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  <div class="section" id="module-hep_ml.metrics">
<span id="metric-functions"></span><h1>Metric functions<a class="headerlink" href="#module-hep_ml.metrics" title="Permalink to this headline">¶</a></h1>
<p>Currently <strong>hep_ml.metrics</strong> module contains metric functions, which measure nonuniformity in predictions.</p>
<p>These metrics are unfortunately more complicated than usual ones
and require more information: not only predictions and classes,
but also mass (or other variables along which we want to have uniformity)</p>
<p>Available metrics of uniformity of predictions (for each of them bin version and knn version are available):</p>
<ul class="simple">
<li><p>SDE  - the standard deviation of efficiency</p></li>
<li><p>Theil - Theil index of Efficiency (Theil index is used in economics)</p></li>
<li><p>CVM  - based on Cramer-von Mises similarity between distributions</p></li>
</ul>
<dl class="simple">
<dt>uniform_label:</dt><dd><ul class="simple">
<li><p>1, if you want to measure non-uniformity in signal predictions</p></li>
<li><p>0, if background.</p></li>
</ul>
</dd>
</dl>
<p>Metrics are following <a class="reference external" href="https://github.com/yandex/rep">REP</a> conventions (first fit, then compute metrics on same dataset).
For these metrics <cite>fit</cite> stage is crucial, since it precomputes information using dataset X,
which is quite long and better to do this once. Different quality metrics with same interface
can be found in <strong>REP</strong> package.</p>
<div class="section" id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h2>
<p>we want to check if our predictions are uniform in mass for background events</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metric</span> <span class="o">=</span> <span class="n">BinBasedCvM</span><span class="p">(</span><span class="n">uniform_features</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mass&#39;</span><span class="p">],</span> <span class="n">uniform_label</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">metric</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">metric</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
</pre></div>
</div>
<p>to check predictions over two variables in signal (for dimensions &gt; 2 always use kNN, not bins):</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metric</span> <span class="o">=</span> <span class="n">KnnBasedCvM</span><span class="p">(</span><span class="n">uniform_features</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mass12&#39;</span><span class="p">,</span> <span class="s1">&#39;mass23&#39;</span><span class="p">],</span> <span class="n">uniform_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">metric</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">metric</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
</pre></div>
</div>
<p>to check uniformity of signal predictions at global signal efficiency of 0.7:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metric</span> <span class="o">=</span> <span class="n">KnnBasedSDE</span><span class="p">(</span><span class="n">uniform_features</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mass12&#39;</span><span class="p">,</span> <span class="s1">&#39;mass23&#39;</span><span class="p">],</span> <span class="n">uniform_label</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">target_rcp</span><span class="o">=</span><span class="p">[</span><span class="mf">0.7</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">metric</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">metric</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
</pre></div>
</div>
<p>Generally kNN versions are slower, but more stable in higher dimensions.
Don’t forget to scale features is those are of different nature.</p>
<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.metrics.BinBasedCvM">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.metrics.</span></span><span class="sig-name descname"><span class="pre">BinBasedCvM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniform_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uniform_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/metrics.html#BinBasedCvM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.metrics.BinBasedCvM" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.metrics.AbstractBinMetric</span></code></p>
<p>Nonuniformity metric based on Cramer-von Mises distance between distributions, computed on bins.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>uniform_features</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – features, in which we compute non-uniformity.</p></li>
<li><p><strong>uniform_label</strong> – label of class, in which uniformity is measured (0 for bck, 1 for signal)</p></li>
<li><p><strong>n_bins</strong> (<em>int</em>) – number of bins used along each axis.</p></li>
<li><p><strong>power</strong> (<em>float</em>) – power used in CvM formula (default is 2.)</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.metrics.BinBasedSDE">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.metrics.</span></span><span class="sig-name descname"><span class="pre">BinBasedSDE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniform_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uniform_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_rcp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/metrics.html#BinBasedSDE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.metrics.BinBasedSDE" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.metrics.AbstractBinMetric</span></code></p>
<p>Standard Deviation of Efficiency, computed using bins.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>uniform_features</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – features, in which we compute non-uniformity.</p></li>
<li><p><strong>uniform_label</strong> – label of class, in which uniformity is measured (0 for bck, 1 for signal)</p></li>
<li><p><strong>n_bins</strong> (<em>int</em>) – number of bins used along each axis.</p></li>
<li><p><strong>target_rcp</strong> (<em>list</em><em>[</em><em>float</em><em>]</em>) – global right-classified-parts.
Thresholds are selected so this part of class was correctly classified.
Default values are [0.5, 0.6, 0.7, 0.8, 0.9]</p></li>
<li><p><strong>power</strong> (<em>float</em>) – power used in SDE formula (default is 2.)</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.metrics.BinBasedTheil">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.metrics.</span></span><span class="sig-name descname"><span class="pre">BinBasedTheil</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniform_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uniform_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_rcp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/metrics.html#BinBasedTheil"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.metrics.BinBasedTheil" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.metrics.AbstractBinMetric</span></code></p>
<p>Theil index of Efficiency, computed using bins.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>uniform_features</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – features, in which we compute non-uniformity.</p></li>
<li><p><strong>uniform_label</strong> – label of class, in which uniformity is measured (0 for bck, 1 for signal)</p></li>
<li><p><strong>n_bins</strong> (<em>int</em>) – number of bins used along each axis.</p></li>
<li><p><strong>target_rcp</strong> (<em>list</em><em>[</em><em>float</em><em>]</em>) – global right-classified-parts.
Thresholds are selected so this part of class was correctly classified.
Default values are [0.5, 0.6, 0.7, 0.8, 0.9]</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.metrics.KnnBasedCvM">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.metrics.</span></span><span class="sig-name descname"><span class="pre">KnnBasedCvM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniform_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uniform_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/metrics.html#KnnBasedCvM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.metrics.KnnBasedCvM" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.metrics.AbstractKnnMetric</span></code></p>
<p>Nonuniformity metric based on Cramer-von Mises distance between distributions, computed on nearest neighbours.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>uniform_features</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – features, in which we compute non-uniformity.</p></li>
<li><p><strong>uniform_label</strong> – label of class, in which uniformity is measured (0 for bck, 1 for signal)</p></li>
<li><p><strong>n_neighbours</strong> (<em>int</em>) – number of neighs</p></li>
<li><p><strong>power</strong> (<em>float</em>) – power used in CvM formula (default is 2.)</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.metrics.KnnBasedSDE">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.metrics.</span></span><span class="sig-name descname"><span class="pre">KnnBasedSDE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniform_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uniform_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_rcp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/metrics.html#KnnBasedSDE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.metrics.KnnBasedSDE" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.metrics.AbstractKnnMetric</span></code></p>
<p>Standard Deviation of Efficiency, computed using k nearest neighbours.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>uniform_features</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – features, in which we compute non-uniformity.</p></li>
<li><p><strong>uniform_label</strong> – label of class, in which uniformity is measured (0 for bck, 1 for signal)</p></li>
<li><p><strong>n_neighbours</strong> (<em>int</em>) – number of neighs</p></li>
<li><p><strong>target_rcp</strong> (<em>list</em><em>[</em><em>float</em><em>]</em>) – global right-classified-parts.
Thresholds are selected so this part of class was correctly classified.
Default values are [0.5, 0.6, 0.7, 0.8, 0.9]</p></li>
<li><p><strong>power</strong> (<em>float</em>) – power used in SDE formula (default is 2.)</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.metrics.KnnBasedTheil">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.metrics.</span></span><span class="sig-name descname"><span class="pre">KnnBasedTheil</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniform_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uniform_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_rcp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/metrics.html#KnnBasedTheil"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.metrics.KnnBasedTheil" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.metrics.AbstractKnnMetric</span></code></p>
<p>Theil index of Efficiency, computed using k nearest neighbours.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>uniform_features</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – features, in which we compute non-uniformity.</p></li>
<li><p><strong>uniform_label</strong> – label of class, in which uniformity is measured (0 for bck, 1 for signal)</p></li>
<li><p><strong>n_neighbours</strong> (<em>int</em>) – number of neighs</p></li>
<li><p><strong>target_rcp</strong> (<em>list</em><em>[</em><em>float</em><em>]</em>) – global right-classified-parts.
Thresholds are selected so this part of class was correctly classified.
Default values are [0.5, 0.6, 0.7, 0.8, 0.9]</p></li>
</ul>
</dd>
</dl>
</dd></dl>

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