import numpy as np import pytest from sklearn.utils._testing import assert_allclose, assert_raises from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors from sklearn.neighbors._ball_tree import kernel_norm from sklearn.pipeline import make_pipeline from sklearn.datasets import make_blobs from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.exceptions import NotFittedError import joblib # XXX Duplicated in test_neighbors_tree, test_kde def compute_kernel_slow(Y, X, kernel, h): d = np.sqrt(((Y[:, None, :] - X) ** 2).sum(-1)) norm = kernel_norm(h, X.shape[1], kernel) / X.shape[0] if kernel == 'gaussian': return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1) elif kernel == 'tophat': return norm * (d < h).sum(-1) elif kernel == 'epanechnikov': return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1) elif kernel == 'exponential': return norm * (np.exp(-d / h)).sum(-1) elif kernel == 'linear': return norm * ((1 - d / h) * (d < h)).sum(-1) elif kernel == 'cosine': return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1) else: raise ValueError('kernel not recognized') def check_results(kernel, bandwidth, atol, rtol, X, Y, dens_true): kde = KernelDensity(kernel=kernel, bandwidth=bandwidth, atol=atol, rtol=rtol) log_dens = kde.fit(X).score_samples(Y) assert_allclose(np.exp(log_dens), dens_true, atol=atol, rtol=max(1E-7, rtol)) assert_allclose(np.exp(kde.score(Y)), np.prod(dens_true), atol=atol, rtol=max(1E-7, rtol)) @pytest.mark.parametrize( 'kernel', ['gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear', 'cosine']) @pytest.mark.parametrize('bandwidth', [0.01, 0.1, 1]) def test_kernel_density(kernel, bandwidth): n_samples, n_features = (100, 3) rng = np.random.RandomState(0) X = rng.randn(n_samples, n_features) Y = rng.randn(n_samples, n_features) dens_true = compute_kernel_slow(Y, X, kernel, bandwidth) for rtol in [0, 1E-5]: for atol in [1E-6, 1E-2]: for breadth_first in (True, False): check_results(kernel, bandwidth, atol, rtol, X, Y, dens_true) def test_kernel_density_sampling(n_samples=100, n_features=3): rng = np.random.RandomState(0) X = rng.randn(n_samples, n_features) bandwidth = 0.2 for kernel in ['gaussian', 'tophat']: # draw a tophat sample kde = KernelDensity(bandwidth=bandwidth, kernel=kernel).fit(X) samp = kde.sample(100) assert X.shape == samp.shape # check that samples are in the right range nbrs = NearestNeighbors(n_neighbors=1).fit(X) dist, ind = nbrs.kneighbors(X, return_distance=True) if kernel == 'tophat': assert np.all(dist < bandwidth) elif kernel == 'gaussian': # 5 standard deviations is safe for 100 samples, but there's a # very small chance this test could fail. assert np.all(dist < 5 * bandwidth) # check unsupported kernels for kernel in ['epanechnikov', 'exponential', 'linear', 'cosine']: kde = KernelDensity(bandwidth=bandwidth, kernel=kernel).fit(X) assert_raises(NotImplementedError, kde.sample, 100) # non-regression test: used to return a scalar X = rng.randn(4, 1) kde = KernelDensity(kernel="gaussian").fit(X) assert kde.sample().shape == (1, 1) @pytest.mark.parametrize('algorithm', ['auto', 'ball_tree', 'kd_tree']) @pytest.mark.parametrize('metric', ['euclidean', 'minkowski', 'manhattan', 'chebyshev', 'haversine']) def test_kde_algorithm_metric_choice(algorithm, metric): # Smoke test for various metrics and algorithms rng = np.random.RandomState(0) X = rng.randn(10, 2) # 2 features required for haversine dist. Y = rng.randn(10, 2) if algorithm == 'kd_tree' and metric not in KDTree.valid_metrics: assert_raises(ValueError, KernelDensity, algorithm=algorithm, metric=metric) else: kde = KernelDensity(algorithm=algorithm, metric=metric) kde.fit(X) y_dens = kde.score_samples(Y) assert y_dens.shape == Y.shape[:1] def test_kde_score(n_samples=100, n_features=3): pass # FIXME # rng = np.random.RandomState(0) # X = rng.random_sample((n_samples, n_features)) # Y = rng.random_sample((n_samples, n_features)) def test_kde_badargs(): assert_raises(ValueError, KernelDensity, algorithm='blah') assert_raises(ValueError, KernelDensity, bandwidth=0) assert_raises(ValueError, KernelDensity, kernel='blah') assert_raises(ValueError, KernelDensity, metric='blah') assert_raises(ValueError, KernelDensity, algorithm='kd_tree', metric='blah') kde = KernelDensity() assert_raises(ValueError, kde.fit, np.random.random((200, 10)), sample_weight=np.random.random((200, 10))) assert_raises(ValueError, kde.fit, np.random.random((200, 10)), sample_weight=-np.random.random(200)) def test_kde_pipeline_gridsearch(): # test that kde plays nice in pipelines and grid-searches X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) pipe1 = make_pipeline(StandardScaler(with_mean=False, with_std=False), KernelDensity(kernel="gaussian")) params = dict(kerneldensity__bandwidth=[0.001, 0.01, 0.1, 1, 10]) search = GridSearchCV(pipe1, param_grid=params) search.fit(X) assert search.best_params_['kerneldensity__bandwidth'] == .1 def test_kde_sample_weights(): n_samples = 400 size_test = 20 weights_neutral = np.full(n_samples, 3.) for d in [1, 2, 10]: rng = np.random.RandomState(0) X = rng.rand(n_samples, d) weights = 1 + (10 * X.sum(axis=1)).astype(np.int8) X_repetitions = np.repeat(X, weights, axis=0) n_samples_test = size_test // d test_points = rng.rand(n_samples_test, d) for algorithm in ['auto', 'ball_tree', 'kd_tree']: for metric in ['euclidean', 'minkowski', 'manhattan', 'chebyshev']: if algorithm != 'kd_tree' or metric in KDTree.valid_metrics: kde = KernelDensity(algorithm=algorithm, metric=metric) # Test that adding a constant sample weight has no effect kde.fit(X, sample_weight=weights_neutral) scores_const_weight = kde.score_samples(test_points) sample_const_weight = kde.sample(random_state=1234) kde.fit(X) scores_no_weight = kde.score_samples(test_points) sample_no_weight = kde.sample(random_state=1234) assert_allclose(scores_const_weight, scores_no_weight) assert_allclose(sample_const_weight, sample_no_weight) # Test equivalence between sampling and (integer) weights kde.fit(X, sample_weight=weights) scores_weight = kde.score_samples(test_points) sample_weight = kde.sample(random_state=1234) kde.fit(X_repetitions) scores_ref_sampling = kde.score_samples(test_points) sample_ref_sampling = kde.sample(random_state=1234) assert_allclose(scores_weight, scores_ref_sampling) assert_allclose(sample_weight, sample_ref_sampling) # Test that sample weights has a non-trivial effect diff = np.max(np.abs(scores_no_weight - scores_weight)) assert diff > 0.001 # Test invariance with respect to arbitrary scaling scale_factor = rng.rand() kde.fit(X, sample_weight=(scale_factor * weights)) scores_scaled_weight = kde.score_samples(test_points) assert_allclose(scores_scaled_weight, scores_weight) def test_sample_weight_invalid(): # Check sample weighting raises errors. kde = KernelDensity() data = np.reshape([1., 2., 3.], (-1, 1)) sample_weight = [0.1, -0.2, 0.3] expected_err = "sample_weight must have positive values" with pytest.raises(ValueError, match=expected_err): kde.fit(data, sample_weight=sample_weight) @pytest.mark.parametrize('sample_weight', [None, [0.1, 0.2, 0.3]]) def test_pickling(tmpdir, sample_weight): # Make sure that predictions are the same before and after pickling. Used # to be a bug because sample_weights wasn't pickled and the resulting tree # would miss some info. kde = KernelDensity() data = np.reshape([1., 2., 3.], (-1, 1)) kde.fit(data, sample_weight=sample_weight) X = np.reshape([1.1, 2.1], (-1, 1)) scores = kde.score_samples(X) file_path = str(tmpdir.join('dump.pkl')) joblib.dump(kde, file_path) kde = joblib.load(file_path) scores_pickled = kde.score_samples(X) assert_allclose(scores, scores_pickled) @pytest.mark.parametrize('method', ['score_samples', 'sample']) def test_check_is_fitted(method): # Check that predict raises an exception in an unfitted estimator. # Unfitted estimators should raise a NotFittedError. rng = np.random.RandomState(0) X = rng.randn(10, 2) kde = KernelDensity() with pytest.raises(NotFittedError): getattr(kde, method)(X)