1471 lines
46 KiB
Python
1471 lines
46 KiB
Python
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# Copyright Anne M. Archibald 2008
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# Released under the scipy license
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import os
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from numpy.testing import (assert_equal, assert_array_equal, assert_,
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assert_almost_equal, assert_array_almost_equal,
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assert_allclose)
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from pytest import raises as assert_raises
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import pytest
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from platform import python_implementation
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import numpy as np
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from scipy.spatial import KDTree, Rectangle, distance_matrix, cKDTree
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from scipy.spatial._ckdtree import cKDTreeNode
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from scipy.spatial import minkowski_distance
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import itertools
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@pytest.fixture(params=[KDTree, cKDTree])
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def kdtree_type(request):
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return request.param
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def KDTreeTest(kls):
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"""Class decorator to create test cases for KDTree and cKDTree
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Tests use the class variable ``kdtree_type`` as the tree constructor.
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"""
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if not kls.__name__.startswith('_Test'):
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raise RuntimeError("Expected a class name starting with _Test")
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for tree in (KDTree, cKDTree):
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test_name = kls.__name__[1:] + '_' + tree.__name__
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if test_name in globals():
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raise RuntimeError("Duplicated test name: " + test_name)
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# Create a new sub-class with kdtree_type defined
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test_case = type(test_name, (kls,), {'kdtree_type': tree})
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globals()[test_name] = test_case
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return kls
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def distance_box(a, b, p, boxsize):
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diff = a - b
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diff[diff > 0.5 * boxsize] -= boxsize
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diff[diff < -0.5 * boxsize] += boxsize
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d = minkowski_distance(diff, 0, p)
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return d
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class ConsistencyTests:
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def distance(self, a, b, p):
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return minkowski_distance(a, b, p)
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def test_nearest(self):
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x = self.x
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d, i = self.kdtree.query(x, 1)
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assert_almost_equal(d**2, np.sum((x-self.data[i])**2))
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eps = 1e-8
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assert_(np.all(np.sum((self.data-x[np.newaxis, :])**2, axis=1) > d**2-eps))
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def test_m_nearest(self):
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x = self.x
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m = self.m
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dd, ii = self.kdtree.query(x, m)
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d = np.amax(dd)
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i = ii[np.argmax(dd)]
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assert_almost_equal(d**2, np.sum((x-self.data[i])**2))
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eps = 1e-8
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assert_equal(np.sum(np.sum((self.data-x[np.newaxis, :])**2, axis=1) < d**2+eps), m)
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def test_points_near(self):
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x = self.x
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d = self.d
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dd, ii = self.kdtree.query(x, k=self.kdtree.n, distance_upper_bound=d)
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eps = 1e-8
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hits = 0
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for near_d, near_i in zip(dd, ii):
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if near_d == np.inf:
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continue
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hits += 1
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assert_almost_equal(near_d**2, np.sum((x-self.data[near_i])**2))
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assert_(near_d < d+eps, "near_d=%g should be less than %g" % (near_d, d))
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assert_equal(np.sum(self.distance(self.data, x, 2) < d**2+eps), hits)
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def test_points_near_l1(self):
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x = self.x
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d = self.d
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dd, ii = self.kdtree.query(x, k=self.kdtree.n, p=1, distance_upper_bound=d)
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eps = 1e-8
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hits = 0
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for near_d, near_i in zip(dd, ii):
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if near_d == np.inf:
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continue
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hits += 1
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assert_almost_equal(near_d, self.distance(x, self.data[near_i], 1))
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assert_(near_d < d+eps, "near_d=%g should be less than %g" % (near_d, d))
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assert_equal(np.sum(self.distance(self.data, x, 1) < d+eps), hits)
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def test_points_near_linf(self):
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x = self.x
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d = self.d
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dd, ii = self.kdtree.query(x, k=self.kdtree.n, p=np.inf, distance_upper_bound=d)
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eps = 1e-8
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hits = 0
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for near_d, near_i in zip(dd, ii):
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if near_d == np.inf:
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continue
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hits += 1
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assert_almost_equal(near_d, self.distance(x, self.data[near_i], np.inf))
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assert_(near_d < d+eps, "near_d=%g should be less than %g" % (near_d, d))
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assert_equal(np.sum(self.distance(self.data, x, np.inf) < d+eps), hits)
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def test_approx(self):
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x = self.x
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k = self.k
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eps = 0.1
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d_real, i_real = self.kdtree.query(x, k)
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d, i = self.kdtree.query(x, k, eps=eps)
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assert_(np.all(d <= d_real*(1+eps)))
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@KDTreeTest
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class _Test_random(ConsistencyTests):
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def setup_method(self):
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self.n = 100
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self.m = 4
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np.random.seed(1234)
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self.data = np.random.randn(self.n, self.m)
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self.kdtree = self.kdtree_type(self.data, leafsize=2)
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self.x = np.random.randn(self.m)
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self.d = 0.2
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self.k = 10
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@KDTreeTest
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class _Test_random_far(_Test_random):
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def setup_method(self):
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super().setup_method()
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self.x = np.random.randn(self.m)+10
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@KDTreeTest
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class _Test_small(ConsistencyTests):
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def setup_method(self):
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self.data = np.array([[0, 0, 0],
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[0, 0, 1],
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[0, 1, 0],
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[0, 1, 1],
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[1, 0, 0],
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[1, 0, 1],
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[1, 1, 0],
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[1, 1, 1]])
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self.kdtree = self.kdtree_type(self.data)
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self.n = self.kdtree.n
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self.m = self.kdtree.m
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np.random.seed(1234)
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self.x = np.random.randn(3)
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self.d = 0.5
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self.k = 4
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def test_nearest(self):
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assert_array_equal(
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self.kdtree.query((0, 0, 0.1), 1),
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(0.1, 0))
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def test_nearest_two(self):
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assert_array_equal(
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self.kdtree.query((0, 0, 0.1), 2),
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([0.1, 0.9], [0, 1]))
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@KDTreeTest
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class _Test_small_nonleaf(_Test_small):
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def setup_method(self):
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super().setup_method()
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self.kdtree = self.kdtree_type(self.data, leafsize=1)
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class Test_vectorization_KDTree:
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def setup_method(self):
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self.data = np.array([[0, 0, 0],
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[0, 0, 1],
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[0, 1, 0],
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[0, 1, 1],
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[1, 0, 0],
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[1, 0, 1],
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[1, 1, 0],
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[1, 1, 1]])
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self.kdtree = KDTree(self.data)
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def test_single_query(self):
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d, i = self.kdtree.query(np.array([0, 0, 0]))
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assert_(isinstance(d, float))
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assert_(np.issubdtype(i, np.signedinteger))
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def test_vectorized_query(self):
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d, i = self.kdtree.query(np.zeros((2, 4, 3)))
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assert_equal(np.shape(d), (2, 4))
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assert_equal(np.shape(i), (2, 4))
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def test_single_query_multiple_neighbors(self):
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s = 23
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kk = self.kdtree.n+s
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d, i = self.kdtree.query(np.array([0, 0, 0]), k=kk)
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assert_equal(np.shape(d), (kk,))
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assert_equal(np.shape(i), (kk,))
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assert_(np.all(~np.isfinite(d[-s:])))
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assert_(np.all(i[-s:] == self.kdtree.n))
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def test_vectorized_query_multiple_neighbors(self):
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s = 23
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kk = self.kdtree.n+s
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d, i = self.kdtree.query(np.zeros((2, 4, 3)), k=kk)
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assert_equal(np.shape(d), (2, 4, kk))
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assert_equal(np.shape(i), (2, 4, kk))
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assert_(np.all(~np.isfinite(d[:, :, -s:])))
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assert_(np.all(i[:, :, -s:] == self.kdtree.n))
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def test_query_raises_for_k_none(self):
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x = 1.0
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with pytest.raises(ValueError, match="k must be an integer or*"):
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self.kdtree.query(x, k=None)
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class Test_vectorization_cKDTree:
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def setup_method(self):
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self.data = np.array([[0, 0, 0],
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[0, 0, 1],
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[0, 1, 0],
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[0, 1, 1],
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[1, 0, 0],
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[1, 0, 1],
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[1, 1, 0],
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[1, 1, 1]])
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self.kdtree = cKDTree(self.data)
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def test_single_query(self):
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d, i = self.kdtree.query([0, 0, 0])
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assert_(isinstance(d, float))
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assert_(isinstance(i, int))
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def test_vectorized_query(self):
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d, i = self.kdtree.query(np.zeros((2, 4, 3)))
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assert_equal(np.shape(d), (2, 4))
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assert_equal(np.shape(i), (2, 4))
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def test_vectorized_query_noncontiguous_values(self):
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np.random.seed(1234)
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qs = np.random.randn(3, 1000).T
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ds, i_s = self.kdtree.query(qs)
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for q, d, i in zip(qs, ds, i_s):
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assert_equal(self.kdtree.query(q), (d, i))
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def test_single_query_multiple_neighbors(self):
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s = 23
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kk = self.kdtree.n+s
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d, i = self.kdtree.query([0, 0, 0], k=kk)
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assert_equal(np.shape(d), (kk,))
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assert_equal(np.shape(i), (kk,))
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assert_(np.all(~np.isfinite(d[-s:])))
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assert_(np.all(i[-s:] == self.kdtree.n))
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def test_vectorized_query_multiple_neighbors(self):
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s = 23
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kk = self.kdtree.n+s
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d, i = self.kdtree.query(np.zeros((2, 4, 3)), k=kk)
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assert_equal(np.shape(d), (2, 4, kk))
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assert_equal(np.shape(i), (2, 4, kk))
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assert_(np.all(~np.isfinite(d[:, :, -s:])))
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assert_(np.all(i[:, :, -s:] == self.kdtree.n))
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class ball_consistency:
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tol = 0.0
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def distance(self, a, b, p):
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return minkowski_distance(a * 1.0, b * 1.0, p)
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def test_in_ball(self):
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x = np.atleast_2d(self.x)
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d = np.broadcast_to(self.d, x.shape[:-1])
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l = self.T.query_ball_point(x, self.d, p=self.p, eps=self.eps)
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for i, ind in enumerate(l):
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dist = self.distance(self.data[ind], x[i], self.p) - d[i]*(1.+self.eps)
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norm = self.distance(self.data[ind], x[i], self.p) + d[i]*(1.+self.eps)
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assert_array_equal(dist < self.tol * norm, True)
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def test_found_all(self):
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x = np.atleast_2d(self.x)
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d = np.broadcast_to(self.d, x.shape[:-1])
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l = self.T.query_ball_point(x, self.d, p=self.p, eps=self.eps)
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for i, ind in enumerate(l):
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c = np.ones(self.T.n, dtype=bool)
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c[ind] = False
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dist = self.distance(self.data[c], x[i], self.p) - d[i]/(1.+self.eps)
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norm = self.distance(self.data[c], x[i], self.p) + d[i]/(1.+self.eps)
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assert_array_equal(dist > -self.tol * norm, True)
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@KDTreeTest
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class _Test_random_ball(ball_consistency):
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def setup_method(self):
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n = 100
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m = 4
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np.random.seed(1234)
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self.data = np.random.randn(n, m)
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self.T = self.kdtree_type(self.data, leafsize=2)
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self.x = np.random.randn(m)
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self.p = 2.
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self.eps = 0
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self.d = 0.2
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@KDTreeTest
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class _Test_random_ball_periodic(ball_consistency):
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def distance(self, a, b, p):
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return distance_box(a, b, p, 1.0)
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def setup_method(self):
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n = 10000
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m = 4
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np.random.seed(1234)
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self.data = np.random.uniform(size=(n, m))
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self.T = self.kdtree_type(self.data, leafsize=2, boxsize=1)
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self.x = np.full(m, 0.1)
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self.p = 2.
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self.eps = 0
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self.d = 0.2
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def test_in_ball_outside(self):
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l = self.T.query_ball_point(self.x + 1.0, self.d, p=self.p, eps=self.eps)
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for i in l:
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assert_(self.distance(self.data[i], self.x, self.p) <= self.d*(1.+self.eps))
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l = self.T.query_ball_point(self.x - 1.0, self.d, p=self.p, eps=self.eps)
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for i in l:
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assert_(self.distance(self.data[i], self.x, self.p) <= self.d*(1.+self.eps))
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|
|
|
||
|
|
def test_found_all_outside(self):
|
||
|
|
c = np.ones(self.T.n, dtype=bool)
|
||
|
|
l = self.T.query_ball_point(self.x + 1.0, self.d, p=self.p, eps=self.eps)
|
||
|
|
c[l] = False
|
||
|
|
assert_(np.all(self.distance(self.data[c], self.x, self.p) >= self.d/(1.+self.eps)))
|
||
|
|
|
||
|
|
l = self.T.query_ball_point(self.x - 1.0, self.d, p=self.p, eps=self.eps)
|
||
|
|
c[l] = False
|
||
|
|
assert_(np.all(self.distance(self.data[c], self.x, self.p) >= self.d/(1.+self.eps)))
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_largep_issue9890(ball_consistency):
|
||
|
|
|
||
|
|
# allow some roundoff errors due to numerical issues
|
||
|
|
tol = 1e-13
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
n = 1000
|
||
|
|
m = 2
|
||
|
|
np.random.seed(123)
|
||
|
|
self.data = np.random.randint(100, 1000, size=(n, m))
|
||
|
|
self.T = self.kdtree_type(self.data)
|
||
|
|
self.x = self.data
|
||
|
|
self.p = 100
|
||
|
|
self.eps = 0
|
||
|
|
self.d = 10
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_approx(_Test_random_ball):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.eps = 0.1
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_approx_periodic(_Test_random_ball):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.eps = 0.1
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_far(_Test_random_ball):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.d = 2.
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_far_periodic(_Test_random_ball_periodic):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.d = 2.
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_l1(_Test_random_ball):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.p = 1
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_random_ball_linf(_Test_random_ball):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.p = np.inf
|
||
|
|
|
||
|
|
|
||
|
|
def test_random_ball_vectorized(kdtree_type):
|
||
|
|
n = 20
|
||
|
|
m = 5
|
||
|
|
np.random.seed(1234)
|
||
|
|
T = kdtree_type(np.random.randn(n, m))
|
||
|
|
|
||
|
|
r = T.query_ball_point(np.random.randn(2, 3, m), 1)
|
||
|
|
assert_equal(r.shape, (2, 3))
|
||
|
|
assert_(isinstance(r[0, 0], list))
|
||
|
|
|
||
|
|
|
||
|
|
def test_query_ball_point_multithreading(kdtree_type):
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 5000
|
||
|
|
k = 2
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T = kdtree_type(points)
|
||
|
|
l1 = T.query_ball_point(points, 0.003, workers=1)
|
||
|
|
l2 = T.query_ball_point(points, 0.003, workers=64)
|
||
|
|
l3 = T.query_ball_point(points, 0.003, workers=-1)
|
||
|
|
|
||
|
|
for i in range(n):
|
||
|
|
if l1[i] or l2[i]:
|
||
|
|
assert_array_equal(l1[i], l2[i])
|
||
|
|
|
||
|
|
for i in range(n):
|
||
|
|
if l1[i] or l3[i]:
|
||
|
|
assert_array_equal(l1[i], l3[i])
|
||
|
|
|
||
|
|
|
||
|
|
class two_trees_consistency:
|
||
|
|
|
||
|
|
def distance(self, a, b, p):
|
||
|
|
return minkowski_distance(a, b, p)
|
||
|
|
|
||
|
|
def test_all_in_ball(self):
|
||
|
|
r = self.T1.query_ball_tree(self.T2, self.d, p=self.p, eps=self.eps)
|
||
|
|
for i, l in enumerate(r):
|
||
|
|
for j in l:
|
||
|
|
assert_(self.distance(self.data1[i], self.data2[j], self.p) <= self.d*(1.+self.eps))
|
||
|
|
|
||
|
|
def test_found_all(self):
|
||
|
|
r = self.T1.query_ball_tree(self.T2, self.d, p=self.p, eps=self.eps)
|
||
|
|
for i, l in enumerate(r):
|
||
|
|
c = np.ones(self.T2.n, dtype=bool)
|
||
|
|
c[l] = False
|
||
|
|
assert_(np.all(self.distance(self.data2[c], self.data1[i], self.p) >= self.d/(1.+self.eps)))
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_two_random_trees(two_trees_consistency):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
n = 50
|
||
|
|
m = 4
|
||
|
|
np.random.seed(1234)
|
||
|
|
self.data1 = np.random.randn(n, m)
|
||
|
|
self.T1 = self.kdtree_type(self.data1, leafsize=2)
|
||
|
|
self.data2 = np.random.randn(n, m)
|
||
|
|
self.T2 = self.kdtree_type(self.data2, leafsize=2)
|
||
|
|
self.p = 2.
|
||
|
|
self.eps = 0
|
||
|
|
self.d = 0.2
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_two_random_trees_periodic(two_trees_consistency):
|
||
|
|
def distance(self, a, b, p):
|
||
|
|
return distance_box(a, b, p, 1.0)
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
n = 50
|
||
|
|
m = 4
|
||
|
|
np.random.seed(1234)
|
||
|
|
self.data1 = np.random.uniform(size=(n, m))
|
||
|
|
self.T1 = self.kdtree_type(self.data1, leafsize=2, boxsize=1.0)
|
||
|
|
self.data2 = np.random.uniform(size=(n, m))
|
||
|
|
self.T2 = self.kdtree_type(self.data2, leafsize=2, boxsize=1.0)
|
||
|
|
self.p = 2.
|
||
|
|
self.eps = 0
|
||
|
|
self.d = 0.2
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_two_random_trees_far(_Test_two_random_trees):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.d = 2
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_two_random_trees_far_periodic(_Test_two_random_trees_periodic):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.d = 2
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_two_random_trees_linf(_Test_two_random_trees):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.p = np.inf
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_two_random_trees_linf_periodic(_Test_two_random_trees_periodic):
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
super().setup_method()
|
||
|
|
self.p = np.inf
|
||
|
|
|
||
|
|
|
||
|
|
class Test_rectangle:
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
self.rect = Rectangle([0, 0], [1, 1])
|
||
|
|
|
||
|
|
def test_min_inside(self):
|
||
|
|
assert_almost_equal(self.rect.min_distance_point([0.5, 0.5]), 0)
|
||
|
|
|
||
|
|
def test_min_one_side(self):
|
||
|
|
assert_almost_equal(self.rect.min_distance_point([0.5, 1.5]), 0.5)
|
||
|
|
|
||
|
|
def test_min_two_sides(self):
|
||
|
|
assert_almost_equal(self.rect.min_distance_point([2, 2]), np.sqrt(2))
|
||
|
|
|
||
|
|
def test_max_inside(self):
|
||
|
|
assert_almost_equal(self.rect.max_distance_point([0.5, 0.5]), 1/np.sqrt(2))
|
||
|
|
|
||
|
|
def test_max_one_side(self):
|
||
|
|
assert_almost_equal(self.rect.max_distance_point([0.5, 1.5]), np.hypot(0.5, 1.5))
|
||
|
|
|
||
|
|
def test_max_two_sides(self):
|
||
|
|
assert_almost_equal(self.rect.max_distance_point([2, 2]), 2*np.sqrt(2))
|
||
|
|
|
||
|
|
def test_split(self):
|
||
|
|
less, greater = self.rect.split(0, 0.1)
|
||
|
|
assert_array_equal(less.maxes, [0.1, 1])
|
||
|
|
assert_array_equal(less.mins, [0, 0])
|
||
|
|
assert_array_equal(greater.maxes, [1, 1])
|
||
|
|
assert_array_equal(greater.mins, [0.1, 0])
|
||
|
|
|
||
|
|
|
||
|
|
def test_distance_l2():
|
||
|
|
assert_almost_equal(minkowski_distance([0, 0], [1, 1], 2), np.sqrt(2))
|
||
|
|
|
||
|
|
|
||
|
|
def test_distance_l1():
|
||
|
|
assert_almost_equal(minkowski_distance([0, 0], [1, 1], 1), 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_distance_linf():
|
||
|
|
assert_almost_equal(minkowski_distance([0, 0], [1, 1], np.inf), 1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_distance_vectorization():
|
||
|
|
np.random.seed(1234)
|
||
|
|
x = np.random.randn(10, 1, 3)
|
||
|
|
y = np.random.randn(1, 7, 3)
|
||
|
|
assert_equal(minkowski_distance(x, y).shape, (10, 7))
|
||
|
|
|
||
|
|
|
||
|
|
class count_neighbors_consistency:
|
||
|
|
def test_one_radius(self):
|
||
|
|
r = 0.2
|
||
|
|
assert_equal(self.T1.count_neighbors(self.T2, r),
|
||
|
|
np.sum([len(l) for l in self.T1.query_ball_tree(self.T2, r)]))
|
||
|
|
|
||
|
|
def test_large_radius(self):
|
||
|
|
r = 1000
|
||
|
|
assert_equal(self.T1.count_neighbors(self.T2, r),
|
||
|
|
np.sum([len(l) for l in self.T1.query_ball_tree(self.T2, r)]))
|
||
|
|
|
||
|
|
def test_multiple_radius(self):
|
||
|
|
rs = np.exp(np.linspace(np.log(0.01), np.log(10), 3))
|
||
|
|
results = self.T1.count_neighbors(self.T2, rs)
|
||
|
|
assert_(np.all(np.diff(results) >= 0))
|
||
|
|
for r, result in zip(rs, results):
|
||
|
|
assert_equal(self.T1.count_neighbors(self.T2, r), result)
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_count_neighbors(count_neighbors_consistency):
|
||
|
|
def setup_method(self):
|
||
|
|
n = 50
|
||
|
|
m = 2
|
||
|
|
np.random.seed(1234)
|
||
|
|
self.T1 = self.kdtree_type(np.random.randn(n, m), leafsize=2)
|
||
|
|
self.T2 = self.kdtree_type(np.random.randn(n, m), leafsize=2)
|
||
|
|
|
||
|
|
|
||
|
|
class sparse_distance_matrix_consistency:
|
||
|
|
|
||
|
|
def distance(self, a, b, p):
|
||
|
|
return minkowski_distance(a, b, p)
|
||
|
|
|
||
|
|
def test_consistency_with_neighbors(self):
|
||
|
|
M = self.T1.sparse_distance_matrix(self.T2, self.r)
|
||
|
|
r = self.T1.query_ball_tree(self.T2, self.r)
|
||
|
|
for i, l in enumerate(r):
|
||
|
|
for j in l:
|
||
|
|
assert_almost_equal(M[i, j],
|
||
|
|
self.distance(self.T1.data[i], self.T2.data[j], self.p),
|
||
|
|
decimal=14)
|
||
|
|
for ((i, j), d) in M.items():
|
||
|
|
assert_(j in r[i])
|
||
|
|
|
||
|
|
def test_zero_distance(self):
|
||
|
|
# raises an exception for bug 870 (FIXME: Does it?)
|
||
|
|
self.T1.sparse_distance_matrix(self.T1, self.r)
|
||
|
|
|
||
|
|
def test_consistency(self):
|
||
|
|
# Test consistency with a distance_matrix
|
||
|
|
M1 = self.T1.sparse_distance_matrix(self.T2, self.r)
|
||
|
|
expected = distance_matrix(self.T1.data, self.T2.data)
|
||
|
|
expected[expected > self.r] = 0
|
||
|
|
assert_array_almost_equal(M1.toarray(), expected, decimal=14)
|
||
|
|
|
||
|
|
def test_against_logic_error_regression(self):
|
||
|
|
# regression test for gh-5077 logic error
|
||
|
|
np.random.seed(0)
|
||
|
|
too_many = np.array(np.random.randn(18, 2), dtype=int)
|
||
|
|
tree = self.kdtree_type(
|
||
|
|
too_many, balanced_tree=False, compact_nodes=False)
|
||
|
|
d = tree.sparse_distance_matrix(tree, 3).toarray()
|
||
|
|
assert_array_almost_equal(d, d.T, decimal=14)
|
||
|
|
|
||
|
|
def test_ckdtree_return_types(self):
|
||
|
|
# brute-force reference
|
||
|
|
ref = np.zeros((self.n, self.n))
|
||
|
|
for i in range(self.n):
|
||
|
|
for j in range(self.n):
|
||
|
|
v = self.data1[i, :] - self.data2[j, :]
|
||
|
|
ref[i, j] = np.dot(v, v)
|
||
|
|
ref = np.sqrt(ref)
|
||
|
|
ref[ref > self.r] = 0.
|
||
|
|
# test return type 'dict'
|
||
|
|
dist = np.zeros((self.n, self.n))
|
||
|
|
r = self.T1.sparse_distance_matrix(self.T2, self.r, output_type='dict')
|
||
|
|
for i, j in r.keys():
|
||
|
|
dist[i, j] = r[(i, j)]
|
||
|
|
assert_array_almost_equal(ref, dist, decimal=14)
|
||
|
|
# test return type 'ndarray'
|
||
|
|
dist = np.zeros((self.n, self.n))
|
||
|
|
r = self.T1.sparse_distance_matrix(self.T2, self.r,
|
||
|
|
output_type='ndarray')
|
||
|
|
for k in range(r.shape[0]):
|
||
|
|
i = r['i'][k]
|
||
|
|
j = r['j'][k]
|
||
|
|
v = r['v'][k]
|
||
|
|
dist[i, j] = v
|
||
|
|
assert_array_almost_equal(ref, dist, decimal=14)
|
||
|
|
# test return type 'dok_matrix'
|
||
|
|
r = self.T1.sparse_distance_matrix(self.T2, self.r,
|
||
|
|
output_type='dok_matrix')
|
||
|
|
assert_array_almost_equal(ref, r.toarray(), decimal=14)
|
||
|
|
# test return type 'coo_matrix'
|
||
|
|
r = self.T1.sparse_distance_matrix(self.T2, self.r,
|
||
|
|
output_type='coo_matrix')
|
||
|
|
assert_array_almost_equal(ref, r.toarray(), decimal=14)
|
||
|
|
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_sparse_distance_matrix(sparse_distance_matrix_consistency):
|
||
|
|
def setup_method(self):
|
||
|
|
n = 50
|
||
|
|
m = 4
|
||
|
|
np.random.seed(1234)
|
||
|
|
data1 = np.random.randn(n, m)
|
||
|
|
data2 = np.random.randn(n, m)
|
||
|
|
self.T1 = self.kdtree_type(data1, leafsize=2)
|
||
|
|
self.T2 = self.kdtree_type(data2, leafsize=2)
|
||
|
|
self.r = 0.5
|
||
|
|
self.p = 2
|
||
|
|
self.data1 = data1
|
||
|
|
self.data2 = data2
|
||
|
|
self.n = n
|
||
|
|
self.m = m
|
||
|
|
|
||
|
|
|
||
|
|
def test_distance_matrix():
|
||
|
|
m = 10
|
||
|
|
n = 11
|
||
|
|
k = 4
|
||
|
|
np.random.seed(1234)
|
||
|
|
xs = np.random.randn(m, k)
|
||
|
|
ys = np.random.randn(n, k)
|
||
|
|
ds = distance_matrix(xs, ys)
|
||
|
|
assert_equal(ds.shape, (m, n))
|
||
|
|
for i in range(m):
|
||
|
|
for j in range(n):
|
||
|
|
assert_almost_equal(minkowski_distance(xs[i], ys[j]), ds[i, j])
|
||
|
|
|
||
|
|
|
||
|
|
def test_distance_matrix_looping():
|
||
|
|
m = 10
|
||
|
|
n = 11
|
||
|
|
k = 4
|
||
|
|
np.random.seed(1234)
|
||
|
|
xs = np.random.randn(m, k)
|
||
|
|
ys = np.random.randn(n, k)
|
||
|
|
ds = distance_matrix(xs, ys)
|
||
|
|
dsl = distance_matrix(xs, ys, threshold=1)
|
||
|
|
assert_equal(ds, dsl)
|
||
|
|
|
||
|
|
|
||
|
|
def check_onetree_query(T, d):
|
||
|
|
r = T.query_ball_tree(T, d)
|
||
|
|
s = set()
|
||
|
|
for i, l in enumerate(r):
|
||
|
|
for j in l:
|
||
|
|
if i < j:
|
||
|
|
s.add((i, j))
|
||
|
|
|
||
|
|
assert_(s == T.query_pairs(d))
|
||
|
|
|
||
|
|
def test_onetree_query(kdtree_type):
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 50
|
||
|
|
k = 4
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T = kdtree_type(points)
|
||
|
|
check_onetree_query(T, 0.1)
|
||
|
|
|
||
|
|
points = np.random.randn(3*n, k)
|
||
|
|
points[:n] *= 0.001
|
||
|
|
points[n:2*n] += 2
|
||
|
|
T = kdtree_type(points)
|
||
|
|
check_onetree_query(T, 0.1)
|
||
|
|
check_onetree_query(T, 0.001)
|
||
|
|
check_onetree_query(T, 0.00001)
|
||
|
|
check_onetree_query(T, 1e-6)
|
||
|
|
|
||
|
|
|
||
|
|
def test_query_pairs_single_node(kdtree_type):
|
||
|
|
tree = kdtree_type([[0, 1]])
|
||
|
|
assert_equal(tree.query_pairs(0.5), set())
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_query_pairs(kdtree_type):
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 50
|
||
|
|
k = 2
|
||
|
|
r = 0.1
|
||
|
|
r2 = r**2
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T = kdtree_type(points)
|
||
|
|
# brute force reference
|
||
|
|
brute = set()
|
||
|
|
for i in range(n):
|
||
|
|
for j in range(i+1, n):
|
||
|
|
v = points[i, :] - points[j, :]
|
||
|
|
if np.dot(v, v) <= r2:
|
||
|
|
brute.add((i, j))
|
||
|
|
l0 = sorted(brute)
|
||
|
|
# test default return type
|
||
|
|
s = T.query_pairs(r)
|
||
|
|
l1 = sorted(s)
|
||
|
|
assert_array_equal(l0, l1)
|
||
|
|
# test return type 'set'
|
||
|
|
s = T.query_pairs(r, output_type='set')
|
||
|
|
l1 = sorted(s)
|
||
|
|
assert_array_equal(l0, l1)
|
||
|
|
# test return type 'ndarray'
|
||
|
|
s = set()
|
||
|
|
arr = T.query_pairs(r, output_type='ndarray')
|
||
|
|
for i in range(arr.shape[0]):
|
||
|
|
s.add((int(arr[i, 0]), int(arr[i, 1])))
|
||
|
|
l2 = sorted(s)
|
||
|
|
assert_array_equal(l0, l2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_query_pairs_eps(kdtree_type):
|
||
|
|
spacing = np.sqrt(2)
|
||
|
|
# irrational spacing to have potential rounding errors
|
||
|
|
x_range = np.linspace(0, 3 * spacing, 4)
|
||
|
|
y_range = np.linspace(0, 3 * spacing, 4)
|
||
|
|
xy_array = [(xi, yi) for xi in x_range for yi in y_range]
|
||
|
|
tree = kdtree_type(xy_array)
|
||
|
|
pairs_eps = tree.query_pairs(r=spacing, eps=.1)
|
||
|
|
# result: 24 with eps, 16 without due to rounding
|
||
|
|
pairs = tree.query_pairs(r=spacing * 1.01)
|
||
|
|
# result: 24
|
||
|
|
assert_equal(pairs, pairs_eps)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ball_point_ints(kdtree_type):
|
||
|
|
# Regression test for #1373.
|
||
|
|
x, y = np.mgrid[0:4, 0:4]
|
||
|
|
points = list(zip(x.ravel(), y.ravel()))
|
||
|
|
tree = kdtree_type(points)
|
||
|
|
assert_equal(sorted([4, 8, 9, 12]),
|
||
|
|
sorted(tree.query_ball_point((2, 0), 1)))
|
||
|
|
points = np.asarray(points, dtype=float)
|
||
|
|
tree = kdtree_type(points)
|
||
|
|
assert_equal(sorted([4, 8, 9, 12]),
|
||
|
|
sorted(tree.query_ball_point((2, 0), 1)))
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_comparisons():
|
||
|
|
# Regression test: node comparisons were done wrong in 0.12 w/Py3.
|
||
|
|
nodes = [KDTree.node() for _ in range(3)]
|
||
|
|
assert_equal(sorted(nodes), sorted(nodes[::-1]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_build_modes(kdtree_type):
|
||
|
|
# check if different build modes for KDTree give similar query results
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 5000
|
||
|
|
k = 4
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T1 = kdtree_type(points).query(points, k=5)[-1]
|
||
|
|
T2 = kdtree_type(points, compact_nodes=False).query(points, k=5)[-1]
|
||
|
|
T3 = kdtree_type(points, balanced_tree=False).query(points, k=5)[-1]
|
||
|
|
T4 = kdtree_type(points, compact_nodes=False,
|
||
|
|
balanced_tree=False).query(points, k=5)[-1]
|
||
|
|
assert_array_equal(T1, T2)
|
||
|
|
assert_array_equal(T1, T3)
|
||
|
|
assert_array_equal(T1, T4)
|
||
|
|
|
||
|
|
def test_kdtree_pickle(kdtree_type):
|
||
|
|
# test if it is possible to pickle a KDTree
|
||
|
|
import pickle
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 50
|
||
|
|
k = 4
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T1 = kdtree_type(points)
|
||
|
|
tmp = pickle.dumps(T1)
|
||
|
|
T2 = pickle.loads(tmp)
|
||
|
|
T1 = T1.query(points, k=5)[-1]
|
||
|
|
T2 = T2.query(points, k=5)[-1]
|
||
|
|
assert_array_equal(T1, T2)
|
||
|
|
|
||
|
|
def test_kdtree_pickle_boxsize(kdtree_type):
|
||
|
|
# test if it is possible to pickle a periodic KDTree
|
||
|
|
import pickle
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 50
|
||
|
|
k = 4
|
||
|
|
points = np.random.uniform(size=(n, k))
|
||
|
|
T1 = kdtree_type(points, boxsize=1.0)
|
||
|
|
tmp = pickle.dumps(T1)
|
||
|
|
T2 = pickle.loads(tmp)
|
||
|
|
T1 = T1.query(points, k=5)[-1]
|
||
|
|
T2 = T2.query(points, k=5)[-1]
|
||
|
|
assert_array_equal(T1, T2)
|
||
|
|
|
||
|
|
def test_kdtree_copy_data(kdtree_type):
|
||
|
|
# check if copy_data=True makes the kd-tree
|
||
|
|
# impervious to data corruption by modification of
|
||
|
|
# the data arrray
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 5000
|
||
|
|
k = 4
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T = kdtree_type(points, copy_data=True)
|
||
|
|
q = points.copy()
|
||
|
|
T1 = T.query(q, k=5)[-1]
|
||
|
|
points[...] = np.random.randn(n, k)
|
||
|
|
T2 = T.query(q, k=5)[-1]
|
||
|
|
assert_array_equal(T1, T2)
|
||
|
|
|
||
|
|
def test_ckdtree_parallel(kdtree_type, monkeypatch):
|
||
|
|
# check if parallel=True also generates correct query results
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 5000
|
||
|
|
k = 4
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
T = kdtree_type(points)
|
||
|
|
T1 = T.query(points, k=5, workers=64)[-1]
|
||
|
|
T2 = T.query(points, k=5, workers=-1)[-1]
|
||
|
|
T3 = T.query(points, k=5)[-1]
|
||
|
|
assert_array_equal(T1, T2)
|
||
|
|
assert_array_equal(T1, T3)
|
||
|
|
|
||
|
|
monkeypatch.setattr(os, 'cpu_count', lambda: None)
|
||
|
|
with pytest.raises(NotImplementedError, match="Cannot determine the"):
|
||
|
|
T.query(points, 1, workers=-1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ckdtree_view():
|
||
|
|
# Check that the nodes can be correctly viewed from Python.
|
||
|
|
# This test also sanity checks each node in the cKDTree, and
|
||
|
|
# thus verifies the internal structure of the kd-tree.
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 100
|
||
|
|
k = 4
|
||
|
|
points = np.random.randn(n, k)
|
||
|
|
kdtree = cKDTree(points)
|
||
|
|
|
||
|
|
# walk the whole kd-tree and sanity check each node
|
||
|
|
def recurse_tree(n):
|
||
|
|
assert_(isinstance(n, cKDTreeNode))
|
||
|
|
if n.split_dim == -1:
|
||
|
|
assert_(n.lesser is None)
|
||
|
|
assert_(n.greater is None)
|
||
|
|
assert_(n.indices.shape[0] <= kdtree.leafsize)
|
||
|
|
else:
|
||
|
|
recurse_tree(n.lesser)
|
||
|
|
recurse_tree(n.greater)
|
||
|
|
x = n.lesser.data_points[:, n.split_dim]
|
||
|
|
y = n.greater.data_points[:, n.split_dim]
|
||
|
|
assert_(x.max() < y.min())
|
||
|
|
|
||
|
|
recurse_tree(kdtree.tree)
|
||
|
|
# check that indices are correctly retrieved
|
||
|
|
n = kdtree.tree
|
||
|
|
assert_array_equal(np.sort(n.indices), range(100))
|
||
|
|
# check that data_points are correctly retrieved
|
||
|
|
assert_array_equal(kdtree.data[n.indices, :], n.data_points)
|
||
|
|
|
||
|
|
# KDTree is specialized to type double points, so no need to make
|
||
|
|
# a unit test corresponding to test_ball_point_ints()
|
||
|
|
|
||
|
|
def test_kdtree_list_k(kdtree_type):
|
||
|
|
# check kdtree periodic boundary
|
||
|
|
n = 200
|
||
|
|
m = 2
|
||
|
|
klist = [1, 2, 3]
|
||
|
|
kint = 3
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.uniform(size=(n, m))
|
||
|
|
kdtree = kdtree_type(data, leafsize=1)
|
||
|
|
|
||
|
|
# check agreement between arange(1, k+1) and k
|
||
|
|
dd, ii = kdtree.query(data, klist)
|
||
|
|
dd1, ii1 = kdtree.query(data, kint)
|
||
|
|
assert_equal(dd, dd1)
|
||
|
|
assert_equal(ii, ii1)
|
||
|
|
|
||
|
|
# now check skipping one element
|
||
|
|
klist = np.array([1, 3])
|
||
|
|
kint = 3
|
||
|
|
dd, ii = kdtree.query(data, kint)
|
||
|
|
dd1, ii1 = kdtree.query(data, klist)
|
||
|
|
assert_equal(dd1, dd[..., klist - 1])
|
||
|
|
assert_equal(ii1, ii[..., klist - 1])
|
||
|
|
|
||
|
|
# check k == 1 special case
|
||
|
|
# and k == [1] non-special case
|
||
|
|
dd, ii = kdtree.query(data, 1)
|
||
|
|
dd1, ii1 = kdtree.query(data, [1])
|
||
|
|
assert_equal(len(dd.shape), 1)
|
||
|
|
assert_equal(len(dd1.shape), 2)
|
||
|
|
assert_equal(dd, np.ravel(dd1))
|
||
|
|
assert_equal(ii, np.ravel(ii1))
|
||
|
|
|
||
|
|
def test_kdtree_box(kdtree_type):
|
||
|
|
# check ckdtree periodic boundary
|
||
|
|
n = 2000
|
||
|
|
m = 3
|
||
|
|
k = 3
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.uniform(size=(n, m))
|
||
|
|
kdtree = kdtree_type(data, leafsize=1, boxsize=1.0)
|
||
|
|
|
||
|
|
# use the standard python KDTree for the simulated periodic box
|
||
|
|
kdtree2 = kdtree_type(data, leafsize=1)
|
||
|
|
|
||
|
|
for p in [1, 2, 3.0, np.inf]:
|
||
|
|
dd, ii = kdtree.query(data, k, p=p)
|
||
|
|
|
||
|
|
dd1, ii1 = kdtree.query(data + 1.0, k, p=p)
|
||
|
|
assert_almost_equal(dd, dd1)
|
||
|
|
assert_equal(ii, ii1)
|
||
|
|
|
||
|
|
dd1, ii1 = kdtree.query(data - 1.0, k, p=p)
|
||
|
|
assert_almost_equal(dd, dd1)
|
||
|
|
assert_equal(ii, ii1)
|
||
|
|
|
||
|
|
dd2, ii2 = simulate_periodic_box(kdtree2, data, k, boxsize=1.0, p=p)
|
||
|
|
assert_almost_equal(dd, dd2)
|
||
|
|
assert_equal(ii, ii2)
|
||
|
|
|
||
|
|
def test_kdtree_box_0boxsize(kdtree_type):
|
||
|
|
# check ckdtree periodic boundary that mimics non-periodic
|
||
|
|
n = 2000
|
||
|
|
m = 2
|
||
|
|
k = 3
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.uniform(size=(n, m))
|
||
|
|
kdtree = kdtree_type(data, leafsize=1, boxsize=0.0)
|
||
|
|
|
||
|
|
# use the standard python KDTree for the simulated periodic box
|
||
|
|
kdtree2 = kdtree_type(data, leafsize=1)
|
||
|
|
|
||
|
|
for p in [1, 2, np.inf]:
|
||
|
|
dd, ii = kdtree.query(data, k, p=p)
|
||
|
|
|
||
|
|
dd1, ii1 = kdtree2.query(data, k, p=p)
|
||
|
|
assert_almost_equal(dd, dd1)
|
||
|
|
assert_equal(ii, ii1)
|
||
|
|
|
||
|
|
def test_kdtree_box_upper_bounds(kdtree_type):
|
||
|
|
data = np.linspace(0, 2, 10).reshape(-1, 2)
|
||
|
|
data[:, 1] += 10
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
kdtree_type(data, leafsize=1, boxsize=1.0)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
kdtree_type(data, leafsize=1, boxsize=(0.0, 2.0))
|
||
|
|
# skip a dimension.
|
||
|
|
kdtree_type(data, leafsize=1, boxsize=(2.0, 0.0))
|
||
|
|
|
||
|
|
def test_kdtree_box_lower_bounds(kdtree_type):
|
||
|
|
data = np.linspace(-1, 1, 10)
|
||
|
|
assert_raises(ValueError, kdtree_type, data, leafsize=1, boxsize=1.0)
|
||
|
|
|
||
|
|
def simulate_periodic_box(kdtree, data, k, boxsize, p):
|
||
|
|
dd = []
|
||
|
|
ii = []
|
||
|
|
x = np.arange(3 ** data.shape[1])
|
||
|
|
nn = np.array(np.unravel_index(x, [3] * data.shape[1])).T
|
||
|
|
nn = nn - 1.0
|
||
|
|
for n in nn:
|
||
|
|
image = data + n * 1.0 * boxsize
|
||
|
|
dd2, ii2 = kdtree.query(image, k, p=p)
|
||
|
|
dd2 = dd2.reshape(-1, k)
|
||
|
|
ii2 = ii2.reshape(-1, k)
|
||
|
|
dd.append(dd2)
|
||
|
|
ii.append(ii2)
|
||
|
|
dd = np.concatenate(dd, axis=-1)
|
||
|
|
ii = np.concatenate(ii, axis=-1)
|
||
|
|
|
||
|
|
result = np.empty([len(data), len(nn) * k], dtype=[
|
||
|
|
('ii', 'i8'),
|
||
|
|
('dd', 'f8')])
|
||
|
|
result['ii'][:] = ii
|
||
|
|
result['dd'][:] = dd
|
||
|
|
result.sort(order='dd')
|
||
|
|
return result['dd'][:, :k], result['ii'][:, :k]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.skipif(python_implementation() == 'PyPy',
|
||
|
|
reason="Fails on PyPy CI runs. See #9507")
|
||
|
|
def test_ckdtree_memuse():
|
||
|
|
# unit test adaptation of gh-5630
|
||
|
|
|
||
|
|
# NOTE: this will fail when run via valgrind,
|
||
|
|
# because rss is no longer a reliable memory usage indicator.
|
||
|
|
|
||
|
|
try:
|
||
|
|
import resource
|
||
|
|
except ImportError:
|
||
|
|
# resource is not available on Windows
|
||
|
|
return
|
||
|
|
# Make some data
|
||
|
|
dx, dy = 0.05, 0.05
|
||
|
|
y, x = np.mgrid[slice(1, 5 + dy, dy),
|
||
|
|
slice(1, 5 + dx, dx)]
|
||
|
|
z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)
|
||
|
|
z_copy = np.empty_like(z)
|
||
|
|
z_copy[:] = z
|
||
|
|
# Place FILLVAL in z_copy at random number of random locations
|
||
|
|
FILLVAL = 99.
|
||
|
|
mask = np.random.randint(0, z.size, np.random.randint(50) + 5)
|
||
|
|
z_copy.flat[mask] = FILLVAL
|
||
|
|
igood = np.vstack(np.nonzero(x != FILLVAL)).T
|
||
|
|
ibad = np.vstack(np.nonzero(x == FILLVAL)).T
|
||
|
|
mem_use = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
|
||
|
|
# burn-in
|
||
|
|
for i in range(10):
|
||
|
|
tree = cKDTree(igood)
|
||
|
|
# count memleaks while constructing and querying cKDTree
|
||
|
|
num_leaks = 0
|
||
|
|
for i in range(100):
|
||
|
|
mem_use = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
|
||
|
|
tree = cKDTree(igood)
|
||
|
|
dist, iquery = tree.query(ibad, k=4, p=2)
|
||
|
|
new_mem_use = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
|
||
|
|
if new_mem_use > mem_use:
|
||
|
|
num_leaks += 1
|
||
|
|
# ideally zero leaks, but errors might accidentally happen
|
||
|
|
# outside cKDTree
|
||
|
|
assert_(num_leaks < 10)
|
||
|
|
|
||
|
|
def test_kdtree_weights(kdtree_type):
|
||
|
|
|
||
|
|
data = np.linspace(0, 1, 4).reshape(-1, 1)
|
||
|
|
tree1 = kdtree_type(data, leafsize=1)
|
||
|
|
weights = np.ones(len(data), dtype='f4')
|
||
|
|
|
||
|
|
nw = tree1._build_weights(weights)
|
||
|
|
assert_array_equal(nw, [4, 2, 1, 1, 2, 1, 1])
|
||
|
|
|
||
|
|
assert_raises(ValueError, tree1._build_weights, weights[:-1])
|
||
|
|
|
||
|
|
for i in range(10):
|
||
|
|
# since weights are uniform, these shall agree:
|
||
|
|
c1 = tree1.count_neighbors(tree1, np.linspace(0, 10, i))
|
||
|
|
c2 = tree1.count_neighbors(tree1, np.linspace(0, 10, i),
|
||
|
|
weights=(weights, weights))
|
||
|
|
c3 = tree1.count_neighbors(tree1, np.linspace(0, 10, i),
|
||
|
|
weights=(weights, None))
|
||
|
|
c4 = tree1.count_neighbors(tree1, np.linspace(0, 10, i),
|
||
|
|
weights=(None, weights))
|
||
|
|
tree1.count_neighbors(tree1, np.linspace(0, 10, i),
|
||
|
|
weights=weights)
|
||
|
|
|
||
|
|
assert_array_equal(c1, c2)
|
||
|
|
assert_array_equal(c1, c3)
|
||
|
|
assert_array_equal(c1, c4)
|
||
|
|
|
||
|
|
for i in range(len(data)):
|
||
|
|
# this tests removal of one data point by setting weight to 0
|
||
|
|
w1 = weights.copy()
|
||
|
|
w1[i] = 0
|
||
|
|
data2 = data[w1 != 0]
|
||
|
|
tree2 = kdtree_type(data2)
|
||
|
|
|
||
|
|
c1 = tree1.count_neighbors(tree1, np.linspace(0, 10, 100),
|
||
|
|
weights=(w1, w1))
|
||
|
|
# "c2 is correct"
|
||
|
|
c2 = tree2.count_neighbors(tree2, np.linspace(0, 10, 100))
|
||
|
|
|
||
|
|
assert_array_equal(c1, c2)
|
||
|
|
|
||
|
|
#this asserts for two different trees, singular weights
|
||
|
|
# crashes
|
||
|
|
assert_raises(ValueError, tree1.count_neighbors,
|
||
|
|
tree2, np.linspace(0, 10, 100), weights=w1)
|
||
|
|
|
||
|
|
def test_kdtree_count_neighbous_multiple_r(kdtree_type):
|
||
|
|
n = 2000
|
||
|
|
m = 2
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.normal(size=(n, m))
|
||
|
|
kdtree = kdtree_type(data, leafsize=1)
|
||
|
|
r0 = [0, 0.01, 0.01, 0.02, 0.05]
|
||
|
|
i0 = np.arange(len(r0))
|
||
|
|
n0 = kdtree.count_neighbors(kdtree, r0)
|
||
|
|
nnc = kdtree.count_neighbors(kdtree, r0, cumulative=False)
|
||
|
|
assert_equal(n0, nnc.cumsum())
|
||
|
|
|
||
|
|
for i, r in zip(itertools.permutations(i0),
|
||
|
|
itertools.permutations(r0)):
|
||
|
|
# permute n0 by i and it shall agree
|
||
|
|
n = kdtree.count_neighbors(kdtree, r)
|
||
|
|
assert_array_equal(n, n0[list(i)])
|
||
|
|
|
||
|
|
def test_len0_arrays(kdtree_type):
|
||
|
|
# make sure len-0 arrays are handled correctly
|
||
|
|
# in range queries (gh-5639)
|
||
|
|
np.random.seed(1234)
|
||
|
|
X = np.random.rand(10, 2)
|
||
|
|
Y = np.random.rand(10, 2)
|
||
|
|
tree = kdtree_type(X)
|
||
|
|
# query_ball_point (single)
|
||
|
|
d, i = tree.query([.5, .5], k=1)
|
||
|
|
z = tree.query_ball_point([.5, .5], 0.1*d)
|
||
|
|
assert_array_equal(z, [])
|
||
|
|
# query_ball_point (multiple)
|
||
|
|
d, i = tree.query(Y, k=1)
|
||
|
|
mind = d.min()
|
||
|
|
z = tree.query_ball_point(Y, 0.1*mind)
|
||
|
|
y = np.empty(shape=(10, ), dtype=object)
|
||
|
|
y.fill([])
|
||
|
|
assert_array_equal(y, z)
|
||
|
|
# query_ball_tree
|
||
|
|
other = kdtree_type(Y)
|
||
|
|
y = tree.query_ball_tree(other, 0.1*mind)
|
||
|
|
assert_array_equal(10*[[]], y)
|
||
|
|
# count_neighbors
|
||
|
|
y = tree.count_neighbors(other, 0.1*mind)
|
||
|
|
assert_(y == 0)
|
||
|
|
# sparse_distance_matrix
|
||
|
|
y = tree.sparse_distance_matrix(other, 0.1*mind, output_type='dok_matrix')
|
||
|
|
assert_array_equal(y == np.zeros((10, 10)), True)
|
||
|
|
y = tree.sparse_distance_matrix(other, 0.1*mind, output_type='coo_matrix')
|
||
|
|
assert_array_equal(y == np.zeros((10, 10)), True)
|
||
|
|
y = tree.sparse_distance_matrix(other, 0.1*mind, output_type='dict')
|
||
|
|
assert_equal(y, {})
|
||
|
|
y = tree.sparse_distance_matrix(other, 0.1*mind, output_type='ndarray')
|
||
|
|
_dtype = [('i', np.intp), ('j', np.intp), ('v', np.float64)]
|
||
|
|
res_dtype = np.dtype(_dtype, align=True)
|
||
|
|
z = np.empty(shape=(0, ), dtype=res_dtype)
|
||
|
|
assert_array_equal(y, z)
|
||
|
|
# query_pairs
|
||
|
|
d, i = tree.query(X, k=2)
|
||
|
|
mind = d[:, -1].min()
|
||
|
|
y = tree.query_pairs(0.1*mind, output_type='set')
|
||
|
|
assert_equal(y, set())
|
||
|
|
y = tree.query_pairs(0.1*mind, output_type='ndarray')
|
||
|
|
z = np.empty(shape=(0, 2), dtype=np.intp)
|
||
|
|
assert_array_equal(y, z)
|
||
|
|
|
||
|
|
def test_kdtree_duplicated_inputs(kdtree_type):
|
||
|
|
# check kdtree with duplicated inputs
|
||
|
|
n = 1024
|
||
|
|
for m in range(1, 8):
|
||
|
|
data = np.ones((n, m))
|
||
|
|
data[n//2:] = 2
|
||
|
|
|
||
|
|
for balanced, compact in itertools.product((False, True), repeat=2):
|
||
|
|
kdtree = kdtree_type(data, balanced_tree=balanced,
|
||
|
|
compact_nodes=compact, leafsize=1)
|
||
|
|
assert kdtree.size == 3
|
||
|
|
|
||
|
|
tree = (kdtree.tree if kdtree_type is cKDTree else
|
||
|
|
kdtree.tree._node)
|
||
|
|
|
||
|
|
assert_equal(
|
||
|
|
np.sort(tree.lesser.indices),
|
||
|
|
np.arange(0, n // 2))
|
||
|
|
assert_equal(
|
||
|
|
np.sort(tree.greater.indices),
|
||
|
|
np.arange(n // 2, n))
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_noncumulative_nondecreasing(kdtree_type):
|
||
|
|
# check kdtree with duplicated inputs
|
||
|
|
|
||
|
|
# it shall not divide more than 3 nodes.
|
||
|
|
# root left (1), and right (2)
|
||
|
|
kdtree = kdtree_type([[0]], leafsize=1)
|
||
|
|
|
||
|
|
assert_raises(ValueError, kdtree.count_neighbors,
|
||
|
|
kdtree, [0.1, 0], cumulative=False)
|
||
|
|
|
||
|
|
def test_short_knn(kdtree_type):
|
||
|
|
|
||
|
|
# The test case is based on github: #6425 by @SteveDoyle2
|
||
|
|
|
||
|
|
xyz = np.array([
|
||
|
|
[0., 0., 0.],
|
||
|
|
[1.01, 0., 0.],
|
||
|
|
[0., 1., 0.],
|
||
|
|
[0., 1.01, 0.],
|
||
|
|
[1., 0., 0.],
|
||
|
|
[1., 1., 0.]],
|
||
|
|
dtype='float64')
|
||
|
|
|
||
|
|
ckdt = kdtree_type(xyz)
|
||
|
|
|
||
|
|
deq, ieq = ckdt.query(xyz, k=4, distance_upper_bound=0.2)
|
||
|
|
|
||
|
|
assert_array_almost_equal(deq,
|
||
|
|
[[0., np.inf, np.inf, np.inf],
|
||
|
|
[0., 0.01, np.inf, np.inf],
|
||
|
|
[0., 0.01, np.inf, np.inf],
|
||
|
|
[0., 0.01, np.inf, np.inf],
|
||
|
|
[0., 0.01, np.inf, np.inf],
|
||
|
|
[0., np.inf, np.inf, np.inf]])
|
||
|
|
|
||
|
|
def test_query_ball_point_vector_r(kdtree_type):
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.normal(size=(100, 3))
|
||
|
|
query = np.random.normal(size=(100, 3))
|
||
|
|
tree = kdtree_type(data)
|
||
|
|
d = np.random.uniform(0, 0.3, size=len(query))
|
||
|
|
|
||
|
|
rvector = tree.query_ball_point(query, d)
|
||
|
|
rscalar = [tree.query_ball_point(qi, di) for qi, di in zip(query, d)]
|
||
|
|
for a, b in zip(rvector, rscalar):
|
||
|
|
assert_array_equal(sorted(a), sorted(b))
|
||
|
|
|
||
|
|
def test_query_ball_point_length(kdtree_type):
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.normal(size=(100, 3))
|
||
|
|
query = np.random.normal(size=(100, 3))
|
||
|
|
tree = kdtree_type(data)
|
||
|
|
d = 0.3
|
||
|
|
|
||
|
|
length = tree.query_ball_point(query, d, return_length=True)
|
||
|
|
length2 = [len(ind) for ind in tree.query_ball_point(query, d, return_length=False)]
|
||
|
|
length3 = [len(tree.query_ball_point(qi, d)) for qi in query]
|
||
|
|
length4 = [tree.query_ball_point(qi, d, return_length=True) for qi in query]
|
||
|
|
assert_array_equal(length, length2)
|
||
|
|
assert_array_equal(length, length3)
|
||
|
|
assert_array_equal(length, length4)
|
||
|
|
|
||
|
|
def test_discontiguous(kdtree_type):
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
data = np.random.normal(size=(100, 3))
|
||
|
|
d_contiguous = np.arange(100) * 0.04
|
||
|
|
d_discontiguous = np.ascontiguousarray(
|
||
|
|
np.arange(100)[::-1] * 0.04)[::-1]
|
||
|
|
query_contiguous = np.random.normal(size=(100, 3))
|
||
|
|
query_discontiguous = np.ascontiguousarray(query_contiguous.T).T
|
||
|
|
assert query_discontiguous.strides[-1] != query_contiguous.strides[-1]
|
||
|
|
assert d_discontiguous.strides[-1] != d_contiguous.strides[-1]
|
||
|
|
|
||
|
|
tree = kdtree_type(data)
|
||
|
|
|
||
|
|
length1 = tree.query_ball_point(query_contiguous,
|
||
|
|
d_contiguous, return_length=True)
|
||
|
|
length2 = tree.query_ball_point(query_discontiguous,
|
||
|
|
d_discontiguous, return_length=True)
|
||
|
|
|
||
|
|
assert_array_equal(length1, length2)
|
||
|
|
|
||
|
|
d1, i1 = tree.query(query_contiguous, 1)
|
||
|
|
d2, i2 = tree.query(query_discontiguous, 1)
|
||
|
|
|
||
|
|
assert_array_equal(d1, d2)
|
||
|
|
assert_array_equal(i1, i2)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("balanced_tree, compact_nodes",
|
||
|
|
[(True, False),
|
||
|
|
(True, True),
|
||
|
|
(False, False),
|
||
|
|
(False, True)])
|
||
|
|
def test_kdtree_empty_input(kdtree_type, balanced_tree, compact_nodes):
|
||
|
|
# https://github.com/scipy/scipy/issues/5040
|
||
|
|
np.random.seed(1234)
|
||
|
|
empty_v3 = np.empty(shape=(0, 3))
|
||
|
|
query_v3 = np.ones(shape=(1, 3))
|
||
|
|
query_v2 = np.ones(shape=(2, 3))
|
||
|
|
|
||
|
|
tree = kdtree_type(empty_v3, balanced_tree=balanced_tree,
|
||
|
|
compact_nodes=compact_nodes)
|
||
|
|
length = tree.query_ball_point(query_v3, 0.3, return_length=True)
|
||
|
|
assert length == 0
|
||
|
|
|
||
|
|
dd, ii = tree.query(query_v2, 2)
|
||
|
|
assert ii.shape == (2, 2)
|
||
|
|
assert dd.shape == (2, 2)
|
||
|
|
assert np.isinf(dd).all()
|
||
|
|
|
||
|
|
N = tree.count_neighbors(tree, [0, 1])
|
||
|
|
assert_array_equal(N, [0, 0])
|
||
|
|
|
||
|
|
M = tree.sparse_distance_matrix(tree, 0.3)
|
||
|
|
assert M.shape == (0, 0)
|
||
|
|
|
||
|
|
@KDTreeTest
|
||
|
|
class _Test_sorted_query_ball_point:
|
||
|
|
def setup_method(self):
|
||
|
|
np.random.seed(1234)
|
||
|
|
self.x = np.random.randn(100, 1)
|
||
|
|
self.ckdt = self.kdtree_type(self.x)
|
||
|
|
|
||
|
|
def test_return_sorted_True(self):
|
||
|
|
idxs_list = self.ckdt.query_ball_point(self.x, 1., return_sorted=True)
|
||
|
|
for idxs in idxs_list:
|
||
|
|
assert_array_equal(idxs, sorted(idxs))
|
||
|
|
|
||
|
|
for xi in self.x:
|
||
|
|
idxs = self.ckdt.query_ball_point(xi, 1., return_sorted=True)
|
||
|
|
assert_array_equal(idxs, sorted(idxs))
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|
|
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def test_return_sorted_None(self):
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|
"""Previous behavior was to sort the returned indices if there were
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|
|
multiple points per query but not sort them if there was a single point
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|
|
per query."""
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|
idxs_list = self.ckdt.query_ball_point(self.x, 1.)
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|
for idxs in idxs_list:
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|
assert_array_equal(idxs, sorted(idxs))
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|
|
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||
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|
idxs_list_single = [self.ckdt.query_ball_point(xi, 1.) for xi in self.x]
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|
idxs_list_False = self.ckdt.query_ball_point(self.x, 1., return_sorted=False)
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|
|
for idxs0, idxs1 in zip(idxs_list_False, idxs_list_single):
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|
assert_array_equal(idxs0, idxs1)
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||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_complex_data():
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|
# Test that KDTree rejects complex input points (gh-9108)
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|
|
points = np.random.rand(10, 2).view(complex)
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||
|
|
|
||
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|
with pytest.raises(TypeError, match="complex data"):
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|
|
t = KDTree(points)
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||
|
|
|
||
|
|
t = KDTree(points.real)
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||
|
|
|
||
|
|
with pytest.raises(TypeError, match="complex data"):
|
||
|
|
t.query(points)
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||
|
|
|
||
|
|
with pytest.raises(TypeError, match="complex data"):
|
||
|
|
t.query_ball_point(points, r=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_tree_access():
|
||
|
|
# Test KDTree.tree can be used to traverse the KDTree
|
||
|
|
np.random.seed(1234)
|
||
|
|
points = np.random.rand(100, 4)
|
||
|
|
t = KDTree(points)
|
||
|
|
root = t.tree
|
||
|
|
|
||
|
|
assert isinstance(root, KDTree.innernode)
|
||
|
|
assert root.children == points.shape[0]
|
||
|
|
|
||
|
|
# Visit the tree and assert some basic properties for each node
|
||
|
|
nodes = [root]
|
||
|
|
while nodes:
|
||
|
|
n = nodes.pop(-1)
|
||
|
|
|
||
|
|
if isinstance(n, KDTree.leafnode):
|
||
|
|
assert isinstance(n.children, int)
|
||
|
|
assert n.children == len(n.idx)
|
||
|
|
assert_array_equal(points[n.idx], n._node.data_points)
|
||
|
|
else:
|
||
|
|
assert isinstance(n, KDTree.innernode)
|
||
|
|
assert isinstance(n.split_dim, int)
|
||
|
|
assert 0 <= n.split_dim < t.m
|
||
|
|
assert isinstance(n.split, float)
|
||
|
|
assert isinstance(n.children, int)
|
||
|
|
assert n.children == n.less.children + n.greater.children
|
||
|
|
nodes.append(n.greater)
|
||
|
|
nodes.append(n.less)
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_attributes():
|
||
|
|
# Test KDTree's attributes are available
|
||
|
|
np.random.seed(1234)
|
||
|
|
points = np.random.rand(100, 4)
|
||
|
|
t = KDTree(points)
|
||
|
|
|
||
|
|
assert isinstance(t.m, int)
|
||
|
|
assert t.n == points.shape[0]
|
||
|
|
|
||
|
|
assert isinstance(t.n, int)
|
||
|
|
assert t.m == points.shape[1]
|
||
|
|
|
||
|
|
assert isinstance(t.leafsize, int)
|
||
|
|
assert t.leafsize == 10
|
||
|
|
|
||
|
|
assert_array_equal(t.maxes, np.amax(points, axis=0))
|
||
|
|
assert_array_equal(t.mins, np.amin(points, axis=0))
|
||
|
|
assert t.data is points
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("kdtree_class", [KDTree, cKDTree])
|
||
|
|
def test_kdtree_count_neighbors_weighted(kdtree_class):
|
||
|
|
np.random.seed(1234)
|
||
|
|
r = np.arange(0.05, 1, 0.05)
|
||
|
|
|
||
|
|
A = np.random.random(21).reshape((7,3))
|
||
|
|
B = np.random.random(45).reshape((15,3))
|
||
|
|
|
||
|
|
wA = np.random.random(7)
|
||
|
|
wB = np.random.random(15)
|
||
|
|
|
||
|
|
kdA = kdtree_class(A)
|
||
|
|
kdB = kdtree_class(B)
|
||
|
|
|
||
|
|
nAB = kdA.count_neighbors(kdB, r, cumulative=False, weights=(wA,wB))
|
||
|
|
|
||
|
|
# Compare against brute-force
|
||
|
|
weights = wA[None, :] * wB[:, None]
|
||
|
|
dist = np.linalg.norm(A[None, :, :] - B[:, None, :], axis=-1)
|
||
|
|
expect = [np.sum(weights[(prev_radius < dist) & (dist <= radius)])
|
||
|
|
for prev_radius, radius in zip(itertools.chain([0], r[:-1]), r)]
|
||
|
|
assert_allclose(nAB, expect)
|
||
|
|
|
||
|
|
|
||
|
|
def test_kdtree_nan():
|
||
|
|
vals = [1, 5, -10, 7, -4, -16, -6, 6, 3, -11]
|
||
|
|
n = len(vals)
|
||
|
|
data = np.concatenate([vals, np.full(n, np.nan)])[:, None]
|
||
|
|
|
||
|
|
query_with_nans = KDTree(data).query_pairs(2)
|
||
|
|
query_without_nans = KDTree(data[:n]).query_pairs(2)
|
||
|
|
assert query_with_nans == query_without_nans
|