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<section id="opendataval-dataval-influence-package">
<h1>opendataval.dataval.influence package<a class="headerlink" href="#opendataval-dataval-influence-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.influence.influence">
<span id="opendataval-dataval-influence-influence-module"></span><h2>opendataval.dataval.influence.influence module<a class="headerlink" href="#module-opendataval.dataval.influence.influence" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.influence.influence.InfluenceFunction">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.influence.influence.</span></span><span class="sig-name descname"><span class="pre">InfluenceFunction</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.influence.influence.InfluenceFunction" 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>Influence Function Data evaluation implementation.</p>
<p>TODO it may be useful to compute gradients of the validation dataset in batches
to save time/space.
TODO H^{-1} implementation, Current implementation is for first-order gradients</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>P. W. Koh and P. Liang,
Understanding Black-box Predictions via Influence Functions,
arXiv.org, 2017. <a class="reference external" href="https://arxiv.org/abs/1703.04730">https://arxiv.org/abs/1703.04730</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>grad_args<span class="classifier">tuple, optional</span></dt><dd><p>Positional arguments passed to the model.grad function</p>
</dd>
<dt>grad_kwargs<span class="classifier">dict[str, Any], optional</span></dt><dd><p>Key word arguments passed to the model.grad function</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.influence.influence.InfluenceFunction.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.influence.influence.InfluenceFunction.evaluate_data_values" title="Link to this definition">#</a></dt>
<dd><p>Return influence (data values) for each training data point.</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 for training input data point</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.influence.influence.InfluenceFunction.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.influence.influence.InfluenceFunction.input_data" title="Link to this definition">#</a></dt>
<dd><p>Store and transform input data for Influence Function Data Valuation.</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.influence.influence.InfluenceFunction.input_model">
<span class="sig-name descname"><span class="pre">input_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="opendataval.model.html#opendataval.model.grad.GradientModel" title="opendataval.model.grad.GradientModel"><span class="pre">GradientModel</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataval.influence.influence.InfluenceFunction.input_model" title="Link to this definition">#</a></dt>
<dd><p>Input the prediction model with gradient.</p>
<section id="id3">
<h4>Parameters<a class="headerlink" href="#id3" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>pred_model<span class="classifier">GradientModel</span></dt><dd><p>Prediction model with a gradient</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.influence.influence.InfluenceFunction.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.influence.influence.InfluenceFunction.train_data_values" title="Link to this definition">#</a></dt>
<dd><p>Trains model to compute influence of each data point (data values).</p>
<section id="id4">
<h4>References<a class="headerlink" href="#id4" title="Link to this heading">#</a></h4>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id5" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Implementation inspired by <a class="reference external" href="https://github.com/uvanlp/valda">valda</a>.
<<a class="reference external" href="https://github.com/uvanlp/valda/blob/main/src/valda/inf_func.py">https://github.com/uvanlp/valda/blob/main/src/valda/inf_func.py</a>></p>
</aside>
</aside>
</section>
<section id="id6">
<h4>Parameters<a class="headerlink" href="#id6" 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.influence.infsub">
<span id="opendataval-dataval-influence-infsub-module"></span><h2>opendataval.dataval.influence.infsub module<a class="headerlink" href="#module-opendataval.dataval.influence.infsub" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.influence.infsub.InfluenceSubsample">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.influence.infsub.</span></span><span class="sig-name descname"><span class="pre">InfluenceSubsample</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.influence.infsub.InfluenceSubsample" 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>Influence computed through subsamples implementation.</p>
<p>Compute influence of each training example on for the validation dataset
through closely-related subsampled influence.</p>
<section id="id7">
<h3>References<a class="headerlink" href="#id7" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id8" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>V. Feldman and C. Zhang,
What Neural Networks Memorize and Why: Discovering the Long Tail via
Influence Estimation,
arXiv.org, 2020. Available: <a class="reference external" href="https://arxiv.org/abs/2008.03703">https://arxiv.org/abs/2008.03703</a>.</p>
</aside>
</aside>
</section>
<section id="id9">
<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>samples<span class="classifier">int, optional</span></dt><dd><p>Number of models to fit to take to find data values, by default 1000</p>
</dd>
<dt>proportion<span class="classifier">float, optional</span></dt><dd><p>Proportion of data points to be in each sample, cardinality of each subset is
<span class="math notranslate nohighlight">\((p)(num_points)\)</span>, by default 0.7 as specified by V. Feldman and C. Zhang</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.influence.infsub.InfluenceSubsample.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.influence.infsub.InfluenceSubsample.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 Influence Subsample data valuator. Finds
the difference of average performance of all sets including data point minus
not-including.</p>
<section id="id10">
<h4>Returns<a class="headerlink" href="#id10" 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.influence.infsub.InfluenceSubsample.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.influence.infsub.InfluenceSubsample.input_data" title="Link to this definition">#</a></dt>
<dd><p>Store and transform input data for Influence Subsample Data Valuation.</p>
<section id="id11">
<h4>Parameters<a class="headerlink" href="#id11" 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.influence.infsub.InfluenceSubsample.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.influence.infsub.InfluenceSubsample.train_data_values" title="Link to this definition">#</a></dt>
<dd><p>Trains model to predict data values.</p>
<p>Trains the Influence Subsample Data Valuator by sampling from subsets of
<span class="math notranslate nohighlight">\((p)(num_points)\)</span> cardinality and computing the performance with the
<span class="math notranslate nohighlight">\(i\)</span> data point and without the <span class="math notranslate nohighlight">\(i\)</span> data point. The form of sampling
is similar to the shapely value when <span class="math notranslate nohighlight">\(p\)</span> is :math:<a href="#id12"><span class="problematic" id="id13">`</span></a>0.5: (V. Feldman).
Likewise, if we sample not from the subsets of a specific cardinality but the
uniform across all subsets, it is similar to the Banzhaf value.</p>
<section id="id14">
<h4>Parameters<a class="headerlink" href="#id14" 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.influence">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-opendataval.dataval.influence" title="Link to this heading">#</a></h2>
<p>NOTE <cite>InfluenceSubsample</cite> was previously named <cite>InfluenceFunctionEval</cite> and may be
referred to as such in the demos. This docstring is here to clarify the confusion
between the naming of <cite>InfluenceFunction</cite> and <cite>InfluenceSubsample</cite>.</p>
</section>
</section>
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<ul>
<li><a class="reference internal" href="#">opendataval.dataval.influence package</a><ul>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-opendataval.dataval.influence.influence">opendataval.dataval.influence.influence module</a><ul>
<li><a class="reference internal" href="#opendataval.dataval.influence.influence.InfluenceFunction"><code class="docutils literal notranslate"><span class="pre">InfluenceFunction</span></code></a><ul>
<li><a class="reference internal" href="#opendataval.dataval.influence.influence.InfluenceFunction.evaluate_data_values"><code class="docutils literal notranslate"><span class="pre">InfluenceFunction.evaluate_data_values()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.influence.influence.InfluenceFunction.input_data"><code class="docutils literal notranslate"><span class="pre">InfluenceFunction.input_data()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.influence.influence.InfluenceFunction.input_model"><code class="docutils literal notranslate"><span class="pre">InfluenceFunction.input_model()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.influence.influence.InfluenceFunction.train_data_values"><code class="docutils literal notranslate"><span class="pre">InfluenceFunction.train_data_values()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-opendataval.dataval.influence.infsub">opendataval.dataval.influence.infsub module</a><ul>
<li><a class="reference internal" href="#opendataval.dataval.influence.infsub.InfluenceSubsample"><code class="docutils literal notranslate"><span class="pre">InfluenceSubsample</span></code></a><ul>
<li><a class="reference internal" href="#opendataval.dataval.influence.infsub.InfluenceSubsample.evaluate_data_values"><code class="docutils literal notranslate"><span class="pre">InfluenceSubsample.evaluate_data_values()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.influence.infsub.InfluenceSubsample.input_data"><code class="docutils literal notranslate"><span class="pre">InfluenceSubsample.input_data()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.influence.infsub.InfluenceSubsample.train_data_values"><code class="docutils literal notranslate"><span class="pre">InfluenceSubsample.train_data_values()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-opendataval.dataval.influence">Module contents</a></li>
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