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<div class="section" id="dscript-models">
<h1>dscript.models<a class="headerlink" href="#dscript-models" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-dscript.models.embedding">
<span id="dscript-models-embedding"></span><h2>dscript.models.embedding<a class="headerlink" href="#module-dscript.models.embedding" title="Permalink to this headline">¶</a></h2>
<p>Embedding model classes.</p>
<dl class="py class">
<dt id="dscript.models.embedding.FullyConnectedEmbed">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.embedding.</span></code><code class="sig-name descname"><span class="pre">FullyConnectedEmbed</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ReLU()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/embedding.html#FullyConnectedEmbed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.FullyConnectedEmbed" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Protein Projection Module. Takes embedding from language model and outputs low-dimensional interaction aware projection.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nin</strong> (<em>int</em>) – Size of language model output</p></li>
<li><p><strong>nout</strong> (<em>int</em>) – Dimension of projection</p></li>
<li><p><strong>dropout</strong> (<em>float</em>) – Proportion of weights to drop out [default: 0.5]</p></li>
<li><p><strong>activation</strong> (<em>torch.nn.Module</em>) – Activation for linear projection model</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="dscript.models.embedding.FullyConnectedEmbed.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/embedding.html#FullyConnectedEmbed.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.FullyConnectedEmbed.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> (<em>torch.Tensor</em>) – Input language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Low dimensional projection of embedding</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="dscript.models.embedding.IdentityEmbed">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.embedding.</span></code><code class="sig-name descname"><span class="pre">IdentityEmbed</span></code><a class="reference internal" href="../_modules/dscript/models/embedding.html#IdentityEmbed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.IdentityEmbed" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Does not reduce the dimension of the language model embeddings, just passes them through to the contact model.</p>
<dl class="py method">
<dt id="dscript.models.embedding.IdentityEmbed.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/embedding.html#IdentityEmbed.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.IdentityEmbed.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> (<em>torch.Tensor</em>) – Input language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Same embedding</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="dscript.models.embedding.SkipLSTM">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.embedding.</span></code><code class="sig-name descname"><span class="pre">SkipLSTM</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</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="reference internal" href="../_modules/dscript/models/embedding.html#SkipLSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.SkipLSTM" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Language model from <a class="reference external" href="https://github.com/tbepler/protein-sequence-embedding-iclr2019">Bepler & Berger</a>.</p>
<p>Loaded with pre-trained weights in embedding function.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nin</strong> (<em>int</em>) – Input dimension of amino acid one-hot [default: 21]</p></li>
<li><p><strong>nout</strong> (<em>int</em>) – Output dimension of final layer [default: 100]</p></li>
<li><p><strong>hidden_dim</strong> (<em>int</em>) – Size of hidden dimension [default: 1024]</p></li>
<li><p><strong>num_layers</strong> (<em>int</em>) – Number of stacked LSTM models [default: 3]</p></li>
<li><p><strong>dropout</strong> (<em>float</em>) – Proportion of weights to drop out [default: 0]</p></li>
<li><p><strong>bidirectional</strong> (<em>bool</em>) – Whether to use biLSTM vs. LSTM</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="dscript.models.embedding.SkipLSTM.to_one_hot">
<code class="sig-name descname"><span class="pre">to_one_hot</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/embedding.html#SkipLSTM.to_one_hot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.SkipLSTM.to_one_hot" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform numeric encoded amino acid vector to one-hot encoded vector</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> (<em>torch.Tensor</em>) – Input numeric amino acid encoding <span class="math notranslate nohighlight">\((N)\)</span></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>One-hot encoding vector <span class="math notranslate nohighlight">\((N \times n_{in})\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="dscript.models.embedding.SkipLSTM.transform">
<code class="sig-name descname"><span class="pre">transform</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/embedding.html#SkipLSTM.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.embedding.SkipLSTM.transform" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> (<em>torch.Tensor</em>) – Input numeric amino acid encoding <span class="math notranslate nohighlight">\((N)\)</span></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Concatenation of all hidden layers <span class="math notranslate nohighlight">\((N \times (n_{in} + 2 \times \text{num_layers} \times \text{hidden_dim}))\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-dscript.models.contact">
<span id="dscript-models-contact"></span><h2>dscript.models.contact<a class="headerlink" href="#module-dscript.models.contact" title="Permalink to this headline">¶</a></h2>
<p>Contact model classes.</p>
<dl class="py class">
<dt id="dscript.models.contact.ContactCNN">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.contact.</span></code><code class="sig-name descname"><span class="pre">ContactCNN</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Sigmoid()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/contact.html#ContactCNN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.contact.ContactCNN" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Residue Contact Prediction Module. Takes embeddings from Projection module and produces contact map, output of Contact module.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>embed_dim</strong> (<em>int</em>) – <p>Output dimension of <a class="reference external" href="#module-dscript.models.embedding">dscript.models.embedding</a> model <span class="math notranslate nohighlight">\(d\)</span> [default: 100]</p>
</p></li>
<li><p><strong>hidden_dim</strong> (<em>int</em>) – Hidden dimension <span class="math notranslate nohighlight">\(h\)</span> [default: 50]</p></li>
<li><p><strong>width</strong> (<em>int</em>) – Width of convolutional filter <span class="math notranslate nohighlight">\(2w+1\)</span> [default: 7]</p></li>
<li><p><strong>activation</strong> (<em>torch.nn.Module</em>) – Activation function for final contact map [default: torch.nn.Sigmoid()]</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="dscript.models.contact.ContactCNN.broadcast">
<code class="sig-name descname"><span class="pre">broadcast</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/contact.html#ContactCNN.broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.contact.ContactCNN.broadcast" title="Permalink to this definition">¶</a></dt>
<dd><p>Calls <a class="reference external" href="#module-dscript.models.contact.FullyConnected">dscript.models.contact.FullyConnected</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>z0</strong> (<em>torch.Tensor</em>) – Projection module embedding <span class="math notranslate nohighlight">\((b \times N \times d)\)</span></p></li>
<li><p><strong>z1</strong> (<em>torch.Tensor</em>) – Projection module embedding <span class="math notranslate nohighlight">\((b \times M \times d)\)</span></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted contact broadcast tensor <span class="math notranslate nohighlight">\((b \times N \times M \times h)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="dscript.models.contact.ContactCNN.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/contact.html#ContactCNN.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.contact.ContactCNN.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>z0</strong> (<em>torch.Tensor</em>) – Projection module embedding <span class="math notranslate nohighlight">\((b \times N \times d)\)</span></p></li>
<li><p><strong>z1</strong> (<em>torch.Tensor</em>) – Projection module embedding <span class="math notranslate nohighlight">\((b \times M \times d)\)</span></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted contact map <span class="math notranslate nohighlight">\((b \times N \times M)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="dscript.models.contact.ContactCNN.predict">
<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">B</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/contact.html#ContactCNN.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.contact.ContactCNN.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict contact map from broadcast tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>B</strong> (<em>torch.Tensor</em>) – Predicted contact broadcast <span class="math notranslate nohighlight">\((b \times N \times M \times h)\)</span></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted contact map <span class="math notranslate nohighlight">\((b \times N \times M)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="dscript.models.contact.FullyConnected">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.contact.</span></code><code class="sig-name descname"><span class="pre">FullyConnected</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ReLU()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/contact.html#FullyConnected"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.contact.FullyConnected" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Performs part 1 of Contact Prediction Module. Takes embeddings from Projection module and produces broadcast tensor.</p>
<p>Input embeddings of dimension <span class="math notranslate nohighlight">\(d\)</span> are combined into a <span class="math notranslate nohighlight">\(2d\)</span> length MLP input <span class="math notranslate nohighlight">\(z_{cat}\)</span>, where <span class="math notranslate nohighlight">\(z_{cat} = [z_0 \ominus z_1 | z_0 \odot z_1]\)</span></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>embed_dim</strong> (<em>int</em>) – <p>Output dimension of <a class="reference external" href="#module-dscript.models.embedding">dscript.models.embedding</a> model <span class="math notranslate nohighlight">\(d\)</span> [default: 100]</p>
</p></li>
<li><p><strong>hidden_dim</strong> (<em>int</em>) – Hidden dimension <span class="math notranslate nohighlight">\(h\)</span> [default: 50]</p></li>
<li><p><strong>activation</strong> (<em>torch.nn.Module</em>) – Activation function for broadcast tensor [default: torch.nn.ReLU()]</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="dscript.models.contact.FullyConnected.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/contact.html#FullyConnected.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.contact.FullyConnected.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>z0</strong> (<em>torch.Tensor</em>) – Projection module embedding <span class="math notranslate nohighlight">\((b \times N \times d)\)</span></p></li>
<li><p><strong>z1</strong> (<em>torch.Tensor</em>) – Projection module embedding <span class="math notranslate nohighlight">\((b \times M \times d)\)</span></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted broadcast tensor <span class="math notranslate nohighlight">\((b \times N \times M \times h)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-dscript.models.interaction">
<span id="dscript-models-interaction"></span><h2>dscript.models.interaction<a class="headerlink" href="#module-dscript.models.interaction" title="Permalink to this headline">¶</a></h2>
<p>Interaction model classes.</p>
<dl class="py class">
<dt id="dscript.models.interaction.LogisticActivation">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.interaction.</span></code><code class="sig-name descname"><span class="pre">LogisticActivation</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/interaction.html#LogisticActivation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.LogisticActivation" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Implementation of Generalized Sigmoid
Applies the element-wise function:</p>
<p><span class="math notranslate nohighlight">\(\sigma(x) = \frac{1}{1 + \exp(-k(x-x_0))}\)</span></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x0</strong> (<em>float</em>) – The value of the sigmoid midpoint</p></li>
<li><p><strong>k</strong> (<em>float</em>) – The slope of the sigmoid - trainable - <span class="math notranslate nohighlight">\(k \geq 0\)</span></p></li>
<li><p><strong>train</strong> (<em>bool</em>) – Whether <span class="math notranslate nohighlight">\(k\)</span> is a trainable parameter</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="dscript.models.interaction.LogisticActivation.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/interaction.html#LogisticActivation.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.LogisticActivation.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the function to the input elementwise</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> (<em>torch.Tensor</em>) – <span class="math notranslate nohighlight">\((N \times *)\)</span> where <span class="math notranslate nohighlight">\(*\)</span> means, any number of additional dimensions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><span class="math notranslate nohighlight">\((N \times *)\)</span>, same shape as the input</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="dscript.models.interaction.ModelInteraction">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">dscript.models.interaction.</span></code><code class="sig-name descname"><span class="pre">ModelInteraction</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contact</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_W</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="reference internal" href="../_modules/dscript/models/interaction.html#ModelInteraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.ModelInteraction" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<p>Main D-SCRIPT model. Contains an embedding and contact model and offers access to those models. Computes pooling operations on contact map to generate interaction probability.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>embedding</strong> (<a class="reference internal" href="#dscript.models.embedding.FullyConnectedEmbed" title="dscript.models.embedding.FullyConnectedEmbed"><em>dscript.models.embedding.FullyConnectedEmbed</em></a>) – Embedding model</p></li>
<li><p><strong>contact</strong> (<a class="reference internal" href="#dscript.models.contact.ContactCNN" title="dscript.models.contact.ContactCNN"><em>dscript.models.contact.ContactCNN</em></a>) – Contact model</p></li>
<li><p><strong>use_cuda</strong> (<em>bool</em>) – Whether the model should be run on GPU</p></li>
<li><p><strong>pool_size</strong> (<em>bool</em>) – width of max-pool [default 9]</p></li>
<li><p><strong>theta_init</strong> (<em>float</em>) – initialization value of <span class="math notranslate nohighlight">\(\theta\)</span> for weight matrix [default: 1]</p></li>
<li><p><strong>lambda_init</strong> (<em>float</em>) – initialization value of <span class="math notranslate nohighlight">\(\lambda\)</span> for weight matrix [default: 0]</p></li>
<li><p><strong>gamma_init</strong> (<em>float</em>) – initialization value of <span class="math notranslate nohighlight">\(\gamma\)</span> for global pooling [default: 0]</p></li>
<li><p><strong>use_W</strong> (<em>bool</em>) – whether to use the weighting matrix [default: True]</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="dscript.models.interaction.ModelInteraction.cpred">
<code class="sig-name descname"><span class="pre">cpred</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/interaction.html#ModelInteraction.cpred"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.ModelInteraction.cpred" title="Permalink to this definition">¶</a></dt>
<dd><p>Project down input language model embeddings into low dimension using projection module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>z0</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p></li>
<li><p><strong>z1</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted contact map <span class="math notranslate nohighlight">\((b \times N \times M)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="dscript.models.interaction.ModelInteraction.embed">
<code class="sig-name descname"><span class="pre">embed</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/interaction.html#ModelInteraction.embed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.ModelInteraction.embed" title="Permalink to this definition">¶</a></dt>
<dd><p>Project down input language model embeddings into low dimension using projection module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>z</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>D-SCRIPT projection <span class="math notranslate nohighlight">\((b \times N \times d)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="dscript.models.interaction.ModelInteraction.map_predict">
<code class="sig-name descname"><span class="pre">map_predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/interaction.html#ModelInteraction.map_predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.ModelInteraction.map_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Project down input language model embeddings into low dimension using projection module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>z0</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p></li>
<li><p><strong>z1</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted contact map, predicted probability of interaction <span class="math notranslate nohighlight">\((b \times N \times d_0), (1)\)</span></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor, torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="dscript.models.interaction.ModelInteraction.predict">
<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dscript/models/interaction.html#ModelInteraction.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dscript.models.interaction.ModelInteraction.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Project down input language model embeddings into low dimension using projection module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>z0</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p></li>
<li><p><strong>z1</strong> (<em>torch.Tensor</em>) – Language model embedding <span class="math notranslate nohighlight">\((b \times N \times d_0)\)</span></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Predicted probability of interaction</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor, torch.Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
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