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import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal, \
assert_equal
from nose import SkipTest
from nose.tools import assert_true
from sklearn.utils.testing import assert_less
from .. import DictionaryLearning, MiniBatchDictionaryLearning, SparseCoder, \
dict_learning_online, sparse_encode
rng = np.random.RandomState(0)
n_samples, n_features = 10, 8
X = rng.randn(n_samples, n_features)
def test_dict_learning_shapes():
n_atoms = 5
dico = DictionaryLearning(n_atoms).fit(X)
assert_true(dico.components_.shape == (n_atoms, n_features))
def test_dict_learning_overcomplete():
n_atoms = 12
X = rng.randn(n_samples, n_features)
dico = DictionaryLearning(n_atoms).fit(X)
assert_true(dico.components_.shape == (n_atoms, n_features))
def test_dict_learning_reconstruction():
n_atoms = 12
dico = DictionaryLearning(n_atoms, transform_algorithm='omp',
transform_alpha=0.001, random_state=0)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)
dico.set_params(transform_algorithm='lasso_lars')
code = dico.transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
# used to test lars here too, but there's no guarantee the number of
# nonzero atoms is right.
def test_dict_learning_nonzero_coefs():
n_atoms = 4
dico = DictionaryLearning(n_atoms, transform_algorithm='lars',
transform_n_nonzero_coefs=3, random_state=0)
code = dico.fit(X).transform(X[1])
assert_true(len(np.flatnonzero(code)) == 3)
dico.set_params(transform_algorithm='omp')
code = dico.transform(X[1])
assert_equal(len(np.flatnonzero(code)), 3)
def test_dict_learning_split():
n_atoms = 5
dico = DictionaryLearning(n_atoms, transform_algorithm='threshold')
code = dico.fit(X).transform(X)
dico.split_sign = True
split_code = dico.transform(X)
assert_array_equal(split_code[:, :n_atoms] - split_code[:, n_atoms:], code)
def test_dict_learning_online_shapes():
# rng = np.random.RandomState(0)
# X = rng.randn(12, 10)
n_atoms = 8
code, dictionary = dict_learning_online(X, n_atoms=n_atoms, alpha=1,
random_state=rng)
assert_equal(code.shape, (n_samples, n_atoms))
assert_equal(dictionary.shape, (n_atoms, n_features))
assert_equal(np.dot(code, dictionary).shape, X.shape)
def test_dict_learning_online_estimator_shapes():
n_atoms = 5
dico = MiniBatchDictionaryLearning(n_atoms, n_iter=20).fit(X)
assert_true(dico.components_.shape == (n_atoms, n_features))
def test_dict_learning_online_overcomplete():
n_atoms = 12
dico = MiniBatchDictionaryLearning(n_atoms, n_iter=20).fit(X)
assert_true(dico.components_.shape == (n_atoms, n_features))
def test_dict_learning_online_initialization():
n_atoms = 12
V = rng.randn(n_atoms, n_features)
dico = MiniBatchDictionaryLearning(n_atoms, n_iter=0, dict_init=V).fit(X)
assert_array_equal(dico.components_, V)
def test_dict_learning_online_partial_fit():
# this test was not actually passing before!
raise SkipTest
n_atoms = 12
V = rng.randn(n_atoms, n_features) # random init
V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
rng1 = np.random.RandomState(0)
rng2 = np.random.RandomState(0)
dico1 = MiniBatchDictionaryLearning(n_atoms, n_iter=10, chunk_size=1,
shuffle=False, dict_init=V,
random_state=rng1).fit(X)
dico2 = MiniBatchDictionaryLearning(n_atoms, n_iter=1, dict_init=V,
random_state=rng2)
for ii, sample in enumerate(X):
dico2.partial_fit(sample, iter_offset=ii * dico2.n_iter)
# if ii == 1: break
assert_true(not np.all(sparse_encode(X, dico1.components_, alpha=100) ==
0))
assert_array_equal(dico1.components_, dico2.components_)
def test_sparse_encode_shapes():
n_atoms = 12
V = rng.randn(n_atoms, n_features) # random init
V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
for algo in ('lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'):
code = sparse_encode(X, V, algorithm=algo)
assert_equal(code.shape, (n_samples, n_atoms))
def test_sparse_encode_error():
n_atoms = 12
V = rng.randn(n_atoms, n_features) # random init
V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
code = sparse_encode(X, V, alpha=0.001)
assert_true(not np.all(code == 0))
assert_less(np.sqrt(np.sum((np.dot(code, V) - X) ** 2)), 0.1)
def test_sparse_coder_estimator():
n_atoms = 12
V = rng.randn(n_atoms, n_features) # random init
V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
code = SparseCoder(dictionary=V, transform_algorithm='lasso_lars',
transform_alpha=0.001).transform(X)
assert_true(not np.all(code == 0))
assert_less(np.sqrt(np.sum((np.dot(code, V) - X) ** 2)), 0.1)
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