def pspam(fn):
queue = PythonLabs.SpamLab.WordQueue(15)
p_good, p_bad = spamicity.load_dicts()
for line in open(fn):
for word in tokenize(line):
p = spamicity.conditional_spam(word, p_good, p_bad)
if p is not None:
p2.append((word, p))
queue.insert(word, p)
return PythonLabs.SpamLab.combined_probability(queue)
def pspam(fn):
queue = PythonLabs.SpamLab.WordQueue(15)
p_good, p_bad = spamicity.load_dicts()
for line in open(fn):
for word in tokenize(line):
p = spamicity.conditional_spam(word, p_good, p_bad)
if p is not None:
p2.append((word, p))
queue.insert(word, p)
return PythonLabs.SpamLab.combined_probability(queue)
def prob_spam(email_file_path):
q = []
p_good, p_bad = spamicity.load_dicts()
assert p_good['looking'] == 0.0530
with open(email_file_path) as f:
#read each line, tokenize, add to queue with spamicity
for line in f:
words = tokenize(line)
for word in words:
prob = spamicity.conditional_spam(word, p_good, p_bad)
#print('word: {} prob: {}'.format(word, prob))
if prob is not None:
p1.append((word, prob))
heapq.heappush(q, (prob, words))
return combine_probs(q)
def prob_spam(email_file_path):
q = []
p_good,p_bad = spamicity.load_dicts()
assert p_good['looking'] == 0.0530
with open(email_file_path) as f:
#read each line, tokenize, add to queue with spamicity
for line in f:
words = tokenize(line)
for word in words:
prob = spamicity.conditional_spam(word,p_good,p_bad)
#print('word: {} prob: {}'.format(word, prob))
if prob is not None:
p1.append((word, prob))
heapq.heappush(q,(prob, words))
return combine_probs(q)
