265 lines
9.8 KiB
Python
265 lines
9.8 KiB
Python
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import numpy as np
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import itertools
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.utils import check_array
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.testing import TempMemmap
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from sklearn.decomposition import DictionaryLearning
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from sklearn.decomposition import MiniBatchDictionaryLearning
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from sklearn.decomposition import SparseCoder
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from sklearn.decomposition import dict_learning_online
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from sklearn.decomposition import sparse_encode
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rng_global = np.random.RandomState(0)
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n_samples, n_features = 10, 8
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X = rng_global.randn(n_samples, n_features)
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def test_sparse_encode_shapes_omp():
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rng = np.random.RandomState(0)
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algorithms = ['omp', 'lasso_lars', 'lasso_cd', 'lars', 'threshold']
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for n_components, n_samples in itertools.product([1, 5], [1, 9]):
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X_ = rng.randn(n_samples, n_features)
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dictionary = rng.randn(n_components, n_features)
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for algorithm, n_jobs in itertools.product(algorithms, [1, 3]):
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code = sparse_encode(X_, dictionary, algorithm=algorithm,
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n_jobs=n_jobs)
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assert_equal(code.shape, (n_samples, n_components))
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def test_dict_learning_shapes():
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n_components = 5
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dico = DictionaryLearning(n_components, random_state=0).fit(X)
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assert_equal(dico.components_.shape, (n_components, n_features))
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n_components = 1
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dico = DictionaryLearning(n_components, random_state=0).fit(X)
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assert_equal(dico.components_.shape, (n_components, n_features))
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assert_equal(dico.transform(X).shape, (X.shape[0], n_components))
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def test_dict_learning_overcomplete():
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n_components = 12
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dico = DictionaryLearning(n_components, random_state=0).fit(X)
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assert_true(dico.components_.shape == (n_components, n_features))
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def test_dict_learning_reconstruction():
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n_components = 12
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dico = DictionaryLearning(n_components, transform_algorithm='omp',
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transform_alpha=0.001, random_state=0)
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code = dico.fit(X).transform(X)
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assert_array_almost_equal(np.dot(code, dico.components_), X)
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dico.set_params(transform_algorithm='lasso_lars')
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code = dico.transform(X)
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assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
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# used to test lars here too, but there's no guarantee the number of
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# nonzero atoms is right.
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def test_dict_learning_reconstruction_parallel():
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# regression test that parallel reconstruction works with n_jobs=-1
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n_components = 12
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dico = DictionaryLearning(n_components, transform_algorithm='omp',
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transform_alpha=0.001, random_state=0, n_jobs=-1)
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code = dico.fit(X).transform(X)
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assert_array_almost_equal(np.dot(code, dico.components_), X)
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dico.set_params(transform_algorithm='lasso_lars')
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code = dico.transform(X)
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assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
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def test_dict_learning_lassocd_readonly_data():
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n_components = 12
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with TempMemmap(X) as X_read_only:
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dico = DictionaryLearning(n_components, transform_algorithm='lasso_cd',
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transform_alpha=0.001, random_state=0,
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n_jobs=-1)
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with ignore_warnings(category=ConvergenceWarning):
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code = dico.fit(X_read_only).transform(X_read_only)
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assert_array_almost_equal(np.dot(code, dico.components_), X_read_only,
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decimal=2)
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def test_dict_learning_nonzero_coefs():
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n_components = 4
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dico = DictionaryLearning(n_components, transform_algorithm='lars',
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transform_n_nonzero_coefs=3, random_state=0)
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code = dico.fit(X).transform(X[np.newaxis, 1])
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assert_true(len(np.flatnonzero(code)) == 3)
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dico.set_params(transform_algorithm='omp')
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code = dico.transform(X[np.newaxis, 1])
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assert_equal(len(np.flatnonzero(code)), 3)
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def test_dict_learning_unknown_fit_algorithm():
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n_components = 5
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dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
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assert_raises(ValueError, dico.fit, X)
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def test_dict_learning_split():
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n_components = 5
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dico = DictionaryLearning(n_components, transform_algorithm='threshold',
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random_state=0)
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code = dico.fit(X).transform(X)
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dico.split_sign = True
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split_code = dico.transform(X)
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assert_array_equal(split_code[:, :n_components] -
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split_code[:, n_components:], code)
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def test_dict_learning_online_shapes():
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rng = np.random.RandomState(0)
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n_components = 8
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code, dictionary = dict_learning_online(X, n_components=n_components,
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alpha=1, random_state=rng)
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assert_equal(code.shape, (n_samples, n_components))
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assert_equal(dictionary.shape, (n_components, n_features))
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assert_equal(np.dot(code, dictionary).shape, X.shape)
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def test_dict_learning_online_verbosity():
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n_components = 5
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# test verbosity
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from sklearn.externals.six.moves import cStringIO as StringIO
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import sys
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old_stdout = sys.stdout
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try:
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sys.stdout = StringIO()
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dico = MiniBatchDictionaryLearning(n_components, n_iter=20, verbose=1,
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random_state=0)
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dico.fit(X)
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dico = MiniBatchDictionaryLearning(n_components, n_iter=20, verbose=2,
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random_state=0)
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dico.fit(X)
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dict_learning_online(X, n_components=n_components, alpha=1, verbose=1,
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random_state=0)
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dict_learning_online(X, n_components=n_components, alpha=1, verbose=2,
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random_state=0)
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finally:
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sys.stdout = old_stdout
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assert_true(dico.components_.shape == (n_components, n_features))
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def test_dict_learning_online_estimator_shapes():
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n_components = 5
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dico = MiniBatchDictionaryLearning(n_components, n_iter=20, random_state=0)
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dico.fit(X)
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assert_true(dico.components_.shape == (n_components, n_features))
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def test_dict_learning_online_overcomplete():
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n_components = 12
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dico = MiniBatchDictionaryLearning(n_components, n_iter=20,
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random_state=0).fit(X)
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assert_true(dico.components_.shape == (n_components, n_features))
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def test_dict_learning_online_initialization():
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n_components = 12
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features)
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dico = MiniBatchDictionaryLearning(n_components, n_iter=0,
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dict_init=V, random_state=0).fit(X)
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assert_array_equal(dico.components_, V)
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def test_dict_learning_online_partial_fit():
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n_components = 12
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features) # random init
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V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
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dict1 = MiniBatchDictionaryLearning(n_components, n_iter=10 * len(X),
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batch_size=1,
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alpha=1, shuffle=False, dict_init=V,
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random_state=0).fit(X)
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dict2 = MiniBatchDictionaryLearning(n_components, alpha=1,
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n_iter=1, dict_init=V,
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random_state=0)
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for i in range(10):
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for sample in X:
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dict2.partial_fit(sample[np.newaxis, :])
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assert_true(not np.all(sparse_encode(X, dict1.components_, alpha=1) ==
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0))
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assert_array_almost_equal(dict1.components_, dict2.components_,
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decimal=2)
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def test_sparse_encode_shapes():
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n_components = 12
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features) # random init
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V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
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for algo in ('lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'):
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code = sparse_encode(X, V, algorithm=algo)
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assert_equal(code.shape, (n_samples, n_components))
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def test_sparse_encode_input():
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n_components = 100
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features) # random init
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V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
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Xf = check_array(X, order='F')
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for algo in ('lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'):
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a = sparse_encode(X, V, algorithm=algo)
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b = sparse_encode(Xf, V, algorithm=algo)
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assert_array_almost_equal(a, b)
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def test_sparse_encode_error():
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n_components = 12
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features) # random init
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V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
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code = sparse_encode(X, V, alpha=0.001)
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assert_true(not np.all(code == 0))
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assert_less(np.sqrt(np.sum((np.dot(code, V) - X) ** 2)), 0.1)
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def test_sparse_encode_error_default_sparsity():
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rng = np.random.RandomState(0)
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X = rng.randn(100, 64)
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D = rng.randn(2, 64)
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code = ignore_warnings(sparse_encode)(X, D, algorithm='omp',
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n_nonzero_coefs=None)
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assert_equal(code.shape, (100, 2))
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def test_unknown_method():
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n_components = 12
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features) # random init
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assert_raises(ValueError, sparse_encode, X, V, algorithm="<unknown>")
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def test_sparse_coder_estimator():
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n_components = 12
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rng = np.random.RandomState(0)
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V = rng.randn(n_components, n_features) # random init
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V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
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code = SparseCoder(dictionary=V, transform_algorithm='lasso_lars',
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transform_alpha=0.001).transform(X)
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assert_true(not np.all(code == 0))
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assert_less(np.sqrt(np.sum((np.dot(code, V) - X) ** 2)), 0.1)
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