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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.component</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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from abc import ABC, abstractmethod
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from typing import Any, List, Union, TYPE_CHECKING
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from miplearn.instance import Instance
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from miplearn.types import LearningSolveStats, TrainingSample
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if TYPE_CHECKING:
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from miplearn.solvers.learning import LearningSolver
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class Component(ABC):
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"""
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A Component is an object which adds functionality to a LearningSolver.
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For better code maintainability, LearningSolver simply delegates most of its
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functionality to Components. Each Component is responsible for exactly one ML
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strategy.
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"""
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def before_solve(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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) -> None:
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"""
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Method called by LearningSolver before the problem is solved.
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Parameters
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----------
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solver
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The solver calling this method.
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instance
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The instance being solved.
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model
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The concrete optimization model being solved.
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"""
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return
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@abstractmethod
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def after_solve(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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stats: LearningSolveStats,
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training_data: TrainingSample,
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) -> None:
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"""
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Method called by LearningSolver after the problem is solved to optimality.
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Parameters
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----------
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solver: LearningSolver
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The solver calling this method.
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instance: Instance
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|
The instance being solved.
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model: Any
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|
The concrete optimization model being solved.
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stats: LearningSolveStats
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A dictionary containing statistics about the solution process, such as
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number of nodes explored and running time. Components are free to add
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their own statistics here. For example, PrimalSolutionComponent adds
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|
statistics regarding the number of predicted variables. All statistics in
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this dictionary are exported to the benchmark CSV file.
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training_data: TrainingSample
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A dictionary containing data that may be useful for training machine
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learning models and accelerating the solution process. Components are
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free to add their own training data here. For example,
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PrimalSolutionComponent adds the current primal solution. The data must
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be pickable.
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"""
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pass
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def fit(
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self,
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training_instances: Union[List[str], List[Instance]],
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) -> None:
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return
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def iteration_cb(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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) -> bool:
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"""
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Method called by LearningSolver at the end of each iteration.
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After solving the MIP, LearningSolver calls `iteration_cb` of each component,
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giving them a chance to modify the problem and resolve it before the solution
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process ends. For example, the lazy constraint component uses `iteration_cb`
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to check that all lazy constraints are satisfied.
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If `iteration_cb` returns False for all components, the solution process
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ends. If it retunrs True for any component, the MIP is solved again.
|
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Parameters
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----------
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solver: LearningSolver
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|
The solver calling this method.
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instance: Instance
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|
The instance being solved.
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model: Any
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|
The concrete optimization model being solved.
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"""
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return False
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def lazy_cb(
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self,
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solver: "LearningSolver",
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instance: Instance,
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model: Any,
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) -> None:
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return</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.component.Component"><code class="flex name class">
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<span>class <span class="ident">Component</span></span>
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</code></dt>
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<dd>
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<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p>
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|
<p>For better code maintainability, LearningSolver simply delegates most of its
|
|
functionality to Components. Each Component is responsible for exactly one ML
|
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strategy.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class Component(ABC):
|
|
"""
|
|
A Component is an object which adds functionality to a LearningSolver.
|
|
|
|
For better code maintainability, LearningSolver simply delegates most of its
|
|
functionality to Components. Each Component is responsible for exactly one ML
|
|
strategy.
|
|
"""
|
|
|
|
def before_solve(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> None:
|
|
"""
|
|
Method called by LearningSolver before the problem is solved.
|
|
|
|
Parameters
|
|
----------
|
|
solver
|
|
The solver calling this method.
|
|
instance
|
|
The instance being solved.
|
|
model
|
|
The concrete optimization model being solved.
|
|
"""
|
|
return
|
|
|
|
@abstractmethod
|
|
def after_solve(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
stats: LearningSolveStats,
|
|
training_data: TrainingSample,
|
|
) -> None:
|
|
"""
|
|
Method called by LearningSolver after the problem is solved to optimality.
|
|
|
|
Parameters
|
|
----------
|
|
solver: LearningSolver
|
|
The solver calling this method.
|
|
instance: Instance
|
|
The instance being solved.
|
|
model: Any
|
|
The concrete optimization model being solved.
|
|
stats: LearningSolveStats
|
|
A dictionary containing statistics about the solution process, such as
|
|
number of nodes explored and running time. Components are free to add
|
|
their own statistics here. For example, PrimalSolutionComponent adds
|
|
statistics regarding the number of predicted variables. All statistics in
|
|
this dictionary are exported to the benchmark CSV file.
|
|
training_data: TrainingSample
|
|
A dictionary containing data that may be useful for training machine
|
|
learning models and accelerating the solution process. Components are
|
|
free to add their own training data here. For example,
|
|
PrimalSolutionComponent adds the current primal solution. The data must
|
|
be pickable.
|
|
"""
|
|
pass
|
|
|
|
def fit(
|
|
self,
|
|
training_instances: Union[List[str], List[Instance]],
|
|
) -> None:
|
|
return
|
|
|
|
def iteration_cb(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> bool:
|
|
"""
|
|
Method called by LearningSolver at the end of each iteration.
|
|
|
|
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
|
|
giving them a chance to modify the problem and resolve it before the solution
|
|
process ends. For example, the lazy constraint component uses `iteration_cb`
|
|
to check that all lazy constraints are satisfied.
|
|
|
|
If `iteration_cb` returns False for all components, the solution process
|
|
ends. If it retunrs True for any component, the MIP is solved again.
|
|
|
|
Parameters
|
|
----------
|
|
solver: LearningSolver
|
|
The solver calling this method.
|
|
instance: Instance
|
|
The instance being solved.
|
|
model: Any
|
|
The concrete optimization model being solved.
|
|
"""
|
|
return False
|
|
|
|
def lazy_cb(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> None:
|
|
return</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Subclasses</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.components.composite.CompositeComponent" href="composite.html#miplearn.components.composite.CompositeComponent">CompositeComponent</a></li>
|
|
<li><a title="miplearn.components.cuts.UserCutsComponent" href="cuts.html#miplearn.components.cuts.UserCutsComponent">UserCutsComponent</a></li>
|
|
<li><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent" href="lazy_dynamic.html#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent">DynamicLazyConstraintsComponent</a></li>
|
|
<li><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent" href="lazy_static.html#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</a></li>
|
|
<li><a title="miplearn.components.objective.ObjectiveValueComponent" href="objective.html#miplearn.components.objective.ObjectiveValueComponent">ObjectiveValueComponent</a></li>
|
|
<li><a title="miplearn.components.primal.PrimalSolutionComponent" href="primal.html#miplearn.components.primal.PrimalSolutionComponent">PrimalSolutionComponent</a></li>
|
|
<li><a title="miplearn.components.relaxation.RelaxationComponent" href="relaxation.html#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</a></li>
|
|
<li><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep" href="steps/convert_tight.html#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></li>
|
|
<li><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="steps/drop_redundant.html#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></li>
|
|
<li><a title="miplearn.components.steps.relax_integrality.RelaxIntegralityStep" href="steps/relax_integrality.html#miplearn.components.steps.relax_integrality.RelaxIntegralityStep">RelaxIntegralityStep</a></li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.components.component.Component.after_solve"><code class="name flex">
|
|
<span>def <span class="ident">after_solve</span></span>(<span>self, solver, instance, model, stats, training_data)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Method called by LearningSolver after the problem is solved to optimality.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>solver</code></strong> : <code>LearningSolver</code></dt>
|
|
<dd>The solver calling this method.</dd>
|
|
<dt><strong><code>instance</code></strong> : <code>Instance</code></dt>
|
|
<dd>The instance being solved.</dd>
|
|
<dt><strong><code>model</code></strong> : <code>Any</code></dt>
|
|
<dd>The concrete optimization model being solved.</dd>
|
|
<dt><strong><code>stats</code></strong> : <code>LearningSolveStats</code></dt>
|
|
<dd>A dictionary containing statistics about the solution process, such as
|
|
number of nodes explored and running time. Components are free to add
|
|
their own statistics here. For example, PrimalSolutionComponent adds
|
|
statistics regarding the number of predicted variables. All statistics in
|
|
this dictionary are exported to the benchmark CSV file.</dd>
|
|
<dt><strong><code>training_data</code></strong> : <code>TrainingSample</code></dt>
|
|
<dd>A dictionary containing data that may be useful for training machine
|
|
learning models and accelerating the solution process. Components are
|
|
free to add their own training data here. For example,
|
|
PrimalSolutionComponent adds the current primal solution. The data must
|
|
be pickable.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def after_solve(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
stats: LearningSolveStats,
|
|
training_data: TrainingSample,
|
|
) -> None:
|
|
"""
|
|
Method called by LearningSolver after the problem is solved to optimality.
|
|
|
|
Parameters
|
|
----------
|
|
solver: LearningSolver
|
|
The solver calling this method.
|
|
instance: Instance
|
|
The instance being solved.
|
|
model: Any
|
|
The concrete optimization model being solved.
|
|
stats: LearningSolveStats
|
|
A dictionary containing statistics about the solution process, such as
|
|
number of nodes explored and running time. Components are free to add
|
|
their own statistics here. For example, PrimalSolutionComponent adds
|
|
statistics regarding the number of predicted variables. All statistics in
|
|
this dictionary are exported to the benchmark CSV file.
|
|
training_data: TrainingSample
|
|
A dictionary containing data that may be useful for training machine
|
|
learning models and accelerating the solution process. Components are
|
|
free to add their own training data here. For example,
|
|
PrimalSolutionComponent adds the current primal solution. The data must
|
|
be pickable.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.component.Component.before_solve"><code class="name flex">
|
|
<span>def <span class="ident">before_solve</span></span>(<span>self, solver, instance, model)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Method called by LearningSolver before the problem is solved.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>solver</code></strong></dt>
|
|
<dd>The solver calling this method.</dd>
|
|
<dt><strong><code>instance</code></strong></dt>
|
|
<dd>The instance being solved.</dd>
|
|
<dt><strong><code>model</code></strong></dt>
|
|
<dd>The concrete optimization model being solved.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def before_solve(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> None:
|
|
"""
|
|
Method called by LearningSolver before the problem is solved.
|
|
|
|
Parameters
|
|
----------
|
|
solver
|
|
The solver calling this method.
|
|
instance
|
|
The instance being solved.
|
|
model
|
|
The concrete optimization model being solved.
|
|
"""
|
|
return</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.component.Component.fit"><code class="name flex">
|
|
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
|
</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: Union[List[str], List[Instance]],
|
|
) -> None:
|
|
return</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.component.Component.iteration_cb"><code class="name flex">
|
|
<span>def <span class="ident">iteration_cb</span></span>(<span>self, solver, instance, model)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Method called by LearningSolver at the end of each iteration.</p>
|
|
<p>After solving the MIP, LearningSolver calls <code>iteration_cb</code> of each component,
|
|
giving them a chance to modify the problem and resolve it before the solution
|
|
process ends. For example, the lazy constraint component uses <code>iteration_cb</code>
|
|
to check that all lazy constraints are satisfied.</p>
|
|
<p>If <code>iteration_cb</code> returns False for all components, the solution process
|
|
ends. If it retunrs True for any component, the MIP is solved again.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>solver</code></strong> : <code>LearningSolver</code></dt>
|
|
<dd>The solver calling this method.</dd>
|
|
<dt><strong><code>instance</code></strong> : <code>Instance</code></dt>
|
|
<dd>The instance being solved.</dd>
|
|
<dt><strong><code>model</code></strong> : <code>Any</code></dt>
|
|
<dd>The concrete optimization model being solved.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def iteration_cb(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> bool:
|
|
"""
|
|
Method called by LearningSolver at the end of each iteration.
|
|
|
|
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
|
|
giving them a chance to modify the problem and resolve it before the solution
|
|
process ends. For example, the lazy constraint component uses `iteration_cb`
|
|
to check that all lazy constraints are satisfied.
|
|
|
|
If `iteration_cb` returns False for all components, the solution process
|
|
ends. If it retunrs True for any component, the MIP is solved again.
|
|
|
|
Parameters
|
|
----------
|
|
solver: LearningSolver
|
|
The solver calling this method.
|
|
instance: Instance
|
|
The instance being solved.
|
|
model: Any
|
|
The concrete optimization model being solved.
|
|
"""
|
|
return False</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.component.Component.lazy_cb"><code class="name flex">
|
|
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def lazy_cb(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> None:
|
|
return</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</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>
|
|
<h4><code><a title="miplearn.components.component.Component" href="#miplearn.components.component.Component">Component</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.components.component.Component.after_solve" href="#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.before_solve" href="#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.fit" href="#miplearn.components.component.Component.fit">fit</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.lazy_cb" href="#miplearn.components.component.Component.lazy_cb">lazy_cb</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
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