mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 17:36:30 +03:00
Switch to new gold.align method (#5334)
* Switch from original `_align` to new simpler alignment algorithm from #4526 * Remove alignment normalizations beyond whitespace and lowercasing
This commit is contained in:
adrianeboyd
GitHub
parent
bf5c13d170
commit
521f361052
1
setup.py
1
setup.py
|
|
@ -31,7 +31,6 @@ PACKAGES = find_packages()
|
|||
|
||||
|
||||
MOD_NAMES = [
|
||||
"spacy._align",
|
||||
"spacy.parts_of_speech",
|
||||
"spacy.strings",
|
||||
"spacy.lexeme",
|
||||
|
|
|
|||
255
spacy/_align.pyx
255
spacy/_align.pyx
|
|
@ -1,255 +0,0 @@
|
|||
# cython: infer_types=True
|
||||
'''Do Levenshtein alignment, for evaluation of tokenized input.
|
||||
|
||||
Random notes:
|
||||
|
||||
r i n g
|
||||
0 1 2 3 4
|
||||
r 1 0 1 2 3
|
||||
a 2 1 1 2 3
|
||||
n 3 2 2 1 2
|
||||
g 4 3 3 2 1
|
||||
|
||||
0,0: (1,1)=min(0+0,1+1,1+1)=0 S
|
||||
1,0: (2,1)=min(1+1,0+1,2+1)=1 D
|
||||
2,0: (3,1)=min(2+1,3+1,1+1)=2 D
|
||||
3,0: (4,1)=min(3+1,4+1,2+1)=3 D
|
||||
0,1: (1,2)=min(1+1,2+1,0+1)=1 D
|
||||
1,1: (2,2)=min(0+1,1+1,1+1)=1 S
|
||||
2,1: (3,2)=min(1+1,1+1,2+1)=2 S or I
|
||||
3,1: (4,2)=min(2+1,2+1,3+1)=3 S or I
|
||||
0,2: (1,3)=min(2+1,3+1,1+1)=2 I
|
||||
1,2: (2,3)=min(1+1,2+1,1+1)=2 S or I
|
||||
2,2: (3,3)
|
||||
3,2: (4,3)
|
||||
At state (i, j) we're asking "How do I transform S[:i+1] to T[:j+1]?"
|
||||
|
||||
We know the costs to transition:
|
||||
|
||||
S[:i] -> T[:j] (at D[i,j])
|
||||
S[:i+1] -> T[:j] (at D[i+1,j])
|
||||
S[:i] -> T[:j+1] (at D[i,j+1])
|
||||
|
||||
Further, now we can transform:
|
||||
S[:i+1] -> S[:i] (DEL) for 1,
|
||||
T[:j+1] -> T[:j] (INS) for 1.
|
||||
S[i+1] -> T[j+1] (SUB) for 0 or 1
|
||||
|
||||
Therefore we have the costs:
|
||||
SUB: Cost(S[:i]->T[:j]) + Cost(S[i]->S[j])
|
||||
i.e. D[i, j] + S[i+1] != T[j+1]
|
||||
INS: Cost(S[:i+1]->T[:j]) + Cost(T[:j+1]->T[:j])
|
||||
i.e. D[i+1,j] + 1
|
||||
DEL: Cost(S[:i]->T[:j+1]) + Cost(S[:i+1]->S[:i])
|
||||
i.e. D[i,j+1] + 1
|
||||
|
||||
Source string S has length m, with index i
|
||||
Target string T has length n, with index j
|
||||
|
||||
Output two alignment vectors: i2j (length m) and j2i (length n)
|
||||
# function LevenshteinDistance(char s[1..m], char t[1..n]):
|
||||
# for all i and j, d[i,j] will hold the Levenshtein distance between
|
||||
# the first i characters of s and the first j characters of t
|
||||
# note that d has (m+1)*(n+1) values
|
||||
# set each element in d to zero
|
||||
ring rang
|
||||
- r i n g
|
||||
- 0 0 0 0 0
|
||||
r 0 0 0 0 0
|
||||
a 0 0 0 0 0
|
||||
n 0 0 0 0 0
|
||||
g 0 0 0 0 0
|
||||
|
||||
# source prefixes can be transformed into empty string by
|
||||
# dropping all characters
|
||||
# d[i, 0] := i
|
||||
ring rang
|
||||
- r i n g
|
||||
- 0 0 0 0 0
|
||||
r 1 0 0 0 0
|
||||
a 2 0 0 0 0
|
||||
n 3 0 0 0 0
|
||||
g 4 0 0 0 0
|
||||
|
||||
# target prefixes can be reached from empty source prefix
|
||||
# by inserting every character
|
||||
# d[0, j] := j
|
||||
- r i n g
|
||||
- 0 1 2 3 4
|
||||
r 1 0 0 0 0
|
||||
a 2 0 0 0 0
|
||||
n 3 0 0 0 0
|
||||
g 4 0 0 0 0
|
||||
|
||||
'''
|
||||
from __future__ import unicode_literals
|
||||
from libc.stdint cimport uint32_t
|
||||
import numpy
|
||||
cimport numpy as np
|
||||
from .compat import unicode_
|
||||
from murmurhash.mrmr cimport hash32
|
||||
|
||||
|
||||
def align(S, T):
|
||||
cdef int m = len(S)
|
||||
cdef int n = len(T)
|
||||
cdef np.ndarray matrix = numpy.zeros((m+1, n+1), dtype='int32')
|
||||
cdef np.ndarray i2j = numpy.zeros((m,), dtype='i')
|
||||
cdef np.ndarray j2i = numpy.zeros((n,), dtype='i')
|
||||
|
||||
cdef np.ndarray S_arr = _convert_sequence(S)
|
||||
cdef np.ndarray T_arr = _convert_sequence(T)
|
||||
|
||||
fill_matrix(<int*>matrix.data,
|
||||
<const int*>S_arr.data, m, <const int*>T_arr.data, n)
|
||||
fill_i2j(i2j, matrix)
|
||||
fill_j2i(j2i, matrix)
|
||||
for i in range(i2j.shape[0]):
|
||||
if i2j[i] >= 0 and len(S[i]) != len(T[i2j[i]]):
|
||||
i2j[i] = -1
|
||||
for j in range(j2i.shape[0]):
|
||||
if j2i[j] >= 0 and len(T[j]) != len(S[j2i[j]]):
|
||||
j2i[j] = -1
|
||||
return matrix[-1,-1], i2j, j2i, matrix
|
||||
|
||||
|
||||
def multi_align(np.ndarray i2j, np.ndarray j2i, i_lengths, j_lengths):
|
||||
'''Let's say we had:
|
||||
|
||||
Guess: [aa bb cc dd]
|
||||
Truth: [aa bbcc dd]
|
||||
i2j: [0, None, -2, 2]
|
||||
j2i: [0, -2, 3]
|
||||
|
||||
We want:
|
||||
|
||||
i2j_multi: {1: 1, 2: 1}
|
||||
j2i_multi: {}
|
||||
'''
|
||||
i2j_miss = _get_regions(i2j, i_lengths)
|
||||
j2i_miss = _get_regions(j2i, j_lengths)
|
||||
|
||||
i2j_multi, j2i_multi = _get_mapping(i2j_miss, j2i_miss, i_lengths, j_lengths)
|
||||
return i2j_multi, j2i_multi
|
||||
|
||||
|
||||
def _get_regions(alignment, lengths):
|
||||
regions = {}
|
||||
start = None
|
||||
offset = 0
|
||||
for i in range(len(alignment)):
|
||||
if alignment[i] < 0:
|
||||
if start is None:
|
||||
start = offset
|
||||
regions.setdefault(start, [])
|
||||
regions[start].append(i)
|
||||
else:
|
||||
start = None
|
||||
offset += lengths[i]
|
||||
return regions
|
||||
|
||||
|
||||
def _get_mapping(miss1, miss2, lengths1, lengths2):
|
||||
i2j = {}
|
||||
j2i = {}
|
||||
for start, region1 in miss1.items():
|
||||
if not region1 or start not in miss2:
|
||||
continue
|
||||
region2 = miss2[start]
|
||||
if sum(lengths1[i] for i in region1) == sum(lengths2[i] for i in region2):
|
||||
j = region2.pop(0)
|
||||
buff = []
|
||||
# Consume tokens from region 1, until we meet the length of the
|
||||
# first token in region2. If we do, align the tokens. If
|
||||
# we exceed the length, break.
|
||||
while region1:
|
||||
buff.append(region1.pop(0))
|
||||
if sum(lengths1[i] for i in buff) == lengths2[j]:
|
||||
for i in buff:
|
||||
i2j[i] = j
|
||||
j2i[j] = buff[-1]
|
||||
j += 1
|
||||
buff = []
|
||||
elif sum(lengths1[i] for i in buff) > lengths2[j]:
|
||||
break
|
||||
else:
|
||||
if buff and sum(lengths1[i] for i in buff) == lengths2[j]:
|
||||
for i in buff:
|
||||
i2j[i] = j
|
||||
j2i[j] = buff[-1]
|
||||
return i2j, j2i
|
||||
|
||||
|
||||
def _convert_sequence(seq):
|
||||
if isinstance(seq, numpy.ndarray):
|
||||
return numpy.ascontiguousarray(seq, dtype='uint32_t')
|
||||
cdef np.ndarray output = numpy.zeros((len(seq),), dtype='uint32')
|
||||
cdef bytes item_bytes
|
||||
for i, item in enumerate(seq):
|
||||
if item == "``":
|
||||
item = '"'
|
||||
elif item == "''":
|
||||
item = '"'
|
||||
if isinstance(item, unicode):
|
||||
item_bytes = item.encode('utf8')
|
||||
else:
|
||||
item_bytes = item
|
||||
output[i] = hash32(<void*><char*>item_bytes, len(item_bytes), 0)
|
||||
return output
|
||||
|
||||
|
||||
cdef void fill_matrix(int* D,
|
||||
const int* S, int m, const int* T, int n) nogil:
|
||||
m1 = m+1
|
||||
n1 = n+1
|
||||
for i in range(m1*n1):
|
||||
D[i] = 0
|
||||
|
||||
for i in range(m1):
|
||||
D[i*n1] = i
|
||||
|
||||
for j in range(n1):
|
||||
D[j] = j
|
||||
|
||||
cdef int sub_cost, ins_cost, del_cost
|
||||
for j in range(n):
|
||||
for i in range(m):
|
||||
i_j = i*n1 + j
|
||||
i1_j1 = (i+1)*n1 + j+1
|
||||
i1_j = (i+1)*n1 + j
|
||||
i_j1 = i*n1 + j+1
|
||||
if S[i] != T[j]:
|
||||
sub_cost = D[i_j] + 1
|
||||
else:
|
||||
sub_cost = D[i_j]
|
||||
del_cost = D[i_j1] + 1
|
||||
ins_cost = D[i1_j] + 1
|
||||
best = min(min(sub_cost, ins_cost), del_cost)
|
||||
D[i1_j1] = best
|
||||
|
||||
|
||||
cdef void fill_i2j(np.ndarray i2j, np.ndarray D) except *:
|
||||
j = D.shape[1]-2
|
||||
cdef int i = D.shape[0]-2
|
||||
while i >= 0:
|
||||
while D[i+1, j] < D[i+1, j+1]:
|
||||
j -= 1
|
||||
if D[i, j+1] < D[i+1, j+1]:
|
||||
i2j[i] = -1
|
||||
else:
|
||||
i2j[i] = j
|
||||
j -= 1
|
||||
i -= 1
|
||||
|
||||
cdef void fill_j2i(np.ndarray j2i, np.ndarray D) except *:
|
||||
i = D.shape[0]-2
|
||||
cdef int j = D.shape[1]-2
|
||||
while j >= 0:
|
||||
while D[i, j+1] < D[i+1, j+1]:
|
||||
i -= 1
|
||||
if D[i+1, j] < D[i+1, j+1]:
|
||||
j2i[j] = -1
|
||||
else:
|
||||
j2i[j] = i
|
||||
i -= 1
|
||||
j -= 1
|
||||
|
|
@ -21,7 +21,6 @@ from .util import minibatch, itershuffle
|
|||
from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek
|
||||
|
||||
|
||||
USE_NEW_ALIGN = False
|
||||
punct_re = re.compile(r"\W")
|
||||
|
||||
|
||||
|
|
@ -73,57 +72,8 @@ def merge_sents(sents):
|
|||
return [(m_deps, (m_cats, m_brackets))]
|
||||
|
||||
|
||||
_ALIGNMENT_NORM_MAP = [("``", "'"), ("''", "'"), ('"', "'"), ("`", "'")]
|
||||
|
||||
|
||||
def _normalize_for_alignment(tokens):
|
||||
tokens = [w.replace(" ", "").lower() for w in tokens]
|
||||
output = []
|
||||
for token in tokens:
|
||||
token = token.replace(" ", "").lower()
|
||||
for before, after in _ALIGNMENT_NORM_MAP:
|
||||
token = token.replace(before, after)
|
||||
output.append(token)
|
||||
return output
|
||||
|
||||
|
||||
def _align_before_v2_2_2(tokens_a, tokens_b):
|
||||
"""Calculate alignment tables between two tokenizations, using the Levenshtein
|
||||
algorithm. The alignment is case-insensitive.
|
||||
|
||||
tokens_a (List[str]): The candidate tokenization.
|
||||
tokens_b (List[str]): The reference tokenization.
|
||||
RETURNS: (tuple): A 5-tuple consisting of the following information:
|
||||
* cost (int): The number of misaligned tokens.
|
||||
* a2b (List[int]): Mapping of indices in `tokens_a` to indices in `tokens_b`.
|
||||
For instance, if `a2b[4] == 6`, that means that `tokens_a[4]` aligns
|
||||
to `tokens_b[6]`. If there's no one-to-one alignment for a token,
|
||||
it has the value -1.
|
||||
* b2a (List[int]): The same as `a2b`, but mapping the other direction.
|
||||
* a2b_multi (Dict[int, int]): A dictionary mapping indices in `tokens_a`
|
||||
to indices in `tokens_b`, where multiple tokens of `tokens_a` align to
|
||||
the same token of `tokens_b`.
|
||||
* b2a_multi (Dict[int, int]): As with `a2b_multi`, but mapping the other
|
||||
direction.
|
||||
"""
|
||||
from . import _align
|
||||
if tokens_a == tokens_b:
|
||||
alignment = numpy.arange(len(tokens_a))
|
||||
return 0, alignment, alignment, {}, {}
|
||||
tokens_a = [w.replace(" ", "").lower() for w in tokens_a]
|
||||
tokens_b = [w.replace(" ", "").lower() for w in tokens_b]
|
||||
cost, i2j, j2i, matrix = _align.align(tokens_a, tokens_b)
|
||||
i2j_multi, j2i_multi = _align.multi_align(i2j, j2i, [len(w) for w in tokens_a],
|
||||
[len(w) for w in tokens_b])
|
||||
for i, j in list(i2j_multi.items()):
|
||||
if i2j_multi.get(i+1) != j and i2j_multi.get(i-1) != j:
|
||||
i2j[i] = j
|
||||
i2j_multi.pop(i)
|
||||
for j, i in list(j2i_multi.items()):
|
||||
if j2i_multi.get(j+1) != i and j2i_multi.get(j-1) != i:
|
||||
j2i[j] = i
|
||||
j2i_multi.pop(j)
|
||||
return cost, i2j, j2i, i2j_multi, j2i_multi
|
||||
return [w.replace(" ", "").lower() for w in tokens]
|
||||
|
||||
|
||||
def align(tokens_a, tokens_b):
|
||||
|
|
@ -144,8 +94,6 @@ def align(tokens_a, tokens_b):
|
|||
* b2a_multi (Dict[int, int]): As with `a2b_multi`, but mapping the other
|
||||
direction.
|
||||
"""
|
||||
if not USE_NEW_ALIGN:
|
||||
return _align_before_v2_2_2(tokens_a, tokens_b)
|
||||
tokens_a = _normalize_for_alignment(tokens_a)
|
||||
tokens_b = _normalize_for_alignment(tokens_b)
|
||||
cost = 0
|
||||
|
|
|
|||
|
|
@ -1,79 +0,0 @@
|
|||
# coding: utf-8
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import pytest
|
||||
from spacy._align import align, multi_align
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"string1,string2,cost",
|
||||
[
|
||||
("hello", "hell", 1),
|
||||
("rat", "cat", 1),
|
||||
("rat", "rat", 0),
|
||||
("rat", "catsie", 4),
|
||||
("t", "catsie", 5),
|
||||
],
|
||||
)
|
||||
def test_align_costs(string1, string2, cost):
|
||||
output_cost, i2j, j2i, matrix = align(string1, string2)
|
||||
assert output_cost == cost
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"string1,string2,i2j",
|
||||
[
|
||||
("hello", "hell", [0, 1, 2, 3, -1]),
|
||||
("rat", "cat", [0, 1, 2]),
|
||||
("rat", "rat", [0, 1, 2]),
|
||||
("rat", "catsie", [0, 1, 2]),
|
||||
("t", "catsie", [2]),
|
||||
],
|
||||
)
|
||||
def test_align_i2j(string1, string2, i2j):
|
||||
output_cost, output_i2j, j2i, matrix = align(string1, string2)
|
||||
assert list(output_i2j) == i2j
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"string1,string2,j2i",
|
||||
[
|
||||
("hello", "hell", [0, 1, 2, 3]),
|
||||
("rat", "cat", [0, 1, 2]),
|
||||
("rat", "rat", [0, 1, 2]),
|
||||
("rat", "catsie", [0, 1, 2, -1, -1, -1]),
|
||||
("t", "catsie", [-1, -1, 0, -1, -1, -1]),
|
||||
],
|
||||
)
|
||||
def test_align_i2j_2(string1, string2, j2i):
|
||||
output_cost, output_i2j, output_j2i, matrix = align(string1, string2)
|
||||
assert list(output_j2i) == j2i
|
||||
|
||||
|
||||
def test_align_strings():
|
||||
words1 = ["hello", "this", "is", "test!"]
|
||||
words2 = ["hellothis", "is", "test", "!"]
|
||||
cost, i2j, j2i, matrix = align(words1, words2)
|
||||
assert cost == 4
|
||||
assert list(i2j) == [-1, -1, 1, -1]
|
||||
assert list(j2i) == [-1, 2, -1, -1]
|
||||
|
||||
|
||||
def test_align_many_to_one():
|
||||
words1 = ["a", "b", "c", "d", "e", "f", "g", "h"]
|
||||
words2 = ["ab", "bc", "e", "fg", "h"]
|
||||
cost, i2j, j2i, matrix = align(words1, words2)
|
||||
assert list(i2j) == [-1, -1, -1, -1, 2, -1, -1, 4]
|
||||
lengths1 = [len(w) for w in words1]
|
||||
lengths2 = [len(w) for w in words2]
|
||||
i2j_multi, j2i_multi = multi_align(i2j, j2i, lengths1, lengths2)
|
||||
assert i2j_multi[0] == 0
|
||||
assert i2j_multi[1] == 0
|
||||
assert i2j_multi[2] == 1
|
||||
assert i2j_multi[3] == 1
|
||||
assert i2j_multi[3] == 1
|
||||
assert i2j_multi[5] == 3
|
||||
assert i2j_multi[6] == 3
|
||||
|
||||
assert j2i_multi[0] == 1
|
||||
assert j2i_multi[1] == 3
|
||||
|
|
@ -177,13 +177,12 @@ def test_roundtrip_docs_to_json():
|
|||
assert cats["BAKING"] == goldparse.cats["BAKING"]
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="skip while we have backwards-compatible alignment")
|
||||
@pytest.mark.parametrize(
|
||||
"tokens_a,tokens_b,expected",
|
||||
[
|
||||
(["a", "b", "c"], ["ab", "c"], (3, [-1, -1, 1], [-1, 2], {0: 0, 1: 0}, {})),
|
||||
(
|
||||
["a", "b", "``", "c"],
|
||||
["a", "b", '"', "c"],
|
||||
['ab"', "c"],
|
||||
(4, [-1, -1, -1, 1], [-1, 3], {0: 0, 1: 0, 2: 0}, {}),
|
||||
),
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user