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from onmt.utils.logging import logger
from onmt.transforms import register_transform
from .transform import Transform
from rapidfuzz import fuzz, process
import numpy as np
import time
class FuzzyMatcher(object):
"""Class for creating and setting up fuzzy matchers."""
def __init__(
self,
tm_path,
corpus_ratio,
threshold=70,
tm_delimiter="\t",
fuzzy_token="｟fuzzy｠",
tm_unit_min_lentgh=4,
tm_unit_max_length=70,
):
self.threshold = threshold
self.corpus_ratio = corpus_ratio
self.tm_delimiter = tm_delimiter
self.fuzzy_token = fuzzy_token
self.tm_unit_min_length = tm_unit_min_lentgh
self.tm_unit_max_length = tm_unit_max_length
self.internal_tm = self._create_tm(tm_path)
def _create_tm(self, tm_path):
"""The TM should be a utf-8 text file with each line
containing a source sentence and its translation, separated
by the `self.tm_delimiter`. A TM size of 200k-250k pairs should
provide enough matches and good performance, but this may
depend on overall system specs (RAM, CPU)
"""
src_segments, tgt_segments = list(), list()
with open(tm_path, mode="r", encoding="utf-8") as file:
pairs = file.readlines()
for pair in pairs:
source, target = map(str, pair.split(self.tm_delimiter))
# Filter out very short or very long sentences
# from the TM for better performance
if (
len(source) < self.tm_unit_min_length
or len(source) > self.tm_unit_max_length
):
continue
src_segments.append(source.strip())
tgt_segments.append(target.strip())
logger.debug(
f"Translation Memory size for FuzzyMatch transform: " f"{len(src_segments)}"
)
return [src_segments, tgt_segments]
def _get_batch_matches(self, batch):
logger.debug(f"Starting fuzzy matching on {len(batch)} examples")
fuzzy_count = 0
start = time.time()
augmented = list()
# We split the `batch` and perform fuzzy matching
# in smaller chunks of 10.000 examples in order to
# reduce memory usage.
# Perfomance is not affected.
chunk_size = 10000
mini_batches = np.array_split(
batch, len(batch) // chunk_size if len(batch) > chunk_size else 1
)
for mini_batch in mini_batches:
plist = list(mini_batch)
if fuzzy_count >= len(batch) * self.corpus_ratio:
augmented.extend(plist)
continue
results = process.cdist(
plist,
self.internal_tm[0],
scorer=fuzz.ratio,
dtype=np.uint8,
score_cutoff=self.threshold,
workers=-1,
)
matches = np.any(results, 1)
argmax = np.argmax(results, axis=1)
for idx, s in enumerate(plist):
# Probably redundant but let's be safe
# in case some examples are already fuzzied
# (e.g. from another pipeline or workflow)
if self.fuzzy_token in s:
continue
# We don't want exact matches
if matches[idx] and results[idx][argmax[idx]] < 100:
if fuzzy_count >= len(batch) * self.corpus_ratio:
break
plist[idx] = s + self.fuzzy_token + self.internal_tm[1][argmax[idx]]
fuzzy_count += 1
augmented.extend(plist)
end = time.time()
logger.debug(
f"FuzzyMatch Transform: Added {fuzzy_count} " f"fuzzies in {end-start} secs"
)
return augmented
@register_transform(name="fuzzymatch")
class FuzzyMatchTransform(Transform):
"""Perform fuzzy matching against a translation memory and
augment source examples with target matches for Neural Fuzzy Repair.
:cite:`bulte-tezcan-2019-neural`
"""
def __init__(self, opts):
super().__init__(opts)
@classmethod
def add_options(cls, parser):
"""Options for fuzzy matching."""
group = parser.add_argument_group("Transform/FuzzyMatching")
group.add("--tm_path", "-tm_path", type=str, help="Path to a flat text TM.")
group.add(
"--fuzzy_corpus_ratio",
"-fuzzy_corpus_ratio",
type=float,
default=0.1,
help="Ratio of corpus to augment with fuzzy matches.",
)
group.add(
"--fuzzy_threshold",
"-fuzzy_threshold",
type=int,
default=70,
help="The fuzzy matching threshold.",
)
group.add(
"--tm_delimiter",
"-tm_delimiter",
type=str,
default="\t",
help="The delimiter used in the flat text TM.",
)
group.add(
"--fuzzy_token",
"-fuzzy_token",
type=str,
default="｟fuzzy｠",
help="The fuzzy token to be added with the matches.",
)
group.add(
"--fuzzymatch_min_length",
"-fuzzymatch_min_length",
type=int,
default=4,
help="Min length for TM entries and examples to match.",
)
group.add(
"--fuzzymatch_max_length",
"-fuzzymatch_max_length",
type=int,
default=70,
help="Max length for TM entries and examples to match.",
)
def _parse_opts(self):
self.tm_path = self.opts.tm_path
self.fuzzy_corpus_ratio = self.opts.fuzzy_corpus_ratio
self.fuzzy_threshold = self.opts.fuzzy_threshold
self.tm_delimiter = self.opts.tm_delimiter
self.fuzzy_token = self.opts.fuzzy_token
self.fuzzymatch_min_length = self.opts.fuzzymatch_min_length
self.fuzzymatch_max_length = self.opts.fuzzymatch_max_length
@classmethod
def get_specials(cls, opts):
"""Add the fuzzy match token to the src vocab."""
return ([opts.fuzzy_token], list())
def warm_up(self, vocabs=None):
"""Create the fuzzy matcher."""
super().warm_up(None)
self.matcher = FuzzyMatcher(
self.tm_path,
self.fuzzy_corpus_ratio,
self.fuzzy_threshold,
self.tm_delimiter,
self.fuzzy_token,
self.fuzzymatch_min_length,
self.fuzzymatch_max_length,
)
def apply(self, example, is_train=False, stats=None, **kwargs):
return example
def batch_apply(self, batch, is_train=False, stats=None, **kwargs):
src_segments = list()
for ex, _, _ in batch:
# Apply a basic filtering to leave out very short or very long
# sentences and speed up things a bit during fuzzy matching
if (
len(" ".join(ex["src"])) > self.fuzzymatch_min_length
and len(" ".join(ex["src"])) < self.fuzzymatch_max_length
):
src_segments.append(" ".join(ex["src"]))
else:
src_segments.append("")
fuzzied_src = self.matcher._get_batch_matches(src_segments)
assert len(src_segments) == len(fuzzied_src)
for idx, (example, _, _) in enumerate(batch):
if fuzzied_src[idx] != "":
example["src"] = fuzzied_src[idx].split()
return batch
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