Data objects#
A data object describing a spatiotemporal graph, i.e., a graph with time series of equal length associated with every node. 

A batch of 

A data object describing a batch of graphs as one big (disconnected) graph. 
 class Data(input: Optional[Mapping] = None, target: Optional[Mapping] = None, edge_index: Optional[Union[Tensor, SparseTensor]] = None, edge_weight: Optional[Tensor] = None, mask: Optional[Tensor] = None, transform: Optional[Mapping] = None, pattern: Optional[Mapping] = None, **kwargs)[source]#
A data object describing a spatiotemporal graph, i.e., a graph with time series of equal length associated with every node.
The data object extends
torch_geometric.data.Data
, thus preserving all its functionalities (see also the accompanying tutorial). Parameters:
input (Mapping, optional) – Named mapping of
Tensor
to be used as input to the model. (default:None
)target (Mapping, optional) – Named mapping of
Tensor
to be used as target of the task. (default:None
)edge_index (Adj, optional) – Graph connectivity either in COO format (a
Tensor
of shape[2, E]
) or as atorch_sparse.SparseTensor
with shape[N, N]
. For dynamic graphs – with timevarying topology – can be a Python list ofTensor
. (default:None
)edge_weight (Tensor, optional) – Weights of the edges (if
edge_index
is not atorch_sparse.SparseTensor
). (default:None
)mask (Tensor, optional) – The optional mask associated with the target. (default:
None
)transform (Mapping, optional) – Named mapping of
Scaler
associated with entries ininput
oroutput
. (default:None
)pattern (Mapping, optional) – Map of the pattern of each entry in
input
oroutput
. (default:None
)**kwargs – Any keyword argument for
Data
.
 numpy(*args: List[str])[source]#
Transform all tensors to numpy arrays, either for all attributes or only the ones given in
*args
.
 rearrange_element(key: str, pattern: str, **axes_lengths)[source]#
Rearrange key in Data according to the provided patter using einops.rearrange.
 rearrange(patterns: Mapping)[source]#
Rearrange all keys in Data according to the provided pattern using einops.rearrange.
 class StaticBatch(input: Optional[Mapping] = None, target: Optional[Mapping] = None, edge_index: Optional[Union[Tensor, SparseTensor]] = None, edge_weight: Optional[Tensor] = None, mask: Optional[Tensor] = None, transform: Optional[Mapping] = None, pattern: Optional[Mapping] = None, size: Optional[int] = None, **kwargs)[source]#
A batch of
tsl.data.Data
objects for multiple spatiotemporal graphs sharing the same topology.The batch object extends
Data
, thus preserving all its functionalities. Parameters:
input (Mapping, optional) – Named mapping of
Tensor
to be used as input to the model. (default:None
)target (Mapping, optional) – Named mapping of
Tensor
to be used as target of the task. (default:None
)edge_index (Adj, optional) – Shared graph connectivity, either in COO format (a
Tensor
of shape[2, E]
) or as atorch_sparse.SparseTensor
with shape[N, N]
. (default:None
)edge_weight (Tensor, optional) – Weights of the edges (if
edge_index
is not atorch_sparse.SparseTensor
). (default:None
)mask (Tensor, optional) – The optional mask associated with the target. (default:
None
)transform (Mapping, optional) – Named mapping of
Scaler
associated with entries ininput
oroutput
. (default:None
)pattern (Mapping, optional) – Map of the pattern of each entry in
input
oroutput
. (default:None
)size (int, optional) – The batch size, i.e., the number of spatiotemporal graphs in the batch. The different samples in the batch share all the same topology, such that there is (at most) only one
edge_index
andedge_weight
. IfNone
, then the batch size is inferred from data (if possible). (default:None
)**kwargs – Any keyword argument for
Data
.
 classmethod from_data_list(data_list: List[Data])[source]#
Constructs a
Batch
object from a Python list ofData
representing temporal signals on a static (shared) graph.
 get_example(idx: int) Data [source]#
Gets the
Data
orHeteroData
object at indexidx
. TheBatch
object must have been created viafrom_data_list()
in order to be able to reconstruct the initial object.
 index_select(idx: Union[slice, List, Tuple, Tensor, ndarray]) List[Data] [source]#
Creates a subset of
Data
objects from specified indicesidx
.Indices
idx
can be a slicing object, e.g.,[2:5]
, a list, a tuple, or atorch.Tensor
ornp.ndarray
of type long or bool.
 class DisjointBatch(*args, **kwargs)[source]#
A data object describing a batch of graphs as one big (disconnected) graph.
Inherits from
tsl.data.Data
. In addition, single graphs can be identified via the assignment vectorbatch
, which maps each node to its respective graph identifier. classmethod from_data_list(data_list: List[Data], force_batch: bool = False, follow_batch: Optional[List[str]] = None, exclude_keys: Optional[List[str]] = None, graph_attributes: Optional[List[str]] = None)[source]#
Constructs a
DisjointBatch
object from a list ofData
objects.The assignment vector
batch
is created on the fly. In addition, creates assignment vectors for each key infollow_batch
. Will exclude any keys given inexclude_keys
. Parameters:
data_list (list) – The list of
tsl.data.Data
objects.force_batch (bool) – If
True
, then add add dummy batch dimension for timevarying elements. (default:False
)follow_batch (list, optional) – Create an assignment vector for each key in
follow_batch
. (default:None
)exclude_keys (list, optional) – Exclude the keys in
exclude_keys
from collate. (default:None
)graph_attributes – Keys in
graph_attributes
with no node dimension will be added to the batch as graph attributes, i.e., the tensors will be stacked on a new dimension (the first one). Note that all graph attributes indexed by a key which is not in this list are repeated along a new node dimension (the second one, if the attribute is timevarying, otherwise the first one). (default:None
)
 get_example(idx: int) Data [source]#
Gets the
Data
orHeteroData
object at indexidx
. TheBatch
object must have been created viafrom_data_list()
in order to be able to reconstruct the initial object.