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)