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Document InternalSolver; only traverse list of variables once

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pull/3/head
Alinson S. Xavier 6 years ago
parent f80263e71e
commit bde5087055
2 changed files with 235 additions and 93 deletions
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280
src/python/miplearn/solvers.py
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@ -3,14 +3,19 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from abc import ABC
from copy import deepcopy
import pyomo.core.kernel.objective
import pyomo.environ as pe
from p_tqdm import p_map
from pyomo.core import Var
from scipy.stats import randint
from . import ObjectiveValueComponent, PrimalSolutionComponent, LazyConstraintsComponent
from . import (ObjectiveValueComponent,
PrimalSolutionComponent,
LazyConstraintsComponent)
from .instance import Instance
logger = logging.getLogger(__name__)
@ -35,51 +40,68 @@ def _parallel_solve(instance_idx):
}
class InternalSolver:
class InternalSolver(ABC):
"""
The MIP solver used internaly by LearningSolver.
Attributes
----------
instance: miplearn.Instance
The MIPLearn instance currently loaded to the solver
model: pyomo.core.ConcreteModel
The Pyomo model currently loaded on the solver
"""
def __init__(self):
self.all_vars = None
self.instance = None
self.is_warm_start_available = False
self.solver = None
self.model = None
self.sense = None
self.var_name_to_var = {}
self._all_vars = None
self._bin_vars = None
self._is_warm_start_available = False
self._pyomo_solver = None
self._obj_sense = None
self._varname_to_var = {}
def solve_lp(self, tee=False):
# Relax domain
from pyomo.core.base.set_types import Reals, Binary
original_domains = []
for (idx, var) in enumerate(self.model.component_data_objects(Var)):
original_domains += [var.domain]
"""
Solves the LP relaxation of the currently loaded instance.
Parameters
----------
tee: bool
If true, prints the solver log to the screen.
Returns
-------
dict
A dictionary of solver statistics containing the following keys:
"Optimal value".
"""
for var in self._bin_vars:
lb, ub = var.bounds
if var.domain == Binary:
var.domain = Reals
var.setlb(lb)
var.setub(ub)
self.solver.update_var(var)
# Solve LP relaxation
results = self.solver.solve(tee=tee)
# Restore domains
for (idx, var) in enumerate(self.model.component_data_objects(Var)):
if original_domains[idx] == Binary:
var.domain = original_domains[idx]
self.solver.update_var(var)
var.setlb(lb)
var.setub(ub)
var.domain = pyomo.core.base.set_types.Reals
self._pyomo_solver.update_var(var)
results = self._pyomo_solver.solve(tee=tee)
for var in self._bin_vars:
var.domain = pyomo.core.base.set_types.Binary
self._pyomo_solver.update_var(var)
return {
"Optimal value": results["Problem"][0]["Lower bound"],
}
def clear_values(self):
for var in self.model.component_objects(Var):
for index in var:
if var[index].fixed:
continue
var[index].value = None
self.is_warm_start_available = False
def get_solution(self):
"""
Returns current solution found by the solver.
If called after `solve`, returns the best primal solution found during
the search. If called after `solve_lp`, returns the optimal solution
to the LP relaxation.
The solution is a dictionary `sol`, where the optimal value of `var[idx]`
is given by `sol[var][idx]`.
"""
solution = {}
for var in self.model.component_objects(Var):
solution[str(var)] = {}
@ -88,60 +110,120 @@ class InternalSolver:
return solution
def set_warm_start(self, solution):
self.clear_values()
"""
Sets the warm start to be used by the solver.
The solution should be a dictionary following the same format as the
one produced by `get_solution`. Only one warm start is currently
supported. Calling this function when a warm start already exists will
remove the previous warm start.
"""
self.clear_warm_start()
count_total, count_fixed = 0, 0
for var_name in solution:
var = self.var_name_to_var[var_name]
var = self._varname_to_var[var_name]
for index in solution[var_name]:
count_total += 1
var[index].value = solution[var_name][index]
if solution[var_name][index] is not None:
count_fixed += 1
if count_fixed > 0:
self.is_warm_start_available = True
self._is_warm_start_available = True
logger.info("Setting start values for %d variables (out of %d)" %
(count_fixed, count_total))
def set_model(self, model):
from pyomo.core.kernel.objective import minimize
def clear_warm_start(self):
"""
Removes any existing warm start from the solver.
"""
for var in self._all_vars:
if not var.fixed:
var.value = None
self._is_warm_start_available = False
def set_instance(self, instance, model=None):
"""
Loads the given instance into the solver.
Parameters
----------
instance: miplearn.Instance
The instance to be loaded.
model: pyomo.core.ConcreteModel
The corresponding Pyomo model. If not provided, it will be
generated by calling `instance.to_model()`.
"""
if model is None:
model = instance.to_model()
assert isinstance(instance, Instance)
assert isinstance(model, pe.ConcreteModel)
self.instance = instance
self.model = model
self.solver.set_instance(model)
if self.solver._objective.sense == minimize:
self.sense = "min"
else:
self.sense = "max"
self.var_name_to_var = {}
self.all_vars = []
self._pyomo_solver.set_instance(model)
# Update objective sense
self._obj_sense = "max"
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
self._obj_sense = "min"
# Update variables
self._all_vars = []
self._bin_vars = []
self._varname_to_var = {}
for var in model.component_objects(Var):
self.var_name_to_var[var.name] = var
self.all_vars += [var[idx] for idx in var]
def set_instance(self, instance):
self.instance = instance
self._varname_to_var[var.name] = var
for idx in var:
self._all_vars += [var[idx]]
if var[idx].domain == pyomo.core.base.set_types.Binary:
self._bin_vars += [var[idx]]
def fix(self, solution):
"""
Fixes the values of a subset of decision variables.
The values should be provided in the dictionary format generated by
`get_solution`. Missing values in the solution indicate variables
that should be left free.
"""
count_total, count_fixed = 0, 0
for var_name in solution:
for index in solution[var_name]:
var = self.var_name_to_var[var_name]
for varname in solution:
for index in solution[varname]:
var = self._varname_to_var[varname]
count_total += 1
if solution[var_name][index] is None:
if solution[varname][index] is None:
continue
count_fixed += 1
var[index].fix(solution[var_name][index])
self.solver.update_var(var[index])
var[index].fix(solution[varname][index])
self._pyomo_solver.update_var(var[index])
logger.info("Fixing values for %d variables (out of %d)" %
(count_fixed, count_total))
def add_constraint(self, cut):
self.solver.add_constraint(cut)
def add_constraint(self, constraint):
"""
Adds a single constraint to the model.
"""
self._pyomo_solver.add_constraint(constraint)
def solve(self, tee=False):
"""
Solves the currently loaded instance.
Parameters
----------
tee: bool
If true, prints the solver log to the screen.
Returns
-------
dict
A dictionary of solver statistics containing the following keys:
"Lower bound", "Upper bound", "Wallclock time", "Nodes" and "Sense".
"""
total_wallclock_time = 0
self.instance.found_violations = []
while True:
logger.debug("Solving MIP...")
results = self.solver.solve(tee=tee)
results = self._pyomo_solver.solve(tee=tee)
total_wallclock_time += results["Solver"][0]["Wallclock time"]
if not hasattr(self.instance, "find_violations"):
break
@ -154,37 +236,37 @@ class InternalSolver:
for v in violations:
cut = self.instance.build_lazy_constraint(self.model, v)
self.add_constraint(cut)
return {
"Lower bound": results["Problem"][0]["Lower bound"],
"Upper bound": results["Problem"][0]["Upper bound"],
"Wallclock time": total_wallclock_time,
"Nodes": 1,
"Sense": self.sense,
"Sense": self._obj_sense,
}
class GurobiSolver(InternalSolver):
def __init__(self):
super().__init__()
self.solver = pe.SolverFactory('gurobi_persistent')
self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
self._pyomo_solver = pe.SolverFactory('gurobi_persistent')
self._pyomo_solver.options["Seed"] = randint(low=0, high=1000).rvs()
def set_threads(self, threads):
self.solver.options["Threads"] = threads
self._pyomo_solver.options["Threads"] = threads
def set_time_limit(self, time_limit):
self.solver.options["TimeLimit"] = time_limit
self._pyomo_solver.options["TimeLimit"] = time_limit
def set_gap_tolerance(self, gap_tolerance):
self.solver.options["MIPGap"] = gap_tolerance
self._pyomo_solver.options["MIPGap"] = gap_tolerance
def solve(self, tee=False):
from gurobipy import GRB
def cb(cb_model, cb_opt, cb_where):
if cb_where == GRB.Callback.MIPSOL:
cb_opt.cbGetSolution(self.all_vars)
cb_opt.cbGetSolution(self._all_vars)
logger.debug("Finding violated constraints...")
violations = self.instance.find_violations(cb_model)
self.instance.found_violations += violations
@ -194,42 +276,58 @@ class GurobiSolver(InternalSolver):
cb_opt.cbLazy(cut)
if hasattr(self.instance, "find_violations"):
self.solver.options["LazyConstraints"] = 1
self.solver.set_callback(cb)
self._pyomo_solver.options["LazyConstraints"] = 1
self._pyomo_solver.set_callback(cb)
self.instance.found_violations = []
results = self.solver.solve(tee=tee, warmstart=self.is_warm_start_available)
self.solver.set_callback(None)
print(self._is_warm_start_available)
results = self._pyomo_solver.solve(tee=tee,
warmstart=self._is_warm_start_available)
self._pyomo_solver.set_callback(None)
node_count = int(self._pyomo_solver._solver_model.getAttr("NodeCount"))
return {
"Lower bound": results["Problem"][0]["Lower bound"],
"Upper bound": results["Problem"][0]["Upper bound"],
"Wallclock time": results["Solver"][0]["Wallclock time"],
"Nodes": self.solver._solver_model.getAttr("NodeCount"),
"Sense": self.sense,
"Nodes": max(1, node_count),
"Sense": self._obj_sense,
}
class CPLEXSolver(InternalSolver):
def __init__(self):
def __init__(self,
presolve=1,
mip_display=4,
threads=None,
time_limit=None,
gap_tolerance=None):
super().__init__()
self.solver = pe.SolverFactory('cplex_persistent')
self.solver.options["randomseed"] = randint(low=0, high=1000).rvs()
self._pyomo_solver = pe.SolverFactory('cplex_persistent')
self._pyomo_solver.options["randomseed"] = randint(low=0, high=1000).rvs()
self._pyomo_solver.options["preprocessing_presolve"] = presolve
self._pyomo_solver.options["mip_display"] = mip_display
if threads is not None:
self.set_threads(threads)
if time_limit is not None:
self.set_time_limit(time_limit)
if gap_tolerance is not None:
self.set_gap_tolerance(gap_tolerance)
def set_threads(self, threads):
self.solver.options["threads"] = threads
self._pyomo_solver.options["threads"] = threads
def set_time_limit(self, time_limit):
self.solver.options["timelimit"] = time_limit
self._pyomo_solver.options["timelimit"] = time_limit
def set_gap_tolerance(self, gap_tolerance):
self.solver.options["mip_tolerances_mipgap"] = gap_tolerance
self._pyomo_solver.options["mip_tolerances_mipgap"] = gap_tolerance
def solve_lp(self, tee=False):
import cplex
lp = self.solver._solver_model
lp = self._pyomo_solver._solver_model
var_types = lp.variables.get_types()
n_vars = len(var_types)
lp.set_problem_type(cplex.Cplex.problem_type.LP)
results = self.solver.solve(tee=tee)
results = self._pyomo_solver.solve(tee=tee)
lp.variables.set_types(zip(range(n_vars), var_types))
return {
"Optimal value": results["Problem"][0]["Lower bound"],
@ -238,8 +336,9 @@ class CPLEXSolver(InternalSolver):
class LearningSolver:
"""
Mixed-Integer Linear Programming (MIP) solver that extracts information from previous runs,
using Machine Learning methods, to accelerate the solution of new (yet unseen) instances.
Mixed-Integer Linear Programming (MIP) solver that extracts information
from previous runs, using Machine Learning methods, to accelerate the
solution of new (yet unseen) instances.
"""
def __init__(self,
@ -300,8 +399,7 @@ class LearningSolver:
self.tee = tee
self.internal_solver = self._create_internal_solver()
self.internal_solver.set_model(model)
self.internal_solver.set_instance(instance)
self.internal_solver.set_instance(instance, model=model)
logger.debug("Solving LP relaxation...")
results = self.internal_solver.solve_lp(tee=tee)

48
src/python/miplearn/tests/test_solver.py
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@ -5,9 +5,10 @@
import pickle
import tempfile
import pyomo.environ as pe
from miplearn import LearningSolver, BranchPriorityComponent
from miplearn.problems.knapsack import KnapsackInstance
from miplearn.solvers import GurobiSolver
from miplearn.solvers import GurobiSolver, CPLEXSolver
def _get_instance():
@ -18,7 +19,50 @@ def _get_instance():
)
def test_solver():
def test_internal_solver():
for solver in [GurobiSolver(), CPLEXSolver(presolve=False)]:
instance = _get_instance()
model = instance.to_model()
solver.set_instance(instance, model)
solver.set_warm_start({
"x": {
0: 1.0,
1: 0.0,
2: 1.0,
3: 1.0,
}
})
stats = solver.solve()
assert stats["Lower bound"] == 1183.0
assert stats["Upper bound"] == 1183.0
assert stats["Sense"] == "max"
assert isinstance(stats["Wallclock time"], float)
assert isinstance(stats["Nodes"], int)
solution = solver.get_solution()
assert solution["x"][0] == 1.0
assert solution["x"][1] == 0.0
assert solution["x"][2] == 1.0
assert solution["x"][3] == 1.0
stats = solver.solve_lp()
assert round(stats["Optimal value"], 3) == 1287.923
solution = solver.get_solution()
assert round(solution["x"][0], 3) == 1.000
assert round(solution["x"][1], 3) == 0.923
assert round(solution["x"][2], 3) == 1.000
assert round(solution["x"][3], 3) == 0.000
model.cut = pe.Constraint(expr=model.x[0] <= 0.5)
solver.add_constraint(model.cut)
solver.solve_lp()
assert model.x[0].value == 0.5
def test_learning_solver():
instance = _get_instance()
for mode in ["exact", "heuristic"]:
for internal_solver in ["cplex", "gurobi", GurobiSolver]:

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