Pushing the docs for revision for branch: master, commit fe13709dfc42f76de11f9f0b680fbef62c2ff433

Author: skoptci

_downloads/56061d60b433157ebf7d13e5f8a92c2a/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 0x7F378A1A7A40
+     random_state: RandomState(MT19937) at 0x7F55B6892A40
             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  2.811 seconds)
+   **Total running time of the script:** ( 0 minutes  2.964 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ask-and-tell.py:

_downloads/80167a555a9152802d82aab42c671ad2/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 0x7F37D9B6CE40
+     random_state: RandomState(MT19937) at 0x7F56082C5E40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7f37da10ce50>, '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 0x7f5608268040>, '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, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F37D9B6CE40, '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, 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F56082C5E40, '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: [-0.35076964213550554]
           x_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [0.9698921802985794], [-0.4464493263345515], [-0.6474638307563569], [-0.35076964213550554], [-0.28714768066245777], [-0.2968537677230516], [-2.0], [2.0], [-1.3149517821938494], [-0.3218160845454081]]
 
@@ -305,7 +305,7 @@ the last iteration:
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7f375571de20>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f5608bf0c70>
 
 
 
@@ -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  2.973 seconds)
+   **Total running time of the script:** ( 0 minutes  3.106 seconds)
 
 
 .. _sphx_glr_download_auto_examples_bayesian-optimization.py:

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

@@ -562,7 +562,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  58.925 seconds)
+   **Total running time of the script:** ( 0 minutes  58.599 seconds)
 
 
 .. _sphx_glr_download_auto_examples_exploration-vs-exploitation.py:

_sources/auto_examples/bayesian-optimization.rst.txt

@@ -207,14 +207,14 @@ Convergence plot
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7f37da3d7c70>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f56089d47c0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  36.309 seconds)
+   **Total running time of the script:** ( 2 minutes  9.208 seconds)
 
 
 .. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

_sources/auto_examples/exploration-vs-exploitation.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 0x7F37D9B6C940
+     random_state: RandomState(MT19937) at 0x7F56082C5940
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7f37da282310>, '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 0x7f560897cd30>, '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 0x7F37D9B6C940, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f37555d8160>], '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 0x7F56082C5940, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f5581ea28b0>], '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 0x7F37D9B6C940
+     random_state: RandomState(MT19937) at 0x7F56082C5940
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7f37da282310>, '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 0x7f560897cd30>, '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 0x7F37D9B6C940, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f37555d8160>], '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 0x7F56082C5940, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f5581ea28b0>], '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  4.123 seconds)
+   **Total running time of the script:** ( 0 minutes  4.101 seconds)
 
 
 .. _sphx_glr_download_auto_examples_interruptible-optimization.py:

_sources/auto_examples/hyperparameter-optimization.rst.txt

@@ -292,7 +292,7 @@ Test different kernels
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.441 seconds)
+   **Total running time of the script:** ( 0 minutes  11.601 seconds)
 
 
 .. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

_sources/auto_examples/interruptible-optimization.rst.txt

@@ -119,7 +119,7 @@ scikit-optimize.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  32.650 seconds)
+   **Total running time of the script:** ( 1 minutes  36.315 seconds)
 
 
 .. _sphx_glr_download_auto_examples_parallel-optimization.py:

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

@@ -190,7 +190,7 @@ parsing a list as the pars argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  24.783 seconds)
+   **Total running time of the script:** ( 0 minutes  24.567 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-with-categorical.py:

_sources/auto_examples/parallel-optimization.rst.txt

@@ -259,7 +259,7 @@ as the minimum argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  41.673 seconds)
+   **Total running time of the script:** ( 3 minutes  40.225 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot.py:

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

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**12:25.889** total execution time for **auto_examples_plots** files:
+**11:50.692** total execution time for **auto_examples_plots** files:
 
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 08:19.434 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:45.900 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 03:41.673 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 03:40.225 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:24.783 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:24.567 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+

_sources/auto_examples/plots/partial-dependence-plot.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:** ( 8 minutes  19.434 seconds)
+   **Total running time of the script:** ( 7 minutes  45.900 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plots_visualizing-results.py:

_sources/auto_examples/plots/sg_execution_times.rst.txt

@@ -5,26 +5,26 @@
 
 Computation times
 =================
-**21:23.214** total execution time for **auto_examples** files:
+**17:22.265** total execution time for **auto_examples** files:
 
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                                         | 14:37.786 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                                         | 10:54.176 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)                         | 02:36.309 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)                         | 02:09.208 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                                     | 01:32.650 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                                     | 01:36.315 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``)               | 01:12.301 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``)               | 01:19.138 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_exploration-vs-exploitation.py` (``exploration-vs-exploitation.py``)                         | 00:58.925 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_exploration-vs-exploitation.py` (``exploration-vs-exploitation.py``)                         | 00:58.599 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_optimizer-with-different-base-estimator.py` (``optimizer-with-different-base-estimator.py``) | 00:12.441 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_optimizer-with-different-base-estimator.py` (``optimizer-with-different-base-estimator.py``) | 00:11.601 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)                           | 00:04.123 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)                           | 00:04.101 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                                     | 00:02.973 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                                     | 00:03.106 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                                   | 00:02.897 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                                   | 00:03.057 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                                       | 00:02.811 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                                       | 00:02.964 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+--------+

_sources/auto_examples/plots/visualizing-results.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  12.301 seconds)
+   **Total running time of the script:** ( 1 minutes  19.138 seconds)
 
 
 .. _sphx_glr_download_auto_examples_sklearn-gridsearchcv-replacement.py:

_sources/auto_examples/sg_execution_times.rst.txt

@@ -174,7 +174,7 @@ And load from file using :class:`skopt.load`:
  .. code-block:: none
 
 
-    -0.19303607922775187
+    -0.18355210652121143
 
 
 
@@ -208,7 +208,7 @@ level (highest in this case):
  .. code-block:: none
 
     Without compression: 80120 bytes
-    Compressed with gz:  18874 bytes
+    Compressed with gz:  18384 bytes
 
 
 
@@ -296,7 +296,7 @@ copy is created:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.897 seconds)
+   **Total running time of the script:** ( 0 minutes  3.057 seconds)
 
 
 .. _sphx_glr_download_auto_examples_store-and-load-results.py:

_sources/auto_examples/sklearn-gridsearchcv-replacement.rst.txt

@@ -206,7 +206,7 @@ Note that this can take a few minutes.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7f375564a820>
+    <matplotlib.legend.Legend object at 0x7f5581ce29d0>
 
 
 
@@ -227,7 +227,7 @@ values that are below the true value simply because they are fluctuations.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 14 minutes  37.786 seconds)
+   **Total running time of the script:** ( 10 minutes  54.176 seconds)
 
 
 .. _sphx_glr_download_auto_examples_strategy-comparison.py: