105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
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import itertools
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import numpy as np
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import pytest
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from numpy.testing import assert_array_almost_equal
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from sklearn.neighbors._ball_tree import BallTree
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from sklearn.utils import check_random_state
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from sklearn.utils.validation import check_array
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from sklearn.utils._testing import _convert_container
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rng = np.random.RandomState(10)
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V_mahalanobis = rng.rand(3, 3)
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V_mahalanobis = np.dot(V_mahalanobis, V_mahalanobis.T)
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DIMENSION = 3
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METRICS = {
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"euclidean": {},
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"manhattan": {},
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"minkowski": dict(p=3),
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"chebyshev": {},
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"seuclidean": dict(V=rng.random_sample(DIMENSION)),
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"wminkowski": dict(p=3, w=rng.random_sample(DIMENSION)),
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"mahalanobis": dict(V=V_mahalanobis),
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}
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DISCRETE_METRICS = ["hamming", "canberra", "braycurtis"]
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BOOLEAN_METRICS = [
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"matching",
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"jaccard",
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"dice",
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"kulsinski",
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"rogerstanimoto",
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"russellrao",
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"sokalmichener",
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"sokalsneath",
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]
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def brute_force_neighbors(X, Y, k, metric, **kwargs):
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from sklearn.metrics import DistanceMetric
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X, Y = check_array(X), check_array(Y)
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D = DistanceMetric.get_metric(metric, **kwargs).pairwise(Y, X)
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ind = np.argsort(D, axis=1)[:, :k]
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dist = D[np.arange(Y.shape[0])[:, None], ind]
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return dist, ind
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@pytest.mark.parametrize("metric", itertools.chain(BOOLEAN_METRICS, DISCRETE_METRICS))
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@pytest.mark.parametrize("array_type", ["list", "array"])
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def test_ball_tree_query_metrics(metric, array_type):
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rng = check_random_state(0)
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if metric in BOOLEAN_METRICS:
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X = rng.random_sample((40, 10)).round(0)
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Y = rng.random_sample((10, 10)).round(0)
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elif metric in DISCRETE_METRICS:
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X = (4 * rng.random_sample((40, 10))).round(0)
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Y = (4 * rng.random_sample((10, 10))).round(0)
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X = _convert_container(X, array_type)
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Y = _convert_container(Y, array_type)
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k = 5
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bt = BallTree(X, leaf_size=1, metric=metric)
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dist1, ind1 = bt.query(Y, k)
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dist2, ind2 = brute_force_neighbors(X, Y, k, metric)
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assert_array_almost_equal(dist1, dist2)
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def test_query_haversine():
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rng = check_random_state(0)
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X = 2 * np.pi * rng.random_sample((40, 2))
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bt = BallTree(X, leaf_size=1, metric="haversine")
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dist1, ind1 = bt.query(X, k=5)
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dist2, ind2 = brute_force_neighbors(X, X, k=5, metric="haversine")
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assert_array_almost_equal(dist1, dist2)
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assert_array_almost_equal(ind1, ind2)
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def test_array_object_type():
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"""Check that we do not accept object dtype array."""
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X = np.array([(1, 2, 3), (2, 5), (5, 5, 1, 2)], dtype=object)
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with pytest.raises(ValueError, match="setting an array element with a sequence"):
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BallTree(X)
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def test_bad_pyfunc_metric():
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def wrong_returned_value(x, y):
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return "1"
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def one_arg_func(x):
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return 1.0 # pragma: no cover
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X = np.ones((5, 2))
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msg = "Custom distance function must accept two vectors and return a float."
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with pytest.raises(TypeError, match=msg):
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BallTree(X, metric=wrong_returned_value)
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msg = "takes 1 positional argument but 2 were given"
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with pytest.raises(TypeError, match=msg):
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BallTree(X, metric=one_arg_func)
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