benjamc: Fix merge errors

development/_modules/smac/acquisition/maximizer/local_and_random_search.html

@@ -1044,6 +1044,7 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
 <span class="k">class</span> <span class="nc">LocalAndSortedRandomSearch</span><span class="p">(</span><span class="n">AbstractAcquisitionMaximizer</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Implement SMAC&#39;s default acquisition function optimization.</span>
 
+<span class="sd">    This optimizer performs local search from the previous best points according to the acquisition</span>
 <span class="sd">    This optimizer performs local search from the previous best points according to the acquisition</span>
 <span class="sd">    function, uses the acquisition function to sort randomly sampled configurations.</span>
 <span class="sd">    Random configurations are interleaved by the main SMAC code.</span>
@@ -1058,6 +1059,10 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
 <span class="sd">        A version of the user-defined ConfigurationSpace where all parameters are uniform (or have their weights removed</span>
 <span class="sd">        in the case of a categorical hyperparameter). Can optionally be given and sampling ratios be defined via the</span>
 <span class="sd">        `prior_sampling_fraction` parameter.</span>
+<span class="sd">    uniform_configspace : ConfigurationSpace</span>
+<span class="sd">        A version of the user-defined ConfigurationSpace where all parameters are uniform (or have their weights removed</span>
+<span class="sd">        in the case of a categorical hyperparameter). Can optionally be given and sampling ratios be defined via the</span>
+<span class="sd">        `prior_sampling_fraction` parameter.</span>
 <span class="sd">    acquisition_function : AbstractAcquisitionFunction | None, defaults to None</span>
 <span class="sd">    challengers : int, defaults to 5000</span>
 <span class="sd">        Number of challengers.</span>
@@ -1070,6 +1075,9 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
 <span class="sd">    prior_sampling_fraction: float, defaults to 0.5</span>
 <span class="sd">        The ratio of random samples that are taken from the user-defined ConfigurationSpace, as opposed to the uniform</span>
 <span class="sd">        version (needs `uniform_configspace`to be defined).</span>
+<span class="sd">    prior_sampling_fraction: float, defaults to 0.5</span>
+<span class="sd">        The ratio of random samples that are taken from the user-defined ConfigurationSpace, as opposed to the uniform</span>
+<span class="sd">        version (needs `uniform_configspace`to be defined).</span>
 <span class="sd">    seed : int, defaults to 0</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
@@ -1092,6 +1100,28 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
             <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
         <span class="p">)</span>
 
+        <span class="k">if</span> <span class="n">uniform_configspace</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">prior_sampling_fraction</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">prior_sampling_fraction</span> <span class="o">=</span> <span class="mf">0.5</span>
+        <span class="k">if</span> <span class="n">uniform_configspace</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">prior_sampling_fraction</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">ValueError</span><span class="p">(</span><span class="s2">&quot;If `prior_sampling_fraction` is given, `uniform_configspace` must be defined.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">uniform_configspace</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">prior_sampling_fraction</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_prior_random_search</span> <span class="o">=</span> <span class="n">RandomSearch</span><span class="p">(</span>
+                <span class="n">acquisition_function</span><span class="o">=</span><span class="n">acquisition_function</span><span class="p">,</span>
+                <span class="n">configspace</span><span class="o">=</span><span class="n">configspace</span><span class="p">,</span>
+                <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+            <span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_random_search</span> <span class="o">=</span> <span class="n">RandomSearch</span><span class="p">(</span>
+                <span class="n">acquisition_function</span><span class="o">=</span><span class="n">acquisition_function</span><span class="p">,</span>
+                <span class="n">configspace</span><span class="o">=</span><span class="n">uniform_configspace</span><span class="p">,</span>
+                <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_random_search</span> <span class="o">=</span> <span class="n">RandomSearch</span><span class="p">(</span>
+                <span class="n">configspace</span><span class="o">=</span><span class="n">configspace</span><span class="p">,</span>
+                <span class="n">acquisition_function</span><span class="o">=</span><span class="n">acquisition_function</span><span class="p">,</span>
+                <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+            <span class="p">)</span>
         <span class="k">if</span> <span class="n">uniform_configspace</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">prior_sampling_fraction</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">prior_sampling_fraction</span> <span class="o">=</span> <span class="mf">0.5</span>
         <span class="k">if</span> <span class="n">uniform_configspace</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">prior_sampling_fraction</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -1126,6 +1156,8 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
         <span class="bp">self</span><span class="o">.</span><span class="n">_local_search_iterations</span> <span class="o">=</span> <span class="n">local_search_iterations</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_prior_sampling_fraction</span> <span class="o">=</span> <span class="n">prior_sampling_fraction</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_configspace</span> <span class="o">=</span> <span class="n">uniform_configspace</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_prior_sampling_fraction</span> <span class="o">=</span> <span class="n">prior_sampling_fraction</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_configspace</span> <span class="o">=</span> <span class="n">uniform_configspace</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">acquisition_function</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">AbstractAcquisitionFunction</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>  <span class="c1"># noqa: D102</span>
@@ -1135,6 +1167,11 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
     <span class="nd">@acquisition_function</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">acquisition_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acquisition_function</span><span class="p">:</span> <span class="n">AbstractAcquisitionFunction</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_acquisition_function</span> <span class="o">=</span> <span class="n">acquisition_function</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_configspace</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_prior_random_search</span><span class="o">.</span><span class="n">_acquisition_function</span> <span class="o">=</span> <span class="n">acquisition_function</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_random_search</span><span class="o">.</span><span class="n">_acquisition_function</span> <span class="o">=</span> <span class="n">acquisition_function</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_random_search</span><span class="o">.</span><span class="n">_acquisition_function</span> <span class="o">=</span> <span class="n">acquisition_function</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_configspace</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_prior_random_search</span><span class="o">.</span><span class="n">_acquisition_function</span> <span class="o">=</span> <span class="n">acquisition_function</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_random_search</span><span class="o">.</span><span class="n">_acquisition_function</span> <span class="o">=</span> <span class="n">acquisition_function</span>
@@ -1160,6 +1197,21 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
                     <span class="s2">&quot;local_search&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local_search</span><span class="o">.</span><span class="n">meta</span><span class="p">,</span>
                 <span class="p">}</span>
             <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_configspace</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">meta</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;random_search&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_search</span><span class="o">.</span><span class="n">meta</span><span class="p">,</span>
+                    <span class="s2">&quot;local_search&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local_search</span><span class="o">.</span><span class="n">meta</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">meta</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;prior_random_search&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prior_random_search</span><span class="o">.</span><span class="n">meta</span><span class="p">,</span>
+                    <span class="s2">&quot;uniform_random_search&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_uniform_random_search</span><span class="o">.</span><span class="n">meta</span><span class="p">,</span>
+                    <span class="s2">&quot;local_search&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local_search</span><span class="o">.</span><span class="n">meta</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
 
         <span class="k">return</span> <span class="n">meta</span>
 
@@ -1194,6 +1246,9 @@ <h1>Source code for smac.acquisition.maximizer.local_and_random_search</h1><div
                 <span class="n">_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
             <span class="p">)</span>
 
+        <span class="c1"># Choose the best self._local_search_iterations random configs to start the local search, and choose only</span>
+        <span class="c1"># incumbent from previous configs</span>
+        <span class="n">random_starting_points</span> <span class="o">=</span> <span class="n">next_configs_by_random_search_sorted</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local_search_iterations</span><span class="p">]</span>
         <span class="c1"># Choose the best self._local_search_iterations random configs to start the local search, and choose only</span>
         <span class="c1"># incumbent from previous configs</span>
         <span class="n">random_starting_points</span> <span class="o">=</span> <span class="n">next_configs_by_random_search_sorted</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local_search_iterations</span><span class="p">]</span>

development/_sources/examples/1_basics/1_quadratic_function.rst.txt

@@ -161,7 +161,7 @@ be applied to problems with large evaluation budgets (up to 1000 evaluations).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.230 seconds)
+   **Total running time of the script:** (0 minutes 4.357 seconds)
 
 
 .. _sphx_glr_download_examples_1_basics_1_quadratic_function.py:

development/_sources/examples/1_basics/2_svm_cv.rst.txt

@@ -387,7 +387,7 @@ types as well as conditional hyperparameters.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.871 seconds)
+   **Total running time of the script:** (0 minutes 2.925 seconds)
 
 
 .. _sphx_glr_download_examples_1_basics_2_svm_cv.py:

development/_sources/examples/1_basics/3_ask_and_tell.rst.txt

@@ -136,7 +136,7 @@ This examples show how to use the Ask-and-Tell interface.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.813 seconds)
+   **Total running time of the script:** (0 minutes 3.960 seconds)
 
 
 .. _sphx_glr_download_examples_1_basics_3_ask_and_tell.py:

development/_sources/examples/1_basics/4_callback.rst.txt

@@ -146,7 +146,7 @@ Furthermore, we print some stages of the optimization process.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.157 seconds)
+   **Total running time of the script:** (0 minutes 0.158 seconds)
 
 
 .. _sphx_glr_download_examples_1_basics_4_callback.py:

development/_sources/examples/1_basics/5_continue.rst.txt

@@ -172,7 +172,7 @@ there already is a previous run with the same meta data, this run will be contin
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.473 seconds)
+   **Total running time of the script:** (0 minutes 1.536 seconds)
 
 
 .. _sphx_glr_download_examples_1_basics_5_continue.py:

development/_sources/examples/1_basics/6_priors.rst.txt

@@ -229,7 +229,7 @@ optimization, you have to change the acquisition function to ``PriorAcquisitionF
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 39.241 seconds)
+   **Total running time of the script:** (0 minutes 39.668 seconds)
 
 
 .. _sphx_glr_download_examples_1_basics_6_priors.py:

development/_sources/examples/1_basics/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:51.785** total execution time for 6 files **from examples/1_basics**:
+**00:52.605** total execution time for 6 files **from examples/1_basics**:
 
 .. container::
 
@@ -33,20 +33,20 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_examples_1_basics_6_priors.py` (``6_priors.py``)
-     - 00:39.241
+     - 00:39.668
      - 0.0
    * - :ref:`sphx_glr_examples_1_basics_1_quadratic_function.py` (``1_quadratic_function.py``)
-     - 00:04.230
+     - 00:04.357
      - 0.0
    * - :ref:`sphx_glr_examples_1_basics_3_ask_and_tell.py` (``3_ask_and_tell.py``)
-     - 00:03.813
+     - 00:03.960
      - 0.0
    * - :ref:`sphx_glr_examples_1_basics_2_svm_cv.py` (``2_svm_cv.py``)
-     - 00:02.871
+     - 00:02.925
      - 0.0
    * - :ref:`sphx_glr_examples_1_basics_5_continue.py` (``5_continue.py``)
-     - 00:01.473
+     - 00:01.536
      - 0.0
    * - :ref:`sphx_glr_examples_1_basics_4_callback.py` (``4_callback.py``)
-     - 00:00.157
+     - 00:00.158
      - 0.0