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Add customizable branch priority; add more metrics to BenchmarkRunner

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This commit is contained in:
Alinson S. Xavier
2020-01-28 06:51:49 -06:00
parent 99ac7aa718
commit f7d20ed52b
4 changed files with 70 additions and 26 deletions
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45
miplearn/solvers.py
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@@ -3,19 +3,21 @@
# Written by Alinson S. Xavier <axavier@anl.gov>
from .transformers import PerVariableTransformer
from .warmstart import KnnWarmStartPredictor
from .warmstart import KnnWarmStartPredictor, LogisticWarmStartPredictor
import pyomo.environ as pe
import numpy as np
from copy import copy, deepcopy
import pickle
from tqdm import tqdm
from joblib import Parallel, delayed
from scipy.stats import randint
import multiprocessing
def _gurobi_factory():
solver = pe.SolverFactory('gurobi_persistent')
solver.options["threads"] = 4
solver.options["Seed"] = randint(low=0, high=1000).rvs()
return solver
class LearningSolver:
@@ -27,7 +29,8 @@ class LearningSolver:
def __init__(self,
threads=4,
internal_solver_factory=_gurobi_factory,
ws_predictor=KnnWarmStartPredictor(),
ws_predictor=LogisticWarmStartPredictor(),
branch_priority=None,
mode="exact"):
self.internal_solver_factory = internal_solver_factory
self.internal_solver = self.internal_solver_factory()
@@ -36,10 +39,14 @@ class LearningSolver:
self.y_train = {}
self.ws_predictors = {}
self.ws_predictor_prototype = ws_predictor
self.branch_priority = branch_priority
def solve(self, instance, tee=False):
# Convert instance into concrete model
# Load model into solver
model = instance.to_model()
is_solver_persistent = hasattr(self.internal_solver, "set_instance")
if is_solver_persistent:
self.internal_solver.set_instance(model)
# Split decision variables according to their category
transformer = PerVariableTransformer()
@@ -56,10 +63,11 @@ class LearningSolver:
else:
self.x_train[category] = np.vstack([self.x_train[category], x])
# Predict warm start
for category in var_split.keys():
var_index_pairs = var_split[category]
# Predict warm starts
if category in self.ws_predictors.keys():
var_index_pairs = var_split[category]
ws = self.ws_predictors[category].predict(x_test[category])
assert ws.shape == (len(var_index_pairs), 2)
for i in range(len(var_index_pairs)):
@@ -75,9 +83,23 @@ class LearningSolver:
elif ws[i,1] == 1:
var[index].value = 1
# Solve MILP
solve_results = self._solve(model, tee=tee)
# Set custom branch priority
if self.branch_priority is not None:
assert is_solver_persistent
from gurobipy import GRB
for (i, (var, index)) in enumerate(var_index_pairs):
gvar = self.internal_solver._pyomo_var_to_solver_var_map[var[index]]
#priority = randint(low=0, high=1000).rvs()
gvar.setAttr(GRB.Attr.BranchPriority, self.branch_priority[index])
if is_solver_persistent:
solve_results = self.internal_solver.solve(tee=tee, warmstart=True)
else:
solve_results = self.internal_solver.solve(model, tee=tee, warmstart=True)
solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
# Update y_train
for category in var_split.keys():
var_index_pairs = var_split[category]
@@ -113,7 +135,7 @@ class LearningSolver:
results = Parallel(n_jobs=n_jobs)(
delayed(_process)(instance)
for instance in tqdm(instances, desc=label)
for instance in tqdm(instances, desc=label, ncols=80)
)
x_train, y_train, results = _merge(results)
@@ -148,10 +170,3 @@ class LearningSolver:
self.x_train = data["x_train"]
self.y_train = data["y_train"]
self.ws_predictors = self.ws_predictors
def _solve(self, model, tee=False):
if hasattr(self.internal_solver, "set_instance"):
self.internal_solver.set_instance(model)
return self.internal_solver.solve(tee=tee, warmstart=True)
else:
return self.internal_solver.solve(model, tee=tee, warmstart=True)