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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.solvers.tests</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 inspect import isclass
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from typing import List, Callable, Any
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from pyomo import environ as pe
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from miplearn.instance import Instance
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from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.internal import InternalSolver
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from miplearn.solvers.pyomo.base import BasePyomoSolver
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from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
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from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
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class InfeasiblePyomoInstance(Instance):
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def to_model(self) -> pe.ConcreteModel:
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model = pe.ConcreteModel()
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model.x = pe.Var([0], domain=pe.Binary)
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model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
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model.eq = pe.Constraint(expr=model.x[0] >= 2)
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return model
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class InfeasibleGurobiInstance(Instance):
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def to_model(self) -> Any:
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import gurobipy as gp
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from gurobipy import GRB
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model = gp.Model()
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x = model.addVars(1, vtype=GRB.BINARY, name="x")
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model.addConstr(x[0] >= 2)
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model.setObjective(x[0])
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return model
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def _is_subclass_or_instance(obj, parent_class):
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return isinstance(obj, parent_class) or (
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isclass(obj) and issubclass(obj, parent_class)
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)
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def _get_knapsack_instance(solver):
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if _is_subclass_or_instance(solver, BasePyomoSolver):
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return KnapsackInstance(
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weights=[23.0, 26.0, 20.0, 18.0],
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prices=[505.0, 352.0, 458.0, 220.0],
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capacity=67.0,
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)
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if _is_subclass_or_instance(solver, GurobiSolver):
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return GurobiKnapsackInstance(
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weights=[23.0, 26.0, 20.0, 18.0],
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prices=[505.0, 352.0, 458.0, 220.0],
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capacity=67.0,
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)
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assert False
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def _get_infeasible_instance(solver):
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if _is_subclass_or_instance(solver, BasePyomoSolver):
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return InfeasiblePyomoInstance()
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if _is_subclass_or_instance(solver, GurobiSolver):
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return InfeasibleGurobiInstance()
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def _get_internal_solvers() -> List[Callable[[], InternalSolver]]:
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return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]</code></pre>
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</details>
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</section>
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<section>
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<h2 class="section-title" id="header-submodules">Sub-modules</h2>
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||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.solvers.tests.test_internal_solver" href="test_internal_solver.html">miplearn.solvers.tests.test_internal_solver</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
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||||
<dt><code class="name"><a title="miplearn.solvers.tests.test_lazy_cb" href="test_lazy_cb.html">miplearn.solvers.tests.test_lazy_cb</a></code></dt>
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<dd>
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||||
<section class="desc"></section>
|
||||
</dd>
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<dt><code class="name"><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</a></code></dt>
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<dd>
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<section class="desc"></section>
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</dd>
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</dl>
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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.solvers.tests.InfeasibleGurobiInstance"><code class="flex name class">
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<span>class <span class="ident">InfeasibleGurobiInstance</span></span>
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||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class InfeasibleGurobiInstance(Instance):
|
||||
def to_model(self) -> Any:
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model()
|
||||
x = model.addVars(1, vtype=GRB.BINARY, name="x")
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||||
model.addConstr(x[0] >= 2)
|
||||
model.setObjective(x[0])
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||||
return model</code></pre>
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</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
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||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.InfeasiblePyomoInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">InfeasiblePyomoInstance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class InfeasiblePyomoInstance(Instance):
|
||||
def to_model(self) -> pe.ConcreteModel:
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var([0], domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
|
||||
model.eq = pe.Constraint(expr=model.x[0] >= 2)
|
||||
return model</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
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||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers" href="../index.html">miplearn.solvers</a></code></li>
|
||||
</ul>
|
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</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver" href="test_internal_solver.html">miplearn.solvers.tests.test_internal_solver</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_lazy_cb" href="test_lazy_cb.html">miplearn.solvers.tests.test_lazy_cb</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></code></h4>
|
||||
</li>
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|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests.test_internal_solver</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from io import StringIO
|
||||
from warnings import warn
|
||||
|
||||
import pyomo.environ as pe
|
||||
|
||||
from miplearn.solvers import _RedirectOutput
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.pyomo.base import BasePyomoSolver
|
||||
from miplearn.solvers.tests import (
|
||||
_get_knapsack_instance,
|
||||
_get_internal_solvers,
|
||||
_get_infeasible_instance,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_redirect_output():
|
||||
import sys
|
||||
|
||||
original_stdout = sys.stdout
|
||||
io = StringIO()
|
||||
with _RedirectOutput([io]):
|
||||
print("Hello world")
|
||||
assert sys.stdout == original_stdout
|
||||
assert io.getvalue() == "Hello world\n"
|
||||
|
||||
|
||||
def test_internal_solver_warm_starts():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
if stats["Warm start value"] is not None:
|
||||
assert stats["Warm start value"] == 725.0
|
||||
else:
|
||||
warn(f"{solver_class.__name__} should set warm start value")
|
||||
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 1.0,
|
||||
2: 1.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Warm start value"] is None
|
||||
|
||||
solver.fix(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Lower bound"] == 725.0
|
||||
assert stats["Upper bound"] == 725.0
|
||||
|
||||
|
||||
def test_internal_solver():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert not solver.is_infeasible()
|
||||
assert round(stats["Optimal value"], 3) == 1287.923
|
||||
assert len(stats["Log"]) > 100
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert round(solution["x"][0], 3) == 1.000
|
||||
assert round(solution["x"][1], 3) == 0.923
|
||||
assert round(solution["x"][2], 3) == 1.000
|
||||
assert round(solution["x"][3], 3) == 0.000
|
||||
|
||||
stats = solver.solve(tee=True)
|
||||
assert not solver.is_infeasible()
|
||||
assert len(stats["Log"]) > 100
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
assert stats["Upper bound"] == 1183.0
|
||||
assert stats["Sense"] == "max"
|
||||
assert isinstance(stats["Wallclock time"], float)
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 1.0
|
||||
assert solution["x"][1] == 0.0
|
||||
assert solution["x"][2] == 1.0
|
||||
assert solution["x"][3] == 1.0
|
||||
|
||||
# Add a brand new constraint
|
||||
if isinstance(solver, BasePyomoSolver):
|
||||
model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
|
||||
solver.add_constraint(model.cut)
|
||||
elif isinstance(solver, GurobiSolver):
|
||||
x = model.getVarByName("x[0]")
|
||||
solver.add_constraint(x <= 0.0, name="cut")
|
||||
else:
|
||||
raise Exception("Illegal state")
|
||||
|
||||
# New constraint should affect solution and should be listed in
|
||||
# constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
assert solver.get_sense() == "max"
|
||||
assert solver.get_constraint_sense("cut") == "<"
|
||||
assert solver.get_constraint_sense("eq_capacity") == "<"
|
||||
|
||||
# Verify slacks
|
||||
assert solver.get_inequality_slacks() == {
|
||||
"cut": 0.0,
|
||||
"eq_capacity": 3.0,
|
||||
}
|
||||
|
||||
if isinstance(solver, GurobiSolver):
|
||||
# Extract the new constraint
|
||||
cobj = solver.extract_constraint("cut")
|
||||
|
||||
# New constraint should no longer affect solution and should no longer
|
||||
# be listed in constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
|
||||
# New constraint should not be satisfied by current solution
|
||||
assert not solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Re-add constraint
|
||||
solver.add_constraint(cobj)
|
||||
|
||||
# Constraint should affect solution again
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
# New constraint should now be satisfied
|
||||
assert solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Relax problem and make cut into an equality constraint
|
||||
solver.relax()
|
||||
solver.set_constraint_sense("cut", "=")
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1030.0
|
||||
assert round(solver.get_dual("eq_capacity")) == 0.0
|
||||
|
||||
|
||||
def test_relax():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
solver.relax()
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1288.0
|
||||
|
||||
|
||||
def test_infeasible_instance():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_infeasible_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
stats = solver.solve()
|
||||
|
||||
assert solver.is_infeasible()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Upper bound"] is None
|
||||
assert stats["Lower bound"] is None
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Optimal value"] is None
|
||||
assert solver.get_value("x", 0) is None
|
||||
|
||||
|
||||
def test_iteration_cb():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
count = 0
|
||||
|
||||
def custom_iteration_cb():
|
||||
nonlocal count
|
||||
count += 1
|
||||
return count < 5
|
||||
|
||||
solver.solve(iteration_cb=custom_iteration_cb)
|
||||
assert count == 5</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance"><code class="name flex">
|
||||
<span>def <span class="ident">test_infeasible_instance</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_infeasible_instance():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_infeasible_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
stats = solver.solve()
|
||||
|
||||
assert solver.is_infeasible()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Upper bound"] is None
|
||||
assert stats["Lower bound"] is None
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Optimal value"] is None
|
||||
assert solver.get_value("x", 0) is None</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver"><code class="name flex">
|
||||
<span>def <span class="ident">test_internal_solver</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_internal_solver():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert not solver.is_infeasible()
|
||||
assert round(stats["Optimal value"], 3) == 1287.923
|
||||
assert len(stats["Log"]) > 100
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert round(solution["x"][0], 3) == 1.000
|
||||
assert round(solution["x"][1], 3) == 0.923
|
||||
assert round(solution["x"][2], 3) == 1.000
|
||||
assert round(solution["x"][3], 3) == 0.000
|
||||
|
||||
stats = solver.solve(tee=True)
|
||||
assert not solver.is_infeasible()
|
||||
assert len(stats["Log"]) > 100
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
assert stats["Upper bound"] == 1183.0
|
||||
assert stats["Sense"] == "max"
|
||||
assert isinstance(stats["Wallclock time"], float)
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 1.0
|
||||
assert solution["x"][1] == 0.0
|
||||
assert solution["x"][2] == 1.0
|
||||
assert solution["x"][3] == 1.0
|
||||
|
||||
# Add a brand new constraint
|
||||
if isinstance(solver, BasePyomoSolver):
|
||||
model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
|
||||
solver.add_constraint(model.cut)
|
||||
elif isinstance(solver, GurobiSolver):
|
||||
x = model.getVarByName("x[0]")
|
||||
solver.add_constraint(x <= 0.0, name="cut")
|
||||
else:
|
||||
raise Exception("Illegal state")
|
||||
|
||||
# New constraint should affect solution and should be listed in
|
||||
# constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
assert solver.get_sense() == "max"
|
||||
assert solver.get_constraint_sense("cut") == "<"
|
||||
assert solver.get_constraint_sense("eq_capacity") == "<"
|
||||
|
||||
# Verify slacks
|
||||
assert solver.get_inequality_slacks() == {
|
||||
"cut": 0.0,
|
||||
"eq_capacity": 3.0,
|
||||
}
|
||||
|
||||
if isinstance(solver, GurobiSolver):
|
||||
# Extract the new constraint
|
||||
cobj = solver.extract_constraint("cut")
|
||||
|
||||
# New constraint should no longer affect solution and should no longer
|
||||
# be listed in constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
|
||||
# New constraint should not be satisfied by current solution
|
||||
assert not solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Re-add constraint
|
||||
solver.add_constraint(cobj)
|
||||
|
||||
# Constraint should affect solution again
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
# New constraint should now be satisfied
|
||||
assert solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Relax problem and make cut into an equality constraint
|
||||
solver.relax()
|
||||
solver.set_constraint_sense("cut", "=")
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1030.0
|
||||
assert round(solver.get_dual("eq_capacity")) == 0.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts"><code class="name flex">
|
||||
<span>def <span class="ident">test_internal_solver_warm_starts</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_internal_solver_warm_starts():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
if stats["Warm start value"] is not None:
|
||||
assert stats["Warm start value"] == 725.0
|
||||
else:
|
||||
warn(f"{solver_class.__name__} should set warm start value")
|
||||
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 1.0,
|
||||
2: 1.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Warm start value"] is None
|
||||
|
||||
solver.fix(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Lower bound"] == 725.0
|
||||
assert stats["Upper bound"] == 725.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_iteration_cb"><code class="name flex">
|
||||
<span>def <span class="ident">test_iteration_cb</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_iteration_cb():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
count = 0
|
||||
|
||||
def custom_iteration_cb():
|
||||
nonlocal count
|
||||
count += 1
|
||||
return count < 5
|
||||
|
||||
solver.solve(iteration_cb=custom_iteration_cb)
|
||||
assert count == 5</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_redirect_output"><code class="name flex">
|
||||
<span>def <span class="ident">test_redirect_output</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_redirect_output():
|
||||
import sys
|
||||
|
||||
original_stdout = sys.stdout
|
||||
io = StringIO()
|
||||
with _RedirectOutput([io]):
|
||||
print("Hello world")
|
||||
assert sys.stdout == original_stdout
|
||||
assert io.getvalue() == "Hello world\n"</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_relax"><code class="name flex">
|
||||
<span>def <span class="ident">test_relax</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_relax():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
solver.relax()
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1288.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</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.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance" href="#miplearn.solvers.tests.test_internal_solver.test_infeasible_instance">test_infeasible_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver">test_internal_solver</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts">test_internal_solver_warm_starts</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_iteration_cb" href="#miplearn.solvers.tests.test_internal_solver.test_iteration_cb">test_iteration_cb</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_redirect_output" href="#miplearn.solvers.tests.test_internal_solver.test_redirect_output">test_redirect_output</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_relax" href="#miplearn.solvers.tests.test_internal_solver.test_relax">test_relax</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
</nav>
|
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|
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|
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<title>miplearn.solvers.tests.test_lazy_cb API documentation</title>
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests.test_lazy_cb</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.tests import _get_knapsack_instance
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_lazy_cb():
|
||||
solver = GurobiSolver()
|
||||
instance = _get_knapsack_instance(solver)
|
||||
model = instance.to_model()
|
||||
|
||||
def lazy_cb(cb_solver, cb_model):
|
||||
logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
|
||||
cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
|
||||
if not cb_solver.is_constraint_satisfied(cobj):
|
||||
cb_solver.add_constraint(cobj)
|
||||
|
||||
solver.set_instance(instance, model)
|
||||
solver.solve(lazy_cb=lazy_cb)
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 0.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.test_lazy_cb.test_lazy_cb"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_cb</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_lazy_cb():
|
||||
solver = GurobiSolver()
|
||||
instance = _get_knapsack_instance(solver)
|
||||
model = instance.to_model()
|
||||
|
||||
def lazy_cb(cb_solver, cb_model):
|
||||
logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
|
||||
cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
|
||||
if not cb_solver.is_constraint_satisfied(cobj):
|
||||
cb_solver.add_constraint(cobj)
|
||||
|
||||
solver.set_instance(instance, model)
|
||||
solver.solve(lazy_cb=lazy_cb)
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 0.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</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.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.solvers.tests.test_lazy_cb.test_lazy_cb" href="#miplearn.solvers.tests.test_lazy_cb.test_lazy_cb">test_lazy_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
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||||
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|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests.test_learning_solver</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import pickle
|
||||
import tempfile
|
||||
import os
|
||||
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
from miplearn.solvers.tests import _get_knapsack_instance, _get_internal_solvers
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_learning_solver():
|
||||
for mode in ["exact", "heuristic"]:
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
mode=mode,
|
||||
)
|
||||
|
||||
solver.solve(instance)
|
||||
data = instance.training_data[0]
|
||||
assert data["Solution"]["x"][0] == 1.0
|
||||
assert data["Solution"]["x"][1] == 0.0
|
||||
assert data["Solution"]["x"][2] == 1.0
|
||||
assert data["Solution"]["x"][3] == 1.0
|
||||
assert data["Lower bound"] == 1183.0
|
||||
assert data["Upper bound"] == 1183.0
|
||||
assert round(data["LP solution"]["x"][0], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][1], 3) == 0.923
|
||||
assert round(data["LP solution"]["x"][2], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][3], 3) == 0.000
|
||||
assert round(data["LP value"], 3) == 1287.923
|
||||
assert len(data["MIP log"]) > 100
|
||||
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
|
||||
# Assert solver is picklable
|
||||
with tempfile.TemporaryFile() as file:
|
||||
pickle.dump(solver, file)
|
||||
|
||||
|
||||
def test_solve_without_lp():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
solve_lp_first=False,
|
||||
)
|
||||
solver.solve(instance)
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
|
||||
|
||||
def test_parallel_solve():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
results = solver.parallel_solve(instances, n_jobs=3)
|
||||
assert len(results) == 10
|
||||
for instance in instances:
|
||||
data = instance.training_data[0]
|
||||
assert len(data["Solution"]["x"].keys()) == 4
|
||||
|
||||
|
||||
def test_solve_fit_from_disk():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
# Create instances and pickle them
|
||||
filenames = []
|
||||
for k in range(3):
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
|
||||
filenames += [file.name]
|
||||
pickle.dump(instance, file)
|
||||
|
||||
# Test: solve
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
solver.solve(filenames[0])
|
||||
with open(filenames[0], "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: parallel_solve
|
||||
solver.parallel_solve(filenames)
|
||||
for filename in filenames:
|
||||
with open(filename, "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: solve (with specified output)
|
||||
output = [f + ".out" for f in filenames]
|
||||
solver.solve(
|
||||
filenames[0],
|
||||
output_filename=output[0],
|
||||
)
|
||||
assert os.path.isfile(output[0])
|
||||
|
||||
# Test: parallel_solve (with specified output)
|
||||
solver.parallel_solve(
|
||||
filenames,
|
||||
output_filenames=output,
|
||||
)
|
||||
for filename in output:
|
||||
assert os.path.isfile(filename)
|
||||
|
||||
# Delete temporary files
|
||||
for filename in filenames:
|
||||
os.remove(filename)
|
||||
for filename in output:
|
||||
os.remove(filename)
|
||||
|
||||
|
||||
def test_simulate_perfect():
|
||||
internal_solver = GurobiSolver
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
|
||||
pickle.dump(instance, tmp)
|
||||
tmp.flush()
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
simulate_perfect=True,
|
||||
)
|
||||
stats = solver.solve(tmp.name)
|
||||
assert stats["Lower bound"] == stats["Predicted LB"]
|
||||
|
||||
|
||||
def test_gap():
|
||||
assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
|
||||
assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
|
||||
assert LearningSolver._compute_gap(ub=None, lb=None) is None</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_gap"><code class="name flex">
|
||||
<span>def <span class="ident">test_gap</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_gap():
|
||||
assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
|
||||
assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
|
||||
assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
|
||||
assert LearningSolver._compute_gap(ub=None, lb=None) is None</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_learning_solver"><code class="name flex">
|
||||
<span>def <span class="ident">test_learning_solver</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_learning_solver():
|
||||
for mode in ["exact", "heuristic"]:
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
mode=mode,
|
||||
)
|
||||
|
||||
solver.solve(instance)
|
||||
data = instance.training_data[0]
|
||||
assert data["Solution"]["x"][0] == 1.0
|
||||
assert data["Solution"]["x"][1] == 0.0
|
||||
assert data["Solution"]["x"][2] == 1.0
|
||||
assert data["Solution"]["x"][3] == 1.0
|
||||
assert data["Lower bound"] == 1183.0
|
||||
assert data["Upper bound"] == 1183.0
|
||||
assert round(data["LP solution"]["x"][0], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][1], 3) == 0.923
|
||||
assert round(data["LP solution"]["x"][2], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][3], 3) == 0.000
|
||||
assert round(data["LP value"], 3) == 1287.923
|
||||
assert len(data["MIP log"]) > 100
|
||||
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
|
||||
# Assert solver is picklable
|
||||
with tempfile.TemporaryFile() as file:
|
||||
pickle.dump(solver, file)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_parallel_solve"><code class="name flex">
|
||||
<span>def <span class="ident">test_parallel_solve</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_parallel_solve():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
results = solver.parallel_solve(instances, n_jobs=3)
|
||||
assert len(results) == 10
|
||||
for instance in instances:
|
||||
data = instance.training_data[0]
|
||||
assert len(data["Solution"]["x"].keys()) == 4</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_simulate_perfect"><code class="name flex">
|
||||
<span>def <span class="ident">test_simulate_perfect</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_simulate_perfect():
|
||||
internal_solver = GurobiSolver
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
|
||||
pickle.dump(instance, tmp)
|
||||
tmp.flush()
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
simulate_perfect=True,
|
||||
)
|
||||
stats = solver.solve(tmp.name)
|
||||
assert stats["Lower bound"] == stats["Predicted LB"]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk"><code class="name flex">
|
||||
<span>def <span class="ident">test_solve_fit_from_disk</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_solve_fit_from_disk():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
# Create instances and pickle them
|
||||
filenames = []
|
||||
for k in range(3):
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
|
||||
filenames += [file.name]
|
||||
pickle.dump(instance, file)
|
||||
|
||||
# Test: solve
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
solver.solve(filenames[0])
|
||||
with open(filenames[0], "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: parallel_solve
|
||||
solver.parallel_solve(filenames)
|
||||
for filename in filenames:
|
||||
with open(filename, "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: solve (with specified output)
|
||||
output = [f + ".out" for f in filenames]
|
||||
solver.solve(
|
||||
filenames[0],
|
||||
output_filename=output[0],
|
||||
)
|
||||
assert os.path.isfile(output[0])
|
||||
|
||||
# Test: parallel_solve (with specified output)
|
||||
solver.parallel_solve(
|
||||
filenames,
|
||||
output_filenames=output,
|
||||
)
|
||||
for filename in output:
|
||||
assert os.path.isfile(filename)
|
||||
|
||||
# Delete temporary files
|
||||
for filename in filenames:
|
||||
os.remove(filename)
|
||||
for filename in output:
|
||||
os.remove(filename)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_solve_without_lp"><code class="name flex">
|
||||
<span>def <span class="ident">test_solve_without_lp</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_solve_without_lp():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
solve_lp_first=False,
|
||||
)
|
||||
solver.solve(instance)
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</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.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_gap" href="#miplearn.solvers.tests.test_learning_solver.test_gap">test_gap</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_learning_solver" href="#miplearn.solvers.tests.test_learning_solver.test_learning_solver">test_learning_solver</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_parallel_solve" href="#miplearn.solvers.tests.test_learning_solver.test_parallel_solve">test_parallel_solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_simulate_perfect" href="#miplearn.solvers.tests.test_learning_solver.test_simulate_perfect">test_simulate_perfect</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk" href="#miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk">test_solve_fit_from_disk</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_solve_without_lp" href="#miplearn.solvers.tests.test_learning_solver.test_solve_without_lp">test_solve_without_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
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Reference in New Issue
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