c34026b101
note: done using ```sh git grep -l "2010-2024 Google" | xargs sed -i 's/2010-2024 Google/2010-2025 Google/' ```
201 lines
8.1 KiB
Python
201 lines
8.1 KiB
Python
#!/usr/bin/env python3
|
|
# Copyright 2010-2025 Google LLC
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""Tests for statistics."""
|
|
|
|
import math
|
|
|
|
from absl.testing import absltest
|
|
from ortools.math_opt.python import model
|
|
from ortools.math_opt.python import statistics
|
|
|
|
|
|
class RangeTest(absltest.TestCase):
|
|
|
|
def test_merge_optional_ranges(self) -> None:
|
|
self.assertIsNone(statistics.merge_optional_ranges(None, None))
|
|
r = statistics.Range(1.0, 3.0)
|
|
self.assertEqual(statistics.merge_optional_ranges(r, None), r)
|
|
self.assertEqual(statistics.merge_optional_ranges(None, r), r)
|
|
# We also test that, since Range is a frozen class, we return the non-None
|
|
# input when only one input is not None.
|
|
self.assertIs(statistics.merge_optional_ranges(r, None), r)
|
|
self.assertIs(statistics.merge_optional_ranges(None, r), r)
|
|
self.assertEqual(
|
|
statistics.merge_optional_ranges(
|
|
statistics.Range(1.0, 3.0), statistics.Range(-2.0, 2.0)
|
|
),
|
|
statistics.Range(-2.0, 3.0),
|
|
)
|
|
|
|
def test_absolute_finite_non_zeros_range(self) -> None:
|
|
self.assertIsNone(statistics.absolute_finite_non_zeros_range(()))
|
|
self.assertIsNone(
|
|
statistics.absolute_finite_non_zeros_range((math.inf, 0.0, -0.0, -math.inf))
|
|
)
|
|
self.assertEqual(
|
|
statistics.absolute_finite_non_zeros_range(
|
|
(math.inf, -5.0e2, 0.0, 1.5e-3, -0.0, -math.inf, 1.25e-6, 3.0e2)
|
|
),
|
|
statistics.Range(minimum=1.25e-6, maximum=5.0e2),
|
|
)
|
|
|
|
|
|
class ModelRangesTest(absltest.TestCase):
|
|
|
|
def test_printing(self) -> None:
|
|
self.assertMultiLineEqual(
|
|
str(
|
|
statistics.ModelRanges(
|
|
objective_terms=None,
|
|
variable_bounds=None,
|
|
linear_constraint_bounds=None,
|
|
linear_constraint_coefficients=None,
|
|
)
|
|
),
|
|
"Objective terms : no finite values\n"
|
|
"Variable bounds : no finite values\n"
|
|
"Linear constraints bounds : no finite values\n"
|
|
"Linear constraints coeffs : no finite values",
|
|
)
|
|
|
|
self.assertMultiLineEqual(
|
|
str(
|
|
statistics.ModelRanges(
|
|
objective_terms=statistics.Range(2.12345e-99, 1.12345e3),
|
|
variable_bounds=statistics.Range(9.12345e-2, 1.12345e2),
|
|
linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99),
|
|
linear_constraint_coefficients=statistics.Range(0.0, 0.0),
|
|
)
|
|
),
|
|
"Objective terms : [2.12e-99 , 1.12e+03 ]\n"
|
|
"Variable bounds : [9.12e-02 , 1.12e+02 ]\n"
|
|
"Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n"
|
|
"Linear constraints coeffs : [0.00e+00 , 0.00e+00 ]",
|
|
)
|
|
|
|
self.assertMultiLineEqual(
|
|
str(
|
|
statistics.ModelRanges(
|
|
objective_terms=statistics.Range(2.12345e-1, 1.12345e3),
|
|
variable_bounds=statistics.Range(9.12345e-2, 1.12345e2),
|
|
linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99),
|
|
linear_constraint_coefficients=statistics.Range(0.0, 1.0e100),
|
|
)
|
|
),
|
|
"Objective terms : [2.12e-01 , 1.12e+03 ]\n"
|
|
"Variable bounds : [9.12e-02 , 1.12e+02 ]\n"
|
|
"Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n"
|
|
"Linear constraints coeffs : [0.00e+00 , 1.00e+100]",
|
|
)
|
|
|
|
self.assertMultiLineEqual(
|
|
str(
|
|
statistics.ModelRanges(
|
|
objective_terms=statistics.Range(2.12345e-100, 1.12345e3),
|
|
variable_bounds=statistics.Range(9.12345e-2, 1.12345e2),
|
|
linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99),
|
|
linear_constraint_coefficients=statistics.Range(0.0, 0.0),
|
|
)
|
|
),
|
|
"Objective terms : [2.12e-100, 1.12e+03 ]\n"
|
|
"Variable bounds : [9.12e-02 , 1.12e+02 ]\n"
|
|
"Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n"
|
|
"Linear constraints coeffs : [0.00e+00 , 0.00e+00 ]",
|
|
)
|
|
|
|
self.assertMultiLineEqual(
|
|
str(
|
|
statistics.ModelRanges(
|
|
objective_terms=statistics.Range(2.12345e-100, 1.12345e3),
|
|
variable_bounds=statistics.Range(9.12345e-2, 1.12345e2),
|
|
linear_constraint_bounds=statistics.Range(2.12345e6, 5.12345e99),
|
|
linear_constraint_coefficients=statistics.Range(0.0, 1.0e100),
|
|
)
|
|
),
|
|
"Objective terms : [2.12e-100, 1.12e+03 ]\n"
|
|
"Variable bounds : [9.12e-02 , 1.12e+02 ]\n"
|
|
"Linear constraints bounds : [2.12e+06 , 5.12e+99 ]\n"
|
|
"Linear constraints coeffs : [0.00e+00 , 1.00e+100]",
|
|
)
|
|
|
|
|
|
class ComputeModelRangesTest(absltest.TestCase):
|
|
|
|
def test_empty(self) -> None:
|
|
mdl = model.Model(name="model")
|
|
self.assertEqual(
|
|
statistics.compute_model_ranges(mdl),
|
|
statistics.ModelRanges(
|
|
objective_terms=None,
|
|
variable_bounds=None,
|
|
linear_constraint_bounds=None,
|
|
linear_constraint_coefficients=None,
|
|
),
|
|
)
|
|
|
|
def test_only_zero_and_infinite_values(self) -> None:
|
|
mdl = model.Model(name="model")
|
|
mdl.add_variable(lb=0.0, ub=math.inf)
|
|
mdl.add_variable(lb=-math.inf, ub=0.0)
|
|
mdl.add_variable(lb=-math.inf, ub=math.inf)
|
|
mdl.add_linear_constraint(lb=0.0, ub=math.inf)
|
|
mdl.add_linear_constraint(lb=-math.inf, ub=0.0)
|
|
mdl.add_linear_constraint(lb=-math.inf, ub=math.inf)
|
|
|
|
self.assertEqual(
|
|
statistics.compute_model_ranges(mdl),
|
|
statistics.ModelRanges(
|
|
objective_terms=None,
|
|
variable_bounds=None,
|
|
linear_constraint_bounds=None,
|
|
linear_constraint_coefficients=None,
|
|
),
|
|
)
|
|
|
|
def test_mixed_values(self) -> None:
|
|
mdl = model.Model(name="model")
|
|
x = mdl.add_variable(lb=0.0, ub=0.0, name="x")
|
|
y = mdl.add_variable(lb=-math.inf, ub=1e-3, name="y")
|
|
mdl.add_variable(lb=-3e2, ub=math.inf, name="z")
|
|
|
|
mdl.objective.is_maximize = False
|
|
mdl.objective.set_linear_coefficient(x, -5.0e4)
|
|
# TODO(b/225219234): add the quadratic term `1.0e-6 * z * x` when the
|
|
# support of quadratic objective is added to the Python API.
|
|
mdl.objective.set_linear_coefficient(y, 3.0)
|
|
|
|
c = mdl.add_linear_constraint(lb=0.0, name="c")
|
|
c.set_coefficient(y, 1.25e-3)
|
|
c.set_coefficient(x, -4.5e3)
|
|
mdl.add_linear_constraint(lb=-math.inf, ub=3e4)
|
|
d = mdl.add_linear_constraint(lb=-1e-5, ub=0.0, name="d")
|
|
d.set_coefficient(y, 2.5e-3)
|
|
|
|
self.assertEqual(
|
|
statistics.compute_model_ranges(mdl),
|
|
statistics.ModelRanges(
|
|
# TODO(b/225219234): update this to Range(1.0e-6, 5.0e4) once the
|
|
# quadratic term is added.
|
|
objective_terms=statistics.Range(3.0, 5.0e4),
|
|
variable_bounds=statistics.Range(1e-3, 3e2),
|
|
linear_constraint_bounds=statistics.Range(1e-5, 3e4),
|
|
linear_constraint_coefficients=statistics.Range(1.25e-3, 4.5e3),
|
|
),
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main()
|