def test_lda_transform_before_fit():
"""
test `transform` before `fit`
"""
rng = np.random.RandomState(0)
X = rng.randint(4, size=(20, 10))
lda = OnlineLDA()
lda.transform(X)
def test_lda_transform_before_fit():
"""
test `transform` before `fit`
"""
rng = np.random.RandomState(0)
X = rng.randint(4, size=(20, 10))
lda = OnlineLDA()
lda.transform(X)
def test_lda_transform_mismatch():
"""
test n_vocab mismatch in fit and transform
"""
rng = np.random.RandomState(0)
X = rng.randint(4, size=(20, 10))
X_2 = rng.randint(4, size=(10, 8))
n_topics = rng.randint(3, 6)
alpha0 = eta0 = 1.0 / n_topics
lda = OnlineLDA(n_topics=n_topics, alpha=alpha0, eta=eta0, random_state=rng)
lda.partial_fit(X)
lda.transform(X_2)
def test_lda_transform_mismatch():
"""
test n_vocab mismatch in fit and transform
"""
rng = np.random.RandomState(0)
X = rng.randint(4, size=(20, 10))
X_2 = rng.randint(4, size=(10, 8))
n_topics = rng.randint(3, 6)
alpha0 = eta0 = 1. / n_topics
lda = OnlineLDA(n_topics=n_topics,
alpha=alpha0,
eta=eta0,
random_state=rng)
lda.partial_fit(X)
lda.transform(X_2)
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)
