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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.threshold</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from abc import abstractmethod, ABC
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import numpy as np
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from sklearn.metrics._ranking import _binary_clf_curve
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from miplearn.classifiers import Classifier
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class DynamicThreshold(ABC):
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@abstractmethod
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def find(
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self,
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clf: Classifier,
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x_train: np.ndarray,
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y_train: np.ndarray,
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) -> float:
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"""
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Given a trained binary classifier `clf` and a training data set,
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returns the numerical threshold (float) satisfying some criterea.
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"""
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pass
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class MinPrecisionThreshold(DynamicThreshold):
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"""
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The smallest possible threshold satisfying a minimum acceptable true
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positive rate (also known as precision).
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"""
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def __init__(self, min_precision: float) -> None:
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self.min_precision = min_precision
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def find(self, clf, x_train, y_train):
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proba = clf.predict_proba(x_train)
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assert isinstance(proba, np.ndarray), "classifier should return numpy array"
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assert proba.shape == (
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x_train.shape[0],
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2,
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), "classifier should return (%d,%d)-shaped array, not %s" % (
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x_train.shape[0],
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2,
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str(proba.shape),
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)
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fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
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precision = tps / (tps + fps)
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for k in reversed(range(len(precision))):
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if precision[k] >= self.min_precision:
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return thresholds[k]
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return 2.0</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.classifiers.threshold.DynamicThreshold"><code class="flex name class">
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<span>class <span class="ident">DynamicThreshold</span></span>
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</code></dt>
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<dd>
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<section class="desc"><p>Helper class that provides a standard way to create an ABC using
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inheritance.</p></section>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">class DynamicThreshold(ABC):
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@abstractmethod
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def find(
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self,
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clf: Classifier,
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x_train: np.ndarray,
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y_train: np.ndarray,
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) -> float:
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"""
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Given a trained binary classifier `clf` and a training data set,
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returns the numerical threshold (float) satisfying some criterea.
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"""
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pass</code></pre>
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</details>
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<h3>Ancestors</h3>
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<ul class="hlist">
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<li>abc.ABC</li>
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</ul>
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<h3>Subclasses</h3>
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<ul class="hlist">
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<li><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></li>
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</ul>
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<h3>Methods</h3>
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<dl>
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<dt id="miplearn.classifiers.threshold.DynamicThreshold.find"><code class="name flex">
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<span>def <span class="ident">find</span></span>(<span>self, clf, x_train, y_train)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>Given a trained binary classifier <code>clf</code> and a training data set,
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returns the numerical threshold (float) satisfying some criterea.</p></section>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">@abstractmethod
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def find(
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self,
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clf: Classifier,
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x_train: np.ndarray,
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y_train: np.ndarray,
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) -> float:
|
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"""
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Given a trained binary classifier `clf` and a training data set,
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returns the numerical threshold (float) satisfying some criterea.
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"""
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pass</code></pre>
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</details>
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</dd>
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</dl>
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</dd>
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<dt id="miplearn.classifiers.threshold.MinPrecisionThreshold"><code class="flex name class">
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<span>class <span class="ident">MinPrecisionThreshold</span></span>
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<span>(</span><span>min_precision)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>The smallest possible threshold satisfying a minimum acceptable true
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positive rate (also known as precision).</p></section>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">class MinPrecisionThreshold(DynamicThreshold):
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"""
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The smallest possible threshold satisfying a minimum acceptable true
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positive rate (also known as precision).
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"""
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def __init__(self, min_precision: float) -> None:
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self.min_precision = min_precision
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def find(self, clf, x_train, y_train):
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proba = clf.predict_proba(x_train)
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assert isinstance(proba, np.ndarray), "classifier should return numpy array"
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assert proba.shape == (
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x_train.shape[0],
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2,
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), "classifier should return (%d,%d)-shaped array, not %s" % (
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x_train.shape[0],
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2,
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str(proba.shape),
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)
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fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
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precision = tps / (tps + fps)
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for k in reversed(range(len(precision))):
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if precision[k] >= self.min_precision:
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return thresholds[k]
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return 2.0</code></pre>
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</details>
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<h3>Ancestors</h3>
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<ul class="hlist">
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<li><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></li>
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<li>abc.ABC</li>
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</ul>
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<h3>Inherited members</h3>
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<ul class="hlist">
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<li><code><b><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></b></code>:
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<ul class="hlist">
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<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</a></code></li>
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</ul>
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</li>
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</ul>
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</dd>
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</dl>
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</section>
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</article>
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
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<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<ul>
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<li>
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<h4><code><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></code></h4>
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<ul class="">
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<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</a></code></li>
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</ul>
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</li>
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<li>
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<h4><code><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></code></h4>
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</li>
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</ul>
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</li>
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</ul>
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