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 et du thème st.set_page_config( page_title="ML Model Interpreter", layout="wide", initial_sidebar_state="expanded" ) # CSS personnalisé st.markdown(""" """, unsafe_allow_html=True) def custom_metric_card(title, value, prefix=""): return f"""

{title}

{prefix}{value:.4f}

""" def plot_with_style(fig): # Style matplotlib plt.style.use('seaborn') fig.patch.set_facecolor('#FFFFFF') for ax in fig.axes: ax.set_facecolor('#F8F9FA') ax.grid(True, linestyle='--', alpha=0.7) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) return fig # [Fonctions load_data et train_models restent identiques] 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'] # 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) 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) 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') plt.tight_layout() return fig def app(): # En-tête principal avec style personnalisé st.markdown('

Interpréteur de Modèles ML

', 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 avec style personnalisé with st.sidebar: st.markdown('

Navigation

', unsafe_allow_html=True) selected_model = st.selectbox( "📊 Sélectionnez un modèle", list(st.session_state.model_results.keys()) ) st.markdown('
', 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'] # Container principal avec padding main_container = st.container() with main_container: if page == "Performance des modèles": st.markdown('

Performance des modèles

', 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('

Métriques détaillées

', unsafe_allow_html=True) col1, col2 = st.columns(2) with col1: st.markdown('

Entraînement

', 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('

Test

', 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) # [Le reste des sections avec style adapté...] if __name__ == "__main__": app()