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Move instance fixtures into the main source; remove duplication

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Alinson S. Xavier
2021-04-09 18:45:06 -05:00
parent f3fd1e0cda
commit a8224b5a38
15 changed files with 188 additions and 225 deletions
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3
tests/fixtures/__init__.py vendored
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.

44
tests/fixtures/infeasible.py vendored
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import Any
from overrides import overrides
from pyomo import environ as pe
from miplearn.instance.base import Instance
from miplearn.solvers.gurobi import GurobiSolver
from miplearn.solvers.pyomo.base import BasePyomoSolver
from tests.solvers import _is_subclass_or_instance
class InfeasiblePyomoInstance(Instance):
@overrides
def to_model(self) -> pe.ConcreteModel:
model = pe.ConcreteModel()
model.x = pe.Var([0], domain=pe.Binary)
model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
model.eq = pe.Constraint(expr=model.x[0] >= 2)
return model
class InfeasibleGurobiInstance(Instance):
@overrides
def to_model(self) -> Any:
import gurobipy as gp
from gurobipy import GRB
model = gp.Model()
x = model.addVars(1, vtype=GRB.BINARY, name="x")
model.addConstr(x[0] >= 2)
model.setObjective(x[0])
return model
def get_infeasible_instance(solver: Any) -> Instance:
if _is_subclass_or_instance(solver, BasePyomoSolver):
return InfeasiblePyomoInstance()
if _is_subclass_or_instance(solver, GurobiSolver):
return InfeasibleGurobiInstance()
assert False

50
tests/fixtures/knapsack.py vendored
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import List, Any, Tuple
from miplearn.instance.base import Instance
from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
from miplearn.solvers.gurobi import GurobiSolver
from miplearn.solvers.internal import InternalSolver
from miplearn.solvers.learning import LearningSolver
from miplearn.solvers.pyomo.base import BasePyomoSolver
from tests.solvers import _is_subclass_or_instance
def get_test_pyomo_instances() -> Tuple[List[Instance], List[Any]]:
instances: List[Instance] = [
KnapsackInstance(
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
),
KnapsackInstance(
weights=[25.0, 30.0, 22.0, 18.0],
prices=[500.0, 365.0, 420.0, 150.0],
capacity=70.0,
),
]
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 get_knapsack_instance(solver: InternalSolver) -> Instance:
if _is_subclass_or_instance(solver, BasePyomoSolver):
return KnapsackInstance(
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
)
elif _is_subclass_or_instance(solver, GurobiSolver):
return GurobiKnapsackInstance(
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
)
else:
assert False

42
tests/fixtures/redundant.py vendored
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import Any
import pyomo.environ as pe
from miplearn.instance.base import Instance
from miplearn.solvers.gurobi import GurobiSolver
from miplearn.solvers.pyomo.base import BasePyomoSolver
from tests.solvers import _is_subclass_or_instance
class PyomoInstanceWithRedundancy(Instance):
def to_model(self) -> pe.ConcreteModel:
model = pe.ConcreteModel()
model.x = pe.Var([0, 1], domain=pe.Binary)
model.OBJ = pe.Objective(expr=model.x[0] + model.x[1], sense=pe.maximize)
model.eq1 = pe.Constraint(expr=model.x[0] + model.x[1] <= 1)
model.eq2 = pe.Constraint(expr=model.x[0] + model.x[1] <= 2)
return model
class GurobiInstanceWithRedundancy(Instance):
def to_model(self) -> Any:
import gurobipy as gp
from gurobipy import GRB
model = gp.Model()
x = model.addVars(2, vtype=GRB.BINARY, name="x")
model.addConstr(x[0] + x[1] <= 1)
model.addConstr(x[0] + x[1] <= 2)
model.setObjective(x[0] + x[1], GRB.MAXIMIZE)
return model
def get_instance_with_redundancy(solver: Any) -> Instance:
if _is_subclass_or_instance(solver, BasePyomoSolver):
return PyomoInstanceWithRedundancy()
if _is_subclass_or_instance(solver, GurobiSolver):
return GurobiInstanceWithRedundancy()
assert False