Update latest version of site

pytorch · Sep 5, 2024 · d0b6e80 · d0b6e80
1 parent 8ef2f38
commit d0b6e80
Show file tree

Hide file tree

Showing 4 changed files with 24 additions and 12 deletions.
diff --git a/v/latest/api/_modules/botorch/models/utils/assorted.html b/v/latest/api/_modules/botorch/models/utils/assorted.html
@@ -200,8 +200,10 @@ <h1>Source code for botorch.models.utils.assorted</h1><div class="highlight"><pr
         <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">Xmin</span> <span class="o">&lt;</span> <span class="o">-</span><span class="n">atol</span><span class="p">)</span> <span class="ow">or</span> <span class="n">torch</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">Xmax</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">atol</span><span class="p">):</span>
             <span class="n">msg</span> <span class="o">=</span> <span class="s2">"contained"</span>
         <span class="k">if</span> <span class="n">msg</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># NOTE: If you update this message, update the warning filters as well.</span>
+            <span class="c1"># See https://github.com/pytorch/botorch/pull/2508.</span>
             <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="sa">f</span><span class="s2">"Data (input features) not </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s2"> to the unit cube. "</span>
+                <span class="sa">f</span><span class="s2">"Data (input features) is not </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s2"> to the unit cube. "</span>
                 <span class="s2">"Please consider min-max scaling the input data."</span>
             <span class="p">)</span>
             <span class="k">if</span> <span class="n">raise_on_fail</span><span class="p">:</span>
@@ -233,9 +235,11 @@ <h1>Source code for botorch.models.utils.assorted</h1><div class="highlight"><pr
         <span class="n">mean_not_zero</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Ymean</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">atol_mean</span>
         <span class="k">if</span> <span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">mean_not_zero</span><span class="p">:</span>
+                <span class="c1"># NOTE: If you update this message, update the warning filters as well.</span>
+                <span class="c1"># See https://github.com/pytorch/botorch/pull/2508.</span>
                 <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">"Data (outcome observations) not standardized (mean = </span><span class="si">{</span><span class="n">Ymean</span><span class="si">}</span><span class="s2">). "</span>
-                    <span class="s2">"Please consider scaling the input to zero mean and unit variance."</span>
+                    <span class="sa">f</span><span class="s2">"Data (outcome observations) is not standardized (mean = </span><span class="si">{</span><span class="n">Ymean</span><span class="si">}</span><span class="s2">)."</span>
+                    <span class="s2">" Please consider scaling the input to zero mean and unit variance."</span>
                 <span class="p">)</span>
                 <span class="k">if</span> <span class="n">raise_on_fail</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="n">InputDataError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
@@ -244,8 +248,10 @@ <h1>Source code for botorch.models.utils.assorted</h1><div class="highlight"><pr
             <span class="n">Ystd</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">Y</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="n">std_not_one</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Ystd</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">atol_std</span>
             <span class="k">if</span> <span class="n">mean_not_zero</span> <span class="ow">or</span> <span class="n">std_not_one</span><span class="p">:</span>
+                <span class="c1"># NOTE: If you update this message, update the warning filters as well.</span>
+                <span class="c1"># See https://github.com/pytorch/botorch/pull/2508.</span>
                 <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="s2">"Data (outcome observations) not standardized "</span>
+                    <span class="s2">"Data (outcome observations) is not standardized "</span>
                     <span class="sa">f</span><span class="s2">"(std = </span><span class="si">{</span><span class="n">Ystd</span><span class="si">}</span><span class="s2">, mean = </span><span class="si">{</span><span class="n">Ymean</span><span class="si">}</span><span class="s2">)."</span>
                     <span class="s2">"Please consider scaling the input to zero mean and unit variance."</span>
                 <span class="p">)</span>

diff --git a/v/latest/api/_modules/botorch/models/utils/assorted/index.html b/v/latest/api/_modules/botorch/models/utils/assorted/index.html
@@ -200,8 +200,10 @@ <h1>Source code for botorch.models.utils.assorted</h1><div class="highlight"><pr
         <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">Xmin</span> <span class="o">&lt;</span> <span class="o">-</span><span class="n">atol</span><span class="p">)</span> <span class="ow">or</span> <span class="n">torch</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">Xmax</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">atol</span><span class="p">):</span>
             <span class="n">msg</span> <span class="o">=</span> <span class="s2">"contained"</span>
         <span class="k">if</span> <span class="n">msg</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># NOTE: If you update this message, update the warning filters as well.</span>
+            <span class="c1"># See https://github.com/pytorch/botorch/pull/2508.</span>
             <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="sa">f</span><span class="s2">"Data (input features) not </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s2"> to the unit cube. "</span>
+                <span class="sa">f</span><span class="s2">"Data (input features) is not </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s2"> to the unit cube. "</span>
                 <span class="s2">"Please consider min-max scaling the input data."</span>
             <span class="p">)</span>
             <span class="k">if</span> <span class="n">raise_on_fail</span><span class="p">:</span>
@@ -233,9 +235,11 @@ <h1>Source code for botorch.models.utils.assorted</h1><div class="highlight"><pr
         <span class="n">mean_not_zero</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Ymean</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">atol_mean</span>
         <span class="k">if</span> <span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">mean_not_zero</span><span class="p">:</span>
+                <span class="c1"># NOTE: If you update this message, update the warning filters as well.</span>
+                <span class="c1"># See https://github.com/pytorch/botorch/pull/2508.</span>
                 <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">"Data (outcome observations) not standardized (mean = </span><span class="si">{</span><span class="n">Ymean</span><span class="si">}</span><span class="s2">). "</span>
-                    <span class="s2">"Please consider scaling the input to zero mean and unit variance."</span>
+                    <span class="sa">f</span><span class="s2">"Data (outcome observations) is not standardized (mean = </span><span class="si">{</span><span class="n">Ymean</span><span class="si">}</span><span class="s2">)."</span>
+                    <span class="s2">" Please consider scaling the input to zero mean and unit variance."</span>
                 <span class="p">)</span>
                 <span class="k">if</span> <span class="n">raise_on_fail</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="n">InputDataError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
@@ -244,8 +248,10 @@ <h1>Source code for botorch.models.utils.assorted</h1><div class="highlight"><pr
             <span class="n">Ystd</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">Y</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="n">std_not_one</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Ystd</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">atol_std</span>
             <span class="k">if</span> <span class="n">mean_not_zero</span> <span class="ow">or</span> <span class="n">std_not_one</span><span class="p">:</span>
+                <span class="c1"># NOTE: If you update this message, update the warning filters as well.</span>
+                <span class="c1"># See https://github.com/pytorch/botorch/pull/2508.</span>
                 <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="s2">"Data (outcome observations) not standardized "</span>
+                    <span class="s2">"Data (outcome observations) is not standardized "</span>
                     <span class="sa">f</span><span class="s2">"(std = </span><span class="si">{</span><span class="n">Ystd</span><span class="si">}</span><span class="s2">, mean = </span><span class="si">{</span><span class="n">Ymean</span><span class="si">}</span><span class="s2">)."</span>
                     <span class="s2">"Please consider scaling the input to zero mean and unit variance."</span>
                 <span class="p">)</span>

diff --git a/v/latest/api/_modules/botorch/utils/testing.html b/v/latest/api/_modules/botorch/utils/testing.html
@@ -89,12 +89,12 @@ <h1>Source code for botorch.utils.testing</h1><div class="highlight"><pre>
             <span class="p">)</span>
             <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
                 <span class="s2">"ignore"</span><span class="p">,</span>
-                <span class="n">message</span><span class="o">=</span><span class="s2">"Data is not standardized."</span><span class="p">,</span>
+                <span class="n">message</span><span class="o">=</span><span class="sa">r</span><span class="s2">"Data \(outcome observations\) is not standardized "</span><span class="p">,</span>
                 <span class="n">category</span><span class="o">=</span><span class="n">InputDataWarning</span><span class="p">,</span>
             <span class="p">)</span>
             <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
                 <span class="s2">"ignore"</span><span class="p">,</span>
-                <span class="n">message</span><span class="o">=</span><span class="s2">"Input data is not contained to the unit cube."</span><span class="p">,</span>
+                <span class="n">message</span><span class="o">=</span><span class="sa">r</span><span class="s2">"Data \(input features\) is not"</span><span class="p">,</span>
                 <span class="n">category</span><span class="o">=</span><span class="n">InputDataWarning</span><span class="p">,</span>
             <span class="p">)</span></div>
 

diff --git a/v/latest/api/_modules/botorch/utils/testing/index.html b/v/latest/api/_modules/botorch/utils/testing/index.html
@@ -89,12 +89,12 @@ <h1>Source code for botorch.utils.testing</h1><div class="highlight"><pre>
             <span class="p">)</span>
             <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
                 <span class="s2">"ignore"</span><span class="p">,</span>
-                <span class="n">message</span><span class="o">=</span><span class="s2">"Data is not standardized."</span><span class="p">,</span>
+                <span class="n">message</span><span class="o">=</span><span class="sa">r</span><span class="s2">"Data \(outcome observations\) is not standardized "</span><span class="p">,</span>
                 <span class="n">category</span><span class="o">=</span><span class="n">InputDataWarning</span><span class="p">,</span>
             <span class="p">)</span>
             <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
                 <span class="s2">"ignore"</span><span class="p">,</span>
-                <span class="n">message</span><span class="o">=</span><span class="s2">"Input data is not contained to the unit cube."</span><span class="p">,</span>
+                <span class="n">message</span><span class="o">=</span><span class="sa">r</span><span class="s2">"Data \(input features\) is not"</span><span class="p">,</span>
                 <span class="n">category</span><span class="o">=</span><span class="n">InputDataWarning</span><span class="p">,</span>
             <span class="p">)</span></div>