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- 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"]
- # Take commandline arguments
- exp_name = sys.argv[1] if len(sys.argv) > 1 else "Default"
-
-
- try:
- exp_id = mlflow.create_experiment(exp_name)
- except Exception as e:
- exp_id = mlflow.get_experiment_by_name(exp_name).experiment_id
-
-
- for alpha in [1,2]:
- for l1_ratio in [0.1, 0.5, 0.8]:
-
-
- 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)
- 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")
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