Python sklearn.decomposition.fastica() Examples

The following are 5 code examples for showing how to use sklearn.decomposition.fastica(). 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_fastica.py    License: MIT License 6 votes vote down vote up
def test_fastica_errors():
    n_features = 3
    n_samples = 10
    rng = np.random.RandomState(0)
    X = rng.random_sample((n_samples, n_features))
    w_init = rng.randn(n_features + 1, n_features + 1)
    assert_raises_regex(ValueError, 'max_iter should be greater than 1',
                        FastICA, max_iter=0)
    assert_raises_regex(ValueError, r'alpha must be in \[1,2\]',
                        fastica, X, fun_args={'alpha': 0})
    assert_raises_regex(ValueError, 'w_init has invalid shape.+'
                        r'should be \(3L?, 3L?\)',
                        fastica, X, w_init=w_init)
    assert_raises_regex(ValueError,
                        'Invalid algorithm.+must be.+parallel.+or.+deflation',
                        fastica, X, algorithm='pizza') 
Example 2
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_fastica.py    License: MIT License 5 votes vote down vote up
def test_non_square_fastica(add_noise=False):
    # Test the FastICA algorithm on very simple data.
    rng = np.random.RandomState(0)

    n_samples = 1000
    # Generate two sources:
    t = np.linspace(0, 100, n_samples)
    s1 = np.sin(t)
    s2 = np.ceil(np.sin(np.pi * t))
    s = np.c_[s1, s2].T
    center_and_norm(s)
    s1, s2 = s

    # Mixing matrix
    mixing = rng.randn(6, 2)
    m = np.dot(mixing, s)

    if add_noise:
        m += 0.1 * rng.randn(6, n_samples)

    center_and_norm(m)

    k_, mixing_, s_ = fastica(m.T, n_components=2, random_state=rng)
    s_ = s_.T

    # Check that the mixing model described in the docstring holds:
    assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))

    center_and_norm(s_)
    s1_, s2_ = s_
    # Check to see if the sources have been estimated
    # in the wrong order
    if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
        s2_, s1_ = s_
    s1_ *= np.sign(np.dot(s1_, s1))
    s2_ *= np.sign(np.dot(s2_, s2))

    # Check that we have estimated the original sources
    if not add_noise:
        assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=3)
        assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=3) 
Example 3
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_fastica(self):
        iris = datasets.load_iris()
        df = pdml.ModelFrame(iris)

        result = df.decomposition.fastica(random_state=self.random_state)
        expected = decomposition.fastica(iris.data,
                                         random_state=self.random_state)

        self.assertEqual(len(result), 3)
        self.assertIsInstance(result[0], pdml.ModelFrame)
        tm.assert_index_equal(result[0].index, df.data.columns)
        self.assert_numpy_array_almost_equal(result[0].values, expected[0])

        self.assertIsInstance(result[1], pdml.ModelFrame)
        self.assert_numpy_array_almost_equal(result[1].values, expected[1])

        self.assertIsInstance(result[2], pdml.ModelFrame)
        tm.assert_index_equal(result[2].index, df.index)
        self.assert_numpy_array_almost_equal(result[2].values, expected[2])

        result = df.decomposition.fastica(return_X_mean=True,
                                          random_state=self.random_state)
        expected = decomposition.fastica(iris.data, return_X_mean=True,
                                         random_state=self.random_state)

        self.assertEqual(len(result), 4)
        self.assertIsInstance(result[0], pdml.ModelFrame)
        tm.assert_index_equal(result[0].index, df.data.columns)
        self.assert_numpy_array_almost_equal(result[0].values, expected[0])

        self.assertIsInstance(result[1], pdml.ModelFrame)
        self.assert_numpy_array_almost_equal(result[1].values, expected[1])

        self.assertIsInstance(result[2], pdml.ModelFrame)
        tm.assert_index_equal(result[2].index, df.index)
        self.assert_numpy_array_almost_equal(result[2].values, expected[2])

        self.assert_numpy_array_almost_equal(result[3], expected[3]) 
Example 4
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_fastica.py    License: MIT License 5 votes vote down vote up
def test_non_square_fastica(add_noise=False):
    # Test the FastICA algorithm on very simple data.
    rng = np.random.RandomState(0)

    n_samples = 1000
    # Generate two sources:
    t = np.linspace(0, 100, n_samples)
    s1 = np.sin(t)
    s2 = np.ceil(np.sin(np.pi * t))
    s = np.c_[s1, s2].T
    center_and_norm(s)
    s1, s2 = s

    # Mixing matrix
    mixing = rng.randn(6, 2)
    m = np.dot(mixing, s)

    if add_noise:
        m += 0.1 * rng.randn(6, n_samples)

    center_and_norm(m)

    k_, mixing_, s_ = fastica(m.T, n_components=2, random_state=rng)
    s_ = s_.T

    # Check that the mixing model described in the docstring holds:
    assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))

    center_and_norm(s_)
    s1_, s2_ = s_
    # Check to see if the sources have been estimated
    # in the wrong order
    if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
        s2_, s1_ = s_
    s1_ *= np.sign(np.dot(s1_, s1))
    s2_ *= np.sign(np.dot(s2_, s2))

    # Check that we have estimated the original sources
    if not add_noise:
        assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=3)
        assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=3) 
Example 5
Project: monasca-analytics   Author: openstack   File: lingam.py    License: Apache License 2.0 4 votes vote down vote up
def _discover_structure(data):

        # Add a random noise uniformly distributed to avoid singularity
        # when performing the ICA
        data += np.random.random_sample(data.shape)

        # Create the ICA node to get the inverse of the mixing matrix
        k, w, _ = decomposition.fastica(data)

        w = np.dot(w, k)
        n = w.shape[0]
        best_nzd = float("inf")
        best_slt = float("inf")
        best_w_permuted = w
        causality_matrix = None
        causal_perm = None

        if n < 9:
            perm = LiNGAM._perms(n)

            for i in range(perm.shape[1]):
                perm_matrix = np.eye(n)
                perm_matrix = perm_matrix[:, perm[:, i]]
                w_permuted = perm_matrix.dot(w)
                cost = LiNGAM._cost_non_zero_diag(w_permuted)
                if cost < best_nzd:
                    best_nzd = cost
                    best_w_permuted = w_permuted

            w_opt = best_w_permuted

            w_opt = w_opt / np.diag(w_opt).reshape((n, 1))
            b_matrix = np.eye(n) - w_opt
            best_b_permuted = b_matrix
            best_i = 0

            for i in range(perm.shape[1]):
                b_permuted = b_matrix[:, perm[:, i]][perm[:, i], :]
                cost = LiNGAM._cost_strictly_lower_triangular(
                    b_permuted)
                if cost < best_slt:
                    best_slt = cost
                    best_i = i
                    best_b_permuted = b_permuted

            causal_perm = perm[:, best_i]
            causality_matrix = b_matrix

            percent_upper = best_slt / np.sum(best_b_permuted ** 2)

            if percent_upper > 0.2:
                # TODO(David): Change that code to raise an exception instead
                logger.error("LiNGAM failed to run on the data set")
                logger.error(
                    "--> B permuted matrix is at best {}% lower triangular"
                    .format(percent_upper))

        return causality_matrix, causal_perm