import warnings import sys import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_allclose, assert_equal, assert_, suppress_warnings) import pytest from pytest import raises as assert_raises from scipy.cluster.vq import (kmeans, kmeans2, py_vq, vq, whiten, ClusterError, _krandinit) from scipy.cluster import _vq from scipy.sparse._sputils import matrix TESTDATA_2D = np.array([ -2.2, 1.17, -1.63, 1.69, -2.04, 4.38, -3.09, 0.95, -1.7, 4.79, -1.68, 0.68, -2.26, 3.34, -2.29, 2.55, -1.72, -0.72, -1.99, 2.34, -2.75, 3.43, -2.45, 2.41, -4.26, 3.65, -1.57, 1.87, -1.96, 4.03, -3.01, 3.86, -2.53, 1.28, -4.0, 3.95, -1.62, 1.25, -3.42, 3.17, -1.17, 0.12, -3.03, -0.27, -2.07, -0.55, -1.17, 1.34, -2.82, 3.08, -2.44, 0.24, -1.71, 2.48, -5.23, 4.29, -2.08, 3.69, -1.89, 3.62, -2.09, 0.26, -0.92, 1.07, -2.25, 0.88, -2.25, 2.02, -4.31, 3.86, -2.03, 3.42, -2.76, 0.3, -2.48, -0.29, -3.42, 3.21, -2.3, 1.73, -2.84, 0.69, -1.81, 2.48, -5.24, 4.52, -2.8, 1.31, -1.67, -2.34, -1.18, 2.17, -2.17, 2.82, -1.85, 2.25, -2.45, 1.86, -6.79, 3.94, -2.33, 1.89, -1.55, 2.08, -1.36, 0.93, -2.51, 2.74, -2.39, 3.92, -3.33, 2.99, -2.06, -0.9, -2.83, 3.35, -2.59, 3.05, -2.36, 1.85, -1.69, 1.8, -1.39, 0.66, -2.06, 0.38, -1.47, 0.44, -4.68, 3.77, -5.58, 3.44, -2.29, 2.24, -1.04, -0.38, -1.85, 4.23, -2.88, 0.73, -2.59, 1.39, -1.34, 1.75, -1.95, 1.3, -2.45, 3.09, -1.99, 3.41, -5.55, 5.21, -1.73, 2.52, -2.17, 0.85, -2.06, 0.49, -2.54, 2.07, -2.03, 1.3, -3.23, 3.09, -1.55, 1.44, -0.81, 1.1, -2.99, 2.92, -1.59, 2.18, -2.45, -0.73, -3.12, -1.3, -2.83, 0.2, -2.77, 3.24, -1.98, 1.6, -4.59, 3.39, -4.85, 3.75, -2.25, 1.71, -3.28, 3.38, -1.74, 0.88, -2.41, 1.92, -2.24, 1.19, -2.48, 1.06, -1.68, -0.62, -1.3, 0.39, -1.78, 2.35, -3.54, 2.44, -1.32, 0.66, -2.38, 2.76, -2.35, 3.95, -1.86, 4.32, -2.01, -1.23, -1.79, 2.76, -2.13, -0.13, -5.25, 3.84, -2.24, 1.59, -4.85, 2.96, -2.41, 0.01, -0.43, 0.13, -3.92, 2.91, -1.75, -0.53, -1.69, 1.69, -1.09, 0.15, -2.11, 2.17, -1.53, 1.22, -2.1, -0.86, -2.56, 2.28, -3.02, 3.33, -1.12, 3.86, -2.18, -1.19, -3.03, 0.79, -0.83, 0.97, -3.19, 1.45, -1.34, 1.28, -2.52, 4.22, -4.53, 3.22, -1.97, 1.75, -2.36, 3.19, -0.83, 1.53, -1.59, 1.86, -2.17, 2.3, -1.63, 2.71, -2.03, 3.75, -2.57, -0.6, -1.47, 1.33, -1.95, 0.7, -1.65, 1.27, -1.42, 1.09, -3.0, 3.87, -2.51, 3.06, -2.6, 0.74, -1.08, -0.03, -2.44, 1.31, -2.65, 2.99, -1.84, 1.65, -4.76, 3.75, -2.07, 3.98, -2.4, 2.67, -2.21, 1.49, -1.21, 1.22, -5.29, 2.38, -2.85, 2.28, -5.6, 3.78, -2.7, 0.8, -1.81, 3.5, -3.75, 4.17, -1.29, 2.99, -5.92, 3.43, -1.83, 1.23, -1.24, -1.04, -2.56, 2.37, -3.26, 0.39, -4.63, 2.51, -4.52, 3.04, -1.7, 0.36, -1.41, 0.04, -2.1, 1.0, -1.87, 3.78, -4.32, 3.59, -2.24, 1.38, -1.99, -0.22, -1.87, 1.95, -0.84, 2.17, -5.38, 3.56, -1.27, 2.9, -1.79, 3.31, -5.47, 3.85, -1.44, 3.69, -2.02, 0.37, -1.29, 0.33, -2.34, 2.56, -1.74, -1.27, -1.97, 1.22, -2.51, -0.16, -1.64, -0.96, -2.99, 1.4, -1.53, 3.31, -2.24, 0.45, -2.46, 1.71, -2.88, 1.56, -1.63, 1.46, -1.41, 0.68, -1.96, 2.76, -1.61, 2.11]).reshape((200, 2)) # Global data X = np.array([[3.0, 3], [4, 3], [4, 2], [9, 2], [5, 1], [6, 2], [9, 4], [5, 2], [5, 4], [7, 4], [6, 5]]) CODET1 = np.array([[3.0000, 3.0000], [6.2000, 4.0000], [5.8000, 1.8000]]) CODET2 = np.array([[11.0/3, 8.0/3], [6.7500, 4.2500], [6.2500, 1.7500]]) LABEL1 = np.array([0, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1]) class TestWhiten: def test_whiten(self): desired = np.array([[5.08738849, 2.97091878], [3.19909255, 0.69660580], [4.51041982, 0.02640918], [4.38567074, 0.95120889], [2.32191480, 1.63195503]]) for tp in np.array, matrix: obs = tp([[0.98744510, 0.82766775], [0.62093317, 0.19406729], [0.87545741, 0.00735733], [0.85124403, 0.26499712], [0.45067590, 0.45464607]]) assert_allclose(whiten(obs), desired, rtol=1e-5) def test_whiten_zero_std(self): desired = np.array([[0., 1.0, 2.86666544], [0., 1.0, 1.32460034], [0., 1.0, 3.74382172]]) for tp in np.array, matrix: obs = tp([[0., 1., 0.74109533], [0., 1., 0.34243798], [0., 1., 0.96785929]]) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_allclose(whiten(obs), desired, rtol=1e-5) assert_equal(len(w), 1) assert_(issubclass(w[-1].category, RuntimeWarning)) def test_whiten_not_finite(self): for tp in np.array, matrix: for bad_value in np.nan, np.inf, -np.inf: obs = tp([[0.98744510, bad_value], [0.62093317, 0.19406729], [0.87545741, 0.00735733], [0.85124403, 0.26499712], [0.45067590, 0.45464607]]) assert_raises(ValueError, whiten, obs) class TestVq: def test_py_vq(self): initc = np.concatenate(([[X[0]], [X[1]], [X[2]]])) for tp in np.array, matrix: label1 = py_vq(tp(X), tp(initc))[0] assert_array_equal(label1, LABEL1) def test_vq(self): initc = np.concatenate(([[X[0]], [X[1]], [X[2]]])) for tp in np.array, matrix: label1, dist = _vq.vq(tp(X), tp(initc)) assert_array_equal(label1, LABEL1) tlabel1, tdist = vq(tp(X), tp(initc)) def test_vq_1d(self): # Test special rank 1 vq algo, python implementation. data = X[:, 0] initc = data[:3] a, b = _vq.vq(data, initc) ta, tb = py_vq(data[:, np.newaxis], initc[:, np.newaxis]) assert_array_equal(a, ta) assert_array_equal(b, tb) def test__vq_sametype(self): a = np.array([1.0, 2.0], dtype=np.float64) b = a.astype(np.float32) assert_raises(TypeError, _vq.vq, a, b) def test__vq_invalid_type(self): a = np.array([1, 2], dtype=int) assert_raises(TypeError, _vq.vq, a, a) def test_vq_large_nfeat(self): X = np.random.rand(20, 20) code_book = np.random.rand(3, 20) codes0, dis0 = _vq.vq(X, code_book) codes1, dis1 = py_vq(X, code_book) assert_allclose(dis0, dis1, 1e-5) assert_array_equal(codes0, codes1) X = X.astype(np.float32) code_book = code_book.astype(np.float32) codes0, dis0 = _vq.vq(X, code_book) codes1, dis1 = py_vq(X, code_book) assert_allclose(dis0, dis1, 1e-5) assert_array_equal(codes0, codes1) def test_vq_large_features(self): X = np.random.rand(10, 5) * 1000000 code_book = np.random.rand(2, 5) * 1000000 codes0, dis0 = _vq.vq(X, code_book) codes1, dis1 = py_vq(X, code_book) assert_allclose(dis0, dis1, 1e-5) assert_array_equal(codes0, codes1) class TestKMean: def test_large_features(self): # Generate a data set with large values, and run kmeans on it to # (regression for 1077). d = 300 n = 100 m1 = np.random.randn(d) m2 = np.random.randn(d) x = 10000 * np.random.randn(n, d) - 20000 * m1 y = 10000 * np.random.randn(n, d) + 20000 * m2 data = np.empty((x.shape[0] + y.shape[0], d), np.double) data[:x.shape[0]] = x data[x.shape[0]:] = y kmeans(data, 2) def test_kmeans_simple(self): np.random.seed(54321) initc = np.concatenate(([[X[0]], [X[1]], [X[2]]])) for tp in np.array, matrix: code1 = kmeans(tp(X), tp(initc), iter=1)[0] assert_array_almost_equal(code1, CODET2) def test_kmeans_lost_cluster(self): # This will cause kmeans to have a cluster with no points. data = TESTDATA_2D initk = np.array([[-1.8127404, -0.67128041], [2.04621601, 0.07401111], [-2.31149087, -0.05160469]]) kmeans(data, initk) with suppress_warnings() as sup: sup.filter(UserWarning, "One of the clusters is empty. Re-run kmeans with a " "different initialization") kmeans2(data, initk, missing='warn') assert_raises(ClusterError, kmeans2, data, initk, missing='raise') def test_kmeans2_simple(self): np.random.seed(12345678) initc = np.concatenate(([[X[0]], [X[1]], [X[2]]])) for tp in np.array, matrix: code1 = kmeans2(tp(X), tp(initc), iter=1)[0] code2 = kmeans2(tp(X), tp(initc), iter=2)[0] assert_array_almost_equal(code1, CODET1) assert_array_almost_equal(code2, CODET2) def test_kmeans2_rank1(self): data = TESTDATA_2D data1 = data[:, 0] initc = data1[:3] code = initc.copy() kmeans2(data1, code, iter=1)[0] kmeans2(data1, code, iter=2)[0] def test_kmeans2_rank1_2(self): data = TESTDATA_2D data1 = data[:, 0] kmeans2(data1, 2, iter=1) def test_kmeans2_high_dim(self): # test kmeans2 when the number of dimensions exceeds the number # of input points data = TESTDATA_2D data = data.reshape((20, 20))[:10] kmeans2(data, 2) def test_kmeans2_init(self): np.random.seed(12345) data = TESTDATA_2D kmeans2(data, 3, minit='points') kmeans2(data[:, :1], 3, minit='points') # special case (1-D) kmeans2(data, 3, minit='++') kmeans2(data[:, :1], 3, minit='++') # special case (1-D) # minit='random' can give warnings, filter those with suppress_warnings() as sup: sup.filter(message="One of the clusters is empty. Re-run.") kmeans2(data, 3, minit='random') kmeans2(data[:, :1], 3, minit='random') # special case (1-D) @pytest.mark.skipif(sys.platform == 'win32', reason='Fails with MemoryError in Wine.') def test_krandinit(self): data = TESTDATA_2D datas = [data.reshape((200, 2)), data.reshape((20, 20))[:10]] k = int(1e6) for data in datas: # check that np.random.Generator can be used (numpy >= 1.17) if hasattr(np.random, 'default_rng'): rng = np.random.default_rng(1234) else: rng = np.random.RandomState(1234) init = _krandinit(data, k, rng) orig_cov = np.cov(data, rowvar=0) init_cov = np.cov(init, rowvar=0) assert_allclose(orig_cov, init_cov, atol=1e-2) def test_kmeans2_empty(self): # Regression test for gh-1032. assert_raises(ValueError, kmeans2, [], 2) def test_kmeans_0k(self): # Regression test for gh-1073: fail when k arg is 0. assert_raises(ValueError, kmeans, X, 0) assert_raises(ValueError, kmeans2, X, 0) assert_raises(ValueError, kmeans2, X, np.array([])) def test_kmeans_large_thres(self): # Regression test for gh-1774 x = np.array([1, 2, 3, 4, 10], dtype=float) res = kmeans(x, 1, thresh=1e16) assert_allclose(res[0], np.array([4.])) assert_allclose(res[1], 2.3999999999999999) def test_kmeans2_kpp_low_dim(self): # Regression test for gh-11462 prev_res = np.array([[-1.95266667, 0.898], [-3.153375, 3.3945]]) np.random.seed(42) res, _ = kmeans2(TESTDATA_2D, 2, minit='++') assert_allclose(res, prev_res) def test_kmeans2_kpp_high_dim(self): # Regression test for gh-11462 n_dim = 100 size = 10 centers = np.vstack([5 * np.ones(n_dim), -5 * np.ones(n_dim)]) np.random.seed(42) data = np.vstack([ np.random.multivariate_normal(centers[0], np.eye(n_dim), size=size), np.random.multivariate_normal(centers[1], np.eye(n_dim), size=size) ]) res, _ = kmeans2(data, 2, minit='++') assert_array_almost_equal(res, centers, decimal=0) def test_kmeans_and_kmeans2_random_seed(self): seed_list = [1234, np.random.RandomState(1234)] # check that np.random.Generator can be used (numpy >= 1.17) if hasattr(np.random, 'default_rng'): seed_list.append(np.random.default_rng(1234)) for seed in seed_list: # test for kmeans res1, _ = kmeans(TESTDATA_2D, 2, seed=seed) res2, _ = kmeans(TESTDATA_2D, 2, seed=seed) assert_allclose(res1, res1) # should be same results # test for kmeans2 for minit in ["random", "points", "++"]: res1, _ = kmeans2(TESTDATA_2D, 2, minit=minit, seed=seed) res2, _ = kmeans2(TESTDATA_2D, 2, minit=minit, seed=seed) assert_allclose(res1, res1) # should be same results