def evaluate_file(self,
detok_hyp: Path,
ref: Union[Path, List[str]],
lowercase=True) -> float:
detok_lines = list(IO.get_lines(detok_hyp))
# takes multiple refs, but here we have only one
ref_liness = [IO.get_lines(ref) if isinstance(ref, Path) else ref]
bleu: BLEUScore = corpus_bleu(sys_stream=detok_lines,
ref_streams=ref_liness,
lowercase=lowercase)
# this should be part of new sacrebleu release (i sent a PR ;)
bleu_str = bleu.format()
bleu_file = detok_hyp.with_name(detok_hyp.name +
('.lc' if lowercase else '.oc') +
'.sacrebleu')
log.info(f'BLEU {detok_hyp} : {bleu_str}')
IO.write_lines(bleu_file, bleu_str)
return bleu.score
def evaluate_file(self,
detok_hyp: Path,
ref: Union[Path, List[str]],
lowercase=True) -> float:
detok_lines = IO.get_lines(detok_hyp)
# takes multiple refs, but here we have only one
ref_liness = [IO.get_lines(ref) if isinstance(ref, Path) else ref]
bleu: BLEU = corpus_bleu(sys_stream=detok_lines,
ref_streams=ref_liness,
lowercase=lowercase)
# this should be part of new sacrebleu release (i sent a PR ;)
bleu_str = f'BLEU = {bleu.score:.2f} {"/".join(f"{p:.1f}" for p in bleu.precisions)}' \
f' (BP = {bleu.bp:.3f} ratio = {(bleu.sys_len / bleu.ref_len):.3f}' \
f' hyp_len = {bleu.sys_len:d} ref_len={bleu.ref_len:d})'
bleu_file = detok_hyp.with_suffix(('.lc' if lowercase else '.oc') +
'.sacrebleu')
log.info(f'BLEU {detok_hyp} : {bleu_str}')
IO.write_lines(bleu_file, bleu_str)
return bleu.score
def moses_detokenize(self,
inp: Path,
out: Path,
col=0,
lang='en',
post_op=None):
log.info(f"detok : {inp} --> {out}")
tok_lines = IO.get_lines(inp, col=col, line_mapper=lambda x: x.split())
with MosesDetokenizer(lang=lang) as detok:
detok_lines = (detok(tok_line) for tok_line in tok_lines)
if post_op:
detok_lines = (post_op(line) for line in detok_lines)
IO.write_lines(out, detok_lines)
def train(cls,
model_type: str,
vocab_size: int,
model_path: str,
files: List[str],
no_split_toks: Optional[List[str]] = None,
char_coverage: float = 0,
dedup=True,
spark=None):
"""
:param model_type: word, char, bpe
:param vocab_size: vocabulary size
:param model_path: where to store vocabulary model
:param files: text for creating vcabulary
:param no_split_toks:
:param char_coverage: character coverage (0, 1]. value <= 0 => default coverage
:return:
"""
assert not no_split_toks, 'not supported in nlcodec yet'
from nlcodec import learn_vocab, term_freq
kwargs = dict(char_coverage=char_coverage) if char_coverage > 0 else {}
if not spark:
inp = IO.get_liness(*files)
else:
# extract and store frequencies to this file
stats_file = model_path + '.termfreqs'
if not Path(stats_file).exists():
log.info("Extracting term frequencies... ")
paths = [f if isinstance(f, Path) else Path(f) for f in files]
wfs, chfs, n_lines = term_freq.word_counts(paths=paths,
dedup=dedup,
spark=spark)
log.info(
f"Lines = {n_lines:,}, Word Types: {len(wfs):,} Char Types:{len(chfs):,}"
)
stats = chfs if model_type == 'char' else wfs
log.info(f"Writing frequencies to {stats_file}")
with IO.writer(stats_file) as out:
term_freq.write_stats(stats=stats,
out=out,
line_count=n_lines)
kwargs['term_freqs'] = True
inp = IO.get_lines(stats_file, delim='\n')
learn_vocab(inp=inp,
level=model_type,
model=model_path,
vocab_size=vocab_size,
**kwargs)
return cls(model_path)
def decode_eval_file(self,
decoder,
src: Union[Path, List[str]],
out_file: Path,
ref: Optional[Union[Path, List[str]]],
lowercase: bool = True,
**dec_args) -> float:
if out_file.exists() and out_file.stat().st_size > 0 and line_count(
out_file) == (len(src)
if isinstance(src, list) else line_count(src)):
log.warning(
f"{out_file} exists and has desired number of lines. Skipped..."
)
else:
if isinstance(src, Path):
log.info(f"decoding {src.name}")
src = list(IO.get_lines(src))
if isinstance(ref, Path):
ref = list(IO.get_lines(ref))
with IO.writer(out_file) as out:
decoder.decode_file(src, out, **dec_args)
detok_hyp = self.detokenize(out_file)
if ref:
return self.evaluate_file(detok_hyp, ref, lowercase=lowercase)
def tune_decoder_params(self,
exp: Experiment,
tune_src: str,
tune_ref: str,
batch_size: int,
trials: int = 10,
lowercase=True,
beam_size=(1, 4, 8),
ensemble=(1, 5, 10),
lp_alpha=(0.0, 0.4, 0.6),
suggested: List[Tuple[int, int, float]] = None,
**fixed_args):
_, _, _, tune_args = inspect.getargvalues(inspect.currentframe())
tune_args.update(fixed_args)
ex_args = ['exp', 'self', 'fixed_args', 'batch_size', 'max_len']
if trials == 0:
ex_args += ['beam_size', 'ensemble', 'lp_alpha']
for x in ex_args:
del tune_args[x] # exclude some args
_, step = exp.get_last_saved_model()
tune_dir = exp.work_dir / f'tune_step{step}'
log.info(f"Tune dir = {tune_dir}")
tune_dir.mkdir(parents=True, exist_ok=True)
tune_src, tune_ref = Path(tune_src), Path(tune_ref)
assert tune_src.exists()
assert tune_ref.exists()
tune_src, tune_ref = list(IO.get_lines(tune_src)), list(
IO.get_lines(tune_ref))
assert len(tune_src) == len(tune_ref)
tune_log = tune_dir / 'scores.json' # resume the tuning
memory: Dict[Tuple, float] = {}
if tune_log.exists():
data = json.load(tune_log.open())
# JSON keys cant be tuples, so they were stringified
memory = {eval(k): v for k, v in data.items()}
beam_sizes, ensembles, lp_alphas = [], [], []
if suggested:
if isinstance(suggested[0], str):
suggested = [eval(x) for x in suggested]
suggested = [(x[0], x[1], round(x[2], 2)) for x in suggested]
suggested_new = [x for x in suggested if x not in memory]
beam_sizes += [x[0] for x in suggested_new]
ensembles += [x[1] for x in suggested_new]
lp_alphas += [x[2] for x in suggested_new]
new_trials = trials - len(memory)
if new_trials > 0:
beam_sizes += [random.choice(beam_size) for _ in range(new_trials)]
ensembles += [random.choice(ensemble) for _ in range(new_trials)]
lp_alphas += [
round(random.choice(lp_alpha), 2) for _ in range(new_trials)
]
# ensembling is somewhat costlier, so try minimize the model ensembling, by grouping them together
grouped_ens = defaultdict(list)
for b, ens, l in zip(beam_sizes, ensembles, lp_alphas):
grouped_ens[ens].append((b, l))
try:
for ens, args in grouped_ens.items():
decoder = Decoder.new(exp, ensemble=ens)
for b_s, lp_a in args:
eff_batch_size = batch_size // b_s # effective batch size
name = f'tune_step{step}_beam{b_s}_ens{ens}_lp{lp_a:.2f}'
log.info(name)
out_file = tune_dir / f'{name}.out.tsv'
score = self.decode_eval_file(decoder,
tune_src,
out_file,
tune_ref,
batch_size=eff_batch_size,
beam_size=b_s,
lp_alpha=lp_a,
lowercase=lowercase,
**fixed_args)
memory[(b_s, ens, lp_a)] = score
best_params = sorted(memory.items(),
key=lambda x: x[1],
reverse=True)[0][0]
return dict(zip(['beam_size', 'ensemble', 'lp_alpha'],
best_params)), tune_args
finally:
# JSON keys cant be tuples, so we stringify them
data = {str(k): v for k, v in memory.items()}
IO.write_lines(tune_log, json.dumps(data))
