alibi.explainers.anchors.anchor_text

Constants

TYPE_CHECKING

TYPE_CHECKING: bool = False

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

DEFAULT_DATA_ANCHOR

DEFAULT_DATA_ANCHOR: dict = {'anchor': [], 'precision': None, 'coverage': None, 'raw': None}

DEFAULT_META_ANCHOR

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

logger

logger: logging.Logger = <Logger alibi.explainers.anchors.anchor_text (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_SAMPLING_UNKNOWN

DEFAULT_SAMPLING_UNKNOWN: dict = {'sample_proba': 0.5}

DEFAULT_SAMPLING_SIMILARITY

DEFAULT_SAMPLING_SIMILARITY: dict = {'sample_proba': 0.5, 'top_n': 100, 'temperature': 1.0, 'use_proba': False}

DEFAULT_SAMPLING_LANGUAGE_MODEL

DEFAULT_SAMPLING_LANGUAGE_MODEL: dict = {'filling': 'parallel', 'sample_proba': 0.5, 'top_n': 100, 'temperature': 1.0...

AnchorText

Inherits from: Explainer, ABC, Base

Constructor

AnchorText(self, predictor: Callable[[List[str]], numpy.ndarray], sampling_strategy: str = 'unknown', nlp: Optional[ForwardRef('spacy.language.Language')] = None, language_model: Optional[ForwardRef('LanguageModel')] = None, seed: int = 0, **kwargs: Any) -> None

Name	Type	Default	Description
predictor	Callable[[.[typing.List[str]]], numpy.ndarray]		A callable that takes a list of text strings representing N data points as inputs and returns N outputs.
sampling_strategy	str	'unknown'	Perturbation distribution method: - 'unknown' - replaces words with UNKs. - 'similarity' - samples according to a similarity score with the corpus embeddings. - 'language_model' - samples according the language model's output distributions.
nlp	Optional[spacy.language.Language]	None	spaCy object when sampling method is 'unknown' or 'similarity'.
language_model	Optional[alibi.utils.lang_model.LanguageModel]	None	Transformers masked language model. This is a model that it adheres to the LanguageModel interface we define in :py:class:alibi.utils.lang_model.LanguageModel.
seed	int	0	If set, ensure identical random streams.
kwargs	typing.Any		Sampling arguments can be passed as kwargs depending on the sampling_strategy. Check default arguments defined in: - :py:data:alibi.explainers.anchor_text.DEFAULT_SAMPLING_UNKNOWN - :py:data:alibi.explainers.anchor_text.DEFAULT_SAMPLING_SIMILARITY - :py:data:alibi.explainers.anchor_text.DEFAULT_SAMPLING_LANGUAGE_MODEL

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

explain

explain(text: str, 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 = True, 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
text	str		Text instance to be explained.
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 anchor 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	True	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 to include in an anchor.
min_samples_start	int	100	Number of samples used for anchor search initialisation.
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 predicted label are stored).
binary_cache_size	int	10000	The anchor search pre-allocates binary_cache_size batches for storing the boolean 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.
**kwargs			Other keyword arguments passed to the anchor beam search and the text sampling and perturbation functions.

Returns

• Type: alibi.api.interfaces.Explanation

reset_predictor

reset_predictor(predictor: Callable) -> None

Name	Type	Default	Description
predictor	Callable		New predictor function.

Returns

• Type: None

sampler

sampler(anchor: Tuple[int, tuple], num_samples: int, compute_labels: bool = True) -> Union[List[Union[numpy.ndarray, float, int]], List[numpy.ndarray]]

Name	Type	Default	Description
anchor	Tuple[int, tuple]		- int - the position of the anchor in the input batch. - tuple - the anchor itself, a list of words to be kept unchanged.
num_samples	int		Number of generated perturbed samples.
compute_labels	bool	True	If True, an array of comparisons between predictions on perturbed samples and instance to be explained is returned.

Returns

• Type: Union[List[Union[numpy.ndarray, float, int]], List[numpy.ndarray]]