432 lines
16 KiB
Python
432 lines
16 KiB
Python
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import pytest
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import numpy as np
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from scipy.optimize import quadratic_assignment, OptimizeWarning
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from scipy.optimize._qap import _calc_score as _score
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from numpy.testing import assert_equal, assert_, assert_warns
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################
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# Common Tests #
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################
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def chr12c():
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A = [
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[0, 90, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[90, 0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 0],
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[10, 0, 0, 0, 43, 0, 0, 0, 0, 0, 0, 0],
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[0, 23, 0, 0, 0, 88, 0, 0, 0, 0, 0, 0],
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[0, 0, 43, 0, 0, 0, 26, 0, 0, 0, 0, 0],
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[0, 0, 0, 88, 0, 0, 0, 16, 0, 0, 0, 0],
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[0, 0, 0, 0, 26, 0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 16, 0, 0, 0, 96, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 29, 0],
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[0, 0, 0, 0, 0, 0, 0, 96, 0, 0, 0, 37],
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[0, 0, 0, 0, 0, 0, 0, 0, 29, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 0],
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]
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B = [
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[0, 36, 54, 26, 59, 72, 9, 34, 79, 17, 46, 95],
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[36, 0, 73, 35, 90, 58, 30, 78, 35, 44, 79, 36],
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[54, 73, 0, 21, 10, 97, 58, 66, 69, 61, 54, 63],
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[26, 35, 21, 0, 93, 12, 46, 40, 37, 48, 68, 85],
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[59, 90, 10, 93, 0, 64, 5, 29, 76, 16, 5, 76],
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[72, 58, 97, 12, 64, 0, 96, 55, 38, 54, 0, 34],
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[9, 30, 58, 46, 5, 96, 0, 83, 35, 11, 56, 37],
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[34, 78, 66, 40, 29, 55, 83, 0, 44, 12, 15, 80],
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[79, 35, 69, 37, 76, 38, 35, 44, 0, 64, 39, 33],
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[17, 44, 61, 48, 16, 54, 11, 12, 64, 0, 70, 86],
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[46, 79, 54, 68, 5, 0, 56, 15, 39, 70, 0, 18],
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[95, 36, 63, 85, 76, 34, 37, 80, 33, 86, 18, 0],
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]
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A, B = np.array(A), np.array(B)
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n = A.shape[0]
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opt_perm = np.array([7, 5, 1, 3, 10, 4, 8, 6, 9, 11, 2, 12]) - [1] * n
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return A, B, opt_perm
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class QAPCommonTests:
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"""
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Base class for `quadratic_assignment` tests.
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"""
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def setup_method(self):
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np.random.seed(0)
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# Test global optima of problem from Umeyama IVB
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# https://pcl.sitehost.iu.edu/rgoldsto/papers/weighted%20graph%20match2.pdf
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# Graph matching maximum is in the paper
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# QAP minimum determined by brute force
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def test_accuracy_1(self):
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# besides testing accuracy, check that A and B can be lists
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A = [[0, 3, 4, 2],
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[0, 0, 1, 2],
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[1, 0, 0, 1],
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[0, 0, 1, 0]]
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B = [[0, 4, 2, 4],
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[0, 0, 1, 0],
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[0, 2, 0, 2],
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[0, 1, 2, 0]]
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0, "maximize": False})
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assert_equal(res.fun, 10)
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assert_equal(res.col_ind, np.array([1, 2, 3, 0]))
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0, "maximize": True})
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if self.method == 'faq':
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# Global optimum is 40, but FAQ gets 37
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assert_equal(res.fun, 37)
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assert_equal(res.col_ind, np.array([0, 2, 3, 1]))
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else:
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assert_equal(res.fun, 40)
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assert_equal(res.col_ind, np.array([0, 3, 1, 2]))
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0, "maximize": True})
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# Test global optima of problem from Umeyama IIIB
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# https://pcl.sitehost.iu.edu/rgoldsto/papers/weighted%20graph%20match2.pdf
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# Graph matching maximum is in the paper
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# QAP minimum determined by brute force
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def test_accuracy_2(self):
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A = np.array([[0, 5, 8, 6],
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[5, 0, 5, 1],
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[8, 5, 0, 2],
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[6, 1, 2, 0]])
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B = np.array([[0, 1, 8, 4],
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[1, 0, 5, 2],
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[8, 5, 0, 5],
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[4, 2, 5, 0]])
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0, "maximize": False})
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if self.method == 'faq':
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# Global optimum is 176, but FAQ gets 178
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assert_equal(res.fun, 178)
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assert_equal(res.col_ind, np.array([1, 0, 3, 2]))
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else:
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assert_equal(res.fun, 176)
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assert_equal(res.col_ind, np.array([1, 2, 3, 0]))
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0, "maximize": True})
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assert_equal(res.fun, 286)
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assert_equal(res.col_ind, np.array([2, 3, 0, 1]))
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def test_accuracy_3(self):
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A, B, opt_perm = chr12c()
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# basic minimization
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0})
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assert_(11156 <= res.fun < 21000)
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assert_equal(res.fun, _score(A, B, res.col_ind))
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# basic maximization
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res = quadratic_assignment(A, B, method=self.method,
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options={"rng": 0, 'maximize': True})
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assert_(74000 <= res.fun < 85000)
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assert_equal(res.fun, _score(A, B, res.col_ind))
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# check ofv with strictly partial match
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seed_cost = np.array([4, 8, 10])
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seed = np.asarray([seed_cost, opt_perm[seed_cost]]).T
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res = quadratic_assignment(A, B, method=self.method,
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options={'partial_match': seed})
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assert_(11156 <= res.fun < 21000)
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assert_equal(res.col_ind[seed_cost], opt_perm[seed_cost])
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# check performance when partial match is the global optimum
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seed = np.asarray([np.arange(len(A)), opt_perm]).T
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res = quadratic_assignment(A, B, method=self.method,
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options={'partial_match': seed})
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assert_equal(res.col_ind, seed[:, 1].T)
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assert_equal(res.fun, 11156)
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assert_equal(res.nit, 0)
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# check performance with zero sized matrix inputs
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empty = np.empty((0, 0))
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res = quadratic_assignment(empty, empty, method=self.method,
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options={"rng": 0})
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assert_equal(res.nit, 0)
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assert_equal(res.fun, 0)
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def test_unknown_options(self):
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A, B, opt_perm = chr12c()
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def f():
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quadratic_assignment(A, B, method=self.method,
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options={"ekki-ekki": True})
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assert_warns(OptimizeWarning, f)
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class TestFAQ(QAPCommonTests):
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method = "faq"
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def test_options(self):
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# cost and distance matrices of QAPLIB instance chr12c
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A, B, opt_perm = chr12c()
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n = len(A)
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# check that max_iter is obeying with low input value
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res = quadratic_assignment(A, B,
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options={'maxiter': 5})
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assert_equal(res.nit, 5)
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# test with shuffle
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res = quadratic_assignment(A, B,
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options={'shuffle_input': True})
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assert_(11156 <= res.fun < 21000)
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# test with randomized init
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res = quadratic_assignment(A, B,
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options={'rng': 1, 'P0': "randomized"})
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assert_(11156 <= res.fun < 21000)
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# check with specified P0
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K = np.ones((n, n)) / float(n)
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K = _doubly_stochastic(K)
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res = quadratic_assignment(A, B,
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options={'P0': K})
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assert_(11156 <= res.fun < 21000)
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def test_specific_input_validation(self):
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A = np.identity(2)
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B = A
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# method is implicitly faq
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# ValueError Checks: making sure single value parameters are of
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# correct value
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with pytest.raises(ValueError, match="Invalid 'P0' parameter"):
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quadratic_assignment(A, B, options={'P0': "random"})
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with pytest.raises(
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ValueError, match="'maxiter' must be a positive integer"):
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quadratic_assignment(A, B, options={'maxiter': -1})
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with pytest.raises(ValueError, match="'tol' must be a positive float"):
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quadratic_assignment(A, B, options={'tol': -1})
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# TypeError Checks: making sure single value parameters are of
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# correct type
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with pytest.raises(TypeError):
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quadratic_assignment(A, B, options={'maxiter': 1.5})
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# test P0 matrix input
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with pytest.raises(
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ValueError,
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match="`P0` matrix must have shape m' x m', where m'=n-m"):
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quadratic_assignment(
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np.identity(4), np.identity(4),
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options={'P0': np.ones((3, 3))}
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)
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K = [[0.4, 0.2, 0.3],
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[0.3, 0.6, 0.2],
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[0.2, 0.2, 0.7]]
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# matrix that isn't quite doubly stochastic
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with pytest.raises(
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ValueError, match="`P0` matrix must be doubly stochastic"):
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quadratic_assignment(
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np.identity(3), np.identity(3), options={'P0': K}
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)
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class Test2opt(QAPCommonTests):
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method = "2opt"
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def test_deterministic(self):
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# np.random.seed(0) executes before every method
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n = 20
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A = np.random.rand(n, n)
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B = np.random.rand(n, n)
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res1 = quadratic_assignment(A, B, method=self.method)
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np.random.seed(0)
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A = np.random.rand(n, n)
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B = np.random.rand(n, n)
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res2 = quadratic_assignment(A, B, method=self.method)
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assert_equal(res1.nit, res2.nit)
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def test_partial_guess(self):
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n = 5
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A = np.random.rand(n, n)
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B = np.random.rand(n, n)
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res1 = quadratic_assignment(A, B, method=self.method,
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options={'rng': 0})
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guess = np.array([np.arange(5), res1.col_ind]).T
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res2 = quadratic_assignment(A, B, method=self.method,
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options={'rng': 0, 'partial_guess': guess})
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fix = [2, 4]
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match = np.array([np.arange(5)[fix], res1.col_ind[fix]]).T
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res3 = quadratic_assignment(A, B, method=self.method,
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options={'rng': 0, 'partial_guess': guess,
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'partial_match': match})
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assert_(res1.nit != n*(n+1)/2)
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assert_equal(res2.nit, n*(n+1)/2) # tests each swap exactly once
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assert_equal(res3.nit, (n-2)*(n-1)/2) # tests free swaps exactly once
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def test_specific_input_validation(self):
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# can't have more seed nodes than cost/dist nodes
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_rm = _range_matrix
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with pytest.raises(
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ValueError,
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match="`partial_guess` can have only as many entries as"):
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quadratic_assignment(np.identity(3), np.identity(3),
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method=self.method,
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options={'partial_guess': _rm(5, 2)})
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# test for only two seed columns
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with pytest.raises(
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ValueError, match="`partial_guess` must have two columns"):
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quadratic_assignment(
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np.identity(3), np.identity(3), method=self.method,
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options={'partial_guess': _range_matrix(2, 3)}
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)
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# test that seed has no more than two dimensions
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with pytest.raises(
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ValueError, match="`partial_guess` must have exactly two"):
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quadratic_assignment(
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np.identity(3), np.identity(3), method=self.method,
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options={'partial_guess': np.random.rand(3, 2, 2)}
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)
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# seeds cannot be negative valued
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with pytest.raises(
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ValueError, match="`partial_guess` must contain only pos"):
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quadratic_assignment(
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np.identity(3), np.identity(3), method=self.method,
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options={'partial_guess': -1 * _range_matrix(2, 2)}
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)
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# seeds can't have values greater than number of nodes
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with pytest.raises(
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ValueError,
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match="`partial_guess` entries must be less than number"):
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quadratic_assignment(
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np.identity(5), np.identity(5), method=self.method,
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options={'partial_guess': 2 * _range_matrix(4, 2)}
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)
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# columns of seed matrix must be unique
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with pytest.raises(
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ValueError,
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match="`partial_guess` column entries must be unique"):
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quadratic_assignment(
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np.identity(3), np.identity(3), method=self.method,
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options={'partial_guess': np.ones((2, 2))}
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)
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class TestQAPOnce():
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def setup_method(self):
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np.random.seed(0)
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# these don't need to be repeated for each method
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def test_common_input_validation(self):
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# test that non square matrices return error
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with pytest.raises(ValueError, match="`A` must be square"):
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quadratic_assignment(
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|
|
np.random.random((3, 4)),
|
||
|
|
np.random.random((3, 3)),
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match="`B` must be square"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.random.random((3, 3)),
|
||
|
|
np.random.random((3, 4)),
|
||
|
|
)
|
||
|
|
# test that cost and dist matrices have no more than two dimensions
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError, match="`A` and `B` must have exactly two"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.random.random((3, 3, 3)),
|
||
|
|
np.random.random((3, 3, 3)),
|
||
|
|
)
|
||
|
|
# test that cost and dist matrices of different sizes return error
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError,
|
||
|
|
match="`A` and `B` matrices must be of equal size"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.random.random((3, 3)),
|
||
|
|
np.random.random((4, 4)),
|
||
|
|
)
|
||
|
|
# can't have more seed nodes than cost/dist nodes
|
||
|
|
_rm = _range_matrix
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError,
|
||
|
|
match="`partial_match` can have only as many seeds as"):
|
||
|
|
quadratic_assignment(np.identity(3), np.identity(3),
|
||
|
|
options={'partial_match': _rm(5, 2)})
|
||
|
|
# test for only two seed columns
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError, match="`partial_match` must have two columns"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.identity(3), np.identity(3),
|
||
|
|
options={'partial_match': _range_matrix(2, 3)}
|
||
|
|
)
|
||
|
|
# test that seed has no more than two dimensions
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError, match="`partial_match` must have exactly two"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.identity(3), np.identity(3),
|
||
|
|
options={'partial_match': np.random.rand(3, 2, 2)}
|
||
|
|
)
|
||
|
|
# seeds cannot be negative valued
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError, match="`partial_match` must contain only pos"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.identity(3), np.identity(3),
|
||
|
|
options={'partial_match': -1 * _range_matrix(2, 2)}
|
||
|
|
)
|
||
|
|
# seeds can't have values greater than number of nodes
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError,
|
||
|
|
match="`partial_match` entries must be less than number"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.identity(5), np.identity(5),
|
||
|
|
options={'partial_match': 2 * _range_matrix(4, 2)}
|
||
|
|
)
|
||
|
|
# columns of seed matrix must be unique
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError,
|
||
|
|
match="`partial_match` column entries must be unique"):
|
||
|
|
quadratic_assignment(
|
||
|
|
np.identity(3), np.identity(3),
|
||
|
|
options={'partial_match': np.ones((2, 2))}
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _range_matrix(a, b):
|
||
|
|
mat = np.zeros((a, b))
|
||
|
|
for i in range(b):
|
||
|
|
mat[:, i] = np.arange(a)
|
||
|
|
return mat
|
||
|
|
|
||
|
|
|
||
|
|
def _doubly_stochastic(P, tol=1e-3):
|
||
|
|
# cleaner implementation of btaba/sinkhorn_knopp
|
||
|
|
|
||
|
|
max_iter = 1000
|
||
|
|
c = 1 / P.sum(axis=0)
|
||
|
|
r = 1 / (P @ c)
|
||
|
|
P_eps = P
|
||
|
|
|
||
|
|
for it in range(max_iter):
|
||
|
|
if ((np.abs(P_eps.sum(axis=1) - 1) < tol).all() and
|
||
|
|
(np.abs(P_eps.sum(axis=0) - 1) < tol).all()):
|
||
|
|
# All column/row sums ~= 1 within threshold
|
||
|
|
break
|
||
|
|
|
||
|
|
c = 1 / (r @ P)
|
||
|
|
r = 1 / (P @ c)
|
||
|
|
P_eps = r[:, None] * P * c
|
||
|
|
|
||
|
|
return P_eps
|