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v/latest/api/_modules/botorch/acquisition/logei.html

@@ -502,9 +502,13 @@ <h1>Source code for botorch.acquisition.logei</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_root</span><span class="p">:</span>
             <span class="n">samples_full</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_posterior_samples</span><span class="p">(</span><span class="n">posterior</span><span class="p">)</span>
             <span class="n">obj_full</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span><span class="p">(</span><span class="n">samples_full</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X_full</span><span class="p">)</span>
+            <span class="c1"># Calculate the positive index for splitting the samples &amp; objective values.</span>
+            <span class="n">split_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">obj_full</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
             <span class="c1"># assigning baseline buffers so `best_f` can be computed in _sample_forward</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">baseline_samples</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="n">samples_full</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</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">baseline_obj</span><span class="p">,</span> <span class="n">obj</span> <span class="o">=</span> <span class="n">obj_full</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">baseline_samples</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="n">samples_full</span><span class="o">.</span><span class="n">split</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="n">split_dim</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">baseline_obj</span><span class="p">,</span> <span class="n">obj</span> <span class="o">=</span> <span class="n">obj_full</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="n">split_dim</span><span class="p">)</span>
             <span class="k">return</span> <span class="n">samples</span><span class="p">,</span> <span class="n">obj</span>
 
         <span class="c1"># handle one-to-many input transforms</span>

v/latest/api/_modules/botorch/acquisition/logei/index.html

@@ -502,9 +502,13 @@ <h1>Source code for botorch.acquisition.logei</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_root</span><span class="p">:</span>
             <span class="n">samples_full</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_posterior_samples</span><span class="p">(</span><span class="n">posterior</span><span class="p">)</span>
             <span class="n">obj_full</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span><span class="p">(</span><span class="n">samples_full</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X_full</span><span class="p">)</span>
+            <span class="c1"># Calculate the positive index for splitting the samples &amp; objective values.</span>
+            <span class="n">split_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">obj_full</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
             <span class="c1"># assigning baseline buffers so `best_f` can be computed in _sample_forward</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">baseline_samples</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="n">samples_full</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</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">baseline_obj</span><span class="p">,</span> <span class="n">obj</span> <span class="o">=</span> <span class="n">obj_full</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">baseline_samples</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="n">samples_full</span><span class="o">.</span><span class="n">split</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="n">split_dim</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">baseline_obj</span><span class="p">,</span> <span class="n">obj</span> <span class="o">=</span> <span class="n">obj_full</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="n">n_baseline</span><span class="p">,</span> <span class="n">q</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="n">split_dim</span><span class="p">)</span>
             <span class="k">return</span> <span class="n">samples</span><span class="p">,</span> <span class="n">obj</span>
 
         <span class="c1"># handle one-to-many input transforms</span>