{ "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": 107, "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": 3, "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": "0c3a6ddc-9345-4a42-b6bf-a20a95de3028", "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": "2831d546-b365-498b-8248-c618bd9c3057", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_519/2459610029.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": 6, "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', '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", " # 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": 7, "id": "c18195fc-ed40-4e39-a59e-c9ecc5a8e6c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape train : (224213, 17)\n", "Shape test : (96096, 17)\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": 8, "id": "7c81390e-598c-4f02-bd56-dd03b00dcb33", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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 is_email_true \\\n", "0 0.000000 0.0 1 True \n", "1 0.000000 0.0 2 True \n", "2 0.000000 0.0 2 True \n", "3 0.000000 0.0 1 True \n", "4 363.061678 0.0 4 True \n", "... ... ... ... ... \n", "96091 0.000000 1.0 2 True \n", "96092 0.000000 1.0 1 True \n", "96093 -1.000000 0.0 1 True \n", "96094 0.000000 1.0 1 True \n", "96095 -1.000000 0.0 2 True \n", "\n", " opt_in gender_female gender_male gender_other nb_campaigns \\\n", "0 False 1 0 0 0.0 \n", "1 True 0 1 0 0.0 \n", "2 True 1 0 0 0.0 \n", "3 False 1 0 0 0.0 \n", "4 False 1 0 0 0.0 \n", "... ... ... ... ... ... \n", "96091 False 0 1 0 15.0 \n", "96092 False 0 1 0 12.0 \n", "96093 True 1 0 0 29.0 \n", "96094 False 0 1 0 20.0 \n", "96095 False 0 1 0 31.0 \n", "\n", " nb_campaigns_opened \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "... ... \n", "96091 5.0 \n", "96092 9.0 \n", "96093 3.0 \n", "96094 4.0 \n", "96095 4.0 \n", "\n", "[96096 rows x 17 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test" ] }, { "cell_type": "code", "execution_count": 9, "id": "c708f439-bb75-4688-bf4f-4c04e13deaae", "metadata": {}, "outputs": [], "source": [ "def load_model(type_of_activity, model):\n", " BUCKET = f\"projet-bdc2324-team1/Output_model/{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" ] }, { "cell_type": "code", "execution_count": 10, "id": "5261a803-05b8-41a0-968c-dc7bde48ddd3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=3, error_score='raise',\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",
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       "                                                                          'purchase_date_min',\n",
       "                                                                          'purchase_date_max',\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=[('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", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", " 'nb_campaigns',\n", " 'nb_...\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: 0.5837086520288036,\n", " 1.0: 3.486549107420539}],\n", " 'LogisticRegression_cv__penalty': ['l1', 'l2']},\n", " scoring=make_scorer(recall_score, response_method='predict'))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logit_cv = load_model(\"sport\", \"LogisticRegression_cv\")\n", "logit_cv" ] }, { "cell_type": "markdown", "id": "006819e7-e9c5-48d9-85ee-aa43d5e4c9c2", "metadata": {}, "source": [ "## Quartile clustering" ] }, { "cell_type": "code", "execution_count": 12, "id": "018d8ff4-3436-4eec-8507-d1a265cbabf1", "metadata": {}, "outputs": [], "source": [ "y_pred = logit_cv.predict(X_test)\n", "y_pred_prob = logit_cv.predict_proba(X_test)[:, 1]" ] }, { "cell_type": "code", "execution_count": 13, "id": "846f53b9-73c2-4a8b-9d9e-f11bf59ce9ba", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_519/375041546.py:3: 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", " X_test_segment[\"has_purchased\"] = y_test\n", "/tmp/ipykernel_519/375041546.py:4: 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", " X_test_segment[\"has_purchased_estim\"] = y_pred\n", "/tmp/ipykernel_519/375041546.py:5: 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", " X_test_segment[\"score\"] = y_pred_prob\n", "/tmp/ipykernel_519/375041546.py:6: 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", " X_test_segment[\"quartile\"] = np.where(X_test['score']<0.25, '1',\n" ] }, { "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", "5 2.0 1.0 60.0 1.0 0.0 \n", "6 5.0 1.0 61.0 1.0 1.0 \n", "7 4.0 1.0 80.0 1.0 0.0 \n", "8 1.0 1.0 10.0 1.0 0.0 \n", "9 3.0 3.0 165.0 1.0 1.0 \n", "\n", " purchase_date_min purchase_date_max time_between_purchase \\\n", "0 5.177187 5.177187 0.000000 \n", "1 426.265613 426.265613 0.000000 \n", "2 436.033437 436.033437 0.000000 \n", "3 5.196412 5.196412 0.000000 \n", "4 478.693148 115.631470 363.061678 \n", "5 5.140069 5.140069 0.000000 \n", "6 105.053773 105.053773 0.000000 \n", "7 63.206030 63.206030 0.000000 \n", "8 44.698090 44.698090 0.000000 \n", "9 266.012106 258.012106 8.000000 \n", "\n", " nb_tickets_internet fidelity ... opt_in gender_female gender_male \\\n", "0 0.0 1 ... False 1 0 \n", "1 0.0 2 ... True 0 1 \n", "2 0.0 2 ... True 1 0 \n", "3 0.0 1 ... False 1 0 \n", "4 0.0 4 ... False 1 0 \n", "5 0.0 1 ... False 0 1 \n", "6 5.0 1 ... False 0 0 \n", "7 0.0 1 ... True 0 1 \n", "8 0.0 1 ... True 0 0 \n", "9 3.0 2 ... False 0 0 \n", "\n", " gender_other nb_campaigns nb_campaigns_opened has_purchased \\\n", "0 0 0.0 0.0 0.0 \n", "1 0 0.0 0.0 1.0 \n", "2 0 0.0 0.0 0.0 \n", "3 0 0.0 0.0 0.0 \n", "4 0 0.0 0.0 1.0 \n", "5 0 0.0 0.0 0.0 \n", "6 1 0.0 0.0 0.0 \n", "7 0 0.0 0.0 0.0 \n", "8 1 0.0 0.0 0.0 \n", "9 1 0.0 0.0 0.0 \n", "\n", " has_purchased_estim score quartile \n", "0 1.0 0.657671 3 \n", "1 0.0 0.266538 2 \n", "2 0.0 0.214668 1 \n", "3 1.0 0.657770 3 \n", "4 1.0 0.894173 4 \n", "5 1.0 0.717482 3 \n", "6 1.0 0.541855 3 \n", "7 0.0 0.461164 2 \n", "8 0.0 0.310828 2 \n", "9 0.0 0.452877 2 \n", "\n", "[10 rows x 21 columns]" ] }, "execution_count": 13, "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": "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": 14, "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": 15, "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": 16, "id": "2dff1def-02df-413e-afce-b4aeaf7752b6", "metadata": {}, "outputs": [], "source": [ "def odd_ratio(score) :\n", " return score / (1 - score)" ] }, { "cell_type": "code", "execution_count": 17, "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": 18, "id": "781b0d40-c954-4c54-830a-e709c8667328", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.172331113516847" ] }, "execution_count": 18, "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": 19, "id": "248cb862-418e-4767-9933-70c4885ecf40", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.070461139075353" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# comparison with bias of the train set\n", "X_train_score = logit_cv.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=6)\n", "bias_train_set" ] }, { "cell_type": "code", "execution_count": 20, "id": "fff6cbe6-7bb3-4732-9b81-b9ac5383bbcf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "betâ test - betâ train = 0.016642008368292337\n" ] } ], "source": [ "print(\"betâ test - betâ train = \",np.log(bias_test_set/bias_train_set))" ] }, { "cell_type": "code", "execution_count": 21, "id": "f506870d-4a8a-4b2c-8f0b-e0789080b20c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean absolute erreur 0.001409799678121875\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": 22, "id": "8213d0e4-063b-49fa-90b7-677fc34f4c01", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_519/1825363704.py:7: 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", " X_test_segment[\"score_adjusted\"] = score_adjusted_train\n" ] } ], "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": 23, "id": "834d3723-2e72-4c65-9c62-e2d595c69461", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE for score : 0.15494387585189107\n", "MSE for ajusted score : 0.08851697393139933\n", "sum of y_has_purchased : 13690.0\n", "sum of adjusted scores : 13825.476109871417\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": 25, "id": "9f30a4dd-a9d8-405a-a7d5-5324ae88cf70", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for score : 0.32116357895490416\n", "MAE for adjusted score : 0.17359227315595824\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": 105, "id": "6f9396db-e213-408c-a596-eaeec3bc79f3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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()\n", "\n", "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\", type_of_activity=\"sport\")" ] }, { "cell_type": "code", "execution_count": 101, "id": "b478d40d-9677-4204-87bd-16fb0bc1fe9a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\")" ] }, { "cell_type": "code", "execution_count": 103, "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": 108, "id": "3a5b5bd9-e033-4436-8c56-bf5fb61df87f", "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[108], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhist_score_adjusted\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 10\u001b[0m FILE_PATH_OUT_S3 \u001b[38;5;241m=\u001b[39m PATH \u001b[38;5;241m+\u001b[39m file_name \u001b[38;5;241m+\u001b[39m type_of_activity \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mFILE_PATH_OUT_S3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms3_file\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43ms3_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_buffer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39mclose()\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": [ "# 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": "code", "execution_count": null, "id": "19881d5a-e2cc-45eb-af56-43441b3a16d3", "metadata": {}, "outputs": [], "source": [] }, { "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": 161, "id": "90c4c2b5-0ede-4001-889f-749cfbd9df04", "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", "
quartilescore (%)score adjusted (%)has purchased (%)
0113.252.511.57
1233.898.009.85
2363.0622.5821.47
3490.5266.2065.01
\n", "
" ], "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": 161, "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": 130, "id": "d6a04d3e-c454-43e4-ae4c-0746e928575b", "metadata": {}, "outputs": [ { "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[130], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtable_adjusted_score\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4\u001b[0m FILE_PATH_OUT_S3 \u001b[38;5;241m=\u001b[39m PATH \u001b[38;5;241m+\u001b[39m file_name \u001b[38;5;241m+\u001b[39m type_of_activity \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----> 5\u001b[0m \u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mFILE_PATH_OUT_S3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfile_out\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_test_table_adjusted_scores\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_out\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\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 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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 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\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." ] } ], "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": 78, "id": "a974589f-7952-4db2-bebf-7b69c6b09372", "metadata": {}, "outputs": [], "source": [ "def project_tickets_CA (df, 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", " df_output.loc[:,\"nb_tickets_projected\"] = df_output.loc[:,nb_tickets] / duration_ratio\n", " df_output.loc[:,\"total_amount_projected\"] = df_output.loc[:,total_amount] / duration_ratio\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", " return df_output\n" ] }, { "cell_type": "code", "execution_count": 143, "id": "dd8a52e1-d06e-4790-8687-8e58e3e6b84e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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96096 rows × 26 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 ... \\\n", "0 0.000000 0.0 1 ... \n", "1 0.000000 0.0 2 ... \n", "2 0.000000 0.0 2 ... \n", "3 0.000000 0.0 1 ... \n", "4 363.061678 0.0 4 ... \n", "... ... ... ... ... \n", "96091 0.000000 1.0 2 ... \n", "96092 0.000000 1.0 1 ... \n", "96093 -1.000000 0.0 1 ... \n", "96094 0.000000 1.0 1 ... \n", "96095 -1.000000 0.0 2 ... \n", "\n", " nb_campaigns_opened has_purchased has_purchased_estim score \\\n", "0 0.0 0.0 1.0 0.657671 \n", "1 0.0 1.0 0.0 0.266538 \n", "2 0.0 0.0 0.0 0.214668 \n", "3 0.0 0.0 1.0 0.657770 \n", "4 0.0 1.0 1.0 0.894173 \n", "... ... ... ... ... \n", "96091 5.0 1.0 1.0 0.623551 \n", "96092 9.0 0.0 1.0 0.682521 \n", "96093 3.0 0.0 0.0 0.117192 \n", "96094 4.0 0.0 1.0 0.625185 \n", "96095 4.0 0.0 0.0 0.319585 \n", "\n", " quartile score_adjusted nb_tickets_projected total_amount_projected \\\n", "0 3 0.240397 2.666667 66.666667 \n", "1 2 0.056482 0.666667 36.666667 \n", "2 1 0.043089 11.333333 53.333333 \n", "3 3 0.240478 2.666667 80.000000 \n", "4 4 0.581920 22.666667 277.333333 \n", "... ... ... ... ... \n", "96091 3 0.214369 0.666667 44.873333 \n", "96092 3 0.261526 0.666667 40.940000 \n", "96093 1 0.021400 0.000000 0.000000 \n", "96094 3 0.215545 0.666667 52.953333 \n", "96095 2 0.071817 0.000000 0.000000 \n", "\n", " nb_tickets_expected total_amount_expected \n", "0 0.641059 16.026472 \n", "1 0.037655 2.071006 \n", "2 0.488340 2.298068 \n", "3 0.641273 19.238202 \n", "4 13.190183 161.385771 \n", "... ... ... \n", "96091 0.142913 9.619467 \n", "96092 0.174351 10.706885 \n", "96093 0.000000 0.000000 \n", "96094 0.143697 11.413840 \n", "96095 0.000000 0.000000 \n", "\n", "[96096 rows x 26 columns]" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test_segment = project_tickets_CA (X_test_segment, \"nb_tickets\", \"total_amount\", \"score_adjusted\", duration_ref=1.5, duration_projection=1)\n", "X_test_segment" ] }, { "cell_type": "code", "execution_count": 144, "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,\n", " duration_ref=1.5, duration_projection=1) :\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", " return df_expected_CA" ] }, { "cell_type": "code", "execution_count": 145, "id": "c8df6c80-43e8-4f00-9cd3-eb9022744313", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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quartilesizesize_perctnb_tickets_expectedtotal_amount_expectedrevenue_recovered_perct
013741038.9384.761867.194.38
122951730.722899.2974461.029.85
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" ], "text/plain": [ " quartile size size_perct nb_tickets_expected total_amount_expected \\\n", "0 1 37410 38.93 84.76 1867.19 \n", "1 2 29517 30.72 2899.29 74461.02 \n", "2 3 20137 20.96 10876.79 344286.66 \n", "3 4 9032 9.40 215194.83 9899417.81 \n", "\n", " revenue_recovered_perct \n", "0 4.38 \n", "1 9.85 \n", "2 22.84 \n", "3 90.11 " ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test_expected_CA = round(summary_expected_CA(df=X_test_segment, 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" ] }, { "cell_type": "code", "execution_count": 164, "id": "ac706ed7-defa-4df1-82e1-06f12fc1b6ad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\\\begin{tabular}{lrrrrr}\\n\\\\toprule\\nquartile & size & size (%) & nb tickets expected & total amount expected & revenue recovered (%) \\\\\\\\\\n\\\\midrule\\n1 & 37410 & 38.930000 & 84.760000 & 1867.190000 & 4.380000 \\\\\\\\\\n2 & 29517 & 30.720000 & 2899.290000 & 74461.020000 & 9.850000 \\\\\\\\\\n3 & 20137 & 20.960000 & 10876.790000 & 344286.660000 & 22.840000 \\\\\\\\\\n4 & 9032 & 9.400000 & 215194.830000 & 9899417.810000 & 90.110000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" ] }, "execution_count": 164, "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": null, "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": "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
013741038.93419.769245.0821.71
122951730.7211549.06296522.0239.24
232013720.9629997.85954751.9163.34
3490329.40244655.8210736011.9597.72
\n", "
" ], "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 }