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<div class="section" id="dscript-commands">
<h1>dscript.commands<a class="headerlink" href="#dscript-commands" title="Permalink to this headline">¶</a></h1>
<div class="section" id="dscript-commands-predict">
<h2>dscript.commands.predict<a class="headerlink" href="#dscript-commands-predict" title="Permalink to this headline">¶</a></h2>
<p>See <a class="reference external" href="../usage.html#prediction">Prediction</a> for full usage details.</p>
<span class="target" id="module-dscript.commands.predict"></span><p>Make new predictions with a pre-trained model. One of –seqs or –embeddings is required.</p>
</div>
<div class="section" id="dscript-commands-embed">
<h2>dscript.commands.embed<a class="headerlink" href="#dscript-commands-embed" title="Permalink to this headline">¶</a></h2>
<p>See <a class="reference external" href="../usage.html#embedding">Embedding</a> for full usage details.</p>
<span class="target" id="module-dscript.commands.embed"></span><p>Generate new embeddings using pre-trained language model.</p>
</div>
<div class="section" id="dscript-commands-train">
<h2>dscript.commands.train<a class="headerlink" href="#dscript-commands-train" title="Permalink to this headline">¶</a></h2>
<p>See <a class="reference external" href="../usage.html#training">Training</a> for full usage details.</p>
<span class="target" id="module-dscript.commands.train"></span><p>Train a new model.</p>
<dl class="py function">
<dt id="dscript.commands.train.interaction_eval">
<code class="sig-prename descclassname"><span class="pre">dscript.commands.train.</span></code><code class="sig-name descname"><span class="pre">interaction_eval</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_iterator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/commands/train.html#interaction_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.commands.train.interaction_eval" title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluate test data set performance.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="dscript.models.html#dscript.models.interaction.ModelInteraction" title="dscript.models.interaction.ModelInteraction"><em>dscript.models.interaction.ModelInteraction</em></a>) – Model to be trained</p></li>
<li><p><strong>test_iterator</strong> (<em>torch.utils.data.DataLoader</em>) – Test data iterator</p></li>
<li><p><strong>tensors</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>torch.Tensor</em><em>]</em>) – Dictionary of protein names to embeddings</p></li>
<li><p><strong>use_cuda</strong> (<em>bool</em>) – Whether to use GPU</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(Loss, number correct, mean square error, precision, recall, F1 Score, AUPR)</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>(torch.Tensor, int, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor)</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="dscript.commands.train.interaction_grad">
<code class="sig-prename descclassname"><span class="pre">dscript.commands.train.</span></code><code class="sig-name descname"><span class="pre">interaction_grad</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.35</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/commands/train.html#interaction_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.commands.train.interaction_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute gradient and backpropagate loss for a batch.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="dscript.models.html#dscript.models.interaction.ModelInteraction" title="dscript.models.interaction.ModelInteraction"><em>dscript.models.interaction.ModelInteraction</em></a>) – Model to be trained</p></li>
<li><p><strong>n0</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – First protein names</p></li>
<li><p><strong>n1</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – Second protein names</p></li>
<li><p><strong>y</strong> (<em>torch.Tensor</em>) – Interaction labels</p></li>
<li><p><strong>tensors</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>torch.Tensor</em><em>]</em>) – Dictionary of protein names to embeddings</p></li>
<li><p><strong>use_cuda</strong> (<em>bool</em>) – Whether to use GPU</p></li>
<li><p><strong>weight</strong> (<em>float</em>) – Weight on the contact map magnitude objective. BCE loss is <span class="math notranslate nohighlight">\(1 - \text{weight}\)</span>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(Loss, number correct, mean square error, batch size)</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>(torch.Tensor, int, torch.Tensor, int)</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="dscript.commands.train.predict_cmap_interaction">
<code class="sig-prename descclassname"><span class="pre">dscript.commands.train.</span></code><code class="sig-name descname"><span class="pre">predict_cmap_interaction</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/commands/train.html#predict_cmap_interaction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.commands.train.predict_cmap_interaction" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict whether a list of protein pairs will interact, as well as their contact map.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="dscript.models.html#dscript.models.interaction.ModelInteraction" title="dscript.models.interaction.ModelInteraction"><em>dscript.models.interaction.ModelInteraction</em></a>) – Model to be trained</p></li>
<li><p><strong>n0</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – First protein names</p></li>
<li><p><strong>n1</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – Second protein names</p></li>
<li><p><strong>tensors</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>torch.Tensor</em><em>]</em>) – Dictionary of protein names to embeddings</p></li>
<li><p><strong>use_cuda</strong> (<em>bool</em>) – Whether to use GPU</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="dscript.commands.train.predict_interaction">
<code class="sig-prename descclassname"><span class="pre">dscript.commands.train.</span></code><code class="sig-name descname"><span class="pre">predict_interaction</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/commands/train.html#predict_interaction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.commands.train.predict_interaction" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict whether a list of protein pairs will interact.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="dscript.models.html#dscript.models.interaction.ModelInteraction" title="dscript.models.interaction.ModelInteraction"><em>dscript.models.interaction.ModelInteraction</em></a>) – Model to be trained</p></li>
<li><p><strong>n0</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – First protein names</p></li>
<li><p><strong>n1</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – Second protein names</p></li>
<li><p><strong>tensors</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>torch.Tensor</em><em>]</em>) – Dictionary of protein names to embeddings</p></li>
<li><p><strong>use_cuda</strong> (<em>bool</em>) – Whether to use GPU</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="dscript-commands-eval">
<h2>dscript.commands.eval<a class="headerlink" href="#dscript-commands-eval" title="Permalink to this headline">¶</a></h2>
<p>See <a class="reference external" href="../usage.html#evaluation">Evaluation</a> for full usage details.</p>
<span class="target" id="module-dscript.commands.eval"></span><p>Evaluate a trained model.</p>
<dl class="py function">
<dt id="dscript.commands.eval.plot_eval_predictions">
<code class="sig-prename descclassname"><span class="pre">dscript.commands.eval.</span></code><code class="sig-name descname"><span class="pre">plot_eval_predictions</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'figure'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/commands/eval.html#plot_eval_predictions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.commands.eval.plot_eval_predictions" title="Permalink to this definition">¶</a></dt>
<dd><p>Plot histogram of positive and negative predictions, precision-recall curve, and receiver operating characteristic curve.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>y</strong> (<em>np.ndarray</em>) – Labels</p></li>
<li><p><strong>phat</strong> (<em>np.ndarray</em>) – Predicted probabilities</p></li>
<li><p><strong>path</strong> (<em>str</em>) – File prefix for plots to be saved to [default: figure]</p></li>
</ul>
</dd>
</dl>
</dd></dl>
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