Pushing the docs for revision for branch: master, commit dca6f6ba4b022b3be4ae9fe9e328965574657ada

Author: skoptci

index.html

@@ -1241,7 +1241,7 @@ <h2>Development</h2>
 <span class="c1"># Dev branch marker is: &#39;X.Y.dev&#39; or &#39;X.Y.devN&#39; where N is an integer.</span>
 <span class="c1"># &#39;X.Y.dev0&#39; is the canonical version of &#39;X.Y.dev&#39;</span>
 <span class="c1">#</span>
-<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;0.7.rc2&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;0.7.rc3&quot;</span>
 
 
 <span class="k">if</span> <span class="n">__SKOPT_SETUP__</span><span class="p">:</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 0x7EFE435F5380</span>
+<span class="err"> random_state: RandomState(MT19937) at 0x7F1D41865380</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 0x7EFE435F5380
+     random_state: RandomState(MT19937) at 0x7F1D41865380
             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 0x7FCD5D96F9E0</span>
+<span class="err"> random_state: RandomState(MT19937) at 0x7FE86A6BC9E0</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 0x7fcd5d8bdef0&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 0x7fe86a60aef0&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 0x7FCD5D96F9E0, &#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 0x7FE86A6BC9E0, &#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 0x7FCD5D96F9E0
+     random_state: RandomState(MT19937) at 0x7FE86A6BC9E0
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7fcd5d8bdef0>, '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 0x7fe86a60aef0>, '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 0x7FCD5D96F9E0, '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 0x7FE86A6BC9E0, '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 0x7f139d41fc90&gt;</span>
+<div class="codehilite"><pre><span></span><span class="err">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f75688b4290&gt;</span>
 </pre></div>
 
 

notebooks/hyperparameter-optimization.md

@@ -117,7 +117,7 @@ plot_convergence(res_gp)
 
 
 
-    <matplotlib.axes._subplots.AxesSubplot at 0x7f139d41fc90>
+    <matplotlib.axes._subplots.AxesSubplot at 0x7f75688b4290>
 
 
 

notebooks/sklearn-gridsearchcv-replacement.html

@@ -1158,8 +1158,8 @@ <h2>Minimal example</h2>
 </pre></div>
 
 
-<div class="codehilite"><pre><span></span><span class="err">val. score: 0.9866369710467706</span>
-<span class="err">test score: 0.9866666666666667</span>
+<div class="codehilite"><pre><span></span><span class="err">val. score: 0.9903489235337788</span>
+<span class="err">test score: 0.9933333333333333</span>
 </pre></div>
 
 
@@ -1263,14 +1263,10 @@ <h2>Advanced example</h2>
   <span class="ss">&quot;the number of iterations.&quot;</span><span class="p">,</span> <span class="n">ConvergenceWarning</span><span class="p">)</span>
 <span class="o">/</span><span class="n">opt</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span><span class="n">envs</span><span class="o">/</span><span class="n">testenv</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="p">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">sklearn</span><span class="o">/</span><span class="n">svm</span><span class="o">/</span><span class="n">_base</span><span class="p">.</span><span class="n">py</span><span class="p">:</span><span class="mi">947</span><span class="p">:</span> <span class="n">ConvergenceWarning</span><span class="p">:</span> <span class="n">Liblinear</span> <span class="n">failed</span> <span class="k">to</span> <span class="n">converge</span><span class="p">,</span> <span class="n">increase</span> <span class="n">the</span> <span class="nb">number</span> <span class="k">of</span> <span class="n">iterations</span><span class="p">.</span>
   <span class="ss">&quot;the number of iterations.&quot;</span><span class="p">,</span> <span class="n">ConvergenceWarning</span><span class="p">)</span>
-<span class="o">/</span><span class="n">opt</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span><span class="n">envs</span><span class="o">/</span><span class="n">testenv</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="p">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">sklearn</span><span class="o">/</span><span class="n">svm</span><span class="o">/</span><span class="n">_base</span><span class="p">.</span><span class="n">py</span><span class="p">:</span><span class="mi">947</span><span class="p">:</span> <span class="n">ConvergenceWarning</span><span class="p">:</span> <span class="n">Liblinear</span> <span class="n">failed</span> <span class="k">to</span> <span class="n">converge</span><span class="p">,</span> <span class="n">increase</span> <span class="n">the</span> <span class="nb">number</span> <span class="k">of</span> <span class="n">iterations</span><span class="p">.</span>
-  <span class="ss">&quot;the number of iterations.&quot;</span><span class="p">,</span> <span class="n">ConvergenceWarning</span><span class="p">)</span>
-<span class="o">/</span><span class="n">opt</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span><span class="n">envs</span><span class="o">/</span><span class="n">testenv</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="p">.</span><span class="mi">7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">sklearn</span><span class="o">/</span><span class="n">svm</span><span class="o">/</span><span class="n">_base</span><span class="p">.</span><span class="n">py</span><span class="p">:</span><span class="mi">947</span><span class="p">:</span> <span class="n">ConvergenceWarning</span><span class="p">:</span> <span class="n">Liblinear</span> <span class="n">failed</span> <span class="k">to</span> <span class="n">converge</span><span class="p">,</span> <span class="n">increase</span> <span class="n">the</span> <span class="nb">number</span> <span class="k">of</span> <span class="n">iterations</span><span class="p">.</span>
-  <span class="ss">&quot;the number of iterations.&quot;</span><span class="p">,</span> <span class="n">ConvergenceWarning</span><span class="p">)</span>
 
 
-<span class="n">val</span><span class="p">.</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9836674090571641</span>
-<span class="n">test</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9777777777777777</span>
+<span class="n">val</span><span class="p">.</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">985894580549369</span>
+<span class="n">test</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9822222222222222</span>
 </pre></div>
 
 
@@ -1306,16 +1302,8 @@ <h2>Progress monitoring and control using <code>callback</code> argument of <cod
 </pre></div>
 
 
-<div class="codehilite"><pre><span></span><span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9666666666666667</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
-<span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9733333333333334</span>
+<div class="codehilite"><pre><span></span><span class="n">best</span> <span class="n">score</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">98</span>
+<span class="n">Interrupting</span><span class="o">!</span>
 
 
 

notebooks/sklearn-gridsearchcv-replacement.md

@@ -46,8 +46,8 @@ print("val. score: %s" % opt.best_score_)
 print("test score: %s" % opt.score(X_test, y_test))
 ```
 
-    val. score: 0.9866369710467706
-    test score: 0.9866666666666667
+    val. score: 0.9903489235337788
+    test score: 0.9933333333333333
 
 
 ## Advanced example 
@@ -153,14 +153,10 @@ print("test score: %s" % opt.score(X_test, y_test))
       "the number of iterations.", ConvergenceWarning)
     /opt/conda/envs/testenv/lib/python3.7/site-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
       "the number of iterations.", ConvergenceWarning)
-    /opt/conda/envs/testenv/lib/python3.7/site-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
-      "the number of iterations.", ConvergenceWarning)
-    /opt/conda/envs/testenv/lib/python3.7/site-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
-      "the number of iterations.", ConvergenceWarning)
 
 
-    val. score: 0.9836674090571641
-    test score: 0.9777777777777777
+    val. score: 0.985894580549369
+    test score: 0.9822222222222222
 
 
 ## Progress monitoring and control using `callback` argument of `fit` method
@@ -200,16 +196,8 @@ def on_step(optim_result):
 searchcv.fit(X, y, callback=on_step)
 ```
 
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9666666666666667
-    best score: 0.9733333333333334
-    best score: 0.9733333333333334
+    best score: 0.98
+    Interrupting!
 
 
 

rtd/_modules/index.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>Overview: module code &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>Overview: module code &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>
 
 

rtd/_modules/skopt/acquisition.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>skopt.acquisition &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>skopt.acquisition &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>
 
 

rtd/_modules/skopt/callbacks.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>skopt.callbacks &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>skopt.callbacks &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>
 
 

rtd/_modules/skopt/optimizer/dummy.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>skopt.optimizer.dummy &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>skopt.optimizer.dummy &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>
 
 

rtd/_modules/skopt/optimizer/forest.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>skopt.optimizer.forest &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>skopt.optimizer.forest &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>
 
 

rtd/_modules/skopt/optimizer/gbrt.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>skopt.optimizer.gbrt &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>skopt.optimizer.gbrt &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>
 
 

rtd/_modules/skopt/optimizer/gp.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>skopt.optimizer.gp &mdash; scikit-optimize 0.7.rc2 documentation</title>
+  <title>skopt.optimizer.gp &mdash; scikit-optimize 0.7.rc3 documentation</title>
 
 
 
@@ -60,7 +60,7 @@
 
 
               <div class="version">
-                0.7.rc2
+                0.7.rc3
               </div>