def test_lda_preplexity():
"""
Test LDA preplexity for batch training
preplexity should be lower after each iteration
"""
n_topics, alpha, eta, X = _build_sparse_mtx()
lda_1 = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=0)
lda_2 = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=0)
distr_1 = lda_1.fit_transform(X, max_iters=1)
prep_1 = lda_1.preplexity(X, distr_1, sub_sampling=False)
distr_2 = lda_2.fit_transform(X, max_iters=10)
prep_2 = lda_2.preplexity(X, distr_2, sub_sampling=False)
assert_greater_equal(prep_1, prep_2)
def test_lda_preplexity():
"""
Test LDA preplexity for batch training
preplexity should be lower after each iteration
"""
n_topics, alpha, eta, X = _build_sparse_mtx()
lda_1 = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=0)
lda_2 = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=0)
distr_1 = lda_1.fit_transform(X, max_iters=1)
prep_1 = lda_1.preplexity(X, distr_1, sub_sampling=False)
distr_2 = lda_2.fit_transform(X, max_iters=10)
prep_2 = lda_2.preplexity(X, distr_2, sub_sampling=False)
assert_greater_equal(prep_1, prep_2)
def test_lda_normalize_docs():
"""
test sum of topic distribution equals to 1 for each doc
"""
rng = np.random.RandomState(0)
n_topics, alpha, eta, X = _build_sparse_mtx()
lda = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=rng)
X_fit = lda.fit_transform(X)
assert_array_almost_equal(X_fit.sum(axis=1), np.ones(X.shape[0]))
def test_lda_normalize_docs():
"""
test sum of topic distribution equals to 1 for each doc
"""
rng = np.random.RandomState(0)
n_topics, alpha, eta, X = _build_sparse_mtx()
lda = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=rng)
X_fit = lda.fit_transform(X)
assert_array_almost_equal(X_fit.sum(axis=1), np.ones(X.shape[0]))
def test_lda_fit_transform():
"""
Test LDA fit_transform & transform
fit_transform and transform result should be the same
"""
rng = np.random.RandomState(0)
n_topics, alpha, eta, X = _build_sparse_mtx()
lda = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=rng)
X_fit = lda.fit_transform(X)
X_trans = lda.transform(X)
assert_array_almost_equal(X_fit, X_trans, 4)
def test_lda_fit_transform():
"""
Test LDA fit_transform & transform
fit_transform and transform result should be the same
"""
rng = np.random.RandomState(0)
n_topics, alpha, eta, X = _build_sparse_mtx()
lda = OnlineLDA(n_topics=n_topics, alpha=alpha, eta=eta, random_state=rng)
X_fit = lda.fit_transform(X)
X_trans = lda.transform(X)
assert_array_almost_equal(X_fit, X_trans, 4)
def test_lda_dense_input():
"""
Test LDA with dense input.
Similar to test_lda()
"""
rng = np.random.RandomState(0)
X = rng.randint(5, size=(20, 10))
n_topics = 3
alpha0 = eta0 = 1.0 / n_topics
lda = OnlineLDA(n_topics=n_topics, alpha=alpha0, eta=eta0, random_state=rng)
X_trans = lda.fit_transform(X)
assert_true((X_trans > 0.0).any())
def test_lda_dense_input():
"""
Test LDA with dense input.
Similar to test_lda()
"""
rng = np.random.RandomState(0)
X = rng.randint(5, size=(20, 10))
n_topics = 3
alpha0 = eta0 = 1. / n_topics
lda = OnlineLDA(n_topics=n_topics,
alpha=alpha0,
eta=eta0,
random_state=rng)
X_trans = lda.fit_transform(X)
assert_true((X_trans > 0.0).any())
