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)
msg = r'sample_weight.shape == \(2,\), expected \(100,\)'
with pytest.raises(ValueError, match=msg):
km.fit(X, sample_weight=np.ones(2))
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.
# NB: This test is largely redundant with respect to test_predict and
# test_predict_equal_labels. This test has the added effect of
# testing idempotence of the fittng procesdure which appears to
# be where it fails on some MacOS setups.
if sys.platform == "darwin":
pytest.xfail(
"Known failures on MacOS, See "
"https://github.com/scikit-learn/scikit-learn/issues/12644")
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_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 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_sample_weight_missing():
from mrex.cluster import KMeans
clf = AdaBoostClassifier(KMeans(), algorithm="SAMME")
assert_raises(ValueError, clf.fit, X, y_regr)
clf = AdaBoostRegressor(KMeans())
assert_raises(ValueError, clf.fit, X, y_regr)
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_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_result_of_kmeans_equal_in_diff_n_jobs():
# PR 9288
rnd = np.random.RandomState(0)
X = rnd.normal(size=(50, 10))
result_1 = KMeans(n_clusters=3, random_state=0, n_jobs=1).fit(X).labels_
result_2 = KMeans(n_clusters=3, random_state=0, n_jobs=2).fit(X).labels_
assert_array_equal(result_1, result_2)
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_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)
with pytest.raises(ValueError, match="n_init"):
KMeans(n_init=0).fit(X)
with pytest.raises(ValueError, match="n_init"):
KMeans(n_init=-1).fit(X)
def test_supervised_cluster_scorers():
# Test clustering scorers against gold standard labeling.
X, y = make_blobs(random_state=0, centers=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
km = KMeans(n_clusters=3)
km.fit(X_train)
for name in CLUSTER_SCORERS:
score1 = get_scorer(name)(km, X_test, y_test)
score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
assert_almost_equal(score1, score2)
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 X_new[c, c] == 0
for c2 in range(n_clusters):
if c != c2:
assert X_new[c, c2] > 0
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]])
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 np.may_share_memory(km.cluster_centers_, init_centers) is False
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)
def test_pipeline_spectral_clustering(seed=36):
# Test using pipeline to do spectral clustering
random_state = np.random.RandomState(seed)
se_rbf = SpectralEmbedding(n_components=n_clusters,
affinity="rbf",
random_state=random_state)
se_knn = SpectralEmbedding(n_components=n_clusters,
affinity="nearest_neighbors",
n_neighbors=5,
random_state=random_state)
for se in [se_rbf, se_knn]:
km = KMeans(n_clusters=n_clusters, random_state=random_state)
km.fit(se.fit_transform(S))
assert_array_almost_equal(
normalized_mutual_info_score(km.labels_, true_labels), 1.0, 2)
def test_sparse_validate_centers():
from mrex.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"
with pytest.raises(ValueError, match=msg):
classifier.fit(X)
def test_scoring_is_not_metric():
assert_raises_regexp(ValueError, 'make_scorer', check_scoring,
LogisticRegression(), f1_score)
assert_raises_regexp(ValueError, 'make_scorer', check_scoring,
LogisticRegression(), roc_auc_score)
assert_raises_regexp(ValueError, 'make_scorer', check_scoring, Ridge(),
r2_score)
assert_raises_regexp(ValueError, 'make_scorer', check_scoring, KMeans(),
cluster_module.adjusted_rand_score)
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 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
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)
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
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_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_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_fit_predict_on_pipeline():
# test that the fit_predict method is implemented on a pipeline
# test that the fit_predict on pipeline yields same results as applying
# transform and clustering steps separately
iris = load_iris()
scaler = StandardScaler()
km = KMeans(random_state=0)
# As pipeline doesn't clone estimators on construction,
# it must have its own estimators
scaler_for_pipeline = StandardScaler()
km_for_pipeline = KMeans(random_state=0)
# first compute the transform and clustering step separately
scaled = scaler.fit_transform(iris.data)
separate_pred = km.fit_predict(scaled)
# use a pipeline to do the transform and clustering in one step
pipe = Pipeline([
('scaler', scaler_for_pipeline),
('Kmeans', km_for_pipeline)
])
pipeline_pred = pipe.fit_predict(iris.data)
assert_array_almost_equal(pipeline_pred, separate_pred)
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_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_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 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 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
def test_max_iter_error():
km = KMeans(max_iter=-1)
assert_raise_message(ValueError, 'Number of iterations should be', km.fit,
X)
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
