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111 lines
3.8 KiB
111 lines
3.8 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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from copy import deepcopy
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import numpy as np
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from tqdm.auto 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.extractors import InstanceFeaturesExtractor
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logger = logging.getLogger(__name__)
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class UserCutsComponent(Component):
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"""
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A component that predicts which user cuts to enforce.
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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=0.05,
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):
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self.violations = set()
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self.count = {}
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self.n_samples = 0
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self.threshold = threshold
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self.classifier_prototype = classifier
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self.classifiers = {}
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def before_solve(self, solver, instance, model):
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instance.found_violated_user_cuts = []
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logger.info("Predicting violated user cuts...")
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violations = self.predict(instance)
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logger.info("Enforcing %d user cuts..." % len(violations))
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for v in violations:
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cut = instance.build_user_cut(model, v)
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solver.internal_solver.add_constraint(cut)
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def after_solve(
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self,
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solver,
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instance,
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model,
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results,
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training_data,
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):
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pass
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def fit(self, training_instances):
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logger.debug("Fitting...")
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features = InstanceFeaturesExtractor().extract(training_instances)
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self.classifiers = {}
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violation_to_instance_idx = {}
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for (idx, instance) in enumerate(training_instances):
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if not hasattr(instance, "found_violated_user_cuts"):
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continue
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for v in instance.found_violated_user_cuts:
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if v not in self.classifiers:
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self.classifiers[v] = deepcopy(self.classifier_prototype)
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violation_to_instance_idx[v] = []
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violation_to_instance_idx[v] += [idx]
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for (v, classifier) in tqdm(
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self.classifiers.items(),
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desc="Fit (user cuts)",
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disable=not sys.stdout.isatty(),
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):
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logger.debug("Training: %s" % (str(v)))
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label = np.zeros(len(training_instances))
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label[violation_to_instance_idx[v]] = 1.0
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classifier.fit(features, label)
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def predict(self, instance):
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violations = []
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features = InstanceFeaturesExtractor().extract([instance])
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for (v, classifier) in self.classifiers.items():
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proba = classifier.predict_proba(features)
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if proba[0][1] > self.threshold:
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violations += [v]
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return violations
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def evaluate(self, instances):
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results = {}
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all_violations = set()
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for instance in instances:
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all_violations |= set(instance.found_violated_user_cuts)
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for idx in tqdm(
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range(len(instances)),
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desc="Evaluate (lazy)",
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disable=not sys.stdout.isatty(),
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):
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instance = instances[idx]
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condition_positive = set(instance.found_violated_user_cuts)
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condition_negative = all_violations - condition_positive
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pred_positive = set(self.predict(instance)) & all_violations
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pred_negative = all_violations - pred_positive
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tp = len(pred_positive & condition_positive)
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tn = len(pred_negative & condition_negative)
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fp = len(pred_positive & condition_negative)
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fn = len(pred_negative & condition_positive)
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results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
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return results
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