Limnoria/others/reverend/thomas.py

# 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)