421 lines
16 KiB
Python
421 lines
16 KiB
Python
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import sys
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import numpy as np
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from scipy.linalg import block_diag
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from scipy.sparse import csr_matrix
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from scipy.special import psi
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from sklearn.decomposition import LatentDirichletAllocation
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from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
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_dirichlet_expectation_2d)
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from sklearn.utils.testing import assert_allclose
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_greater_equal
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from sklearn.utils.testing import assert_raises_regexp
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from sklearn.utils.testing import if_safe_multiprocessing_with_blas
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from sklearn.utils.testing import assert_warns
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from sklearn.exceptions import NotFittedError
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from sklearn.externals.six.moves import xrange
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from sklearn.externals.six import StringIO
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def _build_sparse_mtx():
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# Create 3 topics and each topic has 3 distinct words.
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# (Each word only belongs to a single topic.)
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n_components = 3
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block = n_components * np.ones((3, 3))
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blocks = [block] * n_components
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X = block_diag(*blocks)
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X = csr_matrix(X)
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return (n_components, X)
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def test_lda_default_prior_params():
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# default prior parameter should be `1 / topics`
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# and verbose params should not affect result
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n_components, X = _build_sparse_mtx()
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prior = 1. / n_components
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lda_1 = LatentDirichletAllocation(n_components=n_components,
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doc_topic_prior=prior,
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topic_word_prior=prior, random_state=0)
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lda_2 = LatentDirichletAllocation(n_components=n_components,
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random_state=0)
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topic_distr_1 = lda_1.fit_transform(X)
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topic_distr_2 = lda_2.fit_transform(X)
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assert_almost_equal(topic_distr_1, topic_distr_2)
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def test_lda_fit_batch():
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# Test LDA batch learning_offset (`fit` method with 'batch' learning)
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rng = np.random.RandomState(0)
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components,
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evaluate_every=1, learning_method='batch',
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random_state=rng)
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lda.fit(X)
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correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
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for component in lda.components_:
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# Find top 3 words in each LDA component
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top_idx = set(component.argsort()[-3:][::-1])
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assert_true(tuple(sorted(top_idx)) in correct_idx_grps)
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def test_lda_fit_online():
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# Test LDA online learning (`fit` method with 'online' learning)
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rng = np.random.RandomState(0)
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components,
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learning_offset=10., evaluate_every=1,
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learning_method='online', random_state=rng)
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lda.fit(X)
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correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
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for component in lda.components_:
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# Find top 3 words in each LDA component
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top_idx = set(component.argsort()[-3:][::-1])
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assert_true(tuple(sorted(top_idx)) in correct_idx_grps)
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def test_lda_partial_fit():
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# Test LDA online learning (`partial_fit` method)
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# (same as test_lda_batch)
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rng = np.random.RandomState(0)
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components,
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learning_offset=10., total_samples=100,
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random_state=rng)
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for i in xrange(3):
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lda.partial_fit(X)
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correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
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for c in lda.components_:
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top_idx = set(c.argsort()[-3:][::-1])
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assert_true(tuple(sorted(top_idx)) in correct_idx_grps)
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def test_lda_dense_input():
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# Test LDA with dense input.
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rng = np.random.RandomState(0)
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components,
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learning_method='batch', random_state=rng)
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lda.fit(X.toarray())
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correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
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for component in lda.components_:
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# Find top 3 words in each LDA component
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top_idx = set(component.argsort()[-3:][::-1])
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assert_true(tuple(sorted(top_idx)) in correct_idx_grps)
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def test_lda_transform():
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# Test LDA transform.
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# Transform result cannot be negative and should be normalized
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rng = np.random.RandomState(0)
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X = rng.randint(5, size=(20, 10))
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n_components = 3
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lda = LatentDirichletAllocation(n_components=n_components,
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random_state=rng)
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X_trans = lda.fit_transform(X)
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assert_true((X_trans > 0.0).any())
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assert_array_almost_equal(np.sum(X_trans, axis=1),
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np.ones(X_trans.shape[0]))
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def test_lda_fit_transform():
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# Test LDA fit_transform & transform
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# fit_transform and transform result should be the same
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for method in ('online', 'batch'):
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rng = np.random.RandomState(0)
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X = rng.randint(10, size=(50, 20))
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lda = LatentDirichletAllocation(n_components=5, learning_method=method,
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random_state=rng)
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X_fit = lda.fit_transform(X)
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X_trans = lda.transform(X)
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assert_array_almost_equal(X_fit, X_trans, 4)
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def test_lda_partial_fit_dim_mismatch():
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# test `n_features` mismatch in `partial_fit`
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rng = np.random.RandomState(0)
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n_components = rng.randint(3, 6)
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n_col = rng.randint(6, 10)
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X_1 = np.random.randint(4, size=(10, n_col))
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X_2 = np.random.randint(4, size=(10, n_col + 1))
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lda = LatentDirichletAllocation(n_components=n_components,
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learning_offset=5., total_samples=20,
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random_state=rng)
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lda.partial_fit(X_1)
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assert_raises_regexp(ValueError, r"^The provided data has",
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lda.partial_fit, X_2)
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def test_invalid_params():
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# test `_check_params` method
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X = np.ones((5, 10))
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invalid_models = (
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('n_components', LatentDirichletAllocation(n_components=0)),
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('learning_method',
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LatentDirichletAllocation(learning_method='unknown')),
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('total_samples', LatentDirichletAllocation(total_samples=0)),
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('learning_offset', LatentDirichletAllocation(learning_offset=-1)),
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)
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for param, model in invalid_models:
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regex = r"^Invalid %r parameter" % param
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assert_raises_regexp(ValueError, regex, model.fit, X)
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def test_lda_negative_input():
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# test pass dense matrix with sparse negative input.
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X = -np.ones((5, 10))
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lda = LatentDirichletAllocation()
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regex = r"^Negative values in data passed"
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assert_raises_regexp(ValueError, regex, lda.fit, X)
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def test_lda_no_component_error():
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# test `transform` and `perplexity` before `fit`
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rng = np.random.RandomState(0)
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X = rng.randint(4, size=(20, 10))
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lda = LatentDirichletAllocation()
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regex = r"^no 'components_' attribute"
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assert_raises_regexp(NotFittedError, regex, lda.transform, X)
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assert_raises_regexp(NotFittedError, regex, lda.perplexity, X)
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def test_lda_transform_mismatch():
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# test `n_features` mismatch in partial_fit and transform
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rng = np.random.RandomState(0)
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X = rng.randint(4, size=(20, 10))
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X_2 = rng.randint(4, size=(10, 8))
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n_components = rng.randint(3, 6)
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lda = LatentDirichletAllocation(n_components=n_components,
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random_state=rng)
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lda.partial_fit(X)
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assert_raises_regexp(ValueError, r"^The provided data has",
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lda.partial_fit, X_2)
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@if_safe_multiprocessing_with_blas
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def test_lda_multi_jobs():
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n_components, X = _build_sparse_mtx()
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# Test LDA batch training with multi CPU
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for method in ('online', 'batch'):
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rng = np.random.RandomState(0)
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lda = LatentDirichletAllocation(n_components=n_components, n_jobs=2,
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learning_method=method,
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evaluate_every=1, random_state=rng)
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lda.fit(X)
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correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
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for c in lda.components_:
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top_idx = set(c.argsort()[-3:][::-1])
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assert_true(tuple(sorted(top_idx)) in correct_idx_grps)
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@if_safe_multiprocessing_with_blas
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def test_lda_partial_fit_multi_jobs():
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# Test LDA online training with multi CPU
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rng = np.random.RandomState(0)
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components, n_jobs=2,
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learning_offset=5., total_samples=30,
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random_state=rng)
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for i in range(2):
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lda.partial_fit(X)
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correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
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for c in lda.components_:
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top_idx = set(c.argsort()[-3:][::-1])
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assert_true(tuple(sorted(top_idx)) in correct_idx_grps)
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def test_lda_preplexity_mismatch():
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# test dimension mismatch in `perplexity` method
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rng = np.random.RandomState(0)
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n_components = rng.randint(3, 6)
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n_samples = rng.randint(6, 10)
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X = np.random.randint(4, size=(n_samples, 10))
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lda = LatentDirichletAllocation(n_components=n_components,
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learning_offset=5., total_samples=20,
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random_state=rng)
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lda.fit(X)
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# invalid samples
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invalid_n_samples = rng.randint(4, size=(n_samples + 1, n_components))
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assert_raises_regexp(ValueError, r'Number of samples',
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lda._perplexity_precomp_distr, X, invalid_n_samples)
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# invalid topic number
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invalid_n_components = rng.randint(4, size=(n_samples, n_components + 1))
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assert_raises_regexp(ValueError, r'Number of topics',
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lda._perplexity_precomp_distr, X,
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invalid_n_components)
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def test_lda_perplexity():
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# Test LDA perplexity for batch training
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# perplexity should be lower after each iteration
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n_components, X = _build_sparse_mtx()
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for method in ('online', 'batch'):
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lda_1 = LatentDirichletAllocation(n_components=n_components,
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max_iter=1, learning_method=method,
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total_samples=100, random_state=0)
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lda_2 = LatentDirichletAllocation(n_components=n_components,
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max_iter=10, learning_method=method,
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total_samples=100, random_state=0)
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lda_1.fit(X)
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perp_1 = lda_1.perplexity(X, sub_sampling=False)
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lda_2.fit(X)
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perp_2 = lda_2.perplexity(X, sub_sampling=False)
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assert_greater_equal(perp_1, perp_2)
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perp_1_subsampling = lda_1.perplexity(X, sub_sampling=True)
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perp_2_subsampling = lda_2.perplexity(X, sub_sampling=True)
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assert_greater_equal(perp_1_subsampling, perp_2_subsampling)
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def test_lda_score():
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# Test LDA score for batch training
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# score should be higher after each iteration
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n_components, X = _build_sparse_mtx()
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for method in ('online', 'batch'):
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lda_1 = LatentDirichletAllocation(n_components=n_components,
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max_iter=1, learning_method=method,
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total_samples=100, random_state=0)
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lda_2 = LatentDirichletAllocation(n_components=n_components,
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max_iter=10, learning_method=method,
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total_samples=100, random_state=0)
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lda_1.fit_transform(X)
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score_1 = lda_1.score(X)
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lda_2.fit_transform(X)
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score_2 = lda_2.score(X)
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assert_greater_equal(score_2, score_1)
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def test_perplexity_input_format():
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# Test LDA perplexity for sparse and dense input
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# score should be the same for both dense and sparse input
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components, max_iter=1,
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learning_method='batch',
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total_samples=100, random_state=0)
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lda.fit(X)
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perp_1 = lda.perplexity(X)
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perp_2 = lda.perplexity(X.toarray())
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assert_almost_equal(perp_1, perp_2)
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def test_lda_score_perplexity():
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# Test the relationship between LDA score and perplexity
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components, max_iter=10,
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random_state=0)
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lda.fit(X)
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perplexity_1 = lda.perplexity(X, sub_sampling=False)
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score = lda.score(X)
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perplexity_2 = np.exp(-1. * (score / np.sum(X.data)))
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assert_almost_equal(perplexity_1, perplexity_2)
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def test_lda_fit_perplexity():
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# Test that the perplexity computed during fit is consistent with what is
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# returned by the perplexity method
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n_components, X = _build_sparse_mtx()
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lda = LatentDirichletAllocation(n_components=n_components, max_iter=1,
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learning_method='batch', random_state=0,
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evaluate_every=1)
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lda.fit(X)
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|
|
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||
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# Perplexity computed at end of fit method
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perplexity1 = lda.bound_
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|
|
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||
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# Result of perplexity method on the train set
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perplexity2 = lda.perplexity(X)
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|
|
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assert_almost_equal(perplexity1, perplexity2)
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|
|
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||
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|
|
||
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def test_doc_topic_distr_deprecation():
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|
# Test that the appropriate warning message is displayed when a user
|
||
|
|
# attempts to pass the doc_topic_distr argument to the perplexity method
|
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|
|
n_components, X = _build_sparse_mtx()
|
||
|
|
lda = LatentDirichletAllocation(n_components=n_components, max_iter=1,
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||
|
|
learning_method='batch',
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||
|
|
total_samples=100, random_state=0)
|
||
|
|
distr1 = lda.fit_transform(X)
|
||
|
|
distr2 = None
|
||
|
|
assert_warns(DeprecationWarning, lda.perplexity, X, distr1)
|
||
|
|
assert_warns(DeprecationWarning, lda.perplexity, X, distr2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_lda_empty_docs():
|
||
|
|
"""Test LDA on empty document (all-zero rows)."""
|
||
|
|
Z = np.zeros((5, 4))
|
||
|
|
for X in [Z, csr_matrix(Z)]:
|
||
|
|
lda = LatentDirichletAllocation(max_iter=750).fit(X)
|
||
|
|
assert_almost_equal(lda.components_.sum(axis=0),
|
||
|
|
np.ones(lda.components_.shape[1]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_dirichlet_expectation():
|
||
|
|
"""Test Cython version of Dirichlet expectation calculation."""
|
||
|
|
x = np.logspace(-100, 10, 10000)
|
||
|
|
expectation = np.empty_like(x)
|
||
|
|
_dirichlet_expectation_1d(x, 0, expectation)
|
||
|
|
assert_allclose(expectation, np.exp(psi(x) - psi(np.sum(x))),
|
||
|
|
atol=1e-19)
|
||
|
|
|
||
|
|
x = x.reshape(100, 100)
|
||
|
|
assert_allclose(_dirichlet_expectation_2d(x),
|
||
|
|
psi(x) - psi(np.sum(x, axis=1)[:, np.newaxis]),
|
||
|
|
rtol=1e-11, atol=3e-9)
|
||
|
|
|
||
|
|
|
||
|
|
def check_verbosity(verbose, evaluate_every, expected_lines,
|
||
|
|
expected_perplexities):
|
||
|
|
n_components, X = _build_sparse_mtx()
|
||
|
|
lda = LatentDirichletAllocation(n_components=n_components, max_iter=3,
|
||
|
|
learning_method='batch',
|
||
|
|
verbose=verbose,
|
||
|
|
evaluate_every=evaluate_every,
|
||
|
|
random_state=0)
|
||
|
|
out = StringIO()
|
||
|
|
old_out, sys.stdout = sys.stdout, out
|
||
|
|
try:
|
||
|
|
lda.fit(X)
|
||
|
|
finally:
|
||
|
|
sys.stdout = old_out
|
||
|
|
|
||
|
|
n_lines = out.getvalue().count('\n')
|
||
|
|
n_perplexity = out.getvalue().count('perplexity')
|
||
|
|
assert_equal(expected_lines, n_lines)
|
||
|
|
assert_equal(expected_perplexities, n_perplexity)
|
||
|
|
|
||
|
|
|
||
|
|
def test_verbosity():
|
||
|
|
for verbose, evaluate_every, expected_lines, expected_perplexities in [
|
||
|
|
(False, 1, 0, 0),
|
||
|
|
(False, 0, 0, 0),
|
||
|
|
(True, 0, 3, 0),
|
||
|
|
(True, 1, 3, 3),
|
||
|
|
(True, 2, 3, 1),
|
||
|
|
]:
|
||
|
|
yield (check_verbosity, verbose, evaluate_every, expected_lines,
|
||
|
|
expected_perplexities)
|
||
|
|
|
||
|
|
|
||
|
|
def test_lda_n_topics_deprecation():
|
||
|
|
n_components, X = _build_sparse_mtx()
|
||
|
|
lda = LatentDirichletAllocation(n_topics=10, learning_method='batch')
|
||
|
|
assert_warns(DeprecationWarning, lda.fit, X)
|