add function description
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15
utils_ml.py
15
utils_ml.py
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@ -49,6 +49,9 @@ def load_train_test(type_of_activity, type_of_model):
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def save_file_s3(File_name, type_of_activity, type_of_model, model):
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"""
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save plot into s3 storage
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"""
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image_buffer = io.BytesIO()
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plt.savefig(image_buffer, format='png')
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image_buffer.seek(0)
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@ -60,6 +63,9 @@ def save_file_s3(File_name, type_of_activity, type_of_model, model):
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def save_result_set_s3(result_set, File_name, type_of_activity, type_of_model, model=None, model_path=False):
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"""
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save result into s3 storage
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"""
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if model_path:
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FILE_PATH_OUT_S3 = f"projet-bdc2324-team1/2_Output/2_1_Modeling_results/{type_of_model}/{type_of_activity}/{model}/" + File_name + '.csv'
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else:
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@ -69,6 +75,9 @@ def save_result_set_s3(result_set, File_name, type_of_activity, type_of_model, m
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def save_model_s3(File_name, type_of_activity, type_of_model, model, classifier):
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"""
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save model into pickle file
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"""
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model_bytes = pickle.dumps(classifier)
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FILE_PATH_OUT_S3 = f"projet-bdc2324-team1/2_Output/2_1_Modeling_results/{type_of_model}/{type_of_activity}/{model}/" + File_name + '.pkl'
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with fs.open(FILE_PATH_OUT_S3, 'wb') as f:
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@ -88,6 +97,9 @@ def compute_recall_companies(dataset_test, y_pred, type_of_activity, model):
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def features_target_split(dataset_train, dataset_test):
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"""
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return train and test set
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"""
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features_l = ['nb_campaigns', 'taux_ouverture_mail', 'prop_purchases_internet', 'nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'time_to_open',
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'purchases_10_2021','purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021','purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',
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'purchases_4_2022', 'purchases_5_2021', 'purchases_5_2022', 'purchases_6_2021', 'purchases_6_2022', 'purchases_7_2021', 'purchases_7_2022', 'purchases_8_2021',
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@ -105,6 +117,9 @@ def features_target_split(dataset_train, dataset_test):
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def preprocess(type_of_model, type_of_activity):
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"""
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preprocess variables before running machine learning pipeline
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"""
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numeric_features = ['nb_campaigns', 'taux_ouverture_mail', 'prop_purchases_internet', 'nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',
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'purchases_10_2021','purchases_10_2022', 'purchases_11_2021', 'purchases_12_2021','purchases_1_2022', 'purchases_2_2022', 'purchases_3_2022',
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@ -1,6 +1,10 @@
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def load_files(nb_compagnie):
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"""
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load and preprocess dataframes
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"""
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customer = pd.DataFrame()
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campaigns_brut = pd.DataFrame()
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campaigns_kpi = pd.DataFrame()
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@ -8,7 +12,6 @@ def load_files(nb_compagnie):
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tickets = pd.DataFrame()
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targets = pd.DataFrame()
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# début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle
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for directory_path in nb_compagnie:
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df_customerplus_clean_0 = display_input_databases(directory_path, file_name = "customerplus_cleaned")
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df_campaigns_brut = display_input_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])
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@ -26,7 +29,7 @@ def load_files(nb_compagnie):
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targets_columns.remove('customer_id')
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df_target_KPI[targets_columns] = df_target_KPI[targets_columns].fillna(0)
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# creation de la colonne Number compagnie, qui permettra d'agréger les résultats
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# Create company identifier
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df_tickets_kpi["number_company"]=int(directory_path)
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df_campaigns_brut["number_company"]=int(directory_path)
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df_campaigns_kpi["number_company"]=int(directory_path)
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@ -34,7 +37,7 @@ def load_files(nb_compagnie):
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df_target_information["number_company"]=int(directory_path)
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df_target_KPI["number_company"]=int(directory_path)
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# Traitement des index
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# Clean index
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df_tickets_kpi["customer_id"]= directory_path + '_' + df_tickets_kpi['customer_id'].astype('str')
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df_campaigns_brut["customer_id"]= directory_path + '_' + df_campaigns_brut['customer_id'].astype('str')
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df_campaigns_kpi["customer_id"]= directory_path + '_' + df_campaigns_kpi['customer_id'].astype('str')
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@ -51,7 +54,7 @@ def load_files(nb_compagnie):
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df_target_KPI["customer_id"]= directory_path + '_' + df_target_KPI['customer_id'].astype('str')
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# Concaténation
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# Concatenation
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customer = pd.concat([customer, df_customerplus_clean], ignore_index=True)
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campaigns_kpi = pd.concat([campaigns_kpi, df_campaigns_kpi], ignore_index=True)
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campaigns_brut = pd.concat([campaigns_brut, df_campaigns_brut], ignore_index=True)
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@ -73,6 +76,9 @@ def remove_outlier_total_amount(tickets):
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def save_file_s3(File_name, type_of_activity):
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"""
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save plots into s3 storage
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"""
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image_buffer = io.BytesIO()
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plt.savefig(image_buffer, format='png', pad_inches=1, bbox_inches="tight", dpi = 150)
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image_buffer.seek(0)
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@ -84,7 +90,9 @@ def save_file_s3(File_name, type_of_activity):
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def outlier_detection(tickets, company_list, show_diagram=False):
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"""
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detect anonymous customers
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"""
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outlier_list = list()
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for company in company_list:
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@ -114,6 +122,9 @@ def outlier_detection(tickets, company_list, show_diagram=False):
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def valid_customer_detection(products, campaigns_brut):
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"""
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identify customer that are in our time perimeter
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"""
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products_valid = products[products['purchase_date']>="2021-05-01"]
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consumer_valid_product = products_valid['customer_id'].to_list()
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@ -125,6 +136,9 @@ def valid_customer_detection(products, campaigns_brut):
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def identify_purchase_during_target_periode(products):
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"""
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identify customer who purchased ticket during the target period
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"""
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products_target_period = products[(products['purchase_date']>="2022-11-01")
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& (products['purchase_date']<="2023-11-01")]
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customer_target_period = products_target_period['customer_id'].to_list()
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