From fa50e6885a0d377e4df4beb163c6bec2159feda0 Mon Sep 17 00:00:00 2001
From: Lars Buitinck <L.J.Buitinck@uva.nl>
Date: Tue, 13 Sep 2011 16:50:30 +0200
Subject: [PATCH] rename ari_score adjusted_rand_score
---
doc/modules/classes.rst | 2 +-
doc/modules/clustering.rst | 12 ++++++------
.../plot_adjusted_for_chance_measures.py | 2 +-
examples/cluster/plot_affinity_propagation.py | 3 ++-
examples/cluster/plot_dbscan.py | 3 ++-
examples/cluster/plot_kmeans_digits.py | 11 +++++++----
examples/document_clustering.py | 3 ++-
sklearn/metrics/__init__.py | 2 +-
sklearn/metrics/cluster.py | 17 +++++++++--------
sklearn/metrics/tests/test_cluster.py | 11 ++++++-----
10 files changed, 37 insertions(+), 29 deletions(-)
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
index f3d492a657..42dfd4bffa 100644
--- a/doc/modules/classes.rst
+++ b/doc/modules/classes.rst
@@ -295,7 +295,7 @@ Clustering metrics
:toctree: generated/
:template: function.rst
- metrics.ari_score
+ metrics.adjusted_rand_score
metrics.homogeneity_completeness_v_measure
metrics.homogeneity_score
metrics.completeness_score
diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
index 656ffc3ca4..1923283568 100644
--- a/doc/modules/clustering.rst
+++ b/doc/modules/clustering.rst
@@ -358,34 +358,34 @@ chance normalization**::
>>> labels_true = [0, 0, 0, 1, 1, 1]
>>> labels_pred = [0, 0, 1, 1, 2, 2]
- >>> metrics.ari_score(labels_true, labels_pred) # doctest: +ELLIPSIS
+ >>> metrics.adjusted_rand_score(labels_true, labels_pred) # doctest: +ELLIPSIS
0.24...
One can permute 0 and 1 in the predicted labels and rename `2` by `3` and get
the same score::
>>> labels_pred = [1, 1, 0, 0, 3, 3]
- >>> metrics.ari_score(labels_true, labels_pred) # doctest: +ELLIPSIS
+ >>> metrics.adjusted_rand_score(labels_true, labels_pred) # doctest: +ELLIPSIS
0.24...
-Furthermore, :func:`ari_score` is **symmetric**: swapping the argument
+Furthermore, :func:`adjusted_rand_score` is **symmetric**: swapping the argument
does not change the score. It can thus be used as a **consensus
measure**::
- >>> metrics.ari_score(labels_pred, labels_true) # doctest: +ELLIPSIS
+ >>> metrics.adjusted_rand_score(labels_pred, labels_true) # doctest: +ELLIPSIS
0.24...
Perfect labeling is scored 1.0::
>>> labels_pred = labels_true[:]
- >>> metrics.ari_score(labels_true, labels_pred)
+ >>> metrics.adjusted_rand_score(labels_true, labels_pred)
1.0
Bad (e.g. independent labelings) have negative or close to 0.0 scores::
>>> labels_true = [0, 1, 2, 0, 3, 4, 5, 1]
>>> labels_pred = [1, 1, 0, 0, 2, 2, 2, 2]
- >>> metrics.ari_score(labels_true, labels_pred) # doctest: +ELLIPSIS
+ >>> metrics.adjusted_rand_score(labels_true, labels_pred) # doctest: +ELLIPSIS
-0.12...
diff --git a/examples/cluster/plot_adjusted_for_chance_measures.py b/examples/cluster/plot_adjusted_for_chance_measures.py
index 2ac81f21c4..bed0dcc875 100644
--- a/examples/cluster/plot_adjusted_for_chance_measures.py
+++ b/examples/cluster/plot_adjusted_for_chance_measures.py
@@ -56,7 +56,7 @@ def uniform_labelings_scores(score_func, n_samples, n_clusters_range,
return scores
score_funcs = [
- metrics.ari_score,
+ metrics.adjusted_rand_score,
metrics.v_measure_score,
]
diff --git a/examples/cluster/plot_affinity_propagation.py b/examples/cluster/plot_affinity_propagation.py
index eb0b7c6664..a059a9aece 100644
--- a/examples/cluster/plot_affinity_propagation.py
+++ b/examples/cluster/plot_affinity_propagation.py
@@ -38,7 +38,8 @@ print 'Estimated number of clusters: %d' % n_clusters_
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels)
print "Completeness: %0.3f" % metrics.completeness_score(labels_true, labels)
print "V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels)
-print "Adjusted Rand Index: %0.3f" % metrics.ari_score(labels_true, labels)
+print "Adjusted Rand Index: %0.3f" % \
+ metrics.adjusted_rand_score(labels_true, labels)
##############################################################################
# Plot result
diff --git a/examples/cluster/plot_dbscan.py b/examples/cluster/plot_dbscan.py
index 0bff8ce1c8..5b806ed5df 100644
--- a/examples/cluster/plot_dbscan.py
+++ b/examples/cluster/plot_dbscan.py
@@ -39,7 +39,8 @@ print 'Estimated number of clusters: %d' % n_clusters_
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels)
print "Completeness: %0.3f" % metrics.completeness_score(labels_true, labels)
print "V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels)
-print "Adjusted Rand Index: %0.3f" % metrics.ari_score(labels_true, labels)
+print "Adjusted Rand Index: %0.3f" % \
+ metrics.adjusted_rand_score(labels_true, labels)
##############################################################################
# Plot result
diff --git a/examples/cluster/plot_kmeans_digits.py b/examples/cluster/plot_kmeans_digits.py
index 11f60cf9a6..6d96456e87 100644
--- a/examples/cluster/plot_kmeans_digits.py
+++ b/examples/cluster/plot_kmeans_digits.py
@@ -44,7 +44,8 @@ print "Inertia: %f" % km.inertia_
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)
print "Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)
print "V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)
-print "Adjusted Rand Index: %0.3f" % metrics.ari_score(labels, km.labels_)
+print "Adjusted Rand Index: %0.3f" % \
+ metrics.adjusted_rand_score(labels, km.labels_)
print
print "Raw k-means with random centroid init..."
@@ -55,7 +56,8 @@ print "Inertia: %f" % km.inertia_
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)
print "Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)
print "V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)
-print "Adjusted Rand Index: %0.3f" % metrics.ari_score(labels, km.labels_)
+print "Adjusted Rand Index: %0.3f" % \
+ metrics.adjusted_rand_score(labels, km.labels_)
print
print "Raw k-means with PCA-based centroid init..."
@@ -69,7 +71,8 @@ print "Inertia: %f" % km.inertia_
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)
print "Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)
print "V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)
-print "Adjusted Rand Index: %0.3f" % metrics.ari_score(labels, km.labels_)
+print "Adjusted Rand Index: %0.3f" % \
+ metrics.adjusted_rand_score(labels, km.labels_)
print
# Plot k-means++ form on a 2D plot using PCA
@@ -80,7 +83,7 @@ kmeans = KMeans(init='k-means++', k=n_digits, n_init=10).fit(reduced_data)
print "done in %0.3fs" % (time() - t0)
# Step size of the mesh. Decrease to increase the quality of the VQ.
-h = .02# point in the mesh [x_min, m_max]x[y_min, y_max].
+h = .02 # point in the mesh [x_min, m_max]x[y_min, y_max].
# Plot the decision boundary. For that, we will asign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
diff --git a/examples/document_clustering.py b/examples/document_clustering.py
index 3c30fce678..5459995ffb 100644
--- a/examples/document_clustering.py
+++ b/examples/document_clustering.py
@@ -85,5 +85,6 @@ print
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, mbkm.labels_)
print "Completeness: %0.3f" % metrics.completeness_score(labels, mbkm.labels_)
print "V-measure: %0.3f" % metrics.v_measure_score(labels, mbkm.labels_)
-print "Adjusted Rand-Index: %.3f" % metrics.ari_score(labels, mbkm.labels_)
+print "Adjusted Rand-Index: %.3f" % \
+ metrics.adjusted_rand_score(labels, mbkm.labels_)
print
diff --git a/sklearn/metrics/__init__.py b/sklearn/metrics/__init__.py
index bcf3f7fd5e..4ed164bef2 100644
--- a/sklearn/metrics/__init__.py
+++ b/sklearn/metrics/__init__.py
@@ -9,7 +9,7 @@ from .metrics import confusion_matrix, roc_curve, auc, precision_score, \
precision_recall_curve, explained_variance_score, r2_score, \
zero_one, mean_square_error, hinge_loss
-from .cluster import ari_score
+from .cluster import adjusted_rand_score
from .cluster import homogeneity_completeness_v_measure
from .cluster import homogeneity_score
from .cluster import completeness_score
diff --git a/sklearn/metrics/cluster.py b/sklearn/metrics/cluster.py
index 454340ad9c..96bc80be6c 100644
--- a/sklearn/metrics/cluster.py
+++ b/sklearn/metrics/cluster.py
@@ -12,6 +12,7 @@ from scipy import comb
import numpy as np
+
# the exact version if faster for k == 2: use it by default globally in
# this module instead of the float approximate variant
def comb2(n):
@@ -39,7 +40,7 @@ def check_clusterings(labels_true, labels_pred):
# clustering measures
-def ari_score(labels_true, labels_pred):
+def adjusted_rand_score(labels_true, labels_pred):
"""Rand index adjusted for chance
The Rand Index computes a similarity measure between two clusterings
@@ -59,7 +60,7 @@ def ari_score(labels_true, labels_pred):
ARI is a symmetric measure::
- ari_score(a, b) == ari_score(b, a)
+ adjusted_rand_score(a, b) == adjusted_rand_score(b, a)
Parameters
----------
@@ -80,28 +81,28 @@ def ari_score(labels_true, labels_pred):
Perfectly maching labelings have a score of 1 even
- >>> from sklearn.metrics.cluster import ari_score
- >>> ari_score([0, 0, 1, 1], [0, 0, 1, 1])
+ >>> from sklearn.metrics.cluster import adjusted_rand_score
+ >>> adjusted_rand_score([0, 0, 1, 1], [0, 0, 1, 1])
1.0
- >>> ari_score([0, 0, 1, 1], [1, 1, 0, 0])
+ >>> adjusted_rand_score([0, 0, 1, 1], [1, 1, 0, 0])
1.0
Labelings that assign all classes members to the same clusters
are complete be not always pure, hence penalized::
- >>> ari_score([0, 0, 1, 2], [0, 0, 1, 1]) # doctest: +ELLIPSIS
+ >>> adjusted_rand_score([0, 0, 1, 2], [0, 0, 1, 1]) # doctest: +ELLIPSIS
0.57...
ARI is symmetric, so labelings that have pure clusters with members
coming from the same classes but unnecessary splits are penalized::
- >>> ari_score([0, 0, 1, 1], [0, 0, 1, 2]) # doctest: +ELLIPSIS
+ >>> adjusted_rand_score([0, 0, 1, 1], [0, 0, 1, 2]) # doctest: +ELLIPSIS
0.57...
If classes members are completely split across different clusters, the
assignment is totally incomplete, hence the ARI is very low::
- >>> ari_score([0, 0, 0, 0], [0, 1, 2, 3])
+ >>> adjusted_rand_score([0, 0, 0, 0], [0, 1, 2, 3])
0.0
References
diff --git a/sklearn/metrics/tests/test_cluster.py b/sklearn/metrics/tests/test_cluster.py
index 6966e51723..35a4982ca7 100644
--- a/sklearn/metrics/tests/test_cluster.py
+++ b/sklearn/metrics/tests/test_cluster.py
@@ -1,6 +1,6 @@
import numpy as np
-from sklearn.metrics.cluster import ari_score
+from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import homogeneity_score
from sklearn.metrics.cluster import completeness_score
from sklearn.metrics.cluster import v_measure_score
@@ -12,12 +12,13 @@ from numpy.testing import assert_array_almost_equal
score_funcs = [
- ari_score,
+ adjusted_rand_score,
homogeneity_score,
completeness_score,
v_measure_score,
]
+
def assert_raise_message(exception, message, callable, *args, **kwargs):
"""Helper function to test error messages in exceptions"""
try:
@@ -97,8 +98,8 @@ def test_non_consicutive_labels():
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
- ari_1 = ari_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
- ari_2 = ari_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
+ ari_1 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
+ ari_2 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
assert_almost_equal(ari_1, 0.24, 2)
assert_almost_equal(ari_2, 0.24, 2)
@@ -123,7 +124,7 @@ def test_adjustment_for_chance():
n_runs = 10
scores = uniform_labelings_scores(
- ari_score, n_samples, n_clusters_range, n_runs)
+ adjusted_rand_score, n_samples, n_clusters_range, n_runs)
max_abs_scores = np.abs(scores).max(axis=1)
assert_array_almost_equal(max_abs_scores, [0.02, 0.03, 0.03, 0.02], 2)
--
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