# Natural Language Toolkit: Collocations and Association Measures 
#
# Copyright (C) 2001-2009 NLTK Project
# Author: Joel Nothman <jnothman@student.usyd.edu.au>
#         Steven Bird <sb@csse.unimelb.edu.au> (incorporate into NLTK)
# URL: <http://nltk.org>
# For license information, see LICENSE.TXT
#
"""
Tools to identify X{collocation}s --- words that often appear consecutively
--- within corpora. They may also be used to find other X{association}s between
word occurrences.
See Manning and Schutze ch. 5 at http://nlp.stanford.edu/fsnlp/promo/colloc.pdf
and the Text::NSP Perl package at http://ngram.sourceforge.net

Finding collocations requires first calculating the frequencies of words and
their appearance in the context of other words. Often the collection of words
will then requiring filtering to only retain useful content terms. Each ngram
of words may then be scored according to some X{association measure}, in order
to determine the relative likelihood of each ngram being a collocation.

The L{BigramCollocationFinder} and L{TrigramCollocationFinder} classes provide
these functionalities, dependent on being provided a function which scores a
ngram given appropriate frequency counts. A number of standard association
measures are provided in L{bigram_measures} and L{trigram_measures}.
"""

# Possible TODOs:
# - consider the distinction between f(x,_) and f(x) and whether our
#   approximation is good enough for fragmented data, and mention it
# - add a n-gram collocation finder with measures which only utilise n-gram
#   and unigram counts (raw_freq, pmi, student_t)

import itertools as _itertools
from operator import itemgetter as _itemgetter

from nltk.probability import FreqDist
from nltk.util import ingrams
from nltk.metrics import ContingencyMeasures, BigramAssocMeasures, TrigramAssocMeasures
from nltk.metrics.spearman import *

class AbstractCollocationFinder(object):
    """
    An abstract base class for X{collocation finder}s whose purpose is to
    collect collocation candidate frequencies, filter and rank them.
    """

    def __init__(self, word_fd, ngram_fd):
        """As a minimum, collocation finders require the frequencies of each
        word in a corpus, and the joint frequency of word tuples. This data
        should be provided through L{nltk.probability.FreqDist} objects or an
        identical interface.
        """
        self.word_fd = word_fd
        self.ngram_fd = ngram_fd

    @classmethod
    def from_documents(cls, documents):
        """Constructs a collocation finder given a collection of documents,
        each of which is a list (or iterable) of tokens.
        """
        return cls.from_words(_itertools.chain(*documents))

    @staticmethod
    def _ngram_freqdist(words, n):
        return FreqDist(tuple(words[i:i+n]) for i in range(len(words)-1))

    def _apply_filter(self, fn=lambda ngram, freq: False):
        """Generic filter removes ngrams from the frequency distribution
        if the function returns True when passed an ngram tuple.
        """
        for ngram, freq in self.ngram_fd.items():
            if fn(ngram, freq):
                try:
                    del self.ngram_fd[ngram]
                except KeyError:
                    pass

    def apply_freq_filter(self, min_freq):
        """Removes candidate ngrams which have frequency less than min_freq."""
        self._apply_filter(lambda ng, freq: freq < min_freq)

    def apply_ngram_filter(self, fn):
        """Removes candidate ngrams (w1, w2, ...) where fn(w1, w2, ...)
        evaluates to True.
        """
        self._apply_filter(lambda ng, f: fn(*ng))

    def apply_word_filter(self, fn):
        """Removes candidate ngrams (w1, w2, ...) where any of (fn(w1), fn(w2),
        ...) evaluates to True.
        """
        self._apply_filter(lambda ng, f: any(fn(w) for w in ng))
 
    def _score_ngrams(self, score_fn):
        """Generates of (ngram, score) pairs as determined by the scoring
        function provided.
        """
        for tup in self.ngram_fd:
            score = self.score_ngram(score_fn, *tup)
            if score is not None:
                yield tup, score

    def score_ngrams(self, score_fn):
        """Returns a sequence of (ngram, score) pairs ordered from highest to
        lowest score, as determined by the scoring function provided.
        """
        return sorted(self._score_ngrams(score_fn),
                      key=_itemgetter(1), reverse=True)

    def nbest(self, score_fn, n):
        """Returns the top n ngrams when scored by the given function."""
        return [p for p,s in self.score_ngrams(score_fn)[:n]]

    def above_score(self, score_fn, min_score):
        """Returns a sequence of ngrams, ordered by decreasing score, whose
        scores each exceed the given minimum score.
        """
        for ngram, score in self.score_ngrams(score_fn):
            if score > min_score:
                yield ngram
            else:
                break


class BigramCollocationFinder(AbstractCollocationFinder):
    """A tool for the finding and ranking of bigram collocations or other
    association measures. It is often useful to use from_words() rather than
    constructing an instance directly.
    """

    @classmethod
    def from_words(cls, words):
        """Construct a BigramCollocationFinder for all bigrams in the given
        sequence.
        """
        wfd = FreqDist()
        bfd = FreqDist()

        for w1, w2 in ingrams(words, 2, pad_right=True):
            wfd.inc(w1)
            if w2 is not None:
                bfd.inc((w1, w2))
        return cls(wfd, bfd)

    def score_ngram(self, score_fn, w1, w2):
        """Returns the score for a given bigram using the given scoring
        function.
        """
        n_all = self.word_fd.N()
        n_ii = self.ngram_fd[(w1, w2)]
        if not n_ii:
            return
        n_ix = self.word_fd[w1]
        n_xi = self.word_fd[w2]
        return score_fn(n_ii, (n_ix, n_xi), n_all)


class TrigramCollocationFinder(AbstractCollocationFinder):
    """A tool for the finding and ranking of bigram collocations or other
    association measures. It is often useful to use from_words() rather than
    constructing an instance directly.
    """

    def __init__(self, word_fd, bigram_fd, wildcard_fd, trigram_fd):
        """Construct a TrigramCollocationFinder, given FreqDists for
        appearances of words, bigrams, two words with any word between them,
        and trigrams.
        """
        AbstractCollocationFinder.__init__(self, word_fd, trigram_fd)
        self.wildcard_fd = wildcard_fd
        self.bigram_fd = bigram_fd

    @classmethod
    def from_words(cls, words):
        """Construct a TrigramCollocationFinder for all trigrams in the given
        sequence.
        """
        wfd = FreqDist()
        wildfd = FreqDist()
        bfd = FreqDist()
        tfd = FreqDist()

        for w1, w2, w3 in ingrams(words, 3, pad_right=True):
            wfd.inc(w1)
            if w2 is None:
                continue
            bfd.inc((w1, w2))
            if w3 is None:
                continue
            wildfd.inc((w1, w3))
            tfd.inc((w1, w2, w3))
        return cls(wfd, bfd, wildfd, tfd)

    def bigram_finder(self):
        """Constructs a bigram collocation finder with the bigram and unigram
        data from this finder. Note that this does not include any filtering
        applied to this finder.
        """
        return BigramCollocationFinder(self.word_fd, self.bigram_fd)

    def score_ngram(self, score_fn, w1, w2, w3):
        """Returns the score for a given trigram using the given scoring
        function.
        """
        n_all = self.word_fd.N()
        n_iii = self.ngram_fd[(w1, w2, w3)]
        if not n_iii:
            return
        n_iix = self.bigram_fd[(w1, w2)]
        n_ixi = self.wildcard_fd[(w1, w3)]
        n_xii = self.bigram_fd[(w2, w3)]
        n_ixx = self.word_fd[w1]
        n_xix = self.word_fd[w2]
        n_xxi = self.word_fd[w3]
        return score_fn(n_iii,
                        (n_iix, n_ixi, n_xii),
                        (n_ixx, n_xix, n_xxi),
                        n_all)


def demo(scorer=None, compare_scorer=None):
    """Finds trigram collocations in the files of the WebText corpus."""
    from nltk.metrics import BigramAssocMeasures, spearman_correlation, ranks_from_scores

    if scorer is None:
        scorer = BigramAssocMeasures.likelihood_ratio
    if compare_scorer is None:
        compare_scorer = BigramAssocMeasures.raw_freq

    from nltk import corpus
        
    ignored_words = corpus.stopwords.words('english')
    word_filter = lambda w: len(w) < 3 or w.lower() in ignored_words

    for file in corpus.webtext.files():
        words = [word.lower()
                 for word in corpus.webtext.words(file)]

        cf = BigramCollocationFinder.from_words(words)
        cf.apply_freq_filter(3)
        cf.apply_word_filter(word_filter)

        print file
        print '\t', [' '.join(tup) for tup in cf.nbest(scorer, 15)]
        print '\t Correlation to %s: %0.4f' % (compare_scorer.__name__,
                spearman_correlation(
                    ranks_from_scores(cf.score_ngrams(scorer)),
                    ranks_from_scores(cf.score_ngrams(compare_scorer))))


bigram_measures = BigramAssocMeasures()
trigram_measures = TrigramAssocMeasures()

if __name__ == '__main__':
    import sys
    from nltk.metrics import BigramAssocMeasures
    try:
        scorer = eval('BigramAssocMeasures.' + sys.argv[1])
    except IndexError:
        scorer = None
    try:
        compare_scorer = eval('BigramAssocMeasures.' + sys.argv[2])
    except IndexError:
        compare_scorer = None

    demo(scorer, compare_scorer)


__all__ = ['BigramCollocationFinder', 'TrigramCollocationFinder',
           'bigram_measures', 'trigram_measures']
