def format_review(raw_data):
formatted_data = []
for review in raw_data:
formatted_review = []
tokenized_review = sent_tokenize(review)
tokenized_review = list(flatten(tokenized_review))
tokenized_review[0] = tokenized_review[0].partition(" ")[2]
for sentence in tokenized_review:
formatted_review.append(sentence.split())
formatted_data.append(formatted_review)
debug()
return formatted_data
def main():
input_dictionary = {}
try:
input_reviews = open(sys.argv[1])
except:
print("Please supply an input file to be classified as the first argument and try again")
try:
input_dictionary = pickle.load(open(sys.argv[2], 'rb'))
except:
print("Please supply a pickled data set as the second argument and try again ")
data_dictionary = input_dictionary["freq"]
prior_dictionary = input_dictionary["prior"]
TCount = 0
FCount = 0
correct = 0
incorrect = 0
test_list = list(flatten((('T',)*20,('F',)*20)))
debug()
for review in format_review(input_reviews):
intermediate = classify(data_dictionary, prior_dictionary, review)
probable_class = (max(intermediate, key=lambda k: (intermediate[k])))
if probable_class == 'final_T':
TCount += 1
else:
FCount += 1
if probable_class[6:] == test_list.pop(0):
correct += 1
else:
incorrect += 1
print(TCount)
print(FCount)
print(TCount/(TCount + FCount))
print(FCount/(TCount + FCount))
if TCount > FCount:
print('T')
else:
print('F')
print(correct/(correct + incorrect))
