523 lines
18 KiB
Python
523 lines
18 KiB
Python
import pytest
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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 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
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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 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 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 dico.components_.shape == (n_components, n_features)
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assert 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 dico.components_.shape == (n_components, n_features)
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def test_max_iter():
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def ricker_function(resolution, center, width):
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"""Discrete sub-sampled Ricker (Mexican hat) wavelet"""
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x = np.linspace(0, resolution - 1, resolution)
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x = ((2 / (np.sqrt(3 * width) * np.pi ** .25))
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* (1 - (x - center) ** 2 / width ** 2)
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* np.exp(-(x - center) ** 2 / (2 * width ** 2)))
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return x
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def ricker_matrix(width, resolution, n_components):
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"""Dictionary of Ricker (Mexican hat) wavelets"""
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centers = np.linspace(0, resolution - 1, n_components)
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D = np.empty((n_components, resolution))
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for i, center in enumerate(centers):
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D[i] = ricker_function(resolution, center, width)
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D /= np.sqrt(np.sum(D ** 2, axis=1))[:, np.newaxis]
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return D
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transform_algorithm = 'lasso_cd'
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resolution = 1024
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subsampling = 3 # subsampling factor
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n_components = resolution // subsampling
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# Compute a wavelet dictionary
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D_multi = np.r_[tuple(ricker_matrix(width=w, resolution=resolution,
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n_components=n_components // 5)
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for w in (10, 50, 100, 500, 1000))]
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X = np.linspace(0, resolution - 1, resolution)
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first_quarter = X < resolution / 4
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X[first_quarter] = 3.
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X[np.logical_not(first_quarter)] = -1.
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X = X.reshape(1, -1)
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# check that the underlying model fails to converge
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with pytest.warns(ConvergenceWarning):
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model = SparseCoder(D_multi, transform_algorithm=transform_algorithm,
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transform_max_iter=1)
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model.fit_transform(X)
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# check that the underlying model converges w/o warnings
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with pytest.warns(None) as record:
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model = SparseCoder(D_multi, transform_algorithm=transform_algorithm,
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transform_max_iter=2000)
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model.fit_transform(X)
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assert not record.list
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def test_dict_learning_lars_positive_parameter():
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n_components = 5
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alpha = 1
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err_msg = "Positive constraint not supported for 'lars' coding method."
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with pytest.raises(ValueError, match=err_msg):
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dict_learning(X, n_components, alpha=alpha, positive_code=True)
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@pytest.mark.parametrize("transform_algorithm", [
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"lasso_lars",
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"lasso_cd",
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"threshold",
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])
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@pytest.mark.parametrize("positive_code", [False, True])
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@pytest.mark.parametrize("positive_dict", [False, True])
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def test_dict_learning_positivity(transform_algorithm,
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positive_code,
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positive_dict):
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n_components = 5
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dico = DictionaryLearning(
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n_components, transform_algorithm=transform_algorithm, random_state=0,
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positive_code=positive_code, positive_dict=positive_dict,
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fit_algorithm="cd").fit(X)
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code = dico.transform(X)
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if positive_dict:
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assert (dico.components_ >= 0).all()
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else:
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assert (dico.components_ < 0).any()
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if positive_code:
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assert (code >= 0).all()
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else:
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assert (code < 0).any()
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@pytest.mark.parametrize("positive_dict", [False, True])
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def test_dict_learning_lars_dict_positivity(positive_dict):
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n_components = 5
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dico = DictionaryLearning(
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n_components, transform_algorithm="lars", random_state=0,
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positive_dict=positive_dict, fit_algorithm="cd").fit(X)
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if positive_dict:
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assert (dico.components_ >= 0).all()
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else:
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assert (dico.components_ < 0).any()
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def test_dict_learning_lars_code_positivity():
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n_components = 5
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dico = DictionaryLearning(
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n_components, transform_algorithm="lars", random_state=0,
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positive_code=True, fit_algorithm="cd").fit(X)
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err_msg = "Positive constraint not supported for '{}' coding method."
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err_msg = err_msg.format("lars")
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with pytest.raises(ValueError, match=err_msg):
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dico.transform(X)
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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=4)
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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=4)
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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 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 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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with pytest.raises(ValueError):
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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_almost_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 code.shape == (n_samples, n_components)
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assert dictionary.shape == (n_components, n_features)
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assert np.dot(code, dictionary).shape == X.shape
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def test_dict_learning_online_lars_positive_parameter():
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alpha = 1
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err_msg = "Positive constraint not supported for 'lars' coding method."
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with pytest.raises(ValueError, match=err_msg):
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dict_learning_online(X, alpha=alpha, positive_code=True)
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@pytest.mark.parametrize("transform_algorithm", [
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"lasso_lars",
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"lasso_cd",
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"threshold",
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])
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@pytest.mark.parametrize("positive_code", [False, True])
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@pytest.mark.parametrize("positive_dict", [False, True])
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def test_minibatch_dictionary_learning_positivity(transform_algorithm,
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positive_code,
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positive_dict):
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n_components = 8
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dico = MiniBatchDictionaryLearning(
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n_components, transform_algorithm=transform_algorithm, random_state=0,
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positive_code=positive_code, positive_dict=positive_dict,
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fit_algorithm='cd').fit(X)
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code = dico.transform(X)
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if positive_dict:
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assert (dico.components_ >= 0).all()
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else:
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assert (dico.components_ < 0).any()
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if positive_code:
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assert (code >= 0).all()
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else:
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assert (code < 0).any()
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@pytest.mark.parametrize("positive_dict", [False, True])
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def test_minibatch_dictionary_learning_lars(positive_dict):
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n_components = 8
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dico = MiniBatchDictionaryLearning(
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n_components, transform_algorithm="lars", random_state=0,
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positive_dict=positive_dict, fit_algorithm='cd').fit(X)
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if positive_dict:
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assert (dico.components_ >= 0).all()
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else:
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assert (dico.components_ < 0).any()
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@pytest.mark.parametrize("positive_code", [False, True])
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@pytest.mark.parametrize("positive_dict", [False, True])
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def test_dict_learning_online_positivity(positive_code,
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positive_dict):
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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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method="cd",
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alpha=1, random_state=rng,
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positive_dict=positive_dict,
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positive_code=positive_code)
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if positive_dict:
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assert (dictionary >= 0).all()
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else:
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assert (dictionary < 0).any()
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if positive_code:
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assert (code >= 0).all()
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else:
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assert (code < 0).any()
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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 io import 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 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 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 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_readonly_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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V.setflags(write=False)
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MiniBatchDictionaryLearning(n_components, n_iter=1, dict_init=V,
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random_state=0, shuffle=False).fit(X)
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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 not np.all(sparse_encode(X, dict1.components_, alpha=1) == 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_dict_learning_iter_offset():
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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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dict1 = MiniBatchDictionaryLearning(n_components, n_iter=10,
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dict_init=V, random_state=0,
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shuffle=False)
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dict2 = MiniBatchDictionaryLearning(n_components, n_iter=10,
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dict_init=V, random_state=0,
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shuffle=False)
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dict1.fit(X)
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for sample in X:
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dict2.partial_fit(sample[np.newaxis, :])
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assert dict1.iter_offset_ == dict2.iter_offset_
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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 code.shape == (n_samples, n_components)
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@pytest.mark.parametrize("algo", [
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'lasso_lars',
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'lasso_cd',
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'threshold'
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])
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@pytest.mark.parametrize("positive", [False, True])
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def test_sparse_encode_positivity(algo, positive):
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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, algorithm=algo, positive=positive)
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if positive:
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assert (code >= 0).all()
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else:
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assert (code < 0).any()
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@pytest.mark.parametrize("algo", ['lars', 'omp'])
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def test_sparse_encode_unavailable_positivity(algo):
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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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err_msg = "Positive constraint not supported for '{}' coding method."
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err_msg = err_msg.format(algo)
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with pytest.raises(ValueError, match=err_msg):
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sparse_encode(X, V, algorithm=algo, positive=True)
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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 not np.all(code == 0)
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assert 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 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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with pytest.raises(ValueError):
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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 not np.all(code == 0)
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assert np.sqrt(np.sum((np.dot(code, V) - X) ** 2)) < 0.1
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def test_sparse_coder_parallel_mmap():
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# Non-regression test for:
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# https://github.com/scikit-learn/scikit-learn/issues/5956
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# Test that SparseCoder does not error by passing reading only
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# arrays to child processes
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rng = np.random.RandomState(777)
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n_components, n_features = 40, 64
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init_dict = rng.rand(n_components, n_features)
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# Ensure that `data` is >2M. Joblib memory maps arrays
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# if they are larger than 1MB. The 4 accounts for float32
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# data type
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n_samples = int(2e6) // (4 * n_features)
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data = np.random.rand(n_samples, n_features).astype(np.float32)
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sc = SparseCoder(init_dict, transform_algorithm='omp', n_jobs=2)
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sc.fit_transform(data)
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def test_sparse_coder_n_features_in():
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d = np.array([[1, 2, 3], [1, 2, 3]])
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sc = SparseCoder(d)
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assert sc.n_features_in_ == d.shape[1]
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