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()
def test_author_classification_dummy_dataset(self):
train_set = numpy.array([[0.2, 0.5, 0.2, 0.2, 0.1, 10., 0],
[0.2, 0.3, 0.12, 0.1, 0.1, 10., 0],
[0.2, 0.2, 0.08, 0.2, 0.01, 20., 0],
[0.2, 0.5, 0.1, 0.1, 0.2, 5., 0],
[0.2, 0.1, 0.2, 0.2, 0.3, 20., 0],
[0.7, 0.5, 0.2, 0.8, 0.3, 0.1, 1],
[0.6, 0.8, 5.2, 0.2, 0.6, 0.3, 1],
[0.2, 0.6, 8.2, 0.9, 0.9, 0.1, 1],
[0.5, 0.9, 1.2, 0.1, 0.1, 0.2, 1],
[0.9, 0.1, 0.9, 0.6, 0.3, 0.6, 1]])
attributes = ["retweets", "links", "retweeted", "replies", "mentions", "ff-ratio", "class"]
classifier = TreeClassifier()
classifier.train(train_set, attributes)
example = [0.2, 0.5, 0.2, 0.2, 0.1, 100, 0]
prediction = classifier.classify(example)
self.assertEquals(0, prediction.value)
def test_author_classification_real_dataset(self):
# 0 --> Celebrirty
# 1 --> Media
# 2 --> Journalists
# 3 --> Common people
classifier = TreeClassifier()
train_set = numpy.array([[0.18, 0.57, 0.01, 0.053, 0.0, 52872., 0], #Bill Gates
[0.5, 0.1, 0.0, 0.09, 0.0, 151., 0], #Justin Bieber
[0.096, 0.4, 0.06, 0.2, 0.0, 14052., 0], #Ashton Kutcher
[0.06, 0.051, 0.04, 0.62, 0.0, 216342., 0], #Oprah
[0.026, 0.72, 0.03, 0.07, 0.0, 79183., 0], #Amy Winehouse
[0.006, 0.85, 0.52, 0.0, 0.0, 55025., 1], #BBC
[0.17, 0.77, 0.86, 0.03, 0.0, 5540., 1], #CNN
[0.25, 0.73, 2., 0.0, 0.0, 264., 1], #HuffPost
[0.02, 0.99, 0.79, 0.03, 0.0, 5034., 1], #AlJazeera
[0.13, 0.31, 2.2, 0.33, 0.0, 14., 2], #Ali Velshi
[0.19, 0.19, 2.8, 0.17, 0.0, 4., 2], #MujMash
[0.053, 0.23, 2.5, 0.3, 0.0, 12., 2], #StevePoliti
[0.09, 0.16, 11.3, 0.36, 0.0, 8., 2], #Rachel King
[0.4, 0.36, 8.6, 0.16, 0.0, 0.32, 3], #George Eracleous
[0.2, 0.12, 7.1, 0.35, 0.0, 0.38, 3], #Nik Adhia
[0.26, 0.15, 2.4, 0.31, 0.0, 0.9, 3], #A person
[0.55, 0.13, 13.2, 0.25, 0.0, 0.7, 3]]) #Yet another person
attributes = ["retweets", "links", "retweeted", "replies", "mentions", "ff-ratio", "class"]
classifier.train(train_set, attributes)
celebrity_example = [0.096, 0.3, 0.03, 0.08, 0.0, 94258, 0]
media_example = [0.0, 0.8, 0.89, 0.031, 0.0, 184., 0]
common_example = [0.55, 0.13, 13.2, 0.25, 0.0, 0.7, 0]
journalist_example = [0.24, 0.48, 14.7, 0.11, 0.0, 11.9, 0]
prediction_celebrity = classifier.classify(celebrity_example)
prediction_media = classifier.classify(media_example)
prediction_journalist = classifier.classify(journalist_example)
prediction_common = classifier.classify(common_example)
calculated = [prediction_celebrity, prediction_media, prediction_journalist, prediction_common]
expected = [0, 1, 2, 3]
self.assertEqual(expected, calculated)