Store and retrieve the train step data.

This class simply stores the first step value and returns it.

For most uses, skorch.utils.FirstStepAccumulator is what you want, since the optimizer calls the train step exactly once. However, some optimizerss such as LBFGSs make more than one call. If in that case, you don’t want the first value to be returned (but instead, say, the last value), implement your own accumulator and make sure it is returned by NeuralNet.get_train_step_accumulator method.

Methods

Stores a generator and calls tee on it to create new generators when TeeGenerator is iterated over to let you iterate over the given generator more than once.

Perform a check how incoming data should be indexed and return an appropriate indexing function with signature f(data, index).

This is useful for determining upfront how data should be indexed instead of doing it repeatedly for each batch, thus saving some time.

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 .

Try to access X and y attribute from dataset.

Also works when dataset is a subset.

dataset : skorch.dataset.Dataset or torch.utils.data.Subset

The incoming dataset should be a skorch.dataset.Dataset or a torch.utils.data.Subset of a skorch.dataset.Dataset .

X_indexing : function/callable or None (default=None)

If not None, use this function for indexing into the X data. If None, try to automatically determine how to index data.

y_indexing : function/callable or None (default=None)

If not None, use this function for indexing into the y data. If None, try to automatically determine how to index data.

skorch.utils. duplicate_items ( *collections ) [source] ¶

Search for duplicate items in all collections.

Examples

>>> duplicate_items([1, 2], [3])
set()
>>> duplicate_items({1: 'a', 2: 'a'})
set()
>>> duplicate_items(['a', 'b', 'a'])
{'a'}
>>> duplicate_items([1, 2], {3: 'hi', 4: 'ha'}, (2, 3))
{2, 3}
skorch.utils.freeze_parameter(param)[source]¶
Convenience function to freeze a passed torch parameter.
Used by skorch.callbacks.Freezer
skorch.utils.get_dim(y)[source]¶
Return the number of dimensions of a torch tensor or numpy
array-like object.
skorch.utils.get_map_location(target_device, fallback_device='cpu')[source]¶
Determine the location to map loaded data (e.g., weights)
for a given target device (e.g. ‘cuda’).
skorch.utils.is_skorch_dataset(ds)[source]¶
Checks if the supplied dataset is an instance of
skorch.dataset.Dataset even when it is nested inside
torch.util.data.Subset.
skorch.utils.multi_indexing(data, i, indexing=None)[source]¶
Perform indexing on multiple data structures.
Currently supported data types:
numpy arrays
torch tensors
pandas NDFrame
a dictionary of the former three
a list/tuple of the former three
i can be an integer or a slice.
indexing : function/callable or None (default=None)
If not None, use this function for indexing into the data. If
None, try to automatically determine how to index data.
>>> multi_indexing({'a': [1, 2, 3], 'b': [4, 5, 6]}, np.s_[-2:])
{'a': [2, 3], 'b': [5, 6]}
>>> multi_indexing(pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}), [1, 2])
1  2  5
2  3  6
skorch.utils.noop(*args, **kwargs)[source]¶
No-op function that does nothing and returns None.
This is useful for defining scoring callbacks that do not need a
target extractor.
skorch.utils.params_for(prefix, kwargs)[source]¶
Extract parameters that belong to a given sklearn module prefix from
kwargs. This is useful to obtain parameters that belong to a
submodule.
Examples
>>> kwargs = {'encoder__a': 3, 'encoder__b': 4, 'decoder__a': 5}
>>> params_for('encoder', kwargs)
{'a': 3, 'b': 4}
skorch.utils.to_device(X, device)[source]¶
Generic function to modify the device type of the tensor(s) or module.
PyTorch distribution objects are left untouched, since they don’t support an
API to move between devices.
device : str, torch.device
The compute device to be used. If device=None, return the input
unmodified
skorch.utils.to_numpy(X)[source]¶
Generic function to convert a pytorch tensor to numpy.
This function tries to unpack the tensor(s) from supported
data structures (e.g., dicts, lists, etc.) but doesn’t go
beyond.
Returns X when it already is a numpy array.
device : str, torch.device
The compute device to be used. If set to ‘cuda’, data in torch
tensors will be pushed to cuda tensors before being sent to the
module.
accept_sparse : bool (default=False)
Whether to accept scipy sparse matrices as input. If False,
passing a sparse matrix raises an error. If True, it is
converted to a torch COO tensor.
skorch.utils.unfreeze_parameter(param)[source]¶
Convenience function to unfreeze a passed torch parameter.
Used by skorch.callbacks.Unfreezer