def strip_token(str_, token, is_token_list=False, compat=True):
"""Returns a copy of strings with leading and trailing tokens removed.
Note that besides :attr:`token`, all leading and trailing whitespace
characters are also removed.
If :attr:`is_token_list` is False, then the function assumes tokens in
:attr:`str_` are separated with whitespace character.
Args:
str\_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
token (str): The token to strip, e.g., the '<PAD>' token defined in
:class:`~texar.tf.data.SpecialTokens`.PAD
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
The stripped strings of the same structure/shape as :attr:`str_`.
Example:
.. code-block:: python
str_ = '<PAD> a sentence <PAD> <PAD> '
str_stripped = strip_token(str_, '<PAD>')
# str_stripped == 'a sentence'
str_ = ['<PAD>', 'a', 'sentence', '<PAD>', '<PAD>', '', '']
str_stripped = strip_token(str_, '<PAD>', is_token_list=True)
# str_stripped == 'a sentence'
"""
def _recur_strip(s):
if is_str(s):
if token == "":
return ' '.join(s.strip().split())
else:
return ' '.join(s.strip().split()).\
replace(' '+token, '').replace(token+' ', '')
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str
def str_join(tokens, sep=' ', compat=True):
"""Concats :attr:`tokens` along the last dimension with intervening
occurrences of :attr:`sep`.
Args:
tokens: An `n`-D numpy array or (possibly nested) list of `str`.
sep (str): The string intervening between the tokens.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
An `(n-1)`-D numpy array (or list) of `str`.
"""
def _recur_join(s):
if len(s) == 0:
return ''
elif is_str(s[0]):
return sep.join(s)
else:
s_ = [_recur_join(si) for si in s]
return _maybe_list_to_array(s_, s)
if compat:
tokens = compat_as_text(tokens)
str_ = _recur_join(tokens)
return str_
def strip_bos(str_, bos_token='<BOS>', is_token_list=False, compat=True):
"""Remove all leading BOS tokens.
Note that besides :attr:`bos_token`, all leading and trailing whitespace
characters are also removed.
If :attr:`is_token_list` is False, then the function assumes tokens in
:attr:`str_` are separated with whitespace character.
Args:
str\_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
bos_token (str): The BOS token. Default is '<BOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.BOS
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
Strings of the same structure/shape as :attr:`str_`.
"""
def _recur_strip(s):
if is_str(s):
if bos_token == '':
return ' '.join(s.strip().split())
else:
return ' '.join(s.strip().split()).replace(bos_token + ' ', '')
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str
def strip_eos(str_, eos_token='<EOS>', is_token_list=False, compat=True):
"""Remove the EOS token and all subsequent tokens.
If :attr:`is_token_list` is False, then the function assumes tokens in
:attr:`str_` are separated with whitespace character.
Args:
str\_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
eos_token (str): The EOS token. Default is '<EOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.EOS
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
Strings of the same structure/shape as :attr:`str_`.
"""
def _recur_strip(s):
if is_str(s):
s_tokens = s.split()
if eos_token in s_tokens:
return ' '.join(s_tokens[:s_tokens.index(eos_token)])
else:
return s
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str
def map_ids_to_strs(ids,
vocab,
join=True,
strip_pad='<PAD>',
strip_bos='<BOS>',
strip_eos='<EOS>',
compat=True):
"""Transforms `int` indexes to strings by mapping ids to tokens,
concatenating tokens into sentences, and stripping special tokens, etc.
Args:
ids: An n-D numpy array or (possibly nested) list of `int` indexes.
vocab: An instance of :class:`~texar.tf.data.Vocab`.
join (bool): Whether to concat along the last dimension of the
the tokens into a string separated with a space character.
strip_pad (str): The PAD token to strip from the strings (i.e., remove
the leading and trailing PAD tokens of the strings). Default
is '<PAD>' as defined in
:class:`~texar.tf.data.SpecialTokens`.PAD.
Set to `None` or `False` to disable the stripping.
strip_bos (str): The BOS token to strip from the strings (i.e., remove
the leading BOS tokens of the strings).
Default is '<BOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.BOS.
Set to `None` or `False` to disable the stripping.
strip_eos (str): The EOS token to strip from the strings (i.e., remove
the EOS tokens and all subsequent tokens of the strings).
Default is '<EOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.EOS.
Set to `None` or `False` to disable the stripping.
Returns:
If :attr:`join` is True, returns a `(n-1)`-D numpy array (or list) of
concatenated strings. If :attr:`join` is False, returns an `n`-D numpy
array (or list) of str tokens.
Example:
.. code-block:: python
text_ids = [[1, 9, 6, 2, 0, 0], [1, 28, 7, 8, 2, 0]]
text = map_ids_to_strs(text_ids, data.vocab)
# text == ['a sentence', 'parsed from ids']
text = map_ids_to_strs(
text_ids, data.vocab, join=False,
strip_pad=None, strip_bos=None, strip_eos=None)
# text == [['<BOS>', 'a', 'sentence', '<EOS>', '<PAD>', '<PAD>'],
# ['<BOS>', 'parsed', 'from', 'ids', '<EOS>', '<PAD>']]
"""
tokens = vocab.map_ids_to_tokens_py(ids)
if isinstance(ids, (list, tuple)):
tokens = tokens.tolist()
if compat:
tokens = compat_as_text(tokens)
str_ = str_join(tokens, compat=False)
str_ = strip_special_tokens(str_,
strip_pad=strip_pad,
strip_bos=strip_bos,
strip_eos=strip_eos,
compat=False)
if join:
return str_
else:
return _recur_split(str_, ids)
def strip_special_tokens(str_,
strip_pad='<PAD>',
strip_bos='<BOS>',
strip_eos='<EOS>',
is_token_list=False,
compat=True):
"""Removes special tokens in strings, including:
- Removes EOS and all subsequent tokens
- Removes leading and and trailing PAD tokens
- Removes leading BOS tokens
Note that besides the special tokens, all leading and trailing whitespace
characters are also removed.
This is a joint function of :func:`strip_eos`, :func:`strip_pad`, and
:func:`strip_bos`
Args:
str\_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
strip_pad (str): The PAD token to strip from the strings (i.e., remove
the leading and trailing PAD tokens of the strings). Default
is '<PAD>' as defined in
:class:`~texar.tf.data.SpecialTokens`.PAD.
Set to `None` or `False` to disable the stripping.
strip_bos (str): The BOS token to strip from the strings (i.e., remove
the leading BOS tokens of the strings).
Default is '<BOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.BOS.
Set to `None` or `False` to disable the stripping.
strip_eos (str): The EOS token to strip from the strings (i.e., remove
the EOS tokens and all subsequent tokens of the strings).
Default is '<EOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.EOS.
Set to `None` or `False` to disable the stripping.
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
Strings of the same shape of :attr:`str_` with special tokens stripped.
"""
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
if strip_eos is not None and strip_eos is not False:
s = _strip_eos_(s, strip_eos, is_token_list=False, compat=False)
if strip_pad is not None and strip_pad is not False:
s = strip_token(s, strip_pad, is_token_list=False, compat=False)
if strip_bos is not None and strip_bos is not False:
s = _strip_bos_(s, strip_bos, is_token_list=False, compat=False)
if is_token_list:
s = _recur_split(s, str_)
return s
def corpus_bleu_moses(list_of_references,
hypotheses,
lowercase=False,
return_all=False):
"""Calculates corpus-level BLEU score using the
**MOSES multi-bleu.perl** script.
Args:
list_of_references: A list of lists of references for each hypothesis.
Each reference can be either a string, or a list of string tokens.
List can also be numpy array.
hypotheses: A list of hypothesis sentences.
Each hyperthsis can be either a string, or a list of string tokens.
List can also be numpy array.
lowercase (bool): If `True`, pass the "-lc" flag to the multi-bleu
script.
return_all (bool): If `True`, returns BLEU and all n-gram precisions.
Returns:
If :attr:`return_all` is `False` (default), returns a float32
BLEU score.
If :attr:`return_all` is `True`, returns a list of 5 float32 scores:
`[BLEU, 1-gram precision, ..., 4-gram precision]`.
"""
list_of_references = compat_as_text(list_of_references)
hypotheses = compat_as_text(hypotheses)
if np.size(hypotheses) == 0:
return np.float32(0.) # pylint: disable=no-member
# Get multi-bleu.perl
cur_dir = os.path.dirname(os.path.realpath(__file__))
multi_bleu_path = os.path.abspath(
os.path.join(cur_dir, "..", "..", "..", "bin", "utils",
"multi-bleu.perl"))
# Create a temporary folder containing hyperthesis and reference files
result_path = tempfile.mkdtemp()
# Create hyperthesis file
hfile_path = os.path.join(result_path, 'hyp')
hyps = [_maybe_list_to_str(h) for h in hypotheses]
with open(hfile_path, 'w', encoding='utf-8') as hfile:
text = "\n".join(hyps)
hfile.write(text)
hfile.write("\n")
# Create reference files
max_nrefs = max([len(refs) for refs in list_of_references])
rfile_path = os.path.join(result_path, 'ref')
for rid in range(max_nrefs):
with open(rfile_path + '%d' % rid, 'w', encoding='utf-8') as rfile:
for refs in list_of_references:
if rid < len(refs):
ref = _maybe_list_to_str(refs[rid])
rfile.write(ref + "\n")
else:
rfile.write("\n")
# Calculate BLEU
multi_bleu_cmd = [multi_bleu_path]
if lowercase:
multi_bleu_cmd += ["-lc"]
multi_bleu_cmd += [rfile_path]
with open(hfile_path, "r") as hyp_input:
try:
multi_bleu_ret = subprocess.check_output(multi_bleu_cmd,
stdin=hyp_input,
stderr=subprocess.STDOUT)
multi_bleu_ret = multi_bleu_ret.decode("utf-8")
bleu_score = _parse_multi_bleu_ret(multi_bleu_ret, return_all)
except subprocess.CalledProcessError as error:
if error.output is not None:
tf.logging.warning(
"multi-bleu.perl returned non-zero exit code")
tf.logging.warning(error.output)
if return_all:
bleu_score = [np.float32(0.0)] * 5
else:
bleu_score = np.float32(0.0)
shutil.rmtree(result_path)
return np.float32(bleu_score)
def corpus_bleu(list_of_references,
hypotheses,
max_order=4,
lowercase=False,
smooth=False,
return_all=True):
"""Computes corpus-level BLEU score.
Args:
list_of_references: A list of lists of references for each hypothesis.
Each reference can be either a list of string tokens, or a string
containing tokenized tokens separated with whitespaces.
List can also be numpy array.
hypotheses: A list of hypothesis sentences.
Each hypothesis can be either a list of string tokens, or a
string containing tokenized tokens separated with whitespaces.
List can also be numpy array.
lowercase (bool): If `True`, lowercase reference and hypothesis tokens.
max_order (int): Maximum n-gram order to use when computing BLEU score.
smooth (bool): Whether or not to apply (Lin et al. 2004) smoothing.
return_all (bool): If `True`, returns BLEU and all n-gram precisions.
Returns:
If :attr:`return_all` is `False` (default), returns a float32
BLEU score.
If :attr:`return_all` is `True`, returns a list of float32 scores:
`[BLEU] + n-gram precisions`, which is of length :attr:`max_order` + 1.
"""
list_of_references = compat_as_text(list_of_references)
hypotheses = compat_as_text(hypotheses)
matches_by_order = [0] * max_order
possible_matches_by_order = [0] * max_order
reference_length = 0
hyperthsis_length = 0
for (references, hyperthsis) in zip(list_of_references, hypotheses):
reference_length += min(len(r) for r in references)
hyperthsis_length += len(hyperthsis)
merged_ref_ngram_counts = collections.Counter()
for reference in references:
reference = _maybe_str_to_list(reference)
if lowercase:
reference = _lowercase(reference)
merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
hyperthsis = _maybe_str_to_list(hyperthsis)
if lowercase:
hyperthsis = _lowercase(hyperthsis)
hyperthsis_ngram_counts = _get_ngrams(hyperthsis, max_order)
overlap = hyperthsis_ngram_counts & merged_ref_ngram_counts
for ngram in overlap:
matches_by_order[len(ngram) - 1] += overlap[ngram]
for order in range(1, max_order + 1):
possible_matches = len(hyperthsis) - order + 1
if possible_matches > 0:
possible_matches_by_order[order - 1] += possible_matches
precisions = [0] * max_order
for i in range(0, max_order):
if smooth:
precisions[i] = ((matches_by_order[i] + 1.) /
(possible_matches_by_order[i] + 1.))
else:
if possible_matches_by_order[i] > 0:
precisions[i] = (float(matches_by_order[i]) /
possible_matches_by_order[i])
else:
precisions[i] = 0.0
if min(precisions) > 0:
p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions)
geo_mean = math.exp(p_log_sum)
else:
geo_mean = 0
ratio = float(hyperthsis_length) / reference_length
if ratio > 1.0:
bp = 1.
else:
try:
bp = math.exp(1 - 1. / ratio)
except ZeroDivisionError:
bp = math.exp(1 - 1. / (ratio + 1e-8))
bleu = geo_mean * bp
if return_all:
return [bleu * 100] + [p * 100 for p in precisions]
else:
return bleu * 100