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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>
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<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>
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<dd>
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<section class="desc"></section>
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</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>
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</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>
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<dd>
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<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
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problem.</p>
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<p>In the knapsack problem, for example, this class could hold the number of items,
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their weights and costs, as well as the size of the knapsack. Objects
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implementing this class are able to convert themselves into a concrete
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optimization model, which can be optimized by a solver, or into arrays of
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features, which can be provided as inputs to machine learning models.</p></section>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">class 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</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.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></li>
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<li>abc.ABC</li>
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</ul>
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<h3>Inherited members</h3>
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<ul class="hlist">
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<li><code><b><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
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<ul class="hlist">
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<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>
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<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>
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<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>
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<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>
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<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>
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<li><code><a title="miplearn.instance.Instance.to_model" href="../../instance.html#miplearn.instance.Instance.to_model">to_model</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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<dt id="miplearn.solvers.tests.InfeasiblePyomoInstance"><code class="flex name class">
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<span>class <span class="ident">InfeasiblePyomoInstance</span></span>
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</code></dt>
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<dd>
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<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
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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
|
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features, which can be provided as inputs to machine learning models.</p></section>
|
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">class 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</code></pre>
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</details>
|
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<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></li>
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<li>abc.ABC</li>
|
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</ul>
|
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<h3>Inherited members</h3>
|
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<ul class="hlist">
|
|
<li><code><b><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
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<ul class="hlist">
|
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<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>
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<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>
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<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>
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<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>
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<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>
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<li><code><a title="miplearn.instance.Instance.to_model" href="../../instance.html#miplearn.instance.Instance.to_model">to_model</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>
|
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</section>
|
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</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>
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<ul>
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<li><code><a title="miplearn.solvers" href="../index.html">miplearn.solvers</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
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<ul>
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<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>
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|
<li><code><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</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.solvers.tests.InfeasibleGurobiInstance" href="#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></code></h4>
|
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</li>
|
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<li>
|
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<h4><code><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></code></h4>
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</li>
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</ul>
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</li>
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