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.. "auto_examples/cluster/plot_adjusted_for_chance_measures.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_adjusted_for_chance_measures.py>`
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.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py:
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation metrics.
Non-adjusted measures such as the V-Measure show a dependency between
the number of clusters and the number of samples: the mean V-Measure
of random labeling increases significantly as the number of clusters is
closer to the total number of samples used to compute the measure.
Adjusted for chance measure such as ARI display some random variations
centered around a mean score of 0.0 for any number of samples and
clusters.
Only adjusted measures can hence safely be used as a consensus index
to evaluate the average stability of clustering algorithms for a given
value of k on various overlapping sub-samples of the dataset.
.. GENERATED FROM PYTHON SOURCE LINES 23-138
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_adjusted_for_chance_measures_001.png
:alt: Clustering measures for 2 random uniform labelings with equal number of clusters
:srcset: /auto_examples/cluster/images/sphx_glr_plot_adjusted_for_chance_measures_001.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_adjusted_for_chance_measures_002.png
:alt: Clustering measures for random uniform labeling against reference assignment with 10 classes
:srcset: /auto_examples/cluster/images/sphx_glr_plot_adjusted_for_chance_measures_002.png
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100
done in 0.027s
Computing v_measure_score for 10 values of n_clusters and n_samples=100
done in 0.043s
Computing ami_score for 10 values of n_clusters and n_samples=100
done in 0.235s
Computing mutual_info_score for 10 values of n_clusters and n_samples=100
done in 0.036s
Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000
done in 0.037s
Computing v_measure_score for 10 values of n_clusters and n_samples=1000
done in 0.059s
Computing ami_score for 10 values of n_clusters and n_samples=1000
done in 0.158s
Computing mutual_info_score for 10 values of n_clusters and n_samples=1000
done in 0.047s
|
.. code-block:: default
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from time import time
from sklearn import metrics
def uniform_labelings_scores(
score_func, n_samples, n_clusters_range, fixed_n_classes=None, n_runs=5, seed=42
):
"""Compute score for 2 random uniform cluster labelings.
Both random labelings have the same number of clusters for each value
possible value in ``n_clusters_range``.
When fixed_n_classes is not None the first labeling is considered a ground
truth class assignment with fixed number of classes.
"""
random_labels = np.random.RandomState(seed).randint
scores = np.zeros((len(n_clusters_range), n_runs))
if fixed_n_classes is not None:
labels_a = random_labels(low=0, high=fixed_n_classes, size=n_samples)
for i, k in enumerate(n_clusters_range):
for j in range(n_runs):
if fixed_n_classes is None:
labels_a = random_labels(low=0, high=k, size=n_samples)
labels_b = random_labels(low=0, high=k, size=n_samples)
scores[i, j] = score_func(labels_a, labels_b)
return scores
def ami_score(U, V):
return metrics.adjusted_mutual_info_score(U, V)
score_funcs = [
metrics.adjusted_rand_score,
metrics.v_measure_score,
ami_score,
metrics.mutual_info_score,
]
# 2 independent random clusterings with equal cluster number
n_samples = 100
n_clusters_range = np.linspace(2, n_samples, 10).astype(int)
plt.figure(1)
plots = []
names = []
for score_func in score_funcs:
print(
"Computing %s for %d values of n_clusters and n_samples=%d"
% (score_func.__name__, len(n_clusters_range), n_samples)
)
t0 = time()
scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range)
print("done in %0.3fs" % (time() - t0))
plots.append(
plt.errorbar(n_clusters_range, np.median(scores, axis=1), scores.std(axis=1))[0]
)
names.append(score_func.__name__)
plt.title(
"Clustering measures for 2 random uniform labelings\nwith equal number of clusters"
)
plt.xlabel("Number of clusters (Number of samples is fixed to %d)" % n_samples)
plt.ylabel("Score value")
plt.legend(plots, names)
plt.ylim(bottom=-0.05, top=1.05)
# Random labeling with varying n_clusters against ground class labels
# with fixed number of clusters
n_samples = 1000
n_clusters_range = np.linspace(2, 100, 10).astype(int)
n_classes = 10
plt.figure(2)
plots = []
names = []
for score_func in score_funcs:
print(
"Computing %s for %d values of n_clusters and n_samples=%d"
% (score_func.__name__, len(n_clusters_range), n_samples)
)
t0 = time()
scores = uniform_labelings_scores(
score_func, n_samples, n_clusters_range, fixed_n_classes=n_classes
)
print("done in %0.3fs" % (time() - t0))
plots.append(
plt.errorbar(n_clusters_range, scores.mean(axis=1), scores.std(axis=1))[0]
)
names.append(score_func.__name__)
plt.title(
"Clustering measures for random uniform labeling\n"
"against reference assignment with %d classes" % n_classes
)
plt.xlabel("Number of clusters (Number of samples is fixed to %d)" % n_samples)
plt.ylabel("Score value")
plt.ylim(bottom=-0.05, top=1.05)
plt.legend(plots, names)
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.802 seconds)
.. _sphx_glr_download_auto_examples_cluster_plot_adjusted_for_chance_measures.py:
.. only:: html
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:alt: Launch binder
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.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_adjusted_for_chance_measures.py <plot_adjusted_for_chance_measures.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_adjusted_for_chance_measures.ipynb <plot_adjusted_for_chance_measures.ipynb>`
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_