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250 lines
8.5 KiB
250 lines
8.5 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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 random
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from copy import deepcopy
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import numpy as np
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from tqdm import tqdm
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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.component import Component
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from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
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logger = logging.getLogger(__name__)
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class ConvertTightIneqsIntoEqsStep(Component):
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"""
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Component that predicts which inequality constraints are likely to be binding in
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the LP relaxation of the problem and converts them into equality constraints.
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This component always makes sure that the conversion process does not affect the
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feasibility of the problem. It can also, optionally, make sure that it does not affect
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the optimality, but this may be expensive.
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This component does not work on MIPs. All integrality constraints must be relaxed
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before this component is used.
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"""
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def __init__(
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self,
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classifier=CountingClassifier(),
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threshold=0.95,
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slack_tolerance=0.0,
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check_optimality=False,
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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.check_optimality = check_optimality
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self.converted = []
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self.original_sense = {}
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self.n_restored = 0
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self.n_infeasible_iterations = 0
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self.n_suboptimal_iterations = 0
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def before_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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self.n_restored = 0
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self.n_infeasible_iterations = 0
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self.n_suboptimal_iterations = 0
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logger.info("Predicting tight LP constraints...")
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x, constraints = DropRedundantInequalitiesStep.x(
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instance,
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constraint_ids=solver.internal_solver.get_constraint_ids(),
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)
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y = self.predict(x)
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n_converted = 0
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n_kept = 0
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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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s = solver.internal_solver.get_constraint_sense(cid)
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self.original_sense[cid] = s
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solver.internal_solver.set_constraint_sense(cid, "=")
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self.converted += [cid]
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n_converted += 1
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else:
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n_kept += 1
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stats["ConvertTight: Kept"] = n_kept
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stats["ConvertTight: Converted"] = n_converted
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logger.info(f"Converted {n_converted} inequalities")
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def after_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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if training_data.slacks is None:
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training_data.slacks = solver.internal_solver.get_inequality_slacks()
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stats["ConvertTight: Restored"] = self.n_restored
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stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
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stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
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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(), desc="Fit (rlx:conv_ineqs)"):
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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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@staticmethod
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def _x_train(instances):
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x = {}
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for instance in tqdm(
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instances,
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desc="Extract (drop:x)",
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disable=len(instances) < 5,
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):
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for training_data in instance.training_data:
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cids = training_data.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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x[category] += [instance.get_constraint_features(cid)]
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for category in x.keys():
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x[category] = np.array(x[category])
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return x
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def x(self, instances):
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return self._x_train(instances)
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def y(self, instances):
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y = {}
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for instance in tqdm(
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instances,
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desc="Extract (rlx:conv_ineqs:y)",
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disable=len(instances) < 5,
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):
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for (cid, slack) in instance.training_data[0].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 0 <= slack <= self.slack_tolerance:
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y[category] += [[False, True]]
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else:
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y[category] += [[True, False]]
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for category in y.keys():
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y[category] = np.array(y[category], dtype=np.bool8)
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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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is_infeasible, is_suboptimal = False, False
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restored = []
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def check_pi(msense, csense, pi):
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if csense == "=":
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return True
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if msense == "max":
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if csense == "<":
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return pi >= 0
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else:
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return pi <= 0
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else:
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if csense == ">":
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return pi >= 0
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else:
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return pi <= 0
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def restore(cid):
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nonlocal restored
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csense = self.original_sense[cid]
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solver.internal_solver.set_constraint_sense(cid, csense)
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restored += [cid]
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if solver.internal_solver.is_infeasible():
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for cid in self.converted:
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pi = solver.internal_solver.get_dual(cid)
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if abs(pi) > 0:
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is_infeasible = True
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restore(cid)
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elif self.check_optimality:
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random.shuffle(self.converted)
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n_restored = 0
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for cid in self.converted:
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if n_restored >= 100:
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break
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pi = solver.internal_solver.get_dual(cid)
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csense = self.original_sense[cid]
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msense = solver.internal_solver.get_sense()
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if not check_pi(msense, csense, pi):
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is_suboptimal = True
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restore(cid)
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n_restored += 1
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for cid in restored:
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self.converted.remove(cid)
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if len(restored) > 0:
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self.n_restored += len(restored)
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if is_infeasible:
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self.n_infeasible_iterations += 1
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if is_suboptimal:
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self.n_suboptimal_iterations += 1
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logger.info(f"Restored {len(restored)} inequalities")
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return True
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else:
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return False
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