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<section id="opendataval-dataloader-datasets-package">
<h1>opendataval.dataloader.datasets package<a class="headerlink" href="#opendataval-dataloader-datasets-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.dataloader.datasets.challenge">
<span id="opendataval-dataloader-datasets-challenge-module"></span><h2>opendataval.dataloader.datasets.challenge module<a class="headerlink" href="#module-opendataval.dataloader.datasets.challenge" title="Link to this heading">#</a></h2>
<dl class="py data">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.challenge.CHALLENGE_URL">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.challenge.</span></span><span class="sig-name descname"><span class="pre">CHALLENGE_URL</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'https://opendataval.yongchanstat.com/challenge'</span></em><a class="headerlink" href="#opendataval.dataloader.datasets.challenge.CHALLENGE_URL" title="Link to this definition">#</a></dt>
<dd><p>Backend URL for opendataval to get drive ids to the challenge data set.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.challenge.basename">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.challenge.</span></span><span class="sig-name descname"><span class="pre">basename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.challenge.basename" title="Link to this definition">#</a></dt>
<dd><p>Get basename of file.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.challenge.download_drive">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.challenge.</span></span><span class="sig-name descname"><span class="pre">download_drive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drive_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.challenge.download_drive" title="Link to this definition">#</a></dt>
<dd><p>Downloads file from google drive with set retry attempts.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.challenge.iris_challenge">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.challenge.</span></span><span class="sig-name descname"><span class="pre">iris_challenge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.challenge.iris_challenge" title="Link to this definition">#</a></dt>
<dd></dd></dl>
</section>
<section id="module-opendataval.dataloader.datasets.datasets">
<span id="opendataval-dataloader-datasets-datasets-module"></span><h2>opendataval.dataloader.datasets.datasets module<a class="headerlink" href="#module-opendataval.dataloader.datasets.datasets" title="Link to this heading">#</a></h2>
<p>Default data sets.</p>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_2dplanes">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_2dplanes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_2dplanes" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"2dplanes"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_MiniBooNE">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_MiniBooNE</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_MiniBooNE" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"MiniBooNE"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_adult">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_adult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</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">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_adult" title="Link to this definition">#</a></dt>
<dd><p>Binary category data set registered as <code class="docutils literal notranslate"><span class="pre">"adult"</span></code>. Adult Income data set.</p>
<p>Implementation from DVRL repository.</p>
<section id="references">
<h3>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id1" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>R. Kohavi, Scaling Up the Accuracy of
Naive-Bayes Classifiers: a Decision-Tree Hybrid,
Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining, 1996</p>
</aside>
<aside class="footnote brackets" id="id2" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>J. Yoon, Arik, 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>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_breast_cancer">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_breast_cancer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_breast_cancer" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"breast_cancer"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_creditcard">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_creditcard</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_creditcard" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"creditcard"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_diabetes">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_diabetes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_diabetes" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"diabetes"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_digits">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_digits</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_digits" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"digits"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_echoMonths">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_echoMonths</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_echoMonths" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"echoMonths"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_election">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_election</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_election" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"election"</span></code>.</p>
<p>Presidential election results by MIT Election Data and Science Lab.</p>
<section id="id3">
<h3>References<a class="headerlink" href="#id3" title="Link to this heading">#</a></h3>
<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>M. E. Data and S. Lab,
U.S. President 1976-2020.
Harvard Dataverse, 2017. doi: 10.7910/DVN/42MVDX.</p>
</aside>
</aside>
</section>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_electricity">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_electricity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_electricity" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"electricity"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_fried">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_fried</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_fried" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"fried"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_iris">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_iris</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_iris" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"iris"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_linnerud">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_linnerud</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_linnerud" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"linnerud"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_lowbwt">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_lowbwt</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_lowbwt" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"lowbwt"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_mv">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_mv</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_mv" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"mv"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_nomao">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_nomao</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_nomao" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"nomao"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_pol">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_pol</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_pol" title="Link to this definition">#</a></dt>
<dd><p>Categorical data set registered as <code class="docutils literal notranslate"><span class="pre">"pol"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_stock">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_stock</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_stock" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"stock"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.download_wave_energy">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">download_wave_energy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.download_wave_energy" title="Link to this definition">#</a></dt>
<dd><p>Regression data set registered as <code class="docutils literal notranslate"><span class="pre">"wave_energy"</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.gaussian_classifier">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">gaussian_classifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</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">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_dim</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">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.gaussian_classifier" title="Link to this definition">#</a></dt>
<dd><p>Binary category data set registered as <code class="docutils literal notranslate"><span class="pre">"gaussian_classifier"</span></code>.</p>
<p>Artificially generated gaussian noise data set.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.datasets.load_openml">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.datasets.</span></span><span class="sig-name descname"><span class="pre">load_openml</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_id</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">is_classification</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.datasets.load_openml" title="Link to this definition">#</a></dt>
<dd><p>load openml datasets.</p>
<p>A help function to load openml datasets with OpenML ID.</p>
</dd></dl>
</section>
<section id="module-opendataval.dataloader.datasets.imagesets">
<span id="opendataval-dataloader-datasets-imagesets-module"></span><h2>opendataval.dataloader.datasets.imagesets module<a class="headerlink" href="#module-opendataval.dataloader.datasets.imagesets" title="Link to this heading">#</a></h2>
<p>TorchVision data sets.</p>
<p>Uses <a class="reference external" href="https://github.com/pytorch/vision">torchvision</a>. as a dependency.</p>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.ResnetEmbeding">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">ResnetEmbeding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_class</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">type</span><span class="p"><span class="pre">[</span></span><span class="pre">VisionDataset</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></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">(224,</span> <span class="pre">224)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</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">128</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.ResnetEmbeding" title="Link to this definition">#</a></dt>
<dd><p>Convert PIL color Images into embeddings with ResNet50 model.</p>
<p>Given a PIL Images, passes through ResNet50 (as done by prior Data Valuation papers)
and saves the vector embeddings. The embeddings are extracted from the <code class="docutils literal notranslate"><span class="pre">avgpool</span></code>
layer of ResNet50. The extraction is through the PyTorch forward hook feature.</p>
<section id="id5">
<h3>References<a class="headerlink" href="#id5" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id6" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>K. He, X. Zhang, S. Ren, and J. Sun,
Deep Residual Learning for Image Recognition,
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Jun. 2016, doi: <a class="reference external" href="https://doi.org/10.1109/cvpr.2016.90">https://doi.org/10.1109/cvpr.2016.90</a>.</p>
</aside>
<aside class="footnote brackets" id="id7" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>2<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="parameters">
<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>image_set<span class="classifier">type[VisionDataset]</span></dt><dd><p>Class of Dataset to compute the embeddings of.</p>
</dd>
<dt>size<span class="classifier">tuple[int, int], optional</span></dt><dd><p>Size to resize images to, by default (224, 224)</p>
</dd>
</dl>
</section>
<section id="returns">
<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>Callable</dt><dd><p>Wrapped function when called returns a covariate embedding array and label array</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.VisionAdapter">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">VisionAdapter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_class</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">type</span><span class="p"><span class="pre">[</span></span><span class="pre">VisionDataset</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.VisionAdapter" title="Link to this definition">#</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
<p>Adapter for PyTorch vision data sets. __call__ is called by <code class="xref py py-class docutils literal notranslate"><span class="pre">Register</span></code>.</p>
<p>Adapter for MNIST data sets. __init__ inputs the class and __call__ initializes the
Dataset and extracts labels. __call__ returns tuple[Self, np.array] where Self is
a Dataset of covariates and np.array is an array of labels.</p>
<section id="id8">
<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>dataset_class<span class="classifier">type[VisionDataset]</span></dt><dd><p>Torchvision data set class provided.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py data">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.cifar10">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">cifar10</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><opendataval.dataloader.datasets.imagesets.VisionAdapter</span> <span class="pre">object></span></em><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.cifar10" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification registered as <code class="docutils literal notranslate"><span class="pre">"cifar10"</span></code>, from TorchVision.</p>
</dd></dl>
<dl class="py data">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.cifar100">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">cifar100</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><opendataval.dataloader.datasets.imagesets.VisionAdapter</span> <span class="pre">object></span></em><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.cifar100" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification data set registered as <code class="docutils literal notranslate"><span class="pre">"cifar100"</span></code>, from TorchVision.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.cifar10_embed">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">cifar10_embed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <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">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</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></span><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.cifar10_embed" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification registered as <code class="docutils literal notranslate"><span class="pre">"cifar10-embeddings"</span></code> ResNet50 embeddings</p>
</dd></dl>
<dl class="py data">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.fashion">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">fashion</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><opendataval.dataloader.datasets.imagesets.VisionAdapter</span> <span class="pre">object></span></em><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.fashion" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification data set registered as <code class="docutils literal notranslate"><span class="pre">"fashion"</span></code>, from TorchVision.</p>
</dd></dl>
<dl class="py data">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.numbers">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">numbers</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><opendataval.dataloader.datasets.imagesets.VisionAdapter</span> <span class="pre">object></span></em><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.numbers" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification data set registered as <code class="docutils literal notranslate"><span class="pre">"mnist"</span></code>, from TorchVision.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.show_image">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">show_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imgs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">Image</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Image</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">None</span></span></span><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.show_image" title="Link to this definition">#</a></dt>
<dd><p>Displays an image or a list of images.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.stl10_embed">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">stl10_embed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <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">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</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></span><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.stl10_embed" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification registered as <code class="docutils literal notranslate"><span class="pre">"stl10-embeddings"</span></code> ResNet50 embeddings</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.imagesets.svhn_embed">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.imagesets.</span></span><span class="sig-name descname"><span class="pre">svhn_embed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <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">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</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></span><a class="headerlink" href="#opendataval.dataloader.datasets.imagesets.svhn_embed" title="Link to this definition">#</a></dt>
<dd><p>Vision Classification registered as <code class="docutils literal notranslate"><span class="pre">"svhn-embeddings"</span></code> ResNet50 embeddings</p>
</dd></dl>
</section>
<section id="module-opendataval.dataloader.datasets.nlpsets">
<span id="opendataval-dataloader-datasets-nlpsets-module"></span><h2>opendataval.dataloader.datasets.nlpsets module<a class="headerlink" href="#module-opendataval.dataloader.datasets.nlpsets" title="Link to this heading">#</a></h2>
<p>NLP data sets.</p>
<p>Uses HuggingFace
<a class="reference external" href="https://huggingface.co/docs/transformers/index">transformers</a>. as dependency.</p>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.nlpsets.BertEmbeddings">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.nlpsets.</span></span><span class="sig-name descname"><span class="pre">BertEmbeddings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">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">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></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></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</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">128</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.nlpsets.BertEmbeddings" title="Link to this definition">#</a></dt>
<dd><p>Convert text data into pooled embeddings with DistilBERT model.</p>
<p>Given a data set with a list of string, such as NLP data set function (see below),
converts the sentences into strings. It is the equivalent of training a downstream
task with bert but all the BERT layers are frozen. It is advised to just
train with the raw strings with a BERT model located in models/bert.py or defining
your own model. DistilBERT is just a faster version of BERT</p>
<section id="id9">
<h3>References<a class="headerlink" href="#id9" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id10" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>J. Devlin, M.W. Chang, K. Lee, and K. Toutanova,
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
arXiv.org, 2018. Available: <a class="reference external" href="https://arxiv.org/abs/1810.04805">https://arxiv.org/abs/1810.04805</a>.</p>
</aside>
<aside class="footnote brackets" id="id11" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>V. Sanh, L. Debut, J. Chaumond, and T. Wolf,
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
arXiv.org, 2019. Available: <a class="reference external" href="https://arxiv.org/abs/1910.01108">https://arxiv.org/abs/1910.01108</a>.</p>
</aside>
</aside>
</section>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.nlpsets.bbc_embedding">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.nlpsets.</span></span><span class="sig-name descname"><span class="pre">bbc_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <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">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</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></span><a class="headerlink" href="#opendataval.dataloader.datasets.nlpsets.bbc_embedding" title="Link to this definition">#</a></dt>
<dd><p>Classification data set registered as <code class="docutils literal notranslate"><span class="pre">"bbc-embeddings"</span></code>, BERT text embeddings.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.nlpsets.download_bbc">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.nlpsets.</span></span><span class="sig-name descname"><span class="pre">download_bbc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.nlpsets.download_bbc" title="Link to this definition">#</a></dt>
<dd><p>Classification data set registered as <code class="docutils literal notranslate"><span class="pre">"bbc"</span></code>.</p>
<p>Predicts type of article from the article. Used in NLP data valuation tasks.</p>
<section id="id12">
<h3>References<a class="headerlink" href="#id12" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id13" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>D. Greene and P. Cunningham,
Practical Solutions to the Problem of Diagonal Dominance in
Kernel Document Clustering, Proc. ICML 2006.</p>
</aside>
</aside>
</section>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.nlpsets.download_imdb">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.nlpsets.</span></span><span class="sig-name descname"><span class="pre">download_imdb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.dataloader.datasets.nlpsets.download_imdb" title="Link to this definition">#</a></dt>
<dd><p>Binary category sentiment analysis data set registered as <code class="docutils literal notranslate"><span class="pre">"imdb"</span></code>.</p>
<p>Predicts sentiment analysis of the review as either positive (1) or negative (0).
Used in NLP data valuation tasks.</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>A. Maas, R. Daly, P. Pham, D. Huang, A. Ng, and C. Potts.
Learning Word Vectors for Sentiment Analysis.
The 49th Annual Meeting of the Association for Computational Linguistics (2011).</p>
</aside>
</aside>
</section>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.dataloader.datasets.nlpsets.imdb_embedding">
<span class="sig-prename descclassname"><span class="pre">opendataval.dataloader.datasets.nlpsets.</span></span><span class="sig-name descname"><span class="pre">imdb_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_download</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <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">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</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></span><a class="headerlink" href="#opendataval.dataloader.datasets.nlpsets.imdb_embedding" title="Link to this definition">#</a></dt>
<dd><p>Classification data set registered as <code class="docutils literal notranslate"><span class="pre">"imdb-embeddings"</span></code>, BERT text embeddings.</p>
</dd></dl>
</section>
<section id="module-opendataval.dataloader.datasets">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-opendataval.dataloader.datasets" title="Link to this heading">#</a></h2>
<p>Data sets registered with <a class="reference internal" href="opendataval.dataloader.html#opendataval.dataloader.register.Register" title="opendataval.dataloader.register.Register"><code class="xref py py-class docutils literal notranslate"><span class="pre">Register</span></code></a>.</p>
<section id="data-sets">
<h3>Data sets<a class="headerlink" href="#data-sets" title="Link to this heading">#</a></h3>
<div class="table-wrapper autosummary longtable docutils container">
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#module-opendataval.dataloader.datasets.datasets" title="opendataval.dataloader.datasets.datasets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets</span></code></a></p></td>
<td><p>Default data sets.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#module-opendataval.dataloader.datasets.imagesets" title="opendataval.dataloader.datasets.imagesets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imagesets</span></code></a></p></td>
<td><p>TorchVision data sets.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#module-opendataval.dataloader.datasets.nlpsets" title="opendataval.dataloader.datasets.nlpsets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nlpsets</span></code></a></p></td>
<td><p>NLP data sets.</p></td>
</tr>
</tbody>
</table>
</div>
<p>Catalog of registered data sets that can be used with
<a class="reference internal" href="opendataval.dataloader.html#opendataval.dataloader.fetcher.DataFetcher" title="opendataval.dataloader.fetcher.DataFetcher"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFetcher</span></code></a>. Pass in the <code class="docutils literal notranslate"><span class="pre">str</span></code> name
registering the data set to load the data set as needed.
.</p>
</section>
</section>
</section>
</article>
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<ul>
<li><a class="reference internal" href="#">opendataval.dataloader.datasets package</a><ul>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-opendataval.dataloader.datasets.challenge">opendataval.dataloader.datasets.challenge module</a><ul>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.challenge.CHALLENGE_URL"><code class="docutils literal notranslate"><span class="pre">CHALLENGE_URL</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.challenge.basename"><code class="docutils literal notranslate"><span class="pre">basename()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.challenge.download_drive"><code class="docutils literal notranslate"><span class="pre">download_drive()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.challenge.iris_challenge"><code class="docutils literal notranslate"><span class="pre">iris_challenge()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-opendataval.dataloader.datasets.datasets">opendataval.dataloader.datasets.datasets module</a><ul>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_2dplanes"><code class="docutils literal notranslate"><span class="pre">download_2dplanes()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_MiniBooNE"><code class="docutils literal notranslate"><span class="pre">download_MiniBooNE()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_adult"><code class="docutils literal notranslate"><span class="pre">download_adult()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_breast_cancer"><code class="docutils literal notranslate"><span class="pre">download_breast_cancer()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_creditcard"><code class="docutils literal notranslate"><span class="pre">download_creditcard()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_diabetes"><code class="docutils literal notranslate"><span class="pre">download_diabetes()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_digits"><code class="docutils literal notranslate"><span class="pre">download_digits()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_echoMonths"><code class="docutils literal notranslate"><span class="pre">download_echoMonths()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_election"><code class="docutils literal notranslate"><span class="pre">download_election()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_electricity"><code class="docutils literal notranslate"><span class="pre">download_electricity()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_fried"><code class="docutils literal notranslate"><span class="pre">download_fried()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_iris"><code class="docutils literal notranslate"><span class="pre">download_iris()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_linnerud"><code class="docutils literal notranslate"><span class="pre">download_linnerud()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_lowbwt"><code class="docutils literal notranslate"><span class="pre">download_lowbwt()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_mv"><code class="docutils literal notranslate"><span class="pre">download_mv()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_nomao"><code class="docutils literal notranslate"><span class="pre">download_nomao()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_pol"><code class="docutils literal notranslate"><span class="pre">download_pol()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_stock"><code class="docutils literal notranslate"><span class="pre">download_stock()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.download_wave_energy"><code class="docutils literal notranslate"><span class="pre">download_wave_energy()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.gaussian_classifier"><code class="docutils literal notranslate"><span class="pre">gaussian_classifier()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.datasets.load_openml"><code class="docutils literal notranslate"><span class="pre">load_openml()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-opendataval.dataloader.datasets.imagesets">opendataval.dataloader.datasets.imagesets module</a><ul>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.ResnetEmbeding"><code class="docutils literal notranslate"><span class="pre">ResnetEmbeding()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.VisionAdapter"><code class="docutils literal notranslate"><span class="pre">VisionAdapter</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.cifar10"><code class="docutils literal notranslate"><span class="pre">cifar10</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.cifar100"><code class="docutils literal notranslate"><span class="pre">cifar100</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.cifar10_embed"><code class="docutils literal notranslate"><span class="pre">cifar10_embed()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.fashion"><code class="docutils literal notranslate"><span class="pre">fashion</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.numbers"><code class="docutils literal notranslate"><span class="pre">numbers</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.show_image"><code class="docutils literal notranslate"><span class="pre">show_image()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.stl10_embed"><code class="docutils literal notranslate"><span class="pre">stl10_embed()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.imagesets.svhn_embed"><code class="docutils literal notranslate"><span class="pre">svhn_embed()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-opendataval.dataloader.datasets.nlpsets">opendataval.dataloader.datasets.nlpsets module</a><ul>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.nlpsets.BertEmbeddings"><code class="docutils literal notranslate"><span class="pre">BertEmbeddings()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.nlpsets.bbc_embedding"><code class="docutils literal notranslate"><span class="pre">bbc_embedding()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.nlpsets.download_bbc"><code class="docutils literal notranslate"><span class="pre">download_bbc()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.nlpsets.download_imdb"><code class="docutils literal notranslate"><span class="pre">download_imdb()</span></code></a></li>
<li><a class="reference internal" href="#opendataval.dataloader.datasets.nlpsets.imdb_embedding"><code class="docutils literal notranslate"><span class="pre">imdb_embedding()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-opendataval.dataloader.datasets">Module contents</a><ul>
<li><a class="reference internal" href="#data-sets">Data sets</a></li>
</ul>
</li>
</ul>
</li>
</ul>
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