def main():
vocab = input('Enter choice for vocabulary: ')
# vocab = '0'
ngram = input('Enter choice for NGram: ')
# ngram = '1'
delta = input('Enter smoothing delta value between 0 and 1: ')
# delta = '0.5'
# training_file = 'OriginalDataSet/training-tweets.txt'
training_file = input('Enter training file: ')
# testing_file = 'OriginalDataSet/test-tweets-given.txt'
testing_file = input('Enter test file: ')
classifier = Classifier(vocab, ngram, float(delta), training_file, testing_file)
start = time.time()
classifier.read_data(False)
print('Time taken to read: ', time.time() - start)
start = time.time()
classifier.create_model()
print('Time taken to create model: ', time.time() - start)
start = time.time()
classifier.train_model()
print('Time taken to train model: ', time.time() - start)
# classifier.save_model()
start = time.time()
trace_file = classifier.test_model()
print('Time taken to test model: ', time.time() - start)
evaluation = Evaluation(trace_file)
start = time.time()
evaluation.calculate_performance()
evaluation.print_to_file()
print('Time taken to evaluate model: ', time.time() - start)
def main():
# vocab = input('Enter choice for vocabulary: ')
vocab = '0'
# ngram = input('Enter choice for NGram: ')
ngram = '3'
# delta = input('Enter smoothing delta value between 0 and 1: ')
delta = '0.5'
training_file = 'OriginalDataSet/training-tweets.txt'
# training_file = input('Enter training file: ')
testing_file = 'OriginalDataSet/test-tweets-given.txt'
# testing_file = 'OriginalDataSet/8.txt'
# testing_file = input('Enter test file: ')
classifier = Classifier(vocab, ngram, float(delta), training_file, testing_file)
start = time.time()
classifier.read_data(False)
# classifier.print_dictionary()
print('Time taken to read: ', time.time() - start)
start = time.time()
classifier.create_model()
print('Time taken to create model: ', time.time() - start)
start = time.time()
classifier.train_model()
print('Time taken to train model: ', time.time() - start)
# classifier.save_model()
# classifier.tweet_count()
start = time.time()
trace_file = classifier.test_model()
# trace_file = 'Outputs/trace_2_1_0.5.txt'
print('Time taken to test model: ', time.time() - start)
evaluation = Evaluation(trace_file)
start = time.time()
evaluation.calculate_performance()
evaluation.confusion_matrix_each_lang()
evaluation.print_to_file()
print('Time taken to evaluate model: ', time.time() - start)
