.. 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-with-categorical.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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-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 14-29 .. code-block:: default print(__doc__) import sys from skopt.plots import plot_objective from skopt import forest_minimize import numpy as np np.random.seed(123) import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_val_score from skopt.space import Integer, Categorical from skopt import plots, gp_minimize from skopt.plots import plot_objective .. GENERATED FROM PYTHON SOURCE LINES 30-33 objective function ================== Here we define a function that we evaluate. .. GENERATED FROM PYTHON SOURCE LINES 33-40 .. code-block:: default 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 41-43 Bayesian optimization ===================== .. GENERATED FROM PYTHON SOURCE LINES 43-56 .. code-block:: default 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 57-64 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 64-67 .. code-block:: default _ = 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 68-74 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 74-77 .. code-block:: default _ = 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 78-86 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 86-90 .. code-block:: default _ = 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 91-95 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 95-98 .. code-block:: default _ = 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 14.357 seconds) **Estimated memory usage:** 52 MB .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-with-categorical.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-optimize/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/plots/partial-dependence-plot-with-categorical.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: partial-dependence-plot-with-categorical.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: partial-dependence-plot-with-categorical.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_