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204 lines
8.0 KiB
204 lines
8.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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import sys
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import numpy as np
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from copy import deepcopy
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from tqdm import tqdm
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from miplearn import Component
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components import classifier_evaluation_dict
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from miplearn.components.lazy_static import LazyConstraint
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from miplearn.extractors import InstanceIterator
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logger = logging.getLogger(__name__)
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class RelaxationComponent(Component):
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"""
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A Component that tries to build a relaxation that is simultaneously strong and easy to solve.
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Currently, this component performs the following operations:
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- Drops all integrality constraints
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- Drops all inequality constraints that are not likely to be binding.
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In future versions of MIPLearn, this component may keep some integrality constraints and perform other operations.
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Parameters
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----------
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classifier : Classifier, optional
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Classifier used to predict whether each constraint is binding or not. One deep copy of this classifier
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is made for each constraint category.
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threshold : float, optional
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If the probability that a constraint is binding exceeds this threshold, the constraint is dropped from the
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linear relaxation.
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slack_tolerance : float, optional
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If a constraint has slack greater than this threshold, then the constraint is considered loose. By default,
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this threshold equals a small positive number to compensate for numerical issues.
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check_dropped : bool, optional
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If `check_dropped` is true, then, after the problem is solved, the component verifies that all dropped
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constraints are still satisfied, re-adds the violated ones and resolves the problem. This loop continues until
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either no violations are found, or a maximum number of iterations is reached.
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violation_tolerance : float, optional
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If `check_dropped` is true, a constraint is considered satisfied during the check if its violation is smaller
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than this tolerance.
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max_iterations : int
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If `check_dropped` is true, set the maximum number of iterations in the lazy constraint loop.
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"""
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def __init__(self,
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classifier=CountingClassifier(),
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threshold=0.95,
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slack_tolerance=1e-5,
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check_dropped=False,
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violation_tolerance=1e-5,
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max_iterations=3,
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):
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self.classifiers = {}
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self.classifier_prototype = classifier
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self.threshold = threshold
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self.slack_tolerance = slack_tolerance
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self.pool = []
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self.check_dropped = check_dropped
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self.violation_tolerance = violation_tolerance
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self.max_iterations = max_iterations
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self.current_iteration = 0
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def before_solve(self, solver, instance, _):
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self.current_iteration = 0
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logger.info("Relaxing integrality...")
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solver.internal_solver.relax()
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logger.info("Predicting redundant LP constraints...")
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cids = solver.internal_solver.get_constraint_ids()
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x, constraints = self.x([instance],
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constraint_ids=cids,
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return_constraints=True)
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y = self.predict(x)
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for category in y.keys():
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for i in range(len(y[category])):
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if y[category][i][0] == 1:
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cid = constraints[category][i]
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c = LazyConstraint(cid=cid,
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obj=solver.internal_solver.extract_constraint(cid))
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self.pool += [c]
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logger.info("Extracted %d predicted constraints" % len(self.pool))
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def after_solve(self, solver, instance, model, results):
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instance.slacks = solver.internal_solver.get_constraint_slacks()
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def fit(self, training_instances):
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logger.debug("Extracting x and y...")
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x = self.x(training_instances)
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y = self.y(training_instances)
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logger.debug("Fitting...")
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for category in tqdm(x.keys(),
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desc="Fit (relaxation)"):
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if category not in self.classifiers:
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self.classifiers[category] = deepcopy(self.classifier_prototype)
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self.classifiers[category].fit(x[category], y[category])
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def x(self,
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instances,
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constraint_ids=None,
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return_constraints=False):
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x = {}
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constraints = {}
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for instance in tqdm(InstanceIterator(instances),
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desc="Extract (relaxation:x)",
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disable=len(instances) < 5):
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if constraint_ids is not None:
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cids = constraint_ids
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else:
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cids = instance.slacks.keys()
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for cid in cids:
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category = instance.get_constraint_category(cid)
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if category is None:
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continue
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if category not in x:
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x[category] = []
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constraints[category] = []
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x[category] += [instance.get_constraint_features(cid)]
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constraints[category] += [cid]
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if return_constraints:
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return x, constraints
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else:
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return x
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def y(self, instances):
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y = {}
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for instance in tqdm(InstanceIterator(instances),
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desc="Extract (relaxation:y)",
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disable=len(instances) < 5):
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for (cid, slack) in instance.slacks.items():
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category = instance.get_constraint_category(cid)
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if category is None:
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continue
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if category not in y:
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y[category] = []
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if slack > self.slack_tolerance:
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y[category] += [[1]]
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else:
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y[category] += [[0]]
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return y
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def predict(self, x):
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y = {}
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for (category, x_cat) in x.items():
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if category not in self.classifiers:
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continue
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y[category] = []
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#x_cat = np.array(x_cat)
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proba = self.classifiers[category].predict_proba(x_cat)
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for i in range(len(proba)):
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if proba[i][1] >= self.threshold:
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y[category] += [[1]]
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else:
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y[category] += [[0]]
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return y
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def evaluate(self, instance):
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x = self.x([instance])
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y_true = self.y([instance])
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y_pred = self.predict(x)
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tp, tn, fp, fn = 0, 0, 0, 0
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for category in y_true.keys():
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for i in range(len(y_true[category])):
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if y_pred[category][i][0] == 1:
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if y_true[category][i][0] == 1:
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tp += 1
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else:
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fp += 1
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else:
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if y_true[category][i][0] == 1:
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fn += 1
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else:
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tn += 1
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return classifier_evaluation_dict(tp, tn, fp, fn)
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def iteration_cb(self, solver, instance, model):
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if not self.check_dropped:
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return False
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if self.current_iteration >= self.max_iterations:
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return False
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self.current_iteration += 1
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logger.debug("Checking that dropped constraints are satisfied...")
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constraints_to_add = []
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for c in self.pool:
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if not solver.internal_solver.is_constraint_satisfied(c.obj, self.violation_tolerance):
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constraints_to_add.append(c)
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for c in constraints_to_add:
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self.pool.remove(c)
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solver.internal_solver.add_constraint(c.obj)
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if len(constraints_to_add) > 0:
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logger.info("%8d constraints %8d in the pool" % (len(constraints_to_add), len(self.pool)))
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return True
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else:
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return False
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