PCQRSCANER/venv/Lib/site-packages/nltk/translate/api.py

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2019-12-22 21:51:47 +01:00
# Natural Language Toolkit: API for alignment and translation objects
#
# Copyright (C) 2001-2019 NLTK Project
# Author: Will Zhang <wilzzha@gmail.com>
# Guan Gui <ggui@student.unimelb.edu.au>
# Steven Bird <stevenbird1@gmail.com>
# Tah Wei Hoon <hoon.tw@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import print_function, unicode_literals
import subprocess
from collections import namedtuple
from nltk.compat import python_2_unicode_compatible
@python_2_unicode_compatible
class AlignedSent(object):
"""
Return an aligned sentence object, which encapsulates two sentences
along with an ``Alignment`` between them.
Typically used in machine translation to represent a sentence and
its translation.
>>> from nltk.translate import AlignedSent, Alignment
>>> algnsent = AlignedSent(['klein', 'ist', 'das', 'Haus'],
... ['the', 'house', 'is', 'small'], Alignment.fromstring('0-3 1-2 2-0 3-1'))
>>> algnsent.words
['klein', 'ist', 'das', 'Haus']
>>> algnsent.mots
['the', 'house', 'is', 'small']
>>> algnsent.alignment
Alignment([(0, 3), (1, 2), (2, 0), (3, 1)])
>>> from nltk.corpus import comtrans
>>> print(comtrans.aligned_sents()[54])
<AlignedSent: 'Weshalb also sollten...' -> 'So why should EU arm...'>
>>> print(comtrans.aligned_sents()[54].alignment)
0-0 0-1 1-0 2-2 3-4 3-5 4-7 5-8 6-3 7-9 8-9 9-10 9-11 10-12 11-6 12-6 13-13
:param words: Words in the target language sentence
:type words: list(str)
:param mots: Words in the source language sentence
:type mots: list(str)
:param alignment: Word-level alignments between ``words`` and ``mots``.
Each alignment is represented as a 2-tuple (words_index, mots_index).
:type alignment: Alignment
"""
def __init__(self, words, mots, alignment=None):
self._words = words
self._mots = mots
if alignment is None:
self.alignment = Alignment([])
else:
assert type(alignment) is Alignment
self.alignment = alignment
@property
def words(self):
return self._words
@property
def mots(self):
return self._mots
def _get_alignment(self):
return self._alignment
def _set_alignment(self, alignment):
_check_alignment(len(self.words), len(self.mots), alignment)
self._alignment = alignment
alignment = property(_get_alignment, _set_alignment)
def __repr__(self):
"""
Return a string representation for this ``AlignedSent``.
:rtype: str
"""
words = "[%s]" % (", ".join("'%s'" % w for w in self._words))
mots = "[%s]" % (", ".join("'%s'" % w for w in self._mots))
return "AlignedSent(%s, %s, %r)" % (words, mots, self._alignment)
def _to_dot(self):
"""
Dot representation of the aligned sentence
"""
s = 'graph align {\n'
s += 'node[shape=plaintext]\n'
# Declare node
for w in self._words:
s += '"%s_source" [label="%s"] \n' % (w, w)
for w in self._mots:
s += '"%s_target" [label="%s"] \n' % (w, w)
# Alignment
for u, v in self._alignment:
s += '"%s_source" -- "%s_target" \n' % (self._words[u], self._mots[v])
# Connect the source words
for i in range(len(self._words) - 1):
s += '"%s_source" -- "%s_source" [style=invis]\n' % (
self._words[i],
self._words[i + 1],
)
# Connect the target words
for i in range(len(self._mots) - 1):
s += '"%s_target" -- "%s_target" [style=invis]\n' % (
self._mots[i],
self._mots[i + 1],
)
# Put it in the same rank
s += '{rank = same; %s}\n' % (' '.join('"%s_source"' % w for w in self._words))
s += '{rank = same; %s}\n' % (' '.join('"%s_target"' % w for w in self._mots))
s += '}'
return s
def _repr_svg_(self):
"""
Ipython magic : show SVG representation of this ``AlignedSent``.
"""
dot_string = self._to_dot().encode('utf8')
output_format = 'svg'
try:
process = subprocess.Popen(
['dot', '-T%s' % output_format],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
except OSError:
raise Exception('Cannot find the dot binary from Graphviz package')
out, err = process.communicate(dot_string)
return out.decode('utf8')
def __str__(self):
"""
Return a human-readable string representation for this ``AlignedSent``.
:rtype: str
"""
source = " ".join(self._words)[:20] + "..."
target = " ".join(self._mots)[:20] + "..."
return "<AlignedSent: '%s' -> '%s'>" % (source, target)
def invert(self):
"""
Return the aligned sentence pair, reversing the directionality
:rtype: AlignedSent
"""
return AlignedSent(self._mots, self._words, self._alignment.invert())
@python_2_unicode_compatible
class Alignment(frozenset):
"""
A storage class for representing alignment between two sequences, s1, s2.
In general, an alignment is a set of tuples of the form (i, j, ...)
representing an alignment between the i-th element of s1 and the
j-th element of s2. Tuples are extensible (they might contain
additional data, such as a boolean to indicate sure vs possible alignments).
>>> from nltk.translate import Alignment
>>> a = Alignment([(0, 0), (0, 1), (1, 2), (2, 2)])
>>> a.invert()
Alignment([(0, 0), (1, 0), (2, 1), (2, 2)])
>>> print(a.invert())
0-0 1-0 2-1 2-2
>>> a[0]
[(0, 1), (0, 0)]
>>> a.invert()[2]
[(2, 1), (2, 2)]
>>> b = Alignment([(0, 0), (0, 1)])
>>> b.issubset(a)
True
>>> c = Alignment.fromstring('0-0 0-1')
>>> b == c
True
"""
def __new__(cls, pairs):
self = frozenset.__new__(cls, pairs)
self._len = max(p[0] for p in self) if self != frozenset([]) else 0
self._index = None
return self
@classmethod
def fromstring(cls, s):
"""
Read a giza-formatted string and return an Alignment object.
>>> Alignment.fromstring('0-0 2-1 9-2 21-3 10-4 7-5')
Alignment([(0, 0), (2, 1), (7, 5), (9, 2), (10, 4), (21, 3)])
:type s: str
:param s: the positional alignments in giza format
:rtype: Alignment
:return: An Alignment object corresponding to the string representation ``s``.
"""
return Alignment([_giza2pair(a) for a in s.split()])
def __getitem__(self, key):
"""
Look up the alignments that map from a given index or slice.
"""
if not self._index:
self._build_index()
return self._index.__getitem__(key)
def invert(self):
"""
Return an Alignment object, being the inverted mapping.
"""
return Alignment(((p[1], p[0]) + p[2:]) for p in self)
def range(self, positions=None):
"""
Work out the range of the mapping from the given positions.
If no positions are specified, compute the range of the entire mapping.
"""
image = set()
if not self._index:
self._build_index()
if not positions:
positions = list(range(len(self._index)))
for p in positions:
image.update(f for _, f in self._index[p])
return sorted(image)
def __repr__(self):
"""
Produce a Giza-formatted string representing the alignment.
"""
return "Alignment(%r)" % sorted(self)
def __str__(self):
"""
Produce a Giza-formatted string representing the alignment.
"""
return " ".join("%d-%d" % p[:2] for p in sorted(self))
def _build_index(self):
"""
Build a list self._index such that self._index[i] is a list
of the alignments originating from word i.
"""
self._index = [[] for _ in range(self._len + 1)]
for p in self:
self._index[p[0]].append(p)
def _giza2pair(pair_string):
i, j = pair_string.split("-")
return int(i), int(j)
def _naacl2pair(pair_string):
i, j, p = pair_string.split("-")
return int(i), int(j)
def _check_alignment(num_words, num_mots, alignment):
"""
Check whether the alignments are legal.
:param num_words: the number of source language words
:type num_words: int
:param num_mots: the number of target language words
:type num_mots: int
:param alignment: alignment to be checked
:type alignment: Alignment
:raise IndexError: if alignment falls outside the sentence
"""
assert type(alignment) is Alignment
if not all(0 <= pair[0] < num_words for pair in alignment):
raise IndexError("Alignment is outside boundary of words")
if not all(pair[1] is None or 0 <= pair[1] < num_mots for pair in alignment):
raise IndexError("Alignment is outside boundary of mots")
PhraseTableEntry = namedtuple('PhraseTableEntry', ['trg_phrase', 'log_prob'])
class PhraseTable(object):
"""
In-memory store of translations for a given phrase, and the log
probability of the those translations
"""
def __init__(self):
self.src_phrases = dict()
def translations_for(self, src_phrase):
"""
Get the translations for a source language phrase
:param src_phrase: Source language phrase of interest
:type src_phrase: tuple(str)
:return: A list of target language phrases that are translations
of ``src_phrase``, ordered in decreasing order of
likelihood. Each list element is a tuple of the target
phrase and its log probability.
:rtype: list(PhraseTableEntry)
"""
return self.src_phrases[src_phrase]
def add(self, src_phrase, trg_phrase, log_prob):
"""
:type src_phrase: tuple(str)
:type trg_phrase: tuple(str)
:param log_prob: Log probability that given ``src_phrase``,
``trg_phrase`` is its translation
:type log_prob: float
"""
entry = PhraseTableEntry(trg_phrase=trg_phrase, log_prob=log_prob)
if src_phrase not in self.src_phrases:
self.src_phrases[src_phrase] = []
self.src_phrases[src_phrase].append(entry)
self.src_phrases[src_phrase].sort(key=lambda e: e.log_prob, reverse=True)
def __contains__(self, src_phrase):
return src_phrase in self.src_phrases