alibi.explainers.anchors.anchor_image

Constants

DEFAULT_DATA_ANCHOR_IMG

DEFAULT_DATA_ANCHOR_IMG: dict = {'anchor': [], 'segments': None, 'precision': None, 'coverage': None, 'raw': ...

DEFAULT_META_ANCHOR

DEFAULT_META_ANCHOR: dict = {'name': None, 'type': ['blackbox'], 'explanations': ['local'], 'params': {},...

logger

logger: logging.Logger = <Logger alibi.explainers.anchors.anchor_image (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

DEFAULT_SEGMENTATION_KWARGS

DEFAULT_SEGMENTATION_KWARGS: dict = {'felzenszwalb': {}, 'quickshift': {}, 'slic': {'n_segments': 10, 'compactnes...

AnchorImage

Inherits from: Explainer, ABC, Base

Constructor

AnchorImage(self, predictor: Callable[[numpy.ndarray], numpy.ndarray], image_shape: tuple, dtype: Type[numpy.generic] = <class 'numpy.float32'>, segmentation_fn: Any = 'slic', segmentation_kwargs: Optional[dict] = None, images_background: Optional[numpy.ndarray] = None, seed: Optional[int] = None) -> None

Name	Type	Default	Description
predictor	Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]		A callable that takes a numpy array of N data points as inputs and returns N outputs.
image_shape	tuple		Shape of the image to be explained. The channel axis is expected to be last.
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.
segmentation_fn	typing.Any	'slic'	Any of the built in segmentation function strings: 'felzenszwalb', 'slic' or 'quickshift' or a custom segmentation function (callable) which returns an image mask with labels for each superpixel. The segmentation function is expected to return a segmentation mask containing all integer values from 0 to K-1, where K is the number of image segments (superpixels). See http://scikit-image.org/docs/dev/api/skimage.segmentation.html for more info.
segmentation_kwargs	Optional[dict]	None	Keyword arguments for the built in segmentation functions.
images_background	Optional[numpy.ndarray]	None	Images to overlay superpixels on.
seed	Optional[int]	None	If set, ensures different runs with the same input will yield same explanation.

Methods

explain

explain(image: numpy.ndarray, p_sample: float = 0.5, 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 = 100, 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
image	numpy.ndarray		Image to be explained.
p_sample	float	0.5	The probability of simulating the absence of a superpixel. If the images_background is not provided, the absent superpixels will be replaced by the average value of their constituent pixels. Otherwise, the synthetic instances are created by fixing the present superpixels and superimposing another image from the images_background over the rest of the absent superpixels.
threshold	float	0.95	Minimum anchor precision threshold. The algorithm tries to find an anchor that maximizes the coverage under precision constraint. The precision constraint is formally defined as :math:P(prec(A) \ge t) \ge 1 - \delta, where :math:A is an anchor, :math:t is the threshold parameter, :math:\delta is the delta parameter, and :math:prec(\cdot) denotes the precision of an anchor. In other words, we are seeking for an anchor having its precision greater or equal than the given threshold with a confidence of (1 - delta). A higher value guarantees that the anchors are faithful to the model, but also leads to more computation time. Note that there are cases in which the precision constraint cannot be satisfied due to the quantile-based discretisation of the numerical features. If that is the case, the best (i.e. highest coverage) non-eligible anchor is returned.
delta	float	0.1	Significance threshold. 1 - delta represents the confidence threshold for the anchor precision (see threshold) and the selection of the best anchor candidate in each iteration (see tau).
tau	float	0.15	Multi-armed bandit parameter used to select candidate anchors in each iteration. The multi-armed bandit algorithm tries to find within a tolerance tau the most promising (i.e. according to the precision) beam_size candidate anchor(s) from a list of proposed anchors. Formally, when the beam_size=1, the multi-armed bandit algorithm seeks to find an anchor :math:A such that :math:P(prec(A) \ge prec(A^\star) - \tau) \ge 1 - \delta, where :math:A^\star is the anchor with the highest true precision (which we don't know), :math:\tau is the tau parameter, :math:\delta is the delta parameter, and :math:prec(\cdot) denotes the precision of an anchor. In other words, in each iteration, the algorithm returns with a probability of at least 1 - delta an anchor :math:A with a precision within an error tolerance of tau from the precision of the highest true precision anchor :math:A^\star. A bigger value for tau means faster convergence but also looser anchor conditions.
batch_size	int	100	Batch size used for sampling. The Anchor algorithm will query the black-box model in batches of size batch_size. A larger batch_size gives more confidence in the anchor, again at the expense of computation time since it involves more model prediction calls.
coverage_samples	int	10000	Number of samples used to estimate coverage from during result search.
beam_size	int	1	Number of candidate anchors selected by the multi-armed bandit algorithm in each iteration from a list of proposed anchors. A bigger beam width can lead to a better overall anchor (i.e. prevents the algorithm of getting stuck in a local maximum) at the expense of more computation time.
stop_on_first	bool	False	If True, the beam search algorithm will return the first anchor that has satisfies the probability constraint.
max_anchor_size	Optional[int]	None	Maximum number of features in result.
min_samples_start	int	100	Min number of initial samples.
n_covered_ex	int	10	How many examples where anchors apply to store for each anchor sampled during search (both examples where prediction on samples agrees/disagrees with desired_label are stored).
binary_cache_size	int	10000	The result search pre-allocates binary_cache_size batches for storing the binary arrays returned during sampling.
cache_margin	int	1000	When only max(cache_margin, batch_size) positions in the binary cache remain empty, a new cache of the same size is pre-allocated to continue buffering samples.
verbose	bool	False	Display updates during the anchor search iterations.
verbose_every	int	1	Frequency of displayed iterations during anchor search process.

Returns

• Type: alibi.api.interfaces.Explanation

generate_superpixels

generate_superpixels(image: numpy.ndarray) -> numpy.ndarray

Name	Type	Default	Description
image	numpy.ndarray		A grayscale or RGB image.

Returns

• Type: numpy.ndarray

overlay_mask

overlay_mask(image: numpy.ndarray, segments: numpy.ndarray, mask_features: list, scale: tuple = (0, 255)) -> numpy.ndarray

Name	Type	Default	Description
image	numpy.ndarray		Image to be explained.
segments	numpy.ndarray		Superpixels.
mask_features	list		List with superpixels present in mask.
scale	tuple	(0, 255)	Pixel scale for masked image.

Returns

• Type: numpy.ndarray

reset_predictor

reset_predictor(predictor: Callable) -> None

Name	Type	Default	Description
predictor	Callable		New predictor function.

Returns

• Type: None

AnchorImageSampler

Constructor

AnchorImageSampler(self, predictor: Callable, segmentation_fn: Callable, custom_segmentation: bool, image: numpy.ndarray, images_background: Optional[numpy.ndarray] = None, p_sample: float = 0.5, n_covered_ex: int = 10)

Name	Type	Default	Description
predictor	Callable		A callable that takes a numpy array of N data points as inputs and returns N outputs.
segmentation_fn	Callable		Function used to segment the images. The segmentation function is expected to return a segmentation mask containing all integer values from 0 to K-1, where K is the number of image segments (superpixels).
custom_segmentation	bool
image	numpy.ndarray		Image to be explained.
images_background	Optional[numpy.ndarray]	None	Images to overlay superpixels on.
p_sample	float	0.5	Probability for a pixel to be represented by the average value of its superpixel.
n_covered_ex	int	10	How many examples where anchors apply to store for each anchor sampled during search (both examples where prediction on samples agrees/disagrees with desired_label are stored).

Methods

compare_labels

compare_labels(samples: numpy.ndarray) -> numpy.ndarray

Name	Type	Default	Description
samples	numpy.ndarray		Samples whose labels are to be compared with the instance label.

Returns

• Type: numpy.ndarray

generate_superpixels

generate_superpixels(image: numpy.ndarray) -> numpy.ndarray

Name	Type	Default	Description
image	numpy.ndarray		A grayscale or RGB image.

Returns

• Type: numpy.ndarray

perturbation

perturbation(anchor: tuple, num_samples: int) -> Tuple[numpy.ndarray, numpy.ndarray]

Name	Type	Default	Description
anchor	tuple		Contains the superpixels whose values are not going to be perturbed.
num_samples	int		Number of perturbed samples to be returned.

Returns

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

Functions

scale_image

scale_image(image: numpy.ndarray, scale: tuple = (0, 255)) -> numpy.ndarray

Scales an image in a specified range.

Name	Type	Default	Description
image	numpy.ndarray		Image to be scale.
scale	tuple	(0, 255)	The scaling interval.

Returns

• Type: numpy.ndarray