105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
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### importations ###
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### not necesary ?? As we exec the utils .py file associated
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"""
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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 io
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import s3fs
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import re
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import pickle
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import warnings
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import matplotlib.pyplot as plt
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"""
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### --- beginning of the code --- ###
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### hyperparameters of the code ###
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###################################
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# choose the type of companies for which you want to run the pipeline
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activity = "sport"
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# choose the model we use for the segmentation
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model_name = "LogisticRegression_Benchmark"
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###################################
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# execute file including functions we need
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exec(open('utils_segmentation_2TP.py').read())
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warnings.filterwarnings('ignore')
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# Create filesystem object
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S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
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fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
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# load test set
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dataset_test = load_test_file(type_of_activity)
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# Load Model
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model = load_model(type_of_activity, model_name)
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### Preprocessing of data
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X_test = dataset_test[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',
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'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', #'is_partner',
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'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']]
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y_test = dataset_test[['y_has_purchased']]
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X_test_segment = X_test
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X_test_segment.insert(X_test.shape[1], "country_fr", dataset_test["country_fr"])
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# add y_has_purchased to X_test
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X_test_segment["has_purchased"] = y_test
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# Add prediction and probability to dataset_test
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y_pred = model.predict(X_test)
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X_test_segment["has_purchased_estim"] = y_pred
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y_pred_prob = model.predict_proba(X_test)[:, 1]
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X_test_segment['score'] = y_pred_prob
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X_test_segment["segment"] = np.where(X_test_segment['score']<0.25, '1',
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np.where(X_test_segment['score']<0.5, '2',
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np.where(X_test_segment['score']<0.75, '3', '4')))
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### 1. business KPIs
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business_var = ["nb_tickets", "nb_purchases", "total_amount", "nb_campaigns"]
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X_test_business_fig = df_business_fig(X_test_segment, "segment", business_var)
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# save histogram to Minio
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hist_segment_business_KPIs(X_test_business_fig, "segment", "size", "nb_tickets",
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"nb_purchases", "total_amount", "nb_campaigns")
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save_file_s3_mp(File_name = "segments_business_KPIs_", type_of_activity = activity)
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### 2. description of marketing personae (spider chart)
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# table summarizing variables relative to marketing personae
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X_test_segment_mp = df_segment_mp(X_test_segment, "segment", "gender_female",
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"gender_male", "gender_other", "country_fr")
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# table relative to purchasing behaviour
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X_test_segment_pb = df_segment_pb(X_test_segment, "segment", "nb_tickets_internet", "nb_tickets",
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"nb_campaigns_opened", "nb_campaigns", "opt_in")
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# concatenation of tables to prepare the plot
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X_test_segment_caract = pd.concat([X_test_segment_pb, X_test_segment_mp[['share_known_gender', 'share_of_women', 'country_fr']]], axis=1)
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# visualization and save the graphic to the MinIo
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categories = list(X_test_segment_caract.drop("segment", axis=1).columns)
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radar_mp_plot_all(df=X_test_segment_caract, categories=categories)
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save_file_s3_mp(File_name = "spider_chart_all_", type_of_activity = activity)
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