Pushing the docs to dev/ for branch: master, commit 6ccd1fc11ac874f75aadc95aac822cb10fac90f4

Author: skoptci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -382,6 +382,8 @@ <h1>Source code for skopt.space.transformers</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">X</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">low</span> <span class="o">-</span> <span class="mf">1e-8</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All values should&quot;</span>
                                  <span class="s2">&quot;be greater than </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">low</span><span class="p">)</span>
+        <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">high</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">low</span><span class="p">)</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">X</span> <span class="o">*</span> <span class="mf">0.</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_int</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">low</span><span class="p">)</span> <span class="o">/</span>\
                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">high</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">low</span><span class="p">)</span>

dev/_downloads/d34667f097c619f8afda4bc936e7af21/auto_examples_jupyter.zip

@@ -329,9 +329,8 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="expected_minimum"><a class="viewcode-back" href="../../modules/generated/skopt.utils.expected_minimum.html#skopt.expected_minimum">[docs]</a><span class="k">def</span> <span class="nf">expected_minimum</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">n_random_starts</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Compute the minimum over the predictions of the last surrogate model.</span>
-<span class="sd">    Uses `expected_minimum_random_sampling` with `n_random_starts`=100000,</span>
+    <span class="sd">&quot;&quot;&quot;Compute the minimum over the predictions of the last surrogate model.</span>
+<span class="sd">    Uses `expected_minimum_random_sampling` with `n_random_starts` = 100000,</span>
 <span class="sd">    when the space contains any categorical values.</span>
 
 <span class="sd">    .. note::</span>
@@ -460,17 +459,15 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="cook_estimator"><a class="viewcode-back" href="../../modules/generated/skopt.utils.cook_estimator.html#skopt.cook_estimator">[docs]</a><span class="k">def</span> <span class="nf">cook_estimator</span><span class="p">(</span><span class="n">base_estimator</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Cook a default estimator.</span>
+    <span class="sd">&quot;&quot;&quot;Cook a default estimator.</span>
 
 <span class="sd">    For the special base_estimator called &quot;DUMMY&quot; the return value is None.</span>
 <span class="sd">    This corresponds to sampling points at random, hence there is no need</span>
 <span class="sd">    for an estimator.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    base_estimator : &quot;GP&quot;, &quot;RF&quot;, &quot;ET&quot;, &quot;GBRT&quot;, &quot;DUMMY&quot;</span>
-<span class="sd">                        or sklearn regressor</span>
+<span class="sd">    base_estimator : &quot;GP&quot;, &quot;RF&quot;, &quot;ET&quot;, &quot;GBRT&quot;, &quot;DUMMY&quot; or sklearn regressor</span>
 <span class="sd">        Should inherit from `sklearn.base.RegressorMixin`.</span>
 <span class="sd">        In addition the `predict` method should have an optional `return_std`</span>
 <span class="sd">        argument, which returns `std(Y | x)`` along with `E[Y | x]`.</span>
@@ -535,15 +532,14 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="cook_initial_point_generator"><a class="viewcode-back" href="../../modules/generated/skopt.utils.cook_initial_point_generator.html#skopt.cook_initial_point_generator">[docs]</a><span class="k">def</span> <span class="nf">cook_initial_point_generator</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Cook a default initial point generator.</span>
+    <span class="sd">&quot;&quot;&quot;Cook a default initial point generator.</span>
 
 <span class="sd">    For the special generator called &quot;random&quot; the return value is None.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    generator : &quot;lhs&quot;, &quot;sobol&quot;, &quot;halton&quot;, &quot;hammersly&quot;, &quot;grid&quot;, &quot;random&quot;</span>
-<span class="sd">                        or InitialPointGenerator instance&quot;</span>
+<span class="sd">    generator : &quot;lhs&quot;, &quot;sobol&quot;, &quot;halton&quot;, &quot;hammersly&quot;, &quot;grid&quot;, &quot;random&quot; \</span>
+<span class="sd">            or InitialPointGenerator instance&quot;</span>
 <span class="sd">        Should inherit from `skopt.sampler.InitialPointGenerator`.</span>
 
 <span class="sd">    kwargs : dict</span>
@@ -721,37 +717,30 @@ <h1>Source code for skopt.utils</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">space</span> <span class="o">=</span> <span class="n">Space</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)</span>
     <span class="n">transformed_dimensions</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">if</span> <span class="n">space</span><span class="o">.</span><span class="n">is_categorical</span><span class="p">:</span>
-        <span class="c1"># recreate the space and explicitly set transform to &quot;string&quot;</span>
-        <span class="c1"># this is a special case for GP based regressors</span>
-        <span class="k">for</span> <span class="n">dimension</span> <span class="ow">in</span> <span class="n">space</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">dimension</span> <span class="ow">in</span> <span class="n">space</span><span class="o">.</span><span class="n">dimensions</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension</span><span class="p">,</span> <span class="n">Categorical</span><span class="p">):</span>
             <span class="n">transformed_dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Categorical</span><span class="p">(</span><span class="n">dimension</span><span class="o">.</span><span class="n">categories</span><span class="p">,</span>
                                                       <span class="n">dimension</span><span class="o">.</span><span class="n">prior</span><span class="p">,</span>
                                                       <span class="n">name</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-                                                      <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;string&quot;</span><span class="p">))</span>
-
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">dimension</span> <span class="ow">in</span> <span class="n">space</span><span class="o">.</span><span class="n">dimensions</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension</span><span class="p">,</span> <span class="n">Categorical</span><span class="p">):</span>
-                <span class="n">transformed_dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dimension</span><span class="p">)</span>
-            <span class="c1"># To make sure that GP operates in the [0, 1] space</span>
-            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
-                <span class="n">transformed_dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">Real</span><span class="p">(</span><span class="n">dimension</span><span class="o">.</span><span class="n">low</span><span class="p">,</span> <span class="n">dimension</span><span class="o">.</span><span class="n">high</span><span class="p">,</span> <span class="n">dimension</span><span class="o">.</span><span class="n">prior</span><span class="p">,</span>
-                         <span class="n">name</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-                         <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;normalize&quot;</span><span class="p">,</span>
-                         <span class="n">dtype</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-                    <span class="p">)</span>
-            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension</span><span class="p">,</span> <span class="n">Integer</span><span class="p">):</span>
-                <span class="n">transformed_dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">Integer</span><span class="p">(</span><span class="n">dimension</span><span class="o">.</span><span class="n">low</span><span class="p">,</span> <span class="n">dimension</span><span class="o">.</span><span class="n">high</span><span class="p">,</span>
-                            <span class="n">name</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-                            <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;normalize&quot;</span><span class="p">,</span>
-                            <span class="n">dtype</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-                    <span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unknown dimension type &quot;</span>
-                                   <span class="s2">&quot;(</span><span class="si">%s</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">dimension</span><span class="p">))</span>
+                                                      <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;normalize&quot;</span><span class="p">))</span>
+        <span class="c1"># To make sure that GP operates in the [0, 1] space</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
+            <span class="n">transformed_dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">Real</span><span class="p">(</span><span class="n">dimension</span><span class="o">.</span><span class="n">low</span><span class="p">,</span> <span class="n">dimension</span><span class="o">.</span><span class="n">high</span><span class="p">,</span> <span class="n">dimension</span><span class="o">.</span><span class="n">prior</span><span class="p">,</span>
+                     <span class="n">name</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+                     <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;normalize&quot;</span><span class="p">,</span>
+                     <span class="n">dtype</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension</span><span class="p">,</span> <span class="n">Integer</span><span class="p">):</span>
+            <span class="n">transformed_dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">Integer</span><span class="p">(</span><span class="n">dimension</span><span class="o">.</span><span class="n">low</span><span class="p">,</span> <span class="n">dimension</span><span class="o">.</span><span class="n">high</span><span class="p">,</span>
+                        <span class="n">name</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+                        <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;normalize&quot;</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="n">dimension</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unknown dimension type &quot;</span>
+                               <span class="s2">&quot;(</span><span class="si">%s</span><span class="s2">)&quot;</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">dimension</span><span class="p">))</span>
 
     <span class="k">return</span> <span class="n">Space</span><span class="p">(</span><span class="n">transformed_dimensions</span><span class="p">)</span>
 

dev/_downloads/scikit-optimize-docs.pdf

@@ -175,7 +175,7 @@ and report the value back to the optimizer:
               fun: -0.032758350111535384
         func_vals: array([-0.03275835])
            models: []
-     random_state: RandomState(MT19937) at 0x7FAB4D60ED40
+     random_state: RandomState(MT19937) at 0x7F30E5530D40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='identity')])
             specs: None
                 x: [-1.7121321838148869]
@@ -312,7 +312,7 @@ meantime:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  4.849 seconds)
+   **Total running time of the script:** ( 0 minutes  4.606 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_images/sphx_glr_partial-dependence-plot-with-categorical_001.png

@@ -267,13 +267,13 @@ provide the following information:
                              n_restarts_optimizer=2, noise=0.010000000000000002,
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=822569775)]
-     random_state: RandomState(MT19937) at 0x7FAB30019640
+     random_state: RandomState(MT19937) at 0x7F30E7AE3D40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7fab2b90c310>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
+            specs: {'args': {'func': <function f at 0x7f30c9742b80>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
                              kernel=1**2 * Matern(length_scale=1, nu=2.5),
                              n_restarts_optimizer=2, noise=0.010000000000000002,
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
-                             random_state=822569775), 'n_calls': 15, 'n_random_starts': 5, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7FAB30019640, 'verbose': False, 'callback': None, 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
+                             random_state=822569775), 'n_calls': 15, 'n_random_starts': 5, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F30E7AE3D40, 'verbose': False, 'callback': None, 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
                 x: [-0.35076964188527904]
           x_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [0.9698921802985794], [-0.4464493263345517], [-0.6474638284799423], [-0.35076964188527904], [-0.28714767658880325], [-0.2968537755362253], [-2.0], [2.0], [-1.3149517825054502], [-0.32181607448732485]]
 
@@ -305,7 +305,7 @@ the last iteration:
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7fab29040640>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f30c0f93e50>
 
 
 
@@ -476,7 +476,7 @@ because the GP does not gain anything more by further exploration:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.721 seconds)
+   **Total running time of the script:** ( 0 minutes  3.932 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_images/sphx_glr_partial-dependence-plot-with-categorical_002.png

@@ -562,7 +562,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  34.142 seconds)
+   **Total running time of the script:** ( 0 minutes  35.732 seconds)
 
 **Estimated memory usage:**  8 MB