Pushing the docs for revision for branch: master, commit 2ba88fe9ef0b8cb02fd0bbcba34de4e96650378d

Author: skoptci

_downloads/56061d60b433157ebf7d13e5f8a92c2a/auto_examples_python.zip

@@ -501,7 +501,6 @@ <h1>Source code for skopt.searchcv</h1><div class="highlight"><pre>
 <span class="sd">        Taken from https://github.com/scikit-learn/scikit-learn/blob/0.18.X</span>
 <span class="sd">                    .../sklearn/model_selection/_search.py</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-
         <span class="n">estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span>
         <span class="n">cv</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">_validation</span><span class="o">.</span><span class="n">check_cv</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cv</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="n">is_classifier</span><span class="p">(</span><span class="n">estimator</span><span class="p">))</span>
@@ -692,6 +691,13 @@ <h1>Source code for skopt.searchcv</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">:</span>
             <span class="n">all_cv_results</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
 
+        <span class="n">all_cv_results</span><span class="p">[</span><span class="s2">&quot;rank_test_score&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
+            <span class="n">rankdata</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_cv_results</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]),</span>
+                     <span class="n">method</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_train_score</span><span class="p">:</span>
+            <span class="n">all_cv_results</span><span class="p">[</span><span class="s2">&quot;rank_train_score&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
+                <span class="n">rankdata</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_cv_results</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]),</span>
+                         <span class="n">method</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">cv_results_</span> <span class="o">=</span> <span class="n">all_cv_results</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">best_index_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">])</span>
 

_downloads/80167a555a9152802d82aab42c671ad2/auto_examples_jupyter.zip

@@ -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 0x7F9F02BFF940
+     random_state: RandomState(MT19937) at 0x7F7FE0C92940
             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  3.377 seconds)
+   **Total running time of the script:** ( 0 minutes  3.420 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ask-and-tell.py:

_modules/skopt/searchcv.html

@@ -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=843828734)]
-     random_state: RandomState(MT19937) at 0x7F9F4628D840
+     random_state: RandomState(MT19937) at 0x7F802E52D840
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7f9f52e639d0>, '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 0x7f7fac185670>, '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 0x7F9F4628D840, '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 0x7F802E52D840, '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: [-0.2861162952526968]
           x_iters: [[0.8518212820929092], [-0.2861162952526968], [0.7635394201074472], [0.8766012406190926], [-0.03552426626961047], [-0.34247541478780397], [-0.26155519596964205], [-0.4646415758058908], [-0.31387649972906195], [1.38961533306915], [-1.9998214636995275], [-1.218993447030592], [2.0], [0.37684326379289823], [-1.6363051829417985]]
 
@@ -305,7 +305,7 @@ the last iteration:
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7f9ecd2b8ac0>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f7fabac84f0>
 
 
 
@@ -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  2.867 seconds)
+   **Total running time of the script:** ( 0 minutes  3.003 seconds)
 
 
 .. _sphx_glr_download_auto_examples_bayesian-optimization.py:

_sources/auto_examples/ask-and-tell.rst.txt

@@ -207,14 +207,14 @@ Convergence plot
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7f9f52e68610>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f8030ef3490>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  13.964 seconds)
+   **Total running time of the script:** ( 1 minutes  17.704 seconds)
 
 
 .. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

_sources/auto_examples/bayesian-optimization.rst.txt

@@ -163,13 +163,13 @@ and pass it to the minimizer:
                              n_restarts_optimizer=2, noise='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=655685735)]
-     random_state: RandomState(MT19937) at 0x7F9F4628D840
+     random_state: RandomState(MT19937) at 0x7F802E52D840
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7f9f52c4f310>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
+            specs: {'args': {'func': <function obj_fun at 0x7f8030ce33a0>, 'dimensions': Space([Real(low=-20.0, high=20.0, 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='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
-                             random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F9F4628D840, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f9ece08f4c0>], '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=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F802E52D840, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f7fac26d9a0>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
                 x: [20.0]
           x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]]
 
@@ -321,14 +321,14 @@ The previous results can then be used to continue the optimization process:
                              n_restarts_optimizer=2, noise='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=655685735)]
-     random_state: RandomState(MT19937) at 0x7F9F4628D840
+     random_state: RandomState(MT19937) at 0x7F802E52D840
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7f9f52c4f310>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
+            specs: {'args': {'func': <function obj_fun at 0x7f8030ce33a0>, 'dimensions': Space([Real(low=-20.0, high=20.0, 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='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]], 'y0': array([-0.04682088, -0.08228249, -0.00653801, -0.07133619,  0.09063509,
-            0.07662367,  0.08260541, -0.13236828, -0.17524445,  0.10024491]), 'random_state': RandomState(MT19937) at 0x7F9F4628D840, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f9ece08f4c0>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
+            0.07662367,  0.08260541, -0.13236828, -0.17524445,  0.10024491]), 'random_state': RandomState(MT19937) at 0x7F802E52D840, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f7fac26d9a0>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
                 x: [20.0]
           x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0], [20.0], [20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0]]
 
@@ -350,7 +350,7 @@ for more information on how the results get saved and possible caveats
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  4.289 seconds)
+   **Total running time of the script:** ( 0 minutes  4.179 seconds)
 
 
 .. _sphx_glr_download_auto_examples_interruptible-optimization.py:

_sources/auto_examples/hyperparameter-optimization.rst.txt

@@ -119,7 +119,7 @@ scikit-optimize.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  54.489 seconds)
+   **Total running time of the script:** ( 1 minutes  29.212 seconds)
 
 
 .. _sphx_glr_download_auto_examples_parallel-optimization.py:

_sources/auto_examples/interruptible-optimization.rst.txt

@@ -5,24 +5,24 @@
 
 Computation times
 =================
-**23:19.069** total execution time for **auto_examples** files:
+**21:57.068** total execution time for **auto_examples** files:
 
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                           | 11:03.898 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                           | 08:53.446 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_visualizing-results.py` (``visualizing-results.py``)                           | 08:29.191 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_visualizing-results.py` (``visualizing-results.py``)                           | 08:36.201 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``) | 01:24.289 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                       | 01:29.212 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)           | 01:13.964 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``) | 01:26.739 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                       | 00:54.489 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)           | 01:17.704 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)             | 00:04.289 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)             | 00:04.179 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                         | 00:03.377 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                         | 00:03.420 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                       | 00:02.867 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                     | 00:03.165 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                     | 00:02.704 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                       | 00:03.003 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+

_sources/auto_examples/parallel-optimization.rst.txt

@@ -364,7 +364,7 @@ calculated with `total_iterations` property, as in the code below.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  24.289 seconds)
+   **Total running time of the script:** ( 1 minutes  26.739 seconds)
 
 
 .. _sphx_glr_download_auto_examples_sklearn-gridsearchcv-replacement.py: