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opendataval.dataval.knnshap.html
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<section id="opendataval-dataval-knnshap-package">
<h1>opendataval.dataval.knnshap package<a class="headerlink" href="#opendataval-dataval-knnshap-package" title="Link to this heading">#</a></h1>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
</section>
<section id="module-opendataval.dataval.knnshap.knnshap">
<span id="opendataval-dataval-knnshap-knnshap-module"></span><h2>opendataval.dataval.knnshap.knnshap module<a class="headerlink" href="#module-opendataval.dataval.knnshap.knnshap" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.knnshap.knnshap.KNNShapley">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.knnshap.knnshap.</span></span><span class="sig-name descname"><span class="pre">KNNShapley</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataval.knnshap.knnshap.KNNShapley" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="opendataval.dataval.html#opendataval.dataval.api.DataEvaluator" title="opendataval.dataval.api.DataEvaluator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataEvaluator</span></code></a>, <a class="reference internal" href="opendataval.dataval.html#opendataval.dataval.api.ModelLessMixin" title="opendataval.dataval.api.ModelLessMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModelLessMixin</span></code></a></p>
<p>Data valuation using KNNShapley implementation.</p>
<p>KNN Shapley is a model-less mixin. This means we cannot specify an underlying
prediction model for the DataEvaluator. However, we can specify a pretrained
embedding model.</p>
<section id="references">
<h3>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id1" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>R. Jia et al.,
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1908.08619">https://arxiv.org/abs/1908.08619</a>.</p>
</aside>
</aside>
</section>
<section id="parameters">
<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>k_neighbors<span class="classifier">int, optional</span></dt><dd><p>Number of neighbors to group the data points, by default 10</p>
</dd>
<dt>batch_size<span class="classifier">int, optional</span></dt><dd><p>Batch size of tensors to load at a time during training, by default 32</p>
</dd>
<dt>embedding_model<span class="classifier">Model, optional</span></dt><dd><p>Pre-trained embedding model used by DataEvaluator, by default None</p>
</dd>
<dt>random_state<span class="classifier">RandomState, optional</span></dt><dd><p>Random initial state, by default None</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.knnshap.knnshap.KNNShapley.evaluate_data_values">
<span class="sig-name descname"><span class="pre">evaluate_data_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#opendataval.dataval.knnshap.knnshap.KNNShapley.evaluate_data_values" title="Link to this definition">#</a></dt>
<dd><p>Return data values for each training data point.</p>
<p>Compute data values using KNN Shapley data valuation</p>
<section id="returns">
<h4>Returns<a class="headerlink" href="#returns" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>np.ndarray</dt><dd><p>Predicted data values/selection for training input data point</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.knnshap.knnshap.KNNShapley.match">
<span class="sig-name descname"><span class="pre">match</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#opendataval.dataval.knnshap.knnshap.KNNShapley.match" title="Link to this definition">#</a></dt>
<dd><p><span class="math notranslate nohighlight">\(1.\)</span> for all matching rows and <span class="math notranslate nohighlight">\(0.\)</span> otherwise.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.knnshap.knnshap.KNNShapley.train_data_values">
<span class="sig-name descname"><span class="pre">train_data_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataval.knnshap.knnshap.KNNShapley.train_data_values" title="Link to this definition">#</a></dt>
<dd><p>Trains model to predict data values.</p>
<p>Computes KNN shapley data values, as implemented by the following. Ignores all
positional and key word arguments.</p>
<section id="id2">
<h4>References<a class="headerlink" href="#id2" title="Link to this heading">#</a></h4>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id3" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>PyTorch implementation
<<a class="reference external" href="https://github.com/AI-secure/Shapley-Study/blob/master/shapley/measures/KNN_Shapley.py">https://github.com/AI-secure/Shapley-Study/blob/master/shapley/measures/KNN_Shapley.py</a>></p>
</aside>
</aside>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.knnshap">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-opendataval.dataval.knnshap" title="Link to this heading">#</a></h2>
</section>
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<li><a class="reference internal" href="#">opendataval.dataval.knnshap package</a><ul>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-opendataval.dataval.knnshap.knnshap">opendataval.dataval.knnshap.knnshap module</a><ul>
<li><a class="reference internal" href="#opendataval.dataval.knnshap.knnshap.KNNShapley"><code class="docutils literal notranslate"><span class="pre">KNNShapley</span></code></a><ul>
<li><a class="reference internal" href="#opendataval.dataval.knnshap.knnshap.KNNShapley.evaluate_data_values"><code class="docutils literal notranslate"><span class="pre">KNNShapley.evaluate_data_values()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.knnshap.knnshap.KNNShapley.match"><code class="docutils literal notranslate"><span class="pre">KNNShapley.match()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.knnshap.knnshap.KNNShapley.train_data_values"><code class="docutils literal notranslate"><span class="pre">KNNShapley.train_data_values()</span></code></a></li>
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