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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.tree import plot_tree, export_text | |
| import seaborn as sns | |
| from sklearn.preprocessing import LabelEncoder | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import GradientBoostingClassifier | |
| 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 | |
| def load_data(): | |
| data = pd.read_csv('exported_named_train_good.csv') | |
| data_test = pd.read_csv('exported_named_test_good.csv') | |
| X_train = data.drop("Target", axis=1) | |
| y_train = data['Target'] | |
| X_test = data_test.drop('Target', axis=1) | |
| y_test = data_test['Target'] | |
| return X_train, y_train, X_test, y_test, X_train.columns | |
| def train_models(X_train, y_train, X_test, y_test): | |
| models = { | |
| "Logistic Regression": LogisticRegression(random_state=42), | |
| "Decision Tree": DecisionTreeClassifier(random_state=42), | |
| "Random Forest": RandomForestClassifier(random_state=42), | |
| "Gradient Boost": GradientBoostingClassifier(random_state=42) | |
| } | |
| results = {} | |
| for name, model in models.items(): | |
| model.fit(X_train, y_train) | |
| # Predictions | |
| y_train_pred = model.predict(X_train) | |
| y_test_pred = model.predict(X_test) | |
| # Metrics | |
| results[name] = { | |
| 'model': model, | |
| 'train_metrics': { | |
| 'accuracy': accuracy_score(y_train, y_train_pred), | |
| 'f1': f1_score(y_train, y_train_pred, average='weighted'), | |
| 'precision': precision_score(y_train, y_train_pred), | |
| 'recall': recall_score(y_train, y_train_pred), | |
| 'roc_auc': roc_auc_score(y_train, y_train_pred) | |
| }, | |
| 'test_metrics': { | |
| 'accuracy': accuracy_score(y_test, y_test_pred), | |
| 'f1': f1_score(y_test, y_test_pred, average='weighted'), | |
| 'precision': precision_score(y_test, y_test_pred), | |
| 'recall': recall_score(y_test, y_test_pred), | |
| 'roc_auc': roc_auc_score(y_test, y_test_pred) | |
| } | |
| } | |
| return results | |
| def plot_model_performance(results): | |
| metrics = ['accuracy', 'f1', 'precision', 'recall', 'roc_auc'] | |
| fig, axes = plt.subplots(1, 2, figsize=(15, 6)) | |
| # 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='Training Performance') | |
| 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='Test Performance') | |
| axes[1].set_ylim(0, 1) | |
| plt.tight_layout() | |
| return fig | |
| def plot_feature_importance(model, feature_names, model_type): | |
| plt.figure(figsize=(10, 6)) | |
| 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) | |
| plt.barh(importance_df['feature'], importance_df['importance']) | |
| plt.title(f"Feature Importance - {model_type}") | |
| return plt.gcf() | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.tree import plot_tree, export_text | |
| import seaborn as sns | |
| from sklearn.preprocessing import LabelEncoder | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import GradientBoostingClassifier | |
| 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 | |
| st.set_page_config( | |
| page_title="ML Model Interpreter", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # CSS personnalisé | |
| st.markdown(""" | |
| <style> | |
| .main-header { | |
| color: #0D47A1; | |
| text-align: center; | |
| padding: 1rem; | |
| background: linear-gradient(90deg, #FFFFFF 0%, #90CAF9 50%, #FFFFFF 100%); | |
| border-radius: 10px; | |
| margin-bottom: 2rem; | |
| } | |
| .metric-card { | |
| background-color: white; | |
| padding: 1.5rem; | |
| border-radius: 10px; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| margin-bottom: 1rem; | |
| } | |
| .sub-header { | |
| color: #1E88E5; | |
| border-bottom: 2px solid #90CAF9; | |
| padding-bottom: 0.5rem; | |
| margin-bottom: 1rem; | |
| } | |
| .metric-value { | |
| font-size: 1.5rem; | |
| font-weight: bold; | |
| color: #1E88E5; | |
| } | |
| div[data-testid="stMetricValue"] { | |
| color: #1E88E5; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def custom_metric_card(title, value, prefix=""): | |
| return f""" | |
| <div class="metric-card"> | |
| <h3 style="color: #1E88E5; margin-bottom: 0.5rem;">{title}</h3> | |
| <p class="metric-value">{prefix}{value:.4f}</p> | |
| </div> | |
| """ | |
| 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=0.3, color='#666666') | |
| ax.spines['top'].set_visible(False) | |
| ax.spines['right'].set_visible(False) | |
| 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)) | |
| 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], 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], 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: | |
| 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(): | |
| 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() | |
| # Train models if not in session state | |
| if 'model_results' not in st.session_state: | |
| with st.spinner("🔄 Entraînement des modèles en cours..."): | |
| st.session_state.model_results = train_models(X_train, y_train, X_test, y_test) | |
| # Sidebar | |
| with st.sidebar: | |
| 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()) | |
| ) | |
| st.markdown('<hr style="margin: 1rem 0;">', unsafe_allow_html=True) | |
| page = st.radio( | |
| "📑 Sélectionnez une section", | |
| ["Performance des modèles", | |
| "Interprétation du modèle", | |
| "Analyse des caractéristiques", | |
| "Simulateur de prédictions"] | |
| ) | |
| current_model = st.session_state.model_results[selected_model]['model'] | |
| # 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) | |
| 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() |