debug

Descriptive_statistics/debug.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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', '6'],\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b967f70a-e3ae-423e-9fb0-edfc00ddf826",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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",
+    "# Identify customer who bought during the period of y\n",
+    "consumer_target_period = identify_purchase_during_target_periode(products)\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['customer_id'] = dataset['customer_id'].isin(customer_valid_list) # keep only valid customer\n",
+    "    dataset['has_purchased_target_period'] = np.where(dataset['customer_id'].isin(customer_valid_list), 1, 0)"
+   ]
+  }
+ ],
+ "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
+}

Descriptive_statistics/generate_stat_desc.py

@@ -50,6 +50,8 @@ 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)

Descriptive_statistics/plot.py

@@ -70,7 +70,7 @@ def outlier_detection(tickets, company_list, show_diagram=False):
         
         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())
+        #print('df circulaire : ', df_circulaire.head())
         top = df_circulaire[:1]
         print('top : ', top)
         outlier_list.append(top.index[0])
@@ -101,7 +101,7 @@ def valid_customer_detection(products, campaigns_brut):
 
 
 def identify_purchase_during_target_periode(products):
-    products_target_period = products[products['purchase_date']>="2022-11-01" & products['purchase_date']<="2023-11-01"]
+    products_target_period = products[(products['purchase_date']>="2022-11-01") & (products['purchase_date']<="2023-11-01")]
     consumer_target_period = products_target_period['customer_id'].to_list()
     return consumer_target_period
 
@@ -140,13 +140,46 @@ def mailing_consent(customer, type_of_activity):
     plt.bar(mailing_consent["number_company"], mailing_consent["opt_in"])
 
     plt.xlabel('Company')
-    plt.ylabel('Consent of mailing (%)')
+    plt.ylabel('Company')
     plt.title(f'Consent of mailing for {type_of_activity}')
 
     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('Company')
+    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()