wehub-resource-sync
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from sklearn.datasets import load_iris
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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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from mlflow.models import infer_signature
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X, y = load_iris(return_X_y=True, as_frame=True)
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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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X_test_1, X_test_2, y_test_1, y_test_2 = train_test_split(
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X_test, y_test, test_size=0.5, random_state=42
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)
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model = LogisticRegression().fit(X_train, y_train)
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predictions = model.predict(X_train)
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signature = infer_signature(X_train, predictions)
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with mlflow.start_run() as run:
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model_info = mlflow.sklearn.log_model(model, name="model", signature=signature)
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print(model_info.name)
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# Evaluate the model URI
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mlflow.evaluate(
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model_info.model_uri,
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X_test_1.assign(label=y_test_1),
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targets="label",
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model_type="classifier",
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evaluators=["default"],
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)
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print(mlflow.get_logged_model(model_info.model_id))
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# Evaluate the pyfunc model object
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model = mlflow.pyfunc.load_model(model_info.model_uri)
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assert model.model_id is not None
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mlflow.evaluate(
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model,
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X_test_2.assign(label=y_test_2),
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targets="label",
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model_type="classifier",
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evaluators=["default"],
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)
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print(mlflow.get_logged_model(model_info.model_id))
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