559 lines
20 KiB
Python
559 lines
20 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 ModelBuilder."""
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import math
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import numpy as np
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import numpy.testing as np_testing
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import os
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from ortools.linear_solver.python import model_builder as mb
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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 = mb.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 = mb.ModelSolver(solver_name)
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self.assertEqual(mb.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(
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solver.value(10.0 * x1 + 6 * x2 + 4.0 * x3 - 5.5),
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solver.objective_value,
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places=self.NUM_PLACES,
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)
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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.assertAlmostEqual(100.0,
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solver.activity(c0),
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places=self.NUM_PLACES)
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self.assertAlmostEqual(600.0,
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solver.activity(c1),
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places=self.NUM_PLACES)
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self.assertAlmostEqual(200.0,
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solver.activity(c2),
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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 = mb.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 = mb.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 = mb.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 = mb.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_class_api(self):
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model = mb.ModelBuilder()
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x = model.new_int_var(0, 10, 'x')
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y = model.new_int_var(1, 10, 'y')
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z = model.new_int_var(2, 10, 'z')
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t = model.new_int_var(3, 10, 't')
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e1 = mb.LinearExpr.sum([x, y, z])
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expected_vars = np.array([0, 1, 2], dtype=np.int32)
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np_testing.assert_array_equal(expected_vars, e1.variable_indices)
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np_testing.assert_array_equal(np.array([1, 1, 1], dtype=np.double),
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e1.coefficients)
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self.assertEqual(e1.constant, 0.0)
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self.assertEqual(e1.pretty_string(model.helper), 'x + y + z')
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e2 = mb.LinearExpr.sum([e1, 4.0])
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np_testing.assert_array_equal(expected_vars, e2.variable_indices)
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np_testing.assert_array_equal(np.array([1, 1, 1], dtype=np.double),
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e2.coefficients)
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self.assertEqual(e2.constant, 4.0)
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self.assertEqual(e2.pretty_string(model.helper), 'x + y + z + 4.0')
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e3 = mb.LinearExpr.term(e2, 2)
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np_testing.assert_array_equal(expected_vars, e3.variable_indices)
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np_testing.assert_array_equal(np.array([2, 2, 2], dtype=np.double),
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e3.coefficients)
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self.assertEqual(e3.constant, 8.0)
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self.assertEqual(e3.pretty_string(model.helper),
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'2.0 * x + 2.0 * y + 2.0 * z + 8.0')
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e4 = mb.LinearExpr.weighted_sum([x, t], [-1, 1], constant=2)
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np_testing.assert_array_equal(np.array([0, 3], dtype=np.int32),
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e4.variable_indices)
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np_testing.assert_array_equal(np.array([-1, 1], dtype=np.double),
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e4.coefficients)
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self.assertEqual(e4.constant, 2.0)
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self.assertEqual(e4.pretty_string(model.helper), '-x + t + 2.0')
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e4b = e4 * 3.0
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np_testing.assert_array_equal(np.array([0, 3], dtype=np.int32),
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e4b.variable_indices)
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np_testing.assert_array_equal(np.array([-3, 3], dtype=np.double),
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e4b.coefficients)
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self.assertEqual(e4b.constant, 6.0)
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self.assertEqual(e4b.pretty_string(model.helper),
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'-3.0 * x + 3.0 * t + 6.0')
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e5 = mb.LinearExpr.sum([e1, -3, e4])
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np_testing.assert_array_equal(np.array([0, 1, 2, 0, 3], dtype=np.int32),
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e5.variable_indices)
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np_testing.assert_array_equal(
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np.array([1, 1, 1, -1, 1], dtype=np.double), e5.coefficients)
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self.assertEqual(e5.constant, -1.0)
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self.assertEqual(e5.pretty_string(model.helper),
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'x + y + z - x + t - 1.0')
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e6 = mb.LinearExpr.term(x, 2.0, constant=1.0)
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np_testing.assert_array_equal(np.array([0], dtype=np.int32),
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e6.variable_indices)
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np_testing.assert_array_equal(np.array([2], dtype=np.double),
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e6.coefficients)
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self.assertEqual(e6.constant, 1.0)
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e7 = mb.LinearExpr.term(x, 1.0, constant=0.0)
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self.assertEqual(x, e7)
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e8 = mb.LinearExpr.term(2, 3, constant=4)
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self.assertEqual(e8, 10)
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def test_variables(self):
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model = mb.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_array(shape=10,
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lower_bounds=0.0,
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upper_bounds=5.0,
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name='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 = [3.0, 4.0, 5.0]
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ys = model.new_int_var_array(lower_bounds=lbs,
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upper_bounds=ubs,
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name='ys_')
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self.assertEqual('VariableContainer([12 13 14])', str(ys))
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zs = model.new_int_var_array(lower_bounds=[1.0, 2.0, 3],
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upper_bounds=[4, 4, 4],
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name='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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self.assertTrue(zs[2].is_integral)
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bs = model.new_bool_var_array([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.assertTrue(bs[1, 1].is_integral)
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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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sum_bs = np.sum(bs)
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self.assertEqual(20, sum_bs.variable_indices.size)
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np_testing.assert_array_equal(sum_bs.variable_indices,
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bs.variable_indices.flatten())
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np_testing.assert_array_equal(sum_bs.coefficients, np.ones(20))
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sum_bs_cte = np.sum(bs, 2.2)
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self.assertEqual(20, sum_bs_cte.variable_indices.size)
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np_testing.assert_array_equal(sum_bs_cte.variable_indices,
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bs.variable_indices.flatten())
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np_testing.assert_array_equal(sum_bs.coefficients, np.ones(20))
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self.assertEqual(sum_bs_cte.constant, 2.2)
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times_bs = np.dot(bs[1], 4)
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np_testing.assert_array_equal(times_bs.variable_indices,
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bs[1].variable_indices.flatten())
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np_testing.assert_array_equal(times_bs.coefficients, np.full(5, 4.0))
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times_bs_rev = np.dot(4, bs[2])
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np_testing.assert_array_equal(times_bs_rev.variable_indices,
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bs[2].variable_indices.flatten())
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np_testing.assert_array_equal(times_bs_rev.coefficients,
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np.full(5, 4.0))
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dot_bs = np.dot(bs[2], np.array([1, 2, 3, 4, 5], dtype=np.double))
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np_testing.assert_array_equal(dot_bs.variable_indices,
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bs[2].variable_indices)
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np_testing.assert_array_equal(dot_bs.coefficients, [1, 2, 3, 4, 5])
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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_numpy_var_arrays(self):
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model = mb.ModelBuilder()
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x = model.new_var_array(
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lower_bounds=0.0,
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upper_bounds=4.0,
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shape=[2, 3],
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is_integral=False,
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)
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np_testing.assert_array_equal(x.shape, [2, 3])
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y = model.new_var_array(
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lower_bounds=[[0.0, 1.0, 2.0], [0.0, 0.0, 2.0]],
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upper_bounds=4.0,
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is_integral=False,
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name='y',
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)
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np_testing.assert_array_equal(y.shape, [2, 3])
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z = model.new_var_array(
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lower_bounds=0.0,
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upper_bounds=[[2.0, 1.0, 2.0], [3.0, 4.0, 2.0]],
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is_integral=False,
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name='z',
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)
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np_testing.assert_array_equal(z.shape, [2, 3])
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with self.assertRaises(ValueError):
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x = model.new_var_array(
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lower_bounds=0.0,
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upper_bounds=4.0,
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is_integral=False,
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)
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with self.assertRaises(ValueError):
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x = model.new_var_array(
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lower_bounds=[0, 0],
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upper_bounds=[1, 2, 3],
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is_integral=False,
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)
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with self.assertRaises(ValueError):
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x = model.new_var_array(
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shape=[2, 3],
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lower_bounds=0.0,
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upper_bounds=[1, 2, 3],
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is_integral=False,
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)
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with self.assertRaises(ValueError):
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x = model.new_var_array(
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shape=[2, 3],
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lower_bounds=[1, 2],
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upper_bounds=4.0,
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is_integral=False,
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)
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with self.assertRaises(ValueError):
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x = model.new_var_array(
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shape=[2, 3],
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lower_bounds=0.0,
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upper_bounds=4.0,
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is_integral=[False, True],
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)
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with self.assertRaises(ValueError):
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x = model.new_var_array(
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lower_bounds=[1, 2],
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upper_bounds=4.0,
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is_integral=[False, False, False],
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)
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def test_numpy_num_var_arrays(self):
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model = mb.ModelBuilder()
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x = model.new_num_var_array(
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lower_bounds=0.0,
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upper_bounds=4.0,
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shape=[2, 3],
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)
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np_testing.assert_array_equal(x.shape, [2, 3])
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y = model.new_num_var_array(
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lower_bounds=[[0.0, 1.0, 2.0], [0.0, 0.0, 2.0]],
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upper_bounds=4.0,
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name='y',
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)
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np_testing.assert_array_equal(y.shape, [2, 3])
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z = model.new_num_var_array(
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lower_bounds=0.0,
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upper_bounds=[[2.0, 1.0, 2.0], [3.0, 4.0, 2.0]],
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name='z',
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)
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np_testing.assert_array_equal(z.shape, [2, 3])
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with self.assertRaises(ValueError):
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x = model.new_num_var_array(
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lower_bounds=0.0,
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upper_bounds=4.0,
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)
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with self.assertRaises(ValueError):
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x = model.new_num_var_array(
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lower_bounds=[0, 0],
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upper_bounds=[1, 2, 3],
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)
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with self.assertRaises(ValueError):
|
|
x = model.new_num_var_array(
|
|
shape=[2, 3],
|
|
lower_bounds=0.0,
|
|
upper_bounds=[1, 2, 3],
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
x = model.new_num_var_array(
|
|
shape=[2, 3],
|
|
lower_bounds=[1, 2],
|
|
upper_bounds=4.0,
|
|
)
|
|
|
|
def test_numpy_int_var_arrays(self):
|
|
model = mb.ModelBuilder()
|
|
|
|
x = model.new_int_var_array(
|
|
lower_bounds=0.0,
|
|
upper_bounds=4.0,
|
|
shape=[2, 3],
|
|
)
|
|
np_testing.assert_array_equal(x.shape, [2, 3])
|
|
|
|
y = model.new_int_var_array(
|
|
lower_bounds=[[0.0, 1.0, 2.0], [0.0, 0.0, 2.0]],
|
|
upper_bounds=4.0,
|
|
name='y',
|
|
)
|
|
np_testing.assert_array_equal(y.shape, [2, 3])
|
|
|
|
z = model.new_int_var_array(
|
|
lower_bounds=0.0,
|
|
upper_bounds=[[2.0, 1.0, 2.0], [3.0, 4.0, 2.0]],
|
|
name='z',
|
|
)
|
|
np_testing.assert_array_equal(z.shape, [2, 3])
|
|
|
|
with self.assertRaises(ValueError):
|
|
x = model.new_int_var_array(
|
|
lower_bounds=0.0,
|
|
upper_bounds=4.0,
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
x = model.new_int_var_array(
|
|
lower_bounds=[0, 0],
|
|
upper_bounds=[1, 2, 3],
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
x = model.new_int_var_array(
|
|
shape=[2, 3],
|
|
lower_bounds=0.0,
|
|
upper_bounds=[1, 2, 3],
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
x = model.new_int_var_array(
|
|
shape=[2, 3],
|
|
lower_bounds=[1, 2],
|
|
upper_bounds=4.0,
|
|
)
|
|
|
|
def test_duplicate_variables(self):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_int_var(0.0, 4.0, 'x')
|
|
y = model.new_int_var(0.0, 4.0, 'y')
|
|
z = model.new_int_var(0.0, 4.0, 'z')
|
|
model.add(x + 2 * y == x - z)
|
|
model.minimize(x + y + z)
|
|
solver = mb.ModelSolver('scip')
|
|
self.assertEqual(mb.SolveStatus.OPTIMAL, solver.solve(model))
|
|
|
|
def test_issue_3614(self):
|
|
total_number_of_choices = 5 + 1
|
|
total_unique_products = 3
|
|
standalone_features = list(range(5))
|
|
feature_bundle_incidence_matrix = {}
|
|
for idx in range(len(standalone_features)):
|
|
feature_bundle_incidence_matrix[idx, 0] = 0
|
|
feature_bundle_incidence_matrix[0, 0] = 1
|
|
feature_bundle_incidence_matrix[1, 0] = 1
|
|
|
|
bundle_start_idx = len(standalone_features)
|
|
# Model
|
|
model = mb.ModelBuilder()
|
|
y = {}
|
|
v = {}
|
|
for i in range(total_number_of_choices):
|
|
y[i] = model.new_bool_var(f'y_{i}')
|
|
|
|
for j in range(total_unique_products):
|
|
for i in range(len(standalone_features)):
|
|
v[i, j] = model.new_bool_var(f'v_{(i,j)}')
|
|
model.add(v[i, j] == (y[i] +
|
|
(feature_bundle_incidence_matrix[(i, 0)] *
|
|
y[bundle_start_idx])))
|
|
|
|
solver = mb.ModelSolver('scip')
|
|
status = solver.solve(model)
|
|
self.assertEqual(mb.SolveStatus.OPTIMAL, status)
|
|
|
|
def test_varcompvar(self):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_int_var(0.0, 4.0, 'x')
|
|
y = model.new_int_var(0.0, 4.0, 'y')
|
|
ct = x == y
|
|
self.assertEqual(ct.left.index, x.index)
|
|
self.assertEqual(ct.right.index, y.index)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|