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.
193 lines
7.5 KiB
193 lines
7.5 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.
|
|
|
|
import logging
|
|
import sys
|
|
import numpy as np
|
|
|
|
from copy import deepcopy
|
|
|
|
from tqdm import tqdm
|
|
|
|
from miplearn import Component
|
|
from miplearn.classifiers.counting import CountingClassifier
|
|
from miplearn.components import classifier_evaluation_dict
|
|
from miplearn.components.lazy_static import LazyConstraint
|
|
from miplearn.extractors import InstanceIterator
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class RelaxationComponent(Component):
|
|
"""
|
|
A Component that tries to build a relaxation that is simultaneously strong and easy to solve.
|
|
|
|
Currently, this component performs the following operations:
|
|
- Drops all integrality constraints
|
|
- Drops all inequality constraints that are not likely to be binding.
|
|
|
|
In future versions of MIPLearn, this component may keep some integrality constraints and perform other operations.
|
|
|
|
Parameters
|
|
----------
|
|
classifier : Classifier, optional
|
|
Classifier used to predict whether each constraint is binding or not. One deep copy of this classifier
|
|
is made for each constraint category.
|
|
threshold : float, optional
|
|
If the probability that a constraint is binding exceeds this threshold, the constraint is dropped from the
|
|
linear relaxation.
|
|
slack_tolerance : float, optional
|
|
If a constraint has slack greater than this threshold, then the constraint is considered loose. By default,
|
|
this threshold equals a small positive number to compensate for numerical issues.
|
|
check_dropped : bool, optional
|
|
If `check_dropped` is true, then, after the problem is solved, the component verifies that all dropped
|
|
constraints are still satisfied and re-adds the ones that are not.
|
|
violation_tolerance : float, optional
|
|
If `check_dropped` is true, a constraint is considered satisfied during the check if its violation is smaller
|
|
than this tolerance.
|
|
"""
|
|
|
|
def __init__(self,
|
|
classifier=CountingClassifier(),
|
|
threshold=0.95,
|
|
slack_tolerance=1e-5,
|
|
check_dropped=False,
|
|
violation_tolerance=1e-5,
|
|
):
|
|
self.classifiers = {}
|
|
self.classifier_prototype = classifier
|
|
self.threshold = threshold
|
|
self.slack_tolerance = slack_tolerance
|
|
self.pool = []
|
|
self.check_dropped = check_dropped
|
|
self.violation_tolerance = violation_tolerance
|
|
|
|
def before_solve(self, solver, instance, _):
|
|
logger.info("Relaxing integrality...")
|
|
solver.internal_solver.relax()
|
|
|
|
logger.info("Predicting redundant LP constraints...")
|
|
cids = solver.internal_solver.get_constraint_ids()
|
|
x, constraints = self.x([instance],
|
|
constraint_ids=cids,
|
|
return_constraints=True)
|
|
y = self.predict(x)
|
|
for category in y.keys():
|
|
for i in range(len(y[category])):
|
|
if y[category][i][0] == 1:
|
|
cid = constraints[category][i]
|
|
c = LazyConstraint(cid=cid,
|
|
obj=solver.internal_solver.extract_constraint(cid))
|
|
self.pool += [c]
|
|
logger.info("Extracted %d predicted constraints" % len(self.pool))
|
|
|
|
def after_solve(self, solver, instance, model, results):
|
|
instance.slacks = solver.internal_solver.get_constraint_slacks()
|
|
|
|
def fit(self, training_instances):
|
|
logger.debug("Extracting x and y...")
|
|
x = self.x(training_instances)
|
|
y = self.y(training_instances)
|
|
logger.debug("Fitting...")
|
|
for category in tqdm(x.keys(),
|
|
desc="Fit (relaxation)"):
|
|
if category not in self.classifiers:
|
|
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
|
self.classifiers[category].fit(x[category], y[category])
|
|
|
|
def x(self,
|
|
instances,
|
|
constraint_ids=None,
|
|
return_constraints=False):
|
|
x = {}
|
|
constraints = {}
|
|
for instance in tqdm(InstanceIterator(instances),
|
|
desc="Extract (relaxation:x)",
|
|
disable=len(instances) < 5):
|
|
if constraint_ids is not None:
|
|
cids = constraint_ids
|
|
else:
|
|
cids = instance.slacks.keys()
|
|
for cid in cids:
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in x:
|
|
x[category] = []
|
|
constraints[category] = []
|
|
x[category] += [instance.get_constraint_features(cid)]
|
|
constraints[category] += [cid]
|
|
if return_constraints:
|
|
return x, constraints
|
|
else:
|
|
return x
|
|
|
|
def y(self, instances):
|
|
y = {}
|
|
for instance in tqdm(InstanceIterator(instances),
|
|
desc="Extract (relaxation:y)",
|
|
disable=len(instances) < 5):
|
|
for (cid, slack) in instance.slacks.items():
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in y:
|
|
y[category] = []
|
|
if slack > self.slack_tolerance:
|
|
y[category] += [[1]]
|
|
else:
|
|
y[category] += [[0]]
|
|
return y
|
|
|
|
def predict(self, x):
|
|
y = {}
|
|
for (category, x_cat) in x.items():
|
|
if category not in self.classifiers:
|
|
continue
|
|
y[category] = []
|
|
#x_cat = np.array(x_cat)
|
|
proba = self.classifiers[category].predict_proba(x_cat)
|
|
for i in range(len(proba)):
|
|
if proba[i][1] >= self.threshold:
|
|
y[category] += [[1]]
|
|
else:
|
|
y[category] += [[0]]
|
|
return y
|
|
|
|
def evaluate(self, instance):
|
|
x = self.x([instance])
|
|
y_true = self.y([instance])
|
|
y_pred = self.predict(x)
|
|
tp, tn, fp, fn = 0, 0, 0, 0
|
|
for category in y_true.keys():
|
|
for i in range(len(y_true[category])):
|
|
if y_pred[category][i][0] == 1:
|
|
if y_true[category][i][0] == 1:
|
|
tp += 1
|
|
else:
|
|
fp += 1
|
|
else:
|
|
if y_true[category][i][0] == 1:
|
|
fn += 1
|
|
else:
|
|
tn += 1
|
|
return classifier_evaluation_dict(tp, tn, fp, fn)
|
|
|
|
def iteration_cb(self, solver, instance, model):
|
|
if not self.check_dropped:
|
|
return False
|
|
logger.debug("Checking that dropped constraints are satisfied...")
|
|
constraints_to_add = []
|
|
for c in self.pool:
|
|
if not solver.internal_solver.is_constraint_satisfied(c.obj, self.violation_tolerance):
|
|
constraints_to_add.append(c)
|
|
for c in constraints_to_add:
|
|
self.pool.remove(c)
|
|
solver.internal_solver.add_constraint(c.obj)
|
|
if len(constraints_to_add) > 0:
|
|
logger.info("%8d constraints %8d in the pool" % (len(constraints_to_add), len(self.pool)))
|
|
return True
|
|
else:
|
|
return False
|