def main(version,method,polarity,ngrams,save=False,fold=5):
data, y = features(polarity,version=version,ngrams=ngrams,skew=False,save=save)
print('data: '+str(np.shape(data)))
fp = fake_pred(polarity,version=version,save=save,fold=fold)
results, acc = fp.ott_5fold(data,y,method,sample='prior',skew=.5)
for key in acc:
print(str(version)+' / '+str(polarity)+' accuracy for '+str(key)+': '+str(acc[key]))
def main(version, method, polarity, ngrams, save=False, fold=5):
data, y = features(polarity,
version=version,
ngrams=ngrams,
skew=False,
save=save)
print('data: ' + str(np.shape(data)))
fp = fake_pred(polarity, version=version, save=save, fold=fold)
results, acc = fp.ott_5fold(data, y, method, sample='prior', skew=.5)
for key in acc:
print(
str(version) + ' / ' + str(polarity) + ' accuracy for ' +
str(key) + ': ' + str(acc[key]))
import os, codecs
import numpy as np
import pandas as pd
import nltk
from extract_data import extract, extract_skew
from feature_extract import features
from pred import fake_pred
from scipy.stats import itemfreq
if __name__ == "__main__":
polarities = ['negative','positive']
for polarity in polarities:
data, y = features(polarity)
#print('data: '+str(np.shape(data)))
fp = fake_pred(version='Ott')
results, acc = fp.ott_1fold(data,'NB')
for key in acc:
print(str(polarity)+' accuracy for '+str(key)+': '+str(acc[key]))
