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Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
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.. _sphx_glr_auto_examples_plots_partial-dependence-plot.py:
========================
Partial Dependence Plots
========================
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.
.. 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
Objective function
==================
Plot objective now supports optional use of partial dependence as well as
different methods of defining parameter values for dependency plots
.. code-block:: default
# Here we define a function that we evaluate.
def funny_func(x):
s = 0
for i in range(len(x)):
s += (x[i] * i) ** 2
return s
Optimisation using decision trees
=================================
We run forest_minimize on the function
.. code-block:: default
bounds = [(-1, 1.), ] * 3
n_calls = 150
result = forest_minimize(funny_func, bounds, n_calls=n_calls,
base_estimator="ET",
random_state=4)
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.
.. code-block:: default
_ = plot_objective(result, n_points=10)
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_001.png
:alt: partial dependence plot
:class: sphx-glr-single-img
It is possible to change the location of the red dot, which normally shows
the position of the found minimum. We can set it 'expected_minimum',
which is the minimum value of the surrogate function, obtained by a
minimum search method.
.. code-block:: default
_ = plot_objective(result, n_points=10, minimum='expected_minimum')
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_002.png
:alt: partial dependence plot
:class: sphx-glr-single-img
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.
.. code-block:: default
_ = plot_objective(result, sample_source='result', n_points=10)
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_003.png
:alt: partial dependence plot
:class: sphx-glr-single-img
Modify the shown minimum
========================
Here we try with setting the `minimum` parameters to something other than
"result". First we try with "expected_minimum" which is the set of
parameters that gives the miniumum value of the surrogate function,
using scipys minimum search method.
.. code-block:: default
_ = plot_objective(result, n_points=10, sample_source='expected_minimum',
minimum='expected_minimum')
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_004.png
:alt: partial dependence plot
:class: sphx-glr-single-img
"expected_minimum_random" is a naive way of finding the minimum of the
surrogate by only using random sampling:
.. code-block:: default
_ = plot_objective(result, n_points=10, sample_source='expected_minimum_random',
minimum='expected_minimum_random')
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_005.png
:alt: partial dependence plot
:class: sphx-glr-single-img
We can also specify how many initial samples are used for the two different
"expected_minimum" methods. We set it to a low value in the next examples
to showcase how it affects the minimum for the two methods.
.. code-block:: default
_ = plot_objective(result, n_points=10, sample_source='expected_minimum_random',
minimum='expected_minimum_random',
n_minimum_search=10)
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_006.png
:alt: partial dependence plot
:class: sphx-glr-single-img
.. code-block:: default
_ = plot_objective(result, n_points=10, sample_source="expected_minimum",
minimum='expected_minimum', n_minimum_search=2)
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_007.png
:alt: partial dependence plot
:class: sphx-glr-single-img
Set a minimum location
======================
Lastly we can also define these parameters ourself by parsing a list
as the minimum argument:
.. code-block:: default
_ = plot_objective(result, n_points=10, sample_source=[1, -0.5, 0.5],
minimum=[1, -0.5, 0.5])
.. image:: /auto_examples/plots/images/sphx_glr_partial-dependence-plot_008.png
:alt: partial dependence plot
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 45.822 seconds)
**Estimated memory usage:** 8 MB
.. _sphx_glr_download_auto_examples_plots_partial-dependence-plot.py:
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:download:`Download Python source code: partial-dependence-plot.py `
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