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<dd>
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<dt><code class="name"><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></dt>
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<h1 class="title">Module <code>miplearn.problems.tests</code></h1>
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<summary>
<span>Expand source code</span>
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<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.</code></pre>
</details>
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<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.problems.tests.test_knapsack" href="test_knapsack.html">miplearn.problems.tests.test_knapsack</a></code></dt>
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<section class="desc"></section>
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</dd>
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<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
</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.problems" href="../index.html">miplearn.problems</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.problems.tests.test_knapsack" href="test_knapsack.html">miplearn.problems.tests.test_knapsack</a></code></li>
<li><code><a title="miplearn.problems.tests.test_stab" href="test_stab.html">miplearn.problems.tests.test_stab</a></code></li>
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<h1 class="title">Module <code>miplearn.problems.tests.test_knapsack</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 numpy as np
from scipy.stats import uniform, randint
from miplearn.problems.knapsack import MultiKnapsackGenerator
def test_knapsack_generator():
gen = MultiKnapsackGenerator(
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=1.0),
alpha=uniform(loc=0.50, scale=0.0),
)
instances = gen.generate(100)
w_sum = sum(instance.weights for instance in instances) / len(instances)
b_sum = sum(instance.capacities for instance in instances) / len(instances)
assert round(np.mean(w_sum), -1) == 500.0
assert round(np.mean(b_sum), -3) == 25000.0</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.problems.tests.test_knapsack.test_knapsack_generator"><code class="name flex">
<span>def <span class="ident">test_knapsack_generator</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_knapsack_generator():
gen = MultiKnapsackGenerator(
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=1.0),
alpha=uniform(loc=0.50, scale=0.0),
)
instances = gen.generate(100)
w_sum = sum(instance.weights for instance in instances) / len(instances)
b_sum = sum(instance.capacities for instance in instances) / len(instances)
assert round(np.mean(w_sum), -1) == 500.0
assert round(np.mean(b_sum), -3) == 25000.0</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
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<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="miplearn.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.problems.tests.test_knapsack.test_knapsack_generator" href="#miplearn.problems.tests.test_knapsack.test_knapsack_generator">test_knapsack_generator</a></code></li>
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<header>
<h1 class="title">Module <code>miplearn.problems.tests.test_stab</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 networkx as nx
import numpy as np
from scipy.stats import uniform, randint
from miplearn.problems.stab import MaxWeightStableSetInstance
from miplearn.solvers.learning import LearningSolver
def test_stab():
graph = nx.cycle_graph(5)
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver()
stats = solver.solve(instance)
assert stats[&#34;Lower bound&#34;] == 2.0
def test_stab_generator_fixed_graph():
np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
)
instances = gen.generate(1_000)
weights = np.array([instance.weights for instance in instances])
weights_avg_actual = np.round(np.average(weights, axis=0))
weights_avg_expected = [55.0] * 10
assert list(weights_avg_actual) == weights_avg_expected
def test_stab_generator_random_graph():
np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=0.0),
fix_graph=False,
)
instances = gen.generate(1_000)
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
n_edges = [instance.graph.number_of_edges() for instance in instances]
assert np.round(np.mean(n_nodes)) == 35.0
assert np.round(np.mean(n_edges), -1) == 300.0</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.problems.tests.test_stab.test_stab"><code class="name flex">
<span>def <span class="ident">test_stab</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_stab():
graph = nx.cycle_graph(5)
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver()
stats = solver.solve(instance)
assert stats[&#34;Lower bound&#34;] == 2.0</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph"><code class="name flex">
<span>def <span class="ident">test_stab_generator_fixed_graph</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_stab_generator_fixed_graph():
np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
)
instances = gen.generate(1_000)
weights = np.array([instance.weights for instance in instances])
weights_avg_actual = np.round(np.average(weights, axis=0))
weights_avg_expected = [55.0] * 10
assert list(weights_avg_actual) == weights_avg_expected</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_random_graph"><code class="name flex">
<span>def <span class="ident">test_stab_generator_random_graph</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_stab_generator_random_graph():
np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=0.0),
fix_graph=False,
)
instances = gen.generate(1_000)
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
n_edges = [instance.graph.number_of_edges() for instance in instances]
assert np.round(np.mean(n_nodes)) == 35.0
assert np.round(np.mean(n_edges), -1) == 300.0</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.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.problems.tests.test_stab.test_stab" href="#miplearn.problems.tests.test_stab.test_stab">test_stab</a></code></li>
<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>
<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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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.problems.tests.test_tsp</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 numpy as np
from numpy.linalg import norm
from scipy.spatial.distance import pdist, squareform
from scipy.stats import uniform, randint
from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
from miplearn.solvers.learning import LearningSolver
def test_generator():
instances = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
).generate(100)
assert len(instances) == 100
assert instances[0].n_cities == 100
assert norm(instances[0].distances - instances[0].distances.T) &lt; 1e-6
d = [instance.distances[0, 1] for instance in instances]
assert np.std(d) &gt; 0
def test_instance():
n_cities = 4
distances = np.array(
[
[0.0, 1.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 2.0],
[2.0, 1.0, 0.0, 1.0],
[1.0, 2.0, 1.0, 0.0],
]
)
instance = TravelingSalesmanInstance(n_cities, distances)
solver = LearningSolver()
stats = solver.solve(instance)
x = instance.training_data[0][&#34;Solution&#34;][&#34;x&#34;]
assert x[0, 1] == 1.0
assert x[0, 2] == 0.0
assert x[0, 3] == 1.0
assert x[1, 2] == 1.0
assert x[1, 3] == 0.0
assert x[2, 3] == 1.0
assert stats[&#34;Lower bound&#34;] == 4.0
assert stats[&#34;Upper bound&#34;] == 4.0
def test_subtour():
n_cities = 6
cities = np.array(
[
[0.0, 0.0],
[1.0, 0.0],
[2.0, 0.0],
[3.0, 0.0],
[0.0, 1.0],
[3.0, 1.0],
]
)
distances = squareform(pdist(cities))
instance = TravelingSalesmanInstance(n_cities, distances)
solver = LearningSolver()
solver.solve(instance)
assert hasattr(instance, &#34;found_violated_lazy_constraints&#34;)
assert hasattr(instance, &#34;found_violated_user_cuts&#34;)
x = instance.training_data[0][&#34;Solution&#34;][&#34;x&#34;]
assert x[0, 1] == 1.0
assert x[0, 4] == 1.0
assert x[1, 2] == 1.0
assert x[2, 3] == 1.0
assert x[3, 5] == 1.0
assert x[4, 5] == 1.0
solver.fit([instance])
solver.solve(instance)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.problems.tests.test_tsp.test_generator"><code class="name flex">
<span>def <span class="ident">test_generator</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_generator():
instances = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
).generate(100)
assert len(instances) == 100
assert instances[0].n_cities == 100
assert norm(instances[0].distances - instances[0].distances.T) &lt; 1e-6
d = [instance.distances[0, 1] for instance in instances]
assert np.std(d) &gt; 0</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_tsp.test_instance"><code class="name flex">
<span>def <span class="ident">test_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_instance():
n_cities = 4
distances = np.array(
[
[0.0, 1.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 2.0],
[2.0, 1.0, 0.0, 1.0],
[1.0, 2.0, 1.0, 0.0],
]
)
instance = TravelingSalesmanInstance(n_cities, distances)
solver = LearningSolver()
stats = solver.solve(instance)
x = instance.training_data[0][&#34;Solution&#34;][&#34;x&#34;]
assert x[0, 1] == 1.0
assert x[0, 2] == 0.0
assert x[0, 3] == 1.0
assert x[1, 2] == 1.0
assert x[1, 3] == 0.0
assert x[2, 3] == 1.0
assert stats[&#34;Lower bound&#34;] == 4.0
assert stats[&#34;Upper bound&#34;] == 4.0</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_tsp.test_subtour"><code class="name flex">
<span>def <span class="ident">test_subtour</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_subtour():
n_cities = 6
cities = np.array(
[
[0.0, 0.0],
[1.0, 0.0],
[2.0, 0.0],
[3.0, 0.0],
[0.0, 1.0],
[3.0, 1.0],
]
)
distances = squareform(pdist(cities))
instance = TravelingSalesmanInstance(n_cities, distances)
solver = LearningSolver()
solver.solve(instance)
assert hasattr(instance, &#34;found_violated_lazy_constraints&#34;)
assert hasattr(instance, &#34;found_violated_user_cuts&#34;)
x = instance.training_data[0][&#34;Solution&#34;][&#34;x&#34;]
assert x[0, 1] == 1.0
assert x[0, 4] == 1.0
assert x[1, 2] == 1.0
assert x[2, 3] == 1.0
assert x[3, 5] == 1.0
assert x[4, 5] == 1.0
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.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.problems.tests.test_tsp.test_generator" href="#miplearn.problems.tests.test_tsp.test_generator">test_generator</a></code></li>
<li><code><a title="miplearn.problems.tests.test_tsp.test_instance" href="#miplearn.problems.tests.test_tsp.test_instance">test_instance</a></code></li>
<li><code><a title="miplearn.problems.tests.test_tsp.test_subtour" href="#miplearn.problems.tests.test_tsp.test_subtour">test_subtour</a></code></li>
</ul>
</li>
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