Ajout description marketing personae

0_6_Segmentation.py

@@ -1,40 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-import io
-import s3fs
-import re
-import pickle
-import warnings
-
-
-exec(open('utils_segmentation.py').read())
-warnings.filterwarnings('ignore')
-
-# Create filesystem object
-S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
-fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
-
-# choose the type of companies for which you want to run the pipeline
-type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
-
-# load test set
-dataset_test = load_test_file(type_of_activity)
-
-# Load Model 
-model = load_model(type_of_activity, 'LogisticRegression_Benchmark')
-
-# Processing
-X_test = dataset_test[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 
-            'time_between_purchase', 'nb_tickets_internet',  'is_email_true', 'opt_in', #'is_partner',
-            'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']]
-
-y_test = dataset_test[['y_has_purchased']]
-
-# Prediction
-y_pred_prob = model.predict_proba(X_test)[:, 1]
-
-# Add probability to dataset_test
-dataset_test['Probability_to_buy'] = y_pred_prob
-print('probability added to dataset_test')
-print(dataset_test.head())

0_6_segmentation_V2TP.py

@@ -1,99 +0,0 @@
-### importations ###
-### not necesary ?? As we exec the utils .py file associated 
-
-"""
-import pandas as pd
-import numpy as np
-import os
-import io
-import s3fs
-import re
-import pickle
-import warnings
-import matplotlib.pyplot as plt
-"""
-
-### --- beginning of the code --- ###
-
-
-### hyperparameters of the code ###
-
-###################################
-
-# choose the type of companies for which you want to run the pipeline
-type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
-
-# choose the model we use for the segmentation
-model_name = "LogisticRegression_Benchmark"
-
-###################################
-
-
-# execute file including functions we need
-exec(open('utils_segmentation_V2TP.py').read())
-
-warnings.filterwarnings('ignore')
-
-# Create filesystem object
-S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
-fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
-
-# load test set
-dataset_test = load_test_file(type_of_activity)
-
-# Load Model 
-model = load_model(type_of_activity, model_name)
-
-
-### Preprocessing of data
-X_test = dataset_test[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 
-            'time_between_purchase', 'nb_tickets_internet',  'is_email_true', 'opt_in', #'is_partner',
-            'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened', 'country_fr']]
-
-y_test = dataset_test[['y_has_purchased']]
-
-X_test_segment = X_test
-
-# add y_has_purchased to X_test
-X_test_segment["has_purchased"] = y_test
-
-# Add prediction and probability to dataset_test
-y_pred = model.predict(X_test)
-X_test_segment["has_purchased_estim"] = y_pred
-
-y_pred_prob = model.predict_proba(X_test)[:, 1]
-X_test_segment['score'] = y_pred_prob
-
-X_test_segment["segment"] = np.where(X_test_segment['score']<0.25, '1',
-                   np.where(X_test_segment['score']<0.5, '2',
-                   np.where(X_test_segment['score']<0.75, '3', '4')))
-
-### 1. business KPIs 
-
-business_var = ["nb_tickets", "nb_purchases", "total_amount", "nb_campaigns"]
-X_test_business_fig = df_business_fig(X_test_segment, "segment", business_var)
-
-# save histogram to Minio
-hist_segment_business_KPIs(X_test_business_fig, "segment", "size", "nb_tickets", 
-                           "nb_purchases", "total_amount", "nb_campaigns")
-save_file_s3_mp(File_name = "segments_business_KPI_", type_of_activity = type_of_activity)
-
-
-### 2. description of marketing personae (spider chart)
-
-# table summarizing variables relative to marketing personae
-X_test_segment_mp = df_segment_mp(X_test_segment, "segment", "gender_female", 
-                                  "gender_male", "gender_other", "country_fr")
-
-# table relative to purchasing behaviour
-X_test_segment_pb = df_segment_pb(X_test_segment, "segment", "nb_tickets_internet", "nb_tickets", 
-                                  "nb_campaigns_opened", "nb_campaigns", "opt_in")
-
-# concatenation of tables to prepare the plot
-X_test_segment_caract = pd.concat([X_test_segment_pb, X_test_segment_mp[['share_known_gender', 'share_of_women', 'country_fr']]], axis=1)
-
-# visualization and save the graphic to the MinIo
-categories = list(X_test_segment_caract.drop("segment", axis=1).columns)
-radar_mp_plot_all(df=X_test_segment_caract, categories=categories)
-save_file_s3_mp(File_name = "spider_chart_all_", type_of_activity = type_of_activity)
-

6_Segmentation_and_Marketing_Personae.py

@@ -0,0 +1,82 @@
+
+# Packages
+import pandas as pd
+import numpy as np
+import os
+import io
+import s3fs
+import re
+import pickle
+import warnings
+import matplotlib.pyplot as plt
+from tabulate import tabulate
+
+###################################
+
+# choose the model we use for the segmentation
+model_name = "LogisticRegression_Benchmark"
+
+###################################
+
+
+# execute file including functions we need
+exec(open('utils_segmentation.py').read())
+
+warnings.filterwarnings('ignore')
+
+# Create filesystem object
+S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
+fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
+
+
+# choose the type of companies for which you want to run the pipeline
+# type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
+for type_of_activity in ['musee', 'sport', 'musique'] : 
+    
+
+    # load test set
+    dataset_test = load_test_file(type_of_activity)
+    
+    # Load Model 
+    model = load_model(type_of_activity, model_name)
+    
+    
+    ### Preprocessing of data
+    X_test = dataset_test.drop(columns = 'y_has_purchased')
+    
+    y_test = dataset_test[['y_has_purchased']]
+    
+    X_test_segment = X_test
+    
+    # add y_has_purchased to X_test
+    X_test_segment["has_purchased"] = y_test
+    
+    # Add prediction and probability to dataset_test
+    y_pred = model.predict(X_test)
+    X_test_segment["has_purchased_estim"] = y_pred
+    
+    y_pred_prob = model.predict_proba(X_test)[:, 1]
+    X_test_segment['score'] = y_pred_prob
+    
+    X_test_segment["segment"] = np.where(X_test_segment['score']<0.25, '1',
+                       np.where(X_test_segment['score']<0.5, '2',
+                       np.where(X_test_segment['score']<0.75, '3', '4')))
+    
+    ### 1. business KPIs 
+    
+    business_var = ["nb_tickets", "nb_purchases", "total_amount", "nb_campaigns"]
+    X_test_business_fig = df_business_fig(X_test_segment, "segment", business_var)
+    
+    # save histogram to Minio
+    hist_segment_business_KPIs(X_test_business_fig, "segment", "size", "nb_tickets", 
+                               "nb_purchases", "total_amount", "nb_campaigns", type_of_activity)
+    save_file_s3_mp(File_name = "segments_business_KPI_", type_of_activity = type_of_activity)
+    
+    
+    ### 2. description of marketing personae 
+    ## A. Spider chart
+    radar_mp_plot_all(df = X_test_segment, type_of_activity = type_of_activity)
+    save_file_s3_mp(File_name = "spider_chart_all_", type_of_activity = type_of_activity)
+    
+    ## B. Latex table
+    known_sociodemo_caracteristics(df = X_test_segment, type_of_activity = type_of_activity)

utils_segmentation.py

@@ -1,15 +1,12 @@
-import pandas as pd
+### importations ###
-import numpy as np
-import os
-import io
-import s3fs
-import re
-import pickle
-import warnings
 
 
+
+
+### functions for segmentation and graphics associated ###
+
 def load_model(type_of_activity, model):
-    BUCKET = f"projet-bdc2324-team1/Output_model/{type_of_activity}/{model}/"
+    BUCKET = f"projet-bdc2324-team1/2_Output/2_1_Modeling_results/standard/{type_of_activity}/{model}/"
     filename = model + '.pkl'
     file_path = BUCKET + filename
     with fs.open(file_path, mode="rb") as f:
@@ -20,8 +17,207 @@ def load_model(type_of_activity, model):
 
 
 def load_test_file(type_of_activity):
-    file_path_test = f"projet-bdc2324-team1/Generalization/{type_of_activity}/Test_set.csv"
+    file_path_test = f"projet-bdc2324-team1/1_Temp/1_0_Modelling_Datasets/{type_of_activity}/Test_set.csv"
     with fs.open(file_path_test, mode="rb") as file_in:
         dataset_test = pd.read_csv(file_in, sep=",")
     return dataset_test
 
+
+def save_file_s3_mp(File_name, type_of_activity):
+    image_buffer = io.BytesIO()
+    plt.savefig(image_buffer, format='png', dpi=110)
+    image_buffer.seek(0)
+    PATH = f"projet-bdc2324-team1/2_Output/2_2_Segmentation_and_Marketing_Personae/{type_of_activity}/"
+    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()
+
+def save_txt_file_s3(file_name, type_of_activity, content):
+    FILE_PATH = f"projet-bdc2324-team1/2_Output/2_2_Segmentation_and_Marketing_Personae/{type_of_activity}/"
+    FILE_PATH_OUT_S3 = FILE_PATH + file_name + type_of_activity + '.txt'
+    with fs.open(FILE_PATH_OUT_S3, 'w') as s3_file:
+        s3_file.write(content)
+        
+def df_business_fig(df, segment, list_var) :
+    df_business_kpi = df.groupby(segment)[list_var].sum().reset_index()
+    df_business_kpi.insert(1, "size", df.groupby(segment).size().values)
+    all_var = ["size"] + list_var
+    df_business_kpi[all_var] = 100 * df_business_kpi[all_var] / df_business_kpi[all_var].sum()
+
+    return df_business_kpi
+
+
+def hist_segment_business_KPIs(df, segment, size, nb_tickets, nb_purchases, total_amount, nb_campaigns, type_of_activity) :
+    
+    plt.figure()
+
+    df_plot = df[[segment, size, nb_tickets, nb_purchases, total_amount, nb_campaigns]]
+    
+    x = ["number of\ncustomers", "number of\ntickets", "number of\npurchases", "total\namount", 
+         "number of\ncampaigns"]
+    
+    bottom = np.zeros(5)
+    
+    # types of blue color
+    colors = plt.cm.Blues(np.linspace(0.1, 0.9, 4))
+    
+    for i in range(4) :
+        height = list(df_plot.loc[i,size:].values)
+        plt.bar(x=x, height=height, label = str(df_plot[segment][i]), bottom=bottom, color=colors[i])
+        bottom+=height
+
+    # Ajust margins
+    plt.subplots_adjust(left = 0.125, right = 0.8, bottom = 0.1, top = 0.9)
+   
+    plt.legend(title = "segment", loc = "upper right", bbox_to_anchor=(1.2, 1))
+    plt.ylabel("Fraction represented by the segment (%)")
+    plt.title(f"Relative weight of each segment regarding business KPIs\nfor {type_of_activity} companies", size=12)
+    # plt.show()
+
+
+# def df_segment_mp(df) :
+#     df_mp = df.groupby("segment")[["gender_female", "gender_male", "gender_other", "country_fr"]].mean().reset_index()
+#     df_mp.insert(3, "share_known_gender", df_mp["gender_female"]+df_mp["gender_male"])
+#     df_mp.insert(4, "share_of_women", df_mp["gender_female"]/(df_mp["share_known_gender"]))
+#     return df_mp
+
+
+# def df_segment_pb (df) :
+#     df_pb = df.groupby("segment")[["prop_purchases_internet", "taux_ouverture_mail", "opt_in"]].mean().reset_index()
+#     return df_pb
+
+
+def radar_mp_plot(df, categories, index) :
+    categories = categories
+
+    # true values are used to print the true value in parenthesis
+    tvalues = list(df.loc[index,categories]) 
+
+    max_values = df[categories].max()
+
+    # values are true values / max among the 4 segments, allows to 
+    # put values in relation with the values for other segments
+    # if the point has a maximal abscisse it means that value is maximal for the segment considered
+    # , event if not equal to 1
+    
+    values = list(df.loc[index,categories]/max_values)
+                  
+    # values normalized are used to adjust the value around the circle
+    # for instance if the maximum of values is equal to 0.8, we want the point to be 
+    # at 8/10th of the circle radius, not at the edge 
+    values_normalized = [ max(values) * elt for elt in values]
+
+    # Nb of categories
+    num_categories = len(categories)
+
+    angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()
+    
+    # Initialize graphic
+    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
+    
+    # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle
+    # which is based on max(value)
+    ax.plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)
+    ax.plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5, linewidth=1.2)
+    
+    # fill the sector
+    ax.fill(angles, values_normalized, color='orange', alpha=0.4)
+    
+    # labels
+    ax.set_yticklabels([])
+    ax.set_xticks(angles)
+    ticks = [categories[i].replace("_"," ") + f"\n({round(100 * tvalues[i],2)}%)" for i in range(len(categories))]
+    ax.set_xticklabels(ticks, color="black")
+    
+    ax.spines['polar'].set_visible(False)
+    
+    plt.title(f'Characteristics of the segment {index+1}\n')
+    
+    # plt.show()
+
+
+def radar_mp_plot_all(df, type_of_activity) :
+    
+    # table summarizing variables relative to marketing personae
+    df_mp = df.groupby("segment")[["gender_female", "gender_male", "gender_other", "country_fr"]].mean().reset_index()
+    df_mp.insert(3, "share_known_gender", df_mp["gender_female"]+df_mp["gender_male"])
+    df_mp.insert(4, "share_of_women", df_mp["gender_female"]/(df_mp["share_known_gender"]))
+
+    # table relative to purchasing behaviour
+    df_pb = df.groupby("segment")[["prop_purchases_internet", "taux_ouverture_mail", "opt_in"]].mean().reset_index()
+
+    # concatenation of tables to prepare the plot
+    df_used = pd.concat([df_pb, df_mp[['share_known_gender', 'share_of_women', 'country_fr']]], axis=1)
+
+    # visualization
+    nb_segments = df_used.shape[0]
+    categories = list(df_used.drop("segment", axis=1).columns)
+
+    # Initialize graphic
+    fig, ax = plt.subplots(2,2, figsize=(25, 20), subplot_kw=dict(polar=True))
+    
+    for index in range(nb_segments) :
+        row = index // 2  # Division entière pour obtenir le numéro de ligne
+        col = index % 2 
+    
+        # true values are used to print the true value in parenthesis
+        tvalues = list(df_used.loc[index,categories]) 
+        
+        max_values = df_used[categories].max()
+        
+        # values are true values / max among the 4 segments, allows to 
+        # put values in relation with the values for other segments
+        # if the point has a maximal abscisse it means that value is maximal for the segment considered
+        # , event if not equal to 1
+
+        values = list(df_used.loc[index,categories]/max_values)
+                          
+        # values normalized are used to adjust the value around the circle
+        # for instance if the maximum of values is equal to 0.8, we want the point to be 
+        # at 8/10th of the circle radius, not at the edge 
+        values_normalized = [ max(values) * elt for elt in values]
+        
+        # Nb of categories
+        num_categories = len(categories)
+    
+        angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()
+    
+        # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle
+        # which is based on max(value)
+        ax[row, col].plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)
+        ax[row, col].plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5,
+                          linewidth=1.2)
+        
+        # fill the sector
+        ax[row, col].fill(angles, values_normalized, color='orange', alpha=0.4, label = index)
+        
+        # labels
+        ax[row, col].set_yticklabels([])
+        ax[row, col].set_xticks(angles)
+        ticks = [categories[i].replace("_"," ") + f"\n({round(100 * tvalues[i],2)}%)" for i in range(len(categories))]
+        ax[row, col].set_xticklabels(ticks, color="black", size = 20)
+
+        ax[row, col].spines['polar'].set_visible(False)
+        
+        ax[row, col].set_title(f'Segment {index+1}\n', size = 24)
+        
+    fig.suptitle(f"Characteristics of marketing personae of {type_of_activity} companies", size=32)
+    # plt.show()
+
+def known_sociodemo_caracteristics(df, type_of_activity) :
+    
+    table_share_known = df.groupby("segment")[["is_profession_known", "is_zipcode_known", "categorie_age_inconnue", "gender_other"]].mean().mul(100).reset_index()
+    table_share_known.columns = ['Segment', 'Share of Known Profession (%)', 'Share of Known Zipcode (%)', 'Share of Unknown Age (%)', 'Share of Unknown Gender (%)']
+    table_share_known= table_share_known.pivot_table(index=None, columns='Segment')
+    
+    # Arrondir les valeurs du DataFrame à une décimale
+    table_share_known_rounded = table_share_known.round(1)
+    
+    # Convertir le DataFrame en format LaTeX avec les valeurs arrondies et le symbole '%'
+    latex_table = tabulate(table_share_known_rounded, headers='keys', tablefmt='latex_raw', floatfmt=".1f")
+    latex_table = latex_table.replace('%', '\\%')
+
+    save_txt_file_s3("table_known_socio_demo_caracteristics", type_of_activity, latex_table)
+    
+

utils_segmentation_V2TP.py

@@ -1,201 +0,0 @@
-### importations ###
-
-import pandas as pd
-import numpy as np
-import os
-import io
-import s3fs
-import re
-import pickle
-import warnings
-import matplotlib.pyplot as plt
-
-
-### functions for segmentation and graphics associated ###
-
-def load_model(type_of_activity, model):
-    BUCKET = f"projet-bdc2324-team1/Output_model/{type_of_activity}/{model}/"
-    filename = model + '.pkl'
-    file_path = BUCKET + filename
-    with fs.open(file_path, mode="rb") as f:
-        model_bytes = f.read()
-
-    model = pickle.loads(model_bytes)
-    return model
-
-
-def load_test_file(type_of_activity):
-    file_path_test = f"projet-bdc2324-team1/Generalization/{type_of_activity}/Test_set.csv"
-    with fs.open(file_path_test, mode="rb") as file_in:
-        dataset_test = pd.read_csv(file_in, sep=",")
-    return dataset_test
-
-
-def save_file_s3_mp(File_name, type_of_activity):
-    image_buffer = io.BytesIO()
-    plt.savefig(image_buffer, format='png', dpi=110)
-    image_buffer.seek(0)
-    PATH = f"projet-bdc2324-team1/Output_marketing_personae_analysis/{type_of_activity}/"
-    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()
-
-
-def df_business_fig(df, segment, list_var) :
-    df_business_kpi = df.groupby(segment)[list_var].sum().reset_index()
-    df_business_kpi.insert(1, "size", df.groupby(segment).size().values)
-    all_var = ["size"] + list_var
-    df_business_kpi[all_var] = 100 * df_business_kpi[all_var] / df_business_kpi[all_var].sum()
-
-    return df_business_kpi
-
-
-def hist_segment_business_KPIs(df, segment, size, nb_tickets, nb_purchases, total_amount, nb_campaigns) :
-    
-    plt.figure()
-
-    df_plot = df[[segment, size, nb_tickets, nb_purchases, total_amount, nb_campaigns]]
-    
-    x = ["number of\ncustomers", "number of\ntickets", "number of\npurchases", "total\namount", 
-         "number of\ncampaigns"]
-    
-    bottom = np.zeros(5)
-    
-    # types of blue color
-    colors = plt.cm.Blues(np.linspace(0.1, 0.9, 4))
-    
-    for i in range(4) :
-        height = list(df_plot.loc[i,size:].values)
-        plt.bar(x=x, height=height, label = str(df_plot[segment][i]), bottom=bottom, color=colors[i])
-        bottom+=height
-
-    # Ajust margins
-    plt.subplots_adjust(left = 0.125, right = 0.8, bottom = 0.1, top = 0.9)
-   
-    plt.legend(title = "segment", loc = "upper right", bbox_to_anchor=(1.2, 1))
-    plt.ylabel("Fraction represented by the segment (%)")
-    plt.title(f"Relative weight of each segment regarding business KPIs\nfor {type_of_activity} companies", size=12)
-    # plt.show()
-
-
-def df_segment_mp(df, segment, gender_female, gender_male, gender_other, country_fr) :
-    df_mp = df.groupby(segment)[[gender_female, gender_male, gender_other, country_fr]].mean().reset_index()
-    df_mp.insert(3, "share_known_gender", df_mp[gender_female]+df_mp[gender_male])
-    df_mp.insert(4, "share_of_women", df_mp[gender_female]/(df_mp["share_known_gender"]))
-    return df_mp
-
-
-def df_segment_pb (df, segment, nb_tickets_internet, nb_tickets, nb_campaigns_opened, nb_campaigns, opt_in) :
-    df_used = df
-    df_used["share_tickets_internet"] = df_used[nb_tickets_internet]/df_used[nb_tickets]
-    df_used["share_campaigns_opened"] = df_used[nb_campaigns_opened]/df_used[nb_campaigns]
-    df_pb = df_used.groupby(segment)[["share_tickets_internet", "share_campaigns_opened", opt_in]].mean().reset_index()
-    return df_pb
-
-
-def radar_mp_plot(df, categories, index) :
-    categories = categories
-
-    # true values are used to print the true value in parenthesis
-    tvalues = list(df.loc[index,categories]) 
-
-    max_values = df[categories].max()
-
-    # values are true values / max among the 4 segments, allows to 
-    # put values in relation with the values for other segments
-    # if the point has a maximal abscisse it means that value is maximal for the segment considered
-    # , event if not equal to 1
-    
-    values = list(df.loc[index,categories]/max_values)
-                  
-    # values normalized are used to adjust the value around the circle
-    # for instance if the maximum of values is equal to 0.8, we want the point to be 
-    # at 8/10th of the circle radius, not at the edge 
-    values_normalized = [ max(values) * elt for elt in values]
-
-    # Nb of categories
-    num_categories = len(categories)
-
-    angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()
-    
-    # Initialize graphic
-    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
-    
-    # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle
-    # which is based on max(value)
-    ax.plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)
-    ax.plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5, linewidth=1.2)
-    
-    # fill the sector
-    ax.fill(angles, values_normalized, color='orange', alpha=0.4)
-    
-    # labels
-    ax.set_yticklabels([])
-    ax.set_xticks(angles)
-    ticks = [categories[i].replace("_"," ") + f"\n({round(100 * tvalues[i],2)}%)" for i in range(len(categories))]
-    ax.set_xticklabels(ticks, color="black")
-    
-    ax.spines['polar'].set_visible(False)
-    
-    plt.title(f'Characteristics of the segment {index+1}\n')
-    
-    # plt.show()
-
-
-def radar_mp_plot_all(df, categories) :
-    
-    nb_segments = df.shape[0]
-    categories = categories
-
-    # Initialize graphic
-    fig, ax = plt.subplots(2,2, figsize=(25, 20), subplot_kw=dict(polar=True))
-    
-    for index in range(nb_segments) :
-        row = index // 2  # Division entière pour obtenir le numéro de ligne
-        col = index % 2 
-    
-        # true values are used to print the true value in parenthesis
-        tvalues = list(df.loc[index,categories]) 
-        
-        max_values = df[categories].max()
-        
-        # values are true values / max among the 4 segments, allows to 
-        # put values in relation with the values for other segments
-        # if the point has a maximal abscisse it means that value is maximal for the segment considered
-        # , event if not equal to 1
-
-        values = list(df.loc[index,categories]/max_values)
-                          
-        # values normalized are used to adjust the value around the circle
-        # for instance if the maximum of values is equal to 0.8, we want the point to be 
-        # at 8/10th of the circle radius, not at the edge 
-        values_normalized = [ max(values) * elt for elt in values]
-        
-        # Nb of categories
-        num_categories = len(categories)
-    
-        angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()
-    
-        # we have to draw first a transparent line (alpha=0) of values to adjust the radius of the circle
-        # which is based on max(value)
-        ax[row, col].plot(angles + angles[:1], values + values[:1], color='skyblue', alpha=0, linewidth=1.5)
-        ax[row, col].plot(angles + angles[:1], values_normalized + values_normalized[:1], color='black', alpha = 0.5,
-                          linewidth=1.2)
-        
-        # fill the sector
-        ax[row, col].fill(angles, values_normalized, color='orange', alpha=0.4, label = index)
-        
-        # labels
-        ax[row, col].set_yticklabels([])
-        ax[row, col].set_xticks(angles)
-        ticks = [categories[i].replace("_"," ") + f"\n({round(100 * tvalues[i],2)}%)" for i in range(len(categories))]
-        ax[row, col].set_xticklabels(ticks, color="black", size = 20)
-
-        ax[row, col].spines['polar'].set_visible(False)
-        
-        ax[row, col].set_title(f'Segment {index+1}\n', size = 24)
-        
-    fig.suptitle(f"Characteristics of marketing personae of {type_of_activity} companies", size=32)
-    # plt.show()
-