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LearningSolver: add fix_variables option; simplify ws_predictor option

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This commit is contained in:
Alinson S. Xavier
2020-01-22 21:05:22 -06:00
parent 077d5326bc
commit 3bb6e61e82
1 changed files with 16 additions and 7 deletions
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23
miplearn/solvers.py
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@@ -6,6 +6,7 @@ from .transformers import PerVariableTransformer
from .warmstart import LogisticWarmStartPredictor
import pyomo.environ as pe
import numpy as np
from copy import deepcopy
class LearningSolver:
@@ -17,13 +18,15 @@ class LearningSolver:
def __init__(self,
threads=4,
parent_solver=pe.SolverFactory('cbc'),
ws_predictor_factory=LogisticWarmStartPredictor):
ws_predictor=LogisticWarmStartPredictor(),
fix_variables=False):
self.parent_solver = parent_solver
self.parent_solver.options["threads"] = threads
self.ws_predictor_factory = ws_predictor_factory
self.fix_variables = fix_variables
self.x_train = {}
self.y_train = {}
self.ws_predictors = {}
self.ws_predictor_prototype = ws_predictor
def solve(self, instance, tee=False):
# Convert instance into concrete model
@@ -52,10 +55,16 @@ class LearningSolver:
assert ws.shape == (len(var_index_pairs), 2)
for i in range(len(var_index_pairs)):
var, index = var_index_pairs[i]
if ws[i,0] == 1:
var[index].value = 0
elif ws[i,1] == 1:
var[index].value = 1
if self.fix_variables:
if ws[i,0] == 1:
var[index].fix(0)
elif ws[i,1] == 1:
var[index].fix(1)
else:
if ws[i,0] == 1:
var[index].value = 0
elif ws[i,1] == 1:
var[index].value = 1
# Solve MILP
self._solve(model, tee=tee)
@@ -76,7 +85,7 @@ class LearningSolver:
for category in x_train_dict.keys():
x_train = x_train_dict[category]
y_train = y_train_dict[category]
self.ws_predictors[category] = self.ws_predictor_factory()
self.ws_predictors[category] = deepcopy(self.ws_predictor_prototype)
self.ws_predictors[category].fit(x_train, y_train)
def _solve(self, model, tee=False):