TorchProbabilityIntervalsCredalSet

class probly.representation.credal_set.torch.TorchProbabilityIntervalsCredalSet(lower_bounds: Tensor, upper_bounds: Tensor)

Bases: TorchAxisProtected[Any], TorchCategoricalCredalSet, ProbabilityIntervalsCredalSet

Credal set represented by lower/upper categorical bounds.

property T: Self

Inverts the order of the dimensions of the underlying array.

property barycenter: TorchCategoricalDistribution

Return the barycenter of the credal set.

clone(*, memory_format: memory_format = torch.preserve_format) → Self

Return a copy of the array.

contains(probabilities: Tensor) → Tensor

Check whether probabilities are inside the intervals.

cpu(memory_format: memory_format = torch.preserve_format) → Self

Move the array to the CPU.

cuda(device: device | str | None = None, non_blocking: bool = False, memory_format: memory_format = torch.preserve_format) → Self

Move the array to the GPU.

detach() → Self

Return a detached version of the array.

property device: device

Device of the array.

property dtype: dtype

Data type of the array.

classmethod from_sample(sample: Sample[TorchCategoricalDistribution]) → Self

Create a credal set from a finite sample.

Parameters:

sample – The sample to create the credal set from.

Returns:

The created credal set.

classmethod from_torch_sample(sample: TorchSample[TorchCategoricalDistribution]) → Self

Create a credal set from categorical distribution samples.

gather(dim: int, index: Tensor) → Self

Return a copy with gathered protected values along a batch dimension.

lower() → Tensor

Return the per-class lower probability envelope (alias for lower_bounds).

lower_bounds: torch.Tensor

property mH: Self

The adjoint (conjugate) transposed version of the underlying array.

property mT: Self

The transposed version of the underlying array.

property ndim: int

Number of dimensions.

property num_classes: int

Get the number of classes.

numpy(*, force: bool = False) → NDArray[Any]

Convert to a numpy array.

permitted_functions = {}

permute(*dims: Size | int | tuple[int] | list[int]) → Self

Return a permuted version of the array.

classmethod primary_protected_name() → str

Return the first protected field (dict order).

protected_axes: ClassVar[dict[str, int]] = {'lower_bounds': 1, 'upper_bounds': 1}

property protected_shape: tuple[int, ...]

Protected trailing shape of the primary field.

protected_value() → TorchProtectedValue

Return the primary protected value.

protected_values(func: Callable | None = None) → dict[str, TorchProtectedValue] | None

Return all protected field values as-is.

Optionally takes the torch function that triggered the call for context. This can be used to conditionally modify the returned values or prevent them from being accessed.

reshape(*shape: int | tuple[int, ...]) → Self

Return a copy with reshaped protected values.

resolve_conj() → Self

Return a version of the array with any conjugate operations resolved.

resolve_neg() → Self

Return a version of the array with any negation operations resolved.

property shape: tuple[int, ...]

Shape of the array.

size(dim: int | None = None) → int | Size

Return the size of the array along the given dimension.

to(*args: Any, **kwargs: Any) → Self

Move and/or cast the tensor, mirroring torch.Tensor.to.

to_device(device: Literal['cpu'], /, *, stream: int | Any | None = None) → Self

Move the array to a device.

transpose(dim0: int, dim1: int) → Self

Return a transposed version of the array.

type = 'categorical'

upper() → Tensor

Return the per-class upper probability envelope (alias for upper_bounds).

upper_bounds: torch.Tensor

width() → Tensor

Compute interval width for each class.

with_protected_values(values: dict[str, TorchProtectedValue], func: Callable | None = None) → TorchAxisProtected[T]

Return a copy with updated protected field values.

Examples using probly.representation.credal_set.torch.TorchProbabilityIntervalsCredalSet

Credal Net Visualization

Credal Net Visualization

Credal Net on MNIST

Credal Net on MNIST

Credal Relative Likelihood Visualization

Credal Relative Likelihood Visualization

Credal Relative Likelihood on MNIST

Credal Relative Likelihood on MNIST

Credal Wrapper Output Visualization

Credal Wrapper Output Visualization

Credal Wrapper on MNIST

Credal Wrapper on MNIST