e6fd1b533326c5f42fb45cac57d5e95f919a8955,dask_ml/model_selection/_normalize.py,,normalize_estimator,#Any#,19

Before Change

    Note: Since scikit-learn requires duck-typing, but not sub-typing from
    ``BaseEstimator``, we sometimes need to call this function directly.
    return type(est).__name__, normalize_token(est.get_params())


def normalize_random_state(random_state):
    if isinstance(random_state, np.random.RandomState):

After Change

    attrs = [x for x in dir(est) if x.endswith("_") and not x.startswith("_")]
    exclude = {"cv_results_", "model_history_", "history_", "refit_time_"}

    with warnings.catch_warnings():
        warnings.simplefilter("ignore", FutureWarning)
        for attr in attrs:
            if attr in exclude:
                continue
            try:
                val = getattr(est, attr)
            except (sklearn.exceptions.NotFittedError, AttributeError):
                continue
            base.append(val)
    return tuple(base)


def normalize_random_state(random_state):

Instances