def __init__(self, wd=None, save=None):
if wd:
os.chdir(wd)
self.reddit = str(wd).split("/")[-2]
self.cutoff = compute_cutoff(wd)
self.popular_texts = []
self.unpopular_texts = []
self.corpus = []
self.all_texts = []
self.save = False
self.word_counts = defaultdict(int)
self.vectorizer = None
if save:
if save.lower()[0] == "y":
self.save = True
print 'F1 Score:', F1score
print 'Confusion matrix:'
print confusion
return F1score
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Builds a Naive Bayes model for classification.")
parser.add_argument("filepath", help="Argument must be the filepath where the text files are located")
parser.add_argument("topic_type", help="topic_type is either bow, tfidf or lda")
parser.add_argument("valid_or_test", help="Either v or t to test against validation or test set")
parser.add_argument("--num_topics", default=10, help="The amount of topics to be grabbed from the LDA model")
args = parser.parse_args()
cutoff = compute_cutoff(args.filepath)
print "Classifying the initial data."
classify_initial_data(args.filepath, cutoff, SOURCES)
training_data = make_data(SOURCES, args.filepath)
if args.valid_or_test[0].lower() == "v":
print "Classifying the validation data."
validation_filepath = args.filepath + "/validation"
classify_initial_data(validation_filepath, cutoff, VALIDATION)
validation_data = make_data(VALIDATION, validation_filepath)
create_Naive_Bayes(training_data, validation_data, args.topic_type, args.num_topics)
else:
print "Classifying the testing data."
testing_filepath = args.filepath + "/testing"
