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 __init__(self, order=3, sb="<s>", se="</s>"):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [defaultdict(float) for i in xrange(order - 1)]
self.numerators = [defaultdict(float) for i in xrange(order - 1)]
self.nonZeros = [defaultdict(float) for i in xrange(order - 1)]
self.CoC = [[0.0 for j in xrange(4)] for i in xrange(order)]
self.discounts = [0.0 for i in xrange(order - 1)]
self.UD = 0.
self.UN = defaultdict(float)
def __init__(self, order=3, sb="<s>", se="</s>"):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [defaultdict(float) for i in xrange(order - 1)]
self.numerators = [defaultdict(float) for i in xrange(order - 1)]
#Modified Kneser-Ney requires that we track the individual N_i
# in contrast to Kneser-Ney, which just requires the sum-total.
self.nonZeros = [
defaultdict(lambda: defaultdict(float)) for i in xrange(order - 1)
]
self.CoC = [[0.0 for j in xrange(4)] for i in xrange(order)]
self.discounts = [[0.0 for j in xrange(3)] for i in xrange(order - 1)]
self.UD = 0.
self.UN = defaultdict(float)
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 __init__( self, order=3, sb="<s>", se="</s>" ):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [ defaultdict(float) for i in xrange(order-1) ]
self.numerators = [ defaultdict(float) for i in xrange(order-1) ]
self.nonZeros = [ defaultdict(float) for i in xrange(order-1) ]
self.CoC = [ [ 0.0 for j in xrange(4) ] for i in xrange(order) ]
self.discounts = [ 0.0 for i in xrange(order-1) ]
self.UD = 0.
self.UN = defaultdict(float)
def __init__( self, order=3, sb="<s>", se="</s>" ):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [ defaultdict(float) for i in xrange(order-1) ]
self.numerators = [ defaultdict(float) for i in xrange(order-1) ]
#Modified Kneser-Ney requires that we track the individual N_i
# in contrast to Kneser-Ney, which just requires the sum-total.
self.nonZeros = [ defaultdict(lambda: defaultdict(float)) for i in xrange(order-1) ]
self.CoC = [ [ 0.0 for j in xrange(4) ] for i in xrange(order) ]
self.discounts = [ [ 0.0 for j in xrange(3) ] for i in xrange(order-1) ]
self.UD = 0.
self.UN = defaultdict(float)
class KNSmoother( ):
"""
Stand-alone python implementation of interpolated Fixed Kneser-Ney discounting.
Intended for educational purposes, this should produce results identical
to mitlm's 'estimate-ngram' utility,
mitlm:
$ estimate-ngram -o 3 -t train.corpus -s FixKN
SimpleKN.py:
$ SimpleKN.py -t train.corpus
WARNING: This program has not been optimized in any way and will almost
surely be extremely slow for anything larger than a small toy corpus.
"""
def __init__( self, order=3, sb="<s>", se="</s>" ):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [ defaultdict(float) for i in xrange(order-1) ]
self.numerators = [ defaultdict(float) for i in xrange(order-1) ]
self.nonZeros = [ defaultdict(float) for i in xrange(order-1) ]
self.CoC = [ [ 0.0 for j in xrange(4) ] for i in xrange(order) ]
self.discounts = [ 0.0 for i in xrange(order-1) ]
self.UD = 0.
self.UN = defaultdict(float)
def _compute_counts_of_counts( self ):
"""
Compute counts-of-counts (CoC) for each N-gram order.
Only CoC<=4 are relevant to the computation of
either ModKNFix or KNFix.
"""
for k in self.UN:
if self.UN[k] <= 4:
self.CoC[0][int(self.UN[k]-1)] += 1.
for i,dic in enumerate(self.numerators):
for k in dic:
if dic[k]<=4:
self.CoC[i+1][int(dic[k]-1)] += 1.
return
def _compute_discounts( self ):
"""
Compute the discount parameters. Note that unigram counts
are not discounted in either FixKN or FixModKN.
---------------------------------
Fixed Kneser-Ney smoothing: FixKN
---------------------------------
This is based on the solution described in Kneser-Ney '95,
and reformulated in Chen&Goodman '98.
D = N_1 / ( N_1 + 2(N_2) )
where N_1 refers to the # of N-grams that appear exactly
once, and N_2 refers to the number of N-grams that appear
exactly twice. This is computed for each order.
NOTE: The discount formula for FixKN is identical
for Absolute discounting.
"""
#Uniform discount for each N-gram order
for o in xrange(self.order-1):
self.discounts[o] = self.CoC[o+1][0] / (self.CoC[o+1][0]+2*self.CoC[o+1][1])
return
def _get_discount( self, order, ngram ):
"""
Retrieve the pre-computed discount for this N-gram.
"""
return self.discounts[order]
def kneser_ney_from_counts( self, arpa_file ):
"""
Train the KN-discount language model from an ARPA format
file containing raw count data. This can be generated with,
$ ./SimpleCount.py --train train.corpus -r > counts.arpa
"""
m_ord = c_ord = 0
for line in open(arpa_file, "r"):
ngram, count = line.strip().split("\t")
count = float(count)
ngram = ngram.split(" ")
if len(ngram)==2:
self.UD += 1.0
if len(ngram)==2:
self.UN[" ".join(ngram[1:])] += 1.0
self.nonZeros[len(ngram)-2][" ".join(ngram[:-1])] += 1.0
if ngram[0]==self.sb:
self.numerators[len(ngram)-2][" ".join(ngram)] += count
self.denominators[len(ngram)-2][" ".join(ngram[:-1])] += count
if len(ngram)>2 and len(ngram)<self.order:
self.numerators[len(ngram)-3][" ".join(ngram[1:])] += 1.0
self.denominators[len(ngram)-3][" ".join(ngram[1:-1])] += 1.0
self.nonZeros[len(ngram)-2][" ".join(ngram[:-1])] += 1.0
if ngram[0]==self.sb:
self.numerators[len(ngram)-2][" ".join(ngram)] += count
self.denominators[len(ngram)-2][" ".join(ngram[:-1])] += count
if len(ngram)==self.order:
self.numerators[len(ngram)-3][" ".join(ngram[1:])] += 1.0
self.numerators[len(ngram)-2][" ".join(ngram)] = count
self.denominators[len(ngram)-3][" ".join(ngram[1:-1])] += 1.0
self.denominators[len(ngram)-2][" ".join(ngram[:-1])] += count
self.nonZeros[len(ngram)-2][" ".join(ngram[:-1])] += 1.0
self._compute_counts_of_counts ( )
self._compute_discounts( )
#self._print_raw_counts( )
return
def kneser_ney_discounting( 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 _kn_recurse() subroutine to increment the N-gram
contexts in the current window / on the stack.
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
self.ngrams.push(self.sb)
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._kn_recurse( ngram, len(ngram)-2 )
#Now push the sentence-end token onto the stack
ngram = self.ngrams.push(self.se)
self._kn_recurse( ngram, len(ngram)-2 )
#Clear the stack for the next sentence
self.ngrams.clear()
self._compute_counts_of_counts ( )
self._compute_discounts( )
#self._print_raw_counts( )
return
def _print_raw_counts( self ):
"""
Convenience function for sanity checking the history counts.
"""
print "NUMERATORS:"
for key in sorted(self.UN.iterkeys()):
print " ", key, self.UN[key]
for o in xrange(len(self.numerators)):
print "ORD",o
for key in sorted(self.numerators[o].iterkeys()):
print " ", key, self.numerators[o][key]
print "DENOMINATORS:"
print self.UD
for o in xrange(len(self.denominators)):
print "DORD", o
for key in sorted(self.denominators[o].iterkeys()):
print " ", key, self.denominators[o][key]
print "NONZEROS:"
for o in xrange(len(self.nonZeros)):
print "ZORD", o
for key in sorted(self.nonZeros[o].iterkeys()):
print " ", key, self.nonZeros[o][key]
def _kn_recurse( self, ngram_stack, i ):
"""
Kneser-Ney discount calculation recursion.
"""
if i==-1 and ngram_stack[0]==self.sb:
return
o = len(ngram_stack)
numer = " ".join(ngram_stack[o-(i+2):])
denom = " ".join(ngram_stack[o-(i+2):o-1])
self.numerators[ i][numer] += 1.
self.denominators[i][denom] += 1.
if self.numerators[i][numer]==1.:
self.nonZeros[i][denom] += 1.
if i>0:
self._kn_recurse( ngram_stack, i-1 )
else:
#The <s> (sentence-begin) token is
# NOT counted as a unigram event
if not ngram_stack[-1]==self.sb:
self.UN[ngram_stack[-1]] += 1.
self.UD += 1.
return
def print_ARPA( self ):
"""
Print the interpolated Kneser-Ney LM out in ARPA format,
computing the interpolated probabilities and back-off
weights for each N-gram on-demand. The 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\
----------------------------
"""
#Handle the header info
print "\\data\\"
print "ngram 1=%d" % (len(self.UN)+1)
for o in xrange(0,self.order-1):
print "ngram %d=%d" % (o+2,len(self.numerators[o]) )
#Handle the Unigrams
print "\n\\1-grams:"
d = self.discounts[0]
#KN discount
lmda = self.nonZeros[0][self.sb] * d / self.denominators[0][self.sb]
print "-99.00000\t%s\t%0.6f" % ( self.sb, log(lmda, 10.) )
for key in sorted(self.UN.iterkeys()):
if key==self.se:
print "%0.6f\t%s\t-99" % ( log(self.UN[key]/self.UD, 10.), key )
continue
d = self.discounts[0]
#KN discount
lmda = self.nonZeros[0][key] * d / self.denominators[0][key]
print "%0.6f\t%s\t%0.6f" % ( log(self.UN[key]/self.UD, 10.), key, log(lmda, 10.) )
#Handle the middle-order N-grams
for o in xrange(0,self.order-2):
print "\n\\%d-grams:" % (o+2)
for key in sorted(self.numerators[o].iterkeys()):
if key.endswith(self.se):
#No back-off prob for N-grams ending in </s>
prob = self._compute_interpolated_prob( key )
print "%0.6f\t%s" % ( log(prob, 10.), key )
continue
d = self.discounts[o+1]
#Compute the back-off weight
#KN discount
lmda = self.nonZeros[o+1][key] * d / self.denominators[o+1][key]
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob( key )
print "%0.6f\t%s\t%0.6f" % ( log(prob, 10.), key, log(lmda, 10.))
#Handle the N-order N-grams
print "\n\\%d-grams:" % (self.order)
for key in sorted(self.numerators[self.order-2].iterkeys()):
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob( key )
print "%0.6f\t%s" % ( log(prob, 10.), key )
print "\n\\end\\"
return
def _compute_interpolated_prob( self, ngram ):
"""
Compute the interpolated probability for the input ngram.
Cribbing the notation from the SRILM webpages,
a_z = An N-gram where a is the first word, z is the
last word, and "_" represents 0 or more words in between.
p(a_z) = The estimated conditional probability of the
nth word z given the first n-1 words (a_) of an N-gram.
a_ = The n-1 word prefix of the N-gram a_z.
_z = The n-1 word suffix of the N-gram a_z.
Then we have,
f(a_z) = g(a_z) + bow(a_) p(_z)
p(a_z) = (c(a_z) > 0) ? f(a_z) : bow(a_) p(_z)
The ARPA format is generated by writing, for each N-gram
with 1 < order < max_order:
p(a_z) a_z bow(a_z)
and for the maximum order:
p(a_z) a_z
special care must be taken for certain N-grams containing
the <s> (sentence-begin) and </s> (sentence-end) tokens.
See the implementation for details on how to do this correctly.
The formulation is based on the seminal Chen&Goodman '98 paper.
SRILM notation-cribbing from:
http://www.speech.sri.com/projects/srilm/manpages/ngram-discount.7.html
"""
probability = 0.0
ngram_stack = ngram.split(" ")
probs = [ 1e-99 for i in xrange(len(ngram_stack)) ]
o = len(ngram_stack)
if not ngram_stack[-1]==self.sb:
probs[0] = self.UN[ngram_stack[-1]] / self.UD
for i in xrange(o-1):
dID = " ".join(ngram_stack[o-(i+2):o-1])
nID = " ".join(ngram_stack[o-(i+2):])
if dID in self.denominators[i]:
d = self.discounts[i]
if nID in self.numerators[i]:
#We have an actual N-gram probability for this N-gram
#KN discount
probs[i+1] = (self.numerators[i][nID]-d)/self.denominators[i][dID]
else:
#No actual N-gram prob, we will have to back-off
probs[i+1] = 1e-99
#This break-down takes the following form:
# probs[i+1]: The interpolated N-gram probability, p(a_z)
# lmda: The un-normalized 'back-off' weight, bow(a_)
# probs[i]: The next lower-order, interpolated N-gram
# probability corresponding to p(_z)
#KN discount
lmda = self.nonZeros[i][dID] * d / self.denominators[i][dID]
probs[i+1] = probs[i+1] + lmda * probs[i]
probability = probs[i+1]
if probability == 0.0:
#If we still have nothing, return the unigram probability
probability = probs[0]
return probability
class KNSmoother():
"""
Stand-alone python implementation of interpolated Fixed Kneser-Ney discounting.
Intended for educational purposes, this should produce results identical
to mitlm's 'estimate-ngram' utility,
mitlm:
$ estimate-ngram -o 3 -t train.corpus -s FixKN
SimpleKN.py:
$ SimpleKN.py -t train.corpus
WARNING: This program has not been optimized in any way and will almost
surely be extremely slow for anything larger than a small toy corpus.
"""
def __init__(self, order=3, sb="<s>", se="</s>"):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [defaultdict(float) for i in xrange(order - 1)]
self.numerators = [defaultdict(float) for i in xrange(order - 1)]
self.nonZeros = [defaultdict(float) for i in xrange(order - 1)]
self.CoC = [[0.0 for j in xrange(4)] for i in xrange(order)]
self.discounts = [0.0 for i in xrange(order - 1)]
self.UD = 0.
self.UN = defaultdict(float)
def _compute_counts_of_counts(self):
"""
Compute counts-of-counts (CoC) for each N-gram order.
Only CoC<=4 are relevant to the computation of
either ModKNFix or KNFix.
"""
for k in self.UN:
if self.UN[k] <= 4:
self.CoC[0][int(self.UN[k] - 1)] += 1.
for i, dic in enumerate(self.numerators):
for k in dic:
if dic[k] <= 4:
self.CoC[i + 1][int(dic[k] - 1)] += 1.
return
def _compute_discounts(self):
"""
Compute the discount parameters. Note that unigram counts
are not discounted in either FixKN or FixModKN.
---------------------------------
Fixed Kneser-Ney smoothing: FixKN
---------------------------------
This is based on the solution described in Kneser-Ney '95,
and reformulated in Chen&Goodman '98.
D = N_1 / ( N_1 + 2(N_2) )
where N_1 refers to the # of N-grams that appear exactly
once, and N_2 refers to the number of N-grams that appear
exactly twice. This is computed for each order.
NOTE: The discount formula for FixKN is identical
for Absolute discounting.
"""
#Uniform discount for each N-gram order
for o in xrange(self.order - 1):
self.discounts[o] = self.CoC[o + 1][0] / (self.CoC[o + 1][0] +
2 * self.CoC[o + 1][1])
return
def _get_discount(self, order, ngram):
"""
Retrieve the pre-computed discount for this N-gram.
"""
return self.discounts[order]
def kneser_ney_from_counts(self, arpa_file):
"""
Train the KN-discount language model from an ARPA format
file containing raw count data. This can be generated with,
$ ./SimpleCount.py --train train.corpus -r > counts.arpa
"""
m_ord = c_ord = 0
for line in open(arpa_file, "r"):
ngram, count = line.strip().split("\t")
#In this version, possibly fractional counts
# are simply rounded to the nearest integer.
#The result is a "poor-man's" fractional Kneser-Ney
# which actually works quite well in practice.
count = round(float(count))
if count == 0.0: continue
ngram = ngram.split(" ")
if len(ngram) == 2:
self.UD += 1.0
if len(ngram) == 2:
self.UN[" ".join(ngram[1:])] += 1.0
self.nonZeros[len(ngram) - 2][" ".join(ngram[:-1])] += 1.0
if ngram[0] == self.sb:
self.numerators[len(ngram) - 2][" ".join(ngram)] += count
self.denominators[len(ngram) - 2][" ".join(
ngram[:-1])] += count
if len(ngram) > 2 and len(ngram) < self.order:
self.numerators[len(ngram) - 3][" ".join(ngram[1:])] += 1.0
self.denominators[len(ngram) - 3][" ".join(ngram[1:-1])] += 1.0
self.nonZeros[len(ngram) - 2][" ".join(ngram[:-1])] += 1.0
if ngram[0] == self.sb:
self.numerators[len(ngram) - 2][" ".join(ngram)] += count
self.denominators[len(ngram) - 2][" ".join(
ngram[:-1])] += count
if len(ngram) == self.order:
self.numerators[len(ngram) - 3][" ".join(ngram[1:])] += 1.0
self.numerators[len(ngram) - 2][" ".join(ngram)] = count
self.denominators[len(ngram) - 3][" ".join(ngram[1:-1])] += 1.0
self.denominators[len(ngram) - 2][" ".join(
ngram[:-1])] += count
self.nonZeros[len(ngram) - 2][" ".join(ngram[:-1])] += 1.0
self._compute_counts_of_counts()
self._compute_discounts()
#self._print_raw_counts( )
return
def kneser_ney_discounting(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 _kn_recurse() subroutine to increment the N-gram
contexts in the current window / on the stack.
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
self.ngrams.push(self.sb)
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._kn_recurse(ngram, len(ngram) - 2)
#Now push the sentence-end token onto the stack
ngram = self.ngrams.push(self.se)
self._kn_recurse(ngram, len(ngram) - 2)
#Clear the stack for the next sentence
self.ngrams.clear()
self._compute_counts_of_counts()
self._compute_discounts()
self._print_raw_counts()
return
def _print_raw_counts(self):
"""
Convenience function for sanity checking the history counts.
"""
print "NUMERATORS:"
for key in sorted(self.UN.iterkeys()):
print " ", key, self.UN[key]
for o in xrange(len(self.numerators)):
print "ORD", o
for key in sorted(self.numerators[o].iterkeys()):
print " ", key, self.numerators[o][key]
print "DENOMINATORS:"
print self.UD
for o in xrange(len(self.denominators)):
print "DORD", o
for key in sorted(self.denominators[o].iterkeys()):
print " ", key, self.denominators[o][key]
print "NONZEROS:"
for o in xrange(len(self.nonZeros)):
print "ZORD", o
for key in sorted(self.nonZeros[o].iterkeys()):
print " ", key, self.nonZeros[o][key]
def _kn_recurse(self, ngram_stack, i):
"""
Kneser-Ney discount calculation recursion.
"""
if i == -1 and ngram_stack[0] == self.sb:
return
o = len(ngram_stack)
numer = " ".join(ngram_stack[o - (i + 2):])
denom = " ".join(ngram_stack[o - (i + 2):o - 1])
self.numerators[i][numer] += 1.
self.denominators[i][denom] += 1.
if self.numerators[i][numer] == 1.:
self.nonZeros[i][denom] += 1.
if i > 0:
self._kn_recurse(ngram_stack, i - 1)
else:
#The <s> (sentence-begin) token is
# NOT counted as a unigram event
if not ngram_stack[-1] == self.sb:
self.UN[ngram_stack[-1]] += 1.
self.UD += 1.
return
def print_ARPA(self):
"""
Print the interpolated Kneser-Ney LM out in ARPA format,
computing the interpolated probabilities and back-off
weights for each N-gram on-demand. The 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: The try:/except: blocks are not necessary for normal
circumstances. Neither raw text nor correct counts will
fire the exception blocks. These are needed only when
using "poor-man's" fractional Kneser-Ney, in which case
the rounded counts may not agree.
"""
#Handle the header info
print "\\data\\"
print "ngram 1=%d" % (len(self.UN) + 1)
for o in xrange(0, self.order - 1):
print "ngram %d=%d" % (o + 2, len(self.numerators[o]))
#Handle the Unigrams
print "\n\\1-grams:"
d = self.discounts[0]
#KN discount
try:
lmda = self.nonZeros[0][self.sb] * d / self.denominators[0][
self.sb]
except:
lmda = 1e-99
print "-99.00000\t%s\t%0.6f" % (self.sb, log(lmda, 10.))
for key in sorted(self.UN.iterkeys()):
if key == self.se:
print "%0.6f\t%s\t-99" % (log(self.UN[key] / self.UD,
10.), key)
continue
d = self.discounts[0]
#KN discount
try:
lmda = self.nonZeros[0][key] * d / self.denominators[0][key]
except:
lmda = 1e-99
print "%0.6f\t%s\t%0.6f" % (log(self.UN[key] / self.UD,
10.), key, log(lmda, 10.))
#Handle the middle-order N-grams
for o in xrange(0, self.order - 2):
print "\n\\%d-grams:" % (o + 2)
for key in sorted(self.numerators[o].iterkeys()):
if key.endswith(self.se):
#No back-off prob for N-grams ending in </s>
prob = self._compute_interpolated_prob(key)
try:
print "%0.6f\t%s" % (log(prob, 10.), key)
except:
print "%0.6f\t%s" % (log(1e-99, 10.), key)
continue
d = self.discounts[o + 1]
#Compute the back-off weight
#KN discount
try:
lmda = self.nonZeros[o + 1][key] * d / self.denominators[
o + 1][key]
except:
lmda = 1e-99
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob(key)
if lmda == 0.: lmda = 1e-99
prob = max(prob, 1e-99)
try:
print "%0.6f\t%s\t%0.6f" % (log(prob,
10.), key, log(lmda, 10.))
except:
raise ValueError, "ERROR %0.6f\t%0.6f" % (prob, lmda)
#Handle the N-order N-grams
print "\n\\%d-grams:" % (self.order)
for key in sorted(self.numerators[self.order - 2].iterkeys()):
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob(key)
try:
print "%0.6f\t%s" % (log(prob, 10.), key)
except:
print "%0.6f\t%s" % (log(1e-99, 10.), key)
print "\n\\end\\"
return
def _compute_interpolated_prob(self, ngram):
"""
Compute the interpolated probability for the input ngram.
Cribbing the notation from the SRILM webpages,
a_z = An N-gram where a is the first word, z is the
last word, and "_" represents 0 or more words in between.
p(a_z) = The estimated conditional probability of the
nth word z given the first n-1 words (a_) of an N-gram.
a_ = The n-1 word prefix of the N-gram a_z.
_z = The n-1 word suffix of the N-gram a_z.
Then we have,
f(a_z) = g(a_z) + bow(a_) p(_z)
p(a_z) = (c(a_z) > 0) ? f(a_z) : bow(a_) p(_z)
The ARPA format is generated by writing, for each N-gram
with 1 < order < max_order:
p(a_z) a_z bow(a_z)
and for the maximum order:
p(a_z) a_z
special care must be taken for certain N-grams containing
the <s> (sentence-begin) and </s> (sentence-end) tokens.
See the implementation for details on how to do this correctly.
The formulation is based on the seminal Chen&Goodman '98 paper.
SRILM notation-cribbing from:
http://www.speech.sri.com/projects/srilm/manpages/ngram-discount.7.html
"""
probability = 0.0
ngram_stack = ngram.split(" ")
probs = [1e-99 for i in xrange(len(ngram_stack))]
o = len(ngram_stack)
if not ngram_stack[-1] == self.sb:
probs[0] = self.UN[ngram_stack[-1]] / self.UD
for i in xrange(o - 1):
dID = " ".join(ngram_stack[o - (i + 2):o - 1])
nID = " ".join(ngram_stack[o - (i + 2):])
if dID in self.denominators[i]:
d = self.discounts[i]
if nID in self.numerators[i]:
#We have an actual N-gram probability for this N-gram
#KN discount
try:
probs[i + 1] = (self.numerators[i][nID] -
d) / self.denominators[i][dID]
except:
probs[i + 1] = 1e-99
else:
#No actual N-gram prob, we will have to back-off
probs[i + 1] = 1e-99
#This break-down takes the following form:
# probs[i+1]: The interpolated N-gram probability, p(a_z)
# lmda: The un-normalized 'back-off' weight, bow(a_)
# probs[i]: The next lower-order, interpolated N-gram
# probability corresponding to p(_z)
#KN discount
try:
lmda = self.nonZeros[i][dID] * d / self.denominators[i][dID]
except:
lmda = 1e-99
probs[i + 1] = probs[i + 1] + lmda * probs[i]
probability = probs[i + 1]
if probability == 0.0:
#If we still have nothing, return the unigram probability
probability = probs[0]
return probability
class ModKNSmoother():
"""
Stand-alone python implementation of Fixed Modified Kneser-Ney discounting.
Intended for educational purposes, this should produce results identical
to Google NGramLibrary tools with ngrammake --bins=3. See the included
run-NGramLibrary.sh script to train a model for comparison.
WARNING: This may be slow for very large corpora.
"""
def __init__(self, order=3, sb="<s>", se="</s>"):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [defaultdict(float) for i in xrange(order - 1)]
self.numerators = [defaultdict(float) for i in xrange(order - 1)]
#Modified Kneser-Ney requires that we track the individual N_i
# in contrast to Kneser-Ney, which just requires the sum-total.
self.nonZeros = [
defaultdict(lambda: defaultdict(float)) for i in xrange(order - 1)
]
self.CoC = [[0.0 for j in xrange(4)] for i in xrange(order)]
self.discounts = [[0.0 for j in xrange(3)] for i in xrange(order - 1)]
self.UD = 0.
self.UN = defaultdict(float)
def _compute_counts_of_counts(self):
"""
Compute counts-of-counts (CoC) for each N-gram order.
Only CoC<=4 are relevant to the computation of
either ModKNFix or KNFix.
"""
for k in self.UN:
if self.UN[k] <= 4:
self.CoC[0][int(self.UN[k] - 1)] += 1.
for i, dic in enumerate(self.numerators):
for k in dic:
if dic[k] <= 4:
self.CoC[i + 1][int(dic[k] - 1)] += 1.
return
def _compute_discounts(self):
"""
Compute the discount parameters. Note that unigram counts
are not discounted in either FixKN or FixModKN.
---------------------------------
Fixed Modified Kneser-Ney: FixModKN
---------------------------------
This is the solution proposed by Chen&Goodman '98
Y = N_1 / (N_1 + 2*N_2)
D_1 = 1 - 2*Y * (N_2 / N_1)
D_2 = 2 - 3*Y * (N_3 / N_2)
D_3+ = 3 - 4*Y * (N_4 / N_3)
where N_i again refers to the number of N-grams that appear
exactly 'i' times in the training data. The D_i refer to the
counts-of-counts for the current N-gram. That is, if the
current N-gram, 'a b c' was seen exactly two times in the
training corpus, then discount D_2 would be applied.
"""
for o in xrange(self.order - 1):
Y = self.CoC[o + 1][0] / (self.CoC[o + 1][0] +
2 * self.CoC[o + 1][1])
#Compute all the D_i based on the formula
for i in xrange(3):
if self.CoC[o + 1][i] > 0:
self.discounts[o][i] = (i + 1) - (i + 2) * Y * (
self.CoC[o + 1][i + 1] / self.CoC[o + 1][i])
else:
self.discounts[o][i] = (i + 1)
return
def _get_discount(self, order, ngram):
"""
Compute the discount mass for this N-gram, based on
the precomputed D_i and individual N_i.
"""
c = [0.0, 0.0, 0.0]
for key in self.nonZeros[order][ngram]:
if int(self.nonZeros[order][ngram][key]) == 1:
c[0] += 1.
elif int(self.nonZeros[order][ngram][key]) == 2:
c[1] += 1.
else:
c[2] += 1.
#Compute the discount mass by summing over the D_i*N_i
d = sum([self.discounts[order][i] * c[i] for i in xrange(len(c))])
return d
def kneser_ney_from_counts(self, arpa_file):
"""
Train the KN-discount language model from an ARPA format
file containing raw count data. This can be generated with,
$ ./SimpleCount.py --train train.corpus -r > counts.arpa
"""
m_ord = c_ord = 0
for line in open(arpa_file, "r"):
ngram, count = line.strip().split("\t")
count = float(count)
ngram = ngram.split(" ")
if len(ngram) == 2:
self.UD += 1.0
if len(ngram) == 2:
self.UN[" ".join(ngram[1:])] += 1.0
#Nonzeros based on suffixes
if ngram[0] == self.sb:
self.nonZeros[len(ngram) - 2][" ".join(
ngram[:-1])][ngram[-1]] += count
self.numerators[len(ngram) - 2][" ".join(ngram)] += count
self.denominators[len(ngram) - 2][" ".join(
ngram[:-1])] += count
if len(ngram) > 2 and len(ngram) < self.order:
self.numerators[len(ngram) - 3][" ".join(ngram[1:])] += 1.0
self.denominators[len(ngram) - 3][" ".join(ngram[1:-1])] += 1.0
self.nonZeros[len(ngram) - 3][" ".join(
ngram[1:-1])][ngram[-1]] += 1.0
if ngram[0] == self.sb:
self.numerators[len(ngram) - 2][" ".join(ngram)] += count
self.denominators[len(ngram) - 2][" ".join(
ngram[:-1])] += count
self.nonZeros[len(ngram) - 2][" ".join(
ngram[:-1])][ngram[-1]] += count
if len(ngram) == self.order:
self.numerators[len(ngram) - 3][" ".join(ngram[1:])] += 1.0
self.numerators[len(ngram) - 2][" ".join(ngram)] = count
self.denominators[len(ngram) - 3][" ".join(ngram[1:-1])] += 1.0
self.denominators[len(ngram) - 2][" ".join(
ngram[:-1])] += count
self.nonZeros[len(ngram) - 3][" ".join(
ngram[1:-1])][ngram[-1]] += 1.0
self.nonZeros[len(ngram) - 2][" ".join(
ngram[:-1])][ngram[-1]] += count
self._compute_counts_of_counts()
self._compute_discounts()
#self._print_raw_counts( )
return
def kneser_ney_discounting(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 _kn_recurse() subroutine to increment the N-gram
contexts in the current window / on the stack.
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
self.ngrams.push(self.sb)
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._kn_recurse(ngram, len(ngram) - 2)
#Now push the sentence-end token onto the stack
ngram = self.ngrams.push(self.se)
self._kn_recurse(ngram, len(ngram) - 2)
#Clear the stack for the next sentence
self.ngrams.clear()
self._compute_counts_of_counts()
self._compute_discounts()
return
def print_raw_counts(self):
"""
Convenience function for sanity checking the history counts.
"""
#print "NUMERATORS:"
#for key in sorted(self.UN.iterkeys()):
# print " ", key, self.UN[key]
#for o in xrange(len(self.numerators)):
# print "ORD",o
# for key in sorted(self.numerators[o].iterkeys()):
# print " ", key, self.numerators[o][key]
#print "DENOMINATORS:"
#print self.UD
#for o in xrange(len(self.denominators)):
# print "DORD", o
# for key in sorted(self.denominators[o].iterkeys()):
# print " ", key, self.denominators[o][key]
print "NONZEROS:"
for o in xrange(len(self.nonZeros)):
print "ZORD", o
for denom in sorted(self.nonZeros[o].iterkeys()):
print " Den:", denom
for key in sorted(self.nonZeros[o][denom].iterkeys()):
print " ", key, self.nonZeros[o][denom][key]
def _kn_recurse(self, ngram_stack, i):
"""
Kneser-Ney discount calculation recursion.
"""
if i == -1 and ngram_stack[0] == self.sb:
return
o = len(ngram_stack)
numer = " ".join(ngram_stack[o - (i + 2):])
denom = " ".join(ngram_stack[o - (i + 2):o - 1])
self.numerators[i][numer] += 1.
self.denominators[i][denom] += 1.
#For Modified Kneser-Ney we need to track
# individual nonZeros based on their suffixes
self.nonZeros[i][denom][ngram_stack[-1]] += 1.
if self.numerators[i][numer] == 1.:
if i > 0:
self._kn_recurse(ngram_stack, i - 1)
else:
#The <s> (sentence-begin) token is
# NOT counted as a unigram event
if not ngram_stack[-1] == self.sb:
self.UN[ngram_stack[-1]] += 1.
self.UD += 1.
return
def print_ARPA(self):
"""
Print the interpolated Kneser-Ney LM out in ARPA format,
computing the interpolated probabilities and back-off
weights for each N-gram on-demand. The 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\
----------------------------
"""
#Handle the header info
print "\\data\\"
print "ngram 1=%d" % (len(self.UN) + 1)
for o in xrange(0, self.order - 1):
print "ngram %d=%d" % (o + 2, len(self.numerators[o]))
#Handle the Unigrams
print "\n\\1-grams:"
d = self._get_discount(0, self.sb)
#ModKN discount
lmda = d / self.denominators[0][self.sb]
print "-99.00000\t%s\t%0.7f" % (self.sb, log(lmda, 10.))
for key in sorted(self.UN.iterkeys()):
if key == self.se:
print "%0.7f\t%s\t-99" % (log(self.UN[key] / self.UD,
10.), key)
continue
d = self._get_discount(0, key)
#ModKN discount
lmda = d / self.denominators[0][key]
print "%0.7f\t%s\t%0.7f" % (log(self.UN[key] / self.UD,
10.), key, log(lmda, 10.))
#Handle the middle-order N-grams
for o in xrange(0, self.order - 2):
print "\n\\%d-grams:" % (o + 2)
for key in sorted(self.numerators[o].iterkeys()):
if key.endswith(self.se):
#No back-off prob for N-grams ending in </s>
prob = self._compute_interpolated_prob(key)
print "%0.7f\t%s" % (log(prob, 10.), key)
continue
d = self._get_discount(o + 1, key)
#Compute the back-off weight
#ModKN discount
lmda = d / self.denominators[o + 1][key]
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob(key)
print "%0.7f\t%s\t%0.7f" % (log(prob, 10.), key, log(
lmda, 10.))
#Handle the N-order N-grams
print "\n\\%d-grams:" % (self.order)
for key in sorted(self.numerators[self.order - 2].iterkeys()):
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob(key)
print "%0.7f\t%s" % (log(prob, 10.), key)
print "\n\\end\\"
return
def _compute_interpolated_prob(self, ngram):
"""
Compute the interpolated probability for the input ngram.
Cribbing the notation from the SRILM webpages,
a_z = An N-gram where a is the first word, z is the
last word, and "_" represents 0 or more words in between.
p(a_z) = The estimated conditional probability of the
nth word z given the first n-1 words (a_) of an N-gram.
a_ = The n-1 word prefix of the N-gram a_z.
_z = The n-1 word suffix of the N-gram a_z.
Then we have,
f(a_z) = g(a_z) + bow(a_) p(_z)
p(a_z) = (c(a_z) > 0) ? f(a_z) : bow(a_) p(_z)
The ARPA format is generated by writing, for each N-gram
with 1 < order < max_order:
p(a_z) a_z bow(a_z)
and for the maximum order:
p(a_z) a_z
special care must be taken for certain N-grams containing
the <s> (sentence-begin) and </s> (sentence-end) tokens.
See the implementation for details on how to do this correctly.
The formulation is based on the seminal Chen&Goodman '98 paper.
SRILM notation-cribbing from:
http://www.speech.sri.com/projects/srilm/manpages/ngram-discount.7.html
"""
probability = 0.0
ngram_stack = ngram.split(" ")
probs = [1e-99 for i in xrange(len(ngram_stack))]
o = len(ngram_stack)
if not ngram_stack[-1] == self.sb:
probs[0] = self.UN[ngram_stack[-1]] / self.UD
for i in xrange(o - 1):
dID = " ".join(ngram_stack[o - (i + 2):o - 1])
nID = " ".join(ngram_stack[o - (i + 2):])
if dID in self.denominators[i]:
count = int(self.numerators[i][nID])
d_i = min(count, 3)
probs[i + 1] = (count - self.discounts[i][d_i - 1]
) / self.denominators[i][dID]
#This break-down takes the following form:
# probs[i+1]: The interpolated N-gram probability, p(a_z)
# d: The discount mass for a_: \Sum_i D_i*N_i
# lmda: The un-normalized 'back-off' weight, bow(a_)
# probs[i]: The next lower-order, interpolated N-gram
# probability corresponding to p(_z)
#ModKN discount
d = self._get_discount(i, dID)
lmda = d / self.denominators[i][dID]
probs[i + 1] = probs[i + 1] + lmda * probs[i]
probability = probs[i + 1]
if probability == 0.0:
#If we still have nothing, return the unigram probability
probability = probs[0]
return probability
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
class AbsSmoother( ):
"""
Stand-alone python implementation of interpolated Absolute discounting.
Intended for educational purposes, this should produce results identical
to mitlm's 'estimate-ngram' utility,
mitlm:
$ estimate-ngram -o 3 -t train.corpus -s FixKN
SimpleKN.py:
$ SimpleKN.py -t train.corpus
WARNING: This program has not been optimized in any way and will almost
surely be extremely slow for anything larger than a small toy corpus.
"""
def __init__( self, order=3, sb="<s>", se="</s>" ):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [ defaultdict(float) for i in xrange(order-1) ]
self.numerators = [ defaultdict(float) for i in xrange(order-1) ]
self.nonZeros = [ defaultdict(float) for i in xrange(order-1) ]
self.CoC = [ [ 0.0 for j in xrange(4) ] for i in xrange(order) ]
self.discounts = [ 0.0 for i in xrange(order-1) ]
self.UD = 0.
self.UN = defaultdict(float)
def _compute_counts_of_counts( self ):
"""
Compute counts-of-counts (CoC) for each N-gram order.
Only CoC<=4 are relevant to the computation of
ModKNFix or KNFix or Absolute discounting.
"""
for k in self.UN:
if self.UN[k] <= 4:
self.CoC[0][int(self.UN[k]-1)] += 1.
for i,dic in enumerate(self.numerators):
for k in dic:
if dic[k]<=4:
self.CoC[i+1][int(dic[k]-1)] += 1.
return
def _compute_discounts( self ):
"""
Compute the discount parameters. Note that unigram counts
are not discounted in Absolute, FixKN or FixModKN.
---------------------------------
Fixed Kneser-Ney smoothing: FixKN
---------------------------------
This is based on the solution described in Kneser-Ney '95,
and reformulated in Chen&Goodman '98.
D = N_1 / ( N_1 + 2(N_2) )
where N_1 refers to the # of N-grams that appear exactly
once, and N_2 refers to the number of N-grams that appear
exactly twice. This is computed for each order.
NOTE: The discount formula for FixKN is identical
for Absolute discounting.
"""
#Uniform discount for each N-gram order
for o in xrange(self.order-1):
self.discounts[o] = self.CoC[o+1][0] / (self.CoC[o+1][0]+2*self.CoC[o+1][1])
return
def _get_discount( self, order, ngram ):
"""
Retrieve the pre-computed discount for this N-gram.
"""
return self.discounts[order]
def absolute_discounting( 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 _abs_count() subroutine to increment the N-gram
contexts in the current window / on the stack.
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
self.ngrams.push(self.sb)
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._abs_count( ngram, len(ngram)-2 )
#Now push the sentence-end token onto the stack
ngram = self.ngrams.push(self.se)
self._abs_count( ngram, len(ngram)-2 )
#Clear the stack for the next sentence
self.ngrams.clear()
self._compute_counts_of_counts ( )
self._compute_discounts( )
return
def _abs_count( self, ngram_stack, i ):
"""
Absolute discount calculation recursion.
"""
if i==-1 and ngram_stack[0]==self.sb:
return
o = len(ngram_stack)
numer = " ".join(ngram_stack[o-(i+2):])
denom = " ".join(ngram_stack[o-(i+2):o-1])
self.numerators[ i][numer] += 1.
self.denominators[i][denom] += 1.
if self.numerators[i][numer]==1.:
self.nonZeros[i][denom] += 1.
#The ONLY difference in terms of implementation
# between Kneser-Ney and Absolute discounting
# is the following if/else.
#In Kneser-Ney this is nested inside of the
# preceding if statement.
if i>0:
self._abs_count( ngram_stack, i-1 )
else:
#The <s> (sentence-begin) token is
# NOT counted as a unigram event
if not ngram_stack[-1]==self.sb:
self.UN[ngram_stack[-1]] += 1.
self.UD += 1.
return
def print_ARPA( self ):
"""
Print the interpolated Absolute discounting LM out in ARPA format,
computing the interpolated probabilities and back-off
weights for each N-gram on-demand. The 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\
----------------------------
"""
#Handle the header info
print "\\data\\"
print "ngram 1=%d" % (len(self.UN)+1)
for o in xrange(0,self.order-1):
print "ngram %d=%d" % (o+2,len(self.numerators[o]) )
#Handle the Unigrams
print "\n\\1-grams:"
d = self.discounts[0]
#Abs discount
lmda = self.nonZeros[0][self.sb] * d / self.denominators[0][self.sb]
print "-99.00000\t%s\t%0.6f" % ( self.sb, log(lmda, 10.) )
for key in sorted(self.UN.iterkeys()):
if key==self.se:
print "%0.6f\t%s\t-99" % ( log(self.UN[key]/self.UD, 10.), key )
continue
d = self.discounts[0]
#Abs discount
lmda = self.nonZeros[0][key] * d / self.denominators[0][key]
print "%0.6f\t%s\t%0.6f" % ( log(self.UN[key]/self.UD, 10.), key, log(lmda, 10.) )
#Handle the middle-order N-grams
for o in xrange(0,self.order-2):
print "\n\\%d-grams:" % (o+2)
for key in sorted(self.numerators[o].iterkeys()):
if key.endswith(self.se):
#No back-off prob for N-grams ending in </s>
prob = self._compute_interpolated_prob( key )
print "%0.6f\t%s" % ( log(prob, 10.), key )
continue
d = self.discounts[o+1]
#Compute the back-off weight
#Abs discount
lmda = self.nonZeros[o+1][key] * d / self.denominators[o+1][key]
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob( key )
print "%0.6f\t%s\t%0.6f" % ( log(prob, 10.), key, log(lmda, 10.))
#Handle the N-order N-grams
print "\n\\%d-grams:" % (self.order)
for key in sorted(self.numerators[self.order-2].iterkeys()):
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob( key )
print "%0.6f\t%s" % ( log(prob, 10.), key )
print "\n\\end\\"
return
def _compute_interpolated_prob( self, ngram ):
"""
Compute the interpolated probability for the input ngram.
Cribbing the notation from the SRILM webpages,
a_z = An N-gram where a is the first word, z is the
last word, and "_" represents 0 or more words in between.
p(a_z) = The estimated conditional probability of the
nth word z given the first n-1 words (a_) of an N-gram.
a_ = The n-1 word prefix of the N-gram a_z.
_z = The n-1 word suffix of the N-gram a_z.
Then we have,
f(a_z) = g(a_z) + bow(a_) p(_z)
p(a_z) = (c(a_z) > 0) ? f(a_z) : bow(a_) p(_z)
The ARPA format is generated by writing, for each N-gram
with 1 < order < max_order:
p(a_z) a_z bow(a_z)
and for the maximum order:
p(a_z) a_z
special care must be taken for certain N-grams containing
the <s> (sentence-begin) and </s> (sentence-end) tokens.
See the implementation for details on how to do this correctly.
The formulation is based on the seminal Chen&Goodman '98 paper.
SRILM notation-cribbing from:
http://www.speech.sri.com/projects/srilm/manpages/ngram-discount.7.html
"""
probability = 0.0
ngram_stack = ngram.split(" ")
probs = [ 1e-99 for i in xrange(len(ngram_stack)) ]
o = len(ngram_stack)
if not ngram_stack[-1]==self.sb:
probs[0] = self.UN[ngram_stack[-1]] / self.UD
for i in xrange(o-1):
dID = " ".join(ngram_stack[o-(i+2):o-1])
nID = " ".join(ngram_stack[o-(i+2):])
if dID in self.denominators[i]:
d = self.discounts[i]
if nID in self.numerators[i]:
#We have an actual N-gram probability for this N-gram
#KN discount
probs[i+1] = (self.numerators[i][nID]-d)/self.denominators[i][dID]
else:
#No actual N-gram prob, we will have to back-off
probs[i+1] = 0.0
#This break-down takes the following form:
# probs[i+1]: The interpolated N-gram probability, p(a_z)
# lmda: The un-normalized 'back-off' weight, bow(a_)
# probs[i]: The next lower-order, interpolated N-gram
# probability corresponding to p(_z)
#KN discount
lmda = self.nonZeros[i][dID] * d / self.denominators[i][dID]
probs[i+1] = probs[i+1] + lmda * probs[i]
probability = probs[i+1]
if probability == 0.0:
#If we still have nothing, return the unigram probability
probability = probs[0]
return probability
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
class ModKNSmoother( ):
"""
Stand-alone python implementation of Fixed Modified Kneser-Ney discounting.
Intended for educational purposes, this should produce results identical
to Google NGramLibrary tools with ngrammake --bins=3. See the included
run-NGramLibrary.sh script to train a model for comparison.
WARNING: This may be slow for very large corpora.
"""
def __init__( self, order=3, sb="<s>", se="</s>" ):
self.sb = sb
self.se = se
self.order = order
self.ngrams = NGramStack(order=order)
self.denominators = [ defaultdict(float) for i in xrange(order-1) ]
self.numerators = [ defaultdict(float) for i in xrange(order-1) ]
#Modified Kneser-Ney requires that we track the individual N_i
# in contrast to Kneser-Ney, which just requires the sum-total.
self.nonZeros = [ defaultdict(lambda: defaultdict(float)) for i in xrange(order-1) ]
self.CoC = [ [ 0.0 for j in xrange(4) ] for i in xrange(order) ]
self.discounts = [ [ 0.0 for j in xrange(3) ] for i in xrange(order-1) ]
self.UD = 0.
self.UN = defaultdict(float)
def _compute_counts_of_counts( self ):
"""
Compute counts-of-counts (CoC) for each N-gram order.
Only CoC<=4 are relevant to the computation of
either ModKNFix or KNFix.
"""
for k in self.UN:
if self.UN[k] <= 4:
self.CoC[0][int(self.UN[k]-1)] += 1.
for i,dic in enumerate(self.numerators):
for k in dic:
if dic[k]<=4:
self.CoC[i+1][int(dic[k]-1)] += 1.
return
def _compute_discounts( self ):
"""
Compute the discount parameters. Note that unigram counts
are not discounted in either FixKN or FixModKN.
---------------------------------
Fixed Modified Kneser-Ney: FixModKN
---------------------------------
This is the solution proposed by Chen&Goodman '98
Y = N_1 / (N_1 + 2*N_2)
D_1 = 1 - 2*Y * (N_2 / N_1)
D_2 = 2 - 3*Y * (N_3 / N_2)
D_3+ = 3 - 4*Y * (N_4 / N_3)
where N_i again refers to the number of N-grams that appear
exactly 'i' times in the training data. The D_i refer to the
counts-of-counts for the current N-gram. That is, if the
current N-gram, 'a b c' was seen exactly two times in the
training corpus, then discount D_2 would be applied.
"""
for o in xrange(self.order-1):
Y = self.CoC[o+1][0] / (self.CoC[o+1][0]+2*self.CoC[o+1][1])
#Compute all the D_i based on the formula
for i in xrange(3):
if self.CoC[o+1][i]>0:
self.discounts[o][i] = (i+1) - (i+2)*Y * (self.CoC[o+1][i+1]/self.CoC[o+1][i])
else:
self.discounts[o][i] = (i+1)
return
def _get_discount( self, order, ngram ):
"""
Compute the discount mass for this N-gram, based on
the precomputed D_i and individual N_i.
"""
c = [0.0, 0.0, 0.0]
for key in self.nonZeros[order][ngram]:
if int(self.nonZeros[order][ngram][key])==1:
c[0] += 1.
elif int(self.nonZeros[order][ngram][key])==2:
c[1] += 1.
else:
c[2] += 1.
#Compute the discount mass by summing over the D_i*N_i
d = sum([ self.discounts[order][i]*c[i] for i in xrange(len(c)) ])
return d
def kneser_ney_from_counts( self, arpa_file ):
"""
Train the KN-discount language model from an ARPA format
file containing raw count data. This can be generated with,
$ ./SimpleCount.py --train train.corpus -r > counts.arpa
"""
m_ord = c_ord = 0
for line in open(arpa_file, "r"):
ngram, count = line.strip().split("\t")
count = float(count)
ngram = ngram.split(" ")
if len(ngram)==2:
self.UD += 1.0
if len(ngram)==2:
self.UN[" ".join(ngram[1:])] += 1.0
#Nonzeros based on suffixes
if ngram[0]==self.sb:
self.nonZeros[len(ngram)-2][" ".join(ngram[:-1])][ngram[-1]] += count
self.numerators[len(ngram)-2][" ".join(ngram)] += count
self.denominators[len(ngram)-2][" ".join(ngram[:-1])] += count
if len(ngram)>2 and len(ngram)<self.order:
self.numerators[len(ngram)-3][" ".join(ngram[1:])] += 1.0
self.denominators[len(ngram)-3][" ".join(ngram[1:-1])] += 1.0
self.nonZeros[len(ngram)-3][" ".join(ngram[1:-1])][ngram[-1]] += 1.0
if ngram[0]==self.sb:
self.numerators[len(ngram)-2][" ".join(ngram)] += count
self.denominators[len(ngram)-2][" ".join(ngram[:-1])] += count
self.nonZeros[len(ngram)-2][" ".join(ngram[:-1])][ngram[-1]] += count
if len(ngram)==self.order:
self.numerators[len(ngram)-3][" ".join(ngram[1:])] += 1.0
self.numerators[len(ngram)-2][" ".join(ngram)] = count
self.denominators[len(ngram)-3][" ".join(ngram[1:-1])] += 1.0
self.denominators[len(ngram)-2][" ".join(ngram[:-1])] += count
self.nonZeros[len(ngram)-3][" ".join(ngram[1:-1])][ngram[-1]] += 1.0
self.nonZeros[len(ngram)-2][" ".join(ngram[:-1])][ngram[-1]] += count
self._compute_counts_of_counts ( )
self._compute_discounts( )
#self._print_raw_counts( )
return
def kneser_ney_discounting( 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 _kn_recurse() subroutine to increment the N-gram
contexts in the current window / on the stack.
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
self.ngrams.push(self.sb)
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._kn_recurse( ngram, len(ngram)-2 )
#Now push the sentence-end token onto the stack
ngram = self.ngrams.push(self.se)
self._kn_recurse( ngram, len(ngram)-2 )
#Clear the stack for the next sentence
self.ngrams.clear()
self._compute_counts_of_counts ( )
self._compute_discounts( )
return
def print_raw_counts( self ):
"""
Convenience function for sanity checking the history counts.
"""
#print "NUMERATORS:"
#for key in sorted(self.UN.iterkeys()):
# print " ", key, self.UN[key]
#for o in xrange(len(self.numerators)):
# print "ORD",o
# for key in sorted(self.numerators[o].iterkeys()):
# print " ", key, self.numerators[o][key]
#print "DENOMINATORS:"
#print self.UD
#for o in xrange(len(self.denominators)):
# print "DORD", o
# for key in sorted(self.denominators[o].iterkeys()):
# print " ", key, self.denominators[o][key]
print "NONZEROS:"
for o in xrange(len(self.nonZeros)):
print "ZORD", o
for denom in sorted(self.nonZeros[o].iterkeys()):
print " Den:", denom
for key in sorted(self.nonZeros[o][denom].iterkeys()):
print " ", key, self.nonZeros[o][denom][key]
def _kn_recurse( self, ngram_stack, i ):
"""
Kneser-Ney discount calculation recursion.
"""
if i==-1 and ngram_stack[0]==self.sb:
return
o = len(ngram_stack)
numer = " ".join(ngram_stack[o-(i+2):])
denom = " ".join(ngram_stack[o-(i+2):o-1])
self.numerators[ i][numer] += 1.
self.denominators[i][denom] += 1.
#For Modified Kneser-Ney we need to track
# individual nonZeros based on their suffixes
self.nonZeros[i][denom][ngram_stack[-1]] += 1.
if self.numerators[i][numer]==1.:
if i>0:
self._kn_recurse( ngram_stack, i-1 )
else:
#The <s> (sentence-begin) token is
# NOT counted as a unigram event
if not ngram_stack[-1]==self.sb:
self.UN[ngram_stack[-1]] += 1.
self.UD += 1.
return
def print_ARPA( self ):
"""
Print the interpolated Kneser-Ney LM out in ARPA format,
computing the interpolated probabilities and back-off
weights for each N-gram on-demand. The 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\
----------------------------
"""
#Handle the header info
print "\\data\\"
print "ngram 1=%d" % (len(self.UN)+1)
for o in xrange(0,self.order-1):
print "ngram %d=%d" % (o+2,len(self.numerators[o]) )
#Handle the Unigrams
print "\n\\1-grams:"
d = self._get_discount( 0, self.sb )
#ModKN discount
lmda = d / self.denominators[0][self.sb]
print "-99.00000\t%s\t%0.7f" % ( self.sb, log(lmda, 10.) )
for key in sorted(self.UN.iterkeys()):
if key==self.se:
print "%0.7f\t%s\t-99" % ( log(self.UN[key]/self.UD, 10.), key )
continue
d = self._get_discount( 0, key )
#ModKN discount
lmda = d / self.denominators[0][key]
print "%0.7f\t%s\t%0.7f" % ( log(self.UN[key]/self.UD, 10.), key, log(lmda, 10.) )
#Handle the middle-order N-grams
for o in xrange(0,self.order-2):
print "\n\\%d-grams:" % (o+2)
for key in sorted(self.numerators[o].iterkeys()):
if key.endswith(self.se):
#No back-off prob for N-grams ending in </s>
prob = self._compute_interpolated_prob( key )
print "%0.7f\t%s" % ( log(prob, 10.), key )
continue
d = self._get_discount( o+1, key )
#Compute the back-off weight
#ModKN discount
lmda = d / self.denominators[o+1][key]
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob( key )
print "%0.7f\t%s\t%0.7f" % ( log(prob, 10.), key, log(lmda, 10.))
#Handle the N-order N-grams
print "\n\\%d-grams:" % (self.order)
for key in sorted(self.numerators[self.order-2].iterkeys()):
#Compute the interpolated N-gram probability
prob = self._compute_interpolated_prob( key )
print "%0.7f\t%s" % ( log(prob, 10.), key )
print "\n\\end\\"
return
def _compute_interpolated_prob( self, ngram ):
"""
Compute the interpolated probability for the input ngram.
Cribbing the notation from the SRILM webpages,
a_z = An N-gram where a is the first word, z is the
last word, and "_" represents 0 or more words in between.
p(a_z) = The estimated conditional probability of the
nth word z given the first n-1 words (a_) of an N-gram.
a_ = The n-1 word prefix of the N-gram a_z.
_z = The n-1 word suffix of the N-gram a_z.
Then we have,
f(a_z) = g(a_z) + bow(a_) p(_z)
p(a_z) = (c(a_z) > 0) ? f(a_z) : bow(a_) p(_z)
The ARPA format is generated by writing, for each N-gram
with 1 < order < max_order:
p(a_z) a_z bow(a_z)
and for the maximum order:
p(a_z) a_z
special care must be taken for certain N-grams containing
the <s> (sentence-begin) and </s> (sentence-end) tokens.
See the implementation for details on how to do this correctly.
The formulation is based on the seminal Chen&Goodman '98 paper.
SRILM notation-cribbing from:
http://www.speech.sri.com/projects/srilm/manpages/ngram-discount.7.html
"""
probability = 0.0
ngram_stack = ngram.split(" ")
probs = [ 1e-99 for i in xrange(len(ngram_stack)) ]
o = len(ngram_stack)
if not ngram_stack[-1]==self.sb:
probs[0] = self.UN[ngram_stack[-1]] / self.UD
for i in xrange(o-1):
dID = " ".join(ngram_stack[o-(i+2):o-1])
nID = " ".join(ngram_stack[o-(i+2):])
if dID in self.denominators[i]:
count = int(self.numerators[i][nID])
d_i = min(count, 3)
probs[i+1] = (count-self.discounts[i][d_i-1])/self.denominators[i][dID]
#This break-down takes the following form:
# probs[i+1]: The interpolated N-gram probability, p(a_z)
# d: The discount mass for a_: \Sum_i D_i*N_i
# lmda: The un-normalized 'back-off' weight, bow(a_)
# probs[i]: The next lower-order, interpolated N-gram
# probability corresponding to p(_z)
#ModKN discount
d = self._get_discount( i, dID )
lmda = d / self.denominators[i][dID]
probs[i+1] = probs[i+1] + lmda * probs[i]
probability = probs[i+1]
if probability == 0.0:
#If we still have nothing, return the unigram probability
probability = probs[0]
return probability
