wehub-resource-sync
26 lines
899 B
Python
26 lines
899 B
Python
from sklearn.datasets import make_classification
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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import mlflow
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X, y = make_classification(n_samples=10000, n_classes=10, n_informative=5, random_state=1)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
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with mlflow.start_run() as run:
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model = LogisticRegression(solver="liblinear").fit(X_train, y_train)
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model_info = mlflow.sklearn.log_model(model, name="model")
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result = mlflow.evaluate(
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model_info.model_uri,
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X_test,
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targets=y_test,
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model_type="classifier",
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evaluators="default",
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evaluator_config={"log_model_explainability": True, "explainability_nsamples": 1000},
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)
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print(f"run_id={run.info.run_id}")
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print(f"metrics:\n{result.metrics}")
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print(f"artifacts:\n{result.artifacts}")
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