def __init__(self, n, train, estimator, freqtype, padding, backoff,
*estimator_args, **estimator_kw_args):
"""
Creates an ngram language model to capture patterns in n consecutive
words of training text. An estimator smooths the probabilities derived
from the text and may allow generation of ngrams not seen during
training.
@param n: the order of the language model (ngram size)
@type n: L{int}
@param train: the training text
@type train: L{list} of L{str} (or L{list} of L{str} L{list}s)
@param estimator: a function for generating a probability distribution (must take FreqDist as first argument, and n as second)
@type estimator: a function that takes a L{ConditionalFreqDist} and returns a L{ConditionalProbDist}
@param freqtype: the type to use to store the counts in the underlying frequency distribution
@type freqtype: any numeric type
@param backoff: whether or not we should use Katz back-off
@type backoff: L{bool}
@param estimator_args: Extra arguments for L{estimator}.
These arguments are usually used to specify extra
properties for the probability distributions of individual
conditions, such as the number of bins they contain.
@type estimator_args: (any)
@param estimator_kw_args: Extra keyword arguments for L{estimator}.
@type estimator_kw_args: (any)
"""
self._n = n
cfd = ConditionalFreqDist(counttype=freqtype)
self._ngrams = set()
self._padding = (padding, ) * (n - 1)
self._estimator = estimator
self._freqtype = freqtype
self._estimator_args = estimator_args
self._estimator_kw_args = estimator_kw_args
if train:
for utterance in train:
for ngram in ingrams(
chain(self._padding, utterance, self._padding), n):
self._ngrams.add(ngram)
context = tuple(ngram[:-1])
token = ngram[-1]
cfd[context].inc(token)
self._model = ConditionalProbDist(cfd, estimator, self._freqtype, n,
*estimator_args, **estimator_kw_args)
# recursively construct the lower-order models
self._backoff = PartialCountNgramModel(
n -
1, train, estimator, freqtype, padding, backoff, *estimator_args,
**estimator_kw_args) if (backoff and n > 1) else None
def __init__(self, n, train, estimator, freqtype, padding, backoff, *estimator_args, **estimator_kw_args):
"""
Creates an ngram language model to capture patterns in n consecutive
words of training text. An estimator smooths the probabilities derived
from the text and may allow generation of ngrams not seen during
training.
@param n: the order of the language model (ngram size)
@type n: L{int}
@param train: the training text
@type train: L{list} of L{str} (or L{list} of L{str} L{list}s)
@param estimator: a function for generating a probability distribution (must take FreqDist as first argument, and n as second)
@type estimator: a function that takes a L{ConditionalFreqDist} and returns a L{ConditionalProbDist}
@param freqtype: the type to use to store the counts in the underlying frequency distribution
@type freqtype: any numeric type
@param backoff: whether or not we should use Katz back-off
@type backoff: L{bool}
@param estimator_args: Extra arguments for L{estimator}.
These arguments are usually used to specify extra
properties for the probability distributions of individual
conditions, such as the number of bins they contain.
@type estimator_args: (any)
@param estimator_kw_args: Extra keyword arguments for L{estimator}.
@type estimator_kw_args: (any)
"""
self._n = n
cfd = ConditionalFreqDist(counttype=freqtype)
self._ngrams = set()
self._padding = (padding,) * (n - 1)
self._estimator = estimator
self._freqtype = freqtype
self._estimator_args = estimator_args
self._estimator_kw_args = estimator_kw_args
if train:
for utterance in train:
for ngram in ingrams(chain(self._padding, utterance, self._padding), n):
self._ngrams.add(ngram)
context = tuple(ngram[:-1])
token = ngram[-1]
cfd[context].inc(token)
self._model = ConditionalProbDist(cfd, estimator, self._freqtype, n, *estimator_args, **estimator_kw_args)
# recursively construct the lower-order models
self._backoff = PartialCountNgramModel(n - 1, train, estimator, freqtype, padding, backoff, *estimator_args, **estimator_kw_args) if (backoff and n > 1) else None
class PartialCountNgramModel(NgramModel):
'''
NgramModel that supports storing counts as types other than int.
'''
def __init__(self, n, train, estimator, freqtype, padding, backoff,
*estimator_args, **estimator_kw_args):
"""
Creates an ngram language model to capture patterns in n consecutive
words of training text. An estimator smooths the probabilities derived
from the text and may allow generation of ngrams not seen during
training.
@param n: the order of the language model (ngram size)
@type n: L{int}
@param train: the training text
@type train: L{list} of L{str} (or L{list} of L{str} L{list}s)
@param estimator: a function for generating a probability distribution (must take FreqDist as first argument, and n as second)
@type estimator: a function that takes a L{ConditionalFreqDist} and returns a L{ConditionalProbDist}
@param freqtype: the type to use to store the counts in the underlying frequency distribution
@type freqtype: any numeric type
@param backoff: whether or not we should use Katz back-off
@type backoff: L{bool}
@param estimator_args: Extra arguments for L{estimator}.
These arguments are usually used to specify extra
properties for the probability distributions of individual
conditions, such as the number of bins they contain.
@type estimator_args: (any)
@param estimator_kw_args: Extra keyword arguments for L{estimator}.
@type estimator_kw_args: (any)
"""
self._n = n
cfd = ConditionalFreqDist(counttype=freqtype)
self._ngrams = set()
self._padding = (padding, ) * (n - 1)
self._estimator = estimator
self._freqtype = freqtype
self._estimator_args = estimator_args
self._estimator_kw_args = estimator_kw_args
if train:
for utterance in train:
for ngram in ingrams(
chain(self._padding, utterance, self._padding), n):
self._ngrams.add(ngram)
context = tuple(ngram[:-1])
token = ngram[-1]
cfd[context].inc(token)
self._model = ConditionalProbDist(cfd, estimator, self._freqtype, n,
*estimator_args, **estimator_kw_args)
# recursively construct the lower-order models
self._backoff = PartialCountNgramModel(
n -
1, train, estimator, freqtype, padding, backoff, *estimator_args,
**estimator_kw_args) if (backoff and n > 1) else None
def update(self, samples, increase_amount=1):
'''
Update the underlying frequency distributions given the current list of samples.
'''
cond_samples = []
for utterance in samples:
for ngram in ingrams(
chain(self._padding, utterance, self._padding), self._n):
self._ngrams.add(ngram)
cond_samples.append((tuple(ngram[:-1]), ngram[-1]))
self._model.update(cond_samples, increase_amount)
# Recursively update lower-order models
if self._backoff:
self._backoff.update(samples, increase_amount)
def prob(self, word, context):
"""
Evaluate the probability of this word in this context using Katz Backoff.
@param word: the word to get the probability of
@type word: C{string}
@param context: the context the word is in
@type context: C{list} of C{string}
"""
context = tuple(context)
if not self._backoff or (context + (word, ) in self._ngrams):
return self[context].prob(word)
else:
# print "Alpha: {}\tBackoff prob: {}".format(self._alpha(context), self._backoff.prob(word, context[1:]))
return self._alpha(context) * self._backoff.prob(word, context[1:])
def setUp(cls):
cls.tokens = text
cond_text = list(zip(tags, text))
cls.fd = FreqDist(tags)
cls.cfd = ConditionalFreqDist(cond_text)
cls.cpd = ConditionalProbDist(cls.cfd, MLEProbDist)
class PartialCountNgramModel(NgramModel):
'''
NgramModel that supports storing counts as types other than int.
'''
def __init__(self, n, train, estimator, freqtype, padding, backoff, *estimator_args, **estimator_kw_args):
"""
Creates an ngram language model to capture patterns in n consecutive
words of training text. An estimator smooths the probabilities derived
from the text and may allow generation of ngrams not seen during
training.
@param n: the order of the language model (ngram size)
@type n: L{int}
@param train: the training text
@type train: L{list} of L{str} (or L{list} of L{str} L{list}s)
@param estimator: a function for generating a probability distribution (must take FreqDist as first argument, and n as second)
@type estimator: a function that takes a L{ConditionalFreqDist} and returns a L{ConditionalProbDist}
@param freqtype: the type to use to store the counts in the underlying frequency distribution
@type freqtype: any numeric type
@param backoff: whether or not we should use Katz back-off
@type backoff: L{bool}
@param estimator_args: Extra arguments for L{estimator}.
These arguments are usually used to specify extra
properties for the probability distributions of individual
conditions, such as the number of bins they contain.
@type estimator_args: (any)
@param estimator_kw_args: Extra keyword arguments for L{estimator}.
@type estimator_kw_args: (any)
"""
self._n = n
cfd = ConditionalFreqDist(counttype=freqtype)
self._ngrams = set()
self._padding = (padding,) * (n - 1)
self._estimator = estimator
self._freqtype = freqtype
self._estimator_args = estimator_args
self._estimator_kw_args = estimator_kw_args
if train:
for utterance in train:
for ngram in ingrams(chain(self._padding, utterance, self._padding), n):
self._ngrams.add(ngram)
context = tuple(ngram[:-1])
token = ngram[-1]
cfd[context].inc(token)
self._model = ConditionalProbDist(cfd, estimator, self._freqtype, n, *estimator_args, **estimator_kw_args)
# recursively construct the lower-order models
self._backoff = PartialCountNgramModel(n - 1, train, estimator, freqtype, padding, backoff, *estimator_args, **estimator_kw_args) if (backoff and n > 1) else None
def update(self, samples, increase_amount=1):
'''
Update the underlying frequency distributions given the current list of samples.
'''
cond_samples = []
for utterance in samples:
for ngram in ingrams(chain(self._padding, utterance, self._padding), self._n):
self._ngrams.add(ngram)
cond_samples.append((tuple(ngram[:-1]), ngram[-1]))
self._model.update(cond_samples, increase_amount)
# Recursively update lower-order models
if self._backoff:
self._backoff.update(samples, increase_amount)
def prob(self, word, context):
"""
Evaluate the probability of this word in this context using Katz Backoff.
@param word: the word to get the probability of
@type word: C{string}
@param context: the context the word is in
@type context: C{list} of C{string}
"""
context = tuple(context)
if not self._backoff or (context + (word,) in self._ngrams):
return self[context].prob(word)
else:
# print "Alpha: {}\tBackoff prob: {}".format(self._alpha(context), self._backoff.prob(word, context[1:]))
return self._alpha(context) * self._backoff.prob(word, context[1:])
