def test_algorithms(n_samples=100, n_features=200, n_instances=10):
X = np.random.random((n_samples, n_features))
X[np.where(X < 0.8)] = 0
X = csr_matrix(X)
labels = np.random.randint(10, size=n_samples)
instances = np.linspace(0, n_samples, n_samples / n_instances + 1)[:-1]
instances = np.round(instances)
data = CRFDataset()
data.add_group_from_array(X, labels, instances)
for algorithm in ALGORITHMS:
trainer = CRFTrainer(algorithm=algorithm, quiet=True)
trainer.train(data)
def test_matrix_conversion(n_samples=50, n_features=100, n_instances=10):
"""Test conversion of csr matrix to and from CRFDataset"""
X = np.random.random((n_samples, n_features))
X[np.where(X < 0.8)] = 0
X = csr_matrix(X)
labels = np.random.randint(len(KEYS1), size=n_samples)
instances = np.linspace(0, n_samples, n_samples / n_instances + 1)[:-1]
instances = np.round(instances)
data = CRFDataset()
data.add_group_from_array(X, labels, instances)
mat = data.to_matrix()
assert_array_almost_equal(mat.toarray(), X.toarray())
