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import pytest
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
import pynndescent.distances as dist
import pynndescent.sparse as spdist
from scipy import stats
from scipy.sparse import csr_matrix
from scipy.version import full_version as scipy_full_version
from sklearn.metrics import pairwise_distances
from sklearn.neighbors import BallTree
from sklearn.preprocessing import normalize
import platform
@pytest.mark.parametrize(
"metric",
[
"euclidean",
"manhattan",
"chebyshev",
"minkowski",
"hamming",
"canberra",
"braycurtis",
"cosine",
"correlation",
],
)
def test_spatial_check(spatial_data, metric):
dist_matrix = pairwise_distances(spatial_data, metric=metric)
# scipy is bad sometimes
if metric == "braycurtis":
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 0.0
if metric in ("cosine", "correlation"):
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 1.0
# And because distance between all zero vectors should be zero
dist_matrix[10, 11] = 0.0
dist_matrix[11, 10] = 0.0
dist_function = dist.named_distances[metric]
test_matrix = np.array(
[
[
dist_function(spatial_data[i], spatial_data[j])
for j in range(spatial_data.shape[0])
]
for i in range(spatial_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Distances don't match for metric {}".format(metric),
)
@pytest.mark.parametrize(
"metric",
[
"jaccard",
"matching",
"dice",
"rogerstanimoto",
"russellrao",
"sokalmichener",
"sokalsneath",
"yule",
],
)
def test_binary_check(binary_data, metric):
dist_matrix = pairwise_distances(binary_data, metric=metric)
if metric in ("jaccard", "dice", "sokalsneath", "yule"):
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 0.0
if metric == "russellrao":
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 0.0
# And because distance between all zero vectors should be zero
dist_matrix[10, 11] = 0.0
dist_matrix[11, 10] = 0.0
dist_function = dist.named_distances[metric]
test_matrix = np.array(
[
[
dist_function(binary_data[i], binary_data[j])
for j in range(binary_data.shape[0])
]
for i in range(binary_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Distances don't match for metric {}".format(metric),
)
@pytest.mark.parametrize(
"metric",
[
"euclidean",
"manhattan",
"chebyshev",
"minkowski",
"hamming",
"canberra",
"cosine",
"braycurtis",
"correlation",
],
)
def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
machine = platform.machine()
if (machine.startswith('arm') or machine.startswith('aarch')):
pytest.skip("Skip on arm")
if metric in spdist.sparse_named_distances:
dist_matrix = pairwise_distances(
np.asarray(sparse_spatial_data.todense()).astype(np.float32), metric=metric
)
if metric in ("braycurtis", "dice", "sokalsneath", "yule"):
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 0.0
if metric in ("cosine", "correlation", "russellrao"):
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 1.0
# And because distance between all zero vectors should be zero
dist_matrix[10, 11] = 0.0
dist_matrix[11, 10] = 0.0
dist_function = spdist.sparse_named_distances[metric]
if metric in spdist.sparse_need_n_features:
test_matrix = np.array(
[
[
dist_function(
sparse_spatial_data[i].indices,
sparse_spatial_data[i].data,
sparse_spatial_data[j].indices,
sparse_spatial_data[j].data,
sparse_spatial_data.shape[1],
)
for j in range(sparse_spatial_data.shape[0])
]
for i in range(sparse_spatial_data.shape[0])
]
)
else:
test_matrix = np.array(
[
[
dist_function(
sparse_spatial_data[i].indices,
sparse_spatial_data[i].data,
sparse_spatial_data[j].indices,
sparse_spatial_data[j].data,
)
for j in range(sparse_spatial_data.shape[0])
]
for i in range(sparse_spatial_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Sparse distances don't match for metric {}".format(metric),
decimal=decimal,
)
@pytest.mark.parametrize(
"metric",
[
"jaccard",
"matching",
"dice",
"rogerstanimoto",
"russellrao",
"sokalmichener",
"sokalsneath",
],
)
def test_sparse_binary_check(sparse_binary_data, metric):
if metric in spdist.sparse_named_distances:
dist_matrix = pairwise_distances(np.asarray(sparse_binary_data.todense()), metric=metric)
if metric in ("jaccard", "dice", "sokalsneath"):
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 0.0
if metric == "russellrao":
dist_matrix[np.where(~np.isfinite(dist_matrix))] = 1.0
# And because distance between all zero vectors should be zero
dist_matrix[10, 11] = 0.0
dist_matrix[11, 10] = 0.0
dist_function = spdist.sparse_named_distances[metric]
if metric in spdist.sparse_need_n_features:
test_matrix = np.array(
[
[
dist_function(
sparse_binary_data[i].indices,
sparse_binary_data[i].data,
sparse_binary_data[j].indices,
sparse_binary_data[j].data,
sparse_binary_data.shape[1],
)
for j in range(sparse_binary_data.shape[0])
]
for i in range(sparse_binary_data.shape[0])
]
)
else:
test_matrix = np.array(
[
[
dist_function(
sparse_binary_data[i].indices,
sparse_binary_data[i].data,
sparse_binary_data[j].indices,
sparse_binary_data[j].data,
)
for j in range(sparse_binary_data.shape[0])
]
for i in range(sparse_binary_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Sparse distances don't match for metric {}".format(metric),
)
def test_seuclidean(spatial_data):
v = np.abs(np.random.randn(spatial_data.shape[1]))
dist_matrix = pairwise_distances(spatial_data, metric="seuclidean", V=v)
test_matrix = np.array(
[
[
dist.standardised_euclidean(spatial_data[i], spatial_data[j], v)
for j in range(spatial_data.shape[0])
]
for i in range(spatial_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Distances don't match for metric seuclidean",
)
@pytest.mark.skipif(
scipy_full_version < "1.8", reason="incorrect function in scipy<1.8"
)
def test_weighted_minkowski(spatial_data):
v = np.abs(np.random.randn(spatial_data.shape[1]))
dist_matrix = pairwise_distances(spatial_data, metric="minkowski", w=v, p=3)
test_matrix = np.array(
[
[
dist.weighted_minkowski(spatial_data[i], spatial_data[j], v, p=3)
for j in range(spatial_data.shape[0])
]
for i in range(spatial_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Distances don't match for metric weighted_minkowski",
)
def test_mahalanobis(spatial_data):
v = np.cov(np.transpose(spatial_data))
dist_matrix = pairwise_distances(spatial_data, metric="mahalanobis", VI=v)
test_matrix = np.array(
[
[
dist.mahalanobis(spatial_data[i], spatial_data[j], v)
for j in range(spatial_data.shape[0])
]
for i in range(spatial_data.shape[0])
]
)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Distances don't match for metric mahalanobis",
)
def test_haversine(spatial_data):
tree = BallTree(spatial_data[:, :2], metric="haversine")
dist_matrix, _ = tree.query(spatial_data[:, :2], k=spatial_data.shape[0])
test_matrix = np.array(
[
[
dist.haversine(spatial_data[i, :2], spatial_data[j, :2])
for j in range(spatial_data.shape[0])
]
for i in range(spatial_data.shape[0])
]
)
test_matrix.sort(axis=1)
assert_array_almost_equal(
test_matrix,
dist_matrix,
err_msg="Distances don't match for metric haversine",
)
def test_spearmanr():
x = np.random.randn(100)
y = np.random.randn(100)
scipy_expected = stats.spearmanr(x, y)
r = dist.spearmanr(x, y)
assert_array_almost_equal(r, 1 - scipy_expected.correlation)
def test_alternative_distances():
for distname in dist.fast_distance_alternatives:
true_dist = dist.named_distances[distname]
alt_dist = dist.fast_distance_alternatives[distname]["dist"]
correction = dist.fast_distance_alternatives[distname]["correction"]
for i in range(100):
x = np.random.random(30).astype(np.float32)
y = np.random.random(30).astype(np.float32)
x[x < 0.25] = 0.0
y[y < 0.25] = 0.0
true_distance = true_dist(x, y)
corrected_alt_distance = correction(alt_dist(x, y))
assert np.isclose(true_distance, corrected_alt_distance)
def test_jensen_shannon():
machine = platform.machine()
if (machine.startswith('arm') or machine.startswith('aarch')):
pytest.skip()
test_data = np.random.random(size=(10, 50))
test_data = normalize(test_data, norm="l1")
for i in range(test_data.shape[0]):
for j in range(i + 1, test_data.shape[0]):
m = (test_data[i] + test_data[j]) / 2.0
p = test_data[i]
q = test_data[j]
d1 = (
-np.sum(m * np.log(m))
+ (np.sum(p * np.log(p)) + np.sum(q * np.log(q))) / 2.0
)
d2 = dist.jensen_shannon_divergence(p, q)
assert np.isclose(d1, d2, rtol=1e-4)
def test_sparse_jensen_shannon():
machine = platform.machine()
if (machine.startswith('arm') or machine.startswith('aarch')):
pytest.skip()
test_data = np.random.random(size=(10, 100))
# sparsify
test_data[test_data <= 0.5] = 0.0
sparse_test_data = csr_matrix(test_data)
sparse_test_data = normalize(sparse_test_data, norm="l1")
test_data = normalize(test_data, norm="l1")
for i in range(test_data.shape[0]):
for j in range(i + 1, test_data.shape[0]):
m = (test_data[i] + test_data[j]) / 2.0
p = test_data[i]
q = test_data[j]
d1 = (
-np.sum(m[m > 0] * np.log(m[m > 0]))
+ (
np.sum(p[p > 0] * np.log(p[p > 0]))
+ np.sum(q[q > 0] * np.log(q[q > 0]))
)
/ 2.0
)
d2 = spdist.sparse_jensen_shannon_divergence(
sparse_test_data[i].indices,
sparse_test_data[i].data,
sparse_test_data[j].indices,
sparse_test_data[j].data,
)
assert np.isclose(d1, d2, rtol=1e-3)
@pytest.mark.parametrize("p", [1.0, 2.0, 3.0, 0.5])
def test_wasserstein_1d(p):
test_data = np.random.random(size=(10, 100))
# sparsify
test_data[test_data <= 0.5] = 0.0
sparse_test_data = csr_matrix(test_data)
for i in range(test_data.shape[0]):
for j in range(i + 1, test_data.shape[0]):
d1 = dist.wasserstein_1d(test_data[i], test_data[j], p)
d2 = spdist.sparse_wasserstein_1d(
sparse_test_data[i].indices,
sparse_test_data[i].data,
sparse_test_data[j].indices,
sparse_test_data[j].data,
p,
)
assert np.isclose(d1, d2)
|
