo0SJatmzpsH9MTAybN29m48aN9q+BAwfahzk6MzqpoPj3KCpPmbn+EGezcogKC+axDlUtqUEkL1ZlQsTjzpyBV16B6tXPN4P694e5c+3NIFAmRPKiXIiYKRMiZv6UCafnEHKlIUOGcPfdd9O0aVNatGjBhx9+yP79++2XhD3//PMcOnSIzz//nMDAQOrWrWt6fsmSJQkPD3fY7i8OHDhgyXmnrzPOW6VEJBGhlv4VEjGxKhMiHvX99zBwoLF0PEDr1jBmjLGa2L8oEyKOlAsRM2VCxMyfMmHpb/N9+/blxIkTvPrqqyQmJlK3bl3mzZtHhQoVAEhMTGT//v1WlujVzk3+5klpmdls2H8KgP+0quTx84tcihWZEPG4wECjGVSpErz7LvTqZRoVdCFlQsSRciFipkyImPlTJiybQ8gqBWkOISt8sXIvw+b8SUx4MH+80oWAi/wSIiIiLrJnDyQkwA03GPdtNpg6FW65BcLDra1NRERERLyKW+cQOnr06EUfO7f8nHjG1KlTPX7OpTv+BqB26Rg1g8TrWJEJEbdJSTEmjK5VC/r1g+PHje0BAXDHHflqBikTIo6UCxEzZULEzJ8y4XRDqF69enz33XcO29977z2/nNjZSlYM7vo7NQOATrXiPH5ukcvxswGPUlDl5MBHH0G1avDWW5CRAU2bQmqq04dSJkQcKRciZsqEiJk/ZcLphtCzzz5L3759GThwIGfPnuXQoUN06NCBd999l2nTprmjRrmIatWqefR8WTm5/Hk4BYBrKxfz6LlF8sPTmRBxuV9+gcaN4cEH4dgxYxWx776DRYuMOYOcpEyIOFIuRMyUCREzf8qE05NKP/XUU3Tq1Im77rqL+vXrk5SUxLXXXsumTZuIi9OoEU+Kj4/36Pl+332C7FwbkaFB1C6l+ZfE+3g6EyIutX8/dOlijBAqXNhYVn7QIAgNveJDKhMijpQLETNlQsTMnzLh9AghgMqVK1OnTh327t1LSkoKffr0UTPIAsuXL/fo+V6e8ycArauVIDBQ8weJ9/F0JkSuWkbG+dvly8Ojj8Jjj8Fff8ETT1xVMwiUCZG8KBciZsqEiJk/ZcLphtCvv/5K/fr1+euvv9i0aRMTJkzgscceo0+fPpw8edIdNYoXOJuZw57jZwBoWL6wtcWIiPi67Gz43/+gQgXYsuX89jFjYNw4KKbLckVERETEvZxuCHXo0IG+ffuycuVKatWqxYABA9iwYQMHDx6kXr167qhRLqJ9+/YeO9d3fxyy336wdWWPnVfEGZ7MhMgVmz8f6tc3RgMdPQrvv3/+MRev3qhMiDhSLkTMlAkRM3/KhNMNoYULF/LWW28REhJi31alShVWrFjBQw895NLi5NL27t3rsXO9t3AHAC2rFNPlYuK1PJkJEaclJEC3bsbX1q3GKKDx480NIRdTJkQcKRciZsqEiJk/ZcLphlDbtm3zPlBgIMOGDbvqgiT/9uzZ47FzBf/TBGpZRZcxiPfyZCZEnPLii8aooPnzISQEnnrKmCfo4Ych2On1HfJNmRBxpFyImCkTImb+lAmn/xX66quvXvLxl19++YqLEedcOErLnTYfTCYxOR2Au66t4JFzilwJT2VCxGnFixurh/XsCe++C1WreuS0yoSII+VCxEyZEDHzp0wE2Gw2mzNPaNSokel+VlYWe/bsITg4mCpVqrB+/XqXFuhqKSkpxMbGkpycTEyMlk7PjzfnbWXist0Ujwpl7UudrS5HRMS72Wzw3XcQHQ0dOhjbMjNh1Spo3dra2kRERESkQHOm5+H0JWMbNmwwfW3ZsoXExEQ6duzIk08+ecVFi/O+/fZbt5/DZrMxcdluAB7rUM3t5xO5Gp7IhMgl/fEHdOxojAQaNAiysoztoaGWNIOUCRFHyoWImTIhYuZPmXC6IZSXmJgYXn31Vc0h5GFZ537RcKNNB5Ptt29qUNrt5xO5Gp7IhEiejhyBBx6ARo1g8WIIC4NevYzl5S2kTIg4Ui5EzJQJETN/yoTLZrI8deoUycnJl99RXKZSpUpuP8fu46cBiAwNokhkqNvPJ3I1PJEJEZP0dBgzBkaOhNPG5yV9+8Jbb0HFipaWBsqESF6UCxEzZULEzJ8y4XRDaNy4cab7NpuNxMREvvjiC7p27eqywuTyKnrgl42JS43LxW5uVMbt5xK5Wp7IhIjJkiXwwgvG7ebNjeZQy5aWlnQhZULEkXIhYqZMiJj5UyacvmRszJgxpq9x48axZMkS7r33Xj788EN31CgXsXjxYrefIzDAWG6+eFSY288lcrU8kQkRkpLO377+erj3XvjiC1i50quaQaBMiORFuRAxUyZEzPwpE06PENqzZ4876hAvlZCYAkDnWnEWVyIiYrFDh4zRQHPnwo4dUKwYBATA5MlWVyYiIiIi4jSXTCot1mjt5hVrEpPP2m9Xi4ty67lEXMHdmRA/lZYGI0ZA9erw+efGCKEffrC6qnxRJkQcKRciZsqEiJk/ZeKKJpVes2YN06dPZ//+/WRmZpoemzlzpksKk8s7cuQIZcuWddvxl+342347PCTIbecRcRV3Z0L8TG4ufP01PPecMToIjEvCxowx5gvyAcqEiCPlQsRMmRAx86dMOD1CaOrUqVx33XUkJCQwa9YssrKySEhI4JdffiE2NtYdNcpF7Ny5063H3338DAD1y+r7Kr7B3ZkQP5KVBa1awd13G82gChVg2jRYscJnmkGgTIjkRbkQMVMmRMz8KRNON4RGjhzJmDFjmDt3LqGhofzf//0fW7dupU+fPpQvX94dNcpFBPwz4bO7bNh/CoCbGpR263lEXMXdmRA/EhICDRpAVJSxpPy2bdCnjzFnkA9RJkQcKRciZsqEiJk/ZSLAZrPZnHlCZGQkf/75JxUrVqR48eIsXryYevXqsXXrVjp06EBiYqK7anWJlJQUYmNjSU5OJiYmxupyvFrF54w5MmY/ch0NyxW2thgREXdKTYU334S77oLatY1tJ04YI4Xi462tTUREREQkn5zpeTg9Qqho0aKkpqYCUKZMGbZs2QLAqVOnSEtLu4Jy5UrNnj3bbcc+lppuv12haITbziPiSu7MhBRQOTnw8cdQrZrREHrqqfOPFSvm880gZULEkXIhYqZMiJj5Uyby3RD6z3/+Q2pqKq1bt2bRokUA9OnTh8cff5wHHniAO+64g44dO7qtUHF09uzZy+90haavPWi/XSQy1G3nEXEld2ZCCqDFi6FpUxgwAI4ehapVYeBAcG7grFdTJkQcKRciZsqEiJk/ZSLfq4x99tlnvPXWW7z//vukpxujR55//nlCQkJYsWIFvXr1YtiwYW4rVByVK1fObcfedew0AHExYW47h4iruTMTUoD89Rc88wyc+9+f2Fh45RV45BEILVgNcGVCxJFyIWKmTIiY+VMm8t0QOjfVUNGiRe3bAgMDGTp0KEOHDnV9ZXJZNWvWdMtxs3NyWZRwFIA3e9VzyzlE3MFdmZACZs4coxkUFGSMCBo+HIoXt7oqt1AmRBwpFyJmyoSImT9lwqk5hPxptm1fcO7SPVdbt+8kqRnZRIcF06ZaCbecQ8Qd3JUJ8XHZ2bB37/n7jz1mXCa2aRO8/36BbQaBMiGSF+VCxEyZEDHzp0zke4QQQPXq1S/bFEpKSrqqgsR6C/8ZHdSiSjGCg5yed1xExHssWABDhkBurtEACgkxLgv76COrKxMRERERsZRTDaERI0YQGxvrrlrESS1btnTLcfedMFaLq1Qi0i3HF3EXd2VCfNDWrcaKYT/+aNwvWtTYVr++tXV5mDIh4ki5EDFTJkTM/CkTTjWEbr/9dkqWLOmuWsRJSUlJVKhQwaXHtNls/HHwFADtqut7Lb7FHZkQH3PihDEn0IQJxpLywcHGJWLDhkGRIlZX53HKhIgj5ULETJkQMfOnTOT7eiDNH+R9tm3b5vJj7j5+hr9TMwgIgEblC7v8+CLu5I5MiA/Zu9dYOv79941m0E03wZ9/wujRftkMAmVCJC/KhYiZMiFi5k+ZcHqVMSnYNuw/BUAAEB4SZGktIiJOqVABGjeG48eNJlDHjlZXJCIiIiLitQJsftbpSUlJITY2luTkZGJiYqwu56rk5OQQFOTaps3Qb//gm7UHubVxWUb1aeDSY4u4mzsyIV5s0ybj8rBJk4w5ggD+/tu4rb8HgDIhkhflQsRMmRAx8/VMONPz0BJSPmzevHkuP+bSHX8DULu0bzfLxD+5IxPihY4ehQcfhEaNYNYsePXV84+VKKFm0AWUCRFHyoWImTIhYuZPmXBqUmnxLqdPn3bp8dKzcjiakgFAl9pxLj22iCe4OhPiZdLT4f/+D954A1JTjW233QaPP25tXV5MmRBxpFyImCkTImb+lAk1hHxY6dKlXXq8nUfP/8UvVzTCpccW8QRXZ0K8yMyZ8PTTsGePcb9pUxgzBlq1srYuL6dMiDhSLkTMlAkRM3/KhC4Z82ENGrh2jp+1+5IAaF6pqEuPK+Iprs6EeJEFC4xmUOnS8NlnsGqVmkH5oEyIOFIuRMyUCREzf8qEGkI+7Mcff3Tp8VbtNhpCjcv75/LM4vtcnQmx0OHDxjLy57z6KowYATt2wD33QKB+fOWHMiHiSLkQMVMmRMz8KRP6F7XY/frXcQDa1ShhcSUi4rfS0ozmT7Vq8Mgj57fHxcHLL0NkpHW1iYiIiIgUIJpDyIc1b97cZcdKPptFakY2ANVKRrnsuCKe5MpMiIfl5sKUKfDcc3DwoLHt5Elj8ujoaGtr82HKhIgj5ULETJkQMfOnTGiEkA9LS0tz2bHW/TN/EECxqDCXHVfEk1yZCfGglSuhRQu46y6jGVS+PEydCr/+qmbQVVImRBwpFyJmyoSImT9lQg0hH7ZlyxaXHWvt3pMAdK8X77JjiniaKzMhHvL999CyJaxeDVFRxpLy27ZB374QEGB1dT5PmRBxpFyImCkTImb+lAldMiYA7E8yuqB1SsdaXImI+JUuXaBqVWjTBl5/HUqVsroiERERERG/EGCz2WxWF+FJKSkpxMbGkpycTExMjNXlXJWMjAzCwlxzeVe94QtITc/mv3c0okeD0i45poinuTIT4ga5ucaS8V99BfPnQ/A//yeRlgYREdbWVkApEyKOlAsRM2VCxMzXM+FMz0OXjPmwn3/+2WXHSk03JpQuU6SQy44p4mmuzIS42LJl0KwZ/Oc/8PPP8Pnn5x9TM8htlAkRR8qFiJkyIWLmT5lQQ8iHJScnu+Q4Gdk59ttl1RASH+aqTIgL7doFt94KbdvC+vUQGwvvvQf9+lldmV9QJkQcKRciZsqEiJk/ZUJzCPmwkiVLuuQ4mw6e/wtfQiuMiQ9zVSbEBbKy4MUX4f/+DzIzITAQHnoIRoyAEiWsrs5vKBMijpQLETNlQsTMnzKhhpAPa968uUuOM33tAQAql4gkQKv6iA9zVSbEBYKDYc0aoxnUpQuMGgV161pdld9RJkQcKRciZsqEiJk/ZUKXjPmwuXPnuuQ4P24+AkCVElEuOZ6IVVyVCblCixZBUpJxOyAAxo2DH34wJpBWM8gSyoSII+VCxEyZEDHzp0yoISREhRsDxdrV0GUcInIFtm2DG280RgK99tr57fXqQffuRnNIRERERES8ihpCPqxx48ZXfYz0rByOpKQD0LlW3FUfT8RKrsiEOCEpCR5/3Gj8/PCDcZlYSIjVVckFlAkRR8qFiJkyIWLmT5nQHEI+LDc396qPseNoKjYbRIcFUyJaE0qLb3NFJiQfsrJgwgQYPhxOnjS29egB774LNWpYWpqYKRMijpQLETNlQsTMnzKhEUI+bOPGjVd9jJW7TgBQPT5aE0qLz3NFJiQfhg83RgadPGmMDlq0CL77Ts0gL6RMiDhSLkTMlAkRM3/KhBpCfu7zlfsAaF2tuMWViIhXu/B/SgYPhqpVYeJE2LABOnWyri4REREREbkiATabzWZ1EZ6UkpJCbGwsycnJxMTEWF3OVUlLSyMiIuKqjlHxuR8A+HZgC5pWLOqKskQs44pMyL8cOwYvvwxHjsDs2ee35+ZCoP5PwdspEyKOlAsRM2VCxMzXM+FMz0P/mvdhK1asuKrnn0rLtN+uFhd9teWIWO5qMyEXyMgw5gSqVs0YCTRnDlw4fFbNIJ+gTIg4Ui5EzJQJETN/yoT+Re/DTpw4cVXP/333+efHFtLKQOL7rjYTAthsMGMG1K4NQ4dCSgo0bgxLl0LDhlZXJ05SJkQcKRciZsqEiJk/ZUKrjPmwokWv7hKvdfuM1YH6NC3rinJELHe1mfB7hw7BnXfCsmXG/VKlYORIuOcejQjyUcqEiCPlQsRMmRAx86dMqCHkw9q0aXNVz//jQDIANeN9ey4lkXOuNhN+r3hxOHAAwsPhmWeMEUJRUVZXJVdBmRBxpFyImCkTImb+lAn9l68Pm33hBK9OOnkmk9V7kwBoU72EiyoSsdbVZMIvnT0L778P2dnG/bAw+Ppr2L4dXn1VzaACQJkQcaRciJgpEyJm/pQJjRDyUwv+PAJA8ahQqpSItLgaEfEomw2mToVnnzVGBAUFwcMPG49de621tYmIiIiIiEeoIeTD6tevf8XPnbspEYC6ZWIJCAhwVUkilrqaTPiN33+HJ580/gQoVw7i462tSdxGmRBxpFyImCkTImb+lAldMubDQkKufGWw/UlpALSqWtxV5YhY7moyUeAdOAD9+kGLFkYzKDISXn/duDzsllusrk7cRJkQcaRciJgpEyJm/pQJNYR82Lp1667oeTm5NntD6Do1hKQAudJM+IUHHjDmBwoIgPvug5074cUXoVAhqysTN1ImRBwpFyJmyoSImT9lQpeM+aE/Dyfbb1crqUljRQqk3FzIzDRWDANj+fiMDBg1Cho3trY2ERERERGxXIDNZrNZXYQnpaSkEBsbS3JyMjExvr3cekpKyhW9hq9X7eeFWZspER3Gmhc7uaEyEWtcaSYKnGXLjHmC2rUzGkDit5QJEUfKhYiZMiFi5uuZcKbnoUvGfNjatWuv6Hk/bT0KQPe6mkhWCpYrzUSBsXs39O4NbdvC+vXw+edw+rTVVYmF/D4TInlQLkTMlAkRM3/KhBpCPuzo0aNX9LxNB08BUCwqzIXViFjvSjPh85KTYehQqFULZsyAwEB46CH480+I0mWh/sxvMyFyCcqFiJkyIWLmT5nQHEI+7EqGseXm2jh+OhOAphWKuLokEUv58tDOK7Z0Kdx2G/z9t3G/UycYPRrq1bO2LvEKfpkJkctQLkTMlAkRM3/KhOYQ8mFZWVlOL4m3bl8St05YCcDON7oREqRBYlJwXEkmfF5iIlSvDqVLG/MF3XCDsZKYCH6aCZHLUC5EzJQJETNfz4TmEPIT3377rdPPWbHzBADXVCqqZpAUOFeSCZ+zYwe8+eb5+6VKwS+/wJYtcOONagaJiV9kQsRJyoWImTIhYuZPmVBHwM+s3ZcEQFdNKC3iW06eNFYOq1MHXngBfvrp/GPNmoEP/y+GiIiIiIh4nuYQ8mF16tRx+jnLdx4HoEZctKvLEbHclWTC62VlwQcfwPDhkGQ0dLnhBihf3tKyxDcUyEyIXCXlQsRMmRAx86dMqCHkw6KcXD0oKyfXfrtC8UhXlyNiOWcz4dVsNvjxR3jqKdi2zdhWt64xYXTnztbWJj6jQGVCxEWUCxEzZULEzJ8yoUvGfNiqVauc2j/lbJb9dsloLTkvBY+zmfBqWVnwyCNGM6h4cZgwATZsUDNInFKgMiHiIsqFiJkyIWLmT5nQCCE/cuJMpv22JpQW8ULHj0PhwhAcDKGhxqphK1fCiy8a20VERERERFxEXQEfdv311zu1/x8HTgHQoGysG6oRsZ6zmfAaGRnw3ntQpQp8/PH57b16wbvvqhkkV8xnMyHiRsqFiJkyIWLmT5lQQ8iHbdmyxan91+8/BUCj8kXcUI2I9ZzNhOVsNpg1y1g57JlnICXFuC/iIj6XCREPUC5EzJQJETN/yoQaQj7s0KFDTu0/ZfV+AFpWKeaOckQs52wmLLVhA7Rvb4wC2rUL4uPhk0/ghx+srkwKEJ/KhIiHKBciZsqEiJk/ZUJzCPmwyMj8rxSWdMH8QQ3LFXZDNSLWcyYTlho1yhgRZLNBeLixkthzz4EfrWggnuEzmRDxIOVCxEyZEDHzp0wE2Gw2m9VFeFJKSgqxsbEkJycTExNjdTlXJTc3l8DA/A3ymrXhIE9O+wOAvW/d4M6yRCzjTCYstXYtNG8Ot98Ob70F5ctbXZEUUD6TCREPUi5EzJQJETNfz4QzPQ/ffZXCtGnT8r3v8dTMy+8k4uOcyYTH2GwwbZoxafQ5TZvCzp3w9ddqBolbeWUmRCymXIiYKRMiZv6UCV0y5ie2H00FoE/TshZXIuJHVq+GJ5+E336DkBC45RZjJTE4/6eIiIiIiIgFNELIh9WoUSPf+/6y7ZjxnHjfvkxO5FKcyYRbHTwId98N11xjNIMiImDYMChVyurKxM94TSZEvIhyIWKmTIiY+VMmNELIhxUvXjzf+56bVDoiNMhd5YhYzplMuMWZM/Duu/DOO3D2rLHt3nvhjTegTBlraxO/ZHkmRLyQciFipkyImPlTJjRCyIf9+uuvTj+nXplYN1Qi4h2uJBMulZxszBV09iy0agVr1sDkyWoGiWUsz4SIF1IuRMyUCREzf8qERgj5gdMZ2fbbFYpFWFiJSAGUkAC1axu3S5c2lpQvVgxuvRUCAqytTURERERE5CI0QsiHderUKV/7HUtJt9+ODg9xVzkilstvJlxizx7o0wfq1IHFi89vf+gh6N1bzSDxCh7NhIiPUC5EzJQJETN/yoQaQj5sx44d+drvwMmzbq5ExDvkNxNXJSUFnn8eatWC6dMhMNBYTUzEC3kkEyI+RrkQMVMmRMz8KRNqCPmw/fv352u/o8nGCKH4mHB3liNiufxm4ork5MCkSVCtGrz1FmRkQMeOsGEDPPus+84rchXcmgkRH6VciJgpEyJm/pQJzSHkw8LCwvK136FTxgihanFR7ixHxHL5zcQV6dULvvvOuF29ujF59I036tIw8WpuzYSIj1IuRMyUCREzf8pEgM1ms1ldhCelpKQQGxtLcnIyMTExVpfjEf0/Xc2S7X/zYJvKvNC9ltXliPimqVPh4YfhlVdg0CAIDbW6IhERERERERNneh66ZMyHTZs2zan9/az3J37I2Uxc1MmTMGQIfPrp+W19+8Lu3fDEE2oGic9wWSZEChDlQsRMmRAx86dM6JIxH5abm5uv/ZZs/xuAzrXj3VmOiOXym4mLys6GiRONUUAnTkDJksZKYpGRxqVhRYq4plARD7nqTIgUQMqFiJkyIWLmT5nQCCEfVqVKlcvuczoj2367Zqlod5YjYrn8ZOKi5s+H+vXh0UeNZlDt2vD550YzSMRHXVUmRAoo5ULETJkQMfOnTKgh5MPKlClz2X32Hj8DQFRYMDHhIe4uScRS+cmEgx07oFs342vrVihWDMaPhz/+gOuvd32RIh50RZkQKeCUCxEzZULEzJ8yoYaQD1u2bNll99n192lA8weJf8hPJhwkJxujg0JC4Kmn4K+/jMmjg3VFrfi+K8qESAGnXIiYKRMiZv6UCf3GU8ClphuXjBUK1bdaBIDMTFi1Clq3Nu43awb//S907QpVq1pbm4iIiIiIiIdohJAPa9eu3WX3WbUnCYDmlTQZrhR8l8yEzQZz5kCdOtCpk7Fi2DmPPqpmkBRI+fk5IeJvlAsRM2VCxMyfMqGGkA/bv3//ZfdZv+8kAFVKRLm7HBHLXTQTf/wBHTtCz57GJWFFisCePR6tTcQK+fk5IeJvlAsRM2VCxMyfMqGGkA/bfeEIh4uICjMuFStbpJC7yxGxnEMmjh6FBx6ARo1g8WIIC4MXXoCdO40GkUgBl5+fEyL+RrkQMVMmRMz8KROWN4TGjx9PpUqVCA8Pp0mTJixfvvyi+86cOZPOnTtTokQJYmJiaNGiBQsWLPBgtd4lOB+T3h4/nQFAvTKF3VyNiPVMmcjIgIYNYdIk43Kxvn1h2zZ44w2IjrasRhFPys/PCRF/o1yImCkTImb+lIkAm4XLT02bNo27776b8ePHc9111zFx4kQmTZpEQkIC5cuXd9j/iSeeoHTp0rRv357ChQvz6aef8t5777Fq1SoaNWqUr3OmpKQQGxtLcnIyMTExrn5JXsVms1Hp+XkA/P58R+Jjwy2uSMTNbDYICDh//9VXYe5cGDMGrrvOurpEREREREQ8wJmeh6UjhEaPHs3999/PgAEDqFWrFmPHjqVcuXJMmDAhz/3Hjh3L0KFDadasGdWqVWPkyJFUq1aN77//3sOVe4cZM2Zc8vEjKen220UjQ91djoi11qzheO3asGTJ+W3PPQe//65mkPity/2cEPFHyoWImTIhYuZPmbCsIZSZmcm6devo0qWLaXuXLl347bff8nWM3NxcUlNTKVq06EX3ycjIICUlxfRVUGRmZl7y8b+OnbbfDg22/OpAEfc4eBDuuQeaN6f4tm3w0kvnHwsNhUD93Rf/dbmfEyL+SLkQMVMmRMz8KROWXRx3/PhxcnJyiIuLM22Pi4vjyJEj+TrGqFGjOHPmDH369LnoPm+++SYjRoxw2D59+nQiIiLo1asXP//8M8nJyZQsWZLmzZszd+5cABo3bkxubi4bN24E4Oabb2bFihWcOHGCokWL0qZNG2bPng1A/fr1CQkJYd26dQDccMMNrF27lqNHjxITE0OXLl349ttvAahTpw5RUVGsWrUKgOuvv54tW7Zw6NAhIiMjufHGG5k2bRoANWrUoHjx4vz6668AdOrUiR07drB//357c2vatGnk5uZSpUoVypQpw7JlywA4UaQOACGBNqZPn85tt93GjBkzyMzMpEKFClSpUoVffvkFgFatWnHs2DF27NgBwB133MGcOXNIS0ujbNmy1K5dm4ULFwLQokULkpOTSUhIAOC2225j/vz5pKamEh8fT+PGjZk3z7hUrVmzZqSnp7N582YAbrnlFpYsWcLJkycpXrw4LVq0sI/wOnfZ34YNGwDo0aMHK1eu5Pjx4xQpUoR27doxa9YsAOrVq0d4eDhr1qwBoHv37qxfv54jR44QHR1N165dmT59OgC1a9cmNjaWlStXAkbTMSEhgYMHDxIREcHNN9/MlClTAKhevTolS5ZkxYoVAHTo0IFdu3axb98+QkNDufXWW5k+fTrZ2dlUrlyZ8uXLs+SfESlt2rTh0KFD7Nq1i8DAQPr27cvMmTPJyMigfPnyVK9enZ9++gmA6667juPHj7N9+3YA+vbty9y5czlz5gxlypShbt269vmxrrnmGk6fPs2ff/4JQO/evVm4cCEpKSnExcXRtGlTfvjhBwCaNGlCVlYWmzZtAqBnz54sW7aMpKQkihUrRqtWrZgzZw4ADRs2JDAwkPXr1wNw4403snr1ao4dO0ZsbCwdO3Zk5syZANStW5eIiAhWr14NQLdu3fjjjz84fPgwUVFRdO/enW+++QaAmjVrUrRoUXtjt3Pnzmzbto0DBw5QqFAhevbsydSpU7HZbFSrVo34+Hj73GHt27dn79697Nmzh5CQEHr37s23335LVlYWlSpVomLFiixevNh4v5s0gVGjiJs8meB/PrR3XHstCbffTvEVK6hZsyaLFi0CoGXLliQlJbFt2zYA+vTpw7x58zh9+jSlS5emQYMG/PjjjwA0b96ctLQ0tmzZAuDTnxFhYWH06tXrop8R7dq1Y//+/ezevZvg4GB9RhTAz4isrCxSUlL88jOidevWHDlyhJ07dxIQEMDtt9/O7NmzOXv2LOXKldNnhB9/RiQlJbFlyxZ9Rvj5vyP0GXH+MyImJsb+d1ifEfp3hD4jFpOUlMTBgwd99jMiLS2N/LJsDqHDhw9TpkwZfvvtN1q0aGHf/sYbb/DFF1/Y38yLmTJlCgMGDGDOnDl06tTpovtlZGSQkZFhv5+SkkK5cuUKxBxCR48edWioXeh/i//i3QXbaV6pKN881OKi+4n4nNmz4dFH4dAh4/5118GYMRwtX/6SmRDxN5f7OSHij5QLETNlQsTM1zPhE3MIFS9enKCgIIfRQMeOHbvsmz9t2jTuv/9+vvnmm0s2gwDCwsKIiYkxfRUU5zruF3PwpNEZrF2q4LxmEQDS0oxmUIUKMG0aLF8OzZpdNhMi/kaZEHGkXIiYKRMiZv6UCcsaQqGhoTRp0sQ+zOqcRYsW0bJly4s+b8qUKfTv35+vv/6aG264wd1l+rS9x9UQkgJi3z74Z4g3AHfcAR9/bCwj36ePeWUxERERERERuSzL5hACGDJkCHfffTdNmzalRYsWfPjhh+zfv5+BAwcC8Pzzz3Po0CE+//xzwGgG3XPPPfzf//0f1157rX10UaFChYiNjbXsdVilVatWl3x85e4TAFQqEemJckRcLzUV3nwTRo+GwoVh506IjjYaQP/5j8Pul8uEiL9RJkQcKRciZsqEiJk/ZcLS5Xf69u3L2LFjefXVV2nYsCHLli1j3rx5VKhQAYDExET2799v33/ixIlkZ2fzyCOPUKpUKfvX448/btVLsNSxY8cu+tiFU0OViArzRDkirpOTY4wAqlbNaAhlZEDt2nDy5CWfdqlMiPgjZULEkXIhYqZMiJj5UyYsX4950KBB7N27l4yMDNatW0ebNm3sj02ePNk+qzrAkiVLsNlsDl+TJ0/2fOFe4Nws/XlJOZttv10yRg0h8SGLF0PTpjBgABw9ClWrGpNI//wzlC9/yadeKhMi/kiZEHGkXIiYKRMiZv6UCUsvGRP3OXjKmD8oOiyYiFB9m8VH7NgBHToYt2Nj4eWXjdXEQkOtrUtERERERKSAsWzZeas4swSbL/txcyIPf7WewhEhbHy5i9XliFxcVhaEhJy/378/REbCiBFQvLhlZYmIiIiIiPgan1h2Xq7enDlzLvrY7uNnAGhUrrCHqhFxUnY2jB8PlSrBnj3nt3/6Kfzvf1fUDLpUJkT8kTIh4ki5EDFTJkTM/CkTagj5sLS0tIs+tmzH3wDULeN/q6+JD1iwABo0gEcegUOHYNy4849dxRLyl8qEiD9SJkQcKRciZsqEiJk/ZUINIR9WtmzZiz4WFHjlv1SLuM3WrXDDDdC1KyQkQLFi8P778M47Ljn8pTIh4o+UCRFHyoWImTIhYuZPmdBswz6sdu3aeW7PybWx6WAyAK2rlfBkSSIX99xz8N57xpLywcHw2GMwbBgUKeKyU1wsEyL+SpkQcaRciJgpEyJm/pQJjRDyYQsXLsxz+1/HTnM6I5vwkECaVHDdL9siV6VQIaMZdPPNxuig0aNd2gyCi2dCxF8pEyKOlAsRM2VCxMyfMqERQgXQwZPGNY+5ubp0TCxis8HcuVCyJFxzjbHtmWegTRto397a2kREREREREQjhHxZixYt8ty+ek8SAO1q6HIxscCmTdC5M9x0kzFpdG6usT0iwu3NoItlQsRfKRMijpQLETNlQsTMnzKhhpAPS05OznP7HwdPAXBdVeeX7Ra5YkePwoMPQqNG8PPPEBZmNIaysjxWwsUyIeKvlAkRR8qFiJkyIWLmT5lQQ8iHJSQkOGzLvWBC6cblNX+QeEB6Orz9NlSrBh99ZIwIuu02Y0WxN980GkMeklcmRPyZMiHiSLkQMVMmRMz8KROaQ6iAOXU2i7TMHACqx0dZXI34he++M1YQA2jaFMaMgVatrK1JRERERERELinAZrPZrC7Ck1JSUoiNjSU5OZmYmBiry7kq2dnZBAebe3pr9ybR+4OVFIsMZd2wzhZVJgVeSgqcy09uLvTuDT17wl13QaB1Aw/zyoSIP1MmRBwpFyJmyoSIma9nwpmehy4Z82Hz58932Lb77zOAMVJIxOUOH4b+/aFWLUhNNbYFBsLMmXDPPZY2gyDvTIj4M2VCxJFyIWKmTIiY+VMm1BDyYannfiG/wF9/nwagRly0p8uRgiwtDV57zZgn6LPPjMaQF35Q5pUJEX+mTIg4Ui5EzJQJETN/yoTvjoMS4uPjHbbtOmY0hIpHe24iXynAcnNhyhRjjqCDB41tLVsa8wQ1b25tbXnIKxMi/kyZEHGkXIiYKRMiZv6UCTWEfFjjxo0dtgUEGH/WLuXb8yOJF0hPh/bt4fffjfsVKhirifXpc/4vmpfJKxMi/kyZEHGkXIiYKRMiZv6UCV0y5sPmzZvnsG3PcWMOoZZVinm6HClowsOhUiWIioKRI41l5Pv29dpmEOSdCRF/pkyIOFIuRMyUCREzf8qEGkIFTGJyOgClCxeyuBLxOadPw7BhsHfv+W2jRsHOnfD881BIf6dEREREREQKCl0y5sOaNWtmup+ankVaZg4AcTGaQ0jyKTfXmCj6hRfgyBGjATR1qvFYqVLW1uakf2dCxN8pEyKOlAsRM2VCxMyfMqGGkA9LT0833T93uVhYcCDR4SFWlCS+ZulSePJJ2LDBuF+linFZmI/6dyZE/J0yIeJIuRAxUyZEzPwpE7pkzIdt3rzZdH/5zuMAhAbr2yqXsWsX3HortGtnNINiY+G99+DPP+GWW6yu7or9OxMi/k6ZEHGkXIiYKRMiZv6UCY0QKkAOJKUBUEsrjMnlfP45zJwJgYHw0EMwYgSUKGF1VSIiIiIiIuIhATabzWZ1EZ6UkpJCbGwsycnJxMT4duMkPT2d8PBw+/3W7/zCgaSzvNi9Fg+0qWxhZeJ1srPh2DEoXdq4f/o0DBwIzz0HdetaW5sL/TsTIv5OmRBxpFyImCkTIma+nglneh66tsiHLVmyxHT/VFoWACU1obRcaNEiaNQIbr7ZmEAajKXkv/yyQDWDwDETIv5OmRBxpFyImCkTImb+lAk1hHzYyZMn7bezcnJJTc8GoGG5whZVJF5l2za48Ubo0gW2bIHdu40VxAqwCzMhIsqESF6UCxEzZULEzJ8yoYaQDytevLj99s6jp+23yxeNsKIc8RZJSfD441CvHvzwAwQHwxNPwF9/QY0aVlfnVhdmQkSUCZG8KBciZsqEiJk/ZUKTSvuwFi1a2G//tstYYaxUbDgBAQFWlSRW274dWrSAc13tHj3g3XcLfCPonAszISLKhEhelAsRM2VCxMyfMqERQj7s+++/t99et89oAPRsVMaqcsQbVKsGVaoYcwMtWgTffec3zSAwZ0JElAmRvCgXImbKhIiZP2VCDaEC4s/DKQBcW7mYxZWIR23ZAnfeaawaBsYy8nPmwIYN0KmTtbWJiIiIiIiI11JDyIc1atTIfnt/UhoAZYsUsqoc8aRjx4xl4xs0gClT4J13zj9WurQxb5AfujATIqJMiORFuRAxUyZEzPwpE/75W2MBc/x0hv12yWgtOV+gZWTAuHHw+uuQYowKo3dv6N/f0rJERERERETEt2iEkA/bsGEDABv3n7Jviw4PsagacbuZM6F2bRg61GgGNW4MS5fC9OlQubLV1XmFc5kQEYMyIeJIuRAxUyZEzPwpE2oIFQBLd/wNQLmiulysQJs+HXbvhlKlYPJkWLMG2rSxuioRERERERHxQbpkzIf16NEDgDOZ2QC0qlrCynLE1RITjT9LlTL+fOstqF4dnnkGoqKsq8uLncuEiBiUCRFHyoWImTIhYuZPmdAIIR+2cuVKAJbvPA5A4/KFLaxGXObsWWOOoGrV4Kmnzm+vUAFGjFAz6BLOZUJEDMqEiCPlQsRMmRAx86dMqCHkw44fNxpBUWHGQK+wkCAry5GrZbMZK4bVqAHDhsGZM7B3L6SnW12ZzziXCRExKBMijpQLETNlQsTMnzKhhpAPK1KkCAB7jp8BoJyWnPddv/8OLVvCnXfCgQNQrhx89RX8+iuEh1tdnc84lwkRMSgTIo6UCxEzZULEzJ8yEWCz2WxWF+FJKSkpxMbGkpycTExMjNXlXJX09HTCwsKo9Pw8ABY/3Y5KxSMtrkqc9s030LevcTsyEp57DoYMgYgIa+vyQenp6YSrgSZip0yIOFIuRMyUCREzX8+EMz0PjRDyYbNmzSI1I9t+Py4mzMJq5Ip17w5lykD//rBjB7z0kppBV2jWrFlWlyDiVZQJEUfKhYiZMiFi5k+Z0CpjPu5g0ln77YhQfTu9Xm4ufP45zJ4NM2dCYKAxSXRCAvj4iDURERERERHxHRoh5MPq1avHqbOZVpch+bVsGTRrBvfdB3PmwLffnn9MzSCXqFevntUliHgVZULEkXIhYqZMiJj5UybUEPJh4eHhnErLAqCRlpz3Xrt3Q+/e0LYtrF9vNH/eeQduvtnqygocX77WV8QdlAkRR8qFiJkyIWLmT5lQQ8iHrVmzhjP/zCEUEx5icTXiID0dnn0WatWCGTOMy8MGDoSdO+GZZyBMcz652po1a6wuQcSrKBMijpQLETNlQsTMnzKhSWd8XPJZY4RQdLi+lV4nNBR++gkyM6FzZxg1Cvxo+KGIiIiIiIh4L40Q8mHdu3fnTEYOADGFNELIK/zyC5w5Y9wODITx42HuXFiwQM0gD+jevbvVJYh4FWVCxJFyIWKmTIiY+VMm1BDyYevXryc13RghVCgkyOJq/Nz27dCjB3TsCO++e377NdfADTdAQIB1tfmR9evXW12CiFdRJkQcKRciZsqEiJk/ZULXGfmwI0eO8Nsx41tYPErz0VgiKQlefRX+9z/IzoagIGPuILHEkSNHrC5BxKsoEyKOlAsRM2VCxMyfMqGGkA+Ljo4mNMkY5KUBKB6WlQUffADDhxtNITBGAr33HtSsaWlp/iw6OtrqEkS8ijIh4ki5EDFTJkTM/CkTATabzWZ1EZ6UkpJCbGwsycnJxMTEWF3OVcnOzqbmywvJzrXxSf+mdKgZZ3VJ/uPJJ2HsWON2nTowejR06WJpSWJkIjhYfW6Rc5QJEUfKhYiZMiFi5uuZcKbnoTmEfNj06dPJzjX6eYUjQi2uxg9c2DsdPBjKloUJE2DjRjWDvMT06dOtLkHEqygTIo6UCxEzZULEzJ8y4bttLyH3gv5Eqdhw6wop6P7+G15+GdLS4LPPjG2VKsGePeDDnWMRERERERHxXxoh5MMqVjs/V00RjRByvYwMY06gqlWN+YI+/xx27Dj/uJpBXqd27dpWlyDiVZQJEUfKhYiZMiFi5k+ZUEPIhwWGRdpvh2vZedex2WDWLGNuoGeegZQUaNQIliyB6tWtrk4uITY21uoSRLyKMiHiSLkQMVMmRMz8KRNqCPmwJas3AlAkIsTaQgqS/fuhQwfo1Qt27YL4ePjkE1izBtq2tbo6uYyVK1daXYKIV1EmRBwpFyJmyoSImT9lQte8+LBTmcZa88lnsyyupAApUgS2boXwcHjqKXjuOYiKsroqEREREREREZfSCCEfVqtOPQCKRoZZXIkPO3sWJk2C3FzjfnQ0fP01bNsGr7+uZpCP6aLV3kRMlAkRR8qFiJkyIWLmT5lQQ8iH/bn7IACtqhazuBIfZLPB1KlQsyY88IBx+5wOHaBCBetqkyuWkJBgdQkiXkWZEHGkXIiYKRMiZv6UCV0y5sMOn0gBwoiL0ZLzTlm9Gp58En77zbhftixERl76OeITDh48aHUJIl5FmRBxpFyImCkTImb+lAmNEPJh6RiTSRfWkvP5c+AA3HUXXHON0QyKiIBXX4Xt2+Hmm62uTlwgIiLC6hJEvIoyIeJIuRAxUyZEzPwpEwE2m81mdRGelJKSQmxsLMnJycTExFhdzlW5duTPHElJ561e9bi9eXmry/F+rVvDihXG7XvvhTfegDJlrK1JRERERERExEWc6XlohJAvyzwDQFBggMWFeKncXMjMPH//9deNptCaNTB5sppBBdCUKVOsLkHEqygTIo6UCxEzZULEzJ8yoYaQDzudbTSCapf27ZFObvHrr8alYW+9dX5b27awdCk0bWpdXSIiIiIiIiJeQA0hH2Wz2TidbXz7imgOofP27oW+faFVK1i7FiZMgIyM848HaDRVQVa9enWrSxDxKsqEiCPlQsRMmRAx86dMqCHko05nZNtvF44IsbASL5GSAs8/bywj/803EBgIDz4IGzdCWJjV1YmHlCxZ0uoSRLyKMiHiSLkQMVMmRMz8KRNqCPmov1PPj3qJCA22sBIv8NNPUK2acXlYRgZ07AgbNsDEiRAXZ3V14kErzk0aLiKAMiGSF+VCxEyZEDHzp0z4eSfBd53NyrG6BO9RuTKcOmU0hUaNghtv1KVhIiIiIiIiIpegEUI+KuWscclYuaKFLK7EAjt3wrhx5+9Xrgw//wxbtkCPHmoG+bEOHTpYXYKIV1EmRBwpFyJmyoSImT9lQg0hH3XmnzmEjqVkXGbPAuTkSRgyBOrUgccfh9Wrzz/WqhWEanJtf7dr1y6rSxDxKsqEiCPlQsRMmRAx86dMqCHkozKycwGoVDzS4ko8ICsL3n/fuCRszBjjfrduULiw1ZWJl9m3b5/VJYh4FWVCxJFyIWKmTIiY+VMmNIeQj0pJzwKgVGy4xZW42Y8/wlNPwdatxv3atWH0aLj+emvrEq8UqlFiIibKhIgj5ULETJkQMfOnTATYbDab1UV4UkpKCrGxsSQnJxMTE2N1OVds0vLdvP7DVm5qUJpxdzSyuhz3SEuDSpXg2DEoVgxeew0eeACC1ccUERERERER+Tdneh66ZMxH7Tx6GoCI0CCLK3GxpCQ416OMiIC33zZGCP31Fzz8sJpBcknTp0+3ugQRr6JMiDhSLkTMlAkRM3/KhBpCPioq3GiMHE1Jt7gSF8nMNC4Fq1IFpk07v71/f3jvPc0XJPmSnZ1tdQkiXkWZEHGkXIiYKRMiZv6UCTWEfFROrjGKpnp8tMWVXCWbDebMMVYOe+opOHUKvv7a6qrER1WuXNnqEkS8ijIh4ki5EDFTJkTM/CkTagj5qMwcY5WxiBAfvoTqjz+gY0fo2dO4JCwuDiZNglmzrK5MfFT58uWtLkHEqygTIo6UCxEzZULEzJ8yoYaQj/rrmDGHUEhwgMWVXKG334ZGjWDxYggLg+efh5074f77IaiAzYskHrNkyRKrSxDxKsqEiCPlQsRMmRAx86dM+PDwEv8W888cQqnpPnp94zXXGJeL9e0Lb70FFStaXZGIiIiIiIiI31BDyEcFBhgjg0pGh1lcST7YbPDNN3DyJAwcaGxr1w7+/BNq17a0NClY2rRpY3UJIl5FmRBxpFyImCkTImb+lAk1hHxURrYxh1B0eIjFlVzGmjXw5JPw66/GMvI33QSlSxuPqRkkLnbo0CHKlCljdRkiXkOZEHGkXIiYXU0mcnJyyMrKcnFFItY6dOgQxYoVs7qMSwoNDSUw8OpnAFJDyEet23cSgLBgL50G6uBBeOEF+OIL435EBAwdCrGx1tYlBdquXbto3ry51WWIeA1lQsSRciFidiWZsNlsHDlyhFOnTrmnKBELBQYGsmfPHqvLuKTAwEAqVapEaGjoVR1HDSEfVbVkFBsPnOJsZo7VpZilpcG77xqTRp89a2y7+24YORLKlrW2NinwXNElFylIlAkRR8qFiNmVZOJcM6hkyZJEREQQEOCjC92I5OHUqVMULlzY6jIuKjc3l8OHD5OYmEj58uWvKn8BNpvN5sLavF5KSgqxsbEkJycTExNjdTlX7Mb/LmfLoRQ+va8Z7WuUtLqc83btMi4Fy8yE666DMWOgWTOrqxIRERERERfIyclhx44dlCxZ0usvqxEpqJKTkzl8+DBVq1YlJMQ8jYwzPQ/9F4mPys4x+njBgV7Qjf/rr/O3q1QxRgN98w0sX65mkHjUzJkzrS5BxKsoEyKOlAsRM2czcW7OoIiICHeUI2K5kydPWl3CZZ27VCwn5+quGFJDyEdl5RiTSgdbOex5715j2fjq1WHt2vPbn3oKbrsNNHRUPCwjI8PqEkS8ijIh4ki5EDG70kzoMjEpqHJzc60u4bJclT81hHxUepbxlzQkyIIP4tRUY8LomjWNkUAAy5Z5vg6RfylfvrzVJYh4FWVCxJFyIWKmTIiYhYWFWV2Cx6gh5KMOnTImbA705CVjOTkwaRJUqwZvvgkZGdC+PaxfD0OGeK4OkYuoXr261SWIeBVlQsSRciFipkxIXj7++GO6dOlidRmWsLohlJGRQfny5Vm3bp3bz6WGkI8qGmlcMxga5MFvYffu8MADcPQoVK0Ks2fDzz9Dw4aeq0HkEn766SerSxDxKsqEiCPlQsTMnzLRv39/AgICCAgIIDg4mPLly/Pwww/nOWfMb7/9Rvfu3SlSpAjh4eHUq1ePUaNG5Tlny+LFi+nevTvFihUjIiKC2rVr89RTT3Ho0CFPvCyXy8jI4OWXX2bYsGFWl+I2NpuN4cOHU7p0aQoVKkS7du34888/AWNS5osZO3YsNWrUoFChQpQrV44nn3yS9PT0PPd98803CQgI4IknnjBtP/d38N9f7777LmA0pJ5++mmeffZZ17zYS1BDyEedW24+OjzYcye99VaIjYVRo+DPP+HmmzVPkIiIiIiI+IyuXbuSmJjI3r17mTRpEt9//z2DBg0y7TNr1izatm1L2bJlWbx4Mdu2bePxxx/njTfe4Pbbb+fChbonTpxIp06diI+PZ8aMGSQkJPDBBx+QnJzMqFGjPPa6MjMzXXasGTNmEBUVRevWra/qOOcmIPdG77zzDqNHj+b9999nzZo1xMfH07lzZ1JTUy/6nK+++ornnnuOV155ha1bt/Lxxx8zbdo0nn/+eYd916xZw4cffkj9+vUdHktMTDR9ffLJJwQEBHDrrbfa9+nXrx/Lly9n69atrnnBF6GGkA+y2WyczTIaQmHBQe45yalT8PTTMGPG+W3332+sKDZkCPwzq7mIN7nuuuusLkHEqygTIo6UCxEzV2TCZrORlpltydeFzZn8CAsLIz4+nrJly9KlSxf69u3LwoUL7Y+fOXOGBx54gJtuuokPP/yQhg0bUrFiRQYMGMBnn33Gt99+yzf/zKN68OBBBg8ezODBg/nkk09o164dFStWpE2bNkyaNImXX375onWcOnWKBx98kLi4OMLDw6lbty5z584FYPjw4TT811UYY8eOpWLFivb7/fv3p2fPnrz55puULl2a6tWr8/zzz3Pttdc6nKt+/fq88sor9vuffvoptWrVIjw8nJo1azJ+/HjT/lOnTuWmm24ybVuzZg2dO3emePHixMbG0rZtW9avX2/aJyAggA8++ICbb76ZyMhIXn/9dQC+//57mjRpQnh4OJUrV2bEiBFkZ2fbnzd69Gjq1atHZGQk5cqVY9CgQZw+ffqi793VstlsjB07lhdffJFevXpRt25dPvvsM9LS0vj666+JiorK83krV67kuuuu484776RixYp06dKFO+64g7UXLrAEnD59mn79+vHRRx9RpEgRh+PEx8ebvubMmUP79u2pXLmyfZ9ixYrRsmVLpkyZ4toX/y8eHF4irpKZc37W80IhLm4IZWfDRx/Byy/D8eNQoQLceCOEhUFQEBQv7trzibjQ8ePHNTGiyAWUCRFHyoWImSsycTYrh9ovL3BRRc5JePV6IkKv7Nfa3bt3M3/+fEJCQuzbFi5cyIkTJ3j66acd9u/RowfVq1dnypQp9O3bl+nTp5OZmcnQoUPzPH7hwoXz3J6bm0u3bt1ITU3lyy+/pEqVKiQkJBAU5Nzvdj///DMxMTEsWrTI3hh766232LVrF1WqVAHgzz//ZPPmzXz77bcAfPTRR7zyyiu8//77NGrUiA0bNvDAAw8QGRnJvffeC8Dy5cvp16+f6Vypqance++9jBs3DoBRo0bRvXt3du7cSXR0tH2/V155hTfffJMxY8YQFBTEggULuOuuuxg3bhytW7dm165dPPjgg/Z9AQIDAxk3bhwVK1Zkz549DBo0iKFDhzo0qi7UrVs3li9ffsn352JNpT179nDkyBHTHElhYWG0bduW3377jbvuuivPeYRatWrFl19+yerVq2nevDm7d+9m3rx59vftnEceeYQbbriBTp062ZtiF3P06FF++OEHPvvsM4fHmjdvftnXeLXUEPJBWTnnu+BhIS4c5LVggbFk/D/XTlKrlnF5mB/Nsi6+bfv27TRu3NjqMkS8hjIh4ki5EDHzt0zMnTuXqKgocnJy7HO/jB492v74jh07AKhVq1aez69Zs6Z9n507dxITE0OpUqWcquGnn35i9erVbN261T6p94WjQ/IrMjKSSZMmEXrB1Rv169fn66+/ts//89VXX9GsWTP7eV577TVGjRpFr169AKhUqRIJCQlMnDiRe++9l1OnTnHq1ClKly5tOleHDh1M9ydOnEiRIkVYunQpN954o337nXfeyX/+8x/7/bvvvpvnnnvO3jSpXLkyr732GkOHDrU3hC6cY6dSpUq89tprPPzww5dsCE2aNImzZ8/m+7260JEjRwCIi4szbY+Li2Pfvn2kp6cTGRnp8Lzbb7+dv//+m1atWmGz2cjOzubhhx/mueees+8zdepU1q9fz5o1a/JVy2effUZ0dLT9+3GhMmXKsHfvXidemfPUEPJBmdnnRwi5ZFLpHTvgySdh3jzjfrFiMGIEPPggXNAtFxERERER+bdCIUEkvHq9Zed2Rvv27ZkwYQJpaWlMmjSJHTt28Nhjjznsd7FL0Ww2GwH/zKN64W1nbNy4kbJly171Cm/16tUzNYPAmHvmk08+YdiwYdhsNqZMmWJvuPz9998cOHCA+++/nwceeMD+nOzsbGJjYwHsTZbw8HDTcY8dO8bLL7/ML7/8wtGjR8nJySEtLY39+/eb9mvatKnp/rp161izZg1vvPGGfdu5ZlxaWhoREREsXryYkSNHkpCQQEpKCtnZ2aSnp3PmzJk8GzNgNEuu1r+/d5f7fi5ZsoQ33niD8ePHc8011/DXX3/x+OOPU6pUKYYNG8aBAwd4/PHHWbhwocP7dzGffPIJ/fr1y3P/QoUKkZaW5tyLcpIaQj4oI9uYPyg4MMA1y84fOGA0g4KD4bHHYNgwyONaRxFv17dvX6tLEPEqyoSII+VCxMwVmQgICLjiy7Y8LTIykqpVqwIwbtw42rdvz4gRI3jttdcA7E2arVu30rJlS4fnb9u2jdq1a9v3TU5OJjEx0alRQoUKFbrk44GBgQ4NqbwmaM6rWXLnnXfy3HPPsX79es6ePcuBAwe4/fbbAeNSNTAuG7vmmmtMzzt3uVqxYsUICAhwWHmtf//+/P3334wdO5YKFSoQFhZGixYtHCaz/ndNubm5jBgxIs8RMOHh4ezbt4/u3bszcOBAXnvtNYoWLcqKFSu4//77Lzkp9dVcMhYfHw8YI4Uu/L4dO3aMuLg4ihYtmufzhg0bxt13382AAQMAoyF35swZHnzwQV588UXWrVvHsWPHaNKkif05OTk5LFu2jPfff5+MjAzTZYHLly9n+/btTJs2Lc/zJSUlUaJEiUu+xqulSaV9UEaWEeTsXOcmULPLzITVq8/f79gRRo40LhUbPVrNIPFZ5ybiExGDMiHiSLkQMfP3TLzyyiu89957HD58GIAuXbpQtGjRPFcI++6779i5cyd33HEHAL179yY0NJR33nknz2OfOnUqz+3169fn4MGD9kvP/q1EiRIcOXLE1BTauHFjvl5P2bJladOmDV999RVfffUVnTp1sl8aFRcXR5kyZdi9ezdVq1Y1fVWqVAmA0NBQateuTUJCgum4y5cvZ/DgwXTv3p06deoQFhbG8ePHL1tP48aN2b59u8P5qlatSmBgIGvXriU7O5tRo0Zx7bXXUr16dfv34lImTZrExo0bL/l1MZUqVSI+Pp5FixbZt2VmZrJ06VJatmxJcnJyns9LS0sjMNDcQgkKCsJms2Gz2ejYsSObN2821dC0aVP69evHxo0bHeaI+vjjj2nSpAkNGjTI83xbtmyhUaNGl30vroZvtHHFJPefD4bgACcbQjYbfP+9sXrY4cOwcyec64jmsVSeiK85c+aM1SWIeBVlQsSRciFi5u+ZaNeuHXXq1GHkyJG8//77REZGMnHiRG6//XYefPBBHn30UWJiYvj555955pln6N27N3369AGgXLlyjBkzhkcffZSUlBTuueceKlasyMGDB/n888+JiorKs7HUtm1b2rRpw6233sro0aOpWrUq27ZtIyAggK5du9KuXTv+/vtv3nnnHXr37s38+fP58ccfiYmJyddr6tevH8OHDyczM5MxY8aYHhs+fDiDBw8mJiaGbt26kZGRwdq1azl58iRDhgwB4Prrr2fFihWmuX2qVq3KF198QdOmTUlJSeGZZ5657EgngJdffpkbb7yRcuXKcdtttxEYGMimTZvYvHkzr7/+OlWqVCE7O5v//ve/9OjRg19//ZUPPvjgsse9mkvGAgICeOKJJxg5ciTVqlWjWrVqjBw5koiICO688077qKd77rmHMmXK8OabbwLGpOKjR4+mUaNG9kvGhg0bxk033URQUBDR0dHUrVvXdK7IyEiKFSvmsD0lJYXp06fn+ffjnOXLl9tHrrmLRgj5oHMNobBgJy4X27QJOnWCm282GkGRkcbcQSIFiCuuJRYpSJQJEUfKhYiZMgFDhgzho48+4sCBA4Ax8mfx4sUcOHCANm3aUKNGDUaPHs2LL77I1KlTTfPMDBo0iIULF3Lo0CFuueUWatasyYABA4iJiclzpbJzZsyYQbNmzbjjjjuoXbs2Q4cOJSfHmBqkVq1ajB8/nv/97380aNCA1atXX/JY/3bbbbdx4sQJ0tLS6Nmzp+mxAQMGMGnSJCZPnky9evVo27YtkydPto8QAnjggQeYN2+eaaTMJ598wsmTJ2nUqBF33303gwcPpmTJkpet5frrr2fu3LksWrSIZs2ace211zJ69GgqVKgAQMOGDRk9ejRvv/02devW5auvvrI3YNxp6NChPPHEEwwaNIimTZty6NAhFi5cSHR0tH1epv3795OYmGh/zksvvcRTTz3FSy+9RO3atbn//vu5/vrrmThxotPnnzp1KjabzT7a7N9WrlxJcnIyvXv3vrIXmE8BtovNllVApaSkEBsbS3Jycr47rN5m+5FUrh+7jCIRwWx4+TKTtx09aswJ9PHHkJtrrBj25JPGiCAfff0iF5OUlHTRa35F/JEyIeJIuRAxczYT6enp7Nmzh0qVKuV74lzxPX369KFRo0Y874dXkmRnZxMcbO3FVLfddhuNGjXihRdeyPPxS+XQmZ6HRgj5oJx/5g7K+tcEXg7OnIE6deCjj4xm0G23wdat8OabagZJgbRgwQKrSxDxKsqEiCPlQsRMmZC8vPvuu0RFRVldhiUuNoeQp2RkZNCgQQOefPJJt59Lcwj5oHOXjF22mxcZCffeC8uWwZgx0KqV22sTERERERER31ahQgUee+wxq8vwS2FhYbz00kseOZdGCPmgcyOEwsNCzQ+sWwft2sH69ee3vfEGrFqlZpD4hX8vnyni75QJEUfKhYiZMiFi5k8joyxvCI0fP95+3VuTJk1Yvnz5JfdfunQpTZo0ITw8nMqVK+drBvKCJsc+7dM/fx4+DP37Q7NmsHQpXHidYXg4BFr+bRbxiNOnT1tdgohXUSZEHCkXImbKhIjZucm9/YGlnYJp06bxxBNP8OKLL7JhwwZat25Nt27d2L9/f57779mzh+7du9O6dWs2bNjACy+8wODBg5kxY4aHK7dW7j8jhILTTsNrr0G1avDZZ8ay8nfdZcwZJOKH/vzzT6tLEPEqyoSII+VCxEyZEDE7e/as1SV4jKVzCI0ePZr777+fAQMGADB27FgWLFjAhAkT8lxq7oMPPqB8+fKMHTsWMJbjW7t2Le+99x633nqrJ0u3VHaujet3/MarP0+ElBPGxhYtYOxYaN7c0tpERERERERExPtZNkIoMzOTdevW0aVLF9P2Ll268Ntvv+X5nJUrVzrsf/3117N27VqysrLyfE5GRgYpKSmmL1+XfDaLEmdOEZdyAsqXh6lT4ddf1QwSv9e7d2+rSxDxKsqEiCPlQsRMmRAxK1KkiNUleIxlI4SOHz9OTk4OcXFxpu1xcXEcOXIkz+ccOXIkz/2zs7M5fvw4pUqVcnjOm2++yYgRIxy2T58+nYiICHr16sXPP/9McnIyJUuWpHnz5sydOxeAxo0bk5uby8aNGwG4+eabWbFiBSdOnKBo0aK0adOG2bNnA1C/fn1CQkJYt24dADfccANr167l6NGjxMTE0KVLF7799lsA6tSpQ1RUFKtWrQKMptaWLVs4dOgQkZGR3HjjjUybNg2AGjVqULx4cX799VcAOnXqxN6d21jZoiMfhKYx8JvxTPvuO3KnTqVKlSqUKVOGZcuWAdCuXTv279/P7t27CQ4O5rbbbmPGjBlkZmZSoUIFqlSpwi+//AJAq1atOHbsGDt27ADgjjvuYM6cOaSlpVG2bFlq167NwoULAWjRogXJyckkJCQAcNtttzF//nxSU1OJj4+ncePGzJs3D4BmzZqRnp7O5s2bAbjllltYsmQJJ0+epHjx4rRo0YLvv/8egEaNGgGwYcMGAHr06MHKlSs5fvw4RYoUoV27dsyaNQuAevXqER4ezpo1awDo3r0769ev58iRI0RHR9O1a1emT58OQO3atYmNjWXlypWA0XRMSEjg4MGDREREcPPNNzNlyhQAqlevTsmSJVmxYgUAHTp0YNeuXezbt4/Q0FBuvfVWpk+fTnZ2NpUrV6Z8+fIsWbIEgDZt2nDo0CF27dpFYGAgffv2ZebMmWRkZFC+fHmqV6/OTz/9BMB1113H8ePH2b59OwB9+/Zl7ty5nDlzhjJlylC3bl37EqDXXHMNp0+ftg/n7d27NwsXLiQlJYW4uDiaNm3KDz/8AECTJk3Iyspi06ZNAPTs2ZNly5aRlJREsWLFaNWqFXPmzAGgYcOGBAYGsv6fSchvvPFGVq9ezbFjx4iNjaVjx47MnDkTgLp16xIREcHq1asB6NatG3/88QeHDx8mKiqK7t2788033wBQs2ZNihYtam/sdu7cmW3btnHgwAEKFSpEz549mTp1KjabjWrVqhEfH2+fO6x9+/bs3buXPXv2EBISQu/evfn222/JysqiUqVKVKxYkcWLFwPQunVrjhw5ws6dOwkICOD2229n4sSJFCtWjHLlylGzZk0WLVoEQMuWLUlKSmLbtm0A9OnTh3nz5nH69GlKly5NgwYN+PHHHwFo3rw5aWlpbNmyBcAnPyN27NjB/v37CQsLo1evXkybNo3c3Fx9RvjhZ0Rqaiq33367PiP++YyYPXs2Z8+e1WeEn39GHD16lE6dOukzQv+O0GcExmfEjh07sP0zR2l+PiNWrlxJxYoVyczMJDs7m4yMDAICAihatCgnT54kNzeXsLAwwsLC7P8ZHx0dTVZWFunp6QAUK1aMU6dOkZOTQ2hoKIUKFbIv9R0VFUVOTo79sp2iRYuSnJxMTk4OISEhRERE2PeNjIzEZrORlpYGGL/Ip6amkp2dTUhICJGRkZw6dQqAiIgIAPu+hQsX5syZM2RlZREcHEx0dDQnT5607xsQEMCZM2cAiI2NJS0tjaysLIKCgoiNjSUpKQmAQoUKERQUZJ+LKTY2lrNnz5KZmUlQUBCFCxfmxAnjqo7w8HBCQkJITU0FICYmhoyMDDIyMggMDKRIkSIkJSVhs9kICwsjNDTUvu+F7+Hl3u+oqCiys7Pt7/eF7+Hl3u8iRYqQkpJif78vfA8v9X4HBwcTFRVler8vfA8v9X4XKlSIwMBA0/t94Xt4qfc7JiaG9PR0MjMzHd7DS73f597DC9/vc+9hdnY2RYoUuej7HRoaSnh4uOn9vtjf2X+/35GRkeTm5pre74v9nf33+124cGFOnz5NdnY2ubm52Gw25s6dS1ZWlukz4tz++RFgs9lnKPaow4cPU6ZMGX777TdatGhh3/7GG2/wxRdf2D9wL1S9enXuu+8+nn/+efu2X3/9lVatWpGYmEh8fLzDc859w89JSUmhXLlyJCcnExMT4+JX5VlTpkzhjjvusLoMEa+hTIiYKRMijpQLETNnM5Gens6ePXvsCwOJFDQnTpygWLFiVpdxSZfKYUpKCrGxsfnqeVh2yVjx4sUJCgpyGA107Ngxh1FA58THx+e5f3Bw8EW/YWFhYcTExJi+CoqLvU8i/kqZEDFTJkQcKRciZsqE51SsWNE+H64/ateuHU888YT9vre+HyEhIVaX4DGWNYRCQ0Np0qSJfSjmOYsWLaJly5Z5PqdFixYO+y9cuJCmTZv61TftnKZNm1pdgohXUSZEzJQJEUfKhYiZP2Wif//+BAQEEBAQQHBwMOXLl+fhhx+2X7pUUA0fPtz+ugMCAoiNjaV169YsXbrU0rrWrFnDgw8+aGkNeYmMjLS6BI+xdNn5IUOGMGnSJD755BO2bt3Kk08+yf79+xk4cCAAzz//PPfcc499/4EDB7Jv3z6GDBnC1q1b+eSTT/j44495+umnrXoJljp3vbeIGJQJETNlQsSRciFi5m+Z6Nq1K4mJiezdu5dJkybx/fffM2jQIKvLcrs6deqQmJhIYmIiK1eupFq1atx44432uW2sUKJECfvcTt7k3Bw+/sDShlDfvn0ZO3Ysr776Kg0bNmTZsmXMmzePChUqAJCYmMj+/fvt+1eqVIl58+axZMkSGjZsyGuvvca4ceP8asl5ERERERERr3TmzMW//pngOF/7/jPh7mX3vQJhYWHEx8dTtmxZunTpQt++fe2T3gPk5ORw//33U6lSJQoVKkSNGjX4v//7P9Mx+vfvT8+ePXnvvfcoVaoUxYoV45FHHjGtfH3s2DF69OhBoUKFqFSpEl999ZVDLfv37+fmm28mKiqKmJgY+vTpw9GjR+2PDx8+nIYNG/LJJ59Qvnx5oqKiePjhh8nJyeGdd94hPj6ekiVL8sYbb1z2dQcHBxMfH098fDy1a9dmxIgRnD592r4YAMDo0aOpV68ekZGRlCtXjkGDBtknbwbYt28fPXr0oEiRIkRGRlKnTh37IgAACQkJdO/enaioKOLi4rj77rs5fvz4RWv69yVjAQEBTJo0iVtuuYWIiAiqVavGd999Z3qOs+eQS7O0IQQwaNAg9u7dS0ZGBuvWraNNmzb2xyZPnmxfeeGctm3bsn79ejIyMtizZ499NJE/atKkidUliHgVZULETJkQcaRciJi5NBNRURf/+vd/4pcsefF9u3Uz71uxYt77XaXdu3czf/580/Qjubm5lC1blm+++YaEhARefvllXnjhBftKeOcsXryYXbt2sXjxYj777DMmT57M5MmT7Y/379+fvXv38ssvv/Dtt98yfvx4jh07Zn/cZrPRs2dPkpKSWLp0KYsWLWLXrl307dvXdJ5du3bx448/Mn/+fKZMmcInn3zCDTfcwMGDB1m6dClvv/02L730Er///nu+X3dGRgaTJ0+mcOHC1KhRw749MDCQcePGsWXLFj777DN++eUXhg4dan/8kUceISMjg2XLlrF582befvttov75PiQmJtK2bVsaNmzI2rVrmT9/PkePHqVPnz75rgtgxIgR9OnTh02bNtG9e3f69etnX2HMVee4HH+6ZMyyZefl6l3YgRYRZULk35QJEUfKhYiZv2Vi7ty59mXCzy3LPnr0aPvjISEhjBgxwn6/UqVK/Pbbb3zzzTemxkORIkV4//33CQoKombNmtxwww38/PPPPPDAA+zYsYMff/yR33//nWuuuQaAjz/+mFq1atmf/9NPP7Fp0yb27NlDuXLlAPjiiy+oU6cOa9asoVmzZoDRoPrkk0+Ijo6mdu3atG/fnu3btzNv3jwCAwOpUaMGb7/9NkuWLOHaa6+96OvevHmzvXmTlpZGdHQ006ZNMy26dOGEz5UqVeK1117j4YcfZvz48YAxounWW2+lXr16AFSuXNm+/4QJE2jcuDEjR460b/vkk08oV64cO3bsoHr16pf8vpzTv39/+6p3I0eO5L///S+rV6+ma9euLjvH5Vi0ELsl1BDyYZs2baJOnTpWlyHiNZQJETNlQsSRciFi5tJMXHB5kYOgIPP9C0bLOAj814Use/decUn/1r59eyZMmEBaWhqTJk1ix44dPPbYY6Z9PvjgAyZNmsS+ffs4e/YsmZmZNGzY0LRPnTp1CLrgNZUqVYrNmzcDsHXrVoKDg00TdtesWZPChQvb72/dupVy5crZm0EAtWvXpnDhwmzdutXeEKpYsSLR0dH2feLi4ggKCiLwgvcoLi7ONPooLzVq1LBffpWamsq0adO47bbbWLx4sb3OxYsXM3LkSBISEkhJSSE7O5v09HTOnDlDZGQkgwcP5uGHH2bhwoV06tSJW2+9lfr16wOwbt06Fi9ebG86XWjXrl35btacOx4YI3Wio6Ptr81V57ictLQ0ChUq5JJjeTvLLxkTERERERGRAiAy8uJf4eH53/ffv4xfbL8rKjGSqlWrUr9+fcaNG0dGRoZpRNA333zDk08+yX/+8x8WLlzIxo0bue+++8jMzDQd59+rXAcEBJCbmwucH2ESEBBw0TpsNluej/97e17nudS5LyY0NJSqVatStWpVGjVqxFtvvUWZMmXsc/js27eP7t27U7duXWbMmMG6dev43//+B5wfRTZgwAB2797N3XffzebNm2natCn//e9/AWMkU48ePdi4caPpa+fOnaZpYS7nUq/NVeeQ8zRCyIf17NnT6hJEvIoyIWKmTIg4Ui5EzPw9E6+88grdunXj4YcfpnTp0ixfvpyWLVuaVh7btWuXU8esVasW2dnZrF27lubNmwOwfft20+pVtWvXZv/+/Rw4cMA+SighIYHk5GTTpWXuFBQUxNl/JvBeu3Yt2dnZjBo1yj766N/zJgGUK1eOgQMHMnDgQJ5//nk++ugjHnvsMRo3bsyMGTOoWLEiwcHuaTN44hxgXA7oLzRCyIctW7bM6hJEvIoyIWKmTIg4Ui5EzPw9E+3ataNOnTr2eWmqVq3K2rVrWbBgATt27GDYsGGsWbPGqWPWqFGDrl278sADD7Bq1SrWrVvHgAEDTJchderUifr169OvXz/Wr1/P6tWrueeee2jbtq3pUjNXyc7O5siRIxw5coSdO3fy+uuvk5CQwM033wxAlSpVyM7O5r///S+7d+/miy++4IMPPjAd44knnmDBggXs2bOH9evX88svv9ibV4888ghJSUnccccdrF69mt27d7Nw4UL+85//kJOT45LX4IlzgHFJnb9QQ8iHnZttXUQMyoSImTIh4ki5EDFTJmDIkCF89NFHHDhwgIEDB9KrVy/69u3LNddcw4kTJ0yjhfLr008/pVy5crRt25ZevXrx4IMPUrJkSfvjAQEBzJ49myJFitCmTRs6depE5cqVmTZtmitfmt2ff/5JqVKlKFWqFA0bNuSbb75hwoQJ3HPPPQA0bNiQ0aNH8/bbb1O3bl2++uor3nzzTdMxcnJyeOSRR6hVqxZdu3alRo0a9gmnS5cuza+//kpOTg7XX389devW5fHHHyc2NtY039HV8MQ5wGie+YsAmz9NoQ2kpKQQGxtLcnKyaUZ1X7Rw4UK6dOlidRkiXkOZEDFTJkQcKRciZs5mIj09nT179lCpUiXC/z0vkEgBkJycTGxsrNVlXNKlcuhMz0MjhHxYq1atrC5BxKsoEyJmyoSII+VCxEyZEDHLaxWzgkoNIR82Z84cq0sQ8SrKhIiZMiHiSLkQMVMmRMwunPy7oFNDSERERERERETEz6gh5MMaNmxodQkiXkWZEDFTJkQcKRciZsqEiFlERITVJXiMGkI+zJUzqYsUBMqEiJkyIeJIuRAxu9JM+NnaROJHAgICrC7hslyVP/1E9GHr16+3ugQRr6JMiJgpEyKOlAsRM2czERISAkBaWpo7yhGx3JkzZ6wu4bIyMzMBCAoKuqrjBLuiGBERERERESn4goKCKFy4MMeOHQOMy2t8YUSFSH5lZmaSnp5udRkXlZuby99//01ERATBwVfX0gmw+dlYv5SUFGJjY0lOTiYmJsbqcq5Kamoq0dHRVpch4jWUCREzZULEkXIhYnYlmbDZbBw5csSvVmMS/5Gbm+v1lxcHBgZSqVIlQkNDHR5zpuehEUI+bPXq1XTs2NHqMkS8hjIhYqZMiDhSLkTMriQTAQEBlCpVipIlS5KVSvKi7QAAFmZJREFUleWmykSssXLlSlq0aGF1GZcUGhrqkqaVGkI+7NwwTRExKBMiZsqEiCPlQsTsajIRFBR01XOYiHibI0eOEB4ebnUZHuHd46DkkmJjY60uQcSrKBMiZsqEiCPlQsRMmRAx86dMaA4hH5aRkUFYWJjVZYh4DWVCxEyZEHGkXIiYKRMiZr6eCWd6Hhoh5MNmzpxpdQkiXkWZEDFTJkQcKRciZsqEiJk/ZcLv5hA6NyAqJSXF4kquXlpaWoF4HSKuokyImCkTIo6UCxEzZULEzNczca72/FwM5neXjB08eJBy5cpZXYaIiIiIiIiIiFscOHCAsmXLXnIfv2sI5ebmcvjwYaKjowkICLC6nCuWkpJCuXLlOHDggM/PhSTiCsqEiJkyIeJIuRAxUyZEzApCJmw2G6mpqZQuXfqyS9P73SVjgYGBl+2S+ZKYmBif/Ysq4g7KhIiZMiHiSLkQMVMmRMx8PRP5XSlNk0qLiIiIiIiIiPgZNYRERERERERERPyMGkI+KiwsjFdeeYWwsDCrSxHxCsqEiJkyIeJIuRAxUyZEzPwtE343qbSIiIiIiIiIiL/TCCERERERERERET+jhpCIiIiIiIiIiJ9RQ0hERERERERExM+oISQiIiIiIiIi4mfUEPJi48ePp1KlSoSHh9OkSROWL19+yf2XLl1KkyZNCA8Pp3LlynzwwQceqlTEM5zJxMyZM+ncuTMlSpQgJiaGFi1asGDBAg9WK+J+zv6cOOfXX38lODiYhg0burdAEQ9zNhMZGRm8+OKLVKhQgbCwMKpUqcInn3zioWpFPMPZXHz11Vc0aNCAiIgISpUqxX333ceJEyc8VK2Iey1btowePXpQunRpAgICmD179mWfU5B/z1ZDyEtNmzaNJ554ghdffJENGzbQunVrunXrxv79+/Pcf8+ePXTv3p3WrVuzYcMGXnjhBQYPHsyMGTM8XLmIezibiWXLltG5c2fmzZvHunXraN++PT169GDDhg0erlzEPZzNxDnJycncc889dOzY0UOVinjGlWSiT58+/Pzzz3z88cds376dKVOmULNmTQ9WLeJezuZixYoV3HPPPdx///38+eefTJ8+nTVr1jBgwAAPVy7iHmfOnKFBgwa8//77+dq/oP+erWXnvdQ111xD48aNmTBhgn1brVq16NmzJ2+++abD/s8++yzfffcdW7dutW8bOHAgf/zxBytXrvRIzSLu5Gwm8lKnTh369u3Lyy+/7K4yRTzmSjNx++23U61aNYKCgpg9ezYbN270QLUi7udsJubPn8/tt9/O7t27KVq0qCdLFfEYZ3Px3nvvMWHCBHbt2mXf9t///pd33nmHAwcOeKRmEU8JCAhg1qxZ9OzZ86L7FPTfszVCyAtlZmaybt06unTpYtrepUsXfvvttzyfs3LlSof9r7/+etauXUtWVpbbahXxhCvJxL/l5uaSmpqqf/RLgXClmfj000/ZtWsXr7zyirtLFPGoK8nEd999R9OmTXnnnXcoU6YM1atX5+mnn+bs2bOeKFnE7a4kFy1btuTgwYPMmzcPm83G0aNH+fbbb7nhhhs8UbKI1ynov2cHW12AODp+/Dg5OTnExcWZtsfFxXHkyJE8n3PkyJE898/Ozub48eOUKlXKbfWKuNuVZOLfRo0axZkzZ+jTp487ShTxqCvJxM6dO3nuuedYvnw5wcH68S8Fy5VkYvfu3axYsYLw8HBmzZrF8ePHGTRoEElJSZpHSAqEK8lFy5Yt+eqrr+jbty/p6elkZ2dz00038d///tcTJYt4nYL+e7ZGCHmxgIAA032bzeaw7XL757VdxFc5m4lzpkyZwvDhw5k2bRolS5Z0V3kiHpffTOTk5HDnnXcyYsQIqlev7qnyRDzOmZ8Tubm5BAQE8NVXX9G8eXO6d+/O6NGjmTx5skYJSYHiTC4SEhIYPHgwL7/8MuvWrWP+/Pns2bOHgQMHeqJUEa9UkH/P1n8ReqHixYsTFBTk0Lk/duyYQ3fynPj4+Dz3Dw4OplixYm6rVcQTriQT50ybNo3777+f6dOn06lTJ3eWKeIxzmYiNTWVtWvXsmHDBh599FHA+GXYZrMRHBzMwoUL6dChg0dqF3GHK/k5UapUKcqUKUNsbKx9W61atbDZbBw8eJBq1aq5tWYRd7uSXLz55ptcd911PPPMMwDUr1+fyMhIWrduzeuvv+7zoyFEnFXQf8/WCCEvFBoaSpMmTVi0aJFp+6JFi2jZsmWez2nRooXD/gsXLqRp06aEhIS4rVYRT7iSTIAxMqh///58/fXXuvZdChRnMxETE8PmzZvZuHGj/WvgwIHUqFGDjRs3cs0113iqdBG3uJKfE9dddx2HDx/m9OnT9m07duwgMDCQsmXLurVeEU+4klykpaURGGj+FTEoKAg4PypCxJ8U+N+zbeKVpk6dagsJCbF9/PHHtoSEBNsTTzxhi4yMtO3du9dms9lszz33nO3uu++27797925bRESE7cknn7QlJCTYPv74Y1tISIjt22+/teoliLiUs5n4+uuvbcHBwbb//e9/tsTERPvXqVOnrHoJIi7lbCb+7ZVXXrE1aNDAQ9WKuJ+zmUhNTbWVLVvW1rt3b9uff/5pW7p0qa1atWq2AQMGWPUSRFzO2Vx8+umntuDgYNv48eNtu3btsq1YscLWtGlTW/Pmza16CSIulZqaatuwYYNtw4YNNsA2evRo24YNG2z79u2z2Wz+93u2GkJe7H//+5+tQoUKttDQUFvjxo1tS5cutT9277332tq2bWvaf8mSJbZGjRrZQkNDbRUrVrRNmDDBwxWLuJczmWjbtq0NcPi69957PV+4iJs4+3PiQmoISUHkbCa2bt1q69Spk61QoUK2smXL2oYMGWJLS0vzcNUi7uVsLsaNG2erXbu2rVChQrZSpUrZ+vXrZzt48KCHqxZxj8WLF1/ydwR/+z07wGbT2D8REREREREREX+iOYRERERERERERPyMGkIiIiIiIiIiIn5GDSERERERERERET+jhpCIiIiIiIiIiJ9RQ0hERERERERExM+oISQiIiIiIiIi4mfUEBIRERERERER8TNqCImIiIiIiIiI+Bk1hERERMRrTZ48mcKFC1/1cYYPH05cXBwBAQHMnj37qo/nrfbu3UtAQAAbN2685H7t2rXjiSeesN9PS0vj1ltvJSYmhoCAAE6dOnVF57/77rsZOXLkFT33ajz99NMMHjzY4+cVERHxZWoIiYiI+KGAgIBLfvXv39/qEl1m69atjBgxgokTJ5KYmEi3bt2sLsltypUrR2JiInXr1gVgyZIleTZ4Zs6cyWuvvWa//9lnn7F8+XJ+++03EhMTiY2NdfrcmzZt4ocffuCxxx6zb2vXrl2ef7+ys7MdHg8LC6N69eqMHDmSnJwcU/3nvooVK0aHDh349ddfTeceOnQon376KXv27HG6bhEREX+lhpCIiIgfSkxMtH+NHTuWmJgY07b/+7//s7pEl9m1axcAN998M/Hx8YSFhVlckfsEBQURHx9PcHDwJfcrWrQo0dHR9vu7du2iVq1a1K1bl/j4eAICApw+9/vvv89tt91mOi7AAw88YPq7lZiYaKrv3OPbt29n8ODBvPTSS7z33numY2zfvp3ExESWLFlCiRIluOGGGzh27Jj98ZIlS9KlSxc++OADp+sWERHxV2oIiYiI+KH4+Hj7V2xsLAEBAfb7ISEhDBw4kLJlyxIREUG9evWYMmWK6fkVK1Zk7Nixpm0NGzZk+PDhgDGyIzQ0lOXLl9sfHzVqFMWLFycxMfGidU2ePJny5csTERHBLbfcwokTJxz2+f7772nSpAnh4eFUrlyZESNG2Eec/Nvw4cPp0aMHAIGBgfZGx5o1a+jcuTPFixcnNjaWtm3bsn79evvz8rr06tSpUwQEBLBkyRIAXn31VUqXLm2q8aabbqJNmzbk5ubmWU///v3p2bMnI0aMoGTJksTExPDQQw+RmZlp3ycjI4PBgwdTsmRJwsPDadWqFWvWrLE/fvLkSfr160eJEiUoVKgQ1apV49NPP3Woe+/evbRv3x6AIkWKmEZ+XXjJWLt27Rg1ahTLli0jICCAdu3aATB+/HiqVatGeHg4cXFx9O7dO8/XBJCbm8v06dO56aabHB6LiIgw/X2Lj4/P8/GKFSvy6KOP0rFjR4fL+kqWLEl8fDz16tXjpZdeIjk5mVWrVpn2uemmmxz+noqIiMjFqSEkIiIiJunp6TRp0oS5c+eyZcsWHnzwQe6++26HX8Av5VzD4e677yY5OZk//viDF198kY8++ohSpUrl+ZxVq1bxn//8h0GDBrFx40bat2/P66+/btpnwYIF3HXXXQwePJiEhAQmTpzI5MmTeeONN/I85tNPP21vlpwbnQKQmprKvffey/Lly/n999+pVq0a3bt3JzU1Nd+v8cUXX6RixYoMGDAAgA8++IBly5bxxRdfEBh48X9i/fzzz2zdupXFixczZcoUZs2axYgRI+yPDx06lBkzZvDZZ5+xfv16qlatyvXXX09SUhIAw4YNIyEhgR9//JGtW7cyYcIEihcv7nCecuXKMWPGDOD8CJu8Rn7NnDmTBx54gBYtWpCYmMjMmTNZu3YtgwcP5tVXX2X79u3Mnz+fNm3aXPQ1bdq0iVOnTtG0adP8vXmXUKhQIbKysvJ8LC0tzf79DAkJMT3WvHlzDhw4wL59+666BhEREb9gExEREb/26aef2mJjYy+5T/fu3W1PPfWU/X6FChVsY8aMMe3ToEED2yuvvGK/n5GRYWvUqJGtT58+tjp16tgGDBhwyXPccccdtq5du5q29e3b11Rb69atbSNHjjTt88UXX9hKlSp10ePOmjXLdrl/8mRnZ9uio6Nt33//vc1ms9n27NljA2wbNmyw73Py5EkbYFu8eLF9265du2zR0dG2Z5991hYREWH78ssvL3mee++911a0aFHbmTNn7NsmTJhgi4qKsuXk5NhOnz5tCwkJsX311Vf2xzMzM22lS5e2vfPOOzabzWbr0aOH7b777svz+P+ue/HixTbAdvLkSdN+bdu2tT3++OP2+48//ritbdu29vszZsywxcTE2FJSUi75es6ZNWuWLSgoyJabm+twnpCQEFtkZKT9a8iQIXnWkZOTY/vxxx9toaGhtqFDh5rqP/fcgIAAG2Br0qSJLTMz03Su5ORkG2BbsmRJvmoWERHxd5e+wFxERET8Tk5ODm+99RbTpk3j0KFDZGRkkJGRQWRkpFPHCQ0N5csvv6R+/fpUqFDB4RKzf9u6dSu33HKLaVuLFi2YP3++/f66detYs2aNaURQTk4O6enppKWlERERka/ajh07xssvv8wvv/zC0aNHycnJIS0tjf379+f/BQKVK1fmvffe46GHHqJv377069fvss9p0KCBqc4WLVpw+vRpDhw4QHJyMllZWVx33XX2x0NCQmjevDlbt24F4OGHH+b/27u/kCa7OA7gX7dyxooMGkl/GNlKTCobFTNqhZaaFM0Eb/SplVhIw+xCSjKdF9ESrFEiUSNlFuqNGEwwjRAXUhRWMjcqg0ldBIZFlIqr7b2QjR63aWZv8b77fsCLPc/ZzjnzQvxyfr8nJycHfX19SE9Ph06nw/bt22e17pns3bsXSqUS8fHxyMzMRGZmJrKzs8N+v2NjY5DJZCF7D+Xl5eHcuXOB11OfGldXVweLxRIomxMEAZWVlaIxdrsdcrkcz549w5kzZ9DQ0BB0QmjBggUAJk8RERER0cwYCBEREZFITU0Nrly5ArPZjA0bNkAul6OkpETU50YikcDn84neF6rMp7e3FwAwMjKCkZGRaUOlqZ8XitfrRVVVFQ4dOhR0LyYmZsb3++n1egwPD8NsNkOpVEImkyElJSWwR3/J149rClfG1NPTA6lUCrfbjW/fvs3Y0DmcqKiowHxTgxWfzxe4tm/fPgwNDaG9vR33799HWloaTp48GdSIeS4WLVqEvr4+dHd3o7OzExUVFTAajXjy5ElQoAMAS5cuxejoKCYmJhAdHS26t3jxYqhUqrBz+QMjmUyG5cuXQyqVBo1ZvXo1YmNjsW7dOoyPjyM7OxsOh0PUINxfUqdQKH5x10RERJGFPYSIiIhIxG634+DBg8jPz8emTZsQHx+P169fi8YoFApRc+jPnz8HPfL7zZs3OH36NG7evAmNRoPDhw+HbbYMAOvXr8ejR49E16a+VqvVePnyJVQqVdDPdH17Qu2xuLgYWVlZSEpKgkwmw4cPH0T7AyDa448Npv1aWlrQ2tqK7u5uvH37VvQo93BevHiBsbEx0R4XLlyIlStXQqVSITo6Gg8fPgzc93g8ePr0KRITE0Xr0+v1uH37NsxmM27cuBFyLn8443+M+2zMmzcPe/bsQXV1Nfr7++F2u/HgwYOQY5OTkwEATqdz1vP4A6NVq1aFDIOmEgQBXq8XdXV1ousOhwPz589HUlLSrNdAREQUiRgIERERkYhKpUJXVxd6e3vhcrlw4sQJvH//XjQmNTUVjY2NsNvtcDgcOHLkiOif+e/fv0MQBKSnp+Po0aOor6+Hw+FATU1N2HmLi4vR0dGB6upqvHr1CrW1taJyMQCoqKiA1WqF0WjEwMAAXC4XWlpaUF5ePus9NjY2wuVy4fHjx8jLywuUHAGT5UcajQYmkwlOpxM9PT1Bc7x79w5FRUW4dOkSduzYgYaGBly8eDEoxJpqYmICBQUFgcbQlZWVMBgMkEgkkMvlKCoqQmlpKTo6OuB0OlFYWIjR0VEUFBQEvoO7d+9icHAQAwMDsNlsorDoR0qlElFRUbDZbBgeHsaXL19+6vux2Wy4evUqnj9/jqGhIVitVni9XiQkJIQcr1AooFarRUHWv0UikaCkpAQmk0lUHma327Fz507R75GIiIjCYyBEREREIufPn4darUZGRgZ2796NuLg46HQ60ZiysjJotVrs378fWVlZ0Ol0WLNmTeD+hQsX4Ha7AydX4uLiYLFYUF5eHvKkDQBoNBpYLBZcu3YNycnJ6OzsDAphMjIyYLPZ0NXVha1bt0Kj0eDy5ctQKpWz2uOtW7fw8eNHbN68GYIgBB7zPnWMx+PBli1bcOrUKdETz3w+H/R6PbZt2waDwQBgsu+OwWBAfn7+tMFLWloa1q5dC61Wi9zcXBw4cABGozFw32QyIScnB4IgQK1WY3BwEPfu3cOSJUsATJ76KSsrw8aNG6HVaiGVStHc3BxyrhUrVqCqqgpnz57FsmXLAmudSWxsLFpbW5GamorExERcv34dTU1N056+OX78OO7cufNTnz9Xx44dg8fjQW1tbeBaU1MTCgsL/8j8RERE/wdRvp8p2CciIiKiOdPr9fj06RPa2tr+9lJ+u/HxcSQkJKC5uRkpKSl/dO729naUlpaiv7//l3s4ERERRRqeECIiIiKiOYuJiYHVahX1YvpTvn79ivr6eoZBREREs8C/mkRERET0W+zateuvzJubm/tX5iUiIvovY8kYEREREREREVGEYckYEREREREREVGEYSBERERERERERBRhGAgREREREREREUUYBkJERERERERERBGGgRARERERERERUYRhIEREREREREREFGEYCBERERERERERRRgGQkREREREREREEeYf/+frUaGCeeYAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC curve\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "y_pred_prob = logit_grid.predict_proba(X_test)[:, 1]\n", - "\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize = (14, 8))\n", - "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : modèle logistique')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "3b5c9485-511b-4f6b-b667-154f4f519682", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", - "\n", - "# Tri des prédictions de probabilités et des vraies valeurs\n", - "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", - "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", - "y_test_sorted = y_test.iloc[sorted_indices]\n", - "\n", - "# Calcul du gain cumulatif\n", - "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", - "\n", - "# Tracé de la courbe de lift\n", - "plt.plot(np.linspace(0, 1, len(cumulative_gain)), cumulative_gain, label='Courbe de lift')\n", - "plt.xlabel('Part de clients identifiés sans modèle ')\n", - "plt.ylabel('Part de clients identifiés avec modèle')\n", - "plt.title('Courbe de Lift')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "6e7cfb6c-8049-4bd1-8d82-61a2e97b257d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of the score \n", - "\n", - "plt.hist(y_pred_prob, bins=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "99f7f70e-c3bb-445e-8889-e7547f6ebd1e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# number of observations\n", - "N = len(y_pred_prob)\n", - "\n", - "# sort the data in ascending order \n", - "y_pred_prob_sorted = np.sort(y_pred_prob) \n", - "\n", - "# get the cdf values of y \n", - "steps = np.arange(N) / N\n", - " \n", - "# plotting \n", - "plt.xlabel('X') \n", - "plt.ylabel('P(score<=X)') \n", - " \n", - "plt.title('CDF curve of the predicted probability of purchase (score) for sports companies') \n", - " \n", - "plt.plot(y_pred_prob_sorted, steps) \n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "dd7a4a9c-d7e3-4747-ae59-b2a5a0b77260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
KMeans(n_clusters=3, random_state=0)
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" - ], - "text/plain": [ - "KMeans(n_clusters=3, random_state=0)" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# K-means clustering \n", - "\n", - "kmeans = KMeans(n_clusters=3, random_state=0)\n", - "\n", - "kmeans.fit(y_pred_prob.reshape(-1,1))" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "10b6ece7-adcf-41c0-884b-a4aef42af378", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 0, 2, ..., 0, 2, 0], dtype=int32)" - ] - }, - "execution_count": 179, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_clusters = kmeans.predict(y_pred_prob.reshape(-1,1))\n", - "y_clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "e4b3b16e-03b8-4883-9788-cb7296fe56cd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "seuil cluster 0 : 0.3666817620198657 (55.46%)\n", - "seuil cluster 2 : 0.7518681604748351 (34.86%)\n", - "seuil cluster 1 : 1.0 (9.68%)\n" - ] - } - ], - "source": [ - "# seuils des clusters et part de clients dans chacun d'eux\n", - "\n", - "print(f\"seuil cluster 0 : {y_pred_prob[y_clusters==0].max()} ({round(100 * (y_clusters==0).mean(), 2)}%)\")\n", - "print(f\"seuil cluster 2 : {y_pred_prob[y_clusters==2].max()} ({round(100 * (y_clusters==2).mean(), 2)}%)\")\n", - "print(f\"seuil cluster 1 : {y_pred_prob[y_clusters==1].max()} ({round(100* (y_clusters==1).mean(), 2)}%)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "id": "3e404a5e-6734-4d98-8853-48b09c96e7e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers vente_internet_max \\\n", - "0 4.0 1.0 100.0 1.0 0.0 \n", - "1 1.0 1.0 55.0 1.0 0.0 \n", - "2 17.0 1.0 80.0 1.0 0.0 \n", - "3 4.0 1.0 120.0 1.0 0.0 \n", - "4 34.0 2.0 416.0 1.0 0.0 \n", - "\n", - " purchase_date_min purchase_date_max nb_tickets_internet is_email_true \\\n", - "0 5.177187 5.177187 0.0 True \n", - "1 426.265613 426.265613 0.0 True \n", - "2 436.033437 436.033437 0.0 True \n", - "3 5.196412 5.196412 0.0 True \n", - "4 478.693148 115.631470 0.0 True \n", - "\n", - " opt_in gender_female gender_male nb_campaigns nb_campaigns_opened \\\n", - "0 False 1 0 0.0 0.0 \n", - "1 True 0 1 0.0 0.0 \n", - "2 True 1 0 0.0 0.0 \n", - "3 False 1 0 0.0 0.0 \n", - "4 False 1 0 0.0 0.0 \n", - "\n", - " cluster \n", - "0 2 \n", - "1 0 \n", - "2 2 \n", - "3 2 \n", - "4 1 " - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# les individus des clusters sont-ils semblables ? def des marketing personae\n", - "\n", - "X_test_clustered = X_test.assign(cluster = y_clusters)\n", - "X_test_clustered.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "b6f4638d-23c4-427a-88a4-b09528b3f91b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "cluster \n", - "0 0.0 0.0 0.000 0.0 \n", - "2 2.0 1.0 59.000 1.0 \n", - "1 12.0 4.0 205.075 1.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "cluster \n", - "0 0.0 550.000000 550.000000 \n", - "2 1.0 232.198352 225.296614 \n", - "1 1.0 416.542519 60.404957 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "cluster \n", - "0 0.0 1.0 1.0 0.0 \n", - "2 1.0 1.0 0.0 0.0 \n", - "1 4.0 1.0 0.0 0.0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "cluster \n", - "0 0.0 7.0 0.0 \n", - "2 1.0 3.0 0.0 \n", - "1 1.0 16.0 1.0 " - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_clustered.groupby(\"cluster\").median().iloc[[0,2,1], :]" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "id": "f80474be-c897-47f9-8fdd-f2fb8d724ee2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "cluster \n", - "0 0.132484 0.067283 0.950238 0.025292 \n", - "2 2.956270 1.396973 77.660347 0.999164 \n", - "1 42.274898 10.682943 1859.028185 1.481824 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "cluster \n", - "0 0.007149 545.999770 545.961714 \n", - "2 0.659682 235.984535 229.598802 \n", - "1 0.750376 386.850491 96.427147 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "cluster \n", - "0 0.015142 1.000000 0.522619 0.240389 \n", - "2 1.620787 0.991373 0.255246 0.258321 \n", - "1 12.382663 0.973220 0.163261 0.197892 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "cluster \n", - "0 0.431319 12.712442 2.241721 \n", - "2 0.558162 10.610967 2.741799 \n", - "1 0.609378 19.805442 7.528286 " - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_clustered.groupby(\"cluster\").mean().iloc[[0,2,1], :]" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5aca0-7e8b-4039-9bb2-ff5011c436a6", - "metadata": {}, - "source": [ - "## Random forest" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "da8873e5-c4e7-4580-8567-70e411c029ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ColumnTransformer(transformers=[('cat',\n",
-       "                                 Pipeline(steps=[('onehot',\n",
-       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                sparse_output=False))]),\n",
-       "                                 ['opt_in', 'is_email_true'])])
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" - ], - "text/plain": [ - "ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in', 'is_email_true'])])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preproc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cb46acb-647f-469d-b5e1-510bf1283196", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ce9acf4-3514-4056-a71a-c7654e25b9de", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "dfdd4601-4866-4102-b620-4f10648e7981", - "metadata": {}, - "source": [ - "### Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eeefae73-afe7-4441-a04c-bd6a04beedd2", - "metadata": {}, - "outputs": [], - "source": [ - "# Define models and parameters for GridSearch\n", - "model = {\n", - " 'model': RandomForestClassifier(),\n", - " 'params': {\n", - " 'randforest__n_estimators': [100, 150, 200, 250, 300],\n", - " 'randforest__max_depth': [None, 15, 20, 25, 30, 35, 40],\n", - " }\n", - " }\n", - "\n", - "# Test each model using GridSearchCV\n", - "pipe = Pipeline(steps=[('preprocessor', preproc), ('randforest', model['model'])])\n", - "clf = GridSearchCV(pipe, model['params'], cv=3)\n", - "clf.fit(X_train, y_train)\n", - "\n", - "print(f\"Model: {model['model']}\")\n", - "print(f\"Best parameters: {clf.best_params_}\")\n", - "print('Best classification accuracy in train is: {}'.format(clf.best_score_))\n", - "print('Classification accuracy on test is: {}'.format(clf.score(X_test, y_test)))\n", - "print(\"------\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "2a88f13b-05bc-4a70-b08b-8b07c118cedc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in',\n",
-       "                                                   'is_email_true'])])),\n",
-       "                ('random_forest',\n",
-       "                 RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                      1.0: 3.486549107420539}))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))])" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pipeline - on joue sur : max_depth\n", - "\n", - "param_grid = {\"random_forest__max_depth\" : [None, 10, 20, 40, 50, 60]}\n", - "\n", - "pipeline = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('random_forest', RandomForestClassifier(bootstrap = False, class_weight = weight_dict,\n", - " )) \n", - "])\n", - "\n", - "pipeline.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "494dca83-4d60-4e49-8689-7d7ac612bb83", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'estimator': DecisionTreeClassifier(),\n", - " 'n_estimators': 100,\n", - " 'estimator_params': ('criterion',\n", - " 'max_depth',\n", - " 'min_samples_split',\n", - " 'min_samples_leaf',\n", - " 'min_weight_fraction_leaf',\n", - " 'max_features',\n", - " 'max_leaf_nodes',\n", - " 'min_impurity_decrease',\n", - " 'random_state',\n", - " 'ccp_alpha',\n", - " 'monotonic_cst'),\n", - " 'bootstrap': True,\n", - " 'oob_score': False,\n", - " 'n_jobs': None,\n", - " 'random_state': None,\n", - " 'verbose': 0,\n", - " 'warm_start': False,\n", - " 'class_weight': None,\n", - " 'max_samples': None,\n", - " 'criterion': 'gini',\n", - " 'max_depth': None,\n", - " 'min_samples_split': 2,\n", - " 'min_samples_leaf': 1,\n", - " 'min_weight_fraction_leaf': 0.0,\n", - " 'max_features': 'sqrt',\n", - " 'max_leaf_nodes': None,\n", - " 'min_impurity_decrease': 0.0,\n", - " 'monotonic_cst': None,\n", - " 'ccp_alpha': 0.0}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RandomForestClassifier().__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "ee7cbc1c-7c31-4111-82a3-995141e2f13f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessor',\n",
-       "                                        ColumnTransformer(transformers=[('cat',\n",
-       "                                                                         Pipeline(steps=[('onehot',\n",
-       "                                                                                          OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                                        sparse_output=False))]),\n",
-       "                                                                         ['opt_in',\n",
-       "                                                                          'is_email_true'])])),\n",
-       "                                       ('random_forest',\n",
-       "                                        RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                                             1.0: 3.486549107420539}))]),\n",
-       "             param_grid={'random_forest__max_depth': [None, 10, 20, 40, 50,\n",
-       "                                                      60]},\n",
-       "             scoring=make_scorer(f1_score, response_method='predict'))
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" - ], - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))]),\n", - " param_grid={'random_forest__max_depth': [None, 10, 20, 40, 50,\n", - " 60]},\n", - " scoring=make_scorer(f1_score, response_method='predict'))" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# pipeline on the subsample\n", - "\n", - "random_forest_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "\n", - "random_forest_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "3f149137-6313-4b4e-99d6-b3af7f296ad7", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'random_forest__max_depth': None}\n", - "Best classification F1 score in train is: 0.33107422141513826\n", - "Classification F1 score on test is: 0.31752789604029275\n" - ] - } - ], - "source": [ - "# run the pipeline on the full sample\n", - "\n", - "random_forest_grid.fit(X_train, y_train)\n", - "\n", - "# print results\n", - "print('Returned hyperparameter: {}'.format(random_forest_grid.best_params_))\n", - "print('Best classification F1 score in train is: {}'.format(random_forest_grid.best_score_))\n", - "print('Classification F1 score on test is: {}'.format(random_forest_grid.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "cd79f942-abd0-48c9-aa0d-0d22673abeec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'scoring': make_scorer(f1_score, response_method='predict'),\n", - " 'estimator': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(bootstrap=False,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))]),\n", - " 'n_jobs': None,\n", - " 'refit': True,\n", - " 'cv': 3,\n", - " 'verbose': 0,\n", - " 'pre_dispatch': '2*n_jobs',\n", - " 'error_score': nan,\n", - " 'return_train_score': False,\n", - " 'param_grid': {'random_forest__max_depth': [None, 10, 20, 40, 50, 60]},\n", - " 'multimetric_': False,\n", - " 'best_index_': 0,\n", - " 'best_score_': 0.33107422141513826,\n", - " 'best_params_': {'random_forest__max_depth': None},\n", - " 'best_estimator_': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(bootstrap=False,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))]),\n", - " 'refit_time_': 2.2247676849365234,\n", - " 'feature_names_in_': array(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", - " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'], dtype=object),\n", - " 'scorer_': make_scorer(f1_score, response_method='predict'),\n", - " 'cv_results_': {'mean_fit_time': array([1.64734515, 1.4220806 , 1.43256299, 1.68632547, 1.4271005 ,\n", - " 1.42404906]),\n", - " 'std_fit_time': array([0.32811727, 0.01915 , 0.02151065, 0.2729267 , 0.02447776,\n", - " 0.02384922]),\n", - " 'mean_score_time': array([0.14065607, 0.13571024, 0.13531415, 0.17512798, 0.13398822,\n", - " 0.13499872]),\n", - " 'std_score_time': array([0.00759402, 0.00653712, 0.00743453, 0.04901062, 0.00848726,\n", - " 0.00789539]),\n", - " 'param_random_forest__max_depth': masked_array(data=[None, 10, 20, 40, 50, 60],\n", - " mask=[False, False, False, False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'params': [{'random_forest__max_depth': None},\n", - " {'random_forest__max_depth': 10},\n", - " {'random_forest__max_depth': 20},\n", - " {'random_forest__max_depth': 40},\n", - " {'random_forest__max_depth': 50},\n", - " {'random_forest__max_depth': 60}],\n", - " 'split0_test_score': array([0.19168873, 0.19168873, 0.19168873, 0.19168873, 0.19168873,\n", - " 0.19168873]),\n", - " 'split1_test_score': array([0.34428494, 0.34428494, 0.34428494, 0.34428494, 0.34428494,\n", - " 0.34428494]),\n", - " 'split2_test_score': array([0.45724899, 0.45724899, 0.45724899, 0.45724899, 0.45724899,\n", - " 0.45724899]),\n", - " 'mean_test_score': array([0.33107422, 0.33107422, 0.33107422, 0.33107422, 0.33107422,\n", - " 0.33107422]),\n", - " 'std_test_score': array([0.10881622, 0.10881622, 0.10881622, 0.10881622, 0.10881622,\n", - " 0.10881622]),\n", - " 'rank_test_score': array([1, 1, 1, 1, 1, 1], dtype=int32)},\n", - " 'n_splits_': 3}" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_forest_grid.__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "1806fe6d-cf98-459d-b05a-eb95972281dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.48955211455211456\n", - "F1 Score: 0.31752789604029275\n", - "Recall Score: 0.8335281227173119\n" - ] - } - ], - "source": [ - "# print results for the best model\n", - "\n", - "y_pred = random_forest_grid.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "1a6a8e07-bd93-496b-986e-d219c03b82c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# confusion matrix \n", - "\n", - "draw_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "1e1b3e42-1075-4a4a-bf44-3dadde3dbed1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC curve\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "y_pred_prob = random_forest_grid.predict_proba(X_test)[:, 1]\n", - "\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize = (14, 8))\n", - "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : random forest')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "854f6242-813f-400a-be43-7414a859b355", - "metadata": {}, - "source": [ - "## Naive Bayes " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b083d10d-8510-4a07-974b-e0c324175d7f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/sklearn/utils/validation.py:1229: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", - " y = column_or_1d(y, warn=True)\n" - ] - }, - { - "data": { - "text/html": [ - "
GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GaussianNB()" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "clf = GaussianNB()\n", - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a5459639-be3d-4292-89d2-061f276dc9a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.8780906593406593\n", - "F1 Score: 0.3673381217259815\n", - "Recall Score: 0.24842951059167276\n" - ] - } - ], - "source": [ - "# print results for the best model\n", - "\n", - "y_pred = clf.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e962eeed-4099-407b-a619-a34a539a404a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC curve\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "y_pred_prob = clf.predict_proba(X_test)[:, 1]\n", - "\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize = (14, 8))\n", - "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : naive Bayes')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ad1a0b57-e382-4ae3-90b6-1f790099711b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/numpy/core/fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n", - " return reduction(axis=axis, out=out, **passkwargs)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", - "\n", - "# Tri des prédictions de probabilités et des vraies valeurs\n", - "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", - "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", - "y_test_sorted = y_test.iloc[sorted_indices]\n", - "\n", - "# Calcul du gain cumulatif\n", - "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", - "\n", - "# Tracé de la courbe de lift\n", - "plt.plot(np.linspace(0, 1, len(cumulative_gain)), cumulative_gain, label='Courbe de lift')\n", - "plt.xlabel('Part de clients identifiés sans modèle ')\n", - "plt.ylabel('Part de clients identifiés avec modèle')\n", - "plt.title('Courbe de Lift')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7cbb1fec-97b9-4780-9488-5b8eff5aee0d", - "metadata": {}, - "source": [ - "## From model to segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d97ca3df-3778-469c-a077-495b3ee25051", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([9.0362e+04, 2.7200e+02, 1.6700e+02, 1.0000e+02, 8.6000e+01,\n", - " 5.7000e+01, 6.6000e+01, 6.3000e+01, 4.5000e+01, 5.1000e+01,\n", - " 5.4000e+01, 3.6000e+01, 5.3000e+01, 5.3000e+01, 5.3000e+01,\n", - " 5.1000e+01, 7.7000e+01, 1.1800e+02, 1.2700e+02, 4.2050e+03]),\n", - " array([8.76852176e-09, 5.00000083e-02, 1.00000008e-01, 1.50000007e-01,\n", - " 2.00000007e-01, 2.50000007e-01, 3.00000006e-01, 3.50000006e-01,\n", - " 4.00000005e-01, 4.50000005e-01, 5.00000004e-01, 5.50000004e-01,\n", - " 6.00000004e-01, 6.50000003e-01, 7.00000003e-01, 7.50000002e-01,\n", - " 8.00000002e-01, 8.50000001e-01, 9.00000001e-01, 9.50000000e-01,\n", - " 1.00000000e+00]),\n", - " )" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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"outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# number of observations\n", - "N = len(y_pred_prob)\n", - "\n", - "# sort the data in ascending order \n", - "y_pred_prob_sorted = np.sort(y_pred_prob) \n", - "\n", - "# get the cdf values of y \n", - "steps = np.arange(N) / N\n", - " \n", - "# plotting \n", - "plt.xlabel('X') \n", - "plt.ylabel('P(score<=X)') \n", - " \n", - "plt.title('CDF curve of the predicted probability of purchasec(score) for sports companies') \n", - " \n", - "plt.plot(y_pred_prob_sorted, steps) \n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "e87efb96-71e6-4571-9a48-576ff5ebcbdc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n", - " 0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1. ])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on regarde de plus près les quantiles (on identifie 2 clusters, où est le cut-off ?)\n", - "\n", - "np.linspace(0,1, 21)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "ccd8373c-85c4-451d-b918-7bb84713c9ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(90634,)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_pred_prob_sorted[y_pred_prob < 0.1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "75a2c582-3020-4e2e-9a41-0da75c5dbbed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "score du quantile 0.0 : 1.0\n", - "score du quantile 0.05 : 1.1703610048497538e-08\n", - "score du quantile 0.1 : 1.1916538583855572e-08\n", - "score du quantile 0.15000000000000002 : 1.672960453020865e-08\n", - "score du quantile 0.2 : 2.261530896018714e-08\n", - "score du quantile 0.25 : 4.429426100901144e-08\n", - "score du quantile 0.30000000000000004 : 5.527720441770875e-08\n", - "score du quantile 0.35000000000000003 : 6.583003552085313e-08\n", - "score du quantile 0.4 : 1.0150014636815537e-07\n", - "score du quantile 0.45 : 1.045553983975125e-07\n", - "score du quantile 0.5 : 1.8254643649033717e-07\n", - "score du quantile 0.55 : 1.0036337913333724e-06\n", - "score du quantile 0.6000000000000001 : 3.6006418270834777e-06\n", - "score du quantile 0.65 : 8.750051427856617e-06\n", - "score du quantile 0.7000000000000001 : 1.7761176996762073e-05\n", - "score du quantile 0.75 : 3.658511676930477e-05\n", - "score du quantile 0.8 : 7.449089979671675e-05\n", - "score du quantile 0.8500000000000001 : 0.0001599334998042523\n", - "score du quantile 0.9 : 0.0006156933309033692\n", - "score du quantile 0.9500000000000001 : 0.5161846499348189\n", - "score du quantile 1.0 : 1.0\n" - ] - } - ], - "source": [ - "for step in np.linspace(0,1, 21) :\n", - " score_reached = y_pred_prob_sorted[int(step*N)-1]\n", - " print(f\"score du quantile {step} : {score_reached}\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "3e7d04c4-1add-4ef3-bca5-c2f68356b669", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "score du quantile 0.94 : 0.046364832132301186\n", - "score du quantile 0.941 : 0.060426331367796585\n", - "score du quantile 0.942 : 0.07560789365683944\n", - "score du quantile 0.943 : 0.0961854989484283\n", - "score du quantile 0.944 : 0.12036366182214445\n", - "score du quantile 0.945 : 0.15326229828189683\n", - "score du quantile 0.946 : 0.20141929276940546\n", - "score du quantile 0.947 : 0.26129057078459816\n", - "score du quantile 0.948 : 0.34459110917836233\n", - "score du quantile 0.949 : 0.42441766527261676\n", - "score du quantile 0.95 : 0.5161846499348189\n", - "score du quantile 0.951 : 0.6281715747542238\n", - "score du quantile 0.952 : 0.7161294443763133\n", - "score du quantile 0.953 : 0.8098274658632696\n", - "score du quantile 0.954 : 0.8628210594682936\n", - "score du quantile 0.955 : 0.9031546758694196\n", - "score du quantile 0.956 : 0.9406325197642711\n", - "score du quantile 0.957 : 0.9717094630837765\n", - "score du quantile 0.958 : 0.9853416074407844\n", - "score du quantile 0.959 : 0.99263528504162\n", - "score du quantile 0.96 : 0.9965103675841931\n" - ] - } - ], - "source": [ - "# le saut survient entre le quantile 0.94 et 0.955\n", - "# on peut prendre le quantile 0.95 / score = 0.52 comme cut-off approximatif\n", - "for step in np.linspace(0.94,0.96, 21) :\n", - " score_reached = y_pred_prob_sorted[int(step*N)-1]\n", - " print(f\"score du quantile {step} : {score_reached}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "5d8bb4ea-0030-4d23-8cff-26c9ed54ca71", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
KMeans(n_clusters=2, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "KMeans(n_clusters=2, random_state=0)" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# simple K-means pour déterminer le seuil qui sépare les 2 clusters apparents\n", - "\n", - "from sklearn.cluster import KMeans\n", - "\n", - "kmeans = KMeans(n_clusters=2, random_state=0)\n", - "\n", - "kmeans.fit(y_pred_prob.reshape(-1,1))" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "afbf8247-4cb1-455b-96df-7e9a87407413", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_clusters = kmeans.predict(y_pred_prob.reshape(-1,1))\n", - "y_clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "e4747b82-1967-4043-bcd1-7659dbd87a2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4846" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_clusters[y_clusters==1].size" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "2853083a-99a4-4ae9-9e8d-ddf175cca7ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9495712620712621" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 5% des individus sont dans le cluster 1\n", - "1 - y_clusters.mean()" - ] - }, - { - "cell_type": "markdown", - "id": "d18c8a4c-7d19-4d24-a304-cb26a533303e", - "metadata": {}, - "source": [ - "Intérêt du K-means : permet d'identifier un seuil de passage d'un cluster à l'autre quand le cluster est restreint, comme ici où on isole les clients avec la proba d'achat dans le quantile 0.95, et on les sépare des 95% restant" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "77f59f30-1dc6-43b8-98b7-d179a966786a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "part d'individus dans le cluster 0 : 0.9495712620712621\n", - "seuil de passage du cluster 0 au cluster 1 : 0.4855790414879801\n" - ] - } - ], - "source": [ - "# seuil de split \n", - "\n", - "size_cluster_0 = 1 - y_clusters.mean()\n", - "seuil_cluster = y_pred_prob_sorted[int(1 - y_clusters.mean()*N)]\n", - "\n", - "print(f\"part d'individus dans le cluster 0 : {size_cluster_0}\")\n", - "print(f\"seuil de passage du cluster 0 au cluster 1 : {seuil_cluster}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/Modelization/3_model_cv_sport+CA.ipynb b/Sport/Modelization/3_model_cv_sport+CA.ipynb deleted file mode 100644 index 217ec35..0000000 --- a/Sport/Modelization/3_model_cv_sport+CA.ipynb +++ /dev/null @@ -1,18751 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ff8cc602-e733-4a31-bf46-a31087511fe0", - "metadata": {}, - "source": [ - "# Predict sales - sports companies" - ] - }, - { - "cell_type": "markdown", - "id": "415e466a-1a71-4150-bff7-2f8904766df4", - "metadata": {}, - "source": [ - "## Importations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b5aaf421-850a-4a86-8e99-2c1f0723bd6c", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "from sklearn.naive_bayes import GaussianNB\n", - "\n", - "import pickle\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "c2f44070-451e-4109-9a08-3b80011d610f", - "metadata": {}, - "source": [ - "## Load data " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b5f8135f-b6e7-4d6d-b8e1-da185b944aff", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2668a243-4ff8-40c6-9de2-5c9c07bcf714", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "13eba3e1-3ea5-435b-8b05-6d7d5744cbe2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1481/2459610029.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "street_id 0\n", - "structure_id 222825\n", - "mcp_contact_id 70874\n", - "fidelity 0\n", - "tenant_id 0\n", - "is_partner 0\n", - "deleted_at 224213\n", - "gender 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "last_buying_date 66139\n", - "max_price 66139\n", - "ticket_sum 0\n", - "average_price 66023\n", - "average_purchase_delay 66139\n", - "average_price_basket 66139\n", - "average_ticket_basket 66139\n", - "total_price 116\n", - "purchase_count 0\n", - "first_buying_date 66139\n", - "country 23159\n", - "gender_label 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "country_fr 23159\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "time_to_open 123159\n", - "y_has_purchased 0\n", - "dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train, dataset_test = load_train_test()\n", - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e46622e7-0fc1-43f8-a7e7-34a5e90068b2", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " \"\"\"\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - " \"\"\"\n", - "\n", - " # we suppress fidelity, time between purchase, and gender other (colinearity issue)\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", - " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'is_email_true', \n", - " 'opt_in', 'gender_female', 'gender_male', 'nb_campaigns', 'nb_campaigns_opened']\n", - " \n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cec4f386-e643-4bd8-b8cd-8917d2c1b3d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape train : (224213, 14)\n", - "Shape test : (96096, 14)\n" - ] - } - ], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)\n", - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "c9e8edbd-7ff6-42f9-a8eb-10d27ca19c8a", - "metadata": {}, - "source": [ - "## Logistic" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "639b432a-c39c-4bf8-8ee2-e136d156e0dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute Weights\n", - "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", - " y = y_train['y_has_purchased'])\n", - "\n", - "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n", - "weight_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "34644a00-85a5-41c9-98df-41178cb3ac69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 2.0 1.0 60.00 1.0 \n", - "1 8.0 3.0 140.00 1.0 \n", - "2 2.0 1.0 50.00 1.0 \n", - "3 3.0 1.0 90.00 1.0 \n", - "4 2.0 1.0 78.00 1.0 \n", - "... ... ... ... ... \n", - "224208 0.0 0.0 0.00 0.0 \n", - "224209 1.0 1.0 20.00 1.0 \n", - "224210 0.0 0.0 0.00 0.0 \n", - "224211 1.0 1.0 97.11 1.0 \n", - "224212 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 355.268981 355.268981 \n", - "1 0.0 373.540289 219.262269 \n", - "2 0.0 5.202442 5.202442 \n", - "3 0.0 5.178958 5.178958 \n", - "4 0.0 5.174039 5.174039 \n", - "... ... ... ... \n", - "224208 0.0 550.000000 550.000000 \n", - "224209 1.0 392.501030 392.501030 \n", - "224210 0.0 550.000000 550.000000 \n", - "224211 1.0 172.334074 172.334074 \n", - "224212 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 0.0 True False 0 \n", - "1 0.0 True False 0 \n", - "2 0.0 True False 0 \n", - "3 0.0 True False 0 \n", - "4 0.0 True False 1 \n", - "... ... ... ... ... \n", - "224208 0.0 True False 0 \n", - "224209 1.0 True False 0 \n", - "224210 0.0 True True 0 \n", - "224211 1.0 True False 0 \n", - "224212 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "0 1 0.0 0.0 \n", - "1 1 0.0 0.0 \n", - "2 1 0.0 0.0 \n", - "3 1 0.0 0.0 \n", - "4 0 0.0 0.0 \n", - "... ... ... ... \n", - "224208 1 34.0 3.0 \n", - "224209 1 23.0 6.0 \n", - "224210 1 8.0 4.0 \n", - "224211 1 13.0 5.0 \n", - "224212 1 4.0 4.0 \n", - "\n", - "[224213 rows x 14 columns]" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "295676df-36ac-43d8-8b31-49ff08efd6e7", - "metadata": {}, - "outputs": [], - "source": [ - "# preprocess data \n", - "# numeric features - standardize\n", - "# categorical features - encode\n", - "# encoded features - do nothing\n", - "\n", - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", - " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'nb_campaigns', \n", - " 'nb_campaigns_opened' # , 'gender_male', 'gender_female'\n", - " ]\n", - "\n", - "numeric_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n", - " (\"scaler\", StandardScaler()) \n", - "])\n", - "\n", - "categorical_features = ['opt_in', 'is_email_true'] \n", - "\n", - "# Transformer for the categorical features\n", - "categorical_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", - "])\n", - "\n", - "preproc = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features)\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "f46fb56e-c908-40b4-868f-9684d1ae01c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "nb_tickets_internet 0\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "dtype: int64" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[numeric_features].isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "e729781b-4d65-42c5-bdc5-82b4d653aaf0", - "metadata": {}, - "outputs": [], - "source": [ - "# Set loss\n", - "balanced_scorer = make_scorer(balanced_accuracy_score)\n", - "recall_scorer = make_scorer(recall_score)\n", - "f1_scorer = make_scorer(f1_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "a7ebbe6f-70ba-4276-be18-f10e7bfd7423", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_confusion_matrix(y_test, y_pred):\n", - " conf_matrix = confusion_matrix(y_test, y_pred)\n", - " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - " plt.xlabel('Predicted')\n", - " plt.ylabel('Actual')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", - "\n", - "\n", - "def draw_roc_curve(X_test, y_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - " # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - " fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - " \n", - " # Calcul de l'aire sous la courbe ROC (AUC)\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " plt.figure(figsize = (14, 8))\n", - " plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - " plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - " plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - " plt.xlabel('Taux de faux positifs (FPR)')\n", - " plt.ylabel('Taux de vrais positifs (TPR)')\n", - " plt.title('Courbe ROC : modèle logistique')\n", - " plt.legend(loc=\"lower right\")\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "2334eb51-e6ea-4fd0-89ce-f54cd474d332", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_features_importance(pipeline, model):\n", - " coefficients = pipeline.named_steps['logreg'].coef_[0]\n", - " feature_names = pipeline.named_steps['logreg'].feature_names_in_\n", - " \n", - " # Tracer l'importance des caractéristiques\n", - " plt.figure(figsize=(10, 6))\n", - " plt.barh(feature_names, coefficients, color='skyblue')\n", - " plt.xlabel('Importance des caractéristiques')\n", - " plt.ylabel('Caractéristiques')\n", - " plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n", - " plt.grid(True)\n", - " plt.show()\n", - "\n", - "def draw_prob_distribution(X_test):\n", - " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - " plt.figure(figsize=(8, 6))\n", - " plt.hist(y_pred_prob, bins=10, range=(0, 1), color='blue', alpha=0.7)\n", - " \n", - " plt.xlim(0, 1)\n", - " plt.ylim(0, None)\n", - " \n", - " plt.title('Histogramme des probabilités pour la classe 1')\n", - " plt.xlabel('Probabilité')\n", - " plt.ylabel('Fréquence')\n", - " plt.grid(True)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "83917b97-4d9b-4e3c-ba27-1e546ce885d3", - "metadata": {}, - "outputs": [], - "source": [ - "# Hyperparameter\n", - "\n", - "param_c = np.logspace(-10, 4, 15, base=2)\n", - "# param_penalty_type = ['l1', 'l2', 'elasticnet']\n", - "param_penalty_type = ['l1']\n", - "param_grid = {'logreg__C': param_c,\n", - " 'logreg__penalty': param_penalty_type} " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "3ae25049-920c-4a6d-a59d-c26e3b45dec6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1024" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2 ** 10" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "ba4cde9f-a614-4a43-81b9-e16e78aa6c4c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in',\n",
-       "                                                   'is_email_true'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
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" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))])" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pipeline\n", - "pipeline = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n", - " max_iter=5000)) \n", - "])\n", - "\n", - "pipeline.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "1e4c1be5-176d-4222-9b3c-fe27225afe36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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396260.00.00.000.00.0550.000000550.0000000.0TrueTrue009.00.0
1585600.00.00.000.00.0550.000000550.0000000.0TrueTrue0020.05.0
1704111.01.062.111.01.0350.010093350.0100931.0TrueFalse0140.023.0
2206921.01.084.001.00.05.1587875.1587870.0TrueFalse010.00.0
1827410.00.00.000.00.0550.000000550.0000000.0TrueTrue0119.01.0
.............................................
1942750.00.00.000.00.0550.000000550.0000000.0TrueFalse1038.019.0
1429150.00.00.000.00.0550.000000550.0000000.0TrueTrue0126.08.0
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10000 rows × 14 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "39626 0.0 0.0 0.00 0.0 \n", - "158560 0.0 0.0 0.00 0.0 \n", - "170411 1.0 1.0 62.11 1.0 \n", - "220692 1.0 1.0 84.00 1.0 \n", - "182741 0.0 0.0 0.00 0.0 \n", - "... ... ... ... ... \n", - "194275 0.0 0.0 0.00 0.0 \n", - "142915 0.0 0.0 0.00 0.0 \n", - "95021 7.0 2.0 250.00 1.0 \n", - "197603 0.0 0.0 0.00 0.0 \n", - "88679 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "39626 0.0 550.000000 550.000000 \n", - "158560 0.0 550.000000 550.000000 \n", - "170411 1.0 350.010093 350.010093 \n", - "220692 0.0 5.158787 5.158787 \n", - "182741 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "194275 0.0 550.000000 550.000000 \n", - "142915 0.0 550.000000 550.000000 \n", - "95021 0.0 382.280455 382.279877 \n", - "197603 0.0 550.000000 550.000000 \n", - "88679 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "39626 0.0 True True 0 \n", - "158560 0.0 True True 0 \n", - "170411 1.0 True False 0 \n", - "220692 0.0 True False 0 \n", - "182741 0.0 True True 0 \n", - "... ... ... ... ... \n", - "194275 0.0 True False 1 \n", - "142915 0.0 True True 0 \n", - "95021 0.0 True True 0 \n", - "197603 0.0 True True 0 \n", - "88679 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "39626 0 9.0 0.0 \n", - "158560 0 20.0 5.0 \n", - "170411 1 40.0 23.0 \n", - "220692 1 0.0 0.0 \n", - "182741 1 19.0 1.0 \n", - "... ... ... ... \n", - "194275 0 38.0 19.0 \n", - "142915 1 26.0 8.0 \n", - "95021 0 0.0 0.0 \n", - "197603 1 21.0 0.0 \n", - "88679 1 5.0 0.0 \n", - "\n", - "[10000 rows x 14 columns]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# reduce X_train to reduce the training time\n", - "\n", - "X_train_subsample = X_train.sample(n=10000, random_state=43)\n", - "y_train_subsample = y_train.loc[X_train_subsample.index]\n", - "X_train_subsample" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "2b09c2cd-fd5c-49b3-be66-cec6c5ec1351", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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y_has_purchased
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Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('num',\n",
-       "                                                  Pipeline(steps=[('scaler',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['nb_tickets', 'nb_purchases',\n",
-       "                                                   'total_amount',\n",
-       "                                                   'nb_suppliers',\n",
-       "                                                   'vente_internet_max',\n",
-       "                                                   'purchase_date_min',\n",
-       "                                                   'purchase_date_max',\n",
-       "                                                   'nb_tickets_internet',\n",
-       "                                                   'nb_campaigns',\n",
-       "                                                   'nb_campaigns_opened']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in',\n",
-       "                                                   'is_email_true'])])),\n",
-       "                ('logreg',\n",
-       "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver='saga'))])
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
396260.00.00.000.00.0550.000000550.0000000.0TrueTrue009.00.0
1585600.00.00.000.00.0550.000000550.0000000.0TrueTrue0020.05.0
1704111.01.062.111.01.0350.010093350.0100931.0TrueFalse0140.023.0
2206921.01.084.001.00.05.1587875.1587870.0TrueFalse010.00.0
1827410.00.00.000.00.0550.000000550.0000000.0TrueTrue0119.01.0
.............................................
1942750.00.00.000.00.0550.000000550.0000000.0TrueFalse1038.019.0
1429150.00.00.000.00.0550.000000550.0000000.0TrueTrue0126.08.0
950217.02.0250.001.00.0382.280455382.2798770.0TrueTrue000.00.0
1976030.00.00.000.00.0550.000000550.0000000.0TrueTrue0121.00.0
886790.00.00.000.00.0550.000000550.0000000.0TrueFalse015.00.0
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10000 rows × 14 columns

\n", - "
" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "39626 0.0 0.0 0.00 0.0 \n", - "158560 0.0 0.0 0.00 0.0 \n", - "170411 1.0 1.0 62.11 1.0 \n", - "220692 1.0 1.0 84.00 1.0 \n", - "182741 0.0 0.0 0.00 0.0 \n", - "... ... ... ... ... \n", - "194275 0.0 0.0 0.00 0.0 \n", - "142915 0.0 0.0 0.00 0.0 \n", - "95021 7.0 2.0 250.00 1.0 \n", - "197603 0.0 0.0 0.00 0.0 \n", - "88679 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "39626 0.0 550.000000 550.000000 \n", - "158560 0.0 550.000000 550.000000 \n", - "170411 1.0 350.010093 350.010093 \n", - "220692 0.0 5.158787 5.158787 \n", - "182741 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "194275 0.0 550.000000 550.000000 \n", - "142915 0.0 550.000000 550.000000 \n", - "95021 0.0 382.280455 382.279877 \n", - "197603 0.0 550.000000 550.000000 \n", - "88679 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "39626 0.0 True True 0 \n", - "158560 0.0 True True 0 \n", - "170411 1.0 True False 0 \n", - "220692 0.0 True False 0 \n", - "182741 0.0 True True 0 \n", - "... ... ... ... ... \n", - "194275 0.0 True False 1 \n", - "142915 0.0 True True 0 \n", - "95021 0.0 True True 0 \n", - "197603 0.0 True True 0 \n", - "88679 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "39626 0 9.0 0.0 \n", - "158560 0 20.0 5.0 \n", - "170411 1 40.0 23.0 \n", - "220692 1 0.0 0.0 \n", - "182741 1 19.0 1.0 \n", - "... ... ... ... \n", - "194275 0 38.0 19.0 \n", - "142915 1 26.0 8.0 \n", - "95021 0 0.0 0.0 \n", - "197603 1 21.0 0.0 \n", - "88679 1 5.0 0.0 \n", - "\n", - "[10000 rows x 14 columns]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_subsample" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "0270013a-6523-4cf8-8de0-569c0d1c5db5", - "metadata": {}, - "outputs": [], - "source": [ - "warnings.filterwarnings('ignore')\n", - "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", - "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "7a49d78a-5a9b-44a9-95cf-3fca1b3febfa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 0.0625, 'logreg__penalty': 'l1'}\n", - "Best classification F1 score in train is: 0.462769170101807\n", - "Classification F1 score on test is: 0.46474681703251214\n" - ] - } - ], - "source": [ - "# run the pipeline on the subsample\n", - "\n", - "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "logit_grid.fit(X_train_subsample, y_train_subsample)\n", - "\n", - "# print results\n", - "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n", - "print('Best classification F1 score in train is: {}'.format(logit_grid.best_score_))\n", - "print('Classification F1 score on test is: {}'.format(logit_grid.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "b1d5e71d-1078-4370-86e8-52b1ae378898", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01])" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_c" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "cfe04739-fe9c-4802-9d34-885a8cfce0dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessor',\n",
-       "                                        ColumnTransformer(transformers=[('num',\n",
-       "                                                                         Pipeline(steps=[('scaler',\n",
-       "                                                                                          StandardScaler())]),\n",
-       "                                                                         ['nb_tickets',\n",
-       "                                                                          'nb_purchases',\n",
-       "                                                                          'total_amount',\n",
-       "                                                                          'nb_suppliers',\n",
-       "                                                                          'vente_internet_max',\n",
-       "                                                                          'purchase_date_min',\n",
-       "                                                                          'purchase_date_max',\n",
-       "                                                                          'nb_tickets_internet',\n",
-       "                                                                          'nb_campaigns',\n",
-       "                                                                          'nb_campaigns_opened']),\n",
-       "                                                                        ('cat',\n",
-       "                                                                         Pipeline(steps=[(...\n",
-       "                                                                         1.0: 3.486549107420539},\n",
-       "                                                           max_iter=5000,\n",
-       "                                                           solver='saga'))]),\n",
-       "             param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n",
-       "       1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
-       "       2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
-       "       4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
-       "                         'logreg__penalty': ['l1']},\n",
-       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets',\n", - " 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[(...\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000,\n", - " solver='saga'))]),\n", - " param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']},\n", - " scoring=make_scorer(f1_score, response_method='predict'))" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "logit_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "6debc66c-a56d-41fa-8ef8-ba388e0e14fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']}" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "e394cc04-5d0b-4a64-9aa0-415dc8a3cbbc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", - "Best classification accuracy in train is: 0.42160313383818665\n", - "Classification accuracy on test is: 0.47078982841737305\n" - ] - } - ], - "source": [ - "# run the pipeline on the full sample\n", - "\n", - "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "logit_grid.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "8e6cf558-a4f4-4159-9835-364ee3bb1ed2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", - "Best classification F1 score in train is: 0.42160313383818665\n", - "Classification F1 score on test is: 0.47078982841737305\n" - ] - } - ], - "source": [ - "# print results\n", - "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n", - "print('Best classification F1 score in train is: {}'.format(logit_grid.best_score_))\n", - "print('Classification F1 score on test is: {}'.format(logit_grid.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "e2ff26cb-f137-4a23-9add-bdb61bebdf9c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessor',\n",
-       "                                        ColumnTransformer(transformers=[('num',\n",
-       "                                                                         Pipeline(steps=[('scaler',\n",
-       "                                                                                          StandardScaler())]),\n",
-       "                                                                         ['nb_tickets',\n",
-       "                                                                          'nb_purchases',\n",
-       "                                                                          'total_amount',\n",
-       "                                                                          'nb_suppliers',\n",
-       "                                                                          'vente_internet_max',\n",
-       "                                                                          'purchase_date_min',\n",
-       "                                                                          'purchase_date_max',\n",
-       "                                                                          'nb_tickets_internet',\n",
-       "                                                                          'nb_campaigns',\n",
-       "                                                                          'nb_campaigns_opened']),\n",
-       "                                                                        ('cat',\n",
-       "                                                                         Pipeline(steps=[(...\n",
-       "                                                                         1.0: 3.486549107420539},\n",
-       "                                                           max_iter=5000,\n",
-       "                                                           solver='saga'))]),\n",
-       "             param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n",
-       "       1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
-       "       2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
-       "       4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
-       "                         'logreg__penalty': ['l1']},\n",
-       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets',\n", - " 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[(...\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000,\n", - " solver='saga'))]),\n", - " param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']},\n", - " scoring=make_scorer(f1_score, response_method='predict'))" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "5d553da2-5c2a-491a-b4d2-f31c30c201a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'scoring': make_scorer(f1_score, response_method='predict'),\n", - " 'estimator': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, solver='saga'))]),\n", - " 'n_jobs': None,\n", - " 'refit': True,\n", - " 'cv': 3,\n", - " 'verbose': 0,\n", - " 'pre_dispatch': '2*n_jobs',\n", - " 'error_score': nan,\n", - " 'return_train_score': False,\n", - " 'param_grid': {'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", - " 'logreg__penalty': ['l1']},\n", - " 'multimetric_': False,\n", - " 'best_index_': 5,\n", - " 'best_score_': 0.42160313383818665,\n", - " 'best_params_': {'logreg__C': 0.03125, 'logreg__penalty': 'l1'},\n", - " 'best_estimator_': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler',\n", - " StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'vente_internet_max',\n", - " 'purchase_date_min',\n", - " 'purchase_date_max',\n", - " 'nb_tickets_internet',\n", - " 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('logreg',\n", - " LogisticRegression(C=0.03125,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, penalty='l1',\n", - " solver='saga'))]),\n", - " 'refit_time_': 305.1356477737427,\n", - " 'feature_names_in_': array(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", - " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'], dtype=object),\n", - " 'scorer_': make_scorer(f1_score, response_method='predict'),\n", - " 'cv_results_': {'mean_fit_time': array([ 11.07076669, 13.15744201, 27.35094929, 40.0343461 ,\n", - " 94.58210254, 140.45846391, 159.83818332, 162.80178094,\n", - " 163.94260454, 171.08749111, 169.26621262, 166.36741408,\n", - " 167.91208776, 173.06720233, 170.93666704]),\n", - " 'std_fit_time': array([ 0.09462032, 1.51362591, 6.70859141, 22.68643753, 28.72690872,\n", - " 70.8434823 , 85.23159321, 79.71538593, 82.70486235, 84.79706797,\n", - " 86.79005212, 84.67956107, 83.94889047, 89.68716252, 89.41361431]),\n", - " 'mean_score_time': array([0.11632609, 0.10857773, 0.18140252, 0.1291213 , 0.11651532,\n", - " 0.07535577, 0.12481014, 0.16039928, 0.15685773, 0.07996233,\n", - " 0.12988146, 0.10067987, 0.1194102 , 0.09737802, 0.09390028]),\n", - " 'std_score_time': array([0.02131792, 0.03620144, 0.05853886, 0.06555575, 0.03228018,\n", - " 0.01433186, 0.03501336, 0.05466042, 0.06882891, 0.01002881,\n", - " 0.00495894, 0.00905774, 0.04075337, 0.03269379, 0.01990173]),\n", - " 'param_logreg__C': masked_array(data=[0.0009765625, 0.001953125, 0.00390625, 0.0078125,\n", - " 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1.0, 2.0,\n", - " 4.0, 8.0, 16.0],\n", - " mask=[False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_logreg__penalty': masked_array(data=['l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1',\n", - " 'l1', 'l1', 'l1', 'l1', 'l1', 'l1'],\n", - " mask=[False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'params': [{'logreg__C': 0.0009765625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.001953125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.00390625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.0078125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.015625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.03125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.0625, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.125, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.25, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 0.5, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 1.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 2.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 4.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 8.0, 'logreg__penalty': 'l1'},\n", - " {'logreg__C': 16.0, 'logreg__penalty': 'l1'}],\n", - " 'split0_test_score': array([0.27289073, 0.2738913 , 0.27382853, 0.27409759, 0.27454764,\n", - " 0.27661894, 0.2766145 , 0.27584723, 0.27571682, 0.27576295,\n", - " 0.27580092, 0.27577943, 0.27581248, 0.27581909, 0.27581909]),\n", - " 'split1_test_score': array([0.4714244 , 0.47196015, 0.48362373, 0.48891733, 0.49066854,\n", - " 0.49091122, 0.49086284, 0.49065871, 0.49062783, 0.49049541,\n", - " 0.49048106, 0.49045238, 0.49043804, 0.49043804, 0.4904237 ]),\n", - " 'split2_test_score': array([0.50689906, 0.50092334, 0.4981377 , 0.49759178, 0.49725836,\n", - " 0.49727924, 0.49708801, 0.49738305, 0.49751781, 0.49738248,\n", - " 0.49738248, 0.49738248, 0.49738248, 0.49738248, 0.49738248]),\n", - " 'mean_test_score': array([0.4170714 , 0.4155916 , 0.41852999, 0.42020223, 0.42082484,\n", - " 0.42160313, 0.42152178, 0.42129633, 0.42128749, 0.42121361,\n", - " 0.42122149, 0.42120476, 0.421211 , 0.4212132 , 0.42120842]),\n", - " 'std_test_score': array([0.10297463, 0.1008925 , 0.10249081, 0.10337226, 0.10346859,\n", - " 0.10255226, 0.10249644, 0.10288467, 0.10297243, 0.10288758,\n", - " 0.10286646, 0.10287015, 0.10285136, 0.10284824, 0.10284503]),\n", - " 'rank_test_score': array([14, 15, 13, 12, 11, 1, 2, 3, 4, 6, 5, 10, 8, 7, 9],\n", - " dtype=int32)},\n", - " 'n_splits_': 3}" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid.__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "3573f34e-25d5-4afb-82cc-52323e2f63c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.67553011, 0. , 0.14254288, 0.41574295, 0.03458744,\n", - " 0.64769185, -1.20510095, 0. , 0.01018587, 0.13959519,\n", - " 0.24222266, -0.68253886, 0. , 0. ]])" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# coefficients trouvés pour le modèle optimal\n", - "logit_grid.best_estimator_.named_steps[\"logreg\"].coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "0332a814-61fb-4b71-836a-e8ace70b1a44", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'preprocessor': ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('scaler', StandardScaler())]),\n", - " ['nb_tickets', 'nb_purchases', 'total_amount',\n", - " 'nb_suppliers', 'vente_internet_max',\n", - " 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'nb_campaigns',\n", - " 'nb_campaigns_opened']),\n", - " ('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in', 'is_email_true'])]),\n", - " 'logreg': LogisticRegression(C=0.0625,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539},\n", - " max_iter=5000, penalty='l1', solver='saga')}" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid.best_estimator_.named_steps" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "287615b9-e062-4b84-be61-26b9364b2cf4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.44041477])" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logit_grid.best_estimator_.named_steps[\"logreg\"].intercept_" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "4d50899d-cc0b-4a71-9406-f8b0a277c4a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
02.01.060.001.00.0355.268981355.2689810.0TrueFalse010.00.0
18.03.0140.001.00.0373.540289219.2622690.0TrueFalse010.00.0
22.01.050.001.00.05.2024425.2024420.0TrueFalse010.00.0
33.01.090.001.00.05.1789585.1789580.0TrueFalse010.00.0
42.01.078.001.00.05.1740395.1740390.0TrueFalse100.00.0
.............................................
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2242100.00.00.000.00.0550.000000550.0000000.0TrueTrue018.04.0
2242111.01.097.111.01.0172.334074172.3340741.0TrueFalse0113.05.0
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\n", - "

224213 rows × 14 columns

\n", - "
" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 2.0 1.0 60.00 1.0 \n", - "1 8.0 3.0 140.00 1.0 \n", - "2 2.0 1.0 50.00 1.0 \n", - "3 3.0 1.0 90.00 1.0 \n", - "4 2.0 1.0 78.00 1.0 \n", - "... ... ... ... ... \n", - "224208 0.0 0.0 0.00 0.0 \n", - "224209 1.0 1.0 20.00 1.0 \n", - "224210 0.0 0.0 0.00 0.0 \n", - "224211 1.0 1.0 97.11 1.0 \n", - "224212 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 355.268981 355.268981 \n", - "1 0.0 373.540289 219.262269 \n", - "2 0.0 5.202442 5.202442 \n", - "3 0.0 5.178958 5.178958 \n", - "4 0.0 5.174039 5.174039 \n", - "... ... ... ... \n", - "224208 0.0 550.000000 550.000000 \n", - "224209 1.0 392.501030 392.501030 \n", - "224210 0.0 550.000000 550.000000 \n", - "224211 1.0 172.334074 172.334074 \n", - "224212 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 0.0 True False 0 \n", - "1 0.0 True False 0 \n", - "2 0.0 True False 0 \n", - "3 0.0 True False 0 \n", - "4 0.0 True False 1 \n", - "... ... ... ... ... \n", - "224208 0.0 True False 0 \n", - "224209 1.0 True False 0 \n", - "224210 0.0 True True 0 \n", - "224211 1.0 True False 0 \n", - "224212 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "0 1 0.0 0.0 \n", - "1 1 0.0 0.0 \n", - "2 1 0.0 0.0 \n", - "3 1 0.0 0.0 \n", - "4 0 0.0 0.0 \n", - "... ... ... ... \n", - "224208 1 34.0 3.0 \n", - "224209 1 23.0 6.0 \n", - "224210 1 8.0 4.0 \n", - "224211 1 13.0 5.0 \n", - "224212 1 4.0 4.0 \n", - "\n", - "[224213 rows x 14 columns]" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# c'est la 2ème variable nb_purchases qui a été supprimée par le LASSO\n", - "X_train" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "e53b1f79-762d-4f1f-8505-91de1088af42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "16.0" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# best param : alpha = 32 (alpha =1/4 sur le petit subsample)\n", - "1/logit_grid.best_params_[\"logreg__C\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "41bcaaf6-ab58-4004-a3c5-586d77e872d1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.7510718448218449\n", - "F1 Score: 0.46474681703251214\n", - "Recall Score: 0.7585829072315559\n" - ] - } - ], - "source": [ - "# print results for the best model\n", - "\n", - "y_pred = logit_grid.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "a454bb57-76eb-4a22-9950-0733d39e449f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# confusion matrix \n", - "\n", - "draw_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "25ec1701-ade5-4419-8b46-8a1bb109cf84", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC curve\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "y_pred_prob = logit_grid.predict_proba(X_test)[:, 1]\n", - "\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize = (14, 8))\n", - "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : modèle logistique')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "3b5c9485-511b-4f6b-b667-154f4f519682", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", - "\n", - "# Tri des prédictions de probabilités et des vraies valeurs\n", - "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", - "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", - "y_test_sorted = y_test.iloc[sorted_indices]\n", - "\n", - "# Calcul du gain cumulatif\n", - "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", - "\n", - "# Tracé de la courbe de lift\n", - "plt.plot(np.linspace(0, 1, len(cumulative_gain)), cumulative_gain, label='Courbe de lift')\n", - "plt.xlabel('Part de clients identifiés sans modèle ')\n", - "plt.ylabel('Part de clients identifiés avec modèle')\n", - "plt.title('Courbe de Lift')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "6e7cfb6c-8049-4bd1-8d82-61a2e97b257d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of the score \n", - "\n", - "plt.hist(y_pred_prob, bins=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "99f7f70e-c3bb-445e-8889-e7547f6ebd1e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# number of observations\n", - "N = len(y_pred_prob)\n", - "\n", - "# sort the data in ascending order \n", - "y_pred_prob_sorted = np.sort(y_pred_prob) \n", - "\n", - "# get the cdf values of y \n", - "steps = np.arange(N) / N\n", - " \n", - "# plotting \n", - "plt.xlabel('X') \n", - "plt.ylabel('P(score<=X)') \n", - " \n", - "plt.title('CDF curve of the predicted probability of purchase (score) for sports companies') \n", - " \n", - "plt.plot(y_pred_prob_sorted, steps) \n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "bcb94066-9387-4a5f-af3a-ab86d534c885", - "metadata": {}, - "source": [ - "### K-means clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "dd7a4a9c-d7e3-4747-ae59-b2a5a0b77260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
KMeans(n_clusters=3, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "KMeans(n_clusters=3, random_state=0)" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# K-means clustering \n", - "\n", - "from sklearn.cluster import KMeans\n", - "\n", - "kmeans = KMeans(n_clusters=3, random_state=0)\n", - "\n", - "kmeans.fit(y_pred_prob.reshape(-1,1))" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "10b6ece7-adcf-41c0-884b-a4aef42af378", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 0, 0, ..., 0, 1, 0], dtype=int32)" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_clusters = kmeans.predict(y_pred_prob.reshape(-1,1))\n", - "y_clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "e4b3b16e-03b8-4883-9788-cb7296fe56cd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "seuil cluster 0 : 0.38635624748849917 (60.14%)\n", - "seuil cluster 1 : 0.7395110401019087 (30.69%)\n", - "seuil cluster 2 : 1.0 (9.16%)\n" - ] - } - ], - "source": [ - "# seuils des clusters et part de clients dans chacun d'eux\n", - "\n", - "print(f\"seuil cluster 0 : {y_pred_prob[y_clusters==0].max()} ({round(100 * (y_clusters==0).mean(), 2)}%)\")\n", - "print(f\"seuil cluster 1 : {y_pred_prob[y_clusters==1].max()} ({round(100 * (y_clusters==1).mean(), 2)}%)\")\n", - "print(f\"seuil cluster 2 : {y_pred_prob[y_clusters==2].max()} ({round(100* (y_clusters==2).mean(), 2)}%)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "3e404a5e-6734-4d98-8853-48b09c96e7e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers vente_internet_max \\\n", - "0 4.0 1.0 100.0 1.0 0.0 \n", - "1 1.0 1.0 55.0 1.0 0.0 \n", - "2 17.0 1.0 80.0 1.0 0.0 \n", - "3 4.0 1.0 120.0 1.0 0.0 \n", - "4 34.0 2.0 416.0 1.0 0.0 \n", - "\n", - " purchase_date_min purchase_date_max nb_tickets_internet is_email_true \\\n", - "0 5.177187 5.177187 0.0 True \n", - "1 426.265613 426.265613 0.0 True \n", - "2 436.033437 436.033437 0.0 True \n", - "3 5.196412 5.196412 0.0 True \n", - "4 478.693148 115.631470 0.0 True \n", - "\n", - " opt_in gender_female gender_male nb_campaigns nb_campaigns_opened \\\n", - "0 False 1 0 0.0 0.0 \n", - "1 True 0 1 0.0 0.0 \n", - "2 True 1 0 0.0 0.0 \n", - "3 False 1 0 0.0 0.0 \n", - "4 False 1 0 0.0 0.0 \n", - "\n", - " cluster \n", - "0 1 \n", - "1 0 \n", - "2 0 \n", - "3 1 \n", - "4 2 " - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# les individus des clusters sont-ils semblables ? def des marketing personae\n", - "\n", - "X_test_clustered = X_test.assign(cluster = y_clusters)\n", - "X_test_clustered.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "b6f4638d-23c4-427a-88a4-b09528b3f91b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "cluster \n", - "0 0.311325 0.114404 6.707697 0.102898 \n", - "1 2.926055 1.395389 82.976104 1.000136 \n", - "2 44.841472 11.576993 1942.145881 1.493641 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "cluster \n", - "0 0.048741 527.762945 527.621410 \n", - "1 0.681539 228.303268 217.641649 \n", - "2 0.742562 382.346041 87.811798 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "cluster \n", - "0 0.137313 1.000000 0.561640 0.239934 \n", - "1 1.736769 0.990202 0.145618 0.260553 \n", - "2 12.613786 0.971724 0.132637 0.199182 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "cluster \n", - "0 0.450610 12.881201 2.163647 \n", - "1 0.536871 9.821800 2.811663 \n", - "2 0.621735 20.781399 8.329548 " - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_clustered.groupby(\"cluster\").mean().iloc[[0,1,2], :]" - ] - }, - { - "cell_type": "markdown", - "id": "d0af77f8-ae66-43a5-bf04-b26667f911f6", - "metadata": {}, - "source": [ - "### Quartile clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "2396ec51-4411-4fe3-9d41-449c4ffa75a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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217.01.080.01.00.0436.033437436.0334370.0TrueTrue100.00.00.279592
34.01.0120.01.00.05.1964125.1964120.0TrueFalse100.00.00.696135
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81.01.010.01.00.044.69809044.6980900.0TrueTrue000.00.00.4419332
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "quartile \n", - "1 0.017380 0.008586 0.475141 0.008439 \n", - "2 2.085810 0.880283 49.701732 0.742336 \n", - "3 3.118100 1.478893 88.811284 1.003292 \n", - "4 46.046362 11.842254 2002.607230 1.508685 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "quartile \n", - "1 0.001358 549.044552 549.044465 \n", - "2 0.420866 381.428495 379.188470 \n", - "3 0.703349 198.284116 184.197970 \n", - "4 0.743192 386.401662 85.808238 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "quartile \n", - "1 0.003071 1.000000 0.562157 0.232416 \n", - "2 1.044473 0.998374 0.507083 0.264515 \n", - "3 1.879098 0.988123 0.051777 0.264001 \n", - "4 12.894131 0.971479 0.130751 0.198239 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened score \\\n", - "quartile \n", - "1 0.416536 11.860521 1.648430 0.169233 \n", - "2 0.596435 14.593184 3.725732 0.360811 \n", - "3 0.526534 9.773898 2.978115 0.626785 \n", - "4 0.622418 20.928286 8.367723 0.902055 \n", - "\n", - " has_purchased \n", - "quartile \n", - "1 0.026780 \n", - "2 0.117452 \n", - "3 0.209332 \n", - "4 0.666549 " - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check consistency of quartiles (we have an upward bias, which is explained by the fact that we want a decent recall)\n", - "\n", - "X_test[\"has_purchased\"] = y_test\n", - "X_test.groupby(\"quartile\").mean()" - ] - }, - { - "cell_type": "markdown", - "id": "e6bcaff3-0f47-46da-8873-a321d3382e63", - "metadata": {}, - "source": [ - "Méthode \\\n", - "On étudie le rythme d'achat des clients et on suppose qu'il sera le même dans le futur" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "04218519-bffa-4340-87dc-e11332977067", - "metadata": {}, - "outputs": [], - "source": [ - "# purchasing pace by segment\n", - "\n", - "X_test[\"consumption_lifetime\"] = X_test[\"purchase_date_min\"] - X_test[\"purchase_date_max\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "4ac3610d-8a22-4135-a127-328812c5198c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 30.347912\n", - "std 95.435372\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 547.122986\n", - "Name: consumption_lifetime, dtype: float64" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[\"consumption_lifetime\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "ee86cfb4-e2c4-4485-b27a-ecaec159a0b9", - "metadata": {}, - "outputs": [], - "source": [ - "X_test[\"avg_purchase_delay\"] = (X_test[\"consumption_lifetime\"]/X_test[\"nb_purchases\"]).replace([np.inf, -np.inf], 0)" - ] - }, - { - "cell_type": "raw", - "id": "a2de6e96-4c92-42b2-8569-1c0f920e7a8c", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "256a684d-0117-4daa-ba38-ff48ac946798", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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217.01.080.001.00.0436.033437436.0334370.0TrueTrue...0.00.27959220.00.0000000.000000436.0334370.00000025.6490262
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434.02.0416.001.00.0478.693148115.6314700.0TrueFalse...0.00.91184441.0363.061678181.530839239.34657410.67828514.0792109
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96096 rows × 23 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 4.0 1.0 100.00 1.0 \n", - "1 1.0 1.0 55.00 1.0 \n", - "2 17.0 1.0 80.00 1.0 \n", - "3 4.0 1.0 120.00 1.0 \n", - "4 34.0 2.0 416.00 1.0 \n", - "... ... ... ... ... \n", - "96091 1.0 1.0 67.31 1.0 \n", - "96092 1.0 1.0 61.41 1.0 \n", - "96093 0.0 0.0 0.00 0.0 \n", - "96094 1.0 1.0 79.43 1.0 \n", - "96095 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 5.177187 5.177187 \n", - "1 0.0 426.265613 426.265613 \n", - "2 0.0 436.033437 436.033437 \n", - "3 0.0 5.196412 5.196412 \n", - "4 0.0 478.693148 115.631470 \n", - "... ... ... ... \n", - "96091 1.0 278.442257 278.442257 \n", - "96092 1.0 189.207373 189.207373 \n", - "96093 0.0 550.000000 550.000000 \n", - "96094 1.0 279.312905 279.312905 \n", - "96095 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in ... nb_campaigns_opened \\\n", - "0 0.0 True False ... 0.0 \n", - "1 0.0 True True ... 0.0 \n", - "2 0.0 True True ... 0.0 \n", - "3 0.0 True False ... 0.0 \n", - "4 0.0 True False ... 0.0 \n", - "... ... ... ... ... ... \n", - "96091 1.0 True False ... 5.0 \n", - "96092 1.0 True False ... 9.0 \n", - "96093 0.0 True True ... 3.0 \n", - "96094 1.0 True False ... 4.0 \n", - "96095 0.0 True False ... 4.0 \n", - "\n", - " score quartile has_purchased consumption_lifetime \\\n", - "0 0.695913 3 0.0 0.000000 \n", - "1 0.244205 1 1.0 0.000000 \n", - "2 0.279592 2 0.0 0.000000 \n", - "3 0.696135 3 0.0 0.000000 \n", - "4 0.911844 4 1.0 363.061678 \n", - "... ... ... ... ... \n", - "96091 0.584680 3 1.0 0.000000 \n", - "96092 0.654520 3 0.0 0.000000 \n", - "96093 0.116503 1 0.0 0.000000 \n", - "96094 0.579827 3 0.0 0.000000 \n", - "96095 0.254002 2 0.0 0.000000 \n", - "\n", - " avg_purchase_delay avg_purchase_delay_all avg_tickets_delay \\\n", - "0 0.000000 5.177187 0.000000 \n", - "1 0.000000 426.265613 0.000000 \n", - "2 0.000000 436.033437 0.000000 \n", - "3 0.000000 5.196412 0.000000 \n", - "4 181.530839 239.346574 10.678285 \n", - "... ... ... ... \n", - "96091 0.000000 278.442257 0.000000 \n", - "96092 0.000000 189.207373 0.000000 \n", - "96093 NaN 0.000000 NaN \n", - "96094 0.000000 279.312905 0.000000 \n", - "96095 NaN 0.000000 NaN \n", - "\n", - " avg_tickets_delay_all decile \n", - "0 1.294297 6 \n", - "1 426.265613 2 \n", - "2 25.649026 2 \n", - "3 1.299103 6 \n", - "4 14.079210 9 \n", - "... ... ... \n", - "96091 278.442257 5 \n", - "96092 189.207373 6 \n", - "96093 0.000000 1 \n", - "96094 279.312905 5 \n", - "96095 0.000000 2 \n", - "\n", - "[96096 rows x 23 columns]" - ] - }, - "execution_count": 275, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on prend un decoupage plus fin : deciles\n", - "\n", - "X_test[\"decile\"] = (10 * X_test[\"score\"]).astype(int)\n", - "X_test[\"decile\"] = X_test[\"decile\"].apply(lambda x : x-1 if x==10 else x)\n", - "X_test" - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "id": "b8db5044-74b1-423b-b12f-798606674bfe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "decile\n", - "1 17.863019\n", - "2 3.826401\n", - "3 3.179880\n", - "4 3.392496\n", - "5 3.260982\n", - "6 3.294104\n", - "7 1.850487\n", - "8 1.489675\n", - "9 1.268598\n", - "dtype: float64" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test.groupby(\"decile\")[\"score\"].mean() / X_test.groupby(\"decile\")[\"has_purchased\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "id": "48a5b42e-fabf-44ae-ac88-fcb5a04d5d4f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.006422122322541649" - ] - }, - "execution_count": 277, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# pour les scores entre 0.1 et 0.2, la proba d'achat est de 0.6% elle est largement surestimée ici\n", - "X_test[X_test[\"decile\"]==1][\"has_purchased\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 284, - "id": "1091028b-0d07-4cfd-9081-696e289c29de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_in...scorequartilehas_purchasedconsumption_lifetimeavg_purchase_delayavg_purchase_delay_allavg_tickets_delayavg_tickets_delay_alldecileovershoot_coeff
04.01.0100.001.00.05.1771875.1771870.0TrueFalse...0.69591330.00.0000000.0000005.1771870.0000001.29429763.294104
11.01.055.001.00.0426.265613426.2656130.0TrueTrue...0.24420511.00.0000000.000000426.2656130.000000426.26561323.826401
217.01.080.001.00.0436.033437436.0334370.0TrueTrue...0.27959220.00.0000000.000000436.0334370.00000025.64902623.826401
34.01.0120.001.00.05.1964125.1964120.0TrueFalse...0.69613530.00.0000000.0000005.1964120.0000001.29910363.294104
434.02.0416.001.00.0478.693148115.6314700.0TrueFalse...0.91184441.0363.061678181.530839239.34657410.67828514.07921091.268598
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960911.01.067.311.01.0278.442257278.4422571.0TrueFalse...0.58468031.00.0000000.000000278.4422570.000000278.44225753.260982
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960941.01.079.431.01.0279.312905279.3129051.0TrueFalse...0.57982730.00.0000000.000000279.3129050.000000279.31290553.260982
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96096 rows × 24 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 4.0 1.0 100.00 1.0 \n", - "1 1.0 1.0 55.00 1.0 \n", - "2 17.0 1.0 80.00 1.0 \n", - "3 4.0 1.0 120.00 1.0 \n", - "4 34.0 2.0 416.00 1.0 \n", - "... ... ... ... ... \n", - "96091 1.0 1.0 67.31 1.0 \n", - "96092 1.0 1.0 61.41 1.0 \n", - "96093 0.0 0.0 0.00 0.0 \n", - "96094 1.0 1.0 79.43 1.0 \n", - "96095 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 5.177187 5.177187 \n", - "1 0.0 426.265613 426.265613 \n", - "2 0.0 436.033437 436.033437 \n", - "3 0.0 5.196412 5.196412 \n", - "4 0.0 478.693148 115.631470 \n", - "... ... ... ... \n", - "96091 1.0 278.442257 278.442257 \n", - "96092 1.0 189.207373 189.207373 \n", - "96093 0.0 550.000000 550.000000 \n", - "96094 1.0 279.312905 279.312905 \n", - "96095 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in ... score quartile \\\n", - "0 0.0 True False ... 0.695913 3 \n", - "1 0.0 True True ... 0.244205 1 \n", - "2 0.0 True True ... 0.279592 2 \n", - "3 0.0 True False ... 0.696135 3 \n", - "4 0.0 True False ... 0.911844 4 \n", - "... ... ... ... ... ... ... \n", - "96091 1.0 True False ... 0.584680 3 \n", - "96092 1.0 True False ... 0.654520 3 \n", - "96093 0.0 True True ... 0.116503 1 \n", - "96094 1.0 True False ... 0.579827 3 \n", - "96095 0.0 True False ... 0.254002 2 \n", - "\n", - " has_purchased consumption_lifetime avg_purchase_delay \\\n", - "0 0.0 0.000000 0.000000 \n", - "1 1.0 0.000000 0.000000 \n", - "2 0.0 0.000000 0.000000 \n", - "3 0.0 0.000000 0.000000 \n", - "4 1.0 363.061678 181.530839 \n", - "... ... ... ... \n", - "96091 1.0 0.000000 0.000000 \n", - "96092 0.0 0.000000 0.000000 \n", - "96093 0.0 0.000000 NaN \n", - "96094 0.0 0.000000 0.000000 \n", - "96095 0.0 0.000000 NaN \n", - "\n", - " avg_purchase_delay_all avg_tickets_delay avg_tickets_delay_all \\\n", - "0 5.177187 0.000000 1.294297 \n", - "1 426.265613 0.000000 426.265613 \n", - "2 436.033437 0.000000 25.649026 \n", - "3 5.196412 0.000000 1.299103 \n", - "4 239.346574 10.678285 14.079210 \n", - "... ... ... ... \n", - "96091 278.442257 0.000000 278.442257 \n", - "96092 189.207373 0.000000 189.207373 \n", - "96093 0.000000 NaN 0.000000 \n", - "96094 279.312905 0.000000 279.312905 \n", - "96095 0.000000 NaN 0.000000 \n", - "\n", - " decile overshoot_coeff \n", - "0 6 3.294104 \n", - "1 2 3.826401 \n", - "2 2 3.826401 \n", - "3 6 3.294104 \n", - "4 9 1.268598 \n", - "... ... ... \n", - "96091 5 3.260982 \n", - "96092 6 3.294104 \n", - "96093 1 17.863019 \n", - "96094 5 3.260982 \n", - "96095 2 3.826401 \n", - "\n", - "[96096 rows x 24 columns]" - ] - }, - "execution_count": 284, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create a variable to approximate the overestimation by decile\n", - "\n", - "# dictionnary mapping decile of the score and average overestimation\n", - "mapping_score_overshoot = dict(X_test.groupby(\"decile\")[\"score\"].mean() / X_test.groupby(\"decile\")[\"has_purchased\"].mean())\n", - "X_test[\"overshoot_coeff\"] = X_test[\"decile\"].map(mapping_score_overshoot)\n", - "X_test" - ] - }, - { - "cell_type": "code", - "execution_count": 285, - "id": "4892d585-c80e-472c-b2bc-dc441255a36d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_in...quartilehas_purchasedconsumption_lifetimeavg_purchase_delayavg_purchase_delay_allavg_tickets_delayavg_tickets_delay_alldecileovershoot_coeffajusted_score
04.01.0100.001.00.05.1771875.1771870.0TrueFalse...30.00.0000000.0000005.1771870.0000001.29429763.2941040.211260
11.01.055.001.00.0426.265613426.2656130.0TrueTrue...11.00.0000000.000000426.2656130.000000426.26561323.8264010.063821
217.01.080.001.00.0436.033437436.0334370.0TrueTrue...20.00.0000000.000000436.0334370.00000025.64902623.8264010.073069
34.01.0120.001.00.05.1964125.1964120.0TrueFalse...30.00.0000000.0000005.1964120.0000001.29910363.2941040.211328
434.02.0416.001.00.0478.693148115.6314700.0TrueFalse...41.0363.061678181.530839239.34657410.67828514.07921091.2685980.718781
..................................................................
960911.01.067.311.01.0278.442257278.4422571.0TrueFalse...31.00.0000000.000000278.4422570.000000278.44225753.2609820.179296
960921.01.061.411.01.0189.207373189.2073731.0TrueFalse...30.00.0000000.000000189.2073730.000000189.20737363.2941040.198694
960930.00.00.000.00.0550.000000550.0000000.0TrueTrue...10.00.000000NaN0.000000NaN0.000000117.8630190.006522
960941.01.079.431.01.0279.312905279.3129051.0TrueFalse...30.00.0000000.000000279.3129050.000000279.31290553.2609820.177808
960950.00.00.000.00.0550.000000550.0000000.0TrueFalse...20.00.000000NaN0.000000NaN0.00000023.8264010.066382
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96096 rows × 25 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 4.0 1.0 100.00 1.0 \n", - "1 1.0 1.0 55.00 1.0 \n", - "2 17.0 1.0 80.00 1.0 \n", - "3 4.0 1.0 120.00 1.0 \n", - "4 34.0 2.0 416.00 1.0 \n", - "... ... ... ... ... \n", - "96091 1.0 1.0 67.31 1.0 \n", - "96092 1.0 1.0 61.41 1.0 \n", - "96093 0.0 0.0 0.00 0.0 \n", - "96094 1.0 1.0 79.43 1.0 \n", - "96095 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 5.177187 5.177187 \n", - "1 0.0 426.265613 426.265613 \n", - "2 0.0 436.033437 436.033437 \n", - "3 0.0 5.196412 5.196412 \n", - "4 0.0 478.693148 115.631470 \n", - "... ... ... ... \n", - "96091 1.0 278.442257 278.442257 \n", - "96092 1.0 189.207373 189.207373 \n", - "96093 0.0 550.000000 550.000000 \n", - "96094 1.0 279.312905 279.312905 \n", - "96095 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in ... quartile \\\n", - "0 0.0 True False ... 3 \n", - "1 0.0 True True ... 1 \n", - "2 0.0 True True ... 2 \n", - "3 0.0 True False ... 3 \n", - "4 0.0 True False ... 4 \n", - "... ... ... ... ... ... \n", - "96091 1.0 True False ... 3 \n", - "96092 1.0 True False ... 3 \n", - "96093 0.0 True True ... 1 \n", - "96094 1.0 True False ... 3 \n", - "96095 0.0 True False ... 2 \n", - "\n", - " has_purchased consumption_lifetime avg_purchase_delay \\\n", - "0 0.0 0.000000 0.000000 \n", - "1 1.0 0.000000 0.000000 \n", - "2 0.0 0.000000 0.000000 \n", - "3 0.0 0.000000 0.000000 \n", - "4 1.0 363.061678 181.530839 \n", - "... ... ... ... \n", - "96091 1.0 0.000000 0.000000 \n", - "96092 0.0 0.000000 0.000000 \n", - "96093 0.0 0.000000 NaN \n", - "96094 0.0 0.000000 0.000000 \n", - "96095 0.0 0.000000 NaN \n", - "\n", - " avg_purchase_delay_all avg_tickets_delay avg_tickets_delay_all \\\n", - "0 5.177187 0.000000 1.294297 \n", - "1 426.265613 0.000000 426.265613 \n", - "2 436.033437 0.000000 25.649026 \n", - "3 5.196412 0.000000 1.299103 \n", - "4 239.346574 10.678285 14.079210 \n", - "... ... ... ... \n", - "96091 278.442257 0.000000 278.442257 \n", - "96092 189.207373 0.000000 189.207373 \n", - "96093 0.000000 NaN 0.000000 \n", - "96094 279.312905 0.000000 279.312905 \n", - "96095 0.000000 NaN 0.000000 \n", - "\n", - " decile overshoot_coeff ajusted_score \n", - "0 6 3.294104 0.211260 \n", - "1 2 3.826401 0.063821 \n", - "2 2 3.826401 0.073069 \n", - "3 6 3.294104 0.211328 \n", - "4 9 1.268598 0.718781 \n", - "... ... ... ... \n", - "96091 5 3.260982 0.179296 \n", - "96092 6 3.294104 0.198694 \n", - "96093 1 17.863019 0.006522 \n", - "96094 5 3.260982 0.177808 \n", - "96095 2 3.826401 0.066382 \n", - "\n", - "[96096 rows x 25 columns]" - ] - }, - "execution_count": 285, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[\"ajusted_score\"] = X_test[\"score\"]/X_test[\"overshoot_coeff\"]\n", - "X_test" - ] - }, - { - "cell_type": "code", - "execution_count": 788, - "id": "8332e5c3-32ee-4492-91ee-0e49a15f94a1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE for score : 0.15637498623391197\n", - "MSE for adjusted score : 0.08877832832116543\n" - ] - } - ], - "source": [ - "# difference between proba estimated and y has purchased\n", - "# the calibration allows to half the MSE\n", - "\n", - "MSE_score = ((X_test[\"score\"]-X_test[\"has_purchased\"])**2).mean()\n", - "MSE_ajusted_score = ((X_test[\"score_adjusted\"]-X_test[\"has_purchased\"])**2).mean()\n", - "print(f\"MSE for score : {MSE_score}\")\n", - "print(f\"MSE for adjusted score : {MSE_ajusted_score}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 787, - "id": "89b41b80-c12a-46be-a7d1-59f4f63482e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MAE for score : 0.32574831037767815\n", - "MAE for adjusted score : 0.17556035724742763\n" - ] - } - ], - "source": [ - "# mean absolute error - divided by 2 with out method\n", - "\n", - "MAE_score = abs(X_test[\"score\"]-X_test[\"has_purchased\"]).mean()\n", - "MAE_ajusted_score = abs(X_test[\"score_adjusted\"]-X_test[\"has_purchased\"]).mean()\n", - "print(f\"MAE for score : {MAE_score}\")\n", - "print(f\"MAE for adjusted score : {MAE_ajusted_score}\")" - ] - }, - { - "cell_type": "markdown", - "id": "15f49d36-da8c-4c08-977e-8de4e438ed61", - "metadata": {}, - "source": [ - "New method to adjust - best way to fit the logit model" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "id": "9e2e1f4c-d9dc-495a-9604-4009f1e4c53f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "somme des scores : 36092.22480054577\n", - "nombre d'achats : y_has_purchased 13690.0\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# au global, la prbabilité d'achat est largement surestimée, il ft corriger\n", - "print(\"somme des scores :\", X_test[\"score\"].sum())\n", - "print(\"nombre d'achats : \", y_test.sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 311, - "id": "1573b9fd-c1be-4f9e-94a5-471ad6cb0726", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36092.22480054577" - ] - }, - "execution_count": 311, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1. calcul du biais\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "id": "5d6d5101-95ce-4137-8349-0e3c6321bc84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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434.02.0416.001.00.0478.693148115.6314700.0TrueFalse...363.061678181.530839239.34657410.67828514.07921091.2685980.71878110.3435380.837972
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96096 rows × 27 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 4.0 1.0 100.00 1.0 \n", - "1 1.0 1.0 55.00 1.0 \n", - "2 17.0 1.0 80.00 1.0 \n", - "3 4.0 1.0 120.00 1.0 \n", - "4 34.0 2.0 416.00 1.0 \n", - "... ... ... ... ... \n", - "96091 1.0 1.0 67.31 1.0 \n", - "96092 1.0 1.0 61.41 1.0 \n", - "96093 0.0 0.0 0.00 0.0 \n", - "96094 1.0 1.0 79.43 1.0 \n", - "96095 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 5.177187 5.177187 \n", - "1 0.0 426.265613 426.265613 \n", - "2 0.0 436.033437 436.033437 \n", - "3 0.0 5.196412 5.196412 \n", - "4 0.0 478.693148 115.631470 \n", - "... ... ... ... \n", - "96091 1.0 278.442257 278.442257 \n", - "96092 1.0 189.207373 189.207373 \n", - "96093 0.0 550.000000 550.000000 \n", - "96094 1.0 279.312905 279.312905 \n", - "96095 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in ... consumption_lifetime \\\n", - "0 0.0 True False ... 0.000000 \n", - "1 0.0 True True ... 0.000000 \n", - "2 0.0 True True ... 0.000000 \n", - "3 0.0 True False ... 0.000000 \n", - "4 0.0 True False ... 363.061678 \n", - "... ... ... ... ... ... \n", - "96091 1.0 True False ... 0.000000 \n", - "96092 1.0 True False ... 0.000000 \n", - "96093 0.0 True True ... 0.000000 \n", - "96094 1.0 True False ... 0.000000 \n", - "96095 0.0 True False ... 0.000000 \n", - "\n", - " avg_purchase_delay avg_purchase_delay_all avg_tickets_delay \\\n", - "0 0.000000 5.177187 0.000000 \n", - "1 0.000000 426.265613 0.000000 \n", - "2 0.000000 436.033437 0.000000 \n", - "3 0.000000 5.196412 0.000000 \n", - "4 181.530839 239.346574 10.678285 \n", - "... ... ... ... \n", - "96091 0.000000 278.442257 0.000000 \n", - "96092 0.000000 189.207373 0.000000 \n", - "96093 NaN 0.000000 NaN \n", - "96094 0.000000 279.312905 0.000000 \n", - "96095 NaN 0.000000 NaN \n", - "\n", - " avg_tickets_delay_all decile overshoot_coeff ajusted_score \\\n", - "0 1.294297 6 3.294104 0.211260 \n", - "1 426.265613 2 3.826401 0.063821 \n", - "2 25.649026 2 3.826401 0.073069 \n", - "3 1.299103 6 3.294104 0.211328 \n", - "4 14.079210 9 1.268598 0.718781 \n", - "... ... ... ... ... \n", - "96091 278.442257 5 3.260982 0.179296 \n", - "96092 189.207373 6 3.294104 0.198694 \n", - "96093 0.000000 1 17.863019 0.006522 \n", - "96094 279.312905 5 3.260982 0.177808 \n", - "96095 0.000000 2 3.826401 0.066382 \n", - "\n", - " odd_ratio test_adjusted_score_2 \n", - "0 2.288530 0.533640 \n", - "1 0.323109 0.139085 \n", - "2 0.388102 0.162515 \n", - "3 2.290940 0.533902 \n", - "4 10.343538 0.837972 \n", - "... ... ... \n", - "96091 1.407779 0.413108 \n", - "96092 1.894523 0.486458 \n", - "96093 0.131865 0.061854 \n", - "96094 1.379973 0.408279 \n", - "96095 0.340487 0.145477 \n", - "\n", - "[96096 rows x 27 columns]" - ] - }, - "execution_count": 377, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[\"odd_ratio\"] = X_test[\"score\"]/(1-X_test[\"score\"])\n", - "X_test" - ] - }, - { - "cell_type": "code", - "execution_count": 378, - "id": "b5971afb-a6ef-4433-9cee-13ea978b22c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 9.609600e+04\n", - "mean 2.117164e+11\n", - "std 2.179173e+13\n", - "min 1.207494e-01\n", - "25% 1.476621e-01\n", - "50% 3.337214e-01\n", - "75% 1.430245e+00\n", - "max 2.251800e+15\n", - "Name: odd_ratio, dtype: float64" - ] - }, - "execution_count": 378, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[\"odd_ratio\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 381, - "id": "e878a711-5d7d-455f-9e0f-da50961568d9", - "metadata": {}, - "outputs": [], - "source": [ - "def adjusted_score(odd_ratio, bias) :\n", - " adjusted_score = odd_ratio/(bias+odd_ratio)\n", - " return adjusted_score" - ] - }, - { - "cell_type": "code", - "execution_count": 424, - "id": "bff25885-1191-432a-976c-4b466dbc0ac7", - "metadata": {}, - "outputs": [], - "source": [ - "def obj_function(bias) :\n", - " obj = sum([adjusted_score(element, bias) for element in X_test[\"odd_ratio\"]]) # - y_test.sum()[\"y_has_purchased\"]\n", - " return obj" - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "id": "a9df55fc-e1c6-4462-9fa5-248d47f4957f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "13690.0" - ] - }, - "execution_count": 380, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test.sum()[\"y_has_purchased\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 396, - "id": "ecae3be2-ddf4-4a76-940d-403a176fa8f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "13749.42306555955" - ] - }, - "execution_count": 396, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# le biais optimal se trouve aux alentours de 6\n", - "sum([adjusted_score(element, 6) for element in X_test[\"odd_ratio\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 411, - "id": "5698b75b-759a-4cc5-8466-c513d2ae2aa2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36092.2248005385" - ] - }, - "execution_count": 411, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum([adjusted_score(element, 1) for element in X_test[\"odd_ratio\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 412, - "id": "42840b8b-0314-4b15-afb9-09a9e550a729", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "13690.0" - ] - }, - "execution_count": 412, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test.sum()[\"y_has_purchased\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 425, - "id": "8a61a53c-c98b-4c76-bcfe-a4bb0f3db42a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36092.2248005385" - ] - }, - "execution_count": 425, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obj_function(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 423, - "id": "d29623ca-c9f7-4ef7-b5ea-45b2d2f65096", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3.9020966429798136" - ] - }, - "execution_count": 423, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on devrait trouver un résultat autour de 6.04\n", - "sum([adjusted_score(element, 6.04) for element in X_test[\"odd_ratio\"]]) - y_test.sum()[\"y_has_purchased\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 426, - "id": "6417f2a2-9e22-40c7-8297-2ed0b72e9b1d", - "metadata": {}, - "outputs": [], - "source": [ - "# minimization\n", - "\n", - "from scipy.optimize import minimize\n", - "\n", - "\n", - "y_sum = y_test.sum()[\"y_has_purchased\"]\n", - "initial_guess = 6\n", - "estimated_biais = minimize(lambda bias : (obj_function(bias)-y_sum)**2 ,\n", - "initial_guess , method = \"BFGS\")" - ] - }, - { - "cell_type": "code", - "execution_count": 430, - "id": "937606df-1730-43b6-9a95-7c626aa7a3c5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bias estimated : 6.042826489667565\n" - ] - } - ], - "source": [ - "print(f\"bias estimated : {estimated_biais.x[0]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 435, - "id": "ad6ebcee-f1f6-46fc-8d9a-008762acae28", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_in...avg_purchase_delayavg_purchase_delay_allavg_tickets_delayavg_tickets_delay_alldecileovershoot_coeffajusted_scoreodd_ratiotest_adjusted_score_2score_adjusted
04.01.0100.001.00.05.1771875.1771870.0TrueFalse...0.0000005.1771870.0000001.29429763.2941040.2112602.2885300.5336400.274689
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217.01.080.001.00.0436.033437436.0334370.0TrueTrue...0.000000436.0334370.00000025.64902623.8264010.0730690.3881020.1625150.060349
34.01.0120.001.00.05.1964125.1964120.0TrueFalse...0.0000005.1964120.0000001.29910363.2941040.2113282.2909400.5339020.274899
434.02.0416.001.00.0478.693148115.6314700.0TrueFalse...181.530839239.34657410.67828514.07921091.2685980.71878110.3435380.8379720.631228
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96096 rows × 28 columns

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NaN \n", - "\n", - " avg_purchase_delay_all avg_tickets_delay avg_tickets_delay_all \\\n", - "0 5.177187 0.000000 1.294297 \n", - "1 426.265613 0.000000 426.265613 \n", - "2 436.033437 0.000000 25.649026 \n", - "3 5.196412 0.000000 1.299103 \n", - "4 239.346574 10.678285 14.079210 \n", - "... ... ... ... \n", - "96091 278.442257 0.000000 278.442257 \n", - "96092 189.207373 0.000000 189.207373 \n", - "96093 0.000000 NaN 0.000000 \n", - "96094 279.312905 0.000000 279.312905 \n", - "96095 0.000000 NaN 0.000000 \n", - "\n", - " decile overshoot_coeff ajusted_score odd_ratio \\\n", - "0 6 3.294104 0.211260 2.288530 \n", - "1 2 3.826401 0.063821 0.323109 \n", - "2 2 3.826401 0.073069 0.388102 \n", - "3 6 3.294104 0.211328 2.290940 \n", - "4 9 1.268598 0.718781 10.343538 \n", - "... ... ... ... ... \n", - "96091 5 3.260982 0.179296 1.407779 \n", - "96092 6 3.294104 0.198694 1.894523 \n", - "96093 1 17.863019 0.006522 0.131865 \n", - "96094 5 3.260982 0.177808 1.379973 \n", - "96095 2 3.826401 0.066382 0.340487 \n", - "\n", - " test_adjusted_score_2 score_adjusted \n", - "0 0.533640 0.274689 \n", - "1 0.139085 0.050756 \n", - "2 0.162515 0.060349 \n", - "3 0.533902 0.274899 \n", - "4 0.837972 0.631228 \n", - "... ... ... \n", - "96091 0.413108 0.188948 \n", - "96092 0.486458 0.238685 \n", - "96093 0.061854 0.021356 \n", - "96094 0.408279 0.185910 \n", - "96095 0.145477 0.053340 \n", - "\n", - "[96096 rows x 28 columns]" - ] - }, - "execution_count": 436, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test" - ] - }, - { - "cell_type": "code", - "execution_count": 549, - "id": "0dadc6f7-9c49-4188-9ae4-8b9c84770cf6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Pz9wmNjb2ko87c+ZM/fSnP1VcXJyee+65Fl+Xne8f//iHDMPQgAEDdN1115mvwsJCc9uysrIWvX1br5MmTVJJSYkiIiKUlpam/Px8c11JSYn69OljhpzzlZWVafjw4S7Lhg8frkOHDqm5udlcNmTIEJea4uJirVmzxuU8EhISdPbsWZWXl7fa76VgRgcAcM0ZP368wsLCtGrVKjkcDp09e1ZRUVHmRbNteWhAly5dWmzX1NTk8j4rK0vJycnavHmztm7dqmeeeUa5ubm67777LrjPs2fPytPTU8XFxS6PSZKk6667rs293n777SovL9fWrVv15ptvasKECYqLi9Nrr70mX1/fVrc1DMPluqNv6sHf37/FuUybNk1paWktam+44Qa3z+FiEXQAANeUkydPqqysTCtXrtSdd94pSSoqKnKpiYyM1Nq1a1VfX29+8O/YsaPV/fbq1Uu1tbWqq6szP+QvdI+dAQMGaMCAAXriiSf04IMPavXq1brvvvvk4+PjMiMiSYMHD1Zzc7OqqqrMXs8XGRnZordv61WSAgICNHHiRE2cOFEPPPCAxowZo1OnTumWW27R0aNHdfDgwQvO6kRGRrYYr23btmnAgAEtwtjX3X777dq/f7++853vfGtvlxNfXQEArik9evRQYGCgXnjhBX300Uf629/+ppkzZ7rUJCcnq0uXLpo8ebJKS0u1ZcuWb31odExMjPz8/PSLX/xCH330kTZs2OBygW59fb2mT5+ud955R59++qnee+897d69WwMHDpT01S+Vzpw5o7feekufffaZPv/8cw0YMEAPPfSQHn74Yf3pT39SeXm5du/erfnz52vLli2SpLS0NOXl5WnBggU6ePCgcnJyWr0+R/rq4dq5ubn68MMPdfDgQf3xj39UaGiounfvrhEjRuiuu+7SD37wAxUUFJgzP+f2mZGRobfeekv/9V//pYMHD+rll19WTk7Ot16s/eSTT2r79u167LHHVFJSokOHDumNN97QjBkzWt3uUhF0AADXlC5duig3N1fFxcWKiorSE088oYULF7rUXHfddfrf//1flZaWavDgwZozZ47mz5/f6n579uypdevWacuWLRo0aJBeeeUV8yfi0lf33Tl58qQefvhhDRgwQBMmTNDYsWM1d+5cSdKwYcP06KOPauLEierVq5cWLFggSVq9erUefvhhZWRkKCIiQklJSdq5c6fCwsIkSUOHDtXvf/97Pf/887rtttuUn5+vX/7yl632et1112n+/PkaMmSI7rjjDv3rX//Sli1b1KXLV7Fg48aNuuOOO/Tggw8qMjJSs2bNMmebbr/9dv3hD39Qbm6uoqKi9J//+Z/61a9+5XIh8oXccsstKiws1KFDh3TnnXdq8ODBevrpp9W7d+9Wt7tUPL2cp5e74unlAC7Stf708gMHDuimm27SoUOH2v3rmGsBTy8HAKCDnDp1Sq+99poCAgLMmRVcnbgYGQAAN02ePFnFxcVasWKFbDZbR7eDVhB0AABw06ZNmzq6BVwkvroCAACWRdABAACWRdABAFySa/jHu7iCLtffFUEHANAm3t7ekqTPP/+8gzuBFZ37uzr3d9ZWXIwMAGgTT09Pde/eXVVVVZIkPz+/Fs9AAtxlGIY+//xzVVVVqXv37q0+VuJiEHQAAG0WGhoqSWbYAS6X7t27m39fl4KgAwBoMw8PD/Xu3VvBwcEtntQNtJW3t/clz+ScQ9ABAFwyT0/Py/bBBFxOXIwMAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsy62gk5WVJQ8PD5dXaGioud4wDGVlZcnhcMjX11cjR47U/v37XfbR0NCgGTNmKCgoSP7+/kpKStLRo0ddaqqrq5WSkiK73S673a6UlBSdPn3apebw4cMaP368/P39FRQUpLS0NDU2Nrp5+gAAwMrcntG5+eabVVFRYb727t1rrluwYIGWLFminJwc7d69W6GhoRo9erRqa2vNmvT0dG3atEm5ubkqKirSmTNnlJiYqObmZrMmOTlZJSUlysvLU15enkpKSpSSkmKub25u1rhx41RXV6eioiLl5uZq48aNysjIaOs4AAAAC/JyewMvL5dZnHMMw9CyZcs0Z84c3X///ZKkl19+WSEhIdqwYYOmTZsmp9OpF198UWvXrlVcXJwkad26dQoLC9Obb76phIQElZWVKS8vTzt27FBMTIwkadWqVYqNjdWBAwcUERGh/Px8lZaW6siRI3I4HJKkxYsXa9KkSfr1r3+tgICANg8IAACwDrdndA4dOiSHw6F+/frpRz/6kT755BNJUnl5uSorKxUfH2/W2mw2jRgxQtu2bZMkFRcXq6mpyaXG4XAoKirKrNm+fbvsdrsZciRp6NChstvtLjVRUVFmyJGkhIQENTQ0qLi4+Bt7b2hoUE1NjcsLAABYl1tBJyYmRv/zP/+jv/71r1q1apUqKys1bNgwnTx5UpWVlZKkkJAQl21CQkLMdZWVlfLx8VGPHj1arQkODm5x7ODgYJea84/To0cP+fj4mDUXMm/ePPO6H7vdrrCwMHdOHwAAdDJuBZ2xY8fqBz/4gQYNGqS4uDht3rxZ0ldfUZ3j4eHhso1hGC2Wne/8mgvVt6XmfLNnz5bT6TRfR44cabUvAADQuV3Sz8v9/f01aNAgHTp0yLxu5/wZlaqqKnP2JTQ0VI2Njaqurm615vjx4y2OdeLECZea849TXV2tpqamFjM9X2ez2RQQEODyAgAA1nVJQaehoUFlZWXq3bu3+vXrp9DQUBUUFJjrGxsbVVhYqGHDhkmSoqOj5e3t7VJTUVGhffv2mTWxsbFyOp3atWuXWbNz5045nU6Xmn379qmiosKsyc/Pl81mU3R09KWcEgAAsBC3fnWVmZmp8ePH64YbblBVVZWeffZZ1dTU6JFHHpGHh4fS09OVnZ2t8PBwhYeHKzs7W35+fkpOTpYk2e12TZ48WRkZGQoMDFTPnj2VmZlpfhUmSQMHDtSYMWM0ZcoUrVy5UpI0depUJSYmKiIiQpIUHx+vyMhIpaSkaOHChTp16pQyMzM1ZcoUZmkAAIDJraBz9OhRPfjgg/rss8/Uq1cvDR06VDt27FDfvn0lSbNmzVJ9fb1SU1NVXV2tmJgY5efnq1u3buY+li5dKi8vL02YMEH19fUaNWqU1qxZI09PT7Nm/fr1SktLM3+dlZSUpJycHHO9p6enNm/erNTUVA0fPly+vr5KTk7WokWLLmkwAACAtXgYhmF0dBMdpaamRna7XU6n88rMBL097/Lv80q7e3ZHdwAAQKvc+fzmWVcAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyLinozJs3Tx4eHkpPTzeXGYahrKwsORwO+fr6auTIkdq/f7/Ldg0NDZoxY4aCgoLk7++vpKQkHT161KWmurpaKSkpstvtstvtSklJ0enTp11qDh8+rPHjx8vf319BQUFKS0tTY2PjpZwSAACwkDYHnd27d+uFF17QLbfc4rJ8wYIFWrJkiXJycrR7926FhoZq9OjRqq2tNWvS09O1adMm5ebmqqioSGfOnFFiYqKam5vNmuTkZJWUlCgvL095eXkqKSlRSkqKub65uVnjxo1TXV2dioqKlJubq40bNyojI6OtpwQAACymTUHnzJkzeuihh7Rq1Sr16NHDXG4YhpYtW6Y5c+bo/vvvV1RUlF5++WV9/vnn2rBhgyTJ6XTqxRdf1OLFixUXF6fBgwdr3bp12rt3r958801JUllZmfLy8vT73/9esbGxio2N1apVq/SXv/xFBw4ckCTl5+ertLRU69at0+DBgxUXF6fFixdr1apVqqmpudRxAQAAFtCmoPPYY49p3LhxiouLc1leXl6uyspKxcfHm8tsNptGjBihbdu2SZKKi4vV1NTkUuNwOBQVFWXWbN++XXa7XTExMWbN0KFDZbfbXWqioqLkcDjMmoSEBDU0NKi4uPiCfTc0NKimpsblBQAArMvL3Q1yc3P1j3/8Q7t3726xrrKyUpIUEhLisjwkJESffvqpWePj4+MyE3Su5tz2lZWVCg4ObrH/4OBgl5rzj9OjRw/5+PiYNeebN2+e5s6dezGnCQAALMCtGZ0jR47o8ccf17p169S1a9dvrPPw8HB5bxhGi2XnO7/mQvVtqfm62bNny+l0mq8jR4602hMAAOjc3Ao6xcXFqqqqUnR0tLy8vOTl5aXCwkItX75cXl5e5gzL+TMqVVVV5rrQ0FA1Njaqurq61Zrjx4+3OP6JEydcas4/TnV1tZqamlrM9Jxjs9kUEBDg8gIAANblVtAZNWqU9u7dq5KSEvM1ZMgQPfTQQyopKVH//v0VGhqqgoICc5vGxkYVFhZq2LBhkqTo6Gh5e3u71FRUVGjfvn1mTWxsrJxOp3bt2mXW7Ny5U06n06Vm3759qqioMGvy8/Nls9kUHR3dhqEAAABW49Y1Ot26dVNUVJTLMn9/fwUGBprL09PTlZ2drfDwcIWHhys7O1t+fn5KTk6WJNntdk2ePFkZGRkKDAxUz549lZmZqUGDBpkXNw8cOFBjxozRlClTtHLlSknS1KlTlZiYqIiICElSfHy8IiMjlZKSooULF+rUqVPKzMzUlClTmKkBAACS2nAx8reZNWuW6uvrlZqaqurqasXExCg/P1/dunUza5YuXSovLy9NmDBB9fX1GjVqlNasWSNPT0+zZv369UpLSzN/nZWUlKScnBxzvaenpzZv3qzU1FQNHz5cvr6+Sk5O1qJFiy73KQEAgE7KwzAMo6Ob6Cg1NTWy2+1yOp1XZhbo7XmXf59X2t2zO7oDAABa5c7nN8+6AgAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAlkXQAQAAluVW0FmxYoVuueUWBQQEKCAgQLGxsdq6dau53jAMZWVlyeFwyNfXVyNHjtT+/ftd9tHQ0KAZM2YoKChI/v7+SkpK0tGjR11qqqurlZKSIrvdLrvdrpSUFJ0+fdql5vDhwxo/frz8/f0VFBSktLQ0NTY2unn6AADAytwKOn369NFzzz2n999/X++//77uueceff/73zfDzIIFC7RkyRLl5ORo9+7dCg0N1ejRo1VbW2vuIz09XZs2bVJubq6Kiop05swZJSYmqrm52axJTk5WSUmJ8vLylJeXp5KSEqWkpJjrm5ubNW7cONXV1amoqEi5ubnauHGjMjIyLnU8AACAhXgYhmFcyg569uyphQsX6ic/+YkcDofS09P15JNPSvpq9iYkJETz58/XtGnT5HQ61atXL61du1YTJ06UJB07dkxhYWHasmWLEhISVFZWpsjISO3YsUMxMTGSpB07dig2NlYffvihIiIitHXrViUmJurIkSNyOBySpNzcXE2aNElVVVUKCAi4qN5rampkt9vldDovehu3vD3v8u/zSrt7dkd3AABAq9z5/G7zNTrNzc3Kzc1VXV2dYmNjVV5ersrKSsXHx5s1NptNI0aM0LZt2yRJxcXFampqcqlxOByKiooya7Zv3y673W6GHEkaOnSo7Ha7S01UVJQZciQpISFBDQ0NKi4u/saeGxoaVFNT4/ICAADW5XbQ2bt3r6677jrZbDY9+uij2rRpkyIjI1VZWSlJCgkJcakPCQkx11VWVsrHx0c9evRotSY4OLjFcYODg11qzj9Ojx495OPjY9ZcyLx588zrfux2u8LCwtw8ewAA0Jm4HXQiIiJUUlKiHTt26Gc/+5keeeQRlZaWmus9PDxc6g3DaLHsfOfXXKi+LTXnmz17tpxOp/k6cuRIq30BAIDOze2g4+Pjo+985zsaMmSI5s2bp1tvvVW/+c1vFBoaKkktZlSqqqrM2ZfQ0FA1Njaqurq61Zrjx4+3OO6JEydcas4/TnV1tZqamlrM9HydzWYzfzF27gUAAKzrku+jYxiGGhoa1K9fP4WGhqqgoMBc19jYqMLCQg0bNkySFB0dLW9vb5eaiooK7du3z6yJjY2V0+nUrl27zJqdO3fK6XS61Ozbt08VFRVmTX5+vmw2m6Kjoy/1lAAAgEV4uVP8i1/8QmPHjlVYWJhqa2uVm5urd955R3l5efLw8FB6erqys7MVHh6u8PBwZWdny8/PT8nJyZIku92uyZMnKyMjQ4GBgerZs6cyMzM1aNAgxcXFSZIGDhyoMWPGaMqUKVq5cqUkaerUqUpMTFRERIQkKT4+XpGRkUpJSdHChQt16tQpZWZmasqUKczSAAAAk1tB5/jx40pJSVFFRYXsdrtuueUW5eXlafTo0ZKkWbNmqb6+XqmpqaqurlZMTIzy8/PVrVs3cx9Lly6Vl5eXJkyYoPr6eo0aNUpr1qyRp6enWbN+/XqlpaWZv85KSkpSTk6Oud7T01ObN29Wamqqhg8fLl9fXyUnJ2vRokWXNBgAAMBaLvk+Op0Z99G5AO6jAwC4yrXLfXQAAACudgQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWQQdAABgWV4d3QA6zvZPTrZYtuPLgx3QyTd7YvSAjm4BANCJEXRwVVtacHUFrwshjAHA1YuvrgAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGW5FXTmzZunO+64Q926dVNwcLDuvfdeHThwwKXGMAxlZWXJ4XDI19dXI0eO1P79+11qGhoaNGPGDAUFBcnf319JSUk6evSoS011dbVSUlJkt9tlt9uVkpKi06dPu9QcPnxY48ePl7+/v4KCgpSWlqbGxkZ3TgkAAFiYW0GnsLBQjz32mHbs2KGCggJ9+eWXio+PV11dnVmzYMECLVmyRDk5Odq9e7dCQ0M1evRo1dbWmjXp6enatGmTcnNzVVRUpDNnzigxMVHNzc1mTXJyskpKSpSXl6e8vDyVlJQoJSXFXN/c3Kxx48aprq5ORUVFys3N1caNG5WRkXEp4wEAACzEwzAMo60bnzhxQsHBwSosLNRdd90lwzDkcDiUnp6uJ598UtJXszchISGaP3++pk2bJqfTqV69emnt2rWaOHGiJOnYsWMKCwvTli1blJCQoLKyMkVGRmrHjh2KiYmRJO3YsUOxsbH68MMPFRERoa1btyoxMVFHjhyRw+GQJOXm5mrSpEmqqqpSQEDAt/ZfU1Mju90up9N5UfVue3ve5d/nZbT9k5Mtlu24YWoHdNK5PTF6QEe3AADXFHc+vy/pGh2n0ylJ6tmzpySpvLxclZWVio+PN2tsNptGjBihbdu2SZKKi4vV1NTkUuNwOBQVFWXWbN++XXa73Qw5kjR06FDZ7XaXmqioKDPkSFJCQoIaGhpUXFx8wX4bGhpUU1Pj8gIAANbV5qBjGIZmzpyp733ve4qKipIkVVZWSpJCQkJcakNCQsx1lZWV8vHxUY8ePVqtCQ4ObnHM4OBgl5rzj9OjRw/5+PiYNeebN2+eec2P3W5XWFiYu6cNAAA6kTYHnenTp+uDDz7QK6+80mKdh4eHy3vDMFosO9/5NReqb0vN182ePVtOp9N8HTlypNWeAABA59amoDNjxgy98cYbevvtt9WnTx9zeWhoqCS1mFGpqqoyZ19CQ0PV2Nio6urqVmuOHz/e4rgnTpxwqTn/ONXV1Wpqamox03OOzWZTQECAywsAAFiXW0HHMAxNnz5df/rTn/S3v/1N/fr1c1nfr18/hYaGqqCgwFzW2NiowsJCDRs2TJIUHR0tb29vl5qKigrt27fPrImNjZXT6dSuXbvMmp07d8rpdLrU7Nu3TxUVFWZNfn6+bDaboqOj3TktAABgUV7uFD/22GPasGGD/vznP6tbt27mjIrdbpevr688PDyUnp6u7OxshYeHKzw8XNnZ2fLz81NycrJZO3nyZGVkZCgwMFA9e/ZUZmamBg0apLi4OEnSwIEDNWbMGE2ZMkUrV66UJE2dOlWJiYmKiIiQJMXHxysyMlIpKSlauHChTp06pczMTE2ZMoWZGgAAIMnNoLNixQpJ0siRI12Wr169WpMmTZIkzZo1S/X19UpNTVV1dbViYmKUn5+vbt26mfVLly6Vl5eXJkyYoPr6eo0aNUpr1qyRp6enWbN+/XqlpaWZv85KSkpSTk6Oud7T01ObN29Wamqqhg8fLl9fXyUnJ2vRokVuDQAAALCuS7qPTmfHfXS4j87lwH10AKB9tdt9dAAAAK5mBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZXh3dAK4uQw+/0NEtuG3HDVM7ugUAwFWKGR0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZXh3dAHCphh5+oWMbeDvQ/W3unn35+wAAtMCMDgAAsCyCDgAAsCyCDgAAsCy3g87f//53jR8/Xg6HQx4eHnr99ddd1huGoaysLDkcDvn6+mrkyJHav3+/S01DQ4NmzJihoKAg+fv7KykpSUePHnWpqa6uVkpKiux2u+x2u1JSUnT69GmXmsOHD2v8+PHy9/dXUFCQ0tLS1NjY6O4pAQAAi3I76NTV1enWW29VTk7OBdcvWLBAS5YsUU5Ojnbv3q3Q0FCNHj1atbW1Zk16ero2bdqk3NxcFRUV6cyZM0pMTFRzc7NZk5ycrJKSEuXl5SkvL08lJSVKSUkx1zc3N2vcuHGqq6tTUVGRcnNztXHjRmVkZLh7SgAAwKI8DMMw2ryxh4c2bdqke++9V9JXszkOh0Pp6el68sknJX01exMSEqL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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# histogramme des probas et des probas ajustées\n", - "\n", - "plt.hist(X_test[\"score\"], label = \"score\", alpha=0.5)\n", - "plt.hist(X_test[\"score_adjusted\"], label=\"adjusted score\", alpha=0.5)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 557, - "id": "646a8e9b-99dc-4e06-ab5a-42b21de6917b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.32260447885447885\n", - "0.06268731268731269\n", - "0.14246170496170496\n" - ] - } - ], - "source": [ - "# on passe de 32% de scores supérieurs à 1/2 à 6%\n", - "\n", - "print((X_test[\"score\"]>0.5).mean())\n", - "print((X_test[\"score_adjusted\"]>0.5).mean())\n", - "print(y_test.mean()[\"y_has_purchased\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 437, - "id": "3a60fa17-c960-4702-baa1-a7dc6cd227b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nombre de clients ayant acheté : 13690.0\n", - "somme des scores ajustés : 13690.000010280266\n" - ] - } - ], - "source": [ - "# on vérifie que cette correction a permis d'avoir des résultats cohérents\n", - "\n", - "print(\"nombre de clients ayant acheté :\",y_sum)\n", - "print(\"somme des scores ajustés :\", X_test[\"score_adjusted\"].sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 440, - "id": "3a7479a5-b6a3-47a2-8f78-4259746498f1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE for score : 0.15637498623391197\n", - "MSE for ajusted score : 0.08877832832116543\n" - ] - } - ], - "source": [ - "# cet ajustement permet de plus de réduire drastiquement le MSE \n", - "\n", - "MSE_score = ((X_test[\"score\"]-X_test[\"has_purchased\"])**2).mean()\n", - "MSE_ajusted_score = ((X_test[\"score_adjusted\"]-X_test[\"has_purchased\"])**2).mean()\n", - "print(f\"MSE for score : {MSE_score}\")\n", - "print(f\"MSE for ajusted score : {MSE_ajusted_score}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 518, - "id": "fd963072-26f7-4805-84db-5612a40dcafd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " score score_adjusted has_purchased\n", - "quartile \n", - "1 0.169233 0.033442 0.026780\n", - "2 0.360811 0.088246 0.117452\n", - "3 0.626785 0.222962 0.209332\n", - "4 0.902055 0.652198 0.666549" - ] - }, - "execution_count": 518, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on est bcp plus proche des probas d'achat moyennes\n", - "X_test.groupby(\"quartile\")[[\"score\",\"score_adjusted\", \"has_purchased\"]].mean()" - ] - }, - { - "cell_type": "markdown", - "id": "0552d1c9-7edd-44ed-9954-0bc7810ec2f3", - "metadata": {}, - "source": [ - "Etape suivante : on peut donc calculer le potentiel de CA de chaque segment" - ] - }, - { - "cell_type": "code", - "execution_count": 473, - "id": "86f0740a-80b5-435b-a1ee-ae59d9143666", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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96096 rows × 32 columns

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nb_ticketstotal_amountnb_tickets_expectedtotal_amount_expected
quartile
10.0173800.4751410.0005900.016112
22.08581049.7017320.1345663.298096
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" - ], - "text/plain": [ - " nb_tickets total_amount nb_tickets_expected total_amount_expected\n", - "quartile \n", - "1 0.017380 0.475141 0.000590 0.016112\n", - "2 2.085810 49.701732 0.134566 3.298096\n", - "3 3.118100 88.811284 0.478898 13.258736\n", - "4 46.046362 2002.607230 26.753314 1246.363503" - ] - }, - "execution_count": 474, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# potentiel de CA par segment, et comparaison avec le CA passé/1.5\n", - "\n", - "X_test.groupby(\"quartile\")[[\"nb_tickets\",\"total_amount\",\"nb_tickets_expected\",\"total_amount_expected\"]].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 519, - "id": "f7052cc7-054b-4b9d-935e-81611b1f6a61", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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quartilenb_ticketstotal_amount
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quartilesizenb_ticketstotal_amountnb_tickets_expectedtotal_amount_expectedtotal_amount_recovered
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48520.00.6521980.2014860.3320490.4730520.6402950.8276441.000000
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" - ], - "text/plain": [ - " count mean std min 25% 50% 75% \\\n", - "quartile \n", - "1 47871.0 0.033442 0.013951 0.019591 0.019867 0.023766 0.048136 \n", - "2 17224.0 0.088246 0.028737 0.052283 0.060481 0.082054 0.115089 \n", - "3 22481.0 0.222962 0.048039 0.141993 0.183323 0.219550 0.268865 \n", - "4 8520.0 0.652198 0.201486 0.332049 0.473052 0.640295 0.827644 \n", - "\n", - " max \n", - "quartile \n", - "1 0.052262 \n", - "2 0.141983 \n", - "3 0.331754 \n", - "4 1.000000 " - ] - }, - "execution_count": 539, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# la part de CA recouvrée est tjs supérieure à la part de clients qui reviennent\n", - "# ça semble logique : ceux qui reviennent sont aussi ceux qui consomment le plus \n", - "# se voit srtt sur dernier quartile : on récupère 65% des clients (avec probas ajustées) mais 93% du CA \n", - "X_test.groupby(\"quartile\")[\"score_adjusted\"].describe()" - ] - }, - { - "cell_type": "markdown", - "id": "59a0850a-c40d-472a-9361-e96840e2b046", - "metadata": {}, - "source": [ - "## Study potential of each segment" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "1773bac2-ab5e-4bca-bda5-aa13e36991e5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KFSuMm5ubiYuLM++//76RZN57772L1vliLtbWv//+eyPJDBky5JLLlytXznh5eZk9e/ZY006dOmWCgoLME088cdnttmrVyuH9fanPaz8/PxMXF1eAvTunXbt2pkyZMiY9Pd1h+rPPPmu8vLzMP//8Y4y5dDvKS9euXY23t7fDD9azZ8+aqlWrOnz+792717i5uTn8w8QYY44dO2bCwsJMly5dLrmd/P4AP19WVpY5c+aM+fDDD42rq6u1j8bk/3316quvGklm8eLFl9zWhcfriSeeMH5+fg5twhhj3nzzTSPJ+oGd8wM3Ojra4Z+A69atM5LM3LlzrWk57+NPPvnEYZ0dO3Y0VapUsZ4X5P1Wo0YNh++QHAX5LrlQQb6zrjRwFfX3tDH/1zaWLl3qULYgx+rC304Faf8X+7zOr7Nnz5rMzEwTFRVlnn/+eWt6ftpyQfYxr+0eP37c+Pr6OvxD6tNPPy3we/daxSmFNzhjzCXn16lTRx4eHnr88cf1wQcf5Ooez6/77rsv32Vr1Kih2rVrO0zr1q2bjh49qh9//LFQ28+v7777Tq1atVJERITD9F69eunkyZNavXq1w/ROnTo5PK9Vq5YkOXR7X+jEiRNav369Onfu7DAik7+/v+666y6Hsl9++aVsNpv+9a9/6ezZs9YjLCxMtWvXtk6JqFSpkgIDA/Xiiy/q3Xff1bZt2/K1v6tXr9bp06fVvXt3h+lNmjRRuXLlctWlZs2aqlOnjkNd2rVrl6/TMz799FM1bdpUfn5+cnNzk7u7u2bMmKHt27dfcrmGDRvqp59+0tNPP61vv/1WR48ezde+5bhwhLdq1apJOnftwIXT//nnH+u0wu+++06Scl3w/cADD8jX11dLly61pvXr10/JyclauXKlpHOny8yaNUs9e/aUn5+fpPwfyxxhYWFq2LChw7RatWpdsm1dze1c6ML3+JdffqkSJUrorrvucqhHnTp1FBYWdsn2smPHDh04cEDdunVzOHWmXLlyatKkSZFt51KaNm2qN954QxMnTtRTTz2lf/3rX+rTp0++ls1PW885BeqZZ5657Prq1KmjsmXLWs+9vLxUuXLlXMfo3XffVb169eTl5WVtd+nSpZd9j+Vo2LCh4uPj9frrr2vNmjUXPXXvfKdPn9bSpUt17733ysfHx+EYdOzYUadPn851ulB+LVu2TK1atbKuMZYkV1dXde3a1aHct99+q7Nnz+rhhx922L6Xl5eaN29eZCMybtq0SZ06dVJwcLBcXV3l7u6uhx9+WFlZWfr1118dyubnffXNN9+ocuXKDgNX5ceXX36pli1bKjw83GF/O3ToIElKSkpyKH/nnXfK1dXVoR5S7u8pm82W6zvowjoXxfvtSr5LCvKddSWc8T2dIzAwUHfccUee283vsTpfYdp/fn+TnT17ViNGjFD16tXl4eEhNzc3eXh46Lfffsv1eZbftpyffTx+/LhefPFFVapUSW5ubnJzc5Ofn59OnDiR78+z6w2B6wZ24sQJ/f333woPD79omYoVK2rJkiUKCQnRM888o4oVK6pixYqXvX7mQqVLl8532bCwsItO+/vvvwu03YL6+++/86xrzmt04faDg4Mdnnt6ekqSTp06ddFtpKWlKTs7+5L7mePgwYMyxig0NFTu7u4OjzVr1uivv/6SJNntdiUlJalOnTr697//rRo1aig8PFxDhw695I+mnP3Jb102b96cqx7+/v4yxlh1ycu8efPUpUsX3XLLLZo9e7ZWr16t9evXq3fv3pe978rgwYP15ptvas2aNerQoYOCg4PVqlUrbdiw4ZLL5bhw5CsPD49LTs+pz99//y03NzfrmpccNptNYWFhDm3h7rvvVvny5a1rVuLj43XixAmHH9P5PZY5Lmxb0rn2dam2dTW3cz4fH59cI0sePHhQR44ckYeHR656pKamXrK9FLRdFnY7l9O9e3d5eHgoIyNDAwcOzNcy+W3rhw8flqura577eKH8HKPx48frqaeeUqNGjfT5559rzZo1Wr9+vdq3b5/vY/nxxx+rZ8+eeu+99xQTE6OgoCA9/PDDSk1Nvegyf//9t86ePau333471+vfsWNHSSr0Mfj777/z3QYk6dZbb81Vh48//viK2kCOvXv36rbbbtOff/6pt956Sz/88IPWr19vvecvfI3zc8wOHz5cqMEPDh48qC+++CLXvuZcL3q59/jFvqd8fHxyDcvu6enp0G6L4v12Jd8lBflsuBLO+J7OcanfQ4X5TVHQ9p/X5/XFvPDCC3r55Zd1zz336IsvvtDatWu1fv161a5du9BtOT/72K1bN02aNEmPPvqovv32W61bt07r169XqVKlCvTddD1hlMIb2FdffaWsrKzLDg1822236bbbblNWVpY2bNigt99+W3FxcQoNDdWDDz6Yr20V5J4ReX2550zLeaPmfClceDHvlX6xBgcHKyUlJdf0AwcOSJJKlix5ReuXzv13y2azXXI/c5QsWVI2m00//PCD9aF0vvOnRUdHKyEhQcYYbd68WfHx8Xr11Vfl7e2tl156Kc+65LyeF6vL+Rc5lyxZUt7e3rku1j9//sXMnj1bkZGR+vjjjx3awoXHLy9ubm564YUX9MILL+jIkSNasmSJ/v3vf6tdu3bat2+f00ZXCg4O1tmzZ3X48GGH0GWMUWpqqjUAgCS5uLjomWee0b///W+NGzdOkydPVqtWrVSlShWrTEGO5ZW4Wts5X17v75IlSyo4OFiJiYl5LuPv73/R9V2uXRZ2O56ennm2ubz+kZOVlaXu3bsrMDBQnp6e6tOnj1auXGkF84vJb1svVaqUsrKylJqaWqB/SF1quy1atNCUKVMcph87dizf6yhZsqQmTpyoiRMnau/evVq4cKFeeuklHTp06KKvb2BgoFxdXdWjR4+L9tZFRkbmf0fOExwcnO82IEmfffZZkfZynG/BggU6ceKE5s2b57CNK7mnWKlSpS47qFFeSpYsqVq1aumNN97Ic/6l/ol6pa7kfX3+Ogr7XVKQ76wr+Z3grO9pqWC/h/KjoO2/INufPXu2Hn74YY0YMcJh+l9//aUSJUpYzwvblvOSnp6uL7/8UkOHDnX47ZKRkaF//vmnSLZxLSJw3aD27t2rAQMGyG6364knnsjXMq6urmrUqJGqVq2qOXPm6Mcff9SDDz6Yr//AFMTWrVv1008/OZxW+NFHH8nf31/16tWTJOtDdfPmzQ4/avMa3asg/61v1aqV5s+frwMHDjh8aX344Yfy8fEpkuF5fX191bBhQ82bN09jx461vhSOHTumL774wqFsbGysRo0apT///FNdunTJ1/ptNptq166tCRMmKD4+/pKnYTZu3FheXl6aM2eOwykGq1at0p49exy+vGJjYzVixAgFBwcX+AeUzWaTh4eHwwd9ampqniO3XUqJEiV0//33688//1RcXJx2796t6tWrF2gd+dWqVSuNGTNGs2fP1vPPP29N//zzz3XixIlcIzs9+uijGjZsmLp3764dO3Zo9OjRDvMLcywLwxnbKcx7PDY2VgkJCcrKylKjRo0KtL0qVaqodOnSmjt3rl544QWr3ezZs0erVq1yeG8WZDvly5fX5s2bHaZ99913uUanlKShQ4fqhx9+0KJFi+Tr66vbb79dAwcOvGzvfn7beocOHTRy5EhNmTJFr7766iXXmR82my3XD7vNmzdr9erVDqdI5/dYli1bVs8++6yWLl1qnSqbFx8fH7Vs2VKbNm1SrVq1LhlIC9qOWrZsqYULF+rgwYPWaYVZWVn6+OOPHcq1a9dObm5u+v333wt0+npB5BzP819jY4ymT59e6HV26NBBr7zyir777ruLnmKWl9jYWH399deqWLGiAgMDC739wijI++1i371X8l1SkO+sgvxOuJCzv6eLkjPbf16fK1999ZX+/PNPVapUyZpW2LZ8sW0aY3Jt97333lNWVpbDtKL+/VmcCFw3gC1btljn9B46dEg//PCDZs6cKVdXV82fPz/XKVPne/fdd/Xdd9/pzjvvVNmyZXX69Gnrv1I55+r6+/urXLly+t///qdWrVopKChIJUuWLPRN6MLDw9WpUycNGzZMpUuX1uzZs7V48WKNHj3a6s249dZbVaVKFQ0YMEBnz55VYGCg5s+frxUrVuRaX3R0tObNm6cpU6aofv36cnFxcbgv2fmGDh1qnR//yiuvKCgoSHPmzNFXX32lMWPGyG63F2qfLvTaa6+pffv2atOmjfr376+srCyNHj1avr6+Dv/Badq0qR5//HE98sgj2rBhg26//Xb5+voqJSVFK1asUHR0tJ566il9+eWXmjx5su655x5VqFBBxhjNmzdPR44cUZs2bS5aj8DAQA0YMECvv/66Hn30UT3wwAPat2+fhg0bluu0ibi4OH3++ee6/fbb9fzzz6tWrVrKzs7W3r17tWjRIvXv3/+iX8CxsbGaN2+enn76ad1///3at2+fXnvtNZUuXVq//fbbJV+ru+66y7qXXKlSpbRnzx5NnDhR5cqVU1RUVAFe9YJp06aN2rVrpxdffFFHjx5V06ZNtXnzZg0dOlR169ZVjx49HMqXKFFCDz/8sKZMmaJy5crlOs8/v8fySjljOxUrVpS3t7fmzJmjatWqyc/PT+Hh4Zf8T/qDDz6oOXPmqGPHjurXr58aNmwod3d37d+/X8uWLdPdd9+te++9N89lXVxc9Nprr+nRRx/Vvffeq8cee0xHjhzJs10WZDs9evTQyy+/rFdeeUXNmzfXtm3bNGnSpFzv68WLF2vkyJF6+eWXrWA9cuRIDRgwQC1atLhovaX8t/XbbrtNPXr00Ouvv66DBw8qNjZWnp6e2rRpk3x8fPTcc89d+qDksd3XXntNQ4cOVfPmzbVjxw69+uqrioyMdBgq/GKf14GBgWrZsqW6deumqlWryt/fX+vXr1diYqI6d+58yW2/9dZbatasmW677TY99dRTKl++vI4dO6adO3fqiy++sK6HLGg7+s9//qOFCxfqjjvu0CuvvCIfHx+98847uYYOL1++vF599VUNGTJEf/zxh9q3b6/AwEAdPHhQ69atk6+vr3Wz58Jq06aNPDw89NBDD2nQoEE6ffq0pkyZorS0tEKvMy4uTh9//LHuvvtuvfTSS2rYsKFOnTqlpKQkxcbGqmXLlnku9+qrr2rx4sVq0qSJ+vbtqypVquj06dPavXu3vv76a7377rtOu8dlQd5vOWddfPzxx6pQoYK8vLwUHR19Rd8lBfnOKsjvhLwU9fe0sziz/cfGxio+Pl5Vq1ZVrVq1tHHjRo0dOzZX+ypsW85LQECAbr/9do0dO9b6LZmUlKQZM2Y49KpJ5+4zK0nTpk2Tv7+/vLy8FBkZmecpvde84hmrA0UhZ4SenIeHh4cJCQkxzZs3NyNGjMhz6MwLR79ZvXq1uffee025cuWMp6enCQ4ONs2bNzcLFy50WG7JkiWmbt26xtPT00iyRvzKWV/OMJ6X2pYx50bkuvPOO81nn31matSoYTw8PEz58uXN+PHjcy3/66+/mrZt25qAgABTqlQp89xzz5mvvvoq14g1//zzj7n//vtNiRIljM1mc9im8hit6+effzZ33XWXsdvtxsPDw9SuXTvXqFo5oxx9+umnDtNzRuHJzyhcCxcuNLVq1TIeHh6mbNmyZtSoUXm+JsYY8/7775tGjRoZX19f4+3tbSpWrGgefvhhs2HDBmPMuSGhH3roIVOxYkXj7e1t7Ha7adiwoYmPj79sPbKzs83IkSNNRESE8fDwMLVq1TJffPGFad68ea4Rpo4fP27+85//mCpVqhgPDw9rKOjnn3/eYSSxvEZ9GzVqlClfvrzx9PQ01apVM9OnT79oGzh/2XHjxpkmTZqYkiVLWq9Vnz59zO7duy+5Xxc7RhcbvTOvtnrq1Cnz4osvmnLlyhl3d3dTunRp89RTT5m0tLQ8t7l8+XIjyYwaNeqi9brcsTTm3EhWNWrUyLVsz549LzriprO3M3fuXFO1alXj7u7u8L7p2bOn8fX1zbMOZ86cMW+++aapXbu28fLyMn5+fqZq1armiSeeML/99ttl9+G9994zUVFRxsPDw1SuXNm8//77edYtv9vJyMgwgwYNMhEREcbb29s0b97cJCcnO7S5AwcOmJCQEHPHHXeYrKwsa9ns7Gxz1113mRIlSuQa9exC+W3rWVlZZsKECaZmzZrW+ykmJsZ88cUXVpmcz8QLXfj+zMjIMAMGDDC33HKL8fLyMvXq1TMLFizI8/XK6/P69OnT5sknnzS1atUyAQEBxtvb21SpUsUMHTrUGh32Unbt2mV69+5tbrnlFuPu7m5KlSplmjRpYl5//XWHchdrRxezcuVK07hxY+Pp6WnCwsLMwIEDzbRp0/IcfW7BggWmZcuWJiAgwHh6eppy5cqZ+++/3yxZsuSS28jvqHVffPGF1cZuueUWM3DgQPPNN9/kWrYg76u0tDTTr18/U7ZsWePu7m5CQkLMnXfe6TDkdl6v0+HDh03fvn1NZGSkcXd3N0FBQaZ+/fpmyJAh5vjx48aY//s+Ov+WKhdb58Xex3m12/y+33bv3m3atm1r/P39rVt05Mjvd0leCvKdld/fCRdTlN/Txly8bRTkWF1s+/lp/5f6vM5LWlqa6dOnjwkJCTE+Pj6mWbNm5ocffsjztb5cWy7IPu7fv9/cd999JjAw0Pj7+5v27dubLVu25PnbYuLEiSYyMtK4uroWaBTUa43NmMsMYwcAkCT1799fU6ZM0b59+67P/7ABAICrjlMKAeAy1qxZo19//VWTJ0/WE088QdgCAAD5Rg8XAFyGzWaTj4+POnbsqJkzZ1r33gIAALgcergA4DL4vxQAACgsbnwMAAAAAE5C4AIAAAAAJyFwAQAAAICTcA1XPmVnZ+vAgQPy9/e37kgPAAAA4OZjjNGxY8cUHh4uF5dL92ERuPLpwIEDioiIKO5qAAAAALhG7Nu3T2XKlLlkGQJXPvn7+0s696IGBAQUc20AAAAAFJejR48qIiLCygiXQuDKp5zTCAMCAghcAAAAAPJ1qRGDZgAAAACAkxC4AAAAAMBJCFwAAAAA4CRcwwUAAFBEsrKydObMmeKuBoAi4OHhcdkh3/ODwAUAAHCFjDFKTU3VkSNHirsqAIqIi4uLIiMj5eHhcUXrIXABAABcoZywFRISIh8fn3yNXAbg2pWdna0DBw4oJSVFZcuWvaL3NIELAADgCmRlZVlhKzg4uLirA6CIlCpVSgcOHNDZs2fl7u5e6PUwaAYAAMAVyLlmy8fHp5hrAqAo5ZxKmJWVdUXrIXABAAAUAU4jBG4sRfWeJnABAAAAgJMQuAAAAAAnGzZsmOrUqVPc1bis3bt3y2azKTk5Od/LtGjRQnFxcU6r0/WOQTMAAACcZMLiX6/atp5vU/mqbQtA/tHDBQAAgKsiKytL2dnZxV2NIpeZmVncVcA1jMAFAABwE0pMTFSzZs1UokQJBQcHKzY2Vr///rs1PyYmRi+99JLDMocPH5a7u7uWLVsm6VzQGDRokG655Rb5+vqqUaNGWr58uVU+Pj5eJUqU0Jdffqnq1avL09NTe/bs0fr169WmTRuVLFlSdrtdzZs3148//uiwrV9++UXNmjWTl5eXqlevriVLlshms2nBggVWmT///FNdu3ZVYGCggoODdffdd2v37t0X3efly5fLZrPpq6++Uu3ateXl5aVGjRrp559/tsrkderfxIkTVb58eet5r169dM8992jkyJEKDw9X5crnehf379+vBx98UEFBQfL19VWDBg20du1ah3XNmjVL5cuXl91u14MPPqhjx47l+5hkZmbq2WefVenSpeXl5aXy5ctr5MiR1vz09HQ9/vjjCgkJUUBAgO644w799NNPF309JGndunWqW7euvLy81KBBA23atClXmW3btqljx47y8/NTaGioevToob/++uui65w9e7YaNGggf39/hYWFqVu3bjp06JCkczcJr1Spkt58802HZbZs2SIXFxeH/b1RELgAAABuQidOnNALL7yg9evXa+nSpXJxcdG9995r9UB1795dc+fOlTHGWubjjz9WaGiomjdvLkl65JFHtHLlSiUkJGjz5s164IEH1L59e/3222/WMidPntTIkSP13nvvaevWrQoJCdGxY8fUs2dP/fDDD1qzZo2ioqLUsWNHK3xkZ2frnnvukY+Pj9auXatp06ZpyJAhDvU/efKkWrZsKT8/P33//fdasWKF/Pz81L59+8v2OA0cOFBvvvmm1q9fr5CQEHXq1Mka3j+/li5dqu3bt2vx4sX68ssvdfz4cTVv3lwHDhzQwoUL9dNPP2nQoEEOPXq///67FixYoC+//FJffvmlkpKSNGrUqHwfk//+979auHChPvnkE+3YsUOzZ8+2gqAxRnfeeadSU1P19ddfa+PGjapXr55atWqlf/75J899OHHihGJjY1WlShVt3LhRw4YN04ABAxzKpKSkqHnz5qpTp442bNigxMREHTx4UF26dLnoa5OZmanXXntNP/30kxYsWKBdu3apV69eks6N/Ne7d2/NnDnTYZn3339ft912mypWrJjvY3C94BouAACAm9B9993n8HzGjBkKCQnRtm3bVLNmTXXt2lXPP/+8VqxYodtuu02S9NFHH6lbt25WT8TcuXO1f/9+hYeHS5IGDBigxMREzZw5UyNGjJB07j5lkydPVu3ata1t3XHHHQ7bnjp1qgIDA5WUlKTY2FgtWrRIv//+u5YvX66wsDBJ0htvvKE2bdpYyyQkJMjFxUXvvfeeNXz3zJkzVaJECS1fvlxt27a96L4PHTrUWtcHH3ygMmXKaP78+ZcMERfy9fXVe++9Z92radq0aTp8+LDWr1+voKAgSVKlSpUclsnOzlZ8fLz8/f0lST169NDSpUv1xhtvSLr8Mdm7d6+ioqLUrFkz2Ww2lStXziq7bNky/fzzzzp06JA8PT0lSW+++aYWLFigzz77TI8//niufZgzZ46ysrL0/vvvy8fHRzVq1ND+/fv11FNPWWWmTJmievXqWcdTOheOIiIi9Ouvv1q9e+fr3bu39XeFChX03//+Vw0bNtTx48fl5+enRx55RK+88orWrVunhg0b6syZM5o9e7bGjh2bj1f++kMPFwAAwE3o999/V7du3VShQgUFBAQoMjJSkrR3715JUqlSpdSmTRvNmTNHkrRr1y6tXr1a3bt3lyT9+OOPMsaocuXK8vPzsx5JSUkOp4V5eHioVq1aDts+dOiQnnzySVWuXFl2u112u13Hjx+3tr1jxw5FRERYYUuSGjZs6LCOjRs3aufOnfL397e2HRQUpNOnT1/2tLSYmBjr76CgIFWpUkXbt28v0OsXHR1thS1JSk5OVt26da2wlZfy5ctbYUuSSpcubZ1qJ13+mPTq1UvJycmqUqWK+vbtq0WLFlnLbty4UcePH1dwcLDD8di1a9dFX4/t27erdu3aDjftPv+1yVnvsmXLHNZZtWpVq7552bRpk+6++26VK1dO/v7+atGihcN+lC5dWnfeeafef/99SdKXX36p06dP64EHHrjoa3c9K9YervLly2vPnj25pj/99NN65513ZIzR8OHDNW3aNKWlpalRo0Z65513VKNGDatsRkaGBgwYoLlz5+rUqVNq1aqVJk+erDJlylhl0tLS1LdvXy1cuFCS1KlTJ7399tsqUaKE0/cRAADgWnTXXXcpIiJC06dPV3h4uLKzs1WzZk2H0/G6d++ufv366e2339ZHH32kGjVqWD1V2dnZcnV11caNG+Xq6uqwbj8/P+tvb2/vXDeQ7dWrlw4fPqyJEyeqXLly8vT0VExMjLVtY8xlbzqbnZ2t+vXrW4HwfKVKlSrYi6H/u8mti4uLw2mUkvI83dDX19fhube392W34e7unmub559yeLljUq9ePe3atUvffPONlixZoi5duqh169b67LPPlJ2drdKlSztcQ5fjYr95L9zPvGRnZ+uuu+7S6NGjc80rXbp0rmknTpxQ27Zt1bZtW82ePVulSpXS3r171a5dO4e29eijj6pHjx6aMGGCZs6cqa5duzoEvxtJsQau9evXKysry3q+ZcsWtWnTxkq3Y8aM0fjx4xUfH6/KlSvr9ddfV5s2bbRjxw7rvwNxcXH64osvlJCQoODgYPXv31+xsbEOb/5u3bpp//79SkxMlCQ9/vjj6tGjh7744ourvMcAAADF7++//9b27ds1depU63TBFStW5Cp3zz336IknnlBiYqI++ugj9ejRw5pXt25dZWVl6dChQ9Y68uuHH37Q5MmT1bFjR0nSvn37HAZhqFq1qvbu3auDBw8qNDRU0rnfjeerV6+ePv74Y2uAiIJYs2aNypYtK+ncP+Z//fVXq9emVKlSSk1NdQh9+bknVa1atfTee+/pn3/+uWQv18Xk95gEBASoa9eu6tq1q+6//361b99e//zzj+rVq6fU1FS5ubk5DPBxKdWrV9esWbN06tQpKzCuWbPGoUy9evX0+eefq3z58nJzu3x0+OWXX/TXX39p1KhRioiIkCRt2LAhV7mOHTvK19dXU6ZM0TfffKPvv/8+X3W+HhVr4Lrwvw+jRo1SxYoV1bx5cxljNHHiRA0ZMkSdO3eWdO4c29DQUH300Ud64oknlJ6erhkzZmjWrFlq3bq1pHOjokRERGjJkiVq166dtm/frsTERK1Zs0aNGjWSJE2fPl0xMTHasWOHqlSpcnV3ughdzXt7XAr3/QAA4PqSM6rftGnTVLp0ae3duzfXiITSuV6cu+++Wy+//LK2b9+ubt26WfMqV66s7t276+GHH9a4ceNUt25d/fXXX/ruu+8UHR1tham8VKpUSbNmzVKDBg109OhRDRw40KGHqE2bNqpYsaJ69uypMWPG6NixY9agGTkhqHv37ho7dqzuvvtuvfrqqypTpoz27t2refPmaeDAgQ5nO13o1VdfVXBwsEJDQzVkyBCVLFlS99xzj6RzN/E9fPiwxowZo/vvv1+JiYn65ptvLhvqHnroIY0YMcIavbB06dLatGmTwsPDc52ml5f8HJMJEyaodOnSqlOnjlxcXPTpp58qLCxMJUqUUOvWrRUTE6N77rlHo0ePVpUqVXTgwAF9/fXXuueee9SgQYNc2+zWrZuGDBmiPn366D//+Y92796da/TAZ555RtOnT9dDDz2kgQMHqmTJktq5c6cSEhI0ffr0XL2bZcuWlYeHh95++209+eST2rJli1577bVc23Z1dVWvXr00ePBgVapUKV+v0fXqmrmGKzMzU7Nnz1bv3r1ls9m0a9cupaamOlzw6OnpqebNm2vVqlWSzp1TeubMGYcy4eHhqlmzplVm9erVstvtVtiSpMaNG8tut1tlAAAAbiYuLi5KSEjQxo0bVbNmTT3//PMXHbCge/fu+umnn3TbbbdZvUI5Zs6cqYcfflj9+/dXlSpV1KlTJ61du9bq2biY999/X2lpaapbt6569Oihvn37KiQkxJrv6uqqBQsW6Pjx47r11lv16KOP6j//+Y8kycvLS5Lk4+Oj77//XmXLllXnzp1VrVo19e7dW6dOnbpsOBo1apT69eun+vXrKyUlRQsXLrSux6pWrZomT56sd955R7Vr19a6detyjdyXFw8PDy1atEghISHq2LGjoqOjNWrUqFyB5GLyc0z8/Pw0evRoNWjQQLfeeqt2796tr7/+Wi4uLrLZbPr66691++23q3fv3qpcubIefPBB7d692+olvJCfn5+++OILbdu2TXXr1tWQIUNynToYHh6ulStXKisrS+3atVPNmjXVr18/2e12ubjkjhKlSpVSfHy8Pv30U1WvXl2jRo3KFeJy9OnTR5mZmQ6DbNyIbCY/J29eBZ988om6deumvXv3Kjw8XKtWrVLTpk31559/WiPfSOdOB9yzZ4++/fZbffTRR3rkkUeUkZHhsK62bdsqMjJSU6dO1YgRIxQfH69ff3XsDapcubIeeeQRDR48OM/6ZGRkOKz36NGjioiIUHp6eoG7rZ2FHi4AAIrf6dOntWvXLkVGRlphAEVv5cqVatasmXbu3FnoocOXL1+uli1bKi0tjWv5rwErV65UixYttH///ouGwuJ0qff20aNHZbfb85UNrplh4WfMmKEOHTo4hCtJuS6YzM9FlBeWyav85dYzcuRIDR8+PD9VBwAAQBGbP3++/Pz8FBUVpZ07d6pfv35q2rTpDXmfpptNRkaG9u3bp5dfflldunS5JsNWUbomTincs2ePlixZokcffdSaljMMaGpqqkPZQ4cOWQclLCxMmZmZSktLu2SZgwcP5trm4cOHL3lwBw8erPT0dOuxb9++wu0cAAAACuzYsWN6+umnVbVqVfXq1Uu33nqr/ve//xV3tVAE5s6dqypVqig9PV1jxowp7uo43TURuGbOnKmQkBDdeeed1rTIyEiFhYVp8eLF1rTMzEwlJSWpSZMmkqT69evL3d3doUxKSoq2bNlilYmJiVF6errWrVtnlVm7dq3S09OtMnnx9PRUQECAwwMAAABXx8MPP6zffvtNp0+f1v79+xUfH6/g4OArWmeLFi1kjOF0wmLWq1cvZWVlaePGjbrllluKuzpOV+ynFGZnZ2vmzJnq2bOnw1CTNptNcXFxGjFihKKiohQVFaURI0bIx8fHGiHHbrerT58+6t+/v4KDgxUUFKQBAwYoOjraGrWwWrVqat++vR577DFNnTpV0rnrwGJjY6/rEQoBAAAAXPuKPXAtWbJEe/fuzXN0kkGDBunUqVN6+umnrRsfL1q0yOEO3RMmTJCbm5u6dOli3fg4Pj7eYUSYOXPmqG/fvtZohp06ddKkSZOcv3MAAAAAbmrXzCiF17qCjERytTBKIQAAxY9RCoEbU1GNUnhNXMMFAAAAADciAhcAAAAAOAmBCwAAAACchMAFAACAm94vv/yixo0by8vLS3Xq1Mn3csuXL5fNZtORI0eKtD69evXSPffcU6TrvJbFx8ffsMP1F/sohQAAADesZSOv3rZaDr5627oBDR06VL6+vtqxY4f8/PzyLNOiRQvVqVNHEydOvLqVKwI2m03z58+/qULctYIeLgAAACgzM7O4q1Csfv/9dzVr1kzlypW74hssw9GZM2eKuwrFisAFAABwE2rRooWeffZZvfDCCypZsqTatGkjSdq2bZs6duwoPz8/hYaGqkePHvrrr7+s5bKzszV69GhVqlRJnp6eKlu2rN544w1r/p9//qmuXbsqMDBQwcHBuvvuu7V7925rfs6pcm+++aZKly6t4OBgPfPMMw4/yjMyMjRo0CBFRETI09NTUVFRmjFjhowxqlSpkt58802HfdmyZYtcXFz0+++/57mv2dnZevXVV1WmTBl5enqqTp06SkxMtObbbDZt3LhRr776qmw2m4YNG5ZrHb169VJSUpLeeust2Ww22Ww2h/3auHGjGjRoIB8fHzVp0kQ7duxwWP6LL75Q/fr15eXlpQoVKmj48OE6e/bsxQ/QBRITE9WsWTOVKFFCwcHBio2NddjfzMxMPfvssypdurS8vLxUvnx5jRx5roe1fPnykqR7771XNpvNen6h3bt3y2azKSEhQU2aNJGXl5dq1Kih5cuXW2XyOvVvwYIFstls1vNhw4apTp06ev/991WhQgV5enrKGKMjR47o8ccfV2hoqLy8vFSzZk19+eWXDuv69ttvVa1aNfn5+al9+/ZKSUmx5q1fv15t2rRRyZIlZbfb1bx5c/34448Oyw8bNkxly5aVp6enwsPD1bdvX4fXaNCgQbrlllvk6+urRo0aOeybsxC4AAAAblIffPCB3NzctHLlSk2dOlUpKSlq3ry56tSpow0bNigxMVEHDx5Uly5drGUGDx6s0aNH6+WXX9a2bdv00UcfKTQ0VJJ08uRJtWzZUn5+fvr++++1YsUK64fz+T1oy5Yt0++//65ly5bpgw8+UHx8vOLj4635Dz/8sBISEvTf//5X27dv17vvvis/Pz/ZbDb17t1bM2fOdNiP999/X7fddpsqVqyY536+9dZbGjdunN58801t3rxZ7dq1U6dOnfTbb79JklJSUlSjRg31799fKSkpGjBgQJ7riImJ0WOPPaaUlBSlpKQoIiLCmj9kyBCNGzdOGzZskJubm3r37m3N+/bbb/Wvf/1Lffv21bZt2zR16lTFx8c7BNXLOXHihF544QWtX79eS5culYuLi+69915lZ2dLkv773/9q4cKF+uSTT7Rjxw7Nnj3bClbr16+XJM2cOVMpKSnW84sZOHCg+vfvr02bNqlJkybq1KmT/v7773zXVZJ27typTz75RJ9//rmSk5OVnZ2tDh06aNWqVZo9e7a2bdumUaNGydXV1Vrm5MmTevPNNzVr1ix9//332rt3r8OxOHbsmHr27KkffvhBa9asUVRUlDp27Khjx45Jkj777DNNmDBBU6dO1W+//aYFCxYoOjraWv6RRx7RypUrlZCQoM2bN+uBBx5Q+/btrXbgLFzDBQAAcJOqVKmSxowZYz1/5ZVXVK9ePY0YMcKa9v777ysiIkK//vqrSpcurbfeekuTJk1Sz549JUkVK1ZUs2bNJEkJCQlycXHRe++9Z/V4zJw5UyVKlNDy5cvVtm1bSVJgYKAmTZokV1dXVa1aVXfeeaeWLl2qxx57TL/++qs++eQTLV68WK1bt5YkVahQwarPI488oldeeUXr1q1Tw4YNdebMGc2ePVtjx4696H6++eabevHFF/Xggw9KkkaPHq1ly5Zp4sSJeueddxQWFiY3Nzf5+fkpLCwsz3XY7XZ5eHjIx8cnzzJvvPGGmjdvLkl66aWXdOedd+r06dPy8vLSG2+8oZdeesl6zSpUqKDXXntNgwYN0tChQy91iCz33Xefw/MZM2YoJCRE27ZtU82aNbV3715FRUWpWbNmstlsKleunFW2VKlSkqQSJUpcdP/O9+yzz1rbmzJlihITEzVjxgwNGjQoX3WVzvUmzZo1y9r2okWLtG7dOm3fvl2VK1eW5HhcpXOnHr777rtWcH722Wf16quvWvPvuOMOh/JTp05VYGCgkpKSFBsbq7179yosLEytW7eWu7u7ypYtq4YNG0o6d8ro3LlztX//foWHh0uSBgwYoMTERM2cOdOhzRc1ergAAABuUg0aNHB4vnHjRi1btkx+fn7Wo2rVqpLO/WDdvn27MjIy1KpVqzzXt3HjRu3cuVP+/v7W8kFBQTp9+rTD6W81atRw6NkoXbq0Dh06JElKTk6Wq6urFV4uVLp0ad155516//33JUlffvmlTp8+rQceeCDP8kePHtWBAwfUtGlTh+lNmzbV9u3bL/XyFEitWrUc6ijJ2qec0xXPf11zespOnjyZr/X//vvv6tatmypUqKCAgABFRkZKkvbu3Svp3CmPycnJqlKlivr27atFixYVel9iYmKsv93c3NSgQYMCv1blypWzwpZ07riWKVPGClt58fHxceilPL9dSOdezyeffFKVK1eW3W6X3W7X8ePHrdfggQce0KlTp1ShQgU99thjmj9/vnXa5o8//ihjjCpXruxwHJKSki56KmpRoYcLAADgJuXr6+vwPDs7W3fddZdGjx6dq2zp0qX1xx9/XHJ92dnZql+/vubMmZNr3vk/vt3d3R3m2Ww269Q4b2/vy9b70UcfVY8ePTRhwgTNnDlTXbt2lY+PzyWXOf8aI0kyxuSadiXO36ec9ebsU3Z2toYPH67OnTvnWs7Lyytf67/rrrsUERGh6dOnKzw8XNnZ2apZs6Z1qma9evW0a9cuffPNN1qyZIm6dOmi1q1b67PPPrvSXXPYJxcXFxljHOblNSjGhW0rP8c1r3Zx/rZ69eqlw4cPa+LEiSpXrpw8PT0VExNjvQYRERHasWOHFi9erCVLlujpp5/W2LFjlZSUpOzsbLm6umrjxo0OYV/SRUelLCoELgAAAEg696P9888/V/ny5eXmlvtnYlRUlLy9vbV06VI9+uijeS7/8ccfKyQkRAEBAYWqQ3R0tLKzs5WUlGSdUnihjh07ytfXV1OmTNE333yj77///qLrCwgIUHh4uFasWKHbb7/dmr5q1SrrdLP88vDwUFZWVoGWkc69Ljt27FClSpUKvKwk/f3339q+fbumTp2q2267TZK0YsWKXOUCAgLUtWtXde3aVffff7/at2+vf/75R0FBQXJ3d8933desWWO9VmfPntXGjRv17LPPSjoXnI8dO6YTJ05YoSo5Ofmy66xVq5b279+vX3/99ZK9XJfyww8/aPLkyerYsaMkad++fQ4Dukjngl2nTp3UqVMnPfPMM6patap+/vln1a1bV1lZWTp06JD1Gl4tBC4AAABIkp555hlNnz5dDz30kAYOHKiSJUtq586dSkhI0PTp0+Xl5aUXX3xRgwYNkoeHh5o2barDhw9r69at6tOnj7p3766xY8fq7rvvtkYF3Lt3r+bNm6eBAweqTJkyl61D+fLl1bNnT/Xu3Vv//e9/Vbt2be3Zs0eHDh2yBu9wdXVVr169NHjwYFWqVMnhFLi8DBw4UEOHDlXFihVVp04dzZw5U8nJyXn2xF2ubmvXrtXu3but0yXz45VXXlFsbKwiIiL0wAMPyMXFRZs3b9bPP/+s119//bLL54z4OG3aNJUuXVp79+7VSy+95FBmwoQJKl26tOrUqSMXFxd9+umnCgsLs0YULF++vJYuXaqmTZvK09NTgYGBF93eO++8o6ioKFWrVk0TJkxQWlqaNQhIo0aN5OPjo3//+9967rnntG7dOocBTy6mefPmuv3223Xfffdp/PjxqlSpkn755RfZbDa1b9/+sstL5645nDVrlho0aKCjR49q4MCBDj1n8fHxysrKsuo4a9YseXt7W0P9d+/eXQ8//LDGjRununXr6q+//tJ3332n6OhoK8Q5A9dwAQAAQJIUHh6ulStXKisrS+3atVPNmjXVr18/2e12ubic+9n48ssvq3///nrllVdUrVo1de3a1brOxsfHR99//73Kli2rzp07q1q1aurdu7dOnTpVoB6vKVOm6P7779fTTz+tqlWr6rHHHtOJEyccyvTp00eZmZkOowFeTN++fdW/f3/1799f0dHRSkxM1MKFCxUVFVWAV+fcIAuurq6qXr26SpUqZV07dDnt2rXTl19+qcWLF+vWW29V48aNNX78eIeBLS7FxcVFCQkJ2rhxo2rWrKnnn38+1yAhfn5+Gj16tBo0aKBbb71Vu3fv1tdff20dt3Hjxmnx4sWKiIhQ3bp1L7m9UaNGafTo0apdu7Z++OEH/e9//1PJkiUlSUFBQZo9e7a+/vprRUdHa+7cuXkOo5+Xzz//XLfeeqseeughVa9eXYMGDSpQj+H777+vtLQ01a1bVz169FDfvn0VEhJizS9RooSmT5+upk2bqlatWlq6dKm++OIL675qM2fO1MMPP6z+/furSpUq6tSpk9auXesw2qQz2MyFJ2EiT0ePHpXdbld6enqhu8iL2oTFvxZ3FSRJz7cpXLcwAAA3gtOnT2vXrl2KjIzM9/U4uHIrV65UixYttH//fmtYelyZ3bt3KzIyUps2bVKdOnWKuzrF7lLv7YJkA04pBAAAwHUjIyND+/bt08svv6wuXboQtnDN45RCAAAAXDfmzp2rKlWqKD093eEeYsC1ih4uAAAAXDd69eqlXr16FXc1bkjly5fPNeQ7rhw9XAAAAADgJAQuAACAIkDPAHBjKar3NIELAADgCri7u0uSTp48Wcw1AVCUMjMzJZ2779uV4BouAACAK+Dq6qoSJUo43IvKZrMVc60AXIns7GwdPnxYPj4+cnO7sshE4AIAALhCYWFhkmSFLgDXPxcXF5UtW/aK/4FC4AIAALhCNptNpUuXVkhIiM6cOVPc1QFQBDw8POTicuVXYBG4AAAAioirq+sVX+8B4MbCoBkAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADhJsQeuP//8U//6178UHBwsHx8f1alTRxs3brTmG2M0bNgwhYeHy9vbWy1atNDWrVsd1pGRkaHnnntOJUuWlK+vrzp16qT9+/c7lElLS1OPHj1kt9tlt9vVo0cPHTly5GrsIgAAAICbVLEGrrS0NDVt2lTu7u765ptvtG3bNo0bN04lSpSwyowZM0bjx4/XpEmTtH79eoWFhalNmzY6duyYVSYuLk7z589XQkKCVqxYoePHjys2NlZZWVlWmW7duik5OVmJiYlKTExUcnKyevTocTV3FwAAAMBNxmaMMcW18ZdeekkrV67UDz/8kOd8Y4zCw8MVFxenF198UdK53qzQ0FCNHj1aTzzxhNLT01WqVCnNmjVLXbt2lSQdOHBAERER+vrrr9WuXTtt375d1atX15o1a9SoUSNJ0po1axQTE6NffvlFVapUuWxdjx49KrvdrvT0dAUEBBTRK3BlJiz+tbirIEl6vk3l4q4CAAAAcNUUJBsUaw/XwoUL1aBBAz3wwAMKCQlR3bp1NX36dGv+rl27lJqaqrZt21rTPD091bx5c61atUqStHHjRp05c8ahTHh4uGrWrGmVWb16tex2uxW2JKlx48ay2+1WmQtlZGTo6NGjDg8AAAAAKIhiDVx//PGHpkyZoqioKH377bd68skn1bdvX3344YeSpNTUVElSaGiow3KhoaHWvNTUVHl4eCgwMPCSZUJCQnJtPyQkxCpzoZEjR1rXe9ntdkVERFzZzgIAAAC46RRr4MrOzla9evU0YsQI1a1bV0888YQee+wxTZkyxaGczWZzeG6MyTXtQheWyav8pdYzePBgpaenW499+/bld7cAAAAAQFIxB67SpUurevXqDtOqVaumvXv3SpLCwsIkKVcv1KFDh6xer7CwMGVmZiotLe2SZQ4ePJhr+4cPH87Ve5bD09NTAQEBDg8AAAAAKIhiDVxNmzbVjh07HKb9+uuvKleunCQpMjJSYWFhWrx4sTU/MzNTSUlJatKkiSSpfv36cnd3dyiTkpKiLVu2WGViYmKUnp6udevWWWXWrl2r9PR0qwwAAAAAFDW34tz4888/ryZNmmjEiBHq0qWL1q1bp2nTpmnatGmSzp0GGBcXpxEjRigqKkpRUVEaMWKEfHx81K1bN0mS3W5Xnz591L9/fwUHBysoKEgDBgxQdHS0WrduLelcr1n79u312GOPaerUqZKkxx9/XLGxsfkaoRAAAAAACqNYA9ett96q+fPna/DgwXr11VcVGRmpiRMnqnv37laZQYMG6dSpU3r66aeVlpamRo0aadGiRfL397fKTJgwQW5uburSpYtOnTqlVq1aKT4+Xq6urlaZOXPmqG/fvtZohp06ddKkSZOu3s4CAAAAuOkU6324rifch+viuA8XAAAAbibXzX24AAAAAOBGRuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACchcAEAAACAk7gV58aHDRum4cOHO0wLDQ1VamqqJMkYo+HDh2vatGlKS0tTo0aN9M4776hGjRpW+YyMDA0YMEBz587VqVOn1KpVK02ePFllypSxyqSlpalv375auHChJKlTp056++23VaJECefvpBM13jutuKtwzrLg4q6Bo5aDi7sGAAAAgKRroIerRo0aSklJsR4///yzNW/MmDEaP368Jk2apPXr1yssLExt2rTRsWPHrDJxcXGaP3++EhIStGLFCh0/flyxsbHKysqyynTr1k3JyclKTExUYmKikpOT1aNHj6u6nwAAAABuPsXawyVJbm5uCgsLyzXdGKOJEydqyJAh6ty5syTpgw8+UGhoqD766CM98cQTSk9P14wZMzRr1iy1bt1akjR79mxFRERoyZIlateunbZv367ExEStWbNGjRo1kiRNnz5dMTEx2rFjh6pUqXL1dhYAAADATaXYe7h+++03hYeHKzIyUg8++KD++OMPSdKuXbuUmpqqtm3bWmU9PT3VvHlzrVq1SpK0ceNGnTlzxqFMeHi4atasaZVZvXq17Ha7FbYkqXHjxrLb7VYZAAAAAHCGYu3hatSokT788ENVrlxZBw8e1Ouvv64mTZpo69at1nVcoaGhDsuEhoZqz549kqTU1FR5eHgoMDAwV5mc5VNTUxUSEpJr2yEhIVaZvGRkZCgjI8N6fvTo0cLtJAAAAICbVrEGrg4dOlh/R0dHKyYmRhUrVtQHH3ygxo0bS5JsNpvDMsaYXNMudGGZvMpfbj0jR47MNaAHAAAAABREsZ9SeD5fX19FR0frt99+s67rurAX6tChQ1avV1hYmDIzM5WWlnbJMgcPHsy1rcOHD+fqPTvf4MGDlZ6ebj327dt3RfsGAAAA4OZzTQWujIwMbd++XaVLl1ZkZKTCwsK0ePFia35mZqaSkpLUpEkTSVL9+vXl7u7uUCYlJUVbtmyxysTExCg9PV3r1q2zyqxdu1bp6elWmbx4enoqICDA4QEAAAAABVGspxQOGDBAd911l8qWLatDhw7p9ddf19GjR9WzZ0/ZbDbFxcVpxIgRioqKUlRUlEaMGCEfHx9169ZNkmS329WnTx/1799fwcHBCgoK0oABAxQdHW2NWlitWjW1b99ejz32mKZOnSpJevzxxxUbG8sIhQAAAACcqlgD1/79+/XQQw/pr7/+UqlSpdS4cWOtWbNG5cqVkyQNGjRIp06d0tNPP23d+HjRokXy9/e31jFhwgS5ubmpS5cu1o2P4+Pj5erqapWZM2eO+vbta41m2KlTJ02aNOnq7iwAAACAm47NGGOKuxLXg6NHj8putys9Pf2aOb1w9YwBxV0FSVJMheDiroKjloOLuwYAAAC4gRUkG1xT13ABAAAAwI2EwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATnJFgWvnzp369ttvderUKUmSMaZIKgUAAAAAN4JCBa6///5brVu3VuXKldWxY0elpKRIkh599FH179+/SCsIAAAAANerQgWu559/Xm5ubtq7d698fHys6V27dlViYmKRVQ4AAAAArmduhVlo0aJF+vbbb1WmTBmH6VFRUdqzZ0+RVAwAAAAArneF6uE6ceKEQ89Wjr/++kuenp5XXCkAAAAAuBEUKnDdfvvt+vDDD63nNptN2dnZGjt2rFq2bFlklQMAAACA61mhTikcO3asWrRooQ0bNigzM1ODBg3S1q1b9c8//2jlypVFXUcAAAAAuC4VqoerevXq2rx5sxo2bKg2bdroxIkT6ty5szZt2qSKFSsWdR0BAAAA4LpUqB4uSQoLC9Pw4cOLsi4AAAAAcEMpVA/XzJkz9emnn+aa/umnn+qDDz644koBAAAAwI2gUIFr1KhRKlmyZK7pISEhGjFiRKErM3LkSNlsNsXFxVnTjDEaNmyYwsPD5e3trRYtWmjr1q0Oy2VkZOi5555TyZIl5evrq06dOmn//v0OZdLS0tSjRw/Z7XbZ7Xb16NFDR44cKXRdAQAAAOByChW49uzZo8jIyFzTy5Urp7179xaqIuvXr9e0adNUq1Yth+ljxozR+PHjNWnSJK1fv15hYWFq06aNjh07ZpWJi4vT/PnzlZCQoBUrVuj48eOKjY1VVlaWVaZbt25KTk5WYmKiEhMTlZycrB49ehSqrgAAAACQH4UKXCEhIdq8eXOu6T/99JOCg4MLvL7jx4+re/fumj59ugIDA63pxhhNnDhRQ4YMUefOnVWzZk198MEHOnnypD766CNJUnp6umbMmKFx48apdevWqlu3rmbPnq2ff/5ZS5YskSRt375diYmJeu+99xQTE6OYmBhNnz5dX375pXbs2FGYlwAAAAAALqtQgevBBx9U3759tWzZMmVlZSkrK0vfffed+vXrpwcffLDA63vmmWd05513qnXr1g7Td+3apdTUVLVt29aa5unpqebNm2vVqlWSpI0bN+rMmTMOZcLDw1WzZk2rzOrVq2W329WoUSOrTOPGjWW3260yF8rIyNDRo0cdHgAAAABQEIUapfD111/Xnj171KpVK7m5nVtFdna2Hn744QJfw5WQkKAff/xR69evzzUvNTVVkhQaGuowPTQ0VHv27LHKeHh4OPSM5ZTJWT41NVUhISG51h8SEmKVudDIkSMZhREAAADAFSlU4PLw8NDHH3+s1157TT/99JO8vb0VHR2tcuXKFWg9+/btU79+/bRo0SJ5eXldtJzNZnN4bozJNe1CF5bJq/yl1jN48GC98MIL1vOjR48qIiLiktsEAAAAgPMV+j5cklS5cmVVrly50Mtv3LhRhw4dUv369a1pWVlZ+v777zVp0iTr+qrU1FSVLl3aKnPo0CGr1yssLEyZmZlKS0tz6OU6dOiQmjRpYpU5ePBgru0fPnw4V+9ZDk9PT3l6ehZ63wAAAACgUIErKytL8fHxWrp0qQ4dOqTs7GyH+d99912+1tOqVSv9/PPPDtMeeeQRVa1aVS+++KIqVKigsLAwLV68WHXr1pUkZWZmKikpSaNHj5Yk1a9fX+7u7lq8eLG6dOkiSUpJSdGWLVs0ZswYSVJMTIzS09O1bt06NWzYUJK0du1apaenW6EMAAAAAIpaoQJXv379FB8frzvvvFM1a9a87Ol9F+Pv76+aNWs6TPP19VVwcLA1PS4uTiNGjFBUVJSioqI0YsQI+fj4qFu3bpIku92uPn36qH///goODlZQUJAGDBig6OhoaxCOatWqqX379nrsscc0depUSdLjjz+u2NhYValSpVB1BwAAAIDLKVTgSkhI0CeffKKOHTsWdX1yGTRokE6dOqWnn35aaWlpatSokRYtWiR/f3+rzIQJE+Tm5qYuXbro1KlTatWqleLj4+Xq6mqVmTNnjvr27WuNZtipUydNmjTJ6fUHAAAAcPOyGWNMQRcKDw/X8uXLr+j6revN0aNHZbfblZ6eroCAgOKujiRp9YwBxV0FSVJMhYLfe82pWg4u7hoAAADgBlaQbFCo+3D1799fb731lgqR1QAAAADgplGoUwpXrFihZcuW6ZtvvlGNGjXk7u7uMH/evHlFUjkAAAAAuJ4VKnCVKFFC9957b1HXBQAAAABuKIUKXDNnzizqegAAAADADadQ13BJ0tmzZ7VkyRJNnTpVx44dkyQdOHBAx48fL7LKAQAAAMD1rFA9XHv27FH79u21d+9eZWRkqE2bNvL399eYMWN0+vRpvfvuu0VdTwAAAAC47hSqh6tfv35q0KCB0tLS5O3tbU2/9957tXTp0iKrHAAAAABczwo9SuHKlSvl4eHhML1cuXL6888/i6RiAAAAAHC9K1QPV3Z2trKysnJN379/v/z9/a+4UgAAAABwIyhU4GrTpo0mTpxoPbfZbDp+/LiGDh2qjh07FlXdAAAAAOC6VqhTCidMmKCWLVuqevXqOn36tLp166bffvtNJUuW1Ny5c4u6jgAAAABwXSpU4AoPD1dycrLmzp2rH3/8UdnZ2erTp4+6d+/uMIgGAAAAANzMChW4JMnb21u9e/dW7969i7I+AAAAAHDDKFTg+vDDDy85/+GHHy5UZQAAAADgRlKowNWvXz+H52fOnNHJkyfl4eEhHx8fAhcAAAAAqJCjFKalpTk8jh8/rh07dqhZs2YMmgEAAAAA/1+hAldeoqKiNGrUqFy9XwAAAABwsyqywCVJrq6uOnDgQFGuEgAAAACuW4W6hmvhwoUOz40xSklJ0aRJk9S0adMiqRgAAAAAXO8KFbjuueceh+c2m02lSpXSHXfcoXHjxhVFvQAAAADguleowJWdnV3U9QAAAACAG06RXsMFAAAAAPg/herheuGFF/Jddvz48YXZBAAAAABc9woVuDZt2qQff/xRZ8+eVZUqVSRJv/76q1xdXVWvXj2rnM1mK5paAgAAAMB1qFCB66677pK/v78++OADBQYGSjp3M+RHHnlEt912m/r371+klQQAAACA61GhruEaN26cRo4caYUtSQoMDNTrr7/OKIUAAAAA8P8VKnAdPXpUBw8ezDX90KFDOnbs2BVXCgAAAABuBIUKXPfee68eeeQRffbZZ9q/f7/279+vzz77TH369FHnzp2Luo4AAAAAcF0q1DVc7777rgYMGKB//etfOnPmzLkVubmpT58+Gjt2bJFWEAAAAACuV4UKXD4+Ppo8ebLGjh2r33//XcYYVapUSb6+vkVdPwAAAAC4bl3RjY9TUlKUkpKiypUry9fXV8aYoqoXAAAAAFz38hW4srOzHZ7//fffatWqlSpXrqyOHTsqJSVFkvToo48yJDwAAAAA/H/5Clzjx4/X119/bT1//vnn5e7urr1798rHx8ea3rVrVyUmJhZ9LQEAAADgOpSva7jatGmj+++/XykpKerTp48WLVqkb7/9VmXKlHEoFxUVpT179jilogAAAABwvclXD1ft2rW1bt06ffHFF5KkEydOOPRs5fjrr7/k6elZtDUEAAAAgOtUvgfNCAwM1IIFCyRJt99+uz788ENrns1mU3Z2tsaOHauWLVsWeSUBAAAA4HpUqGHhx44dqxYtWmjDhg3KzMzUoEGDtHXrVv3zzz9auXJlUdcRAAAAAK5LhRoWvnr16tq8ebMaNmyoNm3a6MSJE+rcubM2bdqkihUrFnUdAQAAAOC6VOAerjNnzqht27aaOnWqhg8f7ow6AQAAAMANocA9XO7u7tqyZYtsNpsz6gMAAAAAN4xCnVL48MMPa8aMGUVdFwAAAAC4oRRq0IzMzEy99957Wrx4sRo0aCBfX1+H+ePHjy+SygEAAADA9SzfgWvz5s2qWbOmXFxctGXLFtWrV0+S9OuvvzqU41RDAAAAADgn34Grbt26SklJUUhIiPbs2aP169crODjYmXUDAAAAgOtavq/hKlGihHbt2iVJ2r17t7Kzs51WKQAAAAC4EeS7h+u+++5T8+bNVbp0adlsNjVo0ECurq55lv3jjz+KrIIAAAAAcL3Kd+CaNm2aOnfurJ07d6pv37567LHH5O/v78y6AQAAAMB1rUCjFLZv316StHHjRvXr14/ABQAAAACXUKhh4WfOnFnU9QAAAACAG06hbnwMAAAAALg8AhcAAAAAOEmxBq4pU6aoVq1aCggIUEBAgGJiYvTNN99Y840xGjZsmMLDw+Xt7a0WLVpo69atDuvIyMjQc889p5IlS8rX11edOnXS/v37HcqkpaWpR48estvtstvt6tGjh44cOXI1dhEAAADATaxYA1eZMmU0atQobdiwQRs2bNAdd9yhu+++2wpVY8aM0fjx4zVp0iStX79eYWFhatOmjY4dO2atIy4uTvPnz1dCQoJWrFih48ePKzY2VllZWVaZbt26KTk5WYmJiUpMTFRycrJ69Ohx1fcXAAAAwM3FZowxxV2J8wUFBWns2LHq3bu3wsPDFRcXpxdffFHSud6s0NBQjR49Wk888YTS09NVqlQpzZo1S127dpUkHThwQBEREfr666/Vrl07bd++XdWrV9eaNWvUqFEjSdKaNWsUExOjX375RVWqVMlXvY4ePSq73a709HQFBAQ4Z+cLaPWMAcVdBUlSTIXg4q6Co5aDi7sGAAAAuIEVJBtcM9dwZWVlKSEhQSdOnFBMTIx27dql1NRUtW3b1irj6emp5s2ba9WqVZLODU9/5swZhzLh4eGqWbOmVWb16tWy2+1W2JKkxo0by263W2UAAAAAwBkKNSx8Ufr5558VExOj06dPy8/PT/Pnz1f16tWtMBQaGupQPjQ0VHv27JEkpaamysPDQ4GBgbnKpKamWmVCQkJybTckJMQqk5eMjAxlZGRYz48ePVq4HQQAAABw0yr2Hq4qVaooOTlZa9as0VNPPaWePXtq27Zt1nybzeZQ3hiTa9qFLiyTV/nLrWfkyJHWIBt2u10RERH53SUAAAAAkHQNBC4PDw9VqlRJDRo00MiRI1W7dm299dZbCgsLk6RcvVCHDh2yer3CwsKUmZmptLS0S5Y5ePBgru0ePnw4V+/Z+QYPHqz09HTrsW/fvivaTwAAAAA3n2IPXBcyxigjI0ORkZEKCwvT4sWLrXmZmZlKSkpSkyZNJEn169eXu7u7Q5mUlBRt2bLFKhMTE6P09HStW7fOKrN27Vqlp6dbZfLi6elpDVef8wAAAACAgijWa7j+/e9/q0OHDoqIiNCxY8eUkJCg5cuXKzExUTabTXFxcRoxYoSioqIUFRWlESNGyMfHR926dZMk2e129enTR/3791dwcLCCgoI0YMAARUdHq3Xr1pKkatWqqX379nrsscc0depUSdLjjz+u2NjYfI9QCAAAAACFUayB6+DBg+rRo4dSUlJkt9tVq1YtJSYmqk2bNpKkQYMG6dSpU3r66aeVlpamRo0aadGiRfL397fWMWHCBLm5ualLly46deqUWrVqpfj4eLm6ulpl5syZo759+1qjGXbq1EmTJk26ujsLAAAA4KZzzd2H61rFfbgujvtwAQAA4GZyXd6HCwAAAABuNAQuAAAAAHASAhcAAAAAOAmBCwAAAACchMAFAAAAAE5C4AIAAAAAJyFwAQAAAICTELgAAAAAwEkIXAAAAADgJAQuAAAAAHASAhcAAAAAOAmBCwAAAACchMAFAAAAAE5C4AIAAAAAJyFwAQAAAICTELgAAAAAwEkIXAAAAADgJAQuAAAAAHASAhcAAAAAOAmBCwAAAACchMAFAAAAAE5C4AIAAAAAJyFwAQAAAICTuBV3BXD9W/3H38VdBUtMheDirgIAAABgoYcLAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJO4FefGR44cqXnz5umXX36Rt7e3mjRpotGjR6tKlSpWGWOMhg8frmnTpiktLU2NGjXSO++8oxo1alhlMjIyNGDAAM2dO1enTp1Sq1atNHnyZJUpU8Yqk5aWpr59+2rhwoWSpE6dOuntt99WiRIlrtr+ArhGLRtZ3DW4NrUcXNw1AADgulesPVxJSUl65plntGbNGi1evFhnz55V27ZtdeLECavMmDFjNH78eE2aNEnr169XWFiY2rRpo2PHjlll4uLiNH/+fCUkJGjFihU6fvy4YmNjlZWVZZXp1q2bkpOTlZiYqMTERCUnJ6tHjx5XdX8BAAAA3FxsxhhT3JXIcfjwYYWEhCgpKUm33367jDEKDw9XXFycXnzxRUnnerNCQ0M1evRoPfHEE0pPT1epUqU0a9Ysde3aVZJ04MABRURE6Ouvv1a7du20fft2Va9eXWvWrFGjRo0kSWvWrFFMTIx++eUXhx61izl69KjsdrvS09MVEBDgvBehAFbPGFDcVbjmxFQI5r/yKDh6uPLGewkAgDwVJBtcU9dwpaenS5KCgoIkSbt27VJqaqratm1rlfH09FTz5s21atUqSdLGjRt15swZhzLh4eGqWbOmVWb16tWy2+1W2JKkxo0by263W2UulJGRoaNHjzo8AAAAAKAgrpnAZYzRCy+8oGbNmqlmzZqSpNTUVElSaGioQ9nQ0FBrXmpqqjw8PBQYGHjJMiEhIbm2GRISYpW50MiRI2W3261HRETEle0gAAAAgJtOsQ6acb5nn31Wmzdv1ooVK3LNs9lsDs+NMbmmXejCMnmVv9R6Bg8erBdeeMF6fvToUULX9YLTw/LG6WEAAABX3TXRw/Xcc89p4cKFWrZsmcPIgmFhYZKUqxfq0KFDVq9XWFiYMjMzlZaWdskyBw8ezLXdw4cP5+o9y+Hp6amAgACHBwAAAAAURLEGLmOMnn32Wc2bN0/fffedIiMjHeZHRkYqLCxMixcvtqZlZmYqKSlJTZo0kSTVr19f7u7uDmVSUlK0ZcsWq0xMTIzS09O1bt06q8zatWuVnp5ulQEAAACAolaspxQ+88wz+uijj/S///1P/v7+Vk+W3W6Xt7e3bDab4uLiNGLECEVFRSkqKkojRoyQj4+PunXrZpXt06eP+vfvr+DgYAUFBWnAgAGKjo5W69atJUnVqlVT+/bt9dhjj2nq1KmSpMcff1yxsbH5GqEQAAAAAAqjWAPXlClTJEktWrRwmD5z5kz16tVLkjRo0CCdOnVKTz/9tHXj40WLFsnf398qP2HCBLm5ualLly7WjY/j4+Pl6upqlZkzZ4769u1rjWbYqVMnTZo0ybk7CAAAAOCmdk3dh+taxn24rg8xFYKLuwrXLgbNuDgGWskbbQYAgDxdt/fhAgAAAIAbCYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4iVtxVwAAcI1aNrK4a3Dtajm4uGsAALhO0MMFAAAAAE5C4AIAAAAAJyFwAQAAAICTcA0Xbiir//i7uKsgSYqpEFzcVQAAAMA1gB4uAAAAAHASAhcAAAAAOAmBCwAAAACchMAFAAAAAE5C4AIAAAAAJyFwAQAAAICTELgAAAAAwEkIXAAAAADgJAQuAAAAAHASAhcAAAAAOAmBCwAAAACchMAFAAAAAE5C4AIAAAAAJyFwAQAAAICTuBV3BQBcJctGFncNAAAAbjr0cAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACfhxseAE6z+4+/iroIlpkJwcVcBAADgpkUPFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMUe+D6/vvvdddddyk8PFw2m00LFixwmG+M0bBhwxQeHi5vb2+1aNFCW7dudSiTkZGh5557TiVLlpSvr686deqk/fv3O5RJS0tTjx49ZLfbZbfb1aNHDx05csTJewcAAADgZlbsgevEiROqXbu2Jk2alOf8MWPGaPz48Zo0aZLWr1+vsLAwtWnTRseOHbPKxMXFaf78+UpISNCKFSt0/PhxxcbGKisryyrTrVs3JScnKzExUYmJiUpOTlaPHj2cvn8AAAAAbl5uxV2BDh06qEOHDnnOM8Zo4sSJGjJkiDp37ixJ+uCDDxQaGqqPPvpITzzxhNLT0zVjxgzNmjVLrVu3liTNnj1bERERWrJkidq1a6ft27crMTFRa9asUaNGjSRJ06dPV0xMjHbs2KEqVapcnZ0FAAAAcFMp9h6uS9m1a5dSU1PVtm1ba5qnp6eaN2+uVatWSZI2btyoM2fOOJQJDw9XzZo1rTKrV6+W3W63wpYkNW7cWHa73SoDAAAAAEWt2Hu4LiU1NVWSFBoa6jA9NDRUe/bsscp4eHgoMDAwV5mc5VNTUxUSEpJr/SEhIVaZC2VkZCgjI8N6fvTo0cLvCAAAN4NlI4u7BteuloOLuwYAisk13cOVw2azOTw3xuSadqELy+RV/lLrGTlypDXAht1uV0RERCFqDgAAAOBmdk0HrrCwMEnK1Qt16NAhq9crLCxMmZmZSktLu2SZgwcP5lr/4cOHc/We5Rg8eLDS09Otx759+654fwAAAADcXK7pwBUZGamwsDAtXrzYmpaZmamkpCQ1adJEklS/fn25u7s7lElJSdGWLVusMjExMUpPT9e6deusMmvXrlV6erpV5kKenp4KCAhweAAAAABAQRT7NVzHjx/Xzp07ree7du1ScnKygoKCVLZsWcXFxWnEiBGKiopSVFSURowYIR8fH3Xr1k2SZLfb1adPH/Xv31/BwcEKCgrSgAEDFB0dbY1aWK1aNbVv316PPfaYpk6dKkl6/PHHFRsbywiFAAAAAJym2APXhg0b1LJlS+v5Cy+8IEnq2bOn4uPjNWjQIJ06dUpPP/200tLS1KhRIy1atEj+/v7WMhMmTJCbm5u6dOmiU6dOqVWrVoqPj5erq6tVZs6cOerbt681mmGnTp0ueu8vAAAAACgKNmOMKe5KXA+OHj0qu92u9PT0a+b0wtUzBhR3FXAdiKkQXNxVAG48jDiXN0YpvDjaDHBDKUg2uKav4QIAAACA6xmBCwAAAACcpNiv4QLgXKv/+Lu4q2Dh9EYAAHCzoYcLAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJO4FXcFAOBqW/3H38VdBUtMheDirgIAAHAiAhcAAAW1bGRx1wAAcJ3glEIAAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4iVtxVwAAAOCGt2xkcdfg2tRycHHXAHA6ergAAAAAwEkIXAAAAADgJJxSCOCqWf3H38VdBQAAgKuKHi4AAAAAcBICFwAAAAA4CYELAAAAAJyEwAUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAAAAAJzErbgrAAAAgJvUspHFXYNrV8vBxV0DFBF6uAAAAADASQhcAAAAAOAknFIIAMVo9R9/F3cVLDEVgou7CgAA3HDo4QIAAAAAJyFwAQAAAICTELgAAAAAwEkIXAAAAADgJAQuAAAAAHASAhcAAAAAOMlNF7gmT56syMhIeXl5qX79+vrhhx+Ku0oAAAAAblA3VeD6+OOPFRcXpyFDhmjTpk267bbb1KFDB+3du7e4qwYAAADgBmQzxpjirsTV0qhRI9WrV09TpkyxplWrVk333HOPRo4ceclljx49KrvdrvT0dAUEBDi7qvmyesaA4q4CABQ5bsAMALioloOLuwaSCpYN3K5SnYpdZmamNm7cqJdeeslhetu2bbVq1apc5TMyMpSRkWE9T09Pl3Tuxb1WnDiVcflCAHCdWbL1QHFXwdKwfFBxVwEAcL5r5Ld4TibIT9/VTRO4/vrrL2VlZSk0NNRhemhoqFJTU3OVHzlypIYPH55rekREhNPqCAAAAOBSXi3uCjg4duyY7Hb7JcvcNIErh81mc3hujMk1TZIGDx6sF154wXqenZ2tf/75R8HBwXmWv9qOHj2qiIgI7du375o5xRFXD8cftAHQBkAbAG2g+BhjdOzYMYWHh1+27E0TuEqWLClXV9dcvVmHDh3K1eslSZ6envL09HSYVqJECWdWsVACAgJ4g93EOP6gDYA2ANoAaAPF43I9WzlumlEKPTw8VL9+fS1evNhh+uLFi9WkSZNiqhUAAACAG9lN08MlSS+88IJ69OihBg0aKCYmRtOmTdPevXv15JNPFnfVAAAAANyAbqrA1bVrV/3999969dVXlZKSopo1a+rrr79WuXLlirtqBebp6amhQ4fmOu0RNweOP2gDoA2ANgDawPXhproPFwAAAABcTTfNNVwAAAAAcLURuAAAAADASQhcAAAAAOAkBC4AAAAAcBIC13Vo8uTJioyMlJeXl+rXr68ffvihuKuEIvL999/rrrvuUnh4uGw2mxYsWOAw3xijYcOGKTw8XN7e3mrRooW2bt3qUCYjI0PPPfecSpYsKV9fX3Xq1En79++/inuBwho5cqRuvfVW+fv7KyQkRPfcc4927NjhUIY2cGObMmWKatWqZd3ENCYmRt988401n+N/8xk5cqRsNpvi4uKsabSDG9uwYcNks9kcHmFhYdZ8jv/1h8B1nfn4448VFxenIUOGaNOmTbrtttvUoUMH7d27t7irhiJw4sQJ1a5dW5MmTcpz/pgxYzR+/HhNmjRJ69evV1hYmNq0aaNjx45ZZeLi4jR//nwlJCRoxYoVOn78uGJjY5WVlXW1dgOFlJSUpGeeeUZr1qzR4sWLdfbsWbVt21YnTpywytAGbmxlypTRqFGjtGHDBm3YsEF33HGH7r77buvHFMf/5rJ+/XpNmzZNtWrVcphOO7jx1ahRQykpKdbj559/tuZx/K9DBteVhg0bmieffNJhWtWqVc1LL71UTDWCs0gy8+fPt55nZ2ebsLAwM2rUKGva6dOnjd1uN++++64xxpgjR44Yd3d3k5CQYJX5888/jYuLi0lMTLxqdUfROHTokJFkkpKSjDG0gZtVYGCgee+99zj+N5ljx46ZqKgos3jxYtO8eXPTr18/YwyfAzeDoUOHmtq1a+c5j+N/faKH6zqSmZmpjRs3qm3btg7T27Ztq1WrVhVTrXC17Nq1S6mpqQ7H39PTU82bN7eO/8aNG3XmzBmHMuHh4apZsyZt5DqUnp4uSQoKCpJEG7jZZGVlKSEhQSdOnFBMTAzH/ybzzDPP6M4771Tr1q0dptMObg6//fabwsPDFRkZqQcffFB//PGHJI7/9cqtuCuA/Pvrr7+UlZWl0NBQh+mhoaFKTU0tplrhask5xnkd/z179lhlPDw8FBgYmKsMbeT6YozRCy+8oGbNmqlmzZqSaAM3i59//lkxMTE6ffq0/Pz8NH/+fFWvXt36ocTxv/ElJCToxx9/1Pr163PN43PgxteoUSN9+OGHqly5sg4ePKjXX39dTZo00datWzn+1ykC13XIZrM5PDfG5JqGG1dhjj9t5Prz7LPPavPmzVqxYkWuebSBG1uVKlWUnJysI0eO6PPPP1fPnj2VlJRkzef439j27dunfv36adGiRfLy8rpoOdrBjatDhw7W39HR0YqJiVHFihX1wQcfqHHjxpI4/tcbTim8jpQsWVKurq65/jtx6NChXP/pwI0nZ4SiSx3/sLAwZWZmKi0t7aJlcO177rnntHDhQi1btkxlypSxptMGbg4eHh6qVKmSGjRooJEjR6p27dp66623OP43iY0bN+rQoUOqX7++3Nzc5ObmpqSkJP33v/+Vm5ubdRxpBzcPX19fRUdH67fffuNz4DpF4LqOeHh4qH79+lq8eLHD9MWLF6tJkybFVCtcLZGRkQoLC3M4/pmZmUpKSrKOf/369eXu7u5QJiUlRVu2bKGNXAeMMXr22Wc1b948fffdd4qMjHSYTxu4ORljlJGRwfG/SbRq1Uo///yzkpOTrUeDBg3UvXt3JScnq0KFCrSDm0xGRoa2b9+u0qVL8zlwvSqOkTpQeAkJCcbd3d3MmDHDbNu2zcTFxRlfX1+ze/fu4q4aisCxY8fMpk2bzKZNm4wkM378eLNp0yazZ88eY4wxo0aNMna73cybN8/8/PPP5qGHHjKlS5c2R48etdbx5JNPmjJlypglS5aYH3/80dxxxx2mdu3a5uzZs8W1W8inp556ytjtdrN8+XKTkpJiPU6ePGmVoQ3c2AYPHmy+//57s2vXLrN582bz73//27i4uJhFixYZYzj+N6vzRyk0hnZwo+vfv79Zvny5+eOPP8yaNWtMbGys8ff3t37rcfyvPwSu69A777xjypUrZzw8PEy9evWsIaNx/Vu2bJmRlOvRs2dPY8y54WCHDh1qwsLCjKenp7n99tvNzz//7LCOU6dOmWeffdYEBQUZb29vExsba/bu3VsMe4OCyuvYSzIzZ860ytAGbmy9e/e2Pt9LlSplWrVqZYUtYzj+N6sLAxft4MbWtWtXU7p0aePu7m7Cw8NN586dzdatW635HP/rj80YY4qnbw0AAAAAbmxcwwUAAAAATkLgAgAAAAAnIXABAAAAgJMQuAAAAADASQhcAAAAAOAkBC4AAAAAcBICFwAAAAA4CYELAHBTiY+P1zfffFPc1QAA3CQIXACAa1KLFi0UFxdXpOucN2+exowZo8aNGxfJ+l5++WU9/vjj1vNevXrpnnvuKZJ1X4kBAwaob9++xV0NAIAkmzHGFHclAAC4UIsWLVSnTh1NnDixSNb3xx9/KDY2Vt98843KlSt3xes7ePCgoqKitHnzZpUvX16SlJ6eLmOMSpQoccXrvxKHDh1SxYoVtXnzZkVGRhZrXQDgZkcPFwDghnXmzBnr7woVKmjbtm1FErYkacaMGYqJibHCliTZ7Xanh63z9+liQkJC1LZtW7377rtOrQsA4PIIXACAYnfixAk9/PDD8vPzU+nSpTVu3LhcZWw2mxYsWOAwrUSJEoqPj5ck7d69WzabTZ988olatGghLy8vzZ49W5I0c+ZMVatWTV5eXqpataomT57ssJ4///xTXbt2VWBgoIKDg3X33Xdr9+7dl6xzQkKCOnXq5DDtwlMKMzIy1LdvX4WEhMjLy0vNmjXT+vXrrfnx8fG5AtqCBQtks9ms58OGDVOdOnX0/vvvq0KFCvL09JQxRp999pmio6Pl7e2t4OBgtW7dWidOnLCW69Spk+bOnXvJfQAAOB+BCwBQ7AYOHKhly5Zp/vz5WrRokZYvX66NGzcWal0vvvii+vbtq+3bt6tdu3aaPn26hgwZojfeeEPbt2/XiBEj9PLLL+uDDz6QJJ08eVItW7aUn5+fvv/+e61YsUJ+fn5q3769MjMz89xGWlqatmzZogYNGlyyLoMGDdLnn3+uDz74QD/++KMqVaqkdu3a6Z9//inQPu3cuVOffPKJPv/8cyUnJys1NVUPPfSQevfure3bt2v58uXq3Lmzzr9KoGHDhtq3b5/27NlToG0BAIqWW3FXAABwczt+/LhmzJihDz/8UG3atJEkffDBBypTpkyh1hcXF6fOnTtbz1977TWNGzfOmhYZGalt27Zp6tSp6tmzpxISEuTi4qL33nvP6lmaOXOmSpQooeXLl6tt27a5trFnzx4ZYxQeHn7Repw4cUJTpkxRfHy8OnToIEmaPn26Fi9erBkzZmjgwIH53qfMzEzNmjVLpUqVkiT9+OOPOnv2rDp37mydIhkdHe2wzC233CLpXM9fUZ1GCQAoOAIXAKBY/f7778rMzFRMTIw1LSgoSFWqVCnU+s7vdTp8+LD27dunPn366LHHHrOmnz17Vna7XZK0ceNG7dy5U/7+/g7rOX36tH7//fc8t3Hq1ClJkpeX10Xr8fvvv+vMmTNq2rSpNc3d3V0NGzbU9u3bC7RP5cqVs8KWJNWuXVutWrVSdHS02rVrp7Zt2+r+++9XYGCgVcbb21vSuR48AEDxIXABAIpVfgfLtdlsucrmNYCEr6+v9Xd2drakcz1LjRo1cijn6upqlalfv77mzJmTa13nh5zzlSxZUtK5UwsvVianrudfj5UzPWeai4tLgfcpp+6LFy/WqlWrtGjRIr399tsaMmSI1q5da41KmHPa4sXqBwC4OriGCwBQrCpVqiR3d3etWbPGmpaWlqZff/3VoVypUqWUkpJiPf/tt98u23sTGhqqW265RX/88YcqVark8MgJJvXq1dNvv/2mkJCQXGVyesEuVLFiRQUEBGjbtm2X3C8PDw+tWLHCmnbmzBlt2LBB1apVs/bp2LFjDoNdJCcnX3KfcthsNjVt2lTDhw/Xpk2b5OHhofnz51vzt2zZInd3d9WoUSNf6wMAOAc9XACAYuXn56c+ffpo4MCBCg4OVmhoqIYMGSIXF8f/Cd5xxx2aNGmSGjdurOzsbL344otyd3e/7PqHDRumvn37KiAgQB06dFBGRoY2bNigtLQ0vfDCC+revbvGjh2ru+++W6+++qrKlCmjvXv3at68eRo4cGCe15K5uLiodevWWrFixUVvdOzr66unnnpKAwcOVFBQkMqWLasxY8bo5MmT6tOnjySpUaNG8vHx0b///W8999xzWrdunTXq4qWsXbtWS5cuVdu2bRUSEqK1a9fq8OHDVpCTpB9++EG33XabdWohAKB40MMFACh2Y8eO1e23365OnTqpdevWatasmerXr+9QZty4cYqIiNDtt9+ubt26acCAAfLx8bnsuh999FG99957io+PV3R0tJo3b674+Hirh8vHx0fff/+9ypYtq86dO6tatWrq3bu3Tp06pYCAgIuu9/HHH1dCQoJ12mJeRo0apfvuu089evRQvXr1tHPnTn377bfWtVZBQUGaPXu2vv76a0VHR2vu3LkaNmzYZfcpICBA33//vTp27KjKlSvrP//5j8aNG2cNziFJc+fOdbhuDQBQPGwmvyfPAwAAizFGjRs3VlxcnB566CFJ0kMPPSRXV1fr/l/F5auvvtLAgQO1efNmublxMgsAFCd6uAAAKASbzaZp06bp7NmzOnv2rLZt26bVq1dfE9dMnThxQjNnziRsAcA1gB4uAACuUHJyspo0aaKWLVtq9uzZDsOzAwBubgQuAAAAAHASTikEAAAAACchcAEAAACAkxC4AAAAAMBJCFwAAAAA4CQELgAAAABwEgIXAAAAADgJgQsAAAAAnITABQAAAABOQuACAAAAACf5f4/4AmxYQ7bSAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# is pace of purchase a good measure ? \n", - "# we ll compare the avg purchase delay and the purchase date max\n", - "\n", - "plt.figure(figsize = [10,6])\n", - "\n", - "plt.hist(X_test[X_test[\"avg_purchase_delay\"]>0][\"avg_purchase_delay\"], alpha = 0.5, label = \"average purchase delay\")\n", - "plt.hist(X_test[X_test[\"avg_purchase_delay\"]>0][\"purchase_date_max\"], alpha=0.5, label = \"recency of the last purchase\")\n", - "plt.legend()\n", - "plt.xlabel(\"durée (jours)\")\n", - "plt.ylabel(\"fréquence\")\n", - "plt.title(\"Distribution des délais moyen entre deux achats et de l'ancienneté du dernier achat\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "id": "3ef409fe-dcf7-4c07-9be3-28b3e8ca5546", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = [10,6])\n", - "\n", - "plt.hist(X_test[X_test[\"avg_purchase_delay\"]>0][\"avg_purchase_delay\"], alpha = 0.5, label = \"average purchase delay on the purchasing period\")\n", - "plt.hist(X_test[X_test[\"avg_purchase_delay\"]>0][\"purchase_date_min\"]/X_test[X_test[\"avg_purchase_delay\"]>0][\"nb_purchases\"], alpha=0.5, label = \"average purchase delay on the full period\")\n", - "plt.legend()\n", - "plt.xlabel(\"durée (jours)\")\n", - "plt.ylabel(\"fréquence\")\n", - "plt.title(\"Comparaison entre le délai-type d'achat sur la période d'achat et sur l'ensemble de la période\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2a46a811-9169-43e2-a759-461562f4f250", - "metadata": {}, - "source": [ - "Il vaut mieux prendre le rythme en considérant purchase date min au dénominateur plutôt que le délai entre le \n", - "1er et le dernier achat" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "id": "fad27180-e1f2-4876-b0b8-2254c342fc36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 1.473400e+04\n", - "mean 9.011960e+07\n", - "std 8.222514e+08\n", - "min 0.000000e+00\n", - "25% 7.194159e-01\n", - "50% 3.564579e+00\n", - "75% 2.645439e+01\n", - "max 1.996151e+10\n", - "dtype: float64" - ] - }, - "execution_count": 192, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(X_test[X_test[\"avg_purchase_delay\"]>0][\"purchase_date_max\"]/X_test[X_test[\"avg_purchase_delay\"]>0][\"avg_purchase_delay\"]).describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "c232ced3-c9b2-4e35-b89b-c18f7c99dc7a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.boxplot(X_test[X_test[\"avg_purchase_delay\"]>0][\"purchase_date_max\"]/X_test[X_test[\"avg_purchase_delay\"]>0][\"avg_purchase_delay\"], showfliers=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "cdc917b9-eb2e-443f-8376-9a4ec4d24074", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 14734.000000\n", - "mean 145.979256\n", - "std 123.403697\n", - "min 0.000000\n", - "25% 38.053773\n", - "50% 111.560918\n", - "75% 225.056992\n", - "max 546.378919\n", - "Name: purchase_date_max, dtype: float64" - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[X_test[\"avg_purchase_delay\"]>0][\"purchase_date_max\"].describe()" - ] - }, - { - "cell_type": "markdown", - "id": "d386e36f-deba-43c9-8a51-eba868b39f0e", - "metadata": {}, - "source": [ - "Il est plus pertinent de considérer l'ensemble de la période que de couper à la date du dernier achat \\\n", - "On définit donc avg purchase delay all comme le délai moyen entre deux achats depuis que le client est \n", - "connu et jusqu'a aujourd'hui" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "id": "71b6ff7e-c48c-45b7-bc1a-70dafd11fbf1", - "metadata": {}, - "outputs": [], - "source": [ - "X_test[\"avg_purchase_delay_all\"] = (X_test[\"purchase_date_min\"]/X_test[\"nb_purchases\"]).replace([np.inf, -np.inf], 0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20c757fe-4f3a-406c-b3b9-dd12b57a474c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e65af9b9-9266-4ec5-950f-2fc2ed14140c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0652202-f5bc-4141-a384-07afd96f146b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "7b3b3398-3ddc-41ee-b669-aea86e7f6d4e", - "metadata": {}, - "source": [ - "Il faut aussi étudier le nombre de tickets acheté, pas seulement le nombre d'achats" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "id": "3b01367d-4fb0-46bb-90e8-307e6152e8bb", - "metadata": {}, - "outputs": [], - "source": [ - "# on def avg tickets delay de façon similaire à avg purchase delay mais en utilisant plutôt nb tickets\n", - "\n", - "X_test[\"avg_tickets_delay\"] = (X_test[\"consumption_lifetime\"]/X_test[\"nb_tickets\"]).replace([np.inf, -np.inf], 0)\n", - "X_test[\"avg_tickets_delay_all\"] = (X_test[\"purchase_date_min\"]/X_test[\"nb_tickets\"]).replace([np.inf, -np.inf], 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "id": "0eb59297-0ec2-4181-b743-0264f95a7bee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"187 0.768947 0.384473 2.595480 \n", - "229 0.021655 0.010828 2.572338 \n", - "312 0.118090 0.059045 2.590035 \n", - "439 0.055324 0.027662 2.583779 \n", - "613 0.483681 0.241840 1.642784 \n", - "713 0.122269 0.061134 2.591620 \n", - "967 0.123183 0.061591 1.625538 \n", - "1042 0.007257 0.003628 2.570451 \n", - "1096 0.544213 0.272106 2.595382 \n", - "1124 0.047685 0.023843 2.596152 \n", - "1451 0.008333 0.004167 2.563391 \n", - "1728 0.179028 0.089514 2.568663 \n", - "1740 0.006563 0.003281 2.591748 \n", - "1843 0.004641 0.002321 2.592402 \n", - "1862 0.006331 0.003166 2.598900 \n", - "1984 0.007211 0.003605 2.594734 \n", - "2041 0.224016 0.112008 2.298547 \n", - "2115 0.053553 0.026777 2.591493 \n", - "2384 0.566898 0.283449 2.585885 \n", - "\n", - " avg_tickets_delay avg_tickets_delay_all \n", - "136 0.369051 2.589641 \n", - "187 0.256316 1.730320 \n", - "229 0.005414 1.286169 \n", - "312 0.029523 1.295017 \n", - "439 0.013831 1.291889 \n", - "613 0.120920 0.821392 \n", - "713 0.030567 1.295810 \n", - "967 0.061591 1.625538 \n", - "1042 0.003628 2.570451 \n", - "1096 0.136053 1.297691 \n", - "1124 0.023843 2.596152 \n", - "1451 0.004167 2.563391 \n", - "1728 0.044757 1.284332 \n", - "1740 0.003281 2.591748 \n", - "1843 0.002321 2.592402 \n", - "1862 0.003166 2.598900 \n", - "1984 0.003605 2.594734 \n", - "2041 0.074672 1.532365 \n", - "2115 0.017851 1.727662 \n", - "2384 0.141725 1.292943 \n", - "\n", - "[20 rows x 22 columns]" - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[(X_test[\"avg_purchase_delay\"]>0) & (X_test[\"purchase_date_min\"]<10)].head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "id": "91ec6a21-89dd-40cd-91fc-8dfab132a9e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "y_has_purchased 13690.0\n", - "dtype: float64" - ] - }, - "execution_count": 217, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "3223968c-409e-4110-8dcc-fe319d34d44f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36092.22480054577" - ] - }, - "execution_count": 218, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[\"score\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "id": "0233ab78-81d7-41a2-b948-4bc24f51c9e9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.20933232507450736" - ] - }, - "execution_count": 241, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[X_test[\"quartile\"]==\"3\"][\"has_purchased\"].mean()" - ] - }, - { - "cell_type": "markdown", - "id": "c3bf1a55-7d46-42c7-9436-b68ce8c7ef24", - "metadata": {}, - "source": [ - "Autre méthode \\\n", - "On considère la durée totale sur laquelle les features ont été observées (1 an et demi) sans se soucier de la \n", - "date du 1er achat. \n", - "Et on extrapole le rythme d'achat en considérant que le client devrait acheter nb_tickets/1.5 tickets durant l'année à venir. " - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "id": "d594a3ee-22cb-45b5-a6fa-4439c0aad01c", - "metadata": {}, - "outputs": [], - "source": [ - "period_duration_years = 1.5\n", - "\n", - "expected_tickets_purchased = X_test[\"nb_tickets\"]/period_duration_years\n", - "expected_amount = X_test[\"total_amount\"]/period_duration_years" - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "id": "807f9810-a691-4e51-af51-cdb7f0b4bd40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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224213 rows × 16 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 2.0 1.0 60.00 1.0 \n", - "1 8.0 3.0 140.00 1.0 \n", - "2 2.0 1.0 50.00 1.0 \n", - "3 3.0 1.0 90.00 1.0 \n", - "4 2.0 1.0 78.00 1.0 \n", - "... ... ... ... ... \n", - "224208 0.0 0.0 0.00 0.0 \n", - "224209 1.0 1.0 20.00 1.0 \n", - "224210 0.0 0.0 0.00 0.0 \n", - "224211 1.0 1.0 97.11 1.0 \n", - "224212 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 355.268981 355.268981 \n", - "1 0.0 373.540289 219.262269 \n", - "2 0.0 5.202442 5.202442 \n", - "3 0.0 5.178958 5.178958 \n", - "4 0.0 5.174039 5.174039 \n", - "... ... ... ... \n", - "224208 0.0 550.000000 550.000000 \n", - "224209 1.0 392.501030 392.501030 \n", - "224210 0.0 550.000000 550.000000 \n", - "224211 1.0 172.334074 172.334074 \n", - "224212 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 0.0 True False 0 \n", - "1 0.0 True False 0 \n", - "2 0.0 True False 0 \n", - "3 0.0 True False 0 \n", - "4 0.0 True False 1 \n", - "... ... ... ... ... \n", - "224208 0.0 True False 0 \n", - "224209 1.0 True False 0 \n", - "224210 0.0 True True 0 \n", - "224211 1.0 True False 0 \n", - "224212 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened score odd_ratio \n", - "0 1 0.0 0.0 0.493834 0.975638 \n", - "1 1 0.0 0.0 0.722704 2.606253 \n", - "2 1 0.0 0.0 0.689866 2.224409 \n", - "3 1 0.0 0.0 0.693078 2.258158 \n", - "4 0 0.0 0.0 0.690209 2.227980 \n", - "... ... ... ... ... ... \n", - "224208 1 34.0 3.0 0.250218 0.333721 \n", - "224209 1 23.0 6.0 0.524745 1.104135 \n", - "224210 1 8.0 4.0 0.117175 0.132728 \n", - "224211 1 13.0 5.0 0.643851 1.807814 \n", - "224212 1 4.0 4.0 0.250170 0.333636 \n", - "\n", - "[224213 rows x 16 columns]" - ] - }, - "execution_count": 493, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[\"score\"] = y_pred_prob_train\n", - "# X_train[\"odd_ratio\"] = X_train[\"score\"]/(1-X_train[\"score\"])\n", - "X_train" - ] - }, - { - "cell_type": "code", - "execution_count": 491, - "id": "240afa08-692d-4c2d-93c7-c8c8a46afdb3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 2.241790e+05\n", - "mean 5.824134e+10\n", - "std 1.462083e+13\n", - "min 1.207494e-01\n", - "25% 1.476621e-01\n", - "50% 3.338869e-01\n", - "75% 1.427047e+00\n", - "max 4.503600e+15\n", - "Name: odd_ratio, dtype: float64" - ] - }, - "execution_count": 491, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[\"odd_ratio\"][X_train[\"odd_ratio\"]\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_openedscoreodd_ratio
02.01.060.001.00.0355.268981355.2689810.0TrueFalse010.00.00.4938340.975638
18.03.0140.001.00.0373.540289219.2622690.0TrueFalse010.00.00.7227042.606253
22.01.050.001.00.05.2024425.2024420.0TrueFalse010.00.00.6898662.224409
33.01.090.001.00.05.1789585.1789580.0TrueFalse010.00.00.6930782.258158
42.01.078.001.00.05.1740395.1740390.0TrueFalse100.00.00.6902092.227980
...................................................
2242080.00.00.000.00.0550.000000550.0000000.0TrueFalse0134.03.00.2502180.333721
2242091.01.020.001.01.0392.501030392.5010301.0TrueFalse0123.06.00.5247451.104135
2242100.00.00.000.00.0550.000000550.0000000.0TrueTrue018.04.00.1171750.132728
2242111.01.097.111.01.0172.334074172.3340741.0TrueFalse0113.05.00.6438511.807814
2242120.00.00.000.00.0550.000000550.0000000.0TrueFalse014.04.00.2501700.333636
\n", - "

224213 rows × 16 columns

\n", - "" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 2.0 1.0 60.00 1.0 \n", - "1 8.0 3.0 140.00 1.0 \n", - "2 2.0 1.0 50.00 1.0 \n", - "3 3.0 1.0 90.00 1.0 \n", - "4 2.0 1.0 78.00 1.0 \n", - "... ... ... ... ... \n", - "224208 0.0 0.0 0.00 0.0 \n", - "224209 1.0 1.0 20.00 1.0 \n", - "224210 0.0 0.0 0.00 0.0 \n", - "224211 1.0 1.0 97.11 1.0 \n", - "224212 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 355.268981 355.268981 \n", - "1 0.0 373.540289 219.262269 \n", - "2 0.0 5.202442 5.202442 \n", - "3 0.0 5.178958 5.178958 \n", - "4 0.0 5.174039 5.174039 \n", - "... ... ... ... \n", - "224208 0.0 550.000000 550.000000 \n", - "224209 1.0 392.501030 392.501030 \n", - "224210 0.0 550.000000 550.000000 \n", - "224211 1.0 172.334074 172.334074 \n", - "224212 0.0 550.000000 550.000000 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "0 0.0 True False 0 \n", - "1 0.0 True False 0 \n", - "2 0.0 True False 0 \n", - "3 0.0 True False 0 \n", - "4 0.0 True False 1 \n", - "... ... ... ... ... \n", - "224208 0.0 True False 0 \n", - "224209 1.0 True False 0 \n", - "224210 0.0 True True 0 \n", - "224211 1.0 True False 0 \n", - "224212 0.0 True False 0 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened score odd_ratio \n", - "0 1 0.0 0.0 0.493834 0.975638 \n", - "1 1 0.0 0.0 0.722704 2.606253 \n", - "2 1 0.0 0.0 0.689866 2.224409 \n", - "3 1 0.0 0.0 0.693078 2.258158 \n", - "4 0 0.0 0.0 0.690209 2.227980 \n", - "... ... ... ... ... ... \n", - "224208 1 34.0 3.0 0.250218 0.333721 \n", - "224209 1 23.0 6.0 0.524745 1.104135 \n", - "224210 1 8.0 4.0 0.117175 0.132728 \n", - "224211 1 13.0 5.0 0.643851 1.807814 \n", - "224212 1 4.0 4.0 0.250170 0.333636 \n", - "\n", - "[224213 rows x 16 columns]" - ] - }, - "execution_count": 494, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on utilise le second score comme valeur de remplacement quand score = 1\n", - "X_train_second_score = X_train[\"score\"][X_train[\"score\"]<1].max()\n", - "\n", - "X_train[\"score\"] = X_train[\"score\"].apply(lambda x : X_train_second_score if x==1 else x)\n", - "X_train" - ] - }, - { - "cell_type": "code", - "execution_count": 498, - "id": "b2690332-9f2e-4597-ab13-cef073de367f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9999999999999998" - ] - }, - "execution_count": 498, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[\"score\"].max()" - ] - }, - { - "cell_type": "code", - "execution_count": 499, - "id": "e749e3b5-f5f9-4ab5-a0c1-ee99c5e88a26", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 2.242130e+05\n", - "mean 7.411652e+11\n", - "std 5.734858e+13\n", - "min 1.207494e-01\n", - "25% 1.476621e-01\n", - "50% 3.338869e-01\n", - "75% 1.427525e+00\n", - "max 4.503600e+15\n", - "Name: odd_ratio, dtype: float64" - ] - }, - "execution_count": 499, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[\"odd_ratio\"] = X_train[\"score\"]/(1-X_train[\"score\"])\n", - "X_train[\"odd_ratio\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 500, - "id": "84fea40a-896f-4e74-8d3c-18ecbe9f4c5f", - "metadata": {}, - "outputs": [], - "source": [ - "def obj_function_X_train(bias) :\n", - " obj = sum([adjusted_score(element, bias) for element in X_train[\"odd_ratio\"]]) # - y_test.sum()[\"y_has_purchased\"]\n", - " return obj" - ] - }, - { - "cell_type": "code", - "execution_count": 501, - "id": "9886995b-59d7-4fdf-acb0-981338a4e083", - "metadata": {}, - "outputs": [], - "source": [ - "# minimization\n", - "\n", - "from scipy.optimize import minimize\n", - "\n", - "\n", - "y_train_sum = y_train.sum()[\"y_has_purchased\"]\n", - "initial_guess = 6\n", - "estimated_biais_train = minimize(lambda bias : (obj_function_X_train(bias)-y_train_sum)**2 ,\n", - "initial_guess , method = \"BFGS\")" - ] - }, - { - "cell_type": "code", - "execution_count": 502, - "id": "80cb872f-2aac-4c77-b935-2d05e0199837", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bias estimated on train set: 5.947447991192572\n" - ] - } - ], - "source": [ - "# biais de 5.95 contre 6.04 pour le test set, OK\n", - "print(f\"bias estimated on train set: {estimated_biais_train.x[0]}\")" - ] - }, - { - "cell_type": "markdown", - "id": "25d8c4e0-ca60-4aeb-8aa9-9cfa8efdf52a", - "metadata": {}, - "source": [ - "### construction d'une fonction de généralisation de la méthode de calcul du biais\n", - "\n", - "Le biais est calculé de la façon suivante. \n", - "En notant $\\hat{p(x_i)}$ le score calculé et $p(x_i)$ le vrai score (sans biais), et $\\beta$ le logarithme du biais, on a : \\\n", - "$\\ln{\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}} = \\beta + \\ln{\\frac{p(x_i)}{1-p(x_i)}}$ \\\n", - "$ \\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}} = \\exp(\\beta) . \\frac{p(x_i)}{1-p(x_i)} $ \\\n", - "Ce qu'on appelle biais et qu'on estime dans le code par la suite est : $B=\\exp(\\beta) $. Les probabilités ne sont donc pas biaisées si $B=1$. Il y a surestimation si $B>1$. \n", - "\n", - "On cherche le B qui permette d'ajuster les probabilités de telle sorte que la somme des scores soit égale à la somme des y_has_purchased. Cela revient à résoudre : \n", - "\n", - "\\begin{equation}\n", - "\\sum_{i}{\\frac{\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}}{B+\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}}} = \\sum_{i}{Y_i}\n", - "\\end{equation}\n", - "\n", - "C'est ce que fait la fonction find_bias" - ] - }, - { - "cell_type": "code", - "execution_count": 733, - "id": "41f588ad-b093-47f9-a2c9-52428c61d8d8", - "metadata": {}, - "outputs": [], - "source": [ - "def adjusted_score(odd_ratio, bias) :\n", - " adjusted_score = odd_ratio/(bias+odd_ratio)\n", - " return adjusted_score" - ] - }, - { - "cell_type": "code", - "execution_count": 734, - "id": "208900ab-0211-4e0a-a235-e4ea3a6957ce", - "metadata": {}, - "outputs": [], - "source": [ - "# fonction qui prend un vecteur en entrée et remplace les 1 par la seconde plus grande valeur\n", - "# permet de remplacer les 1 par une valeur de score très proche, et d'ainsi éviter des odd ratio infinis\n", - "\n", - "def adjust_score_1(score) :\n", - " second_best_score = np.array([element for element in score if element !=1]).max()\n", - " new_score = np.array([element if element!=1 else second_best_score for element in score])\n", - " \n", - " return new_score\n" - ] - }, - { - "cell_type": "code", - "execution_count": 735, - "id": "942c3952-577e-4e18-87a8-e15ed3040241", - "metadata": {}, - "outputs": [], - "source": [ - "def odd_ratio(score) :\n", - " return score / (1 - score)" - ] - }, - { - "cell_type": "code", - "execution_count": 768, - "id": "f34e16f6-1596-492e-8ff2-0703173e815e", - "metadata": {}, - "outputs": [], - "source": [ - "# definition of a function that automatically detects the bias\n", - "\n", - "def find_bias(odd_ratios, y_objective, initial_guess=6) :\n", - " \"\"\"\n", - " results = minimize(lambda bias : (sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective)**2 ,\n", - " initial_guess , method = \"BFGS\")\n", - "\n", - " estimated_bias = results.x[0]\n", - " \"\"\"\n", - "\n", - " # faster method\n", - " bias_estimated = fsolve(lambda bias : sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective, x0=6)\n", - " \n", - " return bias_estimated[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 761, - "id": "8cc3a658-5ab5-482b-ba26-b12a3bf9c81b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([6.0428265])" - ] - }, - "execution_count": 761, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# autre méthode : avec fsolve\n", - "\n", - "from scipy.optimize import fsolve\n", - "\n", - "bias_estimated = fsolve(lambda bias : sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective, x0=6)\n", - "bias_estimated" - ] - }, - { - "cell_type": "code", - "execution_count": 760, - "id": "92be0759-2583-411d-a0b0-f09fd53ff367", - "metadata": {}, - "outputs": [], - "source": [ - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": 763, - "id": "58eb3320-fd4a-4b21-9cfe-6b9f7533a730", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "résultat : [6.0428265]\n", - "tps de calcul 2.112041473388672\n", - "résultat : 6.042826489667565\n", - "tps de calcul 3.9603891372680664\n" - ] - } - ], - "source": [ - "# comparaison du temps pris par les deux opérations\n", - "\n", - "temps_debut = time.time()\n", - "bias_estimated_1 = fsolve(lambda bias : sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective, x0=6)\n", - "temps_fin = time.time()\n", - "\n", - "temps_ecoule = temps_fin - temps_debut\n", - "print(\"résultat : \",bias_estimated_1)\n", - "print(\"tps de calcul\", temps_ecoule)\n", - "\n", - "temps_debut = time.time()\n", - "bias_estimated_2 = minimize(lambda bias : (sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective)**2 ,\n", - " x0=6 , method = \"BFGS\").x[0]\n", - "temps_fin = time.time()\n", - "\n", - "temps_ecoule = temps_fin - temps_debut\n", - "print(\"résultat : \",bias_estimated_2)\n", - "print(\"tps de calcul\", temps_ecoule)" - ] - }, - { - "cell_type": "code", - "execution_count": 755, - "id": "5e6c5b4a-4a13-43ed-af96-e5892563057a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.28853049, 0.3231094 , 0.38810178, ..., 0.13186529, 1.37997272,\n", - " 0.34048672])" - ] - }, - "execution_count": 755, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "odd_ratios" - ] - }, - { - "cell_type": "code", - "execution_count": 749, - "id": "6ef9088a-3ae7-419a-b009-cb5aae4ab4c7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36092.2248005385" - ] - }, - "execution_count": 749, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum([adjusted_score(element, 1) for element in list(odd_ratios)]) # - y_objective" - ] - }, - { - "cell_type": "code", - "execution_count": 704, - "id": "5fcd2467-9119-4bba-af38-f7833173c2d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 1]" - ] - }, - "execution_count": 704, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[element for element in np.array([0,1])]" - ] - }, - { - "cell_type": "code", - "execution_count": 544, - "id": "e20820a3-30a4-4e24-8c65-6178c4d7e9c1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.947447991192572" - ] - }, - "execution_count": 544, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the function works well !!\n", - "\n", - "bias_train_set = find_bias(odd_ratios = X_train[\"odd_ratio\"], y_objective = y_train_sum, initial_guess = 6)\n", - "bias_train_set" - ] - }, - { - "cell_type": "code", - "execution_count": 716, - "id": "c17e4a3c-a3de-425b-a3da-1e15e33cb403", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.28853049, 0.3231094 , 0.38810178, ..., 0.13186529, 1.37997272,\n", - " 0.34048672])" - ] - }, - "execution_count": 716, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "odd_ratio = odd_ratio(adjust_score_1(X_test[\"score\"]))\n", - "odd_ratio" - ] - }, - { - "cell_type": "code", - "execution_count": 751, - "id": "0aad15bd-e820-4eda-b229-64bd1f90f7f5", - "metadata": {}, - "outputs": [], - "source": [ - "# definition of the values for the pb\n", - "\n", - "new_score = adjust_score_1(X_test[\"score\"])\n", - "\n", - "odd_ratios = odd_ratio(np.array(new_score))\n", - "\n", - "y_objective = y_test[\"y_has_purchased\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 752, - "id": "498560c3-e446-4dcc-bb19-47f2910d5fbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.69591281, 0.2442046 , 0.27959173, ..., 0.11650264, 0.57982712,\n", - " 0.25400231]),\n", - " array([2.28853049, 0.3231094 , 0.38810178, ..., 0.13186529, 1.37997272,\n", - " 0.34048672]),\n", - " 13690.0)" - ] - }, - "execution_count": 752, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_score, odd_ratios, y_objective" - ] - }, - { - "cell_type": "code", - "execution_count": 769, - "id": "03f4a8f1-f568-4a7d-9501-8a7467a9a864", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.042826497117542" - ] - }, - "execution_count": 769, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# computation with the function defined\n", - "\n", - "bias_test_set = find_bias(odd_ratios = odd_ratios, \n", - " y_objective = y_objective,\n", - " initial_guess=6)\n", - "bias_test_set" - ] - }, - { - "cell_type": "code", - "execution_count": 770, - "id": "d0ea666d-33e8-46e8-9a4d-f17091dbfa93", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.947447998640124" - ] - }, - "execution_count": 770, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "biais_train_set = find_bias(odd_ratios = odd_ratio(adjust_score_1(X_train[\"score\"])), \n", - " y_objective = y_train[\"y_has_purchased\"].sum(),\n", - " initial_guess=6)\n", - "biais_train_set" - ] - }, - { - "cell_type": "code", - "execution_count": 772, - "id": "1c1bdbc6-4fa7-45fb-ba27-b4c02ff1ff9c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.947447991192572" - ] - }, - "execution_count": 772, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bias_train_set" - ] - }, - { - "cell_type": "code", - "execution_count": 776, - "id": "eced1d08-5230-4449-8024-105111fe5873", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "betâ test - betâ train = 0.015909647078591174\n" - ] - } - ], - "source": [ - "# différence des beta (log du biais)\n", - "print(\"betâ test - betâ train = \",np.log(bias_test_set/bias_train_set))" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5aca0-7e8b-4039-9bb2-ff5011c436a6", - "metadata": {}, - "source": [ - "## Random forest" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "da8873e5-c4e7-4580-8567-70e411c029ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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10000 rows × 14 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "43000 0.0 0.0 0.0 0.0 \n", - "183923 0.0 0.0 0.0 0.0 \n", - "97373 0.0 0.0 0.0 0.0 \n", - "66956 7.0 2.0 254.0 1.0 \n", - "116487 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "83146 1.0 1.0 35.0 1.0 \n", - "223586 0.0 0.0 0.0 0.0 \n", - "56489 0.0 0.0 0.0 0.0 \n", - "141236 0.0 0.0 0.0 0.0 \n", - "6999 2.0 1.0 20.0 1.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "43000 0.0 550.000000 550.000000 \n", - "183923 0.0 550.000000 550.000000 \n", - "97373 0.0 550.000000 550.000000 \n", - "66956 1.0 378.343062 370.453947 \n", - "116487 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "83146 1.0 37.474040 37.474040 \n", - "223586 0.0 550.000000 550.000000 \n", - "56489 0.0 550.000000 550.000000 \n", - "141236 0.0 550.000000 550.000000 \n", - "6999 0.0 171.446921 171.446921 \n", - "\n", - " nb_tickets_internet is_email_true opt_in gender_female \\\n", - "43000 0.0 True True 0 \n", - "183923 0.0 True True 0 \n", - "97373 0.0 True False 0 \n", - "66956 7.0 True False 0 \n", - "116487 0.0 True False 1 \n", - "... ... ... ... ... \n", - "83146 1.0 True False 0 \n", - "223586 0.0 True True 0 \n", - "56489 0.0 True True 0 \n", - "141236 0.0 True False 0 \n", - "6999 0.0 True True 1 \n", - "\n", - " gender_male nb_campaigns nb_campaigns_opened \n", - "43000 1 14.0 12.0 \n", - "183923 1 19.0 11.0 \n", - "97373 0 7.0 2.0 \n", - "66956 1 0.0 0.0 \n", - "116487 0 5.0 0.0 \n", - "... ... ... ... \n", - "83146 1 9.0 3.0 \n", - "223586 1 23.0 1.0 \n", - "56489 1 4.0 0.0 \n", - "141236 1 6.0 0.0 \n", - "6999 0 0.0 0.0 \n", - "\n", - "[10000 rows x 14 columns]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_subsample" - ] - }, - { - "cell_type": "markdown", - "id": "fcbb8bea-e9d3-4fd4-8b47-7e796c788a1f", - "metadata": {}, - "source": [ - "### Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "55e0c6d8-9e98-47be-9d5d-41e06505ceba", - "metadata": {}, - "outputs": [], - "source": [ - "# no need to standardize variables in a random forest\n", - "# we just encode categorical variables\n", - "\n", - "categorical_features = ['opt_in', 'is_email_true'] \n", - "\n", - "# Transformer for the categorical features\n", - "categorical_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", - "])\n", - "\n", - "preproc = ColumnTransformer(\n", - " transformers=[\n", - " (\"cat\", categorical_transformer, categorical_features)\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "27af28da-d2bb-4eff-b842-18cec9740c84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
ColumnTransformer(transformers=[('cat',\n",
-       "                                 Pipeline(steps=[('onehot',\n",
-       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                sparse_output=False))]),\n",
-       "                                 ['opt_in', 'is_email_true'])])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in', 'is_email_true'])])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preproc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cb46acb-647f-469d-b5e1-510bf1283196", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ce9acf4-3514-4056-a71a-c7654e25b9de", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "dfdd4601-4866-4102-b620-4f10648e7981", - "metadata": {}, - "source": [ - "### Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eeefae73-afe7-4441-a04c-bd6a04beedd2", - "metadata": {}, - "outputs": [], - "source": [ - "# Define models and parameters for GridSearch\n", - "model = {\n", - " 'model': RandomForestClassifier(),\n", - " 'params': {\n", - " 'randforest__n_estimators': [100, 150, 200, 250, 300],\n", - " 'randforest__max_depth': [None, 15, 20, 25, 30, 35, 40],\n", - " }\n", - " }\n", - "\n", - "# Test each model using GridSearchCV\n", - "pipe = Pipeline(steps=[('preprocessor', preproc), ('randforest', model['model'])])\n", - "clf = GridSearchCV(pipe, model['params'], cv=3)\n", - "clf.fit(X_train, y_train)\n", - "\n", - "print(f\"Model: {model['model']}\")\n", - "print(f\"Best parameters: {clf.best_params_}\")\n", - "print('Best classification accuracy in train is: {}'.format(clf.best_score_))\n", - "print('Classification accuracy on test is: {}'.format(clf.score(X_test, y_test)))\n", - "print(\"------\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "2a88f13b-05bc-4a70-b08b-8b07c118cedc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
-       "                 ColumnTransformer(transformers=[('cat',\n",
-       "                                                  Pipeline(steps=[('onehot',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                 sparse_output=False))]),\n",
-       "                                                  ['opt_in',\n",
-       "                                                   'is_email_true'])])),\n",
-       "                ('random_forest',\n",
-       "                 RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                      1.0: 3.486549107420539}))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))])" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pipeline - on joue sur : max_depth\n", - "\n", - "param_grid = {\"random_forest__max_depth\" : [None, 10, 20, 40, 50, 60]}\n", - "\n", - "pipeline = Pipeline(steps=[\n", - " ('preprocessor', preproc),\n", - " ('random_forest', RandomForestClassifier(bootstrap = False, class_weight = weight_dict,\n", - " )) \n", - "])\n", - "\n", - "pipeline.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "494dca83-4d60-4e49-8689-7d7ac612bb83", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'estimator': DecisionTreeClassifier(),\n", - " 'n_estimators': 100,\n", - " 'estimator_params': ('criterion',\n", - " 'max_depth',\n", - " 'min_samples_split',\n", - " 'min_samples_leaf',\n", - " 'min_weight_fraction_leaf',\n", - " 'max_features',\n", - " 'max_leaf_nodes',\n", - " 'min_impurity_decrease',\n", - " 'random_state',\n", - " 'ccp_alpha',\n", - " 'monotonic_cst'),\n", - " 'bootstrap': True,\n", - " 'oob_score': False,\n", - " 'n_jobs': None,\n", - " 'random_state': None,\n", - " 'verbose': 0,\n", - " 'warm_start': False,\n", - " 'class_weight': None,\n", - " 'max_samples': None,\n", - " 'criterion': 'gini',\n", - " 'max_depth': None,\n", - " 'min_samples_split': 2,\n", - " 'min_samples_leaf': 1,\n", - " 'min_weight_fraction_leaf': 0.0,\n", - " 'max_features': 'sqrt',\n", - " 'max_leaf_nodes': None,\n", - " 'min_impurity_decrease': 0.0,\n", - " 'monotonic_cst': None,\n", - " 'ccp_alpha': 0.0}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RandomForestClassifier().__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "ee7cbc1c-7c31-4111-82a3-995141e2f13f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessor',\n",
-       "                                        ColumnTransformer(transformers=[('cat',\n",
-       "                                                                         Pipeline(steps=[('onehot',\n",
-       "                                                                                          OneHotEncoder(handle_unknown='ignore',\n",
-       "                                                                                                        sparse_output=False))]),\n",
-       "                                                                         ['opt_in',\n",
-       "                                                                          'is_email_true'])])),\n",
-       "                                       ('random_forest',\n",
-       "                                        RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
-       "                                                                             1.0: 3.486549107420539}))]),\n",
-       "             param_grid={'random_forest__max_depth': [None, 10, 20, 40, 50,\n",
-       "                                                      60]},\n",
-       "             scoring=make_scorer(f1_score, response_method='predict'))
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))]),\n", - " param_grid={'random_forest__max_depth': [None, 10, 20, 40, 50,\n", - " 60]},\n", - " scoring=make_scorer(f1_score, response_method='predict'))" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# pipeline on the subsample\n", - "\n", - "random_forest_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", - " )\n", - "\n", - "random_forest_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "3f149137-6313-4b4e-99d6-b3af7f296ad7", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n", - "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'random_forest__max_depth': None}\n", - "Best classification F1 score in train is: 0.33107422141513826\n", - "Classification F1 score on test is: 0.31752789604029275\n" - ] - } - ], - "source": [ - "# run the pipeline on the full sample\n", - "\n", - "random_forest_grid.fit(X_train, y_train)\n", - "\n", - "# print results\n", - "print('Returned hyperparameter: {}'.format(random_forest_grid.best_params_))\n", - "print('Best classification F1 score in train is: {}'.format(random_forest_grid.best_score_))\n", - "print('Classification F1 score on test is: {}'.format(random_forest_grid.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "cd79f942-abd0-48c9-aa0d-0d22673abeec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'scoring': make_scorer(f1_score, response_method='predict'),\n", - " 'estimator': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(bootstrap=False,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))]),\n", - " 'n_jobs': None,\n", - " 'refit': True,\n", - " 'cv': 3,\n", - " 'verbose': 0,\n", - " 'pre_dispatch': '2*n_jobs',\n", - " 'error_score': nan,\n", - " 'return_train_score': False,\n", - " 'param_grid': {'random_forest__max_depth': [None, 10, 20, 40, 50, 60]},\n", - " 'multimetric_': False,\n", - " 'best_index_': 0,\n", - " 'best_score_': 0.33107422141513826,\n", - " 'best_params_': {'random_forest__max_depth': None},\n", - " 'best_estimator_': Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('cat',\n", - " Pipeline(steps=[('onehot',\n", - " OneHotEncoder(handle_unknown='ignore',\n", - " sparse_output=False))]),\n", - " ['opt_in',\n", - " 'is_email_true'])])),\n", - " ('random_forest',\n", - " RandomForestClassifier(bootstrap=False,\n", - " class_weight={0.0: 0.5837086520288036,\n", - " 1.0: 3.486549107420539}))]),\n", - " 'refit_time_': 2.2247676849365234,\n", - " 'feature_names_in_': array(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", - " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", - " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'], dtype=object),\n", - " 'scorer_': make_scorer(f1_score, response_method='predict'),\n", - " 'cv_results_': {'mean_fit_time': array([1.64734515, 1.4220806 , 1.43256299, 1.68632547, 1.4271005 ,\n", - " 1.42404906]),\n", - " 'std_fit_time': array([0.32811727, 0.01915 , 0.02151065, 0.2729267 , 0.02447776,\n", - " 0.02384922]),\n", - " 'mean_score_time': array([0.14065607, 0.13571024, 0.13531415, 0.17512798, 0.13398822,\n", - " 0.13499872]),\n", - " 'std_score_time': array([0.00759402, 0.00653712, 0.00743453, 0.04901062, 0.00848726,\n", - " 0.00789539]),\n", - " 'param_random_forest__max_depth': masked_array(data=[None, 10, 20, 40, 50, 60],\n", - " mask=[False, False, False, False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'params': [{'random_forest__max_depth': None},\n", - " {'random_forest__max_depth': 10},\n", - " {'random_forest__max_depth': 20},\n", - " {'random_forest__max_depth': 40},\n", - " {'random_forest__max_depth': 50},\n", - " {'random_forest__max_depth': 60}],\n", - " 'split0_test_score': array([0.19168873, 0.19168873, 0.19168873, 0.19168873, 0.19168873,\n", - " 0.19168873]),\n", - " 'split1_test_score': array([0.34428494, 0.34428494, 0.34428494, 0.34428494, 0.34428494,\n", - " 0.34428494]),\n", - " 'split2_test_score': array([0.45724899, 0.45724899, 0.45724899, 0.45724899, 0.45724899,\n", - " 0.45724899]),\n", - " 'mean_test_score': array([0.33107422, 0.33107422, 0.33107422, 0.33107422, 0.33107422,\n", - " 0.33107422]),\n", - " 'std_test_score': array([0.10881622, 0.10881622, 0.10881622, 0.10881622, 0.10881622,\n", - " 0.10881622]),\n", - " 'rank_test_score': array([1, 1, 1, 1, 1, 1], dtype=int32)},\n", - " 'n_splits_': 3}" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_forest_grid.__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "1806fe6d-cf98-459d-b05a-eb95972281dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.48955211455211456\n", - "F1 Score: 0.31752789604029275\n", - "Recall Score: 0.8335281227173119\n" - ] - } - ], - "source": [ - "# print results for the best model\n", - "\n", - "y_pred = random_forest_grid.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "1a6a8e07-bd93-496b-986e-d219c03b82c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# confusion matrix \n", - "\n", - "draw_confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "1e1b3e42-1075-4a4a-bf44-3dadde3dbed1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC curve\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "y_pred_prob = random_forest_grid.predict_proba(X_test)[:, 1]\n", - "\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize = (14, 8))\n", - "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : random forest')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "854f6242-813f-400a-be43-7414a859b355", - "metadata": {}, - "source": [ - "## Naive Bayes " - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "id": "b083d10d-8510-4a07-974b-e0c324175d7f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GaussianNB()" - ] - }, - "execution_count": 219, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf = GaussianNB()\n", - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 234, - "id": "a5459639-be3d-4292-89d2-061f276dc9a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.8780906593406593\n", - "F1 Score: 0.3673381217259815\n", - "Recall Score: 0.24842951059167276\n" - ] - } - ], - "source": [ - "# print results for the best model\n", - "\n", - "y_pred = clf.predict(X_test)\n", - "\n", - "# Calculate the F1 score\n", - "acc = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuracy Score: {acc}\")\n", - "\n", - "f1 = f1_score(y_test, y_pred)\n", - "print(f\"F1 Score: {f1}\")\n", - "\n", - "recall = recall_score(y_test, y_pred)\n", - "print(f\"Recall Score: {recall}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "id": "22d3d4d0-36b4-4561-9bc7-3a408914f089", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "somme des probas de y prédites : 4889.8913137503505\n", - "nombre de y valant 1 : y_has_purchased 13690.0\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# le bayes naif sous-estime les probas d'achat (les autres modèles surestiment pr avoir un bon recall) w\n", - "print(f\"somme des probas de y prédites : {y_pred_prob.sum()}\")\n", - "print(f\"nombre de y valant 1 : {y_test.sum()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "id": "e962eeed-4099-407b-a619-a34a539a404a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC curve\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "y_pred_prob = clf.predict_proba(X_test)[:, 1]\n", - "\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize = (14, 8))\n", - "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", - "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", - "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : naive Bayes')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ad1a0b57-e382-4ae3-90b6-1f790099711b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/numpy/core/fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n", - " return reduction(axis=axis, out=out, **passkwargs)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", - "\n", - "# Tri des prédictions de probabilités et des vraies valeurs\n", - "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", - "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", - "y_test_sorted = y_test.iloc[sorted_indices]\n", - "\n", - "# Calcul du gain cumulatif\n", - "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", - "\n", - "# Tracé de la courbe de lift\n", - "plt.plot(np.linspace(0, 1, len(cumulative_gain)), cumulative_gain, label='Courbe de lift')\n", - "plt.xlabel('Part de clients identifiés sans modèle ')\n", - "plt.ylabel('Part de clients identifiés avec modèle')\n", - "plt.title('Courbe de Lift')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7cbb1fec-97b9-4780-9488-5b8eff5aee0d", - "metadata": {}, - "source": [ - "## From model to segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d97ca3df-3778-469c-a077-495b3ee25051", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([9.0362e+04, 2.7200e+02, 1.6700e+02, 1.0000e+02, 8.6000e+01,\n", - " 5.7000e+01, 6.6000e+01, 6.3000e+01, 4.5000e+01, 5.1000e+01,\n", - " 5.4000e+01, 3.6000e+01, 5.3000e+01, 5.3000e+01, 5.3000e+01,\n", - " 5.1000e+01, 7.7000e+01, 1.1800e+02, 1.2700e+02, 4.2050e+03]),\n", - " array([8.76852176e-09, 5.00000083e-02, 1.00000008e-01, 1.50000007e-01,\n", - " 2.00000007e-01, 2.50000007e-01, 3.00000006e-01, 3.50000006e-01,\n", - " 4.00000005e-01, 4.50000005e-01, 5.00000004e-01, 5.50000004e-01,\n", - " 6.00000004e-01, 6.50000003e-01, 7.00000003e-01, 7.50000002e-01,\n", - " 8.00000002e-01, 8.50000001e-01, 9.00000001e-01, 9.50000000e-01,\n", - " 1.00000000e+00]),\n", - " )" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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"outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# number of observations\n", - "N = len(y_pred_prob)\n", - "\n", - "# sort the data in ascending order \n", - "y_pred_prob_sorted = np.sort(y_pred_prob) \n", - "\n", - "# get the cdf values of y \n", - "steps = np.arange(N) / N\n", - " \n", - "# plotting \n", - "plt.xlabel('X') \n", - "plt.ylabel('P(score<=X)') \n", - " \n", - "plt.title('CDF curve of the predicted probability of purchasec(score) for sports companies') \n", - " \n", - "plt.plot(y_pred_prob_sorted, steps) \n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "e87efb96-71e6-4571-9a48-576ff5ebcbdc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n", - " 0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1. ])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on regarde de plus près les quantiles (on identifie 2 clusters, où est le cut-off ?)\n", - "\n", - "np.linspace(0,1, 21)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "ccd8373c-85c4-451d-b918-7bb84713c9ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(90634,)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_pred_prob_sorted[y_pred_prob < 0.1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "75a2c582-3020-4e2e-9a41-0da75c5dbbed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "score du quantile 0.0 : 1.0\n", - "score du quantile 0.05 : 1.1703610048497538e-08\n", - "score du quantile 0.1 : 1.1916538583855572e-08\n", - "score du quantile 0.15000000000000002 : 1.672960453020865e-08\n", - "score du quantile 0.2 : 2.261530896018714e-08\n", - "score du quantile 0.25 : 4.429426100901144e-08\n", - "score du quantile 0.30000000000000004 : 5.527720441770875e-08\n", - "score du quantile 0.35000000000000003 : 6.583003552085313e-08\n", - "score du quantile 0.4 : 1.0150014636815537e-07\n", - "score du quantile 0.45 : 1.045553983975125e-07\n", - "score du quantile 0.5 : 1.8254643649033717e-07\n", - "score du quantile 0.55 : 1.0036337913333724e-06\n", - "score du quantile 0.6000000000000001 : 3.6006418270834777e-06\n", - "score du quantile 0.65 : 8.750051427856617e-06\n", - "score du quantile 0.7000000000000001 : 1.7761176996762073e-05\n", - "score du quantile 0.75 : 3.658511676930477e-05\n", - "score du quantile 0.8 : 7.449089979671675e-05\n", - "score du quantile 0.8500000000000001 : 0.0001599334998042523\n", - "score du quantile 0.9 : 0.0006156933309033692\n", - "score du quantile 0.9500000000000001 : 0.5161846499348189\n", - "score du quantile 1.0 : 1.0\n" - ] - } - ], - "source": [ - "for step in np.linspace(0,1, 21) :\n", - " score_reached = y_pred_prob_sorted[int(step*N)-1]\n", - " print(f\"score du quantile {step} : {score_reached}\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "3e7d04c4-1add-4ef3-bca5-c2f68356b669", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "score du quantile 0.94 : 0.046364832132301186\n", - "score du quantile 0.941 : 0.060426331367796585\n", - "score du quantile 0.942 : 0.07560789365683944\n", - "score du quantile 0.943 : 0.0961854989484283\n", - "score du quantile 0.944 : 0.12036366182214445\n", - "score du quantile 0.945 : 0.15326229828189683\n", - "score du quantile 0.946 : 0.20141929276940546\n", - "score du quantile 0.947 : 0.26129057078459816\n", - "score du quantile 0.948 : 0.34459110917836233\n", - "score du quantile 0.949 : 0.42441766527261676\n", - "score du quantile 0.95 : 0.5161846499348189\n", - "score du quantile 0.951 : 0.6281715747542238\n", - "score du quantile 0.952 : 0.7161294443763133\n", - "score du quantile 0.953 : 0.8098274658632696\n", - "score du quantile 0.954 : 0.8628210594682936\n", - "score du quantile 0.955 : 0.9031546758694196\n", - "score du quantile 0.956 : 0.9406325197642711\n", - "score du quantile 0.957 : 0.9717094630837765\n", - "score du quantile 0.958 : 0.9853416074407844\n", - "score du quantile 0.959 : 0.99263528504162\n", - "score du quantile 0.96 : 0.9965103675841931\n" - ] - } - ], - "source": [ - "# le saut survient entre le quantile 0.94 et 0.955\n", - "# on peut prendre le quantile 0.95 / score = 0.52 comme cut-off approximatif\n", - "for step in np.linspace(0.94,0.96, 21) :\n", - " score_reached = y_pred_prob_sorted[int(step*N)-1]\n", - " print(f\"score du quantile {step} : {score_reached}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "5d8bb4ea-0030-4d23-8cff-26c9ed54ca71", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
KMeans(n_clusters=2, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "KMeans(n_clusters=2, random_state=0)" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# simple K-means pour déterminer le seuil qui sépare les 2 clusters apparents\n", - "\n", - "from sklearn.cluster import KMeans\n", - "\n", - "kmeans = KMeans(n_clusters=2, random_state=0)\n", - "\n", - "kmeans.fit(y_pred_prob.reshape(-1,1))" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "afbf8247-4cb1-455b-96df-7e9a87407413", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_clusters = kmeans.predict(y_pred_prob.reshape(-1,1))\n", - "y_clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "e4747b82-1967-4043-bcd1-7659dbd87a2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4846" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_clusters[y_clusters==1].size" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "2853083a-99a4-4ae9-9e8d-ddf175cca7ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9495712620712621" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 5% des individus sont dans le cluster 1\n", - "1 - y_clusters.mean()" - ] - }, - { - "cell_type": "markdown", - "id": "d18c8a4c-7d19-4d24-a304-cb26a533303e", - "metadata": {}, - "source": [ - "Intérêt du K-means : permet d'identifier un seuil de passage d'un cluster à l'autre quand le cluster est restreint, comme ici où on isole les clients avec la proba d'achat dans le quantile 0.95, et on les sépare des 95% restant" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "77f59f30-1dc6-43b8-98b7-d179a966786a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "part d'individus dans le cluster 0 : 0.9495712620712621\n", - "seuil de passage du cluster 0 au cluster 1 : 0.4855790414879801\n" - ] - } - ], - "source": [ - "# seuil de split \n", - "\n", - "size_cluster_0 = 1 - y_clusters.mean()\n", - "seuil_cluster = y_pred_prob_sorted[int(1 - y_clusters.mean()*N)]\n", - "\n", - "print(f\"part d'individus dans le cluster 0 : {size_cluster_0}\")\n", - "print(f\"seuil de passage du cluster 0 au cluster 1 : {seuil_cluster}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/Modelization/CA_segment_sport.ipynb b/Sport/Modelization/CA_segment_sport.ipynb deleted file mode 100644 index f2c6f73..0000000 --- a/Sport/Modelization/CA_segment_sport.ipynb +++ /dev/null @@ -1,4226 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84b6e27e-4bda-4d38-8689-ec7fc0da1848", - "metadata": {}, - "source": [ - "# Define segment and predict sales associated" - ] - }, - { - "cell_type": "markdown", - "id": "ec059482-45d3-4ae6-99bc-9b4ced115db3", - "metadata": {}, - "source": [ - "## Importations of packages " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9771bf29-d08e-4674-8c23-9a2672fbef8f", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from pandas import DataFrame\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "from sklearn.naive_bayes import GaussianNB\n", - "from scipy.optimize import fsolve\n", - "import io\n", - "\n", - "import pickle\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "048fcd7c-800a-4a6b-b725-faf8410f924a", - "metadata": {}, - "source": [ - "## load databases" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "539ccbdf-f29f-4f04-99c1-8c88d0efe514", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d6017ed0-6233-4888-85a7-05dec50a255b", - "metadata": {}, - "outputs": [], - "source": [ - "type_of_activity = \"sport\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0c3a6ddc-9345-4a42-b6bf-a20a95de3028", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test(type_of_activity):\n", - " # BUCKET = f\"projet-bdc2324-team1/Generalization/{type_of_activity}\"\n", - " BUCKET = f\"projet-bdc2324-team1/1_Temp/1_0_Modelling_Datasets/{type_of_activity}\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2831d546-b365-498b-8248-c618bd9c3057", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_427/290017524.py:8: DtypeWarning: Columns (10,24) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "/tmp/ipykernel_427/290017524.py:12: DtypeWarning: Columns (10,24) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - }, - { - "data": { - "text/plain": [ - "customer_id 0\n", - "street_id 0\n", - "structure_id 222819\n", - "mcp_contact_id 70845\n", - "fidelity 0\n", - " ... \n", - "purchases_8_2021 0\n", - "purchases_8_2022 0\n", - "purchases_9_2021 0\n", - "purchases_9_2022 0\n", - "y_has_purchased 0\n", - "Length: 87, dtype: int64" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train, dataset_test = load_train_test(type_of_activity)\n", - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b8827f7b-b304-4f51-9814-c7a98ed88cf0", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " \n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'fidelity', 'is_email_true', 'opt_in', #'is_partner', 'nb_tickets_internet',, 'vente_internet_max'\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - " # we suppress fidelity, time between purchase, and gender other (colinearity issue)\n", - " \"\"\"\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", - " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'is_email_true', \n", - " 'opt_in', 'gender_female', 'gender_male', 'nb_campaigns', 'nb_campaigns_opened']\n", - " \"\"\"\n", - " \n", - " X_train = dataset_train # [features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test # [features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c18195fc-ed40-4e39-a59e-c9ecc5a8e6c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape train : (224213, 87)\n", - "Shape test : (96096, 87)\n" - ] - } - ], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)\n", - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "74eda066-5e01-43aa-b0cf-cc6d9bbf770e", - "metadata": {}, - "source": [ - "## get results from the logit cross validated model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7c81390e-598c-4f02-bd56-dd03b00dcb33", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "                                                                          'taux_ouverture_mail',\n",
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" - ], - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise',\n", - " estimator=Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('num',\n", - " Pipeline(steps=[('imputer',\n", - " SimpleImputer(fill_value=0,\n", - " strategy='constant')),\n", - " ('scaler',\n", - " StandardScaler())]),\n", - " ['nb_campaigns',\n", - " 'taux_ouverture_mail',\n", - " 'prop_purchases_internet',\n", - " 'nb_tickets',\n", - " 'nb_purchases',\n", - " 'total_amount',\n", - " 'nb_suppliers',\n", - " 'pu...\n", - " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", - " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", - " 4.000000e+00, 8.000000e+00, 1.600000e+01, 3.200000e+01,\n", - " 6.400000e+01]),\n", - " 'LogisticRegression_cv__class_weight': ['balanced',\n", - " {0.0: 0.5834990214856762,\n", - " 1.0: 3.49404706249026}],\n", - " 'LogisticRegression_cv__penalty': ['l1', 'l2']},\n", - " scoring=make_scorer(recall_score, response_method='predict'))" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#model = load_model(type_of_activity, \"LogisticRegression_Benchmark\")\n", - "# model = load_model(type_of_activity, \"randomF_cv\")\n", - "model = load_model(type_of_activity, \"LogisticRegression_cv\")\n", - "model" - ] - }, - { - "cell_type": "markdown", - "id": "006819e7-e9c5-48d9-85ee-aa43d5e4c9c2", - "metadata": {}, - "source": [ - "## Quartile clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "018d8ff4-3436-4eec-8507-d1a265cbabf1", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = model.predict(X_test)\n", - "y_pred_prob = model.predict_proba(X_test)[:, 1]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "846f53b9-73c2-4a8b-9d9e-f11bf59ce9ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity tenant_id \\\n", - "0 5_4317407 969908 NaN 6156473.0 1 1771 \n", - "1 5_477635 109121 NaN 6213652.0 2 1771 \n", - "2 5_411639 92929 NaN 6160271.0 4 1771 \n", - "3 5_326623 79862 NaN 6140109.0 1 1771 \n", - "4 5_383915 85421 NaN 6149409.0 2 1771 \n", - "5 5_233172 141401 NaN 3324.0 1 1771 \n", - "6 5_389999 95759 NaN 6151025.0 1 1771 \n", - "7 5_4292211 78897 NaN 4729841.0 1 1771 \n", - "8 5_353553 84189 NaN 6146995.0 1 1771 \n", - "9 5_401296 3491 NaN 6155457.0 1 1771 \n", - "\n", - " is_partner deleted_at is_email_true opt_in ... purchases_7_2022 \\\n", - "0 False NaN True 0 ... 0.0 \n", - "1 False NaN True 0 ... 0.0 \n", - "2 False NaN True 0 ... 0.0 \n", - "3 False NaN True 1 ... 0.0 \n", - "4 False NaN True 1 ... 0.0 \n", - "5 False NaN True 1 ... 0.0 \n", - "6 False NaN True 0 ... 0.0 \n", - "7 False NaN True 1 ... 0.0 \n", - "8 False NaN True 1 ... 0.0 \n", - "9 False NaN True 0 ... 0.0 \n", - "\n", - " purchases_8_2021 purchases_8_2022 purchases_9_2021 purchases_9_2022 \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 1.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "5 0.0 0.0 0.0 1.0 \n", - "6 0.0 0.0 0.0 0.0 \n", - "7 0.0 0.0 0.0 0.0 \n", - "8 0.0 0.0 0.0 0.0 \n", - "9 0.0 0.0 0.0 0.0 \n", - "\n", - " y_has_purchased has_purchased has_purchased_estim score quartile \n", - "0 0.0 0.0 0.0 0.445019 2 \n", - "1 0.0 0.0 0.0 0.382586 2 \n", - "2 0.0 0.0 1.0 0.916747 4 \n", - "3 0.0 0.0 0.0 0.090534 1 \n", - "4 0.0 0.0 0.0 0.346571 2 \n", - "5 0.0 0.0 1.0 0.924684 4 \n", - "6 0.0 0.0 1.0 0.569031 3 \n", - "7 0.0 0.0 0.0 0.125622 1 \n", - "8 0.0 0.0 0.0 0.229432 1 \n", - "9 0.0 0.0 1.0 0.503987 3 \n", - "\n", - "[10 rows x 91 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment = X_test\n", - "\n", - "X_test_segment[\"has_purchased\"] = y_test\n", - "X_test_segment[\"has_purchased_estim\"] = y_pred\n", - "X_test_segment[\"score\"] = y_pred_prob\n", - "X_test_segment[\"quartile\"] = np.where(X_test['score']<0.25, '1',\n", - " np.where(X_test['score']<0.5, '2',\n", - " np.where(X_test['score']<0.75, '3', '4')))\n", - "X_test_segment.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "fb592fe3-ea40-4e83-8fe9-c52b9ee42f2a", - "metadata": {}, - "outputs": [], - "source": [ - "def df_segment(df, y, model) :\n", - "\n", - " y_pred = model.predict(df)\n", - " y_pred_prob = model.predict_proba(df)[:, 1]\n", - "\n", - " df_segment = df\n", - "\n", - " df_segment[\"has_purchased\"] = y\n", - " df_segment[\"has_purchased_estim\"] = y_pred\n", - " df_segment[\"score\"] = y_pred_prob\n", - " df_segment[\"quartile\"] = np.where(df_segment['score']<0.25, '1',\n", - " np.where(df_segment['score']<0.5, '2',\n", - " np.where(df_segment['score']<0.75, '3', '4')))\n", - "\n", - " return df_segment" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "968645d5-58cc-485a-bd8b-99f4cfc26fec", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1080/2624515794.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_segment[\"has_purchased\"] = y\n", - "/tmp/ipykernel_1080/2624515794.py:9: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_segment[\"has_purchased_estim\"] = y_pred\n", - "/tmp/ipykernel_1080/2624515794.py:10: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_segment[\"score\"] = y_pred_prob\n", - "/tmp/ipykernel_1080/2624515794.py:11: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_segment[\"quartile\"] = np.where(df_segment['score']<0.25, '1',\n" - ] - }, - { - "data": { - "text/html": [ - "
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96096 rows × 21 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 4.0 1.0 100.00 1.0 \n", - "1 1.0 1.0 55.00 1.0 \n", - "2 17.0 1.0 80.00 1.0 \n", - "3 4.0 1.0 120.00 1.0 \n", - "4 34.0 2.0 416.00 1.0 \n", - "... ... ... ... ... \n", - "96091 1.0 1.0 67.31 1.0 \n", - "96092 1.0 1.0 61.41 1.0 \n", - "96093 0.0 0.0 0.00 0.0 \n", - "96094 1.0 1.0 79.43 1.0 \n", - "96095 0.0 0.0 0.00 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 5.177187 5.177187 \n", - "1 0.0 426.265613 426.265613 \n", - "2 0.0 436.033437 436.033437 \n", - "3 0.0 5.196412 5.196412 \n", - "4 0.0 478.693148 115.631470 \n", - "... ... ... ... \n", - "96091 1.0 278.442257 278.442257 \n", - "96092 1.0 189.207373 189.207373 \n", - "96093 0.0 550.000000 550.000000 \n", - "96094 1.0 279.312905 279.312905 \n", - "96095 0.0 550.000000 550.000000 \n", - "\n", - " time_between_purchase nb_tickets_internet fidelity ... opt_in \\\n", - "0 0.000000 0.0 1 ... False \n", - "1 0.000000 0.0 2 ... True \n", - "2 0.000000 0.0 2 ... True \n", - "3 0.000000 0.0 1 ... False \n", - "4 363.061678 0.0 4 ... False \n", - "... ... ... ... ... ... \n", - "96091 0.000000 1.0 2 ... False \n", - "96092 0.000000 1.0 1 ... False \n", - "96093 -1.000000 0.0 1 ... True \n", - "96094 0.000000 1.0 1 ... False \n", - "96095 -1.000000 0.0 2 ... False \n", - "\n", - " gender_female gender_male gender_other nb_campaigns \\\n", - "0 1 0 0 0.0 \n", - "1 0 1 0 0.0 \n", - "2 1 0 0 0.0 \n", - "3 1 0 0 0.0 \n", - "4 1 0 0 0.0 \n", - "... ... ... ... ... \n", - "96091 0 1 0 15.0 \n", - "96092 0 1 0 12.0 \n", - "96093 1 0 0 29.0 \n", - "96094 0 1 0 20.0 \n", - "96095 0 1 0 31.0 \n", - "\n", - " nb_campaigns_opened has_purchased has_purchased_estim score \\\n", - "0 0.0 0.0 0.0 0.006066 \n", - "1 0.0 1.0 0.0 0.288847 \n", - "2 0.0 0.0 0.0 0.103264 \n", - "3 0.0 0.0 0.0 0.008928 \n", - "4 0.0 1.0 1.0 0.992809 \n", - "... ... ... ... ... \n", - "96091 5.0 1.0 0.0 0.351762 \n", - "96092 9.0 0.0 1.0 0.567814 \n", - "96093 3.0 0.0 0.0 0.004652 \n", - "96094 4.0 0.0 0.0 0.293042 \n", - "96095 4.0 0.0 1.0 0.787852 \n", - "\n", - " quartile \n", - "0 1 \n", - "1 2 \n", - "2 1 \n", - "3 1 \n", - "4 4 \n", - "... ... \n", - "96091 2 \n", - "96092 3 \n", - "96093 1 \n", - "96094 2 \n", - "96095 4 \n", - "\n", - "[96096 rows x 21 columns]" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_segment(X_test, y_test, model)" - ] - }, - { - "cell_type": "markdown", - "id": "ad16b8ab-7e01-404b-971e-866e9b9d5aa4", - "metadata": {}, - "source": [ - "## definition of functions to compute the bias of scores and adjust it \n", - "\n", - "Le biais est calculé de la façon suivante. \n", - "En notant $\\hat{p(x_i)}$ le score calculé (estimé par la modélisation) et $p(x_i)$ le vrai score (sans biais), et $\\beta$ le logarithme du biais, on a : \\\n", - "$\\ln{\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}} = \\beta + \\ln{\\frac{p(x_i)}{1-p(x_i)}}$ \\\n", - "$ \\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}} = \\exp(\\beta) . \\frac{p(x_i)}{1-p(x_i)} $ , soit : \\\n", - "$p(x_i) = {\\frac{\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}}{B+\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}}}$ \\\n", - "Ce qu'on appelle biais et qu'on estime dans le code par la suite est : $B=\\exp(\\beta) $. Les probabilités ne sont donc pas biaisées si $B=1$. Il y a surestimation si $B>1$. \n", - "\n", - "On cherche le B qui permette d'ajuster les probabilités de telle sorte que la somme des scores soit égale à la somme des y_has_purchased. Cela revient à résoudre : \n", - "\n", - "\\begin{equation}\n", - "\\sum_{i}{\\frac{\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}}{B+\\frac{\\hat{p(x_i)}}{1-\\hat{p(x_i)}}}} = \\sum_{i}{Y_i}\n", - "\\end{equation}\n", - "\n", - "C'est ce que fait la fonction find_bias. \n", - "\n", - "Note sur les notations : \\\n", - "$\\hat{p(x_i)}$ correspond à ce qu'on appelle le score et $p(x_i)$ à ce qu'on appellera le score adjusted" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "f0379536-a6c5-4b16-bde5-d0319ec1b140", - "metadata": {}, - "outputs": [], - "source": [ - "# compute adjusted score from odd ratios (cf formula above)\n", - "def adjusted_score(odd_ratio, bias) :\n", - " adjusted_score = odd_ratio/(bias+odd_ratio)\n", - " return adjusted_score" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "32a0dfd0-f49d-4785-a56f-706d381bfe41", - "metadata": {}, - "outputs": [], - "source": [ - "# when the score is 1 we cannot compute the odd ratio, so we adjust scores equal to 1\n", - "# we set the second best score instead\n", - "\n", - "def adjust_score_1(score) :\n", - " second_best_score = np.array([element for element in score if element !=1]).max()\n", - " new_score = np.array([element if element!=1 else second_best_score for element in score]) \n", - " return new_score" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2dff1def-02df-413e-afce-b4aeaf7752b6", - "metadata": {}, - "outputs": [], - "source": [ - "def odd_ratio(score) :\n", - " return score / (1 - score)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "683d71fc-7442-4028-869c-49c57592d6e9", - "metadata": {}, - "outputs": [], - "source": [ - "# definition of a function that automatically detects the bias\n", - "\n", - "def find_bias(odd_ratios, y_objective, initial_guess=6) :\n", - " \"\"\"\n", - " results = minimize(lambda bias : (sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective)**2 ,\n", - " initial_guess , method = \"BFGS\")\n", - "\n", - " estimated_bias = results.x[0]\n", - " \"\"\"\n", - "\n", - " # faster method\n", - " bias_estimated = fsolve(lambda bias : sum([adjusted_score(element, bias) for element in list(odd_ratios)]) - y_objective, x0=6)\n", - " \n", - " return bias_estimated[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f17dc6ca-7a48-441b-8c04-11c47b8b9741", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.31861289893787315 0.14317973692973693\n" - ] - }, - { - "data": { - "text/plain": [ - "0.14310053386734936" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(X_test_segment[\"score\"].mean(), y_test[\"y_has_purchased\"].mean())\n", - "y_train[\"y_has_purchased\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "781b0d40-c954-4c54-830a-e709c8667328", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.939748066330849" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# computation with the function defined\n", - "\n", - "bias_test_set = find_bias(odd_ratios = odd_ratio(adjust_score_1(X_test_segment[\"score\"])), \n", - " y_objective = y_test[\"y_has_purchased\"].sum(),\n", - " initial_guess=6)\n", - "bias_test_set" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "248cb862-418e-4767-9933-70c4885ecf40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.01952986090399" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# comparison with bias of the train set\n", - "X_train_score = model.predict_proba(X_train)[:, 1]\n", - "\n", - "bias_train_set = find_bias(odd_ratios = odd_ratio(adjust_score_1(X_train_score)), \n", - " y_objective = y_train[\"y_has_purchased\"].sum(),\n", - " initial_guess=10)\n", - "bias_train_set" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "fff6cbe6-7bb3-4732-9b81-b9ac5383bbcf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "betâ test - betâ train = -0.013342440676233564\n" - ] - } - ], - "source": [ - "print(\"betâ test - betâ train = \",np.log(bias_test_set/bias_train_set))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f506870d-4a8a-4b2c-8f0b-e0789080b20c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean absolute erreur 0.0009061459618344602\n" - ] - } - ], - "source": [ - "# impact of considering a bias computed on train set instead of test set - totally neglectable\n", - "\n", - "score_adjusted_test = adjusted_score(odd_ratio(adjust_score_1(X_test_segment[\"score\"])), bias = bias_test_set)\n", - "score_adjusted_train = adjusted_score(odd_ratio(adjust_score_1(X_test_segment[\"score\"])), bias = bias_train_set)\n", - "\n", - "print(\"mean absolute erreur\",abs(score_adjusted_test-score_adjusted_train).mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "8213d0e4-063b-49fa-90b7-677fc34f4c01", - "metadata": {}, - "outputs": [], - "source": [ - "# adjust scores accordingly \n", - "\n", - "# X_test_segment[\"score_adjusted\"] = adjusted_score(odd_ratio(adjust_score_1(X_test_segment[\"score\"])), bias = bias_test_set)\n", - "\n", - "# actually, we are not supposed to have X_test, so the biais is estimated on X_train\n", - "# X_test_segment[\"score_adjusted\"] = adjusted_score(odd_ratio(adjust_score_1(X_test_segment[\"score\"])), bias = bias_train_set)\n", - "X_test_segment[\"score_adjusted\"] = score_adjusted_train" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "834d3723-2e72-4c65-9c62-e2d595c69461", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE for score : 0.11809894130837426\n", - "MSE for ajusted score : 0.07434720017843571\n", - "sum of y_has_purchased : 13759.0\n", - "sum of adjusted scores : 13671.922997651252\n" - ] - } - ], - "source": [ - "# check \n", - "\n", - "MSE_score = ((X_test_segment[\"score\"]-X_test_segment[\"has_purchased\"])**2).mean()\n", - "MSE_ajusted_score = ((X_test_segment[\"score_adjusted\"]-X_test_segment[\"has_purchased\"])**2).mean()\n", - "print(f\"MSE for score : {MSE_score}\")\n", - "print(f\"MSE for ajusted score : {MSE_ajusted_score}\")\n", - "\n", - "print(\"sum of y_has_purchased :\",y_test[\"y_has_purchased\"].sum())\n", - "print(\"sum of adjusted scores :\", X_test_segment[\"score_adjusted\"].sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9f30a4dd-a9d8-405a-a7d5-5324ae88cf70", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MAE for score : 0.24742788848313355\n", - "MAE for adjusted score : 0.14205672428104504\n" - ] - } - ], - "source": [ - "# mean absolute error - divided by 2 with out method\n", - "\n", - "MAE_score = abs(X_test_segment[\"score\"]-X_test_segment[\"has_purchased\"]).mean()\n", - "MAE_ajusted_score = abs(X_test_segment[\"score_adjusted\"]-X_test_segment[\"has_purchased\"]).mean()\n", - "print(f\"MAE for score : {MAE_score}\")\n", - "print(f\"MAE for adjusted score : {MAE_ajusted_score}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "6f9396db-e213-408c-a596-eaeec3bc79f3", - "metadata": {}, - "outputs": [], - "source": [ - "# visualization\n", - "\n", - "# histogramme des probas et des probas ajustées\n", - "\n", - "def plot_hist_scores(df, score, score_adjusted, type_of_activity) :\n", - "\n", - " plt.figure()\n", - " plt.hist(df[score], label = \"score\", alpha=0.6)\n", - " plt.hist(df[score_adjusted], label=\"adjusted score\", alpha=0.6)\n", - " plt.legend()\n", - " plt.xlabel(\"probability of a future purchase\")\n", - " plt.ylabel(\"count\")\n", - " plt.title(f\"Comparison between score and adjusted score for {type_of_activity} companies\")\n", - " # plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "def64c16-f4dd-493c-909c-d886d7f53947", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Output_expected_CA/sport/hist_score_adjustedsport.png'" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PATH + file_name + type_of_activity + \".png\"" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b478d40d-9677-4204-87bd-16fb0bc1fe9a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\", type_of_activity = type_of_activity)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "add631d7-0757-45a5-bb5b-f7f4b4baa961", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "projet-bdc2324-team1/Output_expected_CA/sport/\n" - ] - } - ], - "source": [ - "# define path so save graphics\n", - "\n", - "# define type of activity \n", - "type_of_activity = \"sport\"\n", - "PATH = f\"projet-bdc2324-team1/Output_expected_CA/{type_of_activity}/\"\n", - "print(PATH)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "3a5b5bd9-e033-4436-8c56-bf5fb61df87f", - "metadata": {}, - "outputs": [], - "source": [ - "# export png \n", - "\n", - "# plot adjusted scores and save (to be tested)\n", - "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\", type_of_activity = type_of_activity)\n", - "\n", - "image_buffer = io.BytesIO()\n", - "plt.savefig(image_buffer, format='png')\n", - "image_buffer.seek(0)\n", - "file_name = \"hist_score_adjusted_\"\n", - "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".png\"\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", - " s3_file.write(image_buffer.read())\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "id": "e6fae260-fab8-4f51-90dc-9b6d7314c77b", - "metadata": {}, - "source": [ - "## Compute number of tickets and CA by segment with the recalibrated score" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "90c4c2b5-0ede-4001-889f-749cfbd9df04", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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quartilescore (%)score adjusted (%)has purchased (%)
0110.201.941.19
1237.089.1210.62
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" - ], - "text/plain": [ - " quartile score (%) score adjusted (%) has purchased (%)\n", - "0 1 10.20 1.94 1.19\n", - "1 2 37.08 9.12 10.62\n", - "2 3 62.07 22.00 28.67\n", - "3 4 90.35 67.16 63.09" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_table_adjusted_scores = (100 * X_test_segment.groupby(\"quartile\")[[\"score\",\"score_adjusted\", \"has_purchased\"]].mean()).round(2).reset_index()\n", - "X_test_table_adjusted_scores = X_test_table_adjusted_scores.rename(columns = {col : f\"{col.replace('_', ' ')} (%)\" for col in X_test_table_adjusted_scores.columns if col in [\"score\",\"score_adjusted\", \"has_purchased\"]})\n", - "X_test_table_adjusted_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "id": "d0b8740c-cf48-4a3e-83cb-23d95059f62f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\\\begin{tabular}{lrrr}\\n\\\\toprule\\nquartile & score (%) & score adjusted (%) & has purchased (%) \\\\\\\\\\n\\\\midrule\\n1 & 13.250000 & 2.510000 & 1.570000 \\\\\\\\\\n2 & 33.890000 & 8.000000 & 9.850000 \\\\\\\\\\n3 & 63.060000 & 22.580000 & 21.470000 \\\\\\\\\\n4 & 90.520000 & 66.200000 & 65.010000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_table_adjusted_scores.to_latex(index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "d6a04d3e-c454-43e4-ae4c-0746e928575b", - "metadata": {}, - "outputs": [], - "source": [ - "# comparison between score and adjusted score - export csv associated\n", - "\n", - "file_name = \"table_adjusted_score_\"\n", - "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".csv\"\n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " X_test_table_adjusted_scores.to_csv(file_out, index = False)" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "a974589f-7952-4db2-bebf-7b69c6b09372", - "metadata": {}, - "outputs": [], - "source": [ - "def project_tickets_CA (df, nb_purchases, nb_tickets, total_amount, score_adjusted, duration_ref, duration_projection) :\n", - " \n", - " duration_ratio = duration_ref/duration_projection\n", - "\n", - " df_output = df\n", - " \n", - " # project number of tickets : at least 1 ticket purchased if the customer purchased\n", - " df_output.loc[:,\"nb_tickets_projected\"] = df_output.loc[:,nb_tickets].apply(lambda x : max(1, x /duration_ratio))\n", - "\n", - " # project amount : if the customer buys a ticket, we expect the amount to be at least the average price of tickets \n", - " # for customers purchasing exactly one ticket\n", - " if df_output.loc[df_output[nb_tickets]==1].shape[0] > 0 :\n", - " avg_price = df_output.loc[df_output[nb_tickets]==1][total_amount].mean()\n", - " else :\n", - " avg_price = df_output[total_amount].mean()\n", - " # df_output.loc[:,\"total_amount_projected\"] = df_output.loc[:,total_amount] / duration_ratio\n", - " # df_output.loc[:,\"total_amount_projected\"] = df_output.loc[:,total_amount].apply(lambda x : max(avg_ticket_price, x/duration_ratio))\n", - "\n", - " # we compute the avg price of ticket for each customer\n", - " df_output[\"avg_ticket_price\"] = df_output[total_amount]/df_output[nb_tickets]\n", - "\n", - " # correct negatives total amounts\n", - " df_output.loc[:,\"total_amount_corrected\"] = np.where(df_output[total_amount] < 0, \n", - " avg_price * df_output[nb_tickets],\n", - " df_output[total_amount])\n", - " \n", - " df_output.loc[:,\"total_amount_projected\"] = np.where(\n", - " # if no ticket bought in the past, we take the average price\n", - " df_output[nb_tickets]==0, avg_price,\n", - " # if avg prices of tickets are negative, we recompute the expected amount based on the avg price of a ticket\n", - " # observed on the whole population\n", - " np.where(X_test_segment[\"avg_ticket_price\"] < 0, avg_price * df_output.loc[:,\"nb_tickets_projected\"],\n", - " # else, the amount projected is the average price of tickets bought by the customer * nb tickets projected\n", - " df_output[\"avg_ticket_price\"] * df_output.loc[:,\"nb_tickets_projected\"])\n", - " )\n", - " \n", - " df_output.loc[:,\"nb_tickets_expected\"] = df_output.loc[:,score_adjusted] * df_output.loc[:,\"nb_tickets_projected\"]\n", - " df_output.loc[:,\"total_amount_expected\"] = df_output.loc[:,score_adjusted] * df_output.loc[:,\"total_amount_projected\"]\n", - "\n", - " df_output.loc[:,\"pace_purchase\"] = (duration_ref/df_output.loc[:,nb_purchases]).apply(lambda x : np.nan if x==np.inf else x)\n", - " \n", - " return df_output\n" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "87fb8e1c-3567-46df-9e98-197b7ca3becd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([25., 92., 45., ..., 0., 0., 0.])" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.where(X_test_segment[\"total_amount\"] < 0, avg_price * X_test_segment[\"nb_tickets\"],\n", - " X_test_segment[\"total_amount\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "dc0cdf9c-c55c-4085-80a6-c2131bb22ad4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 25.00\n", - "1 92.00\n", - "2 45.00\n", - "3 10.00\n", - "4 127.00\n", - " ... \n", - "96091 0.00\n", - "96092 100.89\n", - "96093 0.00\n", - "96094 0.00\n", - "96095 0.00\n", - "Name: total_amount, Length: 96096, dtype: float64" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " X_test_segment[\"total_amount\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "51455654-e6de-4608-8fbe-594d7fcd5b53", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0, 98)" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment.loc[X_test_segment[\"nb_tickets\"]==-1].shape[0°" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "a0d08a46-93d0-425a-9a56-28cf8bfd93e9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 4.410500e+04\n", - "mean 4.640310e+02\n", - "std 1.049793e+04\n", - "min -2.064700e+04\n", - "25% 3.000000e+01\n", - "50% 6.900000e+01\n", - "75% 1.339900e+02\n", - "max 1.209751e+06\n", - "Name: total_amount, dtype: float64" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "duration_ratio = 17/12\n", - "X_test_segment.loc[X_test_segment[\"nb_tickets\"]>0][\"total_amount\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "dc7de319-6d22-44f0-9e58-492088b0dd5f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 183.851977\n", - "std 5021.379770\n", - "min 48.713098\n", - "25% 48.713098\n", - "50% 48.713098\n", - "75% 48.713098\n", - "max 853942.164706\n", - "Name: total_amount, dtype: float64" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "avg_price = X_test_segment.loc[X_test_segment[\"nb_tickets\"]==1][\"total_amount\"].mean()\n", - "X_test_segment[\"total_amount\"].apply(lambda x : max(avg_price, x/duration_ratio)).describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "8aa50962-067b-493a-8766-258547da8bcd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 150.335598\n", - "std 5022.896337\n", - "min -14574.352941\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 42.352941\n", - "max 853942.164706\n", - "Name: total_amount, dtype: float64" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[\"total_amount\"].apply(lambda x : x/duration_ratio).describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "f2f04205-7b8b-4978-9b4f-1c83034628fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1.411765\n", - "1 1.411765\n", - "2 2.117647\n", - "3 0.705882\n", - "4 5.647059\n", - " ... \n", - "96091 0.000000\n", - "96092 1.411765\n", - "96093 0.000000\n", - "96094 0.000000\n", - "96095 0.000000\n", - "Name: nb_tickets, Length: 96096, dtype: float64" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment.loc[:,\"nb_tickets\"]/duration_ratio" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "140e09b9-f6b8-4075-b380-86851e1596f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 176.690937\n", - "std 5022.166115\n", - "min -14574.352941\n", - "25% 48.713098\n", - "50% 48.713098\n", - "75% 48.713098\n", - "max 853942.164706\n", - "dtype: float64" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.Series(np.where(X_test_segment[\"nb_tickets\"]==0, avg_price, X_test_segment[\"nb_tickets_projected\"] * X_test_segment[\"total_amount\"]/X_test_segment[\"nb_tickets\"])).describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "b2c8c7dd-9cd2-40b8-945f-0daf27b3b66b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 162.000000\n", - "mean 51.283951\n", - "std 135.183724\n", - "min 1.000000\n", - "25% 2.000000\n", - "50% 6.000000\n", - "75% 31.500000\n", - "max 1038.000000\n", - "Name: nb_tickets, dtype: float64" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[X_test_segment[\"total_amount\"]<0][\"nb_tickets\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "44ce62e3-fae6-4192-b8dd-386fd84fed22", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 44105.000000\n", - "mean 35.661188\n", - "std 71.477667\n", - "min -216.368182\n", - "25% 10.000000\n", - "50% 25.000000\n", - "75% 48.720000\n", - "max 4000.000000\n", - "Name: avg_ticket_price, dtype: float64" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# code pr projet revenue\n", - "\n", - "X_test_segment[\"avg_ticket_price\"] = X_test_segment[\"total_amount\"]/X_test_segment[\"nb_tickets\"]\n", - "X_test_segment[\"avg_ticket_price\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "e1c0671a-2b5f-48bf-b964-6bee8a4223ac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 180.394197\n", - "std 5025.591726\n", - "min 0.000000\n", - "25% 48.713098\n", - "50% 48.713098\n", - "75% 48.713098\n", - "max 853942.164706\n", - "dtype: float64" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.Series(\n", - " np.where(X_test_segment[\"nb_tickets\"]==0, avg_price,\n", - " \n", - " np.where(X_test_segment[\"avg_ticket_price\"] < 0, avg_price * X_test_segment[\"nb_tickets\"] / duration_ratio,\n", - " X_test_segment[\"avg_ticket_price\"] * X_test_segment[\"nb_tickets\"] / duration_ratio)\n", - " )\n", - ").describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "6c1e0649-3be1-4754-a86c-24b46a12d523", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5058.000000\n", - "mean 13.671807\n", - "std 155.341970\n", - "min 1.000000\n", - "25% 1.000000\n", - "50% 2.000000\n", - "75% 4.000000\n", - "max 8250.000000\n", - "Name: nb_tickets, dtype: float64" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[X_test_segment[\"avg_ticket_price\"] == 0][\"nb_tickets\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a4d1b0a-fe16-49e7-9b61-d822d2ed062a", - "metadata": {}, - "outputs": [], - "source": [ - "df['colonne2'] = np.where(df['colonne1'] > seuil2, df['colonne2'] * 2, # Si colonne1 > seuil2\n", - " np.where(df['colonne1'] > seuil1, df['colonne2'] + 1, df['colonne2'])) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa87726a-dee2-4b15-af2d-b22583a9eb53", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 132, - "id": "dd8a52e1-d06e-4790-8687-8e58e3e6b84e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atis_email_trueopt_in...scorequartilescore_adjustednb_tickets_projectedtotal_amount_projectednb_tickets_expectedtotal_amount_expectedpace_purchaseavg_ticket_pricetotal_amount_corrected
05_4317407969908NaN6156473.011771FalseNaNTrue0...0.44501920.1175511.41176517.6470590.1659552.07443217.00000012.50025.00
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..................................................................
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96096 rows × 99 columns

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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity \\\n", - "0 5_4317407 969908 NaN 6156473.0 1 \n", - "1 5_477635 109121 NaN 6213652.0 2 \n", - "2 5_411639 92929 NaN 6160271.0 4 \n", - "3 5_326623 79862 NaN 6140109.0 1 \n", - "4 5_383915 85421 NaN 6149409.0 2 \n", - "... ... ... ... ... ... \n", - "96091 9_91205 76215 NaN 47280.0 0 \n", - "96092 9_369887 815891 NaN 30764537.0 4 \n", - "96093 9_1007562 1 NaN NaN 0 \n", - "96094 9_15037 12992 NaN 2213448.0 0 \n", - "96095 9_135370 76215 NaN 2164740.0 0 \n", - "\n", - " tenant_id is_partner deleted_at is_email_true opt_in ... \\\n", - "0 1771 False NaN True 0 ... \n", - "1 1771 False NaN True 0 ... \n", - "2 1771 False NaN True 0 ... \n", - "3 1771 False NaN True 1 ... \n", - "4 1771 False NaN True 1 ... \n", - "... ... ... ... ... ... ... \n", - "96091 1490 False NaN True 1 ... \n", - "96092 1490 False NaN True 0 ... \n", - "96093 1490 False NaN True 0 ... \n", - "96094 1490 False NaN True 1 ... \n", - "96095 1490 False NaN True 1 ... \n", - "\n", - " score quartile score_adjusted nb_tickets_projected \\\n", - "0 0.445019 2 0.117551 1.411765 \n", - "1 0.382586 2 0.093333 1.411765 \n", - "2 0.916747 4 0.646556 2.117647 \n", - "3 0.090534 1 0.016268 1.000000 \n", - "4 0.346571 2 0.080976 5.647059 \n", - "... ... ... ... ... \n", - "96091 0.014966 1 0.002518 1.000000 \n", - "96092 0.834257 4 0.455392 1.411765 \n", - "96093 0.062886 1 0.011025 1.000000 \n", - "96094 0.068998 1 0.012162 1.000000 \n", - "96095 0.018486 1 0.003119 1.000000 \n", - "\n", - " total_amount_projected nb_tickets_expected total_amount_expected \\\n", - "0 17.647059 0.165955 2.074432 \n", - "1 64.941176 0.131765 6.061181 \n", - "2 31.764706 1.369178 20.537670 \n", - "3 10.000000 0.016268 0.162683 \n", - "4 89.647059 0.457279 7.259298 \n", - "... ... ... ... \n", - "96091 48.713098 0.002518 0.122642 \n", - "96092 71.216471 0.642906 32.431379 \n", - "96093 48.713098 0.011025 0.537071 \n", - "96094 48.713098 0.012162 0.592451 \n", - "96095 48.713098 0.003119 0.151938 \n", - "\n", - " pace_purchase avg_ticket_price total_amount_corrected \n", - "0 17.000000 12.500 25.00 \n", - "1 8.500000 46.000 92.00 \n", - "2 5.666667 15.000 45.00 \n", - "3 17.000000 10.000 10.00 \n", - "4 8.500000 15.875 127.00 \n", - "... ... ... ... \n", - "96091 NaN NaN 0.00 \n", - "96092 8.500000 50.445 100.89 \n", - "96093 NaN NaN 0.00 \n", - "96094 NaN NaN 0.00 \n", - "96095 NaN NaN 0.00 \n", - "\n", - "[96096 rows x 99 columns]" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# project nb tickets and CA\n", - "\n", - "X_test_segment = project_tickets_CA (X_test_segment, \"nb_purchases\", \"nb_tickets\", \"total_amount\", \"score_adjusted\", \n", - " duration_ref=17, duration_projection=12)\n", - "X_test_segment" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "22222709-218e-43b5-815f-714dfb776230", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 9.609600e+04\n", - "mean 2.182217e+02\n", - "std 7.120650e+03\n", - "min 0.000000e+00\n", - "25% 0.000000e+00\n", - "50% 0.000000e+00\n", - "75% 6.100000e+01\n", - "max 1.209751e+06\n", - "Name: total_amount_corrected, dtype: float64" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[\"total_amount_corrected\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "73404bdd-e2f2-40e0-8bde-224c460426c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 44105.000000\n", - "mean 35.661188\n", - "std 71.477667\n", - "min -216.368182\n", - "25% 10.000000\n", - "50% 25.000000\n", - "75% 48.720000\n", - "max 4000.000000\n", - "Name: avg_ticket_price, dtype: float64" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[\"avg_ticket_price\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "f96536d3-fff7-4ccf-be3d-34e671852cd8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.052634865134865136" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(X_test_segment[\"total_amount_projected\"]==0).mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "884416e8-edec-4f6b-a40f-1a7c5d653160", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 4.442483\n", - "std 64.952589\n", - "min 1.000000\n", - "25% 1.000000\n", - "50% 1.000000\n", - "75% 1.411765\n", - "max 11472.000000\n", - "Name: nb_tickets_projected, dtype: float64" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[\"nb_tickets_projected\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "cb66a8ea-65f7-460f-b3fc-ba76a3b91faa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "quartile\n", - "1 15.330011\n", - "2 15.314322\n", - "3 14.031588\n", - "4 8.562546\n", - "Name: pace_purchase, dtype: float64" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment.groupby(\"quartile\")[\"pace_purchase\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "8a4eec5c-8a4d-4a2b-9afb-1d49c77f78ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 162.000000\n", - "mean 3112.018089\n", - "std 8392.717823\n", - "min 51.843098\n", - "25% 161.889295\n", - "50% 395.635139\n", - "75% 2141.696184\n", - "max 69988.895986\n", - "dtype: float64" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(X_test[((X_test[\"total_amount_corrected\"] - X_test[\"total_amount\"])>0)][\"total_amount_corrected\"]\n", - " -X_test[((X_test[\"total_amount_corrected\"] - X_test[\"total_amount\"])>0)][\"total_amount\"]) .describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "f58f9151-2f91-45df-abb7-1ddcf0652adc", - "metadata": {}, - "outputs": [], - "source": [ - "# generalization with a function\n", - "\n", - "def summary_expected_CA(df, segment, nb_tickets_expected, total_amount_expected, total_amount, pace_purchase,\n", - " duration_ref=17, duration_projection=12) :\n", - " \n", - " # compute nb tickets estimated and total amount expected\n", - " df_expected_CA = df.groupby(segment)[[nb_tickets_expected, total_amount_expected]].sum().reset_index()\n", - " \n", - " # number of customers by segment\n", - " df_expected_CA.insert(1, \"size\", df.groupby(segment).size().values)\n", - " \n", - " # size in percent of all customers\n", - " df_expected_CA.insert(2, \"size_perct\", 100 * df_expected_CA[\"size\"]/df_expected_CA[\"size\"].sum())\n", - " \n", - " # compute share of CA recovered\n", - " duration_ratio=duration_ref/duration_projection\n", - " \n", - " df_expected_CA[\"revenue_recovered_perct\"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \\\n", - " df.groupby(segment)[total_amount].sum().values\n", - "\n", - " df_expected_CA[\"share_future_revenue_perct\"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \\\n", - " df[total_amount].sum()\n", - "\n", - " df_drop_null_pace = df.dropna(subset=[pace_purchase])\n", - " df_expected_CA[\"pace_purchase\"] = df_drop_null_pace.groupby(segment)[pace_purchase].mean().values\n", - " \n", - " return df_expected_CA" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "id": "c8df6c80-43e8-4f00-9cd3-eb9022744313", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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quartilesizesize_perctnb_tickets_expectedtotal_amount_expectedrevenue_recovered_perctshare_future_revenue_perctpace_purchase
015412356.321480.3655345.2111.990.3715.33
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" - ], - "text/plain": [ - " quartile size size_perct nb_tickets_expected total_amount_expected \\\n", - "0 1 54123 56.32 1480.36 55345.21 \n", - "1 2 18181 18.92 4381.84 130503.26 \n", - "2 3 11111 11.56 8827.97 285945.50 \n", - "3 4 12681 13.20 239758.61 10313321.91 \n", - "\n", - " revenue_recovered_perct share_future_revenue_perct pace_purchase \n", - "0 11.99 0.37 15.33 \n", - "1 11.65 0.88 15.31 \n", - "2 24.00 1.93 14.03 \n", - "3 85.74 69.67 8.56 " - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"\n", - "X_test_expected_CA = round(summary_expected_CA(df=X_test_segment, segment=\"quartile\", \n", - " nb_tickets_expected=\"nb_tickets_expected\", total_amount_expected=\"total_amount_expected\", \n", - " total_amount=\"total_amount\", pace_purchase=\"pace_purchase\"),2)\n", - " \"\"\"\n", - "X_test_expected_CA = round(summary_expected_CA(df=X_test_segment, segment=\"quartile\", \n", - " nb_tickets_expected=\"nb_tickets_expected\", total_amount_expected=\"total_amount_expected\", \n", - " total_amount=\"total_amount_corrected\", pace_purchase=\"pace_purchase\"),2)\n", - "X_test_expected_CA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd25c898-9991-4cc4-8e69-160b61fea0c4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "63369c2a-a842-4b03-aa11-230287cb3b69", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "count 96096.000000\n", - "mean 4.442483\n", - "std 64.952589\n", - "min 1.000000\n", - "25% 1.000000\n", - "50% 1.000000\n", - "75% 1.411765\n", - "max 11472.000000\n", - "Name: nb_tickets_projected, dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 2.647860\n", - "std 59.108910\n", - "min 0.001335\n", - "25% 0.015281\n", - "50% 0.044399\n", - "75% 0.230742\n", - "max 11450.589975\n", - "Name: nb_tickets_expected, dtype: float64" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(X_test_segment[\"nb_tickets_projected\"].describe())\n", - "X_test_segment[\"nb_tickets_expected\"].describe()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "72af97dc-8558-4591-adcf-ad404c9cb3f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "quartile\n", - "1 0.029070\n", - "2 0.074526\n", - "3 0.078737\n", - "4 0.817668\n", - "Name: total_amount, dtype: float64" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# we can recover share future revenue by multipling the share of amount by quartile * revenue recovered\n", - "X_test_segment.groupby(\"quartile\")[\"total_amount\"].sum()/X_test_segment[\"total_amount\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "ac706ed7-defa-4df1-82e1-06f12fc1b6ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\\\begin{tabular}{lrrrrrr}\\n\\\\toprule\\nquartile & size & size (%) & nb tickets expected & total amount expected & revenue recovered (%) & pace purchase \\\\\\\\\\n\\\\midrule\\n1 & 53626 & 35.310000 & 398.260000 & 13949.330000 & 2.350000 & 16.480000 \\\\\\\\\\n2 & 55974 & 36.860000 & 3113.770000 & 101639.450000 & 6.240000 & 16.470000 \\\\\\\\\\n3 & 30435 & 20.040000 & 6214.350000 & 208267.220000 & 14.270000 & 15.710000 \\\\\\\\\\n4 & 11839 & 7.800000 & 72929.460000 & 1835702.430000 & 75.380000 & 11.480000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Création du dictionnaire de mapping pour les noms de colonnes\n", - "mapping_dict = {col: col.replace(\"perct\", \"(%)\").replace(\"_\", \" \") for col in X_test_expected_CA.columns}\n", - "\n", - "X_test_expected_CA.rename(columns=mapping_dict).to_latex(index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "771da0cf-c49f-4e7e-b52f-ebcfb0fb2df3", - "metadata": {}, - "outputs": [], - "source": [ - "# export summary table to the MinIO storage\n", - "\n", - "file_name = \"table_expected_CA_\"\n", - "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".csv\"\n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " X_test_expected_CA.to_csv(file_out, index = False)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "c805dc10-4d07-4f7d-a677-5461a92845d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Output_expected_CA/musique/table_expected_CA_musique.csv'" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PATH = f\"projet-bdc2324-team1/Output_expected_CA/{type_of_activity}/\"\n", - "file_name = \"table_expected_CA_\"\n", - "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".csv\"\n", - "FILE_PATH_OUT_S3" - ] - }, - { - "cell_type": "markdown", - "id": "e35ccfff-1845-41f0-9bde-f09b09b67877", - "metadata": {}, - "source": [ - "## Test : vizu tables saved" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "4e9e88e4-ea10-41f4-9bf1-20b55269a20d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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quartilescore (%)score adjusted (%)has purchased (%)
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" - ], - "text/plain": [ - " quartile score (%) score adjusted (%) has purchased (%)\n", - "0 1 13.25 2.51 1.57\n", - "1 2 33.89 8.00 9.85\n", - "2 3 63.06 22.58 21.47\n", - "3 4 90.52 66.20 65.01" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path = 'projet-bdc2324-team1/Output_expected_CA/sport/table_adjusted_scoresport.csv'\n", - "\n", - "with fs.open( path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\")\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "9c471bdd-25c2-420a-a8a1-3add9f003cbc", - "metadata": {}, - "source": [ - "## Just to try, same computation with score instead of score adjusted\n", - "\n", - "seems overestimated : if only 14% of customers come back, how can we recover 22% of the revenue from the segment that is least likely to buy ?? ..." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "53684a24-1809-465f-8e21-b9295e34582a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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quartilesizesize_perctnb_tickets_expectedtotal_amount_expectedperct_revenue_recovered
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" - ], - "text/plain": [ - " quartile size size_perct nb_tickets_expected total_amount_expected \\\n", - "0 1 37410 38.93 419.76 9245.08 \n", - "1 2 29517 30.72 11549.06 296522.02 \n", - "2 3 20137 20.96 29997.85 954751.91 \n", - "3 4 9032 9.40 244655.82 10736011.95 \n", - "\n", - " perct_revenue_recovered \n", - "0 21.71 \n", - "1 39.24 \n", - "2 63.34 \n", - "3 97.72 " - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment_bis = project_tickets_CA (X_test_segment, \"nb_tickets\", \"total_amount\", \"score\", duration_ref=1.5, duration_projection=1)\n", - "\n", - "X_test_expected_CA_bis = round(summary_expected_CA(df=X_test_segment_bis, segment=\"quartile\", nb_tickets_expected=\"nb_tickets_expected\", \n", - " total_amount_expected=\"total_amount_expected\", total_amount=\"total_amount\"),2)\n", - "\n", - "X_test_expected_CA_bis" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "7dc66d1e-da03-4513-96e4-d9a43ac0a2c8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "overall share of revenue recovered : 90.26 %\n" - ] - } - ], - "source": [ - "print(\"overall share of revenue recovered : \", round(100 * duration_ratio * X_test_expected_CA_bis[\"total_amount_expected\"].sum() / \\\n", - "X_test_segment_bis[\"total_amount\"].sum(),2), \"%\")" - ] - }, - { - "cell_type": "markdown", - "id": "673f2969-7b9a-44c1-abf5-5679fca877ce", - "metadata": {}, - "source": [ - "## Last pieces of analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "2365bb13-0f3f-49d5-bf91-52c92abebcee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "overall share of revenue recovered : 77.64%\n" - ] - } - ], - "source": [ - "# global revenue recovered\n", - "global_revenue_recovered = round(100 * duration_ratio * X_test_expected_CA[\"total_amount_expected\"].sum() / \\\n", - "X_test_segment[\"total_amount\"].sum(),2)\n", - "print(f\"overall share of revenue recovered : {global_revenue_recovered}%\")" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "16b17f35-57dd-459a-8989-129143dc0952", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0.018093\n", - "1 0.721519\n", - "2 3.336101\n", - "3 95.924287\n", - "Name: total_amount_expected, dtype: float64" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "100 * X_test_expected_CA[\"total_amount_expected\"]/X_test_expected_CA[\"total_amount_expected\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "id": "dee4a200-eefe-4377-8e80-59ad33edd3c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "quartile\n", - "1 0.320407\n", - "2 5.685020\n", - "3 11.339715\n", - "4 82.654858\n", - "Name: total_amount, dtype: float64" - ] - }, - "execution_count": 166, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# le segment 4 représente 83% du CA actuel et 96% du CA lié aux anciens clients pour l'année prochaine\n", - "100 * X_test_segment.groupby(\"quartile\")[\"total_amount\"].sum()/X_test_segment[\"total_amount\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "c1e6f020-ef18-40b4-bfc1-19f98cb2796e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 96096.000000\n", - "mean 207.475735\n", - "std 4720.046248\n", - "min -48831.800000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 60.000000\n", - "max 624890.000000\n", - "Name: total_amount, dtype: float64" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment[\"total_amount\"].describe() # total amount négatif ???\n" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "id": "d301a50e-7c68-40f0-9245-a4eea64c387b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -4.883180e+04\n", - "1 -6.483180e+04\n", - "2 -7.683860e+04\n", - "3 -8.683860e+04\n", - "4 -9.683860e+04\n", - " ... \n", - "96091 1.802247e+07\n", - "96092 1.839238e+07\n", - "96093 1.877219e+07\n", - "96094 1.931270e+07\n", - "96095 1.993759e+07\n", - "Name: total_amount, Length: 96096, dtype: float64" - ] - }, - "execution_count": 184, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.cumsum(X_test_segment[\"total_amount\"].sort_values()).reset_index()[\"total_amount\"]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/Modelization/segment_analysis_sport_0_6.ipynb b/Sport/Modelization/segment_analysis_sport_0_6.ipynb deleted file mode 100644 index 5821b7f..0000000 --- a/Sport/Modelization/segment_analysis_sport_0_6.ipynb +++ /dev/null @@ -1,2972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c488134e-680f-44e4-8c43-40c246140519", - "metadata": {}, - "source": [ - "# Analysis of segments and marketing personae associated" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9a8b8c3a-8e74-49f3-91d1-cccfc057fdcd", - "metadata": {}, - "outputs": [], - "source": [ - "# importations\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import io\n", - "import s3fs\n", - "import re\n", - "import pickle\n", - "import warnings\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d553c868-695f-4d57-96d6-d5c6629cefb2", - "metadata": {}, - "outputs": [], - "source": [ - "def load_model(type_of_activity, model):\n", - " #BUCKET = f\"projet-bdc2324-team1/Output_model/{type_of_activity}/{model}/\"\n", - " BUCKET = f\"projet-bdc2324-team1/2_Output/2_1_Modeling_results/standard/{type_of_activity}/{model}/\"\n", - " filename = model + '.pkl'\n", - " file_path = BUCKET + filename\n", - " with fs.open(file_path, mode=\"rb\") as f:\n", - " model_bytes = f.read()\n", - "\n", - " model = pickle.loads(model_bytes)\n", - " return model\n", - "\n", - "\n", - "def load_test_file(type_of_activity):\n", - " #file_path_test = f\"projet-bdc2324-team1/Generalization/{type_of_activity}/Test_set.csv\"\n", - " file_path_test = f\"projet-bdc2324-team1/1_Temp/1_0_Modelling_Datasets/{type_of_activity}/Test_set.csv\"\n", - " with fs.open(file_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " return dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3af80fea-a937-4ea8-bece-cfeaa89d1055", - "metadata": {}, - "outputs": [], - "source": [ - "# exec(open('utils_segmentation.py').read())\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "# choose the type of companies for which you want to run the pipeline\n", - "type_of_activity = \"sport\"" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "cc6af7fa-33b2-4d58-ada4-e2ee7262bab9", - "metadata": {}, - "outputs": [], - "source": [ - "# load test set\n", - "dataset_test = load_test_file(type_of_activity)\n", - "\n", - "# Load Model \n", - "model = load_model(type_of_activity, 'LogisticRegression_Benchmark')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "8238ee71-47ec-4621-9813-4b5d2fd03efd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"96095 1490 False NaN True 1 ... \n", - "\n", - " purchases_5_2022 purchases_6_2021 purchases_6_2022 purchases_7_2021 \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "96091 0.0 0.0 0.0 0.0 \n", - "96092 0.0 0.0 0.0 0.0 \n", - "96093 0.0 0.0 0.0 0.0 \n", - "96094 0.0 0.0 0.0 0.0 \n", - "96095 0.0 0.0 0.0 0.0 \n", - "\n", - " purchases_7_2022 purchases_8_2021 purchases_8_2022 purchases_9_2021 \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 1.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "96091 0.0 0.0 0.0 0.0 \n", - "96092 0.0 0.0 0.0 0.0 \n", - "96093 0.0 0.0 0.0 0.0 \n", - "96094 0.0 0.0 0.0 0.0 \n", - "96095 0.0 0.0 0.0 0.0 \n", - "\n", - " purchases_9_2022 y_has_purchased \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "... ... ... \n", - "96091 0.0 0.0 \n", - 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" train_set = pd.DataFrame()\n", - " for file in file_path_list:\n", - " print(file)\n", - " with fs.open(file, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\")\n", - " train_set = pd.concat([train_set, df], ignore_index = True)\n", - " return train_set\n", - "\n", - "def recup_var(df, activity, var) :\n", - " \n", - " df_test = generate_test_set(activity)\n", - " df_train = generate_train_set(activity)\n", - " df_all = pd.concat([df_train, df_test], ignore_index=True)\n", - "\n", - " df_used = df\n", - " \n", - " df_used = df_used.set_index(\"customer_id\")\n", - " df_used[var] = df_all.set_index(\"customer_id\")[var]\n", - " df_used = df_used.reset_index()\n", - "\n", - " return df_used" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "51843556-d785-4d11-abfa-d4e603b32fe7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'street_id', 'structure_id', 'mcp_contact_id',\n", - " 'fidelity', 'tenant_id', 'is_partner', 'deleted_at', 'is_email_true',\n", - " 'opt_in', 'profession', 'last_buying_date', 'max_price', 'ticket_sum',\n", - " 'average_price', 'average_purchase_delay', 'average_price_basket',\n", - " 'average_ticket_basket', 'total_price', 'preferred_category',\n", - " 'preferred_supplier', 'preferred_formula', 'purchase_count',\n", - " 'first_buying_date', 'last_visiting_date', 'zipcode', 'country', 'age',\n", - " 'gender_label', 'gender_female', 'gender_male', 'gender_other',\n", - " 'categorie_age_0_10', 'categorie_age_10_20', 'categorie_age_20_30',\n", - " 'categorie_age_30_40', 'categorie_age_40_50', 'categorie_age_50_60',\n", - " 'categorie_age_60_70', 'categorie_age_70_80', 'categorie_age_plus_80',\n", - " 'categorie_age_inconnue', 'country_fr', 'is_profession_known',\n", - " 'is_zipcode_known', 'nb_campaigns', 'nb_campaigns_opened',\n", - " 'time_to_open', 'taux_ouverture_mail', 'nb_targets', 'target_jeune',\n", - " 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise',\n", - " 'target_famille', 'target_newsletter', 'target_abonne', 'nb_tickets',\n", - " 'nb_purchases', 'total_amount', 'nb_suppliers', 'achat_internet',\n", - " 'purchase_date_min', 'purchase_date_max', 'time_between_purchase',\n", - " 'nb_purchases_internet', 'prop_purchases_internet', 'purchases_10_2021',\n", - " 'purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021',\n", - " 'purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',\n", - " 'purchases_4_2022', 'purchases_5_2021', 'purchases_5_2022',\n", - " 'purchases_6_2021', 'purchases_6_2022', 'purchases_7_2021',\n", - " 'purchases_7_2022', 'purchases_8_2021', 'purchases_8_2022',\n", - " 'purchases_9_2021', 'purchases_9_2022', 'y_has_purchased',\n", - " 'has_purchased'],\n", - " dtype='object')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_test = recup_var(dataset_test, type_of_activity, \"age\")\n", - "dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "e4287c1a-eab6-4897-91d6-d21804518dc4", - "metadata": {}, - 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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity \\\n", - "0 5_4317407 969908 NaN 6156473.0 1 \n", - "1 5_477635 109121 NaN 6213652.0 2 \n", - "2 5_411639 92929 NaN 6160271.0 4 \n", - "3 5_326623 79862 NaN 6140109.0 1 \n", - "4 5_383915 85421 NaN 6149409.0 2 \n", - "... ... ... ... ... ... \n", - "96091 9_91205 76215 NaN 47280.0 0 \n", - "96092 9_369887 815891 NaN 30764537.0 4 \n", - "96093 9_1007562 1 NaN NaN 0 \n", - "96094 9_15037 12992 NaN 2213448.0 0 \n", - "96095 9_135370 76215 NaN 2164740.0 0 \n", - "\n", - " tenant_id is_partner deleted_at is_email_true opt_in ... \\\n", - "0 1771 False NaN True 0 ... \n", - "1 1771 False NaN True 0 ... \n", - "2 1771 False NaN True 0 ... \n", - "3 1771 False NaN True 1 ... \n", - "4 1771 False NaN True 1 ... \n", - "... ... ... ... ... ... ... \n", - "96091 1490 False NaN True 1 ... \n", - "96092 1490 False NaN True 0 ... \n", - "96093 1490 False NaN True 0 ... \n", - "96094 1490 False NaN True 1 ... \n", - 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"\"\"\"\n", - "\n", - "y_test = dataset_test[['y_has_purchased']]\n", - "\n", - "\n", - "# X_test_segment = X_test\n", - "X_test_segment = dataset_test\n", - "\n", - "# X_test_segment.insert(X_test.shape[1], \"country_fr\", dataset_test[\"country_fr\"])\n", - "\n", - "# add y_has_purchased to X_test\n", - "X_test_segment[\"has_purchased\"] = y_test\n", - "\n", - "# Add prediction and probability to dataset_test\n", - "# y_pred = model.predict(X_test)\n", - "y_pred = model.predict(dataset_test)\n", - "\n", - "X_test_segment[\"has_purchased_estim\"] = y_pred\n", - "\n", - "#y_pred_prob = model.predict_proba(X_test)[:, 1]\n", - "y_pred_prob = model.predict_proba(dataset_test)[:, 1]\n", - "\n", - "X_test_segment['score'] = y_pred_prob\n", - "\n", - "X_test_segment[\"segment\"] = np.where(X_test_segment['score']<0.25, '1',\n", - " np.where(X_test_segment['score']<0.5, '2',\n", - " np.where(X_test_segment['score']<0.75, '3', '4')))\n", - "\n", - "X_test_segment" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "d0d3e25f-3f0d-40ca-adb6-6f87e24edc8f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'street_id', 'structure_id', 'mcp_contact_id',\n", - " 'fidelity', 'tenant_id', 'is_partner', 'deleted_at', 'is_email_true',\n", - " 'opt_in', 'profession', 'last_buying_date', 'max_price', 'ticket_sum',\n", - " 'average_price', 'average_purchase_delay', 'average_price_basket',\n", - " 'average_ticket_basket', 'total_price', 'preferred_category',\n", - " 'preferred_supplier', 'preferred_formula', 'purchase_count',\n", - " 'first_buying_date', 'last_visiting_date', 'zipcode', 'country', 'age',\n", - " 'gender_label', 'gender_female', 'gender_male', 'gender_other',\n", - " 'categorie_age_0_10', 'categorie_age_10_20', 'categorie_age_20_30',\n", - " 'categorie_age_30_40', 'categorie_age_40_50', 'categorie_age_50_60',\n", - " 'categorie_age_60_70', 'categorie_age_70_80', 'categorie_age_plus_80',\n", - " 'categorie_age_inconnue', 'country_fr', 'is_profession_known',\n", - " 'is_zipcode_known', 'nb_campaigns', 'nb_campaigns_opened',\n", - " 'time_to_open', 'taux_ouverture_mail', 'nb_targets', 'target_jeune',\n", - " 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise',\n", - " 'target_famille', 'target_newsletter', 'target_abonne', 'nb_tickets',\n", - " 'nb_purchases', 'total_amount', 'nb_suppliers', 'achat_internet',\n", - " 'purchase_date_min', 'purchase_date_max', 'time_between_purchase',\n", - " 'nb_purchases_internet', 'prop_purchases_internet', 'purchases_10_2021',\n", - " 'purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021',\n", - " 'purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',\n", - " 'purchases_4_2022', 'purchases_5_2021', 'purchases_5_2022',\n", - " 'purchases_6_2021', 'purchases_6_2022', 'purchases_7_2021',\n", - " 'purchases_7_2022', 'purchases_8_2021', 'purchases_8_2022',\n", - " 'purchases_9_2021', 'purchases_9_2022', 'y_has_purchased',\n", - " 'has_purchased', 'has_purchased_estim', 'score', 'segment',\n", - " 'share_campaigns_opened'],\n", - " dtype='object')" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_test.columns" - ] - }, - { - "cell_type": "markdown", - "id": "9058c3b2-8fa2-4322-a57b-395da4033eaf", - "metadata": {}, - "source": [ - "## 1. Business KPIs" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3067d919-50c9-49e9-b0a6-b676a5dbae56", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_purchases_internet\n", - "segment \n", - "1 34667.0 14116.0 6.772701e+05 5836.0\n", - "2 36994.0 16853.0 1.215306e+06 10363.0\n", - "3 40121.0 17157.0 1.059581e+06 10628.0\n", - "4 413816.0 101811.0 1.751393e+07 34378.0" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# business figures\n", - "X_test_segment.groupby(\"segment\")[[\"nb_tickets\", \"nb_purchases\", \"total_amount\",\n", - " \"nb_purchases_internet\"]].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "5b1acd28-b346-45b1-8da2-b79ca7f4fa96", - "metadata": {}, - "outputs": [], - "source": [ - "def df_business_fig(df, segment, list_var) :\n", - " df_business_kpi = df.groupby(segment)[list_var].sum().reset_index()\n", - " df_business_kpi.insert(1, \"size\", df.groupby(segment).size().values)\n", - " all_var = [\"size\"] + list_var\n", - " df_business_kpi[all_var] = 100 * df_business_kpi[all_var] / df_business_kpi[all_var].sum()\n", - "\n", - " return df_business_kpi" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "bd63d787-3ef8-4f23-9069-e9b16b4a0de8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentsizenb_ticketsnb_purchasestotal_amountnb_campaigns
0157.8900276.5957259.4146213.30923156.178807
1217.3607647.03845911.2400545.93814713.839223
2310.9099237.63340011.4428065.17725410.487089
3413.83928678.73241567.90251985.57536819.494881
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" - ], - "text/plain": [ - " segment size nb_tickets nb_purchases total_amount nb_campaigns\n", - "0 1 57.890027 6.595725 9.414621 3.309231 56.178807\n", - "1 2 17.360764 7.038459 11.240054 5.938147 13.839223\n", - "2 3 10.909923 7.633400 11.442806 5.177254 10.487089\n", - "3 4 13.839286 78.732415 67.902519 85.575368 19.494881" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "business_var = [\"nb_tickets\", \"nb_purchases\", \"total_amount\", \"nb_campaigns\"]\n", - "X_test_business_fig = df_business_fig(X_test_segment, \"segment\",\n", - " business_var)\n", - "X_test_business_fig" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "d2f618b6-c984-4790-bd8f-29c7d01c6707", - "metadata": {}, - "outputs": [], - "source": [ - "def hist_segment_business_KPIs(df, segment, size, nb_tickets, nb_purchases, total_amount, nb_campaigns) :\n", - " \n", - " plt.figure()\n", - "\n", - " df_plot = df[[segment, size, nb_tickets, nb_purchases, total_amount, nb_campaigns]]\n", - " \n", - " x = [\"number of\\ncustomers\", \"number of\\ntickets\", \"number of\\npurchases\", \"total\\namount\", \n", - " \"number of\\ncampaigns\"]\n", - "\n", - " # liste_var = [size, nb_tickets, nb_purchases, total_amount]\n", - " \n", - " bottom = np.zeros(5)\n", - " \n", - " # Définir une palette de couleurs\n", - " colors = plt.cm.Blues(np.linspace(0.1, 0.9, 4))\n", - " \n", - " for i in range(4) :\n", - " # print(str(df_plot[segment][i]))\n", - " # segment = df_plot[segment][i]\n", - " height = list(df_plot.loc[i,size:].values)\n", - " \n", - " plt.bar(x=x, height=height, label = str(df_plot[segment][i]), bottom=bottom, color=colors[i])\n", - " \n", - " bottom+=height\n", - "\n", - " # Ajuster les marges\n", - " plt.subplots_adjust(left = 0.125, right = 0.8, bottom = 0.1, top = 0.9)\n", - " \n", - " plt.legend(title = \"segment\", loc = \"upper right\", bbox_to_anchor=(1.2, 1))\n", - " plt.ylabel(\"Fraction represented by the segment (%)\")\n", - " plt.title(f\"Relative weight of each segment regarding business KPIs\\nfor {type_of_activity} companies\", size=12)\n", - " # plt.title(\"test\")\n", - " # plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "14b6ae5c-d704-4f5d-9f9b-5646e29ea470", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hist_segment_business_KPIs(X_test_business_fig, \"segment\", \"size\", *business_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f358fba3-f778-4414-bf55-c830be647ddd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Output_marketing_personae_analysis/sport/segments_business_KPIs_sport.csv'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "activity = \"sport\"\n", - "PATH = f\"projet-bdc2324-team1/Output_marketing_personae_analysis/{activity}/\"\n", - "\n", - "file_name = \"segments_business_KPIs_\" + activity\n", - "FILE_PATH_OUT_S3 = PATH + file_name + \".csv\"\n", - "\n", - "FILE_PATH_OUT_S3" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "3eee7b59-f658-402d-95b2-fa051188fd10", - "metadata": {}, - "outputs": [], - "source": [ - "def save_file_s3_mp(File_name, type_of_activity):\n", - " image_buffer = io.BytesIO()\n", - " plt.savefig(image_buffer, format='png')\n", - " image_buffer.seek(0)\n", - " PATH = f\"projet-bdc2324-team1/Output_marketing_personae_analysis/{type_of_activity}/\"\n", - " FILE_PATH_OUT_S3 = PATH + File_name + type_of_activity + '.png'\n", - " with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", - " s3_file.write(image_buffer.read())\n", - " plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "1790cb81-3304-41f1-a371-d8c926d32906", - "metadata": {}, - "outputs": [], - "source": [ - "# save to Minio\n", - "\n", - "activity = \"sport\"\n", - "PATH = f\"projet-bdc2324-team1/Output_marketing_personae_analysis/{activity}/\"\n", - "\n", - "file_name = \"segments_business_KPI_\" + activity\n", - "# file_name = \"segments_business_KPIs_\" + activity\n", - "FILE_PATH_OUT_S3 = PATH + file_name + \".png\"\n", - "\n", - "hist_segment_business_KPIs(X_test_business_fig, \"segment\", \"size\", \"nb_tickets\", \n", - " \"nb_purchases\", \"total_amount\", \"nb_campaigns\")\n", - "\n", - "image_buffer = io.BytesIO()\n", - "plt.savefig(image_buffer, format='png', dpi=110)\n", - "image_buffer.seek(0)\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", - " s3_file.write(image_buffer.read())\n", - "plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "cbf2cc62-1144-48c6-90d8-e12c8e510e02", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hist_segment_business_KPIs(X_test_business_fig, \"segment\", \"size\", \"nb_tickets\", \n", - " \"nb_purchases\", \"total_amount\", \"nb_campaigns\")" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "7a42523d-f80f-488b-ad8f-39dd793cddd6", - "metadata": {}, - "outputs": [], - "source": [ - "# with function\n", - "\n", - "# activity = \"sport\"\n", - "\n", - "hist_segment_business_KPIs(X_test_business_fig, \"segment\", \"size\", \"nb_tickets\", \n", - " \"nb_purchases\", \"total_amount\", \"nb_campaigns\")\n", - "\n", - "save_file_s3_mp(File_name = \"segments_business_KPIs_\", type_of_activity = type_of_activity)" - ] - }, - { - "cell_type": "markdown", - "id": "53d24165-6b98-4b66-9ad8-7514564689d8", - "metadata": {}, - "source": [ - "## 2. Spider plot summarizing sociodemographic characteristics and purchasing behaviour" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "beb31e4b-a01b-4312-879a-fe5757ea061f", - "metadata": {}, - "outputs": [], - "source": [ - "def df_segment_mp(df, segment, gender_female, gender_male, gender_other, country_fr, age) :\n", - " df_mp = df.groupby(segment)[[gender_female, gender_male, gender_other, country_fr, age]].mean().reset_index()\n", - " # df_mp.insert(3, \"share_known_gender\", df_mp[gender_female]+df_mp[gender_male])\n", - " df_mp.insert(4, \"share_of_women\", df_mp[gender_female]/(df_mp[gender_female]+df_mp[gender_male]))\n", - " return df_mp" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "267ebaee-eaef-4720-8ca9-e40c0cf125df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentgender_femalegender_malegender_othershare_of_womencountry_fr
010.2344600.4192160.3463240.3586790.511056
120.2950310.5395910.1653780.3534900.726962
230.2323540.5831740.1844720.2849120.633363
340.2006920.6746370.1246710.2292760.678772
\n", - "
" - ], - "text/plain": [ - " segment gender_female gender_male gender_other share_of_women \\\n", - "0 1 0.234460 0.419216 0.346324 0.358679 \n", - "1 2 0.295031 0.539591 0.165378 0.353490 \n", - "2 3 0.232354 0.583174 0.184472 0.284912 \n", - "3 4 0.200692 0.674637 0.124671 0.229276 \n", - "\n", - " country_fr \n", - "0 0.511056 \n", - "1 0.726962 \n", - "2 0.633363 \n", - "3 0.678772 " - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# description of marketing personae\n", - "\n", - "X_test_segment_mp = X_test_segment.groupby(\"segment\")[['gender_female', 'gender_male', 'gender_other', 'country_fr']].mean().reset_index()\n", - "# X_test_segment_mp.insert(3, \"share_known_gender\", X_test_segment_mp[\"gender_female\"]+X_test_segment_mp[\"gender_male\"])\n", - "X_test_segment_mp.insert(4, \"share_of_women\", X_test_segment_mp[\"gender_female\"]/(X_test_segment_mp[\"gender_female\"]+X_test_segment_mp[\"gender_male\"]))\n", - "X_test_segment_mp" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "5f908232-b0fe-4707-a8c5-5cadb7d8653f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentgender_femalegender_malegender_othershare_of_womencountry_frage
010.2344600.4192160.3463240.3586790.51105640.652136
120.2950310.5395910.1653780.3534900.72696236.204792
230.2323540.5831740.1844720.2849120.63336337.533425
340.2006920.6746370.1246710.2292760.67877239.665371
\n", - "
" - ], - "text/plain": [ - " segment gender_female gender_male gender_other share_of_women \\\n", - "0 1 0.234460 0.419216 0.346324 0.358679 \n", - "1 2 0.295031 0.539591 0.165378 0.353490 \n", - "2 3 0.232354 0.583174 0.184472 0.284912 \n", - "3 4 0.200692 0.674637 0.124671 0.229276 \n", - "\n", - " country_fr age \n", - "0 0.511056 40.652136 \n", - "1 0.726962 36.204792 \n", - "2 0.633363 37.533425 \n", - "3 0.678772 39.665371 " - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment_mp = df_segment_mp(X_test_segment, \"segment\", \"gender_female\", \n", - " \"gender_male\", \"gender_other\", \"country_fr\", \"age\")\n", - "X_test_segment_mp" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "910876fe-e6df-4f8d-9978-5d6fdd893ac0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentprop_purchases_internetshare_campaigns_openedopt_in
010.0904390.1419850.587075
120.5022320.2716230.111611
230.6817530.2992550.122377
340.5282490.3498110.178660
\n", - "
" - ], - "text/plain": [ - " segment prop_purchases_internet share_campaigns_opened opt_in\n", - "0 1 0.090439 0.141985 0.587075\n", - "1 2 0.502232 0.271623 0.111611\n", - "2 3 0.681753 0.299255 0.122377\n", - "3 4 0.528249 0.349811 0.178660" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# purchasing behaviour\n", - "\n", - "# X_test_segment[\"share_tickets_internet\"] = X_test_segment[\"nb_tickets_internet\"]/X_test_segment[\"nb_tickets\"]\n", - "X_test_segment[\"share_campaigns_opened\"] = X_test_segment[\"nb_campaigns_opened\"]/X_test_segment[\"nb_campaigns\"]\n", - "X_test_segment_pb = X_test_segment.groupby(\"segment\")[[\"prop_purchases_internet\", \"share_campaigns_opened\", \"opt_in\"]].mean().reset_index()\n", - "X_test_segment_pb" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "8d3ab073-040c-4480-bd44-33fc88626707", - "metadata": {}, - "outputs": [], - "source": [ - "def df_segment_pb (df, segment, nb_tickets_internet, nb_tickets, nb_campaigns_opened, nb_campaigns, opt_in,\n", - " time_to_open) :\n", - " df_used = df\n", - " df_used[\"share_tickets_internet\"] = df_used[nb_tickets_internet]/df_used[nb_tickets]\n", - " df_used[\"share_campaigns_opened\"] = df_used[nb_campaigns_opened]/df_used[nb_campaigns]\n", - " df_pb = df_used.groupby(segment)[[\"share_tickets_internet\", \"share_campaigns_opened\", \n", - " opt_in, time_to_open]].mean().reset_index()\n", - " df_pb[\"time_to_open_med\"] = df_used.groupby(segment)[[time_to_open]].apply(lambda x: x.dropna().median()).values\n", - " return df_pb" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "33a11ddf-b410-4cf1-9e6b-645de6dad604", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Durée totale en heures : 49.65333333333333\n" - ] - } - ], - "source": [ - "# add : variable time to open\n", - "\n", - "from datetime import timedelta\n", - "\n", - "def str_duration_to_hours(duration_str):\n", - " parts = duration_str.split()\n", - " days = int(parts[0]) if len(parts) > 1 else 0\n", - " time_parts = parts[-1].split(':')\n", - " hours = int(time_parts[0])\n", - " minutes = int(time_parts[1])\n", - " seconds = int(time_parts[2].split('.')[0])\n", - " total_hours = days * 24 + hours + minutes / 60 + seconds / 3600\n", - " return total_hours\n", - "\n", - "# Exemple d'utilisation :\n", - "duration_str = '2 days 01:39:12.750000'\n", - "\n", - "hours = str_duration_to_hours(duration_str)\n", - "print(\"Durée totale en heures :\", hours)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "4760743c-1032-452a-85fa-63d1447a742c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "segment\n", - "1 6.418056\n", - "2 8.031389\n", - "3 13.037500\n", - "4 15.197500\n", - "Name: time_to_open, dtype: float64" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# def of the variable time_to_open\n", - "\n", - "X_test_segment[\"time_to_open\"] = dataset_test[\"time_to_open\"].apply(lambda x : np.nan if pd.isna(x) else str_duration_to_hours(x))\n", - "X_test_segment.groupby(\"segment\")[\"time_to_open\"].median()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "0cb8f47a-bf0f-4285-b2ff-d90de394c787", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentshare_tickets_internetshare_campaigns_openedopt_intime_to_opentime_to_open_med
010.5272700.1365650.73006456.7854986.418056
120.6296480.1942400.27586056.3492728.031389
230.6544880.2922060.05426057.84739013.037500
340.6066180.3707330.12705157.56768415.197500
\n", - "
" - ], - "text/plain": [ - " segment share_tickets_internet share_campaigns_opened opt_in \\\n", - "0 1 0.527270 0.136565 0.730064 \n", - "1 2 0.629648 0.194240 0.275860 \n", - "2 3 0.654488 0.292206 0.054260 \n", - "3 4 0.606618 0.370733 0.127051 \n", - "\n", - " time_to_open time_to_open_med \n", - "0 56.785498 6.418056 \n", - "1 56.349272 8.031389 \n", - "2 57.847390 13.037500 \n", - "3 57.567684 15.197500 " - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment_pb = df_segment_pb(X_test_segment, \"segment\", \"nb_tickets_internet\", \"nb_tickets\", \n", - " \"nb_campaigns_opened\", \"nb_campaigns\", \"opt_in\", \"time_to_open\")\n", - "X_test_segment_pb" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ba2884e3-004a-4554-ab82-6d477dcc4869", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentprop_purchases_internetshare_campaigns_openedopt_inshare_of_womenage
010.0904390.1419850.5870750.35867940.652136
120.5022320.2716230.1116110.35349036.204792
230.6817530.2992550.1223770.28491237.533425
340.5282490.3498110.1786600.22927639.665371
\n", - "
" - ], - "text/plain": [ - " segment prop_purchases_internet share_campaigns_opened opt_in \\\n", - "0 1 0.090439 0.141985 0.587075 \n", - "1 2 0.502232 0.271623 0.111611 \n", - "2 3 0.681753 0.299255 0.122377 \n", - "3 4 0.528249 0.349811 0.178660 \n", - "\n", - " share_of_women age \n", - "0 0.358679 40.652136 \n", - "1 0.353490 36.204792 \n", - "2 0.284912 37.533425 \n", - "3 0.229276 39.665371 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#X_test_segment_caract = pd.concat([X_test_segment_pb.drop(\"time_to_open\", axis=1), X_test_segment_mp[['share_known_gender', 'share_of_women', 'country_fr', 'age']]], axis=1)\n", - "X_test_segment_caract = pd.concat([X_test_segment_pb, X_test_segment_mp[[ 'share_of_women', 'age']]], axis=1)\n", - "X_test_segment_caract" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "id": "23a37e9b-bb29-4122-85cb-cc15cc344ee2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "share_tickets_internet 0.654488\n", - "share_campaigns_opened 0.370733\n", - "opt_in 0.730064\n", - "time_to_open_med 15.197500\n", - "share_known_gender 0.903085\n", - "share_of_women 0.571869\n", - "country_fr 0.805862\n", - "dtype: float64" - ] - }, - "execution_count": 216, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment_caract.loc[:,\"share_tickets_internet\":].max()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "0809e2ae-3487-4b24-8f60-741c683cb9af", - "metadata": {}, - "outputs": [], - "source": [ - "# def d'une fonction associée - KEEP THIS !!!\n", - "\n", - "def radar_mp_plot(df, categories, index, var_not_perc) :\n", - " categories = categories\n", - "\n", - " # true values are used to print the true value in parenthesis\n", - " tvalues = list(df.loc[index,categories]) \n", - "\n", - " max_values = df[categories].max()\n", - "\n", - " # values are true values / max among the 4 segments, allows to \n", - " # put values in relation with the values for other segments\n", - " # if the point has a maximal abscisse it means that value is maximal for the segment considered\n", - " # , event if not equal to 1\n", - " \n", - " values = list(df.loc[index,categories]/max_values)\n", - " \n", - " # values normalized are used to adjust the value around the circle\n", - " # for instance if the maximum of values is equal to 0.8, we want the point to be \n", - " # at 8/10th of the circle radius, not at the edge \n", - " values_normalized = [ max(values) * elt for elt in values]\n", - "\n", - " # Nb of categories\n", - " num_categories = len(categories)\n", - " \n", - " angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n", - " \n", - " # Initialize graphic\n", - " fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n", - " \n", - " # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle\n", - " # which is based on max(value)\n", - " ax.plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)\n", - " ax.plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5, linewidth=1.2)\n", - " \n", - " # fill the sector\n", - " ax.fill(angles, values_normalized, color='orange', alpha=0.4)\n", - " \n", - " # labels\n", - " ax.set_yticklabels([])\n", - " ax.set_xticks(angles)\n", - "\n", - " # define tick labels\n", - " values_printed = [str(round(tvalues[i],2)) if categories[i] in var_not_perc else f\"{round(100 * tvalues[i],2)}%\" for i in range(len(categories))]\n", - " # ticks = [categories[i].replace(\"_\",\" \") + f\"\\n({round(100 * tvalues[i],2)}%)\" for i in range(len(categories))]\n", - " ticks = [categories[i].replace(\"_\",\" \") + f\"\\n({values_printed[i]})\" for i in range(len(categories))]\n", - "\n", - " ax.set_xticklabels(ticks, color=\"black\")\n", - " \n", - " ax.spines['polar'].set_visible(False)\n", - " \n", - " plt.title(f'Characteristics of the segment {index+1}\\n')\n", - " \n", - " # plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 229, - "id": "2fe80072-90d1-4e17-b8a7-ddc3e3be1b12", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['60.66%', '37.07%', '12.71%', '15.2', '20.82%', '63.9%']" - ] - }, - "execution_count": 229, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "var_not_perc = [\"time_to_open_med\", \"age\"]\n", - "\n", - "tvalues = list(X_test_segment_caract.loc[3,categories]) \n", - "\n", - "values_printed = [str(round(tvalues[i],2)) if categories[i] in var_not_perc else f\"{round(100 * tvalues[i],2)}%\" for i in range(len(categories))]\n", - "values_printed" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cd3cb227-28b2-461e-a921-cff721c356e6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['share_tickets_internet',\n", - " 'share_campaigns_opened',\n", - " 'opt_in',\n", - " 'time_to_open_med',\n", - " 'share_of_women',\n", - " 'country_fr',\n", - " 'age']" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(X_test_segment_caract.drop([\"segment\", \"share_known_gender\"], axis=1).columns)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "9a550db7-ddd7-4d6f-bf98-cf0b2ea35d91", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentprop_purchases_internetshare_campaigns_openedopt_inshare_of_womenage
010.0904390.1419850.5870750.35867940.652136
120.5022320.2716230.1116110.35349036.204792
230.6817530.2992550.1223770.28491237.533425
340.5282490.3498110.1786600.22927639.665371
\n", - "
" - ], - "text/plain": [ - " segment prop_purchases_internet share_campaigns_opened opt_in \\\n", - "0 1 0.090439 0.141985 0.587075 \n", - "1 2 0.502232 0.271623 0.111611 \n", - "2 3 0.681753 0.299255 0.122377 \n", - "3 4 0.528249 0.349811 0.178660 \n", - "\n", - " share_of_women age \n", - "0 0.358679 40.652136 \n", - "1 0.353490 36.204792 \n", - "2 0.284912 37.533425 \n", - "3 0.229276 39.665371 " - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment_caract" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "56cb026b-857f-42eb-baed-0ebdf5aee447", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "var_not_perc = [\"age\"]\n", - "\n", - "categories = list(X_test_segment_caract.drop([\"segment\"], axis=1).columns)\n", - "#for i in range(4) :\n", - "# radar_mp_plot(df=X_test_segment_caract, categories=categories, index=i)\n", - "radar_mp_plot(df=X_test_segment_caract, categories=categories, index=3, var_not_perc=var_not_perc)" - ] - }, - { - "cell_type": "code", - "execution_count": 739, - "id": "5b3c4bac-396e-4117-a7d9-f39a3d8f95b4", - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (4005960846.py, line 6)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[739], line 6\u001b[0;36m\u001b[0m\n\u001b[0;31m file_name = \"spider_chart_\" + activity + \"_sgt_\" str(index)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "# export to MinIo\n", - "\n", - "activity = \"sport\"\n", - "PATH = f\"projet-bdc2324-team1/Output_marketing_personae_analysis/{activity}/\"\n", - "\n", - "file_name = \"spider_chart_\" + activity + \"_sgt_\" + str(index)\n", - "FILE_PATH_OUT_S3 = PATH + file_name + \".csv\"\n", - "\n", - "\n", - "radar_mp_plot(df=X_test_segment_caract, categories=categories, index=3)\n", - "\n", - "image_buffer = io.BytesIO()\n", - "plt.savefig(image_buffer, format='png')\n", - "image_buffer.seek(0)\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", - " s3_file.write(image_buffer.read())\n", - "plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 740, - "id": "276de9a5-d506-4c11-a7c2-a23ebbc59fe5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Output_marketing_personae_analysis/sport/spider_chart_sport_sgt_3.csv'" - ] - }, - "execution_count": 740, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "activity = \"sport\"\n", - "PATH = f\"projet-bdc2324-team1/Output_marketing_personae_analysis/{activity}/\"\n", - "\n", - "file_name = \"spider_chart_\" + activity + \"_sgt_\" + str(index)\n", - "FILE_PATH_OUT_S3 = PATH + file_name + \".csv\"\n", - "FILE_PATH_OUT_S3" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "80e47dbc-3efd-4857-8055-876b308cbcb5", - "metadata": {}, - "outputs": [], - "source": [ - "# general function to have the 4 radars in one plot\n", - "\n", - "def radar_mp_plot_all(df, categories, var_not_perc) :\n", - " \n", - " nb_segments = df.shape[0]\n", - " categories = categories\n", - "\n", - " # Initialize graphic\n", - " fig, ax = plt.subplots(2,2, figsize=(20, 21), subplot_kw=dict(polar=True))\n", - " \n", - " for index in range(nb_segments) :\n", - " row = index // 2 # Division entière pour obtenir le numéro de ligne\n", - " col = index % 2 \n", - " \n", - " # df = X_test_segment_caract\n", - " \n", - " # true values are used to print the true value in parenthesis\n", - " tvalues = list(df.loc[index,categories]) \n", - " \n", - " max_values = df[categories].max()\n", - " \n", - " # values are true values / max among the 4 segments, allows to \n", - " # put values in relation with the values for other segments\n", - " # if the point has a maximal abscisse it means that value is maximal for the segment considered\n", - " # , event if not equal to 1\n", - " \n", - " values = list(df.loc[index,categories]/max_values)\n", - " \n", - " # values normalized are used to adjust the value around the circle\n", - " # for instance if the maximum of values is equal to 0.8, we want the point to be \n", - " # at 8/10th of the circle radius, not at the edge \n", - " values_normalized = [ max(values) * elt for elt in values]\n", - " \n", - " # Nb of categories\n", - " num_categories = len(categories)\n", - "\n", - " angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n", - " \n", - " # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle\n", - " # which is based on max(value)\n", - " ax[row, col].plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)\n", - " ax[row, col].plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5, linewidth=1.2)\n", - " \n", - " # fill the sector\n", - " ax[row, col].fill(angles, values_normalized, color='orange', alpha=0.4, label = index)\n", - " \n", - " # labels\n", - " ax[row, col].set_yticklabels([])\n", - " ax[row, col].set_xticks(angles)\n", - " \n", - " # define the ticks\n", - " values_printed = [str(round(tvalues[i],2)) if categories[i] in var_not_perc else f\"{round(100 * tvalues[i],2)}%\" for i in range(len(categories))]\n", - "\n", - " # ticks = [categories[i].replace(\"_\",\" \") + f\"\\n({round(100 * tvalues[i],2)}%)\" for i in range(len(categories))]\n", - " ticks = [categories[i].replace(\"_\",\" \") + f\"\\n({values_printed[i]})\" for i in range(len(categories))]\n", - " ax[row, col].set_xticklabels(ticks, color=\"black\", size = 20)\n", - " \n", - " ax[row, col].spines['polar'].set_visible(False)\n", - " \n", - " # plt.title(f'Characteristics of the segment {index+1}\\n')\n", - " ax[row, col].set_title(f'Segment {index+1}\\n', size = 24)\n", - " \n", - " fig.suptitle(f\"Characteristics of marketing personae of {type_of_activity} companies\", size=32)\n", - "\n", - " plt.tight_layout()\n", - "\n", - " # plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "67d9a15b-bd93-4e63-a193-e9760d710906", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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segmentshare_tickets_internetshare_campaigns_openedopt_intime_to_open_medshare_known_gendershare_of_womencountry_frage
010.5272700.1365650.7300646.4180560.5231290.5718690.33995941.298584
120.6296480.1942400.2758608.0313890.8553910.1827100.80586239.293163
230.6544880.2922060.05426013.0375000.9030850.3230750.70125835.176503
340.6066180.3707330.12705115.1975000.8643730.2082310.63897241.320841
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" - ], - "text/plain": [ - " segment share_tickets_internet share_campaigns_opened opt_in \\\n", - "0 1 0.527270 0.136565 0.730064 \n", - "1 2 0.629648 0.194240 0.275860 \n", - "2 3 0.654488 0.292206 0.054260 \n", - "3 4 0.606618 0.370733 0.127051 \n", - "\n", - " time_to_open_med share_known_gender share_of_women country_fr age \n", - "0 6.418056 0.523129 0.571869 0.339959 41.298584 \n", - "1 8.031389 0.855391 0.182710 0.805862 39.293163 \n", - "2 13.037500 0.903085 0.323075 0.701258 35.176503 \n", - "3 15.197500 0.864373 0.208231 0.638972 41.320841 " - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment_caract" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "edf76688-1b7e-469e-873f-4884d551be66", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "categories = list(X_test_segment_caract.drop([\"segment\"], axis=1).columns)\n", - "var_not_perc = [\"age\"]\n", - "radar_mp_plot_all(df=X_test_segment_caract, categories=categories, var_not_perc=var_not_perc)" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "c48136d1-c980-4f74-a69f-ed4304c83188", - "metadata": {}, - "outputs": [], - "source": [ - "# export to MinIo\n", - "\n", - "# activity = \"sport\"\n", - "# PATH = f\"projet-bdc2324-team1/Output_marketing_personae_analysis/{activity}/\"\n", - "\n", - "file_name = \"spider_chart_all_\" + activity\n", - "FILE_PATH_OUT_S3 = PATH + file_name + \".png\"\n", - "\n", - "radar_mp_plot_all(df=X_test_segment_caract, categories=categories)\n", - "\n", - "image_buffer = io.BytesIO()\n", - "plt.savefig(image_buffer, format='png', dpi=110)\n", - "image_buffer.seek(0)\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", - " s3_file.write(image_buffer.read())\n", - "plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "6e2bb9c7-e97e-424d-991b-d44ef2684c60", - "metadata": {}, - "outputs": [], - "source": [ - "def radar_mp_plot_all(df, type_of_activity) :\n", - " \n", - " # table summarizing variables relative to marketing personae\n", - " df_mp = df.groupby(\"segment\")[[\"gender_female\", \"gender_male\", \"gender_other\", \"age\"]].mean().reset_index()\n", - " #df_mp.insert(3, \"share_known_gender\", df_mp[\"gender_female\"]+df_mp[\"gender_male\"])\n", - " df_mp.insert(4, \"share_of_women\", df_mp[\"gender_female\"]/(df_mp[\"gender_female\"]+df_mp[\"gender_male\"]))\n", - "\n", - " # table relative to purchasing behaviour\n", - " df_pb = df.groupby(\"segment\")[[\"prop_purchases_internet\", \"taux_ouverture_mail\", \"opt_in\"]].mean().reset_index()\n", - "\n", - " # concatenation of tables to prepare the plot\n", - " df_used = pd.concat([df_pb, df_mp[[ 'share_of_women', 'age']]], axis=1)\n", - "\n", - " # visualization\n", - " nb_segments = df_used.shape[0]\n", - " categories = list(df_used.drop(\"segment\", axis=1).columns)\n", - "\n", - " var_not_perc = [\"age\"]\n", - "\n", - " # Initialize graphic\n", - " fig, ax = plt.subplots(2,2, figsize=(20, 20), subplot_kw=dict(polar=True))\n", - " \n", - " for index in range(nb_segments) :\n", - " row = index // 2 # Division entière pour obtenir le numéro de ligne\n", - " col = index % 2 \n", - " \n", - " # true values are used to print the true value in parenthesis\n", - " tvalues = list(df_used.loc[index,categories]) \n", - " \n", - " max_values = df_used[categories].max()\n", - " \n", - " # values are true values / max among the 4 segments, allows to \n", - " # put values in relation with the values for other segments\n", - " # if the point has a maximal abscisse it means that value is maximal for the segment considered\n", - " # , event if not equal to 1\n", - "\n", - " values = list(df_used.loc[index,categories]/max_values)\n", - " \n", - " # values normalized are used to adjust the value around the circle\n", - " # for instance if the maximum of values is equal to 0.8, we want the point to be \n", - " # at 8/10th of the circle radius, not at the edge \n", - " values_normalized = [ max(values) * elt for elt in values]\n", - " \n", - " # Nb of categories\n", - " num_categories = len(categories)\n", - " \n", - " angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n", - " \n", - " # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle\n", - " # which is based on max(value)\n", - " ax[row, col].plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)\n", - " ax[row, col].plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5,\n", - " linewidth=1.2)\n", - " \n", - " # fill the sector\n", - " ax[row, col].fill(angles, values_normalized, color='orange', alpha=0.4, label = index)\n", - " \n", - " # labels\n", - " ax[row, col].set_yticklabels([])\n", - " ax[row, col].set_xticks(angles)\n", - "\n", - " # define the ticks\n", - " values_printed = [round(tvalues[i],2) if categories[i] in var_not_perc else f\"{round(100 * tvalues[i],2)}%\" for i in range(len(categories))] \n", - " print(values_printed)\n", - " ticks = [categories[i].replace(\"_\",\" \") + f\"\\n({values_printed[i]})\" for i in range(len(categories))]\n", - " ax[row, col].set_xticklabels(ticks, color=\"black\", size = 20)\n", - "\n", - " ax[row, col].spines['polar'].set_visible(False)\n", - " \n", - " ax[row, col].set_title(f'Segment {index+1}\\n', size = 24)\n", - " \n", - " fig.suptitle(f\"Characteristics of marketing personae of {type_of_activity} companies\", size=32)\n", - "\n", - " plt.tight_layout()\n", - " # plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "3f1318b0-0177-47ef-9b72-3dc6c1f47a60", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['9.04%', '12.82%', '58.71%', '35.87%', 40.65]\n", - "['50.22%', '19.06%', '11.16%', '35.35%', 36.2]\n", - "['68.18%', '19.45%', '12.24%', '28.49%', 37.53]\n", - "['52.82%', '20.28%', '17.87%', '22.93%', 39.67]\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "radar_mp_plot_all(X_test_segment, type_of_activity)" - ] - }, - { - "cell_type": "markdown", - "id": "a2395680-69fe-4247-8deb-22f8ee15830b", - "metadata": {}, - "source": [ - "## --- end of the main part --- here are just some attempts --- ##" - ] - }, - { - "cell_type": "code", - "execution_count": 489, - "id": "7d9a2aca-d28d-43b3-9b72-5913b20c4f04", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "colors = plt.cm.Blues(np.linspace(0.1, 0.9, 4)) \n", - "colors = [\"blue\", \"green\", \"orange\", \"red\"]\n", - "\n", - "# Initialisez le graphique en étoile\n", - "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n", - "\n", - "for i in range(4) :\n", - "\n", - " # Caractéristiques et valeurs associées (exemple)\n", - " categories = ['share_known_gender', 'share_of_women', 'country_fr']\n", - " values = list(X_test_segment_mp.loc[i,categories]) # Exemple de valeurs, ajustez selon vos données\n", - " \n", - " values_normalized = [ max(values) * elt for elt in values]\n", - " \n", - " # Nombre de caractéristiques\n", - " num_categories = len(categories)\n", - " \n", - " # Créer un angle pour chaque axe\n", - " angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n", - " \n", - " \n", - " # Tracer uniquement le contour du polygone\n", - " ax.plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)\n", - " # ax.plot(angles + angles[:1], values_normalized + values_normalized[:1], color='blue', alpha = 0.3, linewidth=1.5)\n", - " \n", - " # Remplir le secteur central avec une couleur\n", - " ax.fill(angles, values_normalized, color=colors[i], alpha=0.2, label = str(i+1))\n", - "\n", - "# Étiqueter les axes\n", - "ax.set_yticklabels([])\n", - "ax.set_xticks(angles)\n", - "ax.set_xticklabels(categories)\n", - "ax.legend()\n", - "\n", - "# Titre du graphique\n", - "plt.title('Résumé des caractéristiques')\n", - "\n", - "# Afficher le graphique\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "id": "96aa9ff5-c1ed-49eb-8fb7-2319ac0c40be", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# KEEP THIS CODE !!\n", - "\n", - "# Caractéristiques et valeurs associées (exemple)\n", - "categories = ['Force', 'Vitesse', 'Agilité', 'Précision', 'Endurance']\n", - "values = [8, 7, 6, 9, 7] # Exemple de valeurs, ajustez selon vos données\n", - "\n", - "# Plage de valeurs maximales pour chaque caractéristique\n", - "max_range = [20, 20, 20, 20, 20]\n", - "\n", - "values_normalized = [2 * max(values) * x / y for x, y in zip(values, max_range)]\n", - "\n", - "# Nombre de caractéristiques\n", - "num_categories = len(categories)\n", - "\n", - "# Créer un angle pour chaque axe\n", - "angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n", - "\n", - "# Initialisez le graphique en étoile\n", - "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n", - "\n", - "# Tracer uniquement le contour du polygone\n", - "ax.plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)\n", - "ax.plot(angles + angles[:1], values_normalized + values_normalized[:1], color='blue', linewidth=1.5)\n", - "\n", - "# Remplir le secteur central avec une couleur\n", - "ax.fill(angles, values_normalized, color='skyblue', alpha=0.4)\n", - "\n", - "# Étiqueter les axes\n", - "ax.set_yticklabels([])\n", - "ax.set_xticks(angles)\n", - "ax.set_xticklabels(categories)\n", - "\n", - "# Titre du graphique\n", - "plt.title('Résumé des caractéristiques')\n", - "\n", - "# Afficher le graphique\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "adb7ccb3-7dea-4347-9298-37311a2f1fb1", - "metadata": {}, - "outputs": [], - "source": [ - "def radar_chart(values, categories, segment) :\n", - " # Caractéristiques et valeurs associées (exemple)\n", - " categories = categories\n", - " values = values # Exemple de valeurs, ajustez selon vos données\n", - " \n", - " # Nombre de caractéristiques\n", - " num_categories = len(categories)\n", - " \n", - " # Créer un angle pour chaque axe\n", - " angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n", - " \n", - " # Répéter le premier angle pour fermer la figure\n", - " values += values[:1]\n", - " angles += angles[:1]\n", - " \n", - " # Initialisez le graphique en étoile\n", - " fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n", - " \n", - " # Tracer les lignes radiales\n", - " ax.fill(angles, values, color='skyblue', alpha=0.4)\n", - " \n", - " # Tracer les points sur les axes radiaux\n", - " ax.plot(angles, values, color='blue', linewidth=2, linestyle='solid')\n", - "\n", - " # Afficher les valeurs associées sous les noms de variables\n", - " \"\"\"\n", - " for i, angle in enumerate(angles[:-1]):\n", - " x = angle\n", - " y = values[i] + 0.2 # Ajustez la distance des valeurs par rapport au centre\n", - " plt.text(x, y, str(values[i]), color='black', ha='center', fontsize=10)\n", - " \"\"\"\n", - " \n", - " # Remplir le secteur central avec une couleur\n", - " # ax.fill(angles, values, color='skyblue', alpha=0.4)\n", - "\n", - " \n", - " # Étiqueter les axes\n", - " ax.set_yticklabels([])\n", - " #ax.set_xticks(angles[:-1])\n", - " #ax.set_xticklabels(categories, # fontsize=12, ha='right', rotation=45\n", - " # )\n", - " # ax.set_xticklabels(categories, fontsize=10, color='black', ha='right')\n", - "\n", - " labels = [f\"{category} = {round(100 *value,2)}%\" for category, value in zip(categories, values[:-1])]\n", - " ax.set_xticks(angles[:-1])\n", - " ax.set_xticklabels(labels, fontsize=10, color='black', ha='right')\n", - " \n", - " # Titre du graphique\n", - " plt.title(f'Caracteristics of segment {segment}')\n", - " \n", - " # Afficher le graphique\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "8793fb51-812c-4500-b252-2e2d61d6ff48", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "categories= [\"share_known_gender\",\"share_of_women\",\"country_fr\"]\n", - "radar_chart(values=X_test_segment_mp.loc[0,categories].values.tolist(), categories= categories,\n", - " segment = \"1\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Sport/exploration_sport.ipynb b/Sport/exploration_sport.ipynb deleted file mode 100644 index b9d7e59..0000000 --- a/Sport/exploration_sport.ipynb +++ /dev/null @@ -1,2296 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "314bf34b-1f6d-4a99-8f82-aa71ebacdabc", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os\n", - "import s3fs\n", - "import warnings\n", - "from datetime import date, timedelta, datetime\n", - "import numpy as np\n", - "\n", - "exec(open('../0_KPI_functions.py').read())" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a276822a-c389-429e-b249-8a9e47758bfc", - "metadata": {}, - "outputs": [], - "source": [ - "# Ignore warning\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f62b996c-4e17-40ea-83ba-f0cb60be7671", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1',\n", - " 'bdc2324-data/10',\n", - " 'bdc2324-data/101',\n", - " 'bdc2324-data/11',\n", - " 'bdc2324-data/12',\n", - " 'bdc2324-data/13',\n", - " 'bdc2324-data/14',\n", - " 'bdc2324-data/2',\n", - " 'bdc2324-data/3',\n", - " 'bdc2324-data/4',\n", - " 'bdc2324-data/5',\n", - " 'bdc2324-data/6',\n", - " 'bdc2324-data/7',\n", - " 'bdc2324-data/8',\n", - " 'bdc2324-data/9']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "BUCKET = \"bdc2324-data\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "markdown", - "id": "2c829aa8-2006-4e72-889b-7096dd55718b", - "metadata": {}, - "source": [ - "## Look at the time sequence of each company and compute inter time coverage" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "e86864b7-4852-449a-8680-638559d56080", - "metadata": {}, - "outputs": [], - "source": [ - "sport = ['5', '6', '7', '8', '9']" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "7634ec57-4891-4684-8638-1e1643baca28", - "metadata": {}, - "outputs": [], - "source": [ - "def display_covering_time(df, company, datecover):\n", - " \"\"\"\n", - " This function draws the time coverage of each company\n", - " \"\"\"\n", - " min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n", - " max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n", - " datecover[company] = [datetime.strptime(min_date, \"%Y-%m-%d\") + timedelta(days=x) for x in range((datetime.strptime(max_date, \"%Y-%m-%d\") - datetime.strptime(min_date, \"%Y-%m-%d\")).days)]\n", - " print(f'Couverture Company {company} : {min_date} - {max_date}')\n", - " return datecover" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "53c83f51-822c-4e05-8c7c-89aa327603c6", - "metadata": {}, - "outputs": [], - "source": [ - "def compute_time_intersection(datecover):\n", - " timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n", - " intersection = set.intersection(*timestamps_sets)\n", - " intersection_list = list(intersection)\n", - " formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n", - " return sorted(formated_dates)" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "eec152de-078e-44c4-ad6e-74ae6ba5c65a", - "metadata": {}, - "outputs": [], - "source": [ - "def df_coverage_modelization(sport, coverage_train = 0.7):\n", - " \"\"\"\n", - " This function returns start_date, end_of_features and final dates\n", - " that help to construct train and test datasets\n", - " \"\"\"\n", - " datecover = {}\n", - " for company in sport:\n", - " df_products_purchased_reduced = display_databases(company, file_name = \"products_purchased_reduced\",\n", - " datetime_col = ['purchase_date'])\n", - " datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n", - " #print(datecover.keys())\n", - " dt_coverage = compute_time_intersection(datecover)\n", - " start_date = dt_coverage[0]\n", - " end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n", - " final_date = dt_coverage[-1]\n", - " return start_date, end_of_features, final_date\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "348f246a-bc2d-4bbc-ba05-aa825da15a69", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_5/products_purchased_reduced.csv\n", - "Couverture Company 5 : 2019-04-15 - 2023-11-09\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/products_purchased_reduced.csv\n", - "Couverture Company 6 : 2018-06-28 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_7/products_purchased_reduced.csv\n", - "Couverture Company 7 : 2015-02-10 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_8/products_purchased_reduced.csv\n", - "Couverture Company 8 : 2010-09-28 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_9/products_purchased_reduced.csv\n", - "Couverture Company 9 : 2014-09-22 - 2023-10-24\n", - "dict_keys(['5', '6', '7', '8', '9'])\n", - "2019-04-15 2022-06-15 2023-10-23\n" - ] - } - ], - "source": [ - "start_date, end_of_features, final_date = df_coverage_modelization(sport, coverage_train = 0.7)\n", - "print(start_date, end_of_features, final_date )" - ] - }, - { - "cell_type": "markdown", - "id": "34ddc267-4daa-4926-9d54-5b13d4212eaa", - "metadata": {}, - "source": [ - "## Look at common database between Sport companies" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "389387fa-2046-4811-b8dd-6d524e91fe2e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/5',\n", - " 'bdc2324-data/6',\n", - " 'bdc2324-data/7',\n", - " 'bdc2324-data/8',\n", - " 'bdc2324-data/9']" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "companies = fs.ls(BUCKET)\n", - "companies = [company for company in companies if any(company.endswith(end) for end in sport)]\n", - "companies" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "895fc2b3-c768-454d-bedb-54994e4d211a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of databases : 30\n", - "Number of common databases : 23\n" - ] - } - ], - "source": [ - "companies_database = {}\n", - "\n", - "for company in companies:\n", - " companies_database[company.split('/')[-1]] = [file.split('/')[-1].replace(company.split('/')[-1], '') for file in fs.ls(company)] \n", - "\n", - "all_database = companies_database[max(companies_database, key=lambda x: len(companies_database[x]))]\n", - "print(\"Number of databases : \",len(all_database))\n", - "\n", - "data_in_common = set(all_database)\n", - "\n", - "for key in companies_database:\n", - " diff_database = data_in_common.symmetric_difference(companies_database[key])\n", - " data_in_common = data_in_common - diff_database\n", - "\n", - "print(\"Number of common databases : \",len(data_in_common))" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "0c06517d-f5b7-4104-94fa-0e3f843c5881", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'campaign_stats.csv',\n", - " 'campaigns.csv',\n", - " 'categories.csv',\n", - " 'countries.csv',\n", - " 'currencies.csv',\n", - " 'customer_target_mappings.csv',\n", - " 'customersplus.csv',\n", - " 'event_types.csv',\n", - " 'events.csv',\n", - " 'facilities.csv',\n", - " 'link_stats.csv',\n", - " 'pricing_formulas.csv',\n", - " 'product_packs.csv',\n", - " 'products.csv',\n", - " 'products_groups.csv',\n", - " 'purchases.csv',\n", - " 'representation_category_capacities.csv',\n", - " 'representations.csv',\n", - " 'seasons.csv',\n", - " 'suppliers.csv',\n", - " 'target_types.csv',\n", - " 'targets.csv',\n", - " 'tickets.csv'}" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_in_common" - ] - }, - { - "cell_type": "markdown", - "id": "1af245aa-44a7-453b-90f9-0c4bcc415cd0", - "metadata": {}, - "source": [ - "## Investigate errors from data construction for company 6" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "538a5ca2-a50d-4726-93eb-c2b0d0ab8400", - "metadata": {}, - "outputs": [], - "source": [ - "directory_path = '6'" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "1ca3fb71-930a-441c-b35b-b98bca780606", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_6/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_6/products_purchased_reduced.csv\n" - ] - } - ], - "source": [ - "df_customerplus_clean = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - "df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", - "df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "2ad3052c-e9e6-4ef9-abe2-4b8b2306a2b9", - "metadata": {}, - "outputs": [], - "source": [ - "max_date = pd.to_datetime(final_date, utc = True, format = 'ISO8601') \n", - "end_features_date = pd.to_datetime(end_of_features, utc = True, format = 'ISO8601')\n", - "min_date = pd.to_datetime(start_date, utc = True, format = 'ISO8601')" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "146999f2-ab92-4b7c-8c57-2e3ac8c4dd88", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_6/campaigns_information.csv\n" - ] - } - ], - "source": [ - "df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "id": "7448a7b9-3edf-4177-9df2-a260ebbee45e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2022-06-15 00:00:00+0000', tz='UTC')" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "end_features_date" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "id": "d8e954ab-65d4-4f36-8410-69bf664773a7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape campaigns_information : (1333010, 8)\n" - ] - }, - { - "data": { - "text/html": [ - "
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idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
0138NaT2022-08-02 18:31:33+00:00NaNAdhérents non ré-engagés152022-08-02 18:31:36+00:00
1226135NaT2022-08-02 18:31:34+00:00NaNAdhérents non ré-engagés152022-08-02 18:31:36+00:00
233876NaT2022-08-02 18:31:35+00:00NaNAdhérents non ré-engagés152022-08-02 18:31:36+00:00
3426226NaT2022-08-02 18:31:35+00:00NaNAdhérents non ré-engagés152022-08-02 18:31:36+00:00
4525349NaT2022-08-02 18:31:34+00:00NaNAdhérents non ré-engagés152022-08-02 18:31:36+00:00
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" - ], - "text/plain": [ - " id customer_id opened_at sent_at delivered_at \\\n", - "0 1 38 NaT 2022-08-02 18:31:33+00:00 NaN \n", - "1 2 26135 NaT 2022-08-02 18:31:34+00:00 NaN \n", - "2 3 3876 NaT 2022-08-02 18:31:35+00:00 NaN \n", - "3 4 26226 NaT 2022-08-02 18:31:35+00:00 NaN \n", - "4 5 25349 NaT 2022-08-02 18:31:34+00:00 NaN \n", - "\n", - " campaign_name campaign_service_id campaign_sent_at \n", - "0 Adhérents non ré-engagés 15 2022-08-02 18:31:36+00:00 \n", - "1 Adhérents non ré-engagés 15 2022-08-02 18:31:36+00:00 \n", - "2 Adhérents non ré-engagés 15 2022-08-02 18:31:36+00:00 \n", - "3 Adhérents non ré-engagés 15 2022-08-02 18:31:36+00:00 \n", - "4 Adhérents non ré-engagés 15 2022-08-02 18:31:36+00:00 " - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Shape campaigns_information : \", df_campaigns_information.shape)\n", - "df_campaigns_information.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "id": "93eceaf1-ce4c-4dfa-9c51-4fd016d09fc5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2022-08-02 18:31:33+0000', tz='UTC')" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_campaigns_information['sent_at'].min()" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "id": "ea50cab4-1dae-4efe-ae3c-22b6f9ad1d26", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2023-11-07 10:08:16+0000', tz='UTC')" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_campaigns_information['sent_at'].max()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "dcb87bc9-caf5-4655-9cfa-4a3dad504bac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, customer_id, opened_at, sent_at, delivered_at, campaign_name, campaign_service_id, campaign_sent_at]\n", - "Index: []" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Filtre de la base df_campaigns_information\n", - "df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= end_features_date) & (df_campaigns_information['sent_at'] >= min_date)]\n", - "df_campaigns_information" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "abe22e09-a041-4349-be8f-b0784f2f0a98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_dateamountis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasons
49914011083921259025.04caisse2022-02-27 13:44:10.690000+00:000.0Falseligue 1 uber eatsstade de l'aubehonneur basseolympique de marseillesaison 2021-2022
11753552731304136629.04adhésion2022-04-28 15:47:52.790000+00:000.0Falseligue 1 uber eatsstade de l'aubehonneur basseac ajacciosaison 2022-2023
274547400192140477.04adhésion2022-04-28 15:47:54.053000+00:000.0Falseligue 1 uber eatsstade de l'aubehonneur basserc strasbourgsaison 2022-2023
304844133138820259.04adhésion2021-08-03 13:45:01.603000+00:000.0Falseligue 1 uber eatsstade de l'aubevitoux hauteolympique de marseillesaison 2021-2022
311407271326590527.04web [adhésion]2022-05-26 09:15:40.993000+00:000.0Falseligue 1 uber eatsstade de l'aubechampagne bassestade brestois 29saison 2022-2023
\n", - "
" - ], - "text/plain": [ - " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", - "49 91401 108392 1259025.0 4 caisse \n", - "117 535527 31304 136629.0 4 adhésion \n", - "274 547400 192 140477.0 4 adhésion \n", - "304 84413 31388 20259.0 4 adhésion \n", - "311 407271 3265 90527.0 4 web [adhésion] \n", - "\n", - " purchase_date amount is_full_price \\\n", - "49 2022-02-27 13:44:10.690000+00:00 0.0 False \n", - "117 2022-04-28 15:47:52.790000+00:00 0.0 False \n", - "274 2022-04-28 15:47:54.053000+00:00 0.0 False \n", - "304 2021-08-03 13:45:01.603000+00:00 0.0 False \n", - "311 2022-05-26 09:15:40.993000+00:00 0.0 False \n", - "\n", - " name_event_types name_facilities name_categories \\\n", - "49 ligue 1 uber eats stade de l'aube honneur basse \n", - "117 ligue 1 uber eats stade de l'aube honneur basse \n", - "274 ligue 1 uber eats stade de l'aube honneur basse \n", - "304 ligue 1 uber eats stade de l'aube vitoux haute \n", - "311 ligue 1 uber eats stade de l'aube champagne basse \n", - "\n", - " name_events name_seasons \n", - "49 olympique de marseille saison 2021-2022 \n", - "117 ac ajaccio saison 2022-2023 \n", - "274 rc strasbourg saison 2022-2023 \n", - "304 olympique de marseille saison 2021-2022 \n", - "311 stade brestois 29 saison 2022-2023 " - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Filtre de la base df_products_purchased_reduced\n", - "df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]\n", - "df_products_purchased_reduced.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "ae7ef3a6-5b42-4a3c-a108-fec9f2ec4d32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['caisse', 'adhésion', 'web [adhésion]', 'web [grand public]',\n", - " 'itr ticketmaster', 'itr fnac', nan, 'decathlon', 'boutique web',\n", - " 'boutique officielle'], dtype=object)" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_products_purchased_reduced[\"supplier_name\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "id": "942f58a5-8ed4-4b18-a7a2-bd296447fa6a", - "metadata": {}, - "outputs": [], - "source": [ - "# KPI sur le comportement d'achat\n", - "tickets_information_copy = df_products_purchased_reduced.copy()\n", - "# Dummy : Canal de vente en ligne\n", - "liste_mots = ['en ligne', 'internet', 'web', 'net', 'vad', 'online'] # vad = vente à distance\n", - "tickets_information_copy['vente_internet'] = tickets_information_copy['supplier_name'].fillna('').str.contains('|'.join(liste_mots), case=False).astype(int)" - ] - }, - { - "cell_type": "markdown", - "id": "658b57cd-4fb8-4552-a582-972144b2af1c", - "metadata": {}, - "source": [ - "tickets_information_copy['vente_internet'] corrected by handling na" - ] - }, - { - "cell_type": "markdown", - "id": "99a75c34-f393-433a-b3c2-dc3f6f2f3e7e", - "metadata": {}, - "source": [ - "## Investigate train and test" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "970302f5-4de2-46b4-a1ce-a5396f5330ab", - "metadata": {}, - "outputs": [], - "source": [ - "def display_databases(directory_path, file_name):\n", - " \"\"\"\n", - " This function returns the file from s3 storage \n", - " \"\"\"\n", - " file_path = \"projet-bdc2324-team1\" + \"/Generalization/\" + directory_path + \"/\" + file_name + \".csv\"\n", - " print(\"File path : \", file_path)\n", - " with fs.open(file_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\") \n", - " return df " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f5bfae82-04aa-44e1-9869-3f4fd5736b41", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/Generalization/sport/Train_set.csv\n" - ] - }, - { - "data": { - "text/html": [ - "
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customer_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internet...countrygender_labelgender_femalegender_malegender_othercountry_frnb_campaignsnb_campaigns_openedtime_to_openy_has_purchased
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5 rows × 40 columns

\n", - "
" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 5_6046652 0.0 0.0 0.0 0.0 \n", - "1 5_3789159 0.0 0.0 0.0 0.0 \n", - "2 5_5991148 0.0 0.0 0.0 0.0 \n", - "3 5_3848065 0.0 0.0 0.0 0.0 \n", - "4 5_6154495 0.0 0.0 0.0 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 \n", - "\n", - " time_between_purchase nb_tickets_internet ... country gender_label \\\n", - "0 0.0 0.0 ... af other \n", - "1 0.0 0.0 ... fr male \n", - "2 0.0 0.0 ... af other \n", - "3 0.0 0.0 ... fr male \n", - "4 0.0 0.0 ... af other \n", - "\n", - " gender_female gender_male gender_other country_fr nb_campaigns \\\n", - "0 0 0 1 0.0 0.0 \n", - "1 0 1 0 1.0 0.0 \n", - "2 0 0 1 0.0 0.0 \n", - "3 0 1 0 1.0 0.0 \n", - "4 0 0 1 0.0 0.0 \n", - "\n", - " nb_campaigns_opened time_to_open y_has_purchased \n", - "0 0.0 0 0.0 \n", - "1 0.0 0 0.0 \n", - "2 0.0 0 0.0 \n", - "3 0.0 0 0.0 \n", - "4 0.0 0 0.0 \n", - "\n", - "[5 rows x 40 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_sport = display_databases('sport', 'Train_set').fillna(0)\n", - "train_sport.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "56d5b12e-45e8-4312-869d-bde4d24900b6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape : (426449, 40)\n", - "number of na explained variable : 369102\n" - ] - } - ], - "source": [ - "print('shape : ', train_sport.shape) \n", - "print('number of na explained variable : ', train_sport['y_has_purchased'].isna().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "13bff83a-e931-4286-a3f2-1382462703f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", - "sns.countplot(train_sport, x='y_has_purchased')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d056c7b3-0e8c-485c-b2f3-4681077f1c2e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['projet-bdc2324-team1/Generalization/sport/Test_set',\n", - " 'projet-bdc2324-team1/Generalization/sport/Test_set.csv',\n", - " 'projet-bdc2324-team1/Generalization/sport/Train_set',\n", - " 'projet-bdc2324-team1/Generalization/sport/Train_set.csv']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs.ls('projet-bdc2324-team1/Generalization/sport')" - ] - }, - { - "cell_type": "markdown", - "id": "6a9963be-e17b-4cb3-a795-35cece44ce97", - "metadata": {}, - "source": [ - "## Look at y_has_purchased" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "907bb25a-b555-4cfa-bfc9-785120ae4292", - "metadata": {}, - "outputs": [], - "source": [ - "def display_databases(directory_path, file_name, datetime_col = None):\n", - " \"\"\"\n", - " This function returns the file from s3 storage \n", - " \"\"\"\n", - " file_path = \"projet-bdc2324-team1\" + \"/0_Input/Company_\" + directory_path + \"/\" + file_name + \".csv\"\n", - " print(\"File path : \", file_path)\n", - " with fs.open(file_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser) \n", - " return df " - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "d3164f81-0ef2-4f12-bc56-b7a999c4a9cd", - "metadata": {}, - "outputs": [], - "source": [ - "directory_path = '5'\n", - "# start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7)\n", - "min_date = \"2021-05-01\"\n", - "end_features_date = \"2022-11-01\"\n", - "max_date = \"2023-11-01\"" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "7cb31d80-41ca-4c2b-89b6-ee50486e7298", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_5/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_5/products_purchased_reduced.csv\n" - ] - } - ], - "source": [ - "df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - "df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\",\n", - " datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", - "df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\",\n", - " datetime_col = ['purchase_date'])\n", - "\n", - "# Filtre de cohérence pour la mise en pratique de notre méthode\n", - "max_date = pd.to_datetime(max_date, utc = True, format = 'ISO8601') \n", - "end_features_date = pd.to_datetime(end_features_date, utc = True, format = 'ISO8601')\n", - "min_date = pd.to_datetime(min_date, utc = True, format = 'ISO8601')\n", - "\n", - "df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= end_features_date) & (df_campaigns_information['sent_at'] >= min_date)]\n", - "df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - "\n", - "#Filtre de la base df_products_purchased_reduced\n", - "df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "1d63a61e-22b4-4224-89d4-18444276cfaa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, customer_id, opened_at, sent_at, delivered_at, campaign_name, campaign_service_id, campaign_sent_at]\n", - "Index: []" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_campaigns_information.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "a27a80c1-0be2-4199-96e7-566d568b1f51", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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06287839204007545836.0824fov2022-03-31 03:42:59+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
16287840204007545836.0824fov2022-03-31 03:42:59+00:0030.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
26154548227006535225.0824fov2022-02-28 16:31:29+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
36154549227006535225.0824fov2022-02-28 16:31:29+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
46287843407930545838.0824fov2022-03-31 04:00:22+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
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" - ], - "text/plain": [ - " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", - "0 6287839 204007 545836.0 824 fov \n", - "1 6287840 204007 545836.0 824 fov \n", - "2 6154548 227006 535225.0 824 fov \n", - "3 6154549 227006 535225.0 824 fov \n", - "4 6287843 407930 545838.0 824 fov \n", - "\n", - " purchase_date amount is_full_price name_event_types \\\n", - "0 2022-03-31 03:42:59+00:00 55.0 False match rugby \n", - "1 2022-03-31 03:42:59+00:00 30.0 False match rugby \n", - "2 2022-02-28 16:31:29+00:00 55.0 False match rugby \n", - "3 2022-02-28 16:31:29+00:00 55.0 False match rugby \n", - "4 2022-03-31 04:00:22+00:00 55.0 False match rugby \n", - "\n", - " name_facilities name_categories name_events \\\n", - "0 jean bouin centrale sf paris / racing 92 (ercc) \n", - "1 jean bouin centrale sf paris / racing 92 (ercc) \n", - "2 jean bouin centrale sf paris / racing 92 (ercc) \n", - "3 jean bouin centrale sf paris / racing 92 (ercc) \n", - "4 jean bouin centrale sf paris / racing 92 (ercc) \n", - "\n", - " name_seasons start_date_time end_date_time \\\n", - "0 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "1 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "2 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "3 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "4 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "\n", - " open \n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 True " - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_products_purchased_reduced.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "f47357ab-0216-4f70-ab8f-6767819e1cdb", - "metadata": {}, - "outputs": [], - "source": [ - "# Fusion de l'ensemble et creation des KPI\n", - "\n", - "# KPI sur les campagnes publicitaires\n", - "df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n", - "\n", - "# KPI sur le comportement d'achat\n", - "df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n", - "\n", - "# KPI sur les données socio-démographiques\n", - "df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "3d08a2f8-3c83-41c7-98f8-4be268ffa0da", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " customer_id street_id structure_id mcp_contact_id fidelity tenant_id \\\n", - "0 6009745 1372685 NaN NaN 0 1771 \n", - "1 6011228 1372685 NaN NaN 0 1771 \n", - "2 6058950 1372685 NaN NaN 0 1771 \n", - "3 6062404 1372685 NaN NaN 0 1771 \n", - "4 250217 78785 NaN 11035.0 0 1771 \n", - "\n", - " is_partner deleted_at gender is_email_true ... first_buying_date \\\n", - "0 False NaN 2 True ... NaN \n", - "1 False NaN 2 True ... NaN \n", - "2 False NaN 2 True ... NaN \n", - "3 False NaN 2 True ... NaN \n", - "4 False NaN 0 True ... NaN \n", - "\n", - " country gender_label gender_female gender_male gender_other country_fr \\\n", - "0 af other 0 0 1 0.0 \n", - "1 af other 0 0 1 0.0 \n", - "2 af other 0 0 1 0.0 \n", - "3 af other 0 0 1 0.0 \n", - "4 fr female 1 0 0 1.0 \n", - "\n", - " nb_campaigns nb_campaigns_opened time_to_open \n", - "0 NaN NaN NaT \n", - "1 NaN NaN NaT \n", - "2 NaN NaN NaT \n", - "3 NaN NaN NaT \n", - "4 NaN NaN NaT \n", - "\n", - "[5 rows x 30 columns]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fusion avec KPI liés au customer\n", - "df_customer = pd.merge(df_customerplus_clean, df_campaigns_kpi, on = 'customer_id', how = 'left')\n", - "df_customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "bc3d1aed-b2af-48e5-a920-626f2abc3358", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0160516149.03.04470.01.00.0409.69313766.356979343.3361570.0...2021-09-17 06:39:19+00:00frmale0101.00.00.0NaT
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" - ], - "text/plain": [ - " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 160516 149.0 3.0 4470.0 1.0 \n", - "1 160517 1977.0 27.0 1473.0 2.0 \n", - "2 160518 116.0 8.0 439.0 2.0 \n", - "3 160519 34.0 2.0 608.0 1.0 \n", - "4 160520 207.0 5.0 0.0 1.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 409.693137 66.356979 \n", - "1 1.0 431.558519 27.733472 \n", - "2 0.0 427.177720 23.689340 \n", - "3 0.0 483.642940 108.777870 \n", - "4 0.0 431.550012 69.310266 \n", - "\n", - " time_between_purchase nb_tickets_internet ... first_buying_date \\\n", - "0 343.336157 0.0 ... 2021-09-17 06:39:19+00:00 \n", - "1 403.825046 15.0 ... 2021-08-26 09:53:10+00:00 \n", - "2 403.488380 0.0 ... 2021-08-30 19:01:31+00:00 \n", - "3 374.865069 0.0 ... 2019-05-21 08:03:52+00:00 \n", - "4 362.239745 0.0 ... 2019-08-20 15:10:07+00:00 \n", - "\n", - " country gender_label gender_female gender_male gender_other \\\n", - "0 fr male 0 1 0 \n", - "1 fr female 1 0 0 \n", - "2 fr male 0 1 0 \n", - "3 fr female 1 0 0 \n", - "4 fr male 0 1 0 \n", - "\n", - " country_fr nb_campaigns nb_campaigns_opened time_to_open \n", - "0 1.0 0.0 0.0 NaT \n", - "1 1.0 0.0 0.0 NaT \n", - "2 1.0 0.0 0.0 NaT \n", - "3 1.0 0.0 0.0 NaT \n", - "4 1.0 0.0 0.0 NaT \n", - "\n", - "[5 rows x 39 columns]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_customer[['nb_campaigns', 'nb_campaigns_opened']] = df_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)\n", - "# Fusion avec KPI liés au comportement d'achat\n", - "df_customer_product = pd.merge(df_tickets_kpi, df_customer, on = 'customer_id', how = 'outer')\n", - "df_customer_product.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "5549e265-3904-464b-964b-518a84a42503", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_dateamountis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasonsstart_date_timeend_date_timeopen
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [ticket_id, customer_id, purchase_id, event_type_id, supplier_name, purchase_date, amount, is_full_price, name_event_types, name_facilities, name_categories, name_events, name_seasons, start_date_time, end_date_time, open]\n", - "Index: []" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fill NaN values\n", - "df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0)\n", - "\n", - "# 2. Construction of the explained variable \n", - "df_products_purchased_to_predict = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > end_features_date)]\n", - "df_products_purchased_to_predict.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "be182c6c-012f-447d-a57f-03da65da53f7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "['2022-03-31 03:42:59+00:00', '2022-02-28 16:31:29+00:00',\n", - " '2022-03-31 04:00:22+00:00', '2022-03-31 04:09:18+00:00',\n", - " '2022-03-25 15:50:52+00:00', '2022-08-01 10:05:49+00:00',\n", - " '2021-08-26 12:17:40+00:00', '2022-08-02 06:32:37+00:00',\n", - " '2022-06-30 09:16:59+00:00', '2022-07-03 13:53:30+00:00',\n", - " ...\n", - " '2022-01-26 11:34:05+00:00', '2022-01-21 17:07:25+00:00',\n", - " '2022-01-26 13:43:23+00:00', '2022-01-26 14:38:05+00:00',\n", - " '2022-01-26 14:39:19+00:00', '2022-01-26 14:40:12+00:00',\n", - " '2022-01-26 14:41:17+00:00', '2022-01-27 08:16:02+00:00',\n", - " '2022-01-27 08:45:25+00:00', '2022-01-27 11:57:11+00:00']\n", - "Length: 49543, dtype: datetime64[ns, UTC]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_products_purchased_reduced['purchase_date'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "aab1cc7e-79be-403c-b9c1-4f4f333b13ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_dateamountis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasonsstart_date_timeend_date_timeopen
06287839204007545836.0824fov2022-03-31 03:42:59+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
16287840204007545836.0824fov2022-03-31 03:42:59+00:0030.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
26154548227006535225.0824fov2022-02-28 16:31:29+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
36154549227006535225.0824fov2022-02-28 16:31:29+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
46287843407930545838.0824fov2022-03-31 04:00:22+00:0055.0Falsematch rugbyjean bouincentralesf paris / racing 92 (ercc)saison 2021 - 20222022-04-08 22:00:00+02:001901-01-01 00:09:21+00:09True
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"\n", - " name_seasons start_date_time end_date_time \\\n", - "0 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "1 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "2 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "3 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "4 saison 2021 - 2022 2022-04-08 22:00:00+02:00 1901-01-01 00:09:21+00:09 \n", - "\n", - " open \n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 True " - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date)].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "ce59de67-127e-4b0a-b96c-9684d87792dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2022-10-31 23:17:26+0000', tz='UTC')" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_products_purchased_reduced['purchase_date'].max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "184463d1-b0dd-44b9-a9a3-4ab32c8c13c1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/exploratory_analysis/TP_exploratory_analysis-Copy1.ipynb b/exploratory_analysis/TP_exploratory_analysis-Copy1.ipynb deleted file mode 100644 index 021b463..0000000 --- a/exploratory_analysis/TP_exploratory_analysis-Copy1.ipynb +++ /dev/null @@ -1,7990 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6c0589ab-924f-4706-bef7-65500f0c4dd5", - "metadata": {}, - "source": [ - "# Exploratory study of variables : targets, campaign and link stats" - ] - }, - { - "cell_type": "markdown", - "id": "83319f84-427f-43aa-af26-06797244e89c", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "## First steps : package importations, set up working environment and import data" - ] - }, - { - "cell_type": "code", - "execution_count": 253, - "id": "a26f3f09-3961-43fe-b4d9-1abe3b906a2c", - "metadata": {}, - "outputs": [], - "source": [ - "# importations\n", - "\n", - "import os \n", - "import s3fs\n", - "import pandas as pd\n", - "import re\n", - "from datetime import datetime, timezone, timedelta\n", - "import math\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "78478dbf-bd91-45e0-9f2b-2d9e6b0f648c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1',\n", - " 'bdc2324-data/10',\n", - " 'bdc2324-data/101',\n", - " 'bdc2324-data/11',\n", - " 'bdc2324-data/12',\n", - " 'bdc2324-data/13',\n", - " 'bdc2324-data/14',\n", - " 'bdc2324-data/2',\n", - " 'bdc2324-data/3',\n", - " 'bdc2324-data/4',\n", - " 'bdc2324-data/5',\n", - " 'bdc2324-data/6',\n", - " 'bdc2324-data/7',\n", - " 'bdc2324-data/8',\n", - " 'bdc2324-data/9']" - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# bucket for accessing the data\n", - "\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "\n", - "fs = s3fs.S3FileSystem(client_kwargs = {\"endpoint_url\" : S3_ENDPOINT_URL})\n", - "BUCKET = \"bdc2324-data\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a7e1b277-4381-45c0-b1ec-4050af54a3b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1/1campaign_stats.csv',\n", - " 'bdc2324-data/1/1campaigns.csv',\n", - " 'bdc2324-data/1/1categories.csv',\n", - " 'bdc2324-data/1/1countries.csv',\n", - " 'bdc2324-data/1/1currencies.csv',\n", - " 'bdc2324-data/1/1customer_target_mappings.csv',\n", - " 'bdc2324-data/1/1customersplus.csv',\n", - " 'bdc2324-data/1/1event_types.csv',\n", - " 'bdc2324-data/1/1events.csv',\n", - " 'bdc2324-data/1/1facilities.csv',\n", - " 'bdc2324-data/1/1link_stats.csv',\n", - " 'bdc2324-data/1/1pricing_formulas.csv',\n", - " 'bdc2324-data/1/1product_packs.csv',\n", - " 'bdc2324-data/1/1products.csv',\n", - " 'bdc2324-data/1/1products_groups.csv',\n", - " 'bdc2324-data/1/1purchases.csv',\n", - " 'bdc2324-data/1/1representation_category_capacities.csv',\n", - " 'bdc2324-data/1/1representations.csv',\n", - " 'bdc2324-data/1/1seasons.csv',\n", - " 'bdc2324-data/1/1structure_tag_mappings.csv',\n", - " 'bdc2324-data/1/1suppliers.csv',\n", - " 'bdc2324-data/1/1tags.csv',\n", - " 'bdc2324-data/1/1target_types.csv',\n", - " 'bdc2324-data/1/1targets.csv',\n", - " 'bdc2324-data/1/1tickets.csv',\n", - " 'bdc2324-data/1/1type_of_categories.csv',\n", - " 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n", - " 'bdc2324-data/1/1type_ofs.csv']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FILE_PATH_S3 = fs.ls(BUCKET)[0] # focus on the company number 1\n", - "files_path = fs.ls(FILE_PATH_S3)\n", - "files_path" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b26f7d2b-391f-4326-a60b-5b379186b4e8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_624/107044352.py:9: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in)\n" - ] - } - ], - "source": [ - "# loop to create dataframes related to company 1\n", - "\n", - "client_number = files_path[0].split(\"/\")[1]\n", - "df_prefix = \"df\" + str(client_number) + \"_\"\n", - "\n", - "for i in range(len(files_path)) :\n", - " current_path = files_path[i]\n", - " with fs.open(current_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in)\n", - " # the pattern of the name is df1xxx\n", - " nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n", - " globals()[nom_dataframe] = df" - ] - }, - { - "cell_type": "markdown", - "id": "5cb3e9dc-ba6e-408c-b1a6-a2c5a2215f71", - "metadata": {}, - "source": [ - "## Target, target types and customer target mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c6dbd777-b6da-485f-a650-b0a12f3d90c4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id int64\n", - "is_import bool\n", - "name object\n", - "created_at object\n", - "updated_at object\n", - "identifier object\n", - "dtype: object" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1. target types\n", - "df1_target_types.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "04d625e8-b077-450f-a654-1a3b05fc1325", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(df1_target_types[\"created_at\"][0])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "607441b9-33a8-41a7-a089-120dfe266de0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00
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idtarget_type_idnamecreated_atupdated_at
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00
..................
28218111ddcp_promo_ribambelle_2022_mapado_naikko_opt in2022-11-30 15:57:05.681956+01:002022-11-30 16:00:32.649210+01:00
28320061cp 14 mars2023-03-03 18:07:00.223750+01:002023-03-03 18:15:01.390970+01:00
28421931ddcp fichier musique 22023-04-14 14:33:53.628142+02:002023-04-14 15:00:35.608210+02:00
28524291import_mucem2023-06-26 18:32:40.146757+02:002023-06-26 18:45:02.614668+02:00
28624851po_au salon_2e envoi2023-07-03 13:09:48.598072+02:002023-07-03 13:15:03.634600+02:00
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287 rows × 5 columns

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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
011848246454001302021-09-23 09:35:47.617275+02:002021-09-23 09:35:47.617275+02:00NaNNaN
111848256454003452021-09-23 09:35:47.668846+02:002021-09-23 09:35:47.668846+02:00NaNNaN
211848286454021262021-09-23 12:02:51.253269+02:002021-09-23 12:02:51.253269+02:00NaNNaN
311848296454031262021-09-23 12:20:47.394480+02:002021-09-23 12:20:47.394480+02:00NaNNaN
412957706473013462021-09-28 16:02:29.372608+02:002021-09-28 16:02:29.372608+02:00NaNNaN
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" - ], - "text/plain": [ - " id customer_id target_id created_at \\\n", - "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", - "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", - "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", - "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", - "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", - "\n", - " updated_at name extra_field \n", - "0 2021-09-23 09:35:47.617275+02:00 NaN NaN \n", - "1 2021-09-23 09:35:47.668846+02:00 NaN NaN \n", - "2 2021-09-23 12:02:51.253269+02:00 NaN NaN \n", - "3 2021-09-23 12:20:47.394480+02:00 NaN NaN \n", - "4 2021-09-28 16:02:29.372608+02:00 NaN NaN " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3. customer target mapping\n", - "\n", - "df1_customer_target_mappings.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5244543f-1948-4769-be1f-691ad13174a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0.000000\n", - "customer_id 0.000000\n", - "target_id 0.000000\n", - "created_at 0.000022\n", - "updated_at 0.000022\n", - "name 1.000000\n", - "extra_field 1.000000\n", - "dtype: float64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_customer_target_mappings.isna().sum()/df1_customer_target_mappings.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1c59e2ae-ee24-4195-bfea-ae55b92368ec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "768024" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_customer_target_mappings[\"id\"].nunique()\n", - "# df1_customer_target_mappings.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "4ed49f39-e6d3-4785-ba7d-bce918d423ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# les couples customer_id / target_id sont-ils uniques ?\n", - "df1_customer_target_mappings.duplicated(subset = [\"customer_id\", \"target_id\"]).sum() # aucun doublon" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "f8cb1740-2cb0-4b3a-bfb0-d35423dc2cc7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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target_type_idtarget_type_is_importtarget_type_nametarget_type_identifier
069Falsemanual_dynamic_filtere0f4b8693184850fefd6d2a38f10584e
148Truemanual_structure382bca214204a2d3462f5ec2728d5d1e
21Truemanual_import12213df2ce68a624e4c0070521437bac
356Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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" - ], - "text/plain": [ - " target_type_id target_type_is_import target_type_name \\\n", - "0 69 False manual_dynamic_filter \n", - "1 48 True manual_structure \n", - "2 1 True manual_import \n", - "3 56 False manual_static_filter \n", - "\n", - " target_type_identifier \n", - "0 e0f4b8693184850fefd6d2a38f10584e \n", - "1 382bca214204a2d3462f5ec2728d5d1e \n", - "2 12213df2ce68a624e4c0070521437bac \n", - "3 fb27e81baa4debc6a4e1a8639c20e808 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 4.1. merge target with target type\n", - "\n", - "df1_target_types[[\"id\",\"is_import\",\"name\",\"identifier\"]].add_prefix(\"target_type_\")" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "ebabdebd-3d75-4048-b65d-4cbd69bee390", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtarget_type_idnamecreated_atupdated_attarget_type_is_importtarget_type_nametarget_type_identifier
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
...........................
28218111ddcp_promo_ribambelle_2022_mapado_naikko_opt in2022-11-30 15:57:05.681956+01:002022-11-30 16:00:32.649210+01:00Truemanual_import12213df2ce68a624e4c0070521437bac
28320061cp 14 mars2023-03-03 18:07:00.223750+01:002023-03-03 18:15:01.390970+01:00Truemanual_import12213df2ce68a624e4c0070521437bac
28421931ddcp fichier musique 22023-04-14 14:33:53.628142+02:002023-04-14 15:00:35.608210+02:00Truemanual_import12213df2ce68a624e4c0070521437bac
28524291import_mucem2023-06-26 18:32:40.146757+02:002023-06-26 18:45:02.614668+02:00Truemanual_import12213df2ce68a624e4c0070521437bac
28624851po_au salon_2e envoi2023-07-03 13:09:48.598072+02:002023-07-03 13:15:03.634600+02:00Truemanual_import12213df2ce68a624e4c0070521437bac
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287 rows × 8 columns

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" - ], - "text/plain": [ - " id target_type_id name \\\n", - "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", - "1 701 56 consentement optin scolaires \n", - "2 134 56 DDCP Newsletter jeune public \n", - "3 700 56 consentement optout scolaires \n", - "4 964 56 DDCP achat billet nbr dep 19052021 \n", - ".. ... ... ... \n", - "282 1811 1 ddcp_promo_ribambelle_2022_mapado_naikko_opt in \n", - "283 2006 1 cp 14 mars \n", - "284 2193 1 ddcp fichier musique 2 \n", - "285 2429 1 import_mucem \n", - "286 2485 1 po_au salon_2e envoi \n", - "\n", - " created_at updated_at \\\n", - "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", - "1 2021-12-21 16:03:59.840785+01:00 2022-02-18 17:23:44.761388+01:00 \n", - "2 2020-11-10 09:43:19.667471+01:00 2021-03-02 18:38:19.052304+01:00 \n", - "3 2021-12-21 16:01:57.524946+01:00 2022-02-18 17:23:44.807776+01:00 \n", - "4 2022-04-14 10:58:17.142834+02:00 2022-04-14 10:58:23.677264+02:00 \n", - ".. ... ... \n", - "282 2022-11-30 15:57:05.681956+01:00 2022-11-30 16:00:32.649210+01:00 \n", - "283 2023-03-03 18:07:00.223750+01:00 2023-03-03 18:15:01.390970+01:00 \n", - "284 2023-04-14 14:33:53.628142+02:00 2023-04-14 15:00:35.608210+02:00 \n", - "285 2023-06-26 18:32:40.146757+02:00 2023-06-26 18:45:02.614668+02:00 \n", - "286 2023-07-03 13:09:48.598072+02:00 2023-07-03 13:15:03.634600+02:00 \n", - "\n", - " target_type_is_import target_type_name \\\n", - "0 False manual_static_filter \n", - "1 False manual_static_filter \n", - "2 False manual_static_filter \n", - "3 False manual_static_filter \n", - "4 False manual_static_filter \n", - ".. ... ... \n", - "282 True manual_import \n", - "283 True manual_import \n", - "284 True manual_import \n", - "285 True manual_import \n", - "286 True manual_import \n", - "\n", - " target_type_identifier \n", - "0 fb27e81baa4debc6a4e1a8639c20e808 \n", - "1 fb27e81baa4debc6a4e1a8639c20e808 \n", - "2 fb27e81baa4debc6a4e1a8639c20e808 \n", - "3 fb27e81baa4debc6a4e1a8639c20e808 \n", - "4 fb27e81baa4debc6a4e1a8639c20e808 \n", - ".. ... \n", - "282 12213df2ce68a624e4c0070521437bac \n", - "283 12213df2ce68a624e4c0070521437bac \n", - "284 12213df2ce68a624e4c0070521437bac \n", - "285 12213df2ce68a624e4c0070521437bac \n", - "286 12213df2ce68a624e4c0070521437bac \n", - "\n", - "[287 rows x 8 columns]" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# merge\n", - "\n", - "df1_targets_full = pd.merge(df1_targets, df1_target_types[[\"id\",\"is_import\",\"name\",\"identifier\"]].add_prefix(\"target_type_\"), left_on='target_type_id', right_on='target_type_id', how='left')\n", - "df1_targets_full" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "f0b03a5d-b622-496a-bc71-ef92e91f9e51", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
011848246454001302021-09-23 09:35:47.617275+02:002021-09-23 09:35:47.617275+02:00NaNNaN
111848256454003452021-09-23 09:35:47.668846+02:002021-09-23 09:35:47.668846+02:00NaNNaN
211848286454021262021-09-23 12:02:51.253269+02:002021-09-23 12:02:51.253269+02:00NaNNaN
311848296454031262021-09-23 12:20:47.394480+02:002021-09-23 12:20:47.394480+02:00NaNNaN
412957706473013462021-09-28 16:02:29.372608+02:002021-09-28 16:02:29.372608+02:00NaNNaN
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" - ], - "text/plain": [ - " id customer_id target_id created_at \\\n", - "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", - "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", - "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", - "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", - "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", - "\n", - " updated_at name extra_field \n", - "0 2021-09-23 09:35:47.617275+02:00 NaN NaN \n", - "1 2021-09-23 09:35:47.668846+02:00 NaN NaN \n", - "2 2021-09-23 12:02:51.253269+02:00 NaN NaN \n", - "3 2021-09-23 12:20:47.394480+02:00 NaN NaN \n", - "4 2021-09-28 16:02:29.372608+02:00 NaN NaN " - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 4.2. merge df1_customer_target_mappings with df1_targets_full\n", - "\n", - "df1_customer_target_mappings.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "906e01fd-23b3-4da7-bc5e-6618599fbb05", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "17" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Q : les dates de création et de mise à jour de la table customer target mapping sont elles égales ??\n", - "\n", - "# 17 observations for which creation date != update date, ms ce sont que des Nan, OK !\n", - "(df1_customer_target_mappings[\"created_at\"] != df1_customer_target_mappings[\"updated_at\"]).sum() " - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c9265d2f-b636-415e-bc2d-99b932b89424", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
6054841691570661701264NaNNaNNaNNaN
6545491832071651594264NaNNaNNaNNaN
6545501832072663061264NaNNaNNaNNaN
6545511832073663114264NaNNaNNaNNaN
6551621949466663865264NaNNaNNaNNaN
7540382154438664300264NaNNaNNaNNaN
7609292282079665557264NaNNaNNaNNaN
7609302282080665563264NaNNaNNaNNaN
7617872675293661492264NaNNaNNaNNaN
7617982721237665931264NaNNaNNaNNaN
7617992721238665932264NaNNaNNaNNaN
7618002721239665938264NaNNaNNaNNaN
7618012721240665956264NaNNaNNaNNaN
7679182736960666466264NaNNaNNaNNaN
7679192736961666468264NaNNaNNaNNaN
7679682737357666824264NaNNaNNaNNaN
7679842737489107743264NaNNaNNaNNaN
\n", - "
" - ], - "text/plain": [ - " id customer_id target_id created_at updated_at name \\\n", - "605484 1691570 661701 264 NaN NaN NaN \n", - "654549 1832071 651594 264 NaN NaN NaN \n", - "654550 1832072 663061 264 NaN NaN NaN \n", - "654551 1832073 663114 264 NaN NaN NaN \n", - "655162 1949466 663865 264 NaN NaN NaN \n", - "754038 2154438 664300 264 NaN NaN NaN \n", - "760929 2282079 665557 264 NaN NaN NaN \n", - "760930 2282080 665563 264 NaN NaN NaN \n", - "761787 2675293 661492 264 NaN NaN NaN \n", - "761798 2721237 665931 264 NaN NaN NaN \n", - "761799 2721238 665932 264 NaN NaN NaN \n", - "761800 2721239 665938 264 NaN NaN NaN \n", - "761801 2721240 665956 264 NaN NaN NaN \n", - "767918 2736960 666466 264 NaN NaN NaN \n", - "767919 2736961 666468 264 NaN NaN NaN \n", - "767968 2737357 666824 264 NaN NaN NaN \n", - "767984 2737489 107743 264 NaN NaN NaN \n", - "\n", - " extra_field \n", - "605484 NaN \n", - "654549 NaN \n", - "654550 NaN \n", - "654551 NaN \n", - "655162 NaN \n", - "754038 NaN \n", - "760929 NaN \n", - "760930 NaN \n", - "761787 NaN \n", - "761798 NaN \n", - "761799 NaN \n", - "761800 NaN \n", - "761801 NaN \n", - "767918 NaN \n", - "767919 NaN \n", - "767968 NaN \n", - "767984 NaN " - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_customer_target_mappings[df1_customer_target_mappings[\"created_at\"] != df1_customer_target_mappings[\"updated_at\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "63e4ce23-ce13-46fc-82c5-9065a774b4b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
140341626517512642022-01-28 20:00:16.448920+01:002022-01-28 20:00:16.448920+01:00NaNNaN
149341627422132642022-01-28 20:30:17.323634+01:002022-01-28 20:30:17.323634+01:00NaNNaN
1120429205411560592642022-09-29 07:00:43.003440+02:002022-09-29 07:00:43.003440+02:00NaNNaN
1121429205511560632642022-09-29 07:00:43.003440+02:002022-09-29 07:00:43.003440+02:00NaNNaN
40064428048349162642023-03-14 07:01:27.868349+01:002023-03-14 07:01:27.868349+01:00NaNNaN
........................
7618012721240665956264NaNNaNNaNNaN
7679182736960666466264NaNNaNNaNNaN
7679192736961666468264NaNNaNNaNNaN
7679682737357666824264NaNNaNNaNNaN
7679842737489107743264NaNNaNNaNNaN
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1954 rows × 7 columns

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" - ], - "text/plain": [ - " id customer_id target_id created_at \\\n", - "140 3416265 1751 264 2022-01-28 20:00:16.448920+01:00 \n", - "149 3416274 2213 264 2022-01-28 20:30:17.323634+01:00 \n", - "1120 4292054 1156059 264 2022-09-29 07:00:43.003440+02:00 \n", - "1121 4292055 1156063 264 2022-09-29 07:00:43.003440+02:00 \n", - "4006 4428048 34916 264 2023-03-14 07:01:27.868349+01:00 \n", - "... ... ... ... ... \n", - "761801 2721240 665956 264 NaN \n", - "767918 2736960 666466 264 NaN \n", - "767919 2736961 666468 264 NaN \n", - "767968 2737357 666824 264 NaN \n", - "767984 2737489 107743 264 NaN \n", - "\n", - " updated_at name extra_field \n", - "140 2022-01-28 20:00:16.448920+01:00 NaN NaN \n", - "149 2022-01-28 20:30:17.323634+01:00 NaN NaN \n", - "1120 2022-09-29 07:00:43.003440+02:00 NaN NaN \n", - "1121 2022-09-29 07:00:43.003440+02:00 NaN NaN \n", - "4006 2023-03-14 07:01:27.868349+01:00 NaN NaN \n", - "... ... ... ... \n", - "761801 NaN NaN NaN \n", - "767918 NaN NaN NaN \n", - "767919 NaN NaN NaN \n", - "767968 NaN NaN NaN \n", - "767984 NaN NaN NaN \n", - "\n", - "[1954 rows x 7 columns]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# ces données manquantes concernent le target avec id 264, mais les autres valeurs pr ce même target sont bien renseignées\n", - "df1_customer_target_mappings[df1_customer_target_mappings[\"target_id\"]==264]" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "0681b3e6-71bb-4132-b11a-646382f78de6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2021-10-28 11:30:42.717180+02:00'" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Q : les dates de creation / update sont elles-uniques selon le client ou selon la target ?\n", - "\n", - "df1_customer_target_mappings[df1_customer_target_mappings[\"target_id\"]==217][\"updated_at\"].max()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "93e4a125-08dd-42ba-baa6-0dc5996a76af", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtarget_type_idnamecreated_atupdated_attarget_type_is_importtarget_type_nametarget_type_identifier
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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" - ], - "text/plain": [ - " id target_type_id name \\\n", - "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", - "\n", - " created_at updated_at \\\n", - "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", - "\n", - " target_type_is_import target_type_name \\\n", - "0 False manual_static_filter \n", - "\n", - " target_type_identifier \n", - "0 fb27e81baa4debc6a4e1a8639c20e808 " - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_targets_full[df1_targets_full[\"id\"]==217]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "88eac1a6-74b1-4ce1-91a1-c1c69e7a9264", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtarget_type_idnamecreated_atupdated_attarget_type_is_importtarget_type_nametarget_type_identifier
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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0217DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
1701consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
2134DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
3700consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
4964DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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011848246454001302021-09-23 09:35:47.617275+02:00DDCP PROMO Réseau livres2020-11-04 18:40:49.500866+01:002021-03-02 18:38:19.084287+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
111848256454003452021-09-23 09:35:47.668846+02:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
211848286454021262021-09-23 12:02:51.253269+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
311848296454031262021-09-23 12:20:47.394480+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
412957706473013462021-09-28 16:02:29.372608+02:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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76801927375456669833452021-12-14 14:48:05.456842+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802027375466669833462021-12-14 14:48:05.465830+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802127375756669863462021-12-14 23:15:42.757832+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802227375766669873452021-12-15 00:14:59.018215+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802327375776669873462021-12-15 00:14:59.029434+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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768024 rows × 11 columns

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" - ], - "text/plain": [ - " id customer_id target_id created_at \\\n", - "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", - "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", - "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", - "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", - "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", - "... ... ... ... ... \n", - "768019 2737545 666983 345 2021-12-14 14:48:05.456842+01:00 \n", - "768020 2737546 666983 346 2021-12-14 14:48:05.465830+01:00 \n", - "768021 2737575 666986 346 2021-12-14 23:15:42.757832+01:00 \n", - "768022 2737576 666987 345 2021-12-15 00:14:59.018215+01:00 \n", - "768023 2737577 666987 346 2021-12-15 00:14:59.029434+01:00 \n", - "\n", - " target_name target_created_at \\\n", - "0 DDCP PROMO Réseau livres 2020-11-04 18:40:49.500866+01:00 \n", - "1 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", - "2 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", - "3 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", - "4 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "... ... ... \n", - "768019 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", - "768020 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "768021 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "768022 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", - "768023 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "\n", - " target_updated_at target_type_is_import \\\n", - "0 2021-03-02 18:38:19.084287+01:00 False \n", - "1 2021-04-16 17:17:26.069199+02:00 False \n", - "2 2021-04-16 17:17:25.850107+02:00 False \n", - "3 2021-04-16 17:17:25.850107+02:00 False \n", - "4 2021-04-16 17:17:26.080378+02:00 False \n", - "... ... ... \n", - "768019 2021-04-16 17:17:26.069199+02:00 False \n", - "768020 2021-04-16 17:17:26.080378+02:00 False \n", - "768021 2021-04-16 17:17:26.080378+02:00 False \n", - "768022 2021-04-16 17:17:26.069199+02:00 False \n", - "768023 2021-04-16 17:17:26.080378+02:00 False \n", - "\n", - " target_type_id target_type_name target_type_identifier \n", - "0 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "1 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "2 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "3 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "4 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "... ... ... ... \n", - "768019 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768020 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768021 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768022 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768023 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "\n", - "[768024 rows x 11 columns]" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# finally, merge\n", - "\n", - "# pour df1_customer_target_mappings on enlève les colonnes name, extra_field, et updated_at (valeur égale à created_at)\n", - "# note : by making a left join on df1_customer_target_mappings, we suppress 2 targets that have no customer associated\n", - "\n", - "df1_customer_targets = pd.merge(df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\", \"created_at\"]], \n", - " df1_targets_full, left_on='target_id', right_on='target_id', how='left')\n", - "df1_customer_targets" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "id": "95657bda-d060-48ca-8217-3e3f119028c1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idtarget_idcreated_attarget_nametarget_created_attarget_updated_attarget_type_is_importtarget_type_idtarget_type_nametarget_type_identifier
011848246454001302021-09-23 09:35:47.617275+02:00DDCP PROMO Réseau livres2020-11-04 18:40:49.500866+01:002021-03-02 18:38:19.084287+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
111848256454003452021-09-23 09:35:47.668846+02:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
211848286454021262021-09-23 12:02:51.253269+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
311848296454031262021-09-23 12:20:47.394480+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
412957706473013462021-09-28 16:02:29.372608+02:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
....................................
76801927375456669833452021-12-14 14:48:05.456842+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802027375466669833462021-12-14 14:48:05.465830+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802127375756669863462021-12-14 23:15:42.757832+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802227375766669873452021-12-15 00:14:59.018215+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802327375776669873462021-12-15 00:14:59.029434+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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768024 rows × 11 columns

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" - ], - "text/plain": [ - " id customer_id target_id created_at \\\n", - "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", - "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", - "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", - "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", - "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", - "... ... ... ... ... \n", - "768019 2737545 666983 345 2021-12-14 14:48:05.456842+01:00 \n", - "768020 2737546 666983 346 2021-12-14 14:48:05.465830+01:00 \n", - "768021 2737575 666986 346 2021-12-14 23:15:42.757832+01:00 \n", - "768022 2737576 666987 345 2021-12-15 00:14:59.018215+01:00 \n", - "768023 2737577 666987 346 2021-12-15 00:14:59.029434+01:00 \n", - "\n", - " target_name target_created_at \\\n", - "0 DDCP PROMO Réseau livres 2020-11-04 18:40:49.500866+01:00 \n", - "1 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", - "2 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", - "3 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", - "4 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "... ... ... \n", - "768019 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", - "768020 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "768021 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "768022 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", - "768023 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", - "\n", - " target_updated_at target_type_is_import \\\n", - "0 2021-03-02 18:38:19.084287+01:00 False \n", - "1 2021-04-16 17:17:26.069199+02:00 False \n", - "2 2021-04-16 17:17:25.850107+02:00 False \n", - "3 2021-04-16 17:17:25.850107+02:00 False \n", - "4 2021-04-16 17:17:26.080378+02:00 False \n", - "... ... ... \n", - "768019 2021-04-16 17:17:26.069199+02:00 False \n", - "768020 2021-04-16 17:17:26.080378+02:00 False \n", - "768021 2021-04-16 17:17:26.080378+02:00 False \n", - "768022 2021-04-16 17:17:26.069199+02:00 False \n", - "768023 2021-04-16 17:17:26.080378+02:00 False \n", - "\n", - " target_type_id target_type_name target_type_identifier \n", - "0 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "1 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "2 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "3 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "4 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "... ... ... ... \n", - "768019 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768020 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768021 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768022 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "768023 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", - "\n", - "[768024 rows x 11 columns]" - ] - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# rq : on dirait que la date de création des targets est à peine inférieure à la date minimum de création des targets des customers \n", - "# idée : les targets sont créées puis envoyées aux clients, d'où un léger délai \n", - "# mais question substiste : pourquoi les clients ne reçoivent-ils pas la target en même temps ? \n", - "\n", - "# vérifions que la date de création de la target est tjrs inférieure à la date de création minimum pour tous les clients ayant reçu la target\n", - "\n", - "# first step : convert strings into dates\n", - "\n", - "df1_customer_targets[\"created_at\"] = df1_customer_targets[\"created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "df1_customer_targets[\"target_created_at\"] = df1_customer_targets[\"target_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "df1_customer_targets[\"target_updated_at\"] = df1_customer_targets[\"target_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "58b22fab-d13d-456a-8250-1da035572fe9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "target_id\n", - "116 0 days 00:00:00.949028\n", - "117 0 days 00:00:00.037337\n", - "119 0 days 00:00:00.024423\n", - "120 0 days 00:00:00.058732\n", - "122 0 days 00:00:00.027283\n", - " ... \n", - "2779 0 days 00:00:19.087958\n", - "2788 0 days 00:01:36.372927\n", - "2825 0 days 00:00:00.028771\n", - "2830 0 days 00:00:01.587058\n", - "2833 0 days 00:00:00.031071\n", - "Name: creation_delay, Length: 283, dtype: object" - ] - }, - "execution_count": 144, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# second step : compute delay and minimum by target\n", - "\n", - "df1_customer_targets[\"creation_delay\"] = df1_customer_targets[\"created_at\"] -df1_customer_targets[\"target_created_at\"]\n", - "\n", - "\n", - "df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min()" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "4b5c8f3e-9227-466c-a4c0-2280864a5036", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 days 00:00:00.009293\n", - "686 days 23:14:10.435866\n" - ] - } - ], - "source": [ - "print(df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min().min())\n", - "print((df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min()).max())" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "41e4040c-45a0-41ac-be91-4c86ef5ab1a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "target_id\n", - "335 285 days 22:56:30.356536\n", - "339 86 days 21:34:19.282253\n", - "469 7 days 07:24:03.446563\n", - "490 3 days 16:28:38.068677\n", - "502 7 days 20:15:19.326651\n", - "515 1 days 22:49:33.761856\n", - "517 76 days 00:41:25.366394\n", - "528 26 days 06:17:44.689111\n", - "529 6 days 02:41:29.617761\n", - "530 1 days 04:34:33.843116\n", - "642 219 days 16:50:10.816034\n", - "695 668 days 03:31:22.896313\n", - "697 58 days 20:26:26.744823\n", - "699 686 days 23:14:10.435866\n", - "786 625 days 14:47:48.797084\n", - "1747 14 days 04:08:24.295840\n", - "2094 239 days 15:13:18.681637\n", - "2321 167 days 21:19:37.490219\n", - "Name: creation_delay, dtype: object" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# glt, le délai création de la target - création pour le premier client est très court, envoi quasi instantanné\n", - "# mais parfois, le délai est très long, plus d'une année pour les cas extrêmes\n", - "\n", - "min_target_delay = df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min()\n", - "min_target_delay[min_target_delay > timedelta(days=1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "ffb2d1be-b1cb-4285-9584-d96ffeee146e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "target_type_id\n", - "1 0 days 00:00:06.490151\n", - "56 0 days 00:00:00.009293\n", - "69 0 days 00:00:00.032269\n", - "Name: creation_delay, dtype: object" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_customer_targets.groupby(\"target_type_id\")[\"creation_delay\"].min() # les target de type 1 ont un plus grd délai" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "44d5a1f5-0691-43de-bb9f-9915830bbb77", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[56 69 1]\n", - "[56 69 1]\n" - ] - } - ], - "source": [ - "print(df1_customer_targets[\"target_type_id\"].unique())\n", - "print(df1_targets[\"target_type_id\"].unique()) # rq : slt 3 types de target sur les 4 sont dans la table" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "id": "3a21df0d-0199-45d7-9019-e69dab67c9a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcustomer_idtarget_idcreated_attarget_nametarget_created_attarget_updated_attarget_type_is_importtarget_type_idtarget_type_nametarget_type_identifiercreation_delay
011848246454001302021-09-23 09:35:47.617275+02:00DDCP PROMO Réseau livres2020-11-04 18:40:49.500866+01:002021-03-02 18:38:19.084287+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808322 days, 13:54:58.116409
111848256454003452021-09-23 09:35:47.668846+02:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808159 days, 16:18:21.599647
211848286454021262021-09-23 12:02:51.253269+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808322 days, 16:23:58.236697
311848296454031262021-09-23 12:20:47.394480+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808322 days, 16:41:54.377908
412957706473013462021-09-28 16:02:29.372608+02:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808164 days, 22:45:03.292230
511848336456273982021-09-24 18:16:33.432760+02:00DDCP PROMO MD participants ateliers yoga2021-05-26 10:54:12.232999+02:002021-05-26 10:54:22.378253+02:00False69manual_dynamic_filtere0f4b8693184850fefd6d2a38f10584e121 days, 7:22:21.199761
6445281812087366312023-05-06 03:29:43.875970+02:00consentement optin b2b2021-11-30 10:03:37.430645+01:002022-02-18 17:21:30.653027+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808521 days, 16:26:06.445325
7429170211558455022022-09-28 12:55:36.843316+02:00Automation_parrainage_newsletter_générale2021-08-10 15:25:56.142538+02:002021-08-10 15:26:06.275964+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808413 days, 21:29:40.700778
8409640611216514692022-07-31 11:45:19.694236+02:00RI Newsletter Alexandrie (inscriptions formula...2021-07-08 11:31:10.246495+02:002022-01-26 12:14:17.941253+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808388 days, 0:14:09.447741
9445282412087426312023-05-06 03:29:43.901323+02:00consentement optin b2b2021-11-30 10:03:37.430645+01:002022-02-18 17:21:30.653027+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808521 days, 16:26:06.470678
\n", - "
" - ], - "text/plain": [ - " id customer_id target_id created_at \\\n", - "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", - "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", - "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", - "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", - "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", - "5 1184833 645627 398 2021-09-24 18:16:33.432760+02:00 \n", - "6 4452818 1208736 631 2023-05-06 03:29:43.875970+02:00 \n", - "7 4291702 1155845 502 2022-09-28 12:55:36.843316+02:00 \n", - "8 4096406 1121651 469 2022-07-31 11:45:19.694236+02:00 \n", - "9 4452824 1208742 631 2023-05-06 03:29:43.901323+02:00 \n", - "\n", - " target_name \\\n", - "0 DDCP PROMO Réseau livres \n", - "1 Inscrits NL générale site web \n", - "2 DDCP PROMO Art contemporain \n", - "3 DDCP PROMO Art contemporain \n", - "4 Votre première liste \n", - "5 DDCP PROMO MD participants ateliers yoga \n", - "6 consentement optin b2b \n", - "7 Automation_parrainage_newsletter_générale \n", - "8 RI Newsletter Alexandrie (inscriptions formula... \n", - "9 consentement optin b2b \n", - "\n", - " target_created_at target_updated_at \\\n", - "0 2020-11-04 18:40:49.500866+01:00 2021-03-02 18:38:19.084287+01:00 \n", - "1 2021-04-16 17:17:26.069199+02:00 2021-04-16 17:17:26.069199+02:00 \n", - "2 2020-11-04 18:38:53.016572+01:00 2021-04-16 17:17:25.850107+02:00 \n", - "3 2020-11-04 18:38:53.016572+01:00 2021-04-16 17:17:25.850107+02:00 \n", - "4 2021-04-16 17:17:26.080378+02:00 2021-04-16 17:17:26.080378+02:00 \n", - "5 2021-05-26 10:54:12.232999+02:00 2021-05-26 10:54:22.378253+02:00 \n", - "6 2021-11-30 10:03:37.430645+01:00 2022-02-18 17:21:30.653027+01:00 \n", - "7 2021-08-10 15:25:56.142538+02:00 2021-08-10 15:26:06.275964+02:00 \n", - "8 2021-07-08 11:31:10.246495+02:00 2022-01-26 12:14:17.941253+01:00 \n", - "9 2021-11-30 10:03:37.430645+01:00 2022-02-18 17:21:30.653027+01:00 \n", - "\n", - " target_type_is_import target_type_id target_type_name \\\n", - "0 False 56 manual_static_filter \n", - "1 False 56 manual_static_filter \n", - "2 False 56 manual_static_filter \n", - "3 False 56 manual_static_filter \n", - "4 False 56 manual_static_filter \n", - "5 False 69 manual_dynamic_filter \n", - "6 False 56 manual_static_filter \n", - "7 False 56 manual_static_filter \n", - "8 False 56 manual_static_filter \n", - "9 False 56 manual_static_filter \n", - "\n", - " target_type_identifier creation_delay \n", - "0 fb27e81baa4debc6a4e1a8639c20e808 322 days, 13:54:58.116409 \n", - "1 fb27e81baa4debc6a4e1a8639c20e808 159 days, 16:18:21.599647 \n", - "2 fb27e81baa4debc6a4e1a8639c20e808 322 days, 16:23:58.236697 \n", - "3 fb27e81baa4debc6a4e1a8639c20e808 322 days, 16:41:54.377908 \n", - "4 fb27e81baa4debc6a4e1a8639c20e808 164 days, 22:45:03.292230 \n", - "5 e0f4b8693184850fefd6d2a38f10584e 121 days, 7:22:21.199761 \n", - "6 fb27e81baa4debc6a4e1a8639c20e808 521 days, 16:26:06.445325 \n", - "7 fb27e81baa4debc6a4e1a8639c20e808 413 days, 21:29:40.700778 \n", - "8 fb27e81baa4debc6a4e1a8639c20e808 388 days, 0:14:09.447741 \n", - "9 fb27e81baa4debc6a4e1a8639c20e808 521 days, 16:26:06.470678 " - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# final visu : nice table for targets\n", - "\n", - "# pour la suite, on peut supprimer la colonne creation delay, \n", - "# était juste utile pour vérifier que la date de création était postérieure à la date de création de la target\n", - "\n", - "df1_customer_targets.head(10)" - ] - }, - { - "cell_type": "markdown", - "id": "d762394b-3aee-4284-a472-40a6b6f4308a", - "metadata": {}, - "source": [ - "## Campaign stats, campaigns" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "9d338a1a-52a5-49c4-a277-37be3f190e81", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
01319613newsletter enseignants janvier 20227212022-01-14 16:06:42.586321+01:002022-02-03 14:17:27.112963+01:00NaNNaN0.0Falseaba3b6fd5d186d28e06ff97135cade7f2022-01-14 00:00:00+01:00
11319586lsf_janvier_20227172022-01-07 11:30:35.315895+01:002022-02-03 14:17:27.116171+01:00NaNNaN0.0False788d986905533aba051261497ecffcbb2022-01-07 00:00:00+01:00
21319282Invitation à déjeuner au Mucem | Vernissage « ...5912021-09-28 12:50:24.448752+02:002022-02-03 14:17:27.119582+01:00NaNNaN0.0False3493894fa4ea036cfc6433c3e2ee63b02021-09-28 00:00:00+02:00
31319283Vacances de la Toussaint - centres des loisirs5902021-09-28 18:01:04.692073+02:002022-02-03 14:17:27.124408+01:00NaNNaN0.0False08b255a5d42b89b0585260b6f2360bdd2021-09-28 00:00:00+02:00
41319636ddcp_promo_md_livemag7302022-01-27 18:00:41.053069+01:002022-02-03 14:17:27.127607+01:00NaNNaN0.0Falsed5cfead94f5350c12c322b5b664544c12022-01-27 00:00:00+01:00
\n", - "
" - ], - "text/plain": [ - " id name service_id \\\n", - "0 1319613 newsletter enseignants janvier 2022 721 \n", - "1 1319586 lsf_janvier_2022 717 \n", - "2 1319282 Invitation à déjeuner au Mucem | Vernissage « ... 591 \n", - "3 1319283 Vacances de la Toussaint - centres des loisirs 590 \n", - "4 1319636 ddcp_promo_md_livemag 730 \n", - "\n", - " created_at updated_at \\\n", - "0 2022-01-14 16:06:42.586321+01:00 2022-02-03 14:17:27.112963+01:00 \n", - "1 2022-01-07 11:30:35.315895+01:00 2022-02-03 14:17:27.116171+01:00 \n", - "2 2021-09-28 12:50:24.448752+02:00 2022-02-03 14:17:27.119582+01:00 \n", - "3 2021-09-28 18:01:04.692073+02:00 2022-02-03 14:17:27.124408+01:00 \n", - "4 2022-01-27 18:00:41.053069+01:00 2022-02-03 14:17:27.127607+01:00 \n", - "\n", - " process_id report_url category to_be_synced \\\n", - "0 NaN NaN 0.0 False \n", - "1 NaN NaN 0.0 False \n", - "2 NaN NaN 0.0 False \n", - "3 NaN NaN 0.0 False \n", - "4 NaN NaN 0.0 False \n", - "\n", - " identifier sent_at \n", - "0 aba3b6fd5d186d28e06ff97135cade7f 2022-01-14 00:00:00+01:00 \n", - "1 788d986905533aba051261497ecffcbb 2022-01-07 00:00:00+01:00 \n", - "2 3493894fa4ea036cfc6433c3e2ee63b0 2021-09-28 00:00:00+02:00 \n", - "3 08b255a5d42b89b0585260b6f2360bdd 2021-09-28 00:00:00+02:00 \n", - "4 d5cfead94f5350c12c322b5b664544c1 2022-01-27 00:00:00+01:00 " - ] - }, - "execution_count": 189, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1. campaigns\n", - "\n", - "df1_campaigns.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "id": "fad1a58c-cece-45f9-a44f-ca46884a9a81", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0.000000\n", - "name 0.000000\n", - "service_id 0.000000\n", - "created_at 0.000000\n", - "updated_at 0.000000\n", - "process_id 1.000000\n", - "report_url 1.000000\n", - "category 0.002090\n", - "to_be_synced 0.000000\n", - "identifier 0.000000\n", - "sent_at 0.003135\n", - "dtype: float64" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# part de Nan pour chaque variable\n", - "\n", - "df1_campaigns.isna().sum() / df1_campaigns.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "cdeebf18-a3a4-4131-ad88-d45c39ec5786", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id int64\n", - "name object\n", - "service_id int64\n", - "created_at object\n", - "updated_at object\n", - "process_id float64\n", - "report_url float64\n", - "category float64\n", - "to_be_synced bool\n", - "identifier object\n", - "sent_at object\n", - "dtype: object" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "id": "5c9b669a-477b-4f33-86df-b22ff2c21382", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 186, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(df1_campaigns[\"identifier\"][0])" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "id": "b5b0af8d-b9a0-4224-a229-d74d90ac2686", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0., nan])" - ] - }, - "execution_count": 187, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# category\n", - "\n", - "df1_campaigns[\"category\"].isna()" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "4cc618ae-063f-48fc-bce7-8b72d30ad4ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "957\n", - "957\n" - ] - } - ], - "source": [ - "# identifier\n", - "\n", - "print(df1_campaigns[\"identifier\"].nunique())\n", - "print(df1_campaigns.shape[0]) # identifier is unique" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "id": "d13c3f21-ebd7-4e9b-baca-1f3a10ac24a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id 957\n", - "name 855\n", - "service_id 957\n", - "created_at 957\n", - "updated_at 957\n", - "process_id 0\n", - "report_url 0\n", - "category 1\n", - "to_be_synced 2\n", - "identifier 957\n", - "sent_at 737\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "# service id\n", - "\n", - "print(df1_campaigns.nunique()) # on a un identifiant de service par campagne, mais pas un nom unique" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "id": "aea65b10-8a7f-472e-a7f5-455a90d3cfef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
7771319239\"L'Orient sonore\" au Mucem à partir du 22 juillet1842021-09-24 11:56:09.277085+02:002021-09-24 11:56:09.277085+02:00NaNNaN0.0False6cdd60ea0045eb7a6ec44c54d29ed4022020-07-15 00:00:00+02:00
7781319240\"L'Orient sonore\" au Mucem à partir du 22 juillet1812021-09-24 11:56:09.284647+02:002021-09-24 11:56:09.284647+02:00NaNNaN0.0Falsefc221309746013ac554571fbd180e1c82020-07-09 00:00:00+02:00
2551320926Alexandrie NL211162023-01-31 11:08:55.915268+01:002023-01-31 11:08:56.286044+01:00NaNNaN0.0Falsedd77279f7d325eec933f05b1672f6a1f2023-01-31 12:08:54+01:00
1611320910Alexandrie NL210772023-01-24 09:01:00.250855+01:002023-01-24 09:01:00.271292+01:00NaNNaN0.0False062ddb6c727310e76b6200b7c71f63b52023-01-24 10:00:58+01:00
2411320574Alexandrie NL27312022-10-11 07:00:50.971513+02:002022-12-02 17:51:21.670983+01:00NaNNaN0.0False59c33016884a62116be975a9bb8257e32022-10-11 00:00:00+02:00
3171320972Centres_loisirs _vacances de février11242023-02-08 12:01:16.732961+01:002023-02-08 12:01:16.808008+01:00NaNNaN0.0Falsec7635bfd99248a2cdef8249ef7bfbef42023-02-08 13:01:15+01:00
1661320954Centres_loisirs _vacances de février11102023-02-01 09:30:41.267232+01:002023-02-01 09:30:41.354117+01:00NaNNaN0.0False2cbca44843a864533ec05b321ae1f9d12023-02-01 10:30:40+01:00
672148Champ social décembre 20202832021-04-03 18:24:42.186026+02:002021-09-24 11:56:08.182818+02:00NaNNaN0.0False0f49c89d1e7298bb9930789c8ed59d482020-12-03 00:00:00+01:00
56972Champ social décembre 20202842021-03-29 15:41:53.631952+02:002021-09-24 11:56:07.748770+02:00NaNNaN0.0False46ba9f2a6976570b0353203ec44742172020-12-04 00:00:00+01:00
1751319881Champ social mars 20228332022-04-25 10:00:26.029871+02:002022-12-02 17:51:22.319899+01:00NaNNaN0.0False013a006f03dbc5392effeb8f18fda7552022-04-25 00:00:00+02:00
3161319760Champ social mars 20227852022-03-11 13:00:28.333251+01:002022-12-02 17:51:21.991906+01:00NaNNaN0.0False4b04a686b0ad13dce35fa99fa4161c652022-03-11 00:00:00+01:00
3261319798DDCP Newsletter Destination Mucem Est 28042022-03-22 10:21:02.122363+01:002022-12-02 17:51:22.119041+01:00NaNNaN0.0Falsedc5689792e08eb2e219dce49e64c885b2022-03-22 00:00:00+01:00
1771319882DDCP Newsletter Destination Mucem Est 28432022-04-26 09:00:44.083713+02:002022-12-02 17:51:22.454684+01:00NaNNaN0.0False3d8e28caf901313a554cebc7d32e67e52022-04-26 00:00:00+02:00
3471319883DDCP Newsletter Destination Mucem Nord 28452022-04-26 09:00:46.020370+02:002022-12-02 17:51:22.463986+01:00NaNNaN0.0Falseb86e8d03fe992d1b0e19656875ee557c2022-04-26 00:00:00+02:00
3191319768DDCP Newsletter Destination Mucem Nord 27892022-03-17 10:20:51.757178+01:002022-12-02 17:51:22.064760+01:00NaNNaN0.0False68053af2923e00204c3ca7c6a3150cf72022-03-17 00:00:00+01:00
1761319885DDCP Newsletter Destination Mucem Nord Est 28422022-04-26 09:30:57.232149+02:002022-12-02 17:51:22.447304+01:00NaNNaN0.0Falsefc3cf452d3da8402bebb765225ce8c0e2022-04-26 00:00:00+02:00
3241319769DDCP Newsletter Destination Mucem Nord Est 28002022-03-17 10:22:58.736431+01:002022-12-02 17:51:22.107694+01:00NaNNaN0.0False7a53928fa4dd31e82c6ef826f341daec2022-03-17 00:00:00+01:00
2431319884DDCP Newsletter Destination Mucem Sud 28442022-04-26 09:00:46.894528+02:002022-12-02 17:51:22.459272+01:00NaNNaN0.0Falsee97ee2054defb209c35fe4dc945990612022-04-26 00:00:00+02:00
3271319799DDCP Newsletter Destination Mucem Sud 28052022-03-22 10:24:05.787335+01:002022-12-02 17:51:22.123726+01:00NaNNaN0.0False846c260d715e5b854ffad5f70a516c882022-03-22 00:00:00+01:00
6202681DDCP PROMO programmation Orient sonore Pass mu...2262021-04-08 21:10:40.634455+02:002021-09-24 11:56:07.922243+02:00NaNNaN0.0False9cfdf10e8fc047a44b08ed031e1f0ed12020-10-09 00:00:00+02:00
\n", - "
" - ], - "text/plain": [ - " id name service_id \\\n", - "777 1319239 \"L'Orient sonore\" au Mucem à partir du 22 juillet 184 \n", - "778 1319240 \"L'Orient sonore\" au Mucem à partir du 22 juillet 181 \n", - "255 1320926 Alexandrie NL2 1116 \n", - "161 1320910 Alexandrie NL2 1077 \n", - "241 1320574 Alexandrie NL2 731 \n", - "317 1320972 Centres_loisirs _vacances de février 1124 \n", - "166 1320954 Centres_loisirs _vacances de février 1110 \n", - "672 148 Champ social décembre 2020 283 \n", - "569 72 Champ social décembre 2020 284 \n", - "175 1319881 Champ social mars 2022 833 \n", - "316 1319760 Champ social mars 2022 785 \n", - "326 1319798 DDCP Newsletter Destination Mucem Est 2 804 \n", - "177 1319882 DDCP Newsletter Destination Mucem Est 2 843 \n", - "347 1319883 DDCP Newsletter Destination Mucem Nord 2 845 \n", - "319 1319768 DDCP Newsletter Destination Mucem Nord 2 789 \n", - "176 1319885 DDCP Newsletter Destination Mucem Nord Est 2 842 \n", - "324 1319769 DDCP Newsletter Destination Mucem Nord Est 2 800 \n", - "243 1319884 DDCP Newsletter Destination Mucem Sud 2 844 \n", - "327 1319799 DDCP Newsletter Destination Mucem Sud 2 805 \n", - "620 2681 DDCP PROMO programmation Orient sonore Pass mu... 226 \n", - "\n", - " created_at updated_at \\\n", - "777 2021-09-24 11:56:09.277085+02:00 2021-09-24 11:56:09.277085+02:00 \n", - "778 2021-09-24 11:56:09.284647+02:00 2021-09-24 11:56:09.284647+02:00 \n", - "255 2023-01-31 11:08:55.915268+01:00 2023-01-31 11:08:56.286044+01:00 \n", - "161 2023-01-24 09:01:00.250855+01:00 2023-01-24 09:01:00.271292+01:00 \n", - "241 2022-10-11 07:00:50.971513+02:00 2022-12-02 17:51:21.670983+01:00 \n", - "317 2023-02-08 12:01:16.732961+01:00 2023-02-08 12:01:16.808008+01:00 \n", - "166 2023-02-01 09:30:41.267232+01:00 2023-02-01 09:30:41.354117+01:00 \n", - "672 2021-04-03 18:24:42.186026+02:00 2021-09-24 11:56:08.182818+02:00 \n", - "569 2021-03-29 15:41:53.631952+02:00 2021-09-24 11:56:07.748770+02:00 \n", - "175 2022-04-25 10:00:26.029871+02:00 2022-12-02 17:51:22.319899+01:00 \n", - "316 2022-03-11 13:00:28.333251+01:00 2022-12-02 17:51:21.991906+01:00 \n", - "326 2022-03-22 10:21:02.122363+01:00 2022-12-02 17:51:22.119041+01:00 \n", - "177 2022-04-26 09:00:44.083713+02:00 2022-12-02 17:51:22.454684+01:00 \n", - "347 2022-04-26 09:00:46.020370+02:00 2022-12-02 17:51:22.463986+01:00 \n", - "319 2022-03-17 10:20:51.757178+01:00 2022-12-02 17:51:22.064760+01:00 \n", - "176 2022-04-26 09:30:57.232149+02:00 2022-12-02 17:51:22.447304+01:00 \n", - "324 2022-03-17 10:22:58.736431+01:00 2022-12-02 17:51:22.107694+01:00 \n", - "243 2022-04-26 09:00:46.894528+02:00 2022-12-02 17:51:22.459272+01:00 \n", - "327 2022-03-22 10:24:05.787335+01:00 2022-12-02 17:51:22.123726+01:00 \n", - "620 2021-04-08 21:10:40.634455+02:00 2021-09-24 11:56:07.922243+02:00 \n", - "\n", - " process_id report_url category to_be_synced \\\n", - "777 NaN NaN 0.0 False \n", - "778 NaN NaN 0.0 False \n", - "255 NaN NaN 0.0 False \n", - "161 NaN NaN 0.0 False \n", - "241 NaN NaN 0.0 False \n", - "317 NaN NaN 0.0 False \n", - "166 NaN NaN 0.0 False \n", - "672 NaN NaN 0.0 False \n", - "569 NaN NaN 0.0 False \n", - "175 NaN NaN 0.0 False \n", - "316 NaN NaN 0.0 False \n", - "326 NaN NaN 0.0 False \n", - "177 NaN NaN 0.0 False \n", - "347 NaN NaN 0.0 False \n", - "319 NaN NaN 0.0 False \n", - "176 NaN NaN 0.0 False \n", - "324 NaN NaN 0.0 False \n", - "243 NaN NaN 0.0 False \n", - "327 NaN NaN 0.0 False \n", - "620 NaN NaN 0.0 False \n", - "\n", - " identifier sent_at \n", - "777 6cdd60ea0045eb7a6ec44c54d29ed402 2020-07-15 00:00:00+02:00 \n", - "778 fc221309746013ac554571fbd180e1c8 2020-07-09 00:00:00+02:00 \n", - "255 dd77279f7d325eec933f05b1672f6a1f 2023-01-31 12:08:54+01:00 \n", - "161 062ddb6c727310e76b6200b7c71f63b5 2023-01-24 10:00:58+01:00 \n", - "241 59c33016884a62116be975a9bb8257e3 2022-10-11 00:00:00+02:00 \n", - "317 c7635bfd99248a2cdef8249ef7bfbef4 2023-02-08 13:01:15+01:00 \n", - "166 2cbca44843a864533ec05b321ae1f9d1 2023-02-01 10:30:40+01:00 \n", - "672 0f49c89d1e7298bb9930789c8ed59d48 2020-12-03 00:00:00+01:00 \n", - "569 46ba9f2a6976570b0353203ec4474217 2020-12-04 00:00:00+01:00 \n", - "175 013a006f03dbc5392effeb8f18fda755 2022-04-25 00:00:00+02:00 \n", - "316 4b04a686b0ad13dce35fa99fa4161c65 2022-03-11 00:00:00+01:00 \n", - "326 dc5689792e08eb2e219dce49e64c885b 2022-03-22 00:00:00+01:00 \n", - "177 3d8e28caf901313a554cebc7d32e67e5 2022-04-26 00:00:00+02:00 \n", - "347 b86e8d03fe992d1b0e19656875ee557c 2022-04-26 00:00:00+02:00 \n", - "319 68053af2923e00204c3ca7c6a3150cf7 2022-03-17 00:00:00+01:00 \n", - "176 fc3cf452d3da8402bebb765225ce8c0e 2022-04-26 00:00:00+02:00 \n", - "324 7a53928fa4dd31e82c6ef826f341daec 2022-03-17 00:00:00+01:00 \n", - "243 e97ee2054defb209c35fe4dc94599061 2022-04-26 00:00:00+02:00 \n", - "327 846c260d715e5b854ffad5f70a516c88 2022-03-22 00:00:00+01:00 \n", - "620 9cfdf10e8fc047a44b08ed031e1f0ed1 2020-10-09 00:00:00+02:00 " - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# name\n", - "\n", - "df1_campaigns[df1_campaigns.duplicated(subset = [\"name\"], keep=False)].sort_values(\"name\").head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "id": "35ea834e-01a3-4841-a9a9-351c25c5af37", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "175 True\n", - "316 True\n", - "dtype: bool" - ] - }, - "execution_count": 207, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns[df1_campaigns[\"name\"]==\"Champ social mars 2022\"].duplicated(subset=\"name\", keep=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "id": "5e16bf37-c2e0-48c9-8a90-6713f7c6206c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Share of campaigns to synce : 0.52 % \n" - ] - } - ], - "source": [ - "# to be synced \n", - "\n", - "share_campaigns_to_be_synced = round(100 * df1_campaigns[\"to_be_synced\"].mean(),2)\n", - "print(f\"Share of campaigns to synce : {share_campaigns_to_be_synced} % \") # 0.5% of campaigns to synce" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "id": "88a6f9d4-ddd2-4288-9bba-7d9e76c66f51", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
431320752dre_alors_on_sort0712_tech&cult1212_lesreveill...10192022-11-28 09:30:31.189207+01:002022-12-02 17:51:23.474745+01:00NaNNaN0.0True03e0704b5690a2dee1861dc3ad3316c92022-11-28 00:00:00+01:00
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4641320749dre_le_sel_24112210542022-11-24 09:01:37.467710+01:002022-12-02 17:51:23.622812+01:00NaNNaN0.0Truedb576a7d2453575f29eab4bac787b9192022-11-24 00:00:00+01:00
4651320751News hebdo du 28 novembre au 4 décembre10572022-11-27 18:01:44.546081+01:002022-12-02 17:51:23.627178+01:00NaNNaN0.0Trued8700cbd38cc9f30cecb34f0c195b1372022-11-27 00:00:00+01:00
8881319474ddcp_promo_temps fort salammbo6702021-11-25 13:19:41.547780+01:002022-02-03 14:17:27.728648+01:00NaNNaN0.0True17c276c8e723eb46aef576537e9d56d02021-11-25 00:00:00+01:00
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" - ], - "text/plain": [ - " id name service_id \\\n", - "43 1320752 dre_alors_on_sort0712_tech&cult1212_lesreveill... 1019 \n", - "79 1320755 News hebdo du 5 au 4 décembre 2022 1060 \n", - "464 1320749 dre_le_sel_241122 1054 \n", - "465 1320751 News hebdo du 28 novembre au 4 décembre 1057 \n", - "888 1319474 ddcp_promo_temps fort salammbo 670 \n", - "\n", - " created_at updated_at \\\n", - "43 2022-11-28 09:30:31.189207+01:00 2022-12-02 17:51:23.474745+01:00 \n", - "79 2022-12-04 18:01:29.971417+01:00 2022-12-04 18:01:30.037656+01:00 \n", - "464 2022-11-24 09:01:37.467710+01:00 2022-12-02 17:51:23.622812+01:00 \n", - "465 2022-11-27 18:01:44.546081+01:00 2022-12-02 17:51:23.627178+01:00 \n", - "888 2021-11-25 13:19:41.547780+01:00 2022-02-03 14:17:27.728648+01:00 \n", - "\n", - " process_id report_url category to_be_synced \\\n", - "43 NaN NaN 0.0 True \n", - "79 NaN NaN 0.0 True \n", - "464 NaN NaN 0.0 True \n", - "465 NaN NaN 0.0 True \n", - "888 NaN NaN 0.0 True \n", - "\n", - " identifier sent_at \n", - "43 03e0704b5690a2dee1861dc3ad3316c9 2022-11-28 00:00:00+01:00 \n", - "79 299a23a2291e2126b91d54f3601ec162 2022-12-04 19:01:27+01:00 \n", - "464 db576a7d2453575f29eab4bac787b919 2022-11-24 00:00:00+01:00 \n", - "465 d8700cbd38cc9f30cecb34f0c195b137 2022-11-27 00:00:00+01:00 \n", - "888 17c276c8e723eb46aef576537e9d56d0 2021-11-25 00:00:00+01:00 " - ] - }, - "execution_count": 235, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# focus : campaigns to synce - 5 cases\n", - "# la date d'envoie semble cohérente. Pas d'observation particulière sur ces cas ...\n", - "\n", - "df1_campaigns[df1_campaigns[\"to_be_synced\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 234, - "id": "cf9dedd6-2554-4f9e-a09b-f1465718a18d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
431320752dre_alors_on_sort0712_tech&cult1212_lesreveill...10192022-11-28 09:30:31.189207+01:002022-12-02 17:51:23.474745+01:00NaNNaN0.0True03e0704b5690a2dee1861dc3ad3316c92022-11-28 00:00:00+01:00
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4641320749dre_le_sel_24112210542022-11-24 09:01:37.467710+01:002022-12-02 17:51:23.622812+01:00NaNNaN0.0Truedb576a7d2453575f29eab4bac787b9192022-11-24 00:00:00+01:00
4651320751News hebdo du 28 novembre au 4 décembre10572022-11-27 18:01:44.546081+01:002022-12-02 17:51:23.627178+01:00NaNNaN0.0Trued8700cbd38cc9f30cecb34f0c195b1372022-11-27 00:00:00+01:00
8881319474ddcp_promo_temps fort salammbo6702021-11-25 13:19:41.547780+01:002022-02-03 14:17:27.728648+01:00NaNNaN0.0True17c276c8e723eb46aef576537e9d56d02021-11-25 00:00:00+01:00
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
01319613newsletter enseignants janvier 20227212022-01-14 16:06:42.586321+01:002022-02-03 14:17:27.112963+01:00NaNNaN0.0Falseaba3b6fd5d186d28e06ff97135cade7f2022-01-14 00:00:00+01:00
11319586lsf_janvier_20227172022-01-07 11:30:35.315895+01:002022-02-03 14:17:27.116171+01:00NaNNaN0.0False788d986905533aba051261497ecffcbb2022-01-07 00:00:00+01:00
21319282Invitation à déjeuner au Mucem | Vernissage « ...5912021-09-28 12:50:24.448752+02:002022-02-03 14:17:27.119582+01:00NaNNaN0.0False3493894fa4ea036cfc6433c3e2ee63b02021-09-28 00:00:00+02:00
31319283Vacances de la Toussaint - centres des loisirs5902021-09-28 18:01:04.692073+02:002022-02-03 14:17:27.124408+01:00NaNNaN0.0False08b255a5d42b89b0585260b6f2360bdd2021-09-28 00:00:00+02:00
41319636ddcp_promo_md_livemag7302022-01-27 18:00:41.053069+01:002022-02-03 14:17:27.127607+01:00NaNNaN0.0Falsed5cfead94f5350c12c322b5b664544c12022-01-27 00:00:00+01:00
51319614News hebdo du 17 janv au 23 janv 20227122022-01-16 18:01:28.974157+01:002022-02-03 14:17:27.130944+01:00NaNNaN0.0False19bc916108fc6938f52cb96f7e0879412022-01-16 00:00:00+01:00
61319263ddcp_promo_automne_littérature_relance_nn_ouverts5862021-09-24 15:00:04.174247+02:002021-09-24 16:13:10.505400+02:00NaNNaN0.0False605ff764c617d3cd28dbbdd72be8f9a22021-09-24 00:00:00+02:00
71319284Invitation au vernissage de l'exposition \"La C...5932021-09-30 14:47:18.135394+02:002022-02-03 14:17:27.134073+01:00NaNNaN0.0Falseacc3e0404646c57502b480dc052c4fe12021-09-30 00:00:00+02:00
81319625dre_mobilisations_artistiques_et_politiques7042022-01-27 10:01:16.716706+01:002022-02-03 14:17:27.172039+01:00NaNNaN0.0Falsef64eac11f2cd8f0efa196f8ad173178e2022-01-27 00:00:00+01:00
91319285ddcp_promo_soyinka_taubira_infos_pratiques5942021-10-01 12:16:57.031796+02:002022-02-03 14:17:27.137444+01:00NaNNaN0.0False076a0c97d09cf1a0ec3e19c7f2529f2b2021-10-01 00:00:00+02:00
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
7891319243DRE Exposer le récit 13 mars1112021-09-24 11:56:09.307905+02:002021-09-24 11:56:09.307905+02:00NaNNaN0.0False698d51a19d8a121ce581499d7b7016682020-03-03 00:00:00+01:00
7911319245SDR Relance invit petit dej voyage voyages1092021-09-24 11:56:09.323919+02:002021-09-24 11:56:09.323919+02:00NaNNaN0.0False2723d092b63885e0d7c260cc007e8b9d2020-02-24 00:00:00+01:00
7931319247Au Mucem en 2020972021-09-24 11:56:09.339127+02:002021-09-24 11:56:09.339127+02:00NaNNaN0.0Falsee2ef524fbf3d9fe611d5a8e90fefdc9c2020-01-31 00:00:00+01:00
7941319248DRE Giono922021-09-24 11:56:09.346887+02:002021-09-24 11:56:09.346887+02:00NaNNaN0.0False92cc227532d17e56e07902b254dfad102020-01-29 00:00:00+01:00
7961319250Portes ouvertes \"Voyage, voyages\" au Mucem | M...772021-09-24 11:56:09.362114+02:002021-09-24 11:56:09.362114+02:00NaNNaN0.0False28dd2c7955ce926456240b2ff0100bde2020-01-13 00:00:00+01:00
8051319259Save the date | Vernissage \"Voyage, voyages\" a...382021-09-24 11:56:09.432720+02:002021-09-24 11:56:09.432720+02:00NaNNaN0.0Falsea5771bce93e200c36f7cd9dfd0e5deaa2019-11-20 00:00:00+01:00
8061319260Portes ouvertes \"Massilia Toy\" au Mucem | Merc...372021-09-24 11:56:09.440465+02:002021-09-24 11:56:09.440465+02:00NaNNaN0.0Falsea5bfc9e07964f8dddeb95fc584cd965d2019-11-20 00:00:00+01:00
8081319262TENK S-1 Corse172021-09-24 11:56:09.456460+02:002021-09-24 11:56:09.456460+02:00NaNNaN0.0False70efdf2ec9b086079795c442636b55fb2019-11-07 00:00:00+01:00
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
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" - ], - "text/plain": [ - " id campaign_id customer_id opened_at \\\n", - "0 19793 58 112597 NaN \n", - "1 14211 58 113666 NaN \n", - "2 13150 58 280561 NaN \n", - "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", - "4 5175 58 103972 NaN \n", - "\n", - " sent_at delivered_at \\\n", - "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", - "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", - "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", - "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", - "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", - "\n", - " created_at updated_at \\\n", - "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "\n", - " campaign_name campaign_service_id \\\n", - "0 Le Mucem chez vous, gardons le lien #22 404 \n", - "1 Le Mucem chez vous, gardons le lien #22 404 \n", - "2 Le Mucem chez vous, gardons le lien #22 404 \n", - "3 Le Mucem chez vous, gardons le lien #22 404 \n", - "4 Le Mucem chez vous, gardons le lien #22 404 \n", - "\n", - " campaign_created_at campaign_updated_at \\\n", - "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "\n", - " campaign_sent_at campaign_identifier \n", - "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a " - ] - }, - "execution_count": 338, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# merge \n", - "\n", - "# de campaigns on supprile les var valant tjrs NaN et to_be_synced qui semble pas très informatif\n", - "\n", - "df1_campaigns_full = pd.merge(df1_campaign_stats, \n", - " df1_campaigns[[\"id\", \"name\", \"service_id\", \"created_at\", \"updated_at\", \"sent_at\", \"identifier\"]].add_prefix(\"campaign_\"),\n", - " on = \"campaign_id\", how = \"left\")\n", - "df1_campaigns_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 328, - "id": "81e549e9-d165-439a-a824-17f053a33983", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "campaign_id 0\n", - "customer_id 0\n", - "opened_at 5019527\n", - "sent_at 6023\n", - "delivered_at 133590\n", - "created_at 0\n", - "updated_at 0\n", - "campaign_name 0\n", - "campaign_service_id 0\n", - "campaign_created_at 0\n", - "campaign_updated_at 0\n", - "campaign_sent_at 6\n", - "campaign_identifier 0\n", - "dtype: int64" - ] - }, - "execution_count": 328, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "id": "aa249cdc-e0ac-41ec-b6f8-b9459f31eca3", - "metadata": {}, - "outputs": [], - "source": [ - "# lien entre sent at et campaign sent at ? \n", - "# à quoi correspond la date de la campagne, est-ce le premier envoi à un client ?\n", - "\n", - "# first step : transform dates to have the good format\n", - "# VERY time-consuming bc the df has 6M lines !!!!\n", - "\n", - "from dateutil import parser\n", - "\n", - "def convert_to_datetime(column):\n", - " return column.apply(lambda x: parser.parse(str(x)) if pd.notna(x) else pd.NaT)\n", - "\n", - "# Liste des colonnes à convertir\n", - "columns_to_convert = [\"sent_at\", \"delivered_at\", \"created_at\", \"updated_at\", \n", - " \"campaign_sent_at\", \"campaign_created_at\", \"campaign_updated_at\"]\n", - "\n", - "# Appliquer la fonction à chaque colonne spécifiée\n", - "df1_campaigns_full[columns_to_convert] = df1_campaigns_full[columns_to_convert].apply(convert_to_datetime)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 329, - "id": "f2b05227-e8d8-4ca8-8359-dc3471841763", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UTC: 2021-03-28 16:01:09+00:00\n", - "Local: 2021-03-28 18:01:09+02:00\n" - ] - } - ], - "source": [ - "# Exemple d'élément\n", - "date_string = '2021-03-28 18:01:09+02:00'\n", - "\n", - "# Convertir en datetime en utilisant pd.to_datetime avec utc=True\n", - "datetime_object_utc = pd.to_datetime(date_string, utc=True)\n", - "print(\"UTC:\", datetime_object_utc)\n", - "\n", - "# Convertir en datetime en utilisant pd.to_datetime avec utc=False (ou sans spécifier utc)\n", - "datetime_object_local = pd.to_datetime(date_string, utc=False)\n", - "print(\"Local:\", datetime_object_local)" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "id": "63fa4af8-0c28-4b20-97e2-560da4d4b77e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UTC: 2021-03-28 16:00:00+00:00\n", - "Différence en heures: 1.5\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Exemple d'élément\n", - "date_string = '2021-03-28 18:00:00+02:00'\n", - "\n", - "# Convertir en datetime en utilisant pd.to_datetime avec utc=True\n", - "datetime_object_utc = pd.to_datetime(date_string, utc=True)\n", - "\n", - "# Afficher l'objet datetime en UTC\n", - "print(\"UTC:\", datetime_object_utc)\n", - "\n", - "# Effectuer un calcul de différence entre deux dates en UTC\n", - "other_date_string = '2021-03-28 20:30:00+03:00'\n", - "other_datetime_object_utc = pd.to_datetime(other_date_string, utc=True)\n", - "\n", - "# Calculer la différence entre les dates\n", - "time_difference = other_datetime_object_utc - datetime_object_utc\n", - "\n", - "# Afficher la différence\n", - "print(\"Différence en heures:\", time_difference.total_seconds() / 3600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "id": "9388c008-e2a5-463d-95d2-8f5fea0d6a5a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
\n", - "
" - ], - "text/plain": [ - " id campaign_id customer_id opened_at \\\n", - "0 19793 58 112597 NaN \n", - "1 14211 58 113666 NaN \n", - "2 13150 58 280561 NaN \n", - "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", - "4 5175 58 103972 NaN \n", - "\n", - " sent_at delivered_at \\\n", - "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", - "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", - "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", - "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", - "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", - "\n", - " created_at updated_at \\\n", - "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "\n", - " campaign_name campaign_service_id \\\n", - "0 Le Mucem chez vous, gardons le lien #22 404 \n", - "1 Le Mucem chez vous, gardons le lien #22 404 \n", - "2 Le Mucem chez vous, gardons le lien #22 404 \n", - "3 Le Mucem chez vous, gardons le lien #22 404 \n", - "4 Le Mucem chez vous, gardons le lien #22 404 \n", - "\n", - " campaign_created_at campaign_updated_at \\\n", - "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "\n", - " campaign_sent_at campaign_identifier \n", - "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a " - ] - }, - "execution_count": 321, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# etape supp pour s'assurer que les dates non convertibles sont bien des Nan\n", - "\n", - "df1_campaigns_full[columns_to_convert] = df1_campaigns_full[columns_to_convert].apply(pd.to_datetime, errors='coerce')\n", - "df1_campaigns_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 333, - "id": "edb2f622-bf19-4c51-8213-1b8a3dacf72e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_624/1309539541.py:3: FutureWarning: In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless `utc=True`. Please specify `utc=True` to opt in to the new behaviour and silence this warning. To create a `Series` with mixed offsets and `object` dtype, please use `apply` and `datetime.datetime.strptime`\n", - " df1_campaigns_full[\"sent_at\"] = pd.to_datetime(df1_campaigns_full[\"sent_at\"] , utc=False).astype('datetime64[ns]')\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True, at position 18", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[333], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# autre methode\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf1_campaigns_full\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msent_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdatetime64[ns]\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/generic.py:6534\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6530\u001b[0m results \u001b[38;5;241m=\u001b[39m [ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[1;32m 6532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6533\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6534\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6535\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m 6536\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/internals/managers.py:414\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 412\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/internals/managers.py:354\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 354\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 355\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 357\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/internals/blocks.py:616\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m 598\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 612\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m 613\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 614\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 616\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 618\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m 620\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:238\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 235\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 238\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m 240\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:183\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 180\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 183\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:110\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_datetime\n\u001b[0;32m--> 110\u001b[0m dti \u001b[38;5;241m=\u001b[39m \u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 111\u001b[0m dta \u001b[38;5;241m=\u001b[39m dti\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mreshape(arr\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dta\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39m_ndarray\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:1131\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# error: Argument 1 to \"_maybe_cache\" has incompatible type\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;66;03m# \"Union[float, str, datetime, List[Any], Tuple[Any, ...], ExtensionArray,\u001b[39;00m\n\u001b[1;32m 1126\u001b[0m \u001b[38;5;66;03m# ndarray[Any, Any], Series]\"; expected \"Union[List[Any], Tuple[Any, ...],\u001b[39;00m\n\u001b[1;32m 1127\u001b[0m \u001b[38;5;66;03m# Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series], Series]\"\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m argc \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m 1129\u001b[0m Union[\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, ExtensionArray, np\u001b[38;5;241m.\u001b[39mndarray, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries\u001b[39m\u001b[38;5;124m\"\u001b[39m, Index], arg\n\u001b[1;32m 1130\u001b[0m )\n\u001b[0;32m-> 1131\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43margc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1132\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutOfBoundsDatetime:\n\u001b[1;32m 1133\u001b[0m \u001b[38;5;66;03m# caching attempts to create a DatetimeIndex, which may raise\u001b[39;00m\n\u001b[1;32m 1134\u001b[0m \u001b[38;5;66;03m# an OOB. If that's the desired behavior, then just reraise...\u001b[39;00m\n\u001b[1;32m 1135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43munique_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# GH#45319\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:490\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, allow_object)\u001b[0m\n\u001b[1;32m 2343\u001b[0m \u001b[38;5;66;03m# if str-dtype, convert\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mobject_)\n\u001b[0;32m-> 2346\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mtslib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_to_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2349\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", - "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:480\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mconversion.pyx:716\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_timezone\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True, at position 18" - ] - } - ], - "source": [ - "# autre methode\n", - "\n", - "df1_campaigns_full[\"sent_at\"] = pd.to_datetime(df1_campaigns_full[\"sent_at\"] , utc=False).astype('datetime64[ns]')" - ] - }, - { - "cell_type": "code", - "execution_count": 334, - "id": "92bbdf80-e34b-4146-864a-b0dd4e04c5e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " sent_at\n", - "0 2022-01-01 10:34:56+00:00\n", - "1 2022-02-01 13:45:30+00:00\n", - "2 2022-03-01 16:30:00+00:00\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Exemple de DataFrame avec une colonne 'sent_at' contenant des dates en format string\n", - "df1_campaigns_full = pd.DataFrame({\n", - " 'sent_at': ['2022-01-01 12:34:56+02:00', '2022-02-01 15:45:30+02:00', '2022-03-01 18:30:00+02:00']\n", - "})\n", - "\n", - "# Convertir la colonne 'sent_at' en datetime en conservant l'information sur le fuseau horaire (datetime64[ns])\n", - "df1_campaigns_full['sent_at'] = pd.to_datetime(df1_campaigns_full['sent_at'], utc=True)\n", - "\n", - "# Afficher le DataFrame résultant\n", - "print(df1_campaigns_full)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 324, - "id": "a8ad41ed-433c-4f7e-9f67-888dcb54d24e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "campaign_id\n", - "1 2021-03-24 00:00:00+01:00\n", - "2 2021-03-14 00:00:00+01:00\n", - "3 2021-03-15 00:00:00+01:00\n", - "4 2021-03-21 00:00:00+01:00\n", - "5 2021-03-10 00:00:00+01:00\n", - " ... \n", - "1321501 2023-11-06 13:30:12+01:00\n", - "1321503 2023-11-07 17:31:16+01:00\n", - "1321505 2023-11-08 11:15:52+01:00\n", - "1321506 2023-11-08 19:00:25+01:00\n", - "1321507 2023-11-08 19:00:37+01:00\n", - "Name: campaign_sent_at, Length: 949, dtype: datetime64[ns, tzoffset(None, 3600)]\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'bool' object is not callable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[324], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# comparison \u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(df1_campaigns_full\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_id\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_sent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mfirst()) \u001b[38;5;66;03m# envoi des campagnes\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdf1_campaigns_full\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcampaign_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msent_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mmin())\n", - "\u001b[0;31mTypeError\u001b[0m: 'bool' object is not callable" - ] - } - ], - "source": [ - "# comparison \n", - "\n", - "print(df1_campaigns_full.groupby(\"campaign_id\")[\"campaign_sent_at\"].first()) # envoi des campagnes\n", - "print(df1_campaigns_full.groupby(\"campaign_id\")[\"sent_at\"].dropna().min())" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "id": "1771adeb-bbc9-40ef-afb6-49a6b3ff2e79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "campaign_id 0\n", - "customer_id 0\n", - "opened_at 5019527\n", - "sent_at 2741358\n", - "delivered_at 2807002\n", - "created_at 1547090\n", - "updated_at 766803\n", - "campaign_name 0\n", - "campaign_service_id 0\n", - "campaign_created_at 2216183\n", - "campaign_updated_at 2561268\n", - "campaign_sent_at 3504140\n", - "campaign_identifier 0\n", - "dtype: int64" - ] - }, - "execution_count": 325, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "id": "1a5a1d98-a076-4988-aaf3-e753c117e518", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "name 0\n", - "service_id 0\n", - "created_at 0\n", - "updated_at 0\n", - "process_id 957\n", - "report_url 957\n", - "category 2\n", - "to_be_synced 0\n", - "identifier 0\n", - "sent_at 3\n", - "dtype: int64" - ] - }, - "execution_count": 326, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "id": "749df9f0-8a18-49f0-a820-05cc674a5fce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-06-02 10:24:08+02:00\n", - "2020-06-02 10:24:08+02:00\n" - ] - } - ], - "source": [ - "# df1_campaigns_full[\"sent_at\"] = \n", - "print(pd.to_datetime(df1_campaigns_full[\"sent_at\"], errors='coerce').min())\n", - "print(df1_campaigns_full[\"sent_at\"].dropna().min())" - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "id": "f46000b8-4b7b-4121-b0af-8e8a388ce33c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6214808" - ] - }, - "execution_count": 313, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full[\"sent_at\"].apply(lambda x : isinstance(x, datetime)).sum()\n", - "# df1_campaigns_full[\"sent_at\"].tail(30)" - ] - }, - { - "cell_type": "code", - "execution_count": 314, - "id": "0ae4aeca-6edc-44e8-bc72-74f19b62a8f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6214808" - ] - }, - "execution_count": 314, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 340, - "id": "4ef4d3d5-5f0a-4798-86d1-1b56641fcce4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id int64\n", - "campaign_id int64\n", - "customer_id int64\n", - "opened_at object\n", - "sent_at object\n", - "delivered_at object\n", - "created_at object\n", - "updated_at object\n", - "campaign_name object\n", - "campaign_service_id int64\n", - "campaign_created_at object\n", - "campaign_updated_at object\n", - "campaign_sent_at object\n", - "campaign_identifier object\n", - "dtype: object" - ] - }, - "execution_count": 340, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 341, - "id": "8de270ac-c205-4686-8d53-6cd52d8239d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
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" - ], - "text/plain": [ - " id campaign_id customer_id opened_at \\\n", - "0 19793 58 112597 NaN \n", - "1 14211 58 113666 NaN \n", - "2 13150 58 280561 NaN \n", - "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", - "4 5175 58 103972 NaN \n", - "\n", - " sent_at delivered_at \\\n", - "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", - "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", - "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", - "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", - "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", - "\n", - " created_at updated_at \\\n", - "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "\n", - " campaign_name campaign_service_id \\\n", - "0 Le Mucem chez vous, gardons le lien #22 404 \n", - "1 Le Mucem chez vous, gardons le lien #22 404 \n", - "2 Le Mucem chez vous, gardons le lien #22 404 \n", - "3 Le Mucem chez vous, gardons le lien #22 404 \n", - "4 Le Mucem chez vous, gardons le lien #22 404 \n", - "\n", - " campaign_created_at campaign_updated_at \\\n", - "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", - "\n", - " campaign_sent_at campaign_identifier \n", - "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", - "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a " - ] - }, - "execution_count": 341, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 342, - "id": "e2d81bd1-9fd6-40c7-96f9-998771a4fd77", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "time data '2022-05-06 12:00:23+02:00' does not match format '%Y-%m-%d %H:%M:%S.%f%z'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[342], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# conversion colonne par colonne\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# precision a la Ns\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf1_campaigns_full\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcreated_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatetime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrptime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm-\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mH:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mM:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mS.\u001b[39;49m\u001b[38;5;132;43;01m%f\u001b[39;49;00m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotna\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNaT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"updated_at\"] = df1_campaigns_full[\"updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_created_at\"] = df1_campaigns_full[\"campaign_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_updated_at\"] = df1_campaigns_full[\"campaign_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"delivered_at\"] = df1_campaigns_full[\"delivered_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_sent_at\"] = df1_campaigns_full[\"campaign_sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/series.py:4764\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m 4629\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m 4630\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4631\u001b[0m func: AggFuncType,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4636\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 4637\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m 4638\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4639\u001b[0m \u001b[38;5;124;03m Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m 4640\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[38;5;124;03m dtype: float64\u001b[39;00m\n\u001b[1;32m 4756\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 4757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4758\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4759\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4760\u001b[0m \u001b[43m \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4761\u001b[0m \u001b[43m \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4762\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4763\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 4764\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1209\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1209\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1289\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m 1285\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m 1286\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m 1287\u001b[0m \u001b[38;5;66;03m# Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m 1288\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1289\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1290\u001b[0m \u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[1;32m 1291\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;66;03m# See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:1814\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 1812\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 1813\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1814\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1815\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1816\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[1;32m 1817\u001b[0m values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[1;32m 1818\u001b[0m )\n", - "File \u001b[0;32mlib.pyx:2926\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n", - "Cell \u001b[0;32mIn[342], line 4\u001b[0m, in \u001b[0;36m\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# conversion colonne par colonne\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# precision a la Ns\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x : \u001b[43mdatetime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrptime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm-\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mH:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mM:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mS.\u001b[39;49m\u001b[38;5;132;43;01m%f\u001b[39;49;00m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mnotna(x) \u001b[38;5;28;01melse\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mNaT)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"updated_at\"] = df1_campaigns_full[\"updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_created_at\"] = df1_campaigns_full[\"campaign_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_updated_at\"] = df1_campaigns_full[\"campaign_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"delivered_at\"] = df1_campaigns_full[\"delivered_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_sent_at\"] = df1_campaigns_full[\"campaign_sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/_strptime.py:568\u001b[0m, in \u001b[0;36m_strptime_datetime\u001b[0;34m(cls, data_string, format)\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_strptime_datetime\u001b[39m(\u001b[38;5;28mcls\u001b[39m, data_string, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%a\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mb \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mH:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mM:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mS \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 566\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a class cls instance based on the input string and the\u001b[39;00m\n\u001b[1;32m 567\u001b[0m \u001b[38;5;124;03m format string.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 568\u001b[0m tt, fraction, gmtoff_fraction \u001b[38;5;241m=\u001b[39m \u001b[43m_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_string\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 569\u001b[0m tzname, gmtoff \u001b[38;5;241m=\u001b[39m tt[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m:]\n\u001b[1;32m 570\u001b[0m args \u001b[38;5;241m=\u001b[39m tt[:\u001b[38;5;241m6\u001b[39m] \u001b[38;5;241m+\u001b[39m (fraction,)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/_strptime.py:349\u001b[0m, in \u001b[0;36m_strptime\u001b[0;34m(data_string, format)\u001b[0m\n\u001b[1;32m 347\u001b[0m found \u001b[38;5;241m=\u001b[39m format_regex\u001b[38;5;241m.\u001b[39mmatch(data_string)\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m found:\n\u001b[0;32m--> 349\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime data \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m does not match format \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 350\u001b[0m (data_string, \u001b[38;5;28mformat\u001b[39m))\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data_string) \u001b[38;5;241m!=\u001b[39m found\u001b[38;5;241m.\u001b[39mend():\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munconverted data remains: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 353\u001b[0m data_string[found\u001b[38;5;241m.\u001b[39mend():])\n", - "\u001b[0;31mValueError\u001b[0m: time data '2022-05-06 12:00:23+02:00' does not match format '%Y-%m-%d %H:%M:%S.%f%z'" - ] - } - ], - "source": [ - "# conversion colonne par colonne\n", - "\n", - "# precision a la Ns\n", - "df1_campaigns_full[\"created_at\"] = df1_campaigns_full[\"created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "# df1_campaigns_full[\"updated_at\"] = df1_campaigns_full[\"updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "# df1_campaigns_full[\"campaign_created_at\"] = df1_campaigns_full[\"campaign_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "# df1_campaigns_full[\"campaign_updated_at\"] = df1_campaigns_full[\"campaign_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", - "\n", - "# precision a la sec\n", - "# df1_campaigns_full[\"opened_at\"] = df1_campaigns_full[\"opened_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\n", - "# df1_campaigns_full[\"sent_at\"] = df1_campaigns_full[\"sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\n", - "# df1_campaigns_full[\"delivered_at\"] = df1_campaigns_full[\"delivered_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\n", - "# df1_campaigns_full[\"campaign_sent_at\"] = df1_campaigns_full[\"campaign_sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 346, - "id": "5a1fe408-ae4c-4957-a39b-50a4d5423319", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6214778 2023-10-23 09:31:50.168545+02:00\n", - "6214779 2023-10-23 09:31:28.570386+02:00\n", - "6214780 2023-10-23 09:02:26.494195+02:00\n", - "6214781 2023-10-23 09:32:34.454957+02:00\n", - "6214782 2023-10-23 09:31:29.139217+02:00\n", - "6214783 2023-10-23 09:32:06.223901+02:00\n", - "6214784 2023-10-23 09:31:52.702258+02:00\n", - "6214785 2023-10-23 09:31:45.051321+02:00\n", - "6214786 2023-10-23 09:32:55.350092+02:00\n", - "6214787 2023-10-23 09:33:14.007405+02:00\n", - "6214788 2023-10-23 09:32:44.645432+02:00\n", - "6214789 2023-10-23 09:02:27.578671+02:00\n", - "6214790 2023-10-23 09:34:24.879045+02:00\n", - "6214791 2023-10-23 09:34:02.075066+02:00\n", - "6214792 2023-10-23 09:33:20.349918+02:00\n", - "6214793 2023-10-23 09:34:25.631234+02:00\n", - "6214794 2023-10-23 09:34:27.581150+02:00\n", - "6214795 2023-10-23 09:31:45.192200+02:00\n", - "6214796 2023-10-23 09:32:52.018890+02:00\n", - "6214797 2023-10-23 09:02:01.558573+02:00\n", - "6214798 2023-10-23 09:34:48.543213+02:00\n", - "6214799 2023-10-23 09:32:15.109097+02:00\n", - "6214800 2023-10-23 09:34:26.590416+02:00\n", - "6214801 2023-10-23 09:32:02.729363+02:00\n", - "6214802 2023-10-23 09:31:41.055337+02:00\n", - "6214803 2023-10-23 09:32:36.564696+02:00\n", - "6214804 2023-10-23 09:32:50.829641+02:00\n", - "6214805 2023-10-23 09:33:31.102500+02:00\n", - "6214806 2023-10-23 09:31:55.768547+02:00\n", - "6214807 2023-10-23 09:33:57.477892+02:00\n", - "Name: created_at, dtype: object" - ] - }, - "execution_count": 346, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full[\"created_at\"].tail(30)" - ] - }, - { - "cell_type": "code", - "execution_count": 349, - "id": "feb3fc34-51f2-45d5-8f34-9940a14e9060", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "time data \"2023-10-23 09:31:50.168545+02:00\" doesn't match format \"%Y-%m-%d %H:%M:%S%z\", at position 1. You might want to try:\n - passing `format` if your strings have a consistent format;\n - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[349], line 9\u001b[0m\n\u001b[1;32m 4\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate_str\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-05-06 12:00:23+02:00\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2023-10-23 09:31:50.168545+02:00\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 6\u001b[0m })\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Convertir la colonne 'date_str' en datetime en conservant l'information sur le fuseau horaire (datetime64[ns])\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdate_str\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Afficher le DataFrame résultant\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:1112\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1112\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1113\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1114\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:488\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64ns(\n\u001b[1;32m 491\u001b[0m arg,\n\u001b[1;32m 492\u001b[0m dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 496\u001b[0m allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 497\u001b[0m )\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:519\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_array_strptime_with_fallback\u001b[39m(\n\u001b[1;32m 509\u001b[0m arg,\n\u001b[1;32m 510\u001b[0m name,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 514\u001b[0m errors: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 515\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[1;32m 516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;124;03m Call array_strptime, with fallback behavior depending on 'errors'.\u001b[39;00m\n\u001b[1;32m 518\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 519\u001b[0m result, timezones \u001b[38;5;241m=\u001b[39m \u001b[43marray_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(tz \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m tz \u001b[38;5;129;01min\u001b[39;00m timezones):\n\u001b[1;32m 521\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _return_parsed_timezone_results(result, timezones, utc, name)\n", - "File \u001b[0;32mstrptime.pyx:534\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mstrptime.pyx:355\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: time data \"2023-10-23 09:31:50.168545+02:00\" doesn't match format \"%Y-%m-%d %H:%M:%S%z\", at position 1. You might want to try:\n - passing `format` if your strings have a consistent format;\n - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this." - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Exemple de DataFrame avec une colonne 'date_str' contenant des dates en formats différents\n", - "df = pd.DataFrame({\n", - " 'date_str': ['2022-05-06 12:00:23+02:00', '2023-10-23 09:31:50.168545+02:00']\n", - "})\n", - "\n", - "# Convertir la colonne 'date_str' en datetime en conservant l'information sur le fuseau horaire (datetime64[ns])\n", - "df['date'] = pd.to_datetime(df['date_str'], utc=True)\n", - "\n", - "# Afficher le DataFrame résultant\n", - "print(df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 350, - "id": "da01f2d8-3c1e-4d43-92ef-6236a24963d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " date_str date\n", - "0 2022-05-06 12:00:23+02:00 2022-05-06 10:00:23+00:00\n", - "1 023-10-23 09:31:50.168545+02:00 023-10-23 09:31:50.168545+02:00\n" - ] - } - ], - "source": [ - "\n", - "# Exemple de DataFrame avec une colonne 'date_str' contenant des dates en formats différents\n", - "df = pd.DataFrame({\n", - " 'date_str': ['2022-05-06 12:00:23+02:00', '023-10-23 09:31:50.168545+02:00']\n", - "})\n", - "\n", - "# Fonction lambda pour convertir la colonne 'date_str' en datetime avec précision\n", - "def convert_to_datetime_with_precision(x):\n", - " if pd.notna(x):\n", - " # Format avec nanosecondes\n", - " try:\n", - " return pd.to_datetime(x, utc=True)\n", - " except ValueError:\n", - " pass\n", - "\n", - " # Format sans nanosecondes\n", - " try:\n", - " return pd.to_datetime(x, utc=True, format=\"%Y-%m-%d %H:%M:%S%z\")\n", - " except ValueError:\n", - " pass\n", - "\n", - " return x\n", - "\n", - "# Appliquer la fonction lambda à la colonne 'date_str'\n", - "df['date'] = df['date_str'].apply(convert_to_datetime_with_precision)\n", - "\n", - "# Afficher le DataFrame résultant\n", - "print(df)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 351, - "id": "e6ca12c8-be66-4537-b759-036123b74b7b", - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[351], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m columns_to_convert \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelivered_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupdated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_sent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_created_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_updated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m column \u001b[38;5;129;01min\u001b[39;00m columns_to_convert :\n\u001b[0;32m----> 7\u001b[0m df1_campaigns_full[column] \u001b[38;5;241m=\u001b[39m \u001b[43mdf1_campaigns_full\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert_to_datetime_with_precision\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/series.py:4764\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m 4629\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m 4630\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4631\u001b[0m func: AggFuncType,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4636\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 4637\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m 4638\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4639\u001b[0m \u001b[38;5;124;03m Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m 4640\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[38;5;124;03m dtype: float64\u001b[39;00m\n\u001b[1;32m 4756\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 4757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4758\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4759\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4760\u001b[0m \u001b[43m \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4761\u001b[0m \u001b[43m \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4762\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4763\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 4764\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1209\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1209\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1289\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m 1285\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m 1286\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m 1287\u001b[0m \u001b[38;5;66;03m# Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m 1288\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1289\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1290\u001b[0m \u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[1;32m 1291\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;66;03m# See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:1814\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 1812\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 1813\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1814\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1815\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1816\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[1;32m 1817\u001b[0m values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[1;32m 1818\u001b[0m )\n", - "File \u001b[0;32mlib.pyx:2926\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n", - "Cell \u001b[0;32mIn[350], line 11\u001b[0m, in \u001b[0;36mconvert_to_datetime_with_precision\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mnotna(x):\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Format avec nanosecondes\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:1146\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1144\u001b[0m result \u001b[38;5;241m=\u001b[39m convert_listlike(argc, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1146\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43marg\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, \u001b[38;5;28mbool\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, np\u001b[38;5;241m.\u001b[39mbool_):\n\u001b[1;32m 1148\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mbool\u001b[39m(result) \u001b[38;5;66;03m# TODO: avoid this kludge.\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:488\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64ns(\n\u001b[1;32m 491\u001b[0m arg,\n\u001b[1;32m 492\u001b[0m dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 496\u001b[0m allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 497\u001b[0m )\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:521\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m 519\u001b[0m result, timezones \u001b[38;5;241m=\u001b[39m array_strptime(arg, fmt, exact\u001b[38;5;241m=\u001b[39mexact, errors\u001b[38;5;241m=\u001b[39merrors, utc\u001b[38;5;241m=\u001b[39mutc)\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(tz \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m tz \u001b[38;5;129;01min\u001b[39;00m timezones):\n\u001b[0;32m--> 521\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_return_parsed_timezone_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimezones\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _box_as_indexlike(result, utc\u001b[38;5;241m=\u001b[39mutc, name\u001b[38;5;241m=\u001b[39mname)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:344\u001b[0m, in \u001b[0;36m_return_parsed_timezone_results\u001b[0;34m(result, timezones, utc, name)\u001b[0m\n\u001b[1;32m 342\u001b[0m tz_results \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;28mlen\u001b[39m(result), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mobject\u001b[39m)\n\u001b[1;32m 343\u001b[0m non_na_timezones \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n\u001b[0;32m--> 344\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m zone \u001b[38;5;129;01min\u001b[39;00m \u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimezones\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 345\u001b[0m mask \u001b[38;5;241m=\u001b[39m timezones \u001b[38;5;241m==\u001b[39m zone\n\u001b[1;32m 346\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result[mask])\u001b[38;5;241m.\u001b[39mtz_localize(zone)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:401\u001b[0m, in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique\u001b[39m(values):\n\u001b[1;32m 308\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;124;03m Return unique values based on a hash table.\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;124;03m array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)\u001b[39;00m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munique_with_mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:440\u001b[0m, in \u001b[0;36munique_with_mask\u001b[0;34m(values, mask)\u001b[0m\n\u001b[1;32m 438\u001b[0m table \u001b[38;5;241m=\u001b[39m hashtable(\u001b[38;5;28mlen\u001b[39m(values))\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 440\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[43mtable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m uniques \u001b[38;5;241m=\u001b[39m _reconstruct_data(uniques, original\u001b[38;5;241m.\u001b[39mdtype, original)\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m uniques\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# loop over all dates to convert \n", - "\n", - "columns_to_convert = [\"sent_at\", \"delivered_at\", \"created_at\", \"updated_at\", \n", - " \"campaign_sent_at\", \"campaign_created_at\", \"campaign_updated_at\"]\n", - "\n", - "for column in columns_to_convert :\n", - " df1_campaigns_full[column] = df1_campaigns_full[column].apply(convert_to_datetime_with_precision)" - ] - }, - { - "cell_type": "code", - "execution_count": 356, - "id": "61e1f604-23ce-4cb2-8ad3-523c62e80e68", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
408100223728588268NaN2021-03-28 18:00:57+02:002021-03-28 18:43:38+02:002021-03-28 18:43:42.928685+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
40761394552581472NaN2021-03-28 18:00:57+02:002021-03-28 18:03:26+02:002021-03-28 18:03:28.229670+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4081572140705879782021-03-29 08:38:06+02:002021-03-28 18:00:57+02:002021-03-28 18:20:45+02:002021-03-28 18:20:49.431860+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
409483369695832211NaN2021-03-28 18:00:57+02:002021-03-28 18:09:18+02:002021-03-28 18:09:20.571462+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4094827966258309802021-04-04 17:54:51+02:002021-03-28 18:00:57+02:002021-03-28 18:03:29+02:002021-03-28 18:13:33.153720+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
.............................................
89611241758334002021-03-28 21:27:57+02:002021-03-28 18:17:35+02:002021-03-28 18:17:36+02:002021-03-28 18:17:36.735495+02:002021-03-28 19:27:57.503961+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
203801820558106495NaN2021-03-28 18:30:08+02:002021-03-28 18:30:11+02:002021-03-28 18:30:11.453742+02:002021-03-28 18:30:11.474019+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
272982210758104781NaN2021-03-28 18:39:55+02:002021-03-28 18:39:56+02:002021-03-28 18:39:56.430679+02:002021-03-28 18:39:56.435656+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
291072238958111570NaN2021-03-28 18:40:38+02:002021-03-28 18:40:40+02:002021-03-28 18:40:40.975334+02:002021-03-28 18:40:40.979852+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
2796229258581194962021-03-29 21:03:52+02:002021-03-28 20:52:26+02:002021-03-28 20:52:30+02:002021-03-28 20:52:30.261271+02:002021-03-29 19:03:52.527753+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
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26464 rows × 14 columns

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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
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"6214806 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", - "6214807 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", - "\n", - "[6214808 rows x 14 columns]" - ] - }, - "execution_count": 380, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_merge/TP_merge_target_campaigns_links.ipynb b/notebooks_merge/TP_merge_target_campaigns_links.ipynb deleted file mode 100644 index 7aa0f0e..0000000 --- a/notebooks_merge/TP_merge_target_campaigns_links.ipynb +++ /dev/null @@ -1,1768 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5005d8b3-6295-4b22-bd3c-876109be5b3b", - "metadata": {}, - "source": [ - "# Merges and discovery : target, campaigns, links" - ] - }, - { - "cell_type": "markdown", - "id": "8c56d518-3634-4492-b249-0d8ef33dd527", - "metadata": {}, - "source": [ - "## First steps : package importations, set up working environment and import data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "dede42d9-1262-45f7-bd7a-586ae800092a", - "metadata": {}, - "outputs": [], - "source": [ - "# importations\n", - "\n", - "import os \n", - "import s3fs\n", - "import pandas as pd\n", - "import re\n", - "from datetime import datetime, timezone, timedelta\n", - "import math\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6ce34b58-b5ba-4b54-ba4d-fc82ef01b09c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1',\n", - " 'bdc2324-data/10',\n", - " 'bdc2324-data/101',\n", - " 'bdc2324-data/11',\n", - " 'bdc2324-data/12',\n", - " 'bdc2324-data/13',\n", - " 'bdc2324-data/14',\n", - " 'bdc2324-data/2',\n", - " 'bdc2324-data/3',\n", - " 'bdc2324-data/4',\n", - " 'bdc2324-data/5',\n", - " 'bdc2324-data/6',\n", - " 'bdc2324-data/7',\n", - " 'bdc2324-data/8',\n", - " 'bdc2324-data/9']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# bucket for accessing the data\n", - "\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "\n", - "fs = s3fs.S3FileSystem(client_kwargs = {\"endpoint_url\" : S3_ENDPOINT_URL})\n", - "BUCKET = \"bdc2324-data\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8eb13dd3-53c7-4a70-94a4-846168473aa1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1/1campaign_stats.csv',\n", - " 'bdc2324-data/1/1campaigns.csv',\n", - " 'bdc2324-data/1/1categories.csv',\n", - " 'bdc2324-data/1/1countries.csv',\n", - " 'bdc2324-data/1/1currencies.csv',\n", - " 'bdc2324-data/1/1customer_target_mappings.csv',\n", - " 'bdc2324-data/1/1customersplus.csv',\n", - " 'bdc2324-data/1/1event_types.csv',\n", - " 'bdc2324-data/1/1events.csv',\n", - " 'bdc2324-data/1/1facilities.csv',\n", - " 'bdc2324-data/1/1link_stats.csv',\n", - " 'bdc2324-data/1/1pricing_formulas.csv',\n", - " 'bdc2324-data/1/1product_packs.csv',\n", - " 'bdc2324-data/1/1products.csv',\n", - " 'bdc2324-data/1/1products_groups.csv',\n", - " 'bdc2324-data/1/1purchases.csv',\n", - " 'bdc2324-data/1/1representation_category_capacities.csv',\n", - " 'bdc2324-data/1/1representations.csv',\n", - " 'bdc2324-data/1/1seasons.csv',\n", - " 'bdc2324-data/1/1structure_tag_mappings.csv',\n", - " 'bdc2324-data/1/1suppliers.csv',\n", - " 'bdc2324-data/1/1tags.csv',\n", - " 'bdc2324-data/1/1target_types.csv',\n", - " 'bdc2324-data/1/1targets.csv',\n", - " 'bdc2324-data/1/1tickets.csv',\n", - " 'bdc2324-data/1/1type_of_categories.csv',\n", - " 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n", - " 'bdc2324-data/1/1type_ofs.csv']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FILE_PATH_S3 = fs.ls(BUCKET)[0] # focus on the company number 1\n", - "files_path = fs.ls(FILE_PATH_S3)\n", - "files_path" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1ea66c4e-1307-4f19-836e-3104fba2ff41", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_487/2894332003.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in)\n" - ] - } - ], - "source": [ - "# loop to create dataframes related to company 1\n", - "\n", - "client_number = files_path[0].split(\"/\")[1]\n", - "print(client_number)\n", - "df_prefix = \"df\" + str(client_number) + \"_\"\n", - "\n", - "for i in range(len(files_path)) :\n", - " current_path = files_path[i]\n", - " with fs.open(current_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in)\n", - " # the pattern of the name is df1xxx\n", - " nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n", - " globals()[nom_dataframe] = df" - ] - }, - { - "cell_type": "markdown", - "id": "13d70b2c-6580-4caf-b839-10f72b2e0b39", - "metadata": {}, - "source": [ - "## Target, target types and customer target mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4dbc7fea-ac3b-4348-83fb-dfb1a460f936", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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496456DDCP achat billet nbr dep 19052021Falsemanual_static_filter
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" - ], - "text/plain": [ - " id target_type_id name \\\n", - "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", - "1 701 56 consentement optin scolaires \n", - "2 134 56 DDCP Newsletter jeune public \n", - "3 700 56 consentement optout scolaires \n", - "4 964 56 DDCP achat billet nbr dep 19052021 \n", - "\n", - " target_type_is_import target_type_name \n", - "0 False manual_static_filter \n", - "1 False manual_static_filter \n", - "2 False manual_static_filter \n", - "3 False manual_static_filter \n", - "4 False manual_static_filter " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 4.1. merge target with target type\n", - "\n", - "df1_targets_full = pd.merge(df1_targets[[\"id\", \"target_type_id\", \"name\"]], df1_target_types[[\"id\",\"is_import\",\"name\"]].add_prefix(\"target_type_\"), left_on='target_type_id', right_on='target_type_id', how='left')\n", - "df1_targets_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d48c1fff-73c2-4e75-8799-da2b80694be7", - "metadata": {}, - "outputs": [], - "source": [ - "# 4.2. merge df1_customer_target_mappings with df1_targets_full\n", - "\n", - "# change the position of the column target type id\n", - "\n", - "# Spécifiez le nom de la colonne à déplacer et la colonne après laquelle vous souhaitez la placer\n", - "column_to_move = 'target_type_id'\n", - "\n", - "# Récupérez l'index de la colonne de référence\n", - "reference_index = df1_targets_full.columns.get_loc(\"target_type_name\")\n", - "\n", - "# Créez une copie de la colonne que vous voulez déplacer\n", - "column_copy = df1_targets_full[column_to_move].copy()\n", - "\n", - "# Supprimez la colonne d'origine\n", - "df1_targets_full = df1_targets_full.drop(column_to_move, axis=1)\n", - "\n", - "# Utilisez la méthode insert pour déplacer la colonne à la nouvelle position\n", - "df1_targets_full.insert(reference_index - 1, column_to_move, column_copy)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a874514a-c7dc-42d4-a440-dedd3a270e24", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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target_idtarget_nametarget_type_is_importtarget_type_idtarget_type_name
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1701consentement optin scolairesFalse56manual_static_filter
2134DDCP Newsletter jeune publicFalse56manual_static_filter
3700consentement optout scolairesFalse56manual_static_filter
4964DDCP achat billet nbr dep 19052021False56manual_static_filter
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" - ], - "text/plain": [ - " target_id target_name \\\n", - "0 217 DDCP PROMO Art contemporain - salle de chauffe... \n", - "1 701 consentement optin scolaires \n", - "2 134 DDCP Newsletter jeune public \n", - "3 700 consentement optout scolaires \n", - "4 964 DDCP achat billet nbr dep 19052021 \n", - "\n", - " target_type_is_import target_type_id target_type_name \n", - "0 False 56 manual_static_filter \n", - "1 False 56 manual_static_filter \n", - "2 False 56 manual_static_filter \n", - "3 False 56 manual_static_filter \n", - "4 False 56 manual_static_filter " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_targets_full = df1_targets_full.rename(columns=lambda x: 'target_' + x if not x.startswith('target_') else x)\n", - "df1_targets_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0db0172a-5119-4b7f-97f8-36fc5c985205", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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11184825645400345Inscrits NL générale site webFalse56manual_static_filter
21184828645402126DDCP PROMO Art contemporainFalse56manual_static_filter
31184829645403126DDCP PROMO Art contemporainFalse56manual_static_filter
41295770647301346Votre première listeFalse56manual_static_filter
........................
7680192737545666983345Inscrits NL générale site webFalse56manual_static_filter
7680202737546666983346Votre première listeFalse56manual_static_filter
7680212737575666986346Votre première listeFalse56manual_static_filter
7680222737576666987345Inscrits NL générale site webFalse56manual_static_filter
7680232737577666987346Votre première listeFalse56manual_static_filter
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768024 rows × 7 columns

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" - ], - "text/plain": [ - " id customer_id target_id target_name \\\n", - "0 1184824 645400 130 DDCP PROMO Réseau livres \n", - "1 1184825 645400 345 Inscrits NL générale site web \n", - "2 1184828 645402 126 DDCP PROMO Art contemporain \n", - "3 1184829 645403 126 DDCP PROMO Art contemporain \n", - "4 1295770 647301 346 Votre première liste \n", - "... ... ... ... ... \n", - "768019 2737545 666983 345 Inscrits NL générale site web \n", - "768020 2737546 666983 346 Votre première liste \n", - "768021 2737575 666986 346 Votre première liste \n", - "768022 2737576 666987 345 Inscrits NL générale site web \n", - "768023 2737577 666987 346 Votre première liste \n", - "\n", - " target_type_is_import target_type_id target_type_name \n", - "0 False 56 manual_static_filter \n", - "1 False 56 manual_static_filter \n", - "2 False 56 manual_static_filter \n", - "3 False 56 manual_static_filter \n", - "4 False 56 manual_static_filter \n", - "... ... ... ... \n", - "768019 False 56 manual_static_filter \n", - "768020 False 56 manual_static_filter \n", - "768021 False 56 manual_static_filter \n", - "768022 False 56 manual_static_filter \n", - "768023 False 56 manual_static_filter \n", - "\n", - "[768024 rows x 7 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# finally, merge\n", - "\n", - "# pour df1_customer_target_mappings on enlève les colonnes name, extra_field, et updated_at (valeur égale à created_at)\n", - "# note : by making a left join on df1_customer_target_mappings, we suppress 2 targets that have no customer associated\n", - "\n", - "df1_customer_targets = pd.merge(df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\"]], \n", - " df1_targets_full, left_on='target_id', right_on='target_id', how='left')\n", - "df1_customer_targets" - ] - }, - { - "cell_type": "markdown", - "id": "52326267-c5ba-4e21-b8ab-4b4c62de75d1", - "metadata": {}, - "source": [ - "## Campaign stats, campaigns" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "06dca910-5c07-4ee1-bbf2-3b11b48ba1f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
01319613newsletter enseignants janvier 20227212022-01-14 16:06:42.586321+01:002022-02-03 14:17:27.112963+01:00NaNNaN0.0Falseaba3b6fd5d186d28e06ff97135cade7f2022-01-14 00:00:00+01:00
11319586lsf_janvier_20227172022-01-07 11:30:35.315895+01:002022-02-03 14:17:27.116171+01:00NaNNaN0.0False788d986905533aba051261497ecffcbb2022-01-07 00:00:00+01:00
21319282Invitation à déjeuner au Mucem | Vernissage « ...5912021-09-28 12:50:24.448752+02:002022-02-03 14:17:27.119582+01:00NaNNaN0.0False3493894fa4ea036cfc6433c3e2ee63b02021-09-28 00:00:00+02:00
31319283Vacances de la Toussaint - centres des loisirs5902021-09-28 18:01:04.692073+02:002022-02-03 14:17:27.124408+01:00NaNNaN0.0False08b255a5d42b89b0585260b6f2360bdd2021-09-28 00:00:00+02:00
41319636ddcp_promo_md_livemag7302022-01-27 18:00:41.053069+01:002022-02-03 14:17:27.127607+01:00NaNNaN0.0Falsed5cfead94f5350c12c322b5b664544c12022-01-27 00:00:00+01:00
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" - ], - "text/plain": [ - " id name service_id \\\n", - "0 1319613 newsletter enseignants janvier 2022 721 \n", - "1 1319586 lsf_janvier_2022 717 \n", - "2 1319282 Invitation à déjeuner au Mucem | Vernissage « ... 591 \n", - "3 1319283 Vacances de la Toussaint - centres des loisirs 590 \n", - "4 1319636 ddcp_promo_md_livemag 730 \n", - "\n", - " created_at updated_at \\\n", - "0 2022-01-14 16:06:42.586321+01:00 2022-02-03 14:17:27.112963+01:00 \n", - "1 2022-01-07 11:30:35.315895+01:00 2022-02-03 14:17:27.116171+01:00 \n", - "2 2021-09-28 12:50:24.448752+02:00 2022-02-03 14:17:27.119582+01:00 \n", - "3 2021-09-28 18:01:04.692073+02:00 2022-02-03 14:17:27.124408+01:00 \n", - "4 2022-01-27 18:00:41.053069+01:00 2022-02-03 14:17:27.127607+01:00 \n", - "\n", - " process_id report_url category to_be_synced \\\n", - "0 NaN NaN 0.0 False \n", - "1 NaN NaN 0.0 False \n", - "2 NaN NaN 0.0 False \n", - "3 NaN NaN 0.0 False \n", - "4 NaN NaN 0.0 False \n", - "\n", - " identifier sent_at \n", - "0 aba3b6fd5d186d28e06ff97135cade7f 2022-01-14 00:00:00+01:00 \n", - "1 788d986905533aba051261497ecffcbb 2022-01-07 00:00:00+01:00 \n", - "2 3493894fa4ea036cfc6433c3e2ee63b0 2021-09-28 00:00:00+02:00 \n", - "3 08b255a5d42b89b0585260b6f2360bdd 2021-09-28 00:00:00+02:00 \n", - "4 d5cfead94f5350c12c322b5b664544c1 2022-01-27 00:00:00+01:00 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1. campaigns\n", - "df1_campaigns.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "83eaa447-9144-41ed-9e26-f0f23799a8fd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_at
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00
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" - ], - "text/plain": [ - " id campaign_id customer_id opened_at \\\n", - "0 19793 58 112597 NaN \n", - "1 14211 58 113666 NaN \n", - "2 13150 58 280561 NaN \n", - "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", - "4 5175 58 103972 NaN \n", - "\n", - " sent_at delivered_at \\\n", - "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", - "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", - "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", - "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", - "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", - "\n", - " created_at updated_at \n", - "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", - "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 2. campaigns stats\n", - "df1_campaign_stats.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "7f25eb1b-e7c8-4715-bc30-7ac29a7181ac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 00:00:00+01:00
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 00:00:00+01:00
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 00:00:00+01:00
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 00:00:00+01:00
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 00:00:00+01:00
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" - ], - "text/plain": [ - " id campaign_id customer_id opened_at \\\n", - "0 19793 58 112597 NaN \n", - "1 14211 58 113666 NaN \n", - "2 13150 58 280561 NaN \n", - "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", - "4 5175 58 103972 NaN \n", - "\n", - " sent_at delivered_at \\\n", - "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", - "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", - "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", - "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", - "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", - "\n", - " campaign_name campaign_service_id \\\n", - "0 Le Mucem chez vous, gardons le lien #22 404 \n", - "1 Le Mucem chez vous, gardons le lien #22 404 \n", - "2 Le Mucem chez vous, gardons le lien #22 404 \n", - "3 Le Mucem chez vous, gardons le lien #22 404 \n", - "4 Le Mucem chez vous, gardons le lien #22 404 \n", - "\n", - " campaign_sent_at \n", - "0 2021-03-28 00:00:00+01:00 \n", - "1 2021-03-28 00:00:00+01:00 \n", - "2 2021-03-28 00:00:00+01:00 \n", - "3 2021-03-28 00:00:00+01:00 \n", - "4 2021-03-28 00:00:00+01:00 " - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3. merge campaigns and campaigns stats\n", - "\n", - "df1_campaigns_full = pd.merge(df1_campaign_stats[[\"id\", \"campaign_id\", \"customer_id\", \"opened_at\", \"sent_at\", \"delivered_at\"]], \n", - " df1_campaigns[[\"id\", \"name\", \"service_id\", \"sent_at\"]].add_prefix(\"campaign_\"),\n", - " on = \"campaign_id\", how = \"left\")\n", - "df1_campaigns_full.head()" - ] - }, - { - "cell_type": "markdown", - "id": "87fc686a-4a80-40ab-9987-20d2774f3055", - "metadata": {}, - "source": [ - "## Link stats" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "2f9df2d0-8a23-496b-8e92-617285f64530", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
403064340363764284033NaN2021-03-21 18:01:22+01:002021-03-21 18:08:04+01:00Le Mucem chez vous, gardons le lien #213982021-03-21 00:00:00+01:00
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" - ], - "text/plain": [ - " id campaign_id customer_id opened_at \\\n", - "4030643 4036376 4 284033 NaN \n", - "\n", - " sent_at delivered_at \\\n", - "4030643 2021-03-21 18:01:22+01:00 2021-03-21 18:08:04+01:00 \n", - "\n", - " campaign_name campaign_service_id \\\n", - "4030643 Le Mucem chez vous, gardons le lien #21 398 \n", - "\n", - " campaign_sent_at \n", - "4030643 2021-03-21 00:00:00+01:00 " - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_full[ (df1_campaigns_full[\"customer_id\"] == 284033) & (df1_campaigns_full[\"campaign_id\"] == 4)]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/0_Cleaning_and_merge.ipynb b/useless/0_Cleaning_and_merge.ipynb deleted file mode 100644 index 169cd23..0000000 --- a/useless/0_Cleaning_and_merge.ipynb +++ /dev/null @@ -1,2850 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ad414c84-be46-4d2c-be8b-9fc4d24cc672", - "metadata": {}, - "source": [ - "# Business Data Challenge - Team 1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "15103481-8d74-404c-aa09-7601fe7730da", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "ee97665c-39af-4c1c-a62b-c9c79feae18f", - "metadata": {}, - "source": [ - "Configuration de l'accès aux données" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d83bb1a-d341-446e-91f6-1c428607f6d4", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a9b84234-d5df-4c43-a9cd-80cfe2f1e34d", - "metadata": {}, - "outputs": [], - "source": [ - "# Ignore warning\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "9cbd72c5-6f8e-4366-ab66-96c32c6e963a", - "metadata": {}, - "source": [ - "# Exemple sur Company 1" - ] - }, - { - "cell_type": "markdown", - "id": "db26e59a-927c-407e-b54b-1815473b0b34", - "metadata": {}, - "source": [ - "## Chargement données" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "699664b9-eee4-4f8d-a207-e524526560c5", - "metadata": {}, - "outputs": [], - "source": [ - "BUCKET = \"bdc2324-data/1\"\n", - "liste_database = fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed", - "metadata": {}, - "outputs": [], - "source": [ - "# loop to create dataframes from liste\n", - "\n", - "files_path = liste_database\n", - "\n", - "client_number = files_path[0].split(\"/\")[1]\n", - "df_prefix = \"df\" + str(client_number) + \"_\"\n", - "\n", - "for i in range(len(files_path)) :\n", - " current_path = files_path[i]\n", - " with fs.open(current_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in)\n", - " # the pattern of the name is df1xxx\n", - " nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n", - " globals()[nom_dataframe] = df" - ] - }, - { - "cell_type": "markdown", - "id": "4004c8bf-11d9-413d-bb42-2cb8ddde7716", - "metadata": {}, - "source": [ - "## Cleaning functions" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d237be96-8c86-4a91-b7a1-487e87a16c3d", - "metadata": {}, - "outputs": [], - "source": [ - "def cleaning_date(df, column_name):\n", - " \"\"\"\n", - " Nettoie la colonne spécifiée du DataFrame en convertissant les valeurs en datetime avec le format ISO8601.\n", - "\n", - " Parameters:\n", - " - df: DataFrame\n", - " Le DataFrame contenant la colonne à nettoyer.\n", - " - column_name: str\n", - " Le nom de la colonne à nettoyer.\n", - "\n", - " Returns:\n", - " - DataFrame\n", - " Le DataFrame modifié avec la colonne nettoyée.\n", - " \"\"\"\n", - " df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n", - " return df" - ] - }, - { - "cell_type": "markdown", - "id": "398804d8-2225-4fd3-bceb-75ab1588e359", - "metadata": {}, - "source": [ - "## Preprocessing" - ] - }, - { - "cell_type": "markdown", - "id": "568cb180-0dd9-4b27-aecb-05e4c3775ba6", - "metadata": {}, - "source": [ - "## customer_plus" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7e7b90ce-da54-4f00-bc34-64c543b0858f", - "metadata": {}, - "outputs": [], - "source": [ - "def preprocessing_customerplus(customerplus = None):\n", - "\n", - " customerplus_copy = customerplus.copy()\n", - " \n", - " # Passage en format date\n", - " cleaning_date(customerplus_copy, 'first_buying_date')\n", - " cleaning_date(customerplus_copy, 'last_visiting_date')\n", - " \n", - " # Selection des variables\n", - " customerplus_copy.drop(['lastname', 'firstname', 'email', 'civility', 'note', 'created_at', 'updated_at', 'deleted_at', 'extra', 'reference', 'extra_field', 'identifier', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode', 'last_visiting_date'], axis = 1, inplace=True)\n", - " customerplus_copy.rename(columns = {'id' : 'customer_id'}, inplace = True)\n", - "\n", - " return customerplus_copy\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "03329e32-00a5-42c8-9470-75f7b6216ccd", - "metadata": {}, - "outputs": [], - "source": [ - "df1_customerplus_clean = preprocessing_customerplus(df1_customersplus)" - ] - }, - { - "cell_type": "markdown", - "id": "bade04b1-0cdf-4d10-bcca-7dc7e4831656", - "metadata": {}, - "source": [ - "## Ticket area" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b95464b1-26bc-4aac-84b4-45da83b92251", - "metadata": {}, - "outputs": [], - "source": [ - "# Fonction de nettoyage et selection\n", - "def preprocessing_tickets_area(tickets = None, purchases = None, suppliers = None, type_ofs = None):\n", - " # Base des tickets\n", - " tickets = tickets[['id', 'purchase_id', 'product_id', 'is_from_subscription', 'type_of', 'supplier_id']]\n", - " tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n", - "\n", - " # Base des fournisseurs\n", - " suppliers = suppliers[['id', 'name']]\n", - " suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n", - " suppliers['supplier_name'] = suppliers['supplier_name'].fillna('')\n", - "\n", - " # Base des types de billets\n", - " type_ofs = type_ofs[['id', 'name', 'children']]\n", - " type_ofs.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)\n", - "\n", - " # Base des achats\n", - " # Nettoyage de la date d'achat\n", - " cleaning_date(purchases, 'purchase_date')\n", - " # Selection des variables\n", - " purchases = purchases[['id', 'purchase_date', 'customer_id']]\n", - "\n", - " # Fusions \n", - " # Fusion avec fournisseurs\n", - " ticket_information = pd.merge(tickets, suppliers, left_on = 'supplier_id', right_on = 'id', how = 'inner')\n", - " ticket_information.drop(['supplier_id', 'id'], axis = 1, inplace=True)\n", - " \n", - " # Fusion avec type de tickets\n", - " ticket_information = pd.merge(ticket_information, type_ofs, left_on = 'type_of', right_on = 'id', how = 'inner')\n", - " ticket_information.drop(['type_of', 'id'], axis = 1, inplace=True)\n", - " \n", - " # Fusion avec achats\n", - " ticket_information = pd.merge(ticket_information, purchases, left_on = 'purchase_id', right_on = 'id', how = 'inner')\n", - " ticket_information.drop(['id'], axis = 1, inplace=True)\n", - "\n", - " return ticket_information" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3e1d2ba7-ff4f-48eb-93a8-2bb648c70396", - "metadata": {}, - "outputs": [], - "source": [ - "df1_ticket_information = preprocessing_tickets_area(tickets = df1_tickets, purchases = df1_purchases, suppliers = df1_suppliers, type_ofs = df1_type_ofs)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4b18edfc-6450-4c6a-9e7b-ee5a5808c8c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0130708595107462225251Falsevente en ligneAtelierpricing_formula2018-12-28 14:47:50+00:0048187
1130708605107462224914Falsevente en ligneAtelierpricing_formula2018-12-28 14:47:50+00:0048187
2130708615107462224914Falsevente en ligneAtelierpricing_formula2018-12-28 14:47:50+00:0048187
3130708625107462224914Falsevente en ligneAtelierpricing_formula2018-12-28 14:47:50+00:0048187
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019793112597NaT2021-03-28 16:01:09+00:002021-03-28 16:24:18+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
114211113666NaT2021-03-28 16:01:09+00:002021-03-28 16:21:02+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
213150280561NaT2021-03-28 16:00:59+00:002021-03-28 16:08:45+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
370731010072021-03-28 18:11:06+00:002021-03-28 16:00:59+00:002021-03-28 16:09:47+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
45175103972NaT2021-03-28 16:01:06+00:002021-03-28 16:05:03+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
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" - ], - "text/plain": [ - " id customer_id opened_at sent_at \\\n", - "0 19793 112597 NaT 2021-03-28 16:01:09+00:00 \n", - "1 14211 113666 NaT 2021-03-28 16:01:09+00:00 \n", - "2 13150 280561 NaT 2021-03-28 16:00:59+00:00 \n", - "3 7073 101007 2021-03-28 18:11:06+00:00 2021-03-28 16:00:59+00:00 \n", - "4 5175 103972 NaT 2021-03-28 16:01:06+00:00 \n", - "\n", - " delivered_at campaign_name \\\n", - "0 2021-03-28 16:24:18+00:00 Le Mucem chez vous, gardons le lien #22 \n", - "1 2021-03-28 16:21:02+00:00 Le Mucem chez vous, gardons le lien #22 \n", - "2 2021-03-28 16:08:45+00:00 Le Mucem chez vous, gardons le lien #22 \n", - "3 2021-03-28 16:09:47+00:00 Le Mucem chez vous, gardons le lien #22 \n", - "4 2021-03-28 16:05:03+00:00 Le Mucem chez vous, gardons le lien #22 \n", - "\n", - " campaign_service_id campaign_sent_at \n", - "0 404 2021-03-27 23:00:00+00:00 \n", - "1 404 2021-03-27 23:00:00+00:00 \n", - "2 404 2021-03-27 23:00:00+00:00 \n", - "3 404 2021-03-27 23:00:00+00:00 \n", - "4 404 2021-03-27 23:00:00+00:00 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_information.head()" - ] - }, - { - "cell_type": "markdown", - "id": "56520a97-ede8-4920-a211-3b5b136af33d", - "metadata": {}, - "source": [ - "## Product area" - ] - }, - { - "cell_type": "markdown", - "id": "9782e9d3-ba20-46bf-8562-bd0969972ddc", - "metadata": {}, - "source": [ - "Some useful functions" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "30488a40-1b38-4b9a-9d3b-26a0597c5e6d", - "metadata": {}, - "outputs": [], - "source": [ - "BUCKET = \"bdc2324-data\"\n", - "directory_path = '1'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "607eb4b4-eed9-4b50-b823-f75c116dd37c", - "metadata": {}, - "outputs": [], - "source": [ - "def display_databases(file_name):\n", - " \"\"\"\n", - " This function returns the file from s3 storage\n", - " \"\"\"\n", - " file_path = BUCKET + \"/\" + directory_path + \"/\" + file_name\n", - " print(\"File path : \", file_path)\n", - " with fs.open(file_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\")\n", - " \n", - " print(\"Shape : \", df.shape)\n", - " return df\n", - "\n", - "\n", - "def remove_horodates(df):\n", - " \"\"\"\n", - " this function remove horodate columns like created_at and updated_at\n", - " \"\"\"\n", - " df = df.drop(columns = [\"created_at\", \"updated_at\"])\n", - " return df\n", - "\n", - "\n", - "def order_columns_id(df):\n", - " \"\"\"\n", - " this function puts all id columns at the beginning in order to read the dataset easier\n", - " \"\"\"\n", - " substring = 'id'\n", - " id_columns = [col for col in df.columns if substring in col]\n", - " remaining_col = [col for col in df.columns if substring not in col]\n", - " new_order = id_columns + remaining_col\n", - " return df[new_order]\n", - "\n", - "\n", - "def process_df_2(df):\n", - " \"\"\"\n", - " This function organizes dataframe\n", - " \"\"\"\n", - " df = remove_horodates(df)\n", - " print(\"Number of columns : \", len(df.columns))\n", - " df = order_columns_id(df)\n", - " print(\"Columns : \", df.columns)\n", - " return df\n", - "\n", - "def load_dataset(name):\n", - " \"\"\"\n", - " This function loads csv file\n", - " \"\"\"\n", - " df = display_databases(name)\n", - " df = process_df_2(df)\n", - " # drop na :\n", - " #df = df.dropna(axis=1, thresh=len(df))\n", - " # if identifier in table : delete it\n", - " if 'identifier' in df.columns:\n", - " df = df.drop(columns = 'identifier')\n", - " return df" - ] - }, - { - "cell_type": "markdown", - "id": "d23f28c0-bc95-438b-8d14-5b7bb6e267bd", - "metadata": {}, - "source": [ - "Create theme tables" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "350b09b9-451f-4d47-81fe-f34b892db027", - "metadata": {}, - "outputs": [], - "source": [ - "def create_products_table():\n", - " # first merge products and categories\n", - " print(\"first merge products and categories\")\n", - " products = load_dataset(\"1products.csv\")\n", - " categories = load_dataset(\"1categories.csv\")\n", - " # Drop useless columns\n", - " products = products.drop(columns = ['apply_price', 'extra_field', 'amount_consumption'])\n", - " categories = categories.drop(columns = ['extra_field', 'quota'])\n", - "\n", - " #Merge\n", - " products_theme = products.merge(categories, how = 'left', left_on = 'category_id',\n", - " right_on = 'id', suffixes=('_products', '_categories'))\n", - " products_theme = products_theme.rename(columns = {\"name\" : \"name_categories\"})\n", - " \n", - " # Second merge products_theme and type of categories\n", - " print(\"Second merge products_theme and type of categories\")\n", - " type_of_categories = load_dataset(\"1type_of_categories.csv\")\n", - " type_of_categories = type_of_categories.drop(columns = 'id')\n", - " products_theme = products_theme.merge(type_of_categories, how = 'left', left_on = 'category_id',\n", - " right_on = 'category_id' )\n", - "\n", - " # Index cleaning\n", - " products_theme = products_theme.drop(columns = ['id_categories'])\n", - " products_theme = order_columns_id(products_theme)\n", - " return products_theme\n", - "\n", - "\n", - "def create_events_table():\n", - " # first merge events and seasons : \n", - " print(\"first merge events and seasons : \")\n", - " events = load_dataset(\"1events.csv\")\n", - " seasons = load_dataset(\"1seasons.csv\")\n", - "\n", - " # Drop useless columns\n", - " events = events.drop(columns = ['manual_added', 'is_display'])\n", - " seasons = seasons.drop(columns = ['start_date_time'])\n", - " \n", - " events_theme = events.merge(seasons, how = 'left', left_on = 'season_id', right_on = 'id', suffixes=('_events', '_seasons'))\n", - "\n", - " # Secondly merge events_theme and event_types\n", - " print(\"Secondly merge events_theme and event_types : \")\n", - " event_types = load_dataset(\"1event_types.csv\")\n", - " event_types = event_types.drop(columns = ['fidelity_delay'])\n", - " \n", - " events_theme = events_theme.merge(event_types, how = 'left', left_on = 'event_type_id', right_on = 'id', suffixes=('_events', '_event_type'))\n", - " events_theme = events_theme.rename(columns = {\"name\" : \"name_event_types\"})\n", - " events_theme = events_theme.drop(columns = 'id')\n", - "\n", - " # thirdly merge events_theme and facilities\n", - " print(\"thirdly merge events_theme and facilities : \")\n", - " facilities = load_dataset(\"1facilities.csv\")\n", - " facilities = facilities.drop(columns = ['fixed_capacity'])\n", - " \n", - " events_theme = events_theme.merge(facilities, how = 'left', left_on = 'facility_id', right_on = 'id', suffixes=('_events', '_facility'))\n", - " events_theme = events_theme.rename(columns = {\"name\" : \"name_facilities\", \"id_events\" : \"event_id\"})\n", - " events_theme = events_theme.drop(columns = 'id')\n", - "\n", - " # Index cleaning\n", - " events_theme = events_theme.drop(columns = ['id_seasons'])\n", - " events_theme = order_columns_id(events_theme)\n", - " return events_theme\n", - "\n", - "\n", - "def create_representations_table():\n", - " representations = load_dataset(\"1representations.csv\")\n", - " representations = representations.drop(columns = ['serial', 'open', 'satisfaction', 'is_display', 'expected_filling',\n", - " 'max_filling', 'extra_field', 'start_date_time', 'end_date_time', 'name',\n", - " 'representation_type_id'])\n", - " \n", - " representations_capacity = load_dataset(\"1representation_category_capacities.csv\")\n", - " representations_capacity = representations_capacity.drop(columns = ['expected_filling', 'max_filling'])\n", - "\n", - " representations_theme = representations.merge(representations_capacity, how='left',\n", - " left_on='id', right_on='representation_id',\n", - " suffixes=('_representation', '_representation_cap'))\n", - " # index cleaning\n", - " representations_theme = representations_theme.drop(columns = [\"id_representation\"])\n", - " representations_theme = order_columns_id(representations_theme)\n", - " return representations_theme" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0fccc8ef-e575-4857-a401-94a7274394df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "first merge products and categories\n", - "File path : bdc2324-data/1/1products.csv\n", - "Shape : (94803, 14)\n", - "Number of columns : 12\n", - "Columns : Index(['id', 'representation_id', 'pricing_formula_id', 'category_id',\n", - " 'products_group_id', 'product_pack_id', 'identifier', 'amount',\n", - " 'is_full_price', 'apply_price', 'extra_field', 'amount_consumption'],\n", - " dtype='object')\n", - "File path : bdc2324-data/1/1categories.csv\n", - "Shape : (27, 7)\n", - "Number of columns : 5\n", - "Columns : Index(['id', 'identifier', 'name', 'extra_field', 'quota'], dtype='object')\n", - "Second merge products_theme and type of categories\n", - "File path : bdc2324-data/1/1type_of_categories.csv\n", - "Shape : (5, 6)\n", - "Number of columns : 4\n", - "Columns : Index(['id', 'type_of_id', 'category_id', 'identifier'], dtype='object')\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " event_id id_representation_cap representation_id category_id\n", - "0 12384 123058 84820 2\n", - "1 37 2514 269 2\n", - "2 37 384 269 5\n", - "3 37 2515 269 10\n", - "4 37 383 269 1" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "representation_theme = create_representations_table()\n", - "representation_theme.head()" - ] - }, - { - "cell_type": "markdown", - "id": "8fa191d5-c867-4d4d-bbab-f29d7d91ce6a", - "metadata": {}, - "source": [ - "Create uniform product database " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "15a62ed6-35e4-4abc-aeef-a7daeec0a4ba", - "metadata": {}, - "outputs": [], - "source": [ - "def uniform_product_df():\n", - " \"\"\"\n", - " This function returns the uniform product dataset\n", - " \"\"\"\n", - " print(\"Products theme columns : \", products_theme.columns)\n", - " print(\"\\n Representation theme columns : \", representation_theme.columns)\n", - " print(\"\\n Events theme columns : \", events_theme.columns)\n", - "\n", - " products_global = products_theme.merge(representation_theme, how='left',\n", - " on= [\"representation_id\", \"category_id\"])\n", - " \n", - " products_global = products_global.merge(events_theme, how='left', on='event_id',\n", - " suffixes = (\"_representation\", \"_event\"))\n", - " \n", - " products_global = order_columns_id(products_global)\n", - "\n", - " # remove useless columns \n", - " products_global = products_global.drop(columns = ['type_of_id']) # 'name_events', 'name_seasons', 'name_categories'\n", - " return products_global" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "89dc9685-1de9-4ce3-a6c0-8d7f1931a951", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Products theme columns : Index(['id_products', 'representation_id', 'pricing_formula_id', 'category_id',\n", - " 'products_group_id', 'product_pack_id', 'type_of_id', 'amount',\n", - " 'is_full_price', 'name_categories'],\n", - " dtype='object')\n", - "\n", - " Representation theme columns : Index(['event_id', 'id_representation_cap', 'representation_id',\n", - " 'category_id'],\n", - " dtype='object')\n", - "\n", - " Events theme columns : Index(['event_id', 'season_id', 'facility_id', 'event_type_id',\n", - " 'event_type_key_id', 'facility_key_id', 'street_id', 'name_events',\n", - " 'name_seasons', 'name_event_types', 'name_facilities'],\n", - " dtype='object')\n" - ] - }, - { - "data": { - "text/html": [ - "
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id_productsrepresentation_idpricing_formula_idcategory_idproducts_group_idproduct_pack_idevent_idid_representation_capseason_idfacility_id...event_type_key_idfacility_key_idstreet_idamountis_full_pricename_categoriesname_eventsname_seasonsname_event_typesname_facilities
01068291411441106551132878941...5119.0Falseindiv activité trvisite-jeu \"le classico des minots\" (1h30)2017offre muséale individuelmucem
1478273131147113739021...2119.5Falseindiv entrées tpbillet mucem picasso2016offre muséale individuelmucem
22087327513712082513739521...21111.5Falseindiv entrées tpbillet mucem picasso2016offre muséale individuelmucem
3157142825199515677311236512019917541...4118.0Falseindiv entrées trNaNNaNoffre muséale individuelmucem
4134199311175182141...6118.5Falseindiv entrées tpnon défini2017non définimucem
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" - ], - "text/plain": [ - " id_products representation_id pricing_formula_id category_id \\\n", - "0 10682 914 114 41 \n", - "1 478 273 131 1 \n", - "2 20873 275 137 1 \n", - "3 157142 82519 9 5 \n", - "4 1341 9 93 1 \n", - "\n", - " products_group_id product_pack_id event_id id_representation_cap \\\n", - "0 10655 1 132 8789 \n", - "1 471 1 37 390 \n", - "2 20825 1 37 395 \n", - "3 156773 1 12365 120199 \n", - "4 1175 1 8 21 \n", - "\n", - " season_id facility_id ... event_type_key_id facility_key_id street_id \\\n", - "0 4 1 ... 5 1 1 \n", - "1 2 1 ... 2 1 1 \n", - "2 2 1 ... 2 1 1 \n", - "3 1754 1 ... 4 1 1 \n", - "4 4 1 ... 6 1 1 \n", - "\n", - " amount is_full_price name_categories \\\n", - "0 9.0 False indiv activité tr \n", - "1 9.5 False indiv entrées tp \n", - "2 11.5 False indiv entrées tp \n", - "3 8.0 False indiv entrées tr \n", - "4 8.5 False indiv entrées tp \n", - "\n", - " name_events name_seasons \\\n", - "0 visite-jeu \"le classico des minots\" (1h30) 2017 \n", - "1 billet mucem picasso 2016 \n", - "2 billet mucem picasso 2016 \n", - "3 NaN NaN \n", - "4 non défini 2017 \n", - "\n", - " name_event_types name_facilities \n", - "0 offre muséale individuel mucem \n", - "1 offre muséale individuel mucem \n", - "2 offre muséale individuel mucem \n", - "3 offre muséale individuel mucem \n", - "4 non défini mucem \n", - "\n", - "[5 rows x 21 columns]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "products_global = uniform_product_df()\n", - "products_global.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "98f78cd5-b694-4cc6-b033-20170aa13e8d", - "metadata": {}, - "outputs": [], - "source": [ - "# Fusion liée au product\n", - "df1_products_purchased = pd.merge(df1_ticket_information, products_global, left_on = 'product_id', right_on = 'id_products', how = 'inner')\n", - "\n", - "# Selection des variables d'intérêts\n", - "df1_products_purchased_reduced = df1_products_purchased[['ticket_id', 'customer_id', 'purchase_id' ,'event_type_id', 'supplier_name', 'purchase_date', 'type_of_ticket_name', 'amount', 'children', 'is_full_price', 'name_event_types', 'name_facilities', 'name_categories', 'name_events', 'name_seasons']]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "6d2d2aaa-3c28-4e74-88ec-db48830018f6", - "metadata": {}, - "outputs": [], - "source": [ - "#Exportation \n", - "BUCKET_OUT = \"projet-bdc2324-team1\"\n", - "FILE_KEY_OUT_S3 = \"0_Temp/Company 1 - Purchases.csv\"\n", - "FILE_PATH_OUT_S3 = BUCKET_OUT + \"/\" + FILE_KEY_OUT_S3\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " df1_products_purchased_reduced.to_csv(file_out, index = False)" - ] - }, - { - "cell_type": "markdown", - "id": "d7c3668a-c016-4bd0-837e-04af328ff14f", - "metadata": {}, - "source": [ - "# Construction des variables explicatives" - ] - }, - { - "cell_type": "markdown", - "id": "314f1b7f-ae48-4c6f-8469-9ce879043243", - "metadata": {}, - "source": [ - "## KPI campaigns" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e2c88552-b863-47a2-be23-8d2898fb28bc", - "metadata": {}, - "outputs": [], - "source": [ - "def campaigns_kpi_function(campaigns_information = None):\n", - " # Nombre de campagnes de mails\n", - " nb_campaigns = campaigns_information[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index()\n", - " nb_campaigns.rename(columns = {'campaign_name' : 'nb_campaigns'}, inplace = True)\n", - " # Temps d'ouverture en min moyen \n", - " campaigns_information['time_to_open'] = campaigns_information['opened_at'] - campaigns_information['delivered_at']\n", - " time_to_open = campaigns_information[['customer_id', 'time_to_open']].groupby('customer_id').mean().reset_index()\n", - "\n", - " # Nombre de mail ouvert \n", - " opened_campaign = campaigns_information[['customer_id', 'campaign_name', 'opened_at']]\n", - " opened_campaign.dropna(subset=['opened_at'], inplace=True)\n", - " opened_campaign = opened_campaign[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index()\n", - " opened_campaign.rename(columns = {'campaign_name' : 'nb_campaigns_opened' }, inplace = True)\n", - "\n", - " # Fusion des indicateurs\n", - " campaigns_reduced = pd.merge(nb_campaigns, opened_campaign, on = 'customer_id', how = 'left')\n", - " campaigns_reduced = pd.merge(campaigns_reduced, time_to_open, on = 'customer_id', how = 'left')\n", - "\n", - " # Remplir les NaN : nb_campaigns_opened\n", - " campaigns_reduced['nb_campaigns_opened'].fillna(0)\n", - "\n", - " # Remplir les NaT : time_to_open (??)\n", - "\n", - " return campaigns_reduced\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "24537647-bc29-4777-9848-ac4120a4aa60", - "metadata": {}, - "outputs": [], - "source": [ - "df1_campaigns_kpi = campaigns_kpi_function(campaigns_information = df1_campaigns_information) " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "6be2a9a6-056b-4e19-8c26-a18ba3df36b3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " customer_id nb_campaigns nb_campaigns_opened time_to_open\n", - "0 2 4 NaN NaT\n", - "1 3 222 124.0 1 days 00:28:30.169354838\n", - "2 4 7 7.0 1 days 04:31:01.428571428\n", - "3 5 4 NaN NaT\n", - "4 6 20 NaN NaT" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_campaigns_kpi.head()" - ] - }, - { - "cell_type": "markdown", - "id": "d4dcfbe0-c6ce-497e-b75e-dc9e938801b2", - "metadata": {}, - "source": [ - "## KPI tickets" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "043303fe-e90f-4689-a2a9-5d690555a045", - "metadata": {}, - "outputs": [], - "source": [ - "def tickets_kpi_function(tickets_information = None):\n", - "\n", - " tickets_information_copy = tickets_information.copy()\n", - " \n", - " # Dummy : Canal de vente en ligne\n", - " liste_mots = ['en ligne', 'internet', 'web', 'net', 'vad', 'online'] # vad = vente à distance\n", - " tickets_information_copy['vente_internet'] = tickets_information_copy['supplier_name'].str.contains('|'.join(liste_mots), case=False).astype(int)\n", - "\n", - " # Proportion de vente en ligne\n", - " prop_vente_internet = tickets_information_copy[tickets_information_copy['vente_internet'] == 1].groupby(['customer_id', 'event_type_id'])['ticket_id'].count().reset_index()\n", - " prop_vente_internet.rename(columns = {'ticket_id' : 'nb_tickets_internet'}, inplace = True)\n", - "\n", - " # Average amount\n", - " avg_amount = (tickets_information_copy.groupby([\"event_type_id\", 'name_event_types'])\n", - " .agg({\"amount\" : \"mean\"}).reset_index()\n", - " .rename(columns = {'amount' : 'avg_amount'}))\n", - "\n", - " \n", - " tickets_kpi = (tickets_information_copy[['event_type_id', 'customer_id', 'purchase_id' ,'ticket_id','supplier_name', 'purchase_date', 'amount', 'vente_internet']]\n", - " .groupby(['customer_id', 'event_type_id']) \n", - " .agg({'ticket_id': 'count', \n", - " 'purchase_id' : 'nunique',\n", - " 'amount' : 'sum',\n", - " 'supplier_name': 'nunique',\n", - " 'vente_internet' : 'max',\n", - " 'purchase_date' : ['min', 'max']})\n", - " .reset_index()\n", - " )\n", - " \n", - " tickets_kpi.columns = tickets_kpi.columns.map('_'.join)\n", - " \n", - " tickets_kpi.rename(columns = {'ticket_id_count' : 'nb_tickets', \n", - " 'purchase_id_nunique' : 'nb_purchases',\n", - " 'amount_sum' : 'total_amount',\n", - " 'supplier_name_nunique' : 'nb_suppliers', \n", - " 'customer_id_' : 'customer_id',\n", - " 'event_type_id_' : 'event_type_id'}, inplace = True)\n", - " \n", - " tickets_kpi['time_between_purchase'] = tickets_kpi['purchase_date_max'] - tickets_kpi['purchase_date_min']\n", - " tickets_kpi['time_between_purchase'] = tickets_kpi['time_between_purchase'] / np.timedelta64(1, 'D') # En nombre de jours\n", - "\n", - " # Convertir date et en chiffre\n", - " max_date = tickets_kpi['purchase_date_max'].max()\n", - " tickets_kpi['purchase_date_max'] = (max_date - tickets_kpi['purchase_date_max']) / np.timedelta64(1, 'D')\n", - " tickets_kpi['purchase_date_min'] = (max_date - tickets_kpi['purchase_date_min']) / np.timedelta64(1, 'D')\n", - "\n", - " \n", - " tickets_kpi = tickets_kpi.merge(prop_vente_internet, on = ['customer_id', 'event_type_id'], how = 'left')\n", - " tickets_kpi['nb_tickets_internet'] = tickets_kpi['nb_tickets_internet'].fillna(0)\n", - "\n", - " tickets_kpi = tickets_kpi.merge(avg_amount, how='left', on= 'event_type_id')\n", - "\n", - " return tickets_kpi\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "5882234a-1ed5-4269-87a6-0d75613476e3", - "metadata": {}, - "outputs": [], - "source": [ - "df1_tickets_kpi = tickets_kpi_function(tickets_information = df1_products_purchased_reduced)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "5f2046cf-ffde-4521-91e7-b727b8bc17f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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4221431430.0102041.2745491340.308160700.9663890.0offre muséale individuel6.150659
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" - ], - "text/plain": [ - " customer_id event_type_id nb_tickets nb_purchases total_amount \\\n", - "0 1 2 384226 194790 2686540.5 \n", - "1 1 4 453242 228945 3248965.5 \n", - "2 1 5 201750 107110 1459190.0 \n", - "3 1 6 217356 111786 1435871.5 \n", - "4 2 2 143 143 0.0 \n", - "\n", - " nb_suppliers vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 7 1 3262.190868 4.179306 \n", - "1 6 1 3698.198229 5.221840 \n", - "2 6 1 3803.369792 0.146331 \n", - "3 5 1 2502.715509 1408.715532 \n", - "4 1 0 2041.274549 1340.308160 \n", - "\n", - " time_between_purchase nb_tickets_internet name_event_types \\\n", - "0 3258.011562 51.0 offre muséale individuel \n", - "1 3692.976389 2988.0 spectacle vivant \n", - "2 3803.223461 9.0 offre muséale groupe \n", - "3 1093.999977 5.0 formule adhésion \n", - "4 700.966389 0.0 offre muséale individuel \n", - "\n", - " avg_amount \n", - "0 6.150659 \n", - "1 7.762474 \n", - "2 4.452618 \n", - "3 6.439463 \n", - "4 6.150659 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_tickets_kpi.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "a4a2311d-8a72-4030-afd5-218004d5d2a5", - "metadata": {}, - "outputs": [], - "source": [ - "# Exportation vers 'projet-bdc2324-team1'\n", - "BUCKET_OUT = \"projet-bdc2324-team1\"\n", - "FILE_KEY_OUT_S3 = \"0_Temp/Company 1 - Purchasing behaviour.csv\"\n", - "FILE_PATH_OUT_S3 = BUCKET_OUT + \"/\" + FILE_KEY_OUT_S3\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " df1_tickets_kpi.to_csv(file_out, index = False)" - ] - }, - { - "cell_type": "markdown", - "id": "f1d7f7ba-361b-467d-b375-b09c149185f7", - "metadata": {}, - "source": [ - "## Alexis' work" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "83230baa-9a8a-4614-b629-e99c2505c696", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idbirthdatestreet_idis_partnergenderis_email_trueopt_instructure_idprofessionlanguage...nb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetname_event_typesavg_amount
598971NaN2False2TrueFalseNaNNaNNaN...194790.02686540.57.01.03262.1908684.1793063258.01156251.0offre muséale individuel6.150659
599001NaN2False2TrueFalseNaNNaNNaN...111786.01435871.55.01.02502.7155091408.7155321093.9999775.0formule adhésion6.439463
598981NaN2False2TrueFalseNaNNaNNaN...228945.03248965.56.01.03698.1982295.2218403692.9763892988.0spectacle vivant7.762474
598991NaN2False2TrueFalseNaNNaNNaN...107110.01459190.06.01.03803.3697920.1463313803.2234619.0offre muséale groupe4.452618
1346952NaN2False1TrueTrueNaNNaNNaN...164.00.01.00.01705.2611921456.333715248.9274770.0formule adhésion6.439463
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5 rows × 37 columns

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" - ], - "text/plain": [ - " customer_id birthdate street_id is_partner gender is_email_true \\\n", - "59897 1 NaN 2 False 2 True \n", - "59900 1 NaN 2 False 2 True \n", - "59898 1 NaN 2 False 2 True \n", - "59899 1 NaN 2 False 2 True \n", - "134695 2 NaN 2 False 1 True \n", - "\n", - " opt_in structure_id profession language ... nb_purchases \\\n", - "59897 False NaN NaN NaN ... 194790.0 \n", - "59900 False NaN NaN NaN ... 111786.0 \n", - "59898 False NaN NaN NaN ... 228945.0 \n", - "59899 False NaN NaN NaN ... 107110.0 \n", - "134695 True NaN NaN NaN ... 164.0 \n", - "\n", - " total_amount nb_suppliers vente_internet_max purchase_date_min \\\n", - "59897 2686540.5 7.0 1.0 3262.190868 \n", - "59900 1435871.5 5.0 1.0 2502.715509 \n", - "59898 3248965.5 6.0 1.0 3698.198229 \n", - "59899 1459190.0 6.0 1.0 3803.369792 \n", - "134695 0.0 1.0 0.0 1705.261192 \n", - "\n", - " purchase_date_max time_between_purchase nb_tickets_internet \\\n", - "59897 4.179306 3258.011562 51.0 \n", - "59900 1408.715532 1093.999977 5.0 \n", - "59898 5.221840 3692.976389 2988.0 \n", - "59899 0.146331 3803.223461 9.0 \n", - "134695 1456.333715 248.927477 0.0 \n", - "\n", - " name_event_types avg_amount \n", - "59897 offre muséale individuel 6.150659 \n", - "59900 formule adhésion 6.439463 \n", - "59898 spectacle vivant 7.762474 \n", - "59899 offre muséale groupe 4.452618 \n", - "134695 formule adhésion 6.439463 \n", - "\n", - "[5 rows x 37 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Add customer information\n", - "df1_customer = (df1_customerplus_clean.merge(df1_tickets_kpi, how = \"left\", on='customer_id')\n", - " .sort_values(by='customer_id', ascending=True))\n", - "df1_customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "433921de-03ad-4024-9462-ecd267db1756", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idbirthdatestreet_idis_partnergenderis_email_trueopt_instructure_idprofessionlanguage...vente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetname_event_typesavg_amountnb_campaignsnb_campaigns_openedtime_to_open
01NaN2False2TrueFalseNaNNaNNaN...1.03262.1908684.1793063258.01156251.0offre muséale individuel6.150659NaNNaNNaT
11NaN2False2TrueFalseNaNNaNNaN...1.02502.7155091408.7155321093.9999775.0formule adhésion6.439463NaNNaNNaT
21NaN2False2TrueFalseNaNNaNNaN...1.03698.1982295.2218403692.9763892988.0spectacle vivant7.762474NaNNaNNaT
31NaN2False2TrueFalseNaNNaNNaN...1.03803.3697920.1463313803.2234619.0offre muséale groupe4.452618NaNNaNNaT
42NaN2False1TrueTrueNaNNaNNaN...0.01705.2611921456.333715248.9274770.0formule adhésion6.4394634.0NaNNaT
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5 rows × 40 columns

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" - ], - "text/plain": [ - " customer_id birthdate street_id is_partner gender is_email_true \\\n", - "0 1 NaN 2 False 2 True \n", - "1 1 NaN 2 False 2 True \n", - "2 1 NaN 2 False 2 True \n", - "3 1 NaN 2 False 2 True \n", - "4 2 NaN 2 False 1 True \n", - "\n", - " opt_in structure_id profession language ... vente_internet_max \\\n", - "0 False NaN NaN NaN ... 1.0 \n", - "1 False NaN NaN NaN ... 1.0 \n", - "2 False NaN NaN NaN ... 1.0 \n", - "3 False NaN NaN NaN ... 1.0 \n", - "4 True NaN NaN NaN ... 0.0 \n", - "\n", - " purchase_date_min purchase_date_max time_between_purchase \\\n", - "0 3262.190868 4.179306 3258.011562 \n", - "1 2502.715509 1408.715532 1093.999977 \n", - "2 3698.198229 5.221840 3692.976389 \n", - "3 3803.369792 0.146331 3803.223461 \n", - "4 1705.261192 1456.333715 248.927477 \n", - "\n", - " nb_tickets_internet name_event_types avg_amount nb_campaigns \\\n", - "0 51.0 offre muséale individuel 6.150659 NaN \n", - "1 5.0 formule adhésion 6.439463 NaN \n", - "2 2988.0 spectacle vivant 7.762474 NaN \n", - "3 9.0 offre muséale groupe 4.452618 NaN \n", - "4 0.0 formule adhésion 6.439463 4.0 \n", - "\n", - " nb_campaigns_opened time_to_open \n", - "0 NaN NaT \n", - "1 NaN NaT \n", - "2 NaN NaT \n", - "3 NaN NaT \n", - "4 NaN NaT \n", - "\n", - "[5 rows x 40 columns]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Add campaigns information\n", - "\n", - "df1_customer = df1_customer.merge(df1_campaigns_kpi, how='left', on='customer_id')\n", - "df1_customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "25e54131-6835-4e94-86d3-1a78520ed7bc", - "metadata": {}, - "outputs": [], - "source": [ - "## Exportation\n", - "\n", - "# Exportation vers 'projet-bdc2324-team1'\n", - "BUCKET_OUT = \"projet-bdc2324-team1\"\n", - "FILE_KEY_OUT_S3 = \"0_Temp/Company 1 - customer_event.csv\"\n", - "FILE_PATH_OUT_S3 = BUCKET_OUT + \"/\" + FILE_KEY_OUT_S3\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " df1_customer.to_csv(file_out, index = False)" - ] - }, - { - "cell_type": "markdown", - "id": "edae177c-1247-454d-b3d1-08fea37001f7", - "metadata": {}, - "source": [ - "## End of Alexis' work" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "8710611c-7eb8-45ca-bdcc-009f4081f9e2", - "metadata": {}, - "outputs": [], - "source": [ - "# Fusion avec KPI campaigns liés au customer\n", - "#df1_customer = pd.merge(df1_customerplus_clean, df1_campaigns_kpi, on = 'customer_id', how = 'left')\n", - "#df1_customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "a89fad43-ee68-4081-9384-3e9f08ec6a59", - "metadata": {}, - "outputs": [], - "source": [ - "# df1_customer_product = pd.merge(df1_customer, nb_tickets, on = 'customer_id', how = 'left')\n", - "# print(\"shape : \", df1_customer_product.shape)\n", - "# df1_customer_product.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "a19fec00-4ece-400c-937c-ce5cd8daccfd", - "metadata": {}, - "outputs": [], - "source": [ - "# df1_customer_product.to_csv(\"customer_product.csv\", index = False)" - ] - }, - { - "cell_type": "markdown", - "id": "7c3211a5-a851-43bc-a1f0-b39d51857fb7", - "metadata": {}, - "source": [ - "# Fusion des bases locales" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "46de1912-4a66-46e5-8b9e-7768b2d2723b", - "metadata": {}, - "outputs": [], - "source": [ - "# Fusion avec KPI liés au customer\n", - "df1_customer = pd.merge(df1_customerplus_clean, df1_campaigns_kpi, on = 'customer_id', how = 'left')\n", - "\n", - "# Fill NaN values\n", - "df1_customer[['nb_campaigns', 'nb_campaigns_opened']] = df1_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d53825e4-6453-45bc-94f2-7b2504ec4afb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idbirthdatestreet_idis_partnergenderis_email_trueopt_instructure_idprofessionlanguage...average_ticket_baskettotal_pricepurchase_countfirst_buying_datecountryagetenant_idnb_campaignsnb_campaigns_openedtime_to_open
012751NaN2False1TrueTrueNaNNaNNaN...NaNNaN0NaTfrNaN13110.00.0NaT
112825NaN2False2TrueTrueNaNNaNNaN...NaNNaN0NaTfrNaN13110.00.0NaT
211261NaN2False1TrueTrueNaNNaNNaN...NaNNaN0NaTfrNaN13110.00.0NaT
313071NaN2False2TrueTrueNaNNaNNaN...NaNNaN0NaTfrNaN13110.00.0NaT
4653061NaN10False2TrueFalseNaNNaNNaN...NaNNaN0NaTNaNNaN131180.02.00 days 19:53:02.500000
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5 rows × 28 columns

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" - ], - "text/plain": [ - " customer_id birthdate street_id is_partner gender is_email_true \\\n", - "0 12751 NaN 2 False 1 True \n", - "1 12825 NaN 2 False 2 True \n", - "2 11261 NaN 2 False 1 True \n", - "3 13071 NaN 2 False 2 True \n", - "4 653061 NaN 10 False 2 True \n", - "\n", - " opt_in structure_id profession language ... average_ticket_basket \\\n", - "0 True NaN NaN NaN ... NaN \n", - "1 True NaN NaN NaN ... NaN \n", - "2 True NaN NaN NaN ... NaN \n", - "3 True NaN NaN NaN ... NaN \n", - "4 False NaN NaN NaN ... NaN \n", - "\n", - " total_price purchase_count first_buying_date country age tenant_id \\\n", - "0 NaN 0 NaT fr NaN 1311 \n", - "1 NaN 0 NaT fr NaN 1311 \n", - "2 NaN 0 NaT fr NaN 1311 \n", - "3 NaN 0 NaT fr NaN 1311 \n", - "4 NaN 0 NaT NaN NaN 1311 \n", - "\n", - " nb_campaigns nb_campaigns_opened time_to_open \n", - "0 0.0 0.0 NaT \n", - "1 0.0 0.0 NaT \n", - "2 0.0 0.0 NaT \n", - "3 0.0 0.0 NaT \n", - "4 80.0 2.0 0 days 19:53:02.500000 \n", - "\n", - "[5 rows x 28 columns]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "1e42a790-b215-4107-a969-85005da06ebd", - "metadata": {}, - "outputs": [], - "source": [ - "# Fusion avec KPI liés au comportement d'achat\n", - "df1_customer_product = pd.merge(df1_tickets_kpi, df1_customer, on = 'customer_id', how = 'outer')\n", - "\n", - "# Fill NaN values\n", - "df1_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df1_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "d950f24d-a5d1-4f1e-aeaa-ca826470365f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0\n", - "event_type_id 78355\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 78355\n", - "purchase_date_max 78355\n", - "time_between_purchase 78355\n", - "nb_tickets_internet 0\n", - "name_event_types 78355\n", - "avg_amount 78355\n", - "birthdate 149382\n", - "street_id 7\n", - "is_partner 7\n", - "gender 7\n", - "is_email_true 7\n", - "opt_in 7\n", - "structure_id 136874\n", - "profession 150011\n", - "language 155191\n", - "mcp_contact_id 53526\n", - "last_buying_date 78452\n", - "max_price 78452\n", - "ticket_sum 7\n", - "average_price 13127\n", - "fidelity 7\n", - "average_purchase_delay 78452\n", - "average_price_basket 78452\n", - "average_ticket_basket 78452\n", - "total_price 65332\n", - "purchase_count 7\n", - "first_buying_date 78452\n", - "country 8311\n", - "age 149382\n", - "tenant_id 7\n", - "nb_campaigns 7\n", - "nb_campaigns_opened 7\n", - "time_to_open 69024\n", - "dtype: int64" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1_customer_product.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "ebf6d843-dcc0-4e83-b063-94806c0bac17", - "metadata": {}, - "outputs": [], - "source": [ - "## Exportation\n", - "\n", - "# Exportation vers 'projet-bdc2324-team1'\n", - "BUCKET_OUT = \"projet-bdc2324-team1\"\n", - "FILE_KEY_OUT_S3 = \"1_Output/Company 1 - Segmentation base.csv\"\n", - "FILE_PATH_OUT_S3 = BUCKET_OUT + \"/\" + FILE_KEY_OUT_S3\n", - "\n", - "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " df1_customer_product.to_csv(file_out, index = False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/1_Descriptive_Statistics.ipynb b/useless/1_Descriptive_Statistics.ipynb deleted file mode 100644 index 5c4d9eb..0000000 --- a/useless/1_Descriptive_Statistics.ipynb +++ /dev/null @@ -1,2101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3f41343f-7205-41d9-89dd-88039e301413", - "metadata": {}, - "source": [ - "# Statistiques descriptives" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "abfaf341-7b35-4407-9133-d21336c04027", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "from datetime import datetime, date, timedelta\n", - "from dateutil.relativedelta import relativedelta" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7fb72fa3-7940-496f-ac78-c2837f65eefa", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "markdown", - "id": "45d5261f-4d46-49cb-8582-dd2121122b05", - "metadata": {}, - "source": [ - "# 1 - Comportement d'achat" - ] - }, - { - "cell_type": "markdown", - "id": "3479960c-0d23-45f1-8fff-d87395205731", - "metadata": {}, - "source": [ - "## Outlier" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9376af51-4320-44b6-8f30-1e1234371556", - "metadata": {}, - "outputs": [], - "source": [ - "# Chargement des données temporaires\n", - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Temp/Company 1 - Purchasing behaviour.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " tickets_kpi = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1855dcca-cfce-4c54-90ae-55d9a1ab5d45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idevent_type_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetname_event_typesavg_amount
0123842261947902686540.5713262.1908684.1793063258.01156251.0offre muséale individuel6.150659
1144532422289453248965.5613698.1982295.2218403692.9763892988.0spectacle vivant7.762474
2152017501071101459190.0613803.3697920.1463313803.2234619.0offre muséale groupe4.452618
3162173561117861435871.5512502.7155091408.7155321093.9999775.0formule adhésion6.439463
4221431430.0102041.2745491340.308160700.9663890.0offre muséale individuel6.150659
\n", - "
" - ], - "text/plain": [ - " customer_id event_type_id nb_tickets nb_purchases total_amount \\\n", - "0 1 2 384226 194790 2686540.5 \n", - "1 1 4 453242 228945 3248965.5 \n", - "2 1 5 201750 107110 1459190.0 \n", - "3 1 6 217356 111786 1435871.5 \n", - "4 2 2 143 143 0.0 \n", - "\n", - " nb_suppliers vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 7 1 3262.190868 4.179306 \n", - "1 6 1 3698.198229 5.221840 \n", - "2 6 1 3803.369792 0.146331 \n", - "3 5 1 2502.715509 1408.715532 \n", - "4 1 0 2041.274549 1340.308160 \n", - "\n", - " time_between_purchase nb_tickets_internet name_event_types \\\n", - "0 3258.011562 51.0 offre muséale individuel \n", - "1 3692.976389 2988.0 spectacle vivant \n", - "2 3803.223461 9.0 offre muséale groupe \n", - "3 1093.999977 5.0 formule adhésion \n", - "4 700.966389 0.0 offre muséale individuel \n", - "\n", - " avg_amount \n", - "0 6.150659 \n", - "1 7.762474 \n", - "2 4.452618 \n", - "3 6.439463 \n", - "4 6.150659 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tickets_kpi.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0e5d3b2e-1a75-4d46-80e6-c306e9f8de84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'event_type_id', 'nb_tickets', 'nb_purchases',\n", - " 'total_amount', 'nb_suppliers', 'vente_internet_max',\n", - " 'purchase_date_min', 'purchase_date_max', 'time_between_purchase',\n", - " 'nb_tickets_internet', 'name_event_types', 'avg_amount'],\n", - " dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tickets_kpi.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7667e8eb-9a1e-4216-96f4-bf987c6e30b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idevent_type_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetname_event_typesavg_amount
1144532422289453248965.5613698.1982295.2218403692.9763892988.0spectacle vivant7.762474
0123842261947902686540.5713262.1908684.1793063258.01156251.0offre muséale individuel6.150659
3162173561117861435871.5512502.7155091408.7155321093.9999775.0formule adhésion6.439463
2152017501071101459190.0613803.3697920.1463313803.2234619.0offre muséale groupe4.452618
503267336142081140.0312492.1871991442.4051161049.78208313497.0formule adhésion6.439463
50296733211656158471.0312982.237384489.4953242492.7420609815.0offre muséale individuel6.150659
50306733474401620.0211036.392674426.201944610.1907297419.0spectacle vivant7.762474
60416658363412546.5412501.3379051409.3705211091.9673846391.0formule adhésion6.439463
57412651481222423.0613576.106609247.2326973328.8739125321.0offre muséale individuel6.150659
363766348845750963250.011887.298484440.265162447.0333225750.0spectacle vivant7.762474
\n", - "
" - ], - "text/plain": [ - " customer_id event_type_id nb_tickets nb_purchases total_amount \\\n", - "1 1 4 453242 228945 3248965.5 \n", - "0 1 2 384226 194790 2686540.5 \n", - "3 1 6 217356 111786 1435871.5 \n", - "2 1 5 201750 107110 1459190.0 \n", - "5032 6733 6 14208 114 0.0 \n", - "5029 6733 2 11656 158 471.0 \n", - "5030 6733 4 7440 162 0.0 \n", - "60 41 6 6583 634 12546.5 \n", - "57 41 2 6514 812 22423.0 \n", - "36376 63488 4 5750 9 63250.0 \n", - "\n", - " nb_suppliers vente_internet_max purchase_date_min purchase_date_max \\\n", - "1 6 1 3698.198229 5.221840 \n", - "0 7 1 3262.190868 4.179306 \n", - "3 5 1 2502.715509 1408.715532 \n", - "2 6 1 3803.369792 0.146331 \n", - "5032 3 1 2492.187199 1442.405116 \n", - "5029 3 1 2982.237384 489.495324 \n", - "5030 2 1 1036.392674 426.201944 \n", - "60 4 1 2501.337905 1409.370521 \n", - "57 6 1 3576.106609 247.232697 \n", - "36376 1 1 887.298484 440.265162 \n", - "\n", - " time_between_purchase nb_tickets_internet name_event_types \\\n", - "1 3692.976389 2988.0 spectacle vivant \n", - "0 3258.011562 51.0 offre muséale individuel \n", - "3 1093.999977 5.0 formule adhésion \n", - "2 3803.223461 9.0 offre muséale groupe \n", - "5032 1049.782083 13497.0 formule adhésion \n", - "5029 2492.742060 9815.0 offre muséale individuel \n", - "5030 610.190729 7419.0 spectacle vivant \n", - "60 1091.967384 6391.0 formule adhésion \n", - "57 3328.873912 5321.0 offre muséale individuel \n", - "36376 447.033322 5750.0 spectacle vivant \n", - "\n", - " avg_amount \n", - "1 7.762474 \n", - "0 6.150659 \n", - "3 6.439463 \n", - "2 4.452618 \n", - "5032 6.439463 \n", - "5029 6.150659 \n", - "5030 7.762474 \n", - "60 6.439463 \n", - "57 6.150659 \n", - "36376 7.762474 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Présence d'outlier\n", - "tickets_kpi.sort_values(by = ['nb_tickets'], axis = 0, ascending = False).head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9b2e27f2-703d-465b-a0f9-76e996de617c", - "metadata": {}, - "outputs": [], - "source": [ - "# Part du CA par customer\n", - "total_amount_share = tickets_kpi.groupby('customer_id')['total_amount'].sum().reset_index()\n", - "total_amount_share['total_amount_entreprise'] = total_amount_share['total_amount'].sum()\n", - "total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['total_amount_entreprise']\n", - "\n", - "total_amount_share_index = total_amount_share.set_index('customer_id')\n", - "df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "36141803-8865-4210-bd39-0a980301fd0c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Costumer 1 vs others customers\n", - "coupure = 1\n", - "\n", - "top = df_circulaire[:coupure]\n", - "rest = df_circulaire[coupure:]\n", - "\n", - "# Calculez la somme du reste\n", - "rest_sum = rest.sum()\n", - "\n", - "# Créez une nouvelle série avec les cinq plus grandes parts et 'Autre'\n", - "new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])])\n", - "\n", - "# Créez le graphique circulaire\n", - "plt.figure(figsize=(3, 3))\n", - "plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5)\n", - "plt.axis('equal') # Assurez-vous que le graphique est un cercle\n", - "plt.title('Répartition des montants totaux')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "94cf1a25-9ded-48f2-b1b2-75225bdaf49d", - "metadata": {}, - "outputs": [], - "source": [ - "tickets_kpi_filtered = tickets_kpi[tickets_kpi['customer_id'] != 1]" - ] - }, - { - "cell_type": "markdown", - "id": "dbebfa92-310a-417b-a7fa-36ac3593db06", - "metadata": {}, - "source": [ - "## Evolution des commandes" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "06137694-7f50-47ba-8749-68471ececc1e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1235/3643128924.py:11: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " purchases = pd.read_csv(file_in, sep=\",\", parse_dates = ['purchase_date'], date_parser=custom_date_parser)\n", - "/tmp/ipykernel_1235/3643128924.py:19: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", - " campaigns = pd.read_csv(file_in, sep=\",\", parse_dates = ['sent_at'], date_parser=custom_date_parser)\n" - ] - } - ], - "source": [ - "# Importation - Chargement des données temporaires\n", - "def custom_date_parser(date_string):\n", - " return pd.to_datetime(date_string, utc = True, format = 'ISO8601')\n", - "\n", - "# Achat\n", - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Input/Company_1/products_purchased_reduced.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " purchases = pd.read_csv(file_in, sep=\",\", parse_dates = ['purchase_date'], date_parser=custom_date_parser)\n", - " \n", - "# Emails\n", - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Input/Company_1/campaigns_information.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " campaigns = pd.read_csv(file_in, sep=\",\", parse_dates = ['sent_at'], date_parser=custom_date_parser)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e6b962d4-1a30-4133-ac0f-359f7afef42c", - "metadata": {}, - "outputs": [], - "source": [ - "# Mois du premier achat\n", - "purchase_min = purchases.groupby(['customer_id'])['purchase_date'].min().reset_index()\n", - "purchase_min.rename(columns = {'purchase_date' : 'first_purchase_event'}, inplace = True)\n", - "purchase_min['first_purchase_event'] = pd.to_datetime(purchase_min['first_purchase_event'])\n", - "purchase_min['first_purchase_month'] = pd.to_datetime(purchase_min['first_purchase_event'].dt.strftime('%Y-%m'))\n", - "\n", - "# Mois du premier mails\n", - "first_mail_received = campaigns.groupby('customer_id')['sent_at'].min().reset_index()\n", - "first_mail_received.rename(columns = {'sent_at' : 'first_email_reception'}, inplace = True)\n", - "first_mail_received['first_email_reception'] = pd.to_datetime(first_mail_received['first_email_reception'])\n", - "first_mail_received['first_email_month'] = pd.to_datetime(first_mail_received['first_email_reception'].dt.strftime('%Y-%m'))\n", - "\n", - "# Fusion \n", - "known_customer = pd.merge(purchase_min[['customer_id', 'first_purchase_month']], \n", - " first_mail_received[['customer_id', 'first_email_month']], on = 'customer_id', how = 'outer')\n", - "\n", - "# Mois à partir duquel le client est considere comme connu\n", - "known_customer['known_date'] = pd.to_datetime(known_customer[['first_email_month', 'first_purchase_month']].min(axis = 1), utc = True, format = 'ISO8601')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9c56e5ac-cbf4-4343-80ba-be2ab8b60eab", - "metadata": {}, - "outputs": [], - "source": [ - "# Nombre de commande par mois\n", - "purchases_count = pd.merge(purchases[['customer_id', 'purchase_id', 'purchase_date']].drop_duplicates(), known_customer[['customer_id', 'known_date']], on = ['customer_id'], how = 'inner')\n", - "purchases_count['is_customer_known'] = purchases_count['purchase_date'] > purchases_count['known_date'] + pd.DateOffset(months=1)\n", - "purchases_count['purchase_date_month'] = pd.to_datetime(purchases_count['purchase_date'].dt.strftime('%Y-%m'))\n", - "purchases_count = purchases_count[purchases_count['customer_id'] != 1]\n", - "\n", - "# Nombre de commande par mois par type de client\n", - "nb_purchases_graph = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['purchase_id'].count().reset_index()\n", - "nb_purchases_graph.rename(columns = {'purchase_id' : 'nb_purchases'}, inplace = True)\n", - "\n", - "nb_purchases_graph_2 = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['customer_id'].nunique().reset_index()\n", - "nb_purchases_graph_2.rename(columns = {'customer_id' : 'nb_new_customer'}, inplace = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8c1aed44-03d3-49f9-b96c-b06a0df03dde", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Graphique en nombre de commande\n", - "purchases_graph = nb_purchases_graph\n", - "\n", - "purchases_graph_used = purchases_graph[purchases_graph[\"purchase_date_month\"] >= datetime(2021,3,1)]\n", - "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==False]\n", - "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==True]\n", - "\n", - "\n", - "# Création du barplot\n", - "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Nouveau client\")\n", - "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_1[\"nb_purchases\"], \n", - " bottom = purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Ancien client\")\n", - "\n", - "\n", - "# commande pr afficher slt\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y'))\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Mois')\n", - "plt.ylabel(\"Nombre d'achats\")\n", - "plt.title(\"Nombre d'achats - MUCEM\")\n", - "plt.legend()\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "d312276c-4c46-4d29-b6d6-ed110f59890d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# graphique en nombre de client ayant commandé\n", - "purchases_graph = nb_purchases_graph_2\n", - "\n", - "purchases_graph_used = purchases_graph[purchases_graph[\"purchase_date_month\"] >= datetime(2021,4,1)]\n", - "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==False]\n", - "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==True]\n", - "\n", - "\n", - "# Création du barplot\n", - "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_0[\"nb_new_customer\"], width=12, label = \"Nouveau client\")\n", - "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_1[\"nb_new_customer\"], \n", - " bottom = purchases_graph_used_0[\"nb_new_customer\"], width=12, label = \"Ancien client\")\n", - "\n", - "\n", - "# commande pr afficher slt\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y'))\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Mois')\n", - "plt.ylabel(\"Nombre de client ayant commandé\")\n", - "plt.title(\"Nombre de client ayant commandé un ticket pour l'offre 'muséale groupe'\")\n", - "plt.legend()\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "82895dfc-e5ca-4be0-af24-93c1be8f6248", - "metadata": {}, - "source": [ - "### Proportion de tickets de prix 0" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6e27dd83-f188-43a5-b595-618b4922a358", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ticket_id 0.418220\n", - "customer_id 0.418220\n", - "purchase_id 0.418220\n", - "event_type_id 0.418220\n", - "supplier_name 0.418220\n", - "purchase_date 0.418220\n", - "type_of_ticket_name 0.418220\n", - "amount 0.418220\n", - "children 0.418220\n", - "is_full_price 0.418220\n", - "name_event_types 0.418220\n", - "name_facilities 0.418220\n", - "name_categories 0.402548\n", - "name_events 0.175585\n", - "name_seasons 0.418220\n", - "dtype: float64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases[purchases['amount'] == 0].count()/len(purchases)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f663d68b-8a5c-4804-b31a-4477a03ca1e4", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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purchase_idticket_id
count73518.0000007.351800e+04
mean10.0961672.484660e+01
std2367.7026034.636993e+03
min1.0000001.000000e+00
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50%1.0000002.000000e+00
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max641981.0000001.256574e+06
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" - ], - "text/plain": [ - " purchase_id ticket_id\n", - "count 73518.000000 7.351800e+04\n", - "mean 10.096167 2.484660e+01\n", - "std 2367.702603 4.636993e+03\n", - "min 1.000000 1.000000e+00\n", - "25% 1.000000 1.000000e+00\n", - "50% 1.000000 2.000000e+00\n", - "75% 1.000000 3.000000e+00\n", - "max 641981.000000 1.256574e+06" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases.groupby('customer_id')[['purchase_id', 'ticket_id']].nunique().describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d1212b10-3933-450a-b001-9e2cbf308f79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_datetype_of_ticket_nameamountchildrenis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasons
0130708594818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier8.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
1130708604818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
2130708614818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
3130708624818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
4130708634818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
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182666720662815125613580076975vente en ligne2023-11-08 17:23:54+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182666820662816125613680076985vente en ligne2023-11-08 18:32:18+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182666920662817125613680076985vente en ligne2023-11-08 18:32:18+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182667020662818125613780076995vente en ligne2023-11-08 19:30:28+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182667120662819125613780076995vente en ligne2023-11-08 19:30:28+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
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1826672 rows × 15 columns

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" - ], - "text/plain": [ - " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", - "0 13070859 48187 5107462 4 vente en ligne \n", - "1 13070860 48187 5107462 4 vente en ligne \n", - "2 13070861 48187 5107462 4 vente en ligne \n", - "3 13070862 48187 5107462 4 vente en ligne \n", - "4 13070863 48187 5107462 4 vente en ligne \n", - "... ... ... ... ... ... \n", - "1826667 20662815 1256135 8007697 5 vente en ligne \n", - "1826668 20662816 1256136 8007698 5 vente en ligne \n", - "1826669 20662817 1256136 8007698 5 vente en ligne \n", - "1826670 20662818 1256137 8007699 5 vente en ligne \n", - "1826671 20662819 1256137 8007699 5 vente en ligne \n", - "\n", - " purchase_date type_of_ticket_name amount \\\n", - "0 2018-12-28 14:47:50+00:00 Atelier 8.0 \n", - "1 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "2 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "3 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "4 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "... ... ... ... \n", - "1826667 2023-11-08 17:23:54+00:00 Atelier 11.0 \n", - "1826668 2023-11-08 18:32:18+00:00 Atelier 11.0 \n", - "1826669 2023-11-08 18:32:18+00:00 Atelier 11.0 \n", - "1826670 2023-11-08 19:30:28+00:00 Atelier 11.0 \n", - "1826671 2023-11-08 19:30:28+00:00 Atelier 11.0 \n", - "\n", - " children is_full_price name_event_types name_facilities \\\n", - "0 pricing_formula False spectacle vivant mucem \n", - "1 pricing_formula False spectacle vivant mucem \n", - "2 pricing_formula False spectacle vivant mucem \n", - "3 pricing_formula False spectacle vivant mucem \n", - "4 pricing_formula False spectacle vivant mucem \n", - "... ... ... ... ... \n", - "1826667 pricing_formula False offre muséale groupe mucem \n", - "1826668 pricing_formula False offre muséale groupe mucem \n", - "1826669 pricing_formula False offre muséale groupe mucem \n", - "1826670 pricing_formula False offre muséale groupe mucem \n", - "1826671 pricing_formula False offre muséale groupe mucem \n", - "\n", - " name_categories name_events name_seasons \n", - "0 indiv prog enfant l'école des magiciens 2018 \n", - "1 indiv prog enfant l'école des magiciens 2018 \n", - "2 indiv prog enfant l'école des magiciens 2018 \n", - "3 indiv prog enfant l'école des magiciens 2018 \n", - "4 indiv prog enfant l'école des magiciens 2018 \n", - "... ... ... ... \n", - "1826667 indiv entrées tp NaN 2023 \n", - "1826668 indiv entrées tp NaN 2023 \n", - "1826669 indiv entrées tp NaN 2023 \n", - "1826670 indiv entrées tp NaN 2023 \n", - "1826671 indiv entrées tp NaN 2023 \n", - "\n", - "[1826672 rows x 15 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases" - ] - }, - { - "cell_type": "markdown", - "id": "b8a90eaa-c383-4f73-9fd6-6fbbe8eeefb8", - "metadata": {}, - "source": [ - "# 2 - Comportement d'achat bis (Alexis)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "dc45c1cd-2a78-48a6-aa2b-6a501254b6f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(156289, 40)\n" - ] - }, - { - "data": { - "text/html": [ - "
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customer_idbirthdatestreet_idis_partnergenderis_email_trueopt_instructure_idprofessionlanguage...vente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetname_event_typesavg_amountnb_campaignsnb_campaigns_openedtime_to_open
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21NaN2False2TrueFalseNaNNaNNaN...1.03698.1982295.2218403692.9763892988.0spectacle vivant7.762474NaNNaNNaN
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" - ], - "text/plain": [ - " customer_id birthdate street_id is_partner gender is_email_true \\\n", - "0 1 NaN 2 False 2 True \n", - "1 1 NaN 2 False 2 True \n", - "2 1 NaN 2 False 2 True \n", - "3 1 NaN 2 False 2 True \n", - "4 2 NaN 2 False 1 True \n", - "\n", - " opt_in structure_id profession language ... vente_internet_max \\\n", - "0 False NaN NaN NaN ... 1.0 \n", - "1 False NaN NaN NaN ... 1.0 \n", - "2 False NaN NaN NaN ... 1.0 \n", - "3 False NaN NaN NaN ... 1.0 \n", - "4 True NaN NaN NaN ... 0.0 \n", - "\n", - " purchase_date_min purchase_date_max time_between_purchase \\\n", - "0 3262.190868 4.179306 3258.011562 \n", - "1 2502.715509 1408.715532 1093.999977 \n", - "2 3698.198229 5.221840 3692.976389 \n", - "3 3803.369792 0.146331 3803.223461 \n", - "4 1705.261192 1456.333715 248.927477 \n", - "\n", - " nb_tickets_internet name_event_types avg_amount nb_campaigns \\\n", - "0 51.0 offre muséale individuel 6.150659 NaN \n", - "1 5.0 formule adhésion 6.439463 NaN \n", - "2 2988.0 spectacle vivant 7.762474 NaN \n", - "3 9.0 offre muséale groupe 4.452618 NaN \n", - "4 0.0 formule adhésion 6.439463 4.0 \n", - "\n", - " nb_campaigns_opened time_to_open \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - "[5 rows x 40 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Chargement des données temporaires\n", - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Temp/Company 1 - customer_event.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " customer = pd.read_csv(file_in, sep=\",\")\n", - "\n", - "print(customer.shape)\n", - "customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "89fcb455-efb4-4ad4-ab88-efd6c8a76287", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'birthdate', 'street_id', 'is_partner', 'gender',\n", - " 'is_email_true', 'opt_in', 'structure_id', 'profession', 'language',\n", - " 'mcp_contact_id', 'last_buying_date', 'max_price', 'ticket_sum',\n", - " 'average_price', 'fidelity', 'average_purchase_delay',\n", - " 'average_price_basket', 'average_ticket_basket', 'total_price',\n", - " 'purchase_count', 'first_buying_date', 'country', 'age', 'tenant_id',\n", - " 'event_type_id', 'nb_tickets', 'nb_purchases', 'total_amount',\n", - " 'nb_suppliers', 'vente_internet_max', 'purchase_date_min',\n", - " 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',\n", - " 'name_event_types', 'avg_amount', 'nb_campaigns', 'nb_campaigns_opened',\n", - " 'time_to_open'],\n", - " dtype='object')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customer.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d7b2356a-d5fc-4547-b3ff-fded0e304fb6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idaverage_priceaverage_purchase_delayaverage_price_basketaverage_ticket_basketpurchase_counttotal_pricenb_campaignsnb_campaigns_opened
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420.0000000.0000000.0000001.0000003070.04.00.0
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" - ], - "text/plain": [ - " customer_id average_price average_purchase_delay average_price_basket \\\n", - "0 1 7.030122 -67.790969 13.751530 \n", - "4 2 0.000000 0.000000 0.000000 \n", - "6 3 18.333333 30.666667 36.666667 \n", - "7 4 10.250000 5.000000 20.500000 \n", - "9 5 9.500000 0.000000 19.000000 \n", - "\n", - " average_ticket_basket purchase_count total_price nb_campaigns \\\n", - "0 1.956087 641472 8821221.5 0.0 \n", - "4 1.000000 307 0.0 4.0 \n", - "6 2.000000 3 110.0 222.0 \n", - "7 2.000000 2 41.0 7.0 \n", - "9 2.000000 1 19.0 4.0 \n", - "\n", - " nb_campaigns_opened \n", - "0 0.0 \n", - "4 0.0 \n", - "6 124.0 \n", - "7 7.0 \n", - "9 0.0 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "achat = ['customer_id', 'average_price', 'average_purchase_delay', 'average_price_basket',\n", - " 'average_ticket_basket', 'purchase_count', 'total_price', 'nb_campaigns',\n", - " 'nb_campaigns_opened']\n", - "\n", - "customer_achat = customer[achat].drop_duplicates(subset = ['customer_id'])\n", - "customer_achat['nb_campaigns'] = customer_achat['nb_campaigns'].fillna(0)\n", - "customer_achat['nb_campaigns_opened'] = customer_achat['nb_campaigns_opened'].fillna(0)\n", - "customer_achat = customer_achat.fillna(0)\n", - "customer_achat.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "5559748f-1745-4651-a9f6-94702c7ee66f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean5.252070-206.58148611.4515961.7233720.65514816.99406440.9232417.870681
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" - ], - "text/plain": [ - " average_price average_purchase_delay average_price_basket \\\n", - "count 151865.000000 151865.000000 151865.000000 \n", - "mean 5.252070 -206.581486 11.451596 \n", - "std 7.915955 2996.743657 48.271194 \n", - "min 0.000000 -44863.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 \n", - "50% 0.000000 0.000000 0.000000 \n", - "75% 11.000000 0.000000 19.000000 \n", - "max 320.000000 1914.000000 9900.000000 \n", - "\n", - " average_ticket_basket purchase_count total_price nb_campaigns \\\n", - "count 151865.000000 151865.000000 151865.000000 151865.000000 \n", - "mean 1.723372 0.655148 16.994064 40.923241 \n", - "std 7.045950 5.694038 313.099102 70.445724 \n", - "min 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 2.000000 \n", - "50% 0.000000 0.000000 0.000000 5.000000 \n", - "75% 2.000000 1.000000 20.000000 32.000000 \n", - "max 900.000000 1508.000000 64350.000000 439.000000 \n", - "\n", - " nb_campaigns_opened \n", - "count 151865.000000 \n", - "mean 7.870681 \n", - "std 23.119061 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 1.000000 \n", - "75% 3.000000 \n", - "max 434.000000 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customer_wto_outlier = customer_achat[customer_achat['customer_id']!=1]\n", - "\n", - "customer_wto_outlier[['average_price', 'average_purchase_delay', 'average_price_basket',\n", - " 'average_ticket_basket', 'purchase_count', 'total_price', 'nb_campaigns', 'nb_campaigns_opened']].describe()" - ] - }, - { - "cell_type": "markdown", - "id": "b49c9e93-f324-42ee-a262-34ffb44a2261", - "metadata": {}, - "source": [ - "# Event" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4971e35d-a762-4e18-9443-fd9571bd3f1e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Nombre de consommateurs uniques par type d'évènement\n", - "\n", - "event_counts = customer.groupby('name_event_types')['customer_id'].nunique()\n", - "\n", - "event_counts.plot(kind='bar')\n", - "plt.xlabel(\"Type d'évènement\")\n", - "plt.ylabel('Nombre de consommateurs uniques')\n", - "plt.title(\"Nombre de consommateurs uniques par type d'évènement\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "bc65a711-d172-4839-b487-3047280fc3a6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Nombre Total de tickets achetés par Type d'évènements\n", - "\n", - "total_tickets_by_event = customer.groupby('name_event_types')['nb_tickets'].sum()\n", - "\n", - "total_tickets_by_event.plot(kind='bar', figsize=(8, 5))\n", - "plt.xlabel(\"Type d'évènements\")\n", - "plt.ylabel('Nombre Total de tickets achetés')\n", - "plt.title(\"Nombre Total de tickets achetés par Type d'évènements\")\n", - "plt.xticks(rotation=45)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c95cc35c-abfc-47c7-9b8a-ac69bfd60dd8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Nombre de Canaux de Ventes Moyen utilisé par les Consommateurs par type d'évènement\n", - "\n", - "avg_supp_event = customer.groupby('name_event_types')['nb_suppliers'].mean()\n", - "avg_supp_event.plot(kind='bar')\n", - "plt.xlabel(\"Type d'évènement\")\n", - "plt.ylabel('Nombre de Canaux de Ventes Moyen')\n", - "plt.title(\"Nombre de Canaux de Ventes Moyen utilisé par les Consommateurs par type d'évènement\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "49d5fd2d-9bc1-43ac-9270-1efd73759854", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Nombre Total de tickets achetés sur Internet par Type d'évènements\n", - "\n", - "nb_tickets_internet = customer.groupby('name_event_types')['nb_tickets_internet'].sum()\n", - "nb_tickets_internet.plot(kind='bar', figsize=(8, 5))\n", - "plt.xlabel(\"Type d'évènements\")\n", - "plt.ylabel('Nombre Total de tickets achetés sur Internet')\n", - "plt.title(\"Nombre Total de tickets achetés sur Internet par Type d'évènements\")\n", - "plt.xticks(rotation=45)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc071992-cf4d-4b9f-9c3b-3f0e98e20eff", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "4f9561a9-6a94-434e-b8e7-9b708f5b5529", - "metadata": {}, - "source": [ - "# 3 - Caractéristiques Démographiques (peu exploitable)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e50e2583-4b8f-478e-87ac-591dde200af8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['customer_id', 'birthdate', 'street_id', 'is_partner', 'gender',\n", - " 'is_email_true', 'opt_in', 'structure_id', 'profession', 'language',\n", - " 'mcp_contact_id', 'last_buying_date', 'max_price', 'ticket_sum',\n", - " 'average_price', 'fidelity', 'average_purchase_delay',\n", - " 'average_price_basket', 'average_ticket_basket', 'total_price',\n", - " 'purchase_count', 'first_buying_date', 'country', 'age', 'tenant_id',\n", - " 'event_type_id', 'nb_tickets', 'nb_purchases', 'total_amount',\n", - " 'nb_suppliers', 'vente_internet_max', 'purchase_date_min',\n", - " 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',\n", - " 'name_event_types', 'avg_amount', 'nb_campaigns', 'nb_campaigns_opened',\n", - " 'time_to_open'],\n", - " dtype='object')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customer.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c724a315-9fe8-4874-be8f-a8115b17b5e2", - "metadata": {}, - "outputs": [], - "source": [ - "def percent_of_na(df, column):\n", - " na_percentage = df[column].isna().mean() * 100\n", - " non_na_percentage = 100 - na_percentage\n", - " \n", - " labels = ['Valeurs Manquantes', 'Non-Valeurs Manquantes']\n", - " sizes = [na_percentage, non_na_percentage]\n", - " colors = ['#ff9999','#66b3ff']\n", - " explode = (0.1, 0)\n", - "\n", - " plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)\n", - " plt.axis('equal') \n", - " plt.title('Pourcentage de Valeurs Manquantes : {}'.format(column))\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "58af5dcb-673e-4f4d-ad5c-f66ce1e8a22c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "percent_of_na(customer, 'language')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c34164d2-5ab2-4923-a165-30dc5c070233", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/2_Regression_logistique.ipynb b/useless/2_Regression_logistique.ipynb deleted file mode 100644 index 2dc4112..0000000 --- a/useless/2_Regression_logistique.ipynb +++ /dev/null @@ -1,374 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ac01a6ea-bef6-4ace-89ff-1dc03a4215c2", - "metadata": {}, - "source": [ - "# Segmentation des clients par régression logistique" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bca785be-39f7-4583-9bd8-67c1134ae275", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n", - "from sklearn.preprocessing import StandardScaler\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3bf57816-b023-4e84-9450-095620bddebc", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "27002f2f-a78a-414c-8e4f-b15bf6dd9e40", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_23374/1677066092.py:7: DtypeWarning: Columns (11,40) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "/tmp/ipykernel_23374/1677066092.py:12: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "# Importation des données\n", - "BUCKET = \"projet-bdc2324-team1/1_Output/Logistique Regression databases - First approach\"\n", - "\n", - "FILE_PATH_S3 = BUCKET + \"/\" + \"dataset_train.csv\"\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "\n", - "FILE_PATH_S3 = BUCKET + \"/\" + \"dataset_test.csv\"\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c3928b55-8821-46da-b3b5-a036efd6d2cf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
event_type_idname_event_types
02.0offre muséale individuel
14.0spectacle vivant
25.0offre muséale groupe
3NaNNaN
\n", - "
" - ], - "text/plain": [ - " event_type_id name_event_types\n", - "0 2.0 offre muséale individuel\n", - "1 4.0 spectacle vivant\n", - "2 5.0 offre muséale groupe\n", - "3 NaN NaN" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train[['event_type_id', 'name_event_types']].drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7e8a9d4d-7e55-4173-a7f4-8b8baa9610d2", - "metadata": {}, - "outputs": [], - "source": [ - "#Choose type of event \n", - "type_event_choosed = 5\n", - "\n", - "dataset_test = dataset_test[(dataset_test['event_type_id'] == type_event_choosed) | np.isnan(dataset_test['event_type_id'])]\n", - "dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - "dataset_train = dataset_train[(dataset_train['event_type_id'] == type_event_choosed) | np.isnan(dataset_train['event_type_id'])]\n", - "dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b4078b8e-2172-47e6-9f92-106dc3015fc9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "228.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train['y_has_purchased'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e20ced8f-df1c-43bb-8d15-79f414c8225c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0.000000\n", - "event_type_id 0.967882\n", - "nb_tickets 0.000000\n", - "nb_purchases 0.000000\n", - "total_amount 0.000000\n", - "nb_suppliers 0.000000\n", - "vente_internet_max 0.000000\n", - "purchase_date_min 0.967882\n", - "purchase_date_max 0.967882\n", - "time_between_purchase 0.967882\n", - "nb_tickets_internet 0.000000\n", - "name_event_types 0.967882\n", - "avg_amount 0.967882\n", - "street_id 0.000000\n", - "is_partner 0.000000\n", - "gender 0.000000\n", - "is_email_true 0.000000\n", - "opt_in 0.000000\n", - "structure_id 0.856471\n", - "mcp_contact_id 0.297844\n", - "last_buying_date 0.642312\n", - "max_price 0.642312\n", - "ticket_sum 0.000000\n", - "average_price 0.107403\n", - "fidelity 0.000000\n", - "average_purchase_delay 0.642312\n", - "average_price_basket 0.642312\n", - "average_ticket_basket 0.642312\n", - "total_price 0.534909\n", - "purchase_count 0.000000\n", - "first_buying_date 0.642312\n", - "country 0.066622\n", - "tenant_id 0.000000\n", - "gender_label 0.000000\n", - "gender_female 0.000000\n", - "gender_male 0.000000\n", - "gender_other 0.000000\n", - "country_fr 0.066622\n", - "nb_campaigns 0.000000\n", - "nb_campaigns_opened 0.000000\n", - "time_to_open 0.553988\n", - "y_has_purchased 0.000000\n", - "dtype: float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train.isna().sum()/len(dataset_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2ce94258-e2d1-472a-81fc-fc11e247b423", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "121789.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dataset_train) - dataset_train['y_has_purchased'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "34bae3f7-d579-4f80-a38d-a83eb5ea8a7b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.9986037223669636\n", - "Confusion Matrix:\n", - " [[128000 37]\n", - " [ 142 19]]\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 0.0 1.00 1.00 1.00 128037\n", - " 1.0 0.34 0.12 0.18 161\n", - "\n", - " accuracy 1.00 128198\n", - " macro avg 0.67 0.56 0.59 128198\n", - "weighted avg 1.00 1.00 1.00 128198\n", - "\n" - ] - } - ], - "source": [ - "\n", - "reg_columns = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet', 'opt_in', 'fidelity', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - "X_train = dataset_train[reg_columns]\n", - "y_train = dataset_train['y_has_purchased']\n", - "X_test = dataset_test[reg_columns]\n", - "y_test = dataset_test['y_has_purchased']\n", - "\n", - "# Fit and transform the scaler on the training data\n", - "scaler = StandardScaler()\n", - "\n", - "# Transform the test data using the same scaler\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.fit_transform(X_test)\n", - "\n", - "# Create and fit the linear regression model\n", - "logit_model = LogisticRegression(penalty='l1', solver='liblinear', C=1.0)\n", - "logit_model.fit(X_train_scaled, y_train)\n", - "\n", - "y_pred = logit_model.predict(X_test_scaled)\n", - "\n", - "#Evaluation du modèle \n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "class_report = classification_report(y_test, y_pred)\n", - "\n", - "print(\"Accuracy:\", accuracy)\n", - "print(\"Confusion Matrix:\\n\", conf_matrix)\n", - "print(\"Classification Report:\\n\", class_report)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ccc78c36-3287-46e6-89ac-7494c1a7106a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('Actual')\n", - "plt.title('Confusion Matrix')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/2_modelisation_pipeline+visu.ipynb b/useless/2_modelisation_pipeline+visu.ipynb deleted file mode 100644 index 4caa40c..0000000 --- a/useless/2_modelisation_pipeline+visu.ipynb +++ /dev/null @@ -1,2770 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ac01a6ea-bef6-4ace-89ff-1dc03a4215c2", - "metadata": {}, - "source": [ - "# Segmentation des clients par régression logistique" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "bca785be-39f7-4583-9bd8-67c1134ae275", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n", - "from sklearn.preprocessing import StandardScaler\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8be4cda5-fd19-437f-bf23-9af20be537e9", - "metadata": {}, - "outputs": [], - "source": [ - "# import scipy\n", - "import scikitplot as skplt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "14378e7b-240f-4df7-9ce8-5e60920a7729", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.11.4'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import scipy\n", - "scipy.__version__ # il faut cette version pr eviter les pb" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3bf57816-b023-4e84-9450-095620bddebc", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "27002f2f-a78a-414c-8e4f-b15bf6dd9e40", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2186/1677066092.py:7: DtypeWarning: Columns (11,40) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "/tmp/ipykernel_2186/1677066092.py:12: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "# Importation des données\n", - "BUCKET = \"projet-bdc2324-team1/1_Output/Logistique Regression databases - First approach\"\n", - "\n", - "FILE_PATH_S3 = BUCKET + \"/\" + \"dataset_train.csv\"\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "\n", - "FILE_PATH_S3 = BUCKET + \"/\" + \"dataset_test.csv\"\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c3928b55-8821-46da-b3b5-a036efd6d2cf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
event_type_idname_event_types
02.0offre muséale individuel
14.0spectacle vivant
25.0offre muséale groupe
3NaNNaN
\n", - "
" - ], - "text/plain": [ - " event_type_id name_event_types\n", - "0 2.0 offre muséale individuel\n", - "1 4.0 spectacle vivant\n", - "2 5.0 offre muséale groupe\n", - "3 NaN NaN" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train[['event_type_id', 'name_event_types']].drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7e8a9d4d-7e55-4173-a7f4-8b8baa9610d2", - "metadata": {}, - "outputs": [], - "source": [ - "#Choose type of event \n", - "type_event_choosed = 5\n", - "\n", - "dataset_test = dataset_test[(dataset_test['event_type_id'] == type_event_choosed) | np.isnan(dataset_test['event_type_id'])]\n", - "dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - "dataset_train = dataset_train[(dataset_train['event_type_id'] == type_event_choosed) | np.isnan(dataset_train['event_type_id'])]\n", - "dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e20ced8f-df1c-43bb-8d15-79f414c8225c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0.000000\n", - "event_type_id 0.967882\n", - "nb_tickets 0.000000\n", - "nb_purchases 0.000000\n", - "total_amount 0.000000\n", - "nb_suppliers 0.000000\n", - "vente_internet_max 0.000000\n", - "purchase_date_min 0.967882\n", - "purchase_date_max 0.967882\n", - "time_between_purchase 0.967882\n", - "nb_tickets_internet 0.000000\n", - "name_event_types 0.967882\n", - "avg_amount 0.967882\n", - "street_id 0.000000\n", - "is_partner 0.000000\n", - "gender 0.000000\n", - "is_email_true 0.000000\n", - "opt_in 0.000000\n", - "structure_id 0.856471\n", - "mcp_contact_id 0.297844\n", - "last_buying_date 0.642312\n", - "max_price 0.642312\n", - "ticket_sum 0.000000\n", - "average_price 0.107403\n", - "fidelity 0.000000\n", - "average_purchase_delay 0.642312\n", - "average_price_basket 0.642312\n", - "average_ticket_basket 0.642312\n", - "total_price 0.534909\n", - "purchase_count 0.000000\n", - "first_buying_date 0.642312\n", - "country 0.066622\n", - "tenant_id 0.000000\n", - "gender_label 0.000000\n", - "gender_female 0.000000\n", - "gender_male 0.000000\n", - "gender_other 0.000000\n", - "country_fr 0.066622\n", - "nb_campaigns 0.000000\n", - "nb_campaigns_opened 0.000000\n", - "time_to_open 0.553988\n", - "y_has_purchased 0.000000\n", - "dtype: float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train.isna().sum()/len(dataset_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "05e29adb-7eef-416f-8f7b-248229eee0fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "nb_tickets_internet 0\n", - "opt_in 0\n", - "fidelity 0\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet', 'opt_in', 'fidelity', 'nb_campaigns', 'nb_campaigns_opened']].isna().sum()\n", - "# pas de NaN, OK !" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2ce94258-e2d1-472a-81fc-fc11e247b423", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "228.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train['y_has_purchased'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "34bae3f7-d579-4f80-a38d-a83eb5ea8a7b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.9986037223669636\n", - "Confusion Matrix:\n", - " [[128000 37]\n", - " [ 142 19]]\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 0.0 1.00 1.00 1.00 128037\n", - " 1.0 0.34 0.12 0.18 161\n", - "\n", - " accuracy 1.00 128198\n", - " macro avg 0.67 0.56 0.59 128198\n", - "weighted avg 1.00 1.00 1.00 128198\n", - "\n" - ] - } - ], - "source": [ - "\n", - "reg_columns = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet', 'opt_in', 'fidelity', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - "X_train = dataset_train[reg_columns]\n", - "y_train = dataset_train['y_has_purchased']\n", - "X_test = dataset_test[reg_columns]\n", - "y_test = dataset_test['y_has_purchased']\n", - "\n", - "# Fit and transform the scaler on the training data\n", - "scaler = StandardScaler()\n", - "\n", - "# Transform the test data using the same scaler\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.fit_transform(X_test)\n", - "\n", - "# Create and fit the linear regression model\n", - "logit_model = LogisticRegression(penalty='l1', solver='liblinear', C=1.0)\n", - "logit_model.fit(X_train_scaled, y_train)\n", - "\n", - "y_pred = logit_model.predict(X_test_scaled)\n", - "\n", - "#Evaluation du modèle \n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "class_report = classification_report(y_test, y_pred)\n", - "\n", - "print(\"Accuracy:\", accuracy)\n", - "print(\"Confusion Matrix:\\n\", conf_matrix)\n", - "print(\"Classification Report:\\n\", class_report)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ccc78c36-3287-46e6-89ac-7494c1a7106a", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('Actual')\n", - "plt.title('Confusion Matrix')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fe6e14d2-001d-4585-9344-f240b84ce4af", - "metadata": {}, - "source": [ - "## Ajout TP : test d'une nouvelle pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3782988b-52f9-4172-92d4-68948bf259c9", - "metadata": {}, - "outputs": [], - "source": [ - "# etape supp : suppression du client 1 (outlier car client anonyme)\n", - "\n", - "dataset_train = dataset_train[dataset_train[\"customer_id\"]!=1]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9d19f8c0-ed31-46cd-8879-47810fa099d6", - "metadata": {}, - "outputs": [], - "source": [ - "# definition des variables utilisées\n", - "\n", - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'nb_tickets_internet', 'fidelity', 'nb_campaigns', 'nb_campaigns_opened']\n", - "# categorical_features = [\"opt_in\"]\n", - "encoded_features = [\"opt_in\", \"vente_internet_max\"]\n", - "features = numeric_features + encoded_features\n", - "X_train = dataset_train[features]\n", - "y_train = dataset_train['y_has_purchased']\n", - "X_test = dataset_test[features]\n", - "y_test = dataset_test['y_has_purchased']" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "412ddfad-3d20-4fa0-afaa-79ec87b3122d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 122016.000000\n", - "mean 0.307656\n", - "std 3.135563\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 907.000000\n", - "Name: fidelity, dtype: float64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### variable fidelity\n", - "\n", - "X_train[\"fidelity\"].describe() # sûrement un problème d'outlier pour fidelity\n", - "# X_train[\"total_amount\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "97e1cd25-0961-45dd-af7f-78ab1d8088ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersnb_tickets_internetfidelitynb_campaignsnb_campaigns_openedopt_invente_internet_max
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279 rows × 10 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "3 0.0 0.0 0.0 0.0 \n", - "15 2233.0 66.0 25703.0 2.0 \n", - "24 0.0 0.0 0.0 0.0 \n", - "28 557.0 25.0 0.0 2.0 \n", - "34 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "144823 0.0 0.0 0.0 0.0 \n", - "144824 0.0 0.0 0.0 0.0 \n", - "144868 0.0 0.0 0.0 0.0 \n", - "144877 0.0 0.0 0.0 0.0 \n", - "150595 0.0 0.0 0.0 0.0 \n", - "\n", - " nb_tickets_internet fidelity nb_campaigns nb_campaigns_opened \\\n", - "3 0.0 173 2.0 0.0 \n", - "15 2.0 94 130.0 60.0 \n", - "24 0.0 224 16.0 0.0 \n", - "28 175.0 34 32.0 15.0 \n", - "34 0.0 24 0.0 0.0 \n", - "... ... ... ... ... \n", - "144823 0.0 9 0.0 0.0 \n", - "144824 0.0 120 0.0 0.0 \n", - "144868 0.0 907 0.0 0.0 \n", - "144877 0.0 8 0.0 0.0 \n", - "150595 0.0 6 0.0 0.0 \n", - "\n", - " opt_in vente_internet_max \n", - "3 True 0.0 \n", - "15 True 1.0 \n", - "24 True 0.0 \n", - "28 True 1.0 \n", - "34 True 0.0 \n", - "... ... ... \n", - "144823 True 0.0 \n", - "144824 True 0.0 \n", - "144868 True 0.0 \n", - "144877 True 0.0 \n", - "150595 True 0.0 \n", - "\n", - "[279 rows x 10 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[X_train[\"fidelity\"]>5]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "fc17957e-b684-41cd-880f-049a4ffcc7dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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customer_idevent_type_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchase...tenant_idgender_labelgender_femalegender_malegender_othercountry_frnb_campaignsnb_campaigns_openedtime_to_openy_has_purchased
32NaN0.00.00.00.00.0NaNNaNNaN...1311male0101.02.00.0NaN0.0
43NaN0.00.00.00.00.0NaNNaNNaN...1311male0101.0125.071.01 days 04:13:20.4929577460.0
65NaN0.00.00.00.00.0NaNNaNNaN...1311male0101.02.00.0NaN0.0
76NaN0.00.00.00.00.0NaNNaNNaN...1311male0101.017.00.0NaN0.0
87NaN0.00.00.00.00.0NaNNaNNaN...1311female1001.027.013.05 days 18:07:22.6153846150.0
..................................................................
1525541256102NaN0.00.00.00.00.0NaNNaNNaN...1311female1001.00.00.0NaN0.0
1525551256103NaN0.00.00.00.00.0NaNNaNNaN...1311other001NaN0.00.0NaN0.0
1525561256104NaN0.00.00.00.00.0NaNNaNNaN...1311other001NaN0.00.0NaN0.0
1525571256105NaN0.00.00.00.00.0NaNNaNNaN...1311other001NaN0.00.0NaN0.0
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122016 rows × 42 columns

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" - ], - "text/plain": [ - " customer_id event_type_id nb_tickets nb_purchases total_amount \\\n", - "3 2 NaN 0.0 0.0 0.0 \n", - "4 3 NaN 0.0 0.0 0.0 \n", - "6 5 NaN 0.0 0.0 0.0 \n", - "7 6 NaN 0.0 0.0 0.0 \n", - "8 7 NaN 0.0 0.0 0.0 \n", - "... ... ... ... ... ... \n", - "152554 1256102 NaN 0.0 0.0 0.0 \n", - "152555 1256103 NaN 0.0 0.0 0.0 \n", - "152556 1256104 NaN 0.0 0.0 0.0 \n", - "152557 1256105 NaN 0.0 0.0 0.0 \n", - "152558 1256106 NaN 0.0 0.0 0.0 \n", - "\n", - " nb_suppliers vente_internet_max purchase_date_min \\\n", - "3 0.0 0.0 NaN \n", - "4 0.0 0.0 NaN \n", - "6 0.0 0.0 NaN \n", - "7 0.0 0.0 NaN \n", - "8 0.0 0.0 NaN \n", - "... ... ... ... \n", - "152554 0.0 0.0 NaN \n", - "152555 0.0 0.0 NaN \n", - "152556 0.0 0.0 NaN \n", - "152557 0.0 0.0 NaN \n", - "152558 0.0 0.0 NaN \n", - "\n", - " purchase_date_max time_between_purchase ... tenant_id gender_label \\\n", - "3 NaN NaN ... 1311 male \n", - "4 NaN NaN ... 1311 male \n", - "6 NaN NaN ... 1311 male \n", - "7 NaN NaN ... 1311 male \n", - "8 NaN NaN ... 1311 female \n", - "... ... ... ... ... ... \n", - "152554 NaN NaN ... 1311 female \n", - "152555 NaN NaN ... 1311 other \n", - "152556 NaN NaN ... 1311 other \n", - "152557 NaN NaN ... 1311 other \n", - "152558 NaN NaN ... 1311 other \n", - "\n", - " gender_female gender_male gender_other country_fr nb_campaigns \\\n", - "3 0 1 0 1.0 2.0 \n", - "4 0 1 0 1.0 125.0 \n", - "6 0 1 0 1.0 2.0 \n", - "7 0 1 0 1.0 17.0 \n", - "8 1 0 0 1.0 27.0 \n", - "... ... ... ... ... ... \n", - "152554 1 0 0 1.0 0.0 \n", - "152555 0 0 1 NaN 0.0 \n", - "152556 0 0 1 NaN 0.0 \n", - "152557 0 0 1 NaN 0.0 \n", - "152558 0 0 1 NaN 0.0 \n", - "\n", - " nb_campaigns_opened time_to_open y_has_purchased \n", - "3 0.0 NaN 0.0 \n", - "4 71.0 1 days 04:13:20.492957746 0.0 \n", - "6 0.0 NaN 0.0 \n", - "7 0.0 NaN 0.0 \n", - "8 13.0 5 days 18:07:22.615384615 0.0 \n", - "... ... ... ... \n", - "152554 0.0 NaN 0.0 \n", - "152555 0.0 NaN 0.0 \n", - "152556 0.0 NaN 0.0 \n", - "152557 0.0 NaN 0.0 \n", - "152558 0.0 NaN 0.0 \n", - "\n", - "[122016 rows x 42 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# on transforme opt_in en indicatrice\n", - "\n", - "dataset_train[\"opt_in\"] = dataset_train[\"opt_in\"].astype(int)\n", - "dataset_train" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8ad69b5d-e2e2-4d70-b8f0-ea0d37f7fe0c", - "metadata": {}, - "outputs": [], - "source": [ - "# definition des variables utilisées\n", - "\n", - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'nb_tickets_internet', 'fidelity', 'nb_campaigns', 'nb_campaigns_opened']\n", - "# categorical_features = [\"opt_in\"]\n", - "encoded_features = [\"opt_in\", \"vente_internet_max\"]\n", - "features = numeric_features + encoded_features\n", - "X_train = dataset_train[features]\n", - "y_train = dataset_train['y_has_purchased']\n", - "X_test = dataset_test[features]\n", - "y_test = dataset_test['y_has_purchased']" - ] - }, - { - "cell_type": "markdown", - "id": "3ed647a6-db9a-4737-b819-57cb81691ea2", - "metadata": {}, - "source": [ - "### Autre ajout : travail de preprocessing des données - étude des outliers" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "3771eeb1-5221-44e5-a5cd-15475fbe4858", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 128198.000000\n", - "mean 0.582536\n", - "std 181.774597\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 65082.000000\n", - "Name: nb_purchases, dtype: float64" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1. number of purchases\n", - "\n", - "X_train[\"nb_purchases\"].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "63c44b80-88cd-4339-91b9-3764e2690316", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersnb_tickets_internetfidelitynb_campaignsnb_campaigns_openedopt_invente_internet_max
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"147242 0 1.0 \n", - "147414 0 1.0 \n", - "147636 0 1.0 \n", - "147950 0 1.0 \n", - "\n", - "[747 rows x 10 columns]" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[X_train[\"nb_purchases\"]>1]" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "032fbc5a-9044-41bd-b992-78077a6c8432", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.quantile(X_train[\"nb_purchases\"], 0.99)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "cad9f7cb-8b71-49a6-874b-e15cb9d7a204", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "count 128198.000000\n", - "mean 1.946941\n", - "std 343.940117\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 122983.000000\n", - "Name: nb_tickets, dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "23.0" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### 2. nb tickets\n", - "\n", - "print(X_train[\"nb_tickets\"].describe())\n", - "np.quantile(X_train[\"nb_tickets\"], 0.99)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "6bb0c86d-eb61-473d-a29b-c59e7e5af489", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "count 128198.000000\n", - "mean 10.496193\n", - "std 2457.094272\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 878762.500000\n", - "Name: total_amount, dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "44.0" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3. total amount\n", - "\n", - "print(X_train[\"total_amount\"].describe())\n", - "np.quantile(X_train[\"total_amount\"], 0.99)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "ab6fded3-d8a5-4bb4-8f2d-472ea0e5e755", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "count 128198.000000\n", - "mean 2.924687\n", - "std 923.990506\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 1.000000\n", - "max 330831.000000\n", - "Name: fidelity, dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "2.0" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 4. fidelity\n", - "\n", - "print(X_train[\"fidelity\"].describe())\n", - "np.quantile(X_train[\"fidelity\"], 0.99)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "c1f0ac75-71a4-43fb-844b-e006acf5927b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "count 128198.000000\n", - "mean 24.276463\n", - "std 37.899868\n", - "min 0.000000\n", - "25% 1.000000\n", - "50% 4.000000\n", - "75% 28.000000\n", - "max 299.000000\n", - "Name: nb_campaigns, dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "133.0" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 5. nb campaigns - semble pas aberrant meme si forte variance\n", - "\n", - "print(X_train[\"nb_campaigns\"].describe())\n", - "np.quantile(X_train[\"nb_campaigns\"], 0.99)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "8bb01064-1c23-4100-ace8-56f155e0b4ab", - "metadata": {}, - "outputs": [], - "source": [ - "### on retire les outliers - variables : nb purchases, nb tickets, total amount, fidelity\n", - "\n", - "p99_nb_purchases = np.quantile(X_train[\"nb_purchases\"], 0.99)\n", - "p99_nb_tickets = np.quantile(X_train[\"nb_tickets\"], 0.99)\n", - "p99_total_amount = np.quantile(X_train[\"total_amount\"], 0.99)\n", - "p99_fidelity = np.quantile(X_train[\"fidelity\"], 0.99)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "b2b43ab6-16aa-41bc-9a62-47ab769c5bf2", - "metadata": {}, - "outputs": [], - "source": [ - "# filtre - on enlève les valeurs aberrantes sur les variables problématiques (retire 2% des valeurs en tt)\n", - "\n", - "X_train = X_train.loc[(X_train[\"nb_purchases\"] <= p99_nb_purchases) &\n", - "(X_train[\"nb_tickets\"] <= p99_nb_tickets) &\n", - "(X_train[\"total_amount\"] <= p99_total_amount) &\n", - "(X_train[\"fidelity\"] <= p99_fidelity)]" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "b254a671-9e57-4123-ae65-55c852eb64cd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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125792 rows × 42 columns

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" - ], - "text/plain": [ - " customer_id event_type_id nb_tickets nb_purchases total_amount \\\n", - "6 5 NaN 0.0 0.0 0.0 \n", - "7 6 NaN 0.0 0.0 0.0 \n", - "8 7 NaN 0.0 0.0 0.0 \n", - "9 8 NaN 0.0 0.0 0.0 \n", - "10 9 NaN 0.0 0.0 0.0 \n", - "... ... ... ... ... ... \n", - "152645 1256102 NaN 0.0 0.0 0.0 \n", - "152646 1256103 NaN 0.0 0.0 0.0 \n", - "152647 1256104 NaN 0.0 0.0 0.0 \n", - "152648 1256105 NaN 0.0 0.0 0.0 \n", - "152649 1256106 NaN 0.0 0.0 0.0 \n", - "\n", - " nb_suppliers vente_internet_max purchase_date_min \\\n", - "6 0.0 0.0 NaN \n", - "7 0.0 0.0 NaN \n", - "8 0.0 0.0 NaN \n", - "9 0.0 0.0 NaN \n", - "10 0.0 0.0 NaN \n", - "... ... ... ... \n", - "152645 0.0 0.0 NaN \n", - "152646 0.0 0.0 NaN \n", - "152647 0.0 0.0 NaN \n", - "152648 0.0 0.0 NaN \n", - "152649 0.0 0.0 NaN \n", - "\n", - " purchase_date_max time_between_purchase ... tenant_id gender_label \\\n", - "6 NaN NaN ... 1311 male \n", - "7 NaN NaN ... 1311 male \n", - "8 NaN NaN ... 1311 female \n", - "9 NaN NaN ... 1311 female \n", - "10 NaN NaN ... 1311 female \n", - "... ... ... ... ... ... \n", - "152645 NaN NaN ... 1311 female \n", - "152646 NaN NaN ... 1311 other \n", - "152647 NaN NaN ... 1311 other \n", - "152648 NaN NaN ... 1311 other \n", - "152649 NaN NaN ... 1311 other \n", - "\n", - " gender_female gender_male gender_other country_fr nb_campaigns \\\n", - "6 0 1 0 1.0 2.0 \n", - "7 0 1 0 1.0 12.0 \n", - "8 1 0 0 1.0 24.0 \n", - "9 1 0 0 1.0 14.0 \n", - "10 1 0 0 1.0 23.0 \n", - "... ... ... ... ... ... \n", - "152645 1 0 0 1.0 0.0 \n", - "152646 0 0 1 NaN 0.0 \n", - "152647 0 0 1 NaN 0.0 \n", - "152648 0 0 1 NaN 0.0 \n", - "152649 0 0 1 NaN 0.0 \n", - "\n", - " nb_campaigns_opened time_to_open y_has_purchased \n", - "6 0.0 NaN 0.0 \n", - "7 0.0 NaN 0.0 \n", - "8 10.0 5 days 11:58:52 0.0 \n", - "9 7.0 0 days 13:29:25.714285714 0.0 \n", - "10 11.0 0 days 17:17:44.090909090 0.0 \n", - "... ... ... ... \n", - "152645 0.0 NaN 0.0 \n", - "152646 0.0 NaN 0.0 \n", - "152647 0.0 NaN 0.0 \n", - "152648 0.0 NaN 0.0 \n", - "152649 0.0 NaN 0.0 \n", - "\n", - "[125792 rows x 42 columns]" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "dataset_train = dataset_train.loc[(dataset_train[\"nb_purchases\"] <= p99_nb_purchases) &\n", - "(dataset_train[\"nb_tickets\"] <= p99_nb_tickets) &\n", - "(dataset_train[\"total_amount\"] <= p99_total_amount) &\n", - "(dataset_train[\"fidelity\"] <= p99_fidelity)]\n", - "\n", - "dataset_train" - ] - }, - { - "cell_type": "markdown", - "id": "f9487c48-b973-4d9e-abb9-902800ab778f", - "metadata": {}, - "source": [ - "En enlevant les outliers, on supprime la plupart des clients ayant acheté à nouveau ... Il faut trouver un autre moyen de preprocessing qui ne dégrade pas le dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "9fe7513b-f23b-4bee-957d-f98919d6eb30", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19.0" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train[\"y_has_purchased\"].sum() # pb : on passe de 161 à 19 clients ayant acheté ..." - ] - }, - { - "cell_type": "markdown", - "id": "b531aebb-3b2f-4c62-ae01-84bdf8e45f49", - "metadata": {}, - "source": [ - "### Construction de la pipeline pour le modèle de régression logistique et résultats" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1476da0d-cbb5-46ac-9f97-10855eec0108", - "metadata": {}, - "outputs": [], - "source": [ - "# importations pr créer la pipeline\n", - "\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f905cb6f-b0be-4a47-ac8d-7b3e16ff1dce", - "metadata": {}, - "outputs": [], - "source": [ - "# debut de la pipeline\n", - "numeric_transformer = Pipeline(steps=[\n", - " # (\"imputer\", SimpleImputer(strategy=\"mean\")), # NaN remplacés par la moyenne, mais peu importe car on a supprimé les valeurs manquantes\n", - " (\"scaler\", StandardScaler())])\n", - "\"\"\"\n", - "categorical_transformer = Pipeline(steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"Not defined\")),\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore'))]) # to deal with missing categorical data\n", - "\n", - "\"\"\"\n", - "preproc = ColumnTransformer(transformers=[(\"num\", numeric_transformer, numeric_features)])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d322fb8f-1e97-4a44-96ca-c0f5d7ebd383", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Returned hyperparameter: {'logreg__C': 0.0009765625, 'logreg__class_weight': 'balanced'}\n", - "Best classification accuracy in train is: 0.25403118665289387\n", - "Classification accuracy on test is: 0.0495079950799508\n" - ] - } - ], - "source": [ - "# on doit prendre une métrique adaptée aux datasets déséquilibrés\n", - "balanced_scorer = make_scorer(balanced_accuracy_score)\n", - "f1_scorer = make_scorer(f1_score)\n", - "\n", - "parameter_space = np.logspace(-10, 6, 17, base=2)\n", - "\n", - "pipe = Pipeline([('preprocessor', preproc), ('logreg', LogisticRegression(max_iter=500))]) # prendre 5k iter\n", - "# on met plus de poids sur les observations rares (utile pr gérer le déséquilibre du dataset)\n", - "parameters4 = {'logreg__C': parameter_space, 'logreg__class_weight': ['balanced']} \n", - "clf4 = GridSearchCV(pipe, parameters4, cv=3, scoring = f1_scorer)\n", - "clf4.fit(X_train, y_train)\n", - "\n", - "# print results\n", - "# print(clf4.cv_results_)\n", - "print('Returned hyperparameter: {}'.format(clf4.best_params_))\n", - "print('Best classification accuracy in train is: {}'.format(clf4.best_score_))\n", - "print('Classification accuracy on test is: {}'.format(clf4.score(X_test, y_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b32bb668-c816-4055-b786-e548eb71f318", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.9517777188411676\n", - "Confusion Matrix:\n", - " [[121855 6182]\n", - " [ 0 161]]\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 0.0 1.00 0.95 0.98 128037\n", - " 1.0 0.03 1.00 0.05 161\n", - "\n", - " accuracy 0.95 128198\n", - " macro avg 0.51 0.98 0.51 128198\n", - "weighted avg 1.00 0.95 0.97 128198\n", - "\n" - ] - } - ], - "source": [ - "# visualisation des résultats \n", - "\n", - "y_pred = clf4.predict(X_test)\n", - "\n", - "#Evaluation du modèle \n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "class_report = classification_report(y_test, y_pred)\n", - "\n", - "print(\"Accuracy:\", accuracy)\n", - "print(\"Confusion Matrix:\\n\", conf_matrix)\n", - "print(\"Classification Report:\\n\", class_report)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "faebbecb-3f85-4181-8005-2f52180fa37e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# matrice de confusion\n", - "\n", - "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('Actual')\n", - "plt.title('Confusion Matrix')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "dc66d09e-3f7b-4f6d-a60f-c21a3a057c6d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# on trace la courbe ROC\n", - "\n", - "# Prédictions sur l'ensemble de test\n", - "y_pred_prob = clf4.predict_proba(X_test)[:, 1]\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "# Tracé de la courbe ROC\n", - "plt.figure(figsize=(8, 6))\n", - "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')\n", - "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : modèle logistique')\n", - "plt.legend(loc='lower right')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b36a11db-5d7a-487a-9b22-f02339e6d413", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calcul des valeurs de précision et de rappel à différents seuils\n", - "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_prob)\n", - "\n", - "# Calcul de l'aire sous la courbe PR (AUC-PR)\n", - "average_precision = average_precision_score(y_test, y_pred_prob)\n", - "\n", - "# Tracé de la courbe PR\n", - "plt.figure(figsize=(8, 6))\n", - "plt.step(recall, precision, color='b', alpha=0.2, where='post')\n", - "plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\n", - "plt.xlabel('Rappel')\n", - "plt.ylabel('Précision')\n", - "plt.ylim([0.0, 1.05])\n", - "plt.xlim([0.0, 1.0])\n", - "plt.title(f'Courbe PR (AUC-PR = {average_precision:.2f})')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7fb157b6-4e4e-4c7d-8a37-c3ac99323795", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", - "\n", - "# Tri des prédictions de probabilités et des vraies valeurs\n", - "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", - "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", - "y_test_sorted = y_test.iloc[sorted_indices]\n", - "\n", - "# Calcul du gain cumulatif\n", - "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", - "\n", - "# Tracé de la courbe de lift\n", - "plt.plot(np.linspace(0, 1, len(cumulative_gain))[:10000], (cumulative_gain/np.linspace(0, 1, len(cumulative_gain)))[:10000], label='Courbe de lift')\n", - "plt.xlabel('Pourcentage des données')\n", - "plt.ylabel('Gain cumulatif')\n", - "plt.title('Courbe de Lift')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "98b93d38-a5d7-4480-91e6-e79be5de18e7", - "metadata": {}, - "source": [ - "## Random forest" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "771bee72-8b12-4ffb-b3ce-82f7e2ba6a8d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n", - "Best parameters: {'max_depth': 20, 'n_estimators': 100, 'random_state': 20}\n", - "Best classification accuracy in train is: 0.3224906065485776\n", - "Classification accuracy on test is: 0.31906614785992216\n", - "------\n" - ] - } - ], - "source": [ - "# Define models and parameters for GridSearch\n", - "params = {\n", - " 'n_estimators': [100, 150, 200],\n", - " 'max_depth': [5, 20, 30],\n", - " 'random_state' : [20]\n", - " }\n", - "\n", - "\n", - "# define model and pipeline - no preprocessing\n", - "clf = GridSearchCV(RandomForestClassifier(), params, cv=3, scoring=f1_scorer, verbose=True)\n", - "clf.fit(X_train, y_train)\n", - "\n", - "print(f\"Best parameters: {clf.best_params_}\")\n", - "print('Best classification accuracy in train is: {}'.format(clf.best_score_))\n", - "print('Classification accuracy on test is: {}'.format(clf.score(X_test, y_test)))\n", - "print(\"------\")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "bf44a84d-607e-48c3-b8c6-28a07d1b1c14", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.99863492410178\n", - "Confusion Matrix:\n", - " [[127982 55]\n", - " [ 120 41]]\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 0.0 1.00 1.00 1.00 128037\n", - " 1.0 0.43 0.25 0.32 161\n", - "\n", - " accuracy 1.00 128198\n", - " macro avg 0.71 0.63 0.66 128198\n", - "weighted avg 1.00 1.00 1.00 128198\n", - "\n" - ] - } - ], - "source": [ - "# visualisation des résultats \n", - "\n", - "y_pred = clf.predict(X_test)\n", - "\n", - "#Evaluation du modèle \n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "class_report = classification_report(y_test, y_pred)\n", - "\n", - "print(\"Accuracy:\", accuracy)\n", - "print(\"Confusion Matrix:\\n\", conf_matrix)\n", - "print(\"Classification Report:\\n\", class_report)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "0fa2189c-5c0a-405b-b686-b9df3958c85c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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dGxEREcczmeyymc1mfH19bbY/W9gUFxfTt29fMjIyWLNmjbVbAxAUFERRURG5ubk278nJybF2U4KCgjhy5EiZ8x49etQm5sKuS25uLsXFxX8Yk5OTA1Cmk/NHVNiIiIg4motWRV3K+aLmp59+4osvvqB27do2x8PDw/H09GTNmjXWfVlZWezcuZN27doBEBERQV5eHlu2bLHGbN68mby8PJuYnTt3kpWVZY1ZvXo1ZrOZ8PBwa8zXX39tswR89erVBAcH07Bhwwp/JhU2IiIibur06dOkp6eTnp4OQEZGBunp6WRmZvL7779z//338+233/Luu+9SUlJCdnY22dnZ1uLCYrEwaNAg4uPjWbt2Ldu3b+ehhx6iRYsWdO7cGYBmzZrRvXt3YmNjSU1NJTU1ldjYWCIjIwkNDQWga9euhIWFERMTw/bt21m7di2jR48mNjbW2iGKjo7GbDYzcOBAdu7cyfLly0lMTGTUqFGVGorScm+Ra4iWe4uU5ZTl3l2m2uU8BWvGVSr+q6++4u677y6zf8CAAUyaNIlGjRqV+74vv/ySjh07AucmFY8ZM4alS5dSUFBAp06dmDdvHvXr17fGHz9+nJEjR7JixQoAoqKimDNnjnV1FZy7Qd/QoUNZt24d3t7eREdHM2PGDJuhtB07djBs2DC2bNmCn58fTzzxBBMmTFBho8JGpHwqbETKckph03W6Xc5TsHqMXc7jzrQqSkRExNH0EEyn0RwbERERcRvq2IiIiDiaA1Y0SflU2IiIiDiahqKcRiWkiIiIuA11bERERBxNQ1FOo8JGRETE0TQU5TQqIUVERMRtqGMjIiLiaBqKchoVNiIiIo6mwsZp9E2LiIiI21DHRkRExNE0edhpVNiIiIg4moainEaFjYiIiKOpY+M0KiFFRETEbahjIyIi4mgainIaFTYiIiKOpqEop1EJKSIiIm5DHRsREREHM6lj4zQqbERERBxMhY3zaChKRERE3IY6NiIiIo6mho3TqLARERFxMA1FOY+GokRERMRtqGMjIiLiYOrYOI8KGxEREQdTYeM8KmxEREQcTIWN82iOjYiIiLgNdWxEREQcTQ0bp1FhIyIi4mAainIeDUWJiIiI21DHRkRExMHUsXEeFTYiIiIOpsLGeTQUJSIiIm5DHRsREREHU8fGeVTYiIiIOJrqGqfRUJSIiIi4DXVsREREHExDUc6jwkZERMTBVNg4jwobERERB1Nh4zwuLWzy8/NZunQpGzduJDs7G5PJRGBgIO3bt+eBBx7Ax8fHlemJiIjIVcZlk4d3797NjTfeyNixY8nNzaVBgwZcf/315ObmMmbMGEJDQ9m9e7er0hMREbEfk502uSSXdWyGDRvGXXfdxdtvv42Xl5fNsaKiIgYOHMiwYcP48ssvXZShiIiIfWgoynlcVths3ryZb7/9tkxRA+Dl5cUzzzzD7bff7oLMRERE5GrlsqEoPz8/fvrpp4se37dvH35+fk7MSERExDFMJpNdNrk0l3VsYmNjGTBgAM899xxdunQhMDAQk8lEdnY2a9asITExkbi4OFelJyIiYjcqSpzHZYXNpEmT8Pb2ZubMmYwdO9b6l24YBkFBQTz99NOMHTvWVemJiIjIVcilj1QYN24chw8f5ueff2bDhg1s2LCBn3/+mcOHD6uoERERt+Gqoaivv/6a3r17ExwcjMlk4pNPPrE5bhgGkyZNIjg4GG9vbzp27MiuXbtsYgoLCxkxYgT+/v74+PgQFRXFoUOHbGJyc3OJiYnBYrFgsViIiYnhxIkTNjGZmZn07t0bHx8f/P39GTlyJEVFRTYxO3bsoEOHDnh7e1OvXj2ef/55DMOo1Ge+Ip4V1ahRIyIiIoiIiKBRo0auTkdERMS+XLTcOz8/n1tuuYU5c+aUe3zatGnMnDmTOXPmkJaWRlBQEF26dOHUqVPWmLi4OJYvX05ycjIbNmzg9OnTREZGUlJSYo2Jjo4mPT2dlJQUUlJSSE9PJyYmxnq8pKSEXr16kZ+fz4YNG0hOTmbZsmXEx8dbY06ePEmXLl0IDg4mLS2N2bNnM2PGDGbOnFmpz2wyKlsKXQW8Ww13dQoiV6TctPL/4yZyLavmhEkZwU98bJfzHF7wt8t+r8lkYvny5fTp0wc4160JDg4mLi6OcePGAee6M4GBgUydOpXBgweTl5dHnTp1WLJkCf369TuXw+HD1K9fn5UrV9KtWzf27NlDWFgYqamptGnTBoDU1FQiIiL44YcfCA0NZdWqVURGRvLLL78QHBwMQHJyMgMHDiQnJwdfX1/mz59PQkICR44cwWw2AzBlyhRmz57NoUOHKtyxuiI6NiIiIu7MXkNRhYWFnDx50mYrLCy8rJwyMjLIzs6ma9eu1n1ms5kOHTqwceNGALZu3UpxcbFNTHBwMM2bN7fGbNq0CYvFYi1qANq2bYvFYrGJad68ubWoAejWrRuFhYVs3brVGtOhQwdrUXM+5vDhwxw4cKDCn0uFjYiIiIPZq7BJSkqyzmM5vyUlJV1WTtnZ2QAEBgba7A8MDLQey87OxsvLq8ztVy6MCQgIKHP+gIAAm5gLr+Pn54eXl9cfxpx/fT6mIvQQTBEREQez13LvhIQERo0aZbPvfzscl+PC3AzDuGS+F8aUF2+PmPOzZSrz/bm8Y5OSksKGDRusr+fOnUvLli2Jjo4mNzfXhZmJiIhcWcxmM76+vjbb5RY2QUFBQNluSE5OjrVTEhQURFFRUZnfxxfGHDlypMz5jx49ahNz4XVyc3MpLi7+w5icnBygbFfpj7i8sBkzZgwnT54Ezi3zio+Pp2fPnuzfv79MVSoiInJVugIfgtmoUSOCgoJYs2aNdV9RURHr16+nXbt2AISHh+Pp6WkTk5WVxc6dO60xERER5OXlsWXLFmvM5s2bycvLs4nZuXMnWVlZ1pjVq1djNpsJDw+3xnz99dc2S8BXr15NcHAwDRs2rPDncvlQVEZGBmFhYQAsW7aMyMhIEhMT2bZtGz179nRxdiIiIn+eq+48fPr0afbt22d9nZGRQXp6OrVq1aJBgwbExcWRmJhISEgIISEhJCYmUr16daKjowGwWCwMGjSI+Ph4ateuTa1atRg9ejQtWrSgc+fOADRr1ozu3bsTGxvLwoULAXj88ceJjIwkNDQUgK5duxIWFkZMTAzTp0/n+PHjjB49mtjYWHx9fYFzS8YnT57MwIEDeeaZZ/jpp59ITExkwoQJlfr+XF7YeHl5cebMGQC++OILHn74YQBq1apl7eSIiIhI5X377bfcfffd1tfnR0IGDBjA4sWLGTt2LAUFBQwdOpTc3FzatGnD6tWrqVmzpvU9s2bNomrVqvTt25eCggI6derE4sWL8fDwsMa8++67jBw50rp6KioqyubeOR4eHnz++ecMHTqU9u3b4+3tTXR0NDNmzLDGWCwW1qxZw7Bhw2jdujV+fn6MGjWq0qM3Lr+PTVRUFEVFRbRv354XXniBjIwM6tWrx+rVqxk+fDh79+6t9Dl1H5uKaX9rE556uDO3hjWgbh0LfZ96jU+/+h6AqlWrMGlob7rdcTONrq/NydNnWbf5B8a/uoKso3kANKhbix9XPl/uuR8c8yYff7EdgJY3Xc+LT/Yh/OYGlJQYfLI2nXH/WEZ+wX/bjeFhDXhh5L20CquPYcDWXQd59uVP+H7vrwDcGR7CiIfupvXNN+Bboxr7Mo/y8ttfkLzqW0d+RW5H97FxnvlzZ7Ngnu33Xbu2P+u+/gaA8c88zYp/Lbc53uIvt/DOex84LUc5xxn3sblh5Kd2Oc/BV3vb5TzuzOUdmzlz5jB06FA++ugj5s+fT7169QBYtWoV3bt3d3F27s3H28yOvb+yZEUqyf+ItTlWvZoXLZvVZ8rrq/h+76/4+VZn+uj7+PDlwdzx4DQADh3JpWHnBJv3PXpfe0YN6MK/vzl3S+66dSx8vmAEH63exlNTPsDXpxrTx9zH68/HED3mTQBqVDezYt4wPvtqB08mvU9VjyqMH9KLFfOG0bT7c/z+eyltb2nEzp9+ZebiNRz57RQ97ryZN154mJP5Z1n59U4nfFsildekaQivvbHI+rrK//wLF6D9HXfy/Iv/Xarr6enptNzEufQQTOdxeWHToEEDPvvsszL7Z82a5YJsri2rv9nN6m92l3vs5OmzRA6x/dfmqKkfsuHdsdQP8uOX7FxKSw2O/HbKJibq7lv4aPVWazemx53NKf69hLikD6zL9uKSPmDz+wk0ru/P/l+OcWPDQGpZfHhh/mccOnICgJcWruLbD5+hflAtMg4dY/pbq22uM++99XSOaEbU3beosJErVlUPD/zr1LnocS8vrz88LiKV5/JVUdu2bWPHjh3W1//617/o06cPzzzzTJmHY4lr+db0prS0lBOnCso93qpZfVreVJ+3P9lk3Wf2qkpxcYnNQ8wKCosBaNeyCQB7DxzhaO4pBvRph2dVD6qZPRnYJ4Jd+w6TmXX8ovlYaniTe/KMPT6aiEMczDxI54530KPrPYwd/RSHfvnF5vi3aVvoeGcEvXt2Y/KE5/jtt99clKk4mqsegnktcnlhM3jwYOs8mv3799O/f3+qV6/Ohx9+qCd8X0HMXlV5YeS9vL/qW07lny03ZkCfCPbszyL1uwzrvq+2/EhgbV+eergTnlU9uK6mN8+PiAIgqI4FgNNnCun22Cs80PM2clNnceybf9A5ohl/HTGfkpLScq/1184tCb+5Af/816Zyj4u4Wou//IWXEqcy/7U3mTj5RX47doyHH+zPiRPn7gfS/s67SJw6g9ffepv4MePYtXMHsY8O0D/o3NUVuNzbXbm8sNm7dy8tW7YE4MMPP+Suu+5i6dKlLF68mGXLll3y/eU9N8MoLbnk+6TiqlatwpIpj1DFZOLJpPInNlYze9KvR2ubbg3Anv3ZxE5YwsiYThzfNJMDXySScegY2cdOUvr/RUs1sycLJz3Epu/20+HhGdzzyEz27M9i+ewhVDOXnXNwZ3gIr02OYegL77Fnf8Vvsy3iTHfc2YHOXbsRcmMobSPaMXveuWWwKz75BIDuPXpyV4eOhITcSMe772Huwtc5eOAAX6//ynVJi7gBl8+xMQyD0tJzv+C++OILIiMjAahfvz7Hjh275PuTkpKYPHmyzT6PwNvwrHu7/ZO9BlWtWoV3pw7ihnq16fH47It2a/7auSXVq3nx7mdbyhx7P+Vb3k/5loBaNckvKMQwYORD93Dg13Nt9349WtMguBYdBvzDOmQ1IGExWV9Po3fHv/Dhv7daz3VHeFOWvTKYcf/4mKXlXEvkSlW9enVCbryRzMwD5R6vUyeA4OBgMg+Wf1yubhpGch6Xd2xat27Niy++yJIlS1i/fj29evUCzt1EqCK3UE5ISCAvL89mqxoY7ui0rwnni5omDerQ64k5HM/Lv2jswD7t+Hz9Do7lnr5oTM7xU+QXFHF/t1s5W1TM2tQfgHMrsEpLDZt5OKWGgWFAlf/5j8Gd4SEsf3UI419dwVsff2OHTyjiPEVFRezf/zP+/uVPFj5xIpfs7Czq1Cn7MEG5+mmOjfO4vGPz8ssv8+CDD/LJJ5/w7LPP0rRpUwA++ugj662Y/4jZbC7znAxTFY+LRMv/8vH2okn9//5HtmG92vzlxnrknjzD4aN5LJ3+GK1uqs/fnlyARxUTgbXP3bDpeN4Zin//73Bf4/r+3HFrE/qMmF/udZ7odxep3+3n9JkiOrW9icS4Poyf/S/yTp+bhLw29QcS4/rwckJf5ievp4rJxOhHuvJ7SQnrvz03/+rO8BCWz36CuUu/4pO12625FBWXaAKxXJH+MX0qHTreTVDduhw/fpzXF8wn//Rpovr8lTP5+cyfN4fOXbriX6cOh3/9ldmvzOI6Pz/u+f+7uYp7UU3iPC6/Qd/FnD17Fg8Pj8u6r4Nu0Fcxd4aHsPqNJ8vsX7IilRcXrLzozfe6PvYK/9n6k/X15OG9ie51Ozf2nEB5/3d644UYut/RnBrVvfjxwBFe/uda3vs8zSbmnjY38ezgHoQ1rUtpqcF3Pxxi0txP2bLjAACvTX6ImKi2Zc799bc/0S32lcp87GuabtDnPGNHP8W2b9PIzT2BXy0//vKXlgwb8SRNmjbl7NmzxI0Yxg8/7ObUyVPUqVOH225vw7ARTxJUt66rU7/mOOMGfU1Hr7LLefbN6GGX87izK7aw+TNU2IiUT4WNSFnOKGxCxqTY5Tw/TdeNay/F5UNRJSUlzJo1iw8++IDMzMwySx2PH7/4fUxERESuBhqKch6XTx6ePHkyM2fOpG/fvuTl5TFq1Cj+9re/UaVKFSZNmuTq9EREROQq4vLC5t133+X1119n9OjRVK1alQceeIA33niDCRMmkJqa6ur0RERE/jStinIelxc22dnZtGjRAoAaNWqQl3fuydGRkZF8/vnnrkxNRETELkwm+2xyaS4vbK6//nqysrIAaNq0KatXn3vYYVpaWpll3CIiIiJ/xOWFzV//+lfWrl0LwJNPPsn48eMJCQnh4Ycf5tFHH3VxdiIiIn9elSomu2xyaS5fFTVlyhTrn++//36uv/56Nm7cSNOmTYmKinJhZiIiIvahYSTncXlhc6G2bdvStm3ZG7GJiIiIXIpLCpsVK1ZUOFZdGxERudppRZPzuKSw6dOnT4XiTCYTJSUllw4UERG5gqmucR6XFDalpaWuuKyIiIhLqGPjPC5fFSUiIiJiLy4rbNatW0dYWBgnT54scywvL4+bb76Zr7/+2gWZiYiI2JfuPOw8LitsXn75ZWJjY/H19S1zzGKxMHjwYGbNmuWCzEREROxLdx52HpcVNt999x3du1/88etdu3Zl69atTsxIRERErnYuu4/NkSNH8PT0vOjxqlWrcvToUSdmJCIi4hgaRnIel3Vs6tWrx44dOy56/Pvvv6du3bpOzEhERMQxNBTlPC4rbHr27MmECRM4e/ZsmWMFBQVMnDiRyMhIF2QmIiIiVyuXDUU999xzfPzxx9x4440MHz6c0NBQTCYTe/bsYe7cuZSUlPDss8+6Kj0RERG70VCU87issAkMDGTjxo0MGTKEhIQEDMMAzv3ld+vWjXnz5hEYGOiq9EREROxGdY3zuPQhmDfccAMrV64kNzeXffv2YRgGISEh+Pn5uTItERERuUpdEU/39vPz47bbbnN1GiIiIg6hoSjnuSIKGxEREXemusZ5VNiIiIg4mDo2zqOHYIqIiIjbUMdGRETEwdSwcR4VNiIiIg6moSjn0VCUiIiIuA11bERERBxMDRvnUWEjIiLiYBqKch4NRYmIiIjbUMdGRETEwdSwcR4VNiIiIg6moSjn0VCUiIiIuA11bERERBxMHRvnUWEjIiLiYKprnEdDUSIiIg5mMpnsslXG77//znPPPUejRo3w9vamcePGPP/885SWllpjDMNg0qRJBAcH4+3tTceOHdm1a5fNeQoLCxkxYgT+/v74+PgQFRXFoUOHbGJyc3OJiYnBYrFgsViIiYnhxIkTNjGZmZn07t0bHx8f/P39GTlyJEVFRZX7IitAhY2IiIgbmjp1KgsWLGDOnDns2bOHadOmMX36dGbPnm2NmTZtGjNnzmTOnDmkpaURFBREly5dOHXqlDUmLi6O5cuXk5yczIYNGzh9+jSRkZGUlJRYY6Kjo0lPTyclJYWUlBTS09OJiYmxHi8pKaFXr17k5+ezYcMGkpOTWbZsGfHx8Xb/3CbDMAy7n9XFvFsNd3UKIlek3LQ5rk5B5IpTzQmTMu5+ZaNdzvPlk+0qHBsZGUlgYCBvvvmmdd99991H9erVWbJkCYZhEBwcTFxcHOPGjQPOdWcCAwOZOnUqgwcPJi8vjzp16rBkyRL69esHwOHDh6lfvz4rV66kW7du7Nmzh7CwMFJTU2nTpg0AqampRERE8MMPPxAaGsqqVauIjIzkl19+ITg4GIDk5GQGDhxITk4Ovr6+dvl+QB0bERERh3PFUNQdd9zB2rVr2bt3LwDfffcdGzZsoGfPngBkZGSQnZ1N165dre8xm8106NCBjRvPFWJbt26luLjYJiY4OJjmzZtbYzZt2oTFYrEWNQBt27bFYrHYxDRv3txa1AB069aNwsJCtm7dWqnPdSmaPCwiInKVKCwspLCw0Gaf2WzGbDaXiR03bhx5eXncdNNNeHh4UFJSwksvvcQDDzwAQHZ2NgCBgYE27wsMDOTgwYPWGC8vL/z8/MrEnH9/dnY2AQEBZa4fEBBgE3Phdfz8/PDy8rLG2Is6NiIiIg5mMtlnS0pKsk7QPb8lJSWVe83333+fd955h6VLl7Jt2zbefvttZsyYwdtvv31BbradIMMwLtkdujCmvPjLibEHdWxEREQcrIqdfnknJCQwatQom33ldWsAxowZw9NPP03//v0BaNGiBQcPHiQpKYkBAwYQFBQEnOum1K1b1/q+nJwca3clKCiIoqIicnNzbbo2OTk5tGvXzhpz5MiRMtc/evSozXk2b95sczw3N5fi4uIynZw/Sx0bERGRq4TZbMbX19dmu1hhc+bMGapUsf017+HhYV3u3ahRI4KCglizZo31eFFREevXr7cWLeHh4Xh6etrEZGVlsXPnTmtMREQEeXl5bNmyxRqzefNm8vLybGJ27txJVlaWNWb16tWYzWbCw8P/zFdShjo2IiIiDuaKG/T17t2bl156iQYNGnDzzTezfft2Zs6cyaOPPvr/OZmIi4sjMTGRkJAQQkJCSExMpHr16kRHRwNgsVgYNGgQ8fHx1K5dm1q1ajF69GhatGhB586dAWjWrBndu3cnNjaWhQsXAvD4448TGRlJaGgoAF27diUsLIyYmBimT5/O8ePHGT16NLGxsXZdEQUqbERERBzOFY9UmD17NuPHj2fo0KHk5OQQHBzM4MGDmTBhgjVm7NixFBQUMHToUHJzc2nTpg2rV6+mZs2a1phZs2ZRtWpV+vbtS0FBAZ06dWLx4sV4eHhYY959911GjhxpXT0VFRXFnDn/vb2Eh4cHn3/+OUOHDqV9+/Z4e3sTHR3NjBkz7P65dR8bkWuI7mMjUpYz7mPTY/7mSwdVwKohbS4ddI3THBsRERFxGxqKEhERcTA93dt5VNiIiIg4mOoa59FQlIiIiLgNdWxEREQczIRaNs6iwkZERMTBqqiucRoNRYmIiIjbUMdGRETEwbQqynlU2IiIiDiY6hrn0VCUiIiIuA11bERERBysilo2TqPCRkRExMFU1ziPChsREREH0+Rh59EcGxEREXEb6tiIiIg4mBo2zqPCRkRExME0edh5NBQlIiIibkMdGxEREQdTv8Z5VNiIiIg4mFZFOY+GokRERMRtqGMjIiLiYFXUsHGaChU2K1asqPAJo6KiLjsZERERd6ShKOepUGHTp0+fCp3MZDJRUlLyZ/IRERERuWwVKmxKS0sdnYeIiIjbUsPGeTTHRkRExME0FOU8l1XY5Ofns379ejIzMykqKrI5NnLkSLskJiIi4i40edh5Kl3YbN++nZ49e3LmzBny8/OpVasWx44do3r16gQEBKiwEREREZep9H1snnrqKXr37s3x48fx9vYmNTWVgwcPEh4ezowZMxyRo4iIyFXNZDLZZZNLq3Rhk56eTnx8PB4eHnh4eFBYWEj9+vWZNm0azzzzjCNyFBERuaqZ7LTJpVW6sPH09LRWjYGBgWRmZgJgsVisfxYRERFxhUrPsWnVqhXffvstN954I3fffTcTJkzg2LFjLFmyhBYtWjgiRxERkataFQ0jOU2lOzaJiYnUrVsXgBdeeIHatWszZMgQcnJyeO211+yeoIiIyNXOZLLPJpdW6Y5N69atrX+uU6cOK1eutGtCIiIiIpdLN+gTERFxMK1ocp5KFzaNGjX6w7+g/fv3/6mERERE3I3qGuepdGETFxdn87q4uJjt27eTkpLCmDFj7JWXiIiISKVVurB58skny90/d+5cvv322z+dkIiIiLvRqijnqfSqqIvp0aMHy5Yts9fpRERE3IZWRTmP3SYPf/TRR9SqVctepxMREXEbmjzsPJd1g77//QsyDIPs7GyOHj3KvHnz7JqciIiISGVUurC59957bQqbKlWqUKdOHTp27MhNN91k1+QuV27aHFenICIiYmW3eR9ySZUubCZNmuSANERERNyXhqKcp9JFpIeHBzk5OWX2//bbb3h4eNglKREREZHLUemOjWEY5e4vLCzEy8vrTyckIiLibqqoYeM0FS5sXn31VeBcO+2NN96gRo0a1mMlJSV8/fXXV8wcGxERkSuJChvnqXBhM2vWLOBcx2bBggU2w05eXl40bNiQBQsW2D9DERERkQqqcGGTkZEBwN13383HH3+Mn5+fw5ISERFxJ5o87DyVnjz85ZdfqqgRERGphCom+2yV9euvv/LQQw9Ru3ZtqlevTsuWLdm6dav1uGEYTJo0ieDgYLy9venYsSO7du2yOUdhYSEjRozA398fHx8foqKiOHTokE1Mbm4uMTExWCwWLBYLMTExnDhxwiYmMzOT3r174+Pjg7+/PyNHjqSoqKjyH+oSKl3Y3H///UyZMqXM/unTp/P3v//dLkmJiIjIn5Obm0v79u3x9PRk1apV7N69m3/84x9cd9111php06Yxc+ZM5syZQ1paGkFBQXTp0oVTp05ZY+Li4li+fDnJycls2LCB06dPExkZSUlJiTUmOjqa9PR0UlJSSElJIT09nZiYGOvxkpISevXqRX5+Phs2bCA5OZlly5YRHx9v989tMi62zOki6tSpw7p162jRooXN/h07dtC5c2eOHDli1wQvx9nfXZ2BiIhcLarZ7eFCFzf28x/tcp5pvUIrHPv000/zzTff8J///Kfc44ZhEBwcTFxcHOPGjQPOdWcCAwOZOnUqgwcPJi8vjzp16rBkyRL69esHwOHDh6lfvz4rV66kW7du7Nmzh7CwMFJTU2nTpg0AqampRERE8MMPPxAaGsqqVauIjIzkl19+ITg4GIDk5GQGDhxITk4Ovr6+f+ZrsVHpjs3p06fLXdbt6enJyZMn7ZKUiIiIO6liMtllq4wVK1bQunVr/v73vxMQEECrVq14/fXXrcczMjLIzs6ma9eu1n1ms5kOHTqwceNGALZu3UpxcbFNTHBwMM2bN7fGbNq0CYvFYi1qANq2bYvFYrGJad68ubWoAejWrRuFhYU2Q2P2UOnCpnnz5rz//vtl9icnJxMWFmaXpERERNxJFTtthYWFnDx50mYrLCws95r79+9n/vz5hISE8O9//5snnniCkSNH8s9//hOA7OxsAAIDA23eFxgYaD2WnZ2Nl5dXmbm1F8YEBASUuX5AQIBNzIXX8fPzw8vLyxpjL5VuwI0fP5777ruPn3/+mXvuuQeAtWvXsnTpUj766CO7JiciIiL/lZSUxOTJk232TZw4sdzHHZWWltK6dWsSExOBcw+x3rVrF/Pnz+fhhx+2xl24YsswjEuu4rowprz4y4mxh0p3bKKiovjkk0/Yt28fQ4cOJT4+nl9//ZV169bRsGFDuyYnIiLiDkwm+2wJCQnk5eXZbAkJCeVes27dumVGUpo1a0ZmZiYAQUFBAGU6Jjk5OdbuSlBQEEVFReTm5v5hTHnza48ePWoTc+F1cnNzKS4uLtPJ+bMu64GjvXr14ptvviE/P599+/bxt7/9jbi4OMLDw+2anIiIiDuw1xwbs9mMr6+vzWY2m8u9Zvv27fnxR9tJy3v37uWGG24AoFGjRgQFBbFmzRrr8aKiItavX0+7du0ACA8Px9PT0yYmKyuLnTt3WmMiIiLIy8tjy5Yt1pjNmzeTl5dnE7Nz506ysrKsMatXr8ZsNtu9drjsueDr1q3jrbfe4uOPP+aGG27gvvvu480337RnbiIiInKZnnrqKdq1a0diYiJ9+/Zly5YtvPbaa7z22mvAuaGhuLg4EhMTCQkJISQkhMTERKpXr050dDQAFouFQYMGER8fT+3atalVqxajR4+mRYsWdO7cGTjXBerevTuxsbEsXLgQgMcff5zIyEhCQ8+t4uratSthYWHExMQwffp0jh8/zujRo4mNjbXriiioZGFz6NAhFi9ezFtvvUV+fj59+/aluLiYZcuWaeKwiIjIRbjixsO33XYby5cvJyEhgeeff55GjRrx8ssv8+CDD1pjxo4dS0FBAUOHDiU3N5c2bdqwevVqatasaY2ZNWsWVatWpW/fvhQUFNCpUycWL15s82ild999l5EjR1pXT0VFRTFnzhzrcQ8PDz7//HOGDh1K+/bt8fb2Jjo6mhkzZtj9c1f4PjY9e/Zkw4YNREZG8uCDD9K9e3c8PDzw9PTku+++u6IKG93HRkREKsoZ97GZtPon+5yna4hdzuPOKvzXuXr1akaOHMmQIUMICdEXKyIiIleeCk8e/s9//sOpU6do3bo1bdq0Yc6cORw9etSRuYmIiLgFV9yg71pV4cImIiKC119/naysLAYPHkxycjL16tWjtLSUNWvW2DxXQkRERP7LXsu95dIqvdy7evXqPProo2zYsIEdO3YQHx/PlClTCAgIICoqyhE5ioiIiFTIZd3H5rzQ0FCmTZvGoUOHeO+99+yVk4iIiFupYrLPJpdW6ad7Xw20KkpERCrKGauiEtf+bJfzPNOpiV3O486c8NcpIiJybVO3xXn+1FCUiIiIyJVEHRsREREHU8fGeVTYiIiIOJhJa7WdRkNRIiIi4jbUsREREXEwDUU5jwobERERB9NIlPNoKEpERETchjo2IiIiDqYHWDqPChsREREH0xwb59FQlIiIiLgNdWxEREQcTCNRzqPCRkRExMGqoMrGWVTYiIiIOJg6Ns6jOTYiIiLiNtSxERERcTCtinIeFTYiIiIOpvvYOI+GokRERMRtqGMjIiLiYGrYOI8KGxEREQfTUJTzaChKRERE3IY6NiIiIg6mho3zqLARERFxMA2POI++axEREXEb6tiIiIg4mEljUU6jwkZERMTBVNY4jwobERERB9Nyb+fRHBsRERFxG+rYiIiIOJj6Nc6jwkZERMTBNBLlPBqKEhEREbehjo2IiIiDabm386iwERERcTANjziPvmsRERFxG+rYiIiIOJiGopxHhY2IiIiDqaxxHg1FiYiIiNtQx0ZERMTBNBTlPCpsREREHEzDI86jwkZERMTB1LFxHhWRIiIi4jZU2IiIiDiYyU7bn5GUlITJZCIuLs66zzAMJk2aRHBwMN7e3nTs2JFdu3bZvK+wsJARI0bg7++Pj48PUVFRHDp0yCYmNzeXmJgYLBYLFouFmJgYTpw4YROTmZlJ79698fHxwd/fn5EjR1JUVPQnP1VZKmxEREQczGSyz3a50tLSeO211/jLX/5is3/atGnMnDmTOXPmkJaWRlBQEF26dOHUqVPWmLi4OJYvX05ycjIbNmzg9OnTREZGUlJSYo2Jjo4mPT2dlJQUUlJSSE9PJyYmxnq8pKSEXr16kZ+fz4YNG0hOTmbZsmXEx8df/oe6CJNhGIbdz+piZ393dQYiInK1qOaE2ab/2pFtl/Pc2yKo0u85ffo0t956K/PmzePFF1+kZcuWvPzyyxiGQXBwMHFxcYwbNw44150JDAxk6tSpDB48mLy8POrUqcOSJUvo168fAIcPH6Z+/fqsXLmSbt26sWfPHsLCwkhNTaVNmzYApKamEhERwQ8//EBoaCirVq0iMjKSX375heDgYACSk5MZOHAgOTk5+Pr62uX7AXVsREREHK4KJrtshYWFnDx50mYrLCz8w2sPGzaMXr160blzZ5v9GRkZZGdn07VrV+s+s9lMhw4d2LhxIwBbt26luLjYJiY4OJjmzZtbYzZt2oTFYrEWNQBt27bFYrHYxDRv3txa1AB069aNwsJCtm7depnfavmu2MLmyJEjPP/8865OQ0RE5E+z11BUUlKSdR7L+S0pKemi101OTmbbtm3lxmRnn+siBQYG2uwPDAy0HsvOzsbLyws/P78/jAkICChz/oCAAJuYC6/j5+eHl5eXNcZertjCJjs7m8mTJ7s6DRERkStGQkICeXl5NltCQkK5sb/88gtPPvkk77zzDtWqVbvoOS9cim4YxiWXp18YU1785cTYg8vuY/P999//4fEff/zRSZmIiIg4lslOT4sym82YzeYKxW7dupWcnBzCw8Ot+0pKSvj666+ZM2eO9fdsdnY2devWtcbk5ORYuytBQUEUFRWRm5tr07XJycmhXbt21pgjR46Uuf7Ro0dtzrN582ab47m5uRQXF5fp5PxZLitsWrZsiclkory5y+f364ZGIiLiDlzx66xTp07s2LHDZt8jjzzCTTfdxLhx42jcuDFBQUGsWbOGVq1aAVBUVMT69euZOnUqAOHh4Xh6erJmzRr69u0LQFZWFjt37mTatGkAREREkJeXx5YtW7j99tsB2Lx5M3l5edbiJyIigpdeeomsrCxrEbV69WrMZrNN4WUPLitsateuzdSpU+nUqVO5x3ft2kXv3r2dnJWIiIh7qFmzJs2bN7fZ5+PjQ+3ata374+LiSExMJCQkhJCQEBITE6levTrR0dEAWCwWBg0aRHx8PLVr16ZWrVqMHj2aFi1aWCcjN2vWjO7duxMbG8vChQsBePzxx4mMjCQ0NBSArl27EhYWRkxMDNOnT+f48eOMHj2a2NhYu66IAhcWNuHh4Rw+fJgbbrih3OMnTpwot5sjIiJytalip6Eoexs7diwFBQUMHTqU3Nxc2rRpw+rVq6lZs6Y1ZtasWVStWpW+fftSUFBAp06dWLx4MR4eHtaYd999l5EjR1pXT0VFRTFnzhzrcQ8PDz7//HOGDh1K+/bt8fb2Jjo6mhkzZtj9M7nsPjbLly8nPz+fhx56qNzjubm5rFixggEDBlT63LqPjYiIVJQz7mPz791H7XKebmF17HIed6Yb9ImIyDXNGYXN6j32KWy6NlNhcylX7HJvERERkcpy2RwbERGRa4W9lnvLpamwERERcbAqqmucRkNRIiIi4jbUsREREXEwDUU5j8s7NikpKWzYsMH6eu7cubRs2ZLo6Ghyc3NdmJmIiIh92OshmHJpLi9sxowZw8mTJwHYsWMH8fHx9OzZk/379zNq1CgXZyciIiJXE5cPRWVkZBAWFgbAsmXLiIyMJDExkW3bttGzZ08XZyciIvLnaSjKeVzesfHy8uLMmTMAfPHFF9bbMdeqVcvayREREbmaVTHZZ5NLc3nH5o477mDUqFG0b9+eLVu28P777wOwd+9err/+ehdnJyIiIlcTl3ds5syZQ9WqVfnoo4+YP38+9erVA2DVqlV0797dxdlJebZ+m8aIoU/QueMd3HJzKOvWfmE9VlxczKx/TOe+Pr1p07olnTvewbMJY8nJOWJzjqKiIpJeeoEO7dvQpnVLRg57giPZ2c7+KCJO8+brC7nl5lCmJb1k3ffFmtU8ETuIDu3bcMvNofywZ48LMxRHMtnpf3JpLi9sGjRowGeffcZ3333HoEGDrPtnzZrFq6++6sLM5GIKCs4QGhrK089OKHPs7Nmz/LBnN48/MYT3P/yYma/M4eCBAzw5fIhN3LQpL7Fu7RqmzpjF4iVLOXPmDCOGDqakpMRZH0PEaXbu+J6PPnyfG28MtdlfUHCGlq1a8eRTo12UmTiLVkU5j8uHorZt24anpyctWrQA4F//+heLFi0iLCyMSZMm4eXl5eIM5UJ33NmBO+7sUO6xmjVrsvCNRTb7nn7mOR7s/3eyDh+mbnAwp06dYvmyZbw0ZRptI9oBkDh1Ot06dSR100ba33Gnwz+DiLOcyc8nYdwYJk5+kdcXzrc51juqDwC//nrIBZmJM6kmcR6Xd2wGDx7M3r17Adi/fz/9+/enevXqfPjhh4wdO9bF2Yk9nD59GpPJRE1fXwB279rJ778X065de2tMQEAgTZuG8F36dlelKeIQiS8+z113dbAW8SLiWC7v2Ozdu5eWLVsC8OGHH3LXXXexdOlSvvnmG/r378/LL7/8h+8vLCyksLDQZp/hYcZsNjsoY6mMwsJCXpk1gx69IqlRowYAvx07hqenJ74Wi01sLX9/jh075oo0RRxi1crP2bNnN0vf/8jVqYiLVdE4ktO4vGNjGAalpaXAueXe5+9dU79+/Qr9kktKSsJisdhs06cmOTRnqZji4mLGjX6K0lKDZ8dPuvQbDENjyOI2srOymDblJRKnTNc/tASTnTa5NJd3bFq3bs2LL75I586dWb9+PfPnnxuDzsjIIDAw8JLvT0hIKHOHYsND/xFxteLiYsbEx/HroUO8vuhta7cGoLa/P8XFxZzMy7Pp2hz/7TduadnKFemK2N3u3bs4/ttvPND3b9Z9JSUlbP02jeT33iVt+w48PDxcmKGIe3J5YfPyyy/z4IMP8sknn/Dss8/StGlTAD766CPatbv0mLTZXHbY6ezvDklVKuh8UZN58CBvLPon113nZ3M87ObmVK3qyaZN39Ct+7kO3dGjOezb9xNx8WNckbKI3bVp25aPPvnUZt/EZxNo2LgxjwyKVVFzrVG7xWlcXtj85S9/YceOHWX2T58+XT/4V6gz+flkZmZaX/966BA/7NmDxWKhTkAAo58ayZ49u5k9dyGlJSUcO3oUAIvFgqeXFzVr1uSv993HP6ZP5brr/PC1WJg5fSohITdqgqW4DR+fGoSE3Gizz7t6da6zXGfdn3fiBFlZWRw9mgPAgQMZAPj7++Nfp45zExaH0j1onMflhc3FVKtWzdUpyEXs2rWTxx552Pp6xrRzc5qi7v0rTwwbzldfrgOg73332rzvjUX/5Lbb2wAwZtwzeHhUZcyoOAoLz3J7mwhemDtFxaxcU776ch0Tnkuwvh43+ikAnhg6nCHDRrgqLZGrmskwDMOVCZSUlDBr1iw++OADMjMzKSoqsjl+/PjxSp9TQ1EiIlJR1ZzwT/wt+/Pscp7bG1suHXSNc/mqqMmTJzNz5kz69u1LXl4eo0aN4m9/+xtVqlRh0qRJrk5PRETkT9OqKOdxecemSZMmvPrqq/Tq1YuaNWuSnp5u3ZeamsrSpUsrfU51bEREpKKc0bFJs1PH5jZ1bC7J5R2b7Oxs6+MUatSoQV7eub/8yMhIPv/8c1emJiIiYh9q2TiNywub66+/nqysLACaNm3K6tWrAUhLS9NNrURExC3o6d7O4/LC5q9//Str164F4Mknn2T8+PGEhITw8MMP8+ijj7o4OxERkT9PT/d2HpfPsblQamoqGzdupGnTpkRFRV3WOTTHRkREKsoZc2y2Hjhpl/OEN/S1y3nc2RVX2NiDChsREakoZxQ22+xU2NyqwuaSXHKDvhUrVlQ49nK7NiIiIlcMDSM5jUs6NlWqVGxqj8lkoqSkpNLnV8dGREQqyikdm4N26tjcoI7NpbikY1NaWuqKy4qIiLiEVjQ5zxX7rCgRERF3oRVNzuOy5d7r1q0jLCyMkyfLtufy8vK4+eab+frrr12QmYiIiFytXFbYvPzyy8TGxuLrW3a80GKxMHjwYGbNmuWCzEREROxLNx52HpcVNt999x3du3e/6PGuXbuydetWJ2YkIiLiIKpsnMZlhc2RI0fw9PS86PGqVaty9OhRJ2YkIiIiVzuXFTb16tVjx44dFz3+/fffU7duXSdmJCIi4hh6VpTzuKyw6dmzJxMmTODs2bNljhUUFDBx4kQiIyNdkJmIiIh96VlRzuOyRyocOXKEW2+9FQ8PD4YPH05oaCgmk4k9e/Ywd+5cSkpK2LZtG4GBgZU+t27QJyIiFeWMG/TtPHTaLudpfn0Nu5zHnbn0WVEHDx5kyJAh/Pvf/+Z8GiaTiW7dujFv3jwaNmx4WedVYSMiIhWlwsa9XBEPwczNzWXfvn0YhkFISAh+fn5/6nwqbEREpKKcUtj8aqfCpp4Km0u5Igobe1NhIyIiFeWMwmbXr/l2Oc/N9Xzsch535rLJwyIiIiL2pmdFiYiIOJhWNDmPChsREREHU13jPBqKEhEREbehwkZERMTRXPCsqKSkJG677TZq1qxJQEAAffr04ccff7SJMQyDSZMmERwcjLe3Nx07dmTXrl02MYWFhYwYMQJ/f398fHyIiori0KFDNjG5ubnExMRgsViwWCzExMRw4sQJm5jMzEx69+6Nj48P/v7+jBw5kqKiosp9qApQYSMiIuJgrnikwvr16xk2bBipqamsWbOG33//na5du5Kf/98VWtOmTWPmzJnMmTOHtLQ0goKC6NKlC6dOnbLGxMXFsXz5cpKTk9mwYQOnT58mMjKSkpISa0x0dDTp6emkpKSQkpJCeno6MTEx1uMlJSX06tWL/Px8NmzYQHJyMsuWLSM+Pv5PfKvl03JvERG5pjljufcPWWfscp6b6la/7PcePXqUgIAA1q9fz1133YVhGAQHBxMXF8e4ceOAc92ZwMBApk6dyuDBg8nLy6NOnTosWbKEfv36AXD48GHq16/PypUr6datG3v27CEsLIzU1FTatGkDQGpqKhEREfzwww+EhoayatUqIiMj+eWXXwgODgYgOTmZgQMHkpOTg6+v75/8Zv5LHRsREREHs9ezogoLCzl58qTNVlhYWKEc8vLyAKhVqxYAGRkZZGdn07VrV2uM2WymQ4cObNy4EYCtW7dSXFxsExMcHEzz5s2tMZs2bcJisViLGoC2bdtisVhsYpo3b24tagC6detGYWEhW7duvZyv9KJU2IiIiDiYvabYJCUlWeexnN+SkpIueX3DMBg1ahR33HEHzZs3ByA7OxugzDMZAwMDrceys7Px8vIq80SAC2MCAgLKXDMgIMAm5sLr+Pn54eXlZY2xFy33FhERcTQ7rfdOSEhg1KhRNvvMZvMl3zd8+HC+//57NmzYUDa1C26yYxhGmX0XujCmvPjLibEHdWxERESuEmazGV9fX5vtUoXNiBEjWLFiBV9++SXXX3+9dX9QUBBAmY5JTk6OtbsSFBREUVERubm5fxhz5MiRMtc9evSoTcyF18nNzaW4uLhMJ+fPUmEjIiLiYK5YFWUYBsOHD+fjjz9m3bp1NGrUyOZ4o0aNCAoKYs2aNdZ9RUVFrF+/nnbt2gEQHh6Op6enTUxWVhY7d+60xkRERJCXl8eWLVusMZs3byYvL88mZufOnWRlZVljVq9ejdlsJjw8vFKf61K0KkpERK5pzlgVtS+nwC7naRrgXeHYoUOHsnTpUv71r38RGhpq3W+xWPD2PneeqVOnkpSUxKJFiwgJCSExMZGvvvqKH3/8kZo1awIwZMgQPvvsMxYvXkytWrUYPXo0v/32G1u3bsXDwwOAHj16cPjwYRYuXAjA448/zg033MCnn34KnFvu3bJlSwIDA5k+fTrHjx9n4MCB9OnTh9mzZ9vluzlPhY2IiFzT3LWwudjclUWLFjFw4EDgXFdn8uTJLFy4kNzcXNq0acPcuXOtE4wBzp49y5gxY1i6dCkFBQV06tSJefPmUb9+fWvM8ePHGTlyJCtWrAAgKiqKOXPmcN1111ljMjMzGTp0KOvWrcPb25vo6GhmzJhRoTlClaHCRkRErmnOKGx+tlNh06QShc21SquiREREHE1PwXQaTR4WERERt6GOjYiIiINVdkWTXD4VNiIiIg5m53vQyR/QUJSIiIi4DXVsREREHEwNG+dRYSMiIuJoqmycRoWNiIiIg2nysPNojo2IiIi4DXVsREREHEyropxHhY2IiIiDqa5xHg1FiYiIiNtQx0ZERMTBNBTlPCpsREREHE6VjbNoKEpERETchjo2IiIiDqahKOdRYSMiIuJgqmucR0NRIiIi4jbUsREREXEwDUU5jwobERERB9OzopxHhY2IiIijqa5xGs2xEREREbehjo2IiIiDqWHjPCpsREREHEyTh51HQ1EiIiLiNtSxERERcTCtinIeFTYiIiKOprrGaTQUJSIiIm5DHRsREREHU8PGeVTYiIiIOJhWRTmPhqJERETEbahjIyIi4mBaFeU8KmxEREQcTENRzqOhKBEREXEbKmxERETEbWgoSkRExME0FOU8KmxEREQcTJOHnUdDUSIiIuI21LERERFxMA1FOY8KGxEREQdTXeM8GooSERERt6GOjYiIiKOpZeM0KmxEREQcTKuinEdDUSIiIuI21LERERFxMK2Kch4VNiIiIg6musZ5VNiIiIg4miobp9EcGxEREXEb6tiIiIg4mFZFOY8KGxEREQfT5GHn0VCUiIiIuA2TYRiGq5MQ91RYWEhSUhIJCQmYzWZXpyNyxdDPhojjqLARhzl58iQWi4W8vDx8fX1dnY7IFUM/GyKOo6EoERERcRsqbERERMRtqLARERERt6HCRhzGbDYzceJETY4UuYB+NkQcR5OHRURExG2oYyMiIiJuQ4WNiIiIuA0VNiIiIuI2VNhIhZlMJj755BNXpyFyRdHPhciVRYWNAJCdnc2IESNo3LgxZrOZ+vXr07t3b9auXevq1AAwDINJkyYRHByMt7c3HTt2ZNeuXa5OS9zclf5z8fHHH9OtWzf8/f0xmUykp6e7OiURl1NhIxw4cIDw8HDWrVvHtGnT2LFjBykpKdx9990MGzbM1ekBMG3aNGbOnMmcOXNIS0sjKCiILl26cOrUKVenJm7qavi5yM/Pp3379kyZMsXVqYhcOQy55vXo0cOoV6+ecfr06TLHcnNzrX8GjOXLl1tfjx071ggJCTG8vb2NRo0aGc8995xRVFRkPZ6enm507NjRqFGjhlGzZk3j1ltvNdLS0gzDMIwDBw4YkZGRxnXXXWdUr17dCAsLMz7//PNy8ystLTWCgoKMKVOmWPedPXvWsFgsxoIFC/7kpxcp35X+c/G/MjIyDMDYvn37ZX9eEXdR1cV1lbjY8ePHSUlJ4aWXXsLHx6fM8euuu+6i761ZsyaLFy8mODiYHTt2EBsbS82aNRk7diwADz74IK1atWL+/Pl4eHiQnp6Op6cnAMOGDaOoqIivv/4aHx8fdu/eTY0aNcq9TkZGBtnZ2XTt2tW6z2w206FDBzZu3MjgwYP/xDcgUtbV8HMhIuVTYXON27dvH4ZhcNNNN1X6vc8995z1zw0bNiQ+Pp7333/f+h/wzMxMxowZYz13SEiINT4zM5P77ruPFi1aANC4ceOLXic7OxuAwMBAm/2BgYEcPHiw0nmLXMrV8HMhIuXTHJtrnPH/N542mUyVfu9HH33EHXfcQVBQEDVq1GD8+PFkZmZaj48aNYrHHnuMzp07M2XKFH7++WfrsZEjR/Liiy/Svn17Jk6cyPfff3/J612Yo2EYl5W3yKVcTT8XImJLhc01LiQkBJPJxJ49eyr1vtTUVPr370+PHj347LPP2L59O88++yxFRUXWmEmTJrFr1y569erFunXrCAsLY/ny5QA89thj7N+/n5iYGHbs2EHr1q2ZPXt2udcKCgoC/tu5OS8nJ6dMF0fEHq6GnwsRuQiXzvCRK0L37t0rPUlyxowZRuPGjW1iBw0aZFgslotep3///kbv3r3LPfb0008bLVq0KPfY+cnDU6dOte4rLCzU5GFxqCv95+J/afKwyH+pYyPMmzePkpISbr/9dpYtW8ZPP/3Enj17ePXVV4mIiCj3PU2bNiUzM5Pk5GR+/vlnXn31Veu/OgEKCgoYPnw4X331FQcPHuSbb74hLS2NZs2aARAXF8e///1vMjIy2LZtG+vWrbMeu5DJZCIuLo7ExESWL1/Ozp07GThwINWrVyc6Otr+X4gIV/7PBZyb5Jyens7u3bsB+PHHH0lPTy/T3RS5pri6spIrw+HDh41hw4YZN9xwg+Hl5WXUq1fPiIqKMr788ktrDBcsax0zZoxRu3Zto0aNGka/fv2MWbNmWf9lWlhYaPTv39+oX7++4eXlZQQHBxvDhw83CgoKDMMwjOHDhxtNmjQxzGazUadOHSMmJsY4duzYRfMrLS01Jk6caAQFBRlms9m46667jB07djjiqxCxutJ/LhYtWmQAZbaJEyc64NsQuTqYDOP/Z8mJiIiIXOU0FCUiIiJuQ4WNiIiIuA0VNiIiIuI2VNiIiIiI21BhIyIiIm5DhY2IiIi4DRU2IiIi4jZU2Ii4oUmTJtGyZUvr64EDB9KnTx+n53HgwAFMJhPp6elOv7aIXJtU2Ig40cCBAzGZTJhMJjw9PWncuDGjR48mPz/fodd95ZVXWLx4cYViVYyIyNWsqqsTELnWdO/enUWLFlFcXMx//vMfHnvsMfLz85k/f75NXHFxMZ6enna5psVisct5RESudOrYiDiZ2WwmKCiI+vXrEx0dzYMPPsgnn3xiHT566623aNy4MWazGcMwyMvL4/HHHycgIABfX1/uuecevvvuO5tzTpkyhcDAQGrWrMmgQYM4e/aszfELh6JKS0uZOnUqTZs2xWw206BBA1566SUAGjVqBECrVq0wmUx07NjR+r5FixbRrFkzqlWrxk033cS8efNsrrNlyxZatWpFtWrVaN26Ndu3b7fjNycicmnq2Ii4mLe3N8XFxQDs27ePDz74gGXLluHh4QFAr169qFWrFitXrsRisbBw4UI6derE3r17qVWrFh988AETJ05k7ty53HnnnSxZsoRXX32Vxo0bX/SaCQkJvP7668yaNYs77riDrKwsfvjhB+BccXL77bfzxRdfcPPNN+Pl5QXA66+/zsSJE5kzZw6tWrVi+/btxMbG4uPjw4ABA8jPzycyMpJ77rmHd955h4yMDJ588kkHf3siIhdw8UM4Ra4pAwYMMO69917r682bNxu1a9c2+vbta0ycONHw9PQ0cnJyrMfXrl1r+Pr6GmfPnrU5T5MmTYyFCxcahmEYERERxhNPPGFzvE2bNsYtt9xS7nVPnjxpmM1m4/XXXy83x4yMDAMwtm/fbrO/fv36xtKlS232vfDCC0ZERIRhGIaxcOFCo1atWkZ+fr71+Pz588s9l4iIo2goSsTJPvvsM2rUqEG1atWIiIjgrrvuYvbs2QDccMMN1KlTxxq7detWTp8+Te3atalRo4Z1y8jI4OeffwZgz549RERE2Fzjwtf/a8+ePRQWFtKpU6cK53z06FF++eUXBg0aZJPHiy++aJPHLbfcQvXq1SuUh4iII2goSsTJ7r77bubPn4+npyfBwcE2E4R9fHxsYktLS6lbty5fffVVmfNcd911l3V9b2/vSr+ntLQUODcc1aZNG5tj54fMDMO4rHxEROxJhY2Ik/n4+NC0adMKxd56661kZ2dTtWpVGjZsWG5Ms2bNSE1N5eGHH7buS01Nveg5Q0JC8Pb2Zu3atTz22GNljp+fU1NSUmLdFxgYSL169di/fz8PPvhguecNCwtjyZIlFBQUWIunP8pDRMQRNBQlcgXr3LkzERER9OnTh3//+98cOHCAjRs38txzz/Htt98C8OSTT/LWW2/x1ltvsXfvXiZOnMiuXbsues5q1aoxbtw4xo4dyz//+U9+/vlnUlNTefPNNwEICAjA29ublJQUjhw5Ql5eHnDupn9JSUm88sor7N27lx07drBo0SJmzpwJQHR0NFWqVGHQoEHs3r2blStXMmPGDAd/QyIitlTYiFzBTCYTK1eu5K677uLRRx/lxhtvpH///hw4cIDAwEAA+vXrx4QJExg3bhzh4eEcPHiQIUOG/OF5x48fT3x8PBMmTKBZs2b069ePnJwcAKpWrcqrr77KwoULCQ4O5t577wXgscce44033mDx4sW0aNGCDh06sHjxYuvy8Bo1avDpp5+ye/duWrVqxbPPPsvUqVMd+O2IiJRlMjQwLiIiIm5CHRsRERFxGypsRERExG2osBERERG3ocJGRERE3IYKGxEREXEbKmxERETEbaiwEREREbehwkZERETchgobERERcRsqbERERMRtqLARERERt6HCRkRERNzG/wEwexooEpDSGQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# matrice de confusion\n", - "\n", - "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('Actual')\n", - "plt.title('Confusion Matrix')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "311f0208-b79e-4e80-8016-075a98708f6e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# on trace la courbe ROC\n", - "\n", - "# Prédictions sur l'ensemble de test\n", - "y_pred_prob = clf.predict_proba(X_test)[:, 1]\n", - "\n", - "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", - "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n", - "\n", - "# Calcul de l'aire sous la courbe ROC (AUC)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "# Tracé de la courbe ROC\n", - "plt.figure(figsize=(8, 6))\n", - "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')\n", - "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", - "plt.xlabel('Taux de faux positifs (FPR)')\n", - "plt.ylabel('Taux de vrais positifs (TPR)')\n", - "plt.title('Courbe ROC : random forest')\n", - "plt.legend(loc='lower right')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "e20e9ac2-7232-4418-87f0-c7299a6d7de3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calcul des valeurs de précision et de rappel à différents seuils\n", - "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_prob)\n", - "\n", - "# Calcul de l'aire sous la courbe PR (AUC-PR)\n", - "average_precision = average_precision_score(y_test, y_pred_prob)\n", - "\n", - "# Tracé de la courbe PR\n", - "plt.figure(figsize=(8, 6))\n", - "plt.step(recall, precision, color='b', alpha=0.2, where='post')\n", - "plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\n", - "plt.xlabel('Rappel')\n", - "plt.ylabel('Précision')\n", - "plt.ylim([0.0, 1.05])\n", - "plt.xlim([0.0, 1.0])\n", - "plt.title(f'Courbe PR (AUC-PR = {average_precision:.2f})')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "0633df2d-686e-4f9d-823e-e54c23f983f8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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27NkYNGgQDh06VOMoUnp6OkaMGIGXXnoJL7/8Mvbv34+pU6catDl8+DAGDhyIDz74AEuXLsXly5cxceJETJw4EcuWLTOqX0D56bCmTZti8eLF2Ldvn/5JDT4+Pli2bBkGDRpk8ac3MABZCE9+ERHVHadOnYIQAi1atLhtu82bN+PQoUNIS0tDYGAgAGDFihVo3bo19u3bh86dO9f6mM2aNcPcuXOrrX/sscfw3HPPAQA++OADbNq0CV988QUWLlyINWvWwM7ODt98843+PkvLli2Du7s7tm/fjgEDBlTb36JFixASEoL58+dDJpOhefPmOHz4MD7++GN9m08++QSjRo1CdHQ0ACA0NBSff/45+vTpg0WLFhl9Q0I3Nze4uLhALpfDz8/PYJu7u3u1dZbAAGQhNv+8ESKiCg5KOY7NGijZsWujcgTmTjdvTElJQWBgoD78AECrVq3g7u6OlJQUowJQp06dalzfvXv3asuVV1AlJibi1KlTBs/HBIDi4mKcPn36ljV369bNoG83H6Nyv6tWrdKvE0JAp9MhLS0NLVu2rHW/rBUDEBERWZRMJqvVaSgphYaGQiaTISUlpdr8naqEEDWGpKrr7ezsqs090mg01T5T06m2W6nct06nQ0REhEFQqdSgQYNb1nwnOp0OL774IiZPnlxtW+PGjWtdpzXjJGgL4SkwIqK6w9PTEwMHDsRXX31V48TfyvvltGrVCufPn0d6erp+27Fjx5CTk6MfJWnQoAEyMjIMPm/MPXCqTriuXK48NdexY0ecPHkSPj4+aNasmcHr5quwKrVq1arGfVbVsWNHHD16tNo+mzVrZtKr05RKJbRarcn2ZwwGICIiohosXLgQWq0WXbp0wS+//IKTJ08iJSUFn3/+uf6UUWRkJNq2bYvRo0fjwIED2Lt3L5566in06dNHf0rr/vvvx/79+/Hdd9/h5MmTmDFjBo4cOVLrOn766Sd8++23OHHiBGbMmIG9e/di4sSJAIDRo0fD29sbDz30EHbs2IG0tDTEx8fjlVdewYULF2rc30svvYTTp09jypQpSE1NxerVqw3u1wMAb7zxBnbv3o0JEyYgOTkZJ0+exLp16zBp0qS7+CZvrUmTJtiyZQsyMzORnZ1t0n3fCQMQERFRDYKDg3HgwAH069cPU6dORXh4OKKiorBlyxYsWrQIwI07NXt4eOC+++5DZGQkQkJC8MMPP+j3M3DgQLz77ruYNm0aOnfujLy8PDz11FO1ruP999/HmjVr0LZtWyxfvhyrVq1Cq1atAACOjo74+++/0bhxY4wYMQItW7bEs88+i6KiIri6uta4v8aNG+OXX37B+vXr0a5dO3z99deIiYkxaNO2bVvEx8fj5MmT6N27Nzp06IB3330X/v7+xn6Nt/Xpp59i06ZNCAwMRIcOHUy67zuRCWNuilBP5ebmws3NDTk5Obf8B3O3mrz5BwBgcv9QTIkKM+m+iYjqguLiYqSlpSE4ONjoq4eIbvfv517+fnMEyFKYM4mIiKwGAxARERHZHAYgS7nDvSSIiIjIchiAiIiIyOYwAFlIamau1CUQEUmK19zQ3TDXvxsGIDPKLijVv//r6KXbtCQiqr8qH3JZWlp6h5ZE1RUWFgIov2miKVn3vcjruOIyae5uSURkTRQKBRwdHXH58mUolUrY2fH/e9OdCSFQWFiIrKwsuLu7m/xp8QxARERkVjKZDP7+/khLS8O5c+ekLofqGHM9LZ4BiIiIzE6lUiE0NJSnwcgoSqXS5CM/lRiAiIjIIuzs7HgnaLIaPBFrRrzggYiIyDoxABEREZHNYQAiIiIim8MARERERDaHAciMOAWIiIjIOjEAERERkc1hACIiIiKbwwBERERENocByIz45GMiIiLrxABERERENocBiIiIiGwOA5AZ8QwYERGRdWIAIiIiIpvDAEREREQ2hwGIiIiIbA4DEBEREdkcBiAz4iRoIiIi68QAZEaCj0MlIiKySgxAZmQnk0ldAhEREdWAAYiIiIhsDgMQERER2RxJA9DMmTMhk8kMXn5+fvrtQgjMnDkTAQEBcHBwQN++fXH06FGDfZSUlGDSpEnw9vaGk5MThg0bhgsXLli6K0RERFSHSD4C1Lp1a2RkZOhfhw8f1m+bO3cu5s2bhy+//BL79u2Dn58foqKikJeXp28THR2NtWvXYs2aNdi5cyfy8/Px4IMPQqvVStEdA7wKjIiIyDopJC9AoTAY9akkhMCCBQvw9ttvY8SIEQCA5cuXw9fXF6tXr8aLL76InJwcLF26FCtWrEBkZCQAYOXKlQgMDMTmzZsxcOBAi/blZrwKjIiIyDpJPgJ08uRJBAQEIDg4GE888QTOnDkDAEhLS0NmZiYGDBigb6tWq9GnTx8kJCQAABITE6HRaAzaBAQEIDw8XN+mJiUlJcjNzTV4ERERke2QNAB17doV3333Hf766y8sWbIEmZmZ6NGjB65evYrMzEwAgK+vr8FnfH199dsyMzOhUqng4eFxyzY1mTNnDtzc3PSvwMBAE/esHE+BERERWSdJA9DgwYPxyCOPoE2bNoiMjMQff/wBoPxUVyXZTffSEUJUW3ezO7WZPn06cnJy9K/09PR76MWtbUvNMst+iYiI6N5IfgqsKicnJ7Rp0wYnT57Uzwu6eSQnKytLPyrk5+eH0tJSZGdn37JNTdRqNVxdXQ1e5vD3ictm2S8RERHdG6sKQCUlJUhJSYG/vz+Cg4Ph5+eHTZs26beXlpYiPj4ePXr0AABERERAqVQatMnIyMCRI0f0baTEM2BERETWSdKrwF577TUMHToUjRs3RlZWFmbPno3c3FyMHTsWMpkM0dHRiImJQWhoKEJDQxETEwNHR0eMGjUKAODm5oZx48Zh6tSp8PLygqenJ1577TX9KTUiIiKimkgagC5cuID//Oc/uHLlCho0aIBu3bphz549CAoKAgBMmzYNRUVFGD9+PLKzs9G1a1ds3LgRLi4u+n3Mnz8fCoUCI0eORFFREfr374/Y2FjI5XKpuqV38yTo2sxfIiIiIvOTCcFrlXJzc+Hm5oacnByTzgcas/Qf7Dh5Rb98OuYByO0YgIiIiEzhXv5+W9UcoPpOx6xJRERkFRiALIgBiIiIyDowAJlR9TlA0tRBREREhhiAzOjmZ4FpdUxARERE1oAByIJ4CoyIiMg6MACZ0aXcEoNlDgARERFZBwYgMzqVlW+wzDsOEBERWQcGIAviCBAREZF1YACyIM4BIiIisg4MQBbEAERERGQdGIAsiPmHiIjIOjAAWRBHgIiIiKwDA5AFcRI0ERGRdWAAsiAdExAREZFVYACyIJ4BIyIisg4MQBbEOUBERETWgQHIghiAiIiIrAMDkAVdyC6SugQiIiICA5BF/bA/XeoSiIiICAxAlsUzYERERFaBAciCOAeIiIjIOjAAWRDzDxERkXVgALIgwXNgREREVoEByIKGtWsodQlEREQEBiCLslfy6yYiIrIG/ItsQXwUGBERkXVgALIgLRMQERGRVWAAsiAGICIiIuvAAGRBWl4HT0REZBUYgCzoiy0npS6BiIiIwABkUSez8qUugYiIiMAARERERDaIAYiIiIhsDgMQERER2RwGICIiIrI5DEBERERkcxiAiIiIyOYwABEREZHNYQAiIiIim8MARERERDaHAcjCyrQ6qUsgIiKyeQxAFsYHohIREUmPAcjCmH+IiIikxwBkYTomICIiIskxAFmYVscAREREJDUGIAtjACIiIpIeA5CFlTEAERERSY4ByEIUdjIAHAEiIiKyBlYTgObMmQOZTIbo6Gj9OiEEZs6ciYCAADg4OKBv3744evSowedKSkowadIkeHt7w8nJCcOGDcOFCxcsXP2dySsCkIb3ASIiIpKcVQSgffv2YfHixWjbtq3B+rlz52LevHn48ssvsW/fPvj5+SEqKgp5eXn6NtHR0Vi7di3WrFmDnTt3Ij8/Hw8++CC0Wq2lu3FbSnn5V80RICIiIulJHoDy8/MxevRoLFmyBB4eHvr1QggsWLAAb7/9NkaMGIHw8HAsX74chYWFWL16NQAgJycHS5cuxaefforIyEh06NABK1euxOHDh7F582apulSjyhEgzgEiIiKSnuQBaMKECRgyZAgiIyMN1qelpSEzMxMDBgzQr1Or1ejTpw8SEhIAAImJidBoNAZtAgICEB4erm9Tk5KSEuTm5hq8zK1yDlCZlgGIiIhIagopD75mzRocOHAA+/btq7YtMzMTAODr62uw3tfXF+fOndO3UalUBiNHlW0qP1+TOXPm4P3337/X8o2ikFeOAHEOEBERkdQkGwFKT0/HK6+8gpUrV8Le3v6W7WQymcGyEKLaupvdqc306dORk5Ojf6WnpxtXfC292CcEAKBS2EFhxzlARERE1kKyAJSYmIisrCxERERAoVBAoVAgPj4en3/+ORQKhX7k5+aRnKysLP02Pz8/lJaWIjs7+5ZtaqJWq+Hq6mrwMgcfl/JgN6i1X5WrwBiAiIiIpCZZAOrfvz8OHz6M5ORk/atTp04YPXo0kpOTERISAj8/P2zatEn/mdLSUsTHx6NHjx4AgIiICCiVSoM2GRkZOHLkiL6NlETFc79kshunwDgCREREJD3J5gC5uLggPDzcYJ2TkxO8vLz066OjoxETE4PQ0FCEhoYiJiYGjo6OGDVqFADAzc0N48aNw9SpU+Hl5QVPT0+89tpraNOmTbVJ1VLTT4LmHCAiIiLJSToJ+k6mTZuGoqIijB8/HtnZ2ejatSs2btwIFxcXfZv58+dDoVBg5MiRKCoqQv/+/REbGwu5XC5h5dXJK+YA8SowIiIi6clE5XkaG5abmws3Nzfk5OSYdD7QNzvOYPYfKXiofQDOXC7A4Ys5WPZ0Z/Rr4WOyYxAREdmqe/n7Lfl9gGzFjcvgbT5vEhERSY4ByEJu3AiRc4CIiIikxgBkIXwUBhERkfVgALIQPgyViIjIejAAWYAMqHIjRJ4CIyIikhoDkIVUzgHS8aI7IiIiyTEAWQjnABEREVkPBiALqQxAnANEREQkvVoFoNzcXHPXUe/xTtBERETWo1YByMPDA1lZWQCA+++/H9evXzdnTfUS5wARERFZj1oFIGdnZ1y9ehUAsH37dmg0GrMWVR9xDhAREZH1qNXDUCMjI9GvXz+0bNkSAPDwww9DpVLV2Hbr1q2mq64ekcs4B4iIiMha1CoArVy5EsuXL8fp06cRHx+P1q1bw9HR0dy11XmVZ7tkMhnklc8C4xwgIiIiydUqADk4OOCll14CAOzfvx8ff/wx3N3dzVlXvVM5B0jLOUBERESSq1UAqmrbtm3mqKPeu3EZPO8ETUREJLVaBaApU6bggw8+gJOTE6ZMmXLbtvPmzTNJYfVN5RwgToImIiKSXq0CUFJSkv7KrwMHDkBW8cecaq9yDtD/4s/g2Z7B8HW1l7giIiIi21WrAFT1tNf27dvNVUu9VjkHCABe/SEZq5/vJmE1REREts3oR2E8++yzyMvLq7a+oKAAzz77rEmKqo8q7wQNAEnnr0tXCBERERkfgJYvX46ioqJq64uKivDdd9+ZpKj6SF7ltGGRRithJURERFTrq8Byc3MhhIAQAnl5ebC3vzGHRavVYsOGDfDx8TFLkXWdDIBCznlTRERE1qLWAcjd3R0ymQwymQxhYWHVtstkMrz//vsmLa6uE7hxxRfvAE1ERGQ9ah2Atm3bBiEE7r//fvzyyy/w9PTUb1OpVAgKCkJAQIBZiqwPlu1Kk7oEIiIiqlDrANSnTx8AQFpaGgIDA2FnZ/T0IZuWXcgHyBIREVkLo+8EHRQUBAAoLCzE+fPnUVpaarC9bdu2pqmMiIiIyEyMDkCXL1/GM888gz///LPG7Votr3AiIiIi62b0eazo6GhkZ2djz549cHBwQFxcHJYvX47Q0FCsW7fOHDUSERERmZTRI0Bbt27F//3f/6Fz586ws7NDUFAQoqKi4Orqijlz5mDIkCHmqLPOe7xTIH7Yny51GURERIS7GAEqKCjQ3+/H09MTly9fBgC0adMGBw4cMG119YUMeCUyVOoqiIiIqILRAah58+ZITU0FALRv3x7/+9//cPHiRXz99dfw9/c3eYF1mahy658AdweM6NgQAODppJKoIiIiIgLu4hRYdHQ0MjIyAAAzZszAwIEDsWrVKqhUKsTGxpq6vnpl8v2h+PXARWjKdFKXQkREZNOMDkCjR4/Wv+/QoQPOnj2L48ePo3HjxvD29jZpcfWNUlE+4JZXUgYhBGQyPh6DiIhICvd8N0NHR0d07NiR4acWlHY3As/pywUSVkJERGTbajUCNGXKlFrvcN68eXddTL1XZcCnqJT3SyIiIpJKrQJQUlJSrXbGUzq3p6zy+JBSLecBERERSaVWAWjbtm3mrsMmeFS5+stZbfT0KyIiIjIRPtHUAmRVzn35u9kDAEp5JRgREZFkjB6G6Nev321PdW3duvWeCqpPRA3rcovKnwpfXMY5QERERFIxOgC1b9/eYFmj0SA5ORlHjhzB2LFjTVVXvVVQMfk5NuEsOjfxlLgaIiIi22R0AJo/f36N62fOnIn8/Px7LshW/HEoA1+NkroKIiIi22SyOUBPPvkkvv32W1PtjoiIiMhsTBaAdu/eDXt7e1Ptrt4b1NpP6hKIiIhsltGnwEaMGGGwLIRARkYG9u/fj3fffddkhdVXw9sH4Lfkf9E20E3qUoiIiGyW0QHIzc3wD7ednR2aN2+OWbNmYcCAASYrrD6petGcY8X9f3gZPBERkXSMDkDLli0zRx02QyUvP+vIAERERCSde5oDlJ+fj9zcXIMX3SBquBHQkYs5AICF209buBoiIiKqZHQASktLw5AhQ+Dk5AQ3Nzd4eHjAw8MD7u7u8PDwMEeN9cr+c9lSl0BERGTzjA5Ao0ePRnZ2Nr799lts2bIFW7duxdatW7Ft2zaj7wK9aNEitG3bFq6urnB1dUX37t3x559/6rcLITBz5kwEBATAwcEBffv2xdGjRw32UVJSgkmTJsHb2xtOTk4YNmwYLly4YGy3iIiIyIYYPQfo0KFDSExMRPPmze/54I0aNcJHH32EZs2aAQCWL1+Ohx56CElJSWjdujXmzp2LefPmITY2FmFhYZg9ezaioqKQmpoKFxcXAEB0dDTWr1+PNWvWwMvLC1OnTsWDDz6IxMREyOXye67R1LydVbiSXyp1GURERDbN6BGgzp07Iz093SQHHzp0KB544AGEhYUhLCwMH374IZydnbFnzx4IIbBgwQK8/fbbGDFiBMLDw7F8+XIUFhZi9erVAICcnBwsXboUn376KSIjI9GhQwesXLkShw8fxubNm01So6nNGNoaANDY01HiSoiIiGyX0SNA33zzDV566SVcvHgR4eHhUCqVBtvbtm17V4VotVr89NNPKCgoQPfu3ZGWlobMzEyDS+vVajX69OmDhIQEvPjii0hMTIRGozFoExAQgPDwcCQkJGDgwIE1HqukpAQlJSX6ZUtO3nZQlo9Knb9WaLFjEhERkSGjA9Dly5dx+vRpPPPMM/p1MpkMQgjIZDJotcY95fzw4cPo3r07iouL4ezsjLVr16JVq1ZISEgAAPj6+hq09/X1xblz5wAAmZmZUKlU1SZf+/r6IjMz85bHnDNnDt5//32j6rwXVW4DhEMXrlvsuERERFQzowPQs88+iw4dOuD777+Hr68vZFXv8ncXmjdvjuTkZFy/fh2//PILxo4di/j4eP32m/dfGbRu505tpk+fjilTpuiXc3NzERgYeJc9MI696sa8pL1p19AlmE+EJyIisjSjA9C5c+ewbt06/cTle6VSqfT76tSpE/bt24fPPvsMb7zxBoDyUR5/f399+6ysLP2okJ+fH0pLS5GdnW0wCpSVlYUePXrc8phqtRpqtdok9RurY+MbdY78326c/WiIJHUQERHZMqMnQd9///04ePCgOWoBUD56U1JSguDgYPj5+WHTpk36baWlpYiPj9eHm4iICCiVSoM2GRkZOHLkyG0DkKUIVL8TYofG7pYvhIiIiAwYPQI0dOhQvPrqqzh8+DDatGlTbRL0sGHDar2vt956C4MHD0ZgYCDy8vKwZs0abN++HXFxcZDJZIiOjkZMTAxCQ0MRGhqKmJgYODo6YtSoUQDKn0s2btw4TJ06FV5eXvD09MRrr72GNm3aIDIy0tiuWUTlozCIiIhIOkYHoJdeegkAMGvWrGrbjJ0EfenSJYwZMwYZGRlwc3ND27ZtERcXh6ioKADAtGnTUFRUhPHjxyM7Oxtdu3bFxo0b9fcAAoD58+dDoVBg5MiRKCoqQv/+/REbG2uV9wACqs9pIiIiIsuTCVHTE6tsS25uLtzc3JCTkwNXV1eT7Xfh9lOYG5eKxyIa4ZPH2unXN3nzD/37vW/1h4+rvcmOSUREZCvu5e83z8dIbE/aNalLICIisjlGnwKr6dRXVe+9995dF1Nf3XzW650hLTH7jxQAwOW8kho+QUREROZkdABau3atwbJGo0FaWhoUCgWaNm3KAFQLrg43Jo5/8PsxjOsVLGE1REREtsfoAJSUlFRtXW5uLp5++mk8/PDDJimqvuNzwIiIiKRlkjlArq6umDVrFt59911T7K7euNX08q68+zMREZGkTDYJ+vr168jJyTHV7uo1XgpPREQkLaNPgX3++ecGy0IIZGRkYMWKFRg0aJDJCqvvZLJbjxARERGReRkdgObPn2+wbGdnhwYNGmDs2LGYPn26yQqr7/5+vR96z90GANBodVDyDtFEREQWY3QASktLM0cdNse3ys0PC0rK4O6okrAaIiIi22L0sENOTg6uXat+875r164hNzfXJEXVNzJUn/OjUtjpnwtWUFr7x4cQERHRvTM6AD3xxBNYs2ZNtfU//vgjnnjiCZMUZSuc1OXPKysoKZO4EiIiIttidAD6559/0K9fv2rr+/bti3/++cckRdkKJ3X5Gch8BiAiIiKLMjoAlZSUoKys+h9sjUaDoqIikxRlK5wrAlBhCU+BERERWZLRAahz585YvHhxtfVff/01IiIiTFKUrXBUlZ8Ce3lVosSVEBER2RajrwL78MMPERkZiYMHD6J///4AgC1btmDfvn3YuHGjyQusz3KKNACAvOIyJJ3PRofGHhJXREREZBuMHgHq2bMndu/ejcDAQPz4449Yv349mjVrhkOHDqF3797mqLHeupJfqn//YcXT4YmIiMj8jB4BAoD27dtj1apVpq7F5lSOAAHA1YLS27QkIiIiU+Lthy2gNo/+SrtSYP5CiIiICAADkFX5bPNJqUsgIiKyCQxAEgpp4GSwPH/zCYkqISIisi0MQGYk7vC497hX7kMzH2cLVUNERESVGIAkpFLYYfrgFlKXQUREZHOMvgqsoKAAH330EbZs2YKsrCzodDqD7WfOnDFZcbag8nEYABDmy9EgIiIiSzA6AD333HOIj4/HmDFj4O/vD1ltLnGiW+rcxBP2SjsUa3Q4cSkfZVodFHIOzBEREZmT0QHozz//xB9//IGePXuaox6bI7eTYfXz3TBiYQIA4NTlfLTwc5W4KiIiovrN6KEGDw8PeHp6mqOWeutOg2RNG9w49TVowQ4zV0NERERGB6APPvgA7733HgoLC81Rj01yc1BKXQIREZFNMfoU2KefforTp0/D19cXTZo0gVJp+Mf7wIEDJiuOiIiIyByMDkDDhw83Qxn10x1uA3RLWp2A3I6Ty4mIiMzF6AA0Y8YMc9Rh807MHoywd/4EAFwvLIWXs1riioiIiOovXm9tJVSKG7+K2ISz0hVCRERkA2oVgDw9PXHlyhUAN64Cu9WL7t0XW0+htEx354ZERER0V2p1Cmz+/PlwcXEBACxYsMCc9VCFxHPZ6N7US+oyiIiI6qVaBaCxY8fW+J5qq3YTmj2dVLhWUAoA+M+SPbi/hQ++fbqzOQsjIiKySfc0B6ioqAi5ubkGL7p70ZGhBstbj2ehWKOVqBoiIqL6y+gAVFBQgIkTJ8LHxwfOzs7w8PAweNHdG9kpsNq6zJxiCSohIiKq34wOQNOmTcPWrVuxcOFCqNVqfPPNN3j//fcREBCA7777zhw12gx7pRzvPtjKYN2/OUUSVUNERFR/GX0foPXr1+O7775D37598eyzz6J3795o1qwZgoKCsGrVKowePdocddZJd3MfRF9Xw/v/ZFznCBAREZGpGT0CdO3aNQQHBwMAXF1dce3aNQBAr1698Pfff5u2Ohvk42JvsJzBESAiIiKTMzoAhYSE4OzZswCAVq1a4ccffwRQPjLk7u5uytpskquD4aDcfzeekKgSIiKi+svoAPTMM8/g4MGDAIDp06fr5wK9+uqreP31101eoK0J9naqtu7IxRwJKiEiIqq/jJ4D9Oqrr+rf9+vXD8ePH8f+/fvRtGlTtGvXzqTF1RcyI55rqlbIcWzWQCzafhpfbD0FAPh2VxrmjWxvnuKIiIhs0D0/C6xx48YYMWIEw48JOaoUeDUyTL/864GLElZDRERU/9R6BKioqAhbtmzBgw8+CKD89FdJSYl+u1wuxwcffAB7e/tb7YKMYGdnxLARERERGaXWI0Dfffcd/ve//+mXv/zySyQkJCApKQlJSUlYuXIlFi1aZJYibdVnT7TXv8/K5eXwREREplLrALRq1So8++yzButWr16Nbdu2Ydu2bfjkk0/0V4RROXE3NwKq4qH2DfXvp/x48B6rISIiokq1DkAnTpxAWNiNeSn29vaws7vx8S5duuDYsWOmrY703ByVUpdARERUb9Q6AOXk5EChuDFl6PLly2jSpIl+WafTGcwJItOYElUeOu2MuZSMiIiIbqvWAahRo0Y4cuTILbcfOnQIjRo1Murgc+bMQefOneHi4gIfHx8MHz4cqampBm2EEJg5cyYCAgLg4OCAvn374ujRowZtSkpKMGnSJHh7e8PJyQnDhg3DhQsXjKrFWrXwcwEApF3Jl7gSIiKi+qPWAeiBBx7Ae++9h+Li6pNxi4qK8P7772PIkCFGHTw+Ph4TJkzAnj17sGnTJpSVlWHAgAEoKCjQt5k7dy7mzZuHL7/8Evv27YOfnx+ioqKQl5enbxMdHY21a9dizZo12LlzJ/Lz8/Hggw9Cq9UaVY+53MvYTUgDZwBA2uUCiHudVEREREQAAJmo5V/VS5cuoX379lCpVJg4cSLCwsIgk8lw/PhxfPnllygrK0NSUhJ8fX3vupjLly/Dx8cH8fHxuO+++yCEQEBAAKKjo/HGG28AKB/t8fX1xccff4wXX3wROTk5aNCgAVasWIHHH38cAPDvv/8iMDAQGzZswMCBA+943NzcXLi5uSEnJweurq53Xf/NPtt8EvM3n8Doro3x4cNt7mofpWU6tHj3T+gEsPet/vBx5W0GiIiIgHv7+13rESBfX18kJCSgZcuWePPNN/Hwww9j+PDhmD59Olq1aoWdO3feU/gByucZAYCnpycAIC0tDZmZmRgwYIC+jVqtRp8+fZCQkAAASExMhEajMWgTEBCA8PBwfZublZSUIDc31+BlrVQKOwR6OgIAzlwpuENrIiIiqg2jHoURHByMuLg4XLt2DadOlT+moVmzZvrAci+EEJgyZQp69eqF8PBwAEBmZiYAVAtWvr6+OHfunL6NSqWCh4dHtTaVn7/ZnDlz8P77799zzZbS0N0B564W4vnl+9HQwwGvRoVhYGs/qcsiIiKqs+7qURienp7o0qULunTpYpLwAwATJ07EoUOH8P3331fbJrvpCighRLV1N7tdm+nTpyMnJ0f/Sk9Pv/vCb1cDTDNn53hm+XynvJIyHM/Mw4srEk2yXyIiIlt1z88CM4VJkyZh3bp12LZtm8GVZH5+5aMcN4/kZGVl6UeF/Pz8UFpaiuzs7Fu2uZlarYarq6vBy5qF+jhXW8cJ0URERHdP0gAkhMDEiRPx66+/YuvWrQgODjbYHhwcDD8/P2zatEm/rrS0FPHx8ejRowcAICIiAkql0qBNRkYGjhw5om9T133yaPUHzU77+ZAElRAREdUPkgagCRMmYOXKlVi9ejVcXFyQmZmJzMxMFBUVASg/9RUdHY2YmBisXbsWR44cwdNPPw1HR0eMGjUKAODm5oZx48Zh6tSp2LJlC5KSkvDkk0+iTZs2iIyMlLJ7JtPYyxHP9zYMhz8lXkBOkUaiioiIiOo2SQPQokWLkJOTg759+8Lf31//+uGHH/Rtpk2bhujoaIwfPx6dOnXCxYsXsXHjRri4uOjbzJ8/H8OHD8fIkSPRs2dPODo6Yv369ZDL5VJ0qxpT3MT57SGtkDbnATSuuCIMAEYs3HXvOyYiIrJBtb4PUH1mrvsALdh8Ags2n8ST3Rpj9vC7uw/Qza7kl6DT7M365dTZg6BWWEfQIyIisiSL3AeIrIO3s9pgOSUj7xYtiYiI6FYYgOq4g+nXpS6BiIiozmEAMiNznVw8NmsgIluWX+I/Y91R9Pp4K7p8uJmXxhMREdUSA1Ad5KhSoHtTL/3yhewiZOWV4K+jlySsioiIqO5gAKqjRndtXG1dSob1PtOMiIjImjAA1VH2Sjle7tvUYN2+s9ckqoaIiKhuYQCyABlMcCOgGkS29DFYTjh9FUcu5pjlWERERPUJA1AdFhHkiRf7hBise/CLnbiaXyJRRURERHUDA1AdN31wS5z9aIjBui0pWRJVQ0REVDcwANUTq57rqn8/7Rc+KJWIiOh2GIDMyJJ35enZzBufPdFev7w84awFj05ERFS3MADVIw+1b6h/P2PdUVzO41wgIiKimjAA1TOjqtwf6N3fjkhYCRERkfViAKpnYh5ugwA3ewBA3NFMnL1SIHFFRERE1ocByAJk5rkN0C19Nbqj/n3c0UzLHpyIiKgOYACqhzo09sCAVuUPS91wOEPiaoiIiKwPA1A9FTOiDQDg0IUcxB1hCCIiIqqKAaie8nZW69+/tPIAhLDkRflERETWjQGoHvtyVAf9+18OXJSwEiIiIuvCAGROEo+6PNg2QP9+IydDExER6TEA1XN/TO4FANieehnXC0slroaIiMg6MADVc60D3BDm64xSrQ7tZ21iCCIiIgIDkEVY+DZA1XRs7KF/337WJgkrISIisg4MQDbgvaGtDJYPpl+XphAiIiIrwQBkAxxVCux9q79+OZZPiiciIhvHAGQjfFztsW5iTwDA74f+RVZuscQVERERSYcByIa0beSOiCAPaLQCK/85L3U5REREkmEAMiNrvPfyMz2bAAA+33ISZy7nS1sMERGRRBiAbMzA1n769/d/Gi9hJURERNJhALIxSrkdPniotX752L+5ElZDREQkDQYgC5DJpL4TkKEx3ZtgaLvyx2R8vuWkxNUQERFZHgOQjZp8fzPIZEDc0Ux8uzNN6nKIiIgsigHIRoX6uqB/C18AwKzfj2HHycsSV0RERGQ5DEA2bEpUmP79B78fg05njdetERERmR4DkA1rFeCKNS90AwCcuJSPdQf/lbgiIiIiy2AAMiNRBwZUuoV44fWBzQEAH8cdR1GpVuKKiIiIzI8BiDCuVzAaujsgI6cY//v7tNTlEBERmR0DEMFeKcebg1sAABZuP807RBMRUb3HAEQAgAfb+uO+sAYoLdPh7bVHIOrC+TsiIqK7xABEAMpv1vjh8HDYK+2w+8xV/Jx4QeqSiIiIzIYBiPQCPR3xamT5pfEfbkjBpdxiiSsiIiIyDwYgMvBsr2CEN3TF9UINXv/5EE+FERFRvcQARAaUcjsseLw91Ao7/H3iMr7bfU7qkoiIiEyOAciMBOrm6EkzHxe89UBLAMCMdUexmJfGExFRPcMARDV6qnsQQrydAAAxG44j8dw1iSsiIiIyHQYgqpFMJsMPL3bXL7+88gCyOCmaiIjqCQYgC5DJpK7g7jRwUePo+wMR5uuMrLwSdInZgnNXC6Qui4iI6J5JGoD+/vtvDB06FAEBAZDJZPjtt98MtgshMHPmTAQEBMDBwQF9+/bF0aNHDdqUlJRg0qRJ8Pb2hpOTE4YNG4YLF3gPG1NxUivw9ZMR+uU+n2zHlfwSCSsiIiK6d5IGoIKCArRr1w5ffvlljdvnzp2LefPm4csvv8S+ffvg5+eHqKgo5OXl6dtER0dj7dq1WLNmDXbu3In8/Hw8+OCD0Gr5UE9TCWngbBCCxi3fz4emEhFRnSZpABo8eDBmz56NESNGVNsmhMCCBQvw9ttvY8SIEQgPD8fy5ctRWFiI1atXAwBycnKwdOlSfPrpp4iMjESHDh2wcuVKHD58GJs3b7Z0d+q1QeF+2Dq1D9wdlTiYfh2T1yRBq6ubV7kRERFZ7RygtLQ0ZGZmYsCAAfp1arUaffr0QUJCAgAgMTERGo3GoE1AQADCw8P1bWpSUlKC3NxcgxfdWUgDZyx5qhNUCjtsOnYJb689DB1DEBER1UFWG4AyMzMBAL6+vgbrfX199dsyMzOhUqng4eFxyzY1mTNnDtzc3PSvwMBAE1dfrj7eRLlzE0989nh72MmANfvSMXP9Ud4tmoiI6hyrDUCVZDddQiWEqLbuZndqM336dOTk5Ohf6enpJqnVVgxu449PR7aDTAZ8t/scPvwjhSGIiIjqFKsNQH5+fgBQbSQnKytLPyrk5+eH0tJSZGdn37JNTdRqNVxdXQ1eZJyHOzTCRyPaAAC+2ZmG4Okb8PuhfyWuioiIqHasNgAFBwfDz88PmzZt0q8rLS1FfHw8evToAQCIiIiAUqk0aJORkYEjR47o21gDGerojYDu4PHOjfHB8HD98sTVSfh2Z5qEFREREdWOQsqD5+fn49SpU/rltLQ0JCcnw9PTE40bN0Z0dDRiYmIQGhqK0NBQxMTEwNHREaNGjQIAuLm5Ydy4cZg6dSq8vLzg6emJ1157DW3atEFkZKRU3bIpY7oFwUWtQPQPyQCAWb8fQ1ZeCd4Y1PyOpyqJiIikImkA2r9/P/r166dfnjJlCgBg7NixiI2NxbRp01BUVITx48cjOzsbXbt2xcaNG+Hi4qL/zPz586FQKDBy5EgUFRWhf//+iI2NhVwut3h/bNXwDg3xUPsALNx+Gp/8lYqv408jI6cIHz/SFvZK/h6IiMj6yARnryI3Nxdubm7Iyckx6XyguXHHsXD7aTzbMxjvDW1lsv1as5/2p+PNXw9DqxNo09AN/xsTgQB3B6nLIiKieuhe/n5b7Rwgqpse6xSIleO6wtNJhcMXczDsy53Ym8YnyRMRkXVhACKT697UC/83oSda+rviSn4p/rNkDxZuP8WbJhIRkdVgADIjW/5zH+jpiF9e7o5h7QKg1QnMjUvFmG//QVZusdSlERERMQCR+TiqFPjsifaY+0hbOCjl2HXqKgZ9tgMbj976Lt1ERESWwABkAbZ8NbhMJsPIzoFYP6kXWvq74lpBKV5YkYhJ3yfhan6J1OUREZGNYgAii2jm44y143vg5b5NIbeTYf3BfxE1/2/8X/JFPkaDiIgsjgGILMZeKccbg1rgt/E90cLPBdcKSvHKmmSMWboXJy/lSV0eERHZEAYgsrg2jdywbmIvvBoZBpXCDjtPXcHgz3bgg9+PIbdYI3V5RERkAxiASBIqhR1eiQzF5lf7IKqVL8p0Akt3puH+/27H8oSzKC3TSV0iERHVYwxAJKnGXo5Y8lQnLH+2C0IaOOFKfilmrDuK+z/djl8SL0DLewcREZEZMACZEef21l6fsAb4K/o+zB4eDh8XNS5kF2HqTwcx+LO/8fuhfxmEiIjIpBiAyGoo5XZ4slsQ4l/vhzcGtYCbgxInLuVj4uokRM6Lx5q951FSppW6TCIiqgcYgCzAhm8DdFccVHK83Lcp/p7WD9GRoXB3VCLtSgHe/PUw7pu7DYv/Po2cQk6WJiKiu8cARFbLzUGJ6Mgw7HrjfrwzpCX8XO1xKbcEMRuOo9ucLZj+62GkZORKXSYREdVBCqkLILoTJ7UCz/UOwZjuQfgt6SKW7TqL45l5+H7veXy/9zy6NPHEmO5BiGrlC3ulXOpyiYioDmAAojpDrZDj8c6NMbJTIPamXcN3u88h7mgm9p69hr1nr8HNQYlh7QLwWKdGaNPQDTJbfgYJERHdFgMQ1TkymQxdQ7zQNcQLmTnFWP3POfy4/wIyc4uxYs85rNhzDmG+zng0ohGGtguAv5uD1CUTEZGVYQCiOs3PzR5TBjTHK5Fh2HXqCn5OvIC/jmbixKV8xGw4jpgNxxER5IEhbfzxQBt/+LnZS10yERFZAQYgMxLgvWssRW4nw31hDXBfWAPkFGnwx6EMrE26gH1ns5F4rvw16/dj6NzEA4PD/RHVyheBno5Sl01ERBJhAKJ6x81BiVFdG2NU18bIzCnGn0cy8MehDOw/l419Z8tfs34/hlAfZ/Rv6Yv+LX3QIdAdCjkviiQishUMQBbAubjS8XOzxzM9g/FMz2Bk5BRhw+FMbDyaif3nsnEyKx8ns/LxdfxpuDsq0TesAXqHNkDPZt48VUZEVM8xAJHN8HdzwLhewRjXKxg5hRrEn7yMrSmXsP3EZVwv1OC35H/xW/K/AICQBk7o2dQbPZt5o3uIF9wclRJXT0REpsQARDbJzbH8kvlh7QJQptUhKf06th3Pwq7TV3H4wnWcuVyAM5cLsGLPOdjJgPCGbujcxBOdgjwQ0cQDPi4cISIiqssYgMjmKeR26NzEE52beAIAcoo02HPmKhJOXcHOU1dw+nIBDl3IwaELOVi6Mw0AEOTliIggD30oatrAGXZ2PNdJRFRXMAAR3cTNQYmBrf0wsLUfACAzpxh7zlzF/nPXsP9sNlIv5eHc1UKcu1qIXw9cBAC42CvQpqEb2jRyQ9uG7mjbyA2NPBx4M0YiIivFAER0B35u9hjeoSGGd2gIAMgt1uBAxaX1+89mIyk9G3nFZUg4fRUJp6/qP+fuqESbhm5o28gNbRq6oYWfKxp7OnKkiIjICjAAmRNvA1Qvudor0be5D/o29wEAaLQ6nLyUj8MXr+PQhRwcvpiDlIxcXC/UYMfJK9hx8or+sw5KOcL8XNDSzwUt/FzQ3M8VLfxc4OGkkqo7REQ2iQGI6B4p5XZoFeCKVgGueLxz+bqSMi1SM/Mq5g5dR0pGHk5cykORRouD6ddxMP26wT78XO0R5ueCpg2c0LSBc/nLxwkNnNU8jUZEZAYMQBbAP2C2R62Qo20jd7Rt5A4gCABQptXh7NVCpGbm4XhmLlIy8pB6KRfp14qQmVuMzNxi/H3issF+XOwVaNrAGSFVglFIAyc09nTkk++JiO4BAxCRhSjkdmjm44xmPs4Y0tZfvz6vWIMTl/Jw4lI+zlzOx+nLBTh9OR/p1wqRV1yG5PTrSL5pxAgAfF3VCPJ0QqCnI4K8HNHY0xGNK356OakYvImIboMBiEhiLvZKRAR5IiLI02B9SZkW564W4nRWPk5fzseZimB05koB8orLcCm3BJdyS7D37LVq+3RSyRHoWR6GAtwd0NDdAQHuDghwt0dDdwd4O6s5GZuIbBoDEJGVUivkCPN1QZivi8F6IQRyijTll+JfK0T6tUKcu1qA89cKcf5qITJyi1FQqsXxzDwcz8yrcd9KuQx+bvYIcLsRjvzd7RHg7gA/V3v4utrDw1HJUSQiqrcYgIjqGJlMBndHFdwdVWgX6F5te7FGi4vXi3D+aiHSswvx7/Vi/Hu9SP/KzC2GRiuQfq0I6deKbnkcpVwGHxd7NHBRw9dVDV9Xe/i4qOFT8dOXQYmI6jAGIKJ6xl4p10+YrkmZVoesvBL8e70IF68X6QNSRk4RLl4vRlZuMa4WlEKjFbhY0eZ2KoOSt7MKnk4qeDmr4eWsgrdT+U8vZzW8nFTwdlbD00kFlcLOHN0mIjIKAxCRjVHI7SrmAzmg0y3alJbpcDm/BFm5xbiUW4LLecUVc46KkZVX/vNyXolRQamSi70C3hWhqGpAcnNQwsNRBXdHJdwdVfCo+OnmoISc85WIyMQYgMyI90GkukqlsEPDisnTt1M1KF3NL8XVghJcyS/Vv79WUFqxXB6WtDqBvOIy5BWXIe1KQa3rcbVXwMNJBXcHZcXpvyphyUEJj4oA5eaghKuDEq72SrjYK3irACK6JQYgC+D/d6X6qrZBCQB0OoHcYo1BILrxsxTXizS4XliK64UaZBeWIqdQg7ySMgBAbnEZcovLcO4u6nO1V5QHIgel/r2rgwIu9hXLDuVhqTw0lW+rDFBOKgWvliOqpxiAiMgi7OxuTN5u5lPz/KSbabQ65BgEoxvvrxeVIrtQg5yKwJRdqEFukQZ5xeXBSYjyEaor+eWjUHdDJgMclXI42yvgpFbAWV0eisrfy2+sq/hZ+d5JLTdYX7lOreCIFJG1YAAiIqullNvB21kNb2e1UZ/T6QTyS8sqAlH5z9ziMuQVa256X4bc4oo2xZob7Ys10GgFhAAKSrUoKNUCKLnn/qjkdnCqCE6OKjkcVAo4KuUV7+Vw0L8v31653lElh4NSUeW9vGL7jXVKOSeXExmDAYiI6h07O1n5qS575V19XgiBYo0OeSUaFJRoUVBShvySsio/DdcVlJYhv3JdccX60hvtizU6AECpVofSQh2yCzWm7C4AQGEn04chR5WiSpiSQ62wg1oph71CDnulHeyVFT8Vcv17tbLivb5tZbsqn1HIoVbaQa2w460PqM5jACIiuolMVh4mHFRywOXO7e+kTKsrH0mqEqKKSrUoLNWiUKNFUWkZCku1KNJob6wvrXl9kUaLwsr1pVqU6covtyirMsHcFKNVtyOTAeqKgFT5szJcVQ1SqoqXWmEHtUJeviwvX1YZbK+yTWkHtbyGbYoqn5MzhNG9YwAiIjIzhdwObg52cHO4uxGp29FodfowVFglMFUGqGKNDsUabfmrrPJ9+c+SMq3hdo0OxWValFT9WeWz2oqwJQQqPqczeX+MoaoSlCrDU+W6GyFLrg9MSrkMCrkdlHI7qOQyKOV2UCpuLFfbVrG9crl8uwyqym1yO6gUt96mlMsY0qwYAxARUR2mNGO4uplGe7sAVfGzSoAqLdOhtEyHkoqfpVodSjTa8p83b6vcXtPnynQo0Zb/rKpUW/4ZMw943ROlXAaFXUU4UhiGI2VFWFPY3Qhcior2CjsZFBVt5HYy/X707+UVbewqPyPThzC5nQzKivXyin1X7u9G+/Kfypr2eYsaFHb1K9AxAJmRELwTEBHVH5V/pF3spTm+EKI89NQYrHQo1WprDFYlZTqU6crfa7QCGq2u4lX1vQ6lZYbLVbeXagU0ZTVv02jL69Jodbj5f/bL22lRZPppX5JQ2FUJVZXB66YQVtlG/7Ni3bhewejf0lfqLugxAFlC/QnMRESSkclkUCus93YCQghodQJluopAVCVwVQakMu3tt2m0OpRV7KOsYl3le41OQKurbFf+XlPZTicq2uoqtgn9PrU6AY3OcF/lxzDcV5nWsF1NKmsrKTP+9OfQdgH3+hWbFAMQERGRCchkFaeP5KjzdyGvGub0AaxqsKoIYxrtjeBVVrFNpwM0Oh20FUFKWxG22jVyl7pbBhiAiIiIyEDVMFdf8c5ZREREZHPqTQBauHAhgoODYW9vj4iICOzYsUPqkoiIiMhK1YsA9MMPPyA6Ohpvv/02kpKS0Lt3bwwePBjnz5+XujQiIiKyQvUiAM2bNw/jxo3Dc889h5YtW2LBggUIDAzEokWLamxfUlKC3Nxcg5c5rE26aJb9EhER0b2p8wGotLQUiYmJGDBggMH6AQMGICEhocbPzJkzB25ubvpXYGCgWWrr2NgDACxygzIiIiKqvTofgK5cuQKtVgtfX8ObK/n6+iIzM7PGz0yfPh05OTn6V3p6ullqG90tCB881BpjugWZZf9ERER0d+rNZfA3355bCHHLW3ar1Wqo1Wqz19QnrAGABmY/DhERERmnzo8AeXt7Qy6XVxvtycrKqjYqRERERATUgwCkUqkQERGBTZs2GazftGkTevToIVFVREREZM3qxSmwKVOmYMyYMejUqRO6d++OxYsX4/z583jppZekLo2IiIisUL0IQI8//jiuXr2KWbNmISMjA+Hh4diwYQOCgjj5mIiIiKqTCSFqfuSrDcnNzYWbmxtycnLg6uoqdTlERERUC/fy97vOzwEiIiIiMhYDEBEREdkcBiAiIiKyOQxAREREZHMYgIiIiMjmMAARERGRzWEAIiIiIpvDAEREREQ2p17cCfpeVd4LMjc3V+JKiIiIqLYq/27fzT2dGYAA5OXlAQACAwMlroSIiIiMlZeXBzc3N6M+w0dhANDpdPj333/h4uICmUxmsv3m5uYiMDAQ6enp9foRG+xn/cJ+1h+20EeA/axvjOmnEAJ5eXkICAiAnZ1xs3o4AgTAzs4OjRo1Mtv+XV1d6/U/1krsZ/3CftYfttBHgP2sb2rbT2NHfipxEjQRERHZHAYgIiIisjkMQGakVqsxY8YMqNVqqUsxK/azfmE/6w9b6CPAftY3luonJ0ETERGRzeEIEBEREdkcBiAiIiKyOQxAREREZHMYgIiIiMjmMAARERGRzWEAMtLChQsRHBwMe3t7REREYMeOHbdtHx8fj4iICNjb2yMkJARff/11tTa//PILWrVqBbVajVatWmHt2rXmKr/WTN3Po0eP4pFHHkGTJk0gk8mwYMECM1ZfO6bu45IlS9C7d294eHjAw8MDkZGR2Lt3rzm7UCum7uevv/6KTp06wd3dHU5OTmjfvj1WrFhhzi7Uijn+26y0Zs0ayGQyDB8+3MRVG8/U/YyNjYVMJqv2Ki4uNmc37sgcv8/r169jwoQJ8Pf3h729PVq2bIkNGzaYqwt3ZOo+9u3bt8bf5ZAhQ8zZjTsyx+9ywYIFaN68ORwcHBAYGIhXX33V+H+zgmptzZo1QqlUiiVLlohjx46JV155RTg5OYlz587V2P7MmTPC0dFRvPLKK+LYsWNiyZIlQqlUip9//lnfJiEhQcjlchETEyNSUlJETEyMUCgUYs+ePZbqVjXm6OfevXvFa6+9Jr7//nvh5+cn5s+fb6He1MwcfRw1apT46quvRFJSkkhJSRHPPPOMcHNzExcuXLBUt6oxRz+3bdsmfv31V3Hs2DFx6tQpsWDBAiGXy0VcXJylulWNOfpZ6ezZs6Jhw4aid+/e4qGHHjJzT27PHP1ctmyZcHV1FRkZGQYvKZmjnyUlJaJTp07igQceEDt37hRnz54VO3bsEMnJyZbqlgFz9PHq1asGv8MjR44IuVwuli1bZqFeVWeOfq5cuVKo1WqxatUqkZaWJv766y/h7+8voqOjjaqNAcgIXbp0ES+99JLBuhYtWog333yzxvbTpk0TLVq0MFj34osvim7duumXR44cKQYNGmTQZuDAgeKJJ54wUdXGM0c/qwoKCpI8AJm7j0IIUVZWJlxcXMTy5cvvveC7ZIl+CiFEhw4dxDvvvHNvxd4Dc/WzrKxM9OzZU3zzzTdi7Nixkgcgc/Rz2bJlws3NzeS13gtz9HPRokUiJCRElJaWmr7gu2CJ/zbnz58vXFxcRH5+/r0XfJfM0c8JEyaI+++/36DNlClTRK9evYyqjafAaqm0tBSJiYkYMGCAwfoBAwYgISGhxs/s3r27WvuBAwdi//790Gg0t21zq32am7n6aU0s1cfCwkJoNBp4enqapnAjWaKfQghs2bIFqampuO+++0xXvBHM2c9Zs2ahQYMGGDdunOkLN5I5+5mfn4+goCA0atQIDz74IJKSkkzfgVoyVz/XrVuH7t27Y8KECfD19UV4eDhiYmKg1WrN05HbsNT/Bi1duhRPPPEEnJycTFO4kczVz169eiExMVE/xeDMmTPYsGGD0af6GIBq6cqVK9BqtfD19TVY7+vri8zMzBo/k5mZWWP7srIyXLly5bZtbrVPczNXP62Jpfr45ptvomHDhoiMjDRN4UYyZz9zcnLg7OwMlUqFIUOG4IsvvkBUVJTpO1EL5urnrl27sHTpUixZssQ8hRvJXP1s0aIFYmNjsW7dOnz//fewt7dHz549cfLkSfN05A7M1c8zZ87g559/hlarxYYNG/DOO+/g008/xYcffmiejtyGJf43aO/evThy5Aiee+450xVuJHP184knnsAHH3yAXr16QalUomnTpujXrx/efPNNo+pTGNWaIJPJDJaFENXW3an9zeuN3aclmKOf1sacfZw7dy6+//57bN++Hfb29iao9u6Zo58uLi5ITk5Gfn4+tmzZgilTpiAkJAR9+/Y1XeFGMmU/8/Ly8OSTT2LJkiXw9vY2fbH3wNS/z27duqFbt2767T179kTHjh3xxRdf4PPPPzdV2UYzdT91Oh18fHywePFiyOVyRERE4N9//8Unn3yC9957z8TV1445/zdo6dKlCA8PR5cuXUxQ6b0xdT+3b9+ODz/8EAsXLkTXrl1x6tQpvPLKK/D398e7775b67oYgGrJ29sbcrm8WmrNysqqllYr+fn51dheoVDAy8vrtm1utU9zM1c/rYm5+/jf//4XMTEx2Lx5M9q2bWva4o1gzn7a2dmhWbNmAID27dsjJSUFc+bMkSQAmaOfR48exdmzZzF06FD9dp1OBwBQKBRITU1F06ZNTdyT27PUf5t2dnbo3LmzZCNA5uqnv78/lEol5HK5vk3Lli2RmZmJ0tJSqFQqE/fk1sz9uywsLMSaNWswa9Ys0xZuJHP1891338WYMWP0o1tt2rRBQUEBXnjhBbz99tuws6vdyS2eAqsllUqFiIgIbNq0yWD9pk2b0KNHjxo/071792rtN27ciE6dOkGpVN62za32aW7m6qc1MWcfP/nkE3zwwQeIi4tDp06dTF+8ESz5uxRCoKSk5N6Lvgvm6GeLFi1w+PBhJCcn61/Dhg1Dv379kJycjMDAQLP151Ys9fsUQiA5ORn+/v6mKdxI5upnz549cerUKX2QBYATJ07A39/fouEHMP/v8scff0RJSQmefPJJ0xZuJHP1s7CwsFrIkcvlEOUXdtW+QKOmTNu4ysv5li5dKo4dOyaio6OFk5OTOHv2rBBCiDfffFOMGTNG377ycr5XX31VHDt2TCxdurTa5Xy7du0ScrlcfPTRRyIlJUV89NFHVnMZvCn7WVJSIpKSkkRSUpLw9/cXr732mkhKShInT560eP+EME8fP/74Y6FSqcTPP/9scClqXl6exftXyRz9jImJERs3bhSnT58WKSkp4tNPPxUKhUIsWbLE4v2rZI5+3swargIzRz9nzpwp4uLixOnTp0VSUpJ45plnhEKhEP/884/F+1fJHP08f/68cHZ2FhMnThSpqani999/Fz4+PmL27NkW758Q5v0326tXL/H4449brC+3Y45+zpgxQ7i4uIjvv/9enDlzRmzcuFE0bdpUjBw50qjaGICM9NVXX4mgoCChUqlEx44dRXx8vH7b2LFjRZ8+fQzab9++XXTo0EGoVCrRpEkTsWjRomr7/Omnn0Tz5s2FUqkULVq0EL/88ou5u3FHpu5nWlqaAFDtdfN+LMnUfQwKCqqxjzNmzLBAb27N1P18++23RbNmzYS9vb3w8PAQ3bt3F2vWrLFEV27LHP9tVmUNAUgI0/czOjpaNG7cWKhUKtGgQQMxYMAAkZCQYImu3JY5fp8JCQmia9euQq1Wi5CQEPHhhx+KsrIyc3fllszRx9TUVAFAbNy40dzl15qp+6nRaMTMmTNF06ZNhb29vQgMDBTjx48X2dnZRtUlE8KY8SIiIiKiuo9zgIiIiMjmMAARERGRzWEAIiIiIpvDAEREREQ2hwGIiIiIbA4DEBEREdkcBiAiIiKyOQxAREQ26rfffsP3338vdRlEkmAAIiKbtn37dshkMly/ft1ix4yNjYW7u7vFjleTf/75B5MnT0b37t0lrYNIKgxARBJ7+umnIZPJIJPJoFQqERISgtdeew0FBQVSl3bPzp49C5lMhuTkZKlLoSquXbuGcePG4bfffkOTJk2kLodIEgqpCyAiYNCgQVi2bBk0Gg127NiB5557DgUFBVi0aJFZjqfRaG77dHeq3zw9PXHkyBGpyyCSFEeAiKyAWq2Gn58fAgMDMWrUKIwePRq//fYbAKCkpASTJ0+Gj48P7O3t0atXL+zbt0//2ZpOp/z222+QyWT65ZkzZ6J9+/b49ttvERISArVaDSEErl+/jhdeeAG+vr6wt7dHeHg4fv/9d/3nEhIScN9998HBwQGBgYGYPHmywchUkyZNEBMTg2effRYuLi5o3LgxFi9erN8eHBwMAOjQoQNkMhn69u0LANi3bx+ioqLg7e0NNzc39OnTBwcOHDDow/Hjx9GrVy/Y29ujVatW2Lx5M2Qymf57AYCLFy/i8ccfh4eHB7y8vPDQQw/h7Nmzt/2uN2zYgLCwMDg4OKBfv341tr9TvxcuXIjQ0FDY29vD19cXjz766G2PGRsbi8aNG8PR0REPP/wwrl69Wq3NokWL0LRpU6hUKjRv3hwrVqww2C6TyfDNN9/g4YcfhqOjI0JDQ7Fu3Tr99spTeVu2bEGnTp3g6OiIHj16IDU11WA/69evR0REBOzt7RESEoL3338fZWVl+u05OTl44YUX4OPjA1dXV9x///04ePCgfvvBgwfRr18/uLi4wNXVFREREdi/f/9t+09kle7q0a5EZDI1PWV80qRJwsvLSwghxOTJk0VAQIDYsGGDOHr0qBg7dqzw8PAQV69eFUIIsWzZMuHm5mbw+bVr14qq/3nPmDFDODk5iYEDB4oDBw6IgwcPirKyMtGtWzfRunVrsXHjRnH69Gmxfv16sWHDBiGEEIcOHRLOzs5i/vz54sSJE2LXrl2iQ4cO4umnn9bvNygoSHh6eoqvvvpKnDx5UsyZM0fY2dmJlJQUIYQQe/fuFQDE5s2bRUZGhr7mLVu2iBUrVohjx46JY8eOiXHjxglfX1+Rm5srhBBCq9WK5s2bi6ioKJGcnCx27NghunTpIgCItWvXCiGEKCgoEKGhoeLZZ58Vhw4dEseOHROjRo0SzZs3FyUlJTV+1+fPnxdqtVq88sor4vjx42LlypXC19dXANA/SfpO/d63b5+Qy+Vi9erV4uzZs+LAgQPis88+u+Xvd8+ePUImk4k5c+aI1NRU8dlnnwl3d3eD39mvv/4qlEql+Oqrr0Rqaqr49NNPhVwuF1u3btW3ASAaNWokVq9eLU6ePCkmT54snJ2d9d/ptm3bBADRtWtXsX37dnH06FHRu3dv0aNHD/0+4uLihKurq4iNjRWnT58WGzduFE2aNBEzZ84UQgih0+lEz549xdChQ8W+ffvEiRMnxNSpU4WXl5f+OK1btxZPPvmkSElJESdOnBA//vijSE5OvmX/iawVAxCRxG4OQP/884/w8vISI0eOFPn5+UKpVIpVq1bpt5eWloqAgAAxd+5cIUTtA5BSqRRZWVn6dX/99Zews7MTqampNdY1ZswY8cILLxis27Fjh7CzsxNFRUVCiPIA9OSTT+q363Q64ePjIxYtWiSEECItLU0AEElJSbf9DsrKyoSLi4tYv369EEKIP//8UygUCpGRkaFvs2nTJoMAtHTpUtG8eXOh0+n0bUpKSoSDg4P466+/ajzO9OnTRcuWLQ0+88YbbxgEoDv1+5dffhGurq76sHYn//nPf8SgQYMM1j3++OMGv7MePXqI559/3qDNY489Jh544AH9MgDxzjvv6Jfz8/OFTCYTf/75pxDiRgDavHmzvs0ff/whAOh/X7179xYxMTEGx1mxYoXw9/cXQpQHU1dXV1FcXGzQpmnTpuJ///ufEEIIFxcXERsbW6u+E1kzngIjsgK///47nJ2dYW9vj+7du+O+++7DF198gdOnT0Oj0aBnz576tkqlEl26dEFKSopRxwgKCkKDBg30y8nJyWjUqBHCwsJqbJ+YmIjY2Fg4OzvrXwMHDoROp0NaWpq+Xdu2bfXvZTIZ/Pz8kJWVddtasrKy8NJLLyEsLAxubm5wc3NDfn4+zp8/DwBITU1FYGAg/Pz89J/p0qVLtfpOnToFFxcXfX2enp4oLi7G6dOnazxuSkoKunXrZnB68OaroO7U76ioKAQFBSEkJARjxozBqlWrUFhYeMu+pqSkVDvGzcspKSkGv2MA6NmzZ7XfcdXv2snJCS4uLtW+66pt/P39AUDfJjExEbNmzTLo2/PPP4+MjAwUFhYiMTER+fn58PLyMmiTlpam/06nTJmC5557DpGRkfjoo49u+V0TWTtOgiayAv369cOiRYugVCoREBCgn6CckZEBAAZ/sAFACKFfZ2dnByGEwXaNRlPtGE5OTgbLDg4Ot61Jp9PhxRdfxOTJk6tta9y4sf79zZOpZTIZdDrdbff99NNP4/Lly1iwYAGCgoKgVqvRvXt3lJaWAjDs3+3qi4iIwKpVq6ptqxr0qrr5e7rVfm/Xb5VKhQMHDmD79u3YuHEj3nvvPcycORP79u2r8dL22hwTuP3vuFJtvuuqbSo/X9lGp9Ph/fffx4gRI6od397eHjqdDv7+/ti+fXu17ZV9mzlzJkaNGoU//vgDf/75J2bMmIE1a9bg4YcfrlU/iawFAxCRFXByckKzZs2qrW/WrBlUKhV27tyJUaNGASgPN/v370d0dDSA8j/2eXl5KCgo0Iec2lx23rZtW1y4cAEnTpyocRSoY8eOOHr0aI111ZZKpQIAaLVag/U7duzAwoUL8cADDwAA0tPTceXKFf32Fi1a4Pz587h06RJ8fX0BwGDid2V9P/zwg36ybm20atXKYBI1AOzZs6fafu/Ub4VCgcjISERGRmLGjBlwd3fH1q1bawwWrVq1qnaMm5dbtmyJnTt34qmnntKvS0hIQMuWLWvVr9rq2LEjUlNTb9m3jh07IjMzEwqF4raXx4eFhSEsLAyvvvoq/vOf/2DZsmUMQFTn8BQYkRVzcnLCyy+/jNdffx1xcXE4duwYnn/+eRQWFmLcuHEAgK5du8LR0RFvvfUWTp06hdWrVyM2NvaO++7Tpw/uu+8+PPLII9i0aRPS0tLw559/Ii4uDgDwxhtvYPfu3ZgwYQKSk5Nx8uRJrFu3DpMmTap1/T4+PnBwcEBcXBwuXbqEnJwcAOXBbsWKFUhJScE///yD0aNHG4xIRUVFoWnTphg7diwOHTqEXbt24e233wZwY1Rj9OjR8Pb2xkMPPYQdO3YgLS0N8fHxeOWVV3DhwoUa63nppZdw+vRpTJkyBampqTV+V3fq9++//47PP/8cycnJOHfuHL777jvodDo0b968xmNOnjwZcXFxmDt3Lk6cOIEvv/xS/x1Xev311xEbG4uvv/4aJ0+exLx58/Drr7/itddeq/V3XRvvvfcevvvuO8ycORNHjx5FSkoKfvjhB7zzzjsAgMjISHTv3h3Dhw/HX3/9hbNnzyIhIQHvvPMO9u/fj6KiIkycOBHbt2/HuXPnsGvXLuzbt8/kQY3IIqScgERENV8FVlVRUZGYNGmS8Pb2Fmq1WvTs2VPs3bvXoM3atWtFs2bNhL29vXjwwQfF4sWLq02CbteuXbV9X716VTzzzDPCy8tL2Nvbi/DwcPH777/rt+/du1dERUUJZ2dn4eTkJNq2bSs+/PBD/fagoCAxf/58g322a9dOzJgxQ7+8ZMkSERgYKOzs7ESfPn2EEEIcOHBAdOrUSajVahEaGip++umnavtKSUkRPXv2FCqVSrRo0UKsX79eABBxcXH6NhkZGeKpp57SfzchISHi+eefFzk5Obf8PtevXy+aNWsm1Gq16N27t/j2228NJkHfqd87duwQffr0ER4eHsLBwUG0bdtW/PDDD7c8nhDlE7YbNWokHBwcxNChQ8V///vfahPXFy5cKEJCQoRSqRRhYWHiu+++M9iOKhPAK7m5uYlly5YJIW5Mgq7aj6SkJAFApKWl6dfFxcWJHj16CAcHB+Hq6iq6dOkiFi9erN+em5srJk2aJAICAoRSqRSBgYFi9OjR4vz586KkpEQ88cQTIjAwUKhUKhEQECAmTpyon2RNVJfIhKjlCWoiIgnt2rULvXr1wqlTp9C0aVOpyyGiOo4BiIis0tq1a+Hs7IzQ0FCcOnUKr7zyCjw8PLBz506pSyOieoCToInIKuXl5WHatGlIT0+Ht7c3IiMj8emnn0pdFhHVExwBIiIiIpvDq8CIiIjI5jAAERERkc1hACIiIiKbwwBERERENocBiIiIiGwOAxARERHZHAYgIiIisjkMQERERGRz/h9bPtOa1ZOq8wAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", - "\n", - "# Tri des prédictions de probabilités et des vraies valeurs\n", - "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", - "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", - "y_test_sorted = y_test.iloc[sorted_indices]\n", - "\n", - "# Calcul du gain cumulatif\n", - "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", - "\n", - "# Tracé de la courbe de lift\n", - "plt.plot(np.linspace(0, 1, len(cumulative_gain))[:10000], (cumulative_gain/np.linspace(0, 1, len(cumulative_gain)))[:10000], label='Courbe de lift')\n", - "plt.xlabel('Pourcentage des données')\n", - "plt.ylabel('Gain cumulatif')\n", - "plt.title('Courbe de Lift')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "49dc4e25-a79e-44d7-a577-524468336b96", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "52512 0.000000\n", - "87081 0.000000\n", - "2695 0.000000\n", - "51486 0.006211\n", - "15 0.012422\n", - " ... \n", - "86959 1.000000\n", - "86960 1.000000\n", - "86961 1.000000\n", - "86962 1.000000\n", - "65836 1.000000\n", - "Name: y_has_purchased, Length: 128198, dtype: float64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cumulative_gain" - ] - }, - { - "cell_type": "markdown", - "id": "5fde953b-4cce-4879-bb5e-1852511e7054", - "metadata": {}, - "source": [ - "## Sauvegarde des résultats (à reprendre))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7ac941bf-7994-4baf-8d9f-13b93eed73a9", - "metadata": {}, - "outputs": [], - "source": [ - "# sauvegarde\n", - "\n", - "with open('test_logit.pkl', 'wb') as file:\n", - " pickle.dump(clf4, file)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "3ac3def3-00f2-4b31-b6f7-2cae5038b766", - "metadata": {}, - "outputs": [], - "source": [ - "# pour charger les paramètres \n", - "\n", - "# Chargement du modèle à partir du fichier\n", - "with open('test_logit.pkl', 'rb') as file:\n", - " loaded_logit = pickle.load(file)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Computes_log_coeff.ipynb b/useless/Computes_log_coeff.ipynb deleted file mode 100644 index 3c83cbc..0000000 --- a/useless/Computes_log_coeff.ipynb +++ /dev/null @@ -1,436 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "135a67de-cff8-4345-bacc-d9f9fa68a41f", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "\n", - "import statsmodels.api as sm\n", - "\n", - "import pickle\n", - "import warnings" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9a6254df-d496-4957-89ea-9ed2b74049dd", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "922cf05f-8343-4ed0-ad62-3ef1f17c0730", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/1_Temp/1_0_Modelling_Datasets/musee\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test\n", - "\n", - "\n", - "def features_target_split(dataset_train, dataset_test):\n", - " features_l = ['nb_campaigns', 'taux_ouverture_mail', 'prop_purchases_internet', 'nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'time_to_open',\n", - " 'purchases_10_2021','purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021','purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',\n", - " 'purchases_4_2022', 'purchases_5_2021', 'purchases_5_2022', 'purchases_6_2021', 'purchases_6_2022', 'purchases_7_2021', 'purchases_7_2022', 'purchases_8_2021',\n", - " 'purchases_8_2022','purchases_9_2021', 'purchases_9_2022', 'purchase_date_min', 'purchase_date_max', 'nb_targets', 'gender_female', 'gender_male',\n", - " 'achat_internet', 'categorie_age_0_10', 'categorie_age_10_20', 'categorie_age_20_30','categorie_age_30_40',\n", - " 'categorie_age_40_50', 'categorie_age_50_60', 'categorie_age_60_70', 'categorie_age_70_80', 'categorie_age_plus_80','categorie_age_inconnue',\n", - " 'country_fr', 'is_profession_known', 'is_zipcode_known', 'opt_in', 'target_optin', 'target_newsletter', 'target_scolaire', 'target_entreprise', 'target_famille',\n", - " 'target_jeune', 'target_abonne']\n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2584e454-111b-4c39-881b-676841cb5aa1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_498/3950829189.py:7: DtypeWarning: Columns (10,24,25) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - "/tmp/ipykernel_498/3950829189.py:11: DtypeWarning: Columns (10,24,25) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dataset_train, dataset_test = load_train_test()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a32ea7f8-e2d3-44db-8937-5afda9447b58", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3bdc8840-7f45-416f-8ee0-307db201c496", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "const 0\n", - "nb_campaigns 0\n", - "taux_ouverture_mail 0\n", - "prop_purchases_internet 0\n", - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "time_to_open 0\n", - "purchases_10_2021 0\n", - "purchases_10_2022 0\n", - "purchases_11_2021 0\n", - "purchases_12_2021 0\n", - "purchases_1_2022 0\n", - "purchases_2_2022 0\n", - "purchases_3_2022 0\n", - "purchases_4_2022 0\n", - "purchases_5_2021 0\n", - "purchases_5_2022 0\n", - "purchases_6_2021 0\n", - "purchases_6_2022 0\n", - "purchases_7_2021 0\n", - "purchases_7_2022 0\n", - "purchases_8_2021 0\n", - "purchases_8_2022 0\n", - "purchases_9_2021 0\n", - "purchases_9_2022 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "nb_targets 0\n", - "gender_female 0\n", - "gender_male 0\n", - "achat_internet 0\n", - "categorie_age_0_10 0\n", - "categorie_age_10_20 0\n", - "categorie_age_20_30 0\n", - "categorie_age_30_40 0\n", - "categorie_age_40_50 0\n", - "categorie_age_50_60 0\n", - "categorie_age_60_70 0\n", - "categorie_age_70_80 0\n", - "categorie_age_plus_80 0\n", - "categorie_age_inconnue 0\n", - "country_fr 0\n", - "is_profession_known 0\n", - "is_zipcode_known 0\n", - "opt_in 0\n", - "target_optin 0\n", - "target_newsletter 0\n", - "target_scolaire 0\n", - "target_entreprise 0\n", - "target_famille 0\n", - "target_jeune 0\n", - "target_abonne 0\n", - "dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "3c3ac545-52e0-4d0c-afdc-fff70f468a94", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "most_frequent_value = X_train['country_fr'].mode()[0]\n", - "most_frequent_value" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0fcdc5ee-bcea-4436-be9b-92b79d27a230", - "metadata": {}, - "outputs": [], - "source": [ - "X_train['country_fr'] = X_train['country_fr'].fillna(most_frequent_value)\n", - "X_train['time_to_open'] = X_train['time_to_open'].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7ecdaf1a-b5e4-4880-871e-363eae6fe4e1", - "metadata": {}, - "outputs": [], - "source": [ - "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", - " y = y_train['y_has_purchased'])\n", - "\n", - "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a6b56090-cfe9-4772-810c-d36bf12aceca", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.52239696, 0.52239696, 0.52239696, ..., 0.52239696, 0.52239696,\n", - " 0.52239696])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "class_counts = np.bincount(y_train['y_has_purchased'])\n", - "class_weights = len(y_train['y_has_purchased']) / (2 * class_counts)\n", - "\n", - "weights = class_weights[y_train['y_has_purchased'].values.astype(int)]\n", - "weights" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bfaea23e-7d7a-4c0d-96f6-4ab4c7c2ff51", - "metadata": {}, - "outputs": [], - "source": [ - "X_train = sm.add_constant(X_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "4cf97ae5-9dcf-4f4c-91b3-3b1f339a6213", - "metadata": {}, - "outputs": [], - "source": [ - "numeric_features = ['nb_campaigns', 'taux_ouverture_mail', 'prop_purchases_internet', 'nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", - " 'purchases_10_2021','purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021','purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',\n", - " 'purchases_4_2022', 'purchases_5_2021', 'purchases_5_2022', 'purchases_6_2021', 'purchases_6_2022', 'purchases_7_2021', 'purchases_7_2022', 'purchases_8_2021',\n", - " 'purchases_8_2022','purchases_9_2021', 'purchases_9_2022', 'purchase_date_min', 'purchase_date_max', 'nb_targets', 'time_to_open']" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "debb36df-3c2f-4cf7-83a9-ad6e4f6b0470", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "\n", - "X_train_scaled_columns = scaler.fit_transform(X_train[numeric_features])\n", - "\n", - "X_train_scaled = X_train.copy() #\n", - "X_train_scaled[numeric_features] = X_train_scaled_columns" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7eaa6160-20a0-4a78-ac38-0411e19707ed", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/statsmodels/base/optimizer.py:18: FutureWarning: Keyword arguments have been passed to the optimizer that have no effect. The list of allowed keyword arguments for method newton is: tol, ridge_factor. The list of unsupported keyword arguments passed include: weights. After release 0.14, this will raise.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.136180\n", - " Iterations 9\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y_has_purchased No. Observations: 434278\n", - "Model: Logit Df Residuals: 434226\n", - "Method: MLE Df Model: 51\n", - "Date: Thu, 04 Apr 2024 Pseudo R-squ.: 0.2305\n", - "Time: 06:09:09 Log-Likelihood: -59140.\n", - "converged: True LL-Null: -76855.\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "===========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "-------------------------------------------------------------------------------------------\n", - "const -4.0679 1.65e+06 -2.46e-06 1.000 -3.24e+06 3.24e+06\n", - "nb_campaigns 0.0916 0.012 7.352 0.000 0.067 0.116\n", - "taux_ouverture_mail 0.0012 0.011 0.106 0.916 -0.021 0.023\n", - "prop_purchases_internet -0.1995 0.067 -2.972 0.003 -0.331 -0.068\n", - "nb_tickets 0.5956 0.193 3.091 0.002 0.218 0.973\n", - "nb_purchases 0.1598 1.71e+06 9.37e-08 1.000 -3.34e+06 3.34e+06\n", - "total_amount -0.1938 0.071 -2.724 0.006 -0.333 -0.054\n", - "nb_suppliers 0.0282 0.021 1.348 0.178 -0.013 0.069\n", - "time_to_open 0.2785 0.018 15.534 0.000 0.243 0.314\n", - "purchases_10_2021 0.0417 4.76e+04 8.76e-07 1.000 -9.34e+04 9.34e+04\n", - "purchases_10_2022 0.4578 2.72e+05 1.68e-06 1.000 -5.33e+05 5.33e+05\n", - "purchases_11_2021 0.0252 4.92e+04 5.12e-07 1.000 -9.65e+04 9.65e+04\n", - "purchases_12_2021 0.0221 6.3e+04 3.5e-07 1.000 -1.24e+05 1.24e+05\n", - "purchases_1_2022 0.0083 5.49e+04 1.52e-07 1.000 -1.08e+05 1.08e+05\n", - "purchases_2_2022 0.0462 7.59e+04 6.09e-07 1.000 -1.49e+05 1.49e+05\n", - "purchases_3_2022 0.0928 1.07e+05 8.67e-07 1.000 -2.1e+05 2.1e+05\n", - "purchases_4_2022 0.1446 1.65e+05 8.75e-07 1.000 -3.24e+05 3.24e+05\n", - "purchases_5_2021 -0.0427 4.84e+04 -8.83e-07 1.000 -9.48e+04 9.48e+04\n", - "purchases_5_2022 0.1412 1.67e+05 8.46e-07 1.000 -3.27e+05 3.27e+05\n", - "purchases_6_2021 -0.0252 5.55e+04 -4.54e-07 1.000 -1.09e+05 1.09e+05\n", - "purchases_6_2022 0.1246 1.84e+05 6.77e-07 1.000 -3.6e+05 3.6e+05\n", - "purchases_7_2021 -0.0252 5.55e+04 -4.55e-07 1.000 -1.09e+05 1.09e+05\n", - "purchases_7_2022 -0.0074 2.1e+05 -3.54e-08 1.000 -4.12e+05 4.12e+05\n", - "purchases_8_2021 0.0116 5.26e+04 2.21e-07 1.000 -1.03e+05 1.03e+05\n", - "purchases_8_2022 0.0554 2.4e+05 2.31e-07 1.000 -4.7e+05 4.7e+05\n", - "purchases_9_2021 -0.0320 5.47e+04 -5.85e-07 1.000 -1.07e+05 1.07e+05\n", - "purchases_9_2022 0.2349 2.2e+05 1.07e-06 1.000 -4.32e+05 4.32e+05\n", - "purchase_date_min 0.0781 0.025 3.092 0.002 0.029 0.128\n", - "purchase_date_max -0.5228 0.026 -20.021 0.000 -0.574 -0.472\n", - "nb_targets 0.7083 0.010 74.555 0.000 0.690 0.727\n", - "gender_female 0.2961 0.038 7.701 0.000 0.221 0.371\n", - "gender_male 0.0450 0.040 1.137 0.256 -0.033 0.123\n", - "achat_internet 0.1869 0.158 1.186 0.236 -0.122 0.496\n", - "categorie_age_0_10 -0.2713 1.65e+06 -1.64e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_10_20 -0.1238 1.65e+06 -7.48e-08 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_20_30 -0.6322 1.65e+06 -3.82e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_30_40 -0.5004 1.65e+06 -3.02e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_40_50 -0.4020 1.65e+06 -2.43e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_50_60 -0.4101 1.65e+06 -2.48e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_60_70 -0.3232 1.65e+06 -1.95e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_70_80 -0.1635 1.65e+06 -9.88e-08 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_plus_80 -0.4677 1.65e+06 -2.83e-07 1.000 -3.24e+06 3.24e+06\n", - "categorie_age_inconnue -0.7737 1.65e+06 -4.68e-07 1.000 -3.24e+06 3.24e+06\n", - "country_fr 0.7419 0.065 11.422 0.000 0.615 0.869\n", - "is_profession_known -0.5947 0.066 -9.074 0.000 -0.723 -0.466\n", - "is_zipcode_known 1.1374 0.027 41.609 0.000 1.084 1.191\n", - "opt_in -1.0658 0.030 -35.485 0.000 -1.125 -1.007\n", - "target_optin 0.5946 0.034 17.361 0.000 0.527 0.662\n", - "target_newsletter -1.0237 0.035 -29.411 0.000 -1.092 -0.955\n", - "target_scolaire 0.0428 0.036 1.188 0.235 -0.028 0.113\n", - "target_entreprise -0.2645 0.058 -4.589 0.000 -0.377 -0.152\n", - "target_famille 0.5035 0.035 14.548 0.000 0.436 0.571\n", - "target_jeune -0.6795 0.029 -23.590 0.000 -0.736 -0.623\n", - "target_abonne 0.0677 0.037 1.833 0.067 -0.005 0.140\n", - "===========================================================================================\n" - ] - } - ], - "source": [ - "model_logit = sm.Logit(y_train, X_train_scaled)\n", - "\n", - "result = model_logit.fit(weights=weights)\n", - "\n", - "print(result.summary())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75dc92c7-cc1e-40f1-bc74-0b04043b7e44", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Exploration_billet_AJ.ipynb b/useless/Exploration_billet_AJ.ipynb deleted file mode 100644 index f149f5a..0000000 --- a/useless/Exploration_billet_AJ.ipynb +++ /dev/null @@ -1,1964 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5bf5c226", - "metadata": {}, - "source": [ - "# Business Data Challenge - Team 1" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b1a5b9d3", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "import warnings\n", - "import io\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "markdown", - "id": "ecfa2219", - "metadata": {}, - "source": [ - "Configuration de l'accès aux données" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1a094277", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30d77451-2df6-4c07-8b15-66e0e990ff03", - "metadata": {}, - "outputs": [], - "source": [ - "# Import cleaning and merge functions\n", - "\n", - "exec(open('0_Cleaning_and_merge_functions.py').read())\n", - "\n", - "exec(open('0_KPI_functions.py').read())\n", - "\n", - "# Ignore warning\n", - "warnings.filterwarnings('ignore')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f1b44d3e-76bb-4860-b9db-a2840db7cf39", - "metadata": {}, - "outputs": [], - "source": [ - "def load_dataset_2(directory_path, file_name):\n", - " \"\"\"\n", - " This function loads csv file\n", - " \"\"\"\n", - " file_path = \"bdc2324-data\" + \"/\" + directory_path + \"/\" + directory_path + file_name + \".csv\"\n", - " with fs.open(file_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in, sep=\",\")\n", - "\n", - " # drop na :\n", - " #df = df.dropna(axis=1, thresh=len(df))\n", - " # if identifier in table : delete it\n", - " if 'identifier' in df.columns:\n", - " df = df.drop(columns = 'identifier')\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31ab76f0-fbb1-46f6-b359-97228620c207", - "metadata": {}, - "outputs": [], - "source": [ - "def export_in_temporary(df, output_name):\n", - " print('Export of dataset :', output_name)\n", - " FILE_PATH_OUT_S3 = \"ajoubrel-ensae/Temporary\" + \"/\" + output_name + '.csv'\n", - " with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " df.to_csv(file_out, index = False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "108fc5ef-c56a-4f03-a867-943d9d6492fd", - "metadata": {}, - "outputs": [], - "source": [ - "def save_file_s3(File_name, type_of_activity):\n", - " image_buffer = io.BytesIO()\n", - " plt.savefig(image_buffer, format='png')\n", - " image_buffer.seek(0)\n", - " FILE_PATH = f\"projet-bdc2324-team1/stat_desc/{type_of_activity}/\"\n", - " FILE_PATH_OUT_S3 = FILE_PATH + File_name + type_of_activity + '.png'\n", - " with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", - " s3_file.write(image_buffer.read())\n", - " plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "c99b9cb7-00ab-41cf-bde7-38676f5a3d02", - "metadata": {}, - "outputs": [], - "source": [ - "def taux_partner(campany_nb) :\n", - "\n", - " is_partner = load_dataset_2(campany_nb, 'customersplus')[['is_partner']].astype(int)\n", - " percentage_partner = (is_partner['is_partner'].mean()) * 100\n", - " \n", - " return percentage_partner\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "6facc27e-f95d-49c5-afe0-8c34b3a0cb94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0\n" - ] - } - ], - "source": [ - "a = 0\n", - "for nb in [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\"]:\n", - " a += taux_partner(nb)\n", - "\n", - "print(a/14)" - ] - }, - { - "cell_type": "markdown", - "id": "ccf597b0-b459-4ea5-baf0-5ba8c90915e4", - "metadata": {}, - "source": [ - "# Cleaning target area and tags" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fd88e294-e038-4cec-ad94-2bbbc10a4059", - "metadata": {}, - "outputs": [], - "source": [ - "def concatenate_names(names):\n", - " return ', '.join(names)\n", - "\n", - "def targets_KPI(df_target = None):\n", - " \n", - " df_target['target_name'] = df_target['target_name'].fillna('').str.lower()\n", - "\n", - " # Target name cotegory musees / \n", - " df_target['target_jeune'] = df_target['target_name'].str.contains('|'.join(['jeune', 'pass_culture', 'etudiant', '12-25 ans', 'student', 'jeunesse']), case=False).astype(int)\n", - " df_target['target_optin'] = df_target['target_name'].str.contains('|'.join(['optin' ,'opt-in']), case=False).astype(int)\n", - " df_target['target_optout'] = df_target['target_name'].str.contains('|'.join(['optout', 'unsubscribed']), case=False).astype(int)\n", - " df_target['target_scolaire'] = df_target['target_name'].str.contains('|'.join(['scolaire' , 'enseignant', 'chercheur', 'schulen', 'école']), case=False).astype(int)\n", - " df_target['target_entreprise'] = df_target['target_name'].str.contains('|'.join(['b2b', 'btob', 'cse']), case=False).astype(int)\n", - " df_target['target_famille'] = df_target['target_name'].str.contains('|'.join(['famille', 'enfants', 'family']), case=False).astype(int)\n", - " df_target['target_newsletter'] = df_target['target_name'].str.contains('|'.join(['nl', 'newsletter']), case=False).astype(int)\n", - " \n", - " # Target name category for sport compagnies\n", - " df_target['target_abonne'] = ((\n", - " df_target['target_name']\n", - " .str.contains('|'.join(['abo', 'adh']), case=False)\n", - " & ~df_target['target_name'].str.contains('|'.join(['hors abo', 'anciens abo']), case=False)\n", - " ).astype(int))\n", - " \n", - " df_target_categorie = df_target.groupby('customer_id')[['target_jeune', 'target_optin', 'target_optout', 'target_scolaire', 'target_entreprise', 'target_famille', 'target_newsletter', 'target_abonne']].max()\n", - " \n", - " target_agg = df_target.groupby('customer_id').agg(\n", - " nb_targets=('target_name', 'nunique') # Utilisation de tuples pour spécifier les noms de colonnes\n", - " # all_targets=('target_name', concatenate_names),\n", - " # all_target_types=('target_type_name', concatenate_names)\n", - " ).reset_index()\n", - "\n", - " target_agg['nb_targets'] = (target_agg['nb_targets'] - (target_agg['nb_targets'].mean())) / (target_agg['nb_targets'].std())\n", - " \n", - " target_agg = pd.merge(target_agg, df_target_categorie, how='left', on='customer_id')\n", - " \n", - " return target_agg" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1b124018-9637-463e-b512-15743ec9480b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_5/target_information.csv\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " customer_id nb_targets target_jeune target_optin target_optout \\\n", - "0 160516 6.938264 0 1 0 \n", - "1 160517 10.357387 0 1 1 \n", - "2 160518 5.228703 0 1 1 \n", - "3 160519 6.083483 0 1 1 \n", - "4 160520 2.949288 0 1 0 \n", - "... ... ... ... ... ... \n", - "471205 6405875 -0.754762 0 0 1 \n", - "471206 6405905 -0.469835 0 0 1 \n", - "471207 6405909 -0.754762 0 0 1 \n", - "471208 6405917 -0.754762 0 0 1 \n", - "471209 6405963 -0.754762 0 0 1 \n", - "\n", - " target_scolaire target_entreprise target_famille target_newsletter \\\n", - "0 0 1 0 0 \n", - "1 0 0 0 0 \n", - "2 0 0 0 0 \n", - "3 0 0 1 0 \n", - "4 0 0 0 0 \n", - "... ... ... ... ... \n", - "471205 0 0 0 0 \n", - "471206 0 0 0 0 \n", - "471207 0 0 0 0 \n", - "471208 0 0 0 0 \n", - "471209 0 0 0 0 \n", - "\n", - " target_abonne \n", - "0 1 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 \n", - "... ... \n", - "471205 0 \n", - "471206 0 \n", - "471207 0 \n", - "471208 0 \n", - "471209 0 \n", - "\n", - "[471210 rows x 10 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "targets_KPI(display_input_databases('5', file_name = \"target_information\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "7bbca184-1ec1-43b5-ba50-c5e8343d52e7", - "metadata": {}, - "outputs": [], - "source": [ - "def targets_name_category(df_target=None):\n", - " if df_target is None:\n", - " return None\n", - " \n", - " df_target['target_name'] = df_target['target_name'].fillna('').str.lower()\n", - "\n", - " # Target name category for museums\n", - " df_target['target_jeune'] = df_target['target_name'].str.contains('|'.join(['jeune', 'pass_culture', 'etudiant', '12-25 ans', 'student', 'jeunesse']), case=False).astype(int)\n", - " df_target['target_optin'] = df_target['target_name'].str.contains('|'.join(['optin', 'opt-in']), case=False).astype(int)\n", - " df_target['target_optout'] = df_target['target_name'].str.contains('|'.join(['optout', 'unsubscribed']), case=False).astype(int)\n", - " df_target['target_scolaire'] = df_target['target_name'].str.contains('|'.join(['scolaire', 'enseignant', 'chercheur', 'schulen', 'école']), case=False).astype(int)\n", - " df_target['target_entreprise'] = df_target['target_name'].str.contains('|'.join(['b2b', 'btob', 'cse']), case=False).astype(int)\n", - " df_target['target_famille'] = df_target['target_name'].str.contains('|'.join(['famille', 'enfants', 'family']), case=False).astype(int)\n", - " df_target['target_newsletter'] = df_target['target_name'].str.contains('|'.join(['nl', 'newsletter']), case=False).astype(int)\n", - " \n", - " # Target name category for sport companies\n", - " df_target['target_abonne'] = ((df_target['target_name']\n", - " .str.contains('|'.join(['abo', 'adh']), case=False)\n", - " & ~df_target['target_name'].str.contains('|'.join(['hors abo', 'anciens abo']), case=False))\n", - " .astype(int))\n", - "\n", - " list_target_jeune = df_target[df_target['target_jeune'] == 1]['target_name'].unique()\n", - " list_target_optin = df_target[df_target['target_optin'] == 1]['target_name'].unique()\n", - " list_target_optout = df_target[df_target['target_optout'] == 1]['target_name'].unique()\n", - " list_target_scolaire = df_target[df_target['target_scolaire'] == 1]['target_name'].unique()\n", - " list_target_entreprise = df_target[df_target['target_entreprise'] == 1]['target_name'].unique()\n", - " list_target_famille = df_target[df_target['target_famille'] == 1]['target_name'].unique()\n", - " list_target_newsletter = df_target[df_target['target_newsletter'] == 1]['target_name'].unique()\n", - " list_target_abonne = df_target[df_target['target_abonne'] == 1]['target_name'].unique()\n", - "\n", - " list_all = [list_target_jeune, list_target_optin, list_target_optout, list_target_scolaire,\n", - " list_target_entreprise, list_target_famille, list_target_newsletter, list_target_abonne]\n", - "\n", - " category_name = ['target_jeune', 'target_optin', 'target_optout', 'target_scolaire',\n", - " 'target_entreprise', 'target_famille', 'target_newsletter', 'target_abonne']\n", - " \n", - " liste_category = pd.DataFrame({'category_name': category_name,\n", - " 'list_target_name': list_all})\n", - " \n", - " return liste_category\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "fbabcf4d-3ee6-4441-b231-d7ef24b7f160", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_7/target_information.csv\n" - ] - }, - { - "data": { - "text/html": [ - "
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category_namelist_target_name
0target_jeune[jeunesses vaudoises, etudiant hors ssc 22-23, student supporter club, etudiants hors epfl, student supporter club ehl, etudiants]
1target_optin[]
2target_optout[]
3target_scolaire[]
4target_entreprise[prospects b2b, prospects survey b2b, b2b à enlever, prospect b2b fc 06.10, prospect b2b rk 06.10]
5target_famille[family corner - 20.11.22, family corner - saison 19-20]
6target_newsletter[consentements nl lhc, newsletter 2022, b2b à enlever, abonnés newsletter - saison 21-22]
7target_abonne[abonnés 23/24, non renouvellement abo 23-24 debout (23.06), résas abos debout - 29.06, abonnés - assis, sondage reconduction abos, sondage nouveaux abos, abonnés b2c - relance 1, abonnés b2c - relance 2, relance abos assis, non renouvellement abo 23-24 assis (23.06), résas abos assis - 29.06, abonnés - playoffs, abonnés - debout, abonnés non vip - saison 22-23, avantage abonné - ticket, paiements abos, campagneabosconcours - abonnés 21-22 en attente, campagneabosconcours - abonnés 22-23, nouveaux abonnés - saison 22-23, abonnements - relance 15.04, abonnements - relance 13.04, abonnements - relance 11.04, abonnements - relance 07.04, abonnés newsletter - saison 21-22, abonnés 1-3 ans, abonnés 1-3 ans - relance, abonnés - non-renouvellement 22-23, abonnés - renoncement playoffs 22, abonnés 5 ans - relance, abonnés - version finale]
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" - ], - "text/plain": [ - " category_name \\\n", - "0 target_jeune \n", - "1 target_optin \n", - "2 target_optout \n", - "3 target_scolaire \n", - "4 target_entreprise \n", - "5 target_famille \n", - "6 target_newsletter \n", - "7 target_abonne \n", - "\n", - " list_target_name \n", - "0 [jeunesses vaudoises, etudiant hors ssc 22-23, student supporter club, etudiants hors epfl, student supporter club ehl, etudiants] \n", - "1 [] \n", - "2 [] \n", - "3 [] \n", - "4 [prospects b2b, prospects survey b2b, b2b à enlever, prospect b2b fc 06.10, prospect b2b rk 06.10] \n", - "5 [family corner - 20.11.22, family corner - saison 19-20] \n", - "6 [consentements nl lhc, newsletter 2022, b2b à enlever, abonnés newsletter - saison 21-22] \n", - "7 [abonnés 23/24, non renouvellement abo 23-24 debout (23.06), résas abos debout - 29.06, abonnés - assis, sondage reconduction abos, sondage nouveaux abos, abonnés b2c - relance 1, abonnés b2c - relance 2, relance abos assis, non renouvellement abo 23-24 assis (23.06), résas abos assis - 29.06, abonnés - playoffs, abonnés - debout, abonnés non vip - saison 22-23, avantage abonné - ticket, paiements abos, campagneabosconcours - abonnés 21-22 en attente, campagneabosconcours - abonnés 22-23, nouveaux abonnés - saison 22-23, abonnements - relance 15.04, abonnements - relance 13.04, abonnements - relance 11.04, abonnements - relance 07.04, abonnés newsletter - saison 21-22, abonnés 1-3 ans, abonnés 1-3 ans - relance, abonnés - non-renouvellement 22-23, abonnés - renoncement playoffs 22, abonnés 5 ans - relance, abonnés - version finale] " - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option('display.max_colwidth', None)\n", - "targets_name_category(display_input_databases('7', file_name = \"target_information\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c75efea3-b5e8-4a7a-bed4-dd64ae9ff9f2", - "metadata": {}, - "outputs": [], - "source": [ - "#export_inv_temporary(target_agg, 'Target_kpi_concatenate')" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "9d224485-3472-4cc7-9825-1a643bc94fef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/target_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/target_information.csv\n" - ] - } - ], - "source": [ - "companies = {'musee' : ['1', '2', '3', '4'], # , '101'\n", - " 'sport': ['5', '6', '7', '8', '9'],\n", - " 'musique' : ['10', '11', '12', '13', '14']}\n", - "\n", - "nb_compagnie = companies['musique']\n", - "\n", - "def load_files(nb_compagnie):\n", - " targets = pd.DataFrame()\n", - " \n", - " # début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle\n", - " for directory_path in nb_compagnie:\n", - " df_customerplus_clean_0 = display_input_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - " df_target_information = display_input_databases(directory_path, file_name = \"target_information\")\n", - " \n", - " df_target_KPI = targets_KPI(df_target = df_target_information)\n", - " df_target_KPI = pd.merge(df_customerplus_clean_0[['customer_id']], df_target_KPI, how = 'left', on = 'customer_id')\n", - "\n", - " targets_columns = list(df_target_KPI.columns)\n", - " targets_columns.remove('customer_id')\n", - " df_target_KPI[targets_columns] = df_target_KPI[targets_columns].fillna(0)\n", - " \n", - " # creation de la colonne Number compagnie, qui permettra d'agréger les résultats\n", - " df_target_KPI[\"number_company\"]=int(directory_path)\n", - " \n", - " # Traitement des index\n", - " df_target_KPI[\"customer_id\"]= directory_path + '_' + df_target_KPI['customer_id'].astype('str')\n", - " \n", - " # Concaténation\n", - " targets = pd.concat([targets, df_target_KPI], ignore_index=True)\n", - " \n", - " return targets\n", - "\n", - "targets = load_files(nb_compagnie)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "3c911274-0ebd-49af-9487-26524ba20e74", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def target_description(targets, type_of_activity):\n", - "\n", - " describe_target = targets.groupby('number_company').agg(\n", - " prop_target_jeune=('target_jeune', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_scolaire=('target_scolaire', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_entreprise=('target_entreprise', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_famille=('target_famille', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_optin=('target_optin', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_optout=('target_optout', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_newsletter=('target_newsletter', lambda x: (x.sum() / x.count())*100),\n", - " prop_target_abonne=('target_abonne', lambda x: (x.sum() / x.count())*100))\n", - "\n", - " plot = describe_target.plot.bar()\n", - " \n", - " # Adding a title\n", - " plot.set_title(\"Distribution of Targets by Category\")\n", - " \n", - " # Adding labels for x and y axes\n", - " plot.set_xlabel(\"Company Number\")\n", - " plot.set_ylabel(\"Target Proportion\")\n", - "\n", - " plot.set_xticklabels(plot.get_xticklabels(), rotation=0, horizontalalignment='center')\n", - "\n", - " \n", - " # Adding a legend\n", - " plot.legend([\"Youth\", \"School\", \"Enterprise\", \"Family\", \"Optin\", \"Optout\", \"Newsletter\", \"Subscriber\"], title=\"Target Category\")\n", - "\n", - " # save_file_s3(\"target_category_proportion_\", type_of_activity)\n", - " return plot" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "af62ecef-9120-4107-af3e-512588a96800", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "target_description(targets, 'musique')" - ] - }, - { - "cell_type": "markdown", - "id": "5d91263e-8a97-4cb1-8d94-db8ab0b77cdf", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "# Brouillon" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5e864b1-adad-4267-b956-3f7ef371d677", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def display_covering_time(df, company, datecover):\n", - " \"\"\"\n", - " This function draws the time coverage of each company\n", - " \"\"\"\n", - " min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n", - " max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n", - " datecover[company] = [datetime.strptime(min_date, \"%Y-%m-%d\") + timedelta(days=x) for x in range((datetime.strptime(max_date, \"%Y-%m-%d\") - datetime.strptime(min_date, \"%Y-%m-%d\")).days)]\n", - " print(f'Couverture Company {company} : {min_date} - {max_date}')\n", - " return datecover\n", - "\n", - "\n", - "def compute_time_intersection(datecover):\n", - " \"\"\"\n", - " This function returns the time coverage for all companies\n", - " \"\"\"\n", - " timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n", - " intersection = set.intersection(*timestamps_sets)\n", - " intersection_list = list(intersection)\n", - " formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n", - " return sorted(formated_dates)\n", - "\n", - "\n", - "def df_coverage_modelization(sport, coverage_features = 0.7):\n", - " \"\"\"\n", - " This function returns start_date, end_of_features and final dates\n", - " that help to construct train and test datasets\n", - " \"\"\"\n", - " datecover = {}\n", - " for company in sport:\n", - " df_products_purchased_reduced = display_input_databases(company, file_name = \"products_purchased_reduced\",\n", - " datetime_col = ['purchase_date'])\n", - " datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n", - " #print(datecover.keys())\n", - " dt_coverage = compute_time_intersection(datecover)\n", - " start_date = dt_coverage[0]\n", - " end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n", - " final_date = dt_coverage[-1]\n", - " return start_date, end_of_features, final_date\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "2435097a-95a5-43e1-84d0-7f6b701441ba", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "# Bases non communes : mise à plat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8f988fb-5aab-4b57-80d1-e242f7e5b384", - "metadata": {}, - "outputs": [], - "source": [ - "companies = {'musee' : ['1', '2', '3', '4'],\n", - " 'sport': ['5', '6', '7', '8', '9'],\n", - " 'musique' : ['10', '11', '12', '13', '14']}\n", - "\n", - "all_companies = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35ac004f-c191-4f45-a4b1-6d993d9ec38c", - "metadata": {}, - "outputs": [], - "source": [ - "companies_databases = pd.DataFrame()\n", - "\n", - "for i in all_companies:\n", - " company_databases = pd.DataFrame({'company_number' : [i]})\n", - "\n", - " BUCKET = \"bdc2324-data/\"+i\n", - " for base in fs.ls(BUCKET):\n", - " match = re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', base)\n", - " if match:\n", - " nom_base = match.group(3)\n", - " company_databases[nom_base] = 1\n", - "\n", - " companies_databases = pd.concat([companies_databases, company_databases])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8986e477-e6c5-4d6c-83b2-2c90c134b599", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option(\"display.max_columns\", None)\n", - "companies_databases\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fecc3bb-4c03-4144-97c5-615224d9729e", - "metadata": {}, - "outputs": [], - "source": [ - "pd.reset_option(\"display.max_columns\")" - ] - }, - { - "cell_type": "markdown", - "id": "0294ce71-840e-458b-8ffa-cadabbc6da21", - "metadata": {}, - "source": [ - "# Debut Travail 25/02" - ] - }, - { - "cell_type": "markdown", - "id": "ca2c8b6a-4965-422e-ba7c-66423a464fc1", - "metadata": {}, - "source": [ - "## Base communes au types Musée" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5080f66e-f779-410a-876d-b4fe2795e17e", - "metadata": {}, - "outputs": [], - "source": [ - "for i in companies['musique']:\n", - " BUCKET = \"bdc2324-data/\"+i\n", - " liste_base = []\n", - " for base in fs.ls(BUCKET):\n", - " match = re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', base)\n", - " if match:\n", - " nom_base = match.group(3)\n", - " liste_base.append(nom_base)\n", - " globals()['base_'+i] = liste_base\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abd477e1-7479-4c88-a5aa-f987af3f5b79", - "metadata": {}, - "outputs": [], - "source": [ - "# Trouver l'intersection entre les cinq listes\n", - "intersection = set(base_1).intersection(base_2, base_3, base_4, base_101)\n", - "\n", - "# Convertir le résultat en liste si nécessaire\n", - "intersection_liste = list(intersection)\n", - "\n", - "print(intersection_liste)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d93888f-a511-4ee5-8bc3-d5173a7f119e", - "metadata": {}, - "outputs": [], - "source": [ - "# Trouver l'intersection entre les cinq listes\n", - "intersection = set(base_10).intersection(base_12, base_13, base_14, base_11)\n", - "\n", - "# Convertir le résultat en liste si nécessaire\n", - "intersection_liste = list(intersection)\n", - "\n", - "print(intersection_liste)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10e89669-42bb-4652-a4bc-1a3d1caf4d1a", - "metadata": {}, - "outputs": [], - "source": [ - "len(intersection_liste)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d058b21-a538-4f59-aefb-ef7966f73fdc", - "metadata": {}, - "outputs": [], - "source": [ - "df1_tags = load_dataset_2(\"1\", \"tags\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa441f99-733c-4675-8676-bed4682d3324", - "metadata": {}, - "outputs": [], - "source": [ - "df1_structure_tag_mappings = load_dataset_2(\"1\", 'structure_tag_mappings')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6767a750-14a4-4c05-903e-d2f07170825b", - "metadata": {}, - "outputs": [], - "source": [ - "df1_customersplus = load_dataset_2(\"1\", \"customersplus\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "125e9145-a815-46fd-bdf4-07589508b259", - "metadata": {}, - "outputs": [], - "source": [ - "df1_customersplus.groupby('structure_id')['id'].count().reset_index().sort_values('id', ascending=False).head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c17a6976-792f-474d-bcff-c89396eddb3f", - "metadata": {}, - "outputs": [], - "source": [ - "df1_customersplus['structure_id'].isna().sum() / len(df1_customersplus['structure_id'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecfc155a-cb42-46ec-8da5-33fdcd087355", - "metadata": {}, - "outputs": [], - "source": [ - "len(df1_structure_tag_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "071410b8-950d-4fcc-b2b9-57415253c286", - "metadata": {}, - "outputs": [], - "source": [ - "df1_structure_tag_mappings.groupby('tag_id')['structure_id'].count().reset_index().sort_values('structure_id', ascending=False).head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f48d27a9-14e4-4bb9-a60a-73e9438b58fc", - "metadata": {}, - "outputs": [], - "source": [ - "?np.sort_values()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14eaa0ea-02cc-430b-ab9b-38e6637810c3", - "metadata": {}, - "outputs": [], - "source": [ - "def info_colonnes_dataframe(df):\n", - " # Créer une liste pour stocker les informations sur chaque colonne\n", - " infos_colonnes = []\n", - "\n", - " # Parcourir les colonnes du DataFrame\n", - " for nom_colonne, serie in df.items(): # Utiliser items() au lieu de iteritems()\n", - " # Calculer le taux de valeurs manquantes\n", - " taux_na = serie.isna().mean() * 100\n", - "\n", - " # Ajouter les informations à la liste\n", - " infos_colonnes.append({\n", - " 'Nom_colonne': nom_colonne,\n", - " 'Type_colonne': str(serie.dtype),\n", - " 'Taux_NA': taux_na\n", - " })\n", - "\n", - " # Créer une nouvelle DataFrame à partir de la liste d'informations\n", - " df_infos_colonnes = pd.DataFrame(infos_colonnes)\n", - "\n", - " return df_infos_colonnes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b031c32-d4c8-42a5-9a71-a7810f9bf8d8", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "info_colonnes_dataframe(df1_tags)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1a87f27-c4d4-4832-ac20-0c3c54aa4980", - "metadata": {}, - "outputs": [], - "source": [ - "info_colonnes_dataframe(df1_structure_tag_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa5c65a8-2f74-4f3f-85fc-9ac91e0bb361", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "print(df1_tags['name'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a59bf932-5b54-4600-81f5-c55ac93ae510", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', None)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4ab298e-2cae-4865-9f00-4caff5f75ea1", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print(df1_tags['name'])" - ] - }, - { - "cell_type": "markdown", - "id": "76bffba1-5f7e-4308-9224-437ca66148f8", - "metadata": {}, - "source": [ - "## KPI sur target_type" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6daf22e-6583-4431-a467-660a1dd4e5a4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d91d5895", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', None)\n" - ] - }, - { - "cell_type": "markdown", - "id": "c58b17d3", - "metadata": {}, - "source": [ - "Raisonnement : on prends les target_type qui représente 90% des clients et on fait des catégories dessus." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6930bff5", - "metadata": {}, - "outputs": [], - "source": [ - "def print_main_target(tenant_id, nb_print = 40):\n", - " df_target = display_input_databases(tenant_id, \"target_information\")\n", - "\n", - " print('Nombre de ciblage : ', len(df_target))\n", - " nb_customers = df_target['customer_id'].nunique()\n", - " print('Nombre de client avec étiquette target : ', nb_customers) \n", - "\n", - " nb_custumers_per_target = df_target.groupby(\"target_name\")['customer_id'].count().reset_index().sort_values('customer_id', ascending=False)\n", - " nb_custumers_per_target['cumulative_customers'] = nb_custumers_per_target['customer_id'].cumsum()/len(df_target)\n", - " nb_custumers_per_target['customer_id'] = nb_custumers_per_target['customer_id']/nb_customers\n", - "\n", - " return nb_custumers_per_target.head(nb_print)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e7ee1a0", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "pd.set_option(\"max_colwidth\", None)\n", - "print_main_target('1', 60)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19f3a2dd-ba3d-4dec-8e10-fed544ab6a53", - "metadata": {}, - "outputs": [], - "source": [ - "pd.reset_option('display.max_rows')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b57a28ac", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('2', 25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a65991f", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('3', 70)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f34b8bf", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('4', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52b24d66-92ad-4421-a62b-5cba837f1893", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', None)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40fe3676", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "\n", - "\n", - "print_main_target('5', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "820d3600-379b-4245-a977-f1f1fa1f1839", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('6', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86f64a1b-763a-4e43-9601-a38c80392d47", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('7', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fbf2ea42-515a-4cdf-a4c1-50f99c379ed9", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('8', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9684045c-4e25-4952-b099-a559baa5d749", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('9', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf8f7816-e7f3-4b7a-a987-8350a76eb140", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('10', 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76c818a5-3c52-4d97-ac81-b7f3f89092bd", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('11', 100)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "603b11e4-5d76-4699-a1b2-e795929edc04", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('12', 100)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa93aecd-d117-481e-8507-15e49937ce14", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('13', 100)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a115ebcf-4488-47f3-9d7e-75a1fca52f0f", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print_main_target('14', 100)\n" - ] - }, - { - "cell_type": "markdown", - "id": "605cced5-052f-4a99-ac26-020c5d2ab633", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "## KPI sur tags" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "916c3e2b-04d3-4877-b894-8f26f10d926e", - "metadata": {}, - "outputs": [], - "source": [ - "customersplus = load_dataset_2(\"4\", \"customersplus\")[['id', 'structure_id']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46847b24-15a4-464e-969f-f16ed3653f1f", - "metadata": {}, - "outputs": [], - "source": [ - "structure_tag_mappings = load_dataset_2('4', \"structure_tag_mappings\")[['structure_id', 'tag_id']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c10c69d-735f-453e-96bf-750697d965d0", - "metadata": {}, - "outputs": [], - "source": [ - "customersplus[customersplus['structure_id'].notna()]['structure_id'].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b0e77b3-5f16-4484-9564-7d3826583418", - "metadata": {}, - "outputs": [], - "source": [ - "len(customersplus[customersplus['structure_id'].notna()])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfa27722-37f9-435a-8221-8aa6f9a4a107", - "metadata": {}, - "outputs": [], - "source": [ - "structure_tag_mappings['structure_id'].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2daabdd5-31e3-4918-9856-9bbc30cde602", - "metadata": {}, - "outputs": [], - "source": [ - "def tags_information(tenant_id, first_tags):\n", - "\n", - " customersplus = load_dataset_2(tenant_id, \"customersplus\")[['id', 'structure_id']]\n", - " customersplus.rename(columns = {'id' : 'customer_id'}, inplace = True)\n", - " tags = load_dataset_2(tenant_id, \"tags\")[['id', 'name']]\n", - " tags.rename(columns = {'id' : 'tag_id', 'name' : 'tag_name'}, inplace = True)\n", - " structure_tag_mappings = load_dataset_2(tenant_id, \"structure_tag_mappings\")[['structure_id', 'tag_id']]\n", - " \n", - " customer_tags = pd.merge(customersplus, structure_tag_mappings, on = 'structure_id', how = 'left')\n", - " customer_tags = pd.merge(customer_tags, tags, on = 'tag_id', how = 'inner')\n", - " \n", - " nb_customers_with_tag = customer_tags['customer_id'].nunique()\n", - " \n", - " print('Nombre de client avec tag : ', nb_customers_with_tag)\n", - " print('Proportion de clients avec tags : ', nb_customers_with_tag/len(customersplus))\n", - " print('Moyenne de tags par client : ', len(customer_tags)/nb_customers_with_tag)\n", - " \n", - " info = customer_tags.groupby(['tag_id', 'tag_name'])['customer_id'].count().reset_index().sort_values('customer_id', ascending = False).head(first_tags)\n", - "\n", - " return info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b9f5f71-a927-4cc8-bb0c-9538e28d3553", - "metadata": {}, - "outputs": [], - "source": [ - "tags_information(\"1\", 20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd5bef41-1774-4601-86b5-b7c1aea8f1d2", - "metadata": {}, - "outputs": [], - "source": [ - "tags_information(\"2\", 20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c2dc3e6-1418-44db-a8c0-4a9d59ec5232", - "metadata": {}, - "outputs": [], - "source": [ - "load_dataset_2(\"2\", \"tags\")[['id', 'name']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7b2c670-7122-4f67-b1aa-8c80a10f16d8", - "metadata": {}, - "outputs": [], - "source": [ - "tags_information(\"3\", 20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76639995-252d-4a58-83d8-c0c00900c3a9", - "metadata": {}, - "outputs": [], - "source": [ - "tags_information(\"4\", 20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "07e91791-d4d4-42b1-ac18-22d3b0b9f7bd", - "metadata": {}, - "outputs": [], - "source": [ - "tags_information(\"101\", 20)" - ] - }, - { - "cell_type": "markdown", - "id": "87d131cd-ead0-4ef4-a8ee-b09022d08ffa", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "## KPI product" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26582be9-cfd1-48ea-a0a7-31101fdeb9d1", - "metadata": {}, - "outputs": [], - "source": [ - "tenant_id = \"1\"\n", - "\n", - "df_product = display_databases(tenant_id, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n", - "\n", - "df_product.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "533bf499-dd56-4d29-b261-ca1e4928c9c7", - "metadata": {}, - "outputs": [], - "source": [ - "nb_tickets_per_events = df_product.groupby(['name_event_types', 'name_events'])['ticket_id'].count().reset_index().sort_values('ticket_id', ascending = False)\n", - "nb_tickets_per_events['prop_tickets'] = round(nb_tickets_per_events['ticket_id']/len(df_product), 3)\n", - "nb_tickets_per_events" - ] - }, - { - "cell_type": "markdown", - "id": "1ede9eaa-7f0a-4856-9349-b2747d6a4901", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "# Fin travail 25/02" - ] - }, - { - "cell_type": "markdown", - "id": "c437eaec", - "metadata": {}, - "source": [ - "# Exemple sur Company 1" - ] - }, - { - "cell_type": "markdown", - "id": "e855f403", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "## customersplus.csv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91a8f8c4", - "metadata": {}, - "outputs": [], - "source": [ - "a = pd.DataFrame(df1_customersplus.info())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fda171d", - "metadata": {}, - "outputs": [], - "source": [ - "def info_colonnes_dataframe(df):\n", - " # Créer une liste pour stocker les informations sur chaque colonne\n", - " infos_colonnes = []\n", - "\n", - " # Parcourir les colonnes du DataFrame\n", - " for nom_colonne, serie in df.items(): # Utiliser items() au lieu de iteritems()\n", - " # Calculer le taux de valeurs manquantes\n", - " taux_na = serie.isna().mean() * 100\n", - "\n", - " # Ajouter les informations à la liste\n", - " infos_colonnes.append({\n", - " 'Nom_colonne': nom_colonne,\n", - " 'Type_colonne': str(serie.dtype),\n", - " 'Taux_NA': taux_na\n", - " })\n", - "\n", - " # Créer une nouvelle DataFrame à partir de la liste d'informations\n", - " df_infos_colonnes = pd.DataFrame(infos_colonnes)\n", - "\n", - " return df_infos_colonnes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "205eeeab", - "metadata": {}, - "outputs": [], - "source": [ - "def cleaning_date(df, column_name):\n", - " \"\"\"\n", - " Nettoie la colonne spécifiée du DataFrame en convertissant les valeurs en datetime avec le format ISO8601.\n", - "\n", - " Parameters:\n", - " - df: DataFrame\n", - " Le DataFrame contenant la colonne à nettoyer.\n", - " - column_name: str\n", - " Le nom de la colonne à nettoyer.\n", - "\n", - " Returns:\n", - " - DataFrame\n", - " Le DataFrame modifié avec la colonne nettoyée.\n", - " \"\"\"\n", - " df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "634282c5", - "metadata": {}, - "outputs": [], - "source": [ - "a = info_colonnes_dataframe(df1_customersplus)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e8d4133", - "metadata": {}, - "outputs": [], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1268ad5a", - "metadata": {}, - "outputs": [], - "source": [ - "a = pd.DataFrame(df1_customersplus.isna().sum()/len(df1_customersplus)*100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd41dc80", - "metadata": {}, - "outputs": [], - "source": [ - "# Selection des variables\n", - "df1_customersplus_clean = df1_customersplus.copy()\n", - "\n", - "cleaning_date(df1_customersplus_clean, 'first_buying_date')\n", - "cleaning_date(df1_customersplus_clean, 'last_visiting_date')\n", - "\n", - "df1_customersplus_clean.drop(['lastname', 'firstname', 'email', 'civility', 'note', 'created_at', 'updated_at', 'deleted_at', 'extra', 'reference', 'extra_field', 'identifier', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode', 'last_visiting_date'], axis = 1, inplace=True)\n", - "df1_customersplus_clean.rename(columns = {'id' : 'customer_id'}, inplace = True)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2455d2e1", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df1_purchases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f9a159d", - "metadata": {}, - "outputs": [], - "source": [ - "df1_purchases.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db201bf7", - "metadata": {}, - "outputs": [], - "source": [ - "# Nettoyage purchase_date\n", - "df1_purchases['purchase_date'] = pd.to_datetime(df1_purchases['purchase_date'], utc = True)\n", - "df1_purchases['purchase_date'] = pd.to_datetime(df1_purchases['purchase_date'], format = 'ISO8601')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd436fca", - "metadata": {}, - "outputs": [], - "source": [ - "df1_purchases.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83435862", - "metadata": {}, - "outputs": [], - "source": [ - "# Selection des variables\n", - "df1_purchases_clean = df1_purchases[['id', 'purchase_date', 'customer_id']]" - ] - }, - { - "cell_type": "markdown", - "id": "637bdb72", - "metadata": {}, - "source": [ - "# Customer information" - ] - }, - { - "cell_type": "markdown", - "id": "14c52894", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "## Target area - NLP" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d83abfbf", - "metadata": {}, - "outputs": [], - "source": [ - "# Target.csv cleaning\n", - "df1_targets_clean = df1_targets[[\"id\", \"target_type_id\", \"name\"]]\n", - "df1_targets_clean.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n", - "\n", - "# target_type cleaning\n", - "df1_target_types_clean = df1_target_types[[\"id\",\"is_import\",\"name\"]].add_prefix(\"target_type_\")\n", - "\n", - "#customer_target_mappings cleaning\n", - "df1_customer_target_mappings_clean = df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\"]]\n", - "\n", - "# Merge target et target_type\n", - "df1_targets_full = pd.merge(df1_targets_clean, df1_target_types_clean, left_on='target_type_id', right_on='target_type_id', how='inner')\n", - "df1_targets_full.drop(['target_type_id'], axis = 1, inplace=True)\n", - "\n", - "# Merge\n", - "df1_targets_full = pd.merge(df1_customer_target_mappings_clean, df1_targets_full, left_on='target_id', right_on='target_id', how='inner')\n", - "df1_targets_full.drop(['target_id'], axis = 1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90d71b2c", - "metadata": {}, - "outputs": [], - "source": [ - "df1_targets_test = df1_targets_full[['id', 'customer_id']].groupby(['customer_id']).count()\n", - "len(df1_targets_test[df1_targets_test['id'] > 1]) / len(df1_targets_test)\n", - "\n", - "# 99,6% des 151 000 client visés sont catégorisés plusieurs fois et en moyenne 5 fois... \n", - "df1_targets_test.mean()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2301de1e", - "metadata": {}, - "outputs": [], - "source": [ - "df1_targets_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75fbc2f7", - "metadata": {}, - "outputs": [], - "source": [ - "# Catégorisation des target_name\n", - "import pandas as pd\n", - "import nltk\n", - "from nltk.tokenize import word_tokenize\n", - "from nltk.corpus import stopwords\n", - "from nltk.stem import WordNetLemmatizer\n", - "from nltk.probability import FreqDist\n", - "\n", - "# Téléchargement des ressources nécessaires\n", - "nltk.download('punkt')\n", - "nltk.download('stopwords')\n", - "nltk.download('wordnet')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55cddf92", - "metadata": {}, - "outputs": [], - "source": [ - "# Définition des fonctions de tokenisation, suppression des mots vides et lemmatisation\n", - "def preprocess_text(texte):\n", - " # Concaténation des éléments de la liste en une seule chaîne de caractères\n", - " texte_concat = ' '.join(texte)\n", - " \n", - " # Tokenisation des mots\n", - " tokens = word_tokenize(texte_concat.lower())\n", - " \n", - " # Suppression des mots vides (stopwords)\n", - " stop_words = set(stopwords.words('french'))\n", - " filtered_tokens = [word for word in tokens if word not in stop_words]\n", - " \n", - " # Lemmatisation des mots\n", - " lemmatizer = WordNetLemmatizer()\n", - " lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]\n", - " \n", - " return lemmatized_tokens\n", - "\n", - "\n", - "# Appliquer le prétraitement à la colonne de texte\n", - "df1_targets_full['target_name_tokened'] = df1_targets_full['target_name'].apply(preprocess_text)\n", - "\n", - "# Concaténer les listes de mots pour obtenir une liste de tous les mots dans le corpus\n", - "all_words = [word for tokens in df1_targets_full['target_name_tokened'] for word in tokens]\n", - "\n", - "# Calculer la fréquence des mots\n", - "freq_dist = FreqDist(all_words)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7fd98a85", - "metadata": {}, - "outputs": [], - "source": [ - "# Affichage des mots les plus fréquents\n", - "print(\"Mots les plus fréquents:\")\n", - "for mot, freq in freq_dist.most_common(15):\n", - " print(f\"{mot}: {freq}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf94bb1d", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import nltk\n", - "from nltk.tokenize import word_tokenize\n", - "from nltk.corpus import stopwords\n", - "from nltk.stem import WordNetLemmatizer\n", - "\n", - "# Téléchargement des ressources nécessaires\n", - "nltk.download('punkt')\n", - "nltk.download('stopwords')\n", - "nltk.download('wordnet')\n", - "\n", - "# Création de la DataFrame d'exemple\n", - "data = {'texte': [\"Le chat noir mange une souris.\", \"Le chien blanc aboie.\"]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Fonction pour prétraiter le texte\n", - "def preprocess_text(texte):\n", - " # Concaténation des éléments de la liste en une seule chaîne de caractères\n", - " texte_concat = ' '.join(texte)\n", - " \n", - " # Tokenisation des mots\n", - " tokens = word_tokenize(texte_concat.lower())\n", - " \n", - " # Suppression des mots vides (stopwords)\n", - " stop_words = set(stopwords.words('french'))\n", - " filtered_tokens = [word for word in tokens if word not in stop_words]\n", - " \n", - " # Lemmatisation des mots\n", - " lemmatizer = WordNetLemmatizer()\n", - " lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]\n", - " \n", - " return lemmatized_tokens\n", - "\n", - "# Appliquer la fonction de prétraitement à la colonne de texte\n", - "df['texte_preprocessed'] = df['texte'].apply(preprocess_text)\n", - "\n", - "# Afficher le résultat\n", - "print(df)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Identification_entreprise.ipynb b/useless/Identification_entreprise.ipynb deleted file mode 100644 index 815074a..0000000 --- a/useless/Identification_entreprise.ipynb +++ /dev/null @@ -1,1610 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 335, - "id": "482d19ab-5dd1-4e75-b2c1-df734ce5ee66", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 336, - "id": "b1b5a536-b76c-427b-ab6b-f0235c84f5ad", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import s3fs\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 337, - "id": "0469164d-5770-443e-8cf4-d4f1ebd1b853", - "metadata": {}, - "outputs": [], - "source": [ - "entreprise_base=['bdc2324-data/1', 'bdc2324-data/2', 'bdc2324-data/3', 'bdc2324-data/4', 'bdc2324-data/5', 'bdc2324-data/6', 'bdc2324-data/7', 'bdc2324-data/8','bdc2324-data/9','bdc2324-data/10','bdc2324-data/11','bdc2324-data/12','bdc2324-data/13','bdc2324-data/14','bdc2324-data/101']" - ] - }, - { - "cell_type": "code", - "execution_count": 343, - "id": "55fbbad2-537e-4098-9a2d-d3850fab7332", - "metadata": {}, - "outputs": [ - { - "ename": "PermissionError", - "evalue": "The Access Key Id you provided does not exist in our records.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:394\u001b[0m, in \u001b[0;36mS3FileSystem._lsdir\u001b[0;34m(self, path, refresh, max_items)\u001b[0m\n\u001b[1;32m 393\u001b[0m dircache \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 394\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mit\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[43m \u001b[49m\u001b[43mdircache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCommonPrefixes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/paginate.py:269\u001b[0m, in \u001b[0;36mPageIterator.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 269\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 270\u001b[0m parsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_extract_parsed_response(response)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/paginate.py:357\u001b[0m, in \u001b[0;36mPageIterator._make_request\u001b[0;34m(self, current_kwargs)\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_make_request\u001b[39m(\u001b[38;5;28mself\u001b[39m, current_kwargs):\n\u001b[0;32m--> 357\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcurrent_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[0;31mClientError\u001b[0m: An error occurred (InvalidAccessKeyId) when calling the ListObjectsV2 operation: The Access Key Id you provided does not exist in our records.", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[343], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m BUCKET \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbdc2324-data/2\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBUCKET\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:619\u001b[0m, in \u001b[0;36mS3FileSystem.ls\u001b[0;34m(self, path, detail, refresh, **kwargs)\u001b[0m\n\u001b[1;32m 604\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" List single \"directory\" with or without details\u001b[39;00m\n\u001b[1;32m 605\u001b[0m \n\u001b[1;32m 606\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[38;5;124;03m additional arguments passed on\u001b[39;00m\n\u001b[1;32m 617\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 618\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strip_protocol(path)\u001b[38;5;241m.\u001b[39mrstrip(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 619\u001b[0m files \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrefresh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrefresh\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m files:\n\u001b[1;32m 621\u001b[0m files \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ls(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent(path), refresh\u001b[38;5;241m=\u001b[39mrefresh)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:487\u001b[0m, in \u001b[0;36mS3FileSystem._ls\u001b[0;34m(self, path, refresh)\u001b[0m\n\u001b[1;32m 485\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lsbuckets(refresh)\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 487\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_lsdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrefresh\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:409\u001b[0m, in \u001b[0;36mS3FileSystem._lsdir\u001b[0;34m(self, path, refresh, max_items)\u001b[0m\n\u001b[1;32m 407\u001b[0m f[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m f[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKey\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ClientError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m translate_boto_error(e)\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdircache[path] \u001b[38;5;241m=\u001b[39m files\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m files\n", - "\u001b[0;31mPermissionError\u001b[0m: The Access Key Id you provided does not exist in our records." - ] - } - ], - "source": [ - "BUCKET = \"bdc2324-data/2\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 281, - "id": "0b76f171-9ae1-4900-a23e-ec4dd57d461a", - "metadata": {}, - "outputs": [], - "source": [ - "pd.reset_option('display.max_rows')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 341, - "id": "85357844-15f6-4098-9032-18310305c332", - "metadata": {}, - "outputs": [ - { - "ename": "PermissionError", - "evalue": "Forbidden", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:529\u001b[0m, in \u001b[0;36mS3FileSystem.info\u001b[0;34m(self, path, version_id, refresh)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 529\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhead_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbucket\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mversion_id_kw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mversion_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreq_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 532\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 533\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKey\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([bucket, key]),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m: out\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 541\u001b[0m }\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:200\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[0;31mClientError\u001b[0m: An error occurred (403) when calling the HeadObject operation: Forbidden", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[341], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m entreprise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbdc2324-data/2/2\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevents\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.csv\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mentreprise\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file_in:\n\u001b[1;32m 3\u001b[0m df_event\u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_in, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1295\u001b[0m, in \u001b[0;36mAbstractFileSystem.open\u001b[0;34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1294\u001b[0m ac \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mautocommit\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_intrans)\n\u001b[0;32m-> 1295\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1301\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1302\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1304\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compr\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:375\u001b[0m, in \u001b[0;36mS3FileSystem._open\u001b[0;34m(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, requester_pays, **kwargs)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 373\u001b[0m cache_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_cache_type\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mS3File\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43macl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43macl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mversion_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mversion_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43ms3_additional_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 378\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequester_pays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequester_pays\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1096\u001b[0m, in \u001b[0;36mS3File.__init__\u001b[0;34m(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays)\u001b[0m\n\u001b[1;32m 1094\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ms3_additional_kwargs \u001b[38;5;241m=\u001b[39m s3_additional_kwargs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 1095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreq_kw \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRequestPayer\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequester\u001b[39m\u001b[38;5;124m'\u001b[39m} \u001b[38;5;28;01mif\u001b[39;00m requester_pays \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m-> 1096\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1097\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ms3 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;66;03m# compatibility\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwritable():\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1651\u001b[0m, in \u001b[0;36mAbstractBufferedFile.__init__\u001b[0;34m(self, fs, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\u001b[0m\n\u001b[1;32m 1649\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m=\u001b[39m size\n\u001b[1;32m 1650\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1651\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetails\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;241m=\u001b[39m caches[cache_type](\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocksize, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fetch_range, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcache_options\n\u001b[1;32m 1654\u001b[0m )\n\u001b[1;32m 1655\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1664\u001b[0m, in \u001b[0;36mAbstractBufferedFile.details\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1661\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 1662\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdetails\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_details \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1664\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_details \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1665\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_details\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:548\u001b[0m, in \u001b[0;36mS3FileSystem.info\u001b[0;34m(self, path, version_id, refresh)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m(S3FileSystem, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39minfo(path)\n\u001b[1;32m 547\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 548\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ee\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParamValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 550\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to head path \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (path, e))\n", - "\u001b[0;31mPermissionError\u001b[0m: Forbidden" - ] - } - ], - "source": [ - "entreprise = 'bdc2324-data/2/2' + 'events' + '.csv'\n", - "with fs.open(entreprise, mode=\"rb\") as file_in:\n", - " df_event= pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 342, - "id": "e6117d69-9916-4a81-88aa-0340c6af13e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcreated_atupdated_atseason_idfacility_idnameevent_type_idmanual_addedis_displayevent_type_key_idfacility_key_ididentifier
0152023-10-13 13:02:09.517079+02:002023-11-03 10:17:04.761407+01:0012„kreativ mit allen sinnen\"1FalseTrue1208f32b3fd76fcbfcb949502f4a78b052
1112023-10-13 13:02:09.515135+02:002023-11-03 10:17:04.761407+01:0011truffes zauber1FalseTrue110eafeafe7396fea2284da359febb069d
2122023-10-13 13:02:09.515619+02:002023-11-03 10:17:04.761407+01:0011choco-schule li – die führung für oberstufen &...1FalseTrue11235c4d3206c90b61f668e0e8051cdf33
3142023-10-13 13:02:09.516604+02:002023-11-03 10:17:05.663186+01:0011„formen & veredeln\"44FalseTrue12ed3d806039d13f9a7999033ef68ebe81
4102023-10-13 13:02:09.514640+02:002023-11-03 10:17:04.761407+01:0012truffes zauber1FalseTrue121d4c8761a169128962464ec99ba135f8
5182023-10-13 13:02:09.518522+02:002023-11-03 10:17:04.761407+01:0011choco-welt – die öffentliche führung1FalseTrue11e4e2915fd5ba2a5d14fb51d8df063bed
6172023-10-13 13:02:09.518037+02:002023-11-03 10:17:04.761407+01:0011schokoladentour – familien1FalseTrue115bf172dd5a3bf11f2b346eee5588c97a
7412023-10-13 13:07:51.131668+02:002023-11-03 10:17:04.761407+01:0012ausfahrtsticket1FalseTrue1204fe59a3f6db96a83f6c9734905acb7e
832023-10-13 13:02:09.510741+02:002023-11-03 10:17:04.761407+01:0011choco-welt – gruppenführung1FalseTrue118cf7a143170249b3286c2b76b9580f4b
912023-10-13 13:02:09.443323+02:002023-11-03 10:17:04.761407+01:0011schokoladentour – einzelticket1FalseTrue1134c2ab5c6c6750f78d6e475023db1dcb
1092023-10-13 13:02:09.514157+02:002023-11-03 10:17:04.761407+01:0011„formen & veredeln\"1FalseTrue1140d0622668130a47c06aa63742cc1c55
1172023-10-13 13:02:09.513141+02:002023-11-03 10:17:04.761407+01:0012choco-deluxe – die öffentliche führung1FalseTrue12af69a8da972bb9975f78748655a6bdad
124512023-10-13 15:49:57.226957+02:002023-11-03 10:17:04.761407+01:0011weihnachts-special1FalseTrue11e48ae983e2654f7dd1055f0ed25b4155
1362023-10-13 13:02:09.512552+02:002023-11-03 10:17:04.761407+01:0012gutschein schokoladentour1FalseTrue12662a3e0d8e88a64afb792d6aecc20395
1442023-10-13 13:02:09.511362+02:002023-11-03 10:17:04.761407+01:0011choco-deluxe – die öffentliche führung (de)1FalseTrue1198e165773ac25e1ef8ef84ccc8c45eb4
1552023-10-13 13:02:09.511954+02:002023-11-03 10:17:04.761407+01:0011„kreativ mit allen sinnen\"1FalseTrue118180dfe4fc995269bfac5336c13ec931
164532023-10-13 15:49:57.238792+02:002023-11-03 10:17:04.761407+01:0011privater chocolateria workshop1FalseTrue117cc2c03196cdc8adfc4102c87f15056e
177592023-10-31 03:20:00.509720+01:002023-11-03 10:17:04.761407+01:0011choco-schule i – die führung für primarschulkl...1FalseTrue11582a63d22864911766d8e019c277d1b3
18242023-10-13 13:02:09.521575+02:002023-11-03 10:17:04.761407+01:0012choco-welt – die gruppenführung1FalseTrue129fa748c7defa0d4f6976faa875d8c394
19212023-10-13 13:02:09.520019+02:002023-11-03 10:17:04.761407+01:0012chocolateria1FalseTrue12169b7c348566ccfd0e6ccdeeb6ac5f5a
20402023-10-13 13:07:51.131049+02:002023-11-03 10:17:04.761407+01:0012verlängerungspauschale führungen1FalseTrue12d22a3ae3c0712be5dfe9858b97a22034
2122023-10-13 13:02:09.509959+02:002023-11-03 10:17:04.761407+01:0011choco-deluxe – die öffentliche führung (en)1FalseTrue1184d0ef8ed664798bfa6a0d297f45bf2d
22222023-10-13 13:02:09.520492+02:002023-11-03 10:17:04.761407+01:0011weihnacht-special1FalseTrue11634f074cc18efa0e0ce88bdec14f248e
23202023-10-13 13:02:09.519518+02:002023-11-03 10:17:04.761407+01:0012gutschein gruppentarife1FalseTrue12f005c784b0a8db8244177e61e774a9b6
24132023-10-13 13:02:09.516105+02:002023-11-03 10:17:04.761407+01:0011choco-deluxe – die exklusive gruppenführung1FalseTrue11acb6ff9ac2bac1c55043bcb67a72a3a0
251032023-10-13 13:24:59.980586+02:002023-11-03 10:17:04.761407+01:0011choco-schule l – die führung für primarschulkl...1FalseTrue117b44ae19449523c65c1140c8aa4db924
26812023-10-13 13:19:30.509755+02:002023-11-03 10:17:04.761407+01:0011jumper-deluxe (de) – die weihnachtliche führung1FalseTrue11274024d1c45dc56a82612f8c71e727de
272212023-10-13 13:52:42.848999+02:002023-11-03 10:17:04.761407+01:0011jumper-deluxe (en) – the christmas guided tour1FalseTrue11576951a8841585f9bb3a6e4b72289f95
28232023-10-13 13:02:09.521089+02:002023-11-03 10:17:04.761407+01:0012choco-deluxe – die exklusive gruppenführung1FalseTrue12cc601355e3b07a57631806317f239000
291262023-10-13 13:30:21.301532+02:002023-11-03 10:17:04.761407+01:0011choco-schule railaway 10%1FalseTrue11ac0a1b09039cd2f078f2d09404f6c981
30822023-10-13 13:19:30.511942+02:002023-11-03 10:17:04.761407+01:0012gutschein saisonkurs1FalseTrue128f86ea0275633432963ebdceae17ce7c
31162023-10-13 13:02:09.517575+02:002023-11-03 10:17:04.761407+01:0012choco-welt – die öffentliche führung1FalseTrue12944fc565655297b2e67c4ae00f020074
32252023-10-13 13:02:09.522012+02:002023-11-03 10:17:04.761407+01:0012weihnacht-special1FalseTrue1268c8841c32b53e7ab121a070043ec1c3
33192023-10-13 13:02:09.519025+02:002023-11-03 10:17:04.761407+01:0011choco-schule – die führung für schulklassen1FalseTrue1163615b4e41ea135189db55a27c55e481
3482023-10-13 13:02:09.513651+02:002023-11-03 10:17:04.761407+01:0011schokoladentour – gruppenticket1FalseTrue11f227b307bebc96449506e7e344c80e80
351062023-10-13 13:24:59.981928+02:002023-11-03 10:17:04.761407+01:0011valentinstags-special1FalseTrue11c3ff8a48ad090434023c8b84b556babe
361322023-10-13 13:30:21.303904+02:002023-11-03 10:17:04.761407+01:0011muttertags-special1FalseTrue11cb7fd9470daa045117b40a6189e9267f
371082023-10-13 13:24:59.982672+02:002023-11-03 10:17:04.761407+01:0011osterkurs1FalseTrue11225f5c434a1a05e093ee996b02c774f3
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"\n", - " season_id facility_id name \\\n", - "0 1 2 „kreativ mit allen sinnen\" \n", - "1 1 1 truffes zauber \n", - "2 1 1 choco-schule li – die führung für oberstufen &... \n", - "3 1 1 „formen & veredeln\" \n", - "4 1 2 truffes zauber \n", - "5 1 1 choco-welt – die öffentliche führung \n", - "6 1 1 schokoladentour – familien \n", - "7 1 2 ausfahrtsticket \n", - "8 1 1 choco-welt – gruppenführung \n", - "9 1 1 schokoladentour – einzelticket \n", - "10 1 1 „formen & veredeln\" \n", - "11 1 2 choco-deluxe – die öffentliche führung \n", - "12 1 1 weihnachts-special \n", - "13 1 2 gutschein schokoladentour \n", - "14 1 1 choco-deluxe – die öffentliche führung (de) \n", - "15 1 1 „kreativ mit allen sinnen\" \n", - "16 1 1 privater chocolateria workshop \n", - "17 1 1 choco-schule i – die führung für primarschulkl... \n", - "18 1 2 choco-welt – die gruppenführung \n", - "19 1 2 chocolateria \n", - "20 1 2 verlängerungspauschale führungen \n", - "21 1 1 choco-deluxe – die öffentliche führung (en) \n", - "22 1 1 weihnacht-special \n", - "23 1 2 gutschein gruppentarife \n", - "24 1 1 choco-deluxe – die exklusive gruppenführung \n", - "25 1 1 choco-schule l – die führung für primarschulkl... \n", - "26 1 1 jumper-deluxe (de) – die weihnachtliche führung \n", - "27 1 1 jumper-deluxe (en) – the christmas guided tour \n", - "28 1 2 choco-deluxe – die exklusive gruppenführung \n", - "29 1 1 choco-schule railaway 10% \n", - "30 1 2 gutschein saisonkurs \n", - "31 1 2 choco-welt – die öffentliche führung \n", - "32 1 2 weihnacht-special \n", - "33 1 1 choco-schule – die führung für schulklassen \n", - "34 1 1 schokoladentour – gruppenticket \n", - "35 1 1 valentinstags-special \n", - "36 1 1 muttertags-special \n", - "37 1 1 osterkurs \n", - "\n", - " event_type_id manual_added is_display event_type_key_id \\\n", - "0 1 False True 1 \n", - "1 1 False True 1 \n", - "2 1 False True 1 \n", - "3 44 False True 1 \n", - "4 1 False True 1 \n", - "5 1 False True 1 \n", - "6 1 False True 1 \n", - "7 1 False True 1 \n", - 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] - }, - { - "cell_type": "code", - "execution_count": 278, - "id": "f3fcdd71-0f5f-42a7-83e5-c0b9613b9e91", - "metadata": {}, - "outputs": [], - "source": [ - "dfs['df_event_types_101']=df_event_types_101" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "eccdaffd-9971-45a9-be39-6d3a95a91b2f", - "metadata": {}, - "outputs": [ - { - "ename": "IndentationError", - "evalue": "expected an indented block after 'for' statement on line 1 (2015796903.py, line 2)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[72], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m entreprise1 = 'bdc2324-data/i/i' + 'event_types' + '.csv'\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'for' statement on line 1\n" - ] - } - ], - "source": [ - "for i in range(14):\n", - "entreprise_i = 'bdc2324-data/i/i' + 'event_types' + '.csv'\n", - "with fs.open(entreprise1, mode=\"rb\") as file_in:\n", - " df_event_types_'i'= pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "18820c35-7da3-4520-b645-1a467104ddc8", - "metadata": {}, - "outputs": [], - "source": [ - "del dfs" - ] - }, - { - "cell_type": "code", - "execution_count": 293, - "id": "9b4a932f-cbb7-4057-bf96-b5d2fd7036a4", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# Création d'un dictionnaire pour stocker les DataFrames events de chaque compagnie\n", - "dfs = {}\n", - "\n", - "for i in range(1, 15): # Assurez-vous que i varie de 1 à 4\n", - " entreprise_i = f'bdc2324-data/{i}/{i}events.csv' # Utilisation de f-strings pour formater la chaîne\n", - " with fs.open(entreprise_i, mode=\"rb\") as file_in: # Utilisation de fsspec.open pour ouvrir le fichier\n", - " df_events_i = pd.read_csv(file_in, sep=\",\") # Lecture du fichier CSV et assignation à un DataFrame\n", - " dfs[f'df_events_{i}'] = df_events_i # Stockage du DataFrame dans le dictionnaire avec une clé appropriée\n" - ] - }, - { - "cell_type": "code", - "execution_count": 246, - "id": "14ed2fa0-0ec6-4a49-a4d9-183a77326f5d", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', 1000)#afficher les ligne maximales" - ] - }, - { - "cell_type": "code", - "execution_count": 295, - "id": "0ac766c6-1960-4422-bf2c-4ba924394998", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcreated_atupdated_atseason_idfacility_idnameevent_type_idmanual_addedis_displayevent_type_key_idfacility_key_ididentifier
0143702023-04-27 15:40:36.110558+02:002023-10-20 12:55:20.877464+02:0014351044abonnement - saison 2023 - 20241123FalseTrue11231044ee604d3e64a27c663a3a1d9de76596e1
1176342023-07-06 18:02:47.697110+02:002023-10-20 12:55:20.854693+02:001435832sf paris / racing 92824FalseTrue82483222d7950f7cbce0f2c8f3c4d272ed6926
2176352023-07-06 18:02:47.697577+02:002023-10-20 12:55:20.854693+02:001435832sf paris / stade toulousain824FalseTrue8248324ae51c31e231eaca1bc2db3afafe417b
3176322023-07-06 18:02:47.694821+02:002023-10-20 12:55:20.854693+02:001435832sf paris / montpellier hr824FalseTrue824832389c8fb7577d0ab030d53e521fda600c
4176332023-07-06 18:02:47.696477+02:002023-10-20 12:55:20.854693+02:001435832sf paris / castres olympique824FalseTrue82483205c9dc3878a4c5c3bfe87bc7667c52d8
.......................................
21488102023-04-04 18:21:47.463967+02:002023-10-20 12:55:20.854693+02:00672832sf paris / racing 92 (ercc)824FalseTrue824832019a7e2faca12acff64ef458cf0c5975
21588042023-04-04 18:21:47.457687+02:002023-10-20 12:55:20.854693+02:00672832sf paris / stade toulousain824FalseTrue824832ef8b8362079d64a10811ac758ca22a63
21688002023-04-04 18:21:47.453369+02:002023-10-20 12:55:20.854693+02:00672832sf paris / stade rochelais824FalseTrue824832451e36ee5ad882a0c25447e2e129fedd
21788062023-04-04 18:21:47.459782+02:002023-10-20 12:55:20.854693+02:00672832sf paris / section paloise824FalseTrue8248322fbea7b0e293de5bf9e9f11d7a4780f8
21888072023-04-04 18:21:47.460842+02:002023-10-20 12:55:20.854693+02:00672832sf paris / ca brive-correze824FalseTrue82483264af51a1bcd04ca63b4d824379283aeb
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1bdc2324-data/1bdc2324-data/2bdc2324-data/3bdc2324-data/4bdc2324-data/5bdc2324-data/6bdc2324-data/7bdc2324-data/8bdc2324-data/9bdc2324-data/10bdc2324-data/11bdc2324-data/12bdc2324-data/13bdc2324-data/14bdc2324-data/101
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compagnie1compagnie2compagnie3compagnie4compagnie5compagnie6compagnie7compagnie8compagnie9compagnie10compagnie11compagnie12compagnie13compagnie14compagnie101
type_eventmuseummuseummuseummuseumsportsportsportsportsportspectable/theaterspectable/theaterspectable/theaterspectable/theaterspectable/theatermuseum
base_compagniebdc2324-data/1bdc2324-data/2bdc2324-data/3bdc2324-data/4bdc2324-data/5bdc2324-data/6bdc2324-data/7bdc2324-data/8bdc2324-data/9bdc2324-data/10bdc2324-data/11bdc2324-data/12bdc2324-data/13bdc2324-data/14bdc2324-data/101
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"compagnie_act" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "id": "ede8210c-5d79-4159-8132-85afd0950f85", - "metadata": {}, - "outputs": [], - "source": [ - "compagnie_act.to_csv(r'C:\\Users\\fanta\\OneDrive\\Bureau\\BDC\\compagnie_type_event.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "771079f3-d346-4a63-a987-354b811f5b41", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eda1201a-2cc1-45bc-bf67-70f426183757", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Notebook_AR.ipynb b/useless/Notebook_AR.ipynb deleted file mode 100644 index 0f59f90..0000000 --- a/useless/Notebook_AR.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "0c48e17e-3dd5-43ef-be44-a11a3cbeacfe", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Choisissez le type de compagnie : sport ? musique ? musee ? sport\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_5/customerplus_cleaned.csv\n" - ] - }, - { - "ename": "PermissionError", - "evalue": "Forbidden", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:529\u001b[0m, in \u001b[0;36mS3FileSystem.info\u001b[0;34m(self, path, version_id, refresh)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 529\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhead_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbucket\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mversion_id_kw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mversion_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreq_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 532\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 533\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKey\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([bucket, key]),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m: out\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 541\u001b[0m }\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:200\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[0;31mClientError\u001b[0m: An error occurred (403) when calling the HeadObject operation: Forbidden", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 28\u001b[0m\n\u001b[1;32m 25\u001b[0m list_of_comp \u001b[38;5;241m=\u001b[39m companies[type_of_activity] \n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# Load files\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m customer, campaigns_kpi, campaigns_brut, tickets, products \u001b[38;5;241m=\u001b[39m \u001b[43mload_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlist_of_comp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# Identify anonymous customer for each company and remove them from our datasets\u001b[39;00m\n\u001b[1;32m 31\u001b[0m outlier_list \u001b[38;5;241m=\u001b[39m outlier_detection(tickets, list_of_comp)\n", - "File \u001b[0;32m:22\u001b[0m, in \u001b[0;36mload_files\u001b[0;34m(nb_compagnie)\u001b[0m\n", - "File \u001b[0;32m:12\u001b[0m, in \u001b[0;36mdisplay_input_databases\u001b[0;34m(directory_path, file_name, datetime_col)\u001b[0m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1295\u001b[0m, in \u001b[0;36mAbstractFileSystem.open\u001b[0;34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1294\u001b[0m ac \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mautocommit\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_intrans)\n\u001b[0;32m-> 1295\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1301\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1302\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1304\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compr\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:375\u001b[0m, in \u001b[0;36mS3FileSystem._open\u001b[0;34m(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, requester_pays, **kwargs)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 373\u001b[0m cache_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_cache_type\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mS3File\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43macl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43macl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mversion_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mversion_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43ms3_additional_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 378\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequester_pays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequester_pays\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1096\u001b[0m, in 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ee\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParamValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 550\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to head path \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (path, e))\n", - "\u001b[0;31mPermissionError\u001b[0m: Forbidden" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import io\n", - "import s3fs\n", - "import re\n", - "import warnings\n", - "\n", - "# Ignore warning\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "exec(open('0_KPI_functions.py').read())\n", - "exec(open('utils_stat_desc.py').read())\n", - "\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "companies = {'musee' : ['1', '2', '3', '4'], # , '101'\n", - " 'sport': ['5', '6', '7', '8', '9'],\n", - " 'musique' : ['10', '11', '12', '13', '14']}\n", - "\n", - "\n", - "type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')\n", - "list_of_comp = companies[type_of_activity] \n", - "\n", - "# Load files\n", - "customer, campaigns_kpi, campaigns_brut, tickets, products = load_files(list_of_comp)\n", - "\n", - "# Identify anonymous customer for each company and remove them from our datasets\n", - "outlier_list = outlier_detection(tickets, list_of_comp)\n", - "\n", - "# Identify valid customer (customer who bought tickets after starting date or received mails after starting date)\n", - "customer_valid_list = valid_customer_detection(products, campaigns_brut)\n", - "\n", - "databases = [customer, campaigns_kpi, campaigns_brut, tickets, products]\n", - "\n", - "for dataset in databases:\n", - " dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier\n", - " dataset = dataset[dataset['customer_id'].isin(customer_valid_list)] # keep only valid customer\n", - " #print(f'shape of {dataset} : ', dataset.shape)\n", - "\n", - "# Identify customer who bought during the period of y\n", - "customer_target_period = identify_purchase_during_target_periode(products)\n", - "customer['has_purchased_target_period'] = np.where(customer['customer_id'].isin(customer_target_period), 1, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e15380a0-76b8-4914-a927-303ab46a636e", - "metadata": {}, - "outputs": [], - "source": [ - "customer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf475e2b-fa82-40f0-bcbe-7ef40a13caae", - "metadata": {}, - "outputs": [], - "source": [ - "tickets.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "171cf427-18bf-4c0b-9698-3cec5cd61073", - "metadata": {}, - "outputs": [], - "source": [ - "tickets.groupby('number_company')['achat_internet'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c430185e-7995-4287-8621-95c6410be9df", - "metadata": {}, - "outputs": [], - "source": [ - "tickets.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b299c7a4-aa07-4349-bebd-b4f24bda1c8f", - "metadata": {}, - "outputs": [], - "source": [ - "customer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6630f7a-96f5-488d-9797-caacb6d6067a", - "metadata": {}, - "outputs": [], - "source": [ - "print(len(tickets['customer_id']))\n", - "print(len(tickets['customer_id'].unique()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4caa95a-7854-4a21-b291-28d779c4c4db", - "metadata": {}, - "outputs": [], - "source": [ - "has_purchased = customer.groupby('number_company').agg({\n", - " 'has_purchased_target_period' : 'sum',\n", - " 'customer_id' : 'nunique'})\n", - "has_purchased" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "24fda291-764a-4a6f-9cdf-86da49b978e2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ClientError", - "evalue": "An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The Access Key Id you provided does not exist in our records.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[35], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m exec(\u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutils_stat_desc.py\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mread())\n\u001b[0;32m----> 2\u001b[0m \u001b[43mbox_plot_price_tickets\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickets\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtype_of_activity\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m:357\u001b[0m, in \u001b[0;36mbox_plot_price_tickets\u001b[0;34m(tickets, type_of_activity)\u001b[0m\n", - "File \u001b[0;32m:62\u001b[0m, in \u001b[0;36msave_file_s3\u001b[0;34m(File_name, type_of_activity)\u001b[0m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1963\u001b[0m, in \u001b[0;36mAbstractBufferedFile.__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m-> 1963\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1930\u001b[0m, in \u001b[0;36mAbstractBufferedFile.close\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1928\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1929\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforced:\n\u001b[0;32m-> 1930\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflush\u001b[49m\u001b[43m(\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs\u001b[38;5;241m.\u001b[39minvalidate_cache(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1801\u001b[0m, in \u001b[0;36mAbstractBufferedFile.flush\u001b[0;34m(self, force)\u001b[0m\n\u001b[1;32m 1798\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclosed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1799\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m-> 1801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_upload_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 1802\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 1803\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO()\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1252\u001b[0m, in \u001b[0;36mS3File._upload_chunk\u001b[0;34m(self, final)\u001b[0m\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparts\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPartNumber\u001b[39m\u001b[38;5;124m'\u001b[39m: part, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m]})\n\u001b[1;32m 1251\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautocommit \u001b[38;5;129;01mand\u001b[39;00m final:\n\u001b[0;32m-> 1252\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m final\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1267\u001b[0m, in \u001b[0;36mS3File.commit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 1266\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m-> 1267\u001b[0m write_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1268\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mput_object\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1269\u001b[0m \u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbucket\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 1270\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1271\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs\u001b[38;5;241m.\u001b[39mversion_aware:\n\u001b[1;32m 1272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mversion_id \u001b[38;5;241m=\u001b[39m write_result\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1130\u001b[0m, in \u001b[0;36mS3File._call_s3\u001b[0;34m(self, method, *kwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call_s3\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;241m*\u001b[39mkwarglist, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3_additional_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwarglist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1131\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:200\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 197\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCALL: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, akwarglist, kw2))\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 550\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1005\u001b[0m error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1006\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1007\u001b[0m )\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n", - "\u001b[0;31mClientError\u001b[0m: An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The Access Key Id you provided does not exist in our records." - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "exec(open('utils_stat_desc.py').read())\n", - "box_plot_price_tickets(tickets, type_of_activity)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Notebook_Fanta.ipynb b/useless/Notebook_Fanta.ipynb deleted file mode 100644 index f03d2f9..0000000 --- a/useless/Notebook_Fanta.ipynb +++ /dev/null @@ -1,825 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "aa74dbe0-f974-4b5c-94f4-4dba9fbc64fa", - "metadata": {}, - "source": [ - "# Business Data Challenge - Team 1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "94c498e7-7c50-45f9-b3f4-a1ab19b7ccc4", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "7a3b50ac-b1ff-4f3d-9938-e048fdc8e027", - "metadata": {}, - "source": [ - "Configuration de l'accès aux données" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0b029d42-fb02-481e-a407-7e41886198a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1',\n", - " 'bdc2324-data/10',\n", - " 'bdc2324-data/101',\n", - " 'bdc2324-data/11',\n", - " 'bdc2324-data/12',\n", - " 'bdc2324-data/13',\n", - " 'bdc2324-data/14',\n", - " 'bdc2324-data/2',\n", - " 'bdc2324-data/3',\n", - " 'bdc2324-data/4',\n", - " 'bdc2324-data/5',\n", - " 'bdc2324-data/6',\n", - " 'bdc2324-data/7',\n", - " 'bdc2324-data/8',\n", - " 'bdc2324-data/9']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import s3fs\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "BUCKET = \"bdc2324-data\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fbaf9aa7-ff70-4dbe-a969-b801c593510b", - "metadata": {}, - "outputs": [], - "source": [ - "# Chargement des fichiers campaign_stats.csv\n", - "FILE_PATH_S3 = 'bdc2324-data/1/1campaign_stats.csv'\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " campaign_stats_1 = pd.read_csv(file_in, sep=\",\")\n", - "\n", - "FILE_PATH_S3 = 'bdc2324-data/2/2campaign_stats.csv'\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " campaign_stats_2 = pd.read_csv(file_in, sep=\",\")\n", - "\n", - "FILE_PATH_S3 = 'bdc2324-data/3/3campaign_stats.csv'\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " campaign_stats_3 = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1e0418bc-8e97-4a04-b7f3-bda3bef7d36e", - "metadata": {}, - "outputs": [], - "source": [ - "# Conversion des dates 'sent_at'\n", - "campaign_stats_1['sent_at'] = pd.to_datetime(campaign_stats_1['sent_at'], format = 'ISO8601', utc = True)\n", - "campaign_stats_2['sent_at'] = pd.to_datetime(campaign_stats_2['sent_at'], format = 'ISO8601', utc = True)\n", - "campaign_stats_3['sent_at'] = pd.to_datetime(campaign_stats_3['sent_at'], format = 'ISO8601', utc = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cc5c20ba-e827-4e5a-97a5-7f3947e0621c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-09 18:10:45+00:00\n", - "2020-06-02 08:24:08+00:00\n", - "2023-10-12 01:39:48+00:00\n", - "2023-10-10 17:06:29+00:00\n", - "2023-11-01 09:20:48+00:00\n", - "2021-03-31 14:59:02+00:00\n" - ] - } - ], - "source": [ - "# Chaque unites correspond à une période ? --> Non, les dossiers ont juste pour but de réduire la taille des fichiers\n", - "print(campaign_stats_1['sent_at'].max())\n", - "print(campaign_stats_1['sent_at'].min())\n", - "\n", - "print(campaign_stats_2['sent_at'].max())\n", - "print(campaign_stats_2['sent_at'].min())\n", - "\n", - "print(campaign_stats_3['sent_at'].max())\n", - "print(campaign_stats_3['sent_at'].min())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c75632df-b018-4bb8-a99d-83f15af94369", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 2021-03-28 16:01:09+00:00\n", - "1 2021-03-28 16:01:09+00:00\n", - "2 2021-03-28 16:00:59+00:00\n", - "3 2021-03-28 16:00:59+00:00\n", - "4 2021-03-28 16:01:06+00:00\n", - " ... \n", - "6214803 2023-10-23 09:32:33+00:00\n", - "6214804 2023-10-23 09:32:49+00:00\n", - "6214805 2023-10-23 09:33:28+00:00\n", - "6214806 2023-10-23 09:31:53+00:00\n", - "6214807 2023-10-23 09:33:54+00:00\n", - "Name: sent_at, Length: 6214808, dtype: datetime64[ns, UTC]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "campaign_stats_1['sent_at']" - ] - }, - { - "cell_type": "markdown", - "id": "f4c0c63e-0418-4cfe-a57d-7af57bca0c22", - "metadata": {}, - "source": [ - "### Customersplus.csv" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d3bf880d-1065-4d5b-9954-1830aa5081af", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1362/4118060109.py:9: DtypeWarning: Columns (20) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " customers_plus_2 = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "FILE_PATH_S3 = 'bdc2324-data/1/1customersplus.csv'\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " customers_plus_1 = pd.read_csv(file_in, sep=\",\")\n", - "\n", - "FILE_PATH_S3 = 'bdc2324-data/2/2customersplus.csv'\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " customers_plus_2 = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7368f381-db8e-4a4d-9fe2-5947eb55be58", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'lastname', 'firstname', 'birthdate', 'email', 'street_id',\n", - " 'created_at', 'updated_at', 'civility', 'is_partner', 'extra',\n", - " 'deleted_at', 'reference', 'gender', 'is_email_true', 'extra_field',\n", - " 'identifier', 'opt_in', 'structure_id', 'note', 'profession',\n", - " 'language', 'mcp_contact_id', 'need_reload', 'last_buying_date',\n", - " 'max_price', 'ticket_sum', 'average_price', 'fidelity',\n", - " 'average_purchase_delay', 'average_price_basket',\n", - " 'average_ticket_basket', 'total_price', 'preferred_category',\n", - " 'preferred_supplier', 'preferred_formula', 'purchase_count',\n", - " 'first_buying_date', 'last_visiting_date', 'zipcode', 'country', 'age',\n", - " 'tenant_id'],\n", - " dtype='object')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "customers_plus_1.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08091935-b159-47fa-806c-e1444f3b227e", - "metadata": {}, - "outputs": [], - "source": [ - "customers_plus_1.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f8c8868-c1ac-4cee-af08-533d928f6764", - "metadata": {}, - "outputs": [], - "source": [ - "customers_plus_1['id'].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf95daf2-4852-4718-b474-207a1ebd8ac4", - "metadata": {}, - "outputs": [], - "source": [ - "customers_plus_2['id'].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1425c385-3216-4e4f-ae8f-a121624721ba", - "metadata": {}, - "outputs": [], - "source": [ - "common_id = set(customers_plus_2['id']).intersection(customers_plus_1['id'])" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "92533026-e27c-4f1f-81ca-64eda32a34c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "common_id = set(customers_plus_2['id']).intersection(customers_plus_1['id'])\n", - "# Exemple id commun = caractéristiques communes\n", - "print(customers_plus_2[customers_plus_2['id'] == list(common_id)[0]])\n", - "\n", - "print(customers_plus_1[customers_plus_1['id'] == list(common_id)[0]])" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "bf9ebc94-0ba6-443d-8e53-22477a6e79a7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id 0.000000\n", - "lastname 43.461341\n", - "firstname 44.995588\n", - "birthdate 96.419870\n", - "email 8.622075\n", - "street_id 0.000000\n", - "created_at 0.000000\n", - "updated_at 0.000000\n", - "civility 100.000000\n", - "is_partner 0.000000\n", - "extra 100.000000\n", - "deleted_at 100.000000\n", - "reference 100.000000\n", - "gender 0.000000\n", - "is_email_true 0.000000\n", - "extra_field 100.000000\n", - "identifier 0.000000\n", - "opt_in 0.000000\n", - "structure_id 88.072380\n", - "note 99.403421\n", - "profession 95.913503\n", - "language 99.280945\n", - "mcp_contact_id 34.876141\n", - "need_reload 0.000000\n", - "last_buying_date 51.653431\n", - "max_price 51.653431\n", - "ticket_sum 0.000000\n", - "average_price 8.639195\n", - "fidelity 0.000000\n", - "average_purchase_delay 51.653431\n", - "average_price_basket 51.653431\n", - "average_ticket_basket 51.653431\n", - "total_price 43.014236\n", - "preferred_category 100.000000\n", - "preferred_supplier 100.000000\n", - "preferred_formula 100.000000\n", - "purchase_count 0.000000\n", - "first_buying_date 51.653431\n", - "last_visiting_date 100.000000\n", - "zipcode 71.176564\n", - "country 5.459418\n", - "age 96.419870\n", - "tenant_id 0.000000\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "pd.DataFrame(customers_plus_1.isna().mean()*100)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6d62e73f-3925-490f-9fd4-d0e838903cb2", - "metadata": {}, - "outputs": [], - "source": [ - "# Chargement de toutes les données\n", - "liste_base = ['customer_target_mappings', 'customersplus', 'target_types', 'tags', 'events', 'tickets', 'representations', 'purchases', 'products']\n", - "\n", - "for nom_base in liste_base:\n", - " FILE_PATH_S3 = 'bdc2324-data/11/11' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - 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idlastnamefirstnamebirthdateemailstreet_idcreated_atupdated_atcivilityis_partner...tenant_idid_xcustomer_idpurchase_datetype_ofis_from_subscriptionamountis_full_pricestart_date_timeevent_name
0405082lastname405082NaNNaNNaN62023-01-12 06:30:31.197484+01:002023-01-12 06:30:31.197484+01:00NaNFalse...15569924234050822023-01-11 17:08:41+01:003False13.0False2023-02-06 20:00:00+01:00zaide
1405082lastname405082NaNNaNNaN62023-01-12 06:30:31.197484+01:002023-01-12 06:30:31.197484+01:00NaNFalse...15569924234050822023-01-11 17:08:41+01:003False13.0False2023-02-06 20:00:00+01:00zaide
2411168lastname411168NaNNaNNaN62023-03-17 06:30:35.431967+01:002023-03-17 06:30:35.431967+01:00NaNFalse...155610539344111682023-03-16 16:23:10+01:003False62.0False2023-03-19 16:00:00+01:00luisa miller
3411168lastname411168NaNNaNNaN62023-03-17 06:30:35.431967+01:002023-03-17 06:30:35.431967+01:00NaNFalse...155610539344111682023-03-16 16:23:10+01:003False62.0False2023-03-19 16:00:00+01:00luisa miller
44380lastname4380firstname4380NaNNaN12021-04-22 14:51:55.432952+02:002022-04-14 11:41:33.738500+02:00NaNFalse...1556118914143802020-11-26 13:12:53+01:003False51.3False2020-12-01 20:00:00+01:00iphigenie en tauride
..................................................................
31896419095lastname19095firstname190951979-07-16email1909562021-04-22 15:06:30.120537+02:002023-09-12 18:27:36.904104+02:00NaNFalse...15561090839190952019-05-19 21:18:36+02:001False4.5False2019-05-27 20:00:00+02:00entre femmes
31896519095lastname19095firstname190951979-07-16email1909562021-04-22 15:06:30.120537+02:002023-09-12 18:27:36.904104+02:00NaNFalse...15561090839190952019-05-19 21:18:36+02:001False4.5False2019-05-27 20:00:00+02:00entre femmes
31896619095lastname19095firstname190951979-07-16email1909562021-04-22 15:06:30.120537+02:002023-09-12 18:27:36.904104+02:00NaNFalse...15561090839190952019-05-19 21:18:36+02:001False4.5False2019-05-27 20:00:00+02:00entre femmes
31896719095lastname19095firstname190951979-07-16email1909562021-04-22 15:06:30.120537+02:002023-09-12 18:27:36.904104+02:00NaNFalse...15561244277190952019-12-31 11:04:07+01:001False5.5False2020-02-03 20:00:00+01:00a boire et a manger
31896819095lastname19095firstname190951979-07-16email1909562021-04-22 15:06:30.120537+02:002023-09-12 18:27:36.904104+02:00NaNFalse...15561244277190952019-12-31 11:04:07+01:001False5.5False2020-02-03 20:00:00+01:00a boire et a manger
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318969 rows × 52 columns

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"318965 6 2021-04-22 15:06:30.120537+02:00 \n", - "318966 6 2021-04-22 15:06:30.120537+02:00 \n", - "318967 6 2021-04-22 15:06:30.120537+02:00 \n", - "318968 6 2021-04-22 15:06:30.120537+02:00 \n", - "\n", - " updated_at civility is_partner ... \\\n", - "0 2023-01-12 06:30:31.197484+01:00 NaN False ... \n", - "1 2023-01-12 06:30:31.197484+01:00 NaN False ... \n", - "2 2023-03-17 06:30:35.431967+01:00 NaN False ... \n", - "3 2023-03-17 06:30:35.431967+01:00 NaN False ... \n", - "4 2022-04-14 11:41:33.738500+02:00 NaN False ... \n", - "... ... ... ... ... \n", - "318964 2023-09-12 18:27:36.904104+02:00 NaN False ... \n", - "318965 2023-09-12 18:27:36.904104+02:00 NaN False ... \n", - "318966 2023-09-12 18:27:36.904104+02:00 NaN False ... \n", - "318967 2023-09-12 18:27:36.904104+02:00 NaN False ... \n", - "318968 2023-09-12 18:27:36.904104+02:00 NaN False ... \n", - "\n", - " tenant_id id_x customer_id purchase_date type_of \\\n", - "0 1556 992423 405082 2023-01-11 17:08:41+01:00 3 \n", - "1 1556 992423 405082 2023-01-11 17:08:41+01:00 3 \n", - "2 1556 1053934 411168 2023-03-16 16:23:10+01:00 3 \n", - "3 1556 1053934 411168 2023-03-16 16:23:10+01:00 3 \n", - "4 1556 1189141 4380 2020-11-26 13:12:53+01:00 3 \n", - "... ... ... ... ... ... \n", - "318964 1556 1090839 19095 2019-05-19 21:18:36+02:00 1 \n", - "318965 1556 1090839 19095 2019-05-19 21:18:36+02:00 1 \n", - "318966 1556 1090839 19095 2019-05-19 21:18:36+02:00 1 \n", - "318967 1556 1244277 19095 2019-12-31 11:04:07+01:00 1 \n", - "318968 1556 1244277 19095 2019-12-31 11:04:07+01:00 1 \n", - "\n", - " is_from_subscription amount is_full_price start_date_time \\\n", - "0 False 13.0 False 2023-02-06 20:00:00+01:00 \n", - "1 False 13.0 False 2023-02-06 20:00:00+01:00 \n", - "2 False 62.0 False 2023-03-19 16:00:00+01:00 \n", - "3 False 62.0 False 2023-03-19 16:00:00+01:00 \n", - "4 False 51.3 False 2020-12-01 20:00:00+01:00 \n", - "... ... ... ... ... \n", - "318964 False 4.5 False 2019-05-27 20:00:00+02:00 \n", - "318965 False 4.5 False 2019-05-27 20:00:00+02:00 \n", - "318966 False 4.5 False 2019-05-27 20:00:00+02:00 \n", - "318967 False 5.5 False 2020-02-03 20:00:00+01:00 \n", - "318968 False 5.5 False 2020-02-03 20:00:00+01:00 \n", - "\n", - " event_name \n", - "0 zaide \n", - "1 zaide \n", - "2 luisa miller \n", - "3 luisa miller \n", - "4 iphigenie en tauride \n", - "... ... \n", - "318964 entre femmes \n", - "318965 entre femmes \n", - "318966 entre femmes \n", - "318967 a boire et a manger \n", - "318968 a boire et a manger \n", - "\n", - "[318969 rows x 52 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Jointure\n", - "merge_1 = pd.merge(purchases, tickets, left_on='id', right_on='purchase_id', how='inner')[['id_x', 'customer_id','product_id', 'purchase_date', 'type_of', 'is_from_subscription']]\n", - "merge_2 = pd.merge(products, merge_1, left_on='id', right_on='product_id', how='inner')[['id_x', 'customer_id', 'representation_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price']]\n", - "merge_3 = pd.merge(representations, merge_2, left_on='id', right_on='representation_id', how='inner')[['id_x', 'customer_id', 'event_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time']]\n", - "merge_4 = pd.merge(events, merge_3, left_on='id', right_on='event_id', how='inner')[['id_x', 'customer_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time', 'name']]\n", - "merge_4 = merge_4.rename(columns={'name': 'event_name'})\n", - "df_customer_event = pd.merge(customersplus, merge_4, left_on = 'id', right_on = 'customer_id', how = 'inner')[['id_x', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time', 'event_name']]\n", - "df_customer_event" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/TP_access_merge_data.ipynb b/useless/TP_access_merge_data.ipynb deleted file mode 100644 index f6ef912..0000000 --- a/useless/TP_access_merge_data.ipynb +++ /dev/null @@ -1,1215 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5ce2ffc5-66b6-4709-9e2c-7a50f49d1361", - "metadata": {}, - "outputs": [], - "source": [ - "# test\n", - "\n", - "import os \n", - "import s3fs\n", - "import pandas as pd\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "f579ff01-f009-4fb1-ba79-0cb3ce58ab7f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1',\n", - " 'bdc2324-data/10',\n", - " 'bdc2324-data/101',\n", - " 'bdc2324-data/11',\n", - " 'bdc2324-data/12',\n", - " 'bdc2324-data/13',\n", - " 'bdc2324-data/14',\n", - " 'bdc2324-data/2',\n", - " 'bdc2324-data/3',\n", - " 'bdc2324-data/4',\n", - " 'bdc2324-data/5',\n", - " 'bdc2324-data/6',\n", - " 'bdc2324-data/7',\n", - " 'bdc2324-data/8',\n", - " 'bdc2324-data/9']" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "\n", - "fs = s3fs.S3FileSystem(client_kwargs = {\"endpoint_url\" : S3_ENDPOINT_URL})\n", - "BUCKET = \"bdc2324-data\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c8b2c797-271f-43ee-8823-d0aee5b8782d", - "metadata": {}, - "outputs": [], - "source": [ - "FILE_PATH_S3 = fs.ls(BUCKET)[1] # +\".csv\"\n", - "files_path_2 = fs.ls(FILE_PATH_S3)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "18cee687-1462-4169-9bfe-f39786135cdd", - "metadata": {}, - "outputs": [], - "source": [ - "with fs.open(files_path_1[1], mode=\"rb\") as file_in:\n", - " # print(file_in)\n", - " df_campaigns = pd.read_csv(file_in)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "33e8d14c-c649-4b9c-8290-4a2aa635f999", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
01319613newsletter enseignants janvier 20227212022-01-14 16:06:42.586321+01:002022-02-03 14:17:27.112963+01:00NaNNaN0.0Falseaba3b6fd5d186d28e06ff97135cade7f2022-01-14 00:00:00+01:00
11319586lsf_janvier_20227172022-01-07 11:30:35.315895+01:002022-02-03 14:17:27.116171+01:00NaNNaN0.0False788d986905533aba051261497ecffcbb2022-01-07 00:00:00+01:00
21319282Invitation à déjeuner au Mucem | Vernissage « ...5912021-09-28 12:50:24.448752+02:002022-02-03 14:17:27.119582+01:00NaNNaN0.0False3493894fa4ea036cfc6433c3e2ee63b02021-09-28 00:00:00+02:00
31319283Vacances de la Toussaint - centres des loisirs5902021-09-28 18:01:04.692073+02:002022-02-03 14:17:27.124408+01:00NaNNaN0.0False08b255a5d42b89b0585260b6f2360bdd2021-09-28 00:00:00+02:00
41319636ddcp_promo_md_livemag7302022-01-27 18:00:41.053069+01:002022-02-03 14:17:27.127607+01:00NaNNaN0.0Falsed5cfead94f5350c12c322b5b664544c12022-01-27 00:00:00+01:00
....................................
9521320072dre_gaza01068812022-05-26 09:01:35.523639+02:002022-12-02 17:51:22.614046+01:00NaNNaN0.0False7504adad8bb96320eb3afdd4df6e1f602022-05-26 00:00:00+02:00
953661398DDCP Plan Bis 4 - Marketing direct - MJ5C1832021-06-18 10:30:01.259578+02:002021-09-24 11:56:09.082785+02:00NaNNaN0.0Falsecedebb6e872f539bef8c3f919874e9d72020-07-27 00:00:00+02:00
9541320487Invitation portes ouvertes amitiés9882022-09-29 18:01:33.834090+02:002022-12-02 17:51:23.258324+01:00NaNNaN0.0False9908279ebbf1f9b250ba689db6a0222b2022-09-29 00:00:00+02:00
955906903DDCP PROMO La méditerranée des philosophes #3 ...3102021-07-19 14:07:16.177390+02:002021-09-24 11:56:09.086101+02:00NaNNaN0.0False06eb61b839a0cefee4967c67ccb099dc2020-12-23 00:00:00+01:00
956579313ddcp_promo_automation_manuel_pre_visit4812021-06-08 17:38:54.041310+02:002021-09-24 11:56:09.089394+02:00NaNNaN0.0False9461cce28ebe3e76fb4b931c35a169b02021-06-08 00:00:00+02:00
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Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in)\n" - ] - } - ], - "source": [ - "# loop to create dataframes from file 2\n", - "\n", - "files_path = files_path_2\n", - "\n", - "client_number = files_path[0].split(\"/\")[1]\n", - "df_prefix = \"df\" + str(client_number) + \"_\"\n", - "\n", - "for i in range(len(files_path)) :\n", - " current_path = files_path[i]\n", - " with fs.open(current_path, mode=\"rb\") as file_in:\n", - " df = pd.read_csv(file_in)\n", - " # the pattern of the name is df1xxx\n", - " nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n", - " globals()[nom_dataframe] = df" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "7f46e38e-413c-48cb-a171-eb6bc7219d9c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "client number :10\n", - "prefix used : df10_\n" - ] - } - ], - "source": [ - "print(f\"client number :{client_number}\")\n", - "print(f\"prefix used : {df_prefix}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "bdfd388c-7971-4f4d-99ef-c5b0435a4567", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/10/10campaign_stats.csv',\n", - " 'bdc2324-data/10/10campaigns.csv',\n", - " 'bdc2324-data/10/10categories.csv',\n", - " 'bdc2324-data/10/10countries.csv',\n", - " 'bdc2324-data/10/10currencies.csv',\n", - " 'bdc2324-data/10/10customer_target_mappings.csv',\n", - " 'bdc2324-data/10/10customersplus.csv',\n", - " 'bdc2324-data/10/10event_types.csv',\n", - " 'bdc2324-data/10/10events.csv',\n", - " 'bdc2324-data/10/10facilities.csv',\n", - " 'bdc2324-data/10/10link_stats.csv',\n", - " 'bdc2324-data/10/10pricing_formulas.csv',\n", - " 'bdc2324-data/10/10product_packs.csv',\n", - " 'bdc2324-data/10/10products.csv',\n", - " 'bdc2324-data/10/10products_groups.csv',\n", - " 'bdc2324-data/10/10purchases.csv',\n", - " 'bdc2324-data/10/10representation_category_capacities.csv',\n", - " 'bdc2324-data/10/10representation_types.csv',\n", - " 'bdc2324-data/10/10representations.csv',\n", - " 'bdc2324-data/10/10seasons.csv',\n", - " 'bdc2324-data/10/10suppliers.csv',\n", - " 'bdc2324-data/10/10tags.csv',\n", - " 'bdc2324-data/10/10target_types.csv',\n", - " 'bdc2324-data/10/10targets.csv',\n", - " 'bdc2324-data/10/10tickets.csv',\n", - " 'bdc2324-data/10/10type_of_pricing_formulas.csv',\n", - " 'bdc2324-data/10/10type_ofs.csv']" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files_path_2" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "e7bd02dc-1925-46ff-9d59-231d18f9f4f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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98794 rows × 43 columns

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" - ], - "text/plain": [ - " id lastname firstname birthdate email \\\n", - "0 821538 NaN NaN NaN email821538 \n", - "1 809126 NaN NaN NaN email809126 \n", - "2 11005 NaN NaN NaN NaN \n", - "3 17663 lastname17663 firstname17663 NaN NaN \n", - "4 38100 lastname38100 firstname38100 NaN NaN \n", - "... ... ... ... ... ... \n", - "98789 766266 NaN NaN NaN email766266 \n", - "98790 766336 NaN NaN NaN email766336 \n", - "98791 766348 NaN NaN NaN email766348 \n", - "98792 766363 NaN NaN NaN email766363 \n", - "98793 766366 NaN NaN NaN email766366 \n", - "\n", - " street_id created_at \\\n", - "0 139 2023-07-14 11:43:34.261637+02:00 \n", - "1 1063 2023-05-04 17:17:24.456829+02:00 \n", - "2 1063 2017-07-06 03:01:57.242998+02:00 \n", - "3 12731 2018-09-23 02:39:17.778100+02:00 \n", - "4 12395 2019-02-11 11:05:58.581121+01:00 \n", - "... ... ... \n", - "98789 139 2022-12-06 18:26:04.142337+01:00 \n", - "98790 139 2022-12-06 18:28:49.139502+01:00 \n", - "98791 139 2022-12-06 18:28:51.140745+01:00 \n", - "98792 139 2022-12-06 18:29:44.081056+01:00 \n", - "98793 139 2022-12-06 18:29:44.934174+01:00 \n", - "\n", - " updated_at civility is_partner ... \\\n", - "0 2023-07-14 11:43:34.261637+02:00 NaN False ... \n", - "1 2023-05-04 17:17:24.456829+02:00 NaN False ... \n", - "2 2018-11-12 18:01:18.283492+01:00 NaN False ... \n", - "3 2018-09-23 02:39:17.778100+02:00 NaN False ... \n", - "4 2022-12-06 23:15:33.485866+01:00 NaN False ... \n", - "... ... ... ... ... \n", - "98789 2023-05-03 18:01:01.799141+02:00 NaN False ... \n", - "98790 2022-12-06 23:15:33.485866+01:00 NaN False ... \n", - "98791 2022-12-06 23:15:33.485866+01:00 NaN False ... \n", - "98792 2022-12-06 23:15:33.485866+01:00 NaN False ... \n", - "98793 2022-12-06 23:15:33.485866+01:00 NaN False ... \n", - "\n", - " preferred_category preferred_supplier preferred_formula \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 zone tarif 1 NaN invite rp \n", - "3 zone tarif 1 NaN detaxe \n", - "4 NaN NaN NaN \n", - "... ... ... ... \n", - "98789 NaN NaN NaN \n", - "98790 NaN NaN NaN \n", - "98791 NaN NaN NaN \n", - "98792 NaN NaN NaN \n", - "98793 NaN NaN NaN \n", - "\n", - " purchase_count first_buying_date last_visiting_date zipcode country \\\n", - "0 0 NaN NaN NaN NaN \n", - "1 0 NaN NaN NaN fr \n", - "2 14 NaN NaN NaN fr \n", - "3 1 NaN NaN 44220 fr \n", - "4 1 NaN NaN 44100 fr \n", - "... ... ... ... ... ... \n", - "98789 0 NaN NaN NaN NaN \n", - "98790 0 NaN NaN NaN NaN \n", - "98791 0 NaN NaN NaN NaN \n", - "98792 0 NaN NaN NaN NaN \n", - "98793 0 NaN NaN NaN NaN \n", - "\n", - " age tenant_id \n", - "0 NaN 875 \n", - "1 NaN 875 \n", - "2 NaN 875 \n", - "3 NaN 875 \n", - "4 NaN 875 \n", - "... ... ... \n", - "98789 NaN 875 \n", - "98790 NaN 875 \n", - "98791 NaN 875 \n", - "98792 NaN 875 \n", - "98793 NaN 875 \n", - "\n", - "[98794 rows x 43 columns]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df10_0customersplus" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "932812b1-7a24-4f2d-ae48-7fe8e06b9f62", - "metadata": {}, - "outputs": [], - "source": [ - "# how many missing values ?\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Temporary_barplot_example_TP.ipynb b/useless/Temporary_barplot_example_TP.ipynb deleted file mode 100644 index 28c8ed1..0000000 --- a/useless/Temporary_barplot_example_TP.ipynb +++ /dev/null @@ -1,958 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "08977396-ae9a-4c48-9890-e2d3f9bf5c0e", - "metadata": {}, - "source": [ - "# TP : graphique barplot - nombre d'achats par mois" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "225af1ed-6dcd-4116-99d1-f649dfa8f96f", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import matplotlib.dates as mdates\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7fe35156-ea0b-4f9b-b981-1231e26b1baf", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e0a09bf5-5a96-40c2-93be-ba0a6a130266", - "metadata": {}, - "outputs": [], - "source": [ - "## Evolution vente \n", - "\n", - "# Importation\n", - "# Chargement des données temporaires\n", - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Temp/Company 1 - Purchases.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " purchases = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0c686793-b760-4013-9f79-f2eeee86cafb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_datetype_of_ticket_nameamountchildrenis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasons
0130708594818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier8.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
1130708604818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
2130708614818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
3130708624818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
4130708634818751074624vente en ligne2018-12-28 14:47:50+00:00Atelier4.0pricing_formulaFalsespectacle vivantmucemindiv prog enfantl'école des magiciens2018
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182666720662815125613580076975vente en ligne2023-11-08 17:23:54+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182666820662816125613680076985vente en ligne2023-11-08 18:32:18+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182666920662817125613680076985vente en ligne2023-11-08 18:32:18+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182667020662818125613780076995vente en ligne2023-11-08 19:30:28+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
182667120662819125613780076995vente en ligne2023-11-08 19:30:28+00:00Atelier11.0pricing_formulaFalseoffre muséale groupemucemindiv entrées tpNaN2023
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1826672 rows × 15 columns

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" - ], - "text/plain": [ - " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", - "0 13070859 48187 5107462 4 vente en ligne \n", - "1 13070860 48187 5107462 4 vente en ligne \n", - "2 13070861 48187 5107462 4 vente en ligne \n", - "3 13070862 48187 5107462 4 vente en ligne \n", - "4 13070863 48187 5107462 4 vente en ligne \n", - "... ... ... ... ... ... \n", - "1826667 20662815 1256135 8007697 5 vente en ligne \n", - "1826668 20662816 1256136 8007698 5 vente en ligne \n", - "1826669 20662817 1256136 8007698 5 vente en ligne \n", - "1826670 20662818 1256137 8007699 5 vente en ligne \n", - "1826671 20662819 1256137 8007699 5 vente en ligne \n", - "\n", - " purchase_date type_of_ticket_name amount \\\n", - "0 2018-12-28 14:47:50+00:00 Atelier 8.0 \n", - "1 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "2 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "3 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "4 2018-12-28 14:47:50+00:00 Atelier 4.0 \n", - "... ... ... ... \n", - "1826667 2023-11-08 17:23:54+00:00 Atelier 11.0 \n", - "1826668 2023-11-08 18:32:18+00:00 Atelier 11.0 \n", - "1826669 2023-11-08 18:32:18+00:00 Atelier 11.0 \n", - "1826670 2023-11-08 19:30:28+00:00 Atelier 11.0 \n", - "1826671 2023-11-08 19:30:28+00:00 Atelier 11.0 \n", - "\n", - " children is_full_price name_event_types name_facilities \\\n", - "0 pricing_formula False spectacle vivant mucem \n", - "1 pricing_formula False spectacle vivant mucem \n", - "2 pricing_formula False spectacle vivant mucem \n", - "3 pricing_formula False spectacle vivant mucem \n", - "4 pricing_formula False spectacle vivant mucem \n", - "... ... ... ... ... \n", - "1826667 pricing_formula False offre muséale groupe mucem \n", - "1826668 pricing_formula False offre muséale groupe mucem \n", - "1826669 pricing_formula False offre muséale groupe mucem \n", - "1826670 pricing_formula False offre muséale groupe mucem \n", - "1826671 pricing_formula False offre muséale groupe mucem \n", - "\n", - " name_categories name_events name_seasons \n", - "0 indiv prog enfant l'école des magiciens 2018 \n", - "1 indiv prog enfant l'école des magiciens 2018 \n", - "2 indiv prog enfant l'école des magiciens 2018 \n", - "3 indiv prog enfant l'école des magiciens 2018 \n", - "4 indiv prog enfant l'école des magiciens 2018 \n", - "... ... ... ... \n", - "1826667 indiv entrées tp NaN 2023 \n", - "1826668 indiv entrées tp NaN 2023 \n", - "1826669 indiv entrées tp NaN 2023 \n", - "1826670 indiv entrées tp NaN 2023 \n", - "1826671 indiv entrées tp NaN 2023 \n", - "\n", - "[1826672 rows x 15 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "84a11cdd-aeb9-457a-bf7b-b2ad1752b99d", - "metadata": {}, - "outputs": [], - "source": [ - "purchases['purchase_date'] = pd.to_datetime(purchases['purchase_date'])\n", - "\n", - "purchases_filtered = purchases[purchases['event_type_id'] == 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "bea0e516-ee62-4bb4-bdd9-bb2502972d84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
monthfake_categorypurchase_id
02013-06-0111
12013-07-0111
22013-09-0102
32013-10-0111
42013-11-0102
............
1962023-09-0116900
1972023-10-0103621
1982023-10-0118313
1992023-11-010945
2002023-11-0112268
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201 rows × 3 columns

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" - ], - "text/plain": [ - " month fake_category purchase_id\n", - "0 2013-06-01 1 1\n", - "1 2013-07-01 1 1\n", - "2 2013-09-01 0 2\n", - "3 2013-10-01 1 1\n", - "4 2013-11-01 0 2\n", - ".. ... ... ...\n", - "196 2023-09-01 1 6900\n", - "197 2023-10-01 0 3621\n", - "198 2023-10-01 1 8313\n", - "199 2023-11-01 0 945\n", - "200 2023-11-01 1 2268\n", - "\n", - "[201 rows x 3 columns]" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# création de la table pr faire le graphique\n", - "\n", - "purchases_graph = purchases_filtered[['purchase_id', 'purchase_date']].drop_duplicates()\n", - "\n", - "purchases_graph[\"fake_category\"] = np.random.choice([0, 1], size=purchases_graph.shape[0], p = [0.3, 0.7])\n", - "\n", - "purchases_graph['month'] = purchases['purchase_date'].dt.strftime('%Y-%m')\n", - "\n", - "# purchases_graph = purchases_graph.groupby('month')['purchase_id'].count().reset_index()\n", - "purchases_graph = purchases_graph.groupby(['month','fake_category'])['purchase_id'].count().reset_index()\n", - "\n", - "purchases_graph['month'] = pd.to_datetime(purchases_graph['month'])\n", - "\n", - "purchases_graph" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "c9b70757-7b80-4e6d-99f0-58b9812f404f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Créer le graphique (a changé ! le bon est le barplot qui vient après)\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(purchases_graph['month'], purchases_graph['purchase_id'])\n", - "\n", - "# Définir le format de l'axe des x en fonction des dates\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=5)) # Ajustez l'intervalle selon vos besoins\n", - "\n", - "# Rotation des étiquettes de l'axe x pour une meilleure lisibilité\n", - "plt.xticks(rotation=45)\n", - "\n", - "\n", - "# Titres et labels\n", - "plt.title('Évolution des données')\n", - "plt.xlabel('Date')\n", - "plt.ylabel('Valeurs')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0561564e-2c74-4d99-9aa3-26099160520e", - "metadata": {}, - "source": [ - "## TP : second graphique - barplot" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "1753d45c-2737-4082-a5b0-461071a03351", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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monthfake_categorypurchase_id
962019-03-011102
1002019-05-011140
1022019-06-011131
1422021-06-011157
1442021-07-011145
1482021-09-011123
1502021-10-011220
1602022-03-011112
1622022-04-011107
1642022-05-011164
1662022-06-011158
1722022-09-011178
1742022-10-011218
1762022-11-011137
1782022-12-011107
1792023-01-0102052
1802023-01-0115079
1812023-02-0102684
1822023-02-0116350
1832023-03-0102196
1842023-03-0115304
1852023-04-0103595
1862023-04-0118563
1872023-05-0103727
1882023-05-0118653
1892023-06-0102904
1902023-06-0116641
1912023-07-0104247
1922023-07-01110022
1932023-08-0106146
1942023-08-01114593
1952023-09-0102954
1962023-09-0116900
1972023-10-0103621
1982023-10-0118313
1992023-11-010945
2002023-11-0112268
\n", - "
" - ], - "text/plain": [ - " month fake_category purchase_id\n", - "96 2019-03-01 1 102\n", - "100 2019-05-01 1 140\n", - "102 2019-06-01 1 131\n", - "142 2021-06-01 1 157\n", - "144 2021-07-01 1 145\n", - "148 2021-09-01 1 123\n", - "150 2021-10-01 1 220\n", - "160 2022-03-01 1 112\n", - "162 2022-04-01 1 107\n", - "164 2022-05-01 1 164\n", - "166 2022-06-01 1 158\n", - "172 2022-09-01 1 178\n", - "174 2022-10-01 1 218\n", - "176 2022-11-01 1 137\n", - "178 2022-12-01 1 107\n", - "179 2023-01-01 0 2052\n", - "180 2023-01-01 1 5079\n", - "181 2023-02-01 0 2684\n", - "182 2023-02-01 1 6350\n", - "183 2023-03-01 0 2196\n", - "184 2023-03-01 1 5304\n", - "185 2023-04-01 0 3595\n", - "186 2023-04-01 1 8563\n", - "187 2023-05-01 0 3727\n", - "188 2023-05-01 1 8653\n", - "189 2023-06-01 0 2904\n", - "190 2023-06-01 1 6641\n", - "191 2023-07-01 0 4247\n", - "192 2023-07-01 1 10022\n", - "193 2023-08-01 0 6146\n", - "194 2023-08-01 1 14593\n", - "195 2023-09-01 0 2954\n", - "196 2023-09-01 1 6900\n", - "197 2023-10-01 0 3621\n", - "198 2023-10-01 1 8313\n", - "199 2023-11-01 0 945\n", - "200 2023-11-01 1 2268" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "purchases_graph[purchases_graph[\"purchase_id\"]>100] " - ] - }, - { - "cell_type": "markdown", - "id": "4113b464-1349-4e6e-a8c0-8a327eb7ef58", - "metadata": {}, - "source": [ - "à partir de 2023, rupture : passage de plusieurs centaines à + de 7k ventes (et 3k en nov 2023) - on prend slt 2023" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "161efc2b-8439-4fe7-b136-cc70b9e83267", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# début du graphique\n", - "\n", - "purchases_graph_used = purchases_graph[purchases_graph[\"month\"] >= datetime(2023,1,1)]\n", - "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"fake_category\"]==0]\n", - "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"fake_category\"]==1]\n", - "\n", - "\n", - "# Création du barplot\n", - "plt.bar(purchases_graph_used_0[\"month\"], purchases_graph_used_0[\"purchase_id\"], width=12, label = \"categorie 0\")\n", - "plt.bar(purchases_graph_used_0[\"month\"], purchases_graph_used_1[\"purchase_id\"], \n", - " bottom = purchases_graph_used_0[\"purchase_id\"], width=12, label = \"categorie 1\")\n", - "\n", - "\n", - "# commande pr afficher slt\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b'))\n", - "\n", - "\n", - "# Ajout de titres et d'étiquettes\n", - "plt.xlabel('Mois')\n", - "plt.ylabel('Nombre d achats')\n", - "plt.title('Nombre d achats au cours de l année 2023 pour l offre muséale groupe')\n", - "plt.legend()\n", - "\n", - "# Affichage du barplot\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/Traitement_Fanta.ipynb b/useless/Traitement_Fanta.ipynb deleted file mode 100644 index 651faaa..0000000 --- a/useless/Traitement_Fanta.ipynb +++ /dev/null @@ -1,1833 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c4205b5d-e052-4863-a46b-20e4757052a7", - "metadata": {}, - "source": [ - "# Business Data Challenge - Team 1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ae3af8e6-ced8-4994-8877-fa98d4297cc0", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "dd3184e7-54a1-4463-af42-5850d9517a41", - "metadata": {}, - "source": [ - "Configuration de l'accès aux données" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b6035982-9ff4-4013-9792-2d50e10db3d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1/1campaign_stats.csv',\n", - " 'bdc2324-data/1/1campaigns.csv',\n", - " 'bdc2324-data/1/1categories.csv',\n", - " 'bdc2324-data/1/1countries.csv',\n", - " 'bdc2324-data/1/1currencies.csv',\n", - " 'bdc2324-data/1/1customer_target_mappings.csv',\n", - " 'bdc2324-data/1/1customersplus.csv',\n", - " 'bdc2324-data/1/1event_types.csv',\n", - " 'bdc2324-data/1/1events.csv',\n", - " 'bdc2324-data/1/1facilities.csv',\n", - " 'bdc2324-data/1/1link_stats.csv',\n", - " 'bdc2324-data/1/1pricing_formulas.csv',\n", - " 'bdc2324-data/1/1product_packs.csv',\n", - " 'bdc2324-data/1/1products.csv',\n", - " 'bdc2324-data/1/1products_groups.csv',\n", - " 'bdc2324-data/1/1purchases.csv',\n", - " 'bdc2324-data/1/1representation_category_capacities.csv',\n", - " 'bdc2324-data/1/1representations.csv',\n", - " 'bdc2324-data/1/1seasons.csv',\n", - " 'bdc2324-data/1/1structure_tag_mappings.csv',\n", - " 'bdc2324-data/1/1suppliers.csv',\n", - " 'bdc2324-data/1/1tags.csv',\n", - " 'bdc2324-data/1/1target_types.csv',\n", - " 'bdc2324-data/1/1targets.csv',\n", - " 'bdc2324-data/1/1tickets.csv',\n", - " 'bdc2324-data/1/1type_of_categories.csv',\n", - " 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n", - " 'bdc2324-data/1/1type_ofs.csv']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import s3fs\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "BUCKET = \"bdc2324-data/1\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b86c935d-124f-453f-80dd-83ea6770d09c", - "metadata": {}, - "outputs": [], - "source": [ - "dic_base=['campaign_stats','campaigns','categories','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets','type_of_categories','type_of_pricing_formulas','type_ofs']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f6d0b27c-0ecd-406b-b042-6c3802dd68fd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_438/1008972637.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dic_base=['campaign_stats','campaigns','categories','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets','type_of_categories','type_of_pricing_formulas','type_ofs']\n", - "for nom_base in dic_base:\n", - " FILE_PATH_S3_fanta = 'bdc2324-data/1/1' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2a6b5e22-3370-457f-83b7-dd1e13663229", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'bdc2324-data/1/1type_ofs.csv'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FILE_PATH_S3_fanta" - ] - }, - { - "cell_type": "markdown", - "id": "79012186-ea51-4252-843e-36a9bbe3847e", - "metadata": {}, - "source": [ - "# Analyse exploratoire " - ] - }, - { - "cell_type": "markdown", - "id": "1a365f29-4766-47d8-9796-24a5271867b2", - "metadata": {}, - "source": [ - "## I. Base type_of_pricing_formulas" - ] - }, - { - "cell_type": "markdown", - "id": "bcc14f93-2289-44eb-816b-a51049b258df", - "metadata": {}, - "source": [ - "## Detection des valeur manquantes" - ] - }, - { - "cell_type": "raw", - "id": "ab2ec4c4-9d38-4aeb-8202-9116df3cdd66", - "metadata": {}, - "source": [ - "dic_prod_princing=['type_of_pricing_formulas','products_groups','pricing_formulas','product_packs','products']" - ] - }, - { - "cell_type": "markdown", - "id": "88759b4a-2633-478d-abce-29abeac376d1", - "metadata": {}, - "source": [ - "def verifier_donnees_manquantes(base):\n", - " donnees_manquantes = base.isna().sum()\n", - " print(\"Données manquantes pour la base :\")\n", - " print(donnees_manquantes)" - ] - }, - { - "cell_type": "markdown", - "id": "df3075b4-1490-4cf2-a3fe-c6d4e2144ae3", - "metadata": {}, - "source": [ - "for nom_base in dic_prod_princing:\n", - " verifier_donnees_manquantes(nom_base)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e0c67c01-e837-4772-b070-d1be0d895a36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "type_of_id 0\n", - "pricing_formula_id 0\n", - "created_at 0\n", - "updated_at 0\n", - "identifier 0\n", - "dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#detection des Nan d\n", - "\n", - "type_of_pricing_formulas.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83a6a48d-effe-4537-b4bb-d5a540b610f1", - "metadata": {}, - "outputs": [], - "source": [ - "#variable retenu:[[\"id\",\"type_of_id\",\"pricing_formula_id\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3eaffaa6-1164-4ee9-a671-8b5eb3df797d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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568 rows × 6 columns

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idtype_of_idpricing_formula_idcreated_atupdated_atidentifier
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, type_of_id, pricing_formula_id, created_at, updated_at, identifier]\n", - "Index: []" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Identification des doublons\n", - "type_of_pricing_formulas.loc[type_of_pricing_formulas['id'].duplicated(keep=False),:]" - ] - }, - { - "cell_type": "markdown", - "id": "7a40de03-5e18-4d3d-a0f8-da960c29fad8", - "metadata": {}, - "source": [ - "## II.products_groups" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "89909175-6734-4e8e-8632-d6f8ca812388", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "percent_price 0\n", - "max_price 0\n", - "min_price 0\n", - "category_id 0\n", - "pricing_formula_id 0\n", - "representation_id 0\n", - "created_at 0\n", - "updated_at 0\n", - "dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#detection des Nan \n", - "\n", - "products_groups.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0518684-c83c-4f0a-89ea-d7dcfd60051d", - "metadata": {}, - "outputs": [], - "source": [ - "#variable retenu:[[\"id\",\"percent_price\",\"max_price\",\"min_price\",\"category_id\",\"pricing_formula_id\",\"representation_id\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6a187170-96c4-48d2-9568-b270f67e2c27", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id int64\n", - "percent_price float64\n", - "max_price float64\n", - "min_price float64\n", - "category_id int64\n", - "pricing_formula_id int64\n", - "representation_id int64\n", - "created_at object\n", - "updated_at object\n", - "dtype: object" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#type des variables\n", - "\n", - "products_groups.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2fba2cb0-a6a4-43b2-a854-3be07939c28b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idpercent_pricemax_pricemin_pricecategory_idpricing_formula_idrepresentation_idcreated_atupdated_at
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, percent_price, max_price, min_price, category_id, pricing_formula_id, representation_id, created_at, updated_at]\n", - "Index: []" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Identification des doublons\n", - "products_groups.loc[products_groups[['id','pricing_formula_id','representation_id']].duplicated(keep=False),:]" - ] - }, - { - "cell_type": "markdown", - "id": "5312ac13-8fbd-4c3f-a98a-8c28f079a599", - "metadata": {}, - "source": [ - "## III.pricing_formulas" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3383a773-0817-4b23-84e7-8d5d0c74b179", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"4 1175 ... 93.0 \n", - "\n", - " created_at_type_of_pricing_f updated_at_type_of_pricing_f \\\n", - "0 2021-02-15 17:02:27.395376+01:00 2021-02-15 17:02:27.395376+01:00 \n", - "1 2021-02-05 11:52:05.923905+01:00 2021-02-05 11:52:05.923905+01:00 \n", - "2 2021-02-05 11:52:05.939898+01:00 2021-02-05 11:52:05.939898+01:00 \n", - "3 2021-02-05 11:52:06.107939+01:00 2021-02-05 11:52:06.107939+01:00 \n", - "4 2021-02-05 11:52:06.004162+01:00 2021-02-05 11:52:06.004162+01:00 \n", - "\n", - " identifier_merge4 id name_product_pack type_of \\\n", - "0 3706121eb9f43b635bef1433c06f679c 1 NaN 0 \n", - "1 0aceb248607671792298436004b95275 1 NaN 0 \n", - "2 93002d4637331edd81ffc28b6e8e89c0 1 NaN 0 \n", - "3 7d0b25bdfff9f366da8be820608c8191 1 NaN 0 \n", - "4 1dbb0795e8f47cb75ba7cdb08c06be5f 1 NaN 0 \n", - "\n", - " created_at updated_at \\\n", - "0 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "1 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "2 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "3 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "4 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "\n", - " identifier_product_pack \n", - "0 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "1 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "2 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "3 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "4 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "\n", - "[5 rows x 41 columns]" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_product_pricing.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03442997-806f-4285-a139-3bad46bb4522", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d22a0d75-53c5-4b54-9060-c9e7c307fb13", - "metadata": {}, - "outputs": [], - "source": [ - "BUCKET = \"bdc2324-data\"\n", - "directory_path = '2'" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7c229dad-6ebd-4f43-99f1-fb330dc29466", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/2/2campaign_stats.csv',\n", - " 'bdc2324-data/2/2campaigns.csv',\n", - " 'bdc2324-data/2/2categories.csv',\n", - " 'bdc2324-data/2/2contribution_sites.csv',\n", - " 'bdc2324-data/2/2contributions.csv',\n", - " 'bdc2324-data/2/2countries.csv',\n", - " 'bdc2324-data/2/2currencies.csv',\n", - " 'bdc2324-data/2/2customer_target_mappings.csv',\n", - " 'bdc2324-data/2/2customersplus.csv',\n", - " 'bdc2324-data/2/2event_types.csv',\n", - " 'bdc2324-data/2/2events.csv',\n", - " 'bdc2324-data/2/2facilities.csv',\n", - " 'bdc2324-data/2/2link_stats.csv',\n", - " 'bdc2324-data/2/2pricing_formulas.csv',\n", - " 'bdc2324-data/2/2product_packs.csv',\n", - " 'bdc2324-data/2/2products.csv',\n", - " 'bdc2324-data/2/2products_groups.csv',\n", - " 'bdc2324-data/2/2purchases.csv',\n", - " 'bdc2324-data/2/2representation_category_capacities.csv',\n", - " 'bdc2324-data/2/2representations.csv',\n", - " 'bdc2324-data/2/2seasons.csv',\n", - " 'bdc2324-data/2/2structure_tag_mappings.csv',\n", - " 'bdc2324-data/2/2suppliers.csv',\n", - " 'bdc2324-data/2/2tags.csv',\n", - " 'bdc2324-data/2/2target_types.csv',\n", - " 'bdc2324-data/2/2targets.csv',\n", - " 'bdc2324-data/2/2tickets.csv']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BUCKET = \"bdc2324-data/2\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "df3d3548-3d76-4f07-afa1-e240932bc1c7", - "metadata": {}, - "outputs": [], - "source": [ - "dic_base_ent2=['campaign_stats','campaigns','categories','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "90f8d5fc-43f3-4f36-b8cc-89a41785f032", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_438/673681459.py:5: DtypeWarning: Columns (20) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dic_base_ent2=['campaign_stats','campaigns','categories','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']\n", - "for nom_base in dic_base_ent2:\n", - " FILE_PATH_S3_fanta = 'bdc2324-data/2/2' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3e39a584-e02b-41b2-831c-33b920e298e9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "27" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dic_base_ent2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06759646-9419-4841-b12f-bbfceb417f3a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/code_base_train_test.ipynb b/useless/code_base_train_test.ipynb deleted file mode 100644 index 23cdb2d..0000000 --- a/useless/code_base_train_test.ipynb +++ /dev/null @@ -1,460 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "bf34b03c-536f-4f93-93a5-e452552653aa", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Choisissez le type de compagnie : sport ? musique ? musee ? musique\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n", - "Couverture Company 10 : 2016-03-07 - 2023-09-25\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n", - "Couverture Company 11 : 2015-06-26 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n", - "Couverture Company 12 : 2016-06-14 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n", - "Couverture Company 13 : 2010-07-31 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n", - "Couverture Company 14 : 1901-01-01 - 2023-11-08\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset test : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset train : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset test : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset train : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset test : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset train : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset test : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset train : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset test : SUCCESS\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/campaigns_information.csv\n", - "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n", - "Data filtering : SUCCESS\n", - "KPIs construction : SUCCESS\n", - "Explanatory variable construction : SUCCESS\n", - "Explained variable construction : SUCCESS\n", - "Exportation dataset train : SUCCESS\n", - "FIN DE LA GENERATION DES DATASETS : SUCCESS\n" - ] - } - ], - "source": [ - "# Business Data Challenge - Team 1\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "import warnings\n", - "from datetime import date, timedelta, datetime\n", - "\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "\n", - "# Import KPI construction functions\n", - "exec(open('0_KPI_functions.py').read())\n", - "\n", - "# Ignore warning\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "\n", - "def display_covering_time(df, company, datecover):\n", - " \"\"\"\n", - " This function draws the time coverage of each company\n", - " \"\"\"\n", - " min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n", - " max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n", - " datecover[company] = [datetime.strptime(min_date, \"%Y-%m-%d\") + timedelta(days=x) for x in range((datetime.strptime(max_date, \"%Y-%m-%d\") - datetime.strptime(min_date, \"%Y-%m-%d\")).days)]\n", - " print(f'Couverture Company {company} : {min_date} - {max_date}')\n", - " return datecover\n", - "\n", - "\n", - "def compute_time_intersection(datecover):\n", - " \"\"\"\n", - " This function returns the time coverage for all companies\n", - " \"\"\"\n", - " timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n", - " intersection = set.intersection(*timestamps_sets)\n", - " intersection_list = list(intersection)\n", - " formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n", - " return sorted(formated_dates)\n", - "\n", - "\n", - "def df_coverage_modelization(sport, coverage_train = 0.7):\n", - " \"\"\"\n", - " This function returns start_date, end_of_features and final dates\n", - " that help to construct train and test datasets\n", - " \"\"\"\n", - " datecover = {}\n", - " for company in sport:\n", - " df_products_purchased_reduced = display_databases(company, file_name = \"products_purchased_reduced\",\n", - " datetime_col = ['purchase_date'])\n", - " datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n", - " #print(datecover.keys())\n", - " dt_coverage = compute_time_intersection(datecover)\n", - " start_date = dt_coverage[0]\n", - " end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n", - " final_date = dt_coverage[-1]\n", - " return start_date, end_of_features, final_date\n", - " \n", - "\n", - "def dataset_construction(min_date, end_features_date, max_date, directory_path):\n", - " \n", - " # Import customerplus\n", - " df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", - " df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", - " df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n", - " \n", - " # Filtre de cohérence pour la mise en pratique de notre méthode\n", - " max_date = pd.to_datetime(max_date, utc = True, format = 'ISO8601') \n", - " end_features_date = pd.to_datetime(end_features_date, utc = True, format = 'ISO8601')\n", - " min_date = pd.to_datetime(min_date, utc = True, format = 'ISO8601')\n", - "\n", - " #Filtre de la base df_campaigns_information\n", - " df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= end_features_date) & (df_campaigns_information['sent_at'] >= min_date)]\n", - " df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= end_features_date] = np.datetime64('NaT')\n", - " \n", - " #Filtre de la base df_products_purchased_reduced\n", - " df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= end_features_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]\n", - "\n", - " print(\"Data filtering : SUCCESS\")\n", - " \n", - " # Fusion de l'ensemble et creation des KPI\n", - "\n", - " # KPI sur les campagnes publicitaires\n", - " df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n", - "\n", - " # KPI sur le comportement d'achat\n", - " df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n", - "\n", - " # KPI sur les données socio-démographiques\n", - " df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)\n", - " \n", - " print(\"KPIs construction : SUCCESS\")\n", - " \n", - " # Fusion avec KPI liés au customer\n", - " df_customer = pd.merge(df_customerplus_clean, df_campaigns_kpi, on = 'customer_id', how = 'left')\n", - " \n", - " # Fill NaN values\n", - " df_customer[['nb_campaigns', 'nb_campaigns_opened']] = df_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)\n", - " \n", - " # Fusion avec KPI liés au comportement d'achat\n", - " df_customer_product = pd.merge(df_tickets_kpi, df_customer, on = 'customer_id', how = 'outer')\n", - " \n", - " # Fill NaN values\n", - " df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0)\n", - "\n", - " print(\"Explanatory variable construction : SUCCESS\")\n", - "\n", - " # 2. Construction of the explained variable \n", - " df_products_purchased_to_predict = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > end_features_date)]\n", - "\n", - " # Indicatrice d'achat\n", - " df_products_purchased_to_predict['y_has_purchased'] = 1\n", - "\n", - " y = df_products_purchased_to_predict[['customer_id', 'y_has_purchased']].drop_duplicates()\n", - "\n", - " print(\"Explained variable construction : SUCCESS\")\n", - " \n", - " # 3. Merge between explained and explanatory variables\n", - " dataset = pd.merge(df_customer_product, y, on = ['customer_id'], how = 'left')\n", - "\n", - " # 0 if there is no purchase\n", - " dataset[['y_has_purchased']].fillna(0)\n", - "\n", - " # add id_company prefix to customer_id\n", - " dataset['customer_id'] = directory_path + '_' + dataset['customer_id'].astype('str')\n", - " \n", - " return dataset\n", - "\n", - "## Exportation\n", - "\n", - "companies = {'musee' : ['1', '2', '3', '4', '101'],\n", - " 'sport': ['5', '6', '7', '8', '9'],\n", - " 'musique' : ['10', '11', '12', '13', '14']}\n", - "\n", - "type_of_comp = input('Choisissez le type de compagnie : sport ? musique ? musee ?')\n", - "list_of_comp = companies[type_of_comp] \n", - "# Dossier d'exportation\n", - "BUCKET_OUT = f'projet-bdc2324-team1/Generalization/{type_of_comp}'\n", - "\n", - "# Create test dataset and train dataset for sport companies\n", - "\n", - "start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7)\n", - "\n", - "for company in list_of_comp:\n", - " dataset_test = dataset_construction(min_date = start_date, end_features_date = end_of_features,\n", - " max_date = final_date, directory_path = company) \n", - "\n", - " # Exportation\n", - " FILE_KEY_OUT_S3 = \"dataset_test\" + company + \".csv\"\n", - " FILE_PATH_OUT_S3 = BUCKET_OUT + \"/Test_set/\" + FILE_KEY_OUT_S3\n", - " \n", - " with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " dataset_test.to_csv(file_out, index = False)\n", - " \n", - " print(\"Exportation dataset test : SUCCESS\")\n", - "\n", - "# Dataset train\n", - " dataset_train = dataset_construction(min_date = start_date, end_features_date = end_of_features,\n", - " max_date = final_date, directory_path = company)\n", - " # Export\n", - " FILE_KEY_OUT_S3 = \"dataset_train\" + company + \".csv\" \n", - " FILE_PATH_OUT_S3 = BUCKET_OUT + \"/Train_test/\" + FILE_KEY_OUT_S3\n", - " \n", - " with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", - " dataset_train.to_csv(file_out, index = False)\n", - " \n", - " print(\"Exportation dataset train : SUCCESS\")\n", - "\n", - "\n", - "print(\"FIN DE LA GENERATION DES DATASETS : SUCCESS\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3721427e-5957-4556-b278-2e7ffca892f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projet-bdc2324-team1/Generalization/musique/Train_test/dataset_train14.csv'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FILE_PATH_OUT_S3" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f8546992-f425-4d1e-ad75-ad26a8052a18", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'projet' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprojet\u001b[49m\u001b[38;5;241m-\u001b[39mbdc2324\u001b[38;5;241m-\u001b[39mteam1\u001b[38;5;241m/\u001b[39mGeneralization\u001b[38;5;241m/\u001b[39mmusique\u001b[38;5;241m/\u001b[39mTrain_test\n", - "\u001b[0;31mNameError\u001b[0m: name 'projet' is not defined" - ] - } - ], - "source": [ - "projet-bdc2324-team1/Generalization/musique/Train_test" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0dd34710-6da2-4438-9e1d-0ac092c1d28c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(343126, 41)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a3bfeeb6-2db0-4f1d-866c-8721343e97c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "customer_id 0.000000\n", - "nb_tickets 0.000000\n", - "nb_purchases 0.000000\n", - "total_amount 0.000000\n", - "nb_suppliers 0.000000\n", - "vente_internet_max 0.000000\n", - "purchase_date_min 0.858950\n", - "purchase_date_max 0.858950\n", - "time_between_purchase 0.858950\n", - "nb_tickets_internet 0.000000\n", - "street_id 0.000000\n", - "structure_id 0.869838\n", - "mcp_contact_id 0.276677\n", - "fidelity 0.000000\n", - "tenant_id 0.000000\n", - "is_partner 0.000000\n", - "deleted_at 1.000000\n", - "gender 0.000000\n", - "is_email_true 0.000000\n", - "opt_in 0.000000\n", - "last_buying_date 0.709626\n", - "max_price 0.709626\n", - "ticket_sum 0.000000\n", - "average_price 0.709626\n", - "average_purchase_delay 0.709731\n", - "average_price_basket 0.709731\n", - "average_ticket_basket 0.709731\n", - "total_price 0.000000\n", - "purchase_count 0.000000\n", - "first_buying_date 0.709626\n", - "country 0.152090\n", - "gender_label 0.000000\n", - "gender_female 0.000000\n", - "gender_male 0.000000\n", - "gender_other 0.000000\n", - "country_fr 0.152090\n", - "has_tags 0.000000\n", - "nb_campaigns 0.000000\n", - "nb_campaigns_opened 0.000000\n", - "time_to_open 0.848079\n", - "y_has_purchased 1.000000\n", - "dtype: float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " dataset_train.isna().sum()/dataset_train.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "75f9a672-641f-49a2-a8d6-7673845506f5", - "metadata": {}, - "outputs": [], - "source": [ - "#Creation de la variable dependante fictive: 1 si l'individu a effectué un achat au cours de la periode de train et 0 sinon\n", - "\n", - "dataset_train_modif=dataset_train\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c121c1e2-d8e4-4b93-a882-9385581b63c9", - "metadata": {}, - "outputs": [], - "source": [ - "dataset_train_modif[\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/useless/code_valeur manquante.ipynb b/useless/code_valeur manquante.ipynb deleted file mode 100644 index 5ef2b81..0000000 --- a/useless/code_valeur manquante.ipynb +++ /dev/null @@ -1,2880 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c4205b5d-e052-4863-a46b-20e4757052a7", - "metadata": {}, - "source": [ - "# Business Data Challenge - Team 1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ae3af8e6-ced8-4994-8877-fa98d4297cc0", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "dd3184e7-54a1-4463-af42-5850d9517a41", - "metadata": {}, - "source": [ - "Configuration de l'accès aux données" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "b6035982-9ff4-4013-9792-2d50e10db3d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/1/1campaign_stats.csv',\n", - " 'bdc2324-data/1/1campaigns.csv',\n", - " 'bdc2324-data/1/1categories.csv',\n", - " 'bdc2324-data/1/1countries.csv',\n", - " 'bdc2324-data/1/1currencies.csv',\n", - " 'bdc2324-data/1/1customer_target_mappings.csv',\n", - " 'bdc2324-data/1/1customersplus.csv',\n", - " 'bdc2324-data/1/1event_types.csv',\n", - " 'bdc2324-data/1/1events.csv',\n", - " 'bdc2324-data/1/1facilities.csv',\n", - " 'bdc2324-data/1/1link_stats.csv',\n", - " 'bdc2324-data/1/1pricing_formulas.csv',\n", - " 'bdc2324-data/1/1product_packs.csv',\n", - " 'bdc2324-data/1/1products.csv',\n", - " 'bdc2324-data/1/1products_groups.csv',\n", - " 'bdc2324-data/1/1purchases.csv',\n", - " 'bdc2324-data/1/1representation_category_capacities.csv',\n", - " 'bdc2324-data/1/1representations.csv',\n", - " 'bdc2324-data/1/1seasons.csv',\n", - " 'bdc2324-data/1/1structure_tag_mappings.csv',\n", - " 'bdc2324-data/1/1suppliers.csv',\n", - " 'bdc2324-data/1/1tags.csv',\n", - " 'bdc2324-data/1/1target_types.csv',\n", - " 'bdc2324-data/1/1targets.csv',\n", - " 'bdc2324-data/1/1tickets.csv',\n", - " 'bdc2324-data/1/1type_of_categories.csv',\n", - " 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n", - " 'bdc2324-data/1/1type_ofs.csv']" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import s3fs\n", - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", - "\n", - "BUCKET = \"bdc2324-data/1\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b86c935d-124f-453f-80dd-83ea6770d09c", - "metadata": {}, - "outputs": [], - "source": [ - "dic_base=['campaign_stats','campaigns','categories','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets','type_of_categories','type_of_pricing_formulas','type_ofs']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f6d0b27c-0ecd-406b-b042-6c3802dd68fd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_425/1008972637.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dic_base=['campaign_stats','campaigns','categories','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets','type_of_categories','type_of_pricing_formulas','type_ofs']\n", - "for nom_base in dic_base:\n", - " FILE_PATH_S3_fanta = 'bdc2324-data/1/1' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2a6b5e22-3370-457f-83b7-dd1e13663229", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'bdc2324-data/1/1type_ofs.csv'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FILE_PATH_S3_fanta" - ] - }, - { - "cell_type": "markdown", - "id": "79012186-ea51-4252-843e-36a9bbe3847e", - "metadata": {}, - "source": [ - "# Analyse exploratoire " - ] - }, - { - "cell_type": "markdown", - "id": "1a365f29-4766-47d8-9796-24a5271867b2", - "metadata": {}, - "source": [ - "## I. Base type_of_pricing_formulas" - ] - }, - { - "cell_type": "markdown", - "id": "bcc14f93-2289-44eb-816b-a51049b258df", - "metadata": {}, - "source": [ - "## Detection des valeur manquantes" - ] - }, - { - "cell_type": "raw", - "id": "ab2ec4c4-9d38-4aeb-8202-9116df3cdd66", - "metadata": {}, - "source": [ - "dic_prod_princing=['type_of_pricing_formulas','products_groups','pricing_formulas','product_packs','products']" - ] - }, - { - "cell_type": "markdown", - "id": "88759b4a-2633-478d-abce-29abeac376d1", - "metadata": {}, - "source": [ - "def verifier_donnees_manquantes(base):\n", - " donnees_manquantes = base.isna().sum()\n", - " print(\"Données manquantes pour la base :\")\n", - " print(donnees_manquantes)" - ] - }, - { - "cell_type": "markdown", - "id": "df3075b4-1490-4cf2-a3fe-c6d4e2144ae3", - "metadata": {}, - "source": [ - "for nom_base in dic_prod_princing:\n", - " verifier_donnees_manquantes(nom_base)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e0c67c01-e837-4772-b070-d1be0d895a36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "type_of_id 0\n", - "pricing_formula_id 0\n", - "created_at 0\n", - "updated_at 0\n", - "identifier 0\n", - "dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#detection des Nan d\n", - "\n", - "type_of_pricing_formulas.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83a6a48d-effe-4537-b4bb-d5a540b610f1", - "metadata": {}, - "outputs": [], - "source": [ - "#variable retenu:[[\"id\",\"type_of_id\",\"pricing_formula_id\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3eaffaa6-1164-4ee9-a671-8b5eb3df797d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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568 rows × 6 columns

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idtype_of_idpricing_formula_idcreated_atupdated_atidentifier
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, type_of_id, pricing_formula_id, created_at, updated_at, identifier]\n", - "Index: []" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Identification des doublons\n", - "type_of_pricing_formulas.loc[type_of_pricing_formulas['id'].duplicated(keep=False),:]" - ] - }, - { - "cell_type": "markdown", - "id": "7a40de03-5e18-4d3d-a0f8-da960c29fad8", - "metadata": {}, - "source": [ - "## II.products_groups" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "89909175-6734-4e8e-8632-d6f8ca812388", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "percent_price 0\n", - "max_price 0\n", - "min_price 0\n", - "category_id 0\n", - "pricing_formula_id 0\n", - "representation_id 0\n", - "created_at 0\n", - "updated_at 0\n", - "dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#detection des Nan \n", - "\n", - "products_groups.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0518684-c83c-4f0a-89ea-d7dcfd60051d", - "metadata": {}, - "outputs": [], - "source": [ - "#variable retenu:[[\"id\",\"percent_price\",\"max_price\",\"min_price\",\"category_id\",\"pricing_formula_id\",\"representation_id\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6a187170-96c4-48d2-9568-b270f67e2c27", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id int64\n", - "percent_price float64\n", - "max_price float64\n", - "min_price float64\n", - "category_id int64\n", - "pricing_formula_id int64\n", - "representation_id int64\n", - "created_at object\n", - "updated_at object\n", - "dtype: object" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#type des variables\n", - "\n", - "products_groups.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2fba2cb0-a6a4-43b2-a854-3be07939c28b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1502entree mucem tp( expo picasso)2020-09-03 13:43:59.816765+02:002022-02-18 15:57:55.792581+01:00NaN223b09e6c3f1f75dbf8df019af97a555
2504nombre de personnes cinema2020-09-03 13:43:59.818198+02:002021-01-25 19:16:05.187114+01:00NaNba33b7b6d225a75d713a356b49c4d915
3117spectacle tarif e famille tr2020-09-03 13:21:21.400249+02:002023-03-13 11:30:29.525335+01:00NaNa00b61ad933518856f86e63ca91a5750
41496billet nb famille mecene 1a2020-09-03 14:29:33.320952+02:002021-01-25 19:23:06.816402+01:00NaN7f6013803c242253a5ccde80f780984f
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551529billet nb expo gr2020-09-03 13:43:59.835944+02:002022-02-18 15:57:55.792581+01:00NaN7d888e42abe101fc8b21dc88948c8b74
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5535847visite scolaire rep1h002021-06-09 18:10:49.742531+02:002022-02-18 15:55:03.576236+01:00NaNa7bb5a6892d55f0d5ee4ce5786ae5fc6
5545840france billet - entree ts2021-06-09 18:10:49.737576+02:002022-02-18 16:16:00.199543+01:00NaN4c53016fc65847646f600eff853593e5
5555863france billet - entree tp2021-06-09 18:12:49.269924+02:002022-02-18 16:16:00.199543+01:00NaN90e642c0e1ef6bc9f2bc43089798de00
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5 rows × 41 columns

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" - ], - "text/plain": [ - " id_products amount is_full_price representation_id_merge2 \\\n", - "0 10682 9.0 False 914 \n", - "1 478 9.5 False 273 \n", - "2 20873 11.5 False 275 \n", - "3 157142 8.0 False 82519 \n", - "4 1341 8.5 False 9 \n", - "\n", - " pricing_formula_id_merge2 created_at_products \\\n", - "0 114 2020-09-03 14:09:43.119798+02:00 \n", - "1 131 2020-09-03 13:21:22.711773+02:00 \n", - "2 137 2020-09-03 14:46:33.589030+02:00 \n", - "3 9 2022-01-28 19:29:23.525722+01:00 \n", - "4 93 2020-09-03 13:29:30.773089+02:00 \n", - "\n", - " updated_at_products category_id_merge2 apply_price \\\n", - "0 2020-09-03 14:09:43.119798+02:00 41 0.0 \n", - "1 2020-09-03 13:21:22.711773+02:00 1 0.0 \n", - "2 2020-09-03 14:46:33.589030+02:00 1 0.0 \n", - "3 2022-01-28 19:29:23.525722+01:00 5 0.0 \n", - "4 2020-09-03 13:29:30.773089+02:00 1 0.0 \n", - "\n", - " products_group_id ... pricing_formula_id \\\n", - "0 10655 ... 114.0 \n", - "1 471 ... 131.0 \n", - "2 20825 ... 137.0 \n", - "3 156773 ... 9.0 \n", - "4 1175 ... 93.0 \n", - "\n", - " created_at_type_of_pricing_f updated_at_type_of_pricing_f \\\n", - "0 2021-02-15 17:02:27.395376+01:00 2021-02-15 17:02:27.395376+01:00 \n", - "1 2021-02-05 11:52:05.923905+01:00 2021-02-05 11:52:05.923905+01:00 \n", - "2 2021-02-05 11:52:05.939898+01:00 2021-02-05 11:52:05.939898+01:00 \n", - "3 2021-02-05 11:52:06.107939+01:00 2021-02-05 11:52:06.107939+01:00 \n", - "4 2021-02-05 11:52:06.004162+01:00 2021-02-05 11:52:06.004162+01:00 \n", - "\n", - " identifier_merge4 id name_product_pack type_of \\\n", - "0 3706121eb9f43b635bef1433c06f679c 1 NaN 0 \n", - "1 0aceb248607671792298436004b95275 1 NaN 0 \n", - "2 93002d4637331edd81ffc28b6e8e89c0 1 NaN 0 \n", - "3 7d0b25bdfff9f366da8be820608c8191 1 NaN 0 \n", - "4 1dbb0795e8f47cb75ba7cdb08c06be5f 1 NaN 0 \n", - "\n", - " created_at updated_at \\\n", - "0 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "1 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "2 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "3 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "4 2020-09-03 13:11:24.501197+02:00 2020-09-03 13:11:24.501197+02:00 \n", - "\n", - " identifier_product_pack \n", - "0 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "1 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "2 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "3 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "4 a764b4bf13a360c7ac2a35ec4ca96c95 \n", - "\n", - "[5 rows x 41 columns]" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_product_pricing.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03442997-806f-4285-a139-3bad46bb4522", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d22a0d75-53c5-4b54-9060-c9e7c307fb13", - "metadata": {}, - "outputs": [], - "source": [ - "BUCKET = \"bdc2324-data\"\n", - "directory_path = '2'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7c229dad-6ebd-4f43-99f1-fb330dc29466", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/2/2campaign_stats.csv',\n", - " 'bdc2324-data/2/2campaigns.csv',\n", - " 'bdc2324-data/2/2categories.csv',\n", - " 'bdc2324-data/2/2contribution_sites.csv',\n", - " 'bdc2324-data/2/2contributions.csv',\n", - " 'bdc2324-data/2/2countries.csv',\n", - " 'bdc2324-data/2/2currencies.csv',\n", - " 'bdc2324-data/2/2customer_target_mappings.csv',\n", - " 'bdc2324-data/2/2customersplus.csv',\n", - " 'bdc2324-data/2/2event_types.csv',\n", - " 'bdc2324-data/2/2events.csv',\n", - " 'bdc2324-data/2/2facilities.csv',\n", - " 'bdc2324-data/2/2link_stats.csv',\n", - " 'bdc2324-data/2/2pricing_formulas.csv',\n", - " 'bdc2324-data/2/2product_packs.csv',\n", - " 'bdc2324-data/2/2products.csv',\n", - " 'bdc2324-data/2/2products_groups.csv',\n", - " 'bdc2324-data/2/2purchases.csv',\n", - " 'bdc2324-data/2/2representation_category_capacities.csv',\n", - " 'bdc2324-data/2/2representations.csv',\n", - " 'bdc2324-data/2/2seasons.csv',\n", - " 'bdc2324-data/2/2structure_tag_mappings.csv',\n", - " 'bdc2324-data/2/2suppliers.csv',\n", - " 'bdc2324-data/2/2tags.csv',\n", - " 'bdc2324-data/2/2target_types.csv',\n", - " 'bdc2324-data/2/2targets.csv',\n", - " 'bdc2324-data/2/2tickets.csv']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BUCKET = \"bdc2324-data/2\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "df3d3548-3d76-4f07-afa1-e240932bc1c7", - "metadata": {}, - "outputs": [], - "source": [ - "dic_base_ent2=['campaign_stats','campaigns','categories','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "90f8d5fc-43f3-4f36-b8cc-89a41785f032", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_425/673681459.py:5: DtypeWarning: Columns (20) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dic_base_ent2=['campaign_stats','campaigns','categories','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']\n", - "for nom_base in dic_base_ent2:\n", - " FILE_PATH_S3_fanta = 'bdc2324-data/2/2' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3e39a584-e02b-41b2-831c-33b920e298e9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "27" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dic_base_ent2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2b6c6f65", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "def calculer_proportion_valeurs_manquantes_et_exporter(databases, fichier_sortie='proportion_valeurs_manquantes.xlsx'):\n", - " \"\"\"\n", - " Calculer la proportion de valeurs manquantes pour chaque variable dans chaque base de données et exporter les résultats dans un fichier Excel.\n", - "\n", - " Paramètres:\n", - " - databases (dict): Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont les DataFrames pandas.\n", - " - fichier_sortie (str): Le chemin du fichier Excel de sortie.\n", - "\n", - " Retourne:\n", - " - Un fichier Excel où chaque onglet représente une base de données différente avec la proportion de valeurs manquantes pour chaque variable.\n", - " \"\"\"\n", - " with pd.ExcelWriter(fichier_sortie) as writer:\n", - " for nom_db, df in databases.items():\n", - " # Calculer la proportion de valeurs manquantes pour chaque colonne\n", - " proportion_manquantes = df.isnull().mean()\n", - " # Convertir en DataFrame pour un meilleur affichage\n", - " resultats_df = pd.DataFrame(proportion_manquantes, columns=['ProportionValeursManquantes'])\n", - " resultats_df['ProportionValeursManquantes'] = resultats_df['ProportionValeursManquantes'].map(lambda x: f\"{x:.2%}\")\n", - " # Écrire le DataFrame dans un onglet du fichier Excel\n", - " resultats_df.to_excel(writer, sheet_name=nom_db)\n", - "\n", - " print(f\"Les résultats ont été exportés dans le fichier '{fichier_sortie}'.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "06759646-9419-4841-b12f-bbfceb417f3a", - "metadata": {}, - "outputs": [], - "source": [ - "#fonction calcul la proportion de valeur manquante\n", - "\n", - "import pandas as pd\n", - "\n", - "def calculer_proportion_valeurs_manquantes(databases):\n", - " \"\"\"\n", - " Calculer la proportion de valeurs manquantes pour chaque variable dans chaque base de données.\n", - "\n", - " Paramètres:\n", - " - databases (dict): Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont les DataFrames pandas.\n", - "\n", - " Retourne:\n", - " - Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont des DataFrames avec la proportion de valeurs manquantes pour chaque variable.\n", - " \"\"\"\n", - " resultats = {}\n", - " for nom_db, df in databases.items():\n", - " # Calculer la proportion de valeurs manquantes pour chaque colonne\n", - " proportion_manquantes = df.isnull().mean()\n", - " # Convertir en DataFrame pour un meilleur affichage\n", - " resultats_df = pd.DataFrame(proportion_manquantes, columns=['ProportionValeursManquantes'])\n", - " resultats_df['ProportionValeursManquantes'] = resultats_df['ProportionValeursManquantes'].map(lambda x: f\"{x:.2%}\")\n", - " # Ajouter le résultat au dictionnaire\n", - " resultats[nom_db] = resultats_df\n", - " return resultats" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0960daa8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Base de données: Base1\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "campaign_id 0.00%\n", - "customer_id 0.00%\n", - "opened_at 68.67%\n", - "sent_at 0.00%\n", - "delivered_at 1.61%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: Base2\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 0.00%\n", - "service_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "process_id 100.00%\n", - "report_url 100.00%\n", - "category 0.00%\n", - "to_be_synced 0.00%\n", - "identifier 0.00%\n", - "sent_at 0.00%\n", - "\n" - ] - } - ], - "source": [ - "# Exemple d'utilisation\n", - "\n", - "databases = {'Base1': campaign_stats, 'Base2': campaigns}\n", - "\n", - "resultats = calculer_proportion_valeurs_manquantes(databases)\n", - "\n", - "for nom_db, resultat in resultats.items():\n", - " print(f\"Base de données: {nom_db}\")\n", - " print(resultat)\n", - " print()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "77dc02bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Base de données: campaign_stats\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "campaign_id 0.00%\n", - "customer_id 0.00%\n", - "opened_at 68.67%\n", - "sent_at 0.00%\n", - "delivered_at 1.61%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: campaigns\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 0.00%\n", - "service_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "process_id 100.00%\n", - "report_url 100.00%\n", - "category 0.00%\n", - "to_be_synced 0.00%\n", - "identifier 0.00%\n", - "sent_at 0.00%\n", - "\n", - "Base de données: categories\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 100.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "extra_field 100.00%\n", - "quota 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: contribution_sites\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "identifier 0.00%\n", - "facility_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: contributions\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "sent_at 0.00%\n", - "software 100.00%\n", - "satisfaction 39.65%\n", - "extra_field 100.00%\n", - "customer_id 0.00%\n", - "contribution_site_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: countries\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 1.63%\n", - "code 0.41%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: currencies\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: customer_target_mappings\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "customer_id 0.00%\n", - "target_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "name 100.00%\n", - "extra_field 100.00%\n", - "\n", - "Base de données: customersplus\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "lastname 0.02%\n", - "firstname 0.01%\n", - "birthdate 96.75%\n", - "email 1.05%\n", - "street_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "civility 100.00%\n", - "is_partner 0.00%\n", - "extra 100.00%\n", - "deleted_at 100.00%\n", - "reference 100.00%\n", - "gender 0.00%\n", - "is_email_true 0.00%\n", - "extra_field 100.00%\n", - "identifier 0.00%\n", - "opt_in 0.00%\n", - "structure_id 97.57%\n", - "note 97.84%\n", - "profession 100.00%\n", - "language 46.16%\n", - "mcp_contact_id 100.00%\n", - "need_reload 0.00%\n", - "last_buying_date 12.58%\n", - "max_price 12.58%\n", - "ticket_sum 0.00%\n", - "average_price 12.58%\n", - "fidelity 0.00%\n", - "average_purchase_delay 12.58%\n", - "average_price_basket 12.58%\n", - "average_ticket_basket 12.58%\n", - "total_price 0.00%\n", - "preferred_category 100.00%\n", - "preferred_supplier 100.00%\n", - "preferred_formula 100.00%\n", - "purchase_count 0.00%\n", - "first_buying_date 12.58%\n", - "last_visiting_date 100.00%\n", - "zipcode 98.80%\n", - "country 97.64%\n", - "age 96.75%\n", - "tenant_id 0.00%\n", - "\n", - "Base de données: event_types\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "fidelity_delay 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: events\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "season_id 0.00%\n", - "facility_id 0.00%\n", - "name 0.00%\n", - "event_type_id 0.00%\n", - "manual_added 0.00%\n", - "is_display 0.00%\n", - "event_type_key_id 0.00%\n", - "facility_key_id 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: facilities\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 50.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "street_id 0.00%\n", - "fixed_capacity 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: link_stats\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "clicked_at 0.00%\n", - "link_id 0.00%\n", - "customer_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: pricing_formulas\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "extra_field 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: product_packs\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 100.00%\n", - "type_of 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: products\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "amount 0.00%\n", - "is_full_price 0.00%\n", - "representation_id 0.00%\n", - "pricing_formula_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "category_id 0.00%\n", - "apply_price 0.00%\n", - "products_group_id 0.00%\n", - "product_pack_id 0.00%\n", - "extra_field 100.00%\n", - "amount_consumption 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: products_groups\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "percent_price 0.00%\n", - "max_price 0.00%\n", - "min_price 0.00%\n", - "category_id 0.00%\n", - "pricing_formula_id 0.00%\n", - "representation_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: purchases\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "purchase_date 0.00%\n", - "customer_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "number 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: representation_category_capacities\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "representation_id 0.00%\n", - "category_id 0.00%\n", - "expected_filling 100.00%\n", - "max_filling 100.00%\n", - "\n", - "Base de données: representations\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "serial 100.00%\n", - "event_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "start_date_time 0.00%\n", - "open 0.00%\n", - "satisfaction 100.00%\n", - "end_date_time 0.00%\n", - "name 100.00%\n", - "is_display 0.00%\n", - "representation_type_id 100.00%\n", - "expected_filling 100.00%\n", - "max_filling 100.00%\n", - "extra_field 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: seasons\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "start_date_time 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: structure_tag_mappings\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "structure_id 0.00%\n", - "tag_id 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: suppliers\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 20.00%\n", - "manually_added 0.00%\n", - "label 100.00%\n", - "itr 100.00%\n", - "updated_at 0.00%\n", - "created_at 0.00%\n", - "commission 100.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: tags\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "name 50.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: target_types\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "is_import 25.00%\n", - "name 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "identifier 0.00%\n", - "\n", - "Base de données: targets\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "target_type_id 0.00%\n", - "name 5.26%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "\n", - "Base de données: tickets\n", - " ProportionValeursManquantes\n", - "id 0.00%\n", - "number 0.00%\n", - "created_at 0.00%\n", - "updated_at 0.00%\n", - "purchase_id 0.00%\n", - "product_id 0.00%\n", - "is_from_subscription 0.00%\n", - "type_of 0.00%\n", - "supplier_id 0.00%\n", - "barcode 100.00%\n", - "identifier 0.00%\n", - "\n" - ] - } - ], - "source": [ - "# Exemple d'utilisation\n", - "dict={'campaign_stats': campaign_stats,\n", - " 'campaigns': campaigns,\n", - " 'categories': categories,\n", - " 'contribution_sites': contribution_sites,\n", - " 'contributions': contributions,\n", - " 'countries': countries,\n", - " 'currencies': currencies,\n", - " 'customer_target_mappings': customer_target_mappings,\n", - " 'customersplus': customersplus,\n", - " 'event_types': event_types,\n", - " 'events': events,\n", - " 'facilities': facilities,\n", - " 'link_stats': link_stats,\n", - " 'pricing_formulas': pricing_formulas,\n", - " 'product_packs': product_packs,\n", - " 'products': products,\n", - " 'products_groups': products_groups,\n", - " 'purchases': purchases,\n", - " 'representation_category_capacities': representation_category_capacities,\n", - " 'representations': representations,\n", - " 'seasons': seasons,\n", - " 'structure_tag_mappings': structure_tag_mappings,\n", - " 'suppliers': suppliers,\n", - " 'tags': tags,\n", - " 'target_types': target_types,\n", - " 'targets': targets,\n", - " 'tickets': tickets}\n", - "\n", - "resultats = calculer_proportion_valeurs_manquantes(dict)\n", - "\n", - "for nom_db, resultat in resultats.items():\n", - " print(f\"Base de données: {nom_db}\")\n", - " print(resultat)\n", - " print()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "60be9271", - "metadata": {}, - "outputs": [], - "source": [ - "#MEME CODE mais avec l'exportation de result a en format excel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "955fe358", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "def calculer_proportion_valeurs_manquantes_et_exporter(databases, fichier_sortie='proportion_valeurs_manquantes.xlsx'):\n", - " \"\"\"\n", - " Calculer la proportion de valeurs manquantes pour chaque variable dans chaque base de données et exporter les résultats dans un fichier Excel.\n", - "\n", - " Paramètres:\n", - " - databases (dict): Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont les DataFrames pandas.\n", - " - fichier_sortie (str): Le chemin du fichier Excel de sortie.\n", - "\n", - " Retourne:\n", - " - Un fichier Excel où chaque onglet représente une base de données différente avec la proportion de valeurs manquantes pour chaque variable.\n", - " \"\"\"\n", - " with pd.ExcelWriter(fichier_sortie) as writer:\n", - " for nom_db, df in databases.items():\n", - " # Calculer la proportion de valeurs manquantes pour chaque colonne\n", - " proportion_manquantes = df.isnull().mean()\n", - " # Convertir en DataFrame pour un meilleur affichage\n", - " resultats_df = pd.DataFrame(proportion_manquantes, columns=['ProportionValeursManquantes'])\n", - " resultats_df['ProportionValeursManquantes'] = resultats_df['ProportionValeursManquantes'].map(lambda x: f\"{x:.2%}\")\n", - " # Écrire le DataFrame dans un onglet du fichier Excel\n", - " resultats_df.to_excel(writer, sheet_name=nom_db)\n", - "\n", - " print(f\"Les résultats ont été exportés dans le fichier '{fichier_sortie}'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7897b689", - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation\n", - "dict={'campaign_stats': campaign_stats,\n", - " 'campaigns': campaigns,\n", - " 'categories': categories,\n", - " 'contribution_sites': contribution_sites,\n", - " 'contributions': contributions,\n", - " 'countries': countries,\n", - " 'currencies': currencies,\n", - " 'customer_target_mappings': customer_target_mappings,\n", - " 'customersplus': customersplus,\n", - " 'event_types': event_types,\n", - " 'events': events,\n", - " 'facilities': facilities,\n", - " 'link_stats': link_stats,\n", - " 'pricing_formulas': pricing_formulas,\n", - " 'product_packs': product_packs,\n", - " 'products': products,\n", - " 'products_groups': products_groups,\n", - " 'purchases': purchases,\n", - " 'representation_category_capacities': representation_category_capacities,\n", - " 'representations': representations,\n", - " 'seasons': seasons,\n", - " 'structure_tag_mappings': structure_tag_mappings,\n", - " 'suppliers': suppliers,\n", - " 'tags': tags,\n", - " 'target_types': target_types,\n", - " 'targets': targets,\n", - " 'tickets': tickets}\n", - "\n", - "calculer_proportion_valeurs_manquantes_et_exporter(dict, 'proportion_valeurs_manquantes_ent1.xlsx')\n" - ] - }, - { - "cell_type": "markdown", - "id": "514273f4", - "metadata": {}, - "source": [ - "## Entreprise 3" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "69b8f59a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bdc2324-data/3/3campaign_stats.csv',\n", - " 'bdc2324-data/3/3campaigns.csv',\n", - " 'bdc2324-data/3/3categories.csv',\n", - " 'bdc2324-data/3/3consumptions.csv',\n", - " 'bdc2324-data/3/3contribution_sites.csv',\n", - " 'bdc2324-data/3/3contributions.csv',\n", - " 'bdc2324-data/3/3countries.csv',\n", - " 'bdc2324-data/3/3currencies.csv',\n", - " 'bdc2324-data/3/3customer_target_mappings.csv',\n", - " 'bdc2324-data/3/3customersplus.csv',\n", - " 'bdc2324-data/3/3event_types.csv',\n", - " 'bdc2324-data/3/3events.csv',\n", - " 'bdc2324-data/3/3facilities.csv',\n", - " 'bdc2324-data/3/3link_stats.csv',\n", - " 'bdc2324-data/3/3pricing_formulas.csv',\n", - " 'bdc2324-data/3/3product_packs.csv',\n", - " 'bdc2324-data/3/3products.csv',\n", - " 'bdc2324-data/3/3products_groups.csv',\n", - " 'bdc2324-data/3/3purchases.csv',\n", - " 'bdc2324-data/3/3representation_category_capacities.csv',\n", - " 'bdc2324-data/3/3representations.csv',\n", - " 'bdc2324-data/3/3seasons.csv',\n", - " 'bdc2324-data/3/3structure_tag_mappings.csv',\n", - " 'bdc2324-data/3/3suppliers.csv',\n", - " 'bdc2324-data/3/3tags.csv',\n", - " 'bdc2324-data/3/3target_types.csv',\n", - " 'bdc2324-data/3/3targets.csv',\n", - " 'bdc2324-data/3/3tickets.csv']" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BUCKET = \"bdc2324-data/3\"\n", - "fs.ls(BUCKET)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a842d0b-f341-4752-b624-3a339ef0fe1e", - "metadata": {}, - "outputs": [], - "source": [ - "# Chargement des données temporaires\n", - "BUCKET = \"projet-bdc2324-team1\"\n", - "FILE_KEY_S3 = \"0_Temp/Company 1 - Purchasing behaviour.csv\"\n", - "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n", - "\n", - "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", - " tickets_kpi = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9b4c005f", - "metadata": {}, - "outputs": [], - "source": [ - "dic_base_ent3=['campaign_stats','campaigns','categories','consumptions','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "aae542d6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_425/4241072101.py:5: DtypeWarning: Columns (19,20) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n", - "/tmp/ipykernel_425/4241072101.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n" - ] - } - ], - "source": [ - "dic_base_ent3=['campaign_stats','campaigns','categories','consumptions','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']\n", - "for nom_base in dic_base_ent2:\n", - " FILE_PATH_S3_fanta = 'bdc2324-data/3/3' + nom_base + '.csv'\n", - " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", - " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "907c650c-df7e-4e5c-b3cb-6595be061e99", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnamecreated_atupdated_atfidelity_delayidentifier
060873journees des plantes2022-09-13 17:42:18.040557+02:002022-09-13 17:42:18.040557+02:003658568a64f69dd864539e7a682b03ef3a
160876parking2022-09-13 17:42:18.043821+02:002022-09-13 17:42:18.043821+02:00363ac156eead4ae6b40e9c498d532b4448
260997pass arc2022-09-13 18:04:38.812389+02:002022-09-13 18:04:38.812389+02:0036ddffce8b0a072d76a766097b34208482
361233paris pass museum2022-09-13 18:53:15.878739+02:002022-09-13 18:53:15.878739+02:0036394a376e43e498dccf8a448004f2aa84
460911spectacle noel2022-09-13 17:48:50.549760+02:002022-09-13 17:48:50.549760+02:0036a2b0f7c330d4c8d0338e6edf5ce4c81a
561007domaine + spect noel2022-09-13 18:07:25.121513+02:002022-09-13 18:07:25.121513+02:0036c65e9028b505dcfa71acef4e15f6e7be
662374patrivia2022-09-14 02:08:56.789118+02:002022-09-14 02:08:56.789118+02:00368db1f5c8774a8cf07542249278d7d778
772615NaN2023-08-14 06:20:26.491399+02:002023-08-14 06:20:26.491399+02:0036d41d8cd98f00b204e9800998ecf8427e
861011prestation annexe2022-09-13 18:07:25.126517+02:002022-09-13 18:07:25.126517+02:00362588ceebb05d3329f334687b8647887e
960877minibus2022-09-13 17:42:18.045141+02:002022-09-13 17:42:18.045141+02:0036397361c9c0dc82d911aa931223bd7a4e
1061708location espace2022-09-13 21:52:57.785694+02:002022-09-13 21:52:57.785694+02:00363738beebf604a960e016ffad6db1df74
1160931exposition2022-09-13 17:52:36.164774+02:002022-09-13 17:52:36.164774+02:003669033f19d53294c467d0fb7d3a4ad868
1260871domaine2022-09-13 17:42:18.037831+02:002022-09-13 17:42:18.037831+02:0036b81285860b791e63dee94559f0a9e8e4
1360878visite guidee2022-09-13 17:42:18.046593+02:002022-09-13 17:42:18.046593+02:00363474ed518d4fa7b86719680e70039ac2
1460875parc2022-09-13 17:42:18.042824+02:002022-09-13 17:42:18.042824+02:003641c16554f8160b3bd2a6b3809f309e27
1560870spectacle saison2022-09-13 17:42:18.028836+02:002022-09-13 17:42:18.028836+02:00367afdec36b66f94b67e813ac7d092ea0c
1660872domaine + spect saison2022-09-13 17:42:18.039515+02:002022-09-13 17:42:18.039515+02:0036ccd5dffaa070e9c7885ff3e9149f12f0
1760874supplement spectacle2022-09-13 17:42:18.041862+02:002022-09-13 17:42:18.041862+02:0036093d3e6b14b1ad33908a18522d02886b
1861203pique nique en blanc2022-09-13 18:44:16.813045+02:002022-09-13 18:44:16.813045+02:0036ff9a87979d4a564a1d2a1055a1aa186e
1961427restauration2022-09-13 19:26:41.906836+02:002022-09-13 19:26:41.906836+02:0036f1bcd494fa3171bf042e62c311157547
2060879animation culturelle2022-09-13 17:42:18.047567+02:002022-09-13 17:42:18.047567+02:003627b0b018dfa7301abffcb243a876e4c4
2161270ecurie2022-09-13 18:57:52.734356+02:002022-09-13 18:57:52.734356+02:0036a21e22a0d924104179b25069de927909
2260929domaine + spect ete2022-09-13 17:52:36.162726+02:002022-09-13 17:52:36.162726+02:00368bdc1d1d5fba2317af8c4d733d8206d7
2360910spectacle ete2022-09-13 17:48:50.548826+02:002022-09-13 17:48:50.548826+02:003644878d1fd6c7fe384274861294c59017
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" - ], - "text/plain": [ - " id name created_at \\\n", - "0 60873 journees des plantes 2022-09-13 17:42:18.040557+02:00 \n", - "1 60876 parking 2022-09-13 17:42:18.043821+02:00 \n", - "2 60997 pass arc 2022-09-13 18:04:38.812389+02:00 \n", - "3 61233 paris pass museum 2022-09-13 18:53:15.878739+02:00 \n", - "4 60911 spectacle noel 2022-09-13 17:48:50.549760+02:00 \n", - "5 61007 domaine + spect noel 2022-09-13 18:07:25.121513+02:00 \n", - "6 62374 patrivia 2022-09-14 02:08:56.789118+02:00 \n", - "7 72615 NaN 2023-08-14 06:20:26.491399+02:00 \n", - "8 61011 prestation annexe 2022-09-13 18:07:25.126517+02:00 \n", - "9 60877 minibus 2022-09-13 17:42:18.045141+02:00 \n", - "10 61708 location espace 2022-09-13 21:52:57.785694+02:00 \n", - "11 60931 exposition 2022-09-13 17:52:36.164774+02:00 \n", - "12 60871 domaine 2022-09-13 17:42:18.037831+02:00 \n", - "13 60878 visite guidee 2022-09-13 17:42:18.046593+02:00 \n", - "14 60875 parc 2022-09-13 17:42:18.042824+02:00 \n", - "15 60870 spectacle saison 2022-09-13 17:42:18.028836+02:00 \n", - "16 60872 domaine + spect saison 2022-09-13 17:42:18.039515+02:00 \n", - "17 60874 supplement spectacle 2022-09-13 17:42:18.041862+02:00 \n", - "18 61203 pique nique en blanc 2022-09-13 18:44:16.813045+02:00 \n", - "19 61427 restauration 2022-09-13 19:26:41.906836+02:00 \n", - "20 60879 animation culturelle 2022-09-13 17:42:18.047567+02:00 \n", - "21 61270 ecurie 2022-09-13 18:57:52.734356+02:00 \n", - "22 60929 domaine + spect ete 2022-09-13 17:52:36.162726+02:00 \n", - "23 60910 spectacle ete 2022-09-13 17:48:50.548826+02:00 \n", - "\n", - " updated_at fidelity_delay \\\n", - "0 2022-09-13 17:42:18.040557+02:00 36 \n", - "1 2022-09-13 17:42:18.043821+02:00 36 \n", - "2 2022-09-13 18:04:38.812389+02:00 36 \n", - "3 2022-09-13 18:53:15.878739+02:00 36 \n", - "4 2022-09-13 17:48:50.549760+02:00 36 \n", - "5 2022-09-13 18:07:25.121513+02:00 36 \n", - "6 2022-09-14 02:08:56.789118+02:00 36 \n", - "7 2023-08-14 06:20:26.491399+02:00 36 \n", - "8 2022-09-13 18:07:25.126517+02:00 36 \n", - "9 2022-09-13 17:42:18.045141+02:00 36 \n", - "10 2022-09-13 21:52:57.785694+02:00 36 \n", - "11 2022-09-13 17:52:36.164774+02:00 36 \n", - "12 2022-09-13 17:42:18.037831+02:00 36 \n", - "13 2022-09-13 17:42:18.046593+02:00 36 \n", - "14 2022-09-13 17:42:18.042824+02:00 36 \n", - "15 2022-09-13 17:42:18.028836+02:00 36 \n", - "16 2022-09-13 17:42:18.039515+02:00 36 \n", - "17 2022-09-13 17:42:18.041862+02:00 36 \n", - "18 2022-09-13 18:44:16.813045+02:00 36 \n", - "19 2022-09-13 19:26:41.906836+02:00 36 \n", - "20 2022-09-13 17:42:18.047567+02:00 36 \n", - "21 2022-09-13 18:57:52.734356+02:00 36 \n", - "22 2022-09-13 17:52:36.162726+02:00 36 \n", - "23 2022-09-13 17:48:50.548826+02:00 36 \n", - "\n", - " identifier \n", - "0 58568a64f69dd864539e7a682b03ef3a \n", - "1 3ac156eead4ae6b40e9c498d532b4448 \n", - "2 ddffce8b0a072d76a766097b34208482 \n", - "3 394a376e43e498dccf8a448004f2aa84 \n", - "4 a2b0f7c330d4c8d0338e6edf5ce4c81a \n", - "5 c65e9028b505dcfa71acef4e15f6e7be \n", - "6 8db1f5c8774a8cf07542249278d7d778 \n", - "7 d41d8cd98f00b204e9800998ecf8427e \n", - 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