265 lines
8.9 KiB
Python
265 lines
8.9 KiB
Python
"""Testing for Spectral Biclustering methods"""
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import numpy as np
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import pytest
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from scipy.sparse import issparse
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from sklearn.base import BaseEstimator, BiclusterMixin
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from sklearn.cluster import SpectralBiclustering, SpectralCoclustering
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from sklearn.cluster._bicluster import (
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_bistochastic_normalize,
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_log_normalize,
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_scale_normalize,
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)
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from sklearn.datasets import make_biclusters, make_checkerboard
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from sklearn.metrics import consensus_score, v_measure_score
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from sklearn.model_selection import ParameterGrid
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from sklearn.utils._testing import (
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assert_almost_equal,
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assert_array_almost_equal,
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assert_array_equal,
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)
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from sklearn.utils.fixes import CSR_CONTAINERS
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class MockBiclustering(BiclusterMixin, BaseEstimator):
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# Mock object for testing get_submatrix.
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def __init__(self):
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pass
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def get_indices(self, i):
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# Overridden to reproduce old get_submatrix test.
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return (
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np.where([True, True, False, False, True])[0],
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np.where([False, False, True, True])[0],
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)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_get_submatrix(csr_container):
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data = np.arange(20).reshape(5, 4)
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model = MockBiclustering()
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for X in (data, csr_container(data), data.tolist()):
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submatrix = model.get_submatrix(0, X)
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if issparse(submatrix):
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submatrix = submatrix.toarray()
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assert_array_equal(submatrix, [[2, 3], [6, 7], [18, 19]])
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submatrix[:] = -1
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if issparse(X):
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X = X.toarray()
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assert np.all(X != -1)
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def _test_shape_indices(model):
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# Test get_shape and get_indices on fitted model.
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for i in range(model.n_clusters):
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m, n = model.get_shape(i)
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i_ind, j_ind = model.get_indices(i)
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assert len(i_ind) == m
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assert len(j_ind) == n
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_spectral_coclustering(global_random_seed, csr_container):
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# Test Dhillon's Spectral CoClustering on a simple problem.
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param_grid = {
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"svd_method": ["randomized", "arpack"],
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"n_svd_vecs": [None, 20],
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"mini_batch": [False, True],
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"init": ["k-means++"],
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"n_init": [10],
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}
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S, rows, cols = make_biclusters(
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(30, 30), 3, noise=0.1, random_state=global_random_seed
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)
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S -= S.min() # needs to be nonnegative before making it sparse
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S = np.where(S < 1, 0, S) # threshold some values
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for mat in (S, csr_container(S)):
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for kwargs in ParameterGrid(param_grid):
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model = SpectralCoclustering(
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n_clusters=3, random_state=global_random_seed, **kwargs
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)
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model.fit(mat)
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assert model.rows_.shape == (3, 30)
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assert_array_equal(model.rows_.sum(axis=0), np.ones(30))
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assert_array_equal(model.columns_.sum(axis=0), np.ones(30))
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assert consensus_score(model.biclusters_, (rows, cols)) == 1
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_test_shape_indices(model)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_spectral_biclustering(global_random_seed, csr_container):
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# Test Kluger methods on a checkerboard dataset.
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S, rows, cols = make_checkerboard(
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(30, 30), 3, noise=0.5, random_state=global_random_seed
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)
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non_default_params = {
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"method": ["scale", "log"],
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"svd_method": ["arpack"],
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"n_svd_vecs": [20],
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"mini_batch": [True],
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}
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for mat in (S, csr_container(S)):
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for param_name, param_values in non_default_params.items():
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for param_value in param_values:
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model = SpectralBiclustering(
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n_clusters=3,
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n_init=3,
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init="k-means++",
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random_state=global_random_seed,
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)
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model.set_params(**dict([(param_name, param_value)]))
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if issparse(mat) and model.get_params().get("method") == "log":
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# cannot take log of sparse matrix
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with pytest.raises(ValueError):
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model.fit(mat)
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continue
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else:
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model.fit(mat)
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assert model.rows_.shape == (9, 30)
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assert model.columns_.shape == (9, 30)
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assert_array_equal(model.rows_.sum(axis=0), np.repeat(3, 30))
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assert_array_equal(model.columns_.sum(axis=0), np.repeat(3, 30))
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assert consensus_score(model.biclusters_, (rows, cols)) == 1
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_test_shape_indices(model)
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def _do_scale_test(scaled):
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"""Check that rows sum to one constant, and columns to another."""
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row_sum = scaled.sum(axis=1)
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col_sum = scaled.sum(axis=0)
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if issparse(scaled):
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row_sum = np.asarray(row_sum).squeeze()
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col_sum = np.asarray(col_sum).squeeze()
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assert_array_almost_equal(row_sum, np.tile(row_sum.mean(), 100), decimal=1)
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assert_array_almost_equal(col_sum, np.tile(col_sum.mean(), 100), decimal=1)
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def _do_bistochastic_test(scaled):
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"""Check that rows and columns sum to the same constant."""
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_do_scale_test(scaled)
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assert_almost_equal(scaled.sum(axis=0).mean(), scaled.sum(axis=1).mean(), decimal=1)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_scale_normalize(global_random_seed, csr_container):
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generator = np.random.RandomState(global_random_seed)
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X = generator.rand(100, 100)
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for mat in (X, csr_container(X)):
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scaled, _, _ = _scale_normalize(mat)
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_do_scale_test(scaled)
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if issparse(mat):
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assert issparse(scaled)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_bistochastic_normalize(global_random_seed, csr_container):
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generator = np.random.RandomState(global_random_seed)
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X = generator.rand(100, 100)
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for mat in (X, csr_container(X)):
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scaled = _bistochastic_normalize(mat)
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_do_bistochastic_test(scaled)
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if issparse(mat):
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assert issparse(scaled)
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def test_log_normalize(global_random_seed):
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# adding any constant to a log-scaled matrix should make it
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# bistochastic
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generator = np.random.RandomState(global_random_seed)
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mat = generator.rand(100, 100)
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scaled = _log_normalize(mat) + 1
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_do_bistochastic_test(scaled)
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def test_fit_best_piecewise(global_random_seed):
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model = SpectralBiclustering(random_state=global_random_seed)
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vectors = np.array([[0, 0, 0, 1, 1, 1], [2, 2, 2, 3, 3, 3], [0, 1, 2, 3, 4, 5]])
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best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
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assert_array_equal(best, vectors[:2])
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_project_and_cluster(global_random_seed, csr_container):
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model = SpectralBiclustering(random_state=global_random_seed)
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data = np.array([[1, 1, 1], [1, 1, 1], [3, 6, 3], [3, 6, 3]])
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vectors = np.array([[1, 0], [0, 1], [0, 0]])
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for mat in (data, csr_container(data)):
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labels = model._project_and_cluster(mat, vectors, n_clusters=2)
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assert_almost_equal(v_measure_score(labels, [0, 0, 1, 1]), 1.0)
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def test_perfect_checkerboard(global_random_seed):
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# XXX Previously failed on build bot (not reproducible)
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model = SpectralBiclustering(
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3, svd_method="arpack", random_state=global_random_seed
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)
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S, rows, cols = make_checkerboard(
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(30, 30), 3, noise=0, random_state=global_random_seed
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)
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model.fit(S)
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assert consensus_score(model.biclusters_, (rows, cols)) == 1
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S, rows, cols = make_checkerboard(
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(40, 30), 3, noise=0, random_state=global_random_seed
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)
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model.fit(S)
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assert consensus_score(model.biclusters_, (rows, cols)) == 1
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S, rows, cols = make_checkerboard(
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(30, 40), 3, noise=0, random_state=global_random_seed
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)
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model.fit(S)
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assert consensus_score(model.biclusters_, (rows, cols)) == 1
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@pytest.mark.parametrize(
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"params, type_err, err_msg",
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[
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(
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{"n_clusters": 6},
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ValueError,
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"n_clusters should be <= n_samples=5",
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),
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(
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{"n_clusters": (3, 3, 3)},
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ValueError,
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"Incorrect parameter n_clusters",
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),
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(
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{"n_clusters": (3, 6)},
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ValueError,
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"Incorrect parameter n_clusters",
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),
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(
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{"n_components": 3, "n_best": 4},
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ValueError,
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"n_best=4 must be <= n_components=3",
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),
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],
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)
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def test_spectralbiclustering_parameter_validation(params, type_err, err_msg):
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"""Check parameters validation in `SpectralBiClustering`"""
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data = np.arange(25).reshape((5, 5))
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model = SpectralBiclustering(**params)
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with pytest.raises(type_err, match=err_msg):
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model.fit(data)
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@pytest.mark.parametrize("est", (SpectralBiclustering(), SpectralCoclustering()))
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def test_n_features_in_(est):
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X, _, _ = make_biclusters((3, 3), 3, random_state=0)
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assert not hasattr(est, "n_features_in_")
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est.fit(X)
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assert est.n_features_in_ == 3
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