def test_rbm_verbose(): rbm = BernoulliRBM(n_iter=2, verbose=10) old_stdout = sys.stdout sys.stdout = StringIO() try: rbm.fit(Xdigits) finally: sys.stdout = old_stdout
def test_fit(): X = Xdigits.copy() rbm = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, n_iter=7, random_state=9) rbm.fit(X) assert_almost_equal(rbm.score_samples(X).mean(), -21., decimal=0) # in-place tricks shouldn't have modified X assert_array_equal(X, Xdigits)
def test_partial_fit(): X = Xdigits.copy() rbm = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=20, random_state=9) n_samples = X.shape[0] n_batches = int(np.ceil(float(n_samples) / rbm.batch_size)) batch_slices = np.array_split(X, n_batches) for i in range(7): for batch in batch_slices: rbm.partial_fit(batch) assert_almost_equal(rbm.score_samples(X).mean(), -21., decimal=0) assert_array_equal(X, Xdigits)
def test_fit_gibbs(): # Gibbs on the RBM hidden layer should be able to recreate [[0], [1]] # from the same input rng = np.random.RandomState(42) X = np.array([[0.], [1.]]) rbm1 = BernoulliRBM(n_components=2, batch_size=2, n_iter=42, random_state=rng) # you need that much iters rbm1.fit(X) assert_almost_equal(rbm1.components_, np.array([[0.02649814], [0.02009084]]), decimal=4) assert_almost_equal(rbm1.gibbs(X), X) return rbm1
def test_fit_gibbs_sparse(): # Gibbs on the RBM hidden layer should be able to recreate [[0], [1]] from # the same input even when the input is sparse, and test against non-sparse rbm1 = test_fit_gibbs() rng = np.random.RandomState(42) from scipy.sparse import csc_matrix X = csc_matrix([[0.], [1.]]) rbm2 = BernoulliRBM(n_components=2, batch_size=2, n_iter=42, random_state=rng) rbm2.fit(X) assert_almost_equal(rbm2.components_, np.array([[0.02649814], [0.02009084]]), decimal=4) assert_almost_equal(rbm2.gibbs(X), X.toarray()) assert_almost_equal(rbm1.components_, rbm2.components_)
def test_small_sparse_partial_fit(): for sparse in [csc_matrix, csr_matrix]: X_sparse = sparse(Xdigits[:100]) X = Xdigits[:100].copy() rbm1 = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9) rbm2 = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9) rbm1.partial_fit(X_sparse) rbm2.partial_fit(X) assert_almost_equal(rbm1.score_samples(X).mean(), rbm2.score_samples(X).mean(), decimal=0)
def test_sparse_and_verbose(): # Make sure RBM works with sparse input when verbose=True old_stdout = sys.stdout sys.stdout = StringIO() from scipy.sparse import csc_matrix X = csc_matrix([[0.], [1.]]) rbm = BernoulliRBM(n_components=2, batch_size=2, n_iter=1, random_state=42, verbose=True) try: rbm.fit(X) s = sys.stdout.getvalue() # make sure output is sound assert re.match( r"\[BernoulliRBM\] Iteration 1," r" pseudo-likelihood = -?(\d)+(\.\d+)?," r" time = (\d|\.)+s", s) finally: sys.stdout = old_stdout
def test_transform(): X = Xdigits[:100] rbm1 = BernoulliRBM(n_components=16, batch_size=5, n_iter=5, random_state=42) rbm1.fit(X) Xt1 = rbm1.transform(X) Xt2 = rbm1._mean_hiddens(X) assert_array_equal(Xt1, Xt2)
def test_sample_hiddens(): rng = np.random.RandomState(0) X = Xdigits[:100] rbm1 = BernoulliRBM(n_components=2, batch_size=5, n_iter=5, random_state=42) rbm1.fit(X) h = rbm1._mean_hiddens(X[0]) hs = np.mean([rbm1._sample_hiddens(X[0], rng) for i in range(100)], 0) assert_almost_equal(h, hs, decimal=1)
def test_gibbs_smoke(): # Check if we don't get NaNs sampling the full digits dataset. # Also check that sampling again will yield different results. X = Xdigits rbm1 = BernoulliRBM(n_components=42, batch_size=40, n_iter=20, random_state=42) rbm1.fit(X) X_sampled = rbm1.gibbs(X) assert_all_finite(X_sampled) X_sampled2 = rbm1.gibbs(X) assert np.all((X_sampled != X_sampled2).max(axis=1))
# Load Data X, y = datasets.load_digits(return_X_y=True) X = np.asarray(X, 'float32') X, Y = nudge_dataset(X, y) X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001) # 0-1 scaling X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) # Models we will use logistic = linear_model.LogisticRegression(solver='newton-cg', tol=1) rbm = BernoulliRBM(random_state=0, verbose=True) rbm_features_classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)]) # ############################################################################# # Training # Hyper-parameters. These were set by cross-validation, # using a GridSearchCV. Here we are not performing cross-validation to # save time. rbm.learning_rate = 0.06 rbm.n_iter = 10 # More components tend to give better prediction performance, but larger # fitting time rbm.n_components = 100
def test_small_sparse(): # BernoulliRBM should work on small sparse matrices. X = csr_matrix(Xdigits[:4]) BernoulliRBM().fit(X) # no exception
def test_score_samples(): # Test score_samples (pseudo-likelihood) method. # Assert that pseudo-likelihood is computed without clipping. # See Fabian's blog, http://bit.ly/1iYefRk rng = np.random.RandomState(42) X = np.vstack([np.zeros(1000), np.ones(1000)]) rbm1 = BernoulliRBM(n_components=10, batch_size=2, n_iter=10, random_state=rng) rbm1.fit(X) assert (rbm1.score_samples(X) < -300).all() # Sparse vs. dense should not affect the output. Also test sparse input # validation. rbm1.random_state = 42 d_score = rbm1.score_samples(X) rbm1.random_state = 42 s_score = rbm1.score_samples(lil_matrix(X)) assert_almost_equal(d_score, s_score) # Test numerical stability (#2785): would previously generate infinities # and crash with an exception. with np.errstate(under='ignore'): rbm1.score_samples([np.arange(1000) * 100])