torch_concepts.nn.GraphModel¶

class GraphModel(model_graph: ConceptGraph, 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)[source]¶

Concept-based model with explicit graph structure between concepts and tasks.

This model builds a probabilistic model based on a provided concept graph structure. It automatically constructs the necessary variables and CPDs following the graph’s topological order, supporting both root concepts (encoded from inputs) and internal concepts (predicted from parents).

The graph structure defines dependencies between concepts, enabling: - Hierarchical concept learning - Causal reasoning with interventions - Structured prediction with concept dependencies

model_graph¶

Directed acyclic graph defining concept relationships.

Type:: ConceptGraph

root_nodes¶

Concepts with no parents (encoded from inputs).

Type:: List[str]

internal_nodes¶

Concepts with parents (predicted from other concepts).

Type:: List[str]

graph_order¶

Topologically sorted concept names.

Type:: List[str]

probabilistic_model¶

Underlying PGM with variables and CPDs.

Type:: ProbabilisticModel

Parameters:

model_graph – ConceptGraph defining the structure (must be a DAG).
input_size – Size of input features.
annotations – Annotations object with concept metadata and distributions.
encoder – LazyConstructor for encoding root concepts from inputs.
predictor – LazyConstructor for predicting internal concepts from parents.
use_source_exogenous – Whether to use source exogenous features for predictions.
source_exogenous – Optional propagator for source exogenous features.
internal_exogenous – Optional propagator for internal exogenous features.

Raises:

AssertionError – If model_graph is not a DAG.
AssertionError – If node names don’t match annotations labels.

Example

>>> import torch
>>> import pandas as pd
>>> from torch_concepts import Annotations, AxisAnnotation, ConceptGraph
>>> from torch_concepts.nn import GraphModel, LazyConstructor, LinearCC
>>> from torch.distributions import Bernoulli
>>>
>>> # Define concepts and their structure
>>> # Structure: input -> [A, B] -> C -> D
>>> # A and B are root nodes (no parents)
>>> # C depends on A and B
>>> # D depends on C
>>> concept_names = ['A', 'B', 'C', 'D']
>>>
>>> # Create graph structure as adjacency matrix
>>> graph_df = pd.DataFrame(0, index=concept_names, columns=concept_names)
>>> graph_df.loc['A', 'C'] = 1  # A -> C
>>> graph_df.loc['B', 'C'] = 1  # B -> C
>>> graph_df.loc['C', 'D'] = 1  # C -> D
>>>
>>> graph = ConceptGraph(
...     torch.FloatTensor(graph_df.values),
...     node_names=concept_names
... )
>>>
>>> # Create annotations with distributions
>>> annotations = Annotations({
...     1: AxisAnnotation(
...         labels=tuple(concept_names),
...         metadata={
...             'A': {'distribution': Bernoulli},
...             'B': {'distribution': Bernoulli},
...             'C': {'distribution': Bernoulli},
...             'D': {'distribution': Bernoulli}
...         }
...     )
... })
>>>
>>> # Create GraphModel
>>> model = GraphModel(
...     model_graph=graph,
...     input_size=784,
...     annotations=annotations,
...     encoder=LazyConstructor(torch.nn.Linear),
...     predictor=LazyConstructor(LinearCC),
... )
>>>
>>> # Inspect the graph structure
>>> print(model.root_nodes)  # ['A', 'B'] - no parents
>>> print(model.internal_nodes)  # ['C', 'D'] - have parents
>>> print(model.graph_order)  # ['A', 'B', 'C', 'D'] - topological order
>>>
>>> # Check graph properties
>>> print(model.model_graph.is_dag())  # True
>>> print(model.model_graph.get_predecessors('C'))  # ['A', 'B']
>>> print(model.model_graph.get_successors('C'))  # ['D']

References: Dominici, et al. “Causal concept graph models: Beyond causal opacity in deep learning”, ICLR 2025. https://arxiv.org/abs/2405.16507. De Felice, et al. “Causally reliable concept bottleneck models”, NeurIPS https://arxiv.org/abs/2503.04363v1.

__init__(model_graph: ConceptGraph, 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)[source]¶: Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

`__init__`(model_graph, 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`