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())