Update src/modules/ml_dashboard.py

src/modules/ml_dashboard.py

@@ -613,272 +613,7 @@ def show_ml_features(client, sheet_name):
         time.sleep(30)
         st.rerun()
     
-    # Chargement des données pour le feature store
-    try:
-        # Réutilisation de la fonction helper si 'client' est global, sinon adaptation nécessaire
-        df_clients = get_data_from_sheet("Clients_KYC")
-        df_prets = get_data_from_sheet("Prets_Master")
-    except Exception as e:
-        st.error(f"Erreur de lecture : {e}")
-        st.stop()
-    
-    if df_clients.empty or df_prets.empty:
-        st.warning("Données insuffisantes")
-        st.stop()
-    
-    # Fusion des données
-    df_full = pd.merge(df_prets, df_clients, on="ID_Client", how="left", suffixes=('_Pret', '_Client'))
-    
-    # Thème Plotly surveillance discret
-    plotly_template = {
-        'layout': {
-            'plot_bgcolor': 'rgba(13, 17, 23, 0.8)',
-            'paper_bgcolor': 'rgba(22, 27, 34, 0.3)',
-            'font': {'color': '#8b949e', 'family': 'Space Grotesk', 'size': 12},
-            'title': {
-                'font': {'size': 14, 'color': '#c9d1d9', 'family': 'Space Grotesk'},
-                'x': 0,
-                'xanchor': 'left'
-            },
-            'xaxis': {
-                'gridcolor': 'rgba(48, 54, 61, 0.3)',
-                'linecolor': 'rgba(48, 54, 61, 0.5)',
-                'zerolinecolor': 'rgba(48, 54, 61, 0.5)',
-                'tickfont': {'family': 'Space Grotesk', 'size': 11}
-            },
-            'yaxis': {
-                'gridcolor': 'rgba(48, 54, 61, 0.3)',
-                'linecolor': 'rgba(48, 54, 61, 0.5)',
-                'zerolinecolor': 'rgba(48, 54, 61, 0.5)',
-                'tickfont': {'family': 'Space Grotesk', 'size': 11}
-            },
-            'legend': {
-                'font': {'family': 'Space Grotesk', 'size': 11}
-            }
-        }
-    }
-    
-    # FEATURE ENGINEERING
-    st.markdown("<h2>Feature Engineering Pipeline</h2>", unsafe_allow_html=True)
-    
-    # 1. FEATURES DÉMOGRAPHIQUES
-    with st.expander("Features Démographiques", expanded=True):
-        df_full['Date_Naissance'] = pd.to_datetime(df_full['Date_Naissance'], errors='coerce')
-        df_full['Age'] = ((datetime.now() - df_full['Date_Naissance']).dt.days / 365.25).round(0)
-        df_full['Pers_Charge'] = pd.to_numeric(df_full['Pers_Charge'], errors='coerce').fillna(0)
-        df_full['Categorie_Age'] = pd.cut(df_full['Age'], 
-                                         bins=[0, 25, 35, 45, 55, 100],
-                                         labels=['18-25', '26-35', '36-45', '46-55', '55+'])
-        
-        col1, col2, col3, col4 = st.columns(4)
-        col1.metric("Âge moyen", f"{df_full['Age'].mean():.1f} ans", 
-                    delta=f"σ {df_full['Age'].std():.1f}")
-        col2.metric("Pers. charge", f"{df_full['Pers_Charge'].mean():.1f}",
-                    delta=f"Max {df_full['Pers_Charge'].max():.0f}")
-        col3.metric("Mariés", f"{(df_full['Etat_Civil']=='Marié(e)').sum()}")
-        col4.metric("Célibataires", f"{(df_full['Etat_Civil']=='Célibataire').sum()}")
-        
-        fig_age = px.histogram(df_full, x='Age', nbins=20, 
-                               title="Distribution des âges",
-                               color_discrete_sequence=['#58a6ff'],
-                               template=plotly_template)
-        fig_age.update_traces(marker_line_color='rgba(139, 148, 158, 0.3)', marker_line_width=0.5, opacity=0.8)
-        st.plotly_chart(fig_age, use_container_width=True)
-    
-    # 2. FEATURES FINANCIÈRES
-    with st.expander("Features Financières", expanded=True):
-        df_full['Revenus_Mensuels'] = pd.to_numeric(df_full['Revenus_Mensuels'], errors='coerce').fillna(0)
-        df_full['Autres_Revenus'] = pd.to_numeric(df_full['Autres_Revenus'], errors='coerce').fillna(0)
-        df_full['Charges_Estimees'] = pd.to_numeric(df_full['Charges_Estimees'], errors='coerce').fillna(0)
-        df_full['Patrimoine_Declare'] = pd.to_numeric(df_full['Patrimoine_Declare'], errors='coerce').fillna(0)
-        df_full['Montant_Capital'] = pd.to_numeric(df_full['Montant_Capital'], errors='coerce').fillna(0)
-        df_full['Montant_Versement'] = pd.to_numeric(df_full['Montant_Versement'], errors='coerce').fillna(0)
-        
-        df_full['Revenus_Totaux'] = df_full['Revenus_Mensuels'] + df_full['Autres_Revenus']
-        df_full['Revenu_Net'] = df_full['Revenus_Totaux'] - df_full['Charges_Estimees']
-        df_full['Ratio_Charges_Revenus'] = (df_full['Charges_Estimees'] / df_full['Revenus_Totaux']).replace([np.inf, -np.inf], 0).fillna(0)
-        df_full['Taux_Effort_Mensuel'] = ((df_full['Montant_Versement'] * 4.33) / df_full['Revenus_Totaux']).replace([np.inf, -np.inf], 0).fillna(0) * 100
-        df_full['Capacite_Endettement'] = df_full['Revenus_Totaux'] * 0.33
-        df_full['Reste_A_Vivre'] = df_full['Revenus_Totaux'] - df_full['Charges_Estimees'] - (df_full['Montant_Versement'] * 4.33)
-        
-        col1, col2, col3, col4 = st.columns(4)
-        col1.metric("Revenu moyen", f"{df_full['Revenus_Totaux'].mean():,.0f} XOF",
-                    delta=f"Med {df_full['Revenus_Totaux'].median():,.0f}")
-        col2.metric("Taux effort", f"{df_full['Taux_Effort_Mensuel'].mean():.1f}%",
-                    delta="Risque" if df_full['Taux_Effort_Mensuel'].mean() > 33 else "Sain")
-        col3.metric("Reste à vivre", f"{df_full['Reste_A_Vivre'].mean():,.0f} XOF",
-                    delta=f"Min {df_full['Reste_A_Vivre'].min():,.0f}")
-        col4.metric("Patrimoine", f"{df_full['Patrimoine_Declare'].mean():,.0f} XOF")
-        
-        fig_effort = px.box(df_full, y='Taux_Effort_Mensuel', 
-                            title="Taux d'effort mensuel (%)",
-                            color_discrete_sequence=['#58a6ff'],
-                            template=plotly_template)
-        fig_effort.add_hline(y=33, line_dash="dash", line_color="rgba(248, 81, 73, 0.6)", 
-                             line_width=1.5,
-                             annotation_text="Seuil critique 33%",
-                             annotation_font_color="#8b949e",
-                             annotation_font_size=11)
-        fig_effort.update_traces(opacity=0.7)
-        st.plotly_chart(fig_effort, use_container_width=True)
-    
-    # 3. FEATURES PROFESSIONNELLES
-    with st.expander("Features Professionnelles", expanded=True):
-        df_full['Anciennete_Emploi'] = pd.to_numeric(df_full['Anciennete_Emploi'], errors='coerce').fillna(0)
-        df_full['Anciennete_Revenu'] = pd.to_numeric(df_full['Anciennete_Revenu'], errors='coerce').fillna(0)
-        df_full['Stabilite_Pro'] = df_full['Anciennete_Emploi'].apply(
-            lambda x: 'Élevée' if x >= 36 else ('Moyenne' if x >= 12 else 'Faible')
-        )
-        
-        col1, col2, col3 = st.columns(3)
-        col1.metric("Ancienneté", f"{df_full['Anciennete_Emploi'].mean():.1f} mois")
-        col2.metric("CDI / Stable", f"{(df_full['Statut_Pro']=='CDI').sum()}")
-        col3.metric("Indépendants", f"{(df_full['Statut_Pro']=='Indépendant').sum()}")
-        
-        secteur_count = df_full['Secteur_Activite'].value_counts().head(10)
-        fig_secteur = px.bar(x=secteur_count.values, y=secteur_count.index,
-                             orientation='h',
-                             title="Top 10 secteurs d'activité",
-                             color_discrete_sequence=['#58a6ff'],
-                             template=plotly_template)
-        fig_secteur.update_traces(marker_line_color='rgba(139, 148, 158, 0.2)', marker_line_width=0.5, opacity=0.8)
-        st.plotly_chart(fig_secteur, use_container_width=True)
-    
-    # 4. FEATURES DE PRÊTS
-    with st.expander("Features de Prêts & Historique", expanded=True):
-        # Conversion des colonnes en numérique AVANT le groupby
-        df_prets['Montant_Capital'] = pd.to_numeric(df_prets['Montant_Capital'], errors='coerce').fillna(0)
-        df_prets['Duree_Semaines'] = pd.to_numeric(df_prets['Duree_Semaines'], errors='coerce').fillna(0)
-        
-        pret_history = df_prets.groupby('ID_Client').agg({
-            'ID_Pret': 'count',
-            'Montant_Capital': ['sum', 'mean', 'max'],
-            'Duree_Semaines': 'mean',
-            'Statut': lambda x: (x == 'Actif').sum()
-        }).reset_index()
-        
-        pret_history.columns = ['ID_Client', 'Nb_Prets_Total', 'Montant_Total_Emprunte',
-                                'Montant_Moyen_Pret', 'Montant_Max_Pret', 
-                                'Duree_Moyenne_Semaines', 'Nb_Prets_Actifs']
-        
-        df_full = pd.merge(df_full, pret_history, on='ID_Client', how='left')
-        df_full['Ratio_Pret_Revenu'] = (df_full['Montant_Capital'] / df_full['Revenus_Totaux']).replace([np.inf, -np.inf], 0).fillna(0)
-        df_full['Frequence_Emprunt'] = df_full['Nb_Prets_Total']
-        
-        col1, col2, col3, col4 = st.columns(4)
-        col1.metric("Prêts / client", f"{df_full['Nb_Prets_Total'].mean():.1f}",
-                    delta=f"Max {df_full['Nb_Prets_Total'].max():.0f}")
-        col2.metric("Montant moy", f"{df_full['Montant_Moyen_Pret'].mean():,.0f} XOF")
-        col3.metric("Durée moy", f"{df_full['Duree_Moyenne_Semaines'].mean():.0f} sem")
-        col4.metric("Prêts actifs", f"{df_full['Nb_Prets_Actifs'].mean():.1f}")
-        
-        fig_montant = px.scatter(df_full, x='Revenus_Totaux', y='Montant_Capital',
-                                 size='Nb_Prets_Total', color='Statut_Pro',
-                                 title="Montant prêt vs revenus",
-                                 hover_data=['Nom_Complet_Client'],
-                                 color_discrete_sequence=['#58a6ff', '#79c0ff', '#a5d6ff', '#c9d1d9'],
-                                 template=plotly_template)
-        fig_montant.update_traces(opacity=0.7)
-        st.plotly_chart(fig_montant, use_container_width=True)
-    
-    # 5. FEATURES GÉOGRAPHIQUES
-    with st.expander("Features Géographiques", expanded=True):
-        ville_count = df_full['Ville'].value_counts()
-        quartier_count = df_full['Quartier'].value_counts().head(10)
-        
-        col1, col2 = st.columns(2)
-        with col1:
-            st.metric("Villes", len(ville_count))
-            fig_ville = px.pie(values=ville_count.values, names=ville_count.index,
-                              title="Répartition par ville",
-                              color_discrete_sequence=['#58a6ff', '#79c0ff', '#a5d6ff', '#c9d1d9', '#8b949e'],
-                              template=plotly_template)
-            fig_ville.update_traces(textfont_family='Space Grotesk', opacity=0.85)
-            st.plotly_chart(fig_ville, use_container_width=True)
-        
-        with col2:
-            st.metric("Quartiers", len(quartier_count))
-            fig_quartier = px.bar(x=quartier_count.values, y=quartier_count.index,
-                                  orientation='h', title="Top 10 quartiers",
-                                  color_discrete_sequence=['#58a6ff'],
-                                  template=plotly_template)
-            fig_quartier.update_traces(opacity=0.8)
-            st.plotly_chart(fig_quartier, use_container_width=True)
-    
-    # 6. FEATURES TEMPORELLES
-    with st.expander("Features Temporelles", expanded=True):
-        df_full['Date_Creation'] = pd.to_datetime(df_full.get('Date_Creation', pd.Series()), errors='coerce')
-        df_full['Anciennete_Client_Jours'] = (datetime.now() - df_full['Date_Creation']).dt.days
-        df_full['Anciennete_Client_Mois'] = (df_full['Anciennete_Client_Jours'] / 30.44).round(1)
-        
-        col1, col2 = st.columns(2)
-        col1.metric("Ancienneté moy", f"{df_full['Anciennete_Client_Mois'].mean():.1f} mois")
-        col2.metric("Client + ancien", f"{df_full['Anciennete_Client_Mois'].max():.0f} mois")
-        
-        df_full['Mois_Creation'] = df_full['Date_Creation'].dt.to_period('M').astype(str)
-        timeline = df_full.groupby('Mois_Creation').size().reset_index(name='Nb_Prets')
-        fig_timeline = px.line(timeline, x='Mois_Creation', y='Nb_Prets',
-                              title="Évolution temporelle des prêts",
-                              markers=True,
-                              color_discrete_sequence=['#58a6ff'],
-                              template=plotly_template)
-        fig_timeline.update_traces(line_width=2, marker_size=6, opacity=0.9)
-        st.plotly_chart(fig_timeline, use_container_width=True)
-    
-    # MÉTRIQUES GLOBALES
-    st.divider()
-    st.markdown("<h2>Métriques Globales</h2>", unsafe_allow_html=True)
-    col1, col2, col3 = st.columns(3)
-    col1.metric("Taux défaut", "4.2 %", delta="Simulé")
-    col2.metric("Montant moy", "10,000 XOF")
-    col3.metric("Durée moy", "12 semaines")
+    # === APPEL DU MODULE ML FEATURE STORE ===
+    from Analytics.ML_Feature_Store_Analytics import show_ml_feature_store
     
-    # TABLE DATASET
-    st.divider()
-    st.markdown("<h2>Dataset ML Enrichi</h2>", unsafe_allow_html=True)
-    
-    features_cols = [
-        'ID_Pret', 'ID_Client', 'Nom_Complet_Client', 'Age', 'Categorie_Age',
-        'Revenus_Totaux', 'Revenu_Net', 'Ratio_Charges_Revenus', 
-        'Taux_Effort_Mensuel', 'Reste_A_Vivre', 'Patrimoine_Declare',
-        'Stabilite_Pro', 'Anciennete_Emploi', 'Statut_Pro',
-        'Nb_Prets_Total', 'Montant_Moyen_Pret', 'Ratio_Pret_Revenu',
-        'Ville', 'Quartier', 'Anciennete_Client_Mois', 'Statut'
-    ]
-    
-    features_cols = [col for col in features_cols if col in df_full.columns]
-    df_display = df_full[features_cols].copy()
-    numeric_cols = df_display.select_dtypes(include=[np.number]).columns
-    df_display[numeric_cols] = df_display[numeric_cols].round(2)
-    
-    st.dataframe(df_display, use_container_width=True, height=400)
-    
-    # EXPORT
-    st.divider()
-    st.markdown("<h2>Export Dataset ML</h2>", unsafe_allow_html=True)
-    
-    col1, col2 = st.columns([2, 1])
-    with col1:
-        csv = df_full.to_csv(index=False).encode('utf-8')
-        st.download_button(
-            label="Télécharger dataset complet (.csv)",
-            data=csv,
-            file_name=f'dataset_credit_scoring_{datetime.now().strftime("%Y%m%d_%H%M%S")}.csv',
-            mime='text/csv',
-            use_container_width=True
-        )
-    
-    with col2:
-        st.info(f"**{len(df_full)} lignes** \n**{len(df_full.columns)} features**")
-    
-    with st.expander("Liste des features créées"):
-        created_features = [
-            'Age', 'Categorie_Age', 'Revenus_Totaux', 'Revenu_Net',
-            'Ratio_Charges_Revenus', 'Taux_Effort_Mensuel', 'Capacite_Endettement',
-            'Reste_A_Vivre', 'Stabilite_Pro', 'Nb_Prets_Total', 'Montant_Total_Emprunte',
-            'Montant_Moyen_Pret', 'Montant_Max_Pret', 'Ratio_Pret_Revenu',
-            'Frequence_Emprunt', 'Anciennete_Client_Jours', 'Anciennete_Client_Mois'
-        ]
-        st.markdown("**Features engineering:**")
-        for i, feat in enumerate(created_features, 1):
-            st.markdown(f"`{i:02d}` {feat}")
+    show_ml_feature_store(client, sheet_name)