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.. only:: html

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

.. _sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py:


=================================================
Partial Dependence Plots  with categorical values
=================================================

Sigurd Carlsen Feb 2019
Holger Nahrstaedt 2020

.. currentmodule:: skopt

Plot objective now supports optional use of partial dependence as well as
different methods of defining parameter values for dependency plots.

.. GENERATED FROM PYTHON SOURCE LINES 14-30

.. code-block:: Python


    print(__doc__)
    import numpy as np

    from skopt.plots import plot_objective

    np.random.seed(123)
    import numpy as np
    from sklearn.datasets import load_breast_cancer
    from sklearn.model_selection import cross_val_score
    from sklearn.tree import DecisionTreeClassifier

    from skopt import gp_minimize
    from skopt.plots import plot_objective
    from skopt.space import Categorical, Integer








.. GENERATED FROM PYTHON SOURCE LINES 31-34

objective function
==================
Here we define a function that we evaluate.

.. GENERATED FROM PYTHON SOURCE LINES 34-43

.. code-block:: Python



    def objective(params):
        clf = DecisionTreeClassifier(
            **{dim.name: val for dim, val in zip(SPACE, params) if dim.name != 'dummy'}
        )
        return -np.mean(cross_val_score(clf, *load_breast_cancer(return_X_y=True)))









.. GENERATED FROM PYTHON SOURCE LINES 44-46

Bayesian optimization
=====================

.. GENERATED FROM PYTHON SOURCE LINES 46-59

.. code-block:: Python


    SPACE = [
        Integer(1, 20, name='max_depth'),
        Integer(2, 100, name='min_samples_split'),
        Integer(5, 30, name='min_samples_leaf'),
        Integer(1, 30, name='max_features'),
        Categorical(list('abc'), name='dummy'),
        Categorical(['gini', 'entropy'], name='criterion'),
        Categorical(list('def'), name='dummy'),
    ]

    result = gp_minimize(objective, SPACE, n_calls=20)








.. GENERATED FROM PYTHON SOURCE LINES 60-67

Partial dependence plot
=======================

Here we see an example of using partial dependence. Even when setting
n_points all the way down to 10 from the default of 40, this method is
still very slow. This is because partial dependence calculates 250 extra
predictions for each point on the plots.

.. GENERATED FROM PYTHON SOURCE LINES 67-70

.. code-block:: Python


    _ = plot_objective(result, n_points=10)




.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_001.png
   :alt: partial dependence plot with categorical
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 71-77

Plot without partial dependence
===============================
Here we plot without partial dependence. We see that it is a lot faster.
Also the values for the other parameters are set to the default "result"
which is the parameter set of the best observed value so far. In the case
of funny_func this is close to 0 for all parameters.

.. GENERATED FROM PYTHON SOURCE LINES 77-80

.. code-block:: Python


    _ = plot_objective(result, sample_source='result', n_points=10)




.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_002.png
   :alt: partial dependence plot with categorical
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 81-89

Modify the shown minimum
========================
Here we try with setting the other parameters to something other than
"result". When dealing with categorical dimensions we can't use
'expected_minimum'. Therefore we try with "expected_minimum_random"
which is a naive way of finding the minimum of the surrogate by only
using random sampling. `n_minimum_search` sets the number of random samples,
which is used to find the minimum

.. GENERATED FROM PYTHON SOURCE LINES 89-98

.. code-block:: Python


    _ = plot_objective(
        result,
        n_points=10,
        sample_source='expected_minimum_random',
        minimum='expected_minimum_random',
        n_minimum_search=10000,
    )




.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_003.png
   :alt: partial dependence plot with categorical
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 99-103

Set a minimum location
======================
Lastly we can also define these parameters ourselfs by
parsing a list as the pars argument:

.. GENERATED FROM PYTHON SOURCE LINES 103-110

.. code-block:: Python


    _ = plot_objective(
        result,
        n_points=10,
        sample_source=[15, 4, 7, 15, 'b', 'entropy', 'e'],
        minimum=[15, 4, 7, 15, 'b', 'entropy', 'e'],
    )



.. image-sg:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_004.png
   :alt: partial dependence plot with categorical
   :srcset: /auto_examples/plots/images/sphx_glr_partial-dependence-plot-with-categorical_004.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 8.190 seconds)


.. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-with-categorical.py:

.. only:: html

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

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