Pushing the docs to dev/ for branch: master, commit 30a2c9a664a6a93a0ae6bd2c22129cf3b51bf232

Author: skoptci

dev/_downloads/2f6e22007265fe3158cce44853e94a58/strategy-comparison.py

@@ -55,7 +55,8 @@ def plot_branin():
 
     cm = ax.pcolormesh(x_ax, y_ax, fx,
                        norm=LogNorm(vmin=fx.min(),
-                                    vmax=fx.max()))
+                                    vmax=fx.max()),
+                       cmap='viridis_r')
 
     minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])
     ax.plot(minima[:, 0], minima[:, 1], "r.", markersize=14,

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib.colors import LogNorm\n\n\ndef plot_branin():\n    fig, ax = plt.subplots()\n\n    x1_values = np.linspace(-5, 10, 100)\n    x2_values = np.linspace(0, 15, 100)\n    x_ax, y_ax = np.meshgrid(x1_values, x2_values)\n    vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n    fx = np.reshape([branin(val) for val in vals], (100, 100))\n\n    cm = ax.pcolormesh(x_ax, y_ax, fx,\n                       norm=LogNorm(vmin=fx.min(),\n                                    vmax=fx.max()))\n\n    minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])\n    ax.plot(minima[:, 0], minima[:, 1], \"r.\", markersize=14,\n            lw=0, label=\"Minima\")\n\n    cb = fig.colorbar(cm)\n    cb.set_label(\"f(x)\")\n\n    ax.legend(loc=\"best\", numpoints=1)\n\n    ax.set_xlabel(\"$X_0$\")\n    ax.set_xlim([-5, 10])\n    ax.set_ylabel(\"$X_1$\")\n    ax.set_ylim([0, 15])\n\n\nplot_branin()"
+        "from matplotlib.colors import LogNorm\n\n\ndef plot_branin():\n    fig, ax = plt.subplots()\n\n    x1_values = np.linspace(-5, 10, 100)\n    x2_values = np.linspace(0, 15, 100)\n    x_ax, y_ax = np.meshgrid(x1_values, x2_values)\n    vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n    fx = np.reshape([branin(val) for val in vals], (100, 100))\n\n    cm = ax.pcolormesh(x_ax, y_ax, fx,\n                       norm=LogNorm(vmin=fx.min(),\n                                    vmax=fx.max()),\n                       cmap='viridis_r')\n\n    minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])\n    ax.plot(minima[:, 0], minima[:, 1], \"r.\", markersize=14,\n            lw=0, label=\"Minima\")\n\n    cb = fig.colorbar(cm)\n    cb.set_label(\"f(x)\")\n\n    ax.legend(loc=\"best\", numpoints=1)\n\n    ax.set_xlabel(\"$X_0$\")\n    ax.set_xlim([-5, 10])\n    ax.set_ylabel(\"$X_1$\")\n    ax.set_ylim([0, 15])\n\n\nplot_branin()"
       ]
     },
     {

dev/_downloads/6e32b232b2fb7887cfe8ea013442a759/visualizing-results.ipynb

@@ -81,7 +81,8 @@ def plot_branin():
 
     cm = ax.pcolormesh(x_ax, y_ax, fx,
                        norm=LogNorm(vmin=fx.min(),
-                                    vmax=fx.max()))
+                                    vmax=fx.max()),
+                       cmap='viridis_r')
 
     minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])
     ax.plot(minima[:, 0], minima[:, 1], "r.", markersize=14,

dev/_downloads/70b506c92a51b155038b4a3fa37b1f32/visualizing-results.py

@@ -55,7 +55,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib.colors import LogNorm\n\n\ndef plot_branin():\n    fig, ax = plt.subplots()\n\n    x1_values = np.linspace(-5, 10, 100)\n    x2_values = np.linspace(0, 15, 100)\n    x_ax, y_ax = np.meshgrid(x1_values, x2_values)\n    vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n    fx = np.reshape([branin(val) for val in vals], (100, 100))\n\n    cm = ax.pcolormesh(x_ax, y_ax, fx,\n                       norm=LogNorm(vmin=fx.min(),\n                                    vmax=fx.max()))\n\n    minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])\n    ax.plot(minima[:, 0], minima[:, 1], \"r.\", markersize=14,\n            lw=0, label=\"Minima\")\n\n    cb = fig.colorbar(cm)\n    cb.set_label(\"f(x)\")\n\n    ax.legend(loc=\"best\", numpoints=1)\n\n    ax.set_xlabel(\"X1\")\n    ax.set_xlim([-5, 10])\n    ax.set_ylabel(\"X2\")\n    ax.set_ylim([0, 15])\n\n\nplot_branin()"
+        "from matplotlib.colors import LogNorm\n\n\ndef plot_branin():\n    fig, ax = plt.subplots()\n\n    x1_values = np.linspace(-5, 10, 100)\n    x2_values = np.linspace(0, 15, 100)\n    x_ax, y_ax = np.meshgrid(x1_values, x2_values)\n    vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n    fx = np.reshape([branin(val) for val in vals], (100, 100))\n\n    cm = ax.pcolormesh(x_ax, y_ax, fx,\n                       norm=LogNorm(vmin=fx.min(),\n                                    vmax=fx.max()),\n                       cmap='viridis_r')\n\n    minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])\n    ax.plot(minima[:, 0], minima[:, 1], \"r.\", markersize=14,\n            lw=0, label=\"Minima\")\n\n    cb = fig.colorbar(cm)\n    cb.set_label(\"f(x)\")\n\n    ax.legend(loc=\"best\", numpoints=1)\n\n    ax.set_xlabel(\"X1\")\n    ax.set_xlim([-5, 10])\n    ax.set_ylabel(\"X2\")\n    ax.set_ylim([0, 15])\n\n\nplot_branin()"
       ]
     },
     {

dev/_downloads/b4f910a92676697b8c1c26c50df6d7af/strategy-comparison.ipynb

@@ -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 0x7F345C8D6D40
+     random_state: RandomState(MT19937) at 0x7FAB4D60ED40
             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  6.641 seconds)
+   **Total running time of the script:** ( 0 minutes  4.849 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 0x7F345EE89D40
+     random_state: RandomState(MT19937) at 0x7FAB30019640
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            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,
+            specs: {'args': {'func': <function f at 0x7fab2b90c310>, '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 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'}
+                             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 0x7FAB30019640, '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 0x7f3434408730>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7fab29040640>
 
 
 
@@ -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  5.451 seconds)
+   **Total running time of the script:** ( 0 minutes  3.721 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  38.333 seconds)
+   **Total running time of the script:** ( 0 minutes  34.142 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_images/sphx_glr_strategy-comparison_001.png

@@ -207,16 +207,16 @@ Convergence plot
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7f34441e5550>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7fab3001bdc0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  28.214 seconds)
+   **Total running time of the script:** ( 0 minutes  30.931 seconds)
 
-**Estimated memory usage:**  33 MB
+**Estimated memory usage:**  35 MB
 
 
 .. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

dev/_images/sphx_glr_strategy-comparison_thumb.png

@@ -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 0x7F345EE89D40
+     random_state: RandomState(MT19937) at 0x7FAB4FBC0D40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            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,
+            specs: {'args': {'func': <function obj_fun at 0x7fab2b8d9700>, '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 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'}
+                             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 0x7FAB4FBC0D40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fab291903a0>], '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 0x7F345EE89D40
+     random_state: RandomState(MT19937) at 0x7FAB4FBC0D40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            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,
+            specs: {'args': {'func': <function obj_fun at 0x7fab2b8d9700>, '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 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'}
+            0.07662367,  0.08260541, -0.13236828, -0.17524445,  0.10024491]), 'random_state': RandomState(MT19937) at 0x7FAB4FBC0D40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fab291903a0>], '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  4.309 seconds)
+   **Total running time of the script:** ( 0 minutes  3.453 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_images/sphx_glr_visualizing-results_001.png

@@ -292,9 +292,9 @@ Test different kernels
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.471 seconds)
+   **Total running time of the script:** ( 0 minutes  9.445 seconds)
 
-**Estimated memory usage:**  14 MB
+**Estimated memory usage:**  13 MB
 
 
 .. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

dev/_images/sphx_glr_visualizing-results_thumb.png

@@ -119,9 +119,9 @@ scikit-optimize.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  26.307 seconds)
+   **Total running time of the script:** ( 0 minutes  28.610 seconds)
 
-**Estimated memory usage:**  32 MB
+**Estimated memory usage:**  30 MB
 
 
 .. _sphx_glr_download_auto_examples_parallel-optimization.py:

dev/_sources/auto_examples/ask-and-tell.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  32.518 seconds)
+   **Total running time of the script:** ( 0 minutes  25.542 seconds)
 
-**Estimated memory usage:**  35 MB
+**Estimated memory usage:**  34 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-with-categorical.py:

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

@@ -259,7 +259,7 @@ as the minimum argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 4 minutes  21.797 seconds)
+   **Total running time of the script:** ( 3 minutes  47.176 seconds)
 
 **Estimated memory usage:**  9 MB
 

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

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**12:49.286** total execution time for **auto_examples_plots** files:
+**11:29.246** total execution time for **auto_examples_plots** files:
 
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:54.972 | 88.2 MB |
+| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:16.527 | 87.3 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.py` (``partial-dependence-plot.py``)                                   | 03:47.176 | 8.7 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 |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:25.542 | 34.4 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+

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

@@ -112,7 +112,8 @@ function which can be visualised in two dimensions.
 
         cm = ax.pcolormesh(x_ax, y_ax, fx,
                            norm=LogNorm(vmin=fx.min(),
-                                        vmax=fx.max()))
+                                        vmax=fx.max()),
+                           cmap='viridis_r')
 
         minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])
         ax.plot(minima[:, 0], minima[:, 1], "r.", markersize=14,
@@ -359,9 +360,9 @@ in both the placement of samples and the partial dependence plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 7 minutes  54.972 seconds)
+   **Total running time of the script:** ( 7 minutes  16.527 seconds)
 
-**Estimated memory usage:**  88 MB
+**Estimated memory usage:**  87 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_visualizing-results.py:

dev/_sources/auto_examples/interruptible-optimization.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  10.581 seconds)
+   **Total running time of the script:** ( 0 minutes  8.027 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/optimizer-with-different-base-estimator.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  9.865 seconds)
+   **Total running time of the script:** ( 0 minutes  7.839 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/parallel-optimization.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  48.909 seconds)
+   **Total running time of the script:** ( 2 minutes  45.118 seconds)
 
 **Estimated memory usage:**  8 MB