117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
# Author: Brian M. Clapper, G. Varoquaux, Lars Buitinck
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# License: BSD
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from numpy.testing import assert_array_equal
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import pytest
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import numpy as np
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from scipy.optimize import linear_sum_assignment
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from scipy.sparse import random
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from scipy.sparse._sputils import matrix
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from scipy.sparse.csgraph import min_weight_full_bipartite_matching
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from scipy.sparse.csgraph.tests.test_matching import (
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linear_sum_assignment_assertions, linear_sum_assignment_test_cases
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)
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def test_linear_sum_assignment_input_shape():
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with pytest.raises(ValueError, match="expected a matrix"):
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linear_sum_assignment([1, 2, 3])
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def test_linear_sum_assignment_input_object():
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C = [[1, 2, 3], [4, 5, 6]]
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assert_array_equal(linear_sum_assignment(C),
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linear_sum_assignment(np.asarray(C)))
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assert_array_equal(linear_sum_assignment(C),
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linear_sum_assignment(matrix(C)))
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def test_linear_sum_assignment_input_bool():
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I = np.identity(3)
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assert_array_equal(linear_sum_assignment(I.astype(np.bool_)),
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linear_sum_assignment(I))
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def test_linear_sum_assignment_input_string():
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I = np.identity(3)
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with pytest.raises(TypeError, match="Cannot cast array data"):
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linear_sum_assignment(I.astype(str))
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def test_linear_sum_assignment_input_nan():
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I = np.diag([np.nan, 1, 1])
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with pytest.raises(ValueError, match="contains invalid numeric entries"):
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linear_sum_assignment(I)
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def test_linear_sum_assignment_input_neginf():
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I = np.diag([1, -np.inf, 1])
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with pytest.raises(ValueError, match="contains invalid numeric entries"):
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linear_sum_assignment(I)
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def test_linear_sum_assignment_input_inf():
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I = np.identity(3)
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I[:, 0] = np.inf
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with pytest.raises(ValueError, match="cost matrix is infeasible"):
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linear_sum_assignment(I)
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def test_constant_cost_matrix():
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# Fixes #11602
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n = 8
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C = np.ones((n, n))
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row_ind, col_ind = linear_sum_assignment(C)
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assert_array_equal(row_ind, np.arange(n))
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assert_array_equal(col_ind, np.arange(n))
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@pytest.mark.parametrize('num_rows,num_cols', [(0, 0), (2, 0), (0, 3)])
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def test_linear_sum_assignment_trivial_cost(num_rows, num_cols):
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C = np.empty(shape=(num_cols, num_rows))
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row_ind, col_ind = linear_sum_assignment(C)
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assert len(row_ind) == 0
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assert len(col_ind) == 0
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@pytest.mark.parametrize('sign,test_case', linear_sum_assignment_test_cases)
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def test_linear_sum_assignment_small_inputs(sign, test_case):
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linear_sum_assignment_assertions(
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linear_sum_assignment, np.array, sign, test_case)
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# Tests that combine scipy.optimize.linear_sum_assignment and
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# scipy.sparse.csgraph.min_weight_full_bipartite_matching
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def test_two_methods_give_same_result_on_many_sparse_inputs():
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# As opposed to the test above, here we do not spell out the expected
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# output; only assert that the two methods give the same result.
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# Concretely, the below tests 100 cases of size 100x100, out of which
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# 36 are infeasible.
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np.random.seed(1234)
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for _ in range(100):
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lsa_raises = False
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mwfbm_raises = False
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sparse = random(100, 100, density=0.06,
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data_rvs=lambda size: np.random.randint(1, 100, size))
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# In csgraph, zeros correspond to missing edges, so we explicitly
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# replace those with infinities
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dense = np.full(sparse.shape, np.inf)
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dense[sparse.row, sparse.col] = sparse.data
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sparse = sparse.tocsr()
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try:
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row_ind, col_ind = linear_sum_assignment(dense)
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lsa_cost = dense[row_ind, col_ind].sum()
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except ValueError:
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lsa_raises = True
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try:
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row_ind, col_ind = min_weight_full_bipartite_matching(sparse)
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mwfbm_cost = sparse[row_ind, col_ind].sum()
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except ValueError:
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mwfbm_raises = True
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# Ensure that if one method raises, so does the other one.
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assert lsa_raises == mwfbm_raises
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if not lsa_raises:
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assert lsa_cost == mwfbm_cost
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