ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-06 09:28:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Reorganize directories

Browse Source
This commit is contained in:
Alinson S. Xavier
2020-03-05 17:58:56 -06:00
parent 37795fe013
commit 7765d1f822
50 changed files with 168 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
miplearn/components/tests/__init__.py
View File

@@ -1,4 +0,0 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.

49
miplearn/components/tests/test_branching.py
View File

@@ -1,49 +0,0 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import BranchPriorityComponent, LearningSolver
from miplearn.problems.knapsack import KnapsackInstance
import numpy as np
import tempfile
def _get_instances():
return [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
),
] * 2
def test_branching():
instances = _get_instances()
component = BranchPriorityComponent()
for instance in instances:
component.after_solve(None, instance, None)
component.fit(None)
for key in ["default"]:
assert key in component.x_train.keys()
assert key in component.y_train.keys()
assert component.x_train[key].shape == (8, 4)
assert component.y_train[key].shape == (8, 1)
# def test_branch_priority_save_load():
# state_file = tempfile.NamedTemporaryFile(mode="r")
# solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
# solver.parallel_solve(_get_instances(), n_jobs=2)
# solver.fit()
# comp = solver.components["branch-priority"]
# assert comp.x_train["default"].shape == (8, 4)
# assert comp.y_train["default"].shape == (8, 1)
# assert "default" in comp.predictors.keys()
# solver.save_state(state_file.name)
#
# solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
# solver.load_state(state_file.name)
# comp = solver.components["branch-priority"]
# assert comp.x_train["default"].shape == (8, 4)
# assert comp.y_train["default"].shape == (8, 1)
# assert "default" in comp.predictors.keys()

29
miplearn/components/tests/test_objective.py
View File

@@ -1,29 +0,0 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import ObjectiveValueComponent, LearningSolver
from miplearn.problems.knapsack import KnapsackInstance
def _get_instances():
instances = [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
),
]
models = [instance.to_model() for instance in instances]
solver = LearningSolver()
for i in range(len(instances)):
solver.solve(instances[i], models[i])
return instances, models
def test_usage():
instances, models = _get_instances()
comp = ObjectiveValueComponent()
comp.fit(instances)
assert instances[0].lower_bound == 1183.0
assert instances[0].upper_bound == 1183.0
assert comp.predict(instances).tolist() == [[1183.0, 1183.0]]

33
miplearn/components/tests/test_primal.py
View File

@@ -1,33 +0,0 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import LearningSolver, PrimalSolutionComponent
from miplearn.problems.knapsack import KnapsackInstance
import numpy as np
import tempfile
def _get_instances():
instances = [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
),
] * 5
models = [inst.to_model() for inst in instances]
solver = LearningSolver()
for i in range(len(instances)):
solver.solve(instances[i], models[i])
return instances, models
def test_predict():
instances, models = _get_instances()
comp = PrimalSolutionComponent()
comp.fit(instances)
solution = comp.predict(instances[0])
assert "x" in solution
for idx in range(4):
assert idx in solution["x"]