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<article role="main">
<section id="opendataval-model-package">
<h1>opendataval.model package<a class="headerlink" href="#opendataval-model-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.model.api">
<span id="opendataval-model-api-module"></span><h2>opendataval.model.api module<a class="headerlink" href="#module-opendataval.model.api" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.ClassifierSkLearnWrapper">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">ClassifierSkLearnWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</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="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.model.api.ClassifierSkLearnWrapper" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.Model" title="opendataval.model.api.Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">Model</span></code></a></p>
<p>Wrapper for sk-learn classifiers that can have weighted fit methods.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wrapped</span> <span class="o">=</span> <span class="n">ClassifierSkLearnWrapper</span><span class="p">(</span><span class="n">LinearRegression</span><span class="p">(),</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<section id="parameters">
<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>base_model<span class="classifier">BaseModel</span></dt><dd><p>Any sk-learn model that supports <code class="docutils literal notranslate"><span class="pre">sample_weights</span></code></p>
</dd>
<dt>num_classes<span class="classifier">int</span></dt><dd><p>Label dimensionality</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.ClassifierSkLearnWrapper.fit">
<span class="sig-name descname"><span class="pre">fit</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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.model.api.ClassifierSkLearnWrapper.fit" title="Link to this definition">#</a></dt>
<dd><p>Fits the model on the training data.</p>
<p>Fits a sk-learn wrapped classifier Model. If there are less classes in the
sample than num_classes, uses dummy model.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wrapped</span> <span class="o">=</span> <span class="n">ClassifierSkLearnWrapper</span><span class="p">(</span><span class="n">MLPClassifier</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<section id="id1">
<h4>Parameters<a class="headerlink" href="#id1" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data labels</p>
</dd>
<dt>args<span class="classifier">tuple[Any]</span></dt><dd><p>Additional positional args</p>
</dd>
<dt>sample_weights<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, must be passed in as key word arg,
by default None</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any]</span></dt><dd><p>Addition key word args</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.ClassifierSkLearnWrapper.predict">
<span class="sig-name descname"><span class="pre">predict</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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.model.api.ClassifierSkLearnWrapper.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict labels from sk-learn model.</p>
<p>Makes a prediction based on the input tensor. Uses the <cite>.predict_proba(x)</cite>
method on sk-learn classifiers. Output dim will match the input to
the <cite>.train(x, y)</cite> method</p>
<section id="id2">
<h4>Parameters<a class="headerlink" href="#id2" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Input tensor</p>
</dd>
</dl>
</section>
<section id="returns">
<h4>Returns<a class="headerlink" href="#returns" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Output tensor</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.ClassifierUnweightedSkLearnWrapper">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">ClassifierUnweightedSkLearnWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</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="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.model.api.ClassifierUnweightedSkLearnWrapper" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.ClassifierSkLearnWrapper" title="opendataval.model.api.ClassifierSkLearnWrapper"><code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierSkLearnWrapper</span></code></a></p>
<p>Wrapper for sk-learn classifiers that can don’t have weighted fit methods.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wrapped</span> <span class="o">=</span> <span class="n">ClassifierSkLearnWrapper</span><span class="p">(</span><span class="n">KNeighborsClassifier</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<section id="id3">
<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>base_model<span class="classifier">BaseModel</span></dt><dd><p>Any sk-learn model that supports <code class="docutils literal notranslate"><span class="pre">sample_weights</span></code></p>
</dd>
<dt>num_classes<span class="classifier">int</span></dt><dd><p>Label dimensionality</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.ClassifierUnweightedSkLearnWrapper.fit">
<span class="sig-name descname"><span class="pre">fit</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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.model.api.ClassifierUnweightedSkLearnWrapper.fit" title="Link to this definition">#</a></dt>
<dd><p>Fits the model on the training data.</p>
<p>Fits a sk-learn wrapped classifier Model without sample weight.</p>
<section id="id4">
<h4>Parameters<a class="headerlink" href="#id4" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data labels</p>
</dd>
<dt>args<span class="classifier">tuple[Any]</span></dt><dd><p>Additional positional args</p>
</dd>
<dt>sample_weights<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, must be passed in as key word arg,
by default None</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any]</span></dt><dd><p>Addition key word args</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.Model">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">Model</span></span><a class="headerlink" href="#opendataval.model.api.Model" title="Link to this definition">#</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
<p>Abstract class of Models. Provides a template for models.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="opendataval.model.api.Model.Models">
<span class="sig-name descname"><span class="pre">Models</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Self</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'bertclassifier':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.bert.BertClassifier'>,</span> <span class="pre">'classifiermlp':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.mlp.ClassifierMLP'>,</span> <span class="pre">'classifiersklearnwrapper':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.ClassifierSkLearnWrapper'>,</span> <span class="pre">'classifierunweightedsklearnwrapper':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.ClassifierUnweightedSkLearnWrapper'>,</span> <span class="pre">'gradientmodel':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.grad.GradientModel'>,</span> <span class="pre">'lenet':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.lenet.LeNet'>,</span> <span class="pre">'logisticregression':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.logistic_regression.LogisticRegression'>,</span> <span class="pre">'regressionmlp':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.mlp.RegressionMLP'>,</span> <span class="pre">'regressionsklearnwrapper':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.RegressionSkLearnWrapper'>,</span> <span class="pre">'torchclassmixin':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.TorchClassMixin'>,</span> <span class="pre">'torchgradmixin':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.grad.TorchGradMixin'>,</span> <span class="pre">'torchmodel':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.TorchModel'>,</span> <span class="pre">'torchpredictmixin':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.TorchPredictMixin'>,</span> <span class="pre">'torchregressmixin':</span> <span class="pre"><class</span> <span class="pre">'opendataval.model.api.TorchRegressMixin'>}</span></em><a class="headerlink" href="#opendataval.model.api.Model.Models" title="Link to this definition">#</a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.Model.clone">
<span class="sig-name descname"><span class="pre">clone</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">Self</span></span></span><a class="headerlink" href="#opendataval.model.api.Model.clone" title="Link to this definition">#</a></dt>
<dd><p>Clone Model object.</p>
<p>Copy and returns object representing current state. We often take a base
model and train it several times, so we need to have the same initial conditions
Default clone implementation.</p>
<section id="id5">
<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>self<span class="classifier">object</span></dt><dd><p>Returns deep copy of model.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.Model.fit">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fit</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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="n"><span class="pre">sample_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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">Self</span></span></span><a class="headerlink" href="#opendataval.model.api.Model.fit" title="Link to this definition">#</a></dt>
<dd><p>Fits the model on the training data.</p>
<section id="id6">
<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data labels</p>
</dd>
<dt>args<span class="classifier">tuple[Any]</span></dt><dd><p>Additional positional args</p>
</dd>
<dt>sample_weights<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, must be passed in as key word arg,
by default None</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any]</span></dt><dd><p>Addition key word args</p>
</dd>
</dl>
</section>
<section id="id7">
<h3>Returns<a class="headerlink" href="#id7" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>self<span class="classifier">object</span></dt><dd><p>Returns self for api consistency with sklearn.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.Model.predict">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predict</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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">Tensor</span></span></span><a class="headerlink" href="#opendataval.model.api.Model.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict the label from the input covariates data.</p>
<section id="id8">
<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Input data covariates</p>
</dd>
</dl>
</section>
<section id="id9">
<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Output predictions based on the input</p>
</dd>
</dl>
</section>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.RegressionSkLearnWrapper">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">RegressionSkLearnWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_model</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><a class="headerlink" href="#opendataval.model.api.RegressionSkLearnWrapper" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.Model" title="opendataval.model.api.Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">Model</span></code></a></p>
<p>Wrapper for sk-learn regression models.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wrapped</span> <span class="o">=</span> <span class="n">RegressionSkLearnWrapper</span><span class="p">(</span><span class="n">LinearRegression</span><span class="p">)</span>
</pre></div>
</div>
<section id="id10">
<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>base_model<span class="classifier">BaseModel</span></dt><dd><p>Any sk-learn model that supports <code class="docutils literal notranslate"><span class="pre">sample_weights</span></code></p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.RegressionSkLearnWrapper.fit">
<span class="sig-name descname"><span class="pre">fit</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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.model.api.RegressionSkLearnWrapper.fit" title="Link to this definition">#</a></dt>
<dd><p>Fits the model on the training data.</p>
<p>Fits a sk-learn wrapped regression Model. If there is insufficient data to fit
a regression (such as len(x_train)==0), will use DummyRegressor that predicts
np.zeros((num_samples, self.num_classes))</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 | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data labels</p>
</dd>
<dt>args<span class="classifier">tuple[Any]</span></dt><dd><p>Additional positional args</p>
</dd>
<dt>sample_weights<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, must be passed in as key word arg,
by default None</p>
</dd>
<dt>kwargs<span class="classifier">dict[str, Any]</span></dt><dd><p>Addition key word args</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.RegressionSkLearnWrapper.predict">
<span class="sig-name descname"><span class="pre">predict</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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.model.api.RegressionSkLearnWrapper.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict values from sk-learn regression model.</p>
<p>Makes a prediction based on the input tensor. Uses the <cite>.predict(x)</cite>
method on sk-learn regression models. Output dim will match <code class="docutils literal notranslate"><span class="pre">self.num_classes</span></code></p>
<section id="id12">
<h4>Parameters<a class="headerlink" href="#id12" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Input tensor</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>Output tensor</p>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.TorchClassMixin">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">TorchClassMixin</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.model.api.TorchClassMixin" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.TorchModel" title="opendataval.model.api.TorchModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">TorchModel</span></code></a></p>
<p>Classifier Mixin for Torch Neural Networks.</p>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.TorchClassMixin.fit">
<span class="sig-name descname"><span class="pre">fit</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</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">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</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">0.01</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.api.TorchClassMixin.fit" title="Link to this definition">#</a></dt>
<dd><p>Fits the model on the training data.</p>
<p>Fits a torch classifier Model object using ADAM optimizer and cross
categorical entropy loss.</p>
<section id="id14">
<h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data labels</p>
</dd>
<dt>batch_size<span class="classifier">int, optional</span></dt><dd><p>Training batch size, by default 32</p>
</dd>
<dt>epochs<span class="classifier">int, optional</span></dt><dd><p>Number of training epochs, by default 1</p>
</dd>
<dt>sample_weights<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, by default None</p>
</dd>
<dt>lr<span class="classifier">float, optional</span></dt><dd><p>Learning rate for the Model, by default 0.01</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="opendataval.model.api.TorchClassMixin.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#opendataval.model.api.TorchClassMixin.training" title="Link to this definition">#</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.TorchModel">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">TorchModel</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.model.api.TorchModel" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.Model" title="opendataval.model.api.Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">Model</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
<p>Torch Models have a device they belong to and shared behavior</p>
<dl class="py property">
<dt class="sig sig-object py" id="opendataval.model.api.TorchModel.device">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#opendataval.model.api.TorchModel.device" title="Link to this definition">#</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.TorchPredictMixin">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">TorchPredictMixin</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.model.api.TorchPredictMixin" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.TorchModel" title="opendataval.model.api.TorchModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">TorchModel</span></code></a></p>
<p>Torch <code class="docutils literal notranslate"><span class="pre">.predict()</span></code> method mixin for Torch Neural Networks.</p>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.TorchPredictMixin.predict">
<span class="sig-name descname"><span class="pre">predict</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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.model.api.TorchPredictMixin.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict output from input tensor/data set.</p>
<section id="id15">
<h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x<span class="classifier">torch.Tensor</span></dt><dd><p>Input covariates</p>
</dd>
</dl>
</section>
<section id="id16">
<h3>Returns<a class="headerlink" href="#id16" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Predicted tensor output</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="opendataval.model.api.TorchPredictMixin.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#opendataval.model.api.TorchPredictMixin.training" title="Link to this definition">#</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.api.TorchRegressMixin">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">TorchRegressMixin</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.model.api.TorchRegressMixin" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.TorchModel" title="opendataval.model.api.TorchModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">TorchModel</span></code></a></p>
<p>Regressor Mixin for Torch Neural Networks.</p>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.api.TorchRegressMixin.fit">
<span class="sig-name descname"><span class="pre">fit</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</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">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</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">0.01</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.api.TorchRegressMixin.fit" title="Link to this definition">#</a></dt>
<dd><p>Fits the regression model on the training data.</p>
<p>Fits a torch regression Model object using ADAM optimizer and MSE loss.</p>
<section id="id17">
<h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor | Dataset</span></dt><dd><p>Data labels</p>
</dd>
<dt>batch_size<span class="classifier">int, optional</span></dt><dd><p>Training batch size, by default 32</p>
</dd>
<dt>epochs<span class="classifier">int, optional</span></dt><dd><p>Number of training epochs, by default 1</p>
</dd>
<dt>sample_weight<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, by default None</p>
</dd>
<dt>lr<span class="classifier">float, optional</span></dt><dd><p>Learning rate for the Model, by default 0.01</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="opendataval.model.api.TorchRegressMixin.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#opendataval.model.api.TorchRegressMixin.training" title="Link to this definition">#</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="opendataval.model.api.to_numpy">
<span class="sig-prename descclassname"><span class="pre">opendataval.model.api.</span></span><span class="sig-name descname"><span class="pre">to_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</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">Tensor</span><span class="p"><span class="pre">]</span></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></span><a class="headerlink" href="#opendataval.model.api.to_numpy" title="Link to this definition">#</a></dt>
<dd><p>Mini function to move tensor to CPU for sk-learn.</p>
</dd></dl>
</section>
<section id="module-opendataval.model.bert">
<span id="opendataval-model-bert-module"></span><h2>opendataval.model.bert module<a class="headerlink" href="#module-opendataval.model.bert" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.bert.BertClassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.bert.</span></span><span class="sig-name descname"><span class="pre">BertClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained_model_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</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">'distilbert-base-uncased'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</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">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout_rate</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">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_train_layers</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">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.bert.BertClassifier" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.Model" title="opendataval.model.api.Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">Model</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
<p>Fine tune a pre-trained DistilBERT model on a classification task.</p>
<p>DistilBERT is just a smaller/lighter version of BERT meant to be fine-tuned onto
other language tasks</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="id18" 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="id19" 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>
<section id="id20">
<h3>Parameters<a class="headerlink" href="#id20" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>pretrained_model_name<span class="classifier">str</span></dt><dd><p>Huggingface model directory containing the pretrained model for BERT
by default “distilbert-base-uncased” [2]</p>
</dd>
<dt>num_classes<span class="classifier">int, optional</span></dt><dd><p>Number of prediction classes, by default 2</p>
</dd>
<dt>dropout_rate<span class="classifier">float, optional</span></dt><dd><p>Dropout rate for the embeddings of bert, helps in fine tuning, by default 0.2</p>
</dd>
<dt>num_train_layers<span class="classifier">int, optional</span></dt><dd><p>Number of Bert layers to fine-tune. Minimum number is 1, by default 1</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.bert.BertClassifier.fit">
<span class="sig-name descname"><span class="pre">fit</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">Dataset</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">list</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></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">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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>, <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">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</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">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</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">0.001</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.bert.BertClassifier.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit the model on the training data.</p>
<p>Fine tunes a pre-trained BERT model on an input Sequence[str] by tokenizing the
inputs and then fine tuning the last few layers of BERT and the classifier.</p>
<section id="id21">
<h4>Parameters<a class="headerlink" href="#id21" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x_train<span class="classifier">Dataset[str]</span></dt><dd><p>Training data set of sentences or list[str] to be classified</p>
</dd>
<dt>y_train<span class="classifier">torch.Tensor</span></dt><dd><p>Data Labels</p>
</dd>
<dt>sample_weight<span class="classifier">torch.Tensor, optional</span></dt><dd><p>Weights associated with each data point, must be passed in as key word arg,
by default None</p>
</dd>
<dt>batch_size<span class="classifier">int, optional</span></dt><dd><p>Training batch size, by default 2</p>
</dd>
<dt>epochs<span class="classifier">int, optional</span></dt><dd><p>Number of training epochs, by default 1</p>
</dd>
<dt>lr<span class="classifier">float, optional</span></dt><dd><p>Learning rate for the Model, by default 0.01</p>
</dd>
</dl>
</section>
<section id="id22">
<h4>Returns<a class="headerlink" href="#id22" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>self<span class="classifier">object</span></dt><dd><p>Trained BERT classifier</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.bert.BertClassifier.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">input_ids</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">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</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.model.bert.BertClassifier.forward" title="Link to this definition">#</a></dt>
<dd><p>Forward pass through DistilBert with inputs from DistilBERT tokenizer output.</p>
<p>NOTE this is only applicable for a DistilBERT model that doesn’t require
<code class="docutils literal notranslate"><span class="pre">token_type_ids</span></code>.</p>
<section id="id23">
<h4>Parameters<a class="headerlink" href="#id23" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>input_ids<span class="classifier">torch.Tensor</span></dt><dd><p>List of token ids to be fed to a model.
[Input IDs?](<a class="reference external" href="https://huggingface.co/transformers/glossary#input-ids">https://huggingface.co/transformers/glossary#input-ids</a>)</p>
</dd>
<dt>attention_mask<span class="classifier">torch.Tensor</span></dt><dd><p>List of indices specifying which tokens should be attended to by the model,
by default None
[Attention?](<a class="reference external" href="https://huggingface.co/transformers/glossary#attention-mask">https://huggingface.co/transformers/glossary#attention-mask</a>)</p>
</dd>
</dl>
</section>
<section id="id24">
<h4>Returns<a class="headerlink" href="#id24" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Predicted labels for the classification problem</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.bert.BertClassifier.predict">
<span class="sig-name descname"><span class="pre">predict</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">Dataset</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">list</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></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.bert.BertClassifier.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict output from input sentences/tokens.</p>
<section id="id25">
<h4>Parameters<a class="headerlink" href="#id25" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>x<span class="classifier">Dataset[str | list[str]]</span></dt><dd><p>Input data set of sentences or list[str]</p>
</dd>
</dl>
</section>
<section id="id26">
<h4>Returns<a class="headerlink" href="#id26" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>torch.Tensor</dt><dd><p>Predicted labels as a tensor</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.bert.BertClassifier.tokenize">
<span class="sig-name descname"><span class="pre">tokenize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sentences</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">list</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></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">TensorDataset</span></span></span><a class="headerlink" href="#opendataval.model.bert.BertClassifier.tokenize" title="Link to this definition">#</a></dt>
<dd><p>Convert sequence of sentences or tokens into DistilBERT inputs.</p>
<p>Given a sequence of sentences or tokens, computes the <code class="docutils literal notranslate"><span class="pre">input_ids</span></code>,
and <code class="docutils literal notranslate"><span class="pre">attention_masks</span></code> and loads them on their respective
tensor device. Any changes made to the tokenizer should be reflected here
and the <cite>.forward()</cite> method.</p>
<section id="id27">
<h4>Parameters<a class="headerlink" href="#id27" title="Link to this heading">#</a></h4>
<dl class="simple">
<dt>sentences<span class="classifier">Sequence[str | list[str]]</span></dt><dd><p>Sequence of sentences or tokens to be transformed into inputs for BERT.</p>
</dd>
</dl>
</section>
<section id="id28">
<h4>Returns<a class="headerlink" href="#id28" title="Link to this heading">#</a></h4>
<dl>
<dt>TensorDataset</dt><dd><p>2 tensors representing <code class="docutils literal notranslate"><span class="pre">input_ids</span></code> and <code class="docutils literal notranslate"><span class="pre">attention_masks</span></code>.
For more in-depth on what each these represent:</p>
<ul class="simple">
<li><dl class="simple">
<dt><strong>input_ids</strong> – List of token ids to be fed to a model.</dt><dd><p>[Input IDs?](<a class="reference external" href="https://huggingface.co/transformers/glossary#input-ids">https://huggingface.co/transformers/glossary#input-ids</a>)</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><strong>attention_mask</strong> – List of indices specifying which tokens should</dt><dd><p>be attended to by the model (when <cite>return_attention_mask=True</cite> or if
<em>“attention_mask”</em> is in <cite>self.model_input_names</cite>).
[Mask?](<a class="reference external" href="https://huggingface.co/transformers/glossary#attention-mask">https://huggingface.co/transformers/glossary#attention-mask</a>)</p>
</dd>
</dl>
</li>
</ul>
<p>If using a non-DistilBert tokenizer, see the below. The token type ids
aren’t needed for DistilBert models.
- <strong>token_type_ids</strong> – List of token type ids to be fed to a model</p>
<blockquote>
<div><p>(when <cite>return_token_type_ids=True</cite> or if <em>“token_type_ids”</em> is in
<cite>self.model_input_names</cite>).
[Type IDs?](<a class="reference external" href="https://huggingface.co/transformers/glossary#token-type-ids">https://huggingface.co/transformers/glossary#token-type-ids</a>)</p>
</div></blockquote>
</dd>
</dl>
</section>
</dd></dl>
</section>
</dd></dl>
</section>
<section id="module-opendataval.model.grad">
<span id="opendataval-model-grad-module"></span><h2>opendataval.model.grad module<a class="headerlink" href="#module-opendataval.model.grad" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.grad.GradientModel">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.grad.</span></span><span class="sig-name descname"><span class="pre">GradientModel</span></span><a class="headerlink" href="#opendataval.model.grad.GradientModel" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.Model" title="opendataval.model.api.Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">Model</span></code></a></p>
<p>Provides access to gradients of a <a class="reference internal" href="#opendataval.model.api.Model" title="opendataval.model.api.Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">Model</span></code></a></p>
<p>TODO Some data evaluators may benefit from higher-order gradients or hessians.</p>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.grad.GradientModel.grad">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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 class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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">Iterator</span><span class="p"><span class="pre">[</span></span><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="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#opendataval.model.grad.GradientModel.grad" title="Link to this definition">#</a></dt>
<dd><p>Given input data, iterates through the computed gradients of the model.</p>
<p>Will yield a tuple with gradients for each layer of the model for each input
data. The data the underlying model is trained on does not have to be the data
the gradients of the model are computed for. An iterator is used because
storing the computed gradient for each data point use up lots of memory.</p>
<section id="id29">
<h3>Parameters<a class="headerlink" href="#id29" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x_data<span class="classifier">Union[torch.Tensor, Dataset]</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_data<span class="classifier">Union[torch.Tensor, Dataset]</span></dt><dd><p>Data labels</p>
</dd>
</dl>
</section>
<section id="yields">
<h3>Yields<a class="headerlink" href="#yields" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>Iterator[tuple[torch.Tensor, …]]</dt><dd><p>Computed gradients (for each layer as tuple) yielded by data point in order</p>
</dd>
</dl>
</section>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.grad.TorchGradMixin">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.grad.</span></span><span class="sig-name descname"><span class="pre">TorchGradMixin</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.model.grad.TorchGradMixin" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.grad.GradientModel" title="opendataval.model.grad.GradientModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientModel</span></code></a>, <a class="reference internal" href="#opendataval.model.api.TorchModel" title="opendataval.model.api.TorchModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">TorchModel</span></code></a></p>
<p>Gradient Mixin for Torch Neural Networks.</p>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.grad.TorchGradMixin.grad">
<span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dataset</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">Iterator</span><span class="p"><span class="pre">[</span></span><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="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#opendataval.model.grad.TorchGradMixin.grad" title="Link to this definition">#</a></dt>
<dd><p>Given input data, yields the computed gradients for a torch model</p>
<section id="id30">
<h3>Parameters<a class="headerlink" href="#id30" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>x_data<span class="classifier">Union[torch.Tensor, Dataset]</span></dt><dd><p>Data covariates</p>
</dd>
<dt>y_data<span class="classifier">Union[torch.Tensor, Dataset]</span></dt><dd><p>Data labels</p>
</dd>
</dl>
</section>
<section id="id31">
<h3>Yields<a class="headerlink" href="#id31" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>Iterator[tuple[torch.Tensor, …]]</dt><dd><p>Computed gradients (for each layer as tuple) yielded by data point in order</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="opendataval.model.grad.TorchGradMixin.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#opendataval.model.grad.TorchGradMixin.training" title="Link to this definition">#</a></dt>
<dd></dd></dl>
</dd></dl>
</section>
<section id="module-opendataval.model.lenet">
<span id="opendataval-model-lenet-module"></span><h2>opendataval.model.lenet module<a class="headerlink" href="#module-opendataval.model.lenet" title="Link to this heading">#</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="opendataval.model.lenet.LeNet">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">opendataval.model.lenet.</span></span><span class="sig-name descname"><span class="pre">LeNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_classes</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">gray_scale</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">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.lenet.LeNet" title="Link to this definition">#</a></dt>
<dd><p>Bases: <a class="reference internal" href="#opendataval.model.api.TorchClassMixin" title="opendataval.model.api.TorchClassMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">TorchClassMixin</span></code></a>, <a class="reference internal" href="#opendataval.model.api.TorchPredictMixin" title="opendataval.model.api.TorchPredictMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">TorchPredictMixin</span></code></a></p>
<p>LeNet-5 convolutional neural net classifier.</p>
<p>Consists of 2 5x5 convolution kernels and a MLP classifier. LeNet-5 was one of the
earliest conceived CNNs and was typically applied to digit analysis. LeNet-5 can but
doesn’t generalize particularly well to higher dimension (such as color) images.</p>
<section id="id32">
<h3>References<a class="headerlink" href="#id32" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id33" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner,
Gradient-based learning applied to document recognition,
Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998,
doi: <a class="reference external" href="https://doi.org/10.1109/5.726791">https://doi.org/10.1109/5.726791</a>.</p>
</aside>
</aside>
</section>
<section id="id34">
<h3>Parameters<a class="headerlink" href="#id34" title="Link to this heading">#</a></h3>
<dl class="simple">
<dt>num_classes<span class="classifier">int</span></dt><dd><p>Number of prediction classes</p>
</dd>
<dt>gray_scale<span class="classifier">bool, optional</span></dt><dd><p>Whether the input image is gray scaled. LeNet has been noted to not perform
as well with color, so disable gray_scale at your own risk, by default True</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="opendataval.model.lenet.LeNet.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><span class="sig-paren">)</span><a class="headerlink" href="#opendataval.model.lenet.LeNet.forward" title="Link to this definition">#</a></dt>
<dd><p>Forward pass of LeNet-5.</p>
</dd></dl>
</section>