Pushing the docs for revision for branch: master, commit 02ce7ba9f4f3de3f336ab03d34bef0e82bed6c38

Author: skoptci

acquisition.m.html

@@ -1258,7 +1258,7 @@ <h1 class="title"><span class="name">skopt.acquisition</span> module</h1>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Use the probability of improvement to calculate the acquisition values.</span>
 
-<span class="sd">    The conditional probability `P(y=f(x) | x)`form a gaussian with a</span>
+<span class="sd">    The conditional probability `P(y=f(x) | x)` form a gaussian with a</span>
 <span class="sd">    certain mean and standard deviation approximated by the model.</span>
 
 <span class="sd">    The PI condition is derived by computing ``E[u(f(x))]``</span>
@@ -1341,7 +1341,7 @@ <h1 class="title"><span class="name">skopt.acquisition</span> module</h1>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Use the expected improvement to calculate the acquisition values.</span>
 
-<span class="sd">    The conditional probability `P(y=f(x) | x)`form a gaussian with a certain</span>
+<span class="sd">    The conditional probability `P(y=f(x) | x)` form a gaussian with a certain</span>
 <span class="sd">    mean and standard deviation approximated by the model.</span>
 
 <span class="sd">    The EI condition is derived by computing ``E[u(f(x))]``</span>
@@ -1480,7 +1480,7 @@ <h2 class="section-title" id="header-functions">Functions</h2>
 
 
     <div class="desc"><p>Use the expected improvement to calculate the acquisition values.</p>
-<p>The conditional probability <code>P(y=f(x) | x)</code>form a gaussian with a certain
+<p>The conditional probability <code>P(y=f(x) | x)</code> form a gaussian with a certain
 mean and standard deviation approximated by the model.</p>
 <p>The EI condition is derived by computing <code>E[u(f(x))]</code>
 where <code>u(f(x)) = 0</code>, if <code>f(x) &gt; y_opt</code> and <code>u(f(x)) = y_opt - f(x)</code>,
@@ -1529,7 +1529,7 @@ <h2>Returns</h2>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Use the expected improvement to calculate the acquisition values.</span>
 
-<span class="sd">    The conditional probability `P(y=f(x) | x)`form a gaussian with a certain</span>
+<span class="sd">    The conditional probability `P(y=f(x) | x)` form a gaussian with a certain</span>
 <span class="sd">    mean and standard deviation approximated by the model.</span>
 
 <span class="sd">    The EI condition is derived by computing ``E[u(f(x))]``</span>
@@ -1751,7 +1751,7 @@ <h2>Returns</h2>
 
 
     <div class="desc"><p>Use the probability of improvement to calculate the acquisition values.</p>
-<p>The conditional probability <code>P(y=f(x) | x)</code>form a gaussian with a
+<p>The conditional probability <code>P(y=f(x) | x)</code> form a gaussian with a
 certain mean and standard deviation approximated by the model.</p>
 <p>The PI condition is derived by computing <code>E[u(f(x))]</code>
 where <code>u(f(x)) = 1</code>, if <code>f(x) &lt; y_opt</code> and <code>u(f(x)) = 0</code>,
@@ -1801,7 +1801,7 @@ <h2>Returns</h2>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Use the probability of improvement to calculate the acquisition values.</span>
 
-<span class="sd">    The conditional probability `P(y=f(x) | x)`form a gaussian with a</span>
+<span class="sd">    The conditional probability `P(y=f(x) | x)` form a gaussian with a</span>
 <span class="sd">    certain mean and standard deviation approximated by the model.</span>
 
 <span class="sd">    The PI condition is derived by computing ``E[u(f(x))]``</span>

callbacks.m.html

@@ -1329,7 +1329,7 @@ <h2>Early stopping callbacks</h2>
 
 
 <span class="k">class</span> <span class="nc">DeltaXStopper</span><span class="p">(</span><span class="n">EarlyStopper</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Stop the optimization when |x1 - x2| &lt; `delta`</span>
+    <span class="sd">&quot;&quot;&quot;Stop the optimization when ``|x1 - x2| &lt; delta``</span>
 
 <span class="sd">    If the last two positions at which the objective has been evaluated</span>
 <span class="sd">    are less than `delta` apart stop the optimization procedure.</span>
@@ -1705,14 +1705,14 @@ <h3>Instance variables</h3>
       <p id="skopt.callbacks.DeltaXStopper" class="name">class <span class="ident">DeltaXStopper</span></p>
 
 
-    <div class="desc"><p>Stop the optimization when |x1 - x2| &lt; <code>delta</code></p>
+    <div class="desc"><p>Stop the optimization when <code>|x1 - x2| &lt; delta</code></p>
 <p>If the last two positions at which the objective has been evaluated
 are less than <code>delta</code> apart stop the optimization procedure.</p></div>
   <div class="source_cont">
   <p class="source_link"><a href="javascript:void(0);" onclick="toggle('source-skopt.callbacks.DeltaXStopper', this);">Show source &equiv;</a></p>
   <div id="source-skopt.callbacks.DeltaXStopper" class="source">
     <div class="codehilite"><pre><span></span><span class="k">class</span> <span class="nc">DeltaXStopper</span><span class="p">(</span><span class="n">EarlyStopper</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Stop the optimization when |x1 - x2| &lt; `delta`</span>
+    <span class="sd">&quot;&quot;&quot;Stop the optimization when ``|x1 - x2| &lt; delta``</span>
 
 <span class="sd">    If the last two positions at which the objective has been evaluated</span>
 <span class="sd">    are less than `delta` apart stop the optimization procedure.</span>

index.html

@@ -2958,9 +2958,6 @@ <h2>Parameters</h2>
 <div class="codehilite"><pre><span></span><span class="k">For</span> <span class="kt">integer</span><span class="o">/</span><span class="n">None</span> <span class="n">inputs</span><span class="p">,</span> <span class="k">if</span> <span class="n">the</span> <span class="n">estimator</span> <span class="k">is</span> <span class="n">a</span> <span class="n">classifier</span> <span class="k">and</span> <span class="ss">``</span><span class="n">y</span><span class="ss">``</span> <span class="k">is</span>
 <span class="n">either</span> <span class="k">binary</span> <span class="k">or</span> <span class="n">multiclass</span><span class="p">,</span> <span class="p">:</span><span class="n">class</span><span class="p">:</span><span class="ss">`StratifiedKFold`</span> <span class="k">is</span> <span class="n">used</span><span class="p">.</span> <span class="k">In</span> <span class="k">all</span>
 <span class="n">other</span> <span class="n">cases</span><span class="p">,</span> <span class="p">:</span><span class="n">class</span><span class="p">:</span><span class="ss">`KFold`</span> <span class="k">is</span> <span class="n">used</span><span class="p">.</span>
-
-<span class="n">Refer</span> <span class="p">:</span><span class="n">ref</span><span class="p">:</span><span class="ss">`User Guide &lt;cross_validation&gt;`</span> <span class="k">for</span> <span class="n">the</span> <span class="n">various</span>
-<span class="k">cross</span><span class="o">-</span><span class="n">validation</span> <span class="n">strategies</span> <span class="n">that</span> <span class="n">can</span> <span class="n">be</span> <span class="n">used</span> <span class="n">here</span><span class="p">.</span>
 </pre></div>
 
 
@@ -3198,9 +3195,6 @@ <h2>See Also</h2>
 <span class="sd">        either binary or multiclass, :class:`StratifiedKFold` is used. In all</span>
 <span class="sd">        other cases, :class:`KFold` is used.</span>
 
-<span class="sd">        Refer :ref:`User Guide &lt;cross_validation&gt;` for the various</span>
-<span class="sd">        cross-validation strategies that can be used here.</span>
-
 <span class="sd">    refit : boolean, default=True</span>
 <span class="sd">        Refit the best estimator with the entire dataset.</span>
 <span class="sd">        If &quot;False&quot;, it is impossible to make predictions using</span>

notebooks/ask-and-tell.html

@@ -1210,7 +1210,7 @@ <h2>The Setup</h2>
 <div class="codehilite"><pre><span></span><span class="err">          fun: -0.032758350111535384</span>
 <span class="err">    func_vals: array([-0.03275835])</span>
 <span class="err">       models: []</span>
-<span class="err"> random_state: RandomState(MT19937) at 0x7F6DAE7CAC00</span>
+<span class="err"> random_state: RandomState(MT19937) at 0x7F5849A3DC00</span>
 <span class="err">        space: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;identity&#39;)])</span>
 <span class="err">        specs: None</span>
 <span class="err">            x: [-1.7121321838148869]</span>

notebooks/ask-and-tell.md

@@ -111,7 +111,7 @@ opt.tell(next_x, f_val)
               fun: -0.032758350111535384
         func_vals: array([-0.03275835])
            models: []
-     random_state: RandomState(MT19937) at 0x7F6DAE7CAC00
+     random_state: RandomState(MT19937) at 0x7F5849A3DC00
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='identity')])
             specs: None
                 x: [-1.7121321838148869]

notebooks/bayesian-optimization.html

@@ -1277,13 +1277,13 @@ <h2>Toy example</h2>
 <span class="err">                         n_restarts_optimizer=2, noise=0.010000000000000002,</span>
 <span class="err">                         normalize_y=True, optimizer=&#39;fmin_l_bfgs_b&#39;,</span>
 <span class="err">                         random_state=843828734)]</span>
-<span class="err"> random_state: RandomState(MT19937) at 0x7F970768A380</span>
+<span class="err"> random_state: RandomState(MT19937) at 0x7FBAC55F1380</span>
 <span class="err">        space: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;normalize&#39;)])</span>
-<span class="err">        specs: {&#39;args&#39;: {&#39;func&#39;: &lt;function f at 0x7f97073783b0&gt;, &#39;dimensions&#39;: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;normalize&#39;)]), &#39;base_estimator&#39;: GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,</span>
+<span class="err">        specs: {&#39;args&#39;: {&#39;func&#39;: &lt;function f at 0x7fbac52df3b0&gt;, &#39;dimensions&#39;: Space([Real(low=-2.0, high=2.0, prior=&#39;uniform&#39;, transform=&#39;normalize&#39;)]), &#39;base_estimator&#39;: GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,</span>
 <span class="err">                         kernel=1**2 * Matern(length_scale=1, nu=2.5),</span>
 <span class="err">                         n_restarts_optimizer=2, noise=0.010000000000000002,</span>
 <span class="err">                         normalize_y=True, optimizer=&#39;fmin_l_bfgs_b&#39;,</span>
-<span class="err">                         random_state=843828734), &#39;n_calls&#39;: 15, &#39;n_random_starts&#39;: 5, &#39;acq_func&#39;: &#39;EI&#39;, &#39;acq_optimizer&#39;: &#39;auto&#39;, &#39;x0&#39;: None, &#39;y0&#39;: None, &#39;random_state&#39;: RandomState(MT19937) at 0x7F970768A380, &#39;verbose&#39;: False, &#39;callback&#39;: None, &#39;n_points&#39;: 10000, &#39;n_restarts_optimizer&#39;: 5, &#39;xi&#39;: 0.01, &#39;kappa&#39;: 1.96, &#39;n_jobs&#39;: 1, &#39;model_queue_size&#39;: None}, &#39;function&#39;: &#39;base_minimize&#39;}</span>
+<span class="err">                         random_state=843828734), &#39;n_calls&#39;: 15, &#39;n_random_starts&#39;: 5, &#39;acq_func&#39;: &#39;EI&#39;, &#39;acq_optimizer&#39;: &#39;auto&#39;, &#39;x0&#39;: None, &#39;y0&#39;: None, &#39;random_state&#39;: RandomState(MT19937) at 0x7FBAC55F1380, &#39;verbose&#39;: False, &#39;callback&#39;: None, &#39;n_points&#39;: 10000, &#39;n_restarts_optimizer&#39;: 5, &#39;xi&#39;: 0.01, &#39;kappa&#39;: 1.96, &#39;n_jobs&#39;: 1, &#39;model_queue_size&#39;: None}, &#39;function&#39;: &#39;base_minimize&#39;}</span>
 <span class="err">            x: [-0.25728223389301497]</span>
 <span class="err">      x_iters: [[0.8518212820929092], [-0.2861162952526968], [0.7635394201074472], [0.8766012406190926], [-0.03552426626961047], [-0.30626086081540116], [-0.25728223389301497], [-0.22378966711457093], [1.0553495077507762], [-0.25562401712808525], [-0.5244265431906165], [-1.9992867763849413], [1.999866281741757], [-1.2339044799440428], [0.45960222774783643]]</span>
 </pre></div>

notebooks/bayesian-optimization.md

@@ -175,13 +175,13 @@ print(res)
                              n_restarts_optimizer=2, noise=0.010000000000000002,
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=843828734)]
-     random_state: RandomState(MT19937) at 0x7F970768A380
+     random_state: RandomState(MT19937) at 0x7FBAC55F1380
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7f97073783b0>, '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 0x7fbac52df3b0>, '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=843828734), 'n_calls': 15, 'n_random_starts': 5, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F970768A380, '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=843828734), 'n_calls': 15, 'n_random_starts': 5, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7FBAC55F1380, '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: [-0.25728223389301497]
           x_iters: [[0.8518212820929092], [-0.2861162952526968], [0.7635394201074472], [0.8766012406190926], [-0.03552426626961047], [-0.30626086081540116], [-0.25728223389301497], [-0.22378966711457093], [1.0553495077507762], [-0.25562401712808525], [-0.5244265431906165], [-1.9992867763849413], [1.999866281741757], [-1.2339044799440428], [0.45960222774783643]]
 

notebooks/hyperparameter-optimization.html

@@ -1216,7 +1216,7 @@ <h2>Convergence plot</h2>
 </pre></div>
 
 
-<div class="codehilite"><pre><span></span><span class="err">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f34a91864d0&gt;</span>
+<div class="codehilite"><pre><span></span><span class="err">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fa6e8b25f90&gt;</span>
 </pre></div>
 
 

notebooks/hyperparameter-optimization.md

@@ -117,7 +117,7 @@ plot_convergence(res_gp)
 
 
 
-    <matplotlib.axes._subplots.AxesSubplot at 0x7f34a91864d0>
+    <matplotlib.axes._subplots.AxesSubplot at 0x7fa6e8b25f90>