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SimpleCount.py
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SimpleCount.py
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#!/usr/bin/python
# Copyright (c) [2012-], Josef Robert Novak
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted #provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of #conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
# OF THE POSSIBILITY OF SUCH DAMAGE.
from collections import defaultdict
from NGramStack import NGramStack
from math import log
import re
class MLCounter( ):
"""
Stand-alone python implementation of an simple Maximum Likelihood LM.
This class simply counts NGrams in a training corpus and either,
* Dumps the raw, log_10 counts into ARPA format
* Computes an unsmoothed Maximum Likelihood LM
"""
def __init__( self, order=3, sb="<s>", se="</s>", raw=False, ml=False ):
self.sb = sb
self.se = se
self.raw = raw
self.ml = ml
self.order = order
self.ngrams = NGramStack(order=order)
self.counts = [ defaultdict(float) for i in xrange(order) ]
def maximum_likelihood( self, training_file ):
"""
Iterate through the training data using a FIFO stack or
'window' of max-length equal to the specified N-gram order.
Each time a new word is pushed onto the N-gram stack call
the _ml_count() subroutine to increment the N-gram counts.
If pushing a word onto the stack makes len(stack)>max-order,
then the word at the bottom (stack[0]) is popped off.
"""
for line in open(training_file,"r"):
#Split the current line into words.
words = re.split(r"\s+",line.strip())
#Push a sentence-begin token onto the stack
ngram = self.ngrams.push(self.sb)
self._ml_count( ngram )
for word in words:
#Get the current 'window' of N-grams
ngram = self.ngrams.push(word)
#Now count all N-grams in the current window
#These will be of span <= self.order
self._ml_count( ngram )
#Now push the sentence-end token onto the stack
ngram = self.ngrams.push(self.se)
self._ml_count( ngram )
#Clear the stack for the next sentence
self.ngrams.clear()
return
def _ml_count( self, ngram_stack ):
"""
Just count NGrams. The only slightly confusing thing here
is the sentence-begin (<s>). It does NOT count as a
unigram event and thus does not contribute to the unigram tally.
It IS however used as a history denominator.
"""
#Iterate backwards through the stack
for o in xrange(len(ngram_stack),0,-1):
start = len(ngram_stack)-o
self.counts[o-1][" ".join(ngram_stack[start:])] += 1.0
return
def print_ARPA( self ):
"""
Print the raw counts or ML LM out in ARPA format,
ARPA format:
----------------------------
\data\
ngram 1=NUM_1GRAMS
ngram 2=NUM_2GRAMS
...
ngram N=NUM_NGRAMS (max order)
\1-grams:
p(a_z) a_z bow(a_z)
...
\2-grams:
p(a_z) a_z bow(a_z)
...
\N-grams:
p(a_z) a_z
...
\end\
----------------------------
NOTE: Neither the ML model nor the raw counts
will ever have a 'backoff weight'.
"""
#Handle the header info
print "\\data\\"
for o in xrange(0,self.order):
print "ngram %d=%d" % (o+1,len(self.counts[o]) )
#Handle the Unigrams
print "\n\\1-grams:"
for key in sorted(self.counts[0].iterkeys()):
if key==self.sb:
if self.raw:
print "0.00000\t%s" % ( self.sb )
else:
print "-99.00000\t%s" % ( self.sb )
else:
if self.ml:
ml_prob = self.counts[0][key] / ( sum( [ self.counts[0][c] for c in self.counts[0].keys() ] ) - self.counts[0][self.sb] )
if self.raw:
print "%0.6f\t%s" % ( ml_prob, key )
else:
print "%0.6f\t%s" % ( log(ml_prob,10.), key )
elif self.raw:
print "%0.6f\t%s" % (self.counts[0][key], key)
else:
print "%0.6f\t%s" % (log(self.counts[0][key],10.), key)
#Handle the middle-order N-grams
for o in xrange(1,self.order):
print "\n\\%d-grams:" % (o+1)
for key in sorted(self.counts[o].iterkeys()):
if self.ml:
hist = key[:key.rfind(" ")]
ml_prob = self.counts[o][key] / self.counts[o-1][hist]
if self.raw:
print "%0.6f\t%s" % ( ml_prob, key )
else:
print "%0.6f\t%s" % ( log(ml_prob,10.), key )
elif self.raw:
print "%0.6f\t%s" % (self.counts[o][key], key)
else:
print "%0.6f\t%s" % (log(self.counts[o][key],10.), key)
print "\n\\end\\"
return
if __name__=="__main__":
import sys, argparse
example = """%s --train train.corpus""" % sys.argv[0]
parser = argparse.ArgumentParser(description=example)
parser.add_argument('--train', "-t", help="The text corpus to be used to train the LM.", required=True )
parser.add_argument('--order', "-o", help="The maximum N-gram order (3).", required=False, default=3, type=int )
parser.add_argument('--sb', "-b", help="The sentence-begin token (<s>).", required=False, default="<s>" )
parser.add_argument('--se', "-e", help="The sentence-end token (</s>).", required=False, default="</s>" )
parser.add_argument('--ml', "-m", help="Compute the ML model.", action="store_true", default=False )
parser.add_argument('--raw', "-r", help="Output the raw counts, not log_10.", action="store_true", default=False )
parser.add_argument('--verbose', "-v", help="Verbose mode.", action="store_true", default=False )
args = parser.parse_args()
if args.verbose:
for attr, value in args.__dict__.iteritems():
print attr, "=", value
lms = MLCounter( order=args.order, sb=args.sb, se=args.se, ml=args.ml, raw=args.raw )
lms.maximum_likelihood( args.train )
lms.print_ARPA( )