_utils

Helper functions for PyTorch forecasting

Functions

apply_to_list(obj, func)	Apply function to a list of objects or directly if passed value is not a list.
autocorrelation(input[, dim])	Computes the autocorrelation of samples at dimension dim.
concat_sequences(sequences)	Concatenate RNN sequences.
create_mask(size, lengths[, inverse])	Create boolean masks of shape len(lenghts) x size.
detach(x)	Detach object
get_embedding_size(n[, max_size])	Determine empirically good embedding sizes (formula taken from fastai).
groupby_apply(keys, values[, bins, ...])	Groupby apply for torch tensors
integer_histogram(data[, min, max])	Create histogram of integers in predefined range
masked_op(tensor[, op, dim, mask])	Calculate operation on masked tensor.
move_to_device(x, device)	Move object to device.
next_fast_len(size)	Returns the next largest number n >= size whose prime factors are all 2, 3, or 5.
padded_stack(tensors[, side, mode, value])	Stack tensors along first dimension and pad them along last dimension to ensure their size is equal.
profile(function, profile_fname[, filter, ...])	Profile a given function with vmprof.
repr_class(obj, attributes[, ...])	Print class name and parameters.
to_list(value)	Convert value or list to list of values.
unpack_sequence(sequence)	Unpack RNN sequence.
unsqueeze_like(tensor, like)	Unsqueeze last dimensions of tensor to match another tensor's number of dimensions.

Classes

InitialParameterRepresenterMixIn()
OutputMixIn()	MixIn to give namedtuple some access capabilities of a dictionary
TupleOutputMixIn()	MixIn to give output a namedtuple-like access capabilities with to_network_output() function.