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.
60 lines
2.0 KiB
60 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 .component import Component
|
|
from ..extractors import *
|
|
|
|
from abc import ABC, abstractmethod
|
|
from copy import deepcopy
|
|
import numpy as np
|
|
from sklearn.pipeline import make_pipeline
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.preprocessing import StandardScaler
|
|
from sklearn.model_selection import cross_val_score
|
|
from sklearn.metrics import roc_curve
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
from tqdm.auto import tqdm
|
|
import pyomo.environ as pe
|
|
import logging
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class LazyConstraintsComponent(Component):
|
|
"""
|
|
A component that predicts which lazy constraints to enforce.
|
|
"""
|
|
|
|
def __init__(self,
|
|
threshold=0.05):
|
|
self.violations = set()
|
|
self.count = {}
|
|
self.n_samples = 0
|
|
self.threshold = threshold
|
|
|
|
def before_solve(self, solver, instance, model):
|
|
logger.info("Enforcing %d lazy constraints" % len(self.violations))
|
|
for v in self.violations:
|
|
if self.count[v] < self.n_samples * self.threshold:
|
|
continue
|
|
cut = instance.build_lazy_constraint(model, v)
|
|
solver.internal_solver.add_constraint(cut)
|
|
|
|
def after_solve(self, solver, instance, model, results):
|
|
pass
|
|
|
|
def fit(self, training_instances):
|
|
logger.debug("Fitting...")
|
|
self.n_samples = len(training_instances)
|
|
for instance in training_instances:
|
|
if not hasattr(instance, "found_violations"):
|
|
continue
|
|
for v in instance.found_violations:
|
|
self.violations.add(v)
|
|
if v not in self.count.keys():
|
|
self.count[v] = 0
|
|
self.count[v] += 1
|
|
|
|
def predict(self, instance, model=None):
|
|
return self.violations
|