wehub-resource-sync
93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
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# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
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# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
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import logging
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import sys
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import warnings
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from urllib.parse import urlparse
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import ElasticNet
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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from sklearn.model_selection import train_test_split
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import mlflow
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import mlflow.sklearn
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from mlflow.models import infer_signature
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logging.basicConfig(level=logging.WARN)
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logger = logging.getLogger(__name__)
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def eval_metrics(actual, pred):
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rmse = np.sqrt(mean_squared_error(actual, pred))
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mae = mean_absolute_error(actual, pred)
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r2 = r2_score(actual, pred)
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return rmse, mae, r2
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if __name__ == "__main__":
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warnings.filterwarnings("ignore")
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np.random.seed(40)
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# Read the wine-quality csv file from the URL
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csv_url = (
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"https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-red.csv"
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)
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try:
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data = pd.read_csv(csv_url, sep=";")
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except Exception as e:
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logger.exception(
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"Unable to download training & test CSV, check your internet connection. Error: %s", e
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)
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# Split the data into training and test sets. (0.75, 0.25) split.
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train, test = train_test_split(data)
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# The predicted column is "quality" which is a scalar from [3, 9]
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train_x = train.drop(["quality"], axis=1)
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test_x = test.drop(["quality"], axis=1)
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train_y = train[["quality"]]
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test_y = test[["quality"]]
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alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
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l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
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with mlflow.start_run():
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lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
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lr.fit(train_x, train_y)
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predicted_qualities = lr.predict(test_x)
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(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
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print(f"Elasticnet model (alpha={alpha:f}, l1_ratio={l1_ratio:f}):")
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print(f" RMSE: {rmse}")
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print(f" MAE: {mae}")
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print(f" R2: {r2}")
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mlflow.log_param("alpha", alpha)
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mlflow.log_param("l1_ratio", l1_ratio)
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mlflow.log_metric("rmse", rmse)
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mlflow.log_metric("r2", r2)
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mlflow.log_metric("mae", mae)
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predictions = lr.predict(train_x)
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signature = infer_signature(train_x, predictions)
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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# Model registry does not work with file store
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if tracking_url_type_store != "file":
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# Register the model
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# There are other ways to use the Model Registry, which depends on the use case,
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# please refer to the doc for more information:
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# https://mlflow.org/docs/latest/model-registry.html#api-workflow
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mlflow.sklearn.log_model(
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lr, name="model", registered_model_name="ElasticnetWineModel", signature=signature
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)
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else:
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mlflow.sklearn.log_model(lr, name="model", signature=signature)
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