def run():
# Dataset generation
cls = ['Obama', 'Trump']
obama_sentences = inaugural.sents('2009-Obama.txt')
trump_sentences = inaugural.sents('2017-Trump.txt')
labelled_obama = [(s, cls[0]) for s in obama_sentences]
labelled_trump = [(s, cls[1]) for s in trump_sentences]
labelled_data = labelled_obama + labelled_trump
trump_test = [
'We', ',', 'the', 'citizens', 'of', 'America', ',', 'are', 'now',
'joined', 'in', 'a', 'great', 'national', 'effort', 'to', 'rebuild',
'our', 'country', 'and', 'restore', 'its', 'promise', 'for', 'all',
'of', 'our', 'people', '.'
]
obama_test = [
'I', 'stand', 'here', 'today', 'humbled', 'by', 'the', 'task',
'before', 'us', ',', 'grateful', 'for', 'the', 'trust', 'you', 'have',
'bestowed', ',', 'mindful', 'of', 'the', 'sacrifices', 'borne', 'by',
'our', 'ancestors', '.'
]
model = Model(labelled_data, cls)
model.train()
while True:
inp = input("Input a string to test: ")
doc = tokenize(inp)
print(model.test_doc(doc))
def words_per_user_mapper(status_update):
user = status_update["username"]
for word in tokenize(status_update["text"]):
yield (user, (word, 1))
def word_count_old(documents):
"""word count not using MapReduce"""
return Counter(word
for document in documents
for word in tokenize(document))
def wc_mapper(document):
"""for each word in the document, emit (word,1)"""
for word in tokenize(document):
yield (word, 1)
def wc_mapper(document): '''for each word in the doc, emit (word, 1)''' for word in naive_bayes.tokenize(document): yield (word, 1)
def words_per_user_mapper(status_update):
user = status_update["username"]
for word in tokenize(status_update["text"]):
yield (user, (word, 1))
def word_count_old(documents):
"""word count not using MapReduce"""
return Counter(word
for document in documents
for word in tokenize(document))
def wc_mapper(document):
"""for each word in the document, emit (word,1)"""
for word in tokenize(document):
yield (word, 1)
def test_tokenize(self):
self.assertEqual({'hello', 'it\'s', 'time', 'for', '1000s', 'of'},
naive_bayes.tokenize("Hello it's time for 1000s of HELLO"))
def word_count_mapper(document):
for word in tokenize(document):
yield (word, 1)
