143 lines
5.4 KiB
Python
143 lines
5.4 KiB
Python
import numpy as np
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from sklearn.utils.testing import (assert_allclose, assert_raises,
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assert_equal)
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from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors
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from sklearn.neighbors.ball_tree import kernel_norm
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from sklearn.pipeline import make_pipeline
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from sklearn.datasets import make_blobs
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from sklearn.model_selection import GridSearchCV
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from sklearn.preprocessing import StandardScaler
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def compute_kernel_slow(Y, X, kernel, h):
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d = np.sqrt(((Y[:, None, :] - X) ** 2).sum(-1))
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norm = kernel_norm(h, X.shape[1], kernel) / X.shape[0]
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if kernel == 'gaussian':
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return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1)
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elif kernel == 'tophat':
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return norm * (d < h).sum(-1)
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elif kernel == 'epanechnikov':
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return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1)
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elif kernel == 'exponential':
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return norm * (np.exp(-d / h)).sum(-1)
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elif kernel == 'linear':
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return norm * ((1 - d / h) * (d < h)).sum(-1)
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elif kernel == 'cosine':
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return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1)
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else:
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raise ValueError('kernel not recognized')
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def check_results(kernel, bandwidth, atol, rtol, X, Y, dens_true):
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kde = KernelDensity(kernel=kernel, bandwidth=bandwidth,
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atol=atol, rtol=rtol)
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log_dens = kde.fit(X).score_samples(Y)
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assert_allclose(np.exp(log_dens), dens_true,
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atol=atol, rtol=max(1E-7, rtol))
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assert_allclose(np.exp(kde.score(Y)),
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np.prod(dens_true),
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atol=atol, rtol=max(1E-7, rtol))
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def test_kernel_density(n_samples=100, n_features=3):
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rng = np.random.RandomState(0)
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X = rng.randn(n_samples, n_features)
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Y = rng.randn(n_samples, n_features)
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for kernel in ['gaussian', 'tophat', 'epanechnikov',
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'exponential', 'linear', 'cosine']:
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for bandwidth in [0.01, 0.1, 1]:
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dens_true = compute_kernel_slow(Y, X, kernel, bandwidth)
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for rtol in [0, 1E-5]:
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for atol in [1E-6, 1E-2]:
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for breadth_first in (True, False):
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yield (check_results, kernel, bandwidth, atol, rtol,
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X, Y, dens_true)
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def test_kernel_density_sampling(n_samples=100, n_features=3):
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rng = np.random.RandomState(0)
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X = rng.randn(n_samples, n_features)
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bandwidth = 0.2
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for kernel in ['gaussian', 'tophat']:
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# draw a tophat sample
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kde = KernelDensity(bandwidth, kernel=kernel).fit(X)
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samp = kde.sample(100)
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assert_equal(X.shape, samp.shape)
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# check that samples are in the right range
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nbrs = NearestNeighbors(n_neighbors=1).fit(X)
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dist, ind = nbrs.kneighbors(X, return_distance=True)
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if kernel == 'tophat':
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assert np.all(dist < bandwidth)
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elif kernel == 'gaussian':
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# 5 standard deviations is safe for 100 samples, but there's a
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# very small chance this test could fail.
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assert np.all(dist < 5 * bandwidth)
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# check unsupported kernels
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for kernel in ['epanechnikov', 'exponential', 'linear', 'cosine']:
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kde = KernelDensity(bandwidth, kernel=kernel).fit(X)
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assert_raises(NotImplementedError, kde.sample, 100)
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# non-regression test: used to return a scalar
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X = rng.randn(4, 1)
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kde = KernelDensity(kernel="gaussian").fit(X)
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assert_equal(kde.sample().shape, (1, 1))
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def test_kde_algorithm_metric_choice():
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# Smoke test for various metrics and algorithms
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rng = np.random.RandomState(0)
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X = rng.randn(10, 2) # 2 features required for haversine dist.
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Y = rng.randn(10, 2)
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for algorithm in ['auto', 'ball_tree', 'kd_tree']:
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for metric in ['euclidean', 'minkowski', 'manhattan',
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'chebyshev', 'haversine']:
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if algorithm == 'kd_tree' and metric not in KDTree.valid_metrics:
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assert_raises(ValueError, KernelDensity,
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algorithm=algorithm, metric=metric)
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else:
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kde = KernelDensity(algorithm=algorithm, metric=metric)
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kde.fit(X)
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y_dens = kde.score_samples(Y)
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assert_equal(y_dens.shape, Y.shape[:1])
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def test_kde_score(n_samples=100, n_features=3):
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pass
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# FIXME
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# rng = np.random.RandomState(0)
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# X = rng.random_sample((n_samples, n_features))
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# Y = rng.random_sample((n_samples, n_features))
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def test_kde_badargs():
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assert_raises(ValueError, KernelDensity,
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algorithm='blah')
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assert_raises(ValueError, KernelDensity,
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bandwidth=0)
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assert_raises(ValueError, KernelDensity,
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kernel='blah')
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assert_raises(ValueError, KernelDensity,
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metric='blah')
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assert_raises(ValueError, KernelDensity,
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algorithm='kd_tree', metric='blah')
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def test_kde_pipeline_gridsearch():
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# test that kde plays nice in pipelines and grid-searches
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X, _ = make_blobs(cluster_std=.1, random_state=1,
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centers=[[0, 1], [1, 0], [0, 0]])
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pipe1 = make_pipeline(StandardScaler(with_mean=False, with_std=False),
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KernelDensity(kernel="gaussian"))
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params = dict(kerneldensity__bandwidth=[0.001, 0.01, 0.1, 1, 10])
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search = GridSearchCV(pipe1, param_grid=params, cv=5)
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search.fit(X)
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assert_equal(search.best_params_['kerneldensity__bandwidth'], .1)
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