Python sklearn.decomposition.KernelPCA() Examples

The following are 30 code examples for showing how to use sklearn.decomposition.KernelPCA(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_kernel_pca_sparse():
    rng = np.random.RandomState(0)
    X_fit = sp.csr_matrix(rng.random_sample((5, 4)))
    X_pred = sp.csr_matrix(rng.random_sample((2, 4)))

    for eigen_solver in ("auto", "arpack"):
        for kernel in ("linear", "rbf", "poly"):
            # transform fit data
            kpca = KernelPCA(4, kernel=kernel, eigen_solver=eigen_solver,
                             fit_inverse_transform=False)
            X_fit_transformed = kpca.fit_transform(X_fit)
            X_fit_transformed2 = kpca.fit(X_fit).transform(X_fit)
            assert_array_almost_equal(np.abs(X_fit_transformed),
                                      np.abs(X_fit_transformed2))

            # transform new data
            X_pred_transformed = kpca.transform(X_pred)
            assert_equal(X_pred_transformed.shape[1],
                         X_fit_transformed.shape[1])

            # inverse transform
            # X_pred2 = kpca.inverse_transform(X_pred_transformed)
            # assert_equal(X_pred2.shape, X_pred.shape) 
Example 2
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_leave_zero_eig():
    """This test checks that fit().transform() returns the same result as
    fit_transform() in case of non-removed zero eigenvalue.
    Non-regression test for issue #12141 (PR #12143)"""
    X_fit = np.array([[1, 1], [0, 0]])

    # Assert that even with all np warnings on, there is no div by zero warning
    with pytest.warns(None) as record:
        with np.errstate(all='warn'):
            k = KernelPCA(n_components=2, remove_zero_eig=False,
                          eigen_solver="dense")
            # Fit, then transform
            A = k.fit(X_fit).transform(X_fit)
            # Do both at once
            B = k.fit_transform(X_fit)
            # Compare
            assert_array_almost_equal(np.abs(A), np.abs(B))

    for w in record:
        # There might be warnings about the kernel being badly conditioned,
        # but there should not be warnings about division by zero.
        # (Numpy division by zero warning can have many message variants, but
        # at least we know that it is a RuntimeWarning so lets check only this)
        assert not issubclass(w.category, RuntimeWarning) 
Example 3
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_kernel_pca_precomputed():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    for eigen_solver in ("dense", "arpack"):
        X_kpca = KernelPCA(4, eigen_solver=eigen_solver).\
            fit(X_fit).transform(X_pred)
        X_kpca2 = KernelPCA(
            4, eigen_solver=eigen_solver, kernel='precomputed').fit(
                np.dot(X_fit, X_fit.T)).transform(np.dot(X_pred, X_fit.T))

        X_kpca_train = KernelPCA(
            4, eigen_solver=eigen_solver,
            kernel='precomputed').fit_transform(np.dot(X_fit, X_fit.T))
        X_kpca_train2 = KernelPCA(
            4, eigen_solver=eigen_solver, kernel='precomputed').fit(
                np.dot(X_fit, X_fit.T)).transform(np.dot(X_fit, X_fit.T))

        assert_array_almost_equal(np.abs(X_kpca),
                                  np.abs(X_kpca2))

        assert_array_almost_equal(np.abs(X_kpca_train),
                                  np.abs(X_kpca_train2)) 
Example 4
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_gridsearch_pipeline_precomputed():
    # Test if we can do a grid-search to find parameters to separate
    # circles with a perceptron model using a precomputed kernel.
    X, y = make_circles(n_samples=400, factor=.3, noise=.05,
                        random_state=0)
    kpca = KernelPCA(kernel="precomputed", n_components=2)
    pipeline = Pipeline([("kernel_pca", kpca),
                         ("Perceptron", Perceptron(max_iter=5))])
    param_grid = dict(Perceptron__max_iter=np.arange(1, 5))
    grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
    X_kernel = rbf_kernel(X, gamma=2.)
    grid_search.fit(X_kernel, y)
    assert_equal(grid_search.best_score_, 1)


# 0.23. warning about tol not having its correct default value. 
Example 5
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_nested_circles():
    # Test the linear separability of the first 2D KPCA transform
    X, y = make_circles(n_samples=400, factor=.3, noise=.05,
                        random_state=0)

    # 2D nested circles are not linearly separable
    train_score = Perceptron(max_iter=5).fit(X, y).score(X, y)
    assert_less(train_score, 0.8)

    # Project the circles data into the first 2 components of a RBF Kernel
    # PCA model.
    # Note that the gamma value is data dependent. If this test breaks
    # and the gamma value has to be updated, the Kernel PCA example will
    # have to be updated too.
    kpca = KernelPCA(kernel="rbf", n_components=2,
                     fit_inverse_transform=True, gamma=2.)
    X_kpca = kpca.fit_transform(X)

    # The data is perfectly linearly separable in that space
    train_score = Perceptron(max_iter=5).fit(X_kpca, y).score(X_kpca, y)
    assert_equal(train_score, 1.0) 
Example 6
Project: ml-parameter-optimization   Author: arnaudvl   File: ml_tune.py    License: MIT License 6 votes vote down vote up
def dim_reduction_method(self):
        """
        select dimensionality reduction method
        """
        if self.dim_reduction=='pca':
            return PCA()
        elif self.dim_reduction=='factor-analysis':
            return FactorAnalysis()
        elif self.dim_reduction=='fast-ica':
            return FastICA()
        elif self.dim_reduction=='kernel-pca':
            return KernelPCA()
        elif self.dim_reduction=='sparse-pca':
            return SparsePCA()
        elif self.dim_reduction=='truncated-svd':
            return TruncatedSVD()
        elif self.dim_reduction!=None:
            raise ValueError('%s is not a supported dimensionality reduction method. Valid inputs are: \
                             "pca","factor-analysis","fast-ica,"kernel-pca","sparse-pca","truncated-svd".' 
                             %(self.dim_reduction)) 
Example 7
Project: Splunking-Crime   Author: nccgroup   File: KernelPCA.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def __init__(self, options):
        self.handle_options(options)

        out_params = convert_params(
            options.get('params', {}),
            ints=['k', 'degree', 'alpha', 'max_iteration'],
            floats=['gamma', 'tolerance'],
            aliases={'k': 'n_components', 'tolerance': 'tol',
                     'max_iteration': 'max_iter'},
        )

        out_params['kernel'] = 'rbf'

        if 'n_components' not in out_params:
            out_params['n_components'] = min(2, len(options['feature_variables']))
        elif out_params['n_components'] == 0:
            raise RuntimeError('k needs to be greater than zero.')

        self.estimator = _KPCA(**out_params)

    # sklearn's KernelPCA.transform tries to form a complete kernel
    # matrix of its input and the original data the model was fit
    # on. Unfortunately, this might consume a colossal amount of
    # memory for large inputs. We chunk the input to cut down on this. 
Example 8
Project: pandas-ml   Author: pandas-ml   File: test_decomposition.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.decomposition.PCA, decomposition.PCA)
        self.assertIs(df.decomposition.IncrementalPCA,
                      decomposition.IncrementalPCA)
        self.assertIs(df.decomposition.KernelPCA, decomposition.KernelPCA)
        self.assertIs(df.decomposition.FactorAnalysis,
                      decomposition.FactorAnalysis)
        self.assertIs(df.decomposition.FastICA, decomposition.FastICA)
        self.assertIs(df.decomposition.TruncatedSVD, decomposition.TruncatedSVD)
        self.assertIs(df.decomposition.NMF, decomposition.NMF)
        self.assertIs(df.decomposition.SparsePCA, decomposition.SparsePCA)
        self.assertIs(df.decomposition.MiniBatchSparsePCA,
                      decomposition.MiniBatchSparsePCA)
        self.assertIs(df.decomposition.SparseCoder, decomposition.SparseCoder)
        self.assertIs(df.decomposition.DictionaryLearning,
                      decomposition.DictionaryLearning)
        self.assertIs(df.decomposition.MiniBatchDictionaryLearning,
                      decomposition.MiniBatchDictionaryLearning)

        self.assertIs(df.decomposition.LatentDirichletAllocation,
                      decomposition.LatentDirichletAllocation) 
Example 9
Project: pandas-ml   Author: pandas-ml   File: test_decomposition.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def test_Decompositions_KernelPCA(self):
        iris = datasets.load_iris()
        df = pdml.ModelFrame(iris)

        mod1 = df.decomposition.KernelPCA()
        mod2 = decomposition.KernelPCA()

        df.fit(mod1)
        mod2.fit(iris.data, iris.target)

        result = df.transform(mod1)
        expected = mod2.transform(iris.data)

        self.assertIsInstance(result, pdml.ModelFrame)
        tm.assert_series_equal(df.target, result.target)
        self.assert_numpy_array_almost_equal(result.data.values[:, :40],
                                             expected[:, :40]) 
Example 10
Project: MTSAnomalyDetection   Author: jsonbruce   File: main.py    License: Apache License 2.0 6 votes vote down vote up
def to_uts(mts, transformer):
    """PCA Dimension Reduction. Convert MTS to UTS

    Args:
        mts (ndarray): MTS
        transformer (class): PCA, KernelPCA, TSNE

    Returns:
        ndarray: UTS

    """
    model = PCA(n_components=1)
    if transformer == KernelPCA:
        model = KernelPCA(n_components=1, kernel="rbf")
    elif transformer == TSNE:
        model = TSNE(n_components=1, perplexity=40, n_iter=300)

    uts = model.fit_transform(mts)
    uts = uts.reshape(-1)
    return uts 
Example 11
Project: MTSAnomalyDetection   Author: jsonbruce   File: eeg_eye_state.py    License: Apache License 2.0 6 votes vote down vote up
def to_uts(mts, transformer):
    """PCA Dimension Reduction. Convert MTS to UTS

    Args:
        mts (ndarray): MTS
        transformer (class): PCA, KernelPCA, TSNE

    Returns:
        ndarray: UTS

    """
    model = PCA(n_components=1)
    if transformer == KernelPCA:
        model = KernelPCA(n_components=1, kernel="rbf")
    elif transformer == TSNE:
        model = TSNE(n_components=1, perplexity=40, n_iter=300)

    uts = model.fit_transform(mts)
    uts = uts.reshape(-1)
    return uts 
Example 12
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_kernel_pca_sparse():
    rng = np.random.RandomState(0)
    X_fit = sp.csr_matrix(rng.random_sample((5, 4)))
    X_pred = sp.csr_matrix(rng.random_sample((2, 4)))

    for eigen_solver in ("auto", "arpack"):
        for kernel in ("linear", "rbf", "poly"):
            # transform fit data
            kpca = KernelPCA(4, kernel=kernel, eigen_solver=eigen_solver,
                             fit_inverse_transform=False)
            X_fit_transformed = kpca.fit_transform(X_fit)
            X_fit_transformed2 = kpca.fit(X_fit).transform(X_fit)
            assert_array_almost_equal(np.abs(X_fit_transformed),
                                      np.abs(X_fit_transformed2))

            # transform new data
            X_pred_transformed = kpca.transform(X_pred)
            assert_equal(X_pred_transformed.shape[1],
                         X_fit_transformed.shape[1])

            # inverse transform
            # X_pred2 = kpca.inverse_transform(X_pred_transformed)
            # assert_equal(X_pred2.shape, X_pred.shape) 
Example 13
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 6 votes vote down vote up
def test_kernel_pca_precomputed():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    for eigen_solver in ("dense", "arpack"):
        X_kpca = KernelPCA(4, eigen_solver=eigen_solver).\
            fit(X_fit).transform(X_pred)
        X_kpca2 = KernelPCA(
            4, eigen_solver=eigen_solver, kernel='precomputed').fit(
                np.dot(X_fit, X_fit.T)).transform(np.dot(X_pred, X_fit.T))

        X_kpca_train = KernelPCA(
            4, eigen_solver=eigen_solver,
            kernel='precomputed').fit_transform(np.dot(X_fit, X_fit.T))
        X_kpca_train2 = KernelPCA(
            4, eigen_solver=eigen_solver, kernel='precomputed').fit(
                np.dot(X_fit, X_fit.T)).transform(np.dot(X_fit, X_fit.T))

        assert_array_almost_equal(np.abs(X_kpca),
                                  np.abs(X_kpca2))

        assert_array_almost_equal(np.abs(X_kpca_train),
                                  np.abs(X_kpca_train2)) 
Example 14
def reduce_KernelPCA(x, **kwd_params):
    '''
        Reduce the dimensions using Principal Component
        Analysis with different kernels
    '''
    # create the PCA object
    pca = dc.KernelPCA(**kwd_params)

    # learn the principal components from all the features
    return pca.fit(x)

# the file name of the dataset 
Example 15
def reduce_KernelPCA(x, **kwd_params):
    '''
        Reduce the dimensions using Principal Component
        Analysis with different kernels
    '''
    # create the PCA object
    pca = dc.KernelPCA(**kwd_params)

    # learn the principal components from all the features
    return pca.fit(x)

# get the sample 
Example 16
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    def histogram(x, y, **kwargs):
        # Histogram kernel implemented as a callable.
        assert_equal(kwargs, {})    # no kernel_params that we didn't ask for
        return np.minimum(x, y).sum()

    for eigen_solver in ("auto", "dense", "arpack"):
        for kernel in ("linear", "rbf", "poly", histogram):
            # histogram kernel produces singular matrix inside linalg.solve
            # XXX use a least-squares approximation?
            inv = not callable(kernel)

            # transform fit data
            kpca = KernelPCA(4, kernel=kernel, eigen_solver=eigen_solver,
                             fit_inverse_transform=inv)
            X_fit_transformed = kpca.fit_transform(X_fit)
            X_fit_transformed2 = kpca.fit(X_fit).transform(X_fit)
            assert_array_almost_equal(np.abs(X_fit_transformed),
                                      np.abs(X_fit_transformed2))

            # non-regression test: previously, gamma would be 0 by default,
            # forcing all eigenvalues to 0 under the poly kernel
            assert_not_equal(X_fit_transformed.size, 0)

            # transform new data
            X_pred_transformed = kpca.transform(X_pred)
            assert_equal(X_pred_transformed.shape[1],
                         X_fit_transformed.shape[1])

            # inverse transform
            if inv:
                X_pred2 = kpca.inverse_transform(X_pred_transformed)
                assert_equal(X_pred2.shape, X_pred.shape) 
Example 17
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_invalid_parameters():
    assert_raises(ValueError, KernelPCA, 10, fit_inverse_transform=True,
                  kernel='precomputed') 
Example 18
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_consistent_transform():
    # X_fit_ needs to retain the old, unmodified copy of X
    state = np.random.RandomState(0)
    X = state.rand(10, 10)
    kpca = KernelPCA(random_state=state).fit(X)
    transformed1 = kpca.transform(X)

    X_copy = X.copy()
    X[:, 0] = 666
    transformed2 = kpca.transform(X_copy)
    assert_array_almost_equal(transformed1, transformed2) 
Example 19
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_deterministic_output():
    rng = np.random.RandomState(0)
    X = rng.rand(10, 10)
    eigen_solver = ('arpack', 'dense')

    for solver in eigen_solver:
        transformed_X = np.zeros((20, 2))
        for i in range(20):
            kpca = KernelPCA(n_components=2, eigen_solver=solver,
                             random_state=rng)
            transformed_X[i, :] = kpca.fit_transform(X)[0]
        assert_allclose(
            transformed_X, np.tile(transformed_X[0, :], 20).reshape(20, 2)) 
Example 20
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_n_components():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    for eigen_solver in ("dense", "arpack"):
        for c in [1, 2, 4]:
            kpca = KernelPCA(n_components=c, eigen_solver=eigen_solver)
            shape = kpca.fit(X_fit).transform(X_pred).shape

            assert_equal(shape, (2, c)) 
Example 21
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_remove_zero_eig():
    X = np.array([[1 - 1e-30, 1], [1, 1], [1, 1 - 1e-20]])

    # n_components=None (default) => remove_zero_eig is True
    kpca = KernelPCA()
    Xt = kpca.fit_transform(X)
    assert_equal(Xt.shape, (3, 0))

    kpca = KernelPCA(n_components=2)
    Xt = kpca.fit_transform(X)
    assert_equal(Xt.shape, (3, 2))

    kpca = KernelPCA(n_components=2, remove_zero_eig=True)
    Xt = kpca.fit_transform(X)
    assert_equal(Xt.shape, (3, 0)) 
Example 22
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_invalid_kernel():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((2, 4))
    kpca = KernelPCA(kernel="tototiti")
    assert_raises(ValueError, kpca.fit, X_fit) 
Example 23
Project: intro_ds   Author: GenTang   File: kernel_pca.py    License: Apache License 2.0 5 votes vote down vote up
def trainKernelPCA(data):
    """
    使用带有核函数的主成分分析对数据进行降维
    """
    model = KernelPCA(n_components=2, kernel="rbf", gamma=25)
    model.fit(data)
    return model 
Example 24
Project: pandas-ml   Author: pandas-ml   File: test_decomposition.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def test_fit_transform_KernelPCA(self):
        iris = datasets.load_iris()
        df = pdml.ModelFrame(iris)

        mod1 = df.decomposition.KernelPCA()
        mod2 = decomposition.KernelPCA()

        result = df.fit_transform(mod1)
        expected = mod2.fit_transform(iris.data, iris.target)

        self.assertIsInstance(result, pdml.ModelFrame)
        tm.assert_series_equal(df.target, result.target)
        self.assert_numpy_array_almost_equal(result.data.values[:, :40],
                                             expected[:, :40]) 
Example 25
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    def histogram(x, y, **kwargs):
        # Histogram kernel implemented as a callable.
        assert_equal(kwargs, {})    # no kernel_params that we didn't ask for
        return np.minimum(x, y).sum()

    for eigen_solver in ("auto", "dense", "arpack"):
        for kernel in ("linear", "rbf", "poly", histogram):
            # histogram kernel produces singular matrix inside linalg.solve
            # XXX use a least-squares approximation?
            inv = not callable(kernel)

            # transform fit data
            kpca = KernelPCA(4, kernel=kernel, eigen_solver=eigen_solver,
                             fit_inverse_transform=inv)
            X_fit_transformed = kpca.fit_transform(X_fit)
            X_fit_transformed2 = kpca.fit(X_fit).transform(X_fit)
            assert_array_almost_equal(np.abs(X_fit_transformed),
                                      np.abs(X_fit_transformed2))

            # non-regression test: previously, gamma would be 0 by default,
            # forcing all eigenvalues to 0 under the poly kernel
            assert_not_equal(X_fit_transformed.size, 0)

            # transform new data
            X_pred_transformed = kpca.transform(X_pred)
            assert_equal(X_pred_transformed.shape[1],
                         X_fit_transformed.shape[1])

            # inverse transform
            if inv:
                X_pred2 = kpca.inverse_transform(X_pred_transformed)
                assert_equal(X_pred2.shape, X_pred.shape) 
Example 26
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_invalid_parameters():
    assert_raises(ValueError, KernelPCA, 10, fit_inverse_transform=True,
                  kernel='precomputed') 
Example 27
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_consistent_transform():
    # X_fit_ needs to retain the old, unmodified copy of X
    state = np.random.RandomState(0)
    X = state.rand(10, 10)
    kpca = KernelPCA(random_state=state).fit(X)
    transformed1 = kpca.transform(X)

    X_copy = X.copy()
    X[:, 0] = 666
    transformed2 = kpca.transform(X_copy)
    assert_array_almost_equal(transformed1, transformed2) 
Example 28
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_linear_kernel():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    # for a linear kernel, kernel PCA should find the same projection as PCA
    # modulo the sign (direction)
    # fit only the first four components: fifth is near zero eigenvalue, so
    # can be trimmed due to roundoff error
    assert_array_almost_equal(
        np.abs(KernelPCA(4).fit(X_fit).transform(X_pred)),
        np.abs(PCA(4).fit(X_fit).transform(X_pred))) 
Example 29
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_remove_zero_eig():
    X = np.array([[1 - 1e-30, 1], [1, 1], [1, 1 - 1e-20]])

    # n_components=None (default) => remove_zero_eig is True
    kpca = KernelPCA()
    Xt = kpca.fit_transform(X)
    assert_equal(Xt.shape, (3, 0))

    kpca = KernelPCA(n_components=2)
    Xt = kpca.fit_transform(X)
    assert_equal(Xt.shape, (3, 2))

    kpca = KernelPCA(n_components=2, remove_zero_eig=True)
    Xt = kpca.fit_transform(X)
    assert_equal(Xt.shape, (3, 0)) 
Example 30
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_kernel_pca.py    License: MIT License 5 votes vote down vote up
def test_kernel_pca_invalid_kernel():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((2, 4))
    kpca = KernelPCA(kernel="tototiti")
    assert_raises(ValueError, kpca.fit, X_fit)