MLflow-Basic-Operations/wine_temp.py

import os 
import warnings
import sys

import pandas as pd
import numpy as np
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from urllib.parse import urlparse
import mlflow
from mlflow.models.signature import infer_signature
import mlflow.sklearn

import logging

logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)

def eval_metrics(actual, pred):
    rmse = np.sqrt(mean_absolute_error(actual, pred))
    mae = mean_absolute_error(actual, pred)
    r2 = r2_score(actual, pred)
    return rmse, mae, r2


if __name__ == "__main__":
    warnings.filterwarnings("ignore")
    np.random.seed(40)

    # Wine-quality CSV dosyasını URL'den oku
    csv_url = "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-red.csv"

    try:
        data = pd.read_csv(csv_url, sep=";")
    except Exception as e:
        logger.exception("Eğitim ve test CSV'si indirilemedi, internet bağlantınızı kontrol edin. Hata: %s", e)

    # Veriyi eğitim ve test kümelerine ayır (0.75 , 0.25)
    train, test = train_test_split(data, test_size=0.25)

    # Tahmin edilecek sütun "quality", bu sütun [3, 9] aralığında bir skaler
    train_x = train.drop(["quality"], axis=1)
    test_x = test.drop(["quality"], axis=1)
    train_y = train["quality"]
    test_y = test["quality"]

    alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5

    with mlflow.start_run():
        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
        lr.fit(train_x, train_y)

        predicted_qualities = lr.predict(test_x)

        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

        print("Elasticnet model ( alpha = {:f}, l1_ratio={:f} ): ".format(alpha, l1_ratio))
        print(" RMSE: %s" % rmse)
        print(" MAE: %s" % mae)
        print(" R2: %s" % r2)

        mlflow.log_param("alpha", alpha)
        mlflow.log_param("l1_ratio", l1_ratio)
        mlflow.log_metric("rmse", rmse)
        mlflow.log_metric("r2", r2)
        mlflow.log_metric("mae", mae)


        remote_server_uri = "https://dagshub.com/enesagu/MLflow-Basic-Operations.mlflow"
        mlflow.set_tracking_uri(remote_server_uri)

        tracking_url_type_store = urlparse(mlflow.get_artifact_uri()).scheme

        


        # Model kaydı dosya deposuyla çalışmaz
        if tracking_url_type_store != "file":
            # Modeli kaydet
            mlflow.sklearn.log_model(lr, "model", registered_model_name="ElasticnetWineModel")
        else:
            mlflow.sklearn.log_model(lr, "model")