torch_concepts.nn.ProbabilisticModel¶

class ProbabilisticModel(variables: List[Variable], parametric_cpds: List[ParametricCPD])[source]¶

Probabilistic Model for concept-based reasoning.

This class represents a directed acyclic graph (DAG) where nodes are concept variables and edges represent probabilistic dependencies. Each variable has an associated CPD (neural network module) that computes its conditional probability given its parents.

variables¶

List of concept variables in the model.

Type:: List[Variable]

parametric_cpds¶

Dictionary mapping concept names to their CPDs.

Type:: nn.ModuleDict

concept_to_variable¶

Mapping from concept names to variables.

Type:: Dict[str, Variable]

Parameters:

variables – List of Variable objects defining the concepts.
parametric_cpds – List of ParametricCPD objects defining the conditional distributions.

Example

>>> import torch
>>> from torch_concepts import InputVariable, EndogenousVariable
>>> from torch_concepts.nn import ProbabilisticModel
>>> from torch_concepts.nn import ParametricCPD
>>> from torch_concepts.nn import LinearZC
>>> from torch_concepts.nn import LinearCC
>>> from torch_concepts.distributions import Delta
>>>
>>> # Define variables
>>> emb_var = InputVariable(concepts='input', parents=[], distribution=Delta, size=32)
>>> c1_var = EndogenousVariable(concepts='c1', parents=[emb_var], distribution=Delta, size=1)
>>> c2_var = EndogenousVariable(concepts='c2', parents=[c1_var], distribution=Delta, size=1)
>>>
>>> # Define CPDs (neural network modules)
>>> backbone = torch.nn.Linear(in_features=128, out_features=32)
>>> encoder = LinearZC(in_features=32, out_features=1)
>>> predictor = LinearCC(in_features_endogenous=1, out_features=1)
>>>
>>> parametric_cpds = [
...     ParametricCPD(concepts='input', parametrization=backbone),
...     ParametricCPD(concepts='c1', parametrization=encoder),
...     ParametricCPD(concepts='c2', parametrization=predictor)
... ]
>>>
>>> # Create ProbabilisticModel
>>> probabilistic_model = ProbabilisticModel(
...     variables=[emb_var, c1_var, c2_var],
...     parametric_cpds=parametric_cpds
... )
>>>
>>> print(f"Number of variables: {len(probabilistic_model.variables)}")
Number of variables: 3

__init__(variables: List[Variable], parametric_cpds: List[ParametricCPD])[source]¶: Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

`__init__`(variables, parametric_cpds)	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.
`build_cpts`()	Build Conditional Probability Tables (CPTs) for all concepts.
`build_potentials`()	Build potential functions for all concepts in the ProbabilisticModel.
`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_by_distribution`(distribution_class)	Get all variables with a specific distribution type.
`get_extra_state`()	Return any extra state to include in the module's state_dict.
`get_module_of_concept`(concept_name)	Return the neural network module for a given concept.
`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.
`get_variable_parents`(concept_name)	Get the parent variables of a concept.
`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`