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<h1 class="title">Module <code>miplearn.components.tests</code></h1>
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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.</code></pre>
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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.components.tests.test_composite" href="test_composite.html">miplearn.components.tests.test_composite</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.components.tests.test_lazy_dynamic" href="test_lazy_dynamic.html">miplearn.components.tests.test_lazy_dynamic</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.components.tests.test_lazy_static" href="test_lazy_static.html">miplearn.components.tests.test_lazy_static</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.components.tests.test_objective" href="test_objective.html">miplearn.components.tests.test_objective</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.components.tests.test_primal" href="test_primal.html">miplearn.components.tests.test_primal</a></code></dt>
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<section class="desc"></section>
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<li><h3>Super-module</h3>
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<li><code><a title="miplearn.components" href="../index.html">miplearn.components</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.components.tests.test_composite" href="test_composite.html">miplearn.components.tests.test_composite</a></code></li>
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<li><code><a title="miplearn.components.tests.test_lazy_dynamic" href="test_lazy_dynamic.html">miplearn.components.tests.test_lazy_dynamic</a></code></li>
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<li><code><a title="miplearn.components.tests.test_lazy_static" href="test_lazy_static.html">miplearn.components.tests.test_lazy_static</a></code></li>
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||||
<li><code><a title="miplearn.components.tests.test_objective" href="test_objective.html">miplearn.components.tests.test_objective</a></code></li>
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<li><code><a title="miplearn.components.tests.test_primal" href="test_primal.html">miplearn.components.tests.test_primal</a></code></li>
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179
0.2/api/miplearn/components/tests/test_composite.html
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<main>
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<article id="content">
|
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<header>
|
||||
<h1 class="title">Module <code>miplearn.components.tests.test_composite</code></h1>
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</header>
|
||||
<section id="section-intro">
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<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
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</summary>
|
||||
<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 unittest.mock import Mock, call
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from miplearn.components.component import Component
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from miplearn.components.composite import CompositeComponent
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from miplearn.instance import Instance
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from miplearn.solvers.learning import LearningSolver
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def test_composite():
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solver, instance, model = (
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Mock(spec=LearningSolver),
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Mock(spec=Instance),
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Mock(),
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)
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c1 = Mock(spec=Component)
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c2 = Mock(spec=Component)
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cc = CompositeComponent([c1, c2])
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# Should broadcast before_solve
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cc.before_solve(solver, instance, model)
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c1.before_solve.assert_has_calls([call(solver, instance, model)])
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c2.before_solve.assert_has_calls([call(solver, instance, model)])
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# Should broadcast after_solve
|
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cc.after_solve(solver, instance, model, {}, {})
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c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
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c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
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# Should broadcast fit
|
||||
cc.fit([1, 2, 3])
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c1.fit.assert_has_calls([call([1, 2, 3])])
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c2.fit.assert_has_calls([call([1, 2, 3])])
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||||
|
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# Should broadcast lazy_cb
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cc.lazy_cb(solver, instance, model)
|
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c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
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c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
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||||
|
||||
# Should broadcast iteration_cb
|
||||
cc.iteration_cb(solver, instance, model)
|
||||
c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# If at least one child component returns true, iteration_cb should return True
|
||||
c1.iteration_cb = Mock(return_value=True)
|
||||
c2.iteration_cb = Mock(return_value=False)
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||||
assert cc.iteration_cb(solver, instance, model)
|
||||
|
||||
# If all children return False, iteration_cb should return False
|
||||
c1.iteration_cb = Mock(return_value=False)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert not cc.iteration_cb(solver, instance, model)</code></pre>
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||||
</details>
|
||||
</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>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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||||
<dt id="miplearn.components.tests.test_composite.test_composite"><code class="name flex">
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||||
<span>def <span class="ident">test_composite</span></span>(<span>)</span>
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</code></dt>
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<dd>
|
||||
<section class="desc"></section>
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||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_composite():
|
||||
solver, instance, model = (
|
||||
Mock(spec=LearningSolver),
|
||||
Mock(spec=Instance),
|
||||
Mock(),
|
||||
)
|
||||
|
||||
c1 = Mock(spec=Component)
|
||||
c2 = Mock(spec=Component)
|
||||
cc = CompositeComponent([c1, c2])
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||||
|
||||
# Should broadcast before_solve
|
||||
cc.before_solve(solver, instance, model)
|
||||
c1.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
c2.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast after_solve
|
||||
cc.after_solve(solver, instance, model, {}, {})
|
||||
c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
|
||||
# Should broadcast fit
|
||||
cc.fit([1, 2, 3])
|
||||
c1.fit.assert_has_calls([call([1, 2, 3])])
|
||||
c2.fit.assert_has_calls([call([1, 2, 3])])
|
||||
|
||||
# Should broadcast lazy_cb
|
||||
cc.lazy_cb(solver, instance, model)
|
||||
c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast iteration_cb
|
||||
cc.iteration_cb(solver, instance, model)
|
||||
c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# If at least one child component returns true, iteration_cb should return True
|
||||
c1.iteration_cb = Mock(return_value=True)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert cc.iteration_cb(solver, instance, model)
|
||||
|
||||
# If all children return False, iteration_cb should return False
|
||||
c1.iteration_cb = Mock(return_value=False)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert not cc.iteration_cb(solver, instance, model)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
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|
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<nav id="sidebar">
|
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<h1>Index</h1>
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|
||||
<ul></ul>
|
||||
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|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_composite.test_composite" href="#miplearn.components.tests.test_composite.test_composite">test_composite</a></code></li>
|
||||
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|
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Normal file
365
0.2/api/miplearn/components/tests/test_lazy_dynamic.html
Normal file
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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.components.tests.test_lazy_dynamic</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.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import norm
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_lazy_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "c"]
|
||||
classifier = Mock(spec=Classifier)
|
||||
component = DynamicLazyConstraintsComponent(classifier=classifier)
|
||||
|
||||
component.fit(instances)
|
||||
|
||||
# Should create one classifier for each violation
|
||||
assert "a" in component.classifiers
|
||||
assert "b" in component.classifiers
|
||||
assert "c" in component.classifiers
|
||||
|
||||
# Should provide correct x_train to each classifier
|
||||
expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
|
||||
actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
|
||||
actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
|
||||
assert norm(expected_x_train_a - actual_x_train_a) < E
|
||||
assert norm(expected_x_train_b - actual_x_train_b) < E
|
||||
assert norm(expected_x_train_c - actual_x_train_c) < E
|
||||
|
||||
# Should provide correct y_train to each classifier
|
||||
expected_y_train_a = np.array([1.0, 0.0])
|
||||
expected_y_train_b = np.array([1.0, 1.0])
|
||||
expected_y_train_c = np.array([0.0, 1.0])
|
||||
actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
|
||||
actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
|
||||
actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
|
||||
assert norm(expected_y_train_a - actual_y_train_a) < E
|
||||
assert norm(expected_y_train_b - actual_y_train_b) < E
|
||||
assert norm(expected_y_train_c - actual_y_train_c) < E
|
||||
|
||||
|
||||
def test_lazy_before():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].build_lazy_constraint = Mock(return_value="c1")
|
||||
solver = LearningSolver()
|
||||
solver.internal_solver = Mock(spec=InternalSolver)
|
||||
component = DynamicLazyConstraintsComponent(threshold=0.10)
|
||||
component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
|
||||
|
||||
component.before_solve(solver, instances[0], models[0])
|
||||
|
||||
# Should ask classifier likelihood of each constraint being violated
|
||||
expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
|
||||
expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
|
||||
actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
|
||||
actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
|
||||
assert norm(expected_x_test_a - actual_x_test_a) < E
|
||||
assert norm(expected_x_test_b - actual_x_test_b) < E
|
||||
|
||||
# Should ask instance to generate cut for constraints whose likelihood
|
||||
# of being violated exceeds the threshold
|
||||
instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
|
||||
|
||||
# Should ask internal solver to add generated constraint
|
||||
solver.internal_solver.add_constraint.assert_called_once_with("c1")
|
||||
|
||||
|
||||
def test_lazy_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
component = DynamicLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"a": Mock(spec=Classifier),
|
||||
"b": Mock(spec=Classifier),
|
||||
"c": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "d"]
|
||||
assert component.evaluate(instances) == {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"F1 score": 0.8,
|
||||
"Precision": 1.0,
|
||||
"Recall": 2 / 3.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 3,
|
||||
"Condition negative": 1,
|
||||
"False negative": 1,
|
||||
"False positive": 0,
|
||||
"True negative": 1,
|
||||
"True positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 75.0,
|
||||
"Condition negative (%)": 25.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 50.0,
|
||||
},
|
||||
1: {
|
||||
"Accuracy": 0.5,
|
||||
"F1 score": 0.5,
|
||||
"Precision": 0.5,
|
||||
"Recall": 0.5,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 2,
|
||||
"Condition negative": 2,
|
||||
"False negative": 1,
|
||||
"False positive": 1,
|
||||
"True negative": 1,
|
||||
"True positive": 1,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 50.0,
|
||||
"Condition negative (%)": 50.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 25.0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 25.0,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_before"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_before</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_before():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].build_lazy_constraint = Mock(return_value="c1")
|
||||
solver = LearningSolver()
|
||||
solver.internal_solver = Mock(spec=InternalSolver)
|
||||
component = DynamicLazyConstraintsComponent(threshold=0.10)
|
||||
component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
|
||||
|
||||
component.before_solve(solver, instances[0], models[0])
|
||||
|
||||
# Should ask classifier likelihood of each constraint being violated
|
||||
expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
|
||||
expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
|
||||
actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
|
||||
actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
|
||||
assert norm(expected_x_test_a - actual_x_test_a) < E
|
||||
assert norm(expected_x_test_b - actual_x_test_b) < E
|
||||
|
||||
# Should ask instance to generate cut for constraints whose likelihood
|
||||
# of being violated exceeds the threshold
|
||||
instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
|
||||
|
||||
# Should ask internal solver to add generated constraint
|
||||
solver.internal_solver.add_constraint.assert_called_once_with("c1")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_evaluate</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_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
component = DynamicLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"a": Mock(spec=Classifier),
|
||||
"b": Mock(spec=Classifier),
|
||||
"c": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "d"]
|
||||
assert component.evaluate(instances) == {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"F1 score": 0.8,
|
||||
"Precision": 1.0,
|
||||
"Recall": 2 / 3.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 3,
|
||||
"Condition negative": 1,
|
||||
"False negative": 1,
|
||||
"False positive": 0,
|
||||
"True negative": 1,
|
||||
"True positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 75.0,
|
||||
"Condition negative (%)": 25.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 50.0,
|
||||
},
|
||||
1: {
|
||||
"Accuracy": 0.5,
|
||||
"F1 score": 0.5,
|
||||
"Precision": 0.5,
|
||||
"Recall": 0.5,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 2,
|
||||
"Condition negative": 2,
|
||||
"False negative": 1,
|
||||
"False positive": 1,
|
||||
"True negative": 1,
|
||||
"True positive": 1,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 50.0,
|
||||
"Condition negative (%)": 50.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 25.0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 25.0,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_fit"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_fit</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_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "c"]
|
||||
classifier = Mock(spec=Classifier)
|
||||
component = DynamicLazyConstraintsComponent(classifier=classifier)
|
||||
|
||||
component.fit(instances)
|
||||
|
||||
# Should create one classifier for each violation
|
||||
assert "a" in component.classifiers
|
||||
assert "b" in component.classifiers
|
||||
assert "c" in component.classifiers
|
||||
|
||||
# Should provide correct x_train to each classifier
|
||||
expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
|
||||
actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
|
||||
actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
|
||||
assert norm(expected_x_train_a - actual_x_train_a) < E
|
||||
assert norm(expected_x_train_b - actual_x_train_b) < E
|
||||
assert norm(expected_x_train_c - actual_x_train_c) < E
|
||||
|
||||
# Should provide correct y_train to each classifier
|
||||
expected_y_train_a = np.array([1.0, 0.0])
|
||||
expected_y_train_b = np.array([1.0, 1.0])
|
||||
expected_y_train_c = np.array([0.0, 1.0])
|
||||
actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
|
||||
actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
|
||||
actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
|
||||
assert norm(expected_y_train_a - actual_y_train_a) < E
|
||||
assert norm(expected_y_train_b - actual_y_train_b) < E
|
||||
assert norm(expected_y_train_c - actual_y_train_c) < E</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.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_before" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_before">test_lazy_before</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate">test_lazy_evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_fit" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_fit">test_lazy_fit</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
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|
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|
||||
</html>
|
||||
538
0.2/api/miplearn/components/tests/test_lazy_static.html
Normal file
538
0.2/api/miplearn/components/tests/test_lazy_static.html
Normal file
@@ -0,0 +1,538 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
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<meta charset="utf-8">
|
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<meta name="description" content="" />
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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|
||||
<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>
|
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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.components.tests.test_lazy_static</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.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.lazy_static import StaticLazyConstraintsComponent
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_usage_with_solver():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
solver.use_lazy_cb = False
|
||||
solver.gap_tolerance = 1e-4
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.has_static_lazy_constraints = Mock(return_value=True)
|
||||
instance.is_constraint_lazy = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": False,
|
||||
"c2": True,
|
||||
"c3": True,
|
||||
"c4": True,
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
component = StaticLazyConstraintsComponent(
|
||||
threshold=0.90,
|
||||
use_two_phase_gap=False,
|
||||
violation_tolerance=1.0,
|
||||
)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should ask if instance has static lazy constraints
|
||||
instance.has_static_lazy_constraints.assert_called_once()
|
||||
|
||||
# Should ask internal solver for a list of constraints in the model
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should ask if each constraint in the model is lazy
|
||||
instance.is_constraint_lazy.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For the lazy ones, should ask for features
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should also ask for categories
|
||||
assert instance.get_constraint_category.call_count == 3
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask internal solver to remove constraints identified as lazy
|
||||
assert internal.extract_constraint.call_count == 3
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether each lazy constraint should be enforced
|
||||
component.classifiers["type-a"].predict_proba.assert_called_once_with(
|
||||
[[1.0, 0.0], [0.5, 0.5]]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
|
||||
|
||||
# For the ones that should be enforced, should ask solver to re-add them
|
||||
# to the formulation. The remaining ones should remain in the pool.
|
||||
assert internal.add_constraint.call_count == 2
|
||||
internal.add_constraint.assert_has_calls(
|
||||
[
|
||||
call("<c3>"),
|
||||
call("<c4>"),
|
||||
]
|
||||
)
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert should_repeat
|
||||
|
||||
# Should ask internal solver to verify if constraints in the pool are
|
||||
# satisfied and add the ones that are not
|
||||
internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
|
||||
internal.is_constraint_satisfied.reset_mock()
|
||||
internal.add_constraint.assert_called_once_with("<c2>")
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
# The lazy constraint pool should be empty by now, so no calls should be made
|
||||
internal.is_constraint_satisfied.assert_not_called()
|
||||
internal.add_constraint.assert_not_called()
|
||||
|
||||
# Should update instance object
|
||||
assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]
|
||||
|
||||
|
||||
def test_fit():
|
||||
instance_1 = Mock(spec=Instance)
|
||||
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
|
||||
instance_1.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_1.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [1, 1],
|
||||
"c2": [1, 2],
|
||||
"c3": [1, 3],
|
||||
"c4": [1, 4, 0],
|
||||
"c5": [1, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instance_2 = Mock(spec=Instance)
|
||||
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
|
||||
instance_2.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_2.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [2, 1],
|
||||
"c2": [2, 2],
|
||||
"c3": [2, 3],
|
||||
"c4": [2, 4, 0],
|
||||
"c5": [2, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instances = [instance_1, instance_2]
|
||||
component = StaticLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
|
||||
expected_constraints = {
|
||||
"type-a": ["c1", "c2", "c3"],
|
||||
"type-b": ["c4", "c5"],
|
||||
}
|
||||
expected_x = {
|
||||
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
|
||||
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
|
||||
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
|
||||
}
|
||||
assert component._collect_constraints(instances) == expected_constraints
|
||||
assert component.x(instances) == expected_x
|
||||
assert component.y(instances) == expected_y
|
||||
|
||||
component.fit(instances)
|
||||
component.classifiers["type-a"].fit.assert_called_once_with(
|
||||
expected_x["type-a"],
|
||||
expected_y["type-a"],
|
||||
)
|
||||
component.classifiers["type-b"].fit.assert_called_once_with(
|
||||
expected_x["type-b"],
|
||||
expected_y["type-b"],
|
||||
)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_lazy_static.test_fit"><code class="name flex">
|
||||
<span>def <span class="ident">test_fit</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_fit():
|
||||
instance_1 = Mock(spec=Instance)
|
||||
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
|
||||
instance_1.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_1.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [1, 1],
|
||||
"c2": [1, 2],
|
||||
"c3": [1, 3],
|
||||
"c4": [1, 4, 0],
|
||||
"c5": [1, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instance_2 = Mock(spec=Instance)
|
||||
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
|
||||
instance_2.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_2.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [2, 1],
|
||||
"c2": [2, 2],
|
||||
"c3": [2, 3],
|
||||
"c4": [2, 4, 0],
|
||||
"c5": [2, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instances = [instance_1, instance_2]
|
||||
component = StaticLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
|
||||
expected_constraints = {
|
||||
"type-a": ["c1", "c2", "c3"],
|
||||
"type-b": ["c4", "c5"],
|
||||
}
|
||||
expected_x = {
|
||||
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
|
||||
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
|
||||
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
|
||||
}
|
||||
assert component._collect_constraints(instances) == expected_constraints
|
||||
assert component.x(instances) == expected_x
|
||||
assert component.y(instances) == expected_y
|
||||
|
||||
component.fit(instances)
|
||||
component.classifiers["type-a"].fit.assert_called_once_with(
|
||||
expected_x["type-a"],
|
||||
expected_y["type-a"],
|
||||
)
|
||||
component.classifiers["type-b"].fit.assert_called_once_with(
|
||||
expected_x["type-b"],
|
||||
expected_y["type-b"],
|
||||
)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_lazy_static.test_usage_with_solver"><code class="name flex">
|
||||
<span>def <span class="ident">test_usage_with_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_usage_with_solver():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
solver.use_lazy_cb = False
|
||||
solver.gap_tolerance = 1e-4
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.has_static_lazy_constraints = Mock(return_value=True)
|
||||
instance.is_constraint_lazy = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": False,
|
||||
"c2": True,
|
||||
"c3": True,
|
||||
"c4": True,
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
component = StaticLazyConstraintsComponent(
|
||||
threshold=0.90,
|
||||
use_two_phase_gap=False,
|
||||
violation_tolerance=1.0,
|
||||
)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should ask if instance has static lazy constraints
|
||||
instance.has_static_lazy_constraints.assert_called_once()
|
||||
|
||||
# Should ask internal solver for a list of constraints in the model
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should ask if each constraint in the model is lazy
|
||||
instance.is_constraint_lazy.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For the lazy ones, should ask for features
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should also ask for categories
|
||||
assert instance.get_constraint_category.call_count == 3
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask internal solver to remove constraints identified as lazy
|
||||
assert internal.extract_constraint.call_count == 3
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether each lazy constraint should be enforced
|
||||
component.classifiers["type-a"].predict_proba.assert_called_once_with(
|
||||
[[1.0, 0.0], [0.5, 0.5]]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
|
||||
|
||||
# For the ones that should be enforced, should ask solver to re-add them
|
||||
# to the formulation. The remaining ones should remain in the pool.
|
||||
assert internal.add_constraint.call_count == 2
|
||||
internal.add_constraint.assert_has_calls(
|
||||
[
|
||||
call("<c3>"),
|
||||
call("<c4>"),
|
||||
]
|
||||
)
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert should_repeat
|
||||
|
||||
# Should ask internal solver to verify if constraints in the pool are
|
||||
# satisfied and add the ones that are not
|
||||
internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
|
||||
internal.is_constraint_satisfied.reset_mock()
|
||||
internal.add_constraint.assert_called_once_with("<c2>")
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
# The lazy constraint pool should be empty by now, so no calls should be made
|
||||
internal.is_constraint_satisfied.assert_not_called()
|
||||
internal.add_constraint.assert_not_called()
|
||||
|
||||
# Should update instance object
|
||||
assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]</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.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_static.test_fit" href="#miplearn.components.tests.test_lazy_static.test_fit">test_fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_static.test_usage_with_solver" href="#miplearn.components.tests.test_lazy_static.test_usage_with_solver">test_usage_with_solver</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
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|
||||
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|
||||
</html>
|
||||
174
0.2/api/miplearn/components/tests/test_objective.html
Normal file
174
0.2/api/miplearn/components/tests/test_objective.html
Normal file
@@ -0,0 +1,174 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
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<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.components.tests.test_objective API documentation</title>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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|
||||
<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.components.tests.test_objective</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.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Regressor
|
||||
from miplearn.components.objective import ObjectiveValueComponent
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_usage():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = ObjectiveValueComponent()
|
||||
comp.fit(instances)
|
||||
assert instances[0].training_data[0]["Lower bound"] == 1183.0
|
||||
assert instances[0].training_data[0]["Upper bound"] == 1183.0
|
||||
assert np.round(comp.predict(instances), 2).tolist() == [
|
||||
[1183.0, 1183.0],
|
||||
[1070.0, 1070.0],
|
||||
]
|
||||
|
||||
|
||||
def test_obj_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
reg = Mock(spec=Regressor)
|
||||
reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
|
||||
comp = ObjectiveValueComponent(regressor=reg)
|
||||
comp.fit(instances)
|
||||
ev = comp.evaluate(instances)
|
||||
assert ev == {
|
||||
"Lower bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
"Upper bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_objective.test_obj_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_obj_evaluate</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_obj_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
reg = Mock(spec=Regressor)
|
||||
reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
|
||||
comp = ObjectiveValueComponent(regressor=reg)
|
||||
comp.fit(instances)
|
||||
ev = comp.evaluate(instances)
|
||||
assert ev == {
|
||||
"Lower bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
"Upper bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_objective.test_usage"><code class="name flex">
|
||||
<span>def <span class="ident">test_usage</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_usage():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = ObjectiveValueComponent()
|
||||
comp.fit(instances)
|
||||
assert instances[0].training_data[0]["Lower bound"] == 1183.0
|
||||
assert instances[0].training_data[0]["Upper bound"] == 1183.0
|
||||
assert np.round(comp.predict(instances), 2).tolist() == [
|
||||
[1183.0, 1183.0],
|
||||
[1070.0, 1070.0],
|
||||
]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
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<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_objective.test_obj_evaluate" href="#miplearn.components.tests.test_objective.test_obj_evaluate">test_obj_evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_objective.test_usage" href="#miplearn.components.tests.test_objective.test_usage">test_usage</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
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|
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<script>hljs.initHighlightingOnLoad()</script>
|
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|
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|
||||
306
0.2/api/miplearn/components/tests/test_primal.html
Normal file
306
0.2/api/miplearn/components/tests/test_primal.html
Normal file
@@ -0,0 +1,306 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
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<meta charset="utf-8">
|
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|
||||
<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.components.tests.test_primal</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.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.primal import PrimalSolutionComponent
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_predict():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances)
|
||||
solution = comp.predict(instances[0])
|
||||
assert "x" in solution
|
||||
assert 0 in solution["x"]
|
||||
assert 1 in solution["x"]
|
||||
assert 2 in solution["x"]
|
||||
assert 3 in solution["x"]
|
||||
|
||||
|
||||
def test_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
clf_zero = Mock(spec=Classifier)
|
||||
clf_zero.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.0, 1.0], # x[0]
|
||||
[0.0, 1.0], # x[1]
|
||||
[1.0, 0.0], # x[2]
|
||||
[1.0, 0.0], # x[3]
|
||||
]
|
||||
)
|
||||
)
|
||||
clf_one = Mock(spec=Classifier)
|
||||
clf_one.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[1.0, 0.0], # x[0] instances[0]
|
||||
[1.0, 0.0], # x[1] instances[0]
|
||||
[0.0, 1.0], # x[2] instances[0]
|
||||
[1.0, 0.0], # x[3] instances[0]
|
||||
]
|
||||
)
|
||||
)
|
||||
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
|
||||
comp.fit(instances[:1])
|
||||
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
|
||||
assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
|
||||
ev = comp.evaluate(instances[:1])
|
||||
assert ev == {
|
||||
"Fix one": {
|
||||
0: {
|
||||
"Accuracy": 0.5,
|
||||
"Condition negative": 1,
|
||||
"Condition negative (%)": 25.0,
|
||||
"Condition positive": 3,
|
||||
"Condition positive (%)": 75.0,
|
||||
"F1 score": 0.5,
|
||||
"False negative": 2,
|
||||
"False negative (%)": 50.0,
|
||||
"False positive": 0,
|
||||
"False positive (%)": 0.0,
|
||||
"Precision": 1.0,
|
||||
"Predicted negative": 3,
|
||||
"Predicted negative (%)": 75.0,
|
||||
"Predicted positive": 1,
|
||||
"Predicted positive (%)": 25.0,
|
||||
"Recall": 0.3333333333333333,
|
||||
"True negative": 1,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
"Fix zero": {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"Condition negative": 3,
|
||||
"Condition negative (%)": 75.0,
|
||||
"Condition positive": 1,
|
||||
"Condition positive (%)": 25.0,
|
||||
"F1 score": 0.6666666666666666,
|
||||
"False negative": 0,
|
||||
"False negative (%)": 0.0,
|
||||
"False positive": 1,
|
||||
"False positive (%)": 25.0,
|
||||
"Precision": 0.5,
|
||||
"Predicted negative": 2,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Recall": 1.0,
|
||||
"True negative": 2,
|
||||
"True negative (%)": 50.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def test_primal_parallel_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances, n_jobs=2)
|
||||
assert len(comp.classifiers) == 2</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_primal.test_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_evaluate</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_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
clf_zero = Mock(spec=Classifier)
|
||||
clf_zero.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.0, 1.0], # x[0]
|
||||
[0.0, 1.0], # x[1]
|
||||
[1.0, 0.0], # x[2]
|
||||
[1.0, 0.0], # x[3]
|
||||
]
|
||||
)
|
||||
)
|
||||
clf_one = Mock(spec=Classifier)
|
||||
clf_one.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[1.0, 0.0], # x[0] instances[0]
|
||||
[1.0, 0.0], # x[1] instances[0]
|
||||
[0.0, 1.0], # x[2] instances[0]
|
||||
[1.0, 0.0], # x[3] instances[0]
|
||||
]
|
||||
)
|
||||
)
|
||||
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
|
||||
comp.fit(instances[:1])
|
||||
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
|
||||
assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
|
||||
ev = comp.evaluate(instances[:1])
|
||||
assert ev == {
|
||||
"Fix one": {
|
||||
0: {
|
||||
"Accuracy": 0.5,
|
||||
"Condition negative": 1,
|
||||
"Condition negative (%)": 25.0,
|
||||
"Condition positive": 3,
|
||||
"Condition positive (%)": 75.0,
|
||||
"F1 score": 0.5,
|
||||
"False negative": 2,
|
||||
"False negative (%)": 50.0,
|
||||
"False positive": 0,
|
||||
"False positive (%)": 0.0,
|
||||
"Precision": 1.0,
|
||||
"Predicted negative": 3,
|
||||
"Predicted negative (%)": 75.0,
|
||||
"Predicted positive": 1,
|
||||
"Predicted positive (%)": 25.0,
|
||||
"Recall": 0.3333333333333333,
|
||||
"True negative": 1,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
"Fix zero": {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"Condition negative": 3,
|
||||
"Condition negative (%)": 75.0,
|
||||
"Condition positive": 1,
|
||||
"Condition positive (%)": 25.0,
|
||||
"F1 score": 0.6666666666666666,
|
||||
"False negative": 0,
|
||||
"False negative (%)": 0.0,
|
||||
"False positive": 1,
|
||||
"False positive (%)": 25.0,
|
||||
"Precision": 0.5,
|
||||
"Predicted negative": 2,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Recall": 1.0,
|
||||
"True negative": 2,
|
||||
"True negative (%)": 50.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_primal.test_predict"><code class="name flex">
|
||||
<span>def <span class="ident">test_predict</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_predict():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances)
|
||||
solution = comp.predict(instances[0])
|
||||
assert "x" in solution
|
||||
assert 0 in solution["x"]
|
||||
assert 1 in solution["x"]
|
||||
assert 2 in solution["x"]
|
||||
assert 3 in solution["x"]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_primal.test_primal_parallel_fit"><code class="name flex">
|
||||
<span>def <span class="ident">test_primal_parallel_fit</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_primal_parallel_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances, n_jobs=2)
|
||||
assert len(comp.classifiers) == 2</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.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_primal.test_evaluate" href="#miplearn.components.tests.test_primal.test_evaluate">test_evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_primal.test_predict" href="#miplearn.components.tests.test_primal.test_predict">test_predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_primal.test_primal_parallel_fit" href="#miplearn.components.tests.test_primal.test_primal_parallel_fit">test_primal_parallel_fit</a></code></li>
|
||||
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