Source code for pina.condition.data_condition
"""Module for the DataCondition class."""
import torch
from torch_geometric.data import Data
from .condition_interface import ConditionInterface
from ..label_tensor import LabelTensor
from ..graph import Graph
[docs]
class DataCondition(ConditionInterface):
"""
Condition defined by input data and conditional variables. It can be used
in unsupervised learning problems. Based on the type of the input,
different condition implementations are available:
- :class:`TensorDataCondition`: For :class:`torch.Tensor` or
:class:`~pina.label_tensor.LabelTensor` input data.
- :class:`GraphDataCondition`: For :class:`~pina.graph.Graph` or
:class:`~torch_geometric.data.Data` input data.
"""
__slots__ = ["input", "conditional_variables"]
_avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple)
_avail_conditional_variables_cls = (torch.Tensor, LabelTensor)
def __new__(cls, input, conditional_variables=None):
"""
Instantiate the appropriate subclass of :class:`DataCondition` based on
the type of ``input``.
:param input: Input data for the condition.
:type input: torch.Tensor | LabelTensor | Graph |
Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data]
:param conditional_variables: Conditional variables for the condition.
:type conditional_variables: torch.Tensor | LabelTensor, optional
:return: Subclass of DataCondition.
:rtype: pina.condition.data_condition.TensorDataCondition |
pina.condition.data_condition.GraphDataCondition
:raises ValueError: If input is not of type :class:`torch.Tensor`,
:class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`,
or :class:`~torch_geometric.data.Data`.
"""
if cls != DataCondition:
return super().__new__(cls)
if isinstance(input, (torch.Tensor, LabelTensor)):
subclass = TensorDataCondition
return subclass.__new__(subclass, input, conditional_variables)
if isinstance(input, (Graph, Data, list, tuple)):
cls._check_graph_list_consistency(input)
subclass = GraphDataCondition
return subclass.__new__(subclass, input, conditional_variables)
raise ValueError(
"Invalid input types. "
"Please provide either torch_geometric.data.Data or Graph objects."
)
def __init__(self, input, conditional_variables=None):
"""
Initialize the object by storing the input and conditional
variables (if any).
:param input: Input data for the condition.
:type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] |
list[Data] | tuple[Graph] | tuple[Data]
:param conditional_variables: Conditional variables for the condition.
:type conditional_variables: torch.Tensor | LabelTensor
.. note::
If ``input`` consists of a list of :class:`~pina.graph.Graph` or
:class:`~torch_geometric.data.Data`, all elements must have the same
structure (keys and data types)
"""
super().__init__()
self.input = input
self.conditional_variables = conditional_variables
[docs]
class TensorDataCondition(DataCondition):
"""
DataCondition for :class:`torch.Tensor` or
:class:`~pina.label_tensor.LabelTensor` input data
"""
[docs]
class GraphDataCondition(DataCondition):
"""
DataCondition for :class:`~pina.graph.Graph` or
:class:`~torch_geometric.data.Data` input data
"""