.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\plots\partial-dependence-plot-2D.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plots_partial-dependence-plot-2D.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py:


===========================
Partial Dependence Plots 2D
===========================

Hvass-Labs Dec 2017
Holger Nahrstaedt 2020

.. currentmodule:: skopt

Simple example to show the new 2D plots.

.. GENERATED FROM PYTHON SOURCE LINES 13-27

.. code-block:: Python


    print(__doc__)
    from math import exp

    import numpy as np

    from skopt import gp_minimize
    from skopt.plots import plot_histogram, plot_objective, plot_objective_2D
    from skopt.space import Categorical, Integer, Real
    from skopt.utils import point_asdict

    np.random.seed(123)
    import matplotlib.pyplot as plt








.. GENERATED FROM PYTHON SOURCE LINES 28-63

.. code-block:: Python


    dim_learning_rate = Real(name='learning_rate', low=1e-6, high=1e-2, prior='log-uniform')
    dim_num_dense_layers = Integer(name='num_dense_layers', low=1, high=5)
    dim_num_dense_nodes = Integer(name='num_dense_nodes', low=5, high=512)
    dim_activation = Categorical(name='activation', categories=['relu', 'sigmoid'])

    dimensions = [
        dim_learning_rate,
        dim_num_dense_layers,
        dim_num_dense_nodes,
        dim_activation,
    ]

    default_parameters = [1e-4, 1, 64, 'relu']


    def model_fitness(x):
        learning_rate, num_dense_layers, num_dense_nodes, activation = x

        fitness = (
            ((exp(learning_rate) - 1.0) * 1000) ** 2
            + (num_dense_layers) ** 2
            + (num_dense_nodes / 100) ** 2
        )

        fitness *= 1.0 + 0.1 * np.random.rand()

        if activation == 'sigmoid':
            fitness += 10

        return fitness


    print(model_fitness(x=default_parameters))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    1.518471835296799




.. GENERATED FROM PYTHON SOURCE LINES 64-76

.. code-block:: Python


    search_result = gp_minimize(
        func=model_fitness,
        dimensions=dimensions,
        n_calls=30,
        x0=default_parameters,
        random_state=123,
    )

    print(search_result.x)
    print(search_result.fun)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [4.929711467441911e-06, 1, 5, 'relu']
    1.0201137671146678




.. GENERATED FROM PYTHON SOURCE LINES 77-81

.. code-block:: Python


    for fitness, x in sorted(zip(search_result.func_vals, search_result.x_iters)):
        print(fitness, x)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    1.0201137671146678 [4.929711467441911e-06, 1, 5, 'relu']
    1.020825017708778 [5.447929143724181e-06, 1, 5, 'relu']
    1.021667781479872 [0.0001144786138006685, 1, 5, 'relu']
    1.0319553844579283 [3.2015950064095674e-06, 1, 5, 'relu']
    1.0350021584165856 [9.697008005639565e-05, 1, 5, 'relu']
    1.0387852240116218 [3.918631435807059e-06, 1, 5, 'relu']
    1.0558125588274407 [4.826512273477327e-06, 1, 5, 'relu']
    1.0626217337728787 [4.877033239311359e-06, 1, 23, 'relu']
    1.065773703106011 [4.15423808784293e-06, 1, 5, 'relu']
    1.0660997595359294 [1e-06, 1, 5, 'relu']
    1.066913466503469 [0.0001026463063419168, 1, 5, 'relu']
    1.0751293940451896 [1.6006592652941194e-06, 1, 5, 'relu']
    1.087696030687253 [6.1092363610861856e-06, 1, 5, 'relu']
    1.130168257786041 [0.0001280333478556736, 1, 19, 'relu']
    1.1690663250744864 [0.00010510628632493199, 1, 33, 'relu']
    1.4602213686635033 [0.0001, 1, 64, 'relu']
    4.174922707059395 [0.00011226037475857404, 2, 5, 'relu']
    14.337540595777632 [4.961649309025573e-06, 2, 44, 'sigmoid']
    15.811122459303194 [5.768045960755954e-05, 1, 366, 'relu']
    20.75714626376416 [4.6648726500116405e-05, 4, 195, 'relu']
    20.83105097254721 [3.629134387669892e-06, 3, 323, 'relu']
    25.045498550233685 [1.5528231282886148e-05, 3, 380, 'relu']
    25.725698564025883 [0.0010034940899532338, 4, 264, 'relu']
    26.808790139516606 [1e-06, 5, 5, 'relu']
    28.093314338813517 [1e-06, 1, 512, 'relu']
    31.67808942295837 [9.214584006695478e-05, 4, 213, 'sigmoid']
    32.60979725349034 [0.0007109209001237586, 3, 355, 'sigmoid']
    36.436844941374716 [9.52877578124997e-06, 4, 306, 'sigmoid']
    108.24130894769868 [0.01, 1, 5, 'relu']
    117.22558971730295 [0.008953258961145084, 4, 399, 'relu']




.. GENERATED FROM PYTHON SOURCE LINES 82-91

.. code-block:: Python


    space = search_result.space

    print(search_result.x_iters)

    search_space = {name: space[name][1] for name in space.dimension_names}

    print(point_asdict(search_space, default_parameters))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [[0.0001, 1, 64, 'relu'], [0.0007109209001237586, 3, 355, 'sigmoid'], [9.214584006695478e-05, 4, 213, 'sigmoid'], [3.629134387669892e-06, 3, 323, 'relu'], [9.52877578124997e-06, 4, 306, 'sigmoid'], [5.768045960755954e-05, 1, 366, 'relu'], [1.5528231282886148e-05, 3, 380, 'relu'], [4.6648726500116405e-05, 4, 195, 'relu'], [0.008953258961145084, 4, 399, 'relu'], [4.961649309025573e-06, 2, 44, 'sigmoid'], [0.0010034940899532338, 4, 264, 'relu'], [0.00010510628632493199, 1, 33, 'relu'], [0.0001144786138006685, 1, 5, 'relu'], [0.00011226037475857404, 2, 5, 'relu'], [0.0001280333478556736, 1, 19, 'relu'], [5.447929143724181e-06, 1, 5, 'relu'], [4.929711467441911e-06, 1, 5, 'relu'], [4.826512273477327e-06, 1, 5, 'relu'], [0.0001026463063419168, 1, 5, 'relu'], [1e-06, 1, 5, 'relu'], [6.1092363610861856e-06, 1, 5, 'relu'], [1.6006592652941194e-06, 1, 5, 'relu'], [0.01, 1, 5, 'relu'], [1e-06, 5, 5, 'relu'], [1e-06, 1, 512, 'relu'], [3.918631435807059e-06, 1, 5, 'relu'], [9.697008005639565e-05, 1, 5, 'relu'], [3.2015950064095674e-06, 1, 5, 'relu'], [4.15423808784293e-06, 1, 5, 'relu'], [4.877033239311359e-06, 1, 23, 'relu']]
    OrderedDict([('activation', 0.0001), ('learning_rate', 1), ('num_dense_layers', 64), ('num_dense_nodes', 'relu')])




.. GENERATED FROM PYTHON SOURCE LINES 92-97

.. code-block:: Python

    print("Plotting now ...")

    _ = plot_histogram(result=search_result, dimension_identifier='learning_rate', bins=20)
    plt.show()




.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_001.png
   :alt: partial dependence plot 2D
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Plotting now ...




.. GENERATED FROM PYTHON SOURCE LINES 98-105

.. code-block:: Python

    _ = plot_objective_2D(
        result=search_result,
        dimension_identifier1='learning_rate',
        dimension_identifier2='num_dense_nodes',
    )
    plt.show()




.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_002.png
   :alt: partial dependence plot 2D
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 106-114

.. code-block:: Python


    _ = plot_objective_2D(
        result=search_result,
        dimension_identifier1='num_dense_layers',
        dimension_identifier2='num_dense_nodes',
    )
    plt.show()




.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_003.png
   :alt: partial dependence plot 2D
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 115-120

.. code-block:: Python


    _ = plot_objective(
        result=search_result, plot_dims=['num_dense_layers', 'num_dense_nodes']
    )
    plt.show()



.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_004.png
   :alt: partial dependence plot 2D
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-2D_004.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 7.000 seconds)


.. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-2D.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/holgern/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/plots/partial-dependence-plot-2D.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: partial-dependence-plot-2D.ipynb <partial-dependence-plot-2D.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: partial-dependence-plot-2D.py <partial-dependence-plot-2D.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_