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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.problems.tests.test_stab</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import networkx as nx
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import numpy as np
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from scipy.stats import uniform, randint
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from miplearn.problems.stab import MaxWeightStableSetInstance
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from miplearn.solvers.learning import LearningSolver
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def test_stab():
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graph = nx.cycle_graph(5)
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weights = [1.0, 1.0, 1.0, 1.0, 1.0]
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instance = MaxWeightStableSetInstance(graph, weights)
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solver = LearningSolver()
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stats = solver.solve(instance)
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assert stats["Lower bound"] == 2.0
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def test_stab_generator_fixed_graph():
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np.random.seed(42)
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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gen = MaxWeightStableSetGenerator(
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w=uniform(loc=50.0, scale=10.0),
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n=randint(low=10, high=11),
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p=uniform(loc=0.05, scale=0.0),
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fix_graph=True,
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)
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instances = gen.generate(1_000)
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weights = np.array([instance.weights for instance in instances])
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weights_avg_actual = np.round(np.average(weights, axis=0))
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weights_avg_expected = [55.0] * 10
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assert list(weights_avg_actual) == weights_avg_expected
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def test_stab_generator_random_graph():
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np.random.seed(42)
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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gen = MaxWeightStableSetGenerator(
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w=uniform(loc=50.0, scale=10.0),
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n=randint(low=30, high=41),
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p=uniform(loc=0.5, scale=0.0),
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fix_graph=False,
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)
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instances = gen.generate(1_000)
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n_nodes = [instance.graph.number_of_nodes() for instance in instances]
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n_edges = [instance.graph.number_of_edges() for instance in instances]
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assert np.round(np.mean(n_nodes)) == 35.0
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assert np.round(np.mean(n_edges), -1) == 300.0</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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<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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<dt id="miplearn.problems.tests.test_stab.test_stab"><code class="name flex">
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<span>def <span class="ident">test_stab</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
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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">def test_stab():
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graph = nx.cycle_graph(5)
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weights = [1.0, 1.0, 1.0, 1.0, 1.0]
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instance = MaxWeightStableSetInstance(graph, weights)
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solver = LearningSolver()
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stats = solver.solve(instance)
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assert stats["Lower bound"] == 2.0</code></pre>
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</details>
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</dd>
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<dt id="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph"><code class="name flex">
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<span>def <span class="ident">test_stab_generator_fixed_graph</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
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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">def test_stab_generator_fixed_graph():
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np.random.seed(42)
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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gen = MaxWeightStableSetGenerator(
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w=uniform(loc=50.0, scale=10.0),
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n=randint(low=10, high=11),
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p=uniform(loc=0.05, scale=0.0),
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fix_graph=True,
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)
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instances = gen.generate(1_000)
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weights = np.array([instance.weights for instance in instances])
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weights_avg_actual = np.round(np.average(weights, axis=0))
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weights_avg_expected = [55.0] * 10
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assert list(weights_avg_actual) == weights_avg_expected</code></pre>
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</details>
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</dd>
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<dt id="miplearn.problems.tests.test_stab.test_stab_generator_random_graph"><code class="name flex">
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<span>def <span class="ident">test_stab_generator_random_graph</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
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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">def test_stab_generator_random_graph():
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np.random.seed(42)
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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gen = MaxWeightStableSetGenerator(
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w=uniform(loc=50.0, scale=10.0),
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n=randint(low=30, high=41),
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p=uniform(loc=0.5, scale=0.0),
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fix_graph=False,
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)
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instances = gen.generate(1_000)
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n_nodes = [instance.graph.number_of_nodes() for instance in instances]
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n_edges = [instance.graph.number_of_edges() for instance in instances]
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assert np.round(np.mean(n_nodes)) == 35.0
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assert np.round(np.mean(n_edges), -1) == 300.0</code></pre>
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</details>
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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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</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.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-functions">Functions</a></h3>
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<ul class="">
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<li><code><a title="miplearn.problems.tests.test_stab.test_stab" href="#miplearn.problems.tests.test_stab.test_stab">test_stab</a></code></li>
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<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph">test_stab_generator_fixed_graph</a></code></li>
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<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_random_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_random_graph">test_stab_generator_random_graph</a></code></li>
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</ul>
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</li>
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</ul>
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