Pushing the docs to dev/ for branch: master, commit f647c4ed28d297c99b919c436585a8139ac1abab

Author: skoptci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -1484,8 +1484,6 @@ <h1>Source code for skopt.plots</h1><div class="highlight"><pre>
                 <span class="c1"># Use standard of 10^n_parameters. Note this</span>
                 <span class="c1"># becomes very slow for many parameters</span>
                 <span class="n">n_minimum_search</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">**</span> <span class="nb">len</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">n_minimum_search</span> <span class="o">&gt;</span> <span class="mi">100000</span><span class="p">:</span>
-                    <span class="n">n_minimum_search</span> <span class="o">=</span> <span class="mi">100000</span>
                 <span class="n">x_vals</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">expected_minimum_random_sampling</span><span class="p">(</span>
                     <span class="n">result</span><span class="p">,</span>
                     <span class="n">n_random_starts</span><span class="o">=</span><span class="n">n_minimum_search</span><span class="p">,</span>

dev/_downloads/d34667f097c619f8afda4bc936e7af21/auto_examples_jupyter.zip

@@ -180,9 +180,9 @@ and report the value back to the optimizer:
               fun: 0.2071864923643295
         func_vals: array([0.20718649])
            models: []
-     random_state: RandomState(MT19937) at 0x7F9A515CCD40
+     random_state: RandomState(MT19937) at 0x7FAA32E7DD40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7f9a34044cd0>, 'dimensions': [(-2.0, 2.0)], 'base_estimator': 'GP', 'n_random_starts': None, 'n_initial_points': 10, 'initial_point_generator': 'lhs', 'n_jobs': 1, 'acq_func': 'EI', 'acq_optimizer': 'sampling', 'random_state': None, 'model_queue_size': None, 'acq_func_kwargs': None, 'acq_optimizer_kwargs': None}, 'function': 'Optimizer'}
+            specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7faa1c54c7c0>, 'dimensions': [(-2.0, 2.0)], 'base_estimator': 'GP', 'n_random_starts': None, 'n_initial_points': 10, 'initial_point_generator': 'lhs', 'n_jobs': 1, 'acq_func': 'EI', 'acq_optimizer': 'sampling', 'random_state': None, 'model_queue_size': None, 'acq_func_kwargs': None, 'acq_optimizer_kwargs': None}, 'function': 'Optimizer'}
                 x: [-0.7315058981975282]
           x_iters: [[-0.7315058981975282]]
 
@@ -286,9 +286,9 @@ meantime:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.818 seconds)
+   **Total running time of the script:** ( 0 minutes  2.968 seconds)
 
-**Estimated memory usage:**  9 MB
+**Estimated memory usage:**  11 MB
 
 
 .. _sphx_glr_download_auto_examples_ask-and-tell.py:

dev/_downloads/scikit-optimize-docs.pdf

@@ -265,13 +265,13 @@ 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 0x7F9A2F6FFC40
+     random_state: RandomState(MT19937) at 0x7FAA1505AC40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7f9a2faf1c10>, '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 0x7faa0e7c3ee0>, '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 0x7F9A2F6FFC40, '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 0x7FAA1505AC40, '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]]
 
@@ -303,7 +303,7 @@ the last iteration:
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7f9a2d087700>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7faa0e89dd90>
 
 
 
@@ -415,7 +415,7 @@ because the GP does not gain anything more by further exploration:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.401 seconds)
+   **Total running time of the script:** ( 0 minutes  3.333 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_modules/skopt/plots.html

@@ -526,7 +526,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  29.238 seconds)
+   **Total running time of the script:** ( 0 minutes  36.596 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 0x7f9a3403e310>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7faa154553d0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  31.438 seconds)
+   **Total running time of the script:** ( 0 minutes  29.919 seconds)
 
-**Estimated memory usage:**  38 MB
+**Estimated memory usage:**  37 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 0x7F9A357D5540
+     random_state: RandomState(MT19937) at 0x7FAA1709C540
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7f9a2f715d30>, '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 0x7faa1506cd30>, '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 0x7F9A357D5540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f9a2d6eb460>], '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 0x7FAA1709C540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7faa0ea7b9d0>], '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 0x7F9A2F863B40
+     random_state: RandomState(MT19937) at 0x7FAA1712DB40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7f9a2f715d30>, '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 0x7faa1506cd30>, '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 0x7F9A2F863B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f9a2d6eb460>], '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 0x7FAA1712DB40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7faa0ea7b9d0>], '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  3.102 seconds)
+   **Total running time of the script:** ( 0 minutes  3.008 seconds)
 
 **Estimated memory usage:**  8 MB
 

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

@@ -254,9 +254,9 @@ Test different kernels
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.553 seconds)
+   **Total running time of the script:** ( 0 minutes  10.126 seconds)
 
-**Estimated memory usage:**  14 MB
+**Estimated memory usage:**  13 MB
 
 
 .. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

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

@@ -119,9 +119,9 @@ scikit-optimize.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  31.105 seconds)
+   **Total running time of the script:** ( 0 minutes  31.227 seconds)
 
-**Estimated memory usage:**  31 MB
+**Estimated memory usage:**  32 MB
 
 
 .. _sphx_glr_download_auto_examples_parallel-optimization.py:

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

@@ -281,9 +281,9 @@ Simple example to show the new 2D plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  16.349 seconds)
+   **Total running time of the script:** ( 0 minutes  13.539 seconds)
 
-**Estimated memory usage:**  9 MB
+**Estimated memory usage:**  11 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-2D.py:

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

@@ -191,7 +191,7 @@ parsing a list as the pars argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  14.551 seconds)
+   **Total running time of the script:** ( 0 minutes  12.991 seconds)
 
 **Estimated memory usage:**  34 MB
 

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

@@ -258,7 +258,7 @@ as the minimum argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  44.443 seconds)
+   **Total running time of the script:** ( 0 minutes  48.786 seconds)
 
 **Estimated memory usage:**  9 MB
 

dev/_sources/auto_examples/plots/partial-dependence-plot-2D.rst.txt

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**09:40.722** total execution time for **auto_examples_plots** files:
+**09:17.083** total execution time for **auto_examples_plots** files:
 
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 08:25.380 | 87.9 MB |
+| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 08:01.767 | 88.0 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 00:44.443 | 9.1 MB  |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 00:48.786 | 8.9 MB  |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``)                             | 00:16.349 | 9.0 MB  |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``)                             | 00:13.539 | 10.6 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:14.551 | 33.7 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:12.991 | 33.9 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+

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

@@ -360,7 +360,7 @@ in both the placement of samples and the partial dependence plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 8 minutes  25.380 seconds)
+   **Total running time of the script:** ( 8 minutes  1.767 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  8.443 seconds)
+   **Total running time of the script:** ( 0 minutes  8.084 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  8.049 seconds)
+   **Total running time of the script:** ( 0 minutes  8.576 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  53.380 seconds)
+   **Total running time of the script:** ( 2 minutes  44.872 seconds)
 
 **Estimated memory usage:**  8 MB
 

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

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**03:09.872** total execution time for **auto_examples_sampler** files:
+**03:01.532** total execution time for **auto_examples_sampler** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sampler_sampling_comparison.py` (``sampling_comparison.py``)                         | 02:53.380 | 8.0 MB |
+| :ref:`sphx_glr_auto_examples_sampler_sampling_comparison.py` (``sampling_comparison.py``)                         | 02:44.872 | 8.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method-integer.py` (``initial-sampling-method-integer.py``) | 00:08.443 | 8.0 MB |
+| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method.py` (``initial-sampling-method.py``)                 | 00:08.576 | 8.1 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method.py` (``initial-sampling-method.py``)                 | 00:08.049 | 8.1 MB |
+| :ref:`sphx_glr_auto_examples_sampler_initial-sampling-method-integer.py` (``initial-sampling-method-integer.py``) | 00:08.084 | 8.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+