265 lines
8.9 KiB
Python
265 lines
8.9 KiB
Python
|
"""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
|