mirror of
https://github.com/qurator-spk/dinglehopper.git
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Switch between c and own implementation for distance and editops.
This commit is contained in:
Benjamin Rosemann
parent
11916c2dcf
commit
0e263cfac2
5 changed files with 153 additions and 83 deletions
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@ -12,7 +12,7 @@ def seq_align(s1, s2):
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"""Align general sequences."""
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s1 = list(s1)
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s2 = list(s2)
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ops = seq_editops(s1, s2)
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ops = editops(s1, s2)
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i = 0
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j = 0
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@ -2,9 +2,11 @@ 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 itertools import chain
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from typing import Sequence, Tuple, List
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import numpy as np
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from Levenshtein import editops as c_editops, distance as c_distance
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from multimethod import multimethod
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from uniseg.graphemecluster import grapheme_clusters
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from tqdm import tqdm
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@ -79,12 +81,17 @@ def levenshtein_matrix_cache_clear():
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def distance(s1: str, s2: str):
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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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Note that this is different from levenshtein() as this function knows about Unicode
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normalization and grapheme clusters.
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This should be the correct way to compare two Unicode strings.
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"""
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seq1 = list(grapheme_clusters(unicodedata.normalize("NFC", s1)))
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seq2 = list(grapheme_clusters(unicodedata.normalize("NFC", s2)))
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return distance(seq1, seq2)
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if any(len(s) > 1 for s in chain(seq1, seq2)):
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return distance(seq1, seq2)
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else:
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return distance_fast("".join(seq1), "".join(seq2))
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@multimethod
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@ -97,12 +104,24 @@ def distance(s1: List, s2: List):
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return levenshtein(s1, s2)
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def seq_editops(seq1, seq2):
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def distance_fast(s1: str, s2: str):
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"""Compute the Levenshtein edit distance between two Unicode strings
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Also see `distance()`.
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The difference is that this implementation does not care about grapheme clusters or
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unicode normalization, assuming that this already has been done in preprocessing.
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"""
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return c_distance(s1, s2)
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@multimethod
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def editops(seq1: List, seq2: List):
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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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This aims to return the same/similar results as python-Levenshtein's editops(),
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just generalized to arbitrary sequences.
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"""
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seq1 = list(seq1)
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seq2 = list(seq2)
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@ -138,12 +157,27 @@ def seq_editops(seq1, seq2):
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return b
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def editops(word1, word2):
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@multimethod
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def editops(s1: str, s2: str):
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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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s1 = list(grapheme_clusters(unicodedata.normalize("NFC", s1)))
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s2 = list(grapheme_clusters(unicodedata.normalize("NFC", s2)))
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if any(len(s) > 1 for s in chain(s1, s2)):
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return editops(s1, s2)
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else:
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return editops_fast("".join(s1), "".join(s2))
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def editops_fast(s1: str, s2: str):
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"""Return sequence of edit operations transforming one string to another.
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Also see `editops()`.
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The difference is that this implementation does not care about grapheme clusters or
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unicode normalization, assuming that this already has been done in preprocessing.
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"""
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return c_editops(s1, s2)
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@ -1,13 +1,11 @@
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from __future__ import division, print_function
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import unicodedata
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import pytest
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from .. import levenshtein, distance
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from .. import distance, distance_fast
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TEST_PARAMS = "seq1,seq2,expected_dist"
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TEST_PARAMS = "s1,s2,expected_dist"
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TEST_STRINGS = [
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("a", "a", 0),
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@ -24,30 +22,45 @@ TEST_STRINGS = [
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TEST_SEQUENCES = [(["a", "ab"], ["a", "ab", "c"], 1), (["a", "ab"], ["a", "c"], 1)]
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TEST_UNICODE = [
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# Different, decomposed!
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(unicodedata.normalize("NFC", "Schlyñ"), unicodedata.normalize("NFD", "Schlyñ"), 0),
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# Same decomposition
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(
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# This ends with LATIN SMALL LETTER N WITH TILDE, so 6 code points
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"Schlyñ",
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# This, OTOH, ends with LATIN SMALL LETTER M + COMBINING TILDE, 7 code points
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"Schlym̃",
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1,
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),
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]
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@pytest.mark.parametrize(TEST_PARAMS, [*TEST_STRINGS, *TEST_SEQUENCES])
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def test_distance_sequences(seq1, seq2, expected_dist):
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dist = distance(seq1, seq2)
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def test_distance_sequences(s1, s2, expected_dist):
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dist = distance(s1, s2)
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assert dist == expected_dist
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@pytest.mark.parametrize(TEST_PARAMS, TEST_STRINGS)
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def test_distance(seq1, seq2, expected_dist):
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dist = distance(seq1, seq2)
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def test_distance_strings(s1, s2, expected_dist):
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dist = distance(s1, s2)
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assert dist == expected_dist
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def test_distance_unicode_wide():
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word1 = unicodedata.normalize("NFC", "Schlyñ")
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word2 = unicodedata.normalize("NFD", "Schlyñ") # Different, decomposed!
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assert distance(word1, word2) == 0
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@pytest.mark.parametrize(TEST_PARAMS, TEST_STRINGS)
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def test_distance_fast(s1, s2, expected_dist):
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dist = distance_fast(s1, s2)
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assert dist == expected_dist
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word1 = "Schlyñ"
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assert (
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len(word1) == 6
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) # This ends with LATIN SMALL LETTER N WITH TILDE, so 6 code points
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word2 = "Schlym̃"
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assert (
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len(word2) == 7
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) # This, OTOH, ends with LATIN SMALL LETTER M + COMBINING TILDE, 7 code points
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assert distance(word1, word2) == 1
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@pytest.mark.parametrize(TEST_PARAMS, TEST_UNICODE)
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def test_editops_fast_unicode(s1, s2, expected_dist):
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dist = distance_fast(s1, s2)
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assert dist != expected_dist
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@pytest.mark.parametrize(TEST_PARAMS, TEST_UNICODE)
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def test_distance_unicode(s1, s2, expected_dist):
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dist = distance(s1, s2)
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assert dist == expected_dist
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@ -1,63 +1,86 @@
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import unicodedata
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from .. import seq_editops, editops
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import pytest
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from .. import editops, editops_fast
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TEST_PARAMS = "s1,s2,expected_ops"
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TEST_STRINGS = [
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# trivial
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("abc", "abc", []),
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("", "", []),
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# insert
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("bc", "abc", [("insert", 0, 0)]),
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("ac", "abc", [("insert", 1, 1)]),
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("ab", "abc", [("insert", 2, 2)]),
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("", "a", [("insert", 0, 0)]),
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# delete
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("abcdef", "cdef", [("delete", 0, 0), ("delete", 1, 0)]),
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("Xabcdef", "Xcdef", [("delete", 1, 1), ("delete", 2, 1)]),
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("abcdefg", "acdefX", [("delete", 1, 1), ("replace", 6, 5)]),
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("abcde", "aabcd", [("insert", 1, 1), ("delete", 4, 5)]),
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("Foo", "", [("delete", 0, 0), ("delete", 1, 0), ("delete", 2, 0)]),
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(
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"Foolish",
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"Foo",
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[("delete", 3, 3), ("delete", 4, 3), ("delete", 5, 3), ("delete", 6, 3)],
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),
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# multiple
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("bcd", "abce", [("insert", 0, 0), ("replace", 2, 3)]),
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# ambiguous
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("bcd", "abcef", [("insert", 0, 0), ("insert", 2, 3), ("replace", 2, 4)]),
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]
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TEST_SEQUENCES = [
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(["a", "ab"], ["a", "ab", "c"], [("insert", 2, 2)]),
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(["a", "ab"], ["a", "c"], [("replace", 1, 1)]),
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]
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TEST_UNICODE = [
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# In these cases, one of the words has a composed form, the other one does not.
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("Schlyñ", "Schlym̃", [("replace", 5, 5)]),
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("oͤde", "öde", [("replace", 0, 0)]),
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# equal
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(
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unicodedata.lookup("LATIN SMALL LETTER N")
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+ unicodedata.lookup("COMBINING TILDE"),
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unicodedata.lookup("LATIN SMALL LETTER N WITH TILDE"),
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[],
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),
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]
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def test_trivial():
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assert seq_editops("abc", "abc") == []
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assert seq_editops("", "") == []
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@pytest.mark.parametrize(TEST_PARAMS, TEST_STRINGS)
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def test_editops_strings(s1, s2, expected_ops):
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ops = editops(s1, s2)
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assert ops == expected_ops
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def test_insert():
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assert seq_editops("bc", "abc") == [("insert", 0, 0)]
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assert seq_editops("ac", "abc") == [("insert", 1, 1)]
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assert seq_editops("ab", "abc") == [("insert", 2, 2)]
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assert seq_editops("", "a") == [("insert", 0, 0)]
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@pytest.mark.parametrize(TEST_PARAMS, [*TEST_STRINGS, *TEST_SEQUENCES])
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def test_editops_sequences(s1, s2, expected_ops):
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ops = editops(s1, s2)
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assert ops == expected_ops
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def test_multiple():
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assert seq_editops("bcd", "abce") == [("insert", 0, 0), ("replace", 2, 3)]
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@pytest.mark.parametrize(TEST_PARAMS, TEST_STRINGS)
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def test_editops_fast(s1, s2, expected_ops):
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ops = editops_fast(s1, s2)
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assert ops == expected_ops
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def test_delete():
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assert seq_editops("abcdef", "cdef") == [("delete", 0, 0), ("delete", 1, 0)]
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assert seq_editops("Xabcdef", "Xcdef") == [("delete", 1, 1), ("delete", 2, 1)]
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assert seq_editops("abcdefg", "acdefX") == [("delete", 1, 1), ("replace", 6, 5)]
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assert seq_editops("abcde", "aabcd") == [("insert", 1, 1), ("delete", 4, 5)]
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assert seq_editops("Foo", "") == [
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("delete", 0, 0),
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("delete", 1, 0),
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("delete", 2, 0),
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]
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assert seq_editops("Foolish", "Foo") == [
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("delete", 3, 3),
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("delete", 4, 3),
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("delete", 5, 3),
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("delete", 6, 3),
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]
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@pytest.mark.parametrize(TEST_PARAMS, TEST_UNICODE)
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def test_editops_fast_unicode(s1, s2, expected_ops):
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ops = editops_fast(s1, s2)
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assert ops != expected_ops
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def test_ambiguous():
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assert seq_editops("bcd", "abcef") == [
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("insert", 0, 0),
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("replace", 2, 3),
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("insert", 3, 4),
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]
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def test_editops():
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@pytest.mark.parametrize(TEST_PARAMS, TEST_UNICODE)
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def test_editops_unicode(s1, s2, expected_ops):
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"""Test editops() in cases where dealing with grapheme clusters matters"""
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# In these cases, one of the words has a composed form, the other one does not.
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assert editops("Schlyñ", "Schlym̃") == [("replace", 5, 5)]
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assert editops("oͤde", "öde") == [("replace", 0, 0)]
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def test_editops_canonically_equivalent():
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left = unicodedata.lookup("LATIN SMALL LETTER N") + unicodedata.lookup(
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"COMBINING TILDE"
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)
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right = unicodedata.lookup("LATIN SMALL LETTER N WITH TILDE")
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assert left != right
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assert unicodedata.normalize("NFC", left) == unicodedata.normalize("NFC", right)
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assert editops(left, right) == []
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if not expected_ops:
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assert s1 != s2
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assert unicodedata.normalize("NFC", s1) == unicodedata.normalize("NFC", s2)
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ops = editops(s1, s2)
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assert ops == expected_ops
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@ -6,7 +6,7 @@ from multimethod import multimethod
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import uniseg.wordbreak
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from .edit_distance import levenshtein
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from .edit_distance import distance
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from . import ExtractedText
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@ -81,7 +81,7 @@ def word_error_rate_n(reference: Iterable, compared: Iterable) -> Tuple[float, i
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reference_seq = list(reference)
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compared_seq = list(compared)
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d = levenshtein(reference_seq, compared_seq)
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d = distance(reference_seq, compared_seq)
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n = len(reference_seq)
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if d == 0:
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