def test_seuclidean():
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",
)
def test_metrics():
for metric in spatial_distances:
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),
)
for metric in binary_distances:
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 in ("kulsinski", "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),
)
# Handle the few special distances separately
# SEuclidean
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",
)
# Weighted minkowski
dist_matrix = pairwise_distances(spatial_data, metric="wminkowski", 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",
)
# Mahalanobis
v = np.abs(np.random.randn(spatial_data.shape[1], spatial_data.shape[1]))
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",
)
# Haversine
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",
)
