Python sklearn.utils.testing.SkipTest() Examples

def test_type_of_target():
    for group, group_examples in EXAMPLES.items():
        for example in group_examples:
            assert_equal(type_of_target(example), group,
                         msg=('type_of_target(%r) should be %r, got %r'
                              % (example, group, type_of_target(example))))

    for example in NON_ARRAY_LIKE_EXAMPLES:
        msg_regex = r'Expected array-like \(array or non-string sequence\).*'
        assert_raises_regex(ValueError, msg_regex, type_of_target, example)

    for example in MULTILABEL_SEQUENCES:
        msg = ('You appear to be using a legacy multi-label data '
               'representation. Sequence of sequences are no longer supported;'
               ' use a binary array or sparse matrix instead.')
        assert_raises_regex(ValueError, msg, type_of_target, example)

    try:
        from pandas import SparseSeries
    except ImportError:
        raise SkipTest("Pandas not found")

    y = SparseSeries([1, 0, 0, 1, 0])
    msg = "y cannot be class 'SparseSeries'."
    assert_raises_regex(ValueError, msg, type_of_target, y) 

def test_gaussian_kde(n_samples=1000):
    # Compare gaussian KDE results to scipy.stats.gaussian_kde
    from scipy.stats import gaussian_kde
    rng = check_random_state(0)
    x_in = rng.normal(0, 1, n_samples)
    x_out = np.linspace(-5, 5, 30)

    for h in [0.01, 0.1, 1]:
        kdt = KDTree(x_in[:, None])
        try:
            gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
        except TypeError:
            raise SkipTest("Old scipy, does not accept explicit bandwidth.")

        dens_kdt = kdt.kernel_density(x_out[:, None], h) / n_samples
        dens_gkde = gkde.evaluate(x_out)

        assert_array_almost_equal(dens_kdt, dens_gkde, decimal=3) 

def test_gaussian_kde(n_samples=1000):
    # Compare gaussian KDE results to scipy.stats.gaussian_kde
    from scipy.stats import gaussian_kde
    rng = check_random_state(0)
    x_in = rng.normal(0, 1, n_samples)
    x_out = np.linspace(-5, 5, 30)

    for h in [0.01, 0.1, 1]:
        bt = BallTree(x_in[:, None])
        try:
            gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
        except TypeError:
            raise SkipTest("Old version of scipy, doesn't accept "
                           "explicit bandwidth.")

        dens_bt = bt.kernel_density(x_out[:, None], h) / n_samples
        dens_gkde = gkde.evaluate(x_out)

        assert_array_almost_equal(dens_bt, dens_gkde, decimal=3) 

def test_fetch():
    try:
        data1 = fetch(shuffle=True, random_state=42)
    except IOError:
        raise SkipTest("Covertype dataset can not be loaded.")

    data2 = fetch(shuffle=True, random_state=37)

    X1, X2 = data1['data'], data2['data']
    assert_equal((581012, 54), X1.shape)
    assert_equal(X1.shape, X2.shape)

    assert_equal(X1.sum(), X2.sum())

    y1, y2 = data1['target'], data2['target']
    assert_equal((X1.shape[0],), y1.shape)
    assert_equal((X1.shape[0],), y2.shape) 

def test_perfect_checkerboard():
    raise SkipTest("This test is failing on the buildbot, but cannot"
                   " reproduce. Temporarily disabling it until it can be"
                   " reproduced and  fixed.")
    model = SpectralBiclustering(3, svd_method="arpack", random_state=0)

    S, rows, cols = make_checkerboard((30, 30), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert_equal(consensus_score(model.biclusters_,
                                 (rows, cols)), 1)

    S, rows, cols = make_checkerboard((40, 30), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert_equal(consensus_score(model.biclusters_,
                                 (rows, cols)), 1)

    S, rows, cols = make_checkerboard((30, 40), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert_equal(consensus_score(model.biclusters_,
                                 (rows, cols)), 1) 

def test_bayesian_on_diabetes():
    # Test BayesianRidge on diabetes
    raise SkipTest("XFailed Test")
    diabetes = datasets.load_diabetes()
    X, y = diabetes.data, diabetes.target

    clf = BayesianRidge(compute_score=True)

    # Test with more samples than features
    clf.fit(X, y)
    # Test that scores are increasing at each iteration
    assert_array_equal(np.diff(clf.scores_) > 0, True)

    # Test with more features than samples
    X = X[:5, :]
    y = y[:5]
    clf.fit(X, y)
    # Test that scores are increasing at each iteration
    assert_array_equal(np.diff(clf.scores_) > 0, True) 

def test_spectral_embedding_amg_solver(seed=36):
    # Test spectral embedding with amg solver
    try:
        from pyamg import smoothed_aggregation_solver  # noqa
    except ImportError:
        raise SkipTest("pyamg not available.")

    se_amg = SpectralEmbedding(n_components=2, affinity="nearest_neighbors",
                               eigen_solver="amg", n_neighbors=5,
                               random_state=np.random.RandomState(seed))
    se_arpack = SpectralEmbedding(n_components=2, affinity="nearest_neighbors",
                                  eigen_solver="arpack", n_neighbors=5,
                                  random_state=np.random.RandomState(seed))
    embed_amg = se_amg.fit_transform(S)
    embed_arpack = se_arpack.fit_transform(S)
    assert_true(_check_with_col_sign_flipping(embed_amg, embed_arpack, 0.05)) 

def test_type_of_target():
    for group, group_examples in iteritems(EXAMPLES):
        for example in group_examples:
            assert_equal(type_of_target(example), group,
                         msg=('type_of_target(%r) should be %r, got %r'
                              % (example, group, type_of_target(example))))

    for example in NON_ARRAY_LIKE_EXAMPLES:
        msg_regex = 'Expected array-like \(array or non-string sequence\).*'
        assert_raises_regex(ValueError, msg_regex, type_of_target, example)

    for example in MULTILABEL_SEQUENCES:
        msg = ('You appear to be using a legacy multi-label data '
               'representation. Sequence of sequences are no longer supported;'
               ' use a binary array or sparse matrix instead.')
        assert_raises_regex(ValueError, msg, type_of_target, example)

    try:
        from pandas import SparseSeries
    except ImportError:
        raise SkipTest("Pandas not found")

    y = SparseSeries([1, 0, 0, 1, 0])
    msg = "y cannot be class 'SparseSeries'."
    assert_raises_regex(ValueError, msg, type_of_target, y) 

def check_estimators_data_not_an_array(name, estimator_orig, X, y):
    if name in CROSS_DECOMPOSITION:
        raise SkipTest
    # 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)

    y_ = NotAnArray(np.asarray(y))
    X_ = NotAnArray(np.asarray(X))

    # 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_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 = pd.DataFrame([[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]])
            y = pd.Series([1, 1, 1, 2, 2, 2])
            weights = pd.Series([1] * 6)
            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.") 

def fit(self, X, y):
        logger = logging.get_logger(__name__)
        trainer = create_trainer(
            objective='binary',
            metric='auc',
            sampler=self.sampler,
            n_jobs=self.n_jobs,
            create_validation=self.create_validation,
            cv=self.cv,
            random_state=self.random_state,
        )
        optimizer = create_optimizer(
            objective=self.objective,
            trainer=trainer,
            n_trials=self.n_trials,
            random_state=self.random_state,
        )
        X, y = validate_dataset(optimizer, X, y)
        if len(y.unique()) != 2:
            raise SkipTest('binary classification is only supported')

        logger.info('start optimization')
        optimizer.optimize(X, y)
        self._optimizer = optimizer 

def check_estimators_data_not_an_array(name, estimator_orig, X, y):
    if name in CROSS_DECOMPOSITION:
        raise SkipTest
    # 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)

    y_ = NotAnArray(np.asarray(y))
    X_ = NotAnArray(np.asarray(X))

    # 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_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 = pd.DataFrame([[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]])
            y = pd.Series([1, 1, 1, 2, 2, 2])
            weights = pd.Series([1] * 6)
            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.") 

def test_spectral_embedding_amg_solver(seed=36):
    # Test spectral embedding with amg solver
    try:
        from pyamg import smoothed_aggregation_solver  # noqa
    except ImportError:
        raise SkipTest("pyamg not available.")

    se_amg = SpectralEmbedding(n_components=2, affinity="nearest_neighbors",
                               eigen_solver="amg", n_neighbors=5,
                               random_state=np.random.RandomState(seed))
    se_arpack = SpectralEmbedding(n_components=2, affinity="nearest_neighbors",
                                  eigen_solver="arpack", n_neighbors=5,
                                  random_state=np.random.RandomState(seed))
    embed_amg = se_amg.fit_transform(S)
    embed_arpack = se_arpack.fit_transform(S)
    assert _check_with_col_sign_flipping(embed_amg, embed_arpack, 0.05) 

def test_fetch():
    try:
        data1 = fetch(shuffle=True, random_state=42)
    except IOError:
        raise SkipTest("Covertype dataset can not be loaded.")

    data2 = fetch(shuffle=True, random_state=37)

    X1, X2 = data1['data'], data2['data']
    assert_equal((581012, 54), X1.shape)
    assert_equal(X1.shape, X2.shape)

    assert_equal(X1.sum(), X2.sum())

    y1, y2 = data1['target'], data2['target']
    assert_equal((X1.shape[0],), y1.shape)
    assert_equal((X1.shape[0],), y2.shape)

    # test return_X_y option
    fetch_func = partial(fetch)
    check_return_X_y(data1, fetch_func) 

def test_perfect_checkerboard():
    # XXX test always skipped
    raise SkipTest("This test is failing on the buildbot, but cannot"
                   " reproduce. Temporarily disabling it until it can be"
                   " reproduced and  fixed.")
    model = SpectralBiclustering(3, svd_method="arpack", random_state=0)

    S, rows, cols = make_checkerboard((30, 30), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert_equal(consensus_score(model.biclusters_,
                                 (rows, cols)), 1)

    S, rows, cols = make_checkerboard((40, 30), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert_equal(consensus_score(model.biclusters_,
                                 (rows, cols)), 1)

    S, rows, cols = make_checkerboard((30, 40), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert_equal(consensus_score(model.biclusters_,
                                 (rows, cols)), 1) 

def test_stable_cumsum():
    if np_version < (1, 9):
        raise SkipTest("Sum is as unstable as cumsum for numpy < 1.9")
    assert_array_equal(stable_cumsum([1, 2, 3]), np.cumsum([1, 2, 3]))
    r = np.random.RandomState(0).rand(100000)
    assert_warns(RuntimeWarning, stable_cumsum, r, rtol=0, atol=0)

    # test axis parameter
    A = np.random.RandomState(36).randint(1000, size=(5, 5, 5))
    assert_array_equal(stable_cumsum(A, axis=0), np.cumsum(A, axis=0))
    assert_array_equal(stable_cumsum(A, axis=1), np.cumsum(A, axis=1))
    assert_array_equal(stable_cumsum(A, axis=2), np.cumsum(A, axis=2)) 

def custom_check_estimator(Estimator):
    # Same as sklearn.check_estimator, skipping tests that can't succeed.

    from sklearn.utils.estimator_checks import _yield_all_checks
    from sklearn.utils.testing import SkipTest
    from sklearn.exceptions import SkipTestWarning
    from sklearn.utils import estimator_checks

    estimator = Estimator
    name = type(estimator).__name__

    for check in _yield_all_checks(name, estimator):
        if (check is estimator_checks.check_fit2d_1feature or
                check is estimator_checks.check_fit2d_1sample):
            # X is both Fortran and C aligned and numba can't compile.
            # Opened numba issue 3569
            continue
        if check is estimator_checks.check_classifiers_train:
            continue  # probas don't exactly sum to 1 (very close though)
        if (hasattr(check, 'func') and
                check.func is estimator_checks.check_classifiers_train):
            continue  # same, wrapped in a functools.partial object.

        try:
            check(name, estimator)
        except SkipTest as exception:
            # the only SkipTest thrown currently results from not
            # being able to import pandas.
            warnings.warn(str(exception), SkipTestWarning) 

def test_check_dataframe_fit_attribute():
    # check pandas dataframe with 'fit' column does not raise error
    # https://github.com/scikit-learn/scikit-learn/issues/8415
    try:
        import pandas as pd
        X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        X_df = pd.DataFrame(X, columns=['a', 'b', 'fit'])
        check_consistent_length(X_df)
    except ImportError:
        raise SkipTest("Pandas not found") 

def test_20news_vectorized():
    try:
        datasets.fetch_20newsgroups(subset='all',
                                    download_if_missing=False)
    except IOError:
        raise SkipTest("Download 20 newsgroups to run this test")

    # test subset = train
    bunch = datasets.fetch_20newsgroups_vectorized(subset="train")
    assert_true(sp.isspmatrix_csr(bunch.data))
    assert_equal(bunch.data.shape, (11314, 130107))
    assert_equal(bunch.target.shape[0], 11314)
    assert_equal(bunch.data.dtype, np.float64)

    # test subset = test
    bunch = datasets.fetch_20newsgroups_vectorized(subset="test")
    assert_true(sp.isspmatrix_csr(bunch.data))
    assert_equal(bunch.data.shape, (7532, 130107))
    assert_equal(bunch.target.shape[0], 7532)
    assert_equal(bunch.data.dtype, np.float64)

    # test subset = all
    bunch = datasets.fetch_20newsgroups_vectorized(subset='all')
    assert_true(sp.isspmatrix_csr(bunch.data))
    assert_equal(bunch.data.shape, (11314 + 7532, 130107))
    assert_equal(bunch.target.shape[0], 11314 + 7532)
    assert_equal(bunch.data.dtype, np.float64) 

def test_20news_length_consistency():
    """Checks the length consistencies within the bunch

    This is a non-regression test for a bug present in 0.16.1.
    """
    try:
        data = datasets.fetch_20newsgroups(
            subset='all', download_if_missing=False, shuffle=False)
    except IOError:
        raise SkipTest("Download 20 newsgroups to run this test")
    # Extract the full dataset
    data = datasets.fetch_20newsgroups(subset='all')
    assert_equal(len(data['data']), len(data.data))
    assert_equal(len(data['target']), len(data.target))
    assert_equal(len(data['filenames']), len(data.filenames)) 

def test_percent10():
    try:
        data = fetch_kddcup99(download_if_missing=False)
    except IOError:
        raise SkipTest("kddcup99 dataset can not be loaded.")

    assert_equal(data.data.shape, (494021, 41))
    assert_equal(data.target.shape, (494021,))

    data_shuffled = fetch_kddcup99(shuffle=True, random_state=0)
    assert_equal(data.data.shape, data_shuffled.data.shape)
    assert_equal(data.target.shape, data_shuffled.target.shape)

    data = fetch_kddcup99('SA')
    assert_equal(data.data.shape, (100655, 41))
    assert_equal(data.target.shape, (100655,))

    data = fetch_kddcup99('SF')
    assert_equal(data.data.shape, (73237, 4))
    assert_equal(data.target.shape, (73237,))

    data = fetch_kddcup99('http')
    assert_equal(data.data.shape, (58725, 3))
    assert_equal(data.target.shape, (58725,))

    data = fetch_kddcup99('smtp')
    assert_equal(data.data.shape, (9571, 3))
    assert_equal(data.target.shape, (9571,)) 

def test_k_means_plus_plus_init_2_jobs():
    if sys.version_info[:2] < (3, 4):
        raise SkipTest(
            "Possible multi-process bug with some BLAS under Python < 3.4")

    km = KMeans(init="k-means++", n_clusters=n_clusters, n_jobs=2,
                random_state=42).fit(X)
    _check_fitted_model(km) 

def test_dtype_match():
    # Disabled to unblock the 0.19.2 release. See:
    # https://github.com/scikit-learn/scikit-learn/issues/11438
    # Test that np.float32 input data is not cast to np.float64 when possible
    raise SkipTest()
    X_32 = np.array(X).astype(np.float32)
    y_32 = np.array(Y1).astype(np.float32)
    X_64 = np.array(X).astype(np.float64)
    y_64 = np.array(Y1).astype(np.float64)
    X_sparse_32 = sp.csr_matrix(X, dtype=np.float32)

    for solver in ['newton-cg']:
        for multi_class in ['ovr', 'multinomial']:

            # Check type consistency
            lr_32 = LogisticRegression(solver=solver, multi_class=multi_class)
            lr_32.fit(X_32, y_32)
            assert_equal(lr_32.coef_.dtype, X_32.dtype)

            # check consistency with sparsity
            lr_32_sparse = LogisticRegression(solver=solver,
                                              multi_class=multi_class)
            lr_32_sparse.fit(X_sparse_32, y_32)
            assert_equal(lr_32_sparse.coef_.dtype, X_sparse_32.dtype)

            # Check accuracy consistency
            lr_64 = LogisticRegression(solver=solver, multi_class=multi_class)
            lr_64.fit(X_64, y_64)
            assert_equal(lr_64.coef_.dtype, X_64.dtype)
            assert_almost_equal(lr_32.coef_, lr_64.coef_.astype(np.float32)) 

def test_shuffle():
    try:
        dataset = fetch_kddcup99(random_state=0, subset='SA', shuffle=True,
                                 percent10=True, download_if_missing=False)
    except IOError:
        raise SkipTest("kddcup99 dataset can not be loaded.")

    assert(any(dataset.target[-100:] == b'normal.')) 

def test_check_dataframe_fit_attribute():
    # check pandas dataframe with 'fit' column does not raise error
    # https://github.com/scikit-learn/scikit-learn/issues/8415
    try:
        import pandas as pd
        X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        X_df = pd.DataFrame(X, columns=['a', 'b', 'fit'])
        check_consistent_length(X_df)
    except ImportError:
        raise SkipTest("Pandas not found") 

def test_20news():
    try:
        data = datasets.fetch_20newsgroups(
            subset='all', download_if_missing=False, shuffle=False)
    except IOError:
        raise SkipTest("Download 20 newsgroups to run this test")

    # Extract a reduced dataset
    data2cats = datasets.fetch_20newsgroups(
        subset='all', categories=data.target_names[-1:-3:-1], shuffle=False)
    # Check that the ordering of the target_names is the same
    # as the ordering in the full dataset
    assert_equal(data2cats.target_names,
                 data.target_names[-2:])
    # Assert that we have only 0 and 1 as labels
    assert_equal(np.unique(data2cats.target).tolist(), [0, 1])

    # Check that the number of filenames is consistent with data/target
    assert_equal(len(data2cats.filenames), len(data2cats.target))
    assert_equal(len(data2cats.filenames), len(data2cats.data))

    # Check that the first entry of the reduced dataset corresponds to
    # the first entry of the corresponding category in the full dataset
    entry1 = data2cats.data[0]
    category = data2cats.target_names[data2cats.target[0]]
    label = data.target_names.index(category)
    entry2 = data.data[np.where(data.target == label)[0][0]]
    assert_equal(entry1, entry2) 

def check_class_weight_classifiers(name, classifier_orig):
    if name == "NuSVC":
        # the sparse version has a parameter that doesn't do anything
        raise SkipTest
    if name.endswith("NB"):
        # NaiveBayes classifiers have a somewhat different interface.
        # FIXME SOON!
        raise SkipTest

    for n_centers in [2, 3]:
        # 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)
        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)

        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)
        assert_greater(np.mean(y_pred == 0), 0.87) 

def check_pipeline_consistency(name, estimator_orig):
    if estimator_orig._get_tags()['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 = multioutput_estimator_convert_y_2d(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) 

def check_estimator(Estimator):
    """Check if estimator adheres to scikit-learn conventions.

    This estimator will run an extensive test-suite for input validation,
    shapes, etc.
    Additional tests for classifiers, regressors, clustering or transformers
    will be run if the Estimator class inherits from the corresponding mixin
    from sklearn.base.

    This test can be applied to classes or instances.
    Classes currently have some additional tests that related to construction,
    while passing instances allows the testing of multiple options.

    Parameters
    ----------
    estimator : estimator object or class
        Estimator to check. Estimator is a class object or instance.

    """
    if isinstance(Estimator, type):
        # got a class
        name = Estimator.__name__
        check_parameters_default_constructible(name, Estimator)
        check_no_fit_attributes_set_in_init(name, Estimator)
        estimator = Estimator()
    else:
        # got an instance
        estimator = Estimator
        name = type(estimator).__name__

    for check in _yield_all_checks(name, estimator):
        try:
            check(name, estimator)
        except SkipTest as message:
            # the only SkipTest thrown currently results from not
            # being able to import pandas.
            warnings.warn(message, SkipTestWarning)