torch_concepts.nn.BipartiteModel

class BipartiteModel(task_names: List[str] | str, input_size: int, annotations: Annotations, encoder: LazyConstructor | Module, predictor: LazyConstructor | Module, use_source_exogenous: bool | None = None, source_exogenous: LazyConstructor | Module | None = None, internal_exogenous: LazyConstructor | Module | None = None)

Bipartite concept graph model with concepts and tasks in separate layers.

This model implements a bipartite graph structure where concepts only connect to tasks (not to each other), creating a clean separation between concept and task layers. This is useful for multi-task learning with shared concepts.

label_names

All node labels (concepts + tasks).

Type:

List[str]

concept_names

Concept node labels.

Type:

List[str]

task_names

Task node labels.

Type:

List[str]

Parameters:

• task_names – List of task names (must be in annotations labels).

• input_size – Size of input features.

• annotations – Annotations object with concept and task metadata.

• encoder – LazyConstructor for encoding concepts from inputs.

• predictor – LazyConstructor for predicting tasks from concepts.

• use_source_exogenous – Whether to use exogenous features for source nodes.

• source_exogenous – Optional propagator for source exogenous features.

• internal_exogenous – Optional propagator for internal exogenous features.

Example

>>> import torch
>>> from torch_concepts import Annotations, AxisAnnotation
>>> from torch_concepts.nn import BipartiteModel, LazyConstructor, LinearCC
>>> from torch.distributions import Bernoulli
>>>
>>> # Define concepts and tasks
>>> all_labels = ('color', 'shape', 'size', 'task1', 'task2')
>>> metadata = {'color': {'distribution': Bernoulli},
...             'shape': {'distribution': Bernoulli},
...             'size': {'distribution': Bernoulli},
...             'task1': {'distribution': Bernoulli},
...             'task2': {'distribution': Bernoulli}}
>>> annotations = Annotations({
...     1: AxisAnnotation(labels=all_labels, metadata=metadata)
... })
>>>
>>> # Create bipartite model with tasks
>>> task_names = ['task1', 'task2']
>>>
>>> model = BipartiteModel(
...     task_names=task_names,
...     input_size=784,
...     annotations=annotations,
...     encoder=LazyConstructor(torch.nn.Linear),
...     predictor=LazyConstructor(LinearCC)
... )
>>>
>>> # Generate random input
>>> x = torch.randn(8, 784)  # batch_size=8
>>>
>>> # Forward pass (implementation depends on GraphModel)
>>> # Concepts are encoded, then tasks predicted from concepts
>>> print(model.concept_names)  # ['color', 'shape', 'size']
>>> print(model.task_names)     # ['task1', 'task2']
>>> print(model.probabilistic_model)
>>>
>>> # The bipartite structure ensures:
>>> # - Concepts don't predict other concepts
>>> # - Only concepts -> tasks edges exist

__init__(task_names: List[str] | str, input_size: int, annotations: Annotations, encoder: LazyConstructor | Module, predictor: LazyConstructor | Module, use_source_exogenous: bool | None = None, source_exogenous: LazyConstructor | Module | None = None, internal_exogenous: LazyConstructor | Module | None = None)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(task_names, input_size, ...[, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(*input)	Define the computation performed at every call.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training