AlexBarbier/mlflow-tracking-server

1import mlflow2import mlflow.sklearn3from sklearn.datasets import load_iris4from sklearn.ensemble import RandomForestClassifier5from sklearn.model_selection import train_test_split6from sklearn.metrics import accuracy_score, f1_score7 8# Pointe vers ton MLflow sur HF Spaces9mlflow.set_tracking_uri("https://alexbarbier-mlflow-tracking-server.hf.space")10mlflow.set_experiment("iris_classification")11 12# Charge les données13iris = load_iris()14X_train, X_test, y_train, y_test = train_test_split(15    iris.data, iris.target, test_size=0.2, random_state=4216)17 18# Entraîne un modèle19model = RandomForestClassifier(n_estimators=100, random_state=42)20model.fit(X_train, y_train)21 22# Prédit23y_pred = model.predict(X_test)24 25# Log dans MLflow26with mlflow.start_run(run_name="rf_v1"):27    # Log les paramètres28    mlflow.log_param("n_estimators", 100)29    mlflow.log_param("random_state", 42)30    31    # Log les métriques32    accuracy = accuracy_score(y_test, y_pred)33    f1 = f1_score(y_test, y_pred, average="weighted")34    35    mlflow.log_metric("accuracy", accuracy)36    mlflow.log_metric("f1_score", f1)37    38    # Log le modèle39    mlflow.sklearn.log_model(model, "random_forest_model")40    41    print(f"✅ Run logged! Accuracy: {accuracy:.4f}, F1: {f1:.4f}")