torch_concepts.nn.ParametricCPD¶

class ParametricCPD(concepts: str | List[str], parametrization: Module | List[Module])[source]¶

A ParametricCPD represents a conditional probability distribution (CPD) in a probabilistic graphical model.

A ParametricCPD links concepts to neural network modules that compute probability distributions. It can automatically split multiple concepts into separate CPD and supports building conditional probability tables (CPTs) and potential tables for inference.

Parameters:

concepts (Union[str, List[str]]) – A single concept name or a list of concept names. If a list of N concepts is provided, the ParametricCPD automatically splits into N separate ParametricCPD instances.
module (Union[nn.Module, List[nn.Module]]) – A neural network module or list of modules that compute the probability distribution. If concepts is a list of length N, module can be: - A single module (will be replicated for all concepts) - A list of N modules (one per concept)

concepts¶

List of concept names associated with this CPD.

Type:: List[str]

module¶

The neural network module used to compute probabilities.

Type:: nn.Module

variable¶

The Variable instance this CPD is linked to (set by ProbabilisticModel).

Type:: Optional[Variable]

parents¶

List of parent Variables in the graphical model.

Type:: List[Variable]

Examples

>>> import torch
>>> import torch.nn as nn
>>> from torch_concepts.nn import ParametricCPD
>>>
>>> # Create different modules for different concepts
>>> module_a = nn.Linear(in_features=10, out_features=1)
>>> module_b = nn.Sequential(
...     nn.Linear(in_features=10, out_features=5),
...     nn.ReLU(),
...     nn.Linear(in_features=5, out_features=1)
... )
>>>
>>> # Create CPD with different modules
>>> cpd = ParametricCPD(
...     concepts=["binary_concept", "complex_concept"],
...     parametrization=[module_a, module_b]
... )
>>>
>>> print(cpd[0].parametrization)
Linear(in_features=10, out_features=1, bias=True)
>>> print(cpd[1].parametrization)
Sequential(...)

Notes

The ParametricCPD class uses a custom __new__ method to automatically split multiple concepts into separate ParametricCPD instances when a list is provided.
ParametricCPDs are typically created and managed by a ProbabilisticModel rather than directly.
The module should accept an ‘input’ keyword argument in its forward pass.
Supported distributions for CPT/potential building: Bernoulli, Categorical, Delta, Normal.

See also

Variable: Represents a random variable in the probabilistic model.
ProbabilisticModel: Container that manages CPD and variables.

__init__(concepts: str | List[str], parametrization: Module | List[Module])[source]¶: Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

`__init__`(concepts, parametrization)	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_cpt`()
`build_potential`()
`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`(**kwargs)	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`