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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
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