# Natural Language Toolkit: Language Models
#
# Copyright (C) 2001-2014 NLTK Project
# Authors: Steven Bird <stevenbird1@gmail.com>
# Daniel Blanchard <dblanchard@ets.org>
# Ilia Kurenkov <ilia.kurenkov@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
#
# adapted for mtgencode Nov. 2015
# an attempt was made to preserve the exact functionality of this code,
# hampered somewhat by its brokenness
from __future__ import unicode_literals
from math import log
from nltk.probability import ConditionalProbDist, ConditionalFreqDist, LidstoneProbDist
from nltk.util import ngrams
from nltk_model_api import ModelI
from nltk import compat
def _estimator(fdist, **estimator_kwargs):
"""
Default estimator function using a LidstoneProbDist.
"""
# can't be an instance method of NgramModel as they
# can't be pickled either.
return LidstoneProbDist(fdist, 0.001, **estimator_kwargs)
@compat.python_2_unicode_compatible
class NgramModel(ModelI):
"""
A processing interface for assigning a probability to the next word.
"""
def __init__(self, n, train, pad_left=True, pad_right=False,
estimator=None, **estimator_kwargs):
"""
Create 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. See model.doctest for more detailed testing
>>> from nltk.corpus import brown
>>> lm = NgramModel(3, brown.words(categories='news'))
>>> lm
<NgramModel with 91603 3-grams>
>>> lm._backoff
<NgramModel with 62888 2-grams>
>>> lm.entropy(brown.words(categories='humor'))
... # doctest: +ELLIPSIS
12.0399...
:param n: the order of the language model (ngram size)
:type n: int
:param train: the training text
:type train: list(str) or list(list(str))
:param pad_left: whether to pad the left of each sentence with an (n-1)-gram of empty strings
:type pad_left: bool
:param pad_right: whether to pad the right of each sentence with an (n-1)-gram of empty strings
:type pad_right: bool
:param estimator: a function for generating a probability distribution
:type estimator: a function that takes a ConditionalFreqDist and
returns a ConditionalProbDist
:param estimator_kwargs: Extra keyword arguments for the estimator
:type estimator_kwargs: (any)
"""
# protection from cryptic behavior for calling programs
# that use the pre-2.0.2 interface
assert(isinstance(pad_left, bool))
assert(isinstance(pad_right, bool))
self._lpad = ('',) * (n - 1) if pad_left else ()
self._rpad = ('',) * (n - 1) if pad_right else ()
# make sure n is greater than zero, otherwise print it
assert (n > 0), n
# For explicitness save the check whether this is a unigram model
self.is_unigram_model = (n == 1)
# save the ngram order number
self._n = n
# save left and right padding
self._lpad = ('',) * (n - 1) if pad_left else ()
self._rpad = ('',) * (n - 1) if pad_right else ()
if estimator is None:
estimator = _estimator
cfd = ConditionalFreqDist()
# set read-only ngrams set (see property declaration below to reconfigure)
self._ngrams = set()
# If given a list of strings instead of a list of lists, create enclosing list
if (train is not None) and isinstance(train[0], compat.string_types):
train = [train]
# we need to keep track of the number of word types we encounter
vocabulary = set()
for sent in train:
raw_ngrams = ngrams(sent, n, pad_left, pad_right, pad_symbol='')
for ngram in raw_ngrams:
self._ngrams.add(ngram)
context = tuple(ngram[:-1])
token = ngram[-1]
cfd[context][token] += 1
vocabulary.add(token)
# Unless number of bins is explicitly passed, we should use the number
# of word types encountered during training as the bins value.
# If right padding is on, this includes the padding symbol.
if 'bins' not in estimator_kwargs:
estimator_kwargs['bins'] = len(vocabulary)
self._model = ConditionalProbDist(cfd, estimator, **estimator_kwargs)
# recursively construct the lower-order models
if not self.is_unigram_model:
self._backoff = NgramModel(n-1, train,
pad_left, pad_right,
estimator,
**estimator_kwargs)
self._backoff_alphas = dict()
# For each condition (or context)
for ctxt in cfd.conditions():
backoff_ctxt = ctxt[1:]
backoff_total_pr = 0.0
total_observed_pr = 0.0
# this is the subset of words that we OBSERVED following
# this context.
# i.e. Count(word | context) > 0
for words in self._words_following(ctxt, cfd):
# so, _words_following as fixed gives back a whole list now...
for word in words:
total_observed_pr += self.prob(word, ctxt)
# we also need the total (n-1)-gram probability of
# words observed in this n-gram context
backoff_total_pr += self._backoff.prob(word, backoff_ctxt)
assert (0 <= total_observed_pr <= 1), total_observed_pr
# beta is the remaining probability weight after we factor out
# the probability of observed words.
# As a sanity check, both total_observed_pr and backoff_total_pr
# must be GE 0, since probabilities are never negative
beta = 1.0 - total_observed_pr
# backoff total has to be less than one, otherwise we get
# an error when we try subtracting it from 1 in the denominator
assert (0 <= backoff_total_pr < 1), backoff_total_pr
alpha_ctxt = beta / (1.0 - backoff_total_pr)
self._backoff_alphas[ctxt] = alpha_ctxt
# broken
# def _words_following(self, context, cond_freq_dist):
# for ctxt, word in cond_freq_dist.iterkeys():
# if ctxt == context:
# yield word
# fixed
def _words_following(self, context, cond_freq_dist):
for ctxt in cond_freq_dist.iterkeys():
if ctxt == context:
yield cond_freq_dist[ctxt].keys()
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: str
:param context: the context the word is in
:type context: list(str)
"""
context = tuple(context)
if (context + (word,) in self._ngrams) or (self.is_unigram_model):
return self._model[context].prob(word)
else:
return self._alpha(context) * self._backoff.prob(word, context[1:])
def _alpha(self, context):
"""Get the backoff alpha value for the given context
"""
error_message = "Alphas and backoff are not defined for unigram models"
assert not self.is_unigram_model, error_message
if context in self._backoff_alphas:
return self._backoff_alphas[context]
else:
return 1
def logprob(self, word, context):
"""
Evaluate the (negative) log probability of this word in this context.
:param word: the word to get the probability of
:type word: str
:param context: the context the word is in
:type context: list(str)
"""
return -log(self.prob(word, context), 2)
@property
def ngrams(self):
return self._ngrams
@property
def backoff(self):
return self._backoff
@property
def model(self):
return self._model
def choose_random_word(self, context):
'''
Randomly select a word that is likely to appear in this context.
:param context: the context the word is in
:type context: list(str)
'''
return self.generate(1, context)[-1]
# NB, this will always start with same word if the model
# was trained on a single text
def generate(self, num_words, context=()):
'''
Generate random text based on the language model.
:param num_words: number of words to generate
:type num_words: int
:param context: initial words in generated string
:type context: list(str)
'''
text = list(context)
for i in range(num_words):
text.append(self._generate_one(text))
return text
def _generate_one(self, context):
context = (self._lpad + tuple(context))[-self._n + 1:]
if context in self:
return self[context].generate()
elif self._n > 1:
return self._backoff._generate_one(context[1:])
else:
return '.'
def entropy(self, text):
"""
Calculate the approximate cross-entropy of the n-gram model for a
given evaluation text.
This is the average log probability of each word in the text.
:param text: words to use for evaluation
:type text: list(str)
"""
H = 0.0 # entropy is conventionally denoted by "H"
text = list(self._lpad) + text + list(self._rpad)
for i in range(self._n - 1, len(text)):
context = tuple(text[(i - self._n + 1):i])
token = text[i]
H += self.logprob(token, context)
return H / float(len(text) - (self._n - 1))
def perplexity(self, text):
"""
Calculates the perplexity of the given text.
This is simply 2 ** cross-entropy for the text.
:param text: words to calculate perplexity of
:type text: list(str)
"""
return pow(2.0, self.entropy(text))
def __contains__(self, item):
if not isinstance(item, tuple):
item = (item,)
return item in self._model
def __getitem__(self, item):
if not isinstance(item, tuple):
item = (item,)
return self._model[item]
def __repr__(self):
return '<NgramModel with %d %d-grams>' % (len(self._ngrams), self._n)
if __name__ == "__main__":
import doctest
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
