Introduction: For the validation of any model adapted from a paper, it is of utmost importance that the results of benchmark testing on the datasets listed in the paper match between the actual implementation (palmetto) and gensim. This coherence pipeline has been implemented from the work done by Roeder et al. The paper can be found here.
Approach :
In [9]:
import re
import os
from scipy.stats import pearsonr
from datetime import datetime
from gensim.models import CoherenceModel
from gensim.corpora.dictionary import Dictionary
# %load_ext line_profiler # This was used for finding out which line was taking maximum time for indirect confirmation measure
Download the dataset from the link and plug in the location here
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prefix = "/home/devashish/datasets/Movies/movie/"
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import os
if not os.path.exists(prefix):
raise ValueError("SKIP: Please download the movie corpus.")
start = datetime.now()
texts = []
for fil in os.listdir(prefix):
for line in open(prefix + fil):
# lower case all words
lowered = line.lower()
#remove punctuation and split into seperate words
words = re.findall(r'\w+', lowered, flags = re.UNICODE | re.LOCALE)
texts.append(words)
end = datetime.now()
print("Time taken: %s" % (end - start))
In [12]:
start = datetime.now()
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
end = datetime.now()
print "Time taken: %s" % (end - start)
According to the paper the number of documents should be 108952 with a vocabulary of 1625124. The difference is because of a difference in preprocessing. However the results obtained are still very similar.
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print len(corpus)
print dictionary
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topics = [] # list of 100 topics
for l in open('/home/devashish/datasets/Movies/topicsMovie.txt'):
topics.append([l.split()])
topics.pop(100)
Out[14]:
In [16]:
human_scores = []
for l in open('/home/devashish/datasets/Movies/goldMovie.txt'):
human_scores.append(float(l.strip()))
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start = datetime.now()
u_mass = []
flags = []
for n, topic in enumerate(topics):
try:
cm = CoherenceModel(topics=topic, corpus=corpus, dictionary=dictionary, coherence='u_mass')
u_mass.append(cm.get_coherence())
except KeyError:
flags.append(n)
end = datetime.now()
print "Time taken: %s" % (end - start)
In [18]:
start = datetime.now()
c_v = []
for n, topic in enumerate(topics):
try:
cm = CoherenceModel(topics=topic, texts=texts, dictionary=dictionary, coherence='c_v')
c_v.append(cm.get_coherence())
except KeyError:
pass
end = datetime.now()
print "Time taken: %s" % (end - start)
In [19]:
start = datetime.now()
c_uci = []
flags = []
for n, topic in enumerate(topics):
try:
cm = CoherenceModel(topics=topic, texts=texts, dictionary=dictionary, coherence='c_uci')
c_uci.append(cm.get_coherence())
except KeyError:
flags.append(n)
end = datetime.now()
print "Time taken: %s" % (end - start)
In [20]:
start = datetime.now()
c_npmi = []
for n, topic in enumerate(topics):
print n
try:
cm = CoherenceModel(topics=topic, texts=texts, dictionary=dictionary, coherence='c_npmi')
c_npmi.append(cm.get_coherence())
except KeyError:
pass
end = datetime.now()
print "Time taken: %s" % (end - start)
In [21]:
final_scores = []
for n, score in enumerate(human_scores):
if n not in flags:
final_scores.append(score)
One topic encountered a KeyError. This was because of a difference in preprocessing due to which one topic word wasn't found in the dictionary
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print len(u_mass), len(c_v), len(c_uci), len(c_npmi), len(final_scores)
# 1 topic has word(s) that is not in the dictionary. Probably some difference
# in preprocessing
The values in the paper were:
u_mass correlation : 0.093
c_v correlation : 0.548
c_uci correlation : 0.473
c_npmi correlation : 0.438
Our values are also very similar to these values which is good. This validates the correctness of our pipeline.
In [23]:
print pearsonr(u_mass, final_scores)[0]
print pearsonr(c_v, final_scores)[0]
print pearsonr(c_uci, final_scores)[0]
print pearsonr(c_npmi, final_scores)[0]