ai-content-maker/.venv/Lib/site-packages/nltk/classify/rte_classify.py

# Natural Language Toolkit: RTE Classifier
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Ewan Klein <ewan@inf.ed.ac.uk>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

"""
Simple classifier for RTE corpus.

It calculates the overlap in words and named entities between text and
hypothesis, and also whether there are words / named entities in the
hypothesis which fail to occur in the text, since this is an indicator that
the hypothesis is more informative than (i.e not entailed by) the text.

TO DO: better Named Entity classification
TO DO: add lemmatization
"""

from nltk.classify.maxent import MaxentClassifier
from nltk.classify.util import accuracy
from nltk.tokenize import RegexpTokenizer


class RTEFeatureExtractor:
    """
    This builds a bag of words for both the text and the hypothesis after
    throwing away some stopwords, then calculates overlap and difference.
    """

    def __init__(self, rtepair, stop=True, use_lemmatize=False):
        """
        :param rtepair: a ``RTEPair`` from which features should be extracted
        :param stop: if ``True``, stopwords are thrown away.
        :type stop: bool
        """
        self.stop = stop
        self.stopwords = {
            "a",
            "the",
            "it",
            "they",
            "of",
            "in",
            "to",
            "is",
            "have",
            "are",
            "were",
            "and",
            "very",
            ".",
            ",",
        }

        self.negwords = {"no", "not", "never", "failed", "rejected", "denied"}
        # Try to tokenize so that abbreviations, monetary amounts, email
        # addresses, URLs are single tokens.
        tokenizer = RegexpTokenizer(r"[\w.@:/]+|\w+|\$[\d.]+")

        # Get the set of word types for text and hypothesis
        self.text_tokens = tokenizer.tokenize(rtepair.text)
        self.hyp_tokens = tokenizer.tokenize(rtepair.hyp)
        self.text_words = set(self.text_tokens)
        self.hyp_words = set(self.hyp_tokens)

        if use_lemmatize:
            self.text_words = {self._lemmatize(token) for token in self.text_tokens}
            self.hyp_words = {self._lemmatize(token) for token in self.hyp_tokens}

        if self.stop:
            self.text_words = self.text_words - self.stopwords
            self.hyp_words = self.hyp_words - self.stopwords

        self._overlap = self.hyp_words & self.text_words
        self._hyp_extra = self.hyp_words - self.text_words
        self._txt_extra = self.text_words - self.hyp_words

    def overlap(self, toktype, debug=False):
        """
        Compute the overlap between text and hypothesis.

        :param toktype: distinguish Named Entities from ordinary words
        :type toktype: 'ne' or 'word'
        """
        ne_overlap = {token for token in self._overlap if self._ne(token)}
        if toktype == "ne":
            if debug:
                print("ne overlap", ne_overlap)
            return ne_overlap
        elif toktype == "word":
            if debug:
                print("word overlap", self._overlap - ne_overlap)
            return self._overlap - ne_overlap
        else:
            raise ValueError("Type not recognized:'%s'" % toktype)

    def hyp_extra(self, toktype, debug=True):
        """
        Compute the extraneous material in the hypothesis.

        :param toktype: distinguish Named Entities from ordinary words
        :type toktype: 'ne' or 'word'
        """
        ne_extra = {token for token in self._hyp_extra if self._ne(token)}
        if toktype == "ne":
            return ne_extra
        elif toktype == "word":
            return self._hyp_extra - ne_extra
        else:
            raise ValueError("Type not recognized: '%s'" % toktype)

    @staticmethod
    def _ne(token):
        """
        This just assumes that words in all caps or titles are
        named entities.

        :type token: str
        """
        if token.istitle() or token.isupper():
            return True
        return False

    @staticmethod
    def _lemmatize(word):
        """
        Use morphy from WordNet to find the base form of verbs.
        """
        from nltk.corpus import wordnet as wn

        lemma = wn.morphy(word, pos=wn.VERB)
        if lemma is not None:
            return lemma
        return word


def rte_features(rtepair):
    extractor = RTEFeatureExtractor(rtepair)
    features = {}
    features["alwayson"] = True
    features["word_overlap"] = len(extractor.overlap("word"))
    features["word_hyp_extra"] = len(extractor.hyp_extra("word"))
    features["ne_overlap"] = len(extractor.overlap("ne"))
    features["ne_hyp_extra"] = len(extractor.hyp_extra("ne"))
    features["neg_txt"] = len(extractor.negwords & extractor.text_words)
    features["neg_hyp"] = len(extractor.negwords & extractor.hyp_words)
    return features


def rte_featurize(rte_pairs):
    return [(rte_features(pair), pair.value) for pair in rte_pairs]


def rte_classifier(algorithm, sample_N=None):
    from nltk.corpus import rte as rte_corpus

    train_set = rte_corpus.pairs(["rte1_dev.xml", "rte2_dev.xml", "rte3_dev.xml"])
    test_set = rte_corpus.pairs(["rte1_test.xml", "rte2_test.xml", "rte3_test.xml"])

    if sample_N is not None:
        train_set = train_set[:sample_N]
        test_set = test_set[:sample_N]

    featurized_train_set = rte_featurize(train_set)
    featurized_test_set = rte_featurize(test_set)

    # Train the classifier
    print("Training classifier...")
    if algorithm in ["megam"]:  # MEGAM based algorithms.
        clf = MaxentClassifier.train(featurized_train_set, algorithm)
    elif algorithm in ["GIS", "IIS"]:  # Use default GIS/IIS MaxEnt algorithm
        clf = MaxentClassifier.train(featurized_train_set, algorithm)
    else:
        err_msg = str(
            "RTEClassifier only supports these algorithms:\n "
            "'megam', 'GIS', 'IIS'.\n"
        )
        raise Exception(err_msg)
    print("Testing classifier...")
    acc = accuracy(clf, featurized_test_set)
    print("Accuracy: %6.4f" % acc)
    return clf