def test_author_classification_egypt_dataset(self):
TestAuthor.drop_collection()
ws = WarehouseServer()
for author in [author for author in ws.get_authors(type=Author)]:
if len(author.tweets) > 200:
t = TestAuthor()
t.screen_name = author.screen_name
t.tweets = author.tweets
t.save()
authors = ws.get_authors(type=TestAuthor)
for author in authors:
print '-----------------------'
print author.screen_name
vector = author.update_feature_vector()
print vector
classifier = TreeClassifier()
attributes = ["retweets", "links", "retweeted", "replies", "mentions", "ff-ratio", "class"]
train_set = numpy.array([author.get_feature_vector_with_type() for author in TrainingAuthor.objects])
classifier.train(train_set, attributes)
for author in authors:
prediction = "No prediction"
if len(author.feature_vector) > 0:
prediction = classifier.classify(author.get_feature_vector_with_type())
print author.screen_name
print prediction
print '----------------------'
TestAuthor.drop_collection()
