266 lines
10 KiB
Python
266 lines
10 KiB
Python
#!/usr/bin/env python3
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# 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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import numpy as np
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import os
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from ortools.linear_solver.python import model_builder
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import unittest
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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}/../testdata/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}/../testdata/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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# array
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xs = model.new_int_var_ndarray(10, 0.0, 5.0, 'xs_')
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self.assertEqual(10, xs.size)
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self.assertEqual('xs_4', str(xs[4]))
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lbs = np.array([1.0, 2.0, 3.0])
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ubs = np.array([3.0, 4.0, 5.0])
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ys = model.new_int_var_ndarray_with_bounds(lbs, ubs, 'ys_')
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self.assertEqual('VariableContainer([12 13 14])', str(ys))
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zs = model.new_int_var_ndarray_with_bounds([1.0, 2.0, 3], [4, 4, 4],
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'zs_')
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self.assertEqual(3, zs.size)
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self.assertEqual((3,), zs.shape)
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self.assertEqual('zs_1', str(zs[1]))
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self.assertEqual('zs_2(index=17, lb=3.0, ub=4.0, integer)', repr(zs[2]))
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bs = model.new_bool_var_ndarray([4, 5], 'bs_')
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self.assertEqual((4, 5), bs.shape)
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self.assertEqual((5, 4), bs.T.shape)
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self.assertEqual(31, bs.index_at((2, 3)))
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self.assertEqual(20, bs.size)
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self.assertEqual((20,), bs.flatten.shape)
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self.assertEqual((20,), bs.ravel.shape)
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# Slices are [lb, ub) closed - open.
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self.assertEqual(5, bs[3, :].size)
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self.assertEqual(6, bs[1:3, 2:5].size)
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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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def test_duplicate_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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y = model.new_int_var(0.0, 4.0, 'y')
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z = model.new_int_var(0.0, 4.0, 'z')
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model.add(x + 2 * y == x - z)
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model.minimize(x + y + z)
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solver = model_builder.ModelSolver('scip')
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self.assertEqual(model_builder.SolveStatus.OPTIMAL, solver.solve(model))
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def test_issue_3614(self):
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total_number_of_choices = 5 + 1
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total_unique_products = 3
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standalone_features = list(range(5))
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feature_bundle_incidence_matrix = {}
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for idx in range(len(standalone_features)):
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feature_bundle_incidence_matrix[idx, 0] = 0
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feature_bundle_incidence_matrix[0, 0] = 1
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feature_bundle_incidence_matrix[1, 0] = 1
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bundle_start_idx = len(standalone_features)
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# Model
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model = model_builder.ModelBuilder()
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y = {}
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v = {}
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for i in range(total_number_of_choices):
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y[i] = model.new_bool_var(f'y_{i}')
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for j in range(total_unique_products):
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for i in range(len(standalone_features)):
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v[i, j] = model.new_bool_var(f'v_{(i,j)}')
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model.add(v[i, j] == (y[i] +
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(feature_bundle_incidence_matrix[(i, 0)] *
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y[bundle_start_idx])))
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solver = model_builder.ModelSolver('scip')
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status = solver.solve(model)
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self.assertEqual(model_builder.SolveStatus.OPTIMAL, status)
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def test_varcompvar(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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y = model.new_int_var(0.0, 4.0, 'y')
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ct = x == y
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self.assertEqual(ct.left.index, x.index)
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self.assertEqual(ct.right.index, y.index)
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if __name__ == '__main__':
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unittest.main()
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