peal.explainers.interfaces.ExplainerConfig

class peal.explainers.interfaces.ExplainerConfig

Bases: BaseModel

Base pydantic config of an explainer.

Concrete explainers subclass this and add their own fields; the attributes documented inline below are common to all of them.

Methods

Attributes

category	The category of the config.
clustering_strategy
component_bounds_scale	Widen (>1) or tighten (<1) the empirical [c_min, c_max] clamp of the "dynamic" linesearch about its midpoint, the same knob DiDAE's sweep exposes as component_bounds_scale.
component_indices
explanations_dir	The directory where the explanations are stored.
max_samples	The number of samples that counterfactuals are created for.
merge_clusters	How to merge clusters of explanations?
num_attempts	The number of counterfactuals created for the same sample.
parallel_attempts	How many counterfactuals are created for the same factual in parallel.
port	The port the feedback for the explanations shall be given when using the webinterface.
seed	The seed of all randomness to make results reproducible.
temperature	The temperature used for the softmax when creating counterfactuals.
tracking_level	How many intermediate results are cached an visualized.
transition_restrictions	The restriction to interesting counterfactual transitions.
use_clustering	Whether to cluster the explanations and return most salient ones.
validate_generator	Whether to sanity check the used generator before creating explanations.
explainer_type	The type of explanation that shall be used.

explainer_type: str

The type of explanation that shall be used. This is necessary to know which pydantic class to use when loading from yaml.

category: str = 'explainer'

The category of the config. Can not be changed for explainer. This is also necessary to identify which pydantic class to use when loading from yaml.

explanations_dir: str = 'explanations'

The directory where the explanations are stored. This only is used if explainer is executed directly and not e.g. executed via CFKD.

port: int = 8000

The port the feedback for the explanations shall be given when using the webinterface.

tracking_level: int = 2

How many intermediate results are cached an visualized. Goes from 0 = None over 1 = caching only to 2 = essential visualizations to 3 = all.

validate_generator: bool = False

Whether to sanity check the used generator before creating explanations.

max_samples: int | None = None

The number of samples that counterfactuals are created for. If set to None there will be one counterfactual created for every sample in dataset.

temperature: float = 3.0

The temperature used for the softmax when creating counterfactuals. Can be useful for calibration if confidence goes against 0 or 1 too fast.

use_clustering: bool = True

Whether to cluster the explanations and return most salient ones.

merge_clusters: str = 'concatenate'

How to merge clusters of explanations?

num_attempts: int = 1

The number of counterfactuals created for the same sample.

seed: int = 0

The seed of all randomness to make results reproducible.

transition_restrictions: list | None = None

The restriction to interesting counterfactual transitions. Helpful in the case of datasets with a lot of classes and heavy modes like ImageNet.

clustering_strategy: str = 'attempt_nr'

parallel_attempts: int = 1

How many counterfactuals are created for the same factual in parallel.

component_indices: list | None = None

component_bounds_scale: float = 1.0

Widen (>1) or tighten (<1) the empirical [c_min, c_max] clamp of the “dynamic” linesearch about its midpoint, the same knob DiDAE’s sweep exposes as component_bounds_scale. A direction DiDAE only found at x3 is unreachable for CFKD at x1, so DiDAE step 9 forwards its own value here. 1.0 = bounds as written.