You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
50 lines
2.0 KiB
50 lines
2.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
|
# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
|
from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
|
|
from sklearn.linear_model import LinearRegression
|
|
from copy import deepcopy
|
|
import numpy as np
|
|
import logging
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class ObjectiveValueComponent(Component):
|
|
"""
|
|
A Component which predicts the optimal objective value of the problem.
|
|
"""
|
|
def __init__(self,
|
|
regressor=LinearRegression()):
|
|
self.ub_regressor = None
|
|
self.lb_regressor = None
|
|
self.regressor_prototype = regressor
|
|
|
|
def before_solve(self, solver, instance, model):
|
|
if self.ub_regressor is not None:
|
|
lb, ub = self.predict([instance])[0]
|
|
instance.predicted_ub = ub
|
|
instance.predicted_lb = lb
|
|
logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
|
|
|
|
def after_solve(self, solver, instance, model):
|
|
pass
|
|
|
|
def fit(self, training_instances):
|
|
logger.debug("Extracting features...")
|
|
features = InstanceFeaturesExtractor().extract(training_instances)
|
|
ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
|
|
lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
|
|
self.ub_regressor = deepcopy(self.regressor_prototype)
|
|
self.lb_regressor = deepcopy(self.regressor_prototype)
|
|
logger.debug("Fitting ub_regressor...")
|
|
self.ub_regressor.fit(features, ub)
|
|
logger.debug("Fitting ub_regressor...")
|
|
self.lb_regressor.fit(features, lb)
|
|
|
|
def predict(self, instances):
|
|
features = InstanceFeaturesExtractor().extract(instances)
|
|
lb = self.lb_regressor.predict(features)
|
|
ub = self.ub_regressor.predict(features)
|
|
return np.hstack([lb, ub])
|