"""Testing for Spectral Biclustering methods""" import numpy as np from scipy.sparse import csr_matrix, issparse from sklearn.model_selection import ParameterGrid from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_true from sklearn.utils.testing import SkipTest from sklearn.base import BaseEstimator, BiclusterMixin from sklearn.cluster.bicluster import SpectralCoclustering from sklearn.cluster.bicluster import SpectralBiclustering from sklearn.cluster.bicluster import _scale_normalize from sklearn.cluster.bicluster import _bistochastic_normalize from sklearn.cluster.bicluster import _log_normalize from sklearn.metrics import consensus_score from sklearn.datasets import make_biclusters, make_checkerboard class MockBiclustering(BaseEstimator, BiclusterMixin): # Mock object for testing get_submatrix. def __init__(self): pass def get_indices(self, i): # Overridden to reproduce old get_submatrix test. return (np.where([True, True, False, False, True])[0], np.where([False, False, True, True])[0]) def test_get_submatrix(): data = np.arange(20).reshape(5, 4) model = MockBiclustering() for X in (data, csr_matrix(data), data.tolist()): submatrix = model.get_submatrix(0, X) if issparse(submatrix): submatrix = submatrix.toarray() assert_array_equal(submatrix, [[2, 3], [6, 7], [18, 19]]) submatrix[:] = -1 if issparse(X): X = X.toarray() assert_true(np.all(X != -1)) def _test_shape_indices(model): # Test get_shape and get_indices on fitted model. for i in range(model.n_clusters): m, n = model.get_shape(i) i_ind, j_ind = model.get_indices(i) assert_equal(len(i_ind), m) assert_equal(len(j_ind), n) def test_spectral_coclustering(): # Test Dhillon's Spectral CoClustering on a simple problem. param_grid = {'svd_method': ['randomized', 'arpack'], 'n_svd_vecs': [None, 20], 'mini_batch': [False, True], 'init': ['k-means++'], 'n_init': [10], 'n_jobs': [1]} random_state = 0 S, rows, cols = make_biclusters((30, 30), 3, noise=0.5, random_state=random_state) S -= S.min() # needs to be nonnegative before making it sparse S = np.where(S < 1, 0, S) # threshold some values for mat in (S, csr_matrix(S)): for kwargs in ParameterGrid(param_grid): model = SpectralCoclustering(n_clusters=3, random_state=random_state, **kwargs) model.fit(mat) assert_equal(model.rows_.shape, (3, 30)) assert_array_equal(model.rows_.sum(axis=0), np.ones(30)) assert_array_equal(model.columns_.sum(axis=0), np.ones(30)) assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1) _test_shape_indices(model) def test_spectral_biclustering(): # Test Kluger methods on a checkerboard dataset. S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5, random_state=0) non_default_params = {'method': ['scale', 'log'], 'svd_method': ['arpack'], 'n_svd_vecs': [20], 'mini_batch': [True]} for mat in (S, csr_matrix(S)): for param_name, param_values in non_default_params.items(): for param_value in param_values: model = SpectralBiclustering( n_clusters=3, n_init=3, init='k-means++', random_state=0, ) model.set_params(**dict([(param_name, param_value)])) if issparse(mat) and model.get_params().get('method') == 'log': # cannot take log of sparse matrix assert_raises(ValueError, model.fit, mat) continue else: model.fit(mat) assert_equal(model.rows_.shape, (9, 30)) assert_equal(model.columns_.shape, (9, 30)) assert_array_equal(model.rows_.sum(axis=0), np.repeat(3, 30)) assert_array_equal(model.columns_.sum(axis=0), np.repeat(3, 30)) assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1) _test_shape_indices(model) def _do_scale_test(scaled): """Check that rows sum to one constant, and columns to another.""" row_sum = scaled.sum(axis=1) col_sum = scaled.sum(axis=0) if issparse(scaled): row_sum = np.asarray(row_sum).squeeze() col_sum = np.asarray(col_sum).squeeze() assert_array_almost_equal(row_sum, np.tile(row_sum.mean(), 100), decimal=1) assert_array_almost_equal(col_sum, np.tile(col_sum.mean(), 100), decimal=1) def _do_bistochastic_test(scaled): """Check that rows and columns sum to the same constant.""" _do_scale_test(scaled) assert_almost_equal(scaled.sum(axis=0).mean(), scaled.sum(axis=1).mean(), decimal=1) def test_scale_normalize(): generator = np.random.RandomState(0) X = generator.rand(100, 100) for mat in (X, csr_matrix(X)): scaled, _, _ = _scale_normalize(mat) _do_scale_test(scaled) if issparse(mat): assert issparse(scaled) def test_bistochastic_normalize(): generator = np.random.RandomState(0) X = generator.rand(100, 100) for mat in (X, csr_matrix(X)): scaled = _bistochastic_normalize(mat) _do_bistochastic_test(scaled) if issparse(mat): assert issparse(scaled) def test_log_normalize(): # adding any constant to a log-scaled matrix should make it # bistochastic generator = np.random.RandomState(0) mat = generator.rand(100, 100) scaled = _log_normalize(mat) + 1 _do_bistochastic_test(scaled) def test_fit_best_piecewise(): model = SpectralBiclustering(random_state=0) vectors = np.array([[0, 0, 0, 1, 1, 1], [2, 2, 2, 3, 3, 3], [0, 1, 2, 3, 4, 5]]) best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2) assert_array_equal(best, vectors[:2]) def test_project_and_cluster(): model = SpectralBiclustering(random_state=0) data = np.array([[1, 1, 1], [1, 1, 1], [3, 6, 3], [3, 6, 3]]) vectors = np.array([[1, 0], [0, 1], [0, 0]]) for mat in (data, csr_matrix(data)): labels = model._project_and_cluster(data, vectors, n_clusters=2) assert_array_equal(labels, [0, 0, 1, 1]) def test_perfect_checkerboard(): raise SkipTest("This test is failing on the buildbot, but cannot" " reproduce. Temporarily disabling it until it can be" " reproduced and fixed.") model = SpectralBiclustering(3, svd_method="arpack", random_state=0) S, rows, cols = make_checkerboard((30, 30), 3, noise=0, random_state=0) model.fit(S) assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1) S, rows, cols = make_checkerboard((40, 30), 3, noise=0, random_state=0) model.fit(S) assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1) S, rows, cols = make_checkerboard((30, 40), 3, noise=0, random_state=0) model.fit(S) assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1) def test_errors(): data = np.arange(25).reshape((5, 5)) model = SpectralBiclustering(n_clusters=(3, 3, 3)) assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(n_clusters='abc') assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(n_clusters=(3, 'abc')) assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(method='unknown') assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(svd_method='unknown') assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(n_components=0) assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(n_best=0) assert_raises(ValueError, model.fit, data) model = SpectralBiclustering(n_components=3, n_best=4) assert_raises(ValueError, model.fit, data) model = SpectralBiclustering() data = np.arange(27).reshape((3, 3, 3)) assert_raises(ValueError, model.fit, data)