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, 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, 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)
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)