util.py 文件源码

def get_params_for_est(estimator, name):
    '''Choose initialization parameters for an estimator for auto-testing'''
    is_classifier = ClassifierMixin in estimator.__mro__
    is_cluster = ClusterMixin in estimator.__mro__
    is_ensemble = BaseEnsemble in estimator.__mro__
    uses_counts = any(c in name for c in USES_COUNTS)
    as_1d = name in REQUIRES_1D
    args, params, _ = get_args_kwargs_defaults(estimator.__init__)
    est_keys = set(('estimator', 'base_estimator', 'estimators'))
    est_keys = (set(params) | set(args)) & est_keys
    if is_classifier:
        score_func = feat.f_classif
    else:
        score_func = feat.f_regression
    for key in est_keys:
        if name == 'SelectFromModel':
            params[key] = sklearn.linear_model.LassoCV()
        elif is_classifier:
            params[key] = sklearn.tree.DecisionTreeClassifier()
        else:
            params[key] = sklearn.tree.DecisionTreeRegressor()
        if key == 'estimators':
            params[key] = [(str(_), clone(params[key])) for _ in range(10)]
    kw = dict(is_classifier=is_classifier, is_cluster=is_cluster,
              is_ensemble=is_ensemble, uses_counts=uses_counts)
    if 'score_func' in params:
        params['score_func'] = score_func
    X, y = make_X_y(**kw)
    return X, y, params, kw