def test_js_divergence():
    n_samples = 1000
    blobs, _ = datasets.make_blobs(n_samples=n_samples, random_state=8)

    one_component_a = ml.cluster.GaussianMixture(1)
    one_component_b = ml.cluster.GaussianMixture(1)
    two_component = ml.cluster.GaussianMixture(2)

    one_component_a.fit(blobs)
    one_component_b.fit(blobs)
    two_component.fit(blobs)

    confidence_2v1 = ml.confidence.jensen_shannon_divergence(
        one_component_a, two_component)

    confidence_1v1 = ml.confidence.jensen_shannon_divergence(
        one_component_a, one_component_b)

    assert confidence_2v1 > confidence_1v1 

def produce_XOR(sampleSize):
    import sklearn.datasets as dt

    # centers of the blobs
    centers = [(0,0),(3,0),(3,3),(0,3)]

    # create the sample
    x, y = dt.make_blobs(n_samples=sampleSize, n_features=2,
        cluster_std=0.8, centers=centers, shuffle=False
    )

    # and make it XOR like
    y[y == 2] = 0 
    y[y == 3] = 1

    return x, y 

def test_svc():
    """Check that sparse SVC gives the same result as SVC"""
    # many class dataset:
    X_blobs, y_blobs = make_blobs(n_samples=100, centers=10, random_state=0)
    X_blobs = sparse.csr_matrix(X_blobs)

    datasets = [[X_sp, Y, T], [X2_sp, Y2, T2],
                [X_blobs[:80], y_blobs[:80], X_blobs[80:]],
                [iris.data, iris.target, iris.data]]
    kernels = ["linear", "poly", "rbf", "sigmoid"]
    for dataset in datasets:
        for kernel in kernels:
            clf = svm.SVC(gamma=1, kernel=kernel, probability=True,
                          random_state=0, decision_function_shape='ovo')
            sp_clf = svm.SVC(gamma=1, kernel=kernel, probability=True,
                             random_state=0, decision_function_shape='ovo')
            check_svm_model_equal(clf, sp_clf, *dataset) 

def test_grid_search_no_score():
    # Test grid-search on classifier that has no score function.
    clf = LinearSVC(random_state=0)
    X, y = make_blobs(random_state=0, centers=2)
    Cs = [.1, 1, 10]
    clf_no_score = LinearSVCNoScore(random_state=0)
    grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy')
    grid_search.fit(X, y)

    grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs},
                                        scoring='accuracy')
    # smoketest grid search
    grid_search_no_score.fit(X, y)

    # check that best params are equal
    assert_equal(grid_search_no_score.best_params_, grid_search.best_params_)
    # check that we can call score and that it gives the correct result
    assert_equal(grid_search.score(X, y), grid_search_no_score.score(X, y))

    # giving no scoring function raises an error
    grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs})
    assert_raise_message(TypeError, "no scoring", grid_search_no_score.fit,
                         [[1]]) 

def test_unsupervised_grid_search():
    # test grid-search with unsupervised estimator
    X, y = make_blobs(random_state=0)
    km = KMeans(random_state=0)

    # Multi-metric evaluation unsupervised
    scoring = ['adjusted_rand_score', 'fowlkes_mallows_score']
    for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']:
        grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),
                                   scoring=scoring, refit=refit)
        grid_search.fit(X, y)
        # Both ARI and FMS can find the right number :)
        assert_equal(grid_search.best_params_["n_clusters"], 3)

    # Single metric evaluation unsupervised
    grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),
                               scoring='fowlkes_mallows_score')
    grid_search.fit(X, y)
    assert_equal(grid_search.best_params_["n_clusters"], 3)

    # Now without a score, and without y
    grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]))
    grid_search.fit(X)
    assert_equal(grid_search.best_params_["n_clusters"], 4) 

def test_deprecated_grid_search_iid():
    depr_message = ("The default of the `iid` parameter will change from True "
                    "to False in version 0.22")
    X, y = make_blobs(n_samples=54, random_state=0, centers=2)
    grid = GridSearchCV(SVC(gamma='scale', random_state=0),
                        param_grid={'C': [10]}, cv=3)
    # no warning with equally sized test sets
    assert_no_warnings(grid.fit, X, y)

    grid = GridSearchCV(SVC(gamma='scale', random_state=0),
                        param_grid={'C': [10]}, cv=5)
    # warning because 54 % 5 != 0
    assert_warns_message(DeprecationWarning, depr_message, grid.fit, X, y)

    grid = GridSearchCV(SVC(gamma='scale', random_state=0),
                        param_grid={'C': [10]}, cv=2)
    # warning because stratification into two classes and 27 % 2 != 0
    assert_warns_message(DeprecationWarning, depr_message, grid.fit, X, y)

    grid = GridSearchCV(SVC(gamma='scale', random_state=0),
                        param_grid={'C': [10]}, cv=KFold(2))
    # no warning because no stratification and 54 % 2 == 0
    assert_no_warnings(grid.fit, X, y) 

def test_lda_coefs():
    # Test if the coefficients of the solvers are approximately the same.
    n_features = 2
    n_classes = 2
    n_samples = 1000
    X, y = make_blobs(n_samples=n_samples, n_features=n_features,
                      centers=n_classes, random_state=11)

    clf_lda_svd = LinearDiscriminantAnalysis(solver="svd")
    clf_lda_lsqr = LinearDiscriminantAnalysis(solver="lsqr")
    clf_lda_eigen = LinearDiscriminantAnalysis(solver="eigen")

    clf_lda_svd.fit(X, y)
    clf_lda_lsqr.fit(X, y)
    clf_lda_eigen.fit(X, y)

    assert_array_almost_equal(clf_lda_svd.coef_, clf_lda_lsqr.coef_, 1)
    assert_array_almost_equal(clf_lda_svd.coef_, clf_lda_eigen.coef_, 1)
    assert_array_almost_equal(clf_lda_eigen.coef_, clf_lda_lsqr.coef_, 1) 

def test_partial_fit():
    # Test that fit is equivalent to calling partial_fit multiple times
    X, y = make_blobs(n_samples=100)
    brc = Birch(n_clusters=3)
    brc.fit(X)
    brc_partial = Birch(n_clusters=None)
    brc_partial.partial_fit(X[:50])
    brc_partial.partial_fit(X[50:])
    assert_array_almost_equal(brc_partial.subcluster_centers_,
                              brc.subcluster_centers_)

    # Test that same global labels are obtained after calling partial_fit
    # with None
    brc_partial.set_params(n_clusters=3)
    brc_partial.partial_fit(None)
    assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_) 

def test_n_clusters():
    # Test that n_clusters param works properly
    X, y = make_blobs(n_samples=100, centers=10)
    brc1 = Birch(n_clusters=10)
    brc1.fit(X)
    assert_greater(len(brc1.subcluster_centers_), 10)
    assert_equal(len(np.unique(brc1.labels_)), 10)

    # Test that n_clusters = Agglomerative Clustering gives
    # the same results.
    gc = AgglomerativeClustering(n_clusters=10)
    brc2 = Birch(n_clusters=gc)
    brc2.fit(X)
    assert_array_equal(brc1.subcluster_labels_, brc2.subcluster_labels_)
    assert_array_equal(brc1.labels_, brc2.labels_)

    # Test that the wrong global clustering step raises an Error.
    clf = ElasticNet()
    brc3 = Birch(n_clusters=clf)
    assert_raises(ValueError, brc3.fit, X)

    # Test that a small number of clusters raises a warning.
    brc4 = Birch(threshold=10000.)
    assert_warns(ConvergenceWarning, brc4.fit, X) 

def test_branching_factor():
    # Test that nodes have at max branching_factor number of subclusters
    X, y = make_blobs()
    branching_factor = 9

    # Purposefully set a low threshold to maximize the subclusters.
    brc = Birch(n_clusters=None, branching_factor=branching_factor,
                threshold=0.01)
    brc.fit(X)
    check_branching_factor(brc.root_, branching_factor)
    brc = Birch(n_clusters=3, branching_factor=branching_factor,
                threshold=0.01)
    brc.fit(X)
    check_branching_factor(brc.root_, branching_factor)

    # Raises error when branching_factor is set to one.
    brc = Birch(n_clusters=None, branching_factor=1, threshold=0.01)
    assert_raises(ValueError, brc.fit, X) 

def test_make_blobs_error():
    n_samples = [20, 20, 20]
    centers = np.array([[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]])
    cluster_stds = np.array([0.05, 0.2, 0.4])
    wrong_centers_msg = ("Length of `n_samples` not consistent "
                         "with number of centers. Got n_samples = {} "
                         "and centers = {}".format(n_samples, centers[:-1]))
    assert_raise_message(ValueError, wrong_centers_msg,
                         make_blobs, n_samples, centers=centers[:-1])
    wrong_std_msg = ("Length of `clusters_std` not consistent with "
                     "number of centers. Got centers = {} "
                     "and cluster_std = {}".format(centers, cluster_stds[:-1]))
    assert_raise_message(ValueError, wrong_std_msg,
                         make_blobs, n_samples,
                         centers=centers, cluster_std=cluster_stds[:-1])
    wrong_type_msg = ("Parameter `centers` must be array-like. "
                      "Got {!r} instead".format(3))
    assert_raise_message(ValueError, wrong_type_msg,
                         make_blobs, n_samples, centers=3) 

def test_linear_kernel(ax, cost):
    train_x, train_y = make_blobs(
        n_samples=500, centers=2, n_features=2, random_state=1
    )
    train_y[train_y == 0] = -1
    scaler = StandardScaler()
    train_x_scaled = scaler.fit_transform(train_x, train_y)
    train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled))
    mykernel = Kernel(kernel="linear", degree=5, coef0=1, gamma=0.5)
    mysvm = SmoSVM(
        train=train_data,
        kernel_func=mykernel,
        cost=cost,
        tolerance=0.001,
        auto_norm=False,
    )
    mysvm.fit()
    plot_partition_boundary(mysvm, train_data, ax=ax) 

def test_metric_supervised_umap_trustworthiness():
    data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
    embedding = UMAP(
        n_neighbors=10,
        min_dist=0.01,
        target_metric="l1",
        target_weight=0.8,
        n_epochs=100,
        random_state=42,
    ).fit_transform(data, labels)
    trust = trustworthiness(data, embedding, 10)
    assert_greater_equal(
        trust,
        0.95,
        "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
    ) 

def test_string_metric_supervised_umap_trustworthiness():
    data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
    labels = np.array(["this", "that", "other"])[labels]
    embedding = UMAP(
        n_neighbors=10,
        min_dist=0.01,
        target_metric="string",
        target_weight=0.8,
        n_epochs=100,
        random_state=42,
    ).fit_transform(data, labels)
    trust = trustworthiness(data, embedding, 10)
    assert_greater_equal(
        trust,
        0.95,
        "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
    ) 

def test_discrete_metric_supervised_umap_trustworthiness():
    data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
    embedding = UMAP(
        n_neighbors=10,
        min_dist=0.01,
        target_metric="ordinal",
        target_weight=0.8,
        n_epochs=100,
        random_state=42,
    ).fit_transform(data, labels)
    trust = trustworthiness(data, embedding, 10)
    assert_greater_equal(
        trust,
        0.95,
        "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
    ) 

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()

    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

    assert_raises(ValueError, estimator.partial_fit, X[:, :-1], y) 

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_test = np.random.randn(20, 2) + 4
    estimator = clone(estimator_orig)

    if (hasattr(estimator, "decision_function") and
            hasattr(estimator, "predict_proba")):

        estimator.fit(X, y)
        a = estimator.predict_proba(X_test)[:, 1]
        b = estimator.decision_function(X_test)
        assert_array_equal(rankdata(a), rankdata(b)) 

def test_fit_transform(self):
        """ Tests fit_transform against scikit-learn.
        """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        ds_arr = ds.array(x, block_size=(300, 2))

        sc1 = SKScaler()
        scaled_x = sc1.fit_transform(x)
        sc2 = StandardScaler()
        ds_scaled = sc2.fit_transform(ds_arr)

        self.assertTrue(np.allclose(scaled_x, ds_scaled.collect()))
        self.assertTrue(np.allclose(sc1.mean_, sc2.mean_.collect()))
        self.assertTrue(np.allclose(sc1.var_, sc2.var_.collect()))
        self.assertEqual(ds_scaled._top_left_shape,
                         ds_scaled._blocks[0][0].shape)
        self.assertEqual(ds_arr._reg_shape, ds_scaled._reg_shape)
        self.assertEqual(ds_arr._top_left_shape, ds_scaled._top_left_shape)
        self.assertEqual(ds_arr.shape, ds_scaled.shape)
        self.assertEqual(ds_arr._n_blocks, ds_scaled._n_blocks) 

def test_irregular(self):
        """ Test with an irregular array """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        ds_arr = ds.array(x, block_size=(300, 2))
        ds_arr = ds_arr[297:602]
        x = x[297:602]

        sc1 = SKScaler()
        scaled_x = sc1.fit_transform(x)
        sc2 = StandardScaler()
        ds_scaled = sc2.fit_transform(ds_arr)

        self.assertTrue(np.allclose(scaled_x, ds_scaled.collect()))
        self.assertTrue(np.allclose(sc1.mean_, sc2.mean_.collect()))
        self.assertTrue(np.allclose(sc1.var_, sc2.var_.collect()))
        self.assertEqual(ds_scaled._top_left_shape,
                         compss_wait_on(ds_scaled._blocks[0][0]).shape)
        self.assertEqual(ds_arr._reg_shape, ds_scaled._reg_shape)
        self.assertEqual(ds_arr._top_left_shape, ds_scaled._top_left_shape)
        self.assertEqual(ds_arr.shape, ds_scaled.shape)
        self.assertEqual(ds_arr._n_blocks, ds_scaled._n_blocks) 

def test_n_clusters_aniso(self):
        """ Tests that DBSCAN finds the correct number of clusters with
        anisotropicly distributed data.
        """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        dbscan = DBSCAN(n_regions=1, eps=.15)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        y_pred = dbscan.fit_predict(ds_x).collect()
        true_sizes = {19, 496, 491, 488, 6}
        cluster_sizes = {y_pred[y_pred == -1].size,
                         y_pred[y_pred == 0].size,
                         y_pred[y_pred == 1].size,
                         y_pred[y_pred == 2].size,
                         y_pred[y_pred == 3].size}

        self.assertEqual(dbscan.n_clusters, 4)
        self.assertEqual(true_sizes, cluster_sizes) 

def test_n_clusters_aniso_max_samples(self):
        """ Tests that DBSCAN finds the correct number of clusters when
        defining max_samples with anisotropicly distributed data.
        """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        dbscan = DBSCAN(n_regions=1, eps=.15, max_samples=500)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        y_pred = dbscan.fit_predict(ds_x).collect()

        true_sizes = {19, 496, 491, 488, 6}
        cluster_sizes = {y_pred[y_pred == -1].size,
                         y_pred[y_pred == 0].size,
                         y_pred[y_pred == 1].size,
                         y_pred[y_pred == 2].size,
                         y_pred[y_pred == 3].size}

        self.assertEqual(dbscan.n_clusters, 4)
        self.assertEqual(true_sizes, cluster_sizes) 

def test_n_clusters_aniso_grid(self):
        """ Tests that DBSCAN finds the correct number of clusters when
        setting n_regions > 1 with anisotropicly distributed data.
        """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        dbscan = DBSCAN(n_regions=4, eps=.15, max_samples=500)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        y_pred = dbscan.fit_predict(ds_x).collect()
        true_sizes = {19, 496, 491, 488, 6}
        cluster_sizes = {y_pred[y_pred == -1].size,
                         y_pred[y_pred == 0].size,
                         y_pred[y_pred == 1].size,
                         y_pred[y_pred == 2].size,
                         y_pred[y_pred == 3].size}

        self.assertEqual(dbscan.n_clusters, 4)
        self.assertEqual(true_sizes, cluster_sizes) 

def test_n_clusters_aniso_dimensions(self):
        """ Tests that DBSCAN finds the correct number of clusters when
        dimensions is not None.
        """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        dbscan = DBSCAN(n_regions=5, dimensions=[1], eps=.15)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        y_pred = dbscan.fit_predict(ds_x).collect()
        true_sizes = {19, 496, 491, 488, 6}
        cluster_sizes = {y_pred[y_pred == -1].size,
                         y_pred[y_pred == 0].size,
                         y_pred[y_pred == 1].size,
                         y_pred[y_pred == 2].size,
                         y_pred[y_pred == 3].size}

        self.assertEqual(dbscan.n_clusters, 4)
        self.assertEqual(true_sizes, cluster_sizes) 

def test_sparse(self):
        """ Tests that DBSCAN produces the same results with sparse and
        dense data.
        """
        n_samples = 1500
        x, y = make_blobs(n_samples=n_samples, random_state=170)
        dbscan = DBSCAN(n_regions=1, eps=.15)
        transformation = [[0.6, -0.6], [-0.4, 0.8]]
        x = np.dot(x, transformation)
        x = StandardScaler().fit_transform(x)

        dense = ds.array(x, block_size=(300, 2))
        sparse = ds.array(csr_matrix(x), block_size=(300, 2))

        y_dense = dbscan.fit_predict(dense).collect()
        y_sparse = dbscan.fit_predict(sparse).collect()

        self.assertTrue(np.array_equal(y_dense, y_sparse)) 

def test_fit_predict(self):
        """ Tests fit_predict."""
        x, y = make_blobs(n_samples=1500, random_state=170)
        x_filtered = np.vstack(
            (x[y == 0][:500], x[y == 1][:100], x[y == 2][:10]))

        x_train = ds.array(x_filtered, block_size=(300, 2))

        kmeans = KMeans(n_clusters=3, random_state=170)
        labels = kmeans.fit_predict(x_train).collect()

        skmeans = SKMeans(n_clusters=3, random_state=170)
        sklabels = skmeans.fit_predict(x_filtered)

        centers = np.array([[-8.941375656533449, -5.481371322614891],
                            [-4.524023204953875, 0.06235042593214654],
                            [2.332994701667008, 0.37681003933082696]])

        self.assertTrue(np.allclose(centers, kmeans.centers))
        self.assertTrue(np.allclose(labels, sklabels)) 

def test_supervised_cluster_scorers():
    # Test clustering scorers against gold standard labeling.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    km = KMeans(n_clusters=3)
    km.fit(X_train)
    for name in CLUSTER_SCORERS:
        score1 = get_scorer(name)(km, X_test, y_test)
        score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
        assert_almost_equal(score1, score2) 

def test_raises_on_score_list():
    # Test that when a list of scores is returned, we raise proper errors.
    X, y = make_blobs(random_state=0)
    f1_scorer_no_average = make_scorer(f1_score, average=None)
    clf = DecisionTreeClassifier()
    assert_raises(ValueError, cross_val_score, clf, X, y,
                  scoring=f1_scorer_no_average)
    grid_search = GridSearchCV(clf, scoring=f1_scorer_no_average,
                               param_grid={'max_depth': [1, 2]})
    assert_raises(ValueError, grid_search.fit, X, y) 

def test_compute_class_weight_invariance():
    # Test that results with class_weight="balanced" is invariant wrt
    # class imbalance if the number of samples is identical.
    # The test uses a balanced two class dataset with 100 datapoints.
    # It creates three versions, one where class 1 is duplicated
    # resulting in 150 points of class 1 and 50 of class 0,
    # one where there are 50 points in class 1 and 150 in class 0,
    # and one where there are 100 points of each class (this one is balanced
    # again).
    # With balancing class weights, all three should give the same model.
    X, y = make_blobs(centers=2, random_state=0)
    # create dataset where class 1 is duplicated twice
    X_1 = np.vstack([X] + [X[y == 1]] * 2)
    y_1 = np.hstack([y] + [y[y == 1]] * 2)
    # create dataset where class 0 is duplicated twice
    X_0 = np.vstack([X] + [X[y == 0]] * 2)
    y_0 = np.hstack([y] + [y[y == 0]] * 2)
    # duplicate everything
    X_ = np.vstack([X] * 2)
    y_ = np.hstack([y] * 2)
    # results should be identical
    logreg1 = LogisticRegression(class_weight="balanced").fit(X_1, y_1)
    logreg0 = LogisticRegression(class_weight="balanced").fit(X_0, y_0)
    logreg = LogisticRegression(class_weight="balanced").fit(X_, y_)
    assert_array_almost_equal(logreg1.coef_, logreg0.coef_)
    assert_array_almost_equal(logreg.coef_, logreg0.coef_) 

def test_pipeline():
    # check that LocallyLinearEmbedding works fine as a Pipeline
    # only checks that no error is raised.
    # TODO check that it actually does something useful
    from sklearn import pipeline, datasets
    X, y = datasets.make_blobs(random_state=0)
    clf = pipeline.Pipeline(
        [('filter', manifold.LocallyLinearEmbedding(random_state=0)),
         ('clf', neighbors.KNeighborsClassifier())])
    clf.fit(X, y)
    assert_less(.9, clf.score(X, y))


# Test the error raised when the weight matrix is singular 

def test_pipeline():
    # check that Isomap works fine as a transformer in a Pipeline
    # only checks that no error is raised.
    # TODO check that it actually does something useful
    X, y = datasets.make_blobs(random_state=0)
    clf = pipeline.Pipeline(
        [('isomap', manifold.Isomap()),
         ('clf', neighbors.KNeighborsClassifier())])
    clf.fit(X, y)
    assert_less(.9, clf.score(X, y))