added exportation to MinIo option

0_7_CA_segment.py

@@ -25,6 +25,7 @@ from sklearn.naive_bayes import GaussianNB
 from scipy.optimize import fsolve
 import pickle
 import warnings
+import io
 
 # define type of activity 
 type_of_activity = "sport"
@@ -42,19 +43,20 @@ X_test_segment["score_adjusted"] = score_adjusted_train
 
 
 # plot adjusted scores and save (to be tested)
-plot_hist_scores(X_test_segment, score = "score", score_adjusted = "score_adjusted")
+plot_hist_scores(X_test_segment, score = "score", score_adjusted = "score_adjusted", type_of_activity = type_of_activity)
 
 image_buffer = io.BytesIO()
-    plt.savefig(image_buffer, format='png')
-    image_buffer.seek(0)
-    file_name = "hist_score_adjusted"
-    FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".png"
-    with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:
-        s3_file.write(image_buffer.read())
-    plt.close()
+plt.savefig(image_buffer, format='png')
+image_buffer.seek(0)
+file_name = "hist_score_adjusted"
+FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".png"
+with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:
+    s3_file.write(image_buffer.read())
+plt.close()
 
 # comparison between score and adjusted score
-X_test_table_adjusted_scores = X_test_segment.groupby("quartile")[["score","score_adjusted", "has_purchased"]].mean().reset_index().round(2)
+X_test_table_adjusted_scores = (100 * X_test_segment.groupby("quartile")[["score","score_adjusted", "has_purchased"]].mean()).round(2).reset_index()
+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"]})
 
 file_name = "table_adjusted_score"
 FILE_PATH_OUT_S3 = PATH + file_name +  type_of_activity + ".csv"

utils_CA_segment.py

@@ -61,7 +61,7 @@ def find_bias(odd_ratios, y_objective, initial_guess=6) :
     return bias_estimated[0]
     
     
-def plot_hist_scores(df, score, score_adjusted) :
+def plot_hist_scores(df, score, score_adjusted, type_of_activity) :
     """
     Plot a histogram comparing scores and adjusted scores.
 
@@ -69,6 +69,7 @@ def plot_hist_scores(df, score, score_adjusted) :
     - df (DataFrame): DataFrame containing score data.
     - score (str): Name of the column in df representing the original scores.
     - score_adjusted (str): Name of the column in df representing the adjusted scores.
+    - type_of_activity (str) : type of activity of the companies considered.
 
     Returns:
     None
@@ -80,9 +81,10 @@ def plot_hist_scores(df, score, score_adjusted) :
     plt.legend()
     plt.xlabel("probability of a future purchase")
     plt.ylabel("count")
-    plt.title("Comparison between score and adjusted score")
+    plt.title(f"Comparison between score and adjusted score for {type_of_activity} companies")
     plt.show()
 
+
 def project_tickets_CA (df, nb_tickets, total_amount, score_adjusted, duration_ref, duration_projection) : 
     """
     Project ticket counts and total amount for a given duration and adjust based on a score.
@@ -140,7 +142,7 @@ def summary_expected_CA(df, segment, nb_tickets_expected, total_amount_expected,
     # compute share of CA recovered
     duration_ratio=duration_ref/duration_projection
     
-    df_expected_CA["perct_revenue_recovered"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \
+    df_expected_CA["revenue_recovered_perct"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \
     df.groupby(segment)[total_amount].sum().values
     
     return df_expected_CA