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