123 lines
5.3 KiB
Python
123 lines
5.3 KiB
Python
# Business Data Challenge - Team 1
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import pandas as pd
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import numpy as np
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import os
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import s3fs
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import re
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import warnings
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# Import cleaning and merge functions
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exec(open('BDC-team-1/0_KPI_functions.py').read())
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## 2 - Construction of KPIs on a given period
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def explanatory_variables(min_date, max_date, df_campaigns_information = df1_campaigns_information, df_products_purchased_reduced = df1_products_purchased_reduced, df_customerplus_clean = df1_customerplus_clean):
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# Filtre de cohérence pour la mise en pratique de notre méthode
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max_date = pd.to_datetime(max_date, utc = True, format = 'ISO8601')
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min_date = pd.to_datetime(min_date, utc = True, format = 'ISO8601')
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#Filtre de la base df_campaigns_information
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df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= max_date) & (df_campaigns_information['sent_at'] >= min_date)]
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df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= max_date] = np.datetime64('NaT')
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#Filtre de la base df_products_purchased_reduced
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df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]
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print("Data filtering : SUCCESS")
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# Fusion de l'ensemble et creation des KPI
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# KPI sur les campagnes publicitaires
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df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information)
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# KPI sur le comportement d'achat
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df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)
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# KPI sur les données socio-demographique
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## Le genre
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df_customerplus_clean["gender_label"] = df_customerplus_clean["gender"].map({
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0: 'female',
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1: 'male',
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2: 'other'
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})
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gender_dummies = pd.get_dummies(df_customerplus_clean["gender_label"], prefix='gender').astype(int)
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df_customerplus_clean = pd.concat([df_customerplus_clean, gender_dummies], axis=1)
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## Indicatrice si individue vit en France
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df_customerplus_clean["country_fr"] = df_customerplus_clean["country"].apply(lambda x : int(x=="fr") if pd.notna(x) else np.nan)
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print("KPIs construction : SUCCESS")
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# Fusion avec KPI liés au customer
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df_customer = pd.merge(df_customerplus_clean, df_campaigns_kpi, on = 'customer_id', how = 'left')
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# Fill NaN values
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df_customer[['nb_campaigns', 'nb_campaigns_opened']] = df_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)
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# Fusion avec KPI liés au comportement d'achat
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df_customer_product = pd.merge(df_tickets_kpi, df_customer, on = 'customer_id', how = 'outer')
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# Fill NaN values
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df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0)
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print("Explanatory variable construction : SUCCESS")
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return df_customer_product
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# Fonction pour créer les variables expliquée
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def explained_variable(min_date, max_date, df_products_purchased_reduced = df1_products_purchased_reduced):
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# Filtrer la base d'achat
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df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > min_date)]
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# Indicatrice d'achat
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df_products_purchased_reduced['y_has_purchased'] = 1
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y = df_products_purchased_reduced[['customer_id', 'event_type_id', 'y_has_purchased']].drop_duplicates()
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print("Explained variable construction : SUCCESS")
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return y
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## Exportation
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# Dossier d'exportation
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BUCKET_OUT = "projet-bdc2324-team1/1_Output/Logistique Regression databases - First approach"
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# Dataset test
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X_test = explanatory_variables(min_date = "2021-08-01", max_date = "2023-08-01", df_campaigns_information = df1_campaigns_information, df_products_purchased_reduced = df1_products_purchased_reduced, df_customerplus_clean = df1_customerplus_clean)
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y_test = explained_variable(min_date = "2023-08-01", max_date = "2023-11-01", df_products_purchased_reduced = df1_products_purchased_reduced)
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dataset_test = pd.merge(X_test, y_test, on = ['customer_id', 'event_type_id'], how = 'left')
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# Exportation
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FILE_KEY_OUT_S3 = "dataset_test.csv"
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FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
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with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
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dataset_test.to_csv(file_out, index = False)
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print("Exportation dataset test : SUCCESS")
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# Dataset train
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X_train = explanatory_variables(min_date = "2021-05-01", max_date = "2023-05-01", df_campaigns_information = df1_campaigns_information, df_products_purchased_reduced = df1_products_purchased_reduced, df_customerplus_clean = df1_customerplus_clean)
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y_train = explained_variable(min_date = "2023-05-01", max_date = "2023-08-01", df_products_purchased_reduced = df1_products_purchased_reduced)
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dataset_train = pd.merge(X_train, y_train, on = ['customer_id', 'event_type_id'], how = 'left')
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# Exportation
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FILE_KEY_OUT_S3 = "dataset_train.csv"
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FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
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with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
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dataset_train.to_csv(file_out, index = False)
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print("Exportation dataset train : SUCCESS")
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print("FIN DE LA GENERATION DES DATASETS : SUCCESS")
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