8000 [MRG+2] Raise ValueError for metrics.cluster.supervised with too many classes by tomMoral · Pull Request #5445 · scikit-learn/scikit-learn · GitHub
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[MRG+2] Raise ValueError for metrics.cluster.supervised with too many classes #5445

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merged 3 commits into from
Oct 19, 2015
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[MRG+2] Raise ValueError for metrics.cluster.supervised with too many classes #5445

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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
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97 changes: 80 additions & 17 deletions sklearn/metrics/cluster/supervised.py
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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]
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 +291,7 @@ 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):
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@TomDLT TomDLT < A3E2 /strong> Oct 19, 2015

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You seems to be above column 79 here

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It's a 79 line I think.

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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
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4743
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():
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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