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<meta property="og:title" content="Group metric — group_metric" />
<meta property="og:description" content="Group metric enables to extract data from metrics generated for each subgroup (values in protected variable)
The closer metric values are to each other, the less bias particular model has. If parity_loss parameter is set to TRUE, distance between
privileged and unprivileged subgroups will be measured. When plotted shows both fairness metric and chosen performance metric." />
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    <h1>Group metric</h1>
    <small class="dont-index">Source: <a href='https://github.com/ModelOriented/fairmodels/blob/master/R/group_metric.R'><code>R/group_metric.R</code></a></small>
    <div class="hidden name"><code>group_metric.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Group metric enables to extract data from metrics generated for each subgroup (values in protected variable)
The closer metric values are to each other, the less bias particular model has. If <code>parity_loss</code> parameter is set to <code>TRUE</code>, distance between
privileged and unprivileged subgroups will be measured. When plotted shows both fairness metric and chosen performance metric.</p>
    </div>

    <pre class="usage"><span class='fu'>group_metric</span>(
  <span class='no'>x</span>,
  <span class='kw'>fairness_metric</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
  <span class='kw'>performance_metric</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
  <span class='kw'>parity_loss</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
  <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>
)</pre>

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>x</th>
      <td><p>object of class <code>fairness_object</code></p></td>
    </tr>
    <tr>
      <th>fairness_metric</th>
      <td><p>character, fairness metric name</p></td>
    </tr>
    <tr>
      <th>performance_metric</th>
      <td><p>character, performance metric name</p></td>
    </tr>
    <tr>
      <th>parity_loss</th>
      <td><p>logical, if <code>TRUE</code> parity loss will supersede basic metric</p></td>
    </tr>
    <tr>
      <th>verbose</th>
      <td><p>logical, whether to print information about metrics on console or not. Default <code>TRUE</code></p></td>
    </tr>
    </table>

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p><code>group_metric</code> object.
It is a list with following items:</p><ul>
<li><p>group_metric_data - <code>data.frame</code> containing fairness metric scores for each model</p></li>
<li><p>performance_data - <code>data.frame</code> containing performance metric scores for each model</p></li>
<li><p>fairness_metric - name of fairness metric</p></li>
<li><p>performance_metric - name of performance metric</p></li>
</ul>

    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Available metrics:</p>
<p>Fairness metrics:</p>
<ul>
<li><p>TPR</p></li>
<li><p>TNR</p></li>
<li><p>PPV</p></li>
<li><p>NPV</p></li>
<li><p>FNR</p></li>
<li><p>FPR</p></li>
<li><p>FDR</p></li>
<li><p>FOR</p></li>
<li><p>TS</p></li>
<li><p>ACC</p></li>
<li><p>F1</p></li>
<li><p>MCC</p></li>
</ul><p>Performance metrics</p>
<ul>
<li><p>recall</p></li>
<li><p>precision</p></li>
<li><p>accuracy</p></li>
<li><p>f1</p></li>
<li><p>auc</p></li>
</ul>


    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='st'>"german"</span>)

<span class='no'>y_numeric</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='no'>german</span>$<span class='no'>Risk</span>) -<span class='fl'>1</span>

<span class='no'>lm_model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/glm.html'>glm</a></span>(<span class='no'>Risk</span>~<span class='no'>.</span>,
                <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>,
                <span class='kw'>family</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/stats/family.html'>binomial</a></span>(<span class='kw'>link</span><span class='kw'>=</span><span class='st'>"logit"</span>))

<span class='no'>rf_model</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>ranger</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/ranger/man/ranger.html'>ranger</a></span>(<span class='no'>Risk</span> ~<span class='no'>.</span>,
                           <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>,
                           <span class='kw'>probability</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
                           <span class='kw'>num.trees</span> <span class='kw'>=</span> <span class='fl'>200</span>)

<span class='no'>explainer_lm</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>DALEX</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/DALEX/man/explain.html'>explain</a></span>(<span class='no'>lm_model</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>[,-<span class='fl'>1</span>], <span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>y_numeric</span>)</div><div class='output co'>#&gt; Preparation of a new explainer is initiated
#&gt;   -&gt; model label       :  lm  ( <span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  1000  rows  9  cols 
#&gt;   -&gt; target variable   :  1000  values 
#&gt;   -&gt; predict function  :  yhat.glm  will be used ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; predicted values  :  numerical, min =  0.1369187 , mean =  0.7 , max =  0.9832426  
#&gt;   -&gt; model_info        :  package stats , ver. 4.0.2 , task classification ( </span><span style='color: #BBBB00;'> default </span><span> ) 
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.9572803 , mean =  1.940006e-17 , max =  0.8283475  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'><span class='no'>explainer_rf</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>DALEX</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/DALEX/man/explain.html'>explain</a></span>(<span class='no'>rf_model</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>[,-<span class='fl'>1</span>], <span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>y_numeric</span>)</div><div class='output co'>#&gt; Preparation of a new explainer is initiated
#&gt;   -&gt; model label       :  ranger  ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  1000  rows  9  cols 
#&gt;   -&gt; target variable   :  1000  values 
#&gt;   -&gt; predict function  :  yhat.ranger  will be used ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; predicted values  :  numerical, min =  0.071 , mean =  0.6953362 , max =  0.999  
#&gt;   -&gt; model_info        :  package ranger , ver. 0.12.1 , task classification ( </span><span style='color: #BBBB00;'> default </span><span> ) 
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.7145511 , mean =  0.004663826 , max =  0.6161497  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'>
<span class='no'>fobject</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='fairness_check.html'>fairness_check</a></span>(<span class='no'>explainer_lm</span>, <span class='no'>explainer_rf</span>,
                          <span class='kw'>protected</span> <span class='kw'>=</span> <span class='no'>german</span>$<span class='no'>Sex</span>,
                          <span class='kw'>privileged</span> <span class='kw'>=</span> <span class='st'>"male"</span>)</div><div class='output co'>#&gt; Creating fairness object
#&gt; -&gt; Privileged subgroup		: character (</span><span style='color: #00BB00;'> Ok </span><span> )
#&gt; -&gt; Protected variable		: factor (</span><span style='color: #00BB00;'> Ok </span><span> ) 
#&gt; -&gt; Cutoff values for explainers	: 0.5 ( for all subgroups ) 
#&gt; -&gt; Fairness objects		: 0 objects 
#&gt; -&gt; Checking explainers		: 2 in total ( </span><span style='color: #00BB00;'> compatible </span><span> )
#&gt; -&gt; Metric calculation		: 13/13 metrics calculated for all models
#&gt; </span><span style='color: #00BB00;'> Fairness object created succesfully </span><span> </div><div class='input'>
<span class='no'>gm</span> <span class='kw'>&lt;-</span> <span class='fu'>group_metric</span>(<span class='no'>fobject</span>, <span class='st'>"TPR"</span>, <span class='st'>"f1"</span>, <span class='kw'>parity_loss</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; 
#&gt; Creating object with: 
#&gt; Fairness metric:  TPR 
#&gt; Performance metric:  f1 
#&gt; </div><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>gm</span>)</div><div class='img'><img src='group_metric-1.png' alt='' width='700' height='433' /></div><div class='input'>
</div></span></pre>
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