Uploaded Test files
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# Author: Vlad Niculae
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# License: BSD 3 clause
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import sys
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import pytest
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import numpy as np
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import if_safe_multiprocessing_with_blas
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from sklearn.decomposition import SparsePCA, MiniBatchSparsePCA, PCA
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from sklearn.utils import check_random_state
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def generate_toy_data(n_components, n_samples, image_size, random_state=None):
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n_features = image_size[0] * image_size[1]
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rng = check_random_state(random_state)
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U = rng.randn(n_samples, n_components)
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V = rng.randn(n_components, n_features)
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centers = [(3, 3), (6, 7), (8, 1)]
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sz = [1, 2, 1]
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for k in range(n_components):
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img = np.zeros(image_size)
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xmin, xmax = centers[k][0] - sz[k], centers[k][0] + sz[k]
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ymin, ymax = centers[k][1] - sz[k], centers[k][1] + sz[k]
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img[xmin:xmax][:, ymin:ymax] = 1.0
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V[k, :] = img.ravel()
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# Y is defined by : Y = UV + noise
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Y = np.dot(U, V)
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Y += 0.1 * rng.randn(Y.shape[0], Y.shape[1]) # Add noise
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return Y, U, V
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# SparsePCA can be a bit slow. To avoid having test times go up, we
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# test different aspects of the code in the same test
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def test_correct_shapes():
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rng = np.random.RandomState(0)
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X = rng.randn(12, 10)
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spca = SparsePCA(n_components=8, random_state=rng)
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U = spca.fit_transform(X)
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assert spca.components_.shape == (8, 10)
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assert U.shape == (12, 8)
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# test overcomplete decomposition
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spca = SparsePCA(n_components=13, random_state=rng)
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U = spca.fit_transform(X)
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assert spca.components_.shape == (13, 10)
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assert U.shape == (12, 13)
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def test_fit_transform():
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alpha = 1
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
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spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
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random_state=0)
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spca_lars.fit(Y)
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# Test that CD gives similar results
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spca_lasso = SparsePCA(n_components=3, method='cd', random_state=0,
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alpha=alpha)
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spca_lasso.fit(Y)
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assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)
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@if_safe_multiprocessing_with_blas
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def test_fit_transform_parallel():
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alpha = 1
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
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spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
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random_state=0)
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spca_lars.fit(Y)
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U1 = spca_lars.transform(Y)
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# Test multiple CPUs
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spca = SparsePCA(n_components=3, n_jobs=2, method='lars', alpha=alpha,
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random_state=0).fit(Y)
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U2 = spca.transform(Y)
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assert not np.all(spca_lars.components_ == 0)
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assert_array_almost_equal(U1, U2)
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def test_transform_nan():
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# Test that SparsePCA won't return NaN when there is 0 feature in all
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# samples.
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
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Y[:, 0] = 0
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estimator = SparsePCA(n_components=8)
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assert not np.any(np.isnan(estimator.fit_transform(Y)))
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def test_fit_transform_tall():
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 65, (8, 8), random_state=rng) # tall array
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spca_lars = SparsePCA(n_components=3, method='lars', random_state=rng)
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U1 = spca_lars.fit_transform(Y)
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spca_lasso = SparsePCA(n_components=3, method='cd', random_state=rng)
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U2 = spca_lasso.fit(Y).transform(Y)
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assert_array_almost_equal(U1, U2)
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def test_initialization():
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rng = np.random.RandomState(0)
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U_init = rng.randn(5, 3)
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V_init = rng.randn(3, 4)
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model = SparsePCA(n_components=3, U_init=U_init, V_init=V_init, max_iter=0,
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random_state=rng)
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model.fit(rng.randn(5, 4))
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assert_allclose(model.components_,
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V_init / np.linalg.norm(V_init, axis=1)[:, None])
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def test_mini_batch_correct_shapes():
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rng = np.random.RandomState(0)
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X = rng.randn(12, 10)
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pca = MiniBatchSparsePCA(n_components=8, random_state=rng)
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U = pca.fit_transform(X)
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assert pca.components_.shape == (8, 10)
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assert U.shape == (12, 8)
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# test overcomplete decomposition
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pca = MiniBatchSparsePCA(n_components=13, random_state=rng)
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U = pca.fit_transform(X)
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assert pca.components_.shape == (13, 10)
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assert U.shape == (12, 13)
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# XXX: test always skipped
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@pytest.mark.skipif(True, reason="skipping mini_batch_fit_transform.")
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def test_mini_batch_fit_transform():
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alpha = 1
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
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spca_lars = MiniBatchSparsePCA(n_components=3, random_state=0,
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alpha=alpha).fit(Y)
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U1 = spca_lars.transform(Y)
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# Test multiple CPUs
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if sys.platform == 'win32': # fake parallelism for win32
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import joblib
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_mp = joblib.parallel.multiprocessing
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joblib.parallel.multiprocessing = None
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try:
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spca = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
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random_state=0)
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U2 = spca.fit(Y).transform(Y)
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finally:
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joblib.parallel.multiprocessing = _mp
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else: # we can efficiently use parallelism
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spca = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
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random_state=0)
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U2 = spca.fit(Y).transform(Y)
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assert not np.all(spca_lars.components_ == 0)
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assert_array_almost_equal(U1, U2)
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# Test that CD gives similar results
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spca_lasso = MiniBatchSparsePCA(n_components=3, method='cd', alpha=alpha,
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random_state=0).fit(Y)
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assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)
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def test_scaling_fit_transform():
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alpha = 1
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
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spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
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random_state=rng)
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results_train = spca_lars.fit_transform(Y)
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results_test = spca_lars.transform(Y[:10])
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assert_allclose(results_train[0], results_test[0])
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def test_pca_vs_spca():
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
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Z, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
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spca = SparsePCA(alpha=0, ridge_alpha=0, n_components=2)
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pca = PCA(n_components=2)
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pca.fit(Y)
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spca.fit(Y)
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results_test_pca = pca.transform(Z)
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results_test_spca = spca.transform(Z)
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assert_allclose(np.abs(spca.components_.dot(pca.components_.T)),
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np.eye(2), atol=1e-5)
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results_test_pca *= np.sign(results_test_pca[0, :])
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results_test_spca *= np.sign(results_test_spca[0, :])
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assert_allclose(results_test_pca, results_test_spca)
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@pytest.mark.parametrize("spca", [SparsePCA, MiniBatchSparsePCA])
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def test_spca_deprecation_warning(spca):
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
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warn_msg = "'normalize_components' has been deprecated in 0.22"
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with pytest.warns(FutureWarning, match=warn_msg):
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spca(normalize_components=True).fit(Y)
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@pytest.mark.parametrize("spca", [SparsePCA, MiniBatchSparsePCA])
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def test_spca_error_unormalized_components(spca):
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rng = np.random.RandomState(0)
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Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
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err_msg = "normalize_components=False is not supported starting "
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with pytest.raises(NotImplementedError, match=err_msg):
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spca(normalize_components=False).fit(Y)
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@pytest.mark.parametrize("SPCA", [SparsePCA, MiniBatchSparsePCA])
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@pytest.mark.parametrize("n_components", [None, 3])
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def test_spca_n_components_(SPCA, n_components):
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rng = np.random.RandomState(0)
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n_samples, n_features = 12, 10
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X = rng.randn(n_samples, n_features)
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model = SPCA(n_components=n_components).fit(X)
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if n_components is not None:
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assert model.n_components_ == n_components
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else:
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assert model.n_components_ == n_features
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