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Dockerfile

@@ -13,8 +13,6 @@ RUN apt-get update && apt-get install -y \
 COPY requirements.txt requirements.txt
 RUN pip install -r requirements.txt
 
-RUN pip install mlflow==2.15.0 -r requirements.txt
-
 
 
 # Exposition du port MLflow

model.py

@@ -0,0 +1,107 @@
+import mlflow
+import pandas as pd
+import os
+import subprocess
+import numpy as np
+from dotenv import load_dotenv
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
+from xgboost import XGBRegressor
+import boto3
+import joblib
+import io
+
+load_dotenv(dotenv_path='.secrets')
+
+mlflow.set_tracking_uri(os.getenv('BACKEND_STORE_URI'))
+os.environ['AWS_ACCESS_KEY_ID'] = os.getenv('AWS_ACCESS_KEY_ID')
+os.environ['AWS_SECRET_ACCESS_KEY'] = os.getenv('AWS_SECRET_ACCESS_KEY')
+os.environ['MLFLOW_DEFAULT_ARTIFACT_ROOT'] = os.getenv('MLFLOW_DEFAULT_ARTIFACT_ROOT')
+os.environ['S3_BUCKET'] = os.getenv('S3_BUCKET')
+
+# Log configurations au démarrage
+print("=== Configuration MLflow ===")
+print(f"Tracking URI: {mlflow.get_tracking_uri()}")
+print(f"Artifact Store: {os.getenv('MLFLOW_DEFAULT_ARTIFACT_ROOT')}")
+print(f"AWS Access: {'Configuré' if os.getenv('AWS_ACCESS_KEY_ID') else 'Manquant'}")
+
+s3 = boto3.client('s3')
+try:
+   response = s3.list_objects_v2(Bucket=os.getenv('S3_BUCKET'))
+   print("S3 contents:", response.get('Contents', []))
+except Exception as e:
+   print("S3 error:", e)
+
+df = pd.read_csv('/mnt/c/Users/m_bar/dsfs_ft/GETAROUND/pricing_clean.csv')
+feature_list = df.drop('rental_price_per_day', axis=1)
+target = df['rental_price_per_day']
+X = feature_list
+Y = target
+X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=42)
+
+numeric_features = ['mileage', 'engine_power']
+categorical_features = ['model_key','fuel', 'paint_color', 'car_type', 'private_parking_available', 'has_gps', 'has_air_conditioning','automatic_car','has_getaround_connect','has_speed_regulator','winter_tires']
+
+numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])
+categorical_transformer = Pipeline(steps=[('encoder', OneHotEncoder(drop='if_binary', handle_unknown='ignore'))])
+
+preprocessor = ColumnTransformer(transformers =[
+    ('num', numeric_transformer, numeric_features),
+    ('cat', categorical_transformer, categorical_features)
+])
+
+def train_evaluate_model_with_mlflow(model, X_train, X_test, Y_train, Y_test, model_name):
+   print(f"\n=== Démarrage entraînement {model_name} ===")
+   print(f"Tracking URI: {mlflow.get_tracking_uri()}")
+   print(f"Registry URI: {mlflow.get_registry_uri()}")
+   
+   mlflow.set_experiment("price_prediction")
+   print(f"Experiment: price_prediction")
+   s3 = boto3.client('s3')
+
+   with mlflow.start_run() as run:
+       print(f"Run ID: {run.info.run_id}")
+       
+       print("Entraînement du modèle...")
+       model.fit(X_train, Y_train)
+       
+       #save model to S3
+       print("Enregistrement du modèle sur S3...")
+       model_path = f"mlflow/models/{model_name}_{run.info.run_id}.joblib"
+       buffer = io.BytesIO()
+       joblib.dump(model, buffer)
+       s3.put_object(
+           Bucket=os.getenv('S3_BUCKET'),
+           Key=model_path,
+           Body=buffer.getvalue()
+       )
+       print("Modèle enregistré")
+       
+       y_pred = model.predict(X_test)
+       metrics = {
+           "RMSE": np.sqrt(mean_squared_error(Y_test, y_pred)),
+           "MAE": mean_absolute_error(Y_test, y_pred),
+           "R2": r2_score(Y_test, y_pred)
+       }
+       
+       print("\nEnregistrement des métriques...")
+       for name, value in metrics.items():
+           mlflow.log_metric(name, value)
+           print(f"{name}: {value:.2f}")
+       
+       return model, run.info.run_id
+
+if __name__ == "__main__":
+    xgb_best_param = XGBRegressor(learning_rate = 0.1, max_depth = 5, n_estimators =200, n_jobs = 1)
+    xgb_final = Pipeline(steps=[
+        ('preprocessor', preprocessor),
+        ('xgboost_best', xgb_best_param)
+    ])
+    _, run_id = train_evaluate_model_with_mlflow(
+        xgb_final, X_train, X_test, Y_train, Y_test, "xgboost_model"
+    )
+    print(f"Run ID: {run_id}")