import types import warnings import pickle import re from copy import deepcopy from functools import partial, wraps from inspect import signature import numpy as np from scipy import sparse from scipy.stats import rankdata import joblib from . import IS_PYPY from .. import config_context from ._testing import _get_args from ._testing import assert_raise_message from ._testing import assert_array_equal from ._testing import assert_array_almost_equal from ._testing import assert_allclose from ._testing import assert_allclose_dense_sparse from ._testing import set_random_state from ._testing import SkipTest from ._testing import ignore_warnings from ._testing import create_memmap_backed_data from ._testing import raises from . import is_scalar_nan from ..linear_model import LogisticRegression from ..linear_model import Ridge from ..base import ( clone, ClusterMixin, is_classifier, is_regressor, is_outlier_detector, RegressorMixin, _is_pairwise, ) from ..metrics import accuracy_score, adjusted_rand_score, f1_score from ..random_projection import BaseRandomProjection from ..feature_selection import SelectKBest from ..pipeline import make_pipeline from ..exceptions import DataConversionWarning from ..exceptions import NotFittedError from ..exceptions import SkipTestWarning from ..model_selection import train_test_split from ..model_selection import ShuffleSplit from ..model_selection._validation import _safe_split from ..metrics.pairwise import (rbf_kernel, linear_kernel, pairwise_distances) from .import shuffle from ._tags import ( _DEFAULT_TAGS, _safe_tags, ) from .validation import has_fit_parameter, _num_samples from ..preprocessing import StandardScaler from ..preprocessing import scale from ..datasets import ( load_iris, make_blobs, make_multilabel_classification, make_regression, ) REGRESSION_DATASET = None CROSS_DECOMPOSITION = ['PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD'] def _yield_checks(estimator): name = estimator.__class__.__name__ tags = _safe_tags(estimator) pairwise = _is_pairwise(estimator) yield check_no_attributes_set_in_init yield check_estimators_dtypes yield check_fit_score_takes_y yield check_sample_weights_pandas_series yield check_sample_weights_not_an_array yield check_sample_weights_list yield check_sample_weights_shape if has_fit_parameter(estimator, "sample_weight") and not pairwise: # We skip pairwise because the data is not pairwise yield partial(check_sample_weights_invariance, kind='ones') yield partial(check_sample_weights_invariance, kind='zeros') yield check_estimators_fit_returns_self yield partial(check_estimators_fit_returns_self, readonly_memmap=True) # Check that all estimator yield informative messages when # trained on empty datasets if not tags["no_validation"]: yield check_complex_data yield check_dtype_object yield check_estimators_empty_data_messages if name not in CROSS_DECOMPOSITION: # cross-decomposition's "transform" returns X and Y yield check_pipeline_consistency if not tags["allow_nan"] and not tags["no_validation"]: # Test that all estimators check their input for NaN's and infs yield check_estimators_nan_inf if pairwise: # Check that pairwise estimator throws error on non-square input yield check_nonsquare_error yield check_estimators_overwrite_params if hasattr(estimator, 'sparsify'): yield check_sparsify_coefficients yield check_estimator_sparse_data # Test that estimators can be pickled, and once pickled # give the same answer as before. yield check_estimators_pickle yield check_estimator_get_tags_default_keys def _yield_classifier_checks(classifier): tags = _safe_tags(classifier) # test classifiers can handle non-array data and pandas objects yield check_classifier_data_not_an_array # test classifiers trained on a single label always return this label yield check_classifiers_one_label yield check_classifiers_classes yield check_estimators_partial_fit_n_features if tags["multioutput"]: yield check_classifier_multioutput # basic consistency testing yield check_classifiers_train yield partial(check_classifiers_train, readonly_memmap=True) yield partial(check_classifiers_train, readonly_memmap=True, X_dtype='float32') yield check_classifiers_regression_target if tags["multilabel"]: yield check_classifiers_multilabel_representation_invariance if not tags["no_validation"]: yield check_supervised_y_no_nan if not tags['multioutput_only']: yield check_supervised_y_2d if tags["requires_fit"]: yield check_estimators_unfitted if 'class_weight' in classifier.get_params().keys(): yield check_class_weight_classifiers yield check_non_transformer_estimators_n_iter # test if predict_proba is a monotonic transformation of decision_function yield check_decision_proba_consistency @ignore_warnings(category=FutureWarning) def check_supervised_y_no_nan(name, estimator_orig): # Checks that the Estimator targets are not NaN. estimator = clone(estimator_orig) rng = np.random.RandomState(888) X = rng.randn(10, 5) y = np.full(10, np.inf) y = _enforce_estimator_tags_y(estimator, y) match = ( "Input contains NaN, infinity or a value too large for " r"dtype\('float64'\)." ) err_msg = ( f"Estimator {name} should have raised error on fitting " "array y with NaN value." ) with raises(ValueError, match=match, err_msg=err_msg): estimator.fit(X, y) def _yield_regressor_checks(regressor): tags = _safe_tags(regressor) # TODO: test with intercept # TODO: test with multiple responses # basic testing yield check_regressors_train yield partial(check_regressors_train, readonly_memmap=True) yield partial(check_regressors_train, readonly_memmap=True, X_dtype='float32') yield check_regressor_data_not_an_array yield check_estimators_partial_fit_n_features if tags["multioutput"]: yield check_regressor_multioutput yield check_regressors_no_decision_function if not tags["no_validation"] and not tags['multioutput_only']: yield check_supervised_y_2d yield check_supervised_y_no_nan name = regressor.__class__.__name__ if name != 'CCA': # check that the regressor handles int input yield check_regressors_int if tags["requires_fit"]: yield check_estimators_unfitted yield check_non_transformer_estimators_n_iter def _yield_transformer_checks(transformer): tags = _safe_tags(transformer) # All transformers should either deal with sparse data or raise an # exception with type TypeError and an intelligible error message if not tags["no_validation"]: yield check_transformer_data_not_an_array # these don't actually fit the data, so don't raise errors yield check_transformer_general if tags["preserves_dtype"]: yield check_transformer_preserve_dtypes yield partial(check_transformer_general, readonly_memmap=True) if not _safe_tags(transformer, key="stateless"): yield check_transformers_unfitted # Dependent on external solvers and hence accessing the iter # param is non-trivial. external_solver = ['Isomap', 'KernelPCA', 'LocallyLinearEmbedding', 'RandomizedLasso', 'LogisticRegressionCV'] name = transformer.__class__.__name__ if name not in external_solver: yield check_transformer_n_iter def _yield_clustering_checks(clusterer): yield check_clusterer_compute_labels_predict name = clusterer.__class__.__name__ if name not in ('WardAgglomeration', "FeatureAgglomeration"): # this is clustering on the features # let's not test that here. yield check_clustering yield partial(check_clustering, readonly_memmap=True) yield check_estimators_partial_fit_n_features yield check_non_transformer_estimators_n_iter def _yield_outliers_checks(estimator): # checks for outlier detectors that have a fit_predict method if hasattr(estimator, 'fit_predict'): yield check_outliers_fit_predict # checks for estimators that can be used on a test set if hasattr(estimator, 'predict'): yield check_outliers_train yield partial(check_outliers_train, readonly_memmap=True) # test outlier detectors can handle non-array data yield check_classifier_data_not_an_array # test if NotFittedError is raised if _safe_tags(estimator, key="requires_fit"): yield check_estimators_unfitted def _yield_all_checks(estimator): name = estimator.__class__.__name__ tags = _safe_tags(estimator) if "2darray" not in tags["X_types"]: warnings.warn("Can't test estimator {} which requires input " " of type {}".format(name, tags["X_types"]), SkipTestWarning) return if tags["_skip_test"]: warnings.warn("Explicit SKIP via _skip_test tag for estimator " "{}.".format(name), SkipTestWarning) return for check in _yield_checks(estimator): yield check if is_classifier(estimator): for check in _yield_classifier_checks(estimator): yield check if is_regressor(estimator): for check in _yield_regressor_checks(estimator): yield check if hasattr(estimator, 'transform'): for check in _yield_transformer_checks(estimator): yield check if isinstance(estimator, ClusterMixin): for check in _yield_clustering_checks(estimator): yield check if is_outlier_detector(estimator): for check in _yield_outliers_checks(estimator): yield check yield check_parameters_default_constructible yield check_methods_sample_order_invariance yield check_methods_subset_invariance yield check_fit2d_1sample yield check_fit2d_1feature yield check_get_params_invariance yield check_set_params yield check_dict_unchanged yield check_dont_overwrite_parameters yield check_fit_idempotent if not tags["no_validation"]: yield check_n_features_in yield check_fit1d yield check_fit2d_predict1d if tags["requires_y"]: yield check_requires_y_none if tags["requires_positive_X"]: yield check_fit_non_negative def _get_check_estimator_ids(obj): """Create pytest ids for checks. When `obj` is an estimator, this returns the pprint version of the estimator (with `print_changed_only=True`). When `obj` is a function, the name of the function is returned with its keyword arguments. `_get_check_estimator_ids` is designed to be used as the `id` in `pytest.mark.parametrize` where `check_estimator(..., generate_only=True)` is yielding estimators and checks. Parameters ---------- obj : estimator or function Items generated by `check_estimator`. Returns ------- id : str or None See Also -------- check_estimator """ if callable(obj): if not isinstance(obj, partial): return obj.__name__ if not obj.keywords: return obj.func.__name__ kwstring = ",".join(["{}={}".format(k, v) for k, v in obj.keywords.items()]) return "{}({})".format(obj.func.__name__, kwstring) if hasattr(obj, "get_params"): with config_context(print_changed_only=True): return re.sub(r"\s", "", str(obj)) def _construct_instance(Estimator): """Construct Estimator instance if possible.""" required_parameters = getattr(Estimator, "_required_parameters", []) if len(required_parameters): if required_parameters in (["estimator"], ["base_estimator"]): if issubclass(Estimator, RegressorMixin): estimator = Estimator(Ridge()) else: estimator = Estimator(LogisticRegression(C=1)) elif required_parameters in (['estimators'],): # Heterogeneous ensemble classes (i.e. stacking, voting) if issubclass(Estimator, RegressorMixin): estimator = Estimator(estimators=[ ("est1", Ridge(alpha=0.1)), ("est2", Ridge(alpha=1)) ]) else: estimator = Estimator(estimators=[ ("est1", LogisticRegression(C=0.1)), ("est2", LogisticRegression(C=1)) ]) else: msg = (f"Can't instantiate estimator {Estimator.__name__} " f"parameters {required_parameters}") # raise additional warning to be shown by pytest warnings.warn(msg, SkipTestWarning) raise SkipTest(msg) else: estimator = Estimator() return estimator def _maybe_mark_xfail(estimator, check, pytest): # Mark (estimator, check) pairs as XFAIL if needed (see conditions in # _should_be_skipped_or_marked()) # This is similar to _maybe_skip(), but this one is used by # @parametrize_with_checks() instead of check_estimator() should_be_marked, reason = _should_be_skipped_or_marked(estimator, check) if not should_be_marked: return estimator, check else: return pytest.param(estimator, check, marks=pytest.mark.xfail(reason=reason)) def _maybe_skip(estimator, check): # Wrap a check so that it's skipped if needed (see conditions in # _should_be_skipped_or_marked()) # This is similar to _maybe_mark_xfail(), but this one is used by # check_estimator() instead of @parametrize_with_checks which requires # pytest should_be_skipped, reason = _should_be_skipped_or_marked(estimator, check) if not should_be_skipped: return check check_name = (check.func.__name__ if isinstance(check, partial) else check.__name__) @wraps(check) def wrapped(*args, **kwargs): raise SkipTest( f"Skipping {check_name} for {estimator.__class__.__name__}: " f"{reason}" ) return wrapped def _should_be_skipped_or_marked(estimator, check): # Return whether a check should be skipped (when using check_estimator()) # or marked as XFAIL (when using @parametrize_with_checks()), along with a # reason. # Currently, a check should be skipped or marked if # the check is in the _xfail_checks tag of the estimator check_name = (check.func.__name__ if isinstance(check, partial) else check.__name__) xfail_checks = _safe_tags(estimator, key='_xfail_checks') or {} if check_name in xfail_checks: return True, xfail_checks[check_name] return False, 'placeholder reason that will never be used' def parametrize_with_checks(estimators): """Pytest specific decorator for parametrizing estimator checks. The `id` of each check is set to be a pprint version of the estimator and the name of the check with its keyword arguments. This allows to use `pytest -k` to specify which tests to run:: pytest test_check_estimators.py -k check_estimators_fit_returns_self Parameters ---------- estimators : list of estimators instances Estimators to generated checks for. .. versionchanged:: 0.24 Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. Pass an instance instead. .. versionadded:: 0.24 Returns ------- decorator : `pytest.mark.parametrize` Examples -------- >>> from sklearn.utils.estimator_checks import parametrize_with_checks >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.tree import DecisionTreeRegressor >>> @parametrize_with_checks([LogisticRegression(), ... DecisionTreeRegressor()]) ... def test_sklearn_compatible_estimator(estimator, check): ... check(estimator) """ import pytest if any(isinstance(est, type) for est in estimators): msg = ("Passing a class was deprecated in version 0.23 " "and isn't supported anymore from 0.24." "Please pass an instance instead.") raise TypeError(msg) def checks_generator(): for estimator in estimators: name = type(estimator).__name__ for check in _yield_all_checks(estimator): check = partial(check, name) yield _maybe_mark_xfail(estimator, check, pytest) return pytest.mark.parametrize("estimator, check", checks_generator(), ids=_get_check_estimator_ids) def check_estimator(Estimator, generate_only=False): """Check if estimator adheres to scikit-learn conventions. This estimator will run an extensive test-suite for input validation, shapes, etc, making sure that the estimator complies with `scikit-learn` conventions as detailed in :ref:`rolling_your_own_estimator`. Additional tests for classifiers, regressors, clustering or transformers will be run if the Estimator class inherits from the corresponding mixin from sklearn.base. Setting `generate_only=True` returns a generator that yields (estimator, check) tuples where the check can be called independently from each other, i.e. `check(estimator)`. This allows all checks to be run independently and report the checks that are failing. scikit-learn provides a pytest specific decorator, :func:`~sklearn.utils.parametrize_with_checks`, making it easier to test multiple estimators. Parameters ---------- Estimator : estimator object Estimator instance to check. .. versionchanged:: 0.24 Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. generate_only : bool, default=False When `False`, checks are evaluated when `check_estimator` is called. When `True`, `check_estimator` returns a generator that yields (estimator, check) tuples. The check is run by calling `check(estimator)`. .. versionadded:: 0.22 Returns ------- checks_generator : generator Generator that yields (estimator, check) tuples. Returned when `generate_only=True`. """ if isinstance(Estimator, type): msg = ("Passing a class was deprecated in version 0.23 " "and isn't supported anymore from 0.24." "Please pass an instance instead.") raise TypeError(msg) estimator = Estimator name = type(estimator).__name__ def checks_generator(): for check in _yield_all_checks(estimator): check = _maybe_skip(estimator, check) yield estimator, partial(check, name) if generate_only: return checks_generator() for estimator, check in checks_generator(): try: check(estimator) except SkipTest as exception: # SkipTest is thrown when pandas can't be imported, or by checks # that are in the xfail_checks tag warnings.warn(str(exception), SkipTestWarning) def _regression_dataset(): global REGRESSION_DATASET if REGRESSION_DATASET is None: X, y = make_regression( n_samples=200, n_features=10, n_informative=1, bias=5.0, noise=20, random_state=42, ) X = StandardScaler().fit_transform(X) REGRESSION_DATASET = X, y return REGRESSION_DATASET def _set_checking_parameters(estimator): # set parameters to speed up some estimators and # avoid deprecated behaviour params = estimator.get_params() name = estimator.__class__.__name__ if ("n_iter" in params and name != "TSNE"): estimator.set_params(n_iter=5) if "max_iter" in params: if estimator.max_iter is not None: estimator.set_params(max_iter=min(5, estimator.max_iter)) # LinearSVR, LinearSVC if estimator.__class__.__name__ in ['LinearSVR', 'LinearSVC']: estimator.set_params(max_iter=20) # NMF if estimator.__class__.__name__ == 'NMF': # FIXME : init should be removed in 1.1 estimator.set_params(max_iter=500, init='nndsvda') # MLP if estimator.__class__.__name__ in ['MLPClassifier', 'MLPRegressor']: estimator.set_params(max_iter=100) if "n_resampling" in params: # randomized lasso estimator.set_params(n_resampling=5) if "n_estimators" in params: estimator.set_params(n_estimators=min(5, estimator.n_estimators)) if "max_trials" in params: # RANSAC estimator.set_params(max_trials=10) if "n_init" in params: # K-Means estimator.set_params(n_init=2) if name == 'TruncatedSVD': # TruncatedSVD doesn't run with n_components = n_features # This is ugly :-/ estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = min(estimator.n_clusters, 2) if hasattr(estimator, "n_best"): estimator.n_best = 1 if name == "SelectFdr": # be tolerant of noisy datasets (not actually speed) estimator.set_params(alpha=.5) if name == "TheilSenRegressor": estimator.max_subpopulation = 100 if isinstance(estimator, BaseRandomProjection): # Due to the jl lemma and often very few samples, the number # of components of the random matrix projection will be probably # greater than the number of features. # So we impose a smaller number (avoid "auto" mode) estimator.set_params(n_components=2) if isinstance(estimator, SelectKBest): # SelectKBest has a default of k=10 # which is more feature than we have in most case. estimator.set_params(k=1) if name in ('HistGradientBoostingClassifier', 'HistGradientBoostingRegressor'): # The default min_samples_leaf (20) isn't appropriate for small # datasets (only very shallow trees are built) that the checks use. estimator.set_params(min_samples_leaf=5) if name == 'DummyClassifier': # the default strategy prior would output constant predictions and fail # for check_classifiers_predictions estimator.set_params(strategy='stratified') # Speed-up by reducing the number of CV or splits for CV estimators loo_cv = ['RidgeCV'] if name not in loo_cv and hasattr(estimator, 'cv'): estimator.set_params(cv=3) if hasattr(estimator, 'n_splits'): estimator.set_params(n_splits=3) if name == 'OneHotEncoder': estimator.set_params(handle_unknown='ignore') class _NotAnArray: """An object that is convertible to an array. Parameters ---------- data : array-like The data. """ def __init__(self, data): self.data = np.asarray(data) def __array__(self, dtype=None): return self.data def __array_function__(self, func, types, args, kwargs): if func.__name__ == "may_share_memory": return True raise TypeError("Don't want to call array_function {}!".format( func.__name__)) def _is_pairwise_metric(estimator): """Returns True if estimator accepts pairwise metric. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if _pairwise is set to True and False otherwise. """ metric = getattr(estimator, "metric", None) return bool(metric == 'precomputed') def _pairwise_estimator_convert_X(X, estimator, kernel=linear_kernel): if _is_pairwise_metric(estimator): return pairwise_distances(X, metric='euclidean') if _is_pairwise(estimator): return kernel(X, X) return X def _generate_sparse_matrix(X_csr): """Generate sparse matrices with {32,64}bit indices of diverse format. Parameters ---------- X_csr: CSR Matrix Input matrix in CSR format. Returns ------- out: iter(Matrices) In format['dok', 'lil', 'dia', 'bsr', 'csr', 'csc', 'coo', 'coo_64', 'csc_64', 'csr_64'] """ assert X_csr.format == 'csr' yield 'csr', X_csr.copy() for sparse_format in ['dok', 'lil', 'dia', 'bsr', 'csc', 'coo']: yield sparse_format, X_csr.asformat(sparse_format) # Generate large indices matrix only if its supported by scipy X_coo = X_csr.asformat('coo') X_coo.row = X_coo.row.astype('int64') X_coo.col = X_coo.col.astype('int64') yield "coo_64", X_coo for sparse_format in ['csc', 'csr']: X = X_csr.asformat(sparse_format) X.indices = X.indices.astype('int64') X.indptr = X.indptr.astype('int64') yield sparse_format + "_64", X def check_estimator_sparse_data(name, estimator_orig): rng = np.random.RandomState(0) X = rng.rand(40, 10) X[X < .8] = 0 X = _pairwise_estimator_convert_X(X, estimator_orig) X_csr = sparse.csr_matrix(X) y = (4 * rng.rand(40)).astype(int) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) tags = _safe_tags(estimator_orig) for matrix_format, X in _generate_sparse_matrix(X_csr): # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) if name in ['Scaler', 'StandardScaler']: estimator.set_params(with_mean=False) # fit and predict if "64" in matrix_format: err_msg = ( f"Estimator {name} doesn't seem to support {matrix_format} " "matrix, and is not failing gracefully, e.g. by using " "check_array(X, accept_large_sparse=False)" ) else: err_msg = ( f"Estimator {name} doesn't seem to fail gracefully on sparse " "data: error message should state explicitly that sparse " "input is not supported if this is not the case." ) with raises( (TypeError, ValueError), match=["sparse", "Sparse"], may_pass=True, err_msg=err_msg, ): with ignore_warnings(category=FutureWarning): estimator.fit(X, y) if hasattr(estimator, "predict"): pred = estimator.predict(X) if tags['multioutput_only']: assert pred.shape == (X.shape[0], 1) else: assert pred.shape == (X.shape[0],) if hasattr(estimator, 'predict_proba'): probs = estimator.predict_proba(X) if tags['binary_only']: expected_probs_shape = (X.shape[0], 2) else: expected_probs_shape = (X.shape[0], 4) assert probs.shape == expected_probs_shape @ignore_warnings(category=FutureWarning) def check_sample_weights_pandas_series(name, estimator_orig): # check that estimators will accept a 'sample_weight' parameter of # type pandas.Series in the 'fit' function. estimator = clone(estimator_orig) if has_fit_parameter(estimator, "sample_weight"): try: import pandas as pd X = np.array([[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [2, 3], [2, 4], [3, 1], [3, 2], [3, 3], [3, 4]]) X = pd.DataFrame(_pairwise_estimator_convert_X(X, estimator_orig)) y = pd.Series([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2]) weights = pd.Series([1] * 12) if _safe_tags(estimator, key="multioutput_only"): y = pd.DataFrame(y) try: estimator.fit(X, y, sample_weight=weights) except ValueError: raise ValueError("Estimator {0} raises error if " "'sample_weight' parameter is of " "type pandas.Series".format(name)) except ImportError: raise SkipTest("pandas is not installed: not testing for " "input of type pandas.Series to class weight.") @ignore_warnings(category=(FutureWarning)) def check_sample_weights_not_an_array(name, estimator_orig): # check that estimators will accept a 'sample_weight' parameter of # type _NotAnArray in the 'fit' function. estimator = clone(estimator_orig) if has_fit_parameter(estimator, "sample_weight"): X = np.array([[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [2, 3], [2, 4], [3, 1], [3, 2], [3, 3], [3, 4]]) X = _NotAnArray(_pairwise_estimator_convert_X(X, estimator_orig)) y = _NotAnArray([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2]) weights = _NotAnArray([1] * 12) if _safe_tags(estimator, key="multioutput_only"): y = _NotAnArray(y.data.reshape(-1, 1)) estimator.fit(X, y, sample_weight=weights) @ignore_warnings(category=(FutureWarning)) def check_sample_weights_list(name, estimator_orig): # check that estimators will accept a 'sample_weight' parameter of # type list in the 'fit' function. if has_fit_parameter(estimator_orig, "sample_weight"): estimator = clone(estimator_orig) rnd = np.random.RandomState(0) n_samples = 30 X = _pairwise_estimator_convert_X(rnd.uniform(size=(n_samples, 3)), estimator_orig) y = np.arange(n_samples) % 3 y = _enforce_estimator_tags_y(estimator, y) sample_weight = [3] * n_samples # Test that estimators don't raise any exception estimator.fit(X, y, sample_weight=sample_weight) @ignore_warnings(category=FutureWarning) def check_sample_weights_shape(name, estimator_orig): # check that estimators raise an error if sample_weight # shape mismatches the input if (has_fit_parameter(estimator_orig, "sample_weight") and not _is_pairwise(estimator_orig)): estimator = clone(estimator_orig) X = np.array([[1, 3], [1, 3], [1, 3], [1, 3], [2, 1], [2, 1], [2, 1], [2, 1], [3, 3], [3, 3], [3, 3], [3, 3], [4, 1], [4, 1], [4, 1], [4, 1]]) y = np.array([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2]) y = _enforce_estimator_tags_y(estimator, y) estimator.fit(X, y, sample_weight=np.ones(len(y))) with raises(ValueError): estimator.fit(X, y, sample_weight=np.ones(2 * len(y))) with raises(ValueError): estimator.fit(X, y, sample_weight=np.ones((len(y), 2))) @ignore_warnings(category=FutureWarning) def check_sample_weights_invariance(name, estimator_orig, kind="ones"): # For kind="ones" check that the estimators yield same results for # unit weights and no weights # For kind="zeros" check that setting sample_weight to 0 is equivalent # to removing corresponding samples. estimator1 = clone(estimator_orig) estimator2 = clone(estimator_orig) set_random_state(estimator1, random_state=0) set_random_state(estimator2, random_state=0) X1 = np.array([[1, 3], [1, 3], [1, 3], [1, 3], [2, 1], [2, 1], [2, 1], [2, 1], [3, 3], [3, 3], [3, 3], [3, 3], [4, 1], [4, 1], [4, 1], [4, 1]], dtype=np.float64) y1 = np.array([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int) if kind == 'ones': X2 = X1 y2 = y1 sw2 = np.ones(shape=len(y1)) err_msg = (f"For {name} sample_weight=None is not equivalent to " f"sample_weight=ones") elif kind == 'zeros': # Construct a dataset that is very different to (X, y) if weights # are disregarded, but identical to (X, y) given weights. X2 = np.vstack([X1, X1 + 1]) y2 = np.hstack([y1, 3 - y1]) sw2 = np.ones(shape=len(y1) * 2) sw2[len(y1):] = 0 X2, y2, sw2 = shuffle(X2, y2, sw2, random_state=0) err_msg = (f"For {name}, a zero sample_weight is not equivalent " f"to removing the sample") else: # pragma: no cover raise ValueError y1 = _enforce_estimator_tags_y(estimator1, y1) y2 = _enforce_estimator_tags_y(estimator2, y2) estimator1.fit(X1, y=y1, sample_weight=None) estimator2.fit(X2, y=y2, sample_weight=sw2) for method in ["predict", "predict_proba", "decision_function", "transform"]: if hasattr(estimator_orig, method): X_pred1 = getattr(estimator1, method)(X1) X_pred2 = getattr(estimator2, method)(X1) assert_allclose_dense_sparse(X_pred1, X_pred2, err_msg=err_msg) @ignore_warnings(category=(FutureWarning, UserWarning)) def check_dtype_object(name, estimator_orig): # check that estimators treat dtype object as numeric if possible rng = np.random.RandomState(0) X = _pairwise_estimator_convert_X(rng.rand(40, 10), estimator_orig) X = X.astype(object) tags = _safe_tags(estimator_orig) y = (X[:, 0] * 4).astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) estimator.fit(X, y) if hasattr(estimator, "predict"): estimator.predict(X) if hasattr(estimator, "transform"): estimator.transform(X) with raises(Exception, match="Unknown label type", may_pass=True): estimator.fit(X, y.astype(object)) if 'string' not in tags['X_types']: X[0, 0] = {'foo': 'bar'} msg = "argument must be a string.* number" with raises(TypeError, match=msg): estimator.fit(X, y) else: # Estimators supporting string will not call np.asarray to convert the # data to numeric and therefore, the error will not be raised. # Checking for each element dtype in the input array will be costly. # Refer to #11401 for full discussion. estimator.fit(X, y) def check_complex_data(name, estimator_orig): # check that estimators raise an exception on providing complex data X = np.random.sample(10) + 1j * np.random.sample(10) X = X.reshape(-1, 1) y = np.random.sample(10) + 1j * np.random.sample(10) estimator = clone(estimator_orig) with raises(ValueError, match="Complex data not supported"): estimator.fit(X, y) @ignore_warnings def check_dict_unchanged(name, estimator_orig): # this estimator raises # ValueError: Found array with 0 feature(s) (shape=(23, 0)) # while a minimum of 1 is required. # error if name in ['SpectralCoclustering']: return rnd = np.random.RandomState(0) if name in ['RANSACRegressor']: X = 3 * rnd.uniform(size=(20, 3)) else: X = 2 * rnd.uniform(size=(20, 3)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 if hasattr(estimator, "n_best"): estimator.n_best = 1 set_random_state(estimator, 1) estimator.fit(X, y) for method in ["predict", "transform", "decision_function", "predict_proba"]: if hasattr(estimator, method): dict_before = estimator.__dict__.copy() getattr(estimator, method)(X) assert estimator.__dict__ == dict_before, ( 'Estimator changes __dict__ during %s' % method) def _is_public_parameter(attr): return not (attr.startswith('_') or attr.endswith('_')) @ignore_warnings(category=FutureWarning) def check_dont_overwrite_parameters(name, estimator_orig): # check that fit method only changes or sets private attributes if hasattr(estimator_orig.__init__, "deprecated_original"): # to not check deprecated classes return estimator = clone(estimator_orig) rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(20, 3)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(int) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 set_random_state(estimator, 1) dict_before_fit = estimator.__dict__.copy() estimator.fit(X, y) dict_after_fit = estimator.__dict__ public_keys_after_fit = [key for key in dict_after_fit.keys() if _is_public_parameter(key)] attrs_added_by_fit = [key for key in public_keys_after_fit if key not in dict_before_fit.keys()] # check that fit doesn't add any public attribute assert not attrs_added_by_fit, ( 'Estimator adds public attribute(s) during' ' the fit method.' ' Estimators are only allowed to add private attributes' ' either started with _ or ended' ' with _ but %s added' % ', '.join(attrs_added_by_fit)) # check that fit doesn't change any public attribute attrs_changed_by_fit = [key for key in public_keys_after_fit if (dict_before_fit[key] is not dict_after_fit[key])] assert not attrs_changed_by_fit, ( 'Estimator changes public attribute(s) during' ' the fit method. Estimators are only allowed' ' to change attributes started' ' or ended with _, but' ' %s changed' % ', '.join(attrs_changed_by_fit)) @ignore_warnings(category=FutureWarning) def check_fit2d_predict1d(name, estimator_orig): # check by fitting a 2d array and predicting with a 1d array rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(20, 3)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 set_random_state(estimator, 1) estimator.fit(X, y) for method in ["predict", "transform", "decision_function", "predict_proba"]: if hasattr(estimator, method): assert_raise_message(ValueError, "Reshape your data", getattr(estimator, method), X[0]) def _apply_on_subsets(func, X): # apply function on the whole set and on mini batches result_full = func(X) n_features = X.shape[1] result_by_batch = [func(batch.reshape(1, n_features)) for batch in X] # func can output tuple (e.g. score_samples) if type(result_full) == tuple: result_full = result_full[0] result_by_batch = list(map(lambda x: x[0], result_by_batch)) if sparse.issparse(result_full): result_full = result_full.A result_by_batch = [x.A for x in result_by_batch] return np.ravel(result_full), np.ravel(result_by_batch) @ignore_warnings(category=FutureWarning) def check_methods_subset_invariance(name, estimator_orig): # check that method gives invariant results if applied # on mini batches or the whole set rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(20, 3)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 set_random_state(estimator, 1) estimator.fit(X, y) for method in ["predict", "transform", "decision_function", "score_samples", "predict_proba"]: msg = ("{method} of {name} is not invariant when applied " "to a subset.").format(method=method, name=name) if hasattr(estimator, method): result_full, result_by_batch = _apply_on_subsets( getattr(estimator, method), X) assert_allclose(result_full, result_by_batch, atol=1e-7, err_msg=msg) @ignore_warnings(category=FutureWarning) def check_methods_sample_order_invariance(name, estimator_orig): # check that method gives invariant results if applied # on a subset with different sample order rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(20, 3)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(np.int64) if _safe_tags(estimator_orig, key='binary_only'): y[y == 2] = 1 estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 2 set_random_state(estimator, 1) estimator.fit(X, y) idx = np.random.permutation(X.shape[0]) for method in ["predict", "transform", "decision_function", "score_samples", "predict_proba"]: msg = ("{method} of {name} is not invariant when applied to a dataset" "with different sample order.").format(method=method, name=name) if hasattr(estimator, method): assert_allclose_dense_sparse(getattr(estimator, method)(X)[idx], getattr(estimator, method)(X[idx]), atol=1e-9, err_msg=msg) @ignore_warnings def check_fit2d_1sample(name, estimator_orig): # Check that fitting a 2d array with only one sample either works or # returns an informative message. The error message should either mention # the number of samples or the number of classes. rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(1, 10)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 set_random_state(estimator, 1) # min_cluster_size cannot be less than the data size for OPTICS. if name == 'OPTICS': estimator.set_params(min_samples=1) msgs = ["1 sample", "n_samples = 1", "n_samples=1", "one sample", "1 class", "one class"] with raises(ValueError, match=msgs, may_pass=True): estimator.fit(X, y) @ignore_warnings def check_fit2d_1feature(name, estimator_orig): # check fitting a 2d array with only 1 feature either works or returns # informative message rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(10, 1)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = X[:, 0].astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 # ensure two labels in subsample for RandomizedLogisticRegression if name == 'RandomizedLogisticRegression': estimator.sample_fraction = 1 # ensure non skipped trials for RANSACRegressor if name == 'RANSACRegressor': estimator.residual_threshold = 0.5 y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 1) msgs = [r"1 feature\(s\)", "n_features = 1", "n_features=1"] with raises(ValueError, match=msgs, may_pass=True): estimator.fit(X, y) @ignore_warnings def check_fit1d(name, estimator_orig): # check fitting 1d X array raises a ValueError rnd = np.random.RandomState(0) X = 3 * rnd.uniform(size=(20)) y = X.astype(int) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if hasattr(estimator, "n_components"): estimator.n_components = 1 if hasattr(estimator, "n_clusters"): estimator.n_clusters = 1 set_random_state(estimator, 1) with raises(ValueError): estimator.fit(X, y) @ignore_warnings(category=FutureWarning) def check_transformer_general(name, transformer, readonly_memmap=False): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X = StandardScaler().fit_transform(X) X -= X.min() X = _pairwise_estimator_convert_X(X, transformer) if readonly_memmap: X, y = create_memmap_backed_data([X, y]) _check_transformer(name, transformer, X, y) @ignore_warnings(category=FutureWarning) def check_transformer_data_not_an_array(name, transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X = StandardScaler().fit_transform(X) # We need to make sure that we have non negative data, for things # like NMF X -= X.min() - .1 X = _pairwise_estimator_convert_X(X, transformer) this_X = _NotAnArray(X) this_y = _NotAnArray(np.asarray(y)) _check_transformer(name, transformer, this_X, this_y) # try the same with some list _check_transformer(name, transformer, X.tolist(), y.tolist()) @ignore_warnings(category=FutureWarning) def check_transformers_unfitted(name, transformer): X, y = _regression_dataset() transformer = clone(transformer) with raises( (AttributeError, ValueError), err_msg="The unfitted " f"transformer {name} does not raise an error when " "transform is called. Perhaps use " "check_is_fitted in transform.", ): transformer.transform(X) def _check_transformer(name, transformer_orig, X, y): n_samples, n_features = np.asarray(X).shape transformer = clone(transformer_orig) set_random_state(transformer) # fit if name in CROSS_DECOMPOSITION: y_ = np.c_[np.asarray(y), np.asarray(y)] y_[::2, 1] *= 2 if isinstance(X, _NotAnArray): y_ = _NotAnArray(y_) else: y_ = y transformer.fit(X, y_) # fit_transform method should work on non fitted estimator transformer_clone = clone(transformer) X_pred = transformer_clone.fit_transform(X, y=y_) if isinstance(X_pred, tuple): for x_pred in X_pred: assert x_pred.shape[0] == n_samples else: # check for consistent n_samples assert X_pred.shape[0] == n_samples if hasattr(transformer, 'transform'): if name in CROSS_DECOMPOSITION: X_pred2 = transformer.transform(X, y_) X_pred3 = transformer.fit_transform(X, y=y_) else: X_pred2 = transformer.transform(X) X_pred3 = transformer.fit_transform(X, y=y_) if _safe_tags(transformer_orig, key='non_deterministic'): msg = name + ' is non deterministic' raise SkipTest(msg) if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple): for x_pred, x_pred2, x_pred3 in zip(X_pred, X_pred2, X_pred3): assert_allclose_dense_sparse( x_pred, x_pred2, atol=1e-2, err_msg="fit_transform and transform outcomes " "not consistent in %s" % transformer) assert_allclose_dense_sparse( x_pred, x_pred3, atol=1e-2, err_msg="consecutive fit_transform outcomes " "not consistent in %s" % transformer) else: assert_allclose_dense_sparse( X_pred, X_pred2, err_msg="fit_transform and transform outcomes " "not consistent in %s" % transformer, atol=1e-2) assert_allclose_dense_sparse( X_pred, X_pred3, atol=1e-2, err_msg="consecutive fit_transform outcomes " "not consistent in %s" % transformer) assert _num_samples(X_pred2) == n_samples assert _num_samples(X_pred3) == n_samples # raises error on malformed input for transform if hasattr(X, 'shape') and \ not _safe_tags(transformer, key="stateless") and \ X.ndim == 2 and X.shape[1] > 1: # If it's not an array, it does not have a 'T' property with raises( ValueError, err_msg=f"The transformer {name} does not raise an error " "when the number of features in transform is different from " "the number of features in fit." ): transformer.transform(X[:, :-1]) @ignore_warnings def check_pipeline_consistency(name, estimator_orig): if _safe_tags(estimator_orig, key='non_deterministic'): msg = name + ' is non deterministic' raise SkipTest(msg) # check that make_pipeline(est) gives same score as est X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X -= X.min() X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator) pipeline = make_pipeline(estimator) estimator.fit(X, y) pipeline.fit(X, y) funcs = ["score", "fit_transform"] for func_name in funcs: func = getattr(estimator, func_name, None) if func is not None: func_pipeline = getattr(pipeline, func_name) result = func(X, y) result_pipe = func_pipeline(X, y) assert_allclose_dense_sparse(result, result_pipe) @ignore_warnings def check_fit_score_takes_y(name, estimator_orig): # check that all estimators accept an optional y # in fit and score so they can be used in pipelines rnd = np.random.RandomState(0) n_samples = 30 X = rnd.uniform(size=(n_samples, 3)) X = _pairwise_estimator_convert_X(X, estimator_orig) y = np.arange(n_samples) % 3 estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator) funcs = ["fit", "score", "partial_fit", "fit_predict", "fit_transform"] for func_name in funcs: func = getattr(estimator, func_name, None) if func is not None: func(X, y) args = [p.name for p in signature(func).parameters.values()] if args[0] == "self": # if_delegate_has_method makes methods into functions # with an explicit "self", so need to shift arguments args = args[1:] assert args[1] in ["y", "Y"], ( "Expected y or Y as second argument for method " "%s of %s. Got arguments: %r." % (func_name, type(estimator).__name__, args)) @ignore_warnings def check_estimators_dtypes(name, estimator_orig): rnd = np.random.RandomState(0) X_train_32 = 3 * rnd.uniform(size=(20, 5)).astype(np.float32) X_train_32 = _pairwise_estimator_convert_X(X_train_32, estimator_orig) X_train_64 = X_train_32.astype(np.float64) X_train_int_64 = X_train_32.astype(np.int64) X_train_int_32 = X_train_32.astype(np.int32) y = X_train_int_64[:, 0] y = _enforce_estimator_tags_y(estimator_orig, y) methods = ["predict", "transform", "decision_function", "predict_proba"] for X_train in [X_train_32, X_train_64, X_train_int_64, X_train_int_32]: estimator = clone(estimator_orig) set_random_state(estimator, 1) estimator.fit(X_train, y) for method in methods: if hasattr(estimator, method): getattr(estimator, method)(X_train) def check_transformer_preserve_dtypes(name, transformer_orig): # check that dtype are preserved meaning if input X is of some dtype # X_transformed should be from the same dtype. X, y = make_blobs( n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, cluster_std=0.1, ) X = StandardScaler().fit_transform(X) X -= X.min() X = _pairwise_estimator_convert_X(X, transformer_orig) for dtype in _safe_tags(transformer_orig, key="preserves_dtype"): X_cast = X.astype(dtype) transformer = clone(transformer_orig) set_random_state(transformer) X_trans = transformer.fit_transform(X_cast, y) if isinstance(X_trans, tuple): # cross-decompostion returns a tuple of (x_scores, y_scores) # when given y with fit_transform; only check the first element X_trans = X_trans[0] # check that the output dtype is preserved assert X_trans.dtype == dtype, ( f'Estimator transform dtype: {X_trans.dtype} - ' f'original/expected dtype: {dtype.__name__}' ) @ignore_warnings(category=FutureWarning) def check_estimators_empty_data_messages(name, estimator_orig): e = clone(estimator_orig) set_random_state(e, 1) X_zero_samples = np.empty(0).reshape(0, 3) # The precise message can change depending on whether X or y is # validated first. Let us test the type of exception only: err_msg = ( f"The estimator {name} does not raise an error when an " "empty data is used to train. Perhaps use check_array in train." ) with raises(ValueError, err_msg=err_msg): e.fit(X_zero_samples, []) X_zero_features = np.empty(0).reshape(12, 0) # the following y should be accepted by both classifiers and regressors # and ignored by unsupervised models y = _enforce_estimator_tags_y( e, np.array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]) ) msg = ( r"0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* " "is required." ) with raises(ValueError, match=msg): e.fit(X_zero_features, y) @ignore_warnings(category=FutureWarning) def check_estimators_nan_inf(name, estimator_orig): # Checks that Estimator X's do not contain NaN or inf. rnd = np.random.RandomState(0) X_train_finite = _pairwise_estimator_convert_X(rnd.uniform(size=(10, 3)), estimator_orig) X_train_nan = rnd.uniform(size=(10, 3)) X_train_nan[0, 0] = np.nan X_train_inf = rnd.uniform(size=(10, 3)) X_train_inf[0, 0] = np.inf y = np.ones(10) y[:5] = 0 y = _enforce_estimator_tags_y(estimator_orig, y) error_string_fit = "Estimator doesn't check for NaN and inf in fit." error_string_predict = ("Estimator doesn't check for NaN and inf in" " predict.") error_string_transform = ("Estimator doesn't check for NaN and inf in" " transform.") for X_train in [X_train_nan, X_train_inf]: # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) set_random_state(estimator, 1) # try to fit with raises( ValueError, match=["inf", "NaN"], err_msg=error_string_fit ): estimator.fit(X_train, y) # actually fit estimator.fit(X_train_finite, y) # predict if hasattr(estimator, "predict"): with raises( ValueError, match=["inf", "NaN"], err_msg=error_string_predict, ): estimator.predict(X_train) # transform if hasattr(estimator, "transform"): with raises( ValueError, match=["inf", "NaN"], err_msg=error_string_transform, ): estimator.transform(X_train) @ignore_warnings def check_nonsquare_error(name, estimator_orig): """Test that error is thrown when non-square data provided.""" X, y = make_blobs(n_samples=20, n_features=10) estimator = clone(estimator_orig) with raises( ValueError, err_msg=f"The pairwise estimator {name} does not raise an error " "on non-square data", ): estimator.fit(X, y) @ignore_warnings def check_estimators_pickle(name, estimator_orig): """Test that we can pickle all estimators.""" check_methods = ["predict", "transform", "decision_function", "predict_proba"] X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) # some estimators can't do features less than 0 X -= X.min() X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel) tags = _safe_tags(estimator_orig) # include NaN values when the estimator should deal with them if tags['allow_nan']: # set randomly 10 elements to np.nan rng = np.random.RandomState(42) mask = rng.choice(X.size, 10, replace=False) X.reshape(-1)[mask] = np.nan estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator) estimator.fit(X, y) # pickle and unpickle! pickled_estimator = pickle.dumps(estimator) module_name = estimator.__module__ if module_name.startswith('sklearn.') and not ( "test_" in module_name or module_name.endswith("_testing") ): # strict check for sklearn estimators that are not implemented in test # modules. assert b"version" in pickled_estimator unpickled_estimator = pickle.loads(pickled_estimator) result = dict() for method in check_methods: if hasattr(estimator, method): result[method] = getattr(estimator, method)(X) for method in result: unpickled_result = getattr(unpickled_estimator, method)(X) assert_allclose_dense_sparse(result[method], unpickled_result) @ignore_warnings(category=FutureWarning) def check_estimators_partial_fit_n_features(name, estimator_orig): # check if number of features changes between calls to partial_fit. if not hasattr(estimator_orig, 'partial_fit'): return estimator = clone(estimator_orig) X, y = make_blobs(n_samples=50, random_state=1) X -= X.min() y = _enforce_estimator_tags_y(estimator_orig, y) try: if is_classifier(estimator): classes = np.unique(y) estimator.partial_fit(X, y, classes=classes) else: estimator.partial_fit(X, y) except NotImplementedError: return with raises( ValueError, err_msg=f"The estimator {name} does not raise an error when the " "number of features changes between calls to partial_fit.", ): estimator.partial_fit(X[:, :-1], y) @ignore_warnings(category=FutureWarning) def check_classifier_multioutput(name, estimator): n_samples, n_labels, n_classes = 42, 5, 3 tags = _safe_tags(estimator) estimator = clone(estimator) X, y = make_multilabel_classification(random_state=42, n_samples=n_samples, n_labels=n_labels, n_classes=n_classes) estimator.fit(X, y) y_pred = estimator.predict(X) assert y_pred.shape == (n_samples, n_classes), ( "The shape of the prediction for multioutput data is " "incorrect. Expected {}, got {}." .format((n_samples, n_labels), y_pred.shape)) assert y_pred.dtype.kind == 'i' if hasattr(estimator, "decision_function"): decision = estimator.decision_function(X) assert isinstance(decision, np.ndarray) assert decision.shape == (n_samples, n_classes), ( "The shape of the decision function output for " "multioutput data is incorrect. Expected {}, got {}." .format((n_samples, n_classes), decision.shape)) dec_pred = (decision > 0).astype(int) dec_exp = estimator.classes_[dec_pred] assert_array_equal(dec_exp, y_pred) if hasattr(estimator, "predict_proba"): y_prob = estimator.predict_proba(X) if isinstance(y_prob, list) and not tags['poor_score']: for i in range(n_classes): assert y_prob[i].shape == (n_samples, 2), ( "The shape of the probability for multioutput data is" " incorrect. Expected {}, got {}." .format((n_samples, 2), y_prob[i].shape)) assert_array_equal( np.argmax(y_prob[i], axis=1).astype(int), y_pred[:, i] ) elif not tags['poor_score']: assert y_prob.shape == (n_samples, n_classes), ( "The shape of the probability for multioutput data is" " incorrect. Expected {}, got {}." .format((n_samples, n_classes), y_prob.shape)) assert_array_equal(y_prob.round().astype(int), y_pred) if (hasattr(estimator, "decision_function") and hasattr(estimator, "predict_proba")): for i in range(n_classes): y_proba = estimator.predict_proba(X)[:, i] y_decision = estimator.decision_function(X) assert_array_equal(rankdata(y_proba), rankdata(y_decision[:, i])) @ignore_warnings(category=FutureWarning) def check_regressor_multioutput(name, estimator): estimator = clone(estimator) n_samples = n_features = 10 if not _is_pairwise_metric(estimator): n_samples = n_samples + 1 X, y = make_regression(random_state=42, n_targets=5, n_samples=n_samples, n_features=n_features) X = _pairwise_estimator_convert_X(X, estimator) estimator.fit(X, y) y_pred = estimator.predict(X) assert y_pred.dtype == np.dtype('float64'), ( "Multioutput predictions by a regressor are expected to be" " floating-point precision. Got {} instead".format(y_pred.dtype)) assert y_pred.shape == y.shape, ( "The shape of the prediction for multioutput data is incorrect." " Expected {}, got {}.") @ignore_warnings(category=FutureWarning) def check_clustering(name, clusterer_orig, readonly_memmap=False): clusterer = clone(clusterer_orig) X, y = make_blobs(n_samples=50, random_state=1) X, y = shuffle(X, y, random_state=7) X = StandardScaler().fit_transform(X) rng = np.random.RandomState(7) X_noise = np.concatenate([X, rng.uniform(low=-3, high=3, size=(5, 2))]) if readonly_memmap: X, y, X_noise = create_memmap_backed_data([X, y, X_noise]) n_samples, n_features = X.shape # catch deprecation and neighbors warnings if hasattr(clusterer, "n_clusters"): clusterer.set_params(n_clusters=3) set_random_state(clusterer) if name == 'AffinityPropagation': clusterer.set_params(preference=-100) clusterer.set_params(max_iter=100) # fit clusterer.fit(X) # with lists clusterer.fit(X.tolist()) pred = clusterer.labels_ assert pred.shape == (n_samples,) assert adjusted_rand_score(pred, y) > 0.4 if _safe_tags(clusterer, key='non_deterministic'): return set_random_state(clusterer) with warnings.catch_warnings(record=True): pred2 = clusterer.fit_predict(X) assert_array_equal(pred, pred2) # fit_predict(X) and labels_ should be of type int assert pred.dtype in [np.dtype('int32'), np.dtype('int64')] assert pred2.dtype in [np.dtype('int32'), np.dtype('int64')] # Add noise to X to test the possible values of the labels labels = clusterer.fit_predict(X_noise) # There should be at least one sample in every cluster. Equivalently # labels_ should contain all the consecutive values between its # min and its max. labels_sorted = np.unique(labels) assert_array_equal(labels_sorted, np.arange(labels_sorted[0], labels_sorted[-1] + 1)) # Labels are expected to start at 0 (no noise) or -1 (if noise) assert labels_sorted[0] in [0, -1] # Labels should be less than n_clusters - 1 if hasattr(clusterer, 'n_clusters'): n_clusters = getattr(clusterer, 'n_clusters') assert n_clusters - 1 >= labels_sorted[-1] # else labels should be less than max(labels_) which is necessarily true @ignore_warnings(category=FutureWarning) def check_clusterer_compute_labels_predict(name, clusterer_orig): """Check that predict is invariant of compute_labels.""" X, y = make_blobs(n_samples=20, random_state=0) clusterer = clone(clusterer_orig) set_random_state(clusterer) if hasattr(clusterer, "compute_labels"): # MiniBatchKMeans X_pred1 = clusterer.fit(X).predict(X) clusterer.set_params(compute_labels=False) X_pred2 = clusterer.fit(X).predict(X) assert_array_equal(X_pred1, X_pred2) @ignore_warnings(category=FutureWarning) def check_classifiers_one_label(name, classifier_orig): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = ("Classifier can't predict when only one class is " "present.") rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) y = np.ones(10) # catch deprecation warnings with ignore_warnings(category=FutureWarning): classifier = clone(classifier_orig) with raises( ValueError, match="class", may_pass=True, err_msg=error_string_fit ) as cm: classifier.fit(X_train, y) if cm.raised_and_matched: # ValueError was raised with proper error message return assert_array_equal( classifier.predict(X_test), y, err_msg=error_string_predict ) @ignore_warnings # Warnings are raised by decision function def check_classifiers_train( name, classifier_orig, readonly_memmap=False, X_dtype="float64" ): X_m, y_m = make_blobs(n_samples=300, random_state=0) X_m = X_m.astype(X_dtype) X_m, y_m = shuffle(X_m, y_m, random_state=7) X_m = StandardScaler().fit_transform(X_m) # generate binary problem from multi-class one y_b = y_m[y_m != 2] X_b = X_m[y_m != 2] if name in ['BernoulliNB', 'MultinomialNB', 'ComplementNB', 'CategoricalNB']: X_m -= X_m.min() X_b -= X_b.min() if readonly_memmap: X_m, y_m, X_b, y_b = create_memmap_backed_data([X_m, y_m, X_b, y_b]) problems = [(X_b, y_b)] tags = _safe_tags(classifier_orig) if not tags['binary_only']: problems.append((X_m, y_m)) for (X, y) in problems: classes = np.unique(y) n_classes = len(classes) n_samples, n_features = X.shape classifier = clone(classifier_orig) X = _pairwise_estimator_convert_X(X, classifier) y = _enforce_estimator_tags_y(classifier, y) set_random_state(classifier) # raises error on malformed input for fit if not tags["no_validation"]: with raises( ValueError, err_msg=f"The classifier {name} does not raise an error when " "incorrect/malformed input data for fit is passed. The number " "of training examples is not the same as the number of " "labels. Perhaps use check_X_y in fit.", ): classifier.fit(X, y[:-1]) # fit classifier.fit(X, y) # with lists classifier.fit(X.tolist(), y.tolist()) assert hasattr(classifier, "classes_") y_pred = classifier.predict(X) assert y_pred.shape == (n_samples,) # training set performance if not tags['poor_score']: assert accuracy_score(y, y_pred) > 0.83 # raises error on malformed input for predict msg_pairwise = ( "The classifier {} does not raise an error when shape of X in " " {} is not equal to (n_test_samples, n_training_samples)") msg = ("The classifier {} does not raise an error when the number of " "features in {} is different from the number of features in " "fit.") if not tags["no_validation"]: if _is_pairwise(classifier): with raises( ValueError, err_msg=msg_pairwise.format(name, "predict"), ): classifier.predict(X.reshape(-1, 1)) else: with raises(ValueError, err_msg=msg.format(name, "predict")): classifier.predict(X.T) if hasattr(classifier, "decision_function"): try: # decision_function agrees with predict decision = classifier.decision_function(X) if n_classes == 2: if not tags["multioutput_only"]: assert decision.shape == (n_samples,) else: assert decision.shape == (n_samples, 1) dec_pred = (decision.ravel() > 0).astype(int) assert_array_equal(dec_pred, y_pred) else: assert decision.shape == (n_samples, n_classes) assert_array_equal(np.argmax(decision, axis=1), y_pred) # raises error on malformed input for decision_function if not tags["no_validation"]: if _is_pairwise(classifier): with raises( ValueError, err_msg=msg_pairwise.format( name, "decision_function" ), ): classifier.decision_function(X.reshape(-1, 1)) else: with raises( ValueError, err_msg=msg.format(name, "decision_function"), ): classifier.decision_function(X.T) except NotImplementedError: pass if hasattr(classifier, "predict_proba"): # predict_proba agrees with predict y_prob = classifier.predict_proba(X) assert y_prob.shape == (n_samples, n_classes) assert_array_equal(np.argmax(y_prob, axis=1), y_pred) # check that probas for all classes sum to one assert_array_almost_equal(np.sum(y_prob, axis=1), np.ones(n_samples)) if not tags["no_validation"]: # raises error on malformed input for predict_proba if _is_pairwise(classifier_orig): with raises( ValueError, err_msg=msg_pairwise.format(name, "predict_proba"), ): classifier.predict_proba(X.reshape(-1, 1)) else: with raises( ValueError, err_msg=msg.format(name, "predict_proba"), ): classifier.predict_proba(X.T) if hasattr(classifier, "predict_log_proba"): # predict_log_proba is a transformation of predict_proba y_log_prob = classifier.predict_log_proba(X) assert_allclose(y_log_prob, np.log(y_prob), 8, atol=1e-9) assert_array_equal(np.argsort(y_log_prob), np.argsort(y_prob)) def check_outlier_corruption(num_outliers, expected_outliers, decision): # Check for deviation from the precise given contamination level that may # be due to ties in the anomaly scores. if num_outliers < expected_outliers: start = num_outliers end = expected_outliers + 1 else: start = expected_outliers end = num_outliers + 1 # ensure that all values in the 'critical area' are tied, # leading to the observed discrepancy between provided # and actual contamination levels. sorted_decision = np.sort(decision) msg = ('The number of predicted outliers is not equal to the expected ' 'number of outliers and this difference is not explained by the ' 'number of ties in the decision_function values') assert len(np.unique(sorted_decision[start:end])) == 1, msg def check_outliers_train(name, estimator_orig, readonly_memmap=True): n_samples = 300 X, _ = make_blobs(n_samples=n_samples, random_state=0) X = shuffle(X, random_state=7) if readonly_memmap: X = create_memmap_backed_data(X) n_samples, n_features = X.shape estimator = clone(estimator_orig) set_random_state(estimator) # fit estimator.fit(X) # with lists estimator.fit(X.tolist()) y_pred = estimator.predict(X) assert y_pred.shape == (n_samples,) assert y_pred.dtype.kind == 'i' assert_array_equal(np.unique(y_pred), np.array([-1, 1])) decision = estimator.decision_function(X) scores = estimator.score_samples(X) for output in [decision, scores]: assert output.dtype == np.dtype('float') assert output.shape == (n_samples,) # raises error on malformed input for predict with raises(ValueError): estimator.predict(X.T) # decision_function agrees with predict dec_pred = (decision >= 0).astype(int) dec_pred[dec_pred == 0] = -1 assert_array_equal(dec_pred, y_pred) # raises error on malformed input for decision_function with raises(ValueError): estimator.decision_function(X.T) # decision_function is a translation of score_samples y_dec = scores - estimator.offset_ assert_allclose(y_dec, decision) # raises error on malformed input for score_samples with raises(ValueError): estimator.score_samples(X.T) # contamination parameter (not for OneClassSVM which has the nu parameter) if (hasattr(estimator, 'contamination') and not hasattr(estimator, 'novelty')): # proportion of outliers equal to contamination parameter when not # set to 'auto'. This is true for the training set and cannot thus be # checked as follows for estimators with a novelty parameter such as # LocalOutlierFactor (tested in check_outliers_fit_predict) expected_outliers = 30 contamination = expected_outliers / n_samples estimator.set_params(contamination=contamination) estimator.fit(X) y_pred = estimator.predict(X) num_outliers = np.sum(y_pred != 1) # num_outliers should be equal to expected_outliers unless # there are ties in the decision_function values. this can # only be tested for estimators with a decision_function # method, i.e. all estimators except LOF which is already # excluded from this if branch. if num_outliers != expected_outliers: decision = estimator.decision_function(X) check_outlier_corruption(num_outliers, expected_outliers, decision) # raises error when contamination is a scalar and not in [0,1] for contamination in [-0.5, 2.3]: estimator.set_params(contamination=contamination) with raises(ValueError): estimator.fit(X) @ignore_warnings(category=(FutureWarning)) def check_classifiers_multilabel_representation_invariance( name, classifier_orig ): X, y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=5, n_labels=3, length=50, allow_unlabeled=True, random_state=0) X_train, y_train = X[:80], y[:80] X_test = X[80:] y_train_list_of_lists = y_train.tolist() y_train_list_of_arrays = list(y_train) classifier = clone(classifier_orig) set_random_state(classifier) y_pred = classifier.fit(X_train, y_train).predict(X_test) y_pred_list_of_lists = classifier.fit( X_train, y_train_list_of_lists).predict(X_test) y_pred_list_of_arrays = classifier.fit( X_train, y_train_list_of_arrays).predict(X_test) assert_array_equal(y_pred, y_pred_list_of_arrays) assert_array_equal(y_pred, y_pred_list_of_lists) assert y_pred.dtype == y_pred_list_of_arrays.dtype assert y_pred.dtype == y_pred_list_of_lists.dtype assert type(y_pred) == type(y_pred_list_of_arrays) assert type(y_pred) == type(y_pred_list_of_lists) @ignore_warnings(category=FutureWarning) def check_estimators_fit_returns_self( name, estimator_orig, readonly_memmap=False ): """Check if self is returned when calling fit.""" X, y = make_blobs(random_state=0, n_samples=21) # some want non-negative input X -= X.min() X = _pairwise_estimator_convert_X(X, estimator_orig) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) if readonly_memmap: X, y = create_memmap_backed_data([X, y]) set_random_state(estimator) assert estimator.fit(X, y) is estimator @ignore_warnings def check_estimators_unfitted(name, estimator_orig): """Check that predict raises an exception in an unfitted estimator. Unfitted estimators should raise a NotFittedError. """ # Common test for Regressors, Classifiers and Outlier detection estimators X, y = _regression_dataset() estimator = clone(estimator_orig) for method in ('decision_function', 'predict', 'predict_proba', 'predict_log_proba'): if hasattr(estimator, method): with raises(NotFittedError): getattr(estimator, method)(X) @ignore_warnings(category=FutureWarning) def check_supervised_y_2d(name, estimator_orig): tags = _safe_tags(estimator_orig) rnd = np.random.RandomState(0) n_samples = 30 X = _pairwise_estimator_convert_X( rnd.uniform(size=(n_samples, 3)), estimator_orig ) y = np.arange(n_samples) % 3 y = _enforce_estimator_tags_y(estimator_orig, y) estimator = clone(estimator_orig) set_random_state(estimator) # fit estimator.fit(X, y) y_pred = estimator.predict(X) set_random_state(estimator) # Check that when a 2D y is given, a DataConversionWarning is # raised with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always", DataConversionWarning) warnings.simplefilter("ignore", RuntimeWarning) estimator.fit(X, y[:, np.newaxis]) y_pred_2d = estimator.predict(X) msg = "expected 1 DataConversionWarning, got: %s" % ( ", ".join([str(w_x) for w_x in w])) if not tags['multioutput']: # check that we warned if we don't support multi-output assert len(w) > 0, msg assert "DataConversionWarning('A column-vector y" \ " was passed when a 1d array was expected" in msg assert_allclose(y_pred.ravel(), y_pred_2d.ravel()) @ignore_warnings def check_classifiers_predictions(X, y, name, classifier_orig): classes = np.unique(y) classifier = clone(classifier_orig) if name == 'BernoulliNB': X = X > X.mean() set_random_state(classifier) classifier.fit(X, y) y_pred = classifier.predict(X) if hasattr(classifier, "decision_function"): decision = classifier.decision_function(X) assert isinstance(decision, np.ndarray) if len(classes) == 2: dec_pred = (decision.ravel() > 0).astype(int) dec_exp = classifier.classes_[dec_pred] assert_array_equal(dec_exp, y_pred, err_msg="decision_function does not match " "classifier for %r: expected '%s', got '%s'" % (classifier, ", ".join(map(str, dec_exp)), ", ".join(map(str, y_pred)))) elif getattr(classifier, 'decision_function_shape', 'ovr') == 'ovr': decision_y = np.argmax(decision, axis=1).astype(int) y_exp = classifier.classes_[decision_y] assert_array_equal(y_exp, y_pred, err_msg="decision_function does not match " "classifier for %r: expected '%s', got '%s'" % (classifier, ", ".join(map(str, y_exp)), ", ".join(map(str, y_pred)))) # training set performance if name != "ComplementNB": # This is a pathological data set for ComplementNB. # For some specific cases 'ComplementNB' predicts less classes # than expected assert_array_equal(np.unique(y), np.unique(y_pred)) assert_array_equal(classes, classifier.classes_, err_msg="Unexpected classes_ attribute for %r: " "expected '%s', got '%s'" % (classifier, ", ".join(map(str, classes)), ", ".join(map(str, classifier.classes_)))) def _choose_check_classifiers_labels(name, y, y_names): # Semisupervised classifers use -1 as the indicator for an unlabeled # sample. return y if name in ["LabelPropagation", "LabelSpreading", "SelfTrainingClassifier"] else y_names def check_classifiers_classes(name, classifier_orig): X_multiclass, y_multiclass = make_blobs(n_samples=30, random_state=0, cluster_std=0.1) X_multiclass, y_multiclass = shuffle(X_multiclass, y_multiclass, random_state=7) X_multiclass = StandardScaler().fit_transform(X_multiclass) # We need to make sure that we have non negative data, for things # like NMF X_multiclass -= X_multiclass.min() - .1 X_binary = X_multiclass[y_multiclass != 2] y_binary = y_multiclass[y_multiclass != 2] X_multiclass = _pairwise_estimator_convert_X(X_multiclass, classifier_orig) X_binary = _pairwise_estimator_convert_X(X_binary, classifier_orig) labels_multiclass = ["one", "two", "three"] labels_binary = ["one", "two"] y_names_multiclass = np.take(labels_multiclass, y_multiclass) y_names_binary = np.take(labels_binary, y_binary) problems = [(X_binary, y_binary, y_names_binary)] if not _safe_tags(classifier_orig, key='binary_only'): problems.append((X_multiclass, y_multiclass, y_names_multiclass)) for X, y, y_names in problems: for y_names_i in [y_names, y_names.astype('O')]: y_ = _choose_check_classifiers_labels(name, y, y_names_i) check_classifiers_predictions(X, y_, name, classifier_orig) labels_binary = [-1, 1] y_names_binary = np.take(labels_binary, y_binary) y_binary = _choose_check_classifiers_labels(name, y_binary, y_names_binary) check_classifiers_predictions(X_binary, y_binary, name, classifier_orig) @ignore_warnings(category=FutureWarning) def check_regressors_int(name, regressor_orig): X, _ = _regression_dataset() X = _pairwise_estimator_convert_X(X[:50], regressor_orig) rnd = np.random.RandomState(0) y = rnd.randint(3, size=X.shape[0]) y = _enforce_estimator_tags_y(regressor_orig, y) rnd = np.random.RandomState(0) # separate estimators to control random seeds regressor_1 = clone(regressor_orig) regressor_2 = clone(regressor_orig) set_random_state(regressor_1) set_random_state(regressor_2) if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y # fit regressor_1.fit(X, y_) pred1 = regressor_1.predict(X) regressor_2.fit(X, y_.astype(float)) pred2 = regressor_2.predict(X) assert_allclose(pred1, pred2, atol=1e-2, err_msg=name) @ignore_warnings(category=FutureWarning) def check_regressors_train( name, regressor_orig, readonly_memmap=False, X_dtype=np.float64 ): X, y = _regression_dataset() X = X.astype(X_dtype) X = _pairwise_estimator_convert_X(X, regressor_orig) y = scale(y) # X is already scaled regressor = clone(regressor_orig) y = _enforce_estimator_tags_y(regressor, y) if name in CROSS_DECOMPOSITION: rnd = np.random.RandomState(0) y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y if readonly_memmap: X, y, y_ = create_memmap_backed_data([X, y, y_]) if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'): # linear regressors need to set alpha, but not generalized CV ones regressor.alpha = 0.01 if name == 'PassiveAggressiveRegressor': regressor.C = 0.01 # raises error on malformed input for fit with raises( ValueError, err_msg=f"The classifier {name} does not raise an error when " "incorrect/malformed input data for fit is passed. The number of " "training examples is not the same as the number of labels. Perhaps " "use check_X_y in fit.", ): regressor.fit(X, y[:-1]) # fit set_random_state(regressor) regressor.fit(X, y_) regressor.fit(X.tolist(), y_.tolist()) y_pred = regressor.predict(X) assert y_pred.shape == y_.shape # TODO: find out why PLS and CCA fail. RANSAC is random # and furthermore assumes the presence of outliers, hence # skipped if not _safe_tags(regressor, key="poor_score"): assert regressor.score(X, y_) > 0.5 @ignore_warnings def check_regressors_no_decision_function(name, regressor_orig): # check that regressors don't have a decision_function, predict_proba, or # predict_log_proba method. rng = np.random.RandomState(0) regressor = clone(regressor_orig) X = rng.normal(size=(10, 4)) X = _pairwise_estimator_convert_X(X, regressor_orig) y = _enforce_estimator_tags_y(regressor, X[:, 0]) regressor.fit(X, y) funcs = ["decision_function", "predict_proba", "predict_log_proba"] for func_name in funcs: assert not hasattr(regressor, func_name) @ignore_warnings(category=FutureWarning) def check_class_weight_classifiers(name, classifier_orig): if _safe_tags(classifier_orig, key='binary_only'): problems = [2] else: problems = [2, 3] for n_centers in problems: # create a very noisy dataset X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0) # can't use gram_if_pairwise() here, setting up gram matrix manually if _is_pairwise(classifier_orig): X_test = rbf_kernel(X_test, X_train) X_train = rbf_kernel(X_train, X_train) n_centers = len(np.unique(y_train)) if n_centers == 2: class_weight = {0: 1000, 1: 0.0001} else: class_weight = {0: 1000, 1: 0.0001, 2: 0.0001} classifier = clone(classifier_orig).set_params( class_weight=class_weight) if hasattr(classifier, "n_iter"): classifier.set_params(n_iter=100) if hasattr(classifier, "max_iter"): classifier.set_params(max_iter=1000) if hasattr(classifier, "min_weight_fraction_leaf"): classifier.set_params(min_weight_fraction_leaf=0.01) if hasattr(classifier, "n_iter_no_change"): classifier.set_params(n_iter_no_change=20) set_random_state(classifier) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) # XXX: Generally can use 0.89 here. On Windows, LinearSVC gets # 0.88 (Issue #9111) if not _safe_tags(classifier_orig, key='poor_score'): assert np.mean(y_pred == 0) > 0.87 @ignore_warnings(category=FutureWarning) def check_class_weight_balanced_classifiers( name, classifier_orig, X_train, y_train, X_test, y_test, weights ): classifier = clone(classifier_orig) if hasattr(classifier, "n_iter"): classifier.set_params(n_iter=100) if hasattr(classifier, "max_iter"): classifier.set_params(max_iter=1000) set_random_state(classifier) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) classifier.set_params(class_weight='balanced') classifier.fit(X_train, y_train) y_pred_balanced = classifier.predict(X_test) assert (f1_score(y_test, y_pred_balanced, average='weighted') > f1_score(y_test, y_pred, average='weighted')) @ignore_warnings(category=FutureWarning) def check_class_weight_balanced_linear_classifier(name, Classifier): """Test class weights with non-contiguous class labels.""" # this is run on classes, not instances, though this should be changed X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0], [1.0, 0.0]]) y = np.array([1, 1, 1, -1, -1]) classifier = Classifier() if hasattr(classifier, "n_iter"): # This is a very small dataset, default n_iter are likely to prevent # convergence classifier.set_params(n_iter=1000) if hasattr(classifier, "max_iter"): classifier.set_params(max_iter=1000) if hasattr(classifier, 'cv'): classifier.set_params(cv=3) set_random_state(classifier) # Let the model compute the class frequencies classifier.set_params(class_weight='balanced') coef_balanced = classifier.fit(X, y).coef_.copy() # Count each label occurrence to reweight manually n_samples = len(y) n_classes = float(len(np.unique(y))) class_weight = {1: n_samples / (np.sum(y == 1) * n_classes), -1: n_samples / (np.sum(y == -1) * n_classes)} classifier.set_params(class_weight=class_weight) coef_manual = classifier.fit(X, y).coef_.copy() assert_allclose(coef_balanced, coef_manual, err_msg="Classifier %s is not computing" " class_weight=balanced properly." % name) @ignore_warnings(category=FutureWarning) def check_estimators_overwrite_params(name, estimator_orig): X, y = make_blobs(random_state=0, n_samples=21) # some want non-negative input X -= X.min() X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel) estimator = clone(estimator_orig) y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator) # Make a physical copy of the original estimator parameters before fitting. params = estimator.get_params() original_params = deepcopy(params) # Fit the model estimator.fit(X, y) # Compare the state of the model parameters with the original parameters new_params = estimator.get_params() for param_name, original_value in original_params.items(): new_value = new_params[param_name] # We should never change or mutate the internal state of input # parameters by default. To check this we use the joblib.hash function # that introspects recursively any subobjects to compute a checksum. # The only exception to this rule of immutable constructor parameters # is possible RandomState instance but in this check we explicitly # fixed the random_state params recursively to be integer seeds. assert joblib.hash(new_value) == joblib.hash(original_value), ( "Estimator %s should not change or mutate " " the parameter %s from %s to %s during fit." % (name, param_name, original_value, new_value)) @ignore_warnings(category=FutureWarning) def check_no_attributes_set_in_init(name, estimator_orig): """Check setting during init.""" try: # Clone fails if the estimator does not store # all parameters as an attribute during init estimator = clone(estimator_orig) except AttributeError: raise AttributeError(f"Estimator {name} should store all " "parameters as an attribute during init.") if hasattr(type(estimator).__init__, "deprecated_original"): return init_params = _get_args(type(estimator).__init__) if IS_PYPY: # __init__ signature has additional objects in PyPy for key in ['obj']: if key in init_params: init_params.remove(key) parents_init_params = [param for params_parent in (_get_args(parent) for parent in type(estimator).__mro__) for param in params_parent] # Test for no setting apart from parameters during init invalid_attr = (set(vars(estimator)) - set(init_params) - set(parents_init_params)) assert not invalid_attr, ( "Estimator %s should not set any attribute apart" " from parameters during init. Found attributes %s." % (name, sorted(invalid_attr))) @ignore_warnings(category=FutureWarning) def check_sparsify_coefficients(name, estimator_orig): X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, -2], [2, 2], [-2, -2]]) y = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3]) y = _enforce_estimator_tags_y(estimator_orig, y) est = clone(estimator_orig) est.fit(X, y) pred_orig = est.predict(X) # test sparsify with dense inputs est.sparsify() assert sparse.issparse(est.coef_) pred = est.predict(X) assert_array_equal(pred, pred_orig) # pickle and unpickle with sparse coef_ est = pickle.loads(pickle.dumps(est)) assert sparse.issparse(est.coef_) pred = est.predict(X) assert_array_equal(pred, pred_orig) @ignore_warnings(category=FutureWarning) def check_classifier_data_not_an_array(name, estimator_orig): X = np.array([[3, 0], [0, 1], [0, 2], [1, 1], [1, 2], [2, 1], [0, 3], [1, 0], [2, 0], [4, 4], [2, 3], [3, 2]]) X = _pairwise_estimator_convert_X(X, estimator_orig) y = np.array([1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2]) y = _enforce_estimator_tags_y(estimator_orig, y) for obj_type in ["NotAnArray", "PandasDataframe"]: check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type) @ignore_warnings(category=FutureWarning) def check_regressor_data_not_an_array(name, estimator_orig): X, y = _regression_dataset() X = _pairwise_estimator_convert_X(X, estimator_orig) y = _enforce_estimator_tags_y(estimator_orig, y) for obj_type in ["NotAnArray", "PandasDataframe"]: check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type) @ignore_warnings(category=FutureWarning) def check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type): if name in CROSS_DECOMPOSITION: raise SkipTest("Skipping check_estimators_data_not_an_array " "for cross decomposition module as estimators " "are not deterministic.") # separate estimators to control random seeds estimator_1 = clone(estimator_orig) estimator_2 = clone(estimator_orig) set_random_state(estimator_1) set_random_state(estimator_2) if obj_type not in ["NotAnArray", 'PandasDataframe']: raise ValueError("Data type {0} not supported".format(obj_type)) if obj_type == "NotAnArray": y_ = _NotAnArray(np.asarray(y)) X_ = _NotAnArray(np.asarray(X)) else: # Here pandas objects (Series and DataFrame) are tested explicitly # because some estimators may handle them (especially their indexing) # specially. try: import pandas as pd y_ = np.asarray(y) if y_.ndim == 1: y_ = pd.Series(y_) else: y_ = pd.DataFrame(y_) X_ = pd.DataFrame(np.asarray(X)) except ImportError: raise SkipTest("pandas is not installed: not checking estimators " "for pandas objects.") # fit estimator_1.fit(X_, y_) pred1 = estimator_1.predict(X_) estimator_2.fit(X, y) pred2 = estimator_2.predict(X) assert_allclose(pred1, pred2, atol=1e-2, err_msg=name) def check_parameters_default_constructible(name, Estimator): # test default-constructibility # get rid of deprecation warnings Estimator = Estimator.__class__ with ignore_warnings(category=FutureWarning): estimator = _construct_instance(Estimator) # test cloning clone(estimator) # test __repr__ repr(estimator) # test that set_params returns self assert estimator.set_params() is estimator # test if init does nothing but set parameters # this is important for grid_search etc. # We get the default parameters from init and then # compare these against the actual values of the attributes. # this comes from getattr. Gets rid of deprecation decorator. init = getattr(estimator.__init__, 'deprecated_original', estimator.__init__) try: def param_filter(p): """Identify hyper parameters of an estimator.""" return (p.name != 'self' and p.kind != p.VAR_KEYWORD and p.kind != p.VAR_POSITIONAL) init_params = [p for p in signature(init).parameters.values() if param_filter(p)] except (TypeError, ValueError): # init is not a python function. # true for mixins return params = estimator.get_params() # they can need a non-default argument init_params = init_params[len(getattr( estimator, '_required_parameters', [])):] for init_param in init_params: assert init_param.default != init_param.empty, ( "parameter %s for %s has no default value" % (init_param.name, type(estimator).__name__)) allowed_types = { str, int, float, bool, tuple, type(None), type, types.FunctionType, joblib.Memory, } # Any numpy numeric such as np.int32. allowed_types.update(np.core.numerictypes.allTypes.values()) assert type(init_param.default) in allowed_types, ( f"Parameter '{init_param.name}' of estimator " f"'{Estimator.__name__}' is of type " f"{type(init_param.default).__name__} which is not " f"allowed. All init parameters have to be immutable to " f"make cloning possible. Therefore we restrict the set of " f"legal types to " f"{set(type.__name__ for type in allowed_types)}." ) if init_param.name not in params.keys(): # deprecated parameter, not in get_params assert init_param.default is None, ( f"Estimator parameter '{init_param.name}' of estimator " f"'{Estimator.__name__}' is not returned by get_params. " f"If it is deprecated, set its default value to None." ) continue param_value = params[init_param.name] if isinstance(param_value, np.ndarray): assert_array_equal(param_value, init_param.default) else: failure_text = ( f"Parameter {init_param.name} was mutated on init. All " f"parameters must be stored unchanged." ) if is_scalar_nan(param_value): # Allows to set default parameters to np.nan assert param_value is init_param.default, failure_text else: assert param_value == init_param.default, failure_text def _enforce_estimator_tags_y(estimator, y): # Estimators with a `requires_positive_y` tag only accept strictly positive # data if _safe_tags(estimator, key="requires_positive_y"): # Create strictly positive y. The minimal increment above 0 is 1, as # y could be of integer dtype. y += 1 + abs(y.min()) # Estimators with a `binary_only` tag only accept up to two unique y values if _safe_tags(estimator, key="binary_only") and y.size > 0: y = np.where(y == y.flat[0], y, y.flat[0] + 1) # Estimators in mono_output_task_error raise ValueError if y is of 1-D # Convert into a 2-D y for those estimators. if _safe_tags(estimator, key="multioutput_only"): return np.reshape(y, (-1, 1)) return y def _enforce_estimator_tags_x(estimator, X): # Pairwise estimators only accept # X of shape (`n_samples`, `n_samples`) if _is_pairwise(estimator): X = X.dot(X.T) # Estimators with `1darray` in `X_types` tag only accept # X of shape (`n_samples`,) if '1darray' in _safe_tags(estimator, key='X_types'): X = X[:, 0] # Estimators with a `requires_positive_X` tag only accept # strictly positive data if _safe_tags(estimator, key='requires_positive_X'): X -= X.min() return X @ignore_warnings(category=FutureWarning) def check_non_transformer_estimators_n_iter(name, estimator_orig): # Test that estimators that are not transformers with a parameter # max_iter, return the attribute of n_iter_ at least 1. # These models are dependent on external solvers like # libsvm and accessing the iter parameter is non-trivial. # SelfTrainingClassifier does not perform an iteration if all samples are # labeled, hence n_iter_ = 0 is valid. not_run_check_n_iter = ['Ridge', 'SVR', 'NuSVR', 'NuSVC', 'RidgeClassifier', 'SVC', 'RandomizedLasso', 'LogisticRegressionCV', 'LinearSVC', 'LogisticRegression', 'SelfTrainingClassifier'] # Tested in test_transformer_n_iter not_run_check_n_iter += CROSS_DECOMPOSITION if name in not_run_check_n_iter: return # LassoLars stops early for the default alpha=1.0 the iris dataset. if name == 'LassoLars': estimator = clone(estimator_orig).set_params(alpha=0.) else: estimator = clone(estimator_orig) if hasattr(estimator, 'max_iter'): iris = load_iris() X, y_ = iris.data, iris.target y_ = _enforce_estimator_tags_y(estimator, y_) set_random_state(estimator, 0) estimator.fit(X, y_) assert estimator.n_iter_ >= 1 @ignore_warnings(category=FutureWarning) def check_transformer_n_iter(name, estimator_orig): # Test that transformers with a parameter max_iter, return the # attribute of n_iter_ at least 1. estimator = clone(estimator_orig) if hasattr(estimator, "max_iter"): if name in CROSS_DECOMPOSITION: # Check using default data X = [[0., 0., 1.], [1., 0., 0.], [2., 2., 2.], [2., 5., 4.]] y_ = [[0.1, -0.2], [0.9, 1.1], [0.1, -0.5], [0.3, -0.2]] else: X, y_ = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X -= X.min() - 0.1 set_random_state(estimator, 0) estimator.fit(X, y_) # These return a n_iter per component. if name in CROSS_DECOMPOSITION: for iter_ in estimator.n_iter_: assert iter_ >= 1 else: assert estimator.n_iter_ >= 1 @ignore_warnings(category=FutureWarning) def check_get_params_invariance(name, estimator_orig): # Checks if get_params(deep=False) is a subset of get_params(deep=True) e = clone(estimator_orig) shallow_params = e.get_params(deep=False) deep_params = e.get_params(deep=True) assert all(item in deep_params.items() for item in shallow_params.items()) @ignore_warnings(category=FutureWarning) def check_set_params(name, estimator_orig): # Check that get_params() returns the same thing # before and after set_params() with some fuzz estimator = clone(estimator_orig) orig_params = estimator.get_params(deep=False) msg = "get_params result does not match what was passed to set_params" estimator.set_params(**orig_params) curr_params = estimator.get_params(deep=False) assert set(orig_params.keys()) == set(curr_params.keys()), msg for k, v in curr_params.items(): assert orig_params[k] is v, msg # some fuzz values test_values = [-np.inf, np.inf, None] test_params = deepcopy(orig_params) for param_name in orig_params.keys(): default_value = orig_params[param_name] for value in test_values: test_params[param_name] = value try: estimator.set_params(**test_params) except (TypeError, ValueError) as e: e_type = e.__class__.__name__ # Exception occurred, possibly parameter validation warnings.warn("{0} occurred during set_params of param {1} on " "{2}. It is recommended to delay parameter " "validation until fit.".format(e_type, param_name, name)) change_warning_msg = "Estimator's parameters changed after " \ "set_params raised {}".format(e_type) params_before_exception = curr_params curr_params = estimator.get_params(deep=False) try: assert (set(params_before_exception.keys()) == set(curr_params.keys())) for k, v in curr_params.items(): assert params_before_exception[k] is v except AssertionError: warnings.warn(change_warning_msg) else: curr_params = estimator.get_params(deep=False) assert (set(test_params.keys()) == set(curr_params.keys())), msg for k, v in curr_params.items(): assert test_params[k] is v, msg test_params[param_name] = default_value @ignore_warnings(category=FutureWarning) def check_classifiers_regression_target(name, estimator_orig): # Check if classifier throws an exception when fed regression targets X, y = _regression_dataset() X = X + 1 + abs(X.min(axis=0)) # be sure that X is non-negative e = clone(estimator_orig) msg = "Unknown label type: " if not _safe_tags(e, key="no_validation"): with raises(ValueError, match=msg): e.fit(X, y) @ignore_warnings(category=FutureWarning) def check_decision_proba_consistency(name, estimator_orig): # Check whether an estimator having both decision_function and # predict_proba methods has outputs with perfect rank correlation. centers = [(2, 2), (4, 4)] X, y = make_blobs(n_samples=100, random_state=0, n_features=4, centers=centers, cluster_std=1.0, shuffle=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) estimator = clone(estimator_orig) if (hasattr(estimator, "decision_function") and hasattr(estimator, "predict_proba")): estimator.fit(X_train, y_train) # Since the link function from decision_function() to predict_proba() # is sometimes not precise enough (typically expit), we round to the # 10th decimal to avoid numerical issues: we compare the rank # with deterministic ties rather than get platform specific rank # inversions in case of machine level differences. a = estimator.predict_proba(X_test)[:, 1].round(decimals=10) b = estimator.decision_function(X_test).round(decimals=10) assert_array_equal(rankdata(a), rankdata(b)) def check_outliers_fit_predict(name, estimator_orig): # Check fit_predict for outlier detectors. n_samples = 300 X, _ = make_blobs(n_samples=n_samples, random_state=0) X = shuffle(X, random_state=7) n_samples, n_features = X.shape estimator = clone(estimator_orig) set_random_state(estimator) y_pred = estimator.fit_predict(X) assert y_pred.shape == (n_samples,) assert y_pred.dtype.kind == 'i' assert_array_equal(np.unique(y_pred), np.array([-1, 1])) # check fit_predict = fit.predict when the estimator has both a predict and # a fit_predict method. recall that it is already assumed here that the # estimator has a fit_predict method if hasattr(estimator, 'predict'): y_pred_2 = estimator.fit(X).predict(X) assert_array_equal(y_pred, y_pred_2) if hasattr(estimator, "contamination"): # proportion of outliers equal to contamination parameter when not # set to 'auto' expected_outliers = 30 contamination = float(expected_outliers)/n_samples estimator.set_params(contamination=contamination) y_pred = estimator.fit_predict(X) num_outliers = np.sum(y_pred != 1) # num_outliers should be equal to expected_outliers unless # there are ties in the decision_function values. this can # only be tested for estimators with a decision_function # method if (num_outliers != expected_outliers and hasattr(estimator, 'decision_function')): decision = estimator.decision_function(X) check_outlier_corruption(num_outliers, expected_outliers, decision) # raises error when contamination is a scalar and not in [0,1] for contamination in [-0.5, 2.3]: estimator.set_params(contamination=contamination) with raises(ValueError): estimator.fit_predict(X) def check_fit_non_negative(name, estimator_orig): # Check that proper warning is raised for non-negative X # when tag requires_positive_X is present X = np.array([[-1., 1], [-1., 1]]) y = np.array([1, 2]) estimator = clone(estimator_orig) with raises(ValueError): estimator.fit(X, y) def check_fit_idempotent(name, estimator_orig): # Check that est.fit(X) is the same as est.fit(X).fit(X). Ideally we would # check that the estimated parameters during training (e.g. coefs_) are # the same, but having a universal comparison function for those # attributes is difficult and full of edge cases. So instead we check that # predict(), predict_proba(), decision_function() and transform() return # the same results. check_methods = ["predict", "transform", "decision_function", "predict_proba"] rng = np.random.RandomState(0) estimator = clone(estimator_orig) set_random_state(estimator) if 'warm_start' in estimator.get_params().keys(): estimator.set_params(warm_start=False) n_samples = 100 X = rng.normal(loc=100, size=(n_samples, 2)) X = _pairwise_estimator_convert_X(X, estimator) if is_regressor(estimator_orig): y = rng.normal(size=n_samples) else: y = rng.randint(low=0, high=2, size=n_samples) y = _enforce_estimator_tags_y(estimator, y) train, test = next(ShuffleSplit(test_size=.2, random_state=rng).split(X)) X_train, y_train = _safe_split(estimator, X, y, train) X_test, y_test = _safe_split(estimator, X, y, test, train) # Fit for the first time estimator.fit(X_train, y_train) result = {method: getattr(estimator, method)(X_test) for method in check_methods if hasattr(estimator, method)} # Fit again set_random_state(estimator) estimator.fit(X_train, y_train) for method in check_methods: if hasattr(estimator, method): new_result = getattr(estimator, method)(X_test) if np.issubdtype(new_result.dtype, np.floating): tol = 2*np.finfo(new_result.dtype).eps else: tol = 2*np.finfo(np.float64).eps assert_allclose_dense_sparse( result[method], new_result, atol=max(tol, 1e-9), rtol=max(tol, 1e-7), err_msg="Idempotency check failed for method {}".format(method) ) def check_n_features_in(name, estimator_orig): # Make sure that n_features_in_ attribute doesn't exist until fit is # called, and that its value is correct. rng = np.random.RandomState(0) estimator = clone(estimator_orig) set_random_state(estimator) if 'warm_start' in estimator.get_params(): estimator.set_params(warm_start=False) n_samples = 100 X = rng.normal(loc=100, size=(n_samples, 2)) X = _pairwise_estimator_convert_X(X, estimator) if is_regressor(estimator_orig): y = rng.normal(size=n_samples) else: y = rng.randint(low=0, high=2, size=n_samples) y = _enforce_estimator_tags_y(estimator, y) assert not hasattr(estimator, 'n_features_in_') estimator.fit(X, y) if hasattr(estimator, 'n_features_in_'): assert estimator.n_features_in_ == X.shape[1] else: warnings.warn( "As of scikit-learn 0.23, estimators should expose a " "n_features_in_ attribute, unless the 'no_validation' tag is " "True. This attribute should be equal to the number of features " "passed to the fit method. " "An error will be raised from version 1.0 (renaming of 0.25) " "when calling check_estimator(). " "See SLEP010: " "https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep010/proposal.html", # noqa FutureWarning ) def check_requires_y_none(name, estimator_orig): # Make sure that an estimator with requires_y=True fails gracefully when # given y=None rng = np.random.RandomState(0) estimator = clone(estimator_orig) set_random_state(estimator) n_samples = 100 X = rng.normal(loc=100, size=(n_samples, 2)) X = _pairwise_estimator_convert_X(X, estimator) warning_msg = ("As of scikit-learn 0.23, estimators should have a " "'requires_y' tag set to the appropriate value. " "The default value of the tag is False. " "An error will be raised from version 1.0 when calling " "check_estimator() if the tag isn't properly set.") expected_err_msgs = ( "requires y to be passed, but the target y is None", "Expected array-like (array or non-string sequence), got None", "y should be a 1d array" ) try: estimator.fit(X, None) except ValueError as ve: if not any(msg in str(ve) for msg in expected_err_msgs): warnings.warn(warning_msg, FutureWarning) def check_n_features_in_after_fitting(name, estimator_orig): # Make sure that n_features_in are checked after fitting tags = _safe_tags(estimator_orig) if "2darray" not in tags["X_types"] or tags["no_validation"]: return rng = np.random.RandomState(0) estimator = clone(estimator_orig) set_random_state(estimator) if 'warm_start' in estimator.get_params(): estimator.set_params(warm_start=False) n_samples = 100 X = rng.normal(loc=100, size=(n_samples, 2)) X = _pairwise_estimator_convert_X(X, estimator) if is_regressor(estimator): y = rng.normal(size=n_samples) else: y = rng.randint(low=0, high=2, size=n_samples) y = _enforce_estimator_tags_y(estimator, y) estimator.fit(X, y) assert estimator.n_features_in_ == X.shape[1] # check methods will check n_features_in_ check_methods = ["predict", "transform", "decision_function", "predict_proba"] X_bad = X[:, [1]] msg = (f"X has 1 features, but \\w+ is expecting {X.shape[1]} " "features as input") for method in check_methods: if not hasattr(estimator, method): continue with raises(ValueError, match=msg): getattr(estimator, method)(X_bad) # partial_fit will check in the second call if not hasattr(estimator, "partial_fit"): return estimator = clone(estimator_orig) if is_classifier(estimator): estimator.partial_fit(X, y, classes=np.unique(y)) else: estimator.partial_fit(X, y) assert estimator.n_features_in_ == X.shape[1] with raises(ValueError, match=msg): estimator.partial_fit(X_bad, y) def check_estimator_get_tags_default_keys(name, estimator_orig): # check that if _get_tags is implemented, it contains all keys from # _DEFAULT_KEYS estimator = clone(estimator_orig) if not hasattr(estimator, "_get_tags"): return tags_keys = set(estimator._get_tags().keys()) default_tags_keys = set(_DEFAULT_TAGS.keys()) assert tags_keys.intersection(default_tags_keys) == default_tags_keys, ( f"{name}._get_tags() is missing entries for the following default tags" f": {default_tags_keys - tags_keys.intersection(default_tags_keys)}" )