reconnect moved files to git repo

venv/lib/python3.11/site-packages/sklearn/cluster/tests/test_bicluster.py

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+"""Testing for Spectral Biclustering methods"""
+
+import numpy as np
+import pytest
+from scipy.sparse import issparse
+
+from sklearn.base import BaseEstimator, BiclusterMixin
+from sklearn.cluster import SpectralBiclustering, SpectralCoclustering
+from sklearn.cluster._bicluster import (
+    _bistochastic_normalize,
+    _log_normalize,
+    _scale_normalize,
+)
+from sklearn.datasets import make_biclusters, make_checkerboard
+from sklearn.metrics import consensus_score, v_measure_score
+from sklearn.model_selection import ParameterGrid
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+class MockBiclustering(BiclusterMixin, BaseEstimator):
+    # 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],
+        )
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_get_submatrix(csr_container):
+    data = np.arange(20).reshape(5, 4)
+    model = MockBiclustering()
+
+    for X in (data, csr_container(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 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 len(i_ind) == m
+        assert len(j_ind) == n
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_spectral_coclustering(global_random_seed, csr_container):
+    # 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],
+    }
+    S, rows, cols = make_biclusters(
+        (30, 30), 3, noise=0.1, random_state=global_random_seed
+    )
+    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_container(S)):
+        for kwargs in ParameterGrid(param_grid):
+            model = SpectralCoclustering(
+                n_clusters=3, random_state=global_random_seed, **kwargs
+            )
+            model.fit(mat)
+
+            assert 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 consensus_score(model.biclusters_, (rows, cols)) == 1
+
+            _test_shape_indices(model)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_spectral_biclustering(global_random_seed, csr_container):
+    # Test Kluger methods on a checkerboard dataset.
+    S, rows, cols = make_checkerboard(
+        (30, 30), 3, noise=0.5, random_state=global_random_seed
+    )
+
+    non_default_params = {
+        "method": ["scale", "log"],
+        "svd_method": ["arpack"],
+        "n_svd_vecs": [20],
+        "mini_batch": [True],
+    }
+
+    for mat in (S, csr_container(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=global_random_seed,
+                )
+                model.set_params(**dict([(param_name, param_value)]))
+
+                if issparse(mat) and model.get_params().get("method") == "log":
+                    # cannot take log of sparse matrix
+                    with pytest.raises(ValueError):
+                        model.fit(mat)
+                    continue
+                else:
+                    model.fit(mat)
+
+                assert model.rows_.shape == (9, 30)
+                assert 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 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)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_scale_normalize(global_random_seed, csr_container):
+    generator = np.random.RandomState(global_random_seed)
+    X = generator.rand(100, 100)
+    for mat in (X, csr_container(X)):
+        scaled, _, _ = _scale_normalize(mat)
+        _do_scale_test(scaled)
+        if issparse(mat):
+            assert issparse(scaled)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_bistochastic_normalize(global_random_seed, csr_container):
+    generator = np.random.RandomState(global_random_seed)
+    X = generator.rand(100, 100)
+    for mat in (X, csr_container(X)):
+        scaled = _bistochastic_normalize(mat)
+        _do_bistochastic_test(scaled)
+        if issparse(mat):
+            assert issparse(scaled)
+
+
+def test_log_normalize(global_random_seed):
+    # adding any constant to a log-scaled matrix should make it
+    # bistochastic
+    generator = np.random.RandomState(global_random_seed)
+    mat = generator.rand(100, 100)
+    scaled = _log_normalize(mat) + 1
+    _do_bistochastic_test(scaled)
+
+
+def test_fit_best_piecewise(global_random_seed):
+    model = SpectralBiclustering(random_state=global_random_seed)
+    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])
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_project_and_cluster(global_random_seed, csr_container):
+    model = SpectralBiclustering(random_state=global_random_seed)
+    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_container(data)):
+        labels = model._project_and_cluster(mat, vectors, n_clusters=2)
+        assert_almost_equal(v_measure_score(labels, [0, 0, 1, 1]), 1.0)
+
+
+def test_perfect_checkerboard(global_random_seed):
+    # XXX Previously failed on build bot (not reproducible)
+    model = SpectralBiclustering(
+        3, svd_method="arpack", random_state=global_random_seed
+    )
+
+    S, rows, cols = make_checkerboard(
+        (30, 30), 3, noise=0, random_state=global_random_seed
+    )
+    model.fit(S)
+    assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+    S, rows, cols = make_checkerboard(
+        (40, 30), 3, noise=0, random_state=global_random_seed
+    )
+    model.fit(S)
+    assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+    S, rows, cols = make_checkerboard(
+        (30, 40), 3, noise=0, random_state=global_random_seed
+    )
+    model.fit(S)
+    assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+
+@pytest.mark.parametrize(
+    "params, type_err, err_msg",
+    [
+        (
+            {"n_clusters": 6},
+            ValueError,
+            "n_clusters should be <= n_samples=5",
+        ),
+        (
+            {"n_clusters": (3, 3, 3)},
+            ValueError,
+            "Incorrect parameter n_clusters",
+        ),
+        (
+            {"n_clusters": (3, 6)},
+            ValueError,
+            "Incorrect parameter n_clusters",
+        ),
+        (
+            {"n_components": 3, "n_best": 4},
+            ValueError,
+            "n_best=4 must be <= n_components=3",
+        ),
+    ],
+)
+def test_spectralbiclustering_parameter_validation(params, type_err, err_msg):
+    """Check parameters validation in `SpectralBiClustering`"""
+    data = np.arange(25).reshape((5, 5))
+    model = SpectralBiclustering(**params)
+    with pytest.raises(type_err, match=err_msg):
+        model.fit(data)
+
+
+@pytest.mark.parametrize("est", (SpectralBiclustering(), SpectralCoclustering()))
+def test_n_features_in_(est):
+    X, _, _ = make_biclusters((3, 3), 3, random_state=0)
+
+    assert not hasattr(est, "n_features_in_")
+    est.fit(X)
+    assert est.n_features_in_ == 3