8000 [MRG+2] Raise ValueError for metrics.cluster.supervised with too many classes by tomMoral · Pull Request #5445 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

[MRG+2] Raise ValueError for metrics.cluster.supervised with too many classes #5445

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Oct 19, 2015
Merged

[MRG+2] Raise ValueError for metrics.cluster.supervised with too many classes #5445

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
fbd4b79
- Add max_n_classes param to cluster.supervised metric
tomMoral Oct 19, 2015
e916008
Reduce test time.
tomMoral Oct 19, 2015
1dd5518
Correct Typo with Gael comment
tomMoral Oct 19, 2015
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
97 changes: 80 additions & 17 deletions sklearn/metrics/cluster/supervised.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def check_clusterings(labels_true, labels_pred):
return labels_true, labels_pred


def contingency_matrix(labels_true, labels_pred, eps=None):
def contingency_matrix(labels_true, labels_pred, eps=None, max_n_classes=5000):
"""Build a contengency matrix describing the relationship between labels.

Parameters
Expand All @@ -60,6 +60,11 @@ def contingency_matrix(labels_true, labels_pred, eps=None):
matrix. This helps to stop NaN propagation.
If ``None``, nothing is adjusted.

max_n_classes : int, optional (default=5000)
Maximal number of classeses handled for contingency_matrix.
This help to avoid Memory error with regression target
for mutual_information.

Returns
-------
contingency: array, shape=[n_classes_true, n_classes_pred]
10000 Expand All @@ -72,6 +77,14 @@ def contingency_matrix(labels_true, labels_pred, eps=None):
clusters, cluster_idx = np.unique(labels_pred, return_inverse=True)
n_classes = classes.shape[0]
n_clusters = clusters.shape[0]
if n_classes > max_n_classes:
raise ValueError("Too many classes for a clustering metric. If you "
"want to increase the limit, pass parameter "
"max_n_classes to the scoring function")
if n_clusters > max_n_classes:
raise ValueError("Too many clusters for a clustering metric. If you "
"want to increase the limit, pass parameter "
"max_n_classes to the scoring function")
# Using coo_matrix to accelerate simple histogram calculation,
# i.e. bins are consecutive integers
# Currently, coo_matrix is faster than histogram2d for simple cases
Expand All @@ -87,7 +100,7 @@ def contingency_matrix(labels_true, labels_pred, eps=None):

# clustering measures

def adjusted_rand_score(labels_true, labels_pred):
def adjusted_rand_score(labels_true, labels_pred, max_n_classes=5000):
"""Rand index adjusted for chance

The Rand Index computes a similarity measure between two clusterings
Expand Down Expand Up @@ -119,6 +132,11 @@ def adjusted_rand_score(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
Cluster labels to evaluate

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
ari : float
Expand Down Expand Up @@ -180,7 +198,8 @@ def adjusted_rand_score(labels_true, labels_pred):
or classes.shape[0] == clusters.shape[0] == len(labels_true)):
return 1.0

contingency = contingency_matrix(labels_true, labels_pred)
contingency = contingency_matrix(labels_true, labels_pred,
max_n_classes=max_n_classes)

# Compute the ARI using the contingency data
sum_comb_c = sum(comb2(n_c) for n_c in contingency.sum(axis=1))
Expand All @@ -192,7 +211,8 @@ def adjusted_rand_score(labels_true, labels_pred):
return ((sum_comb - prod_comb) / (mean_comb - prod_comb))


def homogeneity_completeness_v_measure(labels_true, labels_pred):
def homogeneity_completeness_v_measure(labels_true, labels_pred,
max_n_classes=5000):
"""Compute the homogeneity and completeness and V-Measure scores at once

Those metrics are based on normalized conditional entropy measures of
Expand Down Expand Up @@ -226,6 +246,11 @@ def homogeneity_completeness_v_measure(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
cluster labels to evaluate

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
homogeneity: float
Expand All @@ -251,7 +276,8 @@ def homogeneity_completeness_v_measure(labels_true, labels_pred):
entropy_C = entropy(labels_true)
entropy_K = entropy(labels_pred)

MI = mutual_info_score(labels_true, labels_pred)
MI = mutual_info_score(labels_true, labels_pred,
max_n_classes=max_n_classes)

homogeneity = MI / (entropy_C) if entropy_C else 1.0
completeness = MI / (entropy_K) if entropy_K else 1.0
Expand All @@ -265,7 +2 10000 91,7 @@ def homogeneity_completeness_v_measure(labels_true, labels_pred):
return homogeneity, completeness, v_measure_score


def homogeneity_score(labels_true, labels_pred):
def homogeneity_score(labels_true, labels_pred, max_n_classes=5000):
"""Homogeneity metric of a cluster labeling given a ground truth

A clustering result satisfies homogeneity if all of its clusters
Expand All @@ -289,6 +315,11 @@ def homogeneity_score(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
cluster labels to evaluate

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
homogeneity: float
Expand Down Expand Up @@ -336,10 +367,11 @@ def homogeneity_score(labels_true, labels_pred):
0.0...

"""
return homogeneity_completeness_v_measure(labels_true, labels_pred)[0]
return homogeneity_completeness_v_measure(labels_true, labels_pred,
max_n_classes)[0]


def completeness_score(labels_true, labels_pred):
def completeness_score(labels_true, labels_pred, max_n_classes=5000):
"""Completeness metric of a cluster labeling given a ground truth

A clustering result satisfies completeness if all the data points
Expand All @@ -363,6 +395,11 @@ def completeness_score(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
cluster labels to evaluate

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
completeness: float
Expand Down Expand Up @@ -406,10 +443,11 @@ def completeness_score(labels_true, labels_pred):
0.0

"""
return homogeneity_completeness_v_measure(labels_true, labels_pred)[1]
return homogeneity_completeness_v_measure(labels_true, labels_pred,
max_n_classes)[1]


def v_measure_score(labels_true, labels_pred):
def v_measure_score(labels_true, labels_pred, max_n_classes=5000):
"""V-measure cluster labeling given a ground truth.

This score is identical to :func:`normalized_mutual_info_score`.
Expand Down Expand Up @@ -437,6 +475,11 @@ def v_measure_score(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
cluster labels to evaluate

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
v_measure: float
Expand Down Expand Up @@ -501,10 +544,12 @@ def v_measure_score(labels_true, labels_pred):
0.0...

"""
return homogeneity_completeness_v_measure(labels_true, labels_pred)[2]
return homogeneity_completeness_v_measure(labels_true, labels_pred,
max_n_classes)[2]


def mutual_info_score(labels_true, labels_pred, contingency=None):
def mutual_info_score(labels_true, labels_pred, contingency=None,
max_n_classes=5000):
"""Mutual Information between two clusterings

The Mutual Information is a measure of the similarity between two labels of
Expand Down Expand Up @@ -544,6 +589,11 @@ def mutual_info_score(labels_true, labels_pred, contingency=None):
If value is ``None``, it will be computed, otherwise the given value is
used, with ``labels_true`` and ``labels_pred`` ignored.

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the mutual_info_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
mi: float
Expand All @@ -556,7 +606,8 @@ def mutual_info_score(labels_true, labels_pred, contingency=None):
"""
if contingency is None:
labels_true, labels_pred = check_clusterings(labels_true, labels_pred)
contingency = contingency_matrix(labels_true, labels_pred)
contingency = contingency_matrix(labels_true, labels_pred,
max_n_classes=max_n_classes)
contingency = np.array(contingency, dtype='float')
contingency_sum = np.sum(contingency)
pi = np.sum(contingency, axis=1)
Expand All @@ -575,7 +626,7 @@ def mutual_info_score(labels_true, labels_pred, contingency=None):
return mi.sum()


def adjusted_mutual_info_score(labels_true, labels_pred):
def adjusted_mutual_info_score(labels_true, labels_pred, max_n_classes=5000):
"""Adjusted Mutual Information between two clusterings

Adjusted Mutual Information (AMI) is an adjustment of the Mutual
Expand Down Expand Up @@ -608,6 +659,11 @@ def adjusted_mutual_info_score(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
A clustering of the data into disjoint subsets.

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
ami: float(upperlimited by 1.0)
Expand Down Expand Up @@ -658,7 +714,8 @@ def adjusted_mutual_info_score(labels_true, labels_pred):
if (classes.shape[0] == clusters.shape[0] == 1
or classes.shape[0] == clusters.shape[0] == 0):
return 1.0
contingency = contingency_matrix(labels_true, labels_pred)
contingency = contingency_matrix(labels_true, labels_pred,
max_n_classes=max_n_classes)
contingency = np.array(contingency, dtype='float')
# Calculate the MI for the two clusterings
mi = mutual_info_score(labels_true, labels_pred,
Expand All @@ -671,7 +728,7 @@ def adjusted_mutual_info_score(labels_true, labels_pred):
return ami


def normalized_mutual_info_score(labels_true, labels_pred):
def normalized_mutual_info_score(labels_true, labels_pred, max_n_classes=5000):
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You seems to be above column 79 here

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

It's a 79 line I think.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

my mistake

"""Normalized Mutual Information between two clusterings

Normalized Mutual Information (NMI) is an normalization of the Mutual
Expand Down Expand Up @@ -701,6 +758,11 @@ def normalized_mutual_info_score(labels_true, labels_pred):
labels_pred : array, shape = [n_samples]
A clustering of the data into disjoint subsets.

max_n_classes: int, optional (default=5000)
Maximal number of classes handled by the adjusted_rand_score
metric. Setting it too high can lead to MemoryError or OS
freeze

Returns
-------
nmi: float
Expand Down Expand Up @@ -739,7 +801,8 @@ def normalized_mutual_info_score(labels_true, labels_pred):
if (classes.shape[0] == clusters.shape[0] == 1
or classes.shape[0] == clusters.shape[0] == 0):
return 1.0
contingency = contingency_matrix(labels_true, labels_pred)
contingency = contingency_matrix(labels_true, labels_pred,
max_n_classes=max_n_classes)
contingency = np.array(contingency, dtype='float')
# Calculate the MI for the two clusterings
mi = mutual_info_score(labels_true, labels_pred,
Expand Down
35 changes: 31 additions & 4 deletions sklearn/metrics/cluster/tests/test_supervised.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -181,10 +181,14 @@ def test_exactly_zero_info_score():
for i in np.logspace(1, 4, 4).astype(np.int):
labels_a, labels_b = np.ones(i, dtype=np.int),\
np.arange(i, dtype=np.int)
assert_equal(normalized_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(v_measure_score(labels_a, labels_b), 0.0)
assert_equal(adjusted_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(normalized_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(normalized_mutual_info_score(labels_a, labels_b,
max_n_classes=1e4), 0.0)
assert_equal(v_measure_score(labels_a, labels_b,
max_n_classes=1e4), 0.0)
assert_equal(adjusted_mutual_info_score(labels_a, labels_b,
max_n_classes=1e4), 0.0)
assert_equal(normalized_mutual_info_score(labels_a, labels_b,
max_n_classes=1e4), 0.0)


def test_v_measure_and_mutual_information(seed=36):
Expand All @@ -196,3 +200,26 @@ def test_v_measure_and_mutual_information(seed=36):
assert_almost_equal(v_measure_score(labels_a, labels_b),
2.0 * mutual_info_score(labels_a, labels_b) /
(entropy(labels_a) + entropy(labels_b)), 0)


def test_max_n_classes():
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You need to create a local random number generator:

rng = np.random.RandomState(seed=0)

and use it after:

labels_true = rng.random(53)

rng = np.random.RandomState(seed=0)
labels_true = rng.rand(53)
labels_pred = rng.rand(53)
labels_zero = np.zeros(53)
labels_true[:2] = 0
labels_zero[:3] = 1
labels_pred[:2] = 0
for score_func in score_funcs:
expected = ("Too many classes for a clustering metric. If you "
"want to increase the limit, pass parameter "
"max_n_classes to the scoring function")
assert_raise_message(ValueError, expected, score_func,
labels_true, labels_pred,
max_n_classes=50)
expected = ("Too many clusters for a clustering metric. If you "
"want to increase the limit, pass parameter "
"max_n_classes to the scoring function")
assert_raise_message(ValueError, expected, score_func,
labels_zero, labels_pred,
max_n_classes=50)
0