2118 lines
76 KiB
Python
2118 lines
76 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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from typing import Any, Callable, Dict, Mapping, Union
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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import numpy.testing as np_testing
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import pandas as pd
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import os
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from google.protobuf import text_format
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from ortools.linear_solver import linear_solver_pb2
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from ortools.linear_solver.python import model_builder as mb
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def build_dict(expr: mb.LinearExprT) -> Dict[mb.Variable, float]:
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res = {}
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for i, c in zip(expr.variable_indices, expr.coefficients):
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if not c:
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continue
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var = mb.Variable(expr.helper, lb=i, ub=None, is_integral=None, name=None)
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res[var] = c
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return res
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class ModelBuilderTest(absltest.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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if not solver.solver_is_supported():
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print(f"Solver {solver_name} is not supported")
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return
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self.assertTrue(pd.isna(solver.value(x1)))
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self.assertTrue(pd.isna(solver.value(x2)))
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self.assertTrue(pd.isna(solver.value(x3)))
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self.assertTrue(pd.isna(solver.reduced_cost(x1)))
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self.assertTrue(pd.isna(solver.reduced_cost(x2)))
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self.assertTrue(pd.isna(solver.dual_value(c0)))
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self.assertTrue(pd.isna(solver.dual_value(c1)))
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self.assertTrue(pd.isna(solver.dual_value(c2)))
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self.assertTrue(pd.isna(solver.activity(c0)))
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self.assertTrue(pd.isna(solver.activity(c1)))
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self.assertTrue(pd.isna(solver.activity(c2)))
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self.assertTrue(pd.isna(solver.objective_value))
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self.assertTrue(pd.isna(solver.best_objective_bound))
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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(
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733.333333 + model.objective_offset,
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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(
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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, solver.value(x1), places=self.NUM_PLACES)
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self.assertAlmostEqual(66.666667, solver.value(x2), 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 = (
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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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)
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self.assertAlmostEqual(
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solver.objective_value, dual_objective_value, places=self.NUM_PLACES
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)
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# x1 and x2 are basic
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self.assertAlmostEqual(0.0, solver.reduced_cost(x1), places=self.NUM_PLACES)
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self.assertAlmostEqual(0.0, solver.reduced_cost(x2), places=self.NUM_PLACES)
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# x3 is non-basic
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x3_expected_reduced_cost = (
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4.0 - 1.0 * solver.dual_value(c0) - 5.0 * solver.dual_value(c1)
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)
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self.assertAlmostEqual(
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x3_expected_reduced_cost,
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solver.reduced_cost(x3),
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places=self.NUM_PLACES,
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)
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self.assertAlmostEqual(100.0, solver.activity(c0), places=self.NUM_PLACES)
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self.assertAlmostEqual(600.0, solver.activity(c1), places=self.NUM_PLACES)
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self.assertAlmostEqual(200.0, solver.activity(c2), places=self.NUM_PLACES)
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self.assertIn("minimal_linear_example", model.export_to_lp_string(False))
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self.assertIn("minimal_linear_example", 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(
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np.array([1, 1, 1], dtype=np.double), e1.coefficients
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)
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self.assertEqual(e1.constant, 0.0)
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self.assertEqual(e1.__str__(), "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(
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np.array([1, 1, 1], dtype=np.double), e2.coefficients
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)
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self.assertEqual(e2.constant, 4.0)
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self.assertEqual(e2.__str__(), "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(
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np.array([2, 2, 2], dtype=np.double), e3.coefficients
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)
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self.assertEqual(e3.constant, 8.0)
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self.assertEqual(e3.__str__(), "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(
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np.array([0, 3], dtype=np.int32), e4.variable_indices
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)
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np_testing.assert_array_equal(
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np.array([-1, 1], dtype=np.double), e4.coefficients
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)
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self.assertEqual(e4.constant, 2.0)
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self.assertEqual(e4.__str__(), "-x + t + 2.0")
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e4b = mb.LinearExpr.weighted_sum([e4 * 3], [1])
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np_testing.assert_array_equal(
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np.array([0, 3], dtype=np.int32), e4b.variable_indices
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)
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np_testing.assert_array_equal(
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np.array([-3, 3], dtype=np.double), e4b.coefficients
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)
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self.assertEqual(e4b.constant, 6.0)
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self.assertEqual(e4b.__str__(), "-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(
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np.array([1, 2, 3], dtype=np.int32), e5.variable_indices
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)
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np_testing.assert_array_equal(
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np.array([1, 1, 1], dtype=np.double), e5.coefficients
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)
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self.assertEqual(e5.constant, -1.0)
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self.assertEqual(e5.__str__(), "y + z + 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(
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np.array([0], dtype=np.int32), e6.variable_indices
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)
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np_testing.assert_array_equal(np.array([2], dtype=np.double), 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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e9 = mb.LinearExpr.term(x * 2 + 3, 1, constant=0)
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e10 = mb.LinearExpr.term(x, 2, constant=3)
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self.assertEqual(
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str(mb._as_flat_linear_expression(e9)),
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str(mb._as_flat_linear_expression(e10)),
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)
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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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# 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 = mb.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 = mb.ModelSolver("sat")
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self.assertEqual(mb.SolveStatus.OPTIMAL, solver.solve(model))
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def test_add_term(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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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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t = model.new_int_var(0.0, 4.0, "t")
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ct = model.add(x + 2 * y == 3)
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self.assertEqual(ct.helper.constraint_var_indices(ct.index), [0, 1])
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self.assertEqual(ct.helper.constraint_coefficients(ct.index), [1, 2])
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ct.add_term(x, 2)
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self.assertEqual(ct.helper.constraint_var_indices(ct.index), [0, 1])
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self.assertEqual(ct.helper.constraint_coefficients(ct.index), [3, 2])
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ct.set_coefficient(x, 5)
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self.assertEqual(ct.helper.constraint_var_indices(ct.index), [0, 1])
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self.assertEqual(ct.helper.constraint_coefficients(ct.index), [5, 2])
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ct.add_term(z, 4)
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self.assertEqual(ct.helper.constraint_var_indices(ct.index), [0, 1, 2])
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self.assertEqual(ct.helper.constraint_coefficients(ct.index), [5, 2, 4])
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ct.set_coefficient(t, -1)
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self.assertEqual(ct.helper.constraint_var_indices(ct.index), [0, 1, 2, 3])
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self.assertEqual(ct.helper.constraint_coefficients(ct.index), [5, 2, 4, -1])
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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 = mb.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(
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v[i, j]
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== (
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y[i]
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+ (
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feature_bundle_incidence_matrix[(i, 0)]
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* y[bundle_start_idx]
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)
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)
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)
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solver = mb.ModelSolver("sat")
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status = solver.solve(model)
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self.assertEqual(mb.SolveStatus.OPTIMAL, status)
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def test_vareqvar(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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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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def test_create_false_ct(self):
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# Create the model.
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model = mb.ModelBuilder()
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x = model.new_num_var(0.0, math.inf, "x")
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ct = model.add(False)
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self.assertTrue(ct.is_always_false())
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self.assertRaises(ValueError, ct.add_term, x, 1)
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model.maximize(x)
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solver = mb.ModelSolver("glop")
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status = solver.solve(model)
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self.assertEqual(status, mb.SolveStatus.INFEASIBLE)
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def test_create_true_ct(self):
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# Create the model.
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model = mb.ModelBuilder()
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x = model.new_num_var(0.0, 5.0, "x")
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ct = model.add(True)
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self.assertEqual(ct.lower_bound, 0.0)
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self.assertEqual(ct.upper_bound, 0.0)
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ct.add_term(var=x, coeff=1)
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model.maximize(x)
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solver = mb.ModelSolver("glop")
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status = solver.solve(model)
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self.assertEqual(status, mb.SolveStatus.OPTIMAL)
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# Note that ct is binding.
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self.assertEqual(0.0, solver.objective_value)
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class InternalHelperTest(absltest.TestCase):
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def test_anonymous_variables(self):
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helper = mb.ModelBuilder().helper
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index = helper.add_var()
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variable = mb.Variable(helper, index, None, None, None)
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self.assertEqual(variable.name, f"variable#{index}")
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def test_anonymous_constraints(self):
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helper = mb.ModelBuilder().helper
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index = helper.add_linear_constraint()
|
|
constraint = mb.LinearConstraint(helper, index)
|
|
self.assertEqual(constraint.name, f"linear_constraint#{index}")
|
|
|
|
|
|
class LinearBaseTest(parameterized.TestCase):
|
|
def setUp(self):
|
|
super().setUp()
|
|
simple_model = mb.ModelBuilder()
|
|
self.x = simple_model.new_var_series(
|
|
name="x", index=pd.Index(range(3), name="i")
|
|
)
|
|
self.y = simple_model.new_var_series(
|
|
name="y", index=pd.Index(range(5), name="i")
|
|
)
|
|
self.simple_model = simple_model
|
|
|
|
@parameterized.named_parameters(
|
|
# Variable / Indexing
|
|
dict(
|
|
testcase_name="x[0]",
|
|
expr=lambda x, y: x[0],
|
|
expected_repr="x[0]",
|
|
),
|
|
dict(
|
|
testcase_name="x[1]",
|
|
expr=lambda x, y: x[1],
|
|
expected_repr="x[1]",
|
|
),
|
|
dict(
|
|
testcase_name="x[2]",
|
|
expr=lambda x, y: x[2],
|
|
expected_repr="x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="y[0]",
|
|
expr=lambda x, y: y[0],
|
|
expected_repr="y[0]",
|
|
),
|
|
dict(
|
|
testcase_name="y[4]",
|
|
expr=lambda x, y: y[4],
|
|
expected_repr="y[4]",
|
|
),
|
|
# Sum
|
|
dict(
|
|
testcase_name="x[0] + 5",
|
|
expr=lambda x, y: x[0] + 5,
|
|
expected_repr="x[0] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="x[0] - 5",
|
|
expr=lambda x, y: x[0] - 5,
|
|
expected_repr="x[0] - 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="5 - x[0]",
|
|
expr=lambda x, y: 5 - x[0],
|
|
expected_repr="-x[0] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="5 + x[0]",
|
|
expr=lambda x, y: 5 + x[0],
|
|
expected_repr="x[0] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="x[0] + y[0]",
|
|
expr=lambda x, y: x[0] + y[0],
|
|
expected_repr="x[0] + y[0]",
|
|
),
|
|
dict(
|
|
testcase_name="x[0] + y[0] + 5",
|
|
expr=lambda x, y: x[0] + y[0] + 5,
|
|
expected_repr="x[0] + y[0] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="5 + x[0] + y[0]",
|
|
expr=lambda x, y: 5 + x[0] + y[0],
|
|
expected_repr="x[0] + y[0] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="5 + x[0] - x[0]",
|
|
expr=lambda x, y: 5 + x[0] - x[0],
|
|
expected_repr="5.0",
|
|
),
|
|
dict(
|
|
testcase_name="5 + x[0] - y[0]",
|
|
expr=lambda x, y: 5 + x[0] - y[0],
|
|
expected_repr="x[0] - y[0] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="x.sum()",
|
|
expr=lambda x, y: x.sum(),
|
|
expected_repr="x[0] + x[1] + x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="x.add(y, fill_value=0).sum() + 5",
|
|
expr=lambda x, y: x.add(y, fill_value=0).sum() + 5,
|
|
expected_repr="x[0] + x[1] + x[2] + y[0] + y[1] + ... + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="sum(x, y + 5)",
|
|
expr=lambda x, y: mb.LinearExpr.sum([x.sum(), y.sum() + 5]),
|
|
expected_repr="x[0] + x[1] + x[2] + y[0] + y[1] + ... + 5.0",
|
|
),
|
|
# Product
|
|
dict(
|
|
testcase_name="- x.sum()",
|
|
expr=lambda x, y: -x.sum(),
|
|
expected_repr="-x[0] - x[1] - x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="5 - x.sum()",
|
|
expr=lambda x, y: 5 - x.sum(),
|
|
expected_repr="-x[0] - x[1] - x[2] + 5.0",
|
|
),
|
|
dict(
|
|
testcase_name="x.sum() / 2.0",
|
|
expr=lambda x, y: x.sum() / 2.0,
|
|
expected_repr="0.5 * x[0] + 0.5 * x[1] + 0.5 * x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="(3 * x).sum()",
|
|
expr=lambda x, y: (3 * x).sum(),
|
|
expected_repr="3.0 * x[0] + 3.0 * x[1] + 3.0 * x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="(x * 3).sum()",
|
|
expr=lambda x, y: (x * 3).sum(),
|
|
expected_repr="3.0 * x[0] + 3.0 * x[1] + 3.0 * x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="x.sum() * 3",
|
|
expr=lambda x, y: x.sum() * 3,
|
|
expected_repr="3.0 * x[0] + 3.0 * x[1] + 3.0 * x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="3 * x.sum()",
|
|
expr=lambda x, y: 3 * x.sum(),
|
|
expected_repr="3.0 * x[0] + 3.0 * x[1] + 3.0 * x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="0 * x.sum() + y.sum()",
|
|
expr=lambda x, y: 0 * x.sum() + y.sum(),
|
|
expected_repr="y[0] + y[1] + y[2] + y[3] + y[4]",
|
|
),
|
|
# LinearExpression
|
|
dict(
|
|
testcase_name="_as_flat_linear_expression(x.sum())",
|
|
expr=lambda x, y: mb._as_flat_linear_expression(x.sum()),
|
|
expected_repr="x[0] + x[1] + x[2]",
|
|
),
|
|
dict(
|
|
testcase_name=(
|
|
"_as_flat_linear_expression(_as_flat_linear_expression(x.sum()))"
|
|
),
|
|
# pylint: disable=g-long-lambda
|
|
expr=lambda x, y: mb._as_flat_linear_expression(
|
|
mb._as_flat_linear_expression(x.sum())
|
|
),
|
|
expected_repr="x[0] + x[1] + x[2]",
|
|
),
|
|
dict(
|
|
testcase_name="""_as_flat_linear_expression(sum([
|
|
_as_flat_linear_expression(x.sum()),
|
|
_as_flat_linear_expression(x.sum()),
|
|
]))""",
|
|
# pylint: disable=g-long-lambda
|
|
expr=lambda x, y: mb._as_flat_linear_expression(
|
|
sum(
|
|
[
|
|
mb._as_flat_linear_expression(x.sum()),
|
|
mb._as_flat_linear_expression(x.sum()),
|
|
]
|
|
)
|
|
),
|
|
expected_repr="2.0 * x[0] + 2.0 * x[1] + 2.0 * x[2]",
|
|
),
|
|
)
|
|
def test_repr(self, expr, expected_repr):
|
|
x = self.x
|
|
y = self.y
|
|
self.assertEqual(repr(expr(x, y)), expected_repr)
|
|
|
|
|
|
class LinearBaseErrorsTest(absltest.TestCase):
|
|
def test_unknown_linear_type(self):
|
|
with self.assertRaisesRegex(TypeError, r"Unrecognized linear expression"):
|
|
|
|
class UnknownLinearType(mb.LinearExpr):
|
|
pass
|
|
|
|
mb._as_flat_linear_expression(UnknownLinearType())
|
|
|
|
def test_division_by_zero(self):
|
|
with self.assertRaises(ZeroDivisionError):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=pd.Index(range(1)))
|
|
print(x / 0)
|
|
|
|
def test_boolean_expression(self):
|
|
with self.assertRaisesRegex(NotImplementedError, r"Cannot use a LinearExpr"):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=pd.Index(range(1)))
|
|
bool(x.sum())
|
|
|
|
|
|
class BoundedLinearBaseTest(parameterized.TestCase):
|
|
def setUp(self):
|
|
super().setUp()
|
|
simple_model = mb.ModelBuilder()
|
|
self.x = simple_model.new_var_series(
|
|
name="x", index=pd.Index(range(3), name="i")
|
|
)
|
|
self.y = simple_model.new_var_series(
|
|
name="y", index=pd.Index(range(5), name="i")
|
|
)
|
|
self.simple_model = simple_model
|
|
|
|
@parameterized.product(
|
|
lhs=(
|
|
lambda x, y: x.sum(),
|
|
lambda x, y: -x.sum(),
|
|
lambda x, y: x.sum() * 0,
|
|
lambda x, y: x.sum() * 3,
|
|
lambda x, y: x[0],
|
|
lambda x, y: x[1],
|
|
lambda x, y: x[2],
|
|
lambda x, y: -math.inf,
|
|
lambda x, y: -1,
|
|
lambda x, y: 0,
|
|
lambda x, y: 1,
|
|
lambda x, y: 1.1,
|
|
lambda x, y: math.inf,
|
|
),
|
|
rhs=(
|
|
lambda x, y: y.sum(),
|
|
lambda x, y: -y.sum(),
|
|
lambda x, y: y.sum() * 0,
|
|
lambda x, y: y.sum() * 3,
|
|
lambda x, y: y[0],
|
|
lambda x, y: y[1],
|
|
lambda x, y: y[2],
|
|
lambda x, y: -math.inf,
|
|
lambda x, y: -1,
|
|
lambda x, y: 0,
|
|
lambda x, y: 1,
|
|
lambda x, y: 1.1,
|
|
lambda x, y: math.inf,
|
|
),
|
|
op=(
|
|
lambda lhs, rhs: lhs == rhs,
|
|
lambda lhs, rhs: lhs <= rhs,
|
|
lambda lhs, rhs: lhs >= rhs,
|
|
),
|
|
)
|
|
def test_repr(self, lhs, rhs, op):
|
|
x = self.x
|
|
y = self.y
|
|
l: mb.LinearExprT = lhs(x, y)
|
|
r: mb.LinearExprT = rhs(x, y)
|
|
result = op(l, r)
|
|
if isinstance(l, mb.LinearExpr) or isinstance(r, mb.LinearExpr):
|
|
self.assertIsInstance(result, mb._BoundedLinearExpr)
|
|
self.assertIn("=", repr(result), msg="is one of ==, <=, or >=")
|
|
else:
|
|
self.assertIsInstance(result, bool)
|
|
|
|
def test_doublesided_bounded_expressions(self):
|
|
x = self.x
|
|
self.assertEqual(
|
|
"0.0 <= x[0] <= 1.0", repr(mb.BoundedLinearExpression(x[0], 0, 1))
|
|
)
|
|
|
|
def test_free_bounded_expressions(self):
|
|
self.assertEqual(
|
|
"x[0] free",
|
|
repr(mb.BoundedLinearExpression(self.x[0], -math.inf, math.inf)),
|
|
)
|
|
|
|
def test_var_eq_var_as_bool(self):
|
|
x = self.x
|
|
y = self.y
|
|
self.assertEqual(x[0], x[0])
|
|
self.assertNotEqual(x[0], x[1])
|
|
self.assertNotEqual(x[0], y[0])
|
|
|
|
self.assertEqual(x[1], x[1])
|
|
self.assertNotEqual(x[1], x[0])
|
|
self.assertNotEqual(x[1], y[1])
|
|
|
|
self.assertEqual(y[0], y[0])
|
|
self.assertNotEqual(y[0], y[1])
|
|
self.assertNotEqual(y[0], x[0])
|
|
|
|
self.assertEqual(y[1], y[1])
|
|
self.assertNotEqual(y[1], y[0])
|
|
self.assertNotEqual(y[1], x[1])
|
|
|
|
|
|
class BoundedLinearBaseErrorsTest(absltest.TestCase):
|
|
def test_bounded_linear_expression_as_bool(self):
|
|
with self.assertRaisesRegex(NotImplementedError, "Boolean value"):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=pd.Index(range(1)))
|
|
bool(mb.BoundedLinearExpression(x, 0, 1))
|
|
|
|
|
|
class ModelBuilderErrorsTest(absltest.TestCase):
|
|
def test_new_var_series_errors(self):
|
|
with self.assertRaisesRegex(TypeError, r"Non-index object"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(name="", index=pd.DataFrame())
|
|
with self.assertRaisesRegex(TypeError, r"invalid type"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(name="x", index=pd.Index([0]), lower_bounds="0")
|
|
with self.assertRaisesRegex(TypeError, r"invalid type"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(name="x", index=pd.Index([0]), upper_bounds="0")
|
|
with self.assertRaisesRegex(TypeError, r"invalid type"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(name="x", index=pd.Index([0]), is_integral="True")
|
|
with self.assertRaisesRegex(ValueError, r"not a valid identifier"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(name="", index=pd.Index([0]))
|
|
with self.assertRaisesRegex(ValueError, r"is greater than"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(
|
|
name="x",
|
|
index=pd.Index([0]),
|
|
lower_bounds=0.2,
|
|
upper_bounds=0.1,
|
|
)
|
|
with self.assertRaisesRegex(ValueError, r"is greater than"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(
|
|
name="x",
|
|
index=pd.Index([0]),
|
|
lower_bounds=0.1,
|
|
upper_bounds=0.2,
|
|
is_integral=True,
|
|
)
|
|
with self.assertRaisesRegex(ValueError, r"index does not match"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(
|
|
name="x", index=pd.Index([0]), lower_bounds=pd.Series([1, 2])
|
|
)
|
|
with self.assertRaisesRegex(ValueError, r"index does not match"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(
|
|
name="x", index=pd.Index([0]), upper_bounds=pd.Series([1, 2])
|
|
)
|
|
with self.assertRaisesRegex(ValueError, r"index does not match"):
|
|
model = mb.ModelBuilder()
|
|
model.new_var_series(
|
|
name="x", index=pd.Index([0]), is_integral=pd.Series([False, True])
|
|
)
|
|
|
|
def test_add_linear_constraints_errors(self):
|
|
with self.assertRaisesRegex(TypeError, r"Not supported"):
|
|
model = mb.ModelBuilder()
|
|
model.add("True", name="c")
|
|
with self.assertRaisesRegex(TypeError, r"invalid type="):
|
|
model = mb.ModelBuilder()
|
|
model.add(pd.Series(["T"]), name="c")
|
|
|
|
|
|
class ModelBuilderVariablesTest(parameterized.TestCase):
|
|
_variable_indices = (
|
|
pd.Index(range(3)),
|
|
pd.Index(range(5), name="i"),
|
|
pd.MultiIndex.from_product(((1, 2), ("a", "b", "c")), names=["i", "j"]),
|
|
pd.MultiIndex.from_product((("a", "b"), (1, 2, 3))),
|
|
)
|
|
_bounds = (
|
|
lambda index: (-math.inf, -10.5),
|
|
lambda index: (-math.inf, -1),
|
|
lambda index: (-math.inf, 0),
|
|
lambda index: (-math.inf, 10),
|
|
lambda index: (-math.inf, math.inf),
|
|
lambda index: (-10, -1.1),
|
|
lambda index: (-10, 0),
|
|
lambda index: (-10, -10),
|
|
lambda index: (-10, 3),
|
|
lambda index: (-9, math.inf),
|
|
lambda index: (-1, 1),
|
|
lambda index: (0, 0),
|
|
lambda index: (0, 1),
|
|
lambda index: (0, math.inf),
|
|
lambda index: (1, 1),
|
|
lambda index: (1, 10.1),
|
|
lambda index: (1, math.inf),
|
|
lambda index: (100.1, math.inf),
|
|
# pylint: disable=g-long-lambda
|
|
lambda index: (
|
|
pd.Series(-math.inf, index=index),
|
|
pd.Series(-10.5, index=index),
|
|
),
|
|
lambda index: (
|
|
pd.Series(-math.inf, index=index),
|
|
pd.Series(-1, index=index),
|
|
),
|
|
lambda index: (
|
|
pd.Series(-math.inf, index=index),
|
|
pd.Series(0, index=index),
|
|
),
|
|
lambda index: (
|
|
pd.Series(-math.inf, index=index),
|
|
pd.Series(10, index=index),
|
|
),
|
|
lambda index: (
|
|
pd.Series(-math.inf, index=index),
|
|
pd.Series(math.inf, index=index),
|
|
),
|
|
lambda index: (pd.Series(-10, index=index), pd.Series(-1.1, index=index)),
|
|
lambda index: (pd.Series(-10, index=index), pd.Series(0, index=index)),
|
|
lambda index: (pd.Series(-10, index=index), pd.Series(-10, index=index)),
|
|
lambda index: (pd.Series(-10, index=index), pd.Series(3, index=index)),
|
|
lambda index: (
|
|
pd.Series(-9, index=index),
|
|
pd.Series(math.inf, index=index),
|
|
),
|
|
lambda index: (pd.Series(-1, index=index), pd.Series(1, index=index)),
|
|
lambda index: (pd.Series(0, index=index), pd.Series(0, index=index)),
|
|
lambda index: (pd.Series(0, index=index), pd.Series(1, index=index)),
|
|
lambda index: (
|
|
pd.Series(0, index=index),
|
|
pd.Series(math.inf, index=index),
|
|
),
|
|
lambda index: (pd.Series(1, index=index), pd.Series(1, index=index)),
|
|
lambda index: (pd.Series(1, index=index), pd.Series(10.1, index=index)),
|
|
lambda index: (
|
|
pd.Series(1, index=index),
|
|
pd.Series(math.inf, index=index),
|
|
),
|
|
lambda index: (
|
|
pd.Series(100.1, index=index),
|
|
pd.Series(math.inf, index=index),
|
|
),
|
|
)
|
|
_is_integer = (
|
|
lambda index: False,
|
|
lambda index: True,
|
|
lambda index: pd.Series(False, index=index),
|
|
lambda index: pd.Series(True, index=index),
|
|
)
|
|
|
|
@parameterized.product(
|
|
index=_variable_indices, bounds=_bounds, is_integer=_is_integer
|
|
)
|
|
def test_new_var_series(self, index, bounds, is_integer):
|
|
model = mb.ModelBuilder()
|
|
variables = model.new_var_series(
|
|
name="test_variable",
|
|
index=index,
|
|
lower_bounds=bounds(index)[0],
|
|
upper_bounds=bounds(index)[1],
|
|
is_integral=is_integer(index),
|
|
)
|
|
self.assertLen(variables, len(index))
|
|
self.assertLen(set(variables), len(index))
|
|
for i in index:
|
|
self.assertEqual(repr(variables[i]), f"test_variable[{i}]")
|
|
|
|
@parameterized.product(
|
|
index=_variable_indices, bounds=_bounds, is_integer=_is_integer
|
|
)
|
|
def test_get_variable_lower_bounds(self, index, bounds, is_integer):
|
|
lower_bound, upper_bound = bounds(index)
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(
|
|
name="x",
|
|
index=index,
|
|
lower_bounds=lower_bound,
|
|
upper_bounds=upper_bound,
|
|
is_integral=is_integer(index),
|
|
)
|
|
y = model.new_var_series(
|
|
name="y",
|
|
index=index,
|
|
lower_bounds=lower_bound,
|
|
upper_bounds=upper_bound,
|
|
is_integral=is_integer(index),
|
|
)
|
|
for lower_bounds in (
|
|
model.get_variable_lower_bounds(x),
|
|
model.get_variable_lower_bounds(y),
|
|
):
|
|
self.assertSequenceAlmostEqual(
|
|
lower_bounds,
|
|
mb._convert_to_series_and_validate_index(lower_bound, index),
|
|
)
|
|
self.assertSequenceAlmostEqual(
|
|
model.get_variable_lower_bounds(),
|
|
pd.concat(
|
|
[
|
|
model.get_variable_lower_bounds(x),
|
|
model.get_variable_lower_bounds(y),
|
|
]
|
|
),
|
|
)
|
|
variables = model.get_variables()
|
|
lower_bounds = model.get_variable_lower_bounds(variables)
|
|
self.assertSequenceAlmostEqual(lower_bounds.index, variables)
|
|
|
|
@parameterized.product(
|
|
index=_variable_indices, bounds=_bounds, is_integer=_is_integer
|
|
)
|
|
def test_get_variable_upper_bounds(self, index, bounds, is_integer):
|
|
lower_bound, upper_bound = bounds(index)
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(
|
|
name="x",
|
|
index=index,
|
|
lower_bounds=lower_bound,
|
|
upper_bounds=upper_bound,
|
|
is_integral=is_integer(index),
|
|
)
|
|
y = model.new_var_series(
|
|
name="y",
|
|
index=index,
|
|
lower_bounds=lower_bound,
|
|
upper_bounds=upper_bound,
|
|
is_integral=is_integer(index),
|
|
)
|
|
for upper_bounds in (
|
|
model.get_variable_upper_bounds(x),
|
|
model.get_variable_upper_bounds(y),
|
|
):
|
|
self.assertSequenceAlmostEqual(
|
|
upper_bounds,
|
|
mb._convert_to_series_and_validate_index(upper_bound, index),
|
|
)
|
|
self.assertSequenceAlmostEqual(
|
|
model.get_variable_upper_bounds(),
|
|
pd.concat(
|
|
[
|
|
model.get_variable_upper_bounds(x),
|
|
model.get_variable_upper_bounds(y),
|
|
]
|
|
),
|
|
)
|
|
variables = model.get_variables()
|
|
upper_bounds = model.get_variable_upper_bounds(variables)
|
|
self.assertSequenceAlmostEqual(upper_bounds.index, variables)
|
|
|
|
|
|
class ModelBuilderLinearConstraintsTest(parameterized.TestCase):
|
|
constraint_test_cases = [
|
|
# pylint: disable=g-long-lambda
|
|
dict(
|
|
testcase_name="True",
|
|
name="true",
|
|
bounded_exprs=lambda x, y: True,
|
|
constraint_count=1,
|
|
lower_bounds=[0.0],
|
|
upper_bounds=[0.0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="pd.Series(True)",
|
|
name="true",
|
|
bounded_exprs=lambda x, y: pd.Series(True),
|
|
constraint_count=1,
|
|
lower_bounds=[0.0],
|
|
upper_bounds=[0.0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="False",
|
|
name="false",
|
|
bounded_exprs=lambda x, y: False,
|
|
constraint_count=1,
|
|
lower_bounds=[1.0],
|
|
upper_bounds=[-1.0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="pd.Series(False)",
|
|
name="false",
|
|
bounded_exprs=lambda x, y: pd.Series(False),
|
|
constraint_count=1,
|
|
lower_bounds=[1.0],
|
|
upper_bounds=[-1.0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] <= 1.5",
|
|
name="x0_le_c",
|
|
bounded_exprs=lambda x, y: x[0] <= 1.5,
|
|
constraint_count=1,
|
|
lower_bounds=[-math.inf],
|
|
upper_bounds=[1.5],
|
|
expression_terms=lambda x, y: [{x[0]: 1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] == 1",
|
|
name="x0_eq_c",
|
|
bounded_exprs=lambda x, y: x[0] == 1,
|
|
constraint_count=1,
|
|
lower_bounds=[1],
|
|
upper_bounds=[1],
|
|
expression_terms=lambda x, y: [{x[0]: 1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] >= -1",
|
|
name="x0_ge_c",
|
|
bounded_exprs=lambda x, y: x[0] >= -1,
|
|
constraint_count=1,
|
|
lower_bounds=[-1],
|
|
upper_bounds=[math.inf],
|
|
expression_terms=lambda x, y: [{x[0]: 1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="-1.5 <= x[0]",
|
|
name="c_le_x0",
|
|
bounded_exprs=lambda x, y: -1.5 <= x[0],
|
|
constraint_count=1,
|
|
lower_bounds=[-1.5],
|
|
upper_bounds=[math.inf],
|
|
expression_terms=lambda x, y: [{x[0]: 1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="0 == x[0]",
|
|
name="c_eq_x0",
|
|
bounded_exprs=lambda x, y: 0 == x[0],
|
|
constraint_count=1,
|
|
lower_bounds=[0],
|
|
upper_bounds=[0],
|
|
expression_terms=lambda x, y: [{x[0]: 1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="10 >= x[0]",
|
|
name="c_ge_x0",
|
|
bounded_exprs=lambda x, y: 10 >= x[0],
|
|
constraint_count=1,
|
|
lower_bounds=[-math.inf],
|
|
upper_bounds=[10],
|
|
expression_terms=lambda x, y: [{x[0]: 1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] <= x[0]",
|
|
name="x0_le_x0",
|
|
bounded_exprs=lambda x, y: x[0] <= x[0],
|
|
constraint_count=1,
|
|
lower_bounds=[-math.inf],
|
|
upper_bounds=[0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] == x[0]",
|
|
name="x0_eq_x0",
|
|
bounded_exprs=lambda x, y: x[0] == x[0],
|
|
constraint_count=1,
|
|
lower_bounds=[0],
|
|
upper_bounds=[0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="pd.Series(x[0] == x[0])",
|
|
name="x0_eq_x0_series",
|
|
bounded_exprs=lambda x, y: pd.Series(x[0] == x[0]),
|
|
constraint_count=1,
|
|
lower_bounds=[0],
|
|
upper_bounds=[0],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] >= x[0]",
|
|
name="x0_ge_x0",
|
|
bounded_exprs=lambda x, y: x[0] >= x[0],
|
|
constraint_count=1,
|
|
lower_bounds=[0],
|
|
upper_bounds=[math.inf],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
# x[0] - x[0] <= 3
|
|
testcase_name="x[0] - 1 <= x[0] + 2",
|
|
name="x0c_le_x0c",
|
|
bounded_exprs=lambda x, y: pd.Series(x[0] - 1 <= x[0] + 2),
|
|
constraint_count=1,
|
|
lower_bounds=[-math.inf],
|
|
upper_bounds=[3],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
# x[0] - x[0] == 3
|
|
testcase_name="x[0] - 1 == x[0] + 2",
|
|
name="x0c_eq_x0c",
|
|
bounded_exprs=lambda x, y: pd.Series(x[0] - 1 == x[0] + 2),
|
|
constraint_count=1,
|
|
lower_bounds=[3],
|
|
upper_bounds=[3],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
# x[0] - x[0] >= 3
|
|
testcase_name="x[0] - 1 >= x[0] + 2",
|
|
name="x0c_ge_x0c",
|
|
bounded_exprs=lambda x, y: pd.Series(x[0] - 1 >= x[0] + 2),
|
|
constraint_count=1,
|
|
lower_bounds=[3],
|
|
upper_bounds=[math.inf],
|
|
expression_terms=lambda x, y: [{}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] <= x[1]",
|
|
name="x0_le_x1",
|
|
bounded_exprs=lambda x, y: x[0] <= x[1],
|
|
constraint_count=1,
|
|
lower_bounds=[-math.inf],
|
|
upper_bounds=[0],
|
|
expression_terms=lambda x, y: [{x[0]: 1, x[1]: -1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] == x[1]",
|
|
name="x0_eq_x1",
|
|
bounded_exprs=lambda x, y: x[0] == x[1],
|
|
constraint_count=1,
|
|
lower_bounds=[0],
|
|
upper_bounds=[0],
|
|
expression_terms=lambda x, y: [{x[0]: 1, x[1]: -1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x[0] >= x[1]",
|
|
name="x0_ge_x1",
|
|
bounded_exprs=lambda x, y: x[0] >= x[1],
|
|
constraint_count=1,
|
|
lower_bounds=[0],
|
|
upper_bounds=[math.inf],
|
|
expression_terms=lambda x, y: [{x[0]: 1, x[1]: -1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
# x[0] - x[1] <= -3
|
|
testcase_name="x[0] + 1 <= x[1] - 2",
|
|
name="x0c_le_x1c",
|
|
bounded_exprs=lambda x, y: x[0] + 1 <= x[1] - 2,
|
|
constraint_count=1,
|
|
lower_bounds=[-math.inf],
|
|
upper_bounds=[-3],
|
|
expression_terms=lambda x, y: [{x[0]: 1, x[1]: -1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
# x[0] - x[1] == -3
|
|
testcase_name="x[0] + 1 == x[1] - 2",
|
|
name="x0c_eq_x1c",
|
|
bounded_exprs=lambda x, y: x[0] + 1 == x[1] - 2,
|
|
constraint_count=1,
|
|
lower_bounds=[-3],
|
|
upper_bounds=[-3],
|
|
expression_terms=lambda x, y: [{x[0]: 1, x[1]: -1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
# x[0] - x[1] >= -3
|
|
testcase_name="x[0] + 1 >= x[1] - 2",
|
|
name="x0c_ge_x1c",
|
|
bounded_exprs=lambda x, y: pd.Series(x[0] + 1 >= x[1] - 2),
|
|
constraint_count=1,
|
|
lower_bounds=[-3],
|
|
upper_bounds=[math.inf],
|
|
expression_terms=lambda x, y: [{x[0]: 1, x[1]: -1}],
|
|
expression_offsets=[0],
|
|
),
|
|
dict(
|
|
testcase_name="x <= 0",
|
|
name="x_le_c",
|
|
bounded_exprs=lambda x, y: x.apply(lambda expr: expr <= 0),
|
|
constraint_count=3,
|
|
lower_bounds=[-math.inf] * 3,
|
|
upper_bounds=[0] * 3,
|
|
expression_terms=lambda x, y: [{xi: 1} for xi in x],
|
|
expression_offsets=[0] * 3,
|
|
),
|
|
dict(
|
|
testcase_name="x >= 0",
|
|
name="x_ge_c",
|
|
bounded_exprs=lambda x, y: x.apply(lambda expr: expr >= 0),
|
|
constraint_count=3,
|
|
lower_bounds=[0] * 3,
|
|
upper_bounds=[math.inf] * 3,
|
|
expression_terms=lambda x, y: [{xi: 1} for xi in x],
|
|
expression_offsets=[0] * 3,
|
|
),
|
|
dict(
|
|
testcase_name="x == 0",
|
|
name="x_eq_c",
|
|
bounded_exprs=lambda x, y: x.apply(lambda expr: expr == 0),
|
|
constraint_count=3,
|
|
lower_bounds=[0] * 3,
|
|
upper_bounds=[0] * 3,
|
|
expression_terms=lambda x, y: [{xi: 1} for xi in x],
|
|
expression_offsets=[0] * 3,
|
|
),
|
|
dict(
|
|
testcase_name="y == 0",
|
|
name="y_eq_c",
|
|
bounded_exprs=(lambda x, y: y.apply(lambda expr: expr == 0)),
|
|
constraint_count=2 * 3,
|
|
lower_bounds=[0] * 2 * 3,
|
|
upper_bounds=[0] * 2 * 3,
|
|
expression_terms=lambda x, y: [{yi: 1} for yi in y],
|
|
expression_offsets=[0] * 3 * 2,
|
|
),
|
|
dict(
|
|
testcase_name='y.groupby("i").sum() == 0',
|
|
name="ygroupbyi_eq_c",
|
|
bounded_exprs=(
|
|
lambda x, y: y.groupby("i").sum().apply(lambda expr: expr == 0)
|
|
),
|
|
constraint_count=2,
|
|
lower_bounds=[0] * 2,
|
|
upper_bounds=[0] * 2,
|
|
expression_terms=lambda x, y: [
|
|
{y[1, "a"]: 1, y[1, "b"]: 1, y[1, "c"]: 1},
|
|
{y[2, "a"]: 1, y[2, "b"]: 1, y[2, "c"]: 1},
|
|
],
|
|
expression_offsets=[0] * 2,
|
|
),
|
|
dict(
|
|
testcase_name='y.groupby("j").sum() == 0',
|
|
name="ygroupbyj_eq_c",
|
|
bounded_exprs=(
|
|
lambda x, y: y.groupby("j").sum().apply(lambda expr: expr == 0)
|
|
),
|
|
constraint_count=3,
|
|
lower_bounds=[0] * 3,
|
|
upper_bounds=[0] * 3,
|
|
expression_terms=lambda x, y: [
|
|
{y[1, "a"]: 1, y[2, "a"]: 1},
|
|
{y[1, "b"]: 1, y[2, "b"]: 1},
|
|
{y[1, "c"]: 1, y[2, "c"]: 1},
|
|
],
|
|
expression_offsets=[0] * 3,
|
|
),
|
|
dict(
|
|
testcase_name='3 * x + y.groupby("i").sum() <= 0',
|
|
name="broadcast_align_fill",
|
|
bounded_exprs=(
|
|
lambda x, y: (3 * x)
|
|
.add(y.groupby("i").sum(), fill_value=0)
|
|
.apply(lambda expr: expr <= 0)
|
|
),
|
|
constraint_count=3,
|
|
lower_bounds=[-math.inf] * 3,
|
|
upper_bounds=[0] * 3,
|
|
expression_terms=lambda x, y: [
|
|
{x[0]: 3},
|
|
{x[1]: 3, y[1, "a"]: 1, y[1, "b"]: 1, y[1, "c"]: 1},
|
|
{x[2]: 3, y[2, "a"]: 1, y[2, "b"]: 1, y[2, "c"]: 1},
|
|
],
|
|
expression_offsets=[0] * 3,
|
|
),
|
|
]
|
|
|
|
def create_test_model(self, name, bounded_exprs):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(
|
|
name="x",
|
|
index=pd.Index(range(3), name="i"),
|
|
)
|
|
y = model.new_var_series(
|
|
name="y",
|
|
index=pd.MultiIndex.from_product(
|
|
((1, 2), ("a", "b", "c")), names=["i", "j"]
|
|
),
|
|
)
|
|
model.add(name=name, ct=bounded_exprs(x, y))
|
|
return model, {"x": x, "y": y}
|
|
|
|
@parameterized.named_parameters(
|
|
# pylint: disable=g-complex-comprehension
|
|
{
|
|
f: tc[f]
|
|
for f in [
|
|
"testcase_name",
|
|
"name",
|
|
"bounded_exprs",
|
|
"constraint_count",
|
|
]
|
|
}
|
|
for tc in constraint_test_cases
|
|
)
|
|
def test_get_linear_constraints(
|
|
self,
|
|
name,
|
|
bounded_exprs,
|
|
constraint_count,
|
|
):
|
|
model, _ = self.create_test_model(name, bounded_exprs)
|
|
linear_constraints = model.get_linear_constraints()
|
|
self.assertIsInstance(linear_constraints, pd.Index)
|
|
self.assertLen(linear_constraints, constraint_count)
|
|
|
|
def test_get_linear_constraints_empty(self):
|
|
linear_constraints = mb.ModelBuilder().get_linear_constraints()
|
|
self.assertIsInstance(linear_constraints, pd.Index)
|
|
self.assertEmpty(linear_constraints)
|
|
|
|
@parameterized.named_parameters(
|
|
# pylint: disable=g-complex-comprehension
|
|
{
|
|
f: tc[f]
|
|
for f in [
|
|
"testcase_name",
|
|
"name",
|
|
"bounded_exprs",
|
|
"lower_bounds",
|
|
]
|
|
}
|
|
for tc in constraint_test_cases
|
|
)
|
|
def test_get_linear_constraint_lower_bounds(
|
|
self,
|
|
name,
|
|
bounded_exprs,
|
|
lower_bounds,
|
|
):
|
|
model, _ = self.create_test_model(name, bounded_exprs)
|
|
for linear_constraint_lower_bounds in (
|
|
model.get_linear_constraint_lower_bounds(),
|
|
model.get_linear_constraint_lower_bounds(model.get_linear_constraints()),
|
|
):
|
|
self.assertSequenceAlmostEqual(linear_constraint_lower_bounds, lower_bounds)
|
|
|
|
@parameterized.named_parameters(
|
|
# pylint: disable=g-complex-comprehension
|
|
{
|
|
f: tc[f]
|
|
for f in [
|
|
"testcase_name",
|
|
"name",
|
|
"bounded_exprs",
|
|
"upper_bounds",
|
|
]
|
|
}
|
|
for tc in constraint_test_cases
|
|
)
|
|
def test_get_linear_constraint_upper_bounds(
|
|
self,
|
|
name,
|
|
bounded_exprs,
|
|
upper_bounds,
|
|
):
|
|
model, _ = self.create_test_model(name, bounded_exprs)
|
|
for linear_constraint_upper_bounds in (
|
|
model.get_linear_constraint_upper_bounds(),
|
|
model.get_linear_constraint_upper_bounds(model.get_linear_constraints()),
|
|
):
|
|
self.assertSequenceAlmostEqual(linear_constraint_upper_bounds, upper_bounds)
|
|
|
|
@parameterized.named_parameters(
|
|
# pylint: disable=g-complex-comprehension
|
|
{
|
|
f: tc[f]
|
|
for f in [
|
|
"testcase_name",
|
|
"name",
|
|
"bounded_exprs",
|
|
"expression_terms",
|
|
"expression_offsets",
|
|
]
|
|
}
|
|
for tc in constraint_test_cases
|
|
)
|
|
def test_get_linear_constraint_expressions(
|
|
self,
|
|
name,
|
|
bounded_exprs,
|
|
expression_terms,
|
|
expression_offsets,
|
|
):
|
|
model, variables = self.create_test_model(name, bounded_exprs)
|
|
x = variables["x"]
|
|
y = variables["y"]
|
|
for linear_constraint_expressions in (
|
|
model.get_linear_constraint_expressions(),
|
|
model.get_linear_constraint_expressions(model.get_linear_constraints()),
|
|
):
|
|
expr_terms = expression_terms(x, y)
|
|
self.assertLen(linear_constraint_expressions, len(expr_terms))
|
|
for expr, expr_term in zip(linear_constraint_expressions, expr_terms):
|
|
self.assertDictEqual(build_dict(expr), expr_term)
|
|
self.assertSequenceAlmostEqual(
|
|
[expr._offset for expr in linear_constraint_expressions],
|
|
expression_offsets,
|
|
)
|
|
|
|
|
|
class ModelBuilderObjectiveTest(parameterized.TestCase):
|
|
_expressions = (
|
|
lambda x, y: -3,
|
|
lambda x, y: 0,
|
|
lambda x, y: 10,
|
|
lambda x, y: x[0],
|
|
lambda x, y: x[1],
|
|
lambda x, y: x[2],
|
|
lambda x, y: y[0],
|
|
lambda x, y: y[1],
|
|
lambda x, y: x[0] + 5,
|
|
lambda x, y: -3 + y[1],
|
|
lambda x, y: 3 * x[0],
|
|
lambda x, y: x[0] * 3 * 5 - 3,
|
|
lambda x, y: x.sum(),
|
|
lambda x, y: 101 + 2 * 3 * x.sum(),
|
|
lambda x, y: x.sum() * 2,
|
|
lambda x, y: sum(y),
|
|
lambda x, y: x.sum() + 2 * y.sum() + 3,
|
|
)
|
|
_variable_indices = (
|
|
pd.Index(range(3)),
|
|
pd.Index(range(3), name="i"),
|
|
pd.Index(range(10), name="i"),
|
|
)
|
|
|
|
def assertLinearExpressionAlmostEqual(
|
|
self,
|
|
expr1: mb._LinearExpression,
|
|
expr2: mb._LinearExpression,
|
|
) -> None:
|
|
"""Test that the two linear expressions are almost equal."""
|
|
self.assertEqual(len(expr1.variable_indices), len(expr2.variable_indices))
|
|
if expr1.variable_indices:
|
|
self.assertSequenceEqual(expr1.variable_indices, expr2.variable_indices)
|
|
self.assertSequenceAlmostEqual(
|
|
expr1.coefficients, expr2.coefficients, places=5
|
|
)
|
|
else:
|
|
self.assertEmpty(expr2.coefficients)
|
|
self.assertAlmostEqual(expr1._offset, expr2._offset)
|
|
|
|
@parameterized.product(
|
|
expression=_expressions,
|
|
variable_indices=_variable_indices,
|
|
is_maximize=(True, False),
|
|
)
|
|
def test_set_objective(
|
|
self,
|
|
expression: Callable[[pd.Series, pd.Series], mb.LinearExprT],
|
|
variable_indices: pd.Index,
|
|
is_maximize: bool,
|
|
):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=variable_indices)
|
|
y = model.new_var_series(name="y", index=variable_indices)
|
|
objective_expression = mb._as_flat_linear_expression(expression(x, y))
|
|
if is_maximize:
|
|
model.maximize(objective_expression)
|
|
else:
|
|
model.minimize(objective_expression)
|
|
got_objective_expression = model.objective_expression()
|
|
self.assertLinearExpressionAlmostEqual(
|
|
got_objective_expression, objective_expression
|
|
)
|
|
|
|
def test_set_new_objective(self):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=pd.Index(range(3)))
|
|
old_objective_expression = 1
|
|
new_objective_expression = mb._as_flat_linear_expression(x.sum() - 2.3)
|
|
|
|
# Set and check for old objective.
|
|
model.maximize(old_objective_expression)
|
|
got_objective_expression = model.objective_expression()
|
|
for var_coeff in got_objective_expression.coefficients:
|
|
self.assertAlmostEqual(var_coeff, 0)
|
|
self.assertAlmostEqual(got_objective_expression._offset, 1)
|
|
|
|
# Set to a new objective and check that it is different.
|
|
model.minimize(new_objective_expression)
|
|
got_objective_expression = model.objective_expression()
|
|
self.assertLinearExpressionAlmostEqual(
|
|
got_objective_expression, new_objective_expression
|
|
)
|
|
|
|
@parameterized.product(
|
|
expression=_expressions,
|
|
variable_indices=_variable_indices,
|
|
)
|
|
def test_minimize(
|
|
self,
|
|
expression: Callable[[pd.Series, pd.Series], mb.LinearExprT],
|
|
variable_indices: pd.Index,
|
|
):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=variable_indices)
|
|
y = model.new_var_series(name="y", index=variable_indices)
|
|
objective_expression = mb._as_flat_linear_expression(expression(x, y))
|
|
model.minimize(objective_expression)
|
|
got_objective_expression = model.objective_expression()
|
|
self.assertLinearExpressionAlmostEqual(
|
|
got_objective_expression, objective_expression
|
|
)
|
|
|
|
@parameterized.product(
|
|
expression=_expressions,
|
|
variable_indices=_variable_indices,
|
|
)
|
|
def test_maximize(
|
|
self,
|
|
expression: Callable[[pd.Series, pd.Series], float],
|
|
variable_indices: pd.Index,
|
|
):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(name="x", index=variable_indices)
|
|
y = model.new_var_series(name="y", index=variable_indices)
|
|
objective_expression = mb._as_flat_linear_expression(expression(x, y))
|
|
model.maximize(objective_expression)
|
|
got_objective_expression = model.objective_expression()
|
|
self.assertLinearExpressionAlmostEqual(
|
|
got_objective_expression, objective_expression
|
|
)
|
|
|
|
|
|
class ModelBuilderProtoTest(absltest.TestCase):
|
|
def test_export_to_proto(self):
|
|
expected = linear_solver_pb2.MPModelProto()
|
|
text_format.Parse(
|
|
"""
|
|
name: "test_name"
|
|
maximize: true
|
|
objective_offset: 0
|
|
variable {
|
|
lower_bound: 0
|
|
upper_bound: 1000
|
|
objective_coefficient: 1
|
|
is_integer: false
|
|
name: "x[0]"
|
|
}
|
|
variable {
|
|
lower_bound: 0
|
|
upper_bound: 1000
|
|
objective_coefficient: 1
|
|
is_integer: false
|
|
name: "x[1]"
|
|
}
|
|
constraint {
|
|
var_index: 0
|
|
coefficient: 1
|
|
lower_bound: -inf
|
|
upper_bound: 10
|
|
name: "Ct[0]"
|
|
}
|
|
constraint {
|
|
var_index: 1
|
|
coefficient: 1
|
|
lower_bound: -inf
|
|
upper_bound: 10
|
|
name: "Ct[1]"
|
|
}
|
|
""",
|
|
expected,
|
|
)
|
|
model = mb.ModelBuilder()
|
|
model.name = "test_name"
|
|
x = model.new_var_series("x", pd.Index(range(2)), 0, 1000)
|
|
model.add(ct=x.apply(lambda expr: expr <= 10), name="Ct")
|
|
model.maximize(x.sum())
|
|
self.assertEqual(str(expected), str(model.export_to_proto()))
|
|
|
|
|
|
class SolverTest(parameterized.TestCase):
|
|
_solvers = (
|
|
{
|
|
"name": "sat",
|
|
"is_integer": True, # CP-SAT supports only pure integer variables.
|
|
},
|
|
{
|
|
"name": "glop",
|
|
"solver_specific_parameters": "use_preprocessing: False",
|
|
"is_integer": False, # GLOP does not properly support integers.
|
|
},
|
|
{
|
|
"name": "scip",
|
|
"is_integer": False,
|
|
},
|
|
{
|
|
"name": "scip",
|
|
"is_integer": True,
|
|
},
|
|
)
|
|
_variable_indices = (
|
|
pd.Index(range(0)), # No variables.
|
|
pd.Index(range(1)), # Single variable.
|
|
pd.Index(range(3)), # Multiple variables.
|
|
)
|
|
_variable_bounds = (-1, 0, 10.1)
|
|
_solve_statuses = (
|
|
mb.SolveStatus.OPTIMAL,
|
|
mb.SolveStatus.INFEASIBLE,
|
|
mb.SolveStatus.UNBOUNDED,
|
|
)
|
|
_set_objectives = (True, False)
|
|
_objective_senses = (True, False)
|
|
_objective_expressions = (
|
|
sum,
|
|
lambda x: sum(x) + 5.2,
|
|
lambda x: -10.1,
|
|
lambda x: 0,
|
|
)
|
|
|
|
def _create_model(
|
|
self,
|
|
variable_indices: pd.Index = pd.Index(range(3)),
|
|
variable_bound: float = 0,
|
|
is_integer: bool = False,
|
|
solve_status: mb.SolveStatus = mb.SolveStatus.OPTIMAL,
|
|
set_objective: bool = True,
|
|
is_maximize: bool = True,
|
|
objective_expression: Callable[[pd.Series], float] = lambda x: x.sum(),
|
|
) -> mb.ModelBuilder:
|
|
"""Constructs an optimization problem.
|
|
|
|
It has the following formulation:
|
|
|
|
```
|
|
maximize / minimize objective_expression(x)
|
|
satisfying constraints
|
|
(if solve_status != UNBOUNDED and objective_sense == MAXIMIZE)
|
|
x[variable_indices] <= variable_bound
|
|
(if solve_status != UNBOUNDED and objective_sense == MINIMIZE)
|
|
x[variable_indices] >= variable_bound
|
|
x[variable_indices] is_integer
|
|
False (if solve_status == INFEASIBLE)
|
|
```
|
|
|
|
Args:
|
|
variable_indices (pd.Index): The indices of the variable(s).
|
|
variable_bound (float): The upper- or lower-bound(s) of the variable(s).
|
|
is_integer (bool): Whether the variables should be integer.
|
|
solve_status (mb.SolveStatus): The solve status to target.
|
|
set_objective (bool): Whether to set the objective of the model.
|
|
is_maximize (bool): Whether to maximize the objective of the model.
|
|
objective_expression (Callable[[pd.Series], float]): The expression to
|
|
maximize or minimize if set_objective=True.
|
|
|
|
Returns:
|
|
mb.ModelBuilder: The resulting problem.
|
|
"""
|
|
model = mb.ModelBuilder()
|
|
# Variable(s)
|
|
x = model.new_var_series(
|
|
name="x",
|
|
index=pd.Index(variable_indices),
|
|
is_integral=is_integer,
|
|
)
|
|
# Constraint(s)
|
|
if solve_status == mb.SolveStatus.INFEASIBLE:
|
|
# Force infeasibility here to test that we get pd.NA later.
|
|
model.add(False, name="bool")
|
|
elif solve_status != mb.SolveStatus.UNBOUNDED:
|
|
if is_maximize:
|
|
model.add(x.apply(lambda xi: xi <= variable_bound), "upper_bound")
|
|
else:
|
|
model.add(x.apply(lambda xi: xi >= variable_bound), "lower_bound")
|
|
# Objective
|
|
if set_objective:
|
|
if is_maximize:
|
|
model.maximize(objective_expression(x))
|
|
else:
|
|
model.minimize(objective_expression(x))
|
|
return model
|
|
|
|
@parameterized.product(
|
|
solver=_solvers,
|
|
variable_indices=_variable_indices,
|
|
variable_bound=_variable_bounds,
|
|
solve_status=_solve_statuses,
|
|
set_objective=_set_objectives,
|
|
is_maximize=_objective_senses,
|
|
objective_expression=_objective_expressions,
|
|
)
|
|
def test_solve_status(
|
|
self,
|
|
solver: Dict[str, Union[str, Mapping[str, Any], bool]],
|
|
variable_indices: pd.Index,
|
|
variable_bound: float,
|
|
solve_status: mb.SolveStatus,
|
|
set_objective: bool,
|
|
is_maximize: bool,
|
|
objective_expression: Callable[[pd.Series], float],
|
|
):
|
|
model = self._create_model(
|
|
variable_indices=variable_indices,
|
|
variable_bound=variable_bound,
|
|
is_integer=solver["is_integer"],
|
|
solve_status=solve_status,
|
|
set_objective=set_objective,
|
|
is_maximize=is_maximize,
|
|
objective_expression=objective_expression,
|
|
)
|
|
model_solver = mb.ModelSolver(solver["name"])
|
|
if not model_solver.solver_is_supported():
|
|
print(f'Solver {solver["name"]} is not supported')
|
|
return
|
|
if solver.get("solver_specific_parameters"):
|
|
model_solver.set_solver_specific_parameters(
|
|
solver.get("solver_specific_parameters")
|
|
)
|
|
got_solve_status = model_solver.solve(model)
|
|
|
|
# pylint: disable=g-explicit-length-test
|
|
# (we disable explicit-length-test here because `variable_indices: pd.Index`
|
|
# evaluates to an ambiguous boolean value.)
|
|
if len(variable_indices) > 0: # Test cases with >=1 variable.
|
|
self.assertNotEmpty(variable_indices)
|
|
if (
|
|
isinstance(
|
|
objective_expression(model.get_variables()),
|
|
(int, float),
|
|
)
|
|
and solve_status != mb.SolveStatus.INFEASIBLE
|
|
):
|
|
# Feasibility implies optimality when objective is a constant term.
|
|
self.assertEqual(got_solve_status, mb.SolveStatus.OPTIMAL)
|
|
elif not set_objective and solve_status != mb.SolveStatus.INFEASIBLE:
|
|
# Feasibility implies optimality when objective is not set.
|
|
self.assertEqual(got_solve_status, mb.SolveStatus.OPTIMAL)
|
|
elif solver["name"] == "sat" and got_solve_status == 8:
|
|
# CP_SAT returns status=8 for some infeasible and unbounded cases.
|
|
self.assertIn(
|
|
solve_status,
|
|
(mb.SolveStatus.INFEASIBLE, mb.SolveStatus.UNBOUNDED),
|
|
)
|
|
else:
|
|
self.assertEqual(got_solve_status, solve_status)
|
|
elif solve_status == mb.SolveStatus.UNBOUNDED:
|
|
# Unbounded problems are optimal when there are no variables.
|
|
self.assertEqual(got_solve_status, mb.SolveStatus.OPTIMAL)
|
|
else:
|
|
self.assertEqual(got_solve_status, solve_status)
|
|
|
|
@parameterized.product(
|
|
solver=_solvers,
|
|
variable_indices=_variable_indices,
|
|
variable_bound=_variable_bounds,
|
|
solve_status=_solve_statuses,
|
|
set_objective=_set_objectives,
|
|
is_maximize=_objective_senses,
|
|
objective_expression=_objective_expressions,
|
|
)
|
|
def test_get_variable_values(
|
|
self,
|
|
solver: Dict[str, Union[str, Mapping[str, Any], bool]],
|
|
variable_indices: pd.Index,
|
|
variable_bound: float,
|
|
solve_status: mb.SolveStatus,
|
|
set_objective: bool,
|
|
is_maximize: bool,
|
|
objective_expression: Callable[[pd.Series], float],
|
|
):
|
|
model = self._create_model(
|
|
variable_indices=variable_indices,
|
|
variable_bound=variable_bound,
|
|
is_integer=solver["is_integer"],
|
|
solve_status=solve_status,
|
|
set_objective=set_objective,
|
|
is_maximize=is_maximize,
|
|
objective_expression=objective_expression,
|
|
)
|
|
model_solver = mb.ModelSolver(solver["name"])
|
|
if not model_solver.solver_is_supported():
|
|
print(f'Solver {solver["name"]} is not supported')
|
|
return
|
|
if solver.get("solver_specific_parameters"):
|
|
model_solver.set_solver_specific_parameters(
|
|
solver.get("solver_specific_parameters")
|
|
)
|
|
got_solve_status = model_solver.solve(model)
|
|
variables = model.get_variables()
|
|
variable_values = model_solver.values(variables)
|
|
# Test the type of `variable_values` (we always get pd.Series)
|
|
self.assertIsInstance(variable_values, pd.Series)
|
|
# Test the index of `variable_values` (match the input variables [if any])
|
|
self.assertSequenceAlmostEqual(
|
|
variable_values.index,
|
|
mb._get_index(model._get_variables(variables)),
|
|
)
|
|
if got_solve_status not in (
|
|
mb.SolveStatus.OPTIMAL,
|
|
mb.SolveStatus.FEASIBLE,
|
|
):
|
|
# self.assertSequenceAlmostEqual does not work here because we cannot do
|
|
# equality comparison for NA values (NAs will propagate and we will get
|
|
# 'TypeError: boolean value of NA is ambiguous')
|
|
for variable_value in variable_values:
|
|
self.assertTrue(pd.isna(variable_value))
|
|
elif set_objective and not isinstance(
|
|
objective_expression(model.get_variables()),
|
|
(int, float),
|
|
):
|
|
# The variable values are only well-defined when the objective is set
|
|
# and depends on the variable(s).
|
|
if not solver["is_integer"]:
|
|
self.assertSequenceAlmostEqual(
|
|
variable_values, [variable_bound] * len(variable_values)
|
|
)
|
|
elif is_maximize:
|
|
self.assertTrue(solver["is_integer"]) # Assert a known assumption.
|
|
self.assertSequenceAlmostEqual(
|
|
variable_values,
|
|
[math.floor(variable_bound)] * len(variable_values),
|
|
)
|
|
else:
|
|
self.assertTrue(solver["is_integer"]) # Assert a known assumption.
|
|
self.assertSequenceAlmostEqual(
|
|
variable_values,
|
|
[math.ceil(variable_bound)] * len(variable_values),
|
|
)
|
|
|
|
@parameterized.product(
|
|
solver=_solvers,
|
|
variable_indices=_variable_indices,
|
|
variable_bound=_variable_bounds,
|
|
solve_status=_solve_statuses,
|
|
set_objective=_set_objectives,
|
|
is_maximize=_objective_senses,
|
|
objective_expression=_objective_expressions,
|
|
)
|
|
def test_get_objective_value(
|
|
self,
|
|
solver: Dict[str, Union[str, Mapping[str, Any], bool]],
|
|
variable_indices: pd.Index,
|
|
variable_bound: float,
|
|
solve_status: mb.SolveStatus,
|
|
set_objective: bool,
|
|
is_maximize: bool,
|
|
objective_expression: Callable[[pd.Series], float],
|
|
):
|
|
model = self._create_model(
|
|
variable_indices=variable_indices,
|
|
variable_bound=variable_bound,
|
|
is_integer=solver["is_integer"],
|
|
solve_status=solve_status,
|
|
set_objective=set_objective,
|
|
is_maximize=is_maximize,
|
|
objective_expression=objective_expression,
|
|
)
|
|
model_solver = mb.ModelSolver(solver["name"])
|
|
if not model_solver.solver_is_supported():
|
|
print(f'Solver {solver["name"]} is not supported')
|
|
return
|
|
if solver.get("solver_specific_parameters"):
|
|
model_solver.set_solver_specific_parameters(
|
|
solver.get("solver_specific_parameters")
|
|
)
|
|
got_status = model_solver.solve(model)
|
|
|
|
# Test objective value
|
|
if got_status not in (mb.SolveStatus.OPTIMAL, mb.SolveStatus.FEASIBLE):
|
|
self.assertTrue(pd.isna(model_solver.objective_value))
|
|
return
|
|
if set_objective:
|
|
variable_values = model_solver.values(model.get_variables())
|
|
self.assertAlmostEqual(
|
|
model_solver.objective_value,
|
|
objective_expression(variable_values),
|
|
)
|
|
else:
|
|
self.assertAlmostEqual(model_solver.objective_value, 0)
|
|
|
|
|
|
class ModelBuilderExamplesTest(absltest.TestCase):
|
|
def test_simple_problem(self):
|
|
# max 5x1 + 4x2 + 3x3
|
|
# s.t 2x1 + 3x2 + x3 <= 5
|
|
# 4x1 + x2 + 2x3 <= 11
|
|
# 3x1 + 4x2 + 2x3 <= 8
|
|
# x1, x2, x3 >= 0
|
|
# Values = (2,0,1)
|
|
# Reduced Costs = (0,-3,0)
|
|
model = mb.ModelBuilder()
|
|
x = model.new_var_series(
|
|
"x", pd.Index(range(3)), lower_bounds=0, is_integral=True
|
|
)
|
|
self.assertLen(model.get_variables(), 3)
|
|
model.maximize(x.dot([5, 4, 3]))
|
|
model.add(x.dot([2, 3, 1]) <= 5)
|
|
model.add(x.dot([4, 1, 2]) <= 11)
|
|
model.add(x.dot([3, 4, 2]) <= 8)
|
|
self.assertLen(model.get_linear_constraints(), 3)
|
|
solver = mb.ModelSolver("glop")
|
|
test_red_cost = solver.reduced_costs(model.get_variables())
|
|
test_dual_values = solver.dual_values(model.get_variables())
|
|
self.assertLen(test_red_cost, 3)
|
|
self.assertLen(test_dual_values, 3)
|
|
for reduced_cost in test_red_cost:
|
|
self.assertTrue(pd.isna(reduced_cost))
|
|
for dual_value in test_dual_values:
|
|
self.assertTrue(pd.isna(dual_value))
|
|
run = solver.solve(model)
|
|
self.assertEqual(run, mb.SolveStatus.OPTIMAL)
|
|
i = solver.values(model.get_variables())
|
|
self.assertSequenceAlmostEqual(i, [2, 0, 1])
|
|
red_cost = solver.reduced_costs(model.get_variables())
|
|
dual_val = solver.dual_values(model.get_linear_constraints())
|
|
self.assertSequenceAlmostEqual(red_cost, [0, -3, 0])
|
|
self.assertSequenceAlmostEqual(dual_val, [1, 0, 1])
|
|
self.assertAlmostEqual(2, solver.value(x[0]))
|
|
self.assertAlmostEqual(0, solver.reduced_cost((x[0])))
|
|
self.assertAlmostEqual(-3, solver.reduced_cost((x[1])))
|
|
self.assertAlmostEqual(0, solver.reduced_cost((x[2])))
|
|
self.assertAlmostEqual(1, solver.dual_value((x[0])))
|
|
self.assertAlmostEqual(0, solver.dual_value((x[1])))
|
|
self.assertAlmostEqual(1, solver.dual_value((x[2])))
|
|
|
|
def test_graph_k_color(self):
|
|
# Assign a color to each vertex of graph, s.t that no two adjacent
|
|
# vertices share the same color. Assume N vertices, and max number of
|
|
# colors is K
|
|
# Consider graph with edges:
|
|
# Edge 1: (0,1)
|
|
# Edge 2: (0,2)
|
|
# Edge 3: (1,3)
|
|
# Trying to color graph with at most 3 colors (0, 1, 2)
|
|
# Two sets of variables:
|
|
# x - pandas series representing coloring status of nodes
|
|
# y - pandas series indicating whether color has been used
|
|
# Min: y0 + y1 + y2
|
|
# s.t: Every vertex must be assigned exactly one color
|
|
# if two vertices are adjacent they cannot have the same color
|
|
|
|
model = mb.ModelBuilder()
|
|
num_colors = 3
|
|
num_nodes = 4
|
|
x = model.new_var_series(
|
|
"x",
|
|
pd.MultiIndex.from_product(
|
|
(range(num_nodes), range(num_colors)),
|
|
names=["node", "color"],
|
|
),
|
|
lower_bounds=0,
|
|
upper_bounds=1,
|
|
is_integral=True,
|
|
)
|
|
y = model.new_var_series(
|
|
"y",
|
|
pd.Index(range(num_colors), name="color"),
|
|
lower_bounds=0,
|
|
upper_bounds=1,
|
|
is_integral=True,
|
|
)
|
|
model.minimize(y.dot([1, 1, 1]))
|
|
# Every vertex must be assigned exactly one color
|
|
model.add(x.groupby("node").sum().apply(lambda expr: expr == 1))
|
|
# If a vertex i is assigned to a color j then color j is used:
|
|
# namely, we re-arrange the terms to express: "x - y <= 0".
|
|
model.add(x.sub(y, fill_value=0).apply(lambda expr: expr <= 0))
|
|
# if two vertices are adjacent they cannot have the same color j
|
|
for j in range(num_colors):
|
|
model.add(x[0, j] + x[1, j] <= 1)
|
|
model.add(x[0, j] + x[2, j] <= 1)
|
|
model.add(x[1, j] + x[3, j] <= 1)
|
|
solver = mb.ModelSolver("sat")
|
|
run = solver.solve(model)
|
|
self.assertEqual(run, mb.SolveStatus.OPTIMAL)
|
|
self.assertEqual(solver.objective_value, 2)
|
|
|
|
def test_knapsack_problem(self):
|
|
# Basic Knapsack Problem: Given N items,
|
|
# with N different weights and values, find the maximum possible value of
|
|
# the Items while meeting a weight requirement
|
|
# We have 3 items:
|
|
# Item x1: Weight = 10, Value = 60
|
|
# Item x2: Weight = 20, Value = 100
|
|
# Item x3: Weight = 30, Value = 120
|
|
# Max: 60x1 + 100x2 + 120x3
|
|
# s.t: 10x1 + 20x2 + 30x3 <= 50
|
|
model = mb.ModelBuilder()
|
|
x = model.new_bool_var_series("x", pd.Index(range(3)))
|
|
self.assertLen(model.get_variables(), 3)
|
|
model.maximize(x.dot([60, 100, 120]))
|
|
model.add(x.dot([10, 20, 30]) <= 50)
|
|
self.assertLen(model.get_linear_constraints(), 1)
|
|
solver = mb.ModelSolver("sat")
|
|
run = solver.solve(model)
|
|
self.assertEqual(run, mb.SolveStatus.OPTIMAL)
|
|
i = solver.values(model.get_variables())
|
|
self.assertEqual(solver.objective_value, 220)
|
|
self.assertSequenceAlmostEqual(i, [0, 1, 1])
|
|
|
|
def test_max_flow_problem(self):
|
|
# Testing max flow problem with 8 nodes
|
|
# 0-8, source 0, target 8
|
|
# Edges:
|
|
# (0,1), (0,2), (0,3), (1,4), (2,4),(2,5), (2,6), (3,5), (4,7), (5,7), (6,7)
|
|
# Variables: flow_var- pandas series of flows, indexed by edges
|
|
# Max: flow[(0,1)] + flow[(0,2)] + flow[(0,3)]
|
|
# S.t: flow[(0,1)] <= 3
|
|
# flow[(0,2)] <= 2
|
|
# flow[(0,3)] <= 2
|
|
# flow[(1,4)] <= 5
|
|
# flow[(1,5)] <= 1
|
|
# flow[(2,4)] <= 1
|
|
# flow[(2,5)] <= 3
|
|
# flow[(2,6)] <= 1
|
|
# flow[(3,5)] <= 1
|
|
# flow[(4,7)] <= 4
|
|
# flow[(5,7)] <= 2
|
|
# flow[(6,7)] <= 4
|
|
# Flow conservation constraints:
|
|
# flow[(0,1)] = flow[(1,4)] + flow[(1,5)]
|
|
# flow[(0,2)] = flow[(2,4)] + flow[(2,5)] + flow[(2,6)]
|
|
# flow[(0,3)] = flow[(3,5)]
|
|
# flow[(1,4)] + flow[(2,4)] = flow[(4,7)]
|
|
# flow[(1,5)] + flow[(2,5)] + flow[(3,5)] = X[(5,7)]
|
|
# flow[(2,6)] = flow[(6,7)]
|
|
|
|
model = mb.ModelBuilder()
|
|
nodes = [1, 2, 3, 4, 5, 6]
|
|
edge_capacities = pd.Series(
|
|
{
|
|
(0, 1): 3,
|
|
(0, 2): 2,
|
|
(0, 3): 2,
|
|
(1, 4): 5,
|
|
(1, 5): 1,
|
|
(2, 4): 1,
|
|
(2, 5): 3,
|
|
(2, 6): 1,
|
|
(3, 5): 1,
|
|
(4, 7): 4,
|
|
(5, 7): 2,
|
|
(6, 7): 4,
|
|
}
|
|
)
|
|
flow_var = model.new_var_series(
|
|
"flow_var",
|
|
pd.MultiIndex.from_tuples(
|
|
edge_capacities.index, names=("source", "target")
|
|
),
|
|
lower_bounds=0,
|
|
is_integral=True,
|
|
)
|
|
self.assertLen(model.get_variables(), 12)
|
|
model.maximize(flow_var[0, :].sum())
|
|
model.add(
|
|
(flow_var - edge_capacities).apply(lambda expr: expr <= 0),
|
|
name="capacity_constraint",
|
|
)
|
|
for node in nodes:
|
|
# must specify constraint name when directly comparing two variables
|
|
model.add(
|
|
flow_var.xs(node, level=0).sum() == flow_var.xs(node, level=1).sum(),
|
|
name="flow_conservation",
|
|
)
|
|
solver = mb.ModelSolver("sat")
|
|
run = solver.solve(model)
|
|
self.assertEqual(run, mb.SolveStatus.OPTIMAL)
|
|
self.assertEqual(solver.objective_value, 6)
|
|
|
|
def test_add_enforced(self):
|
|
model = mb.ModelBuilder()
|
|
x = model.new_int_var(0, 10, "x")
|
|
y = model.new_int_var(0, 10, "y")
|
|
z = model.new_bool_var("z")
|
|
ct = model.add_enforced(x + 2 * y >= 10, z, False)
|
|
self.assertEqual(ct.lower_bound, 10.0)
|
|
self.assertEqual(z.index, ct.indicator_variable.index)
|
|
self.assertFalse(ct.indicator_value)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main()
|