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386 lines
18 KiB
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</head>
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<body>
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.cuts</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># 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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from typing import Any, Dict
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import numpy as np
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from tqdm.auto import tqdm
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from miplearn.classifiers import Classifier
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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: Classifier = CountingClassifier(),
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threshold: float = 0.05,
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):
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self.threshold: float = threshold
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self.classifier_prototype: Classifier = classifier
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self.classifiers: Dict[Any, Classifier] = {}
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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</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.components.cuts.UserCutsComponent"><code class="flex name class">
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<span>class <span class="ident">UserCutsComponent</span></span>
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<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>A component that predicts which user cuts to enforce.</p></section>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">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: Classifier = CountingClassifier(),
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threshold: float = 0.05,
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):
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self.threshold: float = threshold
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self.classifier_prototype: Classifier = classifier
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self.classifiers: Dict[Any, Classifier] = {}
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|
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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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|
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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(
|
|
range(len(instances)),
|
|
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</code></pre>
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</details>
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<h3>Ancestors</h3>
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<ul class="hlist">
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<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
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<li>abc.ABC</li>
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</ul>
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<h3>Methods</h3>
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<dl>
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<dt id="miplearn.components.cuts.UserCutsComponent.evaluate"><code class="name flex">
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<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
|
|
<summary>
|
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<span>Expand source code</span>
|
|
</summary>
|
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<pre><code class="python">def evaluate(self, instances):
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results = {}
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all_violations = set()
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for instance in instances:
|
|
all_violations |= set(instance.found_violated_user_cuts)
|
|
for idx in tqdm(
|
|
range(len(instances)),
|
|
desc="Evaluate (lazy)",
|
|
disable=not sys.stdout.isatty(),
|
|
):
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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)
|
|
tn = len(pred_negative & condition_negative)
|
|
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</code></pre>
|
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</details>
|
|
</dd>
|
|
<dt id="miplearn.components.cuts.UserCutsComponent.fit"><code class="name flex">
|
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<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
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</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def fit(self, training_instances):
|
|
logger.debug("Fitting...")
|
|
features = InstanceFeaturesExtractor().extract(training_instances)
|
|
|
|
self.classifiers = {}
|
|
violation_to_instance_idx = {}
|
|
for (idx, instance) in enumerate(training_instances):
|
|
if not hasattr(instance, "found_violated_user_cuts"):
|
|
continue
|
|
for v in instance.found_violated_user_cuts:
|
|
if v not in self.classifiers:
|
|
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
|
violation_to_instance_idx[v] = []
|
|
violation_to_instance_idx[v] += [idx]
|
|
|
|
for (v, classifier) in tqdm(
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self.classifiers.items(),
|
|
desc="Fit (user cuts)",
|
|
disable=not sys.stdout.isatty(),
|
|
):
|
|
logger.debug("Training: %s" % (str(v)))
|
|
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)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.cuts.UserCutsComponent.predict"><code class="name flex">
|
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<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def predict(self, instance):
|
|
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</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
<h3>Inherited members</h3>
|
|
<ul class="hlist">
|
|
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
|
<ul class="hlist">
|
|
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
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<h4><code><a title="miplearn.components.cuts.UserCutsComponent" href="#miplearn.components.cuts.UserCutsComponent">UserCutsComponent</a></code></h4>
|
|
<ul class="">
|
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<li><code><a title="miplearn.components.cuts.UserCutsComponent.evaluate" href="#miplearn.components.cuts.UserCutsComponent.evaluate">evaluate</a></code></li>
|
|
<li><code><a title="miplearn.components.cuts.UserCutsComponent.fit" href="#miplearn.components.cuts.UserCutsComponent.fit">fit</a></code></li>
|
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<li><code><a title="miplearn.components.cuts.UserCutsComponent.predict" href="#miplearn.components.cuts.UserCutsComponent.predict">predict</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</nav>
|
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</main>
|
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<footer id="footer">
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