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<!DOCTYPE html>

<html>
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<meta charset="utf-8" />
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<title>Overview: module code &#8212; regLM unknown documentation</title>
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<h1>All modules for which code is available</h1>
<ul><li><a href="reglm/dataset.html">reglm.dataset</a></li>
<li><a href="reglm/evolve.html">reglm.evolve</a></li>
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<!DOCTYPE html>

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<title>reglm.metrics &#8212; regLM unknown documentation</title>
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<h1>Source code for reglm.metrics</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">reglm.dataset</span> <span class="kn">import</span> <span class="n">CharDataset</span>


<div class="viewcode-block" id="compute_accuracy"><a class="viewcode-back" href="../../api/reglm.html#reglm.metrics.compute_accuracy">[docs]</a><span class="k">def</span> <span class="nf">compute_accuracy</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">seqs</span><span class="p">,</span> <span class="n">shuffle_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute per-base accuracy of a trained regLM model on labeled sequences</span>

<span class="sd"> Args:</span>
<span class="sd"> model (pl.LightningModule): Trained regLM model</span>
<span class="sd"> seqs (pd.DataFrame): Dataframe containing sequences under &#39;Sequence&#39;</span>
<span class="sd"> and labels under &#39;label&#39;.</span>
<span class="sd"> shuffle_labels (bool): Whether to shuffle the labels among sequences</span>
<span class="sd"> before computing accuracy.</span>
<span class="sd"> batch_size (int): Batch size for inference</span>
<span class="sd"> num_workers (int): Number of workers for inference</span>

<span class="sd"> Returns:</span>
<span class="sd"> seqs (pd.DataFrame): original dataframe with added columns for per-</span>
<span class="sd"> base and average accuracy.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># Extract labels</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">seqs</span><span class="o">.</span><span class="n">label</span>

<span class="c1"># Shuffle labels if needed</span>
<span class="k">if</span> <span class="n">shuffle_labels</span><span class="p">:</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">seqs</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">seqs</span><span class="p">))</span>

<span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>

<span class="c1"># Create dataset</span>
<span class="n">ds</span> <span class="o">=</span> <span class="n">CharDataset</span><span class="p">(</span><span class="n">seqs</span><span class="o">=</span><span class="n">seqs</span><span class="o">.</span><span class="n">Sequence</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>

<span class="c1"># Compute per-base accuracy</span>
<span class="n">acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">compute_accuracy_on_dataset</span><span class="p">(</span>
<span class="n">ds</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span>
<span class="p">)</span>

<span class="c1"># Add results to dataframe</span>
<span class="k">if</span> <span class="n">shuffle_labels</span><span class="p">:</span>
<span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc_shuf&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span>
<span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc_shuf_mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc_shuf&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">avg_acc</span> <span class="o">=</span> <span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc_shuf_mean&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span>
<span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc_mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">avg_acc</span> <span class="o">=</span> <span class="n">seqs</span><span class="p">[</span><span class="s2">&quot;acc_mean&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>

<span class="c1"># Print overall mean</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mean accuracy: </span><span class="si">{</span><span class="n">avg_acc</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

<span class="k">return</span> <span class="n">seqs</span></div>
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