Python testing.SkipTest方法代码示例

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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.") 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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)) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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) 

# 需要导入模块: from sklearn.utils import testing [as 别名]
# 或者: from sklearn.utils.testing import SkipTest [as 别名]
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)