alibi.explainers.anchors.anchor_tabular_distributed

DistributedAnchorBaseBeam

Inherits from: AnchorBaseBeam

Constructor

DistributedAnchorBaseBeam(self, samplers: List[Callable], **kwargs) -> None

Name	Type	Default	Description
samplers	List[Callable]		Objects that can be called with args (result, n_samples) tuple to draw samples.

Methods

draw_samples

draw_samples(anchors: list, batch_size: int) -> Tuple[numpy.ndarray, numpy.ndarray]

Name	Type	Default	Description
anchors	list
batch_size	int
anchors,	batch_size		See :py:meth:alibi.explainers.anchors.anchor_base.AnchorBaseBeam.draw_samples implementation.

Returns

• Type: Tuple[numpy.ndarray, numpy.ndarray]

DistributedAnchorTabular

Inherits from: AnchorTabular, Explainer, FitMixin, ABC, Base

Constructor

DistributedAnchorTabular(self, predictor: Callable, feature_names: List[str], categorical_names: Optional[Dict[int, List[str]]] = None, dtype: Type[numpy.generic] = <class 'numpy.float32'>, ohe: bool = False, seed: Optional[int] = None) -> None

Name	Type	Default	Description
predictor	Callable		A callable that takes a numpy array of N data points as inputs and returns N outputs.
feature_names	List[str]		List with feature names.
categorical_names	Optional[Dict[int, List[str]]]	None	Dictionary where keys are feature columns and values are the categories for the feature.
dtype	type[numpy.generic]	<class 'numpy.float32'>	A numpy scalar type that corresponds to the type of input array expected by predictor. This may be used to construct arrays of the given type to be passed through the predictor. For most use cases this argument should have no effect, but it is exposed for use with predictors that would break when called with an array of unsupported type.
ohe	bool	False	Whether the categorical variables are one-hot encoded (OHE) or not. If not OHE, they are assumed to have ordinal encodings.
seed	Optional[int]	None	Used to set the random number generator for repeatability purposes.

Methods

explain

explain(X: numpy.ndarray, threshold: float = 0.95, delta: float = 0.1, tau: float = 0.15, batch_size: int = 100, coverage_samples: int = 10000, beam_size: int = 1, stop_on_first: bool = False, max_anchor_size: Optional[int] = None, min_samples_start: int = 1, n_covered_ex: int = 10, binary_cache_size: int = 10000, cache_margin: int = 1000, verbose: bool = False, verbose_every: int = 1, kwargs: typing.Any) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	numpy.ndarray
threshold	float	0.95
delta	float	0.1
tau	float	0.15
batch_size	int	100
coverage_samples	int	10000
beam_size	int	1
stop_on_first	bool	False
max_anchor_size	Optional[int]	None
min_samples_start	int	1
n_covered_ex	int	10
binary_cache_size	int	10000
cache_margin	int	1000
verbose	bool	False
verbose_every	int	1
X,	threshold, delta, tau, batch_size, coverage_samples, beam_size, stop_on_first, max_anchor_size, min_samples_start, n_covered_ex, binary_cache_size, cache_margin, verbose, verbose_every, **kwargs		See :py:meth:alibi.explainers.anchors.anchor_tabular.AnchorTabular.explain.

Returns

• Type: alibi.api.interfaces.Explanation

fit

fit(train_data: numpy.ndarray, disc_perc: tuple = (25, 50, 75), kwargs) -> alibi.explainers.anchors.anchor_tabular.AnchorTabular

Name	Type	Default	Description
train_data	numpy.ndarray
disc_perc	tuple	(25, 50, 75)
train_data,	disc_perc, **kwargs		See :py:meth:alibi.explainers.anchors.anchor_tabular.AnchorTabular.fit superclass.

Returns

• Type: alibi.explainers.anchors.anchor_tabular.AnchorTabular

reset_predictor

reset_predictor(predictor: Callable) -> None

Name	Type	Default	Description
predictor	Callable		New model prediction function.

Returns

• Type: None

RemoteSampler

A wrapper that facilitates the use of TabularSampler for distributed sampling.

Constructor

RemoteSampler(self, *args)

Methods

build_lookups

build_lookups(X: numpy.ndarray)

Name	Type	Default	Description
X	numpy.ndarray		See :py:meth:alibi.explainers.anchors.anchor_tabular.TabularSampler.build_lookups.

set_instance_label

set_instance_label(X: numpy.ndarray) -> int

Name	Type	Default	Description
X	numpy.ndarray		The instance to be explained.

Returns

• Type: int

set_n_covered

set_n_covered(n_covered: int) -> None

Name	Type	Default	Description
n_covered	int		Number of examples where the result (and partial anchors) apply.

Returns

• Type: None