Update latest version of site

pytorch · Sep 17, 2024 · fb133b3 · fb133b3
1 parent 0c7cb2d
commit fb133b3
Show file tree

Hide file tree

Showing 11 changed files with 77 additions and 87 deletions.
diff --git a/v/latest/api/_modules/botorch/models/approximate_gp.html b/v/latest/api/_modules/botorch/models/approximate_gp.html
@@ -54,10 +54,10 @@ <h1>Source code for botorch.models.approximate_gp</h1><div class="highlight"><pr
 
 <span class="kn">import</span> <span class="nn">copy</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">TypeVar</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">botorch.acquisition.objective</span> <span class="kn">import</span> <span class="n">PosteriorTransform</span>
 <span class="kn">from</span> <span class="nn">botorch.exceptions.warnings</span> <span class="kn">import</span> <span class="n">UserInputWarning</span>
 <span class="kn">from</span> <span class="nn">botorch.models.gpytorch</span> <span class="kn">import</span> <span class="n">GPyTorchModel</span>
 <span class="kn">from</span> <span class="nn">botorch.models.transforms.input</span> <span class="kn">import</span> <span class="n">InputTransform</span>
@@ -91,9 +91,9 @@ <h1>Source code for botorch.models.approximate_gp</h1><div class="highlight"><pr
 <span class="p">)</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
 <span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">Module</span>
+<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">Self</span>
 
 
-<span class="n">TApproxModel</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"TApproxModel"</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="s2">"ApproximateGPyTorchModel"</span><span class="p">)</span>
 <span class="n">TRANSFORM_WARNING</span> <span class="o">=</span> <span class="p">(</span>
     <span class="s2">"Using an </span><span class="si">{ttype}</span><span class="s2"> transform with `SingleTaskVariationalGP`. If this "</span>
     <span class="s2">"model is trained in minibatches, a </span><span class="si">{ttype}</span><span class="s2"> transform with learnable "</span>
@@ -159,14 +159,14 @@ <h1>Source code for botorch.models.approximate_gp</h1><div class="highlight"><pr
 
 <div class="viewcode-block" id="ApproximateGPyTorchModel.eval">
 <a class="viewcode-back" href="../../../models.html#botorch.models.approximate_gp.ApproximateGPyTorchModel.eval">[docs]</a>
-    <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">TApproxModel</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TApproxModel</span><span class="p">:</span>
+    <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Self</span><span class="p">:</span>
 <span class="w">        </span><span class="sa">r</span><span class="sd">"""Puts the model in `eval` mode."""</span>
         <span class="k">return</span> <span class="n">Module</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="ApproximateGPyTorchModel.train">
 <a class="viewcode-back" href="../../../models.html#botorch.models.approximate_gp.ApproximateGPyTorchModel.train">[docs]</a>
-    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">TApproxModel</span><span class="p">,</span> <span class="n">mode</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TApproxModel</span><span class="p">:</span>
+    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mode</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Self</span><span class="p">:</span>
 <span class="w">        </span><span class="sa">r</span><span class="sd">"""Put the model in `train` mode.</span>
 
 <span class="sd">        Args:</span>
@@ -179,8 +179,16 @@ <h1>Source code for botorch.models.approximate_gp</h1><div class="highlight"><pr
 <div class="viewcode-block" id="ApproximateGPyTorchModel.posterior">
 <a class="viewcode-back" href="../../../models.html#botorch.models.approximate_gp.ApproximateGPyTorchModel.posterior">[docs]</a>
     <span class="k">def</span> <span class="nf">posterior</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">output_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">observation_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">X</span><span class="p">,</span>
+        <span class="n">output_indices</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">observation_noise</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">posterior_transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">PosteriorTransform</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">GPyTorchPosterior</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">output_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>  <span class="c1"># pragma: no cover</span>
+                <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.posterior does not support output indices."</span>
+            <span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>  <span class="c1"># make sure model is in eval mode</span>
 
         <span class="c1"># input transforms are applied at `posterior` in `eval` mode, and at</span>
@@ -194,11 +202,13 @@ <h1>Source code for botorch.models.approximate_gp</h1><div class="highlight"><pr
             <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">X_ndim</span> <span class="o">-</span> <span class="mi">2</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_outputs</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">observation_noise</span><span class="p">:</span>
-            <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">likelihood</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">likelihood</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
 
         <span class="n">posterior</span> <span class="o">=</span> <span class="n">GPyTorchPosterior</span><span class="p">(</span><span class="n">distribution</span><span class="o">=</span><span class="n">dist</span><span class="p">)</span>
         <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">"outcome_transform"</span><span class="p">):</span>
             <span class="n">posterior</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">outcome_transform</span><span class="o">.</span><span class="n">untransform_posterior</span><span class="p">(</span><span class="n">posterior</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">posterior_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">posterior</span> <span class="o">=</span> <span class="n">posterior_transform</span><span class="p">(</span><span class="n">posterior</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">posterior</span></div>