def _validate(self, finetune_enabled=False):
check_true(
isinstance(self.n_components, int),
TypeError("The number of components must be an integer."))
check_true(
self.n_components >= 2,
ValueError("The number of components must be at least two."))
def _validate(self, finetune_enabled=False):
check_true(
self.pretrained is None
or self.pretrained is Constants.default_model,
ValueError(
"Random embeddings cannot be pretrained. Save the TextWiser object instead."
))
def _validate(self, finetune_enabled=False):
import umap # this will fail if umap is not available
check_true(
isinstance(self.n_components, int),
TypeError("The number of components must be an integer."))
check_true(
self.n_components >= 2,
ValueError("The number of components must be at least two."))
def _validate(self, finetune_enabled=False):
check_true(
not self.pretrained
or self.pretrained is Constants.default_model
or (isinstance(self.pretrained, str)
and os.path.exists(self.pretrained))
or hasattr(self.pretrained, 'read'), # file-like
ValueError(
"The pretrained model should be a path to a pickle file or a file-like object."
))
def _validate(self, finetune_enabled=False):
check_true(
isinstance(self.schema, (dict, str, tuple, list)),
TypeError(
"The schema should either be a dictionary, a valid embedding, an embedding-parameters tuple, or the path to a JSON file."
))
check_true(
self.pretrained is None
or self.pretrained is Constants.default_model,
ValueError(
"Compound embeddings cannot be pretrained. Save the TextWiser object instead."
))
def _validate_init_args(embedding, transformations, is_finetuneable,
dtype):
"""
Validates arguments for the constructor.
"""
# Embedding
embedding._validate(finetune_enabled=is_finetuneable)
# Transformation
if transformations:
[transformation._validate() for transformation in transformations]
# words should be pooled
if isinstance(
embedding,
Embedding.Word) and embedding.inline_pool_option is None and (
not transformations or not any([
isinstance(transformation, Transformation.Pool)
for transformation in transformations
])):
warnings.warn(
"Word embeddings are specified but no pool options are specified. Are you sure you don't want to pool them?",
RuntimeWarning)
# words shouldn't be double-pooled
check_false(
isinstance(embedding, Embedding.Word)
and embedding.inline_pool_option is not None and transformations
and any([
isinstance(transformation, Transformation.Pool)
for transformation in transformations
]),
ValueError(
"You cannot specify both `inline_pool_option` and `Pool` transformation for the same"
" embedding at the same time. Please pick one!"))
# dtype
check_true(
isinstance(dtype, torch.dtype) or issubclass(dtype, np.generic),
TypeError("The dtype must be either a numpy or torch type."))
check_true(
not is_finetuneable or isinstance(dtype, torch.dtype),
TypeError(
"The dtype must be torch for model to be fine-tuneable."))
check_true(
not is_finetuneable
or TextWiser._check_finetuneable(embedding, transformations),
ValueError(
"Model must have fine-tuneable weights if `is_finetuneable` is specified."
))
def _validate(self, finetune_enabled=False):
check_true(
isinstance(self.deterministic, bool),
TypeError("The deterministic parameter should be a boolean."))
if self.tokenizer:
doc = "string"
res = self.tokenizer(doc)
check_true(
isinstance(res, list),
TypeError("The tokenizer should return a list of tokens."))
check_true(isinstance(res[0], str),
TypeError("The tokens should be of string type."))
check_true(
not self.pretrained
or self.pretrained is Constants.default_model
or (isinstance(self.pretrained, str)
and os.path.exists(self.pretrained))
or hasattr(self.pretrained, 'read'), # file-like
ValueError(
"The pretrained model should be a path to a pickle file or a file-like object."
))
def _validate(self, finetune_enabled=False):
check_true(
isinstance(self.pool_option, PoolOptions),
TypeError("The pool type must be models.options.PoolOptions"))
def _validate(self, finetune_enabled=False):
check_true(
isinstance(self.word_option, WordOptions),
ValueError(
"The embedding must be one of the supported word embeddings."
))
check_true(
self.pretrained or self.word_option is WordOptions.word2vec,
ValueError(
"Only word2vec embeddings can be trained from scratch."))
check_true(
not finetune_enabled or self._is_finetuneable(),
ValueError(
"The weights can only be fine-tuned if they are not ELMo embeddings."
))
check_false(
not finetune_enabled and self.word_option == WordOptions.char,
ValueError(
"Character embeddings are only available if the model is fine-tuneable."
))
check_true(
not self.sparse or self.word_option == WordOptions.word2vec,
ValueError(
"Sparse embeddings only supported with word2vec embeddings"
))
check_true(
isinstance(self.layers, int)
or all([isinstance(l, int) for l in self.layers]),
ValueError(
"Layers can only be an integer or a list of integers"))
check_true(
not self.inline_pool_option
or isinstance(self.inline_pool_option, PoolOptions),
ValueError(
"Inline pooling should either be None or a pool option."))
if self.tokenizer:
check_true(
self.word_option == WordOptions.word2vec,
ValueError(
"The tokenizer can only be used if word2vec embeddings are used."
))
doc = "string"
res = self.tokenizer(doc)
check_true(
isinstance(res, list),
TypeError("The tokenizer should return a list of tokens."))
check_true(isinstance(res[0], str),
TypeError("The tokens should be of string type."))
def _validate(self, finetune_enabled=False):
import tensorflow # this will fail if tensorflow is not available
import tensorflow_hub # this will fail if tensorflow_hub is not available
check_true(self.pretrained,
ValueError("USE needs to be pretrained."))