# Business Data Challenge - Team 1

import pandas as pd
import numpy as np
import os
import s3fs
import re
import warnings

# Import cleaning and merge functions
exec(open('BDC-team-1/0_KPI_functions.py').read())
## 2 - Construction of KPIs on a given period

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):

    # Filtre de cohérence pour la mise en pratique de notre méthode
    max_date =  pd.to_datetime(max_date, utc = True, format = 'ISO8601') 
    min_date = pd.to_datetime(min_date, utc = True, format = 'ISO8601')

    #Filtre de la base df_campaigns_information
    df_campaigns_information = df_campaigns_information[(df_campaigns_information['sent_at'] <= max_date) & (df_campaigns_information['sent_at'] >= min_date)]
    df_campaigns_information['opened_at'][df_campaigns_information['opened_at'] >= max_date] = np.datetime64('NaT')
       
    #Filtre de la base df_products_purchased_reduced
    df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] >= min_date)]

    print("Data filtering : SUCCESS")
    
    # Fusion de l'ensemble et creation des KPI

    # KPI sur les campagnes publicitaires
    df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) 

    # KPI sur le comportement d'achat
    df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)

    # KPI sur les données socio-demographique    

    ## Le genre
    df_customerplus_clean["gender_label"] = df_customerplus_clean["gender"].map({
        0: 'female',
        1: 'male',
        2: 'other'
    })
    gender_dummies = pd.get_dummies(df_customerplus_clean["gender_label"], prefix='gender').astype(int)
    df_customerplus_clean = pd.concat([df_customerplus_clean, gender_dummies], axis=1)

    ## Indicatrice si individue vit en France
    df_customerplus_clean["country_fr"] = df_customerplus_clean["country"].apply(lambda x : int(x=="fr") if pd.notna(x) else np.nan)

    print("KPIs construction : SUCCESS")
    
    # Fusion avec KPI liés au customer
    df_customer = pd.merge(df_customerplus_clean, df_campaigns_kpi, on = 'customer_id', how = 'left')
    
    # Fill NaN values
    df_customer[['nb_campaigns', 'nb_campaigns_opened']] = df_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)
    
    # Fusion avec KPI liés au comportement d'achat
    df_customer_product = pd.merge(df_tickets_kpi, df_customer, on = 'customer_id', how = 'outer')
    
    # Fill NaN values
    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)

    print("Explanatory variable construction : SUCCESS")
    
    return df_customer_product

# Fonction pour créer les variables expliquée
def explained_variable(min_date, max_date, df_products_purchased_reduced = df1_products_purchased_reduced):

    # Filtrer la base d'achat
    df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > min_date)]

    # Indicatrice d'achat
    df_products_purchased_reduced['y_has_purchased'] = 1

    y = df_products_purchased_reduced[['customer_id', 'event_type_id', 'y_has_purchased']].drop_duplicates()

    print("Explained variable construction : SUCCESS")
    
    return y

## Exportation

# Dossier d'exportation
BUCKET_OUT = "projet-bdc2324-team1/1_Output/Logistique Regression databases - First approach"

# Dataset test
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)

y_test = explained_variable(min_date = "2023-08-01", max_date = "2023-11-01", df_products_purchased_reduced = df1_products_purchased_reduced)

dataset_test = pd.merge(X_test, y_test, on = ['customer_id', 'event_type_id'], how = 'left')

# Exportation
FILE_KEY_OUT_S3 = "dataset_test.csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3

with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
    dataset_test.to_csv(file_out, index = False)

print("Exportation dataset test : SUCCESS")

# Dataset train
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)

y_train = explained_variable(min_date = "2023-05-01", max_date = "2023-08-01", df_products_purchased_reduced = df1_products_purchased_reduced)

dataset_train = pd.merge(X_train, y_train, on = ['customer_id', 'event_type_id'], how = 'left')

# Exportation
FILE_KEY_OUT_S3 = "dataset_train.csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3

with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
    dataset_train.to_csv(file_out, index = False)
    
print("Exportation dataset train : SUCCESS")


print("FIN DE LA GENERATION DES DATASETS : SUCCESS")