415 lines
15 KiB
Python
415 lines
15 KiB
Python
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# Natural Language Toolkit: Ngram Association Measures
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Joel Nothman <jnothman@student.usyd.edu.au>
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# URL: <http://nltk.org>
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# For license information, see LICENSE.TXT
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"""
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Provides scoring functions for a number of association measures through a
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generic, abstract implementation in ``NgramAssocMeasures``, and n-specific
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``BigramAssocMeasures`` and ``TrigramAssocMeasures``.
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"""
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from __future__ import division
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from abc import ABCMeta, abstractmethod
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from six import add_metaclass
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import math as _math
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from functools import reduce
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_log2 = lambda x: _math.log(x, 2.0)
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_ln = _math.log
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_product = lambda s: reduce(lambda x, y: x * y, s)
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_SMALL = 1e-20
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try:
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from scipy.stats import fisher_exact
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except ImportError:
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def fisher_exact(*_args, **_kwargs):
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raise NotImplementedError
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### Indices to marginals arguments:
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NGRAM = 0
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"""Marginals index for the ngram count"""
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UNIGRAMS = -2
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"""Marginals index for a tuple of each unigram count"""
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TOTAL = -1
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"""Marginals index for the number of words in the data"""
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@add_metaclass(ABCMeta)
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class NgramAssocMeasures(object):
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"""
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An abstract class defining a collection of generic association measures.
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Each public method returns a score, taking the following arguments::
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score_fn(count_of_ngram,
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(count_of_n-1gram_1, ..., count_of_n-1gram_j),
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(count_of_n-2gram_1, ..., count_of_n-2gram_k),
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...,
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(count_of_1gram_1, ..., count_of_1gram_n),
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count_of_total_words)
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See ``BigramAssocMeasures`` and ``TrigramAssocMeasures``
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Inheriting classes should define a property _n, and a method _contingency
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which calculates contingency values from marginals in order for all
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association measures defined here to be usable.
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"""
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_n = 0
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@staticmethod
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@abstractmethod
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def _contingency(*marginals):
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"""Calculates values of a contingency table from marginal values."""
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raise NotImplementedError("The contingency table is not available"
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"in the general ngram case")
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@staticmethod
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@abstractmethod
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def _marginals(*contingency):
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"""Calculates values of contingency table marginals from its values."""
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raise NotImplementedError("The contingency table is not available"
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"in the general ngram case")
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@classmethod
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def _expected_values(cls, cont):
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"""Calculates expected values for a contingency table."""
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n_all = sum(cont)
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bits = [1 << i for i in range(cls._n)]
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# For each contingency table cell
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for i in range(len(cont)):
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# Yield the expected value
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yield (_product(sum(cont[x] for x in range(2 ** cls._n)
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if (x & j) == (i & j))
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for j in bits) /
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(n_all ** (cls._n - 1)))
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@staticmethod
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def raw_freq(*marginals):
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"""Scores ngrams by their frequency"""
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return marginals[NGRAM] / marginals[TOTAL]
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@classmethod
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def student_t(cls, *marginals):
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"""Scores ngrams using Student's t test with independence hypothesis
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for unigrams, as in Manning and Schutze 5.3.1.
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"""
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return ((marginals[NGRAM] -
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_product(marginals[UNIGRAMS]) /
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(marginals[TOTAL] ** (cls._n - 1))) /
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(marginals[NGRAM] + _SMALL) ** .5)
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@classmethod
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def chi_sq(cls, *marginals):
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"""Scores ngrams using Pearson's chi-square as in Manning and Schutze
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5.3.3.
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"""
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cont = cls._contingency(*marginals)
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exps = cls._expected_values(cont)
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return sum((obs - exp) ** 2 / (exp + _SMALL)
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for obs, exp in zip(cont, exps))
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@staticmethod
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def mi_like(*marginals, **kwargs):
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"""Scores ngrams using a variant of mutual information. The keyword
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argument power sets an exponent (default 3) for the numerator. No
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logarithm of the result is calculated.
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"""
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return (marginals[NGRAM] ** kwargs.get('power', 3) /
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_product(marginals[UNIGRAMS]))
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@classmethod
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def pmi(cls, *marginals):
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"""Scores ngrams by pointwise mutual information, as in Manning and
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Schutze 5.4.
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"""
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return (_log2(marginals[NGRAM] * marginals[TOTAL] ** (cls._n - 1)) -
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_log2(_product(marginals[UNIGRAMS])))
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@classmethod
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def likelihood_ratio(cls, *marginals):
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"""Scores ngrams using likelihood ratios as in Manning and Schutze 5.3.4.
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"""
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cont = cls._contingency(*marginals)
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return (cls._n *
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sum(obs * _ln(obs / (exp + _SMALL) + _SMALL)
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for obs, exp in zip(cont, cls._expected_values(cont))))
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@classmethod
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def poisson_stirling(cls, *marginals):
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"""Scores ngrams using the Poisson-Stirling measure."""
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exp = (_product(marginals[UNIGRAMS]) /
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(marginals[TOTAL] ** (cls._n - 1)))
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return marginals[NGRAM] * (_log2(marginals[NGRAM] / exp) - 1)
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@classmethod
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def jaccard(cls, *marginals):
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"""Scores ngrams using the Jaccard index."""
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cont = cls._contingency(*marginals)
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return cont[0] / sum(cont[:-1])
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class BigramAssocMeasures(NgramAssocMeasures):
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"""
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A collection of bigram association measures. Each association measure
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is provided as a function with three arguments::
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bigram_score_fn(n_ii, (n_ix, n_xi), n_xx)
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The arguments constitute the marginals of a contingency table, counting
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the occurrences of particular events in a corpus. The letter i in the
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suffix refers to the appearance of the word in question, while x indicates
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the appearance of any word. Thus, for example:
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n_ii counts (w1, w2), i.e. the bigram being scored
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n_ix counts (w1, *)
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n_xi counts (*, w2)
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n_xx counts (*, *), i.e. any bigram
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This may be shown with respect to a contingency table::
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w1 ~w1
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------ ------
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w2 | n_ii | n_oi | = n_xi
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------ ------
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~w2 | n_io | n_oo |
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------ ------
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= n_ix TOTAL = n_xx
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"""
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_n = 2
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@staticmethod
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def _contingency(n_ii, n_ix_xi_tuple, n_xx):
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"""Calculates values of a bigram contingency table from marginal values."""
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(n_ix, n_xi) = n_ix_xi_tuple
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n_oi = n_xi - n_ii
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n_io = n_ix - n_ii
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return (n_ii, n_oi, n_io, n_xx - n_ii - n_oi - n_io)
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@staticmethod
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def _marginals(n_ii, n_oi, n_io, n_oo):
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"""Calculates values of contingency table marginals from its values."""
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return (n_ii, (n_oi + n_ii, n_io + n_ii), n_oo + n_oi + n_io + n_ii)
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@staticmethod
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def _expected_values(cont):
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"""Calculates expected values for a contingency table."""
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n_xx = sum(cont)
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# For each contingency table cell
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for i in range(4):
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yield (cont[i] + cont[i ^ 1]) * (cont[i] + cont[i ^ 2]) / n_xx
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@classmethod
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def phi_sq(cls, *marginals):
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"""Scores bigrams using phi-square, the square of the Pearson correlation
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coefficient.
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"""
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n_ii, n_io, n_oi, n_oo = cls._contingency(*marginals)
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return ((n_ii*n_oo - n_io*n_oi)**2 /
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((n_ii + n_io) * (n_ii + n_oi) * (n_io + n_oo) * (n_oi + n_oo)))
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@classmethod
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def chi_sq(cls, n_ii, n_ix_xi_tuple, n_xx):
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"""Scores bigrams using chi-square, i.e. phi-sq multiplied by the number
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of bigrams, as in Manning and Schutze 5.3.3.
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"""
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(n_ix, n_xi) = n_ix_xi_tuple
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return n_xx * cls.phi_sq(n_ii, (n_ix, n_xi), n_xx)
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@classmethod
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def fisher(cls, *marginals):
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"""Scores bigrams using Fisher's Exact Test (Pedersen 1996). Less
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sensitive to small counts than PMI or Chi Sq, but also more expensive
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to compute. Requires scipy.
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"""
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n_ii, n_io, n_oi, n_oo = cls._contingency(*marginals)
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(odds, pvalue) = fisher_exact([[n_ii, n_io], [n_oi, n_oo]], alternative='less')
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return pvalue
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@staticmethod
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def dice(n_ii, n_ix_xi_tuple, n_xx):
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"""Scores bigrams using Dice's coefficient."""
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(n_ix, n_xi) = n_ix_xi_tuple
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return 2 * n_ii / (n_ix + n_xi)
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class TrigramAssocMeasures(NgramAssocMeasures):
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"""
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A collection of trigram association measures. Each association measure
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is provided as a function with four arguments::
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trigram_score_fn(n_iii,
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(n_iix, n_ixi, n_xii),
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(n_ixx, n_xix, n_xxi),
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n_xxx)
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The arguments constitute the marginals of a contingency table, counting
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the occurrences of particular events in a corpus. The letter i in the
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suffix refers to the appearance of the word in question, while x indicates
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the appearance of any word. Thus, for example:
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n_iii counts (w1, w2, w3), i.e. the trigram being scored
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n_ixx counts (w1, *, *)
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n_xxx counts (*, *, *), i.e. any trigram
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"""
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_n = 3
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@staticmethod
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def _contingency(n_iii, n_iix_tuple, n_ixx_tuple, n_xxx):
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"""Calculates values of a trigram contingency table (or cube) from
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marginal values.
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>>> TrigramAssocMeasures._contingency(1, (1, 1, 1), (1, 73, 1), 2000)
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(1, 0, 0, 0, 0, 72, 0, 1927)
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"""
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(n_iix, n_ixi, n_xii) = n_iix_tuple
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(n_ixx, n_xix, n_xxi) = n_ixx_tuple
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n_oii = n_xii - n_iii
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n_ioi = n_ixi - n_iii
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n_iio = n_iix - n_iii
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n_ooi = n_xxi - n_iii - n_oii - n_ioi
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n_oio = n_xix - n_iii - n_oii - n_iio
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n_ioo = n_ixx - n_iii - n_ioi - n_iio
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n_ooo = n_xxx - n_iii - n_oii - n_ioi - n_iio - n_ooi - n_oio - n_ioo
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return (n_iii, n_oii, n_ioi, n_ooi,
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n_iio, n_oio, n_ioo, n_ooo)
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@staticmethod
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def _marginals(*contingency):
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"""Calculates values of contingency table marginals from its values.
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>>> TrigramAssocMeasures._marginals(1, 0, 0, 0, 0, 72, 0, 1927)
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(1, (1, 1, 1), (1, 73, 1), 2000)
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"""
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n_iii, n_oii, n_ioi, n_ooi, n_iio, n_oio, n_ioo, n_ooo = contingency
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return (n_iii,
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(n_iii + n_iio, n_iii + n_ioi, n_iii + n_oii),
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(n_iii + n_ioi + n_iio + n_ioo,
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n_iii + n_oii + n_iio + n_oio,
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n_iii + n_oii + n_ioi + n_ooi),
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sum(contingency))
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class QuadgramAssocMeasures(NgramAssocMeasures):
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"""
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A collection of quadgram association measures. Each association measure
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is provided as a function with five arguments::
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trigram_score_fn(n_iiii,
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(n_iiix, n_iixi, n_ixii, n_xiii),
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(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),
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(n_ixxx, n_xixx, n_xxix, n_xxxi),
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n_all)
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The arguments constitute the marginals of a contingency table, counting
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the occurrences of particular events in a corpus. The letter i in the
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suffix refers to the appearance of the word in question, while x indicates
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the appearance of any word. Thus, for example:
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n_iiii counts (w1, w2, w3, w4), i.e. the quadgram being scored
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n_ixxi counts (w1, *, *, w4)
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n_xxxx counts (*, *, *, *), i.e. any quadgram
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"""
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_n = 4
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@staticmethod
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def _contingency(n_iiii, n_iiix_tuple, n_iixx_tuple, n_ixxx_tuple, n_xxxx):
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"""Calculates values of a quadgram contingency table from
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marginal values.
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"""
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(n_iiix, n_iixi, n_ixii, n_xiii) = n_iiix_tuple
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(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix) = n_iixx_tuple
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(n_ixxx, n_xixx, n_xxix, n_xxxi) = n_ixxx_tuple
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n_oiii = n_xiii - n_iiii
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n_ioii = n_ixii - n_iiii
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n_iioi = n_iixi - n_iiii
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n_ooii = n_xxii - n_iiii - n_oiii - n_ioii
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n_oioi = n_xixi - n_iiii - n_oiii - n_iioi
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n_iooi = n_ixxi - n_iiii - n_ioii - n_iioi
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n_oooi = n_xxxi - n_iiii - n_oiii - n_ioii - n_iioi - n_ooii - n_iooi - n_oioi
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n_iiio = n_iiix - n_iiii
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||
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n_oiio = n_xiix - n_iiii - n_oiii - n_iiio
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n_ioio = n_ixix - n_iiii - n_ioii - n_iiio
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n_ooio = n_xxix - n_iiii - n_oiii - n_ioii - n_iiio - n_ooii - n_ioio - n_oiio
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n_iioo = n_iixx - n_iiii - n_iioi - n_iiio
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n_oioo = n_xixx - n_iiii - n_oiii - n_iioi - n_iiio - n_oioi - n_oiio - n_iioo
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n_iooo = n_ixxx - n_iiii - n_ioii - n_iioi - n_iiio - n_iooi - n_iioo - n_ioio
|
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n_oooo = n_xxxx - n_iiii - n_oiii - n_ioii - n_iioi - n_ooii - n_oioi - n_iooi - \
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|
n_oooi - n_iiio - n_oiio - n_ioio - n_ooio - n_iioo - n_oioo - n_iooo
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|
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||
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return (n_iiii, n_oiii, n_ioii, n_ooii, n_iioi,
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|
n_oioi, n_iooi, n_oooi, n_iiio, n_oiio,
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||
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|
n_ioio, n_ooio, n_iioo, n_oioo, n_iooo, n_oooo)
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||
|
|
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||
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|
@staticmethod
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def _marginals(*contingency):
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|
"""Calculates values of contingency table marginals from its values.
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|
|
QuadgramAssocMeasures._marginals(1, 0, 2, 46, 552, 825, 2577, 34967, 1, 0, 2, 48, 7250, 9031, 28585, 356653)
|
||
|
|
(1, (2, 553, 3, 1), (7804, 6, 3132, 1378, 49, 2), (38970, 17660, 100, 38970), 440540)
|
||
|
|
"""
|
||
|
|
n_iiii, n_oiii, n_ioii, n_ooii, n_iioi, n_oioi, n_iooi, n_oooi, n_iiio, n_oiio, n_ioio, n_ooio, \
|
||
|
|
n_iioo, n_oioo, n_iooo, n_oooo = contingency
|
||
|
|
|
||
|
|
n_iiix = n_iiii + n_iiio
|
||
|
|
n_iixi = n_iiii + n_iioi
|
||
|
|
n_ixii = n_iiii + n_ioii
|
||
|
|
n_xiii = n_iiii + n_oiii
|
||
|
|
|
||
|
|
n_iixx = n_iiii + n_iioi + n_iiio + n_iioo
|
||
|
|
n_ixix = n_iiii + n_ioii + n_iiio + n_ioio
|
||
|
|
n_ixxi = n_iiii + n_ioii + n_iioi + n_iooi
|
||
|
|
n_xixi = n_iiii + n_oiii + n_iioi + n_oioi
|
||
|
|
n_xxii = n_iiii + n_oiii + n_ioii + n_ooii
|
||
|
|
n_xiix = n_iiii + n_oiii + n_iiio + n_oiio
|
||
|
|
|
||
|
|
n_ixxx = n_iiii + n_ioii + n_iioi + n_iiio + n_iooi + n_iioo + n_ioio + n_iooo
|
||
|
|
n_xixx = n_iiii + n_oiii + n_iioi + n_iiio + n_oioi + n_oiio + n_iioo + n_oioo
|
||
|
|
n_xxix = n_iiii + n_oiii + n_ioii + n_iiio + n_ooii + n_ioio + n_oiio + n_ooio
|
||
|
|
n_xxxi = n_iiii + n_oiii + n_ioii + n_iioi + n_ooii + n_iooi + n_oioi + n_oooi
|
||
|
|
|
||
|
|
n_all = sum(contingency)
|
||
|
|
|
||
|
|
return (n_iiii,
|
||
|
|
(n_iiix, n_iixi, n_ixii, n_xiii),
|
||
|
|
(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),
|
||
|
|
(n_ixxx, n_xixx, n_xxix, n_xxxi),
|
||
|
|
n_all)
|
||
|
|
|
||
|
|
|
||
|
|
class ContingencyMeasures(object):
|
||
|
|
"""Wraps NgramAssocMeasures classes such that the arguments of association
|
||
|
|
measures are contingency table values rather than marginals.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, measures):
|
||
|
|
"""Constructs a ContingencyMeasures given a NgramAssocMeasures class"""
|
||
|
|
self.__class__.__name__ = 'Contingency' + measures.__class__.__name__
|
||
|
|
for k in dir(measures):
|
||
|
|
if k.startswith('__'):
|
||
|
|
continue
|
||
|
|
v = getattr(measures, k)
|
||
|
|
if not k.startswith('_'):
|
||
|
|
v = self._make_contingency_fn(measures, v)
|
||
|
|
setattr(self, k, v)
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _make_contingency_fn(measures, old_fn):
|
||
|
|
"""From an association measure function, produces a new function which
|
||
|
|
accepts contingency table values as its arguments.
|
||
|
|
"""
|
||
|
|
def res(*contingency):
|
||
|
|
return old_fn(*measures._marginals(*contingency))
|
||
|
|
res.__doc__ = old_fn.__doc__
|
||
|
|
res.__name__ = old_fn.__name__
|
||
|
|
return res
|