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<body>
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.classifiers.tests.test_cv</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 numpy as np
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from numpy.linalg import norm
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVC
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from miplearn.classifiers.cv import CrossValidatedClassifier
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E = 0.1
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def test_cv():
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# Training set: label is true if point is inside a 2D circle
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x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
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x_train = StandardScaler().fit_transform(x_train)
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n_samples = x_train.shape[0]
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y_train = np.array(
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[
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1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
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for x1 in range(-10, 11)
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for x2 in range(-10, 11)
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]
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)
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# Support vector machines with linear kernels do not perform well on this
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# data set, so predictor should return the given constant.
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clf = CrossValidatedClassifier(
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classifier=SVC(probability=True, random_state=42),
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threshold=0.90,
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constant=0.0,
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cv=30,
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)
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clf.fit(x_train, y_train)
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assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
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# Support vector machines with quadratic kernels perform almost perfectly
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# on this data set, so predictor should return their prediction.
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clf = CrossValidatedClassifier(
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classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
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threshold=0.90,
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cv=30,
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)
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clf.fit(x_train, y_train)
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print(y_train - clf.predict(x_train))
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assert norm(y_train - clf.predict(x_train)) < E</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.classifiers.tests.test_cv.test_cv"><code class="name flex">
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<span>def <span class="ident">test_cv</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_cv():
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# Training set: label is true if point is inside a 2D circle
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x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
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x_train = StandardScaler().fit_transform(x_train)
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n_samples = x_train.shape[0]
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y_train = np.array(
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[
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1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
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for x1 in range(-10, 11)
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for x2 in range(-10, 11)
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]
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)
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# Support vector machines with linear kernels do not perform well on this
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# data set, so predictor should return the given constant.
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clf = CrossValidatedClassifier(
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classifier=SVC(probability=True, random_state=42),
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threshold=0.90,
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constant=0.0,
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cv=30,
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)
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clf.fit(x_train, y_train)
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assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
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# Support vector machines with quadratic kernels perform almost perfectly
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# on this data set, so predictor should return their prediction.
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clf = CrossValidatedClassifier(
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classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
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threshold=0.90,
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cv=30,
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)
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clf.fit(x_train, y_train)
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print(y_train - clf.predict(x_train))
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assert norm(y_train - clf.predict(x_train)) < E</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.classifiers.tests" href="index.html">miplearn.classifiers.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.classifiers.tests.test_cv.test_cv" href="#miplearn.classifiers.tests.test_cv.test_cv">test_cv</a></code></li>
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</ul>
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</li>
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</ul>
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