def test_evaluate(self):
# the data sources
ukr = self.source.find_clean({"subreddit" : "UkrainianConflict"}, limit=2500, batch_size=1000)
askr = self.source.find_clean({"subreddit" : "AskReddit"}, limit=2500, batch_size=1000)
alll = self.source.find_clean(limit=10000)
tagger = ngrams.make_backoff_tagger()
featuresets = [(cls.ner_feature(doc,tagger=tagger), "YES") for doc in ukr]
featuresets.extend([(cls.ner_feature(doc,tagger=tagger), "NO") for doc in askr])
random.shuffle(featuresets)
trainset, testset = featuresets[1250:], featuresets[:1250]
classifier = NaiveBayesClassifier.train(trainset)
f = open("./UkrainianConflictNVM","w")
for doc in alll:
del (doc["_id"])
truthiness = False
truthiness = classifier.classify(cls.ner_feature(doc,tagger=tagger))
if truthiness:
f.write(json.dumps(doc) + "\n")
f.close()
print(nltk.classify.accuracy(classifier, testset))
def test_classbased(self):
tagger = ngrams.make_backoff_tagger()
params = {
"corpora" : self.source,
"labeled_set" : lambda : self.source.find_clean({"subreddit" : "fitness"}, batch_size=1000, limit=2000),
"unlabeled_set" : lambda : self.source.find_clean({"subreddit" : "AskReddit"}, batch_size=1000, limit=2000),
"feature" : lambda x : cls.ner_feature(x,tagger=tagger),
"exit" : lambda self : self.corpora.exit()
}
pnb_a = annotate.PNBAnnotater(**params)
pnb_a.train()
pnb_a.describe()
ct = 0
for doc, annotation in pnb_a.classify_iter(self.source.find_clean()):
ct += 1
# print(doc)
print(annotation)
if ct == 10:
break
print("------------")
