Pattern matching.
Pattern matching aims at checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact.
Several pattern matching algorithms are implemented in this class. They allow to find an hypothesis pattern in a reference.
Create a new Pattern instance.
def __init__(self):
"""Create a new Pattern instance."""
self._ngram = 3
self._score = 1.0
self._gap = 2
self._interstice = self._gap * 2
Return the score value (float).
def get_score(self):
"""Return the score value (float)."""
return self._score
Return the n value for n-grams (int).
def get_ngram(self):
"""Return the n value for n-grams (int)."""
return self._ngram
Return the gap value (int).
def get_gap(self):
"""Return the gap value (int)."""
return self._gap
Fix the value of n of the n-grams.
NgramRangeError
def set_ngram(self, n):
"""Fix the value of n of the n-grams.
:param n: (int) Value of n (1<n<MAX_NGRAM)
:raises: NgramRangeError
"""
n = int(n)
if 0 < n < sppasPatterns.MAX_NGRAM:
self._ngram = n
else:
raise NgramRangeError(sppasPatterns.MAX_NGRAM, n)
Fix the value of the gap.
GapRangeError
def set_gap(self, g):
"""Fix the value of the gap.
:param g: (int) Value of the gap (0<g<MAX_GAP)
:raises: GapRangeError
"""
g = int(g)
if 0 <= g < sppasPatterns.MAX_GAP:
self._gap = g
self._interstice = 2 * g
else:
raise GapRangeError(sppasPatterns.MAX_GAP, g)
Fix the value of the score.
ScoreRangeError
def set_score(self, s):
"""Fix the value of the score.
:param s: (float) Value of the score (0<s<1)
:raises: ScoreRangeError
"""
s = float(s)
if 0.0 <= s <= 1.0:
self._score = s
else:
raise ScoreRangeError(s)
n-gram matches between ref and hyp.
Search for common n-gram sequences of hyp in ref. The scores are supposed to range in [0;1] values.
def ngram_matches(self, ref, hyp):
"""n-gram matches between ref and hyp.
Search for common n-gram sequences of hyp in ref.
The scores are supposed to range in [0;1] values.
:param ref: (list of tokens) List of references
:param hyp: (list of tuples) List of hypothesis with their scores
:returns: List of matching indexes as tuples (i_ref, i_hyp)
"""
_matches = list()
nman, nasr = self._create_ngrams(ref, hyp)
prev_idxm = 0
for idxa in range(len(nasr)):
match_idxa = list()
for idxm in range(prev_idxm, len(nman)):
if nasr[idxa] == nman[idxm]:
match_idxa.append(idxm)
if len(match_idxa) == 1:
idxm = match_idxa[0]
for i in range(self._ngram):
_matches.append((idxm + i, idxa + i))
return sorted(list(set(_matches)))
n-gram alignment of ref and hyp.
The algorithm is based on the finding of matching n-grams, in the range of a given gap. If 1-gram, keep only hypothesis items with a high confidence score. A gap of search has to be fixed. An interstice value ensure the gap between an item in the ref and in the hyp won't be too far.
Example:
ref: w0 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 | | | | | | | | | | \ | | / | | | \ | | / hyp: w0 w1 w2 wX w3 w5 w6 wX w9
Returned matches:
def ngram_alignments(self, ref, hyp):
"""n-gram alignment of ref and hyp.
The algorithm is based on the finding of matching n-grams, in the
range of a given gap. If 1-gram, keep only hypothesis items with a
high confidence score. A gap of search has to be fixed.
An interstice value ensure the gap between an item in the ref and
in the hyp won't be too far.
:param ref: (list of tokens) List of references
:param hyp: (list of tuples) List of hypothesis with their scores
The scores are supposed to range in [0;1] values.
:returns: List of alignments indexes as tuples (i_ref,i_hyp),
Example:
ref: w0 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12
| | | | | | |
| | | \\ | | /
| | | \\ | | /
hyp: w0 w1 w2 wX w3 w5 w6 wX w9
Returned matches:
- if n=3: [ (0,0), (1,1), (2,2) ]
- if n=2: [(0, 0), (1, 1), (2, 2), (5, 5), (6, 6)]
- if n=1, it depends on the scores in hyp and the value of the gap.
"""
alignment = []
nman, nasr = self._create_ngrams(ref, hyp)
print(nman)
print(nasr)
lastidxa = len(nasr)
lastidxm = len(nman)
lastidx = min(lastidxa, lastidxm)
idxa = 0
idxm = 0
while idxa < lastidxa and idxm < lastidx + self._gap - 1:
found = False
if idxm < lastidxm and nasr[idxa] == nman[idxm]:
for i in range(self._ngram):
alignment.append((idxm + i, idxa + i))
found = True
if idxm < lastidxm:
for gap in range(self._gap):
if not found and idxm < lastidxm - gap - 1:
if nasr[idxa] == nman[idxm + gap + 1]:
idxm = idxm + gap + 1
for i in range(self._ngram):
alignment.append((idxm + i, idxa + i))
found = True
if idxm > 0:
for gap in range(self._gap):
if not found and idxm > gap + 1:
if nasr[idxa] == nman[idxm - gap - 1]:
idxm = idxm - gap - 1
for i in range(self._ngram):
alignment.append((idxm + i, idxa + i))
found = True
idxa += 1
idxm += 1
interstice = math.fabs(idxm - idxa)
if interstice > self._interstice:
vmax = max(idxa, idxm)
idxa = vmax
idxm = vmax
return sorted(list(set(alignment)))
Dynamic Programming alignment of ref and hyp.
The DP alignment algorithm performs a global minimization of a Levenshtein distance function which weights the cost of correct words, insertions, deletions and substitutions as 0, 3, 3 and 4 respectively.
See: | TIME WARPS, STRING EDITS, AND MACROMOLECULES: | THE THEORY AND PRACTICE OF SEQUENCE COMPARISON, | by Sankoff and Kruskal, ISBN 0-201-07809-0
def dp_matching(self, ref, hyp):
"""Dynamic Programming alignment of ref and hyp.
The DP alignment algorithm performs a global minimization of a
Levenshtein distance function which weights the cost of correct words,
insertions, deletions and substitutions as 0, 3, 3 and 4 respectively.
See:
| TIME WARPS, STRING EDITS, AND MACROMOLECULES:
| THE THEORY AND PRACTICE OF SEQUENCE COMPARISON,
| by Sankoff and Kruskal, ISBN 0-201-07809-0
"""
raise NotImplementedError
Create ngrams of the reference and the hypothesis.
def _create_ngrams(self, ref, hyp):
"""Create ngrams of the reference and the hypothesis.
:param ref: (list of tokens) List of references
:param hyp: (list of tuples) List of hypothesis with their scores
"""
nman = list(zip(*[ref[i:] for i in range(self._ngram)]))
if self._ngram > 1:
tab = [token for token, score in hyp]
nasr = list(zip(*[tab[i:] for i in range(self._ngram)]))
else:
nasr = []
for token, score in hyp:
if score >= self._score:
nasr.append((token,))
else:
nasr.append(('<>',))
return (nman, nasr)