xai4tsc.xai.random_baseline

Random-baseline explainers in the frequency and time-frequency domains.

Sanity baselines for evaluation: they ignore the model and return uniform-random relevance shaped like the transformed input. A faithful explainer should beat these on perturbation metrics.

Classes

RandomFrequencyExplainer	Uniform-random baseline relevance in the frequency domain.
RandomTimeFreqExplainer	Uniform-random baseline relevance in the time-frequency domain.

Module Contents

class xai4tsc.xai.random_baseline.RandomFrequencyExplainer(transform: xai4tsc.utils.fourier_transforms.DomainTransform | dict | None = None, seed: int | None = None)

Bases: xai4tsc.xai.base.PerturbationExplainer

Uniform-random baseline relevance in the frequency domain.

Parameters:

• transform (DomainTransform or dict, optional) – Transform from time to frequency domain. Defaults to a full FourierTransform.

• seed (int, optional) – Seed for the random relevance, for reproducible draws.

data_applicability: ClassVar[set[xai4tsc.xai._types.DataType]]

Data domains this explainer applies to. A set of DataType members — {DataType.AGNOSTIC} (any input) or {DataType.TIME_SERIES}.

explanation_domains: ClassVar[set[xai4tsc.xai._types.Domain]]

Signal domains this explainer can produce explanations in. A set of Domain members (capability declaration, mirroring data_applicability). Checked statically by the runner’s config sanity check before any explainer is instantiated. Defaults to {Domain.TIME}; frequency/time-frequency explainers override it. The realized domain of a produced explanation (Explanation.explanation_domain) must be a member of this set.

transform

prng

explain(model: torch.nn.Module, exp: xai4tsc.xai._types.Explanation, device: str | torch.device, targets: list | None, **kwargs: object) → numpy.ndarray

Return uniform-random relevance shaped like the frequency transform.

class xai4tsc.xai.random_baseline.RandomTimeFreqExplainer(transform: xai4tsc.utils.fourier_transforms.DomainTransform | dict | None = None, seed: int | None = None)

Bases: xai4tsc.xai.base.PerturbationExplainer

Uniform-random baseline relevance in the time-frequency domain.

Parameters:

• transform (DomainTransform or dict) – Transform from time to time-frequency domain (e.g. an STFTransform). Required — there is no sensible default window configuration.

• seed (int, optional) – Seed for the random relevance, for reproducible draws.

data_applicability: ClassVar[set[xai4tsc.xai._types.DataType]]

Data domains this explainer applies to. A set of DataType members — {DataType.AGNOSTIC} (any input) or {DataType.TIME_SERIES}.

explanation_domains: ClassVar[set[xai4tsc.xai._types.Domain]]

Signal domains this explainer can produce explanations in. A set of Domain members (capability declaration, mirroring data_applicability). Checked statically by the runner’s config sanity check before any explainer is instantiated. Defaults to {Domain.TIME}; frequency/time-frequency explainers override it. The realized domain of a produced explanation (Explanation.explanation_domain) must be a member of this set.

transform = None

prng

explain(model: torch.nn.Module, exp: xai4tsc.xai._types.Explanation, device: str | torch.device, targets: list | None, **kwargs: object) → numpy.ndarray

Return uniform-random relevance shaped like the time-frequency transform.