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Pyomo: Collect variable bounds, obj_coeff, value, type

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master
Alinson S. Xavier 5 years ago
parent 6b15337e4c
commit 3cfadf4e97
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GPG Key ID: DCA0DAD4D2F58624
6 changed files with 163 additions and 68 deletions
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9
miplearn/solvers/gurobi.py
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@ -26,6 +26,7 @@ from miplearn.types import (
UserCutCallback,
Solution,
VariableName,
Category,
)
logger = logging.getLogger(__name__)
@ -67,6 +68,7 @@ class GurobiSolver(InternalSolver):
self.lazy_cb_frequency = lazy_cb_frequency
self._bin_vars: List["gurobipy.Var"] = []
self._varname_to_var: Dict[str, "gurobipy.Var"] = {}
self._original_vtype: Dict["gurobipy.Var", str] = {}
self._dirty = True
self._has_lp_solution = False
self._has_mip_solution = False
@ -101,6 +103,7 @@ class GurobiSolver(InternalSolver):
def _update_vars(self) -> None:
assert self.model is not None
self._varname_to_var.clear()
self._original_vtype = {}
self._bin_vars.clear()
for var in self.model.getVars():
assert var.varName not in self._varname_to_var, (
@ -112,6 +115,7 @@ class GurobiSolver(InternalSolver):
"Only binary and continuous variables are currently supported. "
"Variable {var.varName} has type {var.vtype}."
)
self._original_vtype[var] = var.vtype
if var.vtype == "B":
self._bin_vars.append(var)
@ -433,7 +437,7 @@ class GurobiSolver(InternalSolver):
var.lower_bound = gp_var.lb
var.upper_bound = gp_var.ub
var.obj_coeff = gp_var.obj
var.type = gp_var.vtype
var.type = self._original_vtype[gp_var]
if self._has_lp_solution:
var.reduced_cost = gp_var.rc
@ -587,8 +591,9 @@ class GurobiTestInstanceKnapsack(PyomoTestInstanceKnapsack):
model = gp.Model("Knapsack")
n = len(self.weights)
x = model.addVars(n, vtype=GRB.BINARY, name="x")
z = model.addVar(vtype=GRB.CONTINUOUS, name="z", ub=self.capacity)
model.addConstr(
gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
gp.quicksum(x[i] * self.weights[i] for i in range(n)) == z,
"eq_capacity",
)
model.setObjective(

1
miplearn/solvers/learning.py
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@ -253,7 +253,6 @@ class LearningSolver:
# -------------------------------------------------------
logger.info("Extracting features (after-mip)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
features.lp_solve = lp_stats
features.mip_solve = mip_stats
instance.features_after_mip.append(features)

99
miplearn/solvers/pyomo/base.py
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@ -12,7 +12,7 @@ import numpy as np
import pyomo
from overrides import overrides
from pyomo import environ as pe
from pyomo.core import Var, Suffix
from pyomo.core import Var, Suffix, Objective
from pyomo.core.base import _GeneralVarData
from pyomo.core.base.constraint import ConstraintList
from pyomo.core.expr.numeric_expr import SumExpression, MonomialTermExpression
@ -64,6 +64,7 @@ class BasePyomoSolver(InternalSolver):
self._termination_condition: str = ""
self._has_lp_solution = False
self._has_mip_solution = False
self._obj: Dict[str, float] = {}
for (key, value) in params.items():
self._pyomo_solver.options[key] = value
@ -223,6 +224,9 @@ class BasePyomoSolver(InternalSolver):
self._all_vars += [var[idx]]
if var[idx].domain == pyomo.core.base.set_types.Binary:
self._bin_vars += [var[idx]]
for obj in self.model.component_objects(Objective):
self._obj = self._parse_pyomo_expr(obj.expr)
break
def _update_constrs(self) -> None:
assert self.model is not None
@ -371,11 +375,42 @@ class BasePyomoSolver(InternalSolver):
for var in self.model.component_objects(pyomo.core.Var):
for idx in var:
varname = f"{var}[{idx}]"
if idx is None:
varname = str(var)
variables[varname] = self._parse_pyomo_variable(var[idx])
return variables
def _parse_pyomo_variable(self, var: pyomo.core.Var) -> Variable:
return Variable()
# Variable type
vtype: Optional[str] = None
if var.domain == pyomo.core.Binary:
vtype = "B"
elif var.domain in [
pyomo.core.Reals,
pyomo.core.NonNegativeReals,
pyomo.core.NonPositiveReals,
pyomo.core.NegativeReals,
pyomo.core.PositiveReals,
]:
vtype = "C"
if vtype is None:
raise Exception(f"unknown variable domain: {var.domain}")
# Bounds
lb, ub = var.bounds
# Objective coefficient
obj_coeff = 0.0
if var.name in self._obj:
obj_coeff = self._obj[var.name]
return Variable(
value=var.value,
type=vtype,
lower_bound=float(lb),
upper_bound=float(ub),
obj_coeff=obj_coeff,
)
@overrides
def get_constraints(self) -> Dict[str, Constraint]:
@ -408,10 +443,10 @@ class BasePyomoSolver(InternalSolver):
assert (
(not has_lb) or (not has_ub) or pyomo_constr.upper() == pyomo_constr.lower()
), "range constraints not supported"
if has_lb:
if not has_ub:
constr.sense = ">"
constr.rhs = pyomo_constr.lower()
elif has_ub:
elif not has_lb:
constr.sense = "<"
constr.rhs = pyomo_constr.upper()
else:
@ -419,22 +454,7 @@ class BasePyomoSolver(InternalSolver):
constr.rhs = pyomo_constr.upper()
# Extract LHS
lhs = {}
if isinstance(pyomo_constr.body, SumExpression):
for term in pyomo_constr.body._args_:
if isinstance(term, MonomialTermExpression):
lhs[term._args_[1].name] = term._args_[0]
elif isinstance(term, _GeneralVarData):
lhs[term.name] = 1.0
else:
raise Exception(f"Unknown term type: {term.__class__.__name__}")
elif isinstance(pyomo_constr.body, _GeneralVarData):
lhs[pyomo_constr.body.name] = 1.0
else:
raise Exception(
f"Unknown expression type: {pyomo_constr.body.__class__.__name__}"
)
constr.lhs = lhs
constr.lhs = self._parse_pyomo_expr(pyomo_constr.body)
# Extract solution attributes
if self._has_lp_solution:
@ -446,6 +466,22 @@ class BasePyomoSolver(InternalSolver):
# Build constraint
return constr
def _parse_pyomo_expr(self, expr):
lhs = {}
if isinstance(expr, SumExpression):
for term in expr._args_:
if isinstance(term, MonomialTermExpression):
lhs[term._args_[1].name] = float(term._args_[0])
elif isinstance(term, _GeneralVarData):
lhs[term.name] = 1.0
else:
raise Exception(f"Unknown term type: {term.__class__.__name__}")
elif isinstance(expr, _GeneralVarData):
lhs[expr.name] = 1.0
else:
raise Exception(f"Unknown expression type: {expr.__class__.__name__}")
return lhs
@overrides
def are_callbacks_supported(self) -> bool:
return False
@ -453,23 +489,20 @@ class BasePyomoSolver(InternalSolver):
@overrides
def get_constraint_attrs(self) -> List[str]:
return [
"category",
"dual_value",
"lazy",
"lhs",
"rhs",
"sense",
"slack",
"user_features",
]
@overrides
def get_variable_attrs(self) -> List[str]:
return [
# "basis_status",
# "category",
# "lower_bound",
# "obj_coeff",
"lower_bound",
"obj_coeff",
# "reduced_cost",
# "sa_lb_down",
# "sa_lb_up",
@ -477,10 +510,9 @@ class BasePyomoSolver(InternalSolver):
# "sa_obj_up",
# "sa_ub_down",
# "sa_ub_up",
# "type",
# "upper_bound",
# "user_features",
# "value",
"type",
"upper_bound",
"value",
]
@ -529,12 +561,13 @@ class PyomoTestInstanceKnapsack(Instance):
model = pe.ConcreteModel()
items = range(len(self.weights))
model.x = pe.Var(items, domain=pe.Binary)
model.z = pe.Var(domain=pe.Reals, bounds=(0, self.capacity))
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.prices[v] for v in items),
sense=pe.maximize,
)
model.eq_capacity = pe.Constraint(
expr=sum(model.x[v] * self.weights[v] for v in items) <= self.capacity
expr=sum(model.x[v] * self.weights[v] for v in items) == model.z
)
return model
@ -552,3 +585,9 @@ class PyomoTestInstanceKnapsack(Instance):
self.weights[item],
self.prices[item],
]
@overrides
def get_variable_category(self, var_name: VariableName) -> Optional[Category]:
if var_name.startswith("x"):
return "default"
return None

84
miplearn/solvers/tests/__init__.py
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@ -117,6 +117,12 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
type="B",
upper_bound=1.0,
),
"z": Variable(
lower_bound=0.0,
obj_coeff=0.0,
type="C",
upper_bound=67.0,
),
},
),
)
@ -127,9 +133,9 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
{
"eq_capacity": Constraint(
lazy=False,
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0},
rhs=67.0,
sense="<",
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
rhs=0.0,
sense="=",
)
},
)
@ -161,7 +167,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
sa_obj_up=inf,
sa_ub_down=0.913043,
sa_ub_up=2.043478,
type="C",
type="B",
upper_bound=1.0,
value=1.0,
),
@ -176,7 +182,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
sa_obj_up=570.869565,
sa_ub_down=0.923077,
sa_ub_up=inf,
type="C",
type="B",
upper_bound=1.0,
value=0.923077,
),
@ -191,7 +197,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
sa_obj_up=inf,
sa_ub_down=0.9,
sa_ub_up=2.2,
type="C",
type="B",
upper_bound=1.0,
value=1.0,
),
@ -206,10 +212,25 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
sa_obj_up=243.692308,
sa_ub_down=0.0,
sa_ub_up=inf,
type="C",
type="B",
upper_bound=1.0,
value=0.0,
),
"z": Variable(
basis_status="U",
lower_bound=0.0,
obj_coeff=0.0,
reduced_cost=13.538462,
sa_lb_down=-inf,
sa_lb_up=67.0,
sa_obj_down=-13.538462,
sa_obj_up=inf,
sa_ub_down=43.0,
sa_ub_up=69.0,
type="C",
upper_bound=67.0,
value=67.0,
),
},
),
)
@ -221,15 +242,21 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
solver,
{
"eq_capacity": Constraint(
basis_status="N",
dual_value=13.538462,
lazy=False,
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0},
rhs=67.0,
sense="<",
lhs={
"x[0]": 23.0,
"x[1]": 26.0,
"x[2]": 20.0,
"x[3]": 18.0,
"z": -1.0,
},
rhs=0.0,
sa_rhs_down=-24.0,
sa_rhs_up=1.9999999999999987,
sense="=",
slack=0.0,
dual_value=13.538462,
sa_rhs_down=43.0,
sa_rhs_up=69.0,
basis_status="N",
)
},
),
@ -238,9 +265,6 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
# Solve MIP
mip_stats = solver.solve(
tee=True,
iteration_cb=None,
lazy_cb=None,
user_cut_cb=None,
)
assert not solver.is_infeasible()
assert mip_stats.mip_log is not None
@ -289,6 +313,13 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
upper_bound=1.0,
value=1.0,
),
"z": Variable(
lower_bound=0.0,
obj_coeff=0.0,
type="C",
upper_bound=67.0,
value=61.0,
),
},
),
)
@ -298,11 +329,12 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
_round_constraints(solver.get_constraints()),
{
"eq_capacity": Constraint(
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0},
rhs=67.0,
sense="<",
slack=6.0,
),
lazy=False,
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
rhs=0.0,
sense="=",
slack=0.0,
)
},
)
@ -317,11 +349,13 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
_round_constraints(solver.get_constraints()),
{
"eq_capacity": Constraint(
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0},
rhs=67.0,
sense="<",
lazy=False,
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
rhs=0.0,
sense="=",
),
"cut": Constraint(
lazy=False,
lhs={"x[0]": 1.0},
rhs=0.0,
sense="<",

2
tests/solvers/test_learning_solver.py
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@ -92,7 +92,7 @@ def test_parallel_solve(
for instance in instances:
data = instance.training_data[0]
assert data.solution is not None
assert len(data.solution.keys()) == 4
assert len(data.solution.keys()) == 5
def test_solve_fit_from_disk(

36
tests/test_features.py
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@ -41,7 +41,7 @@ def test_knapsack() -> None:
sa_obj_up=inf,
sa_ub_down=0.913043,
sa_ub_up=2.043478,
type="C",
type="B",
upper_bound=1.0,
user_features=[23.0, 505.0],
value=1.0,
@ -59,7 +59,7 @@ def test_knapsack() -> None:
sa_obj_up=570.869565,
sa_ub_down=0.923077,
sa_ub_up=inf,
type="C",
type="B",
upper_bound=1.0,
user_features=[26.0, 352.0],
value=0.923077,
@ -86,7 +86,7 @@ def test_knapsack() -> None:
sa_obj_up=inf,
sa_ub_down=0.9,
sa_ub_up=2.2,
type="C",
type="B",
upper_bound=1.0,
user_features=[20.0, 458.0],
value=1.0,
@ -104,12 +104,30 @@ def test_knapsack() -> None:
sa_obj_up=243.692308,
sa_ub_down=0.0,
sa_ub_up=inf,
type="C",
type="B",
upper_bound=1.0,
user_features=[18.0, 220.0],
value=0.0,
alvarez_2017=[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
),
"z": Variable(
basis_status="U",
category=None,
lower_bound=0.0,
obj_coeff=0.0,
reduced_cost=13.538462,
sa_lb_down=-inf,
sa_lb_up=67.0,
sa_obj_down=-13.538462,
sa_obj_up=inf,
sa_ub_down=43.0,
sa_ub_up=69.0,
type="C",
upper_bound=67.0,
user_features=None,
value=67.0,
alvarez_2017=[0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
),
},
)
assert_equals(
@ -120,11 +138,11 @@ def test_knapsack() -> None:
category="eq_capacity",
dual_value=13.538462,
lazy=False,
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0},
rhs=67.0,
sa_rhs_down=43.0,
sa_rhs_up=69.0,
sense="<",
lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
rhs=0.0,
sa_rhs_down=-24.0,
sa_rhs_up=1.9999999999999987,
sense="=",
slack=0.0,
user_features=[0.0],
)

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