Update app.py

app.py

@@ -110,7 +110,7 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve
 import shap
 
-# Configuration de la page et du thème
+# Configuration de la page
 st.set_page_config(
     page_title="ML Model Interpreter",
     layout="wide",
@@ -120,25 +120,15 @@ st.set_page_config(
 # CSS personnalisé
 st.markdown("""
 <style>
-    /* Couleurs principales */
-    :root {
-        --primary-blue: #1E88E5;
-        --light-blue: #90CAF9;
-        --dark-blue: #0D47A1;
-        --white: #FFFFFF;
-    }
-    
-    /* En-tête principal */
     .main-header {
-        color: var(--dark-blue);
+        color: #0D47A1;
         text-align: center;
         padding: 1rem;
-        background: linear-gradient(90deg, var(--white) 0%, var(--light-blue) 50%, var(--white) 100%);
+        background: linear-gradient(90deg, #FFFFFF 0%, #90CAF9 50%, #FFFFFF 100%);
         border-radius: 10px;
         margin-bottom: 2rem;
     }
     
-    /* Carte pour les métriques */
     .metric-card {
         background-color: white;
         padding: 1.5rem;
@@ -147,44 +137,21 @@ st.markdown("""
         margin-bottom: 1rem;
     }
     
-    /* Style pour les sous-titres */
     .sub-header {
-        color: var(--primary-blue);
-        border-bottom: 2px solid var(--light-blue);
+        color: #1E88E5;
+        border-bottom: 2px solid #90CAF9;
         padding-bottom: 0.5rem;
         margin-bottom: 1rem;
     }
     
-    /* Style pour les valeurs de métriques */
     .metric-value {
         font-size: 1.5rem;
         font-weight: bold;
-        color: var(--primary-blue);
-    }
-    
-    /* Style pour la barre latérale */
-    .sidebar .sidebar-content {
-        background-color: var(--white);
-    }
-    
-    /* Style pour les boutons */
-    .stButton > button {
-        background-color: var(--primary-blue);
-        color: white;
-        border-radius: 5px;
-        border: none;
-        padding: 0.5rem 1rem;
+        color: #1E88E5;
     }
     
-    /* Style pour les sliders */
-    .stSlider > div > div {
-        background-color: var(--light-blue);
-    }
-    
-    /* Style pour les selectbox */
-    .stSelectbox > div > div {
-        background-color: white;
-        border: 1px solid var(--light-blue);
+    div[data-testid="stMetricValue"] {
+        color: #1E88E5;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -197,57 +164,80 @@ def custom_metric_card(title, value, prefix=""):
     </div>
     """
 
-def plot_with_style(fig):
-    # Style matplotlib
-    plt.style.use('seaborn')
-    fig.patch.set_facecolor('#FFFFFF')
+def set_plot_style(fig):
+    """Configure le style des graphiques"""
+    colors = ['#1E88E5', '#90CAF9', '#0D47A1', '#42A5F5']
     for ax in fig.axes:
         ax.set_facecolor('#F8F9FA')
-        ax.grid(True, linestyle='--', alpha=0.7)
+        ax.grid(True, linestyle='--', alpha=0.3, color='#666666')
         ax.spines['top'].set_visible(False)
         ax.spines['right'].set_visible(False)
-    return fig
-
-# [Fonctions load_data et train_models restent identiques]
+        ax.tick_params(axis='both', colors='#666666')
+        ax.set_axisbelow(True)
+    return fig, colors
 
 def plot_model_performance(results):
     metrics = ['accuracy', 'f1', 'precision', 'recall', 'roc_auc']
     fig, axes = plt.subplots(1, 2, figsize=(15, 6))
-    
-    # Configuration du style
-    plt.style.use('seaborn')
-    colors = ['#1E88E5', '#90CAF9', '#0D47A1', '#42A5F5']
+    fig, colors = set_plot_style(fig)
     
     # Training metrics
     train_data = {model: [results[model]['train_metrics'][metric] for metric in metrics] 
                  for model in results.keys()}
     train_df = pd.DataFrame(train_data, index=metrics)
-    train_df.plot(kind='bar', ax=axes[0], title='Performance d\'Entraînement',
-                 color=colors)
+    train_df.plot(kind='bar', ax=axes[0], color=colors)
+    axes[0].set_title('Performance d\'Entraînement', color='#0D47A1', pad=20)
     axes[0].set_ylim(0, 1)
     
     # Test metrics
     test_data = {model: [results[model]['test_metrics'][metric] for metric in metrics] 
                 for model in results.keys()}
     test_df = pd.DataFrame(test_data, index=metrics)
-    test_df.plot(kind='bar', ax=axes[1], title='Performance de Test',
-                color=colors)
+    test_df.plot(kind='bar', ax=axes[1], color=colors)
+    axes[1].set_title('Performance de Test', color='#0D47A1', pad=20)
     axes[1].set_ylim(0, 1)
     
     # Style des graphiques
     for ax in axes:
-        ax.set_facecolor('#F8F9FA')
-        ax.grid(True, linestyle='--', alpha=0.7)
-        ax.spines['top'].set_visible(False)
-        ax.spines['right'].set_visible(False)
         plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
+        ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
     
     plt.tight_layout()
     return fig
 
+def plot_feature_importance(model, feature_names, model_type):
+    fig, ax = plt.subplots(figsize=(10, 6))
+    fig, colors = set_plot_style(fig)
+    
+    if model_type in ["Decision Tree", "Random Forest", "Gradient Boost"]:
+        importance = model.feature_importances_
+    elif model_type == "Logistic Regression":
+        importance = np.abs(model.coef_[0])
+    
+    importance_df = pd.DataFrame({
+        'feature': feature_names,
+        'importance': importance
+    }).sort_values('importance', ascending=True)
+    
+    ax.barh(importance_df['feature'], importance_df['importance'], 
+            color='#1E88E5', alpha=0.8)
+    ax.set_title("Importance des Caractéristiques", color='#0D47A1', pad=20)
+    
+    return fig
+
+def plot_correlation_matrix(data):
+    fig, ax = plt.subplots(figsize=(10, 8))
+    fig, _ = set_plot_style(fig)
+    
+    sns.heatmap(data.corr(), annot=True, cmap='coolwarm', center=0,
+                ax=ax, fmt='.2f', square=True)
+    ax.set_title("Matrice de Corrélation", color='#0D47A1', pad=20)
+    
+    return fig
+
 def app():
-    # En-tête principal avec style personnalisé
-    st.markdown('<h1 class="main-header">Interpréteur de Modèles ML</h1>', unsafe_allow_html=True)
+    st.markdown('<h1 class="main-header">Interpréteur de Modèles ML</h1>', 
+                unsafe_allow_html=True)
     
     # Load data
     X_train, y_train, X_test, y_test, feature_names = load_data()
@@ -257,9 +247,11 @@ def app():
         with st.spinner("🔄 Entraînement des modèles en cours..."):
             st.session_state.model_results = train_models(X_train, y_train, X_test, y_test)
     
-    # Sidebar avec style personnalisé
+    # Sidebar
     with st.sidebar:
-        st.markdown('<h2 style="color: #1E88E5;">Navigation</h2>', unsafe_allow_html=True)
+        st.markdown('<h2 style="color: #1E88E5;">Navigation</h2>', 
+                   unsafe_allow_html=True)
+        
         selected_model = st.selectbox(
             "📊 Sélectionnez un modèle",
             list(st.session_state.model_results.keys())
@@ -277,31 +269,43 @@ def app():
     
     current_model = st.session_state.model_results[selected_model]['model']
     
-    # Container principal avec padding
-    main_container = st.container()
-    with main_container:
-        if page == "Performance des modèles":
-            st.markdown('<h2 class="sub-header">Performance des modèles</h2>', unsafe_allow_html=True)
-            
-            # Graphiques de performance
-            performance_fig = plot_model_performance(st.session_state.model_results)
-            st.pyplot(plot_with_style(performance_fig))
-            
-            # Métriques détaillées dans des cartes
-            st.markdown('<h3 class="sub-header">Métriques détaillées</h3>', unsafe_allow_html=True)
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                st.markdown('<h4 style="color: #1E88E5;">Entraînement</h4>', unsafe_allow_html=True)
-                for metric, value in st.session_state.model_results[selected_model]['train_metrics'].items():
-                    st.markdown(custom_metric_card(metric.capitalize(), value), unsafe_allow_html=True)
-            
-            with col2:
-                st.markdown('<h4 style="color: #1E88E5;">Test</h4>', unsafe_allow_html=True)
-                for metric, value in st.session_state.model_results[selected_model]['test_metrics'].items():
-                    st.markdown(custom_metric_card(metric.capitalize(), value), unsafe_allow_html=True)
+    # Main content
+    if page == "Performance des modèles":
+        st.markdown('<h2 class="sub-header">Performance des modèles</h2>', 
+                   unsafe_allow_html=True)
+        
+        performance_fig = plot_model_performance(st.session_state.model_results)
+        st.pyplot(performance_fig)
+        
+        st.markdown('<h3 class="sub-header">Métriques détaillées</h3>', 
+                   unsafe_allow_html=True)
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown('<h4 style="color: #1E88E5;">Entraînement</h4>', 
+                       unsafe_allow_html=True)
+            for metric, value in st.session_state.model_results[selected_model]['train_metrics'].items():
+                st.markdown(custom_metric_card(metric.capitalize(), value), 
+                          unsafe_allow_html=True)
+        
+        with col2:
+            st.markdown('<h4 style="color: #1E88E5;">Test</h4>', 
+                       unsafe_allow_html=True)
+            for metric, value in st.session_state.model_results[selected_model]['test_metrics'].items():
+                st.markdown(custom_metric_card(metric.capitalize(), value), 
+                          unsafe_allow_html=True)
+    
+    elif page == "Analyse des caractéristiques":
+        st.markdown('<h2 class="sub-header">Analyse des caractéristiques</h2>', 
+                   unsafe_allow_html=True)
+        
+        importance_fig = plot_feature_importance(current_model, feature_names, selected_model)
+        st.pyplot(importance_fig)
         
-        # [Le reste des sections avec style adapté...]
+        st.markdown('<h3 class="sub-header">Corrélations</h3>', 
+                   unsafe_allow_html=True)
+        corr_fig = plot_correlation_matrix(X_train)
+        st.pyplot(corr_fig)
 
 if __name__ == "__main__":
     app()