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136 lines
4.4 KiB
Python
136 lines
4.4 KiB
Python
from __future__ import division, print_function
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import unicodedata
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from functools import partial, lru_cache
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from typing import Sequence, Tuple
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import numpy as np
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from uniseg.graphemecluster import grapheme_clusters
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from .ocr_files import ExtractedText
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def levenshtein_matrix(seq1: Sequence, seq2: Sequence):
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"""Compute the matrix commonly computed to produce the Levenshtein distance.
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This is also known as the Wagner-Fischer algorithm. The matrix element at the bottom right contains the desired
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edit distance.
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This algorithm is implemented here because we need an implementation that can work with sequences other than
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strings, e.g. lists of grapheme clusters or lists of word strings.
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"""
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# Internally, we use a cached version. As the cache only works on hashable parameters, we convert the input
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# sequences to tuples to make them hashable.
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return _levenshtein_matrix(tuple(seq1), tuple(seq2))
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@lru_cache(maxsize=10)
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def _levenshtein_matrix(seq1: Tuple, seq2: Tuple):
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"""Compute the matrix commonly computed to produce the Levenshtein distance.
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This is a LRU cached function not meant to be used directly. Use levenshtein_matrix() instead.
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"""
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m = len(seq1)
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n = len(seq2)
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def from_to(start, stop):
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return range(start, stop + 1, 1)
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D = np.zeros((m + 1, n + 1), np.int)
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D[0, 0] = 0
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for i in from_to(1, m):
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D[i, 0] = i
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for j in from_to(1, n):
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D[0, j] = j
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for i in from_to(1, m):
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for j in from_to(1, n):
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D[i, j] = min(
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D[i - 1, j - 1] + 1 * (seq1[i - 1] != seq2[j - 1]), # Same or Substitution
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D[i, j - 1] + 1, # Insertion
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D[i - 1, j] + 1 # Deletion
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)
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return D
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def levenshtein(seq1, seq2):
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"""Compute the Levenshtein edit distance between two sequences"""
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m = len(seq1)
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n = len(seq2)
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D = levenshtein_matrix(seq1, seq2)
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return D[m, n]
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def levenshtein_matrix_cache_clear():
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"""Clear internal Levenshtein matrix cache.
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You want to do this between different input file pairs to decrease memory
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usage by not caching results from prior input files.
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"""
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_levenshtein_matrix.cache_clear()
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def distance(s1, s2):
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"""Compute the Levenshtein edit distance between two Unicode strings
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Note that this is different from levenshtein() as this function knows about Unicode normalization and grapheme
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clusters. This should be the correct way to compare two Unicode strings.
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"""
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# XXX Implicit normalization
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if isinstance(s1, str):
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s1 = ExtractedText.from_str(s1)
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if isinstance(s2, str):
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s2 = ExtractedText.from_str(s2)
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# s1 and s2 are now guaranteed (by ExtractedText) to be in NFC
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seq1 = list(grapheme_clusters(s1.text))
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seq2 = list(grapheme_clusters(s2.text))
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return levenshtein(seq1, seq2)
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def seq_editops(seq1, seq2):
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"""
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Return sequence of edit operations transforming one sequence to another.
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This aims to return the same/similar results as python-Levenshtein's editops(), just generalized to arbitrary
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sequences.
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"""
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seq1 = list(seq1)
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seq2 = list(seq2)
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m = len(seq1)
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n = len(seq2)
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D = levenshtein_matrix(seq1, seq2)
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def _tail_backtrace(i, j, accumulator):
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if i > 0 and D[i - 1, j] + 1 == D[i, j]:
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return partial(_tail_backtrace, i - 1, j, [('delete', i-1, j)] + accumulator)
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if j > 0 and D[i, j - 1] + 1 == D[i, j]:
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return partial(_tail_backtrace, i, j - 1, [('insert', i, j-1)] + accumulator)
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if i > 0 and j > 0 and D[i - 1, j - 1] + 1 == D[i, j]:
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return partial(_tail_backtrace, i - 1, j - 1, [('replace', i-1, j-1)] + accumulator)
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if i > 0 and j > 0 and D[i - 1, j - 1] == D[i, j]:
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return partial(_tail_backtrace, i - 1, j - 1, accumulator) # NOP
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return accumulator
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def backtrace(i, j):
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result = partial(_tail_backtrace, i, j, [])
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while isinstance(result, partial):
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result = result()
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return result
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b = backtrace(m, n)
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return b
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def editops(word1, word2):
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"""
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Return sequence of edit operations transforming one string to another.
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Note that this returns indices to the _grapheme clusters_, not characters!
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"""
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word1 = list(grapheme_clusters(unicodedata.normalize('NFC', word1)))
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word2 = list(grapheme_clusters(unicodedata.normalize('NFC', word2)))
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return seq_editops(word1, word2)
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