"""Testing for K-means""" import re import sys import numpy as np from scipy import sparse as sp from threadpoolctl import threadpool_limits import pytest 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_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils.fixes import _astype_copy_false from sklearn.base import clone from sklearn.exceptions import ConvergenceWarning from sklearn.utils.extmath import row_norms from sklearn.metrics import pairwise_distances_argmin from sklearn.metrics.cluster import v_measure_score from sklearn.cluster import KMeans, k_means from sklearn.cluster import MiniBatchKMeans from sklearn.cluster._kmeans import _labels_inertia from sklearn.cluster._kmeans import _mini_batch_step from sklearn.cluster._k_means_fast import _relocate_empty_clusters_dense from sklearn.cluster._k_means_fast import _relocate_empty_clusters_sparse from sklearn.cluster._k_means_fast import _euclidean_dense_dense_wrapper from sklearn.cluster._k_means_fast import _euclidean_sparse_dense_wrapper from sklearn.cluster._k_means_fast import _inertia_dense from sklearn.cluster._k_means_fast import _inertia_sparse from sklearn.datasets 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("array_constr", [np.array, sp.csr_matrix], ids=["dense", "sparse"]) @pytest.mark.parametrize("algo", ["full", "elkan"]) @pytest.mark.parametrize("dtype", [np.float32, np.float64]) def test_kmeans_results(array_constr, algo, dtype): # Checks that KMeans works as intended on toy dataset by comparing with # expected results computed by hand. X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]], dtype=dtype) sample_weight = [3, 1, 1, 3] init_centers = np.array([[0, 0], [1, 1]], dtype=dtype) expected_labels = [0, 0, 1, 1] expected_inertia = 0.375 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_allclose(kmeans.inertia_, expected_inertia) assert_allclose(kmeans.cluster_centers_, expected_centers) assert kmeans.n_iter_ == expected_n_iter @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=['dense', 'sparse']) @pytest.mark.parametrize("algo", ['full', 'elkan']) def test_relocated_clusters(array_constr, algo): # check that empty clusters are relocated as expected X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]]) # second center too far from others points will be empty at first iter init_centers = np.array([[0.5, 0.5], [3, 3]]) expected_labels = [0, 0, 1, 1] expected_inertia = 0.25 expected_centers = [[0.25, 0], [0.75, 1]] expected_n_iter = 3 kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo) kmeans.fit(X) 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("representation", ["dense", "sparse"]) def test_relocate_empty_clusters(representation): # test for the _relocate_empty_clusters_(dense/sparse) helpers # Synthetic dataset with 3 obvious clusters of different sizes X = np.array( [-10., -9.5, -9, -8.5, -8, -1, 1, 9, 9.5, 10]).reshape(-1, 1) if representation == "sparse": X = sp.csr_matrix(X) sample_weight = np.full(shape=10, fill_value=1.) # centers all initialized to the first point of X centers_old = np.array([-10., -10, -10]).reshape(-1, 1) # With this initialization, all points will be assigned to the first center # At this point a center in centers_new is the weighted sum of the points # it contains if it's not empty, otherwise it is the same as before. centers_new = np.array([-16.5, -10, -10]).reshape(-1, 1) weight_in_clusters = np.array([10., 0, 0]) labels = np.zeros(10, dtype=np.int32) if representation == "dense": _relocate_empty_clusters_dense(X, sample_weight, centers_old, centers_new, weight_in_clusters, labels) else: _relocate_empty_clusters_sparse(X.data, X.indices, X.indptr, sample_weight, centers_old, centers_new, weight_in_clusters, labels) # The relocation scheme will take the 2 points farthest from the center and # assign them to the 2 empty clusters, i.e. points at 10 and at 9.9. The # first center will be updated to contain the other 8 points. assert_array_equal(weight_in_clusters, [8, 1, 1]) assert_allclose(centers_new, [[-36], [10], [9.5]]) @pytest.mark.parametrize("distribution", ["normal", "blobs"]) @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=["dense", "sparse"]) @pytest.mark.parametrize("tol", [1e-2, 1e-8, 1e-100, 0]) def test_kmeans_elkan_results(distribution, array_constr, tol): # Check that results are identical between lloyd and elkan algorithms rnd = np.random.RandomState(0) if distribution == 'normal': X = rnd.normal(size=(5000, 10)) else: X, _ = make_blobs(random_state=rnd) km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1, tol=tol) km_elkan = KMeans(algorithm='elkan', n_clusters=5, random_state=0, n_init=1, tol=tol) km_full.fit(X) km_elkan.fit(X) assert_allclose(km_elkan.cluster_centers_, km_full.cluster_centers_) assert_array_equal(km_elkan.labels_, km_full.labels_) assert km_elkan.n_iter_ == km_full.n_iter_ assert km_elkan.inertia_ == pytest.approx(km_full.inertia_, rel=1e-6) @pytest.mark.parametrize('algorithm', ['full', 'elkan']) def test_kmeans_convergence(algorithm): # Check that KMeans stops when convergence is reached when tol=0. (#16075) rnd = np.random.RandomState(0) X = rnd.normal(size=(5000, 10)) max_iter = 300 km = KMeans(algorithm=algorithm, n_clusters=5, random_state=0, n_init=1, tol=0, max_iter=max_iter).fit(X) assert km.n_iter_ < max_iter @pytest.mark.parametrize('distribution', ['normal', 'blobs']) def test_elkan_results_sparse(distribution): # check that results are identical between lloyd and elkan algorithms # with sparse input rnd = np.random.RandomState(0) if distribution == 'normal': X = sp.random(100, 100, density=0.1, format='csr', random_state=rnd) X.data = rnd.randn(len(X.data)) else: X, _ = make_blobs(n_samples=100, n_features=100, random_state=rnd) X = sp.csr_matrix(X) 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_allclose(km_elkan.cluster_centers_, km_full.cluster_centers_) assert_allclose(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 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 new_inertia > 0.0 assert 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 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 new_inertia_csr > 0.0 assert 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 centers.shape == (n_clusters, n_features) labels = km.labels_ assert np.unique(labels).shape[0] == n_clusters # check that the labels assignment are perfect (up to a permutation) assert v_measure_score(true_labels, labels) == 1.0 assert 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) @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) @pytest.mark.parametrize("init", ["random", "k-means++", centers, lambda X, k, random_state: centers], ids=["random", "k-means++", "ndarray", "callable"]) def test_minibatch_kmeans_partial_fit_init(init): # Check MiniBatchKMeans init with partial_fit km = MiniBatchKMeans(init=init, n_clusters=n_clusters, random_state=0) for i in range(100): # "random" init requires many batches to recover the true labels. km.partial_fit(X) _check_fitted_model(km) 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, 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. # 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") 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) labels_1 = kmeans.fit(X).predict(X) labels_2 = kmeans.fit_predict(X) # Due to randomness in the order in which chunks of data are processed when # using more than one thread, the absolute values of the labels can be # different between the 2 strategies but they should correspond to the same # clustering. assert v_measure_score(labels_1, labels_2) == 1 def test_minibatch_kmeans_verbose(): # Check verbose mode of MiniBatchKMeans for better coverage. km = MiniBatchKMeans(n_clusters=n_clusters, random_state=42, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: km.fit(X) finally: sys.stdout = old_stdout @pytest.mark.parametrize("algorithm", ["full", "elkan"]) @pytest.mark.parametrize("tol", [1e-2, 0]) def test_kmeans_verbose(algorithm, tol, capsys): # Check verbose mode of KMeans for better coverage. X = np.random.RandomState(0).normal(size=(5000, 10)) KMeans(algorithm=algorithm, n_clusters=n_clusters, random_state=42, init="random", n_init=1, tol=tol, verbose=1).fit(X) captured = capsys.readouterr() assert re.search(r"Initialization complete", captured.out) assert re.search(r"Iteration [0-9]+, inertia", captured.out) if tol == 0: assert re.search(r"strict convergence", captured.out) else: assert re.search(r"center shift .* within tolerance", captured.out) def test_minibatch_kmeans_warning_init_size(): # Check that a warning is raised when init_size is smaller than n_clusters with pytest.warns(RuntimeWarning, match=r"init_size.* should be larger than n_clusters"): MiniBatchKMeans(init_size=10, n_clusters=20).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 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 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 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 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 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 v_measure_score(true_labels, labels) == 1.0 def test_minibatch_kmeans_default_init_size(): # Check the internal _init_size attribute of MiniBatchKMeans # default init size should be 3 * batch_size km = MiniBatchKMeans(n_clusters=10, batch_size=5, n_init=1).fit(X) assert km._init_size == 15 # if 3 * batch size < n_clusters, it should then be 3 * n_clusters km = MiniBatchKMeans(n_clusters=10, batch_size=1, n_init=1).fit(X) assert km._init_size == 30 # it should not be larger than n_samples km = MiniBatchKMeans(n_clusters=10, batch_size=5, n_init=1, init_size=n_samples + 1).fit(X) assert km._init_size == n_samples 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 mb_k_means.init_size == 666 assert mb_k_means.init_size_ == n_samples _check_fitted_model(mb_k_means) 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 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 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 X_new[c, c] == 0 for c2 in range(n_clusters): if c != c2: assert 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_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) ) == pytest.approx(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 centers.shape == (n_clusters, n_features) labels = labels assert np.unique(labels).shape[0] == n_clusters # check that the labels assignment are perfect (up to a permutation) assert v_measure_score(true_labels, labels) == 1.0 assert inertia > 0.0 # check warning when centers are passed assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters, sample_weight=None, init=centers) def test_x_squared_norms_init_centroids(): # Test that x_squared_norms can be None in _init_centroids from sklearn.cluster._kmeans 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)) @pytest.mark.parametrize("data", [X, X_csr], ids=["dense", "sparse"]) @pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans]) def test_float_precision(Estimator, data): # Check that the results are the same for single and double precision. km = Estimator(n_init=1, random_state=0) inertia = {} Xt = {} centers = {} labels = {} for dtype in [np.float64, np.float32]: X = data.astype(dtype, **_astype_copy_false(data)) km.fit(X) inertia[dtype] = km.inertia_ Xt[dtype] = km.transform(X) centers[dtype] = km.cluster_centers_ labels[dtype] = km.labels_ # dtype of cluster centers has to be the dtype of the input data assert km.cluster_centers_.dtype == dtype # same with partial_fit if Estimator is MiniBatchKMeans: km.partial_fit(X[0:3]) assert km.cluster_centers_.dtype == dtype # compare arrays with low precision since the difference between 32 and # 64 bit comes from an accumulation of rounding errors. assert_allclose(inertia[np.float32], inertia[np.float64], rtol=1e-5) assert_allclose(Xt[np.float32], Xt[np.float64], rtol=1e-5) assert_allclose(centers[np.float32], centers[np.float64], rtol=1e-5) assert_array_equal(labels[np.float32], labels[np.float64]) 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 @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_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 _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_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]]) def test_minibatch_kmeans_partial_fit_int_data(): # Issue GH #14314 X = np.array([[-1], [1]], dtype=np.int) km = MiniBatchKMeans(n_clusters=2) km.partial_fit(X) assert km.cluster_centers_.dtype.kind == "f" def test_result_of_kmeans_equal_in_diff_n_threads(): # Check that KMeans gives the same results in parallel mode than in # sequential mode. rnd = np.random.RandomState(0) X = rnd.normal(size=(50, 10)) with threadpool_limits(limits=1, user_api="openmp"): result_1 = KMeans( n_clusters=3, random_state=0).fit(X).labels_ with threadpool_limits(limits=2, user_api="openmp"): result_2 = KMeans( n_clusters=3, random_state=0).fit(X).labels_ assert_array_equal(result_1, result_2) @pytest.mark.parametrize("precompute_distances", ["auto", False, True]) def test_precompute_distance_deprecated(precompute_distances): # FIXME: remove in 0.25 depr_msg = ("'precompute_distances' was deprecated in version 0.23 and " "will be removed in 0.25.") X, _ = make_blobs(n_samples=10, n_features=2, centers=2, random_state=0) kmeans = KMeans(n_clusters=2, n_init=1, init='random', random_state=0, precompute_distances=precompute_distances) with pytest.warns(FutureWarning, match=depr_msg): kmeans.fit(X) @pytest.mark.parametrize("n_jobs", [None, 1]) def test_n_jobs_deprecated(n_jobs): # FIXME: remove in 0.25 depr_msg = ("'n_jobs' was deprecated in version 0.23 and will be removed " "in 0.25.") X, _ = make_blobs(n_samples=10, n_features=2, centers=2, random_state=0) kmeans = KMeans(n_clusters=2, n_init=1, init='random', random_state=0, n_jobs=n_jobs) with pytest.warns(FutureWarning, match=depr_msg): kmeans.fit(X) def test_warning_elkan_1_cluster(): X, _ = make_blobs(n_samples=10, n_features=2, centers=1, random_state=0) kmeans = KMeans(n_clusters=1, n_init=1, init='random', random_state=0, algorithm='elkan') with pytest.warns(RuntimeWarning, match="algorithm='elkan' doesn't make sense for a single" " cluster"): kmeans.fit(X) def test_error_wrong_algorithm(): X, _ = make_blobs(n_samples=10, n_features=2, centers=2, random_state=0) kmeans = KMeans(n_clusters=2, n_init=1, init='random', random_state=0, algorithm='wrong') with pytest.raises(ValueError, match="Algorithm must be 'auto', 'full' or 'elkan'"): kmeans.fit(X) @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=['dense', 'sparse']) @pytest.mark.parametrize("algo", ['full', 'elkan']) def test_k_means_1_iteration(array_constr, algo): # check the results after a single iteration (E-step M-step E-step) by # comparing against a pure python implementation. X = np.random.RandomState(0).uniform(size=(100, 5)) init_centers = X[:5] X = array_constr(X) def py_kmeans(X, init): new_centers = init.copy() labels = pairwise_distances_argmin(X, init) for label in range(init.shape[0]): new_centers[label] = X[labels == label].mean(axis=0) labels = pairwise_distances_argmin(X, new_centers) return labels, new_centers py_labels, py_centers = py_kmeans(X, init_centers) cy_kmeans = KMeans(n_clusters=5, n_init=1, init=init_centers, algorithm=algo, max_iter=1).fit(X) cy_labels = cy_kmeans.labels_ cy_centers = cy_kmeans.cluster_centers_ assert_array_equal(py_labels, cy_labels) assert_allclose(py_centers, cy_centers) @pytest.mark.parametrize("dtype", [np.float32, np.float64]) @pytest.mark.parametrize("squared", [True, False]) def test_euclidean_distance(dtype, squared): rng = np.random.RandomState(0) a_sparse = sp.random(1, 100, density=0.5, format="csr", random_state=rng, dtype=dtype) a_dense = a_sparse.toarray().reshape(-1) b = rng.randn(100).astype(dtype, copy=False) b_squared_norm = (b**2).sum() expected = ((a_dense - b)**2).sum() expected = expected if squared else np.sqrt(expected) distance_dense_dense = _euclidean_dense_dense_wrapper(a_dense, b, squared) distance_sparse_dense = _euclidean_sparse_dense_wrapper( a_sparse.data, a_sparse.indices, b, b_squared_norm, squared) assert_allclose(distance_dense_dense, distance_sparse_dense, rtol=1e-6) assert_allclose(distance_dense_dense, expected, rtol=1e-6) assert_allclose(distance_sparse_dense, expected, rtol=1e-6) @pytest.mark.parametrize("dtype", [np.float32, np.float64]) def test_inertia(dtype): rng = np.random.RandomState(0) X_sparse = sp.random(100, 10, density=0.5, format="csr", random_state=rng, dtype=dtype) X_dense = X_sparse.toarray() sample_weight = rng.randn(100).astype(dtype, copy=False) centers = rng.randn(5, 10).astype(dtype, copy=False) labels = rng.randint(5, size=100, dtype=np.int32) distances = ((X_dense - centers[labels])**2).sum(axis=1) expected = np.sum(distances * sample_weight) inertia_dense = _inertia_dense(X_dense, sample_weight, centers, labels) inertia_sparse = _inertia_sparse(X_sparse, sample_weight, centers, labels) assert_allclose(inertia_dense, inertia_sparse, rtol=1e-6) assert_allclose(inertia_dense, expected, rtol=1e-6) assert_allclose(inertia_sparse, expected, rtol=1e-6) def test_sample_weight_unchanged(): # Check that sample_weight is not modified in place by KMeans (#17204) X = np.array([[1], [2], [4]]) sample_weight = np.array([0.5, 0.2, 0.3]) KMeans(n_clusters=2, random_state=0).fit(X, sample_weight=sample_weight) # internally, sample_weight is rescale to sum up to n_samples = 3 assert_array_equal(sample_weight, np.array([0.5, 0.2, 0.3])) @pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans]) @pytest.mark.parametrize("param, match", [ ({"n_init": 0}, r"n_init should be > 0"), ({"max_iter": 0}, r"max_iter should be > 0"), ({"n_clusters": n_samples + 1}, r"n_samples.* should be >= n_clusters"), ({"init": X[:2]}, r"The shape of the initial centers .* does not match " r"the number of clusters"), ({"init": lambda X_, k, random_state: X_[:2]}, r"The shape of the initial centers .* does not match " r"the number of clusters"), ({"init": X[:8, :2]}, r"The shape of the initial centers .* does not match " r"the number of features of the data"), ({"init": lambda X_, k, random_state: X_[:8, :2]}, r"The shape of the initial centers .* does not match " r"the number of features of the data"), ({"init": "wrong"}, r"init should be either 'k-means\+\+', 'random', " r"a ndarray or a callable")] ) def test_wrong_params(Estimator, param, match): # Check that error are raised with clear error message when wrong values # are passed for the parameters with pytest.raises(ValueError, match=match): Estimator(**param).fit(X) @pytest.mark.parametrize("param, match", [ ({"algorithm": "wrong"}, r"Algorithm must be 'auto', 'full' or 'elkan'")] ) def test_kmeans_wrong_params(param, match): # Check that error are raised with clear error message when wrong values # are passed for the KMeans specific parameters with pytest.raises(ValueError, match=match): KMeans(**param).fit(X) @pytest.mark.parametrize("param, match", [ ({"max_no_improvement": -1}, r"max_no_improvement should be >= 0"), ({"batch_size": -1}, r"batch_size should be > 0"), ({"init_size": -1}, r"init_size should be > 0"), ({"reassignment_ratio": -1}, r"reassignment_ratio should be >= 0")] ) def test_minibatch_kmeans_wrong_params(param, match): # Check that error are raised with clear error message when wrong values # are passed for the MiniBatchKMeans specific parameters with pytest.raises(ValueError, match=match): MiniBatchKMeans(**param).fit(X)