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evaluate.py
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evaluate.py
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from nltk.corpus import wordnet as wn
import re
import Stemmer
class Evaluate:
# the 20 news groups
# reordered
ground_truth_labels = [
"science.space",
"science.medicine",
"computer.system.mac.hardware.macintosh.apple",
"talk.politics.guns",
"science.electronics",
"talk.politics.miscellaneous",
"recreation.sport.baseball",
"recreation.automobiles",
"computer.os.ms.windows.miscellaneous.microsoft.operating.system",
"rec.sport.hockey",
"soc.religion.christian",
"talk.religion.miscellaneous",
"alternative.atheism",
"computer.windows.x",
"computer.system.ibm.pc.hardware",
"talk.politics.middle_east",
"recreation.motorcycles",
"computer.graphics",
"miscellaneous.forsale",
"science.cryptography",
]
# original order
og_ground_truth_labels = [
"comp.graphics",
"comp.os.ms-windows.misc",
"comp.sys.ibm.pc.hardware",
"comp.sys.mac.hardware",
"comp.windows.x",
"rec.autos",
"rec.motorcycles",
"rec.sport.baseball",
"rec.sport.hockey",
"sci.crypt",
"sci.electronics",
"sci.med",
"sci.space",
"misc.forsale",
"talk.politics.misc",
"talk.politics.guns",
"talk.politics.mideast",
"talk.religion.misc",
"alt.atheism",
"soc.religion.christian"
]
max_k = 5
def __init__(self, labels = None):
self.labels = labels
# label is correct if it is identical, an inflection, or a Wordnet synonym of the cluster's correct label
stemmer = Stemmer.Stemmer('english')
# split of non alpha
self.p_non_char = p_non_char = re.compile('[\W_]+')
ground_truth_labels = self.ground_truth_labels
ground_truth_labels = map(lambda x: p_non_char.split(x), ground_truth_labels)
# add stemmed version
ground_truth_labels = map(lambda label_set: list(set(label_set + stemmer.stemWords(label_set))), ground_truth_labels)
# add synonyms
new_gt_labels = []
for labels in ground_truth_labels:
new_labels = []
for label in labels:
for synset in wn.synsets(label):
new_labels += map(lambda x: p_non_char.split(x), synset.lemma_names)
new_labels = [item for sublist in new_labels for item in sublist]
new_gt_labels.append(list(set(new_labels + labels)))
self.ground_truth_labels = new_gt_labels
min_labels = min(map(len, labels))
if min_labels < self.max_k:
self.max_k = min_labels
def test(self):
# test candidate labels
self.labels = [
["recreation", "graphics"],
["recreation", "misc"],
["recreation", "hardware"],
["recreation", "hardware"],
["recreation", "windows"],
["recreation", "autos"],
["recreation", "motorcycles"],
["recreation", "baseball"],
["recreation", "hockey"],
["recreation", "crypt"],
["recreation", "electronics"],
["recreation", "med"],
["recreation", "space"],
["recreation", "forsale"],
["recreation", "politics"],
["recreation", "guns"],
["recreation", "mideast"],
["recreation", "religion"],
["recreation", "atheism"],
["recreation", "christian"]
]
self.max_k = 2
self.score()
def score(self):
ground_truth_labels = self.ground_truth_labels
max_k = self.max_k
p_non_char = self.p_non_char
# Match@K
# Is the relative number of clusters for which at least one of the top-k labels is correct.
match_at_k = [0]*max_k
# MRR@K
# Given an ordered list of k proposed labels for a cluster, the reciprocal rank is the
# inverse of the rank of the first correct label, or zero if no label in the list is correct.
# The mean reciprocal rank at k (MRR@K) is the average of the reciprocal ranks of all clusters.
mrr_at_k = [0]*max_k
for k in range(max_k):
for idx, candidate_labels in enumerate(self.labels):
snt = False
for idx_cl, candidate_label in enumerate(candidate_labels[0:k+1]):
# we take highest score for label against any ground truth cluster labels
for gt_labels in ground_truth_labels:
for gt_label in gt_labels:
for term in [candidate_label] + p_non_char.split(candidate_label):
# print "gt: %s =? cn: %s" % (gt_label, term)
if gt_label.lower() == term.lower():
match_at_k[k] += 1
mrr_at_k[k] += 1/float(idx_cl + 1)
snt = True
break
if snt: break
if snt: break
if snt: break
self.match_at_k = map(lambda x: float(x) / len(ground_truth_labels), match_at_k)
self.mrr_at_k = map(lambda x: float(x) / len(ground_truth_labels), mrr_at_k)
def main()
e = Evaluate()
print e.ground_truth_labels
e.test()
print e.match_at_k
print e.mrr_at_k
# main()