Pushing the docs to dev/ for branch: master, commit 3e23b6318dfb34b639a4ce730579b9adbaf1328f

Author: skoptci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -239,7 +239,7 @@ <h1>Source code for skopt.acquisition</h1><div class="highlight"><pre>
 <span class="sd">        exploration over exploitation and vice versa.</span>
 <span class="sd">        If set to &#39;inf&#39;, the acquisition function will only use the variance</span>
 <span class="sd">        which is useful in a pure exploration setting.</span>
-<span class="sd">        Useless if ``method`` is set to &quot;LCB&quot;.</span>
+<span class="sd">        Useless if ``method`` is not set to &quot;LCB&quot;.</span>
 
 <span class="sd">    return_grad : boolean, optional</span>
 <span class="sd">        Whether or not to return the grad. Implemented only for the case where</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 0x7FB9CAD23D40
+     random_state: RandomState(MT19937) at 0x7F40386B8D40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7fb9ad2ffc40>, '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 0x7f4021d966d0>, '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]]
 
@@ -286,7 +286,7 @@ meantime:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.214 seconds)
+   **Total running time of the script:** ( 0 minutes  2.879 seconds)
 
 **Estimated memory usage:**  9 MB
 

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 0x7FB9AD266C40
+     random_state: RandomState(MT19937) at 0x7F401A950440
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7fb9b406a820>, '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 0x7f4021d5d820>, '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 0x7FB9AD266C40, '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 0x7F401A950440, '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 0x7fb9a6a661f0>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f401806be50>
 
 
 
@@ -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  3.754 seconds)
+   **Total running time of the script:** ( 0 minutes  3.432 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_modules/skopt/acquisition.html

@@ -526,7 +526,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  30.356 seconds)
+   **Total running time of the script:** ( 0 minutes  31.500 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 0x7fb9ad327340>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f40201c9100>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  32.428 seconds)
+   **Total running time of the script:** ( 0 minutes  31.788 seconds)
 
-**Estimated memory usage:**  35 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 0x7FB9B4047540
+     random_state: RandomState(MT19937) at 0x7F4020873540
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fb9ad2498b0>, '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 0x7f401a9278b0>, '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 0x7FB9B4047540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fb9a670e400>], '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 0x7F4020873540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f4018170dc0>], '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 0x7FB9ACFA9B40
+     random_state: RandomState(MT19937) at 0x7F401A7BBE40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fb9ad2498b0>, '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 0x7f401a9278b0>, '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 0x7FB9ACFA9B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fb9a670e400>], '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 0x7F401A7BBE40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f4018170dc0>], '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  3.141 seconds)
+   **Total running time of the script:** ( 0 minutes  3.127 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  8.923 seconds)
+   **Total running time of the script:** ( 0 minutes  10.366 seconds)
 
-**Estimated memory usage:**  13 MB
+**Estimated memory usage:**  12 MB
 
 
 .. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py: