189 lines
7.1 KiB
Python
189 lines
7.1 KiB
Python
# Copyright 2010-2022 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for model_builder."""
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import math
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from ortools.linear_solver.python import model_builder
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import unittest
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import os
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class ModelBuilderTest(unittest.TestCase):
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# Number of decimal places to use for numerical tolerance for
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# checking primal, dual, objective values and other values.
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NUM_PLACES = 5
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# pylint: disable=too-many-statements
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def run_minimal_linear_example(self, solver_name):
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"""Minimal Linear Example."""
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model = model_builder.ModelBuilder()
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model.name = 'minimal_linear_example'
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x1 = model.new_num_var(0.0, math.inf, 'x1')
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x2 = model.new_num_var(0.0, math.inf, 'x2')
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x3 = model.new_num_var(0.0, math.inf, 'x3')
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self.assertEqual(3, model.num_variables)
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self.assertFalse(x1.is_integral)
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self.assertEqual(0.0, x1.lower_bound)
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self.assertEqual(math.inf, x2.upper_bound)
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x1.lower_bound = 1.0
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self.assertEqual(1.0, x1.lower_bound)
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model.maximize(10.0 * x1 + 6 * x2 + 4.0 * x3 - 3.5)
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self.assertEqual(4.0, x3.objective_coefficient)
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self.assertEqual(-3.5, model.objective_offset)
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model.objective_offset = -5.5
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self.assertEqual(-5.5, model.objective_offset)
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c0 = model.add(x1 + x2 + x3 <= 100.0)
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self.assertEqual(100, c0.upper_bound)
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c1 = model.add(10 * x1 + 4.0 * x2 + 5.0 * x3 <= 600.0, 'c1')
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self.assertEqual('c1', c1.name)
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c2 = model.add(2.0 * x1 + 2.0 * x2 + 6.0 * x3 <= 300.0)
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self.assertEqual(-math.inf, c2.lower_bound)
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solver = model_builder.ModelSolver(solver_name)
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self.assertEqual(model_builder.SolveStatus.OPTIMAL, solver.solve(model))
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# The problem has an optimal solution.
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self.assertAlmostEqual(733.333333 + model.objective_offset,
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solver.objective_value,
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places=self.NUM_PLACES)
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self.assertAlmostEqual(33.333333,
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solver.value(x1),
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places=self.NUM_PLACES)
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self.assertAlmostEqual(66.666667,
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solver.value(x2),
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places=self.NUM_PLACES)
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self.assertAlmostEqual(0.0, solver.value(x3), places=self.NUM_PLACES)
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dual_objective_value = (solver.dual_value(c0) * c0.upper_bound +
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solver.dual_value(c1) * c1.upper_bound +
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solver.dual_value(c2) * c2.upper_bound +
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model.objective_offset)
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self.assertAlmostEqual(solver.objective_value,
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dual_objective_value,
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places=self.NUM_PLACES)
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# x1 and x2 are basic
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self.assertAlmostEqual(0.0,
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solver.reduced_cost(x1),
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places=self.NUM_PLACES)
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self.assertAlmostEqual(0.0,
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solver.reduced_cost(x2),
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places=self.NUM_PLACES)
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# x3 is non-basic
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x3_expected_reduced_cost = (4.0 - 1.0 * solver.dual_value(c0) -
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5.0 * solver.dual_value(c1))
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self.assertAlmostEqual(x3_expected_reduced_cost,
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solver.reduced_cost(x3),
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places=self.NUM_PLACES)
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self.assertIn('minimal_linear_example',
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model.export_to_lp_string(False))
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self.assertIn('minimal_linear_example',
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model.export_to_mps_string(False))
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def test_minimal_linear_example(self):
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self.run_minimal_linear_example('glop')
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def test_import_from_mps_string(self):
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mps_data = """
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* Generated by MPModelProtoExporter
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* Name : SupportedMaximizationProblem
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* Format : Free
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* Constraints : 0
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* Variables : 1
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* Binary : 0
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* Integer : 0
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* Continuous : 1
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NAME SupportedMaximizationProblem
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OBJSENSE
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MAX
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ROWS
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N COST
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COLUMNS
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X_ONE COST 1
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BOUNDS
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UP BOUND X_ONE 4
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ENDATA
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"""
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model = model_builder.ModelBuilder()
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self.assertTrue(model.import_from_mps_string(mps_data))
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self.assertEqual(model.name, 'SupportedMaximizationProblem')
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def test_import_from_mps_file(self):
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path = os.path.dirname(__file__)
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mps_path = f'{path}/maximization.mps'
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model = model_builder.ModelBuilder()
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self.assertTrue(model.import_from_mps_file(mps_path))
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self.assertEqual(model.name, 'SupportedMaximizationProblem')
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def test_import_from_lp_string(self):
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lp_data = """
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min: x + y;
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bin: b1, b2, b3;
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1 <= x <= 42;
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constraint_num1: 5 b1 + 3b2 + x <= 7;
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4 y + b2 - 3 b3 <= 2;
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constraint_num2: -4 b1 + b2 - 3 z <= -2;
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"""
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model = model_builder.ModelBuilder()
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self.assertTrue(model.import_from_lp_string(lp_data))
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self.assertEqual(6, model.num_variables)
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self.assertEqual(3, model.num_constraints)
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self.assertEqual(1, model.var_from_index(0).lower_bound)
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self.assertEqual(42, model.var_from_index(0).upper_bound)
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self.assertEqual('x', model.var_from_index(0).name)
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def test_import_from_lp_file(self):
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path = os.path.dirname(__file__)
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lp_path = f'{path}/small_model.lp'
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model = model_builder.ModelBuilder()
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self.assertTrue(model.import_from_lp_file(lp_path))
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self.assertEqual(6, model.num_variables)
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self.assertEqual(3, model.num_constraints)
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self.assertEqual(1, model.var_from_index(0).lower_bound)
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self.assertEqual(42, model.var_from_index(0).upper_bound)
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self.assertEqual('x', model.var_from_index(0).name)
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def test_variables(self):
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model = model_builder.ModelBuilder()
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x = model.new_int_var(0.0, 4.0, 'x')
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self.assertEqual(0, x.index)
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self.assertEqual(0.0, x.lower_bound)
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self.assertEqual(4.0, x.upper_bound)
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self.assertEqual('x', x.name)
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x.lower_bound = 1.0
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x.upper_bound = 3.0
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self.assertEqual(1.0, x.lower_bound)
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self.assertEqual(3.0, x.upper_bound)
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self.assertTrue(x.is_integral)
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# Tests the equality operator.
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y = model.new_int_var(0.0, 4.0, 'y')
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x_copy = model.var_from_index(0)
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self.assertEqual(x, x)
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self.assertEqual(x, x_copy)
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self.assertNotEqual(x, y)
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# Tests the hash method.
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var_set = set()
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var_set.add(x)
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self.assertIn(x, var_set)
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self.assertIn(x_copy, var_set)
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self.assertNotIn(y, var_set)
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if __name__ == '__main__':
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unittest.main()
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