def _real_register(class_):
# Assert that the passed kwargs are meaningful
for kwarg_name, values in kwargs.items():
try:
real_args = inspect.getfullargspec(class_).args
except AttributeError:
# pylint: disable=deprecated-method
real_args = inspect.getargspec(class_.__init__).args
if not kwarg_name in real_args:
raise RuntimeError(
('{} is not a valid argument for {}. '
'Only valid arguments can be registered.').format(
kwarg_name, class_.__name__))
if not isinstance(values, (list, tuple)):
raise RuntimeError(
('{} should be a list of '
'valid arguments for {}. ').format(values, kwarg_name))
# Save the kwargs associated with this class_
_REGSITRY_NAME_KWARGS[class_] = kwargs
register_ = registry.get_register_func(Dataset, 'dataset')
return register_(class_)
def _real_register(class_):
# Assert that the passed kwargs are meaningful
for kwarg_name, values in kwargs.items():
try:
real_args = inspect.getfullargspec(class_).args
except AttributeError:
# pylint: disable=deprecated-method
real_args = inspect.getargspec(class_.__init__).args
if not kwarg_name in real_args:
raise RuntimeError(
('{} is not a valid argument for {}. '
'Only valid arguments can be registered.').format(
kwarg_name, class_.__name__))
if not isinstance(values, (list, tuple)):
raise RuntimeError(('{} should be a list of '
'valid arguments for {}. ').format(
values, kwarg_name))
# Save the kwargs associated with this class_
_REGSITRY_NAME_KWARGS[class_] = kwargs
register_ = registry.get_register_func(Dataset, 'dataset')
return register_(class_)
def register(class_):
"""Registers a new word embedding evaluation function.
Once registered, we can create an instance with
:func:`~gluonnlp.embedding.evaluation.create`.
Examples
--------
>>> @gluonnlp.embedding.evaluation.register
... class MySimilarityFunction(gluonnlp.embedding.evaluation.WordEmbeddingSimilarityFunction):
... def __init__(self, eps=1e-10):
... pass
>>> similarity_function = gluonnlp.embedding.evaluation.create('similarity',
... 'MySimilarityFunction')
>>> print(type(similarity_function))
<class 'MySimilarityFunction'>
>>> @gluonnlp.embedding.evaluation.register
... class MyAnalogyFunction(gluonnlp.embedding.evaluation.WordEmbeddingAnalogyFunction):
... def __init__(self, k=1, eps=1E-10):
... pass
>>> analogy_function = gluonnlp.embedding.evaluation.create('analogy', 'MyAnalogyFunction')
>>> print(type(analogy_function))
<class 'MyAnalogyFunction'>
"""
if issubclass(class_, WordEmbeddingSimilarityFunction):
register_ = registry.get_register_func(
WordEmbeddingSimilarityFunction,
'word embedding similarity evaluation function')
elif issubclass(class_, WordEmbeddingAnalogyFunction):
register_ = registry.get_register_func(
WordEmbeddingAnalogyFunction,
'word embedding analogy evaluation function')
else:
raise RuntimeError(
'The custom function must either subclass '
'WordEmbeddingSimilarityFunction or WordEmbeddingAnalogyFunction')
return register_(class_)
def register(class_):
"""Registers a new word embedding evaluation function.
Once registered, we can create an instance with
:func:`~gluonnlp.embedding.evaluation.create`.
Examples
--------
>>> @gluonnlp.embedding.evaluation.register
... class MySimilarityFunction(gluonnlp.embedding.evaluation.WordEmbeddingSimilarityFunction):
... def __init__(self, eps=1e-10):
... pass
>>> similarity_function = gluonnlp.embedding.evaluation.create('similarity',
... 'MySimilarityFunction')
>>> print(type(similarity_function))
<class 'MySimilarityFunction'>
>>> @gluonnlp.embedding.evaluation.register
... class MyAnalogyFunction(gluonnlp.embedding.evaluation.WordEmbeddingAnalogyFunction):
... def __init__(self, k=1, eps=1E-10):
... pass
>>> analogy_function = gluonnlp.embedding.evaluation.create('analogy', 'MyAnalogyFunction')
>>> print(type(analogy_function))
<class 'MyAnalogyFunction'>
"""
if issubclass(class_, WordEmbeddingSimilarityFunction):
register_ = registry.get_register_func(
WordEmbeddingSimilarityFunction,
'word embedding similarity evaluation function')
elif issubclass(class_, WordEmbeddingAnalogyFunction):
register_ = registry.get_register_func(
WordEmbeddingAnalogyFunction,
'word embedding analogy evaluation function')
else:
raise RuntimeError(
'The custom function must either subclass '
'WordEmbeddingSimilarityFunction or WordEmbeddingAnalogyFunction')
return register_(class_)
def register(embedding_cls):
"""Registers a new token embedding.
Once an embedding is registered, we can create an instance of this embedding with
:func:`~gluonnlp.embedding.create`.
Examples
--------
>>> @gluonnlp.embedding.register
... class MyTextEmbed(gluonnlp.embedding.TokenEmbedding):
... def __init__(self, source='my_pretrain_file'):
... pass
>>> embed = gluonnlp.embedding.create('MyTokenEmbed')
>>> print(type(embed))
<class '__main__.MyTokenEmbed'>
"""
register_text_embedding = registry.get_register_func(TokenEmbedding, 'token embedding')
return register_text_embedding(embedding_cls)
def register(embedding_cls):
"""Registers a new token embedding.
Once an embedding is registered, we can create an instance of this embedding with
:func:`~gluonnlp.embedding.create`.
Examples
--------
>>> @gluonnlp.embedding.register
... class MyTextEmbed(gluonnlp.embedding.TokenEmbedding):
... def __init__(self, source='my_pretrain_file'):
... pass
>>> embed = gluonnlp.embedding.create('MyTextEmbed')
>>> print(type(embed))
<class 'MyTextEmbed'>
"""
register_text_embedding = registry.get_register_func(TokenEmbedding, 'token embedding')
return register_text_embedding(embedding_cls)
def register_subword_function(subword_cls):
"""Registers a new subword function."""
register_text_embedding = registry.get_register_func(
SubwordFunction, 'subword function')
return register_text_embedding(subword_cls)
def get_name_value(self):
"""Returns zipped name and value pairs.
Returns
-------
list of tuples
A (name, value) tuple list.
"""
name, value = self.get()
if not isinstance(name, list):
name = [name]
if not isinstance(value, list):
value = [value]
return list(zip(name, value))
# pylint: disable=invalid-name
register = registry.get_register_func(EvalMetric, 'metric')
alias = registry.get_alias_func(EvalMetric, 'metric')
_create = registry.get_create_func(EvalMetric, 'metric')
# pylint: enable=invalid-name
def create(metric, *args, **kwargs):
"""Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.
Parameters
----------
metric : str or callable
Specifies the metric to create.
This argument must be one of the below:
- Name of a metric.
- An instance of `EvalMetric`.
