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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/model_selection/plot_cv_indices.py"
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.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_model_selection_plot_cv_indices.py>`
to download the full example code or to run this example in your browser via JupyterLite or Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_model_selection_plot_cv_indices.py:
Visualizing cross-validation behavior in scikit-learn
=====================================================
Choosing the right cross-validation object is a crucial part of fitting a
model properly. There are many ways to split data into training and test
sets in order to avoid model overfitting, to standardize the number of
groups in test sets, etc.
This example visualizes the behavior of several common scikit-learn objects
for comparison.
.. GENERATED FROM PYTHON SOURCE LINES 14-35
.. code-block:: default
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Patch
from sklearn.model_selection import (
GroupKFold,
GroupShuffleSplit,
KFold,
ShuffleSplit,
StratifiedGroupKFold,
StratifiedKFold,
StratifiedShuffleSplit,
TimeSeriesSplit,
)
rng = np.random.RandomState(1338)
cmap_data = plt.cm.Paired
cmap_cv = plt.cm.coolwarm
n_splits = 4
.. GENERATED FROM PYTHON SOURCE LINES 36-48
Visualize our data
------------------
First, we must understand the structure of our data. It has 100 randomly
generated input datapoints, 3 classes split unevenly across datapoints,
and 10 "groups" split evenly across datapoints.
As we'll see, some cross-validation objects do specific things with
labeled data, others behave differently with grouped data, and others
do not use this information.
To begin, we'll visualize our data.
.. GENERATED FROM PYTHON SOURCE LINES 48-90
.. code-block:: default
# Generate the class/group data
n_points = 100
X = rng.randn(100, 10)
percentiles_classes = [0.1, 0.3, 0.6]
y = np.hstack([[ii] * int(100 * perc) for ii, perc in enumerate(percentiles_classes)])
# Generate uneven groups
group_prior = rng.dirichlet([2] * 10)
groups = np.repeat(np.arange(10), rng.multinomial(100, group_prior))
def visualize_groups(classes, groups, name):
# Visualize dataset groups
fig, ax = plt.subplots()
ax.scatter(
range(len(groups)),
[0.5] * len(groups),
c=groups,
marker="_",
lw=50,
cmap=cmap_data,
)
ax.scatter(
range(len(groups)),
[3.5] * len(groups),
c=classes,
marker="_",
lw=50,
cmap=cmap_data,
)
ax.set(
ylim=[-1, 5],
yticks=[0.5, 3.5],
yticklabels=["Data\ngroup", "Data\nclass"],
xlabel="Sample index",
)
visualize_groups(y, groups, "no groups")
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_001.png
:alt: plot cv indices
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 91-98
Define a function to visualize cross-validation behavior
--------------------------------------------------------
We'll define a function that lets us visualize the behavior of each
cross-validation object. We'll perform 4 splits of the data. On each
split, we'll visualize the indices chosen for the training set
(in blue) and the test set (in red).
.. GENERATED FROM PYTHON SOURCE LINES 98-145
.. code-block:: default
def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):
"""Create a sample plot for indices of a cross-validation object."""
# Generate the training/testing visualizations for each CV split
for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):
# Fill in indices with the training/test groups
indices = np.array([np.nan] * len(X))
indices[tt] = 1
indices[tr] = 0
# Visualize the results
ax.scatter(
range(len(indices)),
[ii + 0.5] * len(indices),
c=indices,
marker="_",
lw=lw,
cmap=cmap_cv,
vmin=-0.2,
vmax=1.2,
)
# Plot the data classes and groups at the end
ax.scatter(
range(len(X)), [ii + 1.5] * len(X), c=y, marker="_", lw=lw, cmap=cmap_data
)
ax.scatter(
range(len(X)), [ii + 2.5] * len(X), c=group, marker="_", lw=lw, cmap=cmap_data
)
# Formatting
yticklabels = list(range(n_splits)) + ["class", "group"]
ax.set(
yticks=np.arange(n_splits + 2) + 0.5,
yticklabels=yticklabels,
xlabel="Sample index",
ylabel="CV iteration",
ylim=[n_splits + 2.2, -0.2],
xlim=[0, 100],
)
ax.set_title("{}".format(type(cv).__name__), fontsize=15)
return ax
.. GENERATED FROM PYTHON SOURCE LINES 146-148
Let's see how it looks for the :class:`~sklearn.model_selection.KFold`
cross-validation object:
.. GENERATED FROM PYTHON SOURCE LINES 148-153
.. code-block:: default
fig, ax = plt.subplots()
cv = KFold(n_splits)
plot_cv_indices(cv, X, y, groups, ax, n_splits)
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_002.png
:alt: KFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<Axes: title={'center': 'KFold'}, xlabel='Sample index', ylabel='CV iteration'>
.. GENERATED FROM PYTHON SOURCE LINES 154-163
As you can see, by default the KFold cross-validation iterator does not
take either datapoint class or group into consideration. We can change this
by using either:
- ``StratifiedKFold`` to preserve the percentage of samples for each class.
- ``GroupKFold`` to ensure that the same group will not appear in two
different folds.
- ``StratifiedGroupKFold`` to keep the constraint of ``GroupKFold`` while
attempting to return stratified folds.
.. GENERATED FROM PYTHON SOURCE LINES 163-177
.. code-block:: default
cvs = [StratifiedKFold, GroupKFold, StratifiedGroupKFold]
for cv in cvs:
fig, ax = plt.subplots(figsize=(6, 3))
plot_cv_indices(cv(n_splits), X, y, groups, ax, n_splits)
ax.legend(
[Patch(color=cmap_cv(0.8)), Patch(color=cmap_cv(0.02))],
["Testing set", "Training set"],
loc=(1.02, 0.8),
)
# Make the legend fit
plt.tight_layout()
fig.subplots_adjust(right=0.7)
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_003.png
:alt: StratifiedKFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_003.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_004.png
:alt: GroupKFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_004.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_005.png
:alt: StratifiedGroupKFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_005.png
:class: sphx-glr-multi-img
.. GENERATED FROM PYTHON SOURCE LINES 178-188
Next we'll visualize this behavior for a number of CV iterators.
Visualize cross-validation indices for many CV objects
------------------------------------------------------
Let's visually compare the cross validation behavior for many
scikit-learn cross-validation objects. Below we will loop through several
common cross-validation objects, visualizing the behavior of each.
Note how some use the group/class information while others do not.
.. GENERATED FROM PYTHON SOURCE LINES 188-215
.. code-block:: default
cvs = [
KFold,
GroupKFold,
ShuffleSplit,
StratifiedKFold,
StratifiedGroupKFold,
GroupShuffleSplit,
StratifiedShuffleSplit,
TimeSeriesSplit,
]
for cv in cvs:
this_cv = cv(n_splits=n_splits)
fig, ax = plt.subplots(figsize=(6, 3))
plot_cv_indices(this_cv, X, y, groups, ax, n_splits)
ax.legend(
[Patch(color=cmap_cv(0.8)), Patch(color=cmap_cv(0.02))],
["Testing set", "Training set"],
loc=(1.02, 0.8),
)
# Make the legend fit
plt.tight_layout()
fig.subplots_adjust(right=0.7)
plt.show()
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_006.png
:alt: KFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_006.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_007.png
:alt: GroupKFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_007.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_008.png
:alt: ShuffleSplit
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_008.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_009.png
:alt: StratifiedKFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_009.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_010.png
:alt: StratifiedGroupKFold
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_010.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_011.png
:alt: GroupShuffleSplit
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_011.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_012.png
:alt: StratifiedShuffleSplit
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_012.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_013.png
:alt: TimeSeriesSplit
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_indices_013.png
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 1.241 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_cv_indices.py:
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:alt: Launch binder
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:target: ../../lite/lab/?path=auto_examples/model_selection/plot_cv_indices.ipynb
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.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_cv_indices.py <plot_cv_indices.py>`
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