Python sklearn.decomposition.SparsePCA() Examples

The following are 17 code examples for showing how to use sklearn.decomposition.SparsePCA(). 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_sparse_pca.py    License: MIT License 6 votes vote down vote up
def generate_toy_data(n_components, n_samples, image_size, random_state=None):
    n_features = image_size[0] * image_size[1]

    rng = check_random_state(random_state)
    U = rng.randn(n_samples, n_components)
    V = rng.randn(n_components, n_features)

    centers = [(3, 3), (6, 7), (8, 1)]
    sz = [1, 2, 1]
    for k in range(n_components):
        img = np.zeros(image_size)
        xmin, xmax = centers[k][0] - sz[k], centers[k][0] + sz[k]
        ymin, ymax = centers[k][1] - sz[k], centers[k][1] + sz[k]
        img[xmin:xmax][:, ymin:ymax] = 1.0
        V[k, :] = img.ravel()

    # Y is defined by : Y = UV + noise
    Y = np.dot(U, V)
    Y += 0.1 * rng.randn(Y.shape[0], Y.shape[1])  # Add noise
    return Y, U, V

# SparsePCA can be a bit slow. To avoid having test times go up, we
# test different aspects of the code in the same test 
Example 2
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 6 votes vote down vote up
def test_pca_vs_spca():
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
    Z, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
    spca = SparsePCA(alpha=0, ridge_alpha=0, n_components=2,
                     normalize_components=True)
    pca = PCA(n_components=2)
    pca.fit(Y)
    spca.fit(Y)
    results_test_pca = pca.transform(Z)
    results_test_spca = spca.transform(Z)
    assert_allclose(np.abs(spca.components_.dot(pca.components_.T)),
                    np.eye(2), atol=1e-5)
    results_test_pca *= np.sign(results_test_pca[0, :])
    results_test_spca *= np.sign(results_test_spca[0, :])
    assert_allclose(results_test_pca, results_test_spca) 
Example 3
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 4
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 5
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_sparse_pca.py    License: MIT License 6 votes vote down vote up
def generate_toy_data(n_components, n_samples, image_size, random_state=None):
    n_features = image_size[0] * image_size[1]

    rng = check_random_state(random_state)
    U = rng.randn(n_samples, n_components)
    V = rng.randn(n_components, n_features)

    centers = [(3, 3), (6, 7), (8, 1)]
    sz = [1, 2, 1]
    for k in range(n_components):
        img = np.zeros(image_size)
        xmin, xmax = centers[k][0] - sz[k], centers[k][0] + sz[k]
        ymin, ymax = centers[k][1] - sz[k], centers[k][1] + sz[k]
        img[xmin:xmax][:, ymin:ymax] = 1.0
        V[k, :] = img.ravel()

    # Y is defined by : Y = UV + noise
    Y = np.dot(U, V)
    Y += 0.1 * rng.randn(Y.shape[0], Y.shape[1])  # Add noise
    return Y, U, V

# SparsePCA can be a bit slow. To avoid having test times go up, we
# test different aspects of the code in the same test 
Example 6
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_sparse_pca.py    License: MIT License 6 votes vote down vote up
def test_fit_transform():
    alpha = 1
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
    spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
                          random_state=0)
    spca_lars.fit(Y)

    # Test that CD gives similar results
    spca_lasso = SparsePCA(n_components=3, method='cd', random_state=0,
                           alpha=alpha)
    spca_lasso.fit(Y)
    assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)

    # Test that deprecated ridge_alpha parameter throws warning
    warning_msg = "The ridge_alpha parameter on transform()"
    assert_warns_message(DeprecationWarning, warning_msg, spca_lars.transform,
                         Y, ridge_alpha=0.01)
    assert_warns_message(DeprecationWarning, warning_msg, spca_lars.transform,
                         Y, ridge_alpha=None) 
Example 7
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_correct_shapes(norm_comp):
    rng = np.random.RandomState(0)
    X = rng.randn(12, 10)
    spca = SparsePCA(n_components=8, random_state=rng,
                     normalize_components=norm_comp)
    U = spca.fit_transform(X)
    assert_equal(spca.components_.shape, (8, 10))
    assert_equal(U.shape, (12, 8))
    # test overcomplete decomposition
    spca = SparsePCA(n_components=13, random_state=rng,
                     normalize_components=norm_comp)
    U = spca.fit_transform(X)
    assert_equal(spca.components_.shape, (13, 10))
    assert_equal(U.shape, (12, 13)) 
Example 8
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_fit_transform(norm_comp):
    alpha = 1
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
    spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
                          random_state=0, normalize_components=norm_comp)
    spca_lars.fit(Y)

    # Test that CD gives similar results
    spca_lasso = SparsePCA(n_components=3, method='cd', random_state=0,
                           alpha=alpha, normalize_components=norm_comp)
    spca_lasso.fit(Y)
    assert_array_almost_equal(spca_lasso.components_, spca_lars.components_) 
Example 9
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_fit_transform_parallel(norm_comp):
    alpha = 1
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
    spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
                          random_state=0, normalize_components=norm_comp)
    spca_lars.fit(Y)
    U1 = spca_lars.transform(Y)
    # Test multiple CPUs
    spca = SparsePCA(n_components=3, n_jobs=2, method='lars', alpha=alpha,
                     random_state=0, normalize_components=norm_comp).fit(Y)
    U2 = spca.transform(Y)
    assert not np.all(spca_lars.components_ == 0)
    assert_array_almost_equal(U1, U2) 
Example 10
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_transform_nan(norm_comp):
    # Test that SparsePCA won't return NaN when there is 0 feature in all
    # samples.
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
    Y[:, 0] = 0
    estimator = SparsePCA(n_components=8, normalize_components=norm_comp)
    assert not np.any(np.isnan(estimator.fit_transform(Y))) 
Example 11
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_initialization(norm_comp):
    rng = np.random.RandomState(0)
    U_init = rng.randn(5, 3)
    V_init = rng.randn(3, 4)
    model = SparsePCA(n_components=3, U_init=U_init, V_init=V_init, max_iter=0,
                      random_state=rng, normalize_components=norm_comp)
    model.fit(rng.randn(5, 4))
    if norm_comp:
        assert_allclose(model.components_,
                        V_init / np.linalg.norm(V_init, axis=1)[:, None])
    else:
        assert_allclose(model.components_, V_init) 
Example 12
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_scaling_fit_transform():
    alpha = 1
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
    spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
                          random_state=rng, normalize_components=True)
    results_train = spca_lars.fit_transform(Y)
    results_test = spca_lars.transform(Y[:10])
    assert_allclose(results_train[0], results_test[0]) 
Example 13
Project: CatLearn   Author: SUNCAT-Center   File: feature_extraction.py    License: GNU General Public License v3.0 5 votes vote down vote up
def spca(components, train_matrix, test_matrix):
    """Sparse principal component analysis routine.

    Parameters
    ----------
    components : int
        The number of components to be returned.
    train_matrix : array
        The training features.
    test_matrix : array
        The test features.

    Returns
    -------
    new_train : array
        Extracted training features.
    new_test : array
        Extracted test features.
    """
    msg = 'The number of components must be a positive int greater than 0.'
    assert components > 0, msg

    pca = SparsePCA(n_components=components)
    model = pca.fit(X=train_matrix)
    new_train = model.transform(train_matrix)
    new_test = model.transform(test_matrix)

    return new_train, new_test 
Example 14
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_correct_shapes():
    rng = np.random.RandomState(0)
    X = rng.randn(12, 10)
    spca = SparsePCA(n_components=8, random_state=rng)
    U = spca.fit_transform(X)
    assert_equal(spca.components_.shape, (8, 10))
    assert_equal(U.shape, (12, 8))
    # test overcomplete decomposition
    spca = SparsePCA(n_components=13, random_state=rng)
    U = spca.fit_transform(X)
    assert_equal(spca.components_.shape, (13, 10))
    assert_equal(U.shape, (12, 13)) 
Example 15
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_fit_transform_parallel():
    alpha = 1
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
    spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
                          random_state=0)
    spca_lars.fit(Y)
    U1 = spca_lars.transform(Y)
    # Test multiple CPUs
    spca = SparsePCA(n_components=3, n_jobs=2, method='lars', alpha=alpha,
                     random_state=0).fit(Y)
    U2 = spca.transform(Y)
    assert_true(not np.all(spca_lars.components_ == 0))
    assert_array_almost_equal(U1, U2) 
Example 16
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_transform_nan():
    # Test that SparsePCA won't return NaN when there is 0 feature in all
    # samples.
    rng = np.random.RandomState(0)
    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
    Y[:, 0] = 0
    estimator = SparsePCA(n_components=8)
    assert_false(np.any(np.isnan(estimator.fit_transform(Y)))) 
Example 17
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_sparse_pca.py    License: MIT License 5 votes vote down vote up
def test_initialization():
    rng = np.random.RandomState(0)
    U_init = rng.randn(5, 3)
    V_init = rng.randn(3, 4)
    model = SparsePCA(n_components=3, U_init=U_init, V_init=V_init, max_iter=0,
                      random_state=rng)
    model.fit(rng.randn(5, 4))
    assert_array_equal(model.components_, V_init)