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<section id="opendataval-dataval-margcontrib-package">
<h1>opendataval.dataval.margcontrib package<a class="headerlink" href="#opendataval-dataval-margcontrib-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.margcontrib.banzhaf">
<span id="opendataval-dataval-margcontrib-banzhaf-module"></span><h2>opendataval.dataval.margcontrib.banzhaf module<a class="headerlink" href="#module-opendataval.dataval.margcontrib.banzhaf" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.banzhaf.DataBanzhaf">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.banzhaf.</span></span><span class="sig-name descname"><span class="pre">DataBanzhaf</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.margcontrib.banzhaf.DataBanzhaf" 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.ModelMixin" title="opendataval.dataval.api.ModelMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModelMixin</span></code></a></p>
<p>Data Banzhaf implementation.</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>J. T. Wang and R. Jia,
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning,
arXiv.org, 2022. Available: <a class="reference external" href="https://arxiv.org/abs/2205.15466">https://arxiv.org/abs/2205.15466</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>num_models<span class="classifier">int, optional</span></dt><dd><p>Number of models to take to compute Banzhaf values, by default 1000</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.margcontrib.banzhaf.DataBanzhaf.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.margcontrib.banzhaf.DataBanzhaf.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 the Data Banzhaf data valuator. Finds difference
of average performance of all sets including data point minus not-including.</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 every training data point</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.banzhaf.DataBanzhaf.input_data">
<span class="sig-name descname"><span class="pre">input_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_train</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_train</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_valid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_valid</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><a class="headerlink" href="#opendataval.dataval.margcontrib.banzhaf.DataBanzhaf.input_data" title="Link to this definition">#</a></dt>
<dd><p>Store and transform input data for Data Banzhaf.</p>
<section id="id2">
<h4>Parameters<a class="headerlink" href="#id2" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor</span></dt><dd><p>Data labels</p>
</dd>
<dt>x_valid<span class="classifier">torch.Tensor</span></dt><dd><p>Test+Held-out covariates</p>
</dd>
<dt>y_valid<span class="classifier">torch.Tensor</span></dt><dd><p>Test+Held-out labels</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.banzhaf.DataBanzhaf.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.margcontrib.banzhaf.DataBanzhaf.train_data_values" title="Link to this definition">#</a></dt>
<dd><p>Trains model to predict data values.</p>
<p>Trains the Data Banzhaf value by sampling from the powerset. We compute
average performance of all subsets including and not including a data point.</p>
<section id="id3">
<h4>References<a class="headerlink" href="#id3" title="Link to this heading">#</a></h4>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id4" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>J. T. Wang and R. Jia,
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning,
arXiv.org, 2022. Available: <a class="reference external" href="https://arxiv.org/abs/2205.15466">https://arxiv.org/abs/2205.15466</a>.</p>
</aside>
</aside>
</section>
<section id="id5">
<h4>Parameters<a class="headerlink" href="#id5" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>args<span class="classifier">tuple[Any], optional</span></dt><dd><p>Training positional args</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any], optional</span></dt><dd><p>Training key word arguments</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.banzhaf.DataBanzhafMargContrib">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.banzhaf.</span></span><span class="sig-name descname"><span class="pre">DataBanzhafMargContrib</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.margcontrib.banzhaf.DataBanzhafMargContrib" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.dataval.margcontrib.shap.ShapEvaluator" title="opendataval.dataval.margcontrib.shap.ShapEvaluator"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShapEvaluator</span></code></a></p>
<p>Data Banzhaf implementation using the marginal contributions.</p>
<p>Data Banzhaf implementation using the ShapEvaluator, which already computes the
marginal contributions for other evaluators. This approach may not be as efficient
as the previous approach, but is recommended to minimize compute time if
you cache a previous computation.</p>
<section id="id6">
<h3>References<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id7" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>J. T. Wang and R. Jia,
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning,
arXiv.org, 2022. Available: <a class="reference external" href="https://arxiv.org/abs/2205.15466">https://arxiv.org/abs/2205.15466</a>.</p>
</aside>
</aside>
</section>
<section id="id8">
<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>sampler<span class="classifier">Sampler, optional</span></dt><dd><p>Sampler used to compute the marginal contributions. Can be found in
<code class="xref py py-mod docutils literal notranslate"><span class="pre">sampler</span></code>, by default uses <a href="#id9"><span class="problematic" id="id10">*</span></a>args, <a href="#id11"><span class="problematic" id="id12">**</span></a>kwargs for
<a class="reference internal" href="#opendataval.dataval.margcontrib.sampler.GrTMCSampler" title="opendataval.dataval.margcontrib.sampler.GrTMCSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">GrTMCSampler</span></code></a>.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.banzhaf.DataBanzhafMargContrib.compute_weight">
<span class="sig-name descname"><span class="pre">compute_weight</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">float</span></span></span><a class="headerlink" href="#opendataval.dataval.margcontrib.banzhaf.DataBanzhafMargContrib.compute_weight" title="Link to this definition">#</a></dt>
<dd><p>Compute weights for each cardinality of training set.</p>
<p>Banzhaf weights each data point according to the number of combinations of
<span class="math notranslate nohighlight">\(j\)</span> cardinality to number of data points</p>
<section id="id13">
<h4>Returns<a class="headerlink" href="#id13" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>np.ndarray</dt><dd><p>Weights by cardinality of subset</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.margcontrib.betashap">
<span id="opendataval-dataval-margcontrib-betashap-module"></span><h2>opendataval.dataval.margcontrib.betashap module<a class="headerlink" href="#module-opendataval.dataval.margcontrib.betashap" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.betashap.BetaShapley">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.betashap.</span></span><span class="sig-name descname"><span class="pre">BetaShapley</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.margcontrib.betashap.BetaShapley" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.dataval.margcontrib.shap.ShapEvaluator" title="opendataval.dataval.margcontrib.shap.ShapEvaluator"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShapEvaluator</span></code></a></p>
<p>Beta Shapley implementation. Must specify alpha/beta values for beta function.</p>
<section id="id14">
<h3>References<a class="headerlink" href="#id14" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id15" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Y. Kwon and J. Zou,
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for
Machine Learning,
arXiv.org, 2021. Available: <a class="reference external" href="https://arxiv.org/abs/2110.14049">https://arxiv.org/abs/2110.14049</a>.</p>
</aside>
</aside>
</section>
<section id="id16">
<h3>Parameters<a class="headerlink" href="#id16" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>sampler<span class="classifier">Sampler, optional</span></dt><dd><p>Sampler used to compute the marginal contributions. Can be found in
<code class="xref py py-mod docutils literal notranslate"><span class="pre">sampler</span></code>, by default uses <a href="#id17"><span class="problematic" id="id18">*</span></a>args, <a href="#id19"><span class="problematic" id="id20">**</span></a>kwargs for
<a class="reference internal" href="#opendataval.dataval.margcontrib.sampler.GrTMCSampler" title="opendataval.dataval.margcontrib.sampler.GrTMCSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">GrTMCSampler</span></code></a>.</p>
</dd>
<dt>alpha<span class="classifier">int, optional</span></dt><dd><p>Alpha parameter for beta distribution used in the weight function, by default 4</p>
</dd>
<dt>beta<span class="classifier">int, optional</span></dt><dd><p>Beta parameter for beta distribution used in the weight function, by default 1</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.betashap.BetaShapley.compute_weight">
<span class="sig-name descname"><span class="pre">compute_weight</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.margcontrib.betashap.BetaShapley.compute_weight" title="Link to this definition">#</a></dt>
<dd><p>Compute weights for each cardinality of training set.</p>
<p>Uses <span class="math notranslate nohighlight">\(\alpha\)</span>, <span class="math notranslate nohighlight">\(beta\)</span> are parameters to the beta distribution.
[1] BetaShap weight computation, <span class="math notranslate nohighlight">\(j\)</span> is cardinality, Equation (3) and (5).</p>
<div class="math-wrapper docutils container">
<div class="math notranslate nohighlight">
\[w(j) := \frac{1}{n} w^{(n)}(j) \tbinom{n-1}{j-1}
\propto \frac{Beta(j + \beta - 1, n - j + \alpha)}{Beta(\alpha, \beta)}
\tbinom{n-1}{j-1}\]</div>
</div>
<section id="id21">
<h4>References<a class="headerlink" href="#id21" title="Link to this heading">#</a></h4>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id22" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Y. Kwon and J. Zou,
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for
Machine Learning,
arXiv.org, 2021. Available: <a class="reference external" href="https://arxiv.org/abs/2110.14049">https://arxiv.org/abs/2110.14049</a>.</p>
</aside>
</aside>
</section>
<section id="id23">
<h4>Returns<a class="headerlink" href="#id23" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>np.ndarray</dt><dd><p>Weights by cardinality of subset</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.margcontrib.datashap">
<span id="opendataval-dataval-margcontrib-datashap-module"></span><h2>opendataval.dataval.margcontrib.datashap module<a class="headerlink" href="#module-opendataval.dataval.margcontrib.datashap" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.datashap.DataShapley">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.datashap.</span></span><span class="sig-name descname"><span class="pre">DataShapley</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.margcontrib.datashap.DataShapley" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.dataval.margcontrib.shap.ShapEvaluator" title="opendataval.dataval.margcontrib.shap.ShapEvaluator"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShapEvaluator</span></code></a></p>
<p>Data Shapley implementation.</p>
<section id="id24">
<h3>References<a class="headerlink" href="#id24" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id25" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>A. Ghorbani and J. Zou,
Data Shapley: Equitable Valuation of Data for Machine Learning,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1904.02868">https://arxiv.org/abs/1904.02868</a>.</p>
</aside>
</aside>
</section>
<section id="id26">
<h3>Parameters<a class="headerlink" href="#id26" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>sampler<span class="classifier">Sampler, optional</span></dt><dd><p>Sampler used to compute the marginal contributions. Can be found in
<code class="xref py py-mod docutils literal notranslate"><span class="pre">sampler</span></code>, by default uses <a href="#id27"><span class="problematic" id="id28">*</span></a>args, <a href="#id29"><span class="problematic" id="id30">**</span></a>kwargs for
<a class="reference internal" href="#opendataval.dataval.margcontrib.sampler.GrTMCSampler" title="opendataval.dataval.margcontrib.sampler.GrTMCSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">GrTMCSampler</span></code></a>.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.datashap.DataShapley.compute_weight">
<span class="sig-name descname"><span class="pre">compute_weight</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">float</span></span></span><a class="headerlink" href="#opendataval.dataval.margcontrib.datashap.DataShapley.compute_weight" title="Link to this definition">#</a></dt>
<dd><p>Compute weights (uniform) for each cardinality of training set.</p>
<p>Shapley values take a simple average of the marginal contributions across
all different cardinalities.</p>
<section id="id31">
<h4>Returns<a class="headerlink" href="#id31" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>np.ndarray</dt><dd><p>Weights by cardinality of subset</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.margcontrib.loo">
<span id="opendataval-dataval-margcontrib-loo-module"></span><h2>opendataval.dataval.margcontrib.loo module<a class="headerlink" href="#module-opendataval.dataval.margcontrib.loo" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.loo.LeaveOneOut">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.loo.</span></span><span class="sig-name descname"><span class="pre">LeaveOneOut</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.margcontrib.loo.LeaveOneOut" 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.ModelMixin" title="opendataval.dataval.api.ModelMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModelMixin</span></code></a></p>
<p>Leave One Out data valuation implementation.</p>
<section id="id32">
<h3>References<a class="headerlink" href="#id32" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id33" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>R. Cook,
Detection of Influential Observation in Linear Regression,
Technometrics, Vol. 19, No. 1 (Feb., 1977), pp. 15-18 (4 pages).</p>
</aside>
</aside>
</section>
<section id="id34">
<h3>Parameters<a class="headerlink" href="#id34" title="Link to this heading">#</a></h3>
<dl class="simple">
<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.margcontrib.loo.LeaveOneOut.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.margcontrib.loo.LeaveOneOut.evaluate_data_values" title="Link to this definition">#</a></dt>
<dd><p>Compute data values using Leave One Out data valuation.</p>
<section id="id35">
<h4>Returns<a class="headerlink" href="#id35" 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.margcontrib.loo.LeaveOneOut.input_data">
<span class="sig-name descname"><span class="pre">input_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_train</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_train</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_valid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_valid</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><a class="headerlink" href="#opendataval.dataval.margcontrib.loo.LeaveOneOut.input_data" title="Link to this definition">#</a></dt>
<dd><p>Store and transform input data for Leave-One-Out data valuation.</p>
<section id="id36">
<h4>Parameters<a class="headerlink" href="#id36" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor</span></dt><dd><p>Data labels</p>
</dd>
<dt>x_valid<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Test+Held-out covariates</p>
</dd>
<dt>y_valid<span class="classifier">torch.Tensor</span></dt><dd><p>Test+Held-out labels</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.loo.LeaveOneOut.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.margcontrib.loo.LeaveOneOut.train_data_values" title="Link to this definition">#</a></dt>
<dd><p>Trains model to predict data values.</p>
<p>Compute the data values using the Leave-One-Out data valuation.
Equivalently, LOO can be computed from the marginal contributions as it’s a
semivalue.</p>
<section id="id37">
<h4>Parameters<a class="headerlink" href="#id37" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>args<span class="classifier">tuple[Any], optional</span></dt><dd><p>Training positional args</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any], optional</span></dt><dd><p>Training key word arguments</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.margcontrib.sampler">
<span id="opendataval-dataval-margcontrib-sampler-module"></span><h2>opendataval.dataval.margcontrib.sampler module<a class="headerlink" href="#module-opendataval.dataval.margcontrib.sampler" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.GrTMCSampler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.sampler.</span></span><span class="sig-name descname"><span class="pre">GrTMCSampler</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.margcontrib.sampler.GrTMCSampler" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.dataval.margcontrib.sampler.Sampler" title="opendataval.dataval.margcontrib.sampler.Sampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code></a></p>
<p>TMC Sampler with terminator for semivalue-based methods of computing data values.</p>
<p>Evaluators that share marginal contributions should share a sampler.</p>
<section id="id38">
<h3>References<a class="headerlink" href="#id38" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id39" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>A. Ghorbani and J. Zou,
Data Shapley: Equitable Valuation of Data for Machine Learning,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1904.02868">https://arxiv.org/abs/1904.02868</a>.</p>
</aside>
<aside class="footnote brackets" id="id40" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>Y. Kwon and J. Zou,
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for
Machine Learning,
arXiv.org, 2021. Available: <a class="reference external" href="https://arxiv.org/abs/2110.14049">https://arxiv.org/abs/2110.14049</a>.</p>
</aside>
</aside>
</section>
<section id="id41">
<h3>Parameters<a class="headerlink" href="#id41" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>gr_threshold<span class="classifier">float, optional</span></dt><dd><p>Convergence threshold for the Gelman-Rubin statistic.
Shapley values are NP-hard so we resort to MCMC sampling, by default 1.05</p>
</dd>
<dt>max_mc_epochs<span class="classifier">int, optional</span></dt><dd><p>Max number of outer epochs of MCMC sampling, by default 100</p>
</dd>
<dt>models_per_epoch<span class="classifier">int, optional</span></dt><dd><p>Number of model fittings to take per epoch prior to checking GR convergence,
by default 100</p>
</dd>
<dt>min_models<span class="classifier">int, optional</span></dt><dd><p>Minimum samples before checking MCMC convergence, by default 1000</p>
</dd>
<dt>min_cardinality<span class="classifier">int, optional</span></dt><dd><p>Minimum cardinality of a training set, must be passed as kwarg, by default 5</p>
</dd>
<dt>cache_name<span class="classifier">str, optional</span></dt><dd><p>Unique cache_name of the model to cache marginal contributions, set to None to
disable caching, by default “” which is set to a unique value for a object</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 attribute">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.GrTMCSampler.CACHE">
<span class="sig-name descname"><span class="pre">CACHE</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ndarray</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.GrTMCSampler.CACHE" title="Link to this definition">#</a></dt>
<dd><p>Cached marginal contributions.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.GrTMCSampler.GR_MAX">
<span class="sig-name descname"><span class="pre">GR_MAX</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">100</span></em><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.GrTMCSampler.GR_MAX" title="Link to this definition">#</a></dt>
<dd><p>Default maximum Gelman-Rubin statistic. Used for burn-in.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.GrTMCSampler.compute_marginal_contribution">
<span class="sig-name descname"><span class="pre">compute_marginal_contribution</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.margcontrib.sampler.GrTMCSampler.compute_marginal_contribution" title="Link to this definition">#</a></dt>
<dd><p>Compute the marginal contributions for semivalue based data evaluators.</p>
<p>Computes the marginal contribution by sampling.
Checks MCMC convergence every 100 iterations using Gelman-Rubin Statistic.
NOTE if the marginal contribution has not been calculated, will look it up in
a cache of already trained ShapEvaluators, otherwise will train from scratch.</p>
<section id="id42">
<h4>Parameters<a class="headerlink" href="#id42" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>args<span class="classifier">tuple[Any], optional</span></dt><dd><p>Training positional args</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any], optional</span></dt><dd><p>Training key word arguments</p>
</dd>
</dl>
</section>
<section id="notes">
<h4>Notes<a class="headerlink" href="#notes" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>marginal_increment_array_stack<span class="classifier">np.ndarray</span></dt><dd><p>Marginal increments when one data point is added.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.GrTMCSampler.set_coalition">
<span class="sig-name descname"><span class="pre">set_coalition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coalition</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><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.GrTMCSampler.set_coalition" title="Link to this definition">#</a></dt>
<dd><p>Initializes storage to find marginal contribution of each data point</p>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.MonteCarloSampler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.sampler.</span></span><span class="sig-name descname"><span class="pre">MonteCarloSampler</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.margcontrib.sampler.MonteCarloSampler" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.dataval.margcontrib.sampler.Sampler" title="opendataval.dataval.margcontrib.sampler.Sampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code></a></p>
<p>Monte Carlo sampler for semivalue-based methods of computing data values.</p>
<p>Evaluators that share marginal contributions should share a sampler. We take
mc_epochs permutations and compute the marginal contributions. Simplest
implementation but the least practical.</p>
<section id="id43">
<h3>Parameters<a class="headerlink" href="#id43" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>mc_epochs<span class="classifier">int, optional</span></dt><dd><p>Number of outer epochs of MCMC sampling, by default 1000</p>
</dd>
<dt>min_cardinality<span class="classifier">int, optional</span></dt><dd><p>Minimum cardinality of a training set, must be passed as kwarg, by default 5</p>
</dd>
<dt>cache_name<span class="classifier">str, optional</span></dt><dd><p>Unique cache_name of the model to cache marginal contributions, set to None to
disable caching, by default “” which is set to a unique value for a object</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 attribute">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.MonteCarloSampler.CACHE">
<span class="sig-name descname"><span class="pre">CACHE</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ndarray</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.MonteCarloSampler.CACHE" title="Link to this definition">#</a></dt>
<dd><p>Cached marginal contributions.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.MonteCarloSampler.compute_marginal_contribution">
<span class="sig-name descname"><span class="pre">compute_marginal_contribution</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.margcontrib.sampler.MonteCarloSampler.compute_marginal_contribution" title="Link to this definition">#</a></dt>
<dd><p>Trains model to predict data values.</p>
<p>Uses permutation sampling to find the marginal contribution of each data point,
takes self.mc_epochs number of permutations.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.MonteCarloSampler.set_coalition">
<span class="sig-name descname"><span class="pre">set_coalition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coalition</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><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.MonteCarloSampler.set_coalition" title="Link to this definition">#</a></dt>
<dd><p>Initializes storage to find marginal contribution of each data point</p>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.Sampler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.sampler.</span></span><span class="sig-name descname"><span class="pre">Sampler</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.margcontrib.sampler.Sampler" title="Link to this definition">#</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code>, <a class="reference internal" href="opendataval.html#opendataval.util.ReprMixin" title="opendataval.util.ReprMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReprMixin</span></code></a></p>
<p>Abstract Sampler class for marginal contribution based data evaluators.</p>
<p>Many marginal contribution based data evaluators depend on a sampling method as
they typically can be very computationally expensive. The Sampler class provides
a blue print of required methods to be used and the following samplers provide ways
of caching computed marginal contributions if given a <cite>“cache_name”</cite>.</p>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.Sampler.compute_marginal_contribution">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_marginal_contribution</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> <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.margcontrib.sampler.Sampler.compute_marginal_contribution" title="Link to this definition">#</a></dt>
<dd><p>Given args and kwargs for the value func, computes marginal contribution.</p>
<section id="id44">
<h3>Returns<a class="headerlink" href="#id44" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>np.ndarray</dt><dd><p>Marginal contribution array per data point for each coalition size. Dim 0 is
the index of the added data point, Dim 1 is the cardinality when the data
point is added.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.Sampler.set_coalition">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_coalition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coalition</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">Self</span></span></span><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.Sampler.set_coalition" title="Link to this definition">#</a></dt>
<dd><p>Given the coalition, initializes data structures to compute marginal contrib.</p>
<section id="id45">
<h3>Parameters<a class="headerlink" href="#id45" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>coalition<span class="classifier">torch.Tensor</span></dt><dd><p>Coalition of data to compute the marginal contribution of each data point.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.Sampler.set_evaluator">
<span class="sig-name descname"><span class="pre">set_evaluator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.Sampler.set_evaluator" title="Link to this definition">#</a></dt>
<dd><p>Sets the evaluator function to evaluate the utility of a coalition</p>
<section id="id46">
<h3>Parameters<a class="headerlink" href="#id46" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>value_func<span class="classifier">Callable[[list[int], …], float]</span></dt><dd><p>T his function sets the utility function which computes the utility for a
given coalition of indices.</p>
</dd>
</dl>
<p>The following is an example of how the api would work in a DataEvaluator:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">set_evaluator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_evaluate_model</span><span class="p">)</span>
</pre></div>
</div>
</section>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.TMCSampler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.sampler.</span></span><span class="sig-name descname"><span class="pre">TMCSampler</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.margcontrib.sampler.TMCSampler" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.dataval.margcontrib.sampler.Sampler" title="opendataval.dataval.margcontrib.sampler.Sampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code></a></p>
<p>TMCShapley sampler for semivalue-based methods of computing data values.</p>
<p>Evaluators that share marginal contributions should share a sampler.</p>
<section id="id47">
<h3>References<a class="headerlink" href="#id47" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id48" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>A. Ghorbani and J. Zou,
Data Shapley: Equitable Valuation of Data for Machine Learning,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1904.02868">https://arxiv.org/abs/1904.02868</a>.</p>
</aside>
</aside>
</section>
<section id="id49">
<h3>Parameters<a class="headerlink" href="#id49" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>mc_epochs<span class="classifier">int, optional</span></dt><dd><p>Number of outer epochs of MCMC sampling, by default 1000</p>
</dd>
<dt>min_cardinality<span class="classifier">int, optional</span></dt><dd><p>Minimum cardinality of a training set, must be passed as kwarg, by default 5</p>
</dd>
<dt>cache_name<span class="classifier">str, optional</span></dt><dd><p>Unique cache_name of the model to cache marginal contributions, set to None to
disable caching, by default “” which is set to a unique value for a object</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 attribute">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.TMCSampler.CACHE">
<span class="sig-name descname"><span class="pre">CACHE</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ndarray</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.TMCSampler.CACHE" title="Link to this definition">#</a></dt>
<dd><p>Cached marginal contributions.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.TMCSampler.compute_marginal_contribution">
<span class="sig-name descname"><span class="pre">compute_marginal_contribution</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.margcontrib.sampler.TMCSampler.compute_marginal_contribution" title="Link to this definition">#</a></dt>
<dd><p>Computes marginal contribution through TMC Shapley.</p>
<p>Uses TMC-Shapley sampling to find the marginal contribution of each data point,
takes self.mc_epochs number of samples.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.sampler.TMCSampler.set_coalition">
<span class="sig-name descname"><span class="pre">set_coalition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coalition</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><a class="headerlink" href="#opendataval.dataval.margcontrib.sampler.TMCSampler.set_coalition" title="Link to this definition">#</a></dt>
<dd><p>Initializes storage to find marginal contribution of each data point</p>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.margcontrib.shap">
<span id="opendataval-dataval-margcontrib-shap-module"></span><h2>opendataval.dataval.margcontrib.shap module<a class="headerlink" href="#module-opendataval.dataval.margcontrib.shap" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.shap.ShapEvaluator">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.margcontrib.shap.</span></span><span class="sig-name descname"><span class="pre">ShapEvaluator</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.margcontrib.shap.ShapEvaluator" 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.ModelMixin" title="opendataval.dataval.api.ModelMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModelMixin</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
<p>Abstract class for all semivalue-based methods of computing data values.</p>
<section id="id50">
<h3>References<a class="headerlink" href="#id50" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id51" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>A. Ghorbani and J. Zou,
Data Shapley: Equitable Valuation of Data for Machine Learning,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1904.02868">https://arxiv.org/abs/1904.02868</a>.</p>
</aside>
<aside class="footnote brackets" id="id52" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>Y. Kwon and J. Zou,
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for
Machine Learning,
arXiv.org, 2021. Available: <a class="reference external" href="https://arxiv.org/abs/2110.14049">https://arxiv.org/abs/2110.14049</a>.</p>
</aside>
</aside>
</section>
<section id="attributes">
<h3>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>sampler<span class="classifier">Sampler, optional</span></dt><dd><p>Sampler used to compute the marginal contribution, by default uses
TMC-Shapley with a Gelman-Rubin statistic terminator. Samplers are found in
<code class="xref py py-mod docutils literal notranslate"><span class="pre">sampler</span></code></p>
</dd>
</dl>
</section>
<section id="id53">
<h3>Parameters<a class="headerlink" href="#id53" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>sampler<span class="classifier">Sampler, optional</span></dt><dd><p>Sampler used to compute the marginal contributions. Can be found in
opendataval/margcontrib/sampler.py, by default GrTMCSampler and uses additonal
arguments as constructor for sampler.</p>
</dd>
<dt>gr_threshold<span class="classifier">float, optional</span></dt><dd><p>Convergence threshold for the Gelman-Rubin statistic.
Shapley values are NP-hard so we resort to MCMC sampling, by default 1.05</p>
</dd>
<dt>max_mc_epochs<span class="classifier">int, optional</span></dt><dd><p>Max number of outer epochs of MCMC sampling, by default 100</p>
</dd>
<dt>models_per_epoch<span class="classifier">int, optional</span></dt><dd><p>Number of model fittings to take per epoch prior to checking GR convergence,
by default 100</p>
</dd>
<dt>min_models<span class="classifier">int, optional</span></dt><dd><p>Minimum samples before checking MCMC convergence, by default 1000</p>
</dd>
<dt>min_cardinality<span class="classifier">int, optional</span></dt><dd><p>Minimum cardinality of a training set, must be passed as kwarg, by default 5</p>
</dd>
<dt>cache_name<span class="classifier">str, optional</span></dt><dd><p>Unique cache_name of the model to cache marginal contributions, set to None to
disable caching, by default “” which is set to a unique value for a object</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.margcontrib.shap.ShapEvaluator.compute_weight">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_weight</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.margcontrib.shap.ShapEvaluator.compute_weight" title="Link to this definition">#</a></dt>
<dd><p>Compute the weights for each cardinality of training set.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.shap.ShapEvaluator.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.margcontrib.shap.ShapEvaluator.evaluate_data_values" title="Link to this definition">#</a></dt>
<dd><p>Return data values for each training data point.</p>
<p>Multiplies the marginal contribution with their respective weights to get
data values for semivalue-based estimators</p>
<section id="id54">
<h4>Returns<a class="headerlink" href="#id54" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>np.ndarray</dt><dd><p>Predicted data values/selection for every input data point</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.margcontrib.shap.ShapEvaluator.input_data">