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              <p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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  <section id="evaluation">
<span id="id1"></span><h1>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">¶</a></h1>
<p>Slideflow includes several tools for evaluating trained models. In the next sections, we’ll review how to evaluate a model on a held-out test set, generate predictions without ground-truth labels, and visualize predictions with heatmaps.</p>
<section id="evaluating-a-test-set">
<h2>Evaluating a test set<a class="headerlink" href="#evaluating-a-test-set" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="../project/#slideflow.Project.evaluate" title="slideflow.Project.evaluate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.evaluate()</span></code></a> provides an easy interface for evaluating model performance on a held-out test set. Locate the saved model to evaluate (which will be in the project <code class="docutils literal notranslate"><span class="pre">models/</span></code> folder). <a class="reference internal" href="../training/#training-with-project"><span class="std std-ref">As with training</span></a>, the dataset to evaluate can be specified using either the <code class="docutils literal notranslate"><span class="pre">filters</span></code> or <code class="docutils literal notranslate"><span class="pre">dataset</span></code> arguments. If neither is provided, all slides in the project will be evaluated.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Method 1: specifying filters</span>
<span class="n">P</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
  <span class="n">model</span><span class="o">=</span><span class="s2">&quot;/path/to/trained_model_epoch1&quot;</span><span class="p">,</span>
  <span class="n">outcomes</span><span class="o">=</span><span class="s2">&quot;tumor_type&quot;</span><span class="p">,</span>
  <span class="n">filters</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;dataset&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]}</span>
<span class="p">)</span>

<span class="c1"># Method 2: specify a dataset</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span><span class="n">tile_px</span><span class="o">=</span><span class="mi">299</span><span class="p">,</span> <span class="n">tile_um</span><span class="o">=</span><span class="s1">&#39;10x&#39;</span><span class="p">)</span>
<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">filter</span><span class="p">({</span><span class="s2">&quot;dataset&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]})</span>
<span class="n">P</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
  <span class="n">model</span><span class="o">=</span><span class="s2">&quot;/path/to/trained_model_epoch1&quot;</span><span class="p">,</span>
  <span class="n">outcomes</span><span class="o">=</span><span class="s2">&quot;tumor_type&quot;</span><span class="p">,</span>
  <span class="n">dataset</span><span class="o">=</span><span class="n">test_dataset</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Results are returned from the <code class="docutils literal notranslate"><span class="pre">Project.evaluate()</span></code> function as a dictionary and saved in the project evaluation directory. Tile-, slide-, and patient- level predictions are also saved in the corresponding project evaluation folder, <code class="docutils literal notranslate"><span class="pre">eval/</span></code>.</p>
</section>
<section id="generating-predictions">
<h2>Generating predictions<a class="headerlink" href="#generating-predictions" title="Permalink to this heading">¶</a></h2>
<section id="for-a-dataset">
<h3>For a dataset<a class="headerlink" href="#for-a-dataset" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="../project/#slideflow.Project.predict" title="slideflow.Project.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.predict()</span></code></a> provides an interface for generating model predictions on an entire dataset. As above, locate the saved model from which to generate predictions, and specify the dataset with either <code class="docutils literal notranslate"><span class="pre">filters</span></code> or <code class="docutils literal notranslate"><span class="pre">dataset</span></code> arguments.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">dfs</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
  <span class="n">model</span><span class="o">=</span><span class="s2">&quot;/path/to/trained_model_epoch1&quot;</span><span class="p">,</span>
  <span class="n">filters</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;dataset&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]}</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dfs</span><span class="p">[</span><span class="s1">&#39;patient&#39;</span><span class="p">])</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>                        patient  ...  cohort-y_pred1
0    TCGA-05-4244-01Z-00-DX1...  ...        0.032608
1    TCGA-05-4245-01Z-00-DX1...  ...        0.216634
2    TCGA-05-4249-01Z-00-DX1...  ...        0.000858
3    TCGA-05-4250-01Z-00-DX1...  ...        0.015915
4    TCGA-05-4382-01Z-00-DX1...  ...        0.020700
..                          ...  ...             ...
936  TCGA-O2-A52S-01Z-00-DX1...  ...        0.983500
937  TCGA-O2-A52V-01Z-00-DX1...  ...        0.773328
938  TCGA-O2-A52W-01Z-00-DX1...  ...        0.858558
939  TCGA-S2-AA1A-01Z-00-DX1...  ...        0.000212
940  TCGA-XC-AA0X-01Z-00-DX1...  ...        0.632612
</pre></div>
</div>
<p>Results are returned as a dictionary of pandas DataFrames (with the keys <code class="docutils literal notranslate"><span class="pre">'tile'</span></code>, <code class="docutils literal notranslate"><span class="pre">'slide'</span></code>, and <code class="docutils literal notranslate"><span class="pre">'patient'</span></code> for each level of prediction) and saved in the project evaluation directory, <code class="docutils literal notranslate"><span class="pre">eval/</span></code>.</p>
</section>
<section id="for-a-single-slide">
<h3>For a single slide<a class="headerlink" href="#for-a-single-slide" title="Permalink to this heading">¶</a></h3>
<p>You can also generate predictions for a single slide with either <a class="reference internal" href="../slide/#slideflow.slide.predict" title="slideflow.slide.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.slide.predict()</span></code></a> or <a class="reference internal" href="../slide/#slideflow.WSI.predict" title="slideflow.WSI.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.WSI.predict()</span></code></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">slideflow</span> <span class="k">as</span> <span class="nn">sf</span>

<span class="n">slide</span> <span class="o">=</span> <span class="s1">&#39;/path/to/slide.svs&#39;</span>
<span class="n">model</span> <span class="o">=</span> <span class="s1">&#39;/path/to/model_epoch1&#39;</span>
<span class="n">sf</span><span class="o">.</span><span class="n">slide</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">slide</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([0.84378019, 0.15622007])
</pre></div>
</div>
<p>The returned array has the shape <code class="docutils literal notranslate"><span class="pre">(num_classes,)</span></code>, indicating the whole-slide prediction for each outcome category. If the model was trained with uncertainty quantification, this function will return two arrays; the first with predictions, the second with estimated uncertainty.</p>
</section>
</section>
<section id="heatmaps">
<span id="generate-heatmaps"></span><h2>Heatmaps<a class="headerlink" href="#heatmaps" title="Permalink to this heading">¶</a></h2>
<section id="id2">
<h3>For a dataset<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h3>
<p>Predictive heatmaps can be created for an entire dataset using <a class="reference internal" href="../project/#slideflow.Project.generate_heatmaps" title="slideflow.Project.generate_heatmaps"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.generate_heatmaps()</span></code></a>. Heatmaps will be saved and exported in the project directory. See the linked API documentation for arguments and customization.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">generate_heatmaps</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;/path/to/trained_model_epoch1&quot;</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="id3">
<h3>For a single slide<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="../heatmap/#slideflow.Heatmap" title="slideflow.Heatmap"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.Heatmap</span></code></a> provides more granular control for calculating and displaying a heatmap for a given slide. The required arguments are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">slide</span></code>: Either a path to a slide, or a <code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.WSI</span></code> object.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model</span></code>: Path to a saved Slideflow model.</p></li>
</ul>
<p>Additional keyword arguments can be used to customize and optimize the heatmap. In this example, we’ll increase the batch size to 64 and allow multiprocessing by setting <code class="docutils literal notranslate"><span class="pre">num_processes</span></code> equal to our CPU core count, 16.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">heatmap</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">Heatmap</span><span class="p">(</span>
  <span class="n">slide</span><span class="o">=</span><span class="s1">&#39;/path/to/slide.svs&#39;</span><span class="p">,</span>
  <span class="n">model</span><span class="o">=</span><span class="s1">&#39;/path/to/model&#39;</span>
  <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
  <span class="n">num_processes</span><span class="o">=</span><span class="mi">16</span>
<span class="p">)</span>
</pre></div>
</div>
<p>If <code class="docutils literal notranslate"><span class="pre">slide</span></code> is a <code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.WSI</span></code>, the heatmap will be calculated only within non-masked areas and ROIs, if applicable.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">slideflow.slide</span> <span class="kn">import</span> <span class="n">qc</span>

<span class="c1"># Prepare the slide</span>
<span class="n">wsi</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">WSI</span><span class="p">(</span><span class="s1">&#39;slide.svs&#39;</span><span class="p">,</span> <span class="n">tile_px</span><span class="o">=</span><span class="mi">299</span><span class="p">,</span> <span class="n">tile_um</span><span class="o">=</span><span class="mi">302</span><span class="p">,</span> <span class="n">rois</span><span class="o">=</span><span class="s1">&#39;/path&#39;</span><span class="p">)</span>
<span class="n">wsi</span><span class="o">.</span><span class="n">qc</span><span class="p">([</span><span class="n">qc</span><span class="o">.</span><span class="n">Otsu</span><span class="p">(),</span> <span class="n">qc</span><span class="o">.</span><span class="n">Gaussian</span><span class="p">()])</span>

<span class="c1"># Generate a heatmap</span>
<span class="n">heatmap</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">Heatmap</span><span class="p">(</span>
  <span class="n">slide</span><span class="o">=</span><span class="n">wsi</span><span class="p">,</span>
  <span class="n">model</span><span class="o">=</span><span class="s1">&#39;/path/to/model&#39;</span>
  <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
  <span class="n">num_processes</span><span class="o">=</span><span class="mi">16</span>
<span class="p">)</span>
</pre></div>
</div>
<p>If <code class="docutils literal notranslate"><span class="pre">slide</span></code> is a path to a slide, Regions of Interest can be provided through the optional <code class="docutils literal notranslate"><span class="pre">roi_dir</span></code> or <code class="docutils literal notranslate"><span class="pre">rois</span></code> arguments.</p>
<p>Once generated, heatmaps can be rendered and displayed (ie. in a Jupyter notebook) with <a class="reference internal" href="../heatmap/#slideflow.Heatmap.plot" title="slideflow.Heatmap.plot"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Heatmap.plot()</span></code></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">heatmap</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">class_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;inferno&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Insets showing zoomed-in portions of the heatmap can be added with <a class="reference internal" href="../heatmap/#slideflow.Heatmap.add_inset" title="slideflow.Heatmap.add_inset"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Heatmap.add_inset()</span></code></a>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">heatmap</span><span class="o">.</span><span class="n">add_inset</span><span class="p">(</span><span class="n">zoom</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="p">(</span><span class="mi">10000</span><span class="p">,</span> <span class="mi">10500</span><span class="p">),</span> <span class="n">y</span><span class="o">=</span><span class="p">(</span><span class="mi">2500</span><span class="p">,</span> <span class="mi">3000</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">heatmap</span><span class="o">.</span><span class="n">add_inset</span><span class="p">(</span><span class="n">zoom</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="p">(</span><span class="mi">12000</span><span class="p">,</span> <span class="mi">12500</span><span class="p">),</span> <span class="n">y</span><span class="o">=</span><span class="p">(</span><span class="mi">7500</span><span class="p">,</span> <span class="mi">8000</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">heatmap</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">class_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">mpp</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../_images/heatmap_inset.jpg" src="../_images/heatmap_inset.jpg" />
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>Save rendered heatmaps for each outcome category with <a class="reference internal" href="../heatmap/#slideflow.Heatmap.save" title="slideflow.Heatmap.save"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Heatmap.save()</span></code></a>. The spatial map of predictions, as calculated across the input slide, can be accessed through <code class="docutils literal notranslate"><span class="pre">Heatmap.predictions</span></code>. You can save the numpy array with calculated predictions (and uncertainty, if applicable) as an *.npz file using <a class="reference internal" href="../heatmap/#slideflow.Heatmap.save_npz" title="slideflow.Heatmap.save_npz"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Heatmap.save_npz()</span></code></a>.</p>
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