def __init__(self):
self.bayes = Bayes()
class Test:
def __init__(self):
self.bayes = Bayes()
def status(self):
args = ['', 'status']
self.bayes.run(args)
def reset(self):
args = ['', 'reset']
self.bayes.run(args)
def learn(self, dataset, count_spam, count_ham):
b = self.bayes
args = ['', 'learn', 'spam', '%s/spam' % dataset, int(count_spam)]
b.run(args)
args = ['', 'learn', 'ham', '%s/ham' % dataset, int(count_ham)]
b.run(args)
def test(self, dataset, threshold):
b = self.bayes
print '>> Classifying spam ...'
args = ['./test.py', 'classify', '%s/spam' % dataset, 'spam', 'ham', threshold]
result = b.run(args)
tp = result['positive']
fn = result['count'] - tp
print '>> Classifying ham ...'
args = ['./test.py', 'classify', '%s/ham' % dataset, 'spam', 'ham', threshold]
result = b.run(args)
fp = result['positive']
tn = result['count'] - fp
precison = 1.0 * tp / (tp + fp)
recall = 1.0 * tp / (tp + fn)
fscore = 2/(1/precison + 1/recall)
fpr = 1.0*fp/(fp+tn)
tnr = 1 - fpr
# self.status()
print "Classifying Results:"
print "========================================\n"
print 'POSITIVE: %6d, TP: %6d, FN: %6d' % (tp + fn, tp, fn)
print 'NEGATIVE: %6d, TN: %6d, FP: %6d\n' % (tn + fp, tn, fp)
print 'TPR: %1.4f, FPR: %1.4f, TNR: %1.4f' % (recall, fpr, tnr)
print ' R: %1.4f, P: %1.4f, F: %1.4f' % (recall, precison, fscore)
print "\n========================================\n"
def tfidf(self, dataset):
b = self.bayes
print '>> TFIDFing spam ...'
args = ['./test.py', 'tfidf', '%s/spam' % dataset, 'spam', 'ham']
result = b.run(args)
print '>> TFIDF ham ...'
args = ['./test.py', 'tfidf', '%s/ham' % dataset, 'spam', 'ham']
result = b.run(args)
def chi(self, dataset):
b = self.bayes
print '>> TFIDFing spam ...'
args = ['./test.py', 'chi', '%s/spam' % dataset, 'spam', 'ham']
result = b.run(args)
print '>> TFIDF ham ...'
args = ['./test.py', 'chi', '%s/ham' % dataset, 'spam', 'ham']
result = b.run(args)
