import pytest import numpy as np from scipy.optimize import quadratic_assignment, OptimizeWarning from scipy.optimize._qap import _calc_score as _score from numpy.testing import assert_equal, assert_, assert_warns ################ # Common Tests # ################ def chr12c(): A = [ [0, 90, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0], [90, 0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 0], [10, 0, 0, 0, 43, 0, 0, 0, 0, 0, 0, 0], [0, 23, 0, 0, 0, 88, 0, 0, 0, 0, 0, 0], [0, 0, 43, 0, 0, 0, 26, 0, 0, 0, 0, 0], [0, 0, 0, 88, 0, 0, 0, 16, 0, 0, 0, 0], [0, 0, 0, 0, 26, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 16, 0, 0, 0, 96, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 29, 0], [0, 0, 0, 0, 0, 0, 0, 96, 0, 0, 0, 37], [0, 0, 0, 0, 0, 0, 0, 0, 29, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 0], ] B = [ [0, 36, 54, 26, 59, 72, 9, 34, 79, 17, 46, 95], [36, 0, 73, 35, 90, 58, 30, 78, 35, 44, 79, 36], [54, 73, 0, 21, 10, 97, 58, 66, 69, 61, 54, 63], [26, 35, 21, 0, 93, 12, 46, 40, 37, 48, 68, 85], [59, 90, 10, 93, 0, 64, 5, 29, 76, 16, 5, 76], [72, 58, 97, 12, 64, 0, 96, 55, 38, 54, 0, 34], [9, 30, 58, 46, 5, 96, 0, 83, 35, 11, 56, 37], [34, 78, 66, 40, 29, 55, 83, 0, 44, 12, 15, 80], [79, 35, 69, 37, 76, 38, 35, 44, 0, 64, 39, 33], [17, 44, 61, 48, 16, 54, 11, 12, 64, 0, 70, 86], [46, 79, 54, 68, 5, 0, 56, 15, 39, 70, 0, 18], [95, 36, 63, 85, 76, 34, 37, 80, 33, 86, 18, 0], ] A, B = np.array(A), np.array(B) n = A.shape[0] opt_perm = np.array([7, 5, 1, 3, 10, 4, 8, 6, 9, 11, 2, 12]) - [1] * n return A, B, opt_perm class QAPCommonTests: """ Base class for `quadratic_assignment` tests. """ def setup_method(self): np.random.seed(0) # Test global optima of problem from Umeyama IVB # https://pcl.sitehost.iu.edu/rgoldsto/papers/weighted%20graph%20match2.pdf # Graph matching maximum is in the paper # QAP minimum determined by brute force def test_accuracy_1(self): # besides testing accuracy, check that A and B can be lists A = [[0, 3, 4, 2], [0, 0, 1, 2], [1, 0, 0, 1], [0, 0, 1, 0]] B = [[0, 4, 2, 4], [0, 0, 1, 0], [0, 2, 0, 2], [0, 1, 2, 0]] res = quadratic_assignment(A, B, method=self.method, options={"rng": 0, "maximize": False}) assert_equal(res.fun, 10) assert_equal(res.col_ind, np.array([1, 2, 3, 0])) res = quadratic_assignment(A, B, method=self.method, options={"rng": 0, "maximize": True}) if self.method == 'faq': # Global optimum is 40, but FAQ gets 37 assert_equal(res.fun, 37) assert_equal(res.col_ind, np.array([0, 2, 3, 1])) else: assert_equal(res.fun, 40) assert_equal(res.col_ind, np.array([0, 3, 1, 2])) res = quadratic_assignment(A, B, method=self.method, options={"rng": 0, "maximize": True}) # Test global optima of problem from Umeyama IIIB # https://pcl.sitehost.iu.edu/rgoldsto/papers/weighted%20graph%20match2.pdf # Graph matching maximum is in the paper # QAP minimum determined by brute force def test_accuracy_2(self): A = np.array([[0, 5, 8, 6], [5, 0, 5, 1], [8, 5, 0, 2], [6, 1, 2, 0]]) B = np.array([[0, 1, 8, 4], [1, 0, 5, 2], [8, 5, 0, 5], [4, 2, 5, 0]]) res = quadratic_assignment(A, B, method=self.method, options={"rng": 0, "maximize": False}) if self.method == 'faq': # Global optimum is 176, but FAQ gets 178 assert_equal(res.fun, 178) assert_equal(res.col_ind, np.array([1, 0, 3, 2])) else: assert_equal(res.fun, 176) assert_equal(res.col_ind, np.array([1, 2, 3, 0])) res = quadratic_assignment(A, B, method=self.method, options={"rng": 0, "maximize": True}) assert_equal(res.fun, 286) assert_equal(res.col_ind, np.array([2, 3, 0, 1])) def test_accuracy_3(self): A, B, opt_perm = chr12c() # basic minimization res = quadratic_assignment(A, B, method=self.method, options={"rng": 0}) assert_(11156 <= res.fun < 21000) assert_equal(res.fun, _score(A, B, res.col_ind)) # basic maximization res = quadratic_assignment(A, B, method=self.method, options={"rng": 0, 'maximize': True}) assert_(74000 <= res.fun < 85000) assert_equal(res.fun, _score(A, B, res.col_ind)) # check ofv with strictly partial match seed_cost = np.array([4, 8, 10]) seed = np.asarray([seed_cost, opt_perm[seed_cost]]).T res = quadratic_assignment(A, B, method=self.method, options={'partial_match': seed}) assert_(11156 <= res.fun < 21000) assert_equal(res.col_ind[seed_cost], opt_perm[seed_cost]) # check performance when partial match is the global optimum seed = np.asarray([np.arange(len(A)), opt_perm]).T res = quadratic_assignment(A, B, method=self.method, options={'partial_match': seed}) assert_equal(res.col_ind, seed[:, 1].T) assert_equal(res.fun, 11156) assert_equal(res.nit, 0) # check performance with zero sized matrix inputs empty = np.empty((0, 0)) res = quadratic_assignment(empty, empty, method=self.method, options={"rng": 0}) assert_equal(res.nit, 0) assert_equal(res.fun, 0) def test_unknown_options(self): A, B, opt_perm = chr12c() def f(): quadratic_assignment(A, B, method=self.method, options={"ekki-ekki": True}) assert_warns(OptimizeWarning, f) class TestFAQ(QAPCommonTests): method = "faq" def test_options(self): # cost and distance matrices of QAPLIB instance chr12c A, B, opt_perm = chr12c() n = len(A) # check that max_iter is obeying with low input value res = quadratic_assignment(A, B, options={'maxiter': 5}) assert_equal(res.nit, 5) # test with shuffle res = quadratic_assignment(A, B, options={'shuffle_input': True}) assert_(11156 <= res.fun < 21000) # test with randomized init res = quadratic_assignment(A, B, options={'rng': 1, 'P0': "randomized"}) assert_(11156 <= res.fun < 21000) # check with specified P0 K = np.ones((n, n)) / float(n) K = _doubly_stochastic(K) res = quadratic_assignment(A, B, options={'P0': K}) assert_(11156 <= res.fun < 21000) def test_specific_input_validation(self): A = np.identity(2) B = A # method is implicitly faq # ValueError Checks: making sure single value parameters are of # correct value with pytest.raises(ValueError, match="Invalid 'P0' parameter"): quadratic_assignment(A, B, options={'P0': "random"}) with pytest.raises( ValueError, match="'maxiter' must be a positive integer"): quadratic_assignment(A, B, options={'maxiter': -1}) with pytest.raises(ValueError, match="'tol' must be a positive float"): quadratic_assignment(A, B, options={'tol': -1}) # TypeError Checks: making sure single value parameters are of # correct type with pytest.raises(TypeError): quadratic_assignment(A, B, options={'maxiter': 1.5}) # test P0 matrix input with pytest.raises( ValueError, match="`P0` matrix must have shape m' x m', where m'=n-m"): quadratic_assignment( np.identity(4), np.identity(4), options={'P0': np.ones((3, 3))} ) K = [[0.4, 0.2, 0.3], [0.3, 0.6, 0.2], [0.2, 0.2, 0.7]] # matrix that isn't quite doubly stochastic with pytest.raises( ValueError, match="`P0` matrix must be doubly stochastic"): quadratic_assignment( np.identity(3), np.identity(3), options={'P0': K} ) class Test2opt(QAPCommonTests): method = "2opt" def test_deterministic(self): # np.random.seed(0) executes before every method n = 20 A = np.random.rand(n, n) B = np.random.rand(n, n) res1 = quadratic_assignment(A, B, method=self.method) np.random.seed(0) A = np.random.rand(n, n) B = np.random.rand(n, n) res2 = quadratic_assignment(A, B, method=self.method) assert_equal(res1.nit, res2.nit) def test_partial_guess(self): n = 5 A = np.random.rand(n, n) B = np.random.rand(n, n) res1 = quadratic_assignment(A, B, method=self.method, options={'rng': 0}) guess = np.array([np.arange(5), res1.col_ind]).T res2 = quadratic_assignment(A, B, method=self.method, options={'rng': 0, 'partial_guess': guess}) fix = [2, 4] match = np.array([np.arange(5)[fix], res1.col_ind[fix]]).T res3 = quadratic_assignment(A, B, method=self.method, options={'rng': 0, 'partial_guess': guess, 'partial_match': match}) assert_(res1.nit != n*(n+1)/2) assert_equal(res2.nit, n*(n+1)/2) # tests each swap exactly once assert_equal(res3.nit, (n-2)*(n-1)/2) # tests free swaps exactly once def test_specific_input_validation(self): # can't have more seed nodes than cost/dist nodes _rm = _range_matrix with pytest.raises( ValueError, match="`partial_guess` can have only as many entries as"): quadratic_assignment(np.identity(3), np.identity(3), method=self.method, options={'partial_guess': _rm(5, 2)}) # test for only two seed columns with pytest.raises( ValueError, match="`partial_guess` must have two columns"): quadratic_assignment( np.identity(3), np.identity(3), method=self.method, options={'partial_guess': _range_matrix(2, 3)} ) # test that seed has no more than two dimensions with pytest.raises( ValueError, match="`partial_guess` must have exactly two"): quadratic_assignment( np.identity(3), np.identity(3), method=self.method, options={'partial_guess': np.random.rand(3, 2, 2)} ) # seeds cannot be negative valued with pytest.raises( ValueError, match="`partial_guess` must contain only pos"): quadratic_assignment( np.identity(3), np.identity(3), method=self.method, options={'partial_guess': -1 * _range_matrix(2, 2)} ) # seeds can't have values greater than number of nodes with pytest.raises( ValueError, match="`partial_guess` entries must be less than number"): quadratic_assignment( np.identity(5), np.identity(5), method=self.method, options={'partial_guess': 2 * _range_matrix(4, 2)} ) # columns of seed matrix must be unique with pytest.raises( ValueError, match="`partial_guess` column entries must be unique"): quadratic_assignment( np.identity(3), np.identity(3), method=self.method, options={'partial_guess': np.ones((2, 2))} ) class TestQAPOnce(): def setup_method(self): np.random.seed(0) # these don't need to be repeated for each method def test_common_input_validation(self): # test that non square matrices return error with pytest.raises(ValueError, match="`A` must be square"): quadratic_assignment( 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