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# Natural Language Toolkit: Language Models
#
# Copyright (C) 2001-2023 NLTK Project
# Authors: Ilia Kurenkov <[email protected]>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
"""Language Model Interface."""
import random
import warnings
from abc import ABCMeta, abstractmethod
from bisect import bisect
from itertools import accumulate
from nltk.lm.counter import NgramCounter
from nltk.lm.util import log_base2
from nltk.lm.vocabulary import Vocabulary
class Smoothing(metaclass=ABCMeta):
"""Ngram Smoothing Interface
Implements Chen & Goodman 1995's idea that all smoothing algorithms have
certain features in common. This should ideally allow smoothing algorithms to
work both with Backoff and Interpolation.
"""
def __init__(self, vocabulary, counter):
"""
:param vocabulary: The Ngram vocabulary object.
:type vocabulary: nltk.lm.vocab.Vocabulary
:param counter: The counts of the vocabulary items.
:type counter: nltk.lm.counter.NgramCounter
"""
self.vocab = vocabulary
self.counts = counter
@abstractmethod
def unigram_score(self, word):
raise NotImplementedError()
@abstractmethod
def alpha_gamma(self, word, context):
raise NotImplementedError()
def _mean(items):
"""Return average (aka mean) for sequence of items."""
return sum(items) / len(items)
def _random_generator(seed_or_generator):
if isinstance(seed_or_generator, random.Random):
return seed_or_generator
return random.Random(seed_or_generator)
def _weighted_choice(population, weights, random_generator=None):
"""Like random.choice, but with weights.
Heavily inspired by python 3.6 `random.choices`.
"""
if not population:
raise ValueError("Can't choose from empty population")
if len(population) != len(weights):
raise ValueError("The number of weights does not match the population")
cum_weights = list(accumulate(weights))
total = cum_weights[-1]
threshold = random_generator.random()
return population[bisect(cum_weights, total * threshold)]
class LanguageModel(metaclass=ABCMeta):
"""ABC for Language Models.
Cannot be directly instantiated itself.
"""
def __init__(self, order, vocabulary=None, counter=None):
"""Creates new LanguageModel.
:param vocabulary: If provided, this vocabulary will be used instead
of creating a new one when training.
:type vocabulary: `nltk.lm.Vocabulary` or None
:param counter: If provided, use this object to count ngrams.
:type counter: `nltk.lm.NgramCounter` or None
:param ngrams_fn: If given, defines how sentences in training text are turned to ngram
sequences.
:type ngrams_fn: function or None
:param pad_fn: If given, defines how sentences in training text are padded.
:type pad_fn: function or None
"""
self.order = order
if vocabulary and not isinstance(vocabulary, Vocabulary):
warnings.warn(
f"The `vocabulary` argument passed to {self.__class__.__name__!r} "
"must be an instance of `nltk.lm.Vocabulary`.",
stacklevel=3,
)
self.vocab = Vocabulary() if vocabulary is None else vocabulary
self.counts = NgramCounter() if counter is None else counter
def fit(self, text, vocabulary_text=None):
"""Trains the model on a text.
:param text: Training text as a sequence of sentences.
"""
if not self.vocab:
if vocabulary_text is None:
raise ValueError(
"Cannot fit without a vocabulary or text to create it from."
)
self.vocab.update(vocabulary_text)
self.counts.update(self.vocab.lookup(sent) for sent in text)
def score(self, word, context=None):
"""Masks out of vocab (OOV) words and computes their model score.
For model-specific logic of calculating scores, see the `unmasked_score`
method.
"""
return self.unmasked_score(
self.vocab.lookup(word), self.vocab.lookup(context) if context else None
)
@abstractmethod
def unmasked_score(self, word, context=None):
"""Score a word given some optional context.
Concrete models are expected to provide an implementation.
Note that this method does not mask its arguments with the OOV label.
Use the `score` method for that.
:param str word: Word for which we want the score
:param tuple(str) context: Context the word is in.
If `None`, compute unigram score.
:param context: tuple(str) or None
:rtype: float
"""
raise NotImplementedError()
def logscore(self, word, context=None):
"""Evaluate the log score of this word in this context.
The arguments are the same as for `score` and `unmasked_score`.
"""
return log_base2(self.score(word, context))
def context_counts(self, context):
"""Helper method for retrieving counts for a given context.
Assumes context has been checked and oov words in it masked.
:type context: tuple(str) or None
"""
return (
self.counts[len(context) + 1][context] if context else self.counts.unigrams
)
def entropy(self, text_ngrams):
"""Calculate cross-entropy of model for given evaluation text.
:param Iterable(tuple(str)) text_ngrams: A sequence of ngram tuples.
:rtype: float
"""
return -1 * _mean(
[self.logscore(ngram[-1], ngram[:-1]) for ngram in text_ngrams]
)
def perplexity(self, text_ngrams):
"""Calculates the perplexity of the given text.
This is simply 2 ** cross-entropy for the text, so the arguments are the same.
"""
return pow(2.0, self.entropy(text_ngrams))
def generate(self, num_words=1, text_seed=None, random_seed=None):
"""Generate words from the model.
:param int num_words: How many words to generate. By default 1.
:param text_seed: Generation can be conditioned on preceding context.
:param random_seed: A random seed or an instance of `random.Random`. If provided,
makes the random sampling part of generation reproducible.
:return: One (str) word or a list of words generated from model.
Examples:
>>> from nltk.lm import MLE
>>> lm = MLE(2)
>>> lm.fit([[("a", "b"), ("b", "c")]], vocabulary_text=['a', 'b', 'c'])
>>> lm.fit([[("a",), ("b",), ("c",)]])
>>> lm.generate(random_seed=3)
'a'
>>> lm.generate(text_seed=['a'])
'b'
"""
text_seed = [] if text_seed is None else list(text_seed)
random_generator = _random_generator(random_seed)
# This is the base recursion case.
if num_words == 1:
context = (
text_seed[-self.order + 1 :]
if len(text_seed) >= self.order
else text_seed
)
samples = self.context_counts(self.vocab.lookup(context))
while context and not samples:
context = context[1:] if len(context) > 1 else []
samples = self.context_counts(self.vocab.lookup(context))
# Sorting samples achieves two things:
# - reproducible randomness when sampling
# - turns Mapping into Sequence which `_weighted_choice` expects
samples = sorted(samples)
return _weighted_choice(
samples,
tuple(self.score(w, context) for w in samples),
random_generator,
)
# We build up text one word at a time using the preceding context.
generated = []
for _ in range(num_words):
generated.append(
self.generate(
num_words=1,
text_seed=text_seed + generated,
random_seed=random_generator,
)
)
return generated
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