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<!doctype html>
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<section id="opendataval-dataval-dvrl-package">
<h1>opendataval.dataval.dvrl package<a class="headerlink" href="#opendataval-dataval-dvrl-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.dvrl.dvrl">
<span id="opendataval-dataval-dvrl-dvrl-module"></span><h2>opendataval.dataval.dvrl.dvrl module<a class="headerlink" href="#module-opendataval.dataval.dvrl.dvrl" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.dvrl.dvrl.DVRL">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.dvrl.dvrl.</span></span><span class="sig-name descname"><span class="pre">DVRL</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.dvrl.dvrl.DVRL" 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 valuation using reinforcement learning class, implemented with PyTorch.</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. Yoon, S. Arik, and T. Pfister,
Data Valuation using Reinforcement Learning,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1909.11671">https://arxiv.org/abs/1909.11671</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>hidden_dim<span class="classifier">int, optional</span></dt><dd><p>Hidden dimensions for the RL Multilayer Perceptron Value Estimator (VE)
(details in <a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DataValueEstimatorRL" title="opendataval.dataval.dvrl.dvrl.DataValueEstimatorRL"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataValueEstimatorRL</span></code></a> class), by default 100</p>
</dd>
<dt>layer_number<span class="classifier">int, optional</span></dt><dd><p>Number of hidden layers for the Value Estimator (VE), by default 5</p>
</dd>
<dt>comb_dim<span class="classifier">int, optional</span></dt><dd><p>After concat inputs how many layers, much less than <cite>hidden_dim</cite>, by default 10</p>
</dd>
<dt>rl_epochs<span class="classifier">int, optional</span></dt><dd><p>Number of training epochs for the VE, by default 1000</p>
</dd>
<dt>rl_batch_size<span class="classifier">int, optional</span></dt><dd><p>Batch size for training the VE, by default 32</p>
</dd>
<dt>lr<span class="classifier">float, optional</span></dt><dd><p>Learning rate for the VE, by default 0.01</p>
</dd>
<dt>threshold<span class="classifier">float, optional</span></dt><dd><p>Search rate threshold, the VE may get stuck in certain bounds close to
<span class="math notranslate nohighlight">\([0, 1]\)</span>, thus outside of <span class="math notranslate nohighlight">\([1-threshold, threshold]\)</span> we encourage
searching, by default 0.9</p>
</dd>
<dt>device<span class="classifier">torch.device, optional</span></dt><dd><p>Tensor device for acceleration, by default torch.device(“cpu”)</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.dvrl.dvrl.DVRL.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.dvrl.dvrl.DVRL.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 for DVRL using the Value Estimator MLP.</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.dvrl.dvrl.DVRL.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.dvrl.dvrl.DVRL.input_data" title="Link to this definition">#</a></dt>
<dd><p>Store and transform input data for DVRL.</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.dvrl.dvrl.DVRL.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="n"><span class="pre">num_workers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</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.dvrl.dvrl.DVRL.train_data_values" title="Link to this definition">#</a></dt>
<dd><p>Trains model to predict data values.</p>
<p>Trains the VE to assign probabilities of each data point being selected
using a signal from the evaluation performance.</p>
<section id="id3">
<h4>Parameters<a class="headerlink" href="#id3" 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>num_workers<span class="classifier">int, optional</span></dt><dd><p>Number of workers used to load data, by default 0, loaded in main process</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.dvrl.dvrl.DataValueEstimatorRL">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.dvrl.dvrl.</span></span><span class="sig-name descname"><span class="pre">DataValueEstimatorRL</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comb_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataval.dvrl.dvrl.DataValueEstimatorRL" title="Link to this definition">#</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
<p>Value Estimator model.</p>
<p>Here, we assume a simple multi-layer perceptron architecture for the data
value evaluator model. For data types like tabular, multi-layer perceptron
is already efficient at extracting the relevant information.
For high-dimensional data types like images or text,
it is important to introduce inductive biases to the architecture to
extract information efficiently. In such cases, there are two options:
(i) Input the encoded representations (e.g. the last layer activations of
ResNet for images, or the last layer activations of BERT for text) and use
the multi-layer perceptron on top of it. The encoded representations can
simply come from a pre-trained predictor model using the entire dataset.
(ii) Modify the data value evaluator model definition below to have the
appropriate inductive bias (e.g. using convolutions layers for images,
or attention layers text).</p>
<section id="id4">
<h3>References<a class="headerlink" href="#id4" title="Link to this heading">#</a></h3>
<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>J. Yoon, Sercan O, and T. Pfister,
Data Valuation using Reinforcement Learning,
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1909.11671">https://arxiv.org/abs/1909.11671</a>.</p>
</aside>
</aside>
</section>
<section id="id6">
<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x_dim<span class="classifier">int</span></dt><dd><p>Data covariates dimension, can be flatten dimension size</p>
</dd>
<dt>y_dim<span class="classifier">int</span></dt><dd><p>Data labels dimension, can be flatten dimension size</p>
</dd>
<dt>hidden_dim<span class="classifier">int</span></dt><dd><p>Hidden dimensions for the Value Estimator</p>
</dd>
<dt>layer_number<span class="classifier">int</span></dt><dd><p>Number of hidden layers for the Value Estimator</p>
</dd>
<dt>comb_dim<span class="classifier">int</span></dt><dd><p>After concat inputs how many layers, much less than <cite>hidden_dim</cite>, by default 10</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.dvrl.dvrl.DataValueEstimatorRL.forward">
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</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</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_hat</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.dvrl.dvrl.DataValueEstimatorRL.forward" title="Link to this definition">#</a></dt>
<dd><p>Forward pass of inputs through value estimator for data values of input.</p>
<p>Forward pass through Value Estimator. Returns selection probabilities.
Concats the difference between labels and predicted labels to compute
selection probabilities.</p>
<section id="id7">
<h4>Parameters<a class="headerlink" href="#id7" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x<span class="classifier">torch.Tensor</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y<span class="classifier">torch.Tensor</span></dt><dd><p>Data labels</p>
</dd>
<dt>y_hat<span class="classifier">torch.Tensor</span></dt><dd><p>Data label predictions (from prediction model)</p>
</dd>
</dl>
</section>
<section id="id8">
<h4>Returns<a class="headerlink" href="#id8" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Selection probabilities per covariate data point</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataval.dvrl.dvrl.DveLoss">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataval.dvrl.dvrl.</span></span><span class="sig-name descname"><span class="pre">DveLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploration_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataval.dvrl.dvrl.DveLoss" title="Link to this definition">#</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
<p>Compute Loss for Value Estimator.</p>
<p>Custom loss function for the value estimator RL Model. Uses BCE Loss and
checks average is within threshold to encourage exploration</p>
<section id="id9">
<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>threshold<span class="classifier">float, optional</span></dt><dd><p>Search rate threshold, the VE may get stuck in certain bounds close to
<span class="math notranslate nohighlight">\([0, 1]\)</span>, thus outside of <span class="math notranslate nohighlight">\([1-threshold, threshold]\)</span> we encourage
searching, by default 0.9</p>
</dd>
<dt>exploration_weight<span class="classifier">float, optional</span></dt><dd><p>Large constant to encourage exploration in the Value Estimator, by default 1e3</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.dataval.dvrl.dvrl.DveLoss.forward">
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred_dataval</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">selector_input</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">reward_input</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</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.dvrl.dvrl.DveLoss.forward" title="Link to this definition">#</a></dt>
<dd><p>Compute the loss for the Value Estimator.</p>
<p>Uses REINFORCE Algorithm to compute a loss for the Value Estimator.
<cite>pred_dataval</cite> is the data values. <cite>selector_input</cite> is a bernoulli random
variable with <cite>p=pred_dataval</cite>. Computes a BCE between <cite>pred_dataval</cite> and
<cite>selector_input</cite> and multiplies by the reward signal. Adds an additional loss
if the Value Estimator is getting stuck outside the threshold.</p>
<section id="id10">
<h4>References<a class="headerlink" href="#id10" title="Link to this heading">#</a></h4>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id11" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>R. J. Williams,
Simple statistical gradient-following algorithms for connectionist
reinforcement learning,
Machine Learning, vol. 8, no. 3-4, pp. 229-256, May 1992,
doi: <a class="reference external" href="https://doi.org/10.1007/bf00992696">https://doi.org/10.1007/bf00992696</a>.</p>
</aside>
</aside>
</section>
<section id="id12">
<h4>Parameters<a class="headerlink" href="#id12" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>pred_dataval<span class="classifier">torch.Tensor</span></dt><dd><p>Predicted values from value estimator</p>
</dd>
<dt>selector_input<span class="classifier">torch.Tensor</span></dt><dd><p><cite>1</cite> for selected <cite>0</cite> for not selected, bernoulli random variable</p>
</dd>
<dt>reward_input<span class="classifier">float</span></dt><dd><p>Reward/performance signal of prediction model trained on <cite>selector_input</cite>.
If positive, indicates better than naive model of full sample.</p>
</dd>
</dl>
</section>
<section id="id13">
<h4>Returns<a class="headerlink" href="#id13" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Computed loss tensor for Value Estimator</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.dataval.dvrl">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-opendataval.dataval.dvrl" title="Link to this heading">#</a></h2>
</section>
</section>
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<li><a class="reference internal" href="#">opendataval.dataval.dvrl package</a><ul>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-opendataval.dataval.dvrl.dvrl">opendataval.dataval.dvrl.dvrl module</a><ul>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DVRL"><code class="docutils literal notranslate"><span class="pre">DVRL</span></code></a><ul>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DVRL.evaluate_data_values"><code class="docutils literal notranslate"><span class="pre">DVRL.evaluate_data_values()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DVRL.input_data"><code class="docutils literal notranslate"><span class="pre">DVRL.input_data()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DVRL.train_data_values"><code class="docutils literal notranslate"><span class="pre">DVRL.train_data_values()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DataValueEstimatorRL"><code class="docutils literal notranslate"><span class="pre">DataValueEstimatorRL</span></code></a><ul>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DataValueEstimatorRL.forward"><code class="docutils literal notranslate"><span class="pre">DataValueEstimatorRL.forward()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DveLoss"><code class="docutils literal notranslate"><span class="pre">DveLoss</span></code></a><ul>
<li><a class="reference internal" href="#opendataval.dataval.dvrl.dvrl.DveLoss.forward"><code class="docutils literal notranslate"><span class="pre">DveLoss.forward()</span></code></a></li>
</ul>
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<li><a class="reference internal" href="#module-opendataval.dataval.dvrl">Module contents</a></li>
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