100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
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# 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 os
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import warnings
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import sys
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import pandas as pd
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import numpy as np
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import ElasticNet
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from urllib.parse import urlparse
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import mlflow
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import mlflow.sklearn
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import logging
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logging.basicConfig(level=logging.WARN)
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logger = logging.getLogger(__name__)
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mlflow.set_tracking_uri("http://localhost:5001")
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mlflow.set_experiment("s123456")
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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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"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/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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#alpha = 0.5
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l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
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#l1_ratio = 0.5
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with mlflow.start_run() as run:
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print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
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print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
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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("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
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print(" RMSE: %s" % rmse)
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print(" MAE: %s" % mae)
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print(" R2: %s" % 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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# Infer model signature to log it
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signature = mlflow.models.signature.infer_signature(train_x, lr.predict(train_x))
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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(lr, "wines-model", registered_model_name="ElasticnetWineModel", signature=signature)
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else:
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mlflow.sklearn.log_model(lr, "model", signature=signature)
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