# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality # P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. # Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009. import os import warnings import sys import pandas as pd import numpy as np from sklearn.metrics import mean_squared_error, 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 import mlflow.sklearn import logging logging.basicConfig(level=logging.WARN) logger = logging.getLogger(__name__) mlflow.set_tracking_uri("http://172.17.0.1:5000") mlflow.set_experiment("s123456-new") def eval_metrics(actual, pred): rmse = np.sqrt(mean_squared_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) # Read the wine-quality csv file from the URL csv_url = ( "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv" ) try: data = pd.read_csv(csv_url, sep=";") except Exception as e: logger.exception( "Unable to download training & test CSV, check your internet connection. Error: %s", e ) # Split the data into training and test sets. (0.75, 0.25) split. train, test = train_test_split(data) # The predicted column is "quality" which is a scalar from [3, 9] 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 #alpha = 0.5 l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 #l1_ratio = 0.5 with mlflow.start_run() as run: print("MLflow run experiment_id: {0}".format(run.info.experiment_id)) print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri)) 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):" % (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) # Infer model signature to log it signature = mlflow.models.signature.infer_signature(train_x, lr.predict(train_x)) tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme # Model registry does not work with file store if tracking_url_type_store != "file": # Register the model # There are other ways to use the Model Registry, which depends on the use case, # please refer to the doc for more information: # https://mlflow.org/docs/latest/model-registry.html#api-workflow mlflow.sklearn.log_model(lr, "wines-model", registered_model_name="ElasticnetWineModel_s123456", signature=signature) else: mlflow.sklearn.log_model(lr, "model", signature=signature)