wehub-resource-sync
127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
# ruff: noqa
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"""
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python examples/demo.py
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"""
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import logging
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import tempfile
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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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# 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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logger = logging.getLogger(__name__)
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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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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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alpha = 0.5
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l1_ratio = 0.5
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# Start a run to represent the training job
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with mlflow.start_run() as training_run:
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# Load the training dataset with MLflow. We will link training metrics to this dataset.
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train_dataset: mlflow.data.pandas_dataset.PandasDataset = mlflow.data.from_pandas(
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train, name="train_dataset"
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)
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train_x = train_dataset.df.drop(["quality"], axis=1)
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train_y = train_dataset.df[["quality"]]
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# Fit a model to the training dataset
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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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# Evaluate the model on the training dataset and log metrics
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predictions = lr.predict(train_x)
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(rmse, mae, r2) = eval_metrics(train_y, predictions)
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mlflow.log_metrics(
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metrics={
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"rmse": rmse,
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"r2": r2,
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"mae": mae,
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},
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dataset=train_dataset,
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)
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# Log the model, specifying its ElasticNet parameters (alpha, l1_ratio)
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model = mlflow.sklearn.log_model(
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sk_model=lr,
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name="elasticnet",
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params={
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"alpha": alpha,
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"l1_ratio": l1_ratio,
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},
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)
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# Fetch the model ID, and print the model
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model_id = model.model_id
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print("\n")
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print(model)
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print("\n")
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print(model_id)
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# Start a run to represent the test dataset evaluation job
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with mlflow.start_run() as evaluation_run:
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# Load the test dataset with MLflow. We will link test metrics to this dataset.
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test_dataset: mlflow.data.pandas_dataset.PandasDataset = mlflow.data.from_pandas(
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test, name="test_dataset"
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)
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test_x = test_dataset.df.drop(["quality"], axis=1)
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test_y = test_dataset.df[["quality"]]
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# Load the model
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model = mlflow.sklearn.load_model(f"models:/{model_id}")
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# Evaluate the model on the training dataset and log metrics
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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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mlflow.log_metrics(
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metrics={
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"rmse": rmse,
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"r2": r2,
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"mae": mae,
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},
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dataset=test_dataset,
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# Specify the ID of the model logged above
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model_id=model_id,
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)
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model = mlflow.get_logged_model(model_id)
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training_run = mlflow.get_run(training_run.info.run_id)
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print(training_run)
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print("\n")
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print(training_run.outputs)
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evaluation_run = mlflow.get_run(evaluation_run.info.run_id)
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print(evaluation_run)
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print("\n")
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print(evaluation_run.inputs)
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print(f"models:/{model_id}")
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mlflow.register_model(model_uri=f"models:/{model_id}", name="registered_elasticnet")
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mlflow.MlflowClient().get_model_version("registered_elasticnet", 1)
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