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import itertools
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_almost_equal, assert_equal
from sklearn.neighbors._ball_tree import BallTree, BallTree32, BallTree64
from sklearn.utils import check_random_state
from sklearn.utils._testing import _convert_container
from sklearn.utils.validation import check_array
rng = np.random.RandomState(10)
V_mahalanobis = rng.rand(3, 3)
V_mahalanobis = np.dot(V_mahalanobis, V_mahalanobis.T)
DIMENSION = 3
METRICS = {
"euclidean": {},
"manhattan": {},
"minkowski": dict(p=3),
"chebyshev": {},
}
DISCRETE_METRICS = ["hamming", "canberra", "braycurtis"]
BOOLEAN_METRICS = [
"jaccard",
"dice",
"rogerstanimoto",
"russellrao",
"sokalmichener",
"sokalsneath",
]
BALL_TREE_CLASSES = [
BallTree64,
BallTree32,
]
def brute_force_neighbors(X, Y, k, metric, **kwargs):
from sklearn.metrics import DistanceMetric
X, Y = check_array(X), check_array(Y)
D = DistanceMetric.get_metric(metric, **kwargs).pairwise(Y, X)
ind = np.argsort(D, axis=1)[:, :k]
dist = D[np.arange(Y.shape[0])[:, None], ind]
return dist, ind
def test_BallTree_is_BallTree64_subclass():
assert issubclass(BallTree, BallTree64)
@pytest.mark.parametrize("metric", itertools.chain(BOOLEAN_METRICS, DISCRETE_METRICS))
@pytest.mark.parametrize("array_type", ["list", "array"])
@pytest.mark.parametrize("BallTreeImplementation", BALL_TREE_CLASSES)
def test_ball_tree_query_metrics(metric, array_type, BallTreeImplementation):
rng = check_random_state(0)
if metric in BOOLEAN_METRICS:
X = rng.random_sample((40, 10)).round(0)
Y = rng.random_sample((10, 10)).round(0)
elif metric in DISCRETE_METRICS:
X = (4 * rng.random_sample((40, 10))).round(0)
Y = (4 * rng.random_sample((10, 10))).round(0)
X = _convert_container(X, array_type)
Y = _convert_container(Y, array_type)
k = 5
bt = BallTreeImplementation(X, leaf_size=1, metric=metric)
dist1, ind1 = bt.query(Y, k)
dist2, ind2 = brute_force_neighbors(X, Y, k, metric)
assert_array_almost_equal(dist1, dist2)
@pytest.mark.parametrize(
"BallTreeImplementation, decimal_tol", zip(BALL_TREE_CLASSES, [6, 5])
)
def test_query_haversine(BallTreeImplementation, decimal_tol):
rng = check_random_state(0)
X = 2 * np.pi * rng.random_sample((40, 2))
bt = BallTreeImplementation(X, leaf_size=1, metric="haversine")
dist1, ind1 = bt.query(X, k=5)
dist2, ind2 = brute_force_neighbors(X, X, k=5, metric="haversine")
assert_array_almost_equal(dist1, dist2, decimal=decimal_tol)
assert_array_almost_equal(ind1, ind2)
@pytest.mark.parametrize("BallTreeImplementation", BALL_TREE_CLASSES)
def test_array_object_type(BallTreeImplementation):
"""Check that we do not accept object dtype array."""
X = np.array([(1, 2, 3), (2, 5), (5, 5, 1, 2)], dtype=object)
with pytest.raises(ValueError, match="setting an array element with a sequence"):
BallTreeImplementation(X)
@pytest.mark.parametrize("BallTreeImplementation", BALL_TREE_CLASSES)
def test_bad_pyfunc_metric(BallTreeImplementation):
def wrong_returned_value(x, y):
return "1"
def one_arg_func(x):
return 1.0 # pragma: no cover
X = np.ones((5, 2))
msg = "Custom distance function must accept two vectors and return a float."
with pytest.raises(TypeError, match=msg):
BallTreeImplementation(X, metric=wrong_returned_value)
msg = "takes 1 positional argument but 2 were given"
with pytest.raises(TypeError, match=msg):
BallTreeImplementation(X, metric=one_arg_func)
@pytest.mark.parametrize("metric", itertools.chain(METRICS, BOOLEAN_METRICS))
def test_ball_tree_numerical_consistency(global_random_seed, metric):
# Results on float64 and float32 versions of a dataset must be
# numerically close.
X_64, X_32, Y_64, Y_32 = get_dataset_for_binary_tree(
random_seed=global_random_seed, features=50
)
metric_params = METRICS.get(metric, {})
bt_64 = BallTree64(X_64, leaf_size=1, metric=metric, **metric_params)
bt_32 = BallTree32(X_32, leaf_size=1, metric=metric, **metric_params)
# Test consistency with respect to the `query` method
k = 5
dist_64, ind_64 = bt_64.query(Y_64, k=k)
dist_32, ind_32 = bt_32.query(Y_32, k=k)
assert_allclose(dist_64, dist_32, rtol=1e-5)
assert_equal(ind_64, ind_32)
assert dist_64.dtype == np.float64
assert dist_32.dtype == np.float32
# Test consistency with respect to the `query_radius` method
r = 2.38
ind_64 = bt_64.query_radius(Y_64, r=r)
ind_32 = bt_32.query_radius(Y_32, r=r)
for _ind64, _ind32 in zip(ind_64, ind_32):
assert_equal(_ind64, _ind32)
# Test consistency with respect to the `query_radius` method
# with return distances being true
ind_64, dist_64 = bt_64.query_radius(Y_64, r=r, return_distance=True)
ind_32, dist_32 = bt_32.query_radius(Y_32, r=r, return_distance=True)
for _ind64, _ind32, _dist_64, _dist_32 in zip(ind_64, ind_32, dist_64, dist_32):
assert_equal(_ind64, _ind32)
assert_allclose(_dist_64, _dist_32, rtol=1e-5)
assert _dist_64.dtype == np.float64
assert _dist_32.dtype == np.float32
@pytest.mark.parametrize("metric", itertools.chain(METRICS, BOOLEAN_METRICS))
def test_kernel_density_numerical_consistency(global_random_seed, metric):
# Test consistency with respect to the `kernel_density` method
X_64, X_32, Y_64, Y_32 = get_dataset_for_binary_tree(random_seed=global_random_seed)
metric_params = METRICS.get(metric, {})
bt_64 = BallTree64(X_64, leaf_size=1, metric=metric, **metric_params)
bt_32 = BallTree32(X_32, leaf_size=1, metric=metric, **metric_params)
kernel = "gaussian"
h = 0.1
density64 = bt_64.kernel_density(Y_64, h=h, kernel=kernel, breadth_first=True)
density32 = bt_32.kernel_density(Y_32, h=h, kernel=kernel, breadth_first=True)
assert_allclose(density64, density32, rtol=1e-5)
assert density64.dtype == np.float64
assert density32.dtype == np.float32
def test_two_point_correlation_numerical_consistency(global_random_seed):
# Test consistency with respect to the `two_point_correlation` method
X_64, X_32, Y_64, Y_32 = get_dataset_for_binary_tree(random_seed=global_random_seed)
bt_64 = BallTree64(X_64, leaf_size=10)
bt_32 = BallTree32(X_32, leaf_size=10)
r = np.linspace(0, 1, 10)
counts_64 = bt_64.two_point_correlation(Y_64, r=r, dualtree=True)
counts_32 = bt_32.two_point_correlation(Y_32, r=r, dualtree=True)
assert_allclose(counts_64, counts_32)
def get_dataset_for_binary_tree(random_seed, features=3):
rng = np.random.RandomState(random_seed)
_X = rng.rand(100, features)
_Y = rng.rand(5, features)
X_64 = _X.astype(dtype=np.float64, copy=False)
Y_64 = _Y.astype(dtype=np.float64, copy=False)
X_32 = _X.astype(dtype=np.float32, copy=False)
Y_32 = _Y.astype(dtype=np.float32, copy=False)
return X_64, X_32, Y_64, Y_32
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