def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
"""Checks whether the net is initialized.
Note: This calls ``sklearn.utils.validation.check_is_fitted``
under the hood, using exactly the same arguments and logic. The
only difference is that this function has an adapted error message
and raises a ``skorch.exception.NotInitializedError`` instead of
an ``sklearn.exceptions.NotFittedError``.
"""
if msg is None:
msg = ("This %(name)s instance is not initialized yet. Call "
"'initialize' or 'fit' with appropriate arguments "
"before using this method.")
if not isinstance(attributes, (list, tuple)):
attributes = [attributes]
if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
raise NotInitializedError(msg % {'name': type(estimator).__name__})
def load_params(self, f):
"""Load only the module's parameters, not the whole object.
To save and load the whole object, use pickle.
Parameters
----------
f : file-like object or str
See ``torch.load`` documentation.
Example
-------
>>> before = NeuralNetClassifier(mymodule)
>>> before.save_params('path/to/file')
>>> after = NeuralNetClassifier(mymodule).initialize()
>>> after.load_params('path/to/file')
"""
if not hasattr(self, 'module_'):
raise NotInitializedError(
"Cannot load parameters of an un-initialized model. "
"Please initialize first by calling .initialize() "
"or by fitting the model with .fit(...).")
cuda_req_not_met = (self.use_cuda and not torch.cuda.is_available())
if not self.use_cuda or cuda_req_not_met:
# Eiher we want to load the model to the CPU in which case
# we are loading in a way where it doesn't matter if the data
# was on the GPU or not or the model was on the GPU but there
# is no CUDA device available.
if cuda_req_not_met:
warnings.warn(
"Model configured to use CUDA but no CUDA devices "
"available. Loading on CPU instead.",
ResourceWarning)
self.use_cuda = False
model = torch.load(f, lambda storage, loc: storage)
else:
model = torch.load(f)
self.module_.load_state_dict(model)
def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
"""Checks whether the net is initialized.
Note: This calls ``sklearn.utils.validation.check_is_fitted``
under the hood, using exactly the same arguments and logic. The
only difference is that this function has an adapted error message
and raises a ``skorch.exception.NotInitializedError`` instead of
an ``sklearn.exceptions.NotFittedError``.
"""
if msg is None:
msg = ("This %(name)s instance is not initialized yet. Call "
"'initialize' or 'fit' with appropriate arguments "
"before using this method.")
try:
sklearn_check_is_fitted(
estimator=estimator,
attributes=attributes,
msg=msg,
all_or_any=all_or_any,
)
except NotFittedError as e:
raise NotInitializedError(str(e))
def save_params(self, f):
"""Save only the module's parameters, not the whole object.
To save the whole object, use pickle.
Parameters
----------
f : file-like object or str
See ``torch.save`` documentation.
Example
-------
>>> before = NeuralNetClassifier(mymodule)
>>> before.save_params('path/to/file')
>>> after = NeuralNetClassifier(mymodule).initialize()
>>> after.load_params('path/to/file')
"""
if not hasattr(self, 'module_'):
raise NotInitializedError(
"Cannot save parameters of an un-initialized model. "
"Please initialize first by calling .initialize() "
"or by fitting the model with .fit(...).")
torch.save(self.module_.state_dict(), f)