torch_concepts.nn.UncertaintyInterventionPolicy¶

class UncertaintyInterventionPolicy(out_features: int, max_uncertainty_point: float = 0.0)[source]¶

Uncertainty-based intervention policy using distance from a maximum uncertainty point.

This policy measures uncertainty as the distance of concept endogenous from a maximum uncertainty point. Values closer to this point are considered more uncertain, while values further from this point are considered more certain.

out_features¶

Number of output features.

Type:: int

max_uncertainty_point¶

The point where uncertainty is maximum.

Type:: float

Parameters:

out_features – Number of output concept features.
max_uncertainty_point – The value representing maximum uncertainty (default: 0.0). Values closer to this point are more uncertain, values further away are more certain.

Example

>>> import torch
>>> from torch_concepts.nn import UncertaintyInterventionPolicy
>>>
>>> # Create uncertainty policy with default max uncertainty point (0.0)
>>> policy = UncertaintyInterventionPolicy(out_features=10)
>>>
>>> # Generate concept endogenous with varying confidence
>>> endogenous = torch.tensor([
...     [3.0, -2.5, 0.1, -0.2, 4.0],  # High confidence for 1st, 2nd, 5th
...     [0.5, 0.3, -0.4, 2.0, -1.5]   # Mixed confidence
... ])
>>>
>>> # Apply policy - returns distance from max uncertainty point (certainty scores)
>>> scores = policy(endogenous)
>>> print(scores)
>>> # tensor([[3.0, 2.5, 0.1, 0.2, 4.0],
>>> #         [0.5, 0.3, 0.4, 2.0, 1.5]])
>>>
>>> # Higher scores = higher certainty = lower intervention priority
>>> # For intervention, you'd typically intervene on LOW scores
>>> print(scores[0].argmin())  # tensor(2) - most uncertain concept
>>> print(scores[0].argmax())  # tensor(4) - most certain concept
>>>
>>> # Use custom max uncertainty point (e.g., 0.5 for probabilities)
>>> policy_prob = UncertaintyInterventionPolicy(out_features=5, max_uncertainty_point=0.5)
>>> probs = torch.tensor([[0.1, 0.5, 0.9, 0.45, 0.55]])
>>> certainty = policy_prob(probs)
>>> print(certainty)
>>> # tensor([[0.4, 0.0, 0.4, 0.05, 0.05]])
>>> # Values at 0.5 are most uncertain, values at 0.1 or 0.9 are most certain

__init__(out_features: int, max_uncertainty_point: float = 0.0)[source]¶

Methods

`__init__`(out_features[, max_uncertainty_point])
`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`(endogenous)	Compute certainty scores as distance from maximum uncertainty point.
`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`