def trainNaiveBayes(spamdir, hamdir):
allFeatures = []
classifications = []
for f in listdir_fullpath(spamdir):
allFeatures += [featuresForMail(f)]
classifications += [True]
for f in listdir_fullpath(hamdir):
allFeatures += [featuresForMail(f)]
classifications += [False]
features = zip(allFeatures, classifications)
classi = NaiveBayesClassifier.train(features)
return classi
from features import featuresForMail
from sys import argv
# do cross validation on training/testing data
if __name__ == '__main__':
if len(argv) == 3:
hamdir = argv[1] + '/ham'
spamdir = argv[2] + '/spam'
classi = trainNaiveBayes(hamdir, spamdir)
total = 0
good = 0
for f in listdir_fullpath(argv[1] + '/ham'):
features = featuresForMail(f)
total += 1
if classi.classify(features) == False:
good += 1
for f in listdir_fullpath(argv[1] + '/spam'):
features = featuresForMail(f)
total += 1
if classi.classify(features) == True:
good += 1
print "accuracy: " + str(float(int(float(good)/float(total)*10000))/100) + "%"
else:
print "usage: python applyCrossval.py <training directory> <testing directory>"
