scikit-learn¶

The registry supports models created using scikit-learn (models derived from sklearn.base.BaseEstimator or sklearn.pipeline.Pipeline).

The following additional options can be used in the options dictionary when you call log_model:

Option	Description
`target_methods`	A list of the names of the methods available on the model object. scikit-learn models have the following target methods by default, assuming the method exists: `predict`, `transform`, `predict_proba`, `predict_log_proba`, `decision_function`.

You must specify either the sample_input_data or signatures parameter when logging a scikit-learn model so that the registry knows the signatures of the target methods.

Example¶

from sklearn import datasets, ensemble

iris_X, iris_y = datasets.load_iris(return_X_y=True, as_frame=True)
clf = ensemble.RandomForestClassifier(random_state=42)
clf.fit(iris_X, iris_y)
model_ref = registry.log_model(
    clf,
    model_name="RandomForestClassifier",
    version_name="v1",
    sample_input_data=iris_X,
    options={
        "method_options": {
            "predict": {"case_sensitive": True},
            "predict_proba": {"case_sensitive": True},
            "predict_log_proba": {"case_sensitive": True},
        }
    },
)
model_ref.run(iris_X[-10:], function_name='"predict_proba"')

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Pipeline:

from sklearn import datasets, ensemble, pipeline, preprocessing

iris_X, iris_y = datasets.load_iris(return_X_y=True, as_frame=True)
pipe = pipeline.Pipeline([
    ('scaler', preprocessing.StandardScaler()),
    ('classifier', ensemble.RandomForestClassifier(random_state=42)),
])
pipe.fit(iris_X, iris_y)
model_ref = registry.log_model(
    pipe,
    model_name="Pipeline",
    version_name="v1",
    sample_input_data=iris_X,
    options={
        "method_options": {
            "predict": {"case_sensitive": True},
            "predict_proba": {"case_sensitive": True},
            "predict_log_proba": {"case_sensitive": True},
        }
    },
)
model_ref.run(iris_X[-10:], function_name='"predict_proba"')

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Note

You can combine scikit-learn preprocessing with a XGBoost model as a scikit-learn pipeline.