Limnoria/others/reverend/thomas.py
2004-10-03 09:08:36 +00:00

310 lines
9.6 KiB
Python
Executable File

# This module is part of the Divmod project and is Copyright 2003 Amir Bakhtiar:
# amir@divmod.org. This is free software; you can redistribute it and/or
# modify it under the terms of version 2.1 of the GNU Lesser General Public
# License as published by the Free Software Foundation.
#
import operator
import string
import math
from sets import Set
from splitter import Splitter
class BayesData(dict):
def __init__(self, name='', pool=None):
self.name = name
self.training = []
self.pool = pool
self.tokenCount = 0
self.trainCount = 0
def trainedOn(self, item):
return item in self.training
def __repr__(self):
return '<BayesDict: %s, %s tokens>' % (self.name, self.tokenCount)
class Bayes(object):
def __init__(self, tokenizer=None, combiner=None, dataClass=None):
if dataClass is None:
self.dataClass = BayesData
else:
self.dataClass = dataClass
self.corpus = self.dataClass('__Corpus__')
self.pools = {}
self.pools['__Corpus__'] = self.corpus
self.trainCount = 0
self.splitter = Splitter()
self.dirty = True
# The tokenizer takes an object and returns
# a list of strings
if tokenizer is None:
self.tokenizer = self.getTokens
else:
self.tokenizer = tokenizer
# The combiner combines probabilities
if combiner is None:
self.combiner = self.robinson
else:
self.combiner = combiner
def split(self, text):
return self.splitter.split(text)
def commit(self):
self.save()
def newPool(self, poolName):
"""Create a new pool, without actually doing any
training.
"""
self.dirty = True # not always true, but it's simple
return self.pools.setdefault(poolName, self.dataClass(poolName))
def removePool(self, poolName):
del(self.pools[poolName])
self.dirty = True
def renamePool(self, poolName, newName):
self.pools[newName] = self.pools[poolName]
self.pools[newName].name = newName
self.removePool(poolName)
self.dirty = True
def mergePools(self, destPool, sourcePool):
"""Merge an existing pool into another.
The data from sourcePool is merged into destPool.
The arguments are the names of the pools to be merged.
The pool named sourcePool is left in tact and you may
want to call removePool() to get rid of it.
"""
sp = self.pools[sourcePool]
dp = self.pools[destPool]
for tok, count in sp.items():
if dp.get(tok):
dp[tok] += count
else:
dp[tok] = count
dp.tokenCount += 1
self.dirty = True
def poolData(self, poolName):
"""Return a list of the (token, count) tuples.
"""
return self.pools[poolName].items()
def poolTokens(self, poolName):
"""Return a list of the tokens in this pool.
"""
return [tok for tok, count in self.poolData(poolName)]
def save(self, fname='bayesdata.dat'):
from cPickle import dump
fp = open(fname, 'wb')
dump(self.pools, fp)
fp.close()
def load(self, fname='bayesdata.dat'):
from cPickle import load
fp = open(fname, 'rb')
self.pools = load(fp)
fp.close()
self.corpus = self.pools['__Corpus__']
self.dirty = True
def poolNames(self):
"""Return a sorted list of Pool names.
Does not include the system pool '__Corpus__'.
"""
pools = self.pools.keys()
pools.remove('__Corpus__')
pools = [pool for pool in pools]
pools.sort()
return pools
def buildCache(self):
""" merges corpora and computes probabilities
"""
self.cache = {}
for pname, pool in self.pools.items():
# skip our special pool
if pname == '__Corpus__':
continue
poolCount = len(pool)
themCount = max(len(self.corpus) - poolCount, 1)
cacheDict = self.cache.setdefault(pname, self.dataClass(pname))
for word, totCount in self.corpus.items():
# for every word in the copus
# check to see if this pool contains this word
thisCount = float(pool.get(word, 0.0))
otherCount = float(totCount) - thisCount
if not poolCount:
goodMetric = 1.0
else:
goodMetric = min(1.0, otherCount/poolCount)
badMetric = min(1.0, thisCount/themCount)
f = badMetric / (goodMetric + badMetric)
# PROBABILITY_THRESHOLD
if abs(f-0.5) >= 0.1 :
# GOOD_PROB, BAD_PROB
cacheDict[word] = max(0.0001, min(0.9999, f))
def poolProbs(self):
if self.dirty:
self.buildCache()
self.dirty = False
return self.cache
def getTokens(self, obj):
"""Hopefully it's a string and we'll just split it
on non-alphanumeric stuff.
Override this in your subclass for objects other
than text.
Alternatively, you can pass in a tokenizer as part of
instance creation.
"""
return self.split(obj)
def getProbs(self, pool, words):
""" extracts the probabilities of tokens in a message
"""
probs = [(word, pool[word]) for word in words if word in pool]
probs.sort(lambda x,y: cmp(y[1],x[1]))
return probs[:2048]
def train(self, pool, item, uid=None):
"""Train Bayes by telling him that item belongs
in pool. uid is optional and may be used to uniquely
identify the item that is being trained on.
"""
tokens = self.tokenizer(item)
pool = self.pools.setdefault(pool, self.dataClass(pool))
self._train(pool, tokens)
self.corpus.trainCount += 1
pool.trainCount += 1
if uid:
pool.training.append(uid)
self.dirty = True
def untrain(self, pool, item, uid=None):
tokens = self.tokenizer(item)
pool = self.pools.get(pool, None)
if not pool:
return
self._untrain(pool, tokens)
# I guess we want to count this as additional training?
self.corpus.trainCount += 1
pool.trainCount += 1
if uid:
pool.training.remove(uid)
self.dirty = True
def _train(self, pool, tokens):
wc = 0
for token in tokens:
count = pool.get(token, 0)
pool[token] = count + 1
count = self.corpus.get(token, 0)
self.corpus[token] = count + 1
wc += 1
pool.tokenCount += wc
self.corpus.tokenCount += wc
def _untrain(self, pool, tokens):
for token in tokens:
count = pool.get(token, 0)
if count:
if count == 1:
del(pool[token])
else:
pool[token] = count - 1
pool.tokenCount -= 1
count = self.corpus.get(token, 0)
if count:
if count == 1:
del(self.corpus[token])
else:
self.corpus[token] = count - 1
self.corpus.tokenCount -= 1
def trainedOn(self, msg):
for p in self.cache.values():
if msg in p.training:
return True
return False
def guess(self, msg):
tokens = Set(self.tokenizer(msg))
pools = self.poolProbs()
res = {}
for pname, pprobs in pools.items():
p = self.getProbs(pprobs, tokens)
if len(p) != 0:
res[pname]=self.combiner(p, pname)
res = res.items()
res.sort(lambda x,y: cmp(y[1], x[1]))
return res
def robinson(self, probs, ignore):
""" computes the probability of a message being spam (Robinson's method)
P = 1 - prod(1-p)^(1/n)
Q = 1 - prod(p)^(1/n)
S = (1 + (P-Q)/(P+Q)) / 2
Courtesy of http://christophe.delord.free.fr/en/index.html
"""
nth = 1./len(probs)
P = 1.0 - reduce(operator.mul, map(lambda p: 1.0-p[1], probs), 1.0) ** nth
Q = 1.0 - reduce(operator.mul, map(lambda p: p[1], probs)) ** nth
S = (P - Q) / (P + Q)
return (1 + S) / 2
def robinsonFisher(self, probs, ignore):
""" computes the probability of a message being spam (Robinson-Fisher method)
H = C-1( -2.ln(prod(p)), 2*n )
S = C-1( -2.ln(prod(1-p)), 2*n )
I = (1 + H - S) / 2
Courtesy of http://christophe.delord.free.fr/en/index.html
"""
n = len(probs)
try: H = chi2P(-2.0 * math.log(reduce(operator.mul, map(lambda p: p[1], probs), 1.0)), 2*n)
except OverflowError: H = 0.0
try: S = chi2P(-2.0 * math.log(reduce(operator.mul, map(lambda p: 1.0-p[1], probs), 1.0)), 2*n)
except OverflowError: S = 0.0
return (1 + H - S) / 2
def __repr__(self):
return '<Bayes: %s>' % [self.pools[p] for p in self.poolNames()]
def __len__(self):
return len(self.corpus)
def chi2P(chi, df):
""" return P(chisq >= chi, with df degree of freedom)
df must be even
"""
assert df & 1 == 0
m = chi / 2.0
sum = term = math.exp(-m)
for i in range(1, df/2):
term *= m/i
sum += term
return min(sum, 1.0)