Pushing the docs to 0.7/ for branch: 0.7.X, commit 8caf37d9f0b630de9a994539f557d34937c03804

Author: skoptci

0.7/_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 0x7FC3FBE26D40
+     random_state: RandomState(MT19937) at 0x7F833EAFAD40
             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  5.358 seconds)
+   **Total running time of the script:** ( 0 minutes  5.175 seconds)
 
 **Estimated memory usage:**  8 MB
 

0.7/_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 0x7FC3DE03E840
+     random_state: RandomState(MT19937) at 0x7F8322CE7B40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7fc3d38709d0>, '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 0x7f832819e430>, '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 0x7FC3DE03E840, '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 0x7F8322CE7B40, '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 0x7fc3dc9f3970>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f831a6b7ac0>
 
 
 
@@ -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  4.146 seconds)
+   **Total running time of the script:** ( 0 minutes  3.780 seconds)
 
 **Estimated memory usage:**  8 MB
 

0.7/_downloads/scikit-optimize-docs.pdf

@@ -562,7 +562,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  43.133 seconds)
+   **Total running time of the script:** ( 0 minutes  34.924 seconds)
 
 **Estimated memory usage:**  8 MB
 

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

@@ -207,16 +207,16 @@ Convergence plot
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7fc3dfff8790>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f8320cacdc0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  35.734 seconds)
+   **Total running time of the script:** ( 0 minutes  30.711 seconds)
 
-**Estimated memory usage:**  37 MB
+**Estimated memory usage:**  33 MB
 
 
 .. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

0.7/_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 0x7FC3DFF9FB40
+     random_state: RandomState(MT19937) at 0x7F8322CE7B40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fc3de066d30>, '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 0x7f8320d43d30>, '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 0x7FC3DFF9FB40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fc3d3810af0>], '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 0x7F8322CE7B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f831a509100>], '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 0x7FC3DFF9FB40
+     random_state: RandomState(MT19937) at 0x7F8322CE7B40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fc3de066d30>, '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 0x7f8320d43d30>, '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 0x7FC3DFF9FB40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fc3d3810af0>], '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 0x7F8322CE7B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f831a509100>], '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  3.889 seconds)
+   **Total running time of the script:** ( 0 minutes  3.680 seconds)
 
 **Estimated memory usage:**  8 MB
 

0.7/_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  11.806 seconds)
+   **Total running time of the script:** ( 0 minutes  9.161 seconds)
 
-**Estimated memory usage:**  12 MB
+**Estimated memory usage:**  13 MB
 
 
 .. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

0.7/_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  33.788 seconds)
+   **Total running time of the script:** ( 0 minutes  27.735 seconds)
 
-**Estimated memory usage:**  30 MB
+**Estimated memory usage:**  29 MB
 
 
 .. _sphx_glr_download_auto_examples_parallel-optimization.py:

0.7/_sources/auto_examples/interruptible-optimization.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  33.105 seconds)
+   **Total running time of the script:** ( 0 minutes  25.236 seconds)
 
 **Estimated memory usage:**  34 MB
 

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

@@ -259,9 +259,9 @@ as the minimum argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  20.554 seconds)
+   **Total running time of the script:** ( 4 minutes  7.363 seconds)
 
-**Estimated memory usage:**  8 MB
+**Estimated memory usage:**  9 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot.py:

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

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**15:02.871** total execution time for **auto_examples_plots** files:
+**12:02.776** total execution time for **auto_examples_plots** files:
 
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 09:09.212 | 87.0 MB |
+| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:30.177 | 87.8 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 05:20.554 | 8.3 MB  |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 04:07.363 | 8.6 MB  |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:33.105 | 33.5 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:25.236 | 34.3 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+

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

@@ -359,9 +359,9 @@ in both the placement of samples and the partial dependence plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 9 minutes  9.212 seconds)
+   **Total running time of the script:** ( 7 minutes  30.177 seconds)
 
-**Estimated memory usage:**  87 MB
+**Estimated memory usage:**  88 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_visualizing-results.py:

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

@@ -5,26 +5,26 @@
 
 Computation times
 =================
-**07:05.894** total execution time for **auto_examples** files:
+**06:10.853** total execution time for **auto_examples** files:
 
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                                         | 03:44.902 | 65.2 MB |
+| :ref:`sphx_glr_auto_examples_strategy-comparison.py` (``strategy-comparison.py``)                                         | 03:22.036 | 65.1 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``)               | 00:59.828 | 9.0 MB  |
+| :ref:`sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py` (``sklearn-gridsearchcv-replacement.py``)               | 00:50.878 | 8.8 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_exploration-vs-exploitation.py` (``exploration-vs-exploitation.py``)                         | 00:43.133 | 8.2 MB  |
+| :ref:`sphx_glr_auto_examples_exploration-vs-exploitation.py` (``exploration-vs-exploitation.py``)                         | 00:34.924 | 8.2 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)                         | 00:35.734 | 36.6 MB |
+| :ref:`sphx_glr_auto_examples_hyperparameter-optimization.py` (``hyperparameter-optimization.py``)                         | 00:30.711 | 32.8 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                                     | 00:33.788 | 29.6 MB |
+| :ref:`sphx_glr_auto_examples_parallel-optimization.py` (``parallel-optimization.py``)                                     | 00:27.735 | 29.1 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_optimizer-with-different-base-estimator.py` (``optimizer-with-different-base-estimator.py``) | 00:11.806 | 11.8 MB |
+| :ref:`sphx_glr_auto_examples_optimizer-with-different-base-estimator.py` (``optimizer-with-different-base-estimator.py``) | 00:09.161 | 13.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                                       | 00:05.358 | 8.3 MB  |
+| :ref:`sphx_glr_auto_examples_ask-and-tell.py` (``ask-and-tell.py``)                                                       | 00:05.175 | 8.4 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                                     | 00:04.146 | 8.2 MB  |
+| :ref:`sphx_glr_auto_examples_bayesian-optimization.py` (``bayesian-optimization.py``)                                     | 00:03.780 | 8.2 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)                           | 00:03.889 | 8.3 MB  |
+| :ref:`sphx_glr_auto_examples_interruptible-optimization.py` (``interruptible-optimization.py``)                           | 00:03.680 | 8.3 MB  |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                                   | 00:03.311 | 11.9 MB |
+| :ref:`sphx_glr_auto_examples_store-and-load-results.py` (``store-and-load-results.py``)                                   | 00:02.774 | 10.7 MB |
 +---------------------------------------------------------------------------------------------------------------------------+-----------+---------+

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

@@ -358,7 +358,7 @@ calculated with `total_iterations` property, as in the code below.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  59.828 seconds)
+   **Total running time of the script:** ( 0 minutes  50.878 seconds)
 
 **Estimated memory usage:**  9 MB
 

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

@@ -115,8 +115,6 @@ We will use the same optimization problem as in the
       warnings.warn("The objective has been evaluated "
     /home/circleci/project/skopt/optimizer/optimizer.py:409: UserWarning: The objective has been evaluated at this point before.
       warnings.warn("The objective has been evaluated "
-    /home/circleci/project/skopt/optimizer/optimizer.py:409: UserWarning: The objective has been evaluated at this point before.
-      warnings.warn("The objective has been evaluated "
 
 
 
@@ -174,7 +172,7 @@ And load from file using :class:`skopt.load`:
  .. code-block:: none
 
 
-    -0.09233683633169375
+    -0.2423455753391654
 
 
 
@@ -208,7 +206,7 @@ level (highest in this case):
  .. code-block:: none
 
     Without compression: 80120 bytes
-    Compressed with gz:  18683 bytes
+    Compressed with gz:  19024 bytes
 
 
 
@@ -296,9 +294,9 @@ copy is created:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.311 seconds)
+   **Total running time of the script:** ( 0 minutes  2.774 seconds)
 
-**Estimated memory usage:**  12 MB
+**Estimated memory usage:**  11 MB
 
 
 .. _sphx_glr_download_auto_examples_store-and-load-results.py:

0.7/_sources/auto_examples/sg_execution_times.rst.txt

@@ -206,7 +206,7 @@ Note that this can take a few minutes.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fc3d3840610>
+    <matplotlib.legend.Legend object at 0x7f8322d9f2e0>
 
 
 
@@ -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:** ( 3 minutes  44.902 seconds)
+   **Total running time of the script:** ( 3 minutes  22.036 seconds)
 
 **Estimated memory usage:**  65 MB