#!/usr/bin/env python3 # Copyright 2010-2025 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from absl.testing import absltest from ortools.math_opt.python import model from ortools.math_opt.python import statistics class RangeTest(absltest.TestCase): def test_merge_optional_ranges(self) -> None: self.assertIsNone(statistics.merge_optional_ranges(None, None)) r = statistics.Range(1.0, 3.0) self.assertEqual(statistics.merge_optional_ranges(r, None), r) self.assertEqual(statistics.merge_optional_ranges(None, r), r) # We also test that, since Range is a frozen class, we return the non-None # input when only one input is not None. self.assertIs(statistics.merge_optional_ranges(r, None), r) self.assertIs(statistics.merge_optional_ranges(None, r), r) self.assertEqual( statistics.merge_optional_ranges( statistics.Range(1.0, 3.0), statistics.Range(-2.0, 2.0) ), statistics.Range(-2.0, 3.0), ) def test_absolute_finite_non_zeros_range(self) -> None: self.assertIsNone(statistics.absolute_finite_non_zeros_range(())) self.assertIsNone( statistics.absolute_finite_non_zeros_range((math.inf, 0.0, -0.0, -math.inf)) ) self.assertEqual( statistics.absolute_finite_non_zeros_range( (math.inf, -5.0e2, 0.0, 1.5e-3, -0.0, -math.inf, 1.25e-6, 3.0e2) ), statistics.Range(minimum=1.25e-6, maximum=5.0e2), ) class ModelRangesTest(absltest.TestCase): def test_printing(self) -> None: self.assertMultiLineEqual( str( statistics.ModelRanges( objective_terms=None, variable_bounds=None, linear_constraint_bounds=None, linear_constraint_coefficients=None, ) ), "Objective terms : no finite values\n" "Variable bounds : no finite values\n" "Linear constraints bounds : no finite values\n" "Linear constraints coeffs : no finite values", ) self.assertMultiLineEqual( str( statistics.ModelRanges( objective_terms=statistics.Range(2.12345e-99, 1.12345e3), variable_bounds=statistics.Range(9.12345e-2, 1.12345e2), linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99), linear_constraint_coefficients=statistics.Range(0.0, 0.0), ) ), "Objective terms : [2.12e-99 , 1.12e+03 ]\n" "Variable bounds : [9.12e-02 , 1.12e+02 ]\n" "Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n" "Linear constraints coeffs : [0.00e+00 , 0.00e+00 ]", ) self.assertMultiLineEqual( str( statistics.ModelRanges( objective_terms=statistics.Range(2.12345e-1, 1.12345e3), variable_bounds=statistics.Range(9.12345e-2, 1.12345e2), linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99), linear_constraint_coefficients=statistics.Range(0.0, 1.0e100), ) ), "Objective terms : [2.12e-01 , 1.12e+03 ]\n" "Variable bounds : [9.12e-02 , 1.12e+02 ]\n" "Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n" "Linear constraints coeffs : [0.00e+00 , 1.00e+100]", ) self.assertMultiLineEqual( str( statistics.ModelRanges( objective_terms=statistics.Range(2.12345e-100, 1.12345e3), variable_bounds=statistics.Range(9.12345e-2, 1.12345e2), linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99), linear_constraint_coefficients=statistics.Range(0.0, 0.0), ) ), "Objective terms : [2.12e-100, 1.12e+03 ]\n" "Variable bounds : [9.12e-02 , 1.12e+02 ]\n" "Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n" "Linear constraints coeffs : [0.00e+00 , 0.00e+00 ]", ) self.assertMultiLineEqual( str( statistics.ModelRanges( objective_terms=statistics.Range(2.12345e-100, 1.12345e3), variable_bounds=statistics.Range(9.12345e-2, 1.12345e2), linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99), linear_constraint_coefficients=statistics.Range(0.0, 1.0e100), ) ), "Objective terms : [2.12e-100, 1.12e+03 ]\n" "Variable bounds : [9.12e-02 , 1.12e+02 ]\n" "Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n" "Linear constraints coeffs : [0.00e+00 , 1.00e+100]", ) class ComputeModelRangesTest(absltest.TestCase): def test_empty(self) -> None: mdl = model.Model(name="model") self.assertEqual( statistics.compute_model_ranges(mdl), statistics.ModelRanges( objective_terms=None, variable_bounds=None, linear_constraint_bounds=None, linear_constraint_coefficients=None, ), ) def test_only_zero_and_infinite_values(self) -> None: mdl = model.Model(name="model") mdl.add_variable(lb=0.0, ub=math.inf) mdl.add_variable(lb=-math.inf, ub=0.0) mdl.add_variable(lb=-math.inf, ub=math.inf) mdl.add_linear_constraint(lb=0.0, ub=math.inf) mdl.add_linear_constraint(lb=-math.inf, ub=0.0) mdl.add_linear_constraint(lb=-math.inf, ub=math.inf) self.assertEqual( statistics.compute_model_ranges(mdl), statistics.ModelRanges( objective_terms=None, variable_bounds=None, linear_constraint_bounds=None, linear_constraint_coefficients=None, ), ) def test_mixed_values(self) -> None: mdl = model.Model(name="model") x = mdl.add_variable(lb=0.0, ub=0.0, name="x") y = mdl.add_variable(lb=-math.inf, ub=1e-3, name="y") mdl.add_variable(lb=-3e2, ub=math.inf, name="z") mdl.objective.is_maximize = False mdl.objective.set_linear_coefficient(x, -5.0e4) # TODO(b/225219234): add the quadratic term `1.0e-6 * z * x` when the # support of quadratic objective is added to the Python API. mdl.objective.set_linear_coefficient(y, 3.0) c = mdl.add_linear_constraint(lb=0.0, name="c") c.set_coefficient(y, 1.25e-3) c.set_coefficient(x, -4.5e3) mdl.add_linear_constraint(lb=-math.inf, ub=3e4) d = mdl.add_linear_constraint(lb=-1e-5, ub=0.0, name="d") d.set_coefficient(y, 2.5e-3) self.assertEqual( statistics.compute_model_ranges(mdl), statistics.ModelRanges( # TODO(b/225219234): update this to Range(1.0e-6, 5.0e4) once the # quadratic term is added. objective_terms=statistics.Range(3.0, 5.0e4), variable_bounds=statistics.Range(1e-3, 3e2), linear_constraint_bounds=statistics.Range(1e-5, 3e4), linear_constraint_coefficients=statistics.Range(1.25e-3, 4.5e3), ), ) if __name__ == "__main__": absltest.main()