Difference between revisions of "Talk:Machine Learning"

Feb. 27, 2014

Folks met and hacked on the noisebridge discuss mailing list. We created a 102MB text dump, and a python script to parse it, File:Py-piper-parser.txt. We wrote pseudo code to implement a Naive Bayesian filter to protect the world from trolls. Will implement soon.

March 6, 2014

Group compared notes on list-scraping code and then delved into details of algorithm via tracing simple example.

user presented w message
user labels spam/not spam or scale
user presented with rating: 50% naive

very beginning
first step: assign prior (50% of all messages are spam)
also have a likelihood that each word is spam or not spam
- combines to total 50%  (also consider log-likelihood)
next step: algorithm decides per message
this is end of phase zero (PERFORMANCE PHASE)
algorithm performing without human intervention

next: TRAINING PHASE
first step of training phase
examine message, assign a rating (spamicity ie 7/7 or drama-tag or spam/not spam)
then save rating associated with message
ie write to vector (each index corresponds to a message)

spamicity vector:
msg0 msg1 msg2 msg3
1     0    0    3

then update dictionary
what dictionary?
temporary dictionary in the first step
every dictionary item has a frequency count

now ...
after (say) 1000 messages, algorithm guesses mostly correctly
'spam or not-spam' (is this TESTING PHASE ?)
training continues via occasional instances of (human) correction
update dictionary with each word in (human?) rated message

one possibly viable dictionary structure:
{'word':[counted_in_spam, counted_in_not_spam]}

so, algorithm might operate as per this trace:

msg: 'foo bar' SPAM
msg: 'foo foo bar foo bar' HAM
msg: 'bar bar bar foo' ... WHAT IS THIS ????
Can consult algorithm because now we have SPAM and HAM
so can get bayes-informed result

dictionary
after msg
{'foo': [1, 0], 'bar': [1, 0]}

after msg
{'foo': [1, 3], 'bar': [1, 2]}

NOW WE ARE AT msg WHAT IS THIS ???

THIS LOOKS EASIER TO SOLVE THIS TIME !!!!!!!

WE HAVE A VECTOR OF SPAM/HAM
IT LOOKS LIKE THIS: ['s', 'h']
OR IF YOU LIKE BINARY [True, False]
OR [0, 1]
OK I GET IT !!!!

WE HAVE A VECTOR AND A DICTIONARY and a message NOW WHAT ???
{'foo': [1, 3], 'bar': [1, 2]}
[0, 1]
msg: 'bar bar bar foo' ... WHAT IS THIS ????

A: probability of 'foo' | spam = 1
B: probability of spam = 0.5
C: probability of 'foo' | ham = 3
... wtf ???
this gets normalized later ? maybe
sam hopes this cancels out without being painful
D: probability of ham = 0.5

= 0.25 likelihood given 'foo'

(1 * 0.5) / ((1 * 0.5) + (3 * 0.5))
A * B / ((A * B) + (C * D))
= 0.25 likelihood given 'foo'

1(.5)
1 = prob of foo in message | spam
.5 = prob of any word | spam
.5 = prob of 'foo' in (first) word | spam
.5 = A (normed)

3 = prob of foo in message(?) | not spam
1/5 = prob of word | ham
.6 = prob of foo in (first) word | ham
.6 = C (normed)

likelihood given 'bar'
(1 * 0.5) / ((1 * 0.5) + (2 * 0.5))
= 0.3333... (1/3)

(1/3.0)**3 * (1/4.0) / (((1/3.0)**3 * (1/4.0)) + ((2/3.0)**3 * (3/4.0)))
= 0.04
The way this is not fully bayesian... p(foo) & p(bar) are interacting...
Also, are we normalizing correctly?

If we normalized,
we take into account the following:
avg freq of words in spam
avg freq of words in ham

but this is not fully bayesian
because ...  so far ...
we have been assuming independence
between words (at the full-message level)

import re
from StringIO import StringIO
from gzip import GzipFile
from time import gmtime
from urllib import urlopen
from contextlib import closing

def decompress_url(u):
with closing(urlopen(u)) as f:
with GzipFile(fileobj = fs) as g:

def date_in_discuss(m, y):
if 1 <= m <= 12:
if y > 2007:
now = gmtime()
yy, mm = now.tm_year, now.tm_mon
if (y < yy) or ((y == yy) and (m <= mm)):
return True
elif (y == 2007) and (m >= 11):
return True
return False

def datestr(m, y):
try:
ms = ('January', 'February', 'March',
'April', 'May', 'June', 'July',
'August', 'September', 'October',
'November', 'December')[m - 1]
return '-'.join((str(y), ms))
except IndexError:
return None

def nb_gz_url(m, y, listname='noisebridge-discuss'):
if not date_in_discuss(m, y):
return None
a = 'https://www.noisebridge.net/'
b = 'pipermail/'
c = '/'.join((listname, ''))
d = datestr(m, y)
e = '.txt.gz'
return ''.join((a, b, c, d, e))

def all_nb_gz_urls():
now = gmtime()
yy, mm = now.tm_year, now.tm_mon
y, m = 2007, 11
while (y < yy) or ((y == yy) and (m <= mm)):
yield nb_gz_url(m, y)
if m < 12:
m += 1
else:
m = 1
y += 1

def get_month(month, year):
u = nb_gz_url(month, year)
s = decompress_url(u)
return s

def spew():
for u in all_nb_gz_urls():
yield decompress_url(u)

def dump_uncompressed(filename='nb_wtf.txt'):
with open(filename, 'w') as f:
for s in spew():
f.write(s)

def compiled_pattern(key, cache={}):
try:
return cache[key]
except KeyError:
if key == 'msg_start':
p = msg_start_pattern()
elif key == 'msg_stop':
p = msg_stop_pattern()
else:
return None
cache[key] = re.compile(p)
return cache[key]

def msg_start_pattern():
# ... and so it begins:
# 'From jacob at appelbaum.net  Tue Nov 20 20:20:07 2007'
# -> r'^From .*\s+\w{3}\s+\w{3}\s+\d+\s+\d{2}:\d{2}:\d{2} \d{4}\$'
# (return compiled regex roughly equivalent to the above)
space = r'\s+'
datestr = space.join((r'\w{3}', r'\w{3}', r'\d+',
r'\d{2}:\d{2}:\d{2} \d{4}'))
pattern = ''.join(('^', 'From .*', space, datestr, '\$'))
return re.compile(pattern)

def msg_stop_pattern():
anchor = lambda s: ''.join(('^', s, '\$'))
htmldelim = anchor('-------------- next part --------------')
listdelim = anchor('_______________________________________________')
pattern = '|'.join((htmldelim, listdelim))
return re.compile(pattern)

def msglists(s):
# yields list of strings for each msg in string s
msg = []
p = compiled_pattern('msg_start')
for r in s.splitlines():
if p.match(r):
if msg:
yield msg
msg = []
msg.append(r)
if msg:
yield msg

def msg2dict(msg):
# msg is list of strings
# return dict with headers, contents, cruft
d = dict()
p = compiled_pattern('msg_start')
if not (msg and p.match(msg)):
d['bogus'] = msg
return d
cruft = ''
ss = iter(msg)
d['fromkey'] = next(ss)
for s in ss:
t = s.split(':', 1)
if len(t) != 2:
try:
except IndexError:
print 'this happened ???'
else:
k, v = t
if k == 'Message-ID':
break
# skip blank line(s)
s = next(ss)
while not s:
s = next(ss)
contents = [s.rstrip()]
cruft = []
p = compiled_pattern('msg_stop')
for s in ss:
if p.match(s):
cruft.append(s)
break
else:
contents.append(s.rstrip())
d['contents'] = contents
if cruft:
cruft.extend([s.rstrip() for s in ss])
d['cruft'] = cruft
return d

def msg2smtp(msg):
smtp = dict()
msgd = msg2dict(msg)
q = (('From', 'fromline'),
('Date', 'dateline'),
('Subject', 'subjectline'))
for k, v in q:
try:
except KeyError:
continue
smtp['messageline'] = '\n'.join(msgd['contents'])
return smtp

def dicterator(s):
for msg in msglists(s):
yield msg2dict(msg)

def smtperator(s):
for msg in msglists(s):
yield msg2smtp(msg)

Word parsing python script

Function 'get_words' takes list of dictionary of emails. Yields lists of words of in the message, for each message:

def get_words(lst):
for d in lst:
m = d['messageline']
yield m.split()

Plans to improve by using nltk