101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
# importations
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import pandas as pd
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from pandas import DataFrame
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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 matplotlib.pyplot as plt
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from scipy.optimize import fsolve
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import pickle
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import warnings
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import io
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# ignore warnings
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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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# importation of functions defined
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exec(open('utils_sales_forecast.py').read())
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# from utils_CA_segment import *
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# define type of activity
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type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
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PATH = f"projet-bdc2324-team1/2_Output/2_3_Sales_Forecast/{type_of_activity}/"
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# type of model for the score
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# type_of_model = "LogisticRegression_cv"
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type_of_model = "LogisticRegression_Benchmark"
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# load train and test sets
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dataset_train, dataset_test = load_train_test(type_of_activity)
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# make features - define X train and X test
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X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)
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# choose model - logit cross validated
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model = load_model(type_of_activity, type_of_model)
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# create table X test segment from X test
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X_test_segment = df_segment(X_test, y_test, model)
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# comparison with bias of the train set - X train to be defined
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X_train_score = model.predict_proba(X_train)[:, 1]
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bias_train_set = find_bias(odd_ratios = odd_ratio(adjust_score_1(X_train_score)),
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y_objective = y_train["y_has_purchased"].sum(),
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initial_guess=10)
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print("Bias estimated :", np.log(bias_train_set))
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# create a score adjusted with the bias computed
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score_adjusted_train = adjusted_score(odd_ratio(adjust_score_1(X_test_segment["score"])), bias = bias_train_set)
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X_test_segment["score_adjusted"] = score_adjusted_train
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print("The score was successfully adjusted")
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MAE_score = abs(X_test_segment["score"]-X_test_segment["has_purchased"]).mean()
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MAE_ajusted_score = abs(X_test_segment["score_adjusted"]-X_test_segment["has_purchased"]).mean()
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print(f"MAE for score : {MAE_score}")
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print(f"MAE for adjusted score : {MAE_ajusted_score}")
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### 1. plot adjusted scores and save (to be tested)
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plot_hist_scores(X_test_segment, score = "score", score_adjusted = "score_adjusted", type_of_activity = type_of_activity)
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save_file_s3_ca("hist_score_adjusted_", type_of_activity)
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### 2. comparison between score and adjusted score
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X_test_table_adjusted_scores = (100 * X_test_segment.groupby("quartile")[["score","score_adjusted", "has_purchased"]].mean()).round(2).reset_index()
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X_test_table_adjusted_scores = X_test_table_adjusted_scores.rename(columns = {col : f"{col} (%)" for col in X_test_table_adjusted_scores.columns if col in ["score","score_adjusted", "has_purchased"]})
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print(X_test_table_adjusted_scores)
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# save table
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file_name = "table_adjusted_score_"
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FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".csv"
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with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
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X_test_table_adjusted_scores.to_csv(file_out, index = False)
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# project revenue
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X_test_segment = project_tickets_CA (X_test_segment, "nb_purchases", "nb_tickets", "total_amount", "score_adjusted",
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duration_ref=17, duration_projection=12)
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### 3. table summarizing projections (nb tickets, revenue)
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X_test_expected_CA = round(summary_expected_CA(df=X_test_segment, segment="quartile",
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nb_tickets_expected="nb_tickets_expected", total_amount_expected="total_amount_expected",
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total_amount="total_amount", pace_purchase="pace_purchase"),2)
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# rename columns
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mapping_dict = {col: col.replace("perct", "(%)").replace("_", " ") for col in X_test_expected_CA.columns}
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X_test_expected_CA = X_test_expected_CA.rename(columns=mapping_dict)
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# save table
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file_name = "table_expected_CA_"
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FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".csv"
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with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
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X_test_expected_CA.to_csv(file_out, index = False)
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