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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.relaxation</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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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components.component import Component
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from miplearn.components.composite import CompositeComponent
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from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
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from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
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from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
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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
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to solve. Currently, this component is composed by three steps:
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- RelaxIntegralityStep
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- DropRedundantInequalitiesStep
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- ConvertTightIneqsIntoEqsStep
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Parameters
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----------
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redundant_classifier : Classifier, optional
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Classifier used to predict if a constraint is likely redundant. One deep
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copy of this classifier is made for each constraint category.
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redundant_threshold : float, optional
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If the probability that a constraint is redundant exceeds this threshold, the
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constraint is dropped from the linear relaxation.
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tight_classifier : Classifier, optional
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Classifier used to predict if a constraint is likely to be tight. One deep
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copy of this classifier is made for each constraint category.
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tight_threshold : float, optional
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If the probability that a constraint is tight exceeds this threshold, the
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constraint is converted into an equality constraint.
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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
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considered loose. By default, this threshold equals a small positive number to
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compensate for numerical issues.
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check_feasibility : bool, optional
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If true, 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
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problem. This loop continues until either no violations are found, or a maximum
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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
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check if its violation is smaller than this tolerance.
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max_check_iterations : int
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If `check_dropped` is true, set the maximum number of iterations in the lazy
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constraint loop.
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"""
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def __init__(
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self,
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redundant_classifier=CountingClassifier(),
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redundant_threshold=0.95,
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tight_classifier=CountingClassifier(),
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tight_threshold=0.95,
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slack_tolerance=1e-5,
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check_feasibility=False,
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violation_tolerance=1e-5,
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max_check_iterations=3,
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):
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self.steps = [
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RelaxIntegralityStep(),
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DropRedundantInequalitiesStep(
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classifier=redundant_classifier,
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threshold=redundant_threshold,
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slack_tolerance=slack_tolerance,
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violation_tolerance=violation_tolerance,
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max_iterations=max_check_iterations,
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check_feasibility=check_feasibility,
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),
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ConvertTightIneqsIntoEqsStep(
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classifier=tight_classifier,
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threshold=tight_threshold,
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slack_tolerance=slack_tolerance,
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),
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]
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self.composite = CompositeComponent(self.steps)
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def before_solve(self, solver, instance, model):
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self.composite.before_solve(solver, instance, model)
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def after_solve(self, solver, instance, model, stats, training_data):
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self.composite.after_solve(solver, instance, model, stats, training_data)
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def fit(self, training_instances):
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self.composite.fit(training_instances)
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def iteration_cb(self, solver, instance, model):
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return self.composite.iteration_cb(solver, instance, model)</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.relaxation.RelaxationComponent"><code class="flex name class">
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<span>class <span class="ident">RelaxationComponent</span></span>
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<span>(</span><span>redundant_classifier=CountingClassifier(mean=None), redundant_threshold=0.95, tight_classifier=CountingClassifier(mean=None), tight_threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_check_iterations=3)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>A Component that tries to build a relaxation that is simultaneously strong and easy
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to solve. Currently, this component is composed by three steps:</p>
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<ul>
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<li>RelaxIntegralityStep</li>
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<li>DropRedundantInequalitiesStep</li>
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<li>ConvertTightIneqsIntoEqsStep</li>
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</ul>
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<h2 id="parameters">Parameters</h2>
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<dl>
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<dt><strong><code>redundant_classifier</code></strong> : <code>Classifier</code>, optional</dt>
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<dd>Classifier used to predict if a constraint is likely redundant. One deep
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copy of this classifier is made for each constraint category.</dd>
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<dt><strong><code>redundant_threshold</code></strong> : <code>float</code>, optional</dt>
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<dd>If the probability that a constraint is redundant exceeds this threshold, the
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constraint is dropped from the linear relaxation.</dd>
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<dt><strong><code>tight_classifier</code></strong> : <code>Classifier</code>, optional</dt>
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<dd>Classifier used to predict if a constraint is likely to be tight. One deep
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copy of this classifier is made for each constraint category.</dd>
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<dt><strong><code>tight_threshold</code></strong> : <code>float</code>, optional</dt>
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<dd>If the probability that a constraint is tight exceeds this threshold, the
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constraint is converted into an equality constraint.</dd>
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<dt><strong><code>slack_tolerance</code></strong> : <code>float</code>, optional</dt>
|
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<dd>If a constraint has slack greater than this threshold, then the constraint is
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considered loose. By default, this threshold equals a small positive number to
|
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compensate for numerical issues.</dd>
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<dt><strong><code>check_feasibility</code></strong> : <code>bool</code>, optional</dt>
|
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<dd>If true, 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
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problem. This loop continues until either no violations are found, or a maximum
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number of iterations is reached.</dd>
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<dt><strong><code>violation_tolerance</code></strong> : <code>float</code>, optional</dt>
|
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<dd>If <code>check_dropped</code> is true, a constraint is considered satisfied during the
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check if its violation is smaller than this tolerance.</dd>
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<dt><strong><code>max_check_iterations</code></strong> : <code>int</code></dt>
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<dd>If <code>check_dropped</code> is true, set the maximum number of iterations in the lazy
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constraint loop.</dd>
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</dl></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 RelaxationComponent(Component):
|
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"""
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A Component that tries to build a relaxation that is simultaneously strong and easy
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to solve. Currently, this component is composed by three steps:
|
|
|
|
- RelaxIntegralityStep
|
|
- DropRedundantInequalitiesStep
|
|
- ConvertTightIneqsIntoEqsStep
|
|
|
|
Parameters
|
|
----------
|
|
redundant_classifier : Classifier, optional
|
|
Classifier used to predict if a constraint is likely redundant. One deep
|
|
copy of this classifier is made for each constraint category.
|
|
redundant_threshold : float, optional
|
|
If the probability that a constraint is redundant exceeds this threshold, the
|
|
constraint is dropped from the linear relaxation.
|
|
tight_classifier : Classifier, optional
|
|
Classifier used to predict if a constraint is likely to be tight. One deep
|
|
copy of this classifier is made for each constraint category.
|
|
tight_threshold : float, optional
|
|
If the probability that a constraint is tight exceeds this threshold, the
|
|
constraint is converted into an equality constraint.
|
|
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_feasibility : bool, optional
|
|
If true, 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
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|
problem. This loop continues until either no violations are found, or a maximum
|
|
number of iterations is reached.
|
|
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.
|
|
max_check_iterations : int
|
|
If `check_dropped` is true, set the maximum number of iterations in the lazy
|
|
constraint loop.
|
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"""
|
|
|
|
def __init__(
|
|
self,
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|
redundant_classifier=CountingClassifier(),
|
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redundant_threshold=0.95,
|
|
tight_classifier=CountingClassifier(),
|
|
tight_threshold=0.95,
|
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slack_tolerance=1e-5,
|
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check_feasibility=False,
|
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violation_tolerance=1e-5,
|
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max_check_iterations=3,
|
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):
|
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self.steps = [
|
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RelaxIntegralityStep(),
|
|
DropRedundantInequalitiesStep(
|
|
classifier=redundant_classifier,
|
|
threshold=redundant_threshold,
|
|
slack_tolerance=slack_tolerance,
|
|
violation_tolerance=violation_tolerance,
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|
max_iterations=max_check_iterations,
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|
check_feasibility=check_feasibility,
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),
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ConvertTightIneqsIntoEqsStep(
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classifier=tight_classifier,
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threshold=tight_threshold,
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slack_tolerance=slack_tolerance,
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),
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]
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self.composite = CompositeComponent(self.steps)
|
|
|
|
def before_solve(self, solver, instance, model):
|
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self.composite.before_solve(solver, instance, model)
|
|
|
|
def after_solve(self, solver, instance, model, stats, training_data):
|
|
self.composite.after_solve(solver, instance, model, stats, training_data)
|
|
|
|
def fit(self, training_instances):
|
|
self.composite.fit(training_instances)
|
|
|
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def iteration_cb(self, solver, instance, model):
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return self.composite.iteration_cb(solver, instance, model)</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.relaxation.RelaxationComponent.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>
|
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<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def fit(self, training_instances):
|
|
self.composite.fit(training_instances)</code></pre>
|
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</details>
|
|
</dd>
|
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</dl>
|
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<h3>Inherited members</h3>
|
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<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">
|
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<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
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<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
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<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
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</ul>
|
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</li>
|
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</ul>
|
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</dd>
|
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</dl>
|
|
</section>
|
|
</article>
|
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
|
|
<ul></ul>
|
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</div>
|
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<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
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<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
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</ul>
|
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</li>
|
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<li><h3><a href="#header-classes">Classes</a></h3>
|
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<ul>
|
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<li>
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<h4><code><a title="miplearn.components.relaxation.RelaxationComponent" href="#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</a></code></h4>
|
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<ul class="">
|
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<li><code><a title="miplearn.components.relaxation.RelaxationComponent.fit" href="#miplearn.components.relaxation.RelaxationComponent.fit">fit</a></code></li>
|
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</ul>
|
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</li>
|
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</ul>
|
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</li>
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</ul>
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