| import pandas as pd |
| import mlflow |
| import joblib |
| import io |
| import boto3 |
| from sklearn.model_selection import train_test_split |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder |
| from sklearn.compose import ColumnTransformer |
| from sklearn.pipeline import Pipeline |
| from sklearn.metrics import f1_score, recall_score, precision_score |
| from xgboost import XGBClassifier |
| import psycopg2 |
| from sqlalchemy import create_engine |
| import os |
|
|
| def run_retraining(): |
| db_user = os.getenv("DB_USER") |
| db_password = os.getenv("DB_PASS") |
| db_host = os.getenv("DB_HOST") |
| db_name = os.getenv("DB_NAME") |
| engine = create_engine(f"postgresql+psycopg2://{db_user}:{db_password}@{db_host}/{db_name}") |
| df = pd.read_sql("SELECT * FROM fraud_transaction", engine) |
| df=pd.DataFrame(df) |
| X=df.drop(columns=["is_fraud", "is_fraud_pred", "timestamp","id", "inserted_at"]) |
| y=df["is_fraud"] |
| X_train, X_test, y_train,y_test=train_test_split(X,y, test_size=0.2, random_state=42, stratify=y) |
| numeric_features = X.select_dtypes(include=['int64', 'float64', "int32"]).columns |
| categoric_features = X.select_dtypes(include=['object']).columns |
| numeric_transformer = StandardScaler() |
| categorical_transformer = OneHotEncoder() |
| preprocessor = ColumnTransformer( |
| transformers=[ |
| ('num', numeric_transformer, numeric_features), |
| ('cat', categorical_transformer, categoric_features)]) |
| xgb = XGBClassifier(n_estimators=1200,max_depth=10, learning_rate=0.2, random_state=42) |
| xgb_final = Pipeline(steps=[ |
| ('preprocessor', preprocessor), |
| ('xgboost_best', xgb) |
| ]) |
| mlflow.set_tracking_uri("https://gdleds-mlflow-fraud.hf.space") |
| mlflow.set_experiment("fraud_detection") |
|
|
| with mlflow.start_run() as run: |
| xgb_final.fit(X_train, y_train) |
| y_pred = xgb_final.predict(X_test) |
|
|
| f1 = f1_score(y_test, y_pred) |
|
|
| mlflow.log_metric("F1", f1) |
| mlflow.log_metric("precision", precision_score(y_test, y_pred)) |
| mlflow.log_metric("recall", recall_score(y_test, y_pred)) |
|
|
| |
| s3 = boto3.client("s3") |
| bucket = os.getenv("S3_BUCKET") |
| key = f"mlflow/models/xgboost_model_{run.info.run_id}.joblib" |
|
|
| buffer = io.BytesIO() |
| joblib.dump(xgb_final, buffer) |
|
|
| s3.put_object( |
| Bucket=bucket, |
| Key=key, |
| Body=buffer.getvalue() |
| ) |
|
|
| return { |
| "status": "success", |
| "run_id": run.info.run_id, |
| "f1_score": f1, |
| "model_path": key |
| } |
|
|
