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"""Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
import pytest
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import if_safe_multiprocessing_with_blas
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.validation import _num_samples
from sklearn.base import clone
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.extmath import row_norms
from sklearn.metrics.cluster import v_measure_score
from sklearn.cluster import KMeans, k_means
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster.k_means_ import _labels_inertia
from sklearn.cluster.k_means_ import _mini_batch_step
from sklearn.datasets.samples_generator import make_blobs
from io import StringIO
from sklearn.metrics.cluster import homogeneity_score
# non centered, sparse centers to check the
centers = np.array([
[0.0, 5.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 4.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 5.0, 1.0],
])
n_samples = 100
n_clusters, n_features = centers.shape
X, true_labels = make_blobs(n_samples=n_samples, centers=centers,
cluster_std=1., random_state=42)
X_csr = sp.csr_matrix(X)
@pytest.mark.parametrize("representation, algo",
[('dense', 'full'),
('dense', 'elkan'),
('sparse', 'full')])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_kmeans_results(representation, algo, dtype):
# cheks that kmeans works as intended
array_constr = {'dense': np.array, 'sparse': sp.csr_matrix}[representation]
X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]], dtype=dtype)
sample_weight = [3, 1, 1, 3] # will be rescaled to [1.5, 0.5, 0.5, 1.5]
init_centers = np.array([[0, 0], [1, 1]], dtype=dtype)
expected_labels = [0, 0, 1, 1]
expected_inertia = 0.1875
expected_centers = np.array([[0.125, 0], [0.875, 1]], dtype=dtype)
expected_n_iter = 2
kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo)
kmeans.fit(X, sample_weight=sample_weight)
assert_array_equal(kmeans.labels_, expected_labels)
assert_almost_equal(kmeans.inertia_, expected_inertia)
assert_array_almost_equal(kmeans.cluster_centers_, expected_centers)
assert kmeans.n_iter_ == expected_n_iter
@pytest.mark.parametrize('distribution', ['normal', 'blobs'])
def test_elkan_results(distribution):
# check that results are identical between lloyd and elkan algorithms
rnd = np.random.RandomState(0)
if distribution == 'normal':
X = rnd.normal(size=(50, 10))
else:
X, _ = make_blobs(random_state=rnd)
km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1)
km_elkan = KMeans(algorithm='elkan', n_clusters=5,
random_state=0, n_init=1)
km_full.fit(X)
km_elkan.fit(X)
assert_array_almost_equal(km_elkan.cluster_centers_,
km_full.cluster_centers_)
assert_array_equal(km_elkan.labels_, km_full.labels_)
def test_labels_assignment_and_inertia():
# pure numpy implementation as easily auditable reference gold
# implementation
rng = np.random.RandomState(42)
noisy_centers = centers + rng.normal(size=centers.shape)
labels_gold = np.full(n_samples, -1, dtype=np.int)
mindist = np.empty(n_samples)
mindist.fill(np.infty)
for center_id in range(n_clusters):
dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1)
labels_gold[dist < mindist] = center_id
mindist = np.minimum(dist, mindist)
inertia_gold = mindist.sum()
assert (mindist >= 0.0).all()
assert (labels_gold != -1).all()
sample_weight = None
# perform label assignment using the dense array input
x_squared_norms = (X ** 2).sum(axis=1)
labels_array, inertia_array = _labels_inertia(
X, sample_weight, x_squared_norms, noisy_centers)
assert_array_almost_equal(inertia_array, inertia_gold)
assert_array_equal(labels_array, labels_gold)
# perform label assignment using the sparse CSR input
x_squared_norms_from_csr = row_norms(X_csr, squared=True)
labels_csr, inertia_csr = _labels_inertia(
X_csr, sample_weight, x_squared_norms_from_csr, noisy_centers)
assert_array_almost_equal(inertia_csr, inertia_gold)
assert_array_equal(labels_csr, labels_gold)
def test_minibatch_update_consistency():
# Check that dense and sparse minibatch update give the same results
rng = np.random.RandomState(42)
old_centers = centers + rng.normal(size=centers.shape)
new_centers = old_centers.copy()
new_centers_csr = old_centers.copy()
weight_sums = np.zeros(new_centers.shape[0], dtype=np.double)
weight_sums_csr = np.zeros(new_centers.shape[0], dtype=np.double)
x_squared_norms = (X ** 2).sum(axis=1)
x_squared_norms_csr = row_norms(X_csr, squared=True)
buffer = np.zeros(centers.shape[1], dtype=np.double)
buffer_csr = np.zeros(centers.shape[1], dtype=np.double)
# extract a small minibatch
X_mb = X[:10]
X_mb_csr = X_csr[:10]
x_mb_squared_norms = x_squared_norms[:10]
x_mb_squared_norms_csr = x_squared_norms_csr[:10]
sample_weight_mb = np.ones(X_mb.shape[0], dtype=np.double)
# step 1: compute the dense minibatch update
old_inertia, incremental_diff = _mini_batch_step(
X_mb, sample_weight_mb, x_mb_squared_norms, new_centers, weight_sums,
buffer, 1, None, random_reassign=False)
assert_greater(old_inertia, 0.0)
# compute the new inertia on the same batch to check that it decreased
labels, new_inertia = _labels_inertia(
X_mb, sample_weight_mb, x_mb_squared_norms, new_centers)
assert_greater(new_inertia, 0.0)
assert_less(new_inertia, old_inertia)
# check that the incremental difference computation is matching the
# final observed value
effective_diff = np.sum((new_centers - old_centers) ** 2)
assert_almost_equal(incremental_diff, effective_diff)
# step 2: compute the sparse minibatch update
old_inertia_csr, incremental_diff_csr = _mini_batch_step(
X_mb_csr, sample_weight_mb, x_mb_squared_norms_csr, new_centers_csr,
weight_sums_csr, buffer_csr, 1, None, random_reassign=False)
assert_greater(old_inertia_csr, 0.0)
# compute the new inertia on the same batch to check that it decreased
labels_csr, new_inertia_csr = _labels_inertia(
X_mb_csr, sample_weight_mb, x_mb_squared_norms_csr, new_centers_csr)
assert_greater(new_inertia_csr, 0.0)
assert_less(new_inertia_csr, old_inertia_csr)
# check that the incremental difference computation is matching the
# final observed value
effective_diff = np.sum((new_centers_csr - old_centers) ** 2)
assert_almost_equal(incremental_diff_csr, effective_diff)
# step 3: check that sparse and dense updates lead to the same results
assert_array_equal(labels, labels_csr)
assert_array_almost_equal(new_centers, new_centers_csr)
assert_almost_equal(incremental_diff, incremental_diff_csr)
assert_almost_equal(old_inertia, old_inertia_csr)
assert_almost_equal(new_inertia, new_inertia_csr)
def _check_fitted_model(km):
# check that the number of clusters centers and distinct labels match
# the expectation
centers = km.cluster_centers_
assert_equal(centers.shape, (n_clusters, n_features))
labels = km.labels_
assert_equal(np.unique(labels).shape[0], n_clusters)
# check that the labels assignment are perfect (up to a permutation)
assert_equal(v_measure_score(true_labels, labels), 1.0)
assert_greater(km.inertia_, 0.0)
# check error on dataset being too small
assert_raise_message(ValueError, "n_samples=1 should be >= n_clusters=%d"
% km.n_clusters, km.fit, [[0., 1.]])
def test_k_means_new_centers():
# Explore the part of the code where a new center is reassigned
X = np.array([[0, 0, 1, 1],
[0, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 1, 0, 0]])
labels = [0, 1, 2, 1, 1, 2]
bad_centers = np.array([[+0, 1, 0, 0],
[.2, 0, .2, .2],
[+0, 0, 0, 0]])
km = KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10,
random_state=1)
for this_X in (X, sp.coo_matrix(X)):
km.fit(this_X)
this_labels = km.labels_
# Reorder the labels so that the first instance is in cluster 0,
# the second in cluster 1, ...
this_labels = np.unique(this_labels, return_index=True)[1][this_labels]
np.testing.assert_array_equal(this_labels, labels)
@if_safe_multiprocessing_with_blas
def test_k_means_plus_plus_init_2_jobs():
km = KMeans(init="k-means++", n_clusters=n_clusters, n_jobs=2,
random_state=42).fit(X)
_check_fitted_model(km)
def test_k_means_precompute_distances_flag():
# check that a warning is raised if the precompute_distances flag is not
# supported
km = KMeans(precompute_distances="wrong")
assert_raises(ValueError, km.fit, X)
def test_k_means_plus_plus_init_not_precomputed():
km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42,
precompute_distances=False).fit(X)
_check_fitted_model(km)
def test_k_means_random_init_not_precomputed():
km = KMeans(init="random", n_clusters=n_clusters, random_state=42,
precompute_distances=False).fit(X)
_check_fitted_model(km)
@pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse'])
@pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()])
def test_k_means_init(data, init):
km = KMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=1)
km.fit(data)
_check_fitted_model(km)
def test_k_means_n_init():
rnd = np.random.RandomState(0)
X = rnd.normal(size=(40, 2))
# two regression tests on bad n_init argument
# previous bug: n_init <= 0 threw non-informative TypeError (#3858)
assert_raises_regex(ValueError, "n_init", KMeans(n_init=0).fit, X)
assert_raises_regex(ValueError, "n_init", KMeans(n_init=-1).fit, X)
@pytest.mark.parametrize('Class', [KMeans, MiniBatchKMeans])
def test_k_means_explicit_init_shape(Class):
# test for sensible errors when giving explicit init
# with wrong number of features or clusters
rnd = np.random.RandomState(0)
X = rnd.normal(size=(40, 3))
# mismatch of number of features
km = Class(n_init=1, init=X[:, :2], n_clusters=len(X))
msg = "does not match the number of features of the data"
assert_raises_regex(ValueError, msg, km.fit, X)
# for callable init
km = Class(n_init=1,
init=lambda X_, k, random_state: X_[:, :2],
n_clusters=len(X))
assert_raises_regex(ValueError, msg, km.fit, X)
# mismatch of number of clusters
msg = "does not match the number of clusters"
km = Class(n_init=1, init=X[:2, :], n_clusters=3)
assert_raises_regex(ValueError, msg, km.fit, X)
# for callable init
km = Class(n_init=1,
init=lambda X_, k, random_state: X_[:2, :],
n_clusters=3)
assert_raises_regex(ValueError, msg, km.fit, X)
def test_k_means_fortran_aligned_data():
# Check the KMeans will work well, even if X is a fortran-aligned data.
X = np.asfortranarray([[0, 0], [0, 1], [0, 1]])
centers = np.array([[0, 0], [0, 1]])
labels = np.array([0, 1, 1])
km = KMeans(n_init=1, init=centers, precompute_distances=False,
random_state=42, n_clusters=2)
km.fit(X)
assert_array_almost_equal(km.cluster_centers_, centers)
assert_array_equal(km.labels_, labels)
@pytest.mark.parametrize('algo', ['full', 'elkan'])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
@pytest.mark.parametrize('constructor', [np.asarray, sp.csr_matrix])
@pytest.mark.parametrize('seed, max_iter, tol', [
(0, 2, 1e-7), # strict non-convergence
(1, 2, 1e-1), # loose non-convergence
(3, 300, 1e-7), # strict convergence
(4, 300, 1e-1), # loose convergence
])
def test_k_means_fit_predict(algo, dtype, constructor, seed, max_iter, tol):
# check that fit.predict gives same result as fit_predict
# There's a very small chance of failure with elkan on unstructured dataset
# because predict method uses fast euclidean distances computation which
# may cause small numerical instabilities.
if not (algo == 'elkan' and constructor is sp.csr_matrix):
rng = np.random.RandomState(seed)
X = make_blobs(n_samples=1000, n_features=10, centers=10,
random_state=rng)[0].astype(dtype, copy=False)
X = constructor(X)
kmeans = KMeans(algorithm=algo, n_clusters=10, random_state=seed,
tol=tol, max_iter=max_iter, n_jobs=1)
labels_1 = kmeans.fit(X).predict(X)
labels_2 = kmeans.fit_predict(X)
assert_array_equal(labels_1, labels_2)
def test_mb_kmeans_verbose():
mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters,
random_state=42, verbose=1)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
mb_k_means.fit(X)
finally:
sys.stdout = old_stdout
def test_minibatch_init_with_large_k():
mb_k_means = MiniBatchKMeans(init='k-means++', init_size=10, n_clusters=20)
# Check that a warning is raised, as the number clusters is larger
# than the init_size
assert_warns(RuntimeWarning, mb_k_means.fit, X)
def test_minibatch_k_means_init_multiple_runs_with_explicit_centers():
mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters,
random_state=42, n_init=10)
assert_warns(RuntimeWarning, mb_k_means.fit, X)
@pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse'])
@pytest.mark.parametrize('init', ["random", 'k-means++', centers.copy()])
def test_minibatch_k_means_init(data, init):
mb_k_means = MiniBatchKMeans(init=init, n_clusters=n_clusters,
random_state=42, n_init=10)
mb_k_means.fit(data)
_check_fitted_model(mb_k_means)
def test_minibatch_sensible_reassign_fit():
# check if identical initial clusters are reassigned
# also a regression test for when there are more desired reassignments than
# samples.
zeroed_X, true_labels = make_blobs(n_samples=100, centers=5,
cluster_std=1., random_state=42)
zeroed_X[::2, :] = 0
mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=10, random_state=42,
init="random")
mb_k_means.fit(zeroed_X)
# there should not be too many exact zero cluster centers
assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10)
# do the same with batch-size > X.shape[0] (regression test)
mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=201,
random_state=42, init="random")
mb_k_means.fit(zeroed_X)
# there should not be too many exact zero cluster centers
assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10)
def test_minibatch_sensible_reassign_partial_fit():
zeroed_X, true_labels = make_blobs(n_samples=n_samples, centers=5,
cluster_std=1., random_state=42)
zeroed_X[::2, :] = 0
mb_k_means = MiniBatchKMeans(n_clusters=20, random_state=42, init="random")
for i in range(100):
mb_k_means.partial_fit(zeroed_X)
# there should not be too many exact zero cluster centers
assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10)
def test_minibatch_reassign():
# Give a perfect initialization, but a large reassignment_ratio,
# as a result all the centers should be reassigned and the model
# should no longer be good
sample_weight = np.ones(X.shape[0], dtype=X.dtype)
for this_X in (X, X_csr):
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100,
random_state=42)
mb_k_means.fit(this_X)
score_before = mb_k_means.score(this_X)
try:
old_stdout = sys.stdout
sys.stdout = StringIO()
# Turn on verbosity to smoke test the display code
_mini_batch_step(this_X, sample_weight, (X ** 2).sum(axis=1),
mb_k_means.cluster_centers_,
mb_k_means.counts_,
np.zeros(X.shape[1], np.double),
False, distances=np.zeros(X.shape[0]),
random_reassign=True, random_state=42,
reassignment_ratio=1, verbose=True)
finally:
sys.stdout = old_stdout
assert_greater(score_before, mb_k_means.score(this_X))
# Give a perfect initialization, with a small reassignment_ratio,
# no center should be reassigned
for this_X in (X, X_csr):
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100,
init=centers.copy(),
random_state=42, n_init=1)
mb_k_means.fit(this_X)
clusters_before = mb_k_means.cluster_centers_
# Turn on verbosity to smoke test the display code
_mini_batch_step(this_X, sample_weight, (X ** 2).sum(axis=1),
mb_k_means.cluster_centers_,
mb_k_means.counts_,
np.zeros(X.shape[1], np.double),
False, distances=np.zeros(X.shape[0]),
random_reassign=True, random_state=42,
reassignment_ratio=1e-15)
assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_)
def test_minibatch_with_many_reassignments():
# Test for the case that the number of clusters to reassign is bigger
# than the batch_size
n_samples = 550
rnd = np.random.RandomState(42)
X = rnd.uniform(size=(n_samples, 10))
# Check that the fit works if n_clusters is bigger than the batch_size.
# Run the test with 550 clusters and 550 samples, because it turned out
# that this values ensure that the number of clusters to reassign
# is always bigger than the batch_size
n_clusters = 550
MiniBatchKMeans(n_clusters=n_clusters,
batch_size=100,
init_size=n_samples,
random_state=42).fit(X)
def test_sparse_mb_k_means_callable_init():
def test_init(X, k, random_state):
return centers
# Small test to check that giving the wrong number of centers
# raises a meaningful error
msg = "does not match the number of clusters"
assert_raises_regex(ValueError, msg, MiniBatchKMeans(init=test_init,
random_state=42).fit,
X_csr)
# Now check that the fit actually works
mb_k_means = MiniBatchKMeans(n_clusters=3, init=test_init,
random_state=42).fit(X_csr)
_check_fitted_model(mb_k_means)
def test_mini_batch_k_means_random_init_partial_fit():
km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42)
# use the partial_fit API for online learning
for X_minibatch in np.array_split(X, 10):
km.partial_fit(X_minibatch)
# compute the labeling on the complete dataset
labels = km.predict(X)
assert_equal(v_measure_score(true_labels, labels), 1.0)
def test_minibatch_default_init_size():
mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters,
batch_size=10, random_state=42,
n_init=1).fit(X)
assert_equal(mb_k_means.init_size_, 3 * mb_k_means.batch_size)
_check_fitted_model(mb_k_means)
def test_minibatch_tol():
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=10,
random_state=42, tol=.01).fit(X)
_check_fitted_model(mb_k_means)
def test_minibatch_set_init_size():
mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters,
init_size=666, random_state=42,
n_init=1).fit(X)
assert_equal(mb_k_means.init_size, 666)
assert_equal(mb_k_means.init_size_, n_samples)
_check_fitted_model(mb_k_means)
@pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
def test_k_means_invalid_init(Estimator):
km = Estimator(init="invalid", n_init=1, n_clusters=n_clusters)
assert_raises(ValueError, km.fit, X)
def test_k_means_copyx():
# Check if copy_x=False returns nearly equal X after de-centering.
my_X = X.copy()
km = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42)
km.fit(my_X)
_check_fitted_model(km)
# check if my_X is centered
assert_array_almost_equal(my_X, X)
def test_k_means_non_collapsed():
# Check k_means with a bad initialization does not yield a singleton
# Starting with bad centers that are quickly ignored should not
# result in a repositioning of the centers to the center of mass that
# would lead to collapsed centers which in turns make the clustering
# dependent of the numerical unstabilities.
my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]])
array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]])
km = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1)
km.fit(my_X)
# centers must not been collapsed
assert_equal(len(np.unique(km.labels_)), 3)
centers = km.cluster_centers_
assert np.linalg.norm(centers[0] - centers[1]) >= 0.1
assert np.linalg.norm(centers[0] - centers[2]) >= 0.1
assert np.linalg.norm(centers[1] - centers[2]) >= 0.1
@pytest.mark.parametrize('algo', ['full', 'elkan'])
def test_score(algo):
# Check that fitting k-means with multiple inits gives better score
km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42, n_init=1,
algorithm=algo)
s1 = km1.fit(X).score(X)
km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42, n_init=1,
algorithm=algo)
s2 = km2.fit(X).score(X)
assert s2 > s1
@pytest.mark.parametrize('Estimator', [KMeans, MiniBatchKMeans])
@pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse'])
@pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()])
def test_predict(Estimator, data, init):
k_means = Estimator(n_clusters=n_clusters, init=init,
n_init=10, random_state=0).fit(data)
# sanity check: re-predict labeling for training set samples
assert_array_equal(k_means.predict(data), k_means.labels_)
# sanity check: predict centroid labels
pred = k_means.predict(k_means.cluster_centers_)
assert_array_equal(pred, np.arange(n_clusters))
# re-predict labels for training set using fit_predict
pred = k_means.fit_predict(data)
assert_array_equal(pred, k_means.labels_)
@pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()])
def test_predict_minibatch_dense_sparse(init):
# check that models trained on sparse input also works for dense input at
# predict time
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init=init,
n_init=10, random_state=0).fit(X_csr)
assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_)
def test_int_input():
X_list = [[0, 0], [10, 10], [12, 9], [-1, 1], [2, 0], [8, 10]]
for dtype in [np.int32, np.int64]:
X_int = np.array(X_list, dtype=dtype)
X_int_csr = sp.csr_matrix(X_int)
init_int = X_int[:2]
fitted_models = [
KMeans(n_clusters=2).fit(X_int),
KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_int),
# mini batch kmeans is very unstable on such a small dataset hence
# we use many inits
MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int),
MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(
X_int_csr),
MiniBatchKMeans(n_clusters=2, batch_size=2,
init=init_int, n_init=1).fit(X_int),
MiniBatchKMeans(n_clusters=2, batch_size=2,
init=init_int, n_init=1).fit(X_int_csr),
]
for km in fitted_models:
assert_equal(km.cluster_centers_.dtype, np.float64)
expected_labels = [0, 1, 1, 0, 0, 1]
scores = np.array([v_measure_score(expected_labels, km.labels_)
for km in fitted_models])
assert_array_almost_equal(scores, np.ones(scores.shape[0]))
def test_transform():
km = KMeans(n_clusters=n_clusters)
km.fit(X)
X_new = km.transform(km.cluster_centers_)
for c in range(n_clusters):
assert_equal(X_new[c, c], 0)
for c2 in range(n_clusters):
if c != c2:
assert_greater(X_new[c, c2], 0)
def test_fit_transform():
X1 = KMeans(n_clusters=3, random_state=51).fit(X).transform(X)
X2 = KMeans(n_clusters=3, random_state=51).fit_transform(X)
assert_array_almost_equal(X1, X2)
@pytest.mark.parametrize('algo', ['full', 'elkan'])
def test_predict_equal_labels(algo):
km = KMeans(random_state=13, n_jobs=1, n_init=1, max_iter=1,
algorithm=algo)
km.fit(X)
assert_array_equal(km.predict(X), km.labels_)
def test_full_vs_elkan():
km1 = KMeans(algorithm='full', random_state=13).fit(X)
km2 = KMeans(algorithm='elkan', random_state=13).fit(X)
assert homogeneity_score(km1.predict(X), km2.predict(X)) == 1.0
def test_n_init():
# Check that increasing the number of init increases the quality
n_runs = 5
n_init_range = [1, 5, 10]
inertia = np.zeros((len(n_init_range), n_runs))
for i, n_init in enumerate(n_init_range):
for j in range(n_runs):
km = KMeans(n_clusters=n_clusters, init="random", n_init=n_init,
random_state=j).fit(X)
inertia[i, j] = km.inertia_
inertia = inertia.mean(axis=1)
failure_msg = ("Inertia %r should be decreasing"
" when n_init is increasing.") % list(inertia)
for i in range(len(n_init_range) - 1):
assert inertia[i] >= inertia[i + 1], failure_msg
def test_k_means_function():
# test calling the k_means function directly
# catch output
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters,
sample_weight=None,
verbose=True)
finally:
sys.stdout = old_stdout
centers = cluster_centers
assert_equal(centers.shape, (n_clusters, n_features))
labels = labels
assert_equal(np.unique(labels).shape[0], n_clusters)
# check that the labels assignment are perfect (up to a permutation)
assert_equal(v_measure_score(true_labels, labels), 1.0)
assert_greater(inertia, 0.0)
# check warning when centers are passed
assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters,
sample_weight=None, init=centers)
# to many clusters desired
assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1,
sample_weight=None)
# kmeans for algorithm='elkan' raises TypeError on sparse matrix
assert_raise_message(TypeError, "algorithm='elkan' not supported for "
"sparse input X", k_means, X=X_csr, n_clusters=2,
sample_weight=None, algorithm="elkan")
def test_x_squared_norms_init_centroids():
# Test that x_squared_norms can be None in _init_centroids
from sklearn.cluster.k_means_ import _init_centroids
X_norms = np.sum(X**2, axis=1)
precompute = _init_centroids(
X, 3, "k-means++", random_state=0, x_squared_norms=X_norms)
assert_array_almost_equal(
precompute,
_init_centroids(X, 3, "k-means++", random_state=0))
def test_max_iter_error():
km = KMeans(max_iter=-1)
assert_raise_message(ValueError, 'Number of iterations should be',
km.fit, X)
@pytest.mark.parametrize('Estimator', [KMeans, MiniBatchKMeans])
@pytest.mark.parametrize('is_sparse', [False, True])
def test_float_precision(Estimator, is_sparse):
estimator = Estimator(n_init=1, random_state=30)
inertia = {}
X_new = {}
centers = {}
for dtype in [np.float64, np.float32]:
if is_sparse:
X_test = sp.csr_matrix(X_csr, dtype=dtype)
else:
X_test = X.astype(dtype)
estimator.fit(X_test)
# dtype of cluster centers has to be the dtype of the input
# data
assert_equal(estimator.cluster_centers_.dtype, dtype)
inertia[dtype] = estimator.inertia_
X_new[dtype] = estimator.transform(X_test)
centers[dtype] = estimator.cluster_centers_
# ensure the extracted row is a 2d array
assert_equal(estimator.predict(X_test[:1]),
estimator.labels_[0])
if hasattr(estimator, 'partial_fit'):
estimator.partial_fit(X_test[0:3])
# dtype of cluster centers has to stay the same after
# partial_fit
assert_equal(estimator.cluster_centers_.dtype, dtype)
# compare arrays with low precision since the difference between
# 32 and 64 bit sometimes makes a difference up to the 4th decimal
# place
assert_array_almost_equal(inertia[np.float32], inertia[np.float64],
decimal=4)
assert_array_almost_equal(X_new[np.float32], X_new[np.float64],
decimal=4)
assert_array_almost_equal(centers[np.float32], centers[np.float64],
decimal=4)
def test_k_means_init_centers():
# This test is used to check KMeans won't mutate the user provided input
# array silently even if input data and init centers have the same type
X_small = np.array([[1.1, 1.1], [-7.5, -7.5], [-1.1, -1.1], [7.5, 7.5]])
init_centers = np.array([[0.0, 0.0], [5.0, 5.0], [-5.0, -5.0]])
for dtype in [np.int32, np.int64, np.float32, np.float64]:
X_test = dtype(X_small)
init_centers_test = dtype(init_centers)
assert_array_equal(init_centers, init_centers_test)
km = KMeans(init=init_centers_test, n_clusters=3, n_init=1)
km.fit(X_test)
assert_equal(False, np.may_share_memory(km.cluster_centers_,
init_centers))
@pytest.mark.parametrize("data", [X, X_csr], ids=["dense", "sparse"])
def test_k_means_init_fitted_centers(data):
# Get a local optimum
centers = KMeans(n_clusters=3).fit(X).cluster_centers_
# Fit starting from a local optimum shouldn't change the solution
new_centers = KMeans(n_clusters=3, init=centers,
n_init=1).fit(X).cluster_centers_
assert_array_almost_equal(centers, new_centers)
def test_sparse_validate_centers():
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
# Get a local optimum
centers = KMeans(n_clusters=4).fit(X).cluster_centers_
# Test that a ValueError is raised for validate_center_shape
classifier = KMeans(n_clusters=3, init=centers, n_init=1)
msg = r"The shape of the initial centers \(\(4L?, 4L?\)\) " \
"does not match the number of clusters 3"
assert_raises_regex(ValueError, msg, classifier.fit, X)
def test_less_centers_than_unique_points():
X = np.asarray([[0, 0],
[0, 1],
[1, 0],
[1, 0]]) # last point is duplicated
km = KMeans(n_clusters=4).fit(X)
# only three distinct points, so only three clusters
# can have points assigned to them
assert_equal(set(km.labels_), set(range(3)))
# k_means should warn that fewer labels than cluster
# centers have been used
msg = ("Number of distinct clusters (3) found smaller than "
"n_clusters (4). Possibly due to duplicate points in X.")
assert_warns_message(ConvergenceWarning, msg, k_means, X,
sample_weight=None, n_clusters=4)
def _sort_centers(centers):
return np.sort(centers, axis=0)
def test_weighted_vs_repeated():
# a sample weight of N should yield the same result as an N-fold
# repetition of the sample
rng = np.random.RandomState(0)
sample_weight = rng.randint(1, 5, size=n_samples)
X_repeat = np.repeat(X, sample_weight, axis=0)
estimators = [KMeans(init="k-means++", n_clusters=n_clusters,
random_state=42),
KMeans(init="random", n_clusters=n_clusters,
random_state=42),
KMeans(init=centers.copy(), n_clusters=n_clusters,
random_state=42),
MiniBatchKMeans(n_clusters=n_clusters, batch_size=10,
random_state=42)]
for estimator in estimators:
est_weighted = clone(estimator).fit(X, sample_weight=sample_weight)
est_repeated = clone(estimator).fit(X_repeat)
repeated_labels = np.repeat(est_weighted.labels_, sample_weight)
assert_almost_equal(v_measure_score(est_repeated.labels_,
repeated_labels), 1.0)
if not isinstance(estimator, MiniBatchKMeans):
assert_almost_equal(_sort_centers(est_weighted.cluster_centers_),
_sort_centers(est_repeated.cluster_centers_))
def test_unit_weights_vs_no_weights():
# not passing any sample weights should be equivalent
# to all weights equal to one
sample_weight = np.ones(n_samples)
for estimator in [KMeans(n_clusters=n_clusters, random_state=42),
MiniBatchKMeans(n_clusters=n_clusters, random_state=42)]:
est_1 = clone(estimator).fit(X)
est_2 = clone(estimator).fit(X, sample_weight=sample_weight)
assert_almost_equal(v_measure_score(est_1.labels_, est_2.labels_), 1.0)
assert_almost_equal(_sort_centers(est_1.cluster_centers_),
_sort_centers(est_2.cluster_centers_))
def test_scaled_weights():
# scaling all sample weights by a common factor
# shouldn't change the result
sample_weight = np.ones(n_samples)
for estimator in [KMeans(n_clusters=n_clusters, random_state=42),
MiniBatchKMeans(n_clusters=n_clusters, random_state=42)]:
est_1 = clone(estimator).fit(X)
est_2 = clone(estimator).fit(X, sample_weight=0.5*sample_weight)
assert_almost_equal(v_measure_score(est_1.labels_, est_2.labels_), 1.0)
assert_almost_equal(_sort_centers(est_1.cluster_centers_),
_sort_centers(est_2.cluster_centers_))
def test_sample_weight_length():
# check that an error is raised when passing sample weights
# with an incompatible shape
km = KMeans(n_clusters=n_clusters, random_state=42)
assert_raises_regex(ValueError, r'len\(sample_weight\)', km.fit, X,
sample_weight=np.ones(2))
def test_check_sample_weight():
from sklearn.cluster.k_means_ import _check_sample_weight
sample_weight = None
checked_sample_weight = _check_sample_weight(X, sample_weight)
assert_equal(_num_samples(X), _num_samples(checked_sample_weight))
assert_almost_equal(checked_sample_weight.sum(), _num_samples(X))
assert_equal(X.dtype, checked_sample_weight.dtype)
def test_iter_attribute():
# Regression test on bad n_iter_ value. Previous bug n_iter_ was one off
# it's right value (#11340).
estimator = KMeans(algorithm="elkan", max_iter=1)
estimator.fit(np.random.rand(10, 10))
assert estimator.n_iter_ == 1
def test_k_means_empty_cluster_relocated():
# check that empty clusters are correctly relocated when using sample
# weights (#13486)
X = np.array([[-1], [1]])
sample_weight = [1.9, 0.1]
init = np.array([[-1], [10]])
km = KMeans(n_clusters=2, init=init, n_init=1)
km.fit(X, sample_weight=sample_weight)
assert len(set(km.labels_)) == 2
assert_allclose(km.cluster_centers_, [[-1], [1]])
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