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alibi.explainers.anchors.text_samplers

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.

logger

logger: logging.Logger = <Logger alibi.explainers.anchors.text_samplers (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.

AnchorTextSampler

Constructor

AnchorTextSampler(self, /, *args, **kwargs)

Methods

set_text

set_text(text: str) -> None
Name
Type
Default
Description

text

str

Returns

  • Type: None

Neighbors

Constructor

Neighbors(self, nlp_obj: 'spacy.language.Language', n_similar: int = 500, w_prob: float = -15.0) -> None
Name
Type
Default
Description

nlp_obj

spacy.language.Language

spaCy model.

n_similar

int

500

Number of similar words to return.

w_prob

float

-15.0

Smoothed log probability estimate of token's type.

Methods

neighbors

neighbors(word: str, tag: str, top_n: int) -> dict
Name
Type
Default
Description

word

str

Word for which we need to find similar words.

tag

str

Part of speech tag for the words.

top_n

int

Return only top_n neighbors.

Returns

  • Type: dict

SimilaritySampler

Inherits from: AnchorTextSampler

Constructor

SimilaritySampler(self, nlp: 'spacy.language.Language', perturb_opts: Dict)
Name
Type
Default
Description

nlp

spacy.language.Language

spaCy object.

perturb_opts

Dict

Perturbation options.

Methods

find_similar_words

find_similar_words() -> None

This function queries a spaCy nlp model to find n similar words with the same part of speech for each word in the instance to be explained. For each word the search procedure returns a dictionary containing a numpy array of words ('words') and a numpy array of word similarities ('similarities').

Returns

  • Type: None

perturb_sentence_similarity

perturb_sentence_similarity(present: tuple, n: int, sample_proba: float = 0.5, forbidden: frozenset = frozenset(), forbidden_tags: frozenset = frozenset({'PRP$'}), forbidden_words: frozenset = frozenset({'be'}), temperature: float = 1.0, pos: frozenset = frozenset({'VERB', 'ADJ', 'ADP', 'NOUN', 'ADV', 'DET'}), use_proba: bool = False, kwargs) -> Tuple[numpy.ndarray, numpy.ndarray]
Name
Type
Default
Description

present

tuple

Word index in the text for the words in the proposed anchor.

n

int

Number of samples used when sampling from the corpus.

sample_proba

float

0.5

Sample probability for a word if use_proba=False.

forbidden

frozenset

frozenset()

Forbidden lemmas.

forbidden_tags

frozenset

frozenset({'PRP$'})

Forbidden POS tags.

forbidden_words

frozenset

frozenset({'be'})

Forbidden words.

temperature

float

1.0

Sample weight hyper-parameter if use_proba=True.

pos

frozenset

frozenset({'VERB', 'ADJ', 'ADP', 'NOUN', 'ADV', 'DET'})

POS that can be changed during perturbation.

use_proba

bool

False

Bool whether to sample according to a similarity score with the corpus embeddings.

**kwargs

Other arguments. Not used.

Returns

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

set_data_type

set_data_type() -> None

Working with numpy arrays of strings requires setting the data type to avoid truncating examples. This function estimates the longest sentence expected during the sampling process, which is used to set the number of characters for the samples and examples arrays. This depends on the perturbation method used for sampling.

Returns

  • Type: None

set_text

set_text(text: str) -> None
Name
Type
Default
Description

text

str

Text to be processed.

Returns

  • Type: None

UnknownSampler

Inherits from: AnchorTextSampler

Constructor

UnknownSampler(self, nlp: 'spacy.language.Language', perturb_opts: Dict)
Name
Type
Default
Description

nlp

spacy.language.Language

spaCy object.

perturb_opts

Dict

Perturbation options.

Methods

set_data_type

set_data_type() -> None

Working with numpy arrays of strings requires setting the data type to avoid truncating examples. This function estimates the longest sentence expected during the sampling process, which is used to set the number of characters for the samples and examples arrays. This depends on the perturbation method used for sampling.

Returns

  • Type: None

set_text

set_text(text: str) -> None
Name
Type
Default
Description

text

str

Text to be processed.

Returns

  • Type: None

Functions

load_spacy_lexeme_prob

load_spacy_lexeme_prob(nlp: spacy.language.Language) -> spacy.language.Language

This utility function loads the lexeme_prob table for a spacy model if it is not present. This is required to enable support for different spacy versions.

Name
Type
Default
Description

nlp

spacy.language.Language

Returns

  • Type: spacy.language.Language

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