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<section id="module-scpanel.GATclassifier">
<span id="scpanel-gatclassifier"></span><h1>scpanel.GATclassifier<a class="headerlink" href="#module-scpanel.GATclassifier" title="Link to this heading"></a></h1>
<section id="attributes">
<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#scpanel.GATclassifier.seed" title="scpanel.GATclassifier.seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</section>
<section id="classes">
<h2>Classes<a class="headerlink" href="#classes" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#scpanel.GATclassifier.ClusterData" title="scpanel.GATclassifier.ClusterData"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ClusterData</span></code></a></p></td>
<td><p>Clusters/partitions a graph data object into multiple subgraphs, as</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#scpanel.GATclassifier.ClusterLoader" title="scpanel.GATclassifier.ClusterLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ClusterLoader</span></code></a></p></td>
<td><p>The data loader scheme from the <a href="#id1"><span class="problematic" id="id2">`</span></a>"Cluster-GCN: An Efficient Algorithm</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#scpanel.GATclassifier.GAT" title="scpanel.GATclassifier.GAT"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GAT</span></code></a></p></td>
<td><p>Base class for all neural network modules.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#scpanel.GATclassifier.GATclassifier" title="scpanel.GATclassifier.GATclassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GATclassifier</span></code></a></p></td>
<td><p>A pytorch regressor</p></td>
</tr>
</tbody>
</table>
</section>
<section id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#scpanel.GATclassifier.scipysparse2torchsparse" title="scpanel.GATclassifier.scipysparse2torchsparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipysparse2torchsparse</span></code></a>(→ Tuple[torch.Tensor, ...)</p></td>
<td><p>Input: scipy csr_matrix</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-contents">
<h2>Module Contents<a class="headerlink" href="#module-contents" title="Link to this heading"></a></h2>
<dl class="py data">
<dt class="sig sig-object py" id="scpanel.GATclassifier.seed">
<span class="sig-prename descclassname"><span class="pre">scpanel.GATclassifier.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">42</span></em><a class="headerlink" href="#scpanel.GATclassifier.seed" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="scpanel.GATclassifier.scipysparse2torchsparse">
<span class="sig-prename descclassname"><span class="pre">scpanel.GATclassifier.</span></span><span class="sig-name descname"><span class="pre">scipysparse2torchsparse</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">scipy.sparse._csr.csr_matrix</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">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#scpanel.GATclassifier.scipysparse2torchsparse" title="Link to this definition"></a></dt>
<dd><p>Input: scipy csr_matrix
Returns: torch tensor in experimental sparse format</p>
<p>REF: Code adatped from [PyTorch discussion forum](<a class="reference external" href="https://discuss.pytorch.org/t/better-way-to-forward-sparse-matrix/21915">https://discuss.pytorch.org/t/better-way-to-forward-sparse-matrix/21915</a>>)</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">scpanel.GATclassifier.</span></span><span class="sig-name descname"><span class="pre">ClusterData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch_geometric.data.data.Data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_parts</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">recursive</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><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="#scpanel.GATclassifier.ClusterData" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code></p>
<p>Clusters/partitions a graph data object into multiple subgraphs, as
motivated by the <a class="reference external" href="https://arxiv.org/abs/1905.07953">“Cluster-GCN: An Efficient Algorithm for Training Deep
and Large Graph Convolutional Networks”</a> paper.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>torch_geometric.data.Data</em>) – The graph data object.</p></li>
<li><p><strong>num_parts</strong> (<em>int</em>) – The number of partitions.</p></li>
<li><p><strong>recursive</strong> (<em>bool</em><em>, </em><em>optional</em>) – If set to <code class="xref py py-obj docutils literal notranslate"><span class="pre">True</span></code>, will use multilevel
recursive bisection instead of multilevel k-way partitioning.
(default: <code class="xref py py-obj docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
<li><p><strong>save_dir</strong> (<em>string</em><em>, </em><em>optional</em>) – If set, will save the partitioned data to
the <a class="reference internal" href="#scpanel.GATclassifier.ClusterData.save_dir" title="scpanel.GATclassifier.ClusterData.save_dir"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_dir</span></code></a> directory for faster re-use.</p></li>
</ul>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.num_parts">
<span class="sig-name descname"><span class="pre">num_parts</span></span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.num_parts" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.recursive">
<span class="sig-name descname"><span class="pre">recursive</span></span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.recursive" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.save_dir">
<span class="sig-name descname"><span class="pre">save_dir</span></span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.save_dir" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.process">
<span class="sig-name descname"><span class="pre">process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch_geometric.data.data.Data</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.process" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.__len__">
<span class="sig-name descname"><span class="pre">__len__</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">int</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.__len__" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.__getitem__">
<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.__getitem__" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterData.__repr__">
<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.GATclassifier.ClusterData.__repr__" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="scpanel.GATclassifier.ClusterLoader">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">scpanel.GATclassifier.</span></span><span class="sig-name descname"><span class="pre">ClusterLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cluster_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#scpanel.GATclassifier.ClusterData" title="scpanel.GATclassifier.ClusterData"><span class="pre">ClusterData</span></a></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">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <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="#scpanel.GATclassifier.ClusterLoader" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></p>
<p>The data loader scheme from the <a class="reference external" href="https://arxiv.org/abs/1905.07953">“Cluster-GCN: An Efficient Algorithm
for Training Deep and Large Graph Convolutional Networks”</a> paper which merges partioned subgraphs
and their between-cluster links from a large-scale graph data object to
form a mini-batch.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cluster_data</strong> (<em>torch_geometric.data.ClusterData</em>) – The already
partioned data object.</p></li>
<li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – How many samples per batch to load.
(default: <code class="xref py py-obj docutils literal notranslate"><span class="pre">1</span></code>)</p></li>
<li><p><strong>shuffle</strong> (<em>bool</em><em>, </em><em>optional</em>) – If set to <code class="xref py py-obj docutils literal notranslate"><span class="pre">True</span></code>, the data will be
reshuffled at every epoch. (default: <code class="xref py py-obj docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">scpanel.GATclassifier.</span></span><span class="sig-name descname"><span class="pre">GAT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_nodes</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">nFeatures</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">nHiddenUnits</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">nHeads</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">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.GATclassifier.GAT" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
<p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
</pre></div>
</div>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call <code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> call to the parent class
must be made before assignment on the child.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Variables<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>training</strong> (<em>bool</em>) – Boolean represents whether this module is in training or
evaluation mode.</p>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.n_nodes">
<span class="sig-name descname"><span class="pre">n_nodes</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.n_nodes" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.nFeatures">
<span class="sig-name descname"><span class="pre">nFeatures</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.nFeatures" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.nHiddenUnits">
<span class="sig-name descname"><span class="pre">nHiddenUnits</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.nHiddenUnits" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.nHeads">
<span class="sig-name descname"><span class="pre">nHeads</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.nHeads" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.alpha">
<span class="sig-name descname"><span class="pre">alpha</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.alpha" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.dropout">
<span class="sig-name descname"><span class="pre">dropout</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.dropout" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.gat1">
<span class="sig-name descname"><span class="pre">gat1</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.gat1" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.gat2">
<span class="sig-name descname"><span class="pre">gat2</span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.gat2" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GAT.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">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch_geometric.data.data.Data</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">torch.Tensor</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.GAT.forward" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">scpanel.GATclassifier.</span></span><span class="sig-name descname"><span class="pre">GATclassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_nodes</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">nFeatures</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</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">nHiddenUnits</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">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nHeads</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">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</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">dropout</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">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><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">rs</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">random.randint(1,</span> <span class="pre">1000000)</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>, <em class="sig-param"><span class="n"><span class="pre">WeightDecay</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">0.0005</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BatchSize</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">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NumParts</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">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nEpochs</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">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fastmode</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>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</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">'cpu'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code></p>
<p>A pytorch regressor</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier._history">
<span class="sig-name descname"><span class="pre">_history</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier._history" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier._model">
<span class="sig-name descname"><span class="pre">_model</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier._model" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier._build_model">
<span class="sig-name descname"><span class="pre">_build_model</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">None</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier._build_model" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier._train_model">
<span class="sig-name descname"><span class="pre">_train_model</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">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">pandas.core.arrays.categorical.Categorical</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">scipy.sparse._csr.csr_matrix</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier._train_model" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier.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</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">pandas.core.arrays.categorical.Categorical</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">scipy.sparse._csr.csr_matrix</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#scpanel.GATclassifier.GATclassifier" title="scpanel.GATclassifier.GATclassifier"><span class="pre">GATclassifier</span></a></span></span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier.fit" title="Link to this definition"></a></dt>
<dd><p>Trains the pytorch regressor.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier.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">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">pandas.core.arrays.categorical.Categorical</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">scipy.sparse._csr.csr_matrix</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">torch.Tensor</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier.predict" title="Link to this definition"></a></dt>
<dd><p>Makes a prediction using the trained pytorch model</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</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">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">pandas.core.arrays.categorical.Categorical</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">scipy.sparse._csr.csr_matrix</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">numpy.ndarray</span></span></span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier.predict_proba" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.GATclassifier.GATclassifier.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</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">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.GATclassifier.GATclassifier.score" title="Link to this definition"></a></dt>
<dd><p>Scores the data using the trained pytorch model. Under current implementation
returns negative mae.</p>
</dd></dl>
</dd></dl>
</section>
</section>
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Datasets
Models
