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<title>7. Collectors &amp; Extractors &#8212; MIPLearn 0.3</title>
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User Guide
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1. Benchmark Problems
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<a class="reference internal" href="../../guide/collectors/">
2. Training Data Collectors
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3. Feature Extractors
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4. Primal Components
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5. Solvers
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API Reference
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6. Benchmark Problems
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7. Collectors &amp; Extractors
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8. Components
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9. Solvers
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.classifiers.minprob">
7.1. miplearn.classifiers.minprob
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.classifiers.singleclass">
7.2. miplearn.classifiers.singleclass
</a>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.collectors.basic">
7.3. miplearn.collectors.basic
</a>
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<a class="reference internal nav-link" href="#miplearn-features-fields">
7.4. miplearn.features.fields
</a>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#miplearn-features-alvlouweh2017">
7.5. miplearn.features.AlvLouWeh2017
</a>
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<div>
<div class="section" id="collectors-extractors">
<h1><span class="section-number">7. </span>Collectors &amp; Extractors<a class="headerlink" href="#collectors-extractors" title="Permalink to this headline"></a></h1>
<div class="section" id="module-miplearn.classifiers.minprob">
<span id="miplearn-classifiers-minprob"></span><h2><span class="section-number">7.1. </span>miplearn.classifiers.minprob<a class="headerlink" href="#module-miplearn.classifiers.minprob" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.classifiers.minprob.MinProbabilityClassifier">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.classifiers.minprob.</span></code><code class="sig-name descname"><span class="pre">MinProbabilityClassifier</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">base_clf:</span> <span class="pre">Any,</span> <span class="pre">thresholds:</span> <span class="pre">List[float],</span> <span class="pre">clone_fn:</span> <span class="pre">Callable[[Any],</span> <span class="pre">Any]</span> <span class="pre">=</span> <span class="pre">&lt;function</span> <span class="pre">clone&gt;</span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.classifiers.minprob.MinProbabilityClassifier" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code></p>
<p>Meta-classifier that returns NaN for predictions made by a base classifier that
have probability below a given threshold. More specifically, this meta-classifier
calls base_clf.predict_proba and compares the result against the provided
thresholds. If the probability for one of the classes is above its threshold,
the meta-classifier returns that prediction. Otherwise, it returns NaN.</p>
<dl class="py method">
<dt id="miplearn.classifiers.minprob.MinProbabilityClassifier.fit">
<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#miplearn.classifiers.minprob.MinProbabilityClassifier.fit" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="miplearn.classifiers.minprob.MinProbabilityClassifier.predict">
<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">numpy.ndarray</span><a class="headerlink" href="#miplearn.classifiers.minprob.MinProbabilityClassifier.predict" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-miplearn.classifiers.singleclass">
<span id="miplearn-classifiers-singleclass"></span><h2><span class="section-number">7.2. </span>miplearn.classifiers.singleclass<a class="headerlink" href="#module-miplearn.classifiers.singleclass" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.classifiers.singleclass.SingleClassFix">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.classifiers.singleclass.</span></code><code class="sig-name descname"><span class="pre">SingleClassFix</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">base_clf:</span> <span class="pre">sklearn.base.BaseEstimator</span></em>, <em class="sig-param"><span class="pre">clone_fn:</span> <span class="pre">Callable</span> <span class="pre">=</span> <span class="pre">&lt;function</span> <span class="pre">clone&gt;</span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.classifiers.singleclass.SingleClassFix" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code></p>
<p>Some sklearn classifiers, such as logistic regression, have issues with datasets
that contain a single class. This meta-classifier fixes the issue. If the
training data contains a single class, this meta-classifier always returns that
class as a prediction. Otherwise, it fits the provided base classifier,
and returns its predictions instead.</p>
<dl class="py method">
<dt id="miplearn.classifiers.singleclass.SingleClassFix.fit">
<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#miplearn.classifiers.singleclass.SingleClassFix.fit" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="miplearn.classifiers.singleclass.SingleClassFix.predict">
<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">numpy.ndarray</span><a class="headerlink" href="#miplearn.classifiers.singleclass.SingleClassFix.predict" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-miplearn.collectors.basic">
<span id="miplearn-collectors-basic"></span><h2><span class="section-number">7.3. </span>miplearn.collectors.basic<a class="headerlink" href="#module-miplearn.collectors.basic" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.collectors.basic.BasicCollector">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.collectors.basic.</span></code><code class="sig-name descname"><span class="pre">BasicCollector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_limit_sec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">inf</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.collectors.basic.BasicCollector" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<dl class="py method">
<dt id="miplearn.collectors.basic.BasicCollector.collect">
<code class="sig-name descname"><span class="pre">collect</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filenames</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#miplearn.collectors.basic.BasicCollector.collect" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="miplearn-features-fields">
<h2><span class="section-number">7.4. </span>miplearn.features.fields<a class="headerlink" href="#miplearn-features-fields" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="miplearn-features-alvlouweh2017">
<h2><span class="section-number">7.5. </span>miplearn.features.AlvLouWeh2017<a class="headerlink" href="#miplearn-features-alvlouweh2017" title="Permalink to this headline"></a></h2>
</div>
</div>
</div>
<div class='prev-next-bottom'>
<a class='left-prev' id="prev-link" href="../problems/" title="previous page"><span class="section-number">6. </span>Benchmark Problems</a>
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