3659 lines
181 KiB
Plaintext
3659 lines
181 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "markdown",
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||
"id": "84b6e27e-4bda-4d38-8689-ec7fc0da1848",
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"metadata": {},
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"source": [
|
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"# Define segment and predict sales associated"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec059482-45d3-4ae6-99bc-9b4ced115db3",
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||
"metadata": {},
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"source": [
|
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"## Importations of packages "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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||
"id": "9771bf29-d08e-4674-8c23-9a2672fbef8f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from pandas import DataFrame\n",
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"import numpy as np\n",
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"import os\n",
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"import s3fs\n",
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"import re\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n",
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"from sklearn.utils import class_weight\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n",
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"from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
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"from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from scipy.optimize import fsolve\n",
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"import io\n",
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"\n",
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"import pickle\n",
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"import warnings"
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]
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},
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{
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||
"cell_type": "markdown",
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||
"id": "048fcd7c-800a-4a6b-b725-faf8410f924a",
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"metadata": {},
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"source": [
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"## load databases"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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||
"id": "539ccbdf-f29f-4f04-99c1-8c88d0efe514",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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"# Create filesystem object\n",
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"S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
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"fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 270,
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||
"id": "d6017ed0-6233-4888-85a7-05dec50a255b",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"type_of_activity = \"musee\""
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 73,
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||
"id": "0c3a6ddc-9345-4a42-b6bf-a20a95de3028",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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"def load_train_test(type_of_activity):\n",
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" BUCKET = f\"projet-bdc2324-team1/Generalization/{type_of_activity}\"\n",
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" File_path_train = BUCKET + \"/Train_set.csv\"\n",
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" File_path_test = BUCKET + \"/Test_set.csv\"\n",
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" \n",
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" with fs.open( File_path_train, mode=\"rb\") as file_in:\n",
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" dataset_train = pd.read_csv(file_in, sep=\",\")\n",
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" # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n",
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"\n",
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" with fs.open(File_path_test, mode=\"rb\") as file_in:\n",
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" dataset_test = pd.read_csv(file_in, sep=\",\")\n",
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" # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n",
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" \n",
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" return dataset_train, dataset_test"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 271,
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||
"id": "2831d546-b365-498b-8248-c618bd9c3057",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"name": "stderr",
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||
"output_type": "stream",
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||
"text": [
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"/tmp/ipykernel_1080/2350085345.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" dataset_train = pd.read_csv(file_in, sep=\",\")\n"
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||
]
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||
},
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{
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"data": {
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"text/plain": [
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"customer_id 0\n",
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"nb_tickets 0\n",
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"nb_purchases 0\n",
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"total_amount 0\n",
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"nb_suppliers 0\n",
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"vente_internet_max 0\n",
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"purchase_date_min 0\n",
|
||
"purchase_date_max 0\n",
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||
"time_between_purchase 0\n",
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||
"nb_tickets_internet 0\n",
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||
"street_id 0\n",
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||
"structure_id 389658\n",
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||
"mcp_contact_id 150354\n",
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||
"fidelity 0\n",
|
||
"tenant_id 0\n",
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||
"is_partner 0\n",
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||
"deleted_at 434278\n",
|
||
"gender 0\n",
|
||
"is_email_true 0\n",
|
||
"opt_in 0\n",
|
||
"last_buying_date 183987\n",
|
||
"max_price 183987\n",
|
||
"ticket_sum 0\n",
|
||
"average_price 94783\n",
|
||
"average_purchase_delay 183987\n",
|
||
"average_price_basket 183987\n",
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||
"average_ticket_basket 183987\n",
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||
"total_price 89204\n",
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||
"purchase_count 0\n",
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||
"first_buying_date 183987\n",
|
||
"country 141237\n",
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||
"gender_label 0\n",
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||
"gender_female 0\n",
|
||
"gender_male 0\n",
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||
"gender_other 0\n",
|
||
"country_fr 141237\n",
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||
"nb_campaigns 0\n",
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||
"nb_campaigns_opened 0\n",
|
||
"time_to_open 258182\n",
|
||
"y_has_purchased 0\n",
|
||
"dtype: int64"
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||
]
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||
},
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||
"execution_count": 271,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
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||
"dataset_train, dataset_test = load_train_test(type_of_activity)\n",
|
||
"dataset_train.isna().sum()"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"id": "b8827f7b-b304-4f51-9814-c7a98ed88cf0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def features_target_split(dataset_train, dataset_test):\n",
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||
" \n",
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||
" features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n",
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||
" '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",
|
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" 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",
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" \n",
|
||
" X_train = dataset_train[features_l]\n",
|
||
" y_train = dataset_train[['y_has_purchased']]\n",
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||
"\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": 272,
|
||
"id": "c18195fc-ed40-4e39-a59e-c9ecc5a8e6c3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Shape train : (434278, 17)\n",
|
||
"Shape test : (186120, 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": 79,
|
||
"id": "7c81390e-598c-4f02-bd56-dd03b00dcb33",
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||
"metadata": {},
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||
"outputs": [
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||
{
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>nb_tickets</th>\n",
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" <th>nb_purchases</th>\n",
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" <th>total_amount</th>\n",
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||
" <th>nb_suppliers</th>\n",
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||
" <th>vente_internet_max</th>\n",
|
||
" <th>purchase_date_min</th>\n",
|
||
" <th>purchase_date_max</th>\n",
|
||
" <th>time_between_purchase</th>\n",
|
||
" <th>nb_tickets_internet</th>\n",
|
||
" <th>fidelity</th>\n",
|
||
" <th>is_email_true</th>\n",
|
||
" <th>opt_in</th>\n",
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||
" <th>gender_female</th>\n",
|
||
" <th>gender_male</th>\n",
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||
" <th>gender_other</th>\n",
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" <th>nb_campaigns</th>\n",
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" <th>nb_campaigns_opened</th>\n",
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|
||
" <tr>\n",
|
||
" <th>96093</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>29.0</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96094</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>79.43</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>279.312905</td>\n",
|
||
" <td>279.312905</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>20.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96095</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>31.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>96096 rows × 17 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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": 79,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_test"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 80,
|
||
"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": 286,
|
||
"id": "5261a803-05b8-41a0-968c-dc7bde48ddd3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<style>#sk-container-id-12 {\n",
|
||
" /* Definition of color scheme common for light and dark mode */\n",
|
||
" --sklearn-color-text: black;\n",
|
||
" --sklearn-color-line: gray;\n",
|
||
" /* Definition of color scheme for unfitted estimators */\n",
|
||
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
|
||
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
|
||
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
|
||
" --sklearn-color-unfitted-level-3: chocolate;\n",
|
||
" /* Definition of color scheme for fitted estimators */\n",
|
||
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
|
||
" --sklearn-color-fitted-level-1: #d4ebff;\n",
|
||
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
|
||
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
|
||
"\n",
|
||
" /* Specific color for light theme */\n",
|
||
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
|
||
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
|
||
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
|
||
" --sklearn-color-icon: #696969;\n",
|
||
"\n",
|
||
" @media (prefers-color-scheme: dark) {\n",
|
||
" /* Redefinition of color scheme for dark theme */\n",
|
||
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
|
||
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
|
||
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
|
||
" --sklearn-color-icon: #878787;\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 pre {\n",
|
||
" padding: 0;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 input.sk-hidden--visually {\n",
|
||
" border: 0;\n",
|
||
" clip: rect(1px 1px 1px 1px);\n",
|
||
" clip: rect(1px, 1px, 1px, 1px);\n",
|
||
" height: 1px;\n",
|
||
" margin: -1px;\n",
|
||
" overflow: hidden;\n",
|
||
" padding: 0;\n",
|
||
" position: absolute;\n",
|
||
" width: 1px;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-dashed-wrapped {\n",
|
||
" border: 1px dashed var(--sklearn-color-line);\n",
|
||
" margin: 0 0.4em 0.5em 0.4em;\n",
|
||
" box-sizing: border-box;\n",
|
||
" padding-bottom: 0.4em;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-container {\n",
|
||
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
|
||
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
|
||
" so we also need the `!important` here to be able to override the\n",
|
||
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
|
||
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
|
||
" display: inline-block !important;\n",
|
||
" position: relative;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-text-repr-fallback {\n",
|
||
" display: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"div.sk-parallel-item,\n",
|
||
"div.sk-serial,\n",
|
||
"div.sk-item {\n",
|
||
" /* draw centered vertical line to link estimators */\n",
|
||
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
|
||
" background-size: 2px 100%;\n",
|
||
" background-repeat: no-repeat;\n",
|
||
" background-position: center center;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Parallel-specific style estimator block */\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-parallel-item::after {\n",
|
||
" content: \"\";\n",
|
||
" width: 100%;\n",
|
||
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
|
||
" flex-grow: 1;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-parallel {\n",
|
||
" display: flex;\n",
|
||
" align-items: stretch;\n",
|
||
" justify-content: center;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" position: relative;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-parallel-item {\n",
|
||
" display: flex;\n",
|
||
" flex-direction: column;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
|
||
" align-self: flex-end;\n",
|
||
" width: 50%;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
|
||
" align-self: flex-start;\n",
|
||
" width: 50%;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
|
||
" width: 0;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Serial-specific style estimator block */\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-serial {\n",
|
||
" display: flex;\n",
|
||
" flex-direction: column;\n",
|
||
" align-items: center;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" padding-right: 1em;\n",
|
||
" padding-left: 1em;\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
|
||
"clickable and can be expanded/collapsed.\n",
|
||
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
|
||
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
|
||
"*/\n",
|
||
"\n",
|
||
"/* Pipeline and ColumnTransformer style (default) */\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-toggleable {\n",
|
||
" /* Default theme specific background. It is overwritten whether we have a\n",
|
||
" specific estimator or a Pipeline/ColumnTransformer */\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Toggleable label */\n",
|
||
"#sk-container-id-12 label.sk-toggleable__label {\n",
|
||
" cursor: pointer;\n",
|
||
" display: block;\n",
|
||
" width: 100%;\n",
|
||
" margin-bottom: 0;\n",
|
||
" padding: 0.5em;\n",
|
||
" box-sizing: border-box;\n",
|
||
" text-align: center;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
|
||
" /* Arrow on the left of the label */\n",
|
||
" content: \"▸\";\n",
|
||
" float: left;\n",
|
||
" margin-right: 0.25em;\n",
|
||
" color: var(--sklearn-color-icon);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Toggleable content - dropdown */\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-toggleable__content {\n",
|
||
" max-height: 0;\n",
|
||
" max-width: 0;\n",
|
||
" overflow: hidden;\n",
|
||
" text-align: left;\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-toggleable__content pre {\n",
|
||
" margin: 0.2em;\n",
|
||
" border-radius: 0.25em;\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
|
||
" /* Expand drop-down */\n",
|
||
" max-height: 200px;\n",
|
||
" max-width: 100%;\n",
|
||
" overflow: auto;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
|
||
" content: \"▾\";\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Pipeline/ColumnTransformer-specific style */\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Estimator-specific style */\n",
|
||
"\n",
|
||
"/* Colorize estimator box */\n",
|
||
"#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
|
||
"#sk-container-id-12 div.sk-label label {\n",
|
||
" /* The background is the default theme color */\n",
|
||
" color: var(--sklearn-color-text-on-default-background);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* On hover, darken the color of the background */\n",
|
||
"#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Label box, darken color on hover, fitted */\n",
|
||
"#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Estimator label */\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-label label {\n",
|
||
" font-family: monospace;\n",
|
||
" font-weight: bold;\n",
|
||
" display: inline-block;\n",
|
||
" line-height: 1.2em;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-label-container {\n",
|
||
" text-align: center;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Estimator-specific */\n",
|
||
"#sk-container-id-12 div.sk-estimator {\n",
|
||
" font-family: monospace;\n",
|
||
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
||
" border-radius: 0.25em;\n",
|
||
" box-sizing: border-box;\n",
|
||
" margin-bottom: 0.5em;\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-estimator.fitted {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* on hover */\n",
|
||
"#sk-container-id-12 div.sk-estimator:hover {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
|
||
"\n",
|
||
"/* Common style for \"i\" and \"?\" */\n",
|
||
"\n",
|
||
".sk-estimator-doc-link,\n",
|
||
"a:link.sk-estimator-doc-link,\n",
|
||
"a:visited.sk-estimator-doc-link {\n",
|
||
" float: right;\n",
|
||
" font-size: smaller;\n",
|
||
" line-height: 1em;\n",
|
||
" font-family: monospace;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" border-radius: 1em;\n",
|
||
" height: 1em;\n",
|
||
" width: 1em;\n",
|
||
" text-decoration: none !important;\n",
|
||
" margin-left: 1ex;\n",
|
||
" /* unfitted */\n",
|
||
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
||
" color: var(--sklearn-color-unfitted-level-1);\n",
|
||
"}\n",
|
||
"\n",
|
||
".sk-estimator-doc-link.fitted,\n",
|
||
"a:link.sk-estimator-doc-link.fitted,\n",
|
||
"a:visited.sk-estimator-doc-link.fitted {\n",
|
||
" /* fitted */\n",
|
||
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
||
" color: var(--sklearn-color-fitted-level-1);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* On hover */\n",
|
||
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
|
||
".sk-estimator-doc-link:hover,\n",
|
||
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
|
||
".sk-estimator-doc-link:hover {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
||
" color: var(--sklearn-color-background);\n",
|
||
" text-decoration: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
|
||
".sk-estimator-doc-link.fitted:hover,\n",
|
||
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
|
||
".sk-estimator-doc-link.fitted:hover {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
||
" color: var(--sklearn-color-background);\n",
|
||
" text-decoration: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Span, style for the box shown on hovering the info icon */\n",
|
||
".sk-estimator-doc-link span {\n",
|
||
" display: none;\n",
|
||
" z-index: 9999;\n",
|
||
" position: relative;\n",
|
||
" font-weight: normal;\n",
|
||
" right: .2ex;\n",
|
||
" padding: .5ex;\n",
|
||
" margin: .5ex;\n",
|
||
" width: min-content;\n",
|
||
" min-width: 20ex;\n",
|
||
" max-width: 50ex;\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" box-shadow: 2pt 2pt 4pt #999;\n",
|
||
" /* unfitted */\n",
|
||
" background: var(--sklearn-color-unfitted-level-0);\n",
|
||
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
|
||
"}\n",
|
||
"\n",
|
||
".sk-estimator-doc-link.fitted span {\n",
|
||
" /* fitted */\n",
|
||
" background: var(--sklearn-color-fitted-level-0);\n",
|
||
" border: var(--sklearn-color-fitted-level-3);\n",
|
||
"}\n",
|
||
"\n",
|
||
".sk-estimator-doc-link:hover span {\n",
|
||
" display: block;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
|
||
"\n",
|
||
"#sk-container-id-12 a.estimator_doc_link {\n",
|
||
" float: right;\n",
|
||
" font-size: 1rem;\n",
|
||
" line-height: 1em;\n",
|
||
" font-family: monospace;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" border-radius: 1rem;\n",
|
||
" height: 1rem;\n",
|
||
" width: 1rem;\n",
|
||
" text-decoration: none;\n",
|
||
" /* unfitted */\n",
|
||
" color: var(--sklearn-color-unfitted-level-1);\n",
|
||
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 a.estimator_doc_link.fitted {\n",
|
||
" /* fitted */\n",
|
||
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
||
" color: var(--sklearn-color-fitted-level-1);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* On hover */\n",
|
||
"#sk-container-id-12 a.estimator_doc_link:hover {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
||
" color: var(--sklearn-color-background);\n",
|
||
" text-decoration: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
||
"}\n",
|
||
"</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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: 0.5223906809346011,\n",
|
||
" 1.0: 11.665359406898034}],\n",
|
||
" 'LogisticRegression_cv__penalty': ['l1', 'l2']},\n",
|
||
" scoring=make_scorer(recall_score, response_method='predict'))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>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: 0.5223906809346011,\n",
|
||
" 1.0: 11.665359406898034}],\n",
|
||
" 'LogisticRegression_cv__penalty': ['l1', 'l2']},\n",
|
||
" scoring=make_scorer(recall_score, response_method='predict'))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>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",
|
||
" 'nb_campaigns',\n",
|
||
" 'nb_campaigns_opened']),\n",
|
||
" ('cat',\n",
|
||
" Pipeline(steps=[('onehot',\n",
|
||
" OneHotEncoder(handle_unknown='ignore',\n",
|
||
" sparse_output=False))]),\n",
|
||
" ['opt_in', 'gender_male',\n",
|
||
" 'gender_female'])])),\n",
|
||
" ('LogisticRegression_cv',\n",
|
||
" LogisticRegression(max_iter=5000, solver='saga'))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>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",
|
||
" 'time_between_purchase',\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', 'gender_male', 'gender_female'])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'nb_campaigns', 'nb_campaigns_opened']</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>['opt_in', 'gender_male', 'gender_female']</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown='ignore', sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=5000, solver='saga')</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
|
||
],
|
||
"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: 0.5223906809346011,\n",
|
||
" 1.0: 11.665359406898034}],\n",
|
||
" 'LogisticRegression_cv__penalty': ['l1', 'l2']},\n",
|
||
" scoring=make_scorer(recall_score, response_method='predict'))"
|
||
]
|
||
},
|
||
"execution_count": 286,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"model = load_model(type_of_activity, \"LogisticRegression_cv\")\n",
|
||
"# model = load_model(type_of_activity, \"randomF_cv\")\n",
|
||
"model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "006819e7-e9c5-48d9-85ee-aa43d5e4c9c2",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Quartile clustering"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 287,
|
||
"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": 288,
|
||
"id": "846f53b9-73c2-4a8b-9d9e-f11bf59ce9ba",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_1080/375041546.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_1080/375041546.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_1080/375041546.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_1080/375041546.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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>nb_tickets</th>\n",
|
||
" <th>nb_purchases</th>\n",
|
||
" <th>total_amount</th>\n",
|
||
" <th>nb_suppliers</th>\n",
|
||
" <th>vente_internet_max</th>\n",
|
||
" <th>purchase_date_min</th>\n",
|
||
" <th>purchase_date_max</th>\n",
|
||
" <th>time_between_purchase</th>\n",
|
||
" <th>nb_tickets_internet</th>\n",
|
||
" <th>fidelity</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>gender_female</th>\n",
|
||
" <th>gender_male</th>\n",
|
||
" <th>gender_other</th>\n",
|
||
" <th>nb_campaigns</th>\n",
|
||
" <th>nb_campaigns_opened</th>\n",
|
||
" <th>has_purchased</th>\n",
|
||
" <th>has_purchased_estim</th>\n",
|
||
" <th>score</th>\n",
|
||
" <th>quartile</th>\n",
|
||
" <th>score_adjusted</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>1.0</td>\n",
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||
" <td>1.0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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|
||
" <td>0.367961</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.010594</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>269.0</td>\n",
|
||
" <td>8.0</td>\n",
|
||
" <td>50.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>378.208090</td>\n",
|
||
" <td>39.389595</td>\n",
|
||
" <td>338.818495</td>\n",
|
||
" <td>66.0</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>65.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.998731</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.397108</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.211997</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.014916</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.246563</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.024670</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.108575</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.025205</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.257244</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.046644</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.203196</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.023026</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>8.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.240049</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.003825</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>11.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>456.255104</td>\n",
|
||
" <td>456.255104</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.340098</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.006850</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.234470</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.003745</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>10 rows × 22 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" nb_tickets nb_purchases total_amount nb_suppliers vente_internet_max \\\n",
|
||
"0 2.0 1.0 22.0 1.0 1.0 \n",
|
||
"1 269.0 8.0 50.0 2.0 1.0 \n",
|
||
"2 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"3 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"4 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"5 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"6 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"7 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"8 1.0 1.0 11.0 1.0 1.0 \n",
|
||
"9 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" purchase_date_min purchase_date_max time_between_purchase \\\n",
|
||
"0 307.203553 307.203553 0.000000 \n",
|
||
"1 378.208090 39.389595 338.818495 \n",
|
||
"2 550.000000 550.000000 -1.000000 \n",
|
||
"3 550.000000 550.000000 -1.000000 \n",
|
||
"4 550.000000 550.000000 -1.000000 \n",
|
||
"5 550.000000 550.000000 -1.000000 \n",
|
||
"6 550.000000 550.000000 -1.000000 \n",
|
||
"7 550.000000 550.000000 -1.000000 \n",
|
||
"8 456.255104 456.255104 0.000000 \n",
|
||
"9 550.000000 550.000000 -1.000000 \n",
|
||
"\n",
|
||
" nb_tickets_internet fidelity ... gender_female gender_male \\\n",
|
||
"0 2.0 1 ... 0 0 \n",
|
||
"1 66.0 10 ... 0 0 \n",
|
||
"2 0.0 0 ... 0 1 \n",
|
||
"3 0.0 0 ... 1 0 \n",
|
||
"4 0.0 0 ... 0 0 \n",
|
||
"5 0.0 0 ... 1 0 \n",
|
||
"6 0.0 1 ... 0 1 \n",
|
||
"7 0.0 0 ... 0 1 \n",
|
||
"8 1.0 1 ... 0 0 \n",
|
||
"9 0.0 0 ... 0 1 \n",
|
||
"\n",
|
||
" gender_other nb_campaigns nb_campaigns_opened has_purchased \\\n",
|
||
"0 1 0.0 0.0 0.0 \n",
|
||
"1 1 65.0 1.0 1.0 \n",
|
||
"2 0 4.0 2.0 0.0 \n",
|
||
"3 0 2.0 0.0 0.0 \n",
|
||
"4 1 4.0 0.0 0.0 \n",
|
||
"5 0 7.0 0.0 0.0 \n",
|
||
"6 0 2.0 0.0 0.0 \n",
|
||
"7 0 10.0 8.0 0.0 \n",
|
||
"8 1 3.0 3.0 0.0 \n",
|
||
"9 0 10.0 6.0 0.0 \n",
|
||
"\n",
|
||
" has_purchased_estim score quartile score_adjusted \n",
|
||
"0 0.0 0.367961 2 0.010594 \n",
|
||
"1 1.0 0.998731 4 0.397108 \n",
|
||
"2 0.0 0.211997 1 0.014916 \n",
|
||
"3 0.0 0.246563 1 0.024670 \n",
|
||
"4 0.0 0.108575 1 0.025205 \n",
|
||
"5 0.0 0.257244 2 0.046644 \n",
|
||
"6 0.0 0.203196 1 0.023026 \n",
|
||
"7 0.0 0.240049 1 0.003825 \n",
|
||
"8 0.0 0.340098 2 0.006850 \n",
|
||
"9 0.0 0.234470 1 0.003745 \n",
|
||
"\n",
|
||
"[10 rows x 22 columns]"
|
||
]
|
||
},
|
||
"execution_count": 288,
|
||
"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": 86,
|
||
"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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|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>nb_tickets</th>\n",
|
||
" <th>nb_purchases</th>\n",
|
||
" <th>total_amount</th>\n",
|
||
" <th>nb_suppliers</th>\n",
|
||
" <th>vente_internet_max</th>\n",
|
||
" <th>purchase_date_min</th>\n",
|
||
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|
||
" <th>time_between_purchase</th>\n",
|
||
" <th>nb_tickets_internet</th>\n",
|
||
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|
||
" <th>...</th>\n",
|
||
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|
||
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|
||
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|
||
" <th>gender_other</th>\n",
|
||
" <th>nb_campaigns</th>\n",
|
||
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|
||
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|
||
" <th>has_purchased_estim</th>\n",
|
||
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|
||
" <th>quartile</th>\n",
|
||
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|
||
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|
||
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|
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|
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||
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||
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||
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||
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|
||
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|
||
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|
||
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||
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|
||
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||
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||
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|
||
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||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
" <tr>\n",
|
||
" <th>96094</th>\n",
|
||
" <td>1.0</td>\n",
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||
" <td>1.0</td>\n",
|
||
" <td>79.43</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
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|
||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
"</table>\n",
|
||
"<p>96096 rows × 21 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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": 89,
|
||
"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": 90,
|
||
"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": 91,
|
||
"id": "2dff1def-02df-413e-afce-b4aeaf7752b6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def odd_ratio(score) :\n",
|
||
" return score / (1 - score)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 92,
|
||
"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": 289,
|
||
"id": "f17dc6ca-7a48-441b-8c04-11c47b8b9741",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.3940650533525649 0.04284869976359338\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.04286194557403322"
|
||
]
|
||
},
|
||
"execution_count": 289,
|
||
"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": 290,
|
||
"id": "781b0d40-c954-4c54-830a-e709c8667328",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"22.577005337484817"
|
||
]
|
||
},
|
||
"execution_count": 290,
|
||
"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": 291,
|
||
"id": "248cb862-418e-4767-9933-70c4885ecf40",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"22.690061493186622"
|
||
]
|
||
},
|
||
"execution_count": 291,
|
||
"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=6)\n",
|
||
"bias_train_set"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 292,
|
||
"id": "fff6cbe6-7bb3-4732-9b81-b9ac5383bbcf",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"betâ test - betâ train = -0.0049950835646278635\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"betâ test - betâ train = \",np.log(bias_test_set/bias_train_set))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 293,
|
||
"id": "f506870d-4a8a-4b2c-8f0b-e0789080b20c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"mean absolute erreur 0.00017894295558797563\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": 294,
|
||
"id": "8213d0e4-063b-49fa-90b7-677fc34f4c01",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_1080/1825363704.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": 295,
|
||
"id": "834d3723-2e72-4c65-9c62-e2d595c69461",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MSE for score : 0.18391062438077188\n",
|
||
"MSE for ajusted score : 0.037093800862222845\n",
|
||
"sum of y_has_purchased : 7975.0\n",
|
||
"sum of adjusted scores : 7941.695137104767\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": 296,
|
||
"id": "9f30a4dd-a9d8-405a-a7d5-5324ae88cf70",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAE for score : 0.38422988971624206\n",
|
||
"MAE for adjusted score : 0.07284616452278603\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": 103,
|
||
"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": 297,
|
||
"id": "b478d40d-9677-4204-87bd-16fb0bc1fe9a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"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": 298,
|
||
"id": "90c4c2b5-0ede-4001-889f-749cfbd9df04",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>quartile</th>\n",
|
||
" <th>score (%)</th>\n",
|
||
" <th>score adjusted (%)</th>\n",
|
||
" <th>has purchased (%)</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>17.78</td>\n",
|
||
" <td>0.96</td>\n",
|
||
" <td>0.67</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>36.12</td>\n",
|
||
" <td>2.49</td>\n",
|
||
" <td>2.83</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>63.14</td>\n",
|
||
" <td>7.29</td>\n",
|
||
" <td>7.04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>86.03</td>\n",
|
||
" <td>29.21</td>\n",
|
||
" <td>29.20</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" quartile score (%) score adjusted (%) has purchased (%)\n",
|
||
"0 1 17.78 0.96 0.67\n",
|
||
"1 2 36.12 2.49 2.83\n",
|
||
"2 3 63.14 7.29 7.04\n",
|
||
"3 4 86.03 29.21 29.20"
|
||
]
|
||
},
|
||
"execution_count": 298,
|
||
"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": 106,
|
||
"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",
|
||
" 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",
|
||
" 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": 107,
|
||
"id": "dd8a52e1-d06e-4790-8687-8e58e3e6b84e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_1080/3982240549.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",
|
||
" df_output.loc[:,\"nb_tickets_projected\"] = df_output.loc[:,nb_tickets] / duration_ratio\n",
|
||
"/tmp/ipykernel_1080/3982240549.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_output.loc[:,\"total_amount_projected\"] = df_output.loc[:,total_amount] / duration_ratio\n",
|
||
"/tmp/ipykernel_1080/3982240549.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_output.loc[:,\"nb_tickets_expected\"] = df_output.loc[:,score_adjusted] * df_output.loc[:,\"nb_tickets_projected\"]\n",
|
||
"/tmp/ipykernel_1080/3982240549.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_output.loc[:,\"total_amount_expected\"] = df_output.loc[:,score_adjusted] * df_output.loc[:,\"total_amount_projected\"]\n",
|
||
"/tmp/ipykernel_1080/3982240549.py:13: 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_output.loc[:,\"pace_purchase\"] = (duration_ref/df_output.loc[:,nb_purchases]).apply(lambda x : np.nan if x==np.inf else x)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>nb_tickets</th>\n",
|
||
" <th>nb_purchases</th>\n",
|
||
" <th>total_amount</th>\n",
|
||
" <th>nb_suppliers</th>\n",
|
||
" <th>vente_internet_max</th>\n",
|
||
" <th>purchase_date_min</th>\n",
|
||
" <th>purchase_date_max</th>\n",
|
||
" <th>time_between_purchase</th>\n",
|
||
" <th>nb_tickets_internet</th>\n",
|
||
" <th>fidelity</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>has_purchased</th>\n",
|
||
" <th>has_purchased_estim</th>\n",
|
||
" <th>score</th>\n",
|
||
" <th>quartile</th>\n",
|
||
" <th>score_adjusted</th>\n",
|
||
" <th>nb_tickets_projected</th>\n",
|
||
" <th>total_amount_projected</th>\n",
|
||
" <th>nb_tickets_expected</th>\n",
|
||
" <th>total_amount_expected</th>\n",
|
||
" <th>pace_purchase</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>5.177187</td>\n",
|
||
" <td>5.177187</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.006066</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.001713</td>\n",
|
||
" <td>2.823529</td>\n",
|
||
" <td>70.588235</td>\n",
|
||
" <td>0.004836</td>\n",
|
||
" <td>0.120890</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>55.00</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>426.265613</td>\n",
|
||
" <td>426.265613</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.288847</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.102477</td>\n",
|
||
" <td>0.705882</td>\n",
|
||
" <td>38.823529</td>\n",
|
||
" <td>0.072337</td>\n",
|
||
" <td>3.978520</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>17.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>80.00</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>436.033437</td>\n",
|
||
" <td>436.033437</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.103264</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.031356</td>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>56.470588</td>\n",
|
||
" <td>0.376274</td>\n",
|
||
" <td>1.770701</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>120.00</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>5.196412</td>\n",
|
||
" <td>5.196412</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.008928</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.002526</td>\n",
|
||
" <td>2.823529</td>\n",
|
||
" <td>84.705882</td>\n",
|
||
" <td>0.007132</td>\n",
|
||
" <td>0.213968</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>34.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>416.00</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>478.693148</td>\n",
|
||
" <td>115.631470</td>\n",
|
||
" <td>363.061678</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.992809</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.974880</td>\n",
|
||
" <td>24.000000</td>\n",
|
||
" <td>293.647059</td>\n",
|
||
" <td>23.397112</td>\n",
|
||
" <td>286.270541</td>\n",
|
||
" <td>8.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96091</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>67.31</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>278.442257</td>\n",
|
||
" <td>278.442257</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.351762</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.132353</td>\n",
|
||
" <td>0.705882</td>\n",
|
||
" <td>47.512941</td>\n",
|
||
" <td>0.093426</td>\n",
|
||
" <td>6.288478</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96092</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>61.41</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>189.207373</td>\n",
|
||
" <td>189.207373</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.567814</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.269714</td>\n",
|
||
" <td>0.705882</td>\n",
|
||
" <td>43.348235</td>\n",
|
||
" <td>0.190387</td>\n",
|
||
" <td>11.691645</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96093</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.004652</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.001312</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96094</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>79.43</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>279.312905</td>\n",
|
||
" <td>279.312905</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.293042</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.104362</td>\n",
|
||
" <td>0.705882</td>\n",
|
||
" <td>56.068235</td>\n",
|
||
" <td>0.073668</td>\n",
|
||
" <td>5.851420</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96095</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>550.000000</td>\n",
|
||
" <td>-1.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.787852</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.510753</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>96096 rows × 27 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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",
|
||
" has_purchased has_purchased_estim score quartile score_adjusted \\\n",
|
||
"0 0.0 0.0 0.006066 1 0.001713 \n",
|
||
"1 1.0 0.0 0.288847 2 0.102477 \n",
|
||
"2 0.0 0.0 0.103264 1 0.031356 \n",
|
||
"3 0.0 0.0 0.008928 1 0.002526 \n",
|
||
"4 1.0 1.0 0.992809 4 0.974880 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"96091 1.0 0.0 0.351762 2 0.132353 \n",
|
||
"96092 0.0 1.0 0.567814 3 0.269714 \n",
|
||
"96093 0.0 0.0 0.004652 1 0.001312 \n",
|
||
"96094 0.0 0.0 0.293042 2 0.104362 \n",
|
||
"96095 0.0 1.0 0.787852 4 0.510753 \n",
|
||
"\n",
|
||
" nb_tickets_projected total_amount_projected nb_tickets_expected \\\n",
|
||
"0 2.823529 70.588235 0.004836 \n",
|
||
"1 0.705882 38.823529 0.072337 \n",
|
||
"2 12.000000 56.470588 0.376274 \n",
|
||
"3 2.823529 84.705882 0.007132 \n",
|
||
"4 24.000000 293.647059 23.397112 \n",
|
||
"... ... ... ... \n",
|
||
"96091 0.705882 47.512941 0.093426 \n",
|
||
"96092 0.705882 43.348235 0.190387 \n",
|
||
"96093 0.000000 0.000000 0.000000 \n",
|
||
"96094 0.705882 56.068235 0.073668 \n",
|
||
"96095 0.000000 0.000000 0.000000 \n",
|
||
"\n",
|
||
" total_amount_expected pace_purchase \n",
|
||
"0 0.120890 17.0 \n",
|
||
"1 3.978520 17.0 \n",
|
||
"2 1.770701 17.0 \n",
|
||
"3 0.213968 17.0 \n",
|
||
"4 286.270541 8.5 \n",
|
||
"... ... ... \n",
|
||
"96091 6.288478 17.0 \n",
|
||
"96092 11.691645 17.0 \n",
|
||
"96093 0.000000 NaN \n",
|
||
"96094 5.851420 17.0 \n",
|
||
"96095 0.000000 NaN \n",
|
||
"\n",
|
||
"[96096 rows x 27 columns]"
|
||
]
|
||
},
|
||
"execution_count": 107,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"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": 108,
|
||
"id": "cb66a8ea-65f7-460f-b3fc-ba76a3b91faa",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"quartile\n",
|
||
"1 15.578346\n",
|
||
"2 15.403993\n",
|
||
"3 12.415869\n",
|
||
"4 5.983541\n",
|
||
"Name: pace_purchase, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 108,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_test_segment.groupby(\"quartile\")[\"pace_purchase\"].mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 109,
|
||
"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_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": 110,
|
||
"id": "c8df6c80-43e8-4f00-9cd3-eb9022744313",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>quartile</th>\n",
|
||
" <th>size</th>\n",
|
||
" <th>size_perct</th>\n",
|
||
" <th>nb_tickets_expected</th>\n",
|
||
" <th>total_amount_expected</th>\n",
|
||
" <th>revenue_recovered_perct</th>\n",
|
||
" <th>pace_purchase</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>53565</td>\n",
|
||
" <td>55.74</td>\n",
|
||
" <td>1067.91</td>\n",
|
||
" <td>27626.39</td>\n",
|
||
" <td>3.42</td>\n",
|
||
" <td>15.58</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>24607</td>\n",
|
||
" <td>25.61</td>\n",
|
||
" <td>4748.18</td>\n",
|
||
" <td>169941.72</td>\n",
|
||
" <td>15.27</td>\n",
|
||
" <td>15.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>9716</td>\n",
|
||
" <td>10.11</td>\n",
|
||
" <td>11629.33</td>\n",
|
||
" <td>309933.79</td>\n",
|
||
" <td>32.41</td>\n",
|
||
" <td>12.42</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>8208</td>\n",
|
||
" <td>8.54</td>\n",
|
||
" <td>215729.86</td>\n",
|
||
" <td>10042427.50</td>\n",
|
||
" <td>89.69</td>\n",
|
||
" <td>5.98</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" quartile size size_perct nb_tickets_expected total_amount_expected \\\n",
|
||
"0 1 53565 55.74 1067.91 27626.39 \n",
|
||
"1 2 24607 25.61 4748.18 169941.72 \n",
|
||
"2 3 9716 10.11 11629.33 309933.79 \n",
|
||
"3 4 8208 8.54 215729.86 10042427.50 \n",
|
||
"\n",
|
||
" revenue_recovered_perct pace_purchase \n",
|
||
"0 3.42 15.58 \n",
|
||
"1 15.27 15.40 \n",
|
||
"2 32.41 12.42 \n",
|
||
"3 89.69 5.98 "
|
||
]
|
||
},
|
||
"execution_count": 110,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>quartile</th>\n",
|
||
" <th>score (%)</th>\n",
|
||
" <th>score adjusted (%)</th>\n",
|
||
" <th>has purchased (%)</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>13.25</td>\n",
|
||
" <td>2.51</td>\n",
|
||
" <td>1.57</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>33.89</td>\n",
|
||
" <td>8.00</td>\n",
|
||
" <td>9.85</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>63.06</td>\n",
|
||
" <td>22.58</td>\n",
|
||
" <td>21.47</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>90.52</td>\n",
|
||
" <td>66.20</td>\n",
|
||
" <td>65.01</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>quartile</th>\n",
|
||
" <th>size</th>\n",
|
||
" <th>size_perct</th>\n",
|
||
" <th>nb_tickets_expected</th>\n",
|
||
" <th>total_amount_expected</th>\n",
|
||
" <th>perct_revenue_recovered</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>37410</td>\n",
|
||
" <td>38.93</td>\n",
|
||
" <td>419.76</td>\n",
|
||
" <td>9245.08</td>\n",
|
||
" <td>21.71</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>29517</td>\n",
|
||
" <td>30.72</td>\n",
|
||
" <td>11549.06</td>\n",
|
||
" <td>296522.02</td>\n",
|
||
" <td>39.24</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>20137</td>\n",
|
||
" <td>20.96</td>\n",
|
||
" <td>29997.85</td>\n",
|
||
" <td>954751.91</td>\n",
|
||
" <td>63.34</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>9032</td>\n",
|
||
" <td>9.40</td>\n",
|
||
" <td>244655.82</td>\n",
|
||
" <td>10736011.95</td>\n",
|
||
" <td>97.72</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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
|
||
}
|