Pushing the docs to dev/ for branch: master, commit d4e67c0a275660e59c0652aa8bc09096b688ac3b

Author: skoptci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.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 0x7FCC1BAC6D40
+     random_state: RandomState(MT19937) at 0x7F345C8D6D40
             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  5.265 seconds)
+   **Total running time of the script:** ( 0 minutes  6.641 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_downloads/d34667f097c619f8afda4bc936e7af21/auto_examples_jupyter.zip

@@ -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 0x7FCC1E078D40
+     random_state: RandomState(MT19937) at 0x7F345EE89D40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7fcbf3504790>, '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 0x7f3434336e50>, '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 0x7FCC1E078D40, '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 0x7F345EE89D40, '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 0x7fcbffcd6d90>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f3434408730>
 
 
 
@@ -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  3.018 seconds)
+   **Total running time of the script:** ( 0 minutes  5.451 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_downloads/scikit-optimize-docs.pdf

@@ -562,7 +562,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  31.667 seconds)
+   **Total running time of the script:** ( 0 minutes  38.333 seconds)
 
 **Estimated memory usage:**  8 MB
 

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

@@ -207,16 +207,16 @@ Convergence plot
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7fcbfe0f6a00>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f34441e5550>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  29.798 seconds)
+   **Total running time of the script:** ( 0 minutes  28.214 seconds)
 
-**Estimated memory usage:**  34 MB
+**Estimated memory usage:**  33 MB
 
 
 .. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

dev/_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 0x7FCC1E078D40
+     random_state: RandomState(MT19937) at 0x7F345EE89D40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fcbfddcd700>, '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 0x7f344401a700>, '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, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7FCC1E078D40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fcbf35d5e20>], '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, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F345EE89D40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f343427e3d0>], '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 0x7FCC1E078D40
+     random_state: RandomState(MT19937) at 0x7F345EE89D40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fcbfddcd700>, '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 0x7f344401a700>, '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, 'n_initial_points': 10, 'initial_point_generator': 'random', '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 0x7FCC1E078D40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fcbf35d5e20>], '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 0x7F345EE89D40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f343427e3d0>], '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  3.873 seconds)
+   **Total running time of the script:** ( 0 minutes  4.309 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/exploration-vs-exploitation.rst.txt

@@ -292,9 +292,9 @@ Test different kernels
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.858 seconds)
+   **Total running time of the script:** ( 0 minutes  11.471 seconds)
 
-**Estimated memory usage:**  13 MB
+**Estimated memory usage:**  14 MB
 
 
 .. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

dev/_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  27.681 seconds)
+   **Total running time of the script:** ( 0 minutes  26.307 seconds)
 
 **Estimated memory usage:**  32 MB
 

dev/_sources/auto_examples/interruptible-optimization.rst.txt

@@ -190,9 +190,9 @@ parsing a list as the pars argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  23.398 seconds)
+   **Total running time of the script:** ( 0 minutes  32.518 seconds)
 
-**Estimated memory usage:**  34 MB
+**Estimated memory usage:**  35 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-with-categorical.py:

dev/_sources/auto_examples/optimizer-with-different-base-estimator.rst.txt

@@ -259,7 +259,7 @@ as the minimum argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  47.127 seconds)
+   **Total running time of the script:** ( 4 minutes  21.797 seconds)
 
 **Estimated memory usage:**  9 MB
 

dev/_sources/auto_examples/parallel-optimization.rst.txt

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**11:48.780** total execution time for **auto_examples_plots** files:
+**12:49.286** total execution time for **auto_examples_plots** files:
 
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:38.256 | 87.6 MB |
+| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:54.972 | 88.2 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 03:47.127 | 8.8 MB  |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 04:21.797 | 9.0 MB  |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:23.398 | 34.1 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:32.518 | 34.6 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+

dev/_sources/auto_examples/plots/partial-dependence-plot-with-categorical.rst.txt

@@ -359,7 +359,7 @@ in both the placement of samples and the partial dependence plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 7 minutes  38.256 seconds)
+   **Total running time of the script:** ( 7 minutes  54.972 seconds)
 
 **Estimated memory usage:**  88 MB
 

dev/_sources/auto_examples/plots/partial-dependence-plot.rst.txt

@@ -429,7 +429,7 @@ It can be seen that random has the worst performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.051 seconds)
+   **Total running time of the script:** ( 0 minutes  10.581 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/plots/sg_execution_times.rst.txt

@@ -340,7 +340,7 @@ It can be seen that random has the worst performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.979 seconds)
+   **Total running time of the script:** ( 0 minutes  9.865 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/plots/visualizing-results.rst.txt

@@ -283,7 +283,7 @@ n_random_starts = 10 produces the best results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  53.192 seconds)
+   **Total running time of the script:** ( 2 minutes  48.909 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/sampler/initial-sampling-method-integer.rst.txt

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**03:09.221** total execution time for **auto_examples_sampler** files:
+**03:09.355** total execution time for **auto_examples_sampler** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sampler_sampling_comparison.py` (``sampling_comparison.py``)                         | 02:53.192 | 8.0 MB |
+| :ref:`sphx_glr_auto_examples_sampler_sampling_comparison.py` (``sampling_comparison.py``)                         | 02:48.909 | 8.2 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method-integer.py` (``initial-sampling-method-integer.py``) | 00:08.051 | 8.0 MB |
+| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method-integer.py` (``initial-sampling-method-integer.py``) | 00:10.581 | 8.2 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method.py` (``initial-sampling-method.py``)                 | 00:07.979 | 8.0 MB |
+| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method.py` (``initial-sampling-method.py``)                 | 00:09.865 | 8.3 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+

dev/_sources/auto_examples/sampler/initial-sampling-method.rst.txt

@@ -5,26 +5,26 @@
 
 Computation times
 =================
-**06:22.206** total execution time for **auto_examples** files:
+**06:08.638** total execution time for **auto_examples** files:
 
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                                         | 03:35.037 | 65.0 MB |
+| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                                         | 03:04.296 | 65.2 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``)               | 00:53.738 | 8.6 MB  |
+| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``)               | 01:01.104 | 8.8 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_exploration-vs-exploitation.py` (``exploration-vs-exploitation.py``)                         | 00:31.667 | 8.0 MB  |
+| :ref:`sphx_glr_auto_examples_exploration-vs-exploitation.py` (``exploration-vs-exploitation.py``)                         | 00:38.333 | 8.2 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)                         | 00:29.798 | 34.1 MB |
+| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)                         | 00:28.214 | 32.6 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                                     | 00:27.681 | 31.5 MB |
+| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                                     | 00:26.307 | 31.8 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_optimizer-with-different-base-estimator.py` (``optimizer-with-different-base-estimator.py``) | 00:08.858 | 12.8 MB |
+| :ref:`sphx_glr_auto_examples_optimizer-with-different-base-estimator.py` (``optimizer-with-different-base-estimator.py``) | 00:11.471 | 13.7 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                                       | 00:05.265 | 8.1 MB  |
+| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                                       | 00:06.641 | 8.2 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)                           | 00:03.873 | 8.1 MB  |
+| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                                     | 00:05.451 | 8.2 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                                   | 00:03.271 | 11.7 MB |
+| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)                           | 00:04.309 | 8.3 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                                     | 00:03.018 | 8.0 MB  |
+| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                                   | 00:02.513 | 11.8 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+