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])