Package org.tribuo.classification.explanations.lime

Class LIMEText

java.lang.Object

org.tribuo.classification.explanations.lime.LIMEBase

org.tribuo.classification.explanations.lime.LIMEText

All Implemented Interfaces:

TabularExplainer<Regressor>, TextExplainer<Regressor>

public class LIMEText
extends LIMEBase
implements TextExplainer<Regressor>

Uses a Tribuo TextFeatureExtractor to explain the prediction for a given piece of text.

LIME uses a naive sampling procedure which blanks out words and trains the linear model on a set of binary features, each of which is the presence of a word+position combination. Words are not permuted, and new words are not added (so it's only explaining when the absence of a word would change the prediction).

See:

 Ribeiro MT, Singh S, Guestrin C.
 "Why should I trust you?: Explaining the predictions of any classifier"
 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2016.
 

Field Summary

Fields inherited from class org.tribuo.classification.explanations.lime.LIMEBase

DISTANCE_DELTA, evaluator, explanationTrainer, innerModel, kernelWidth, numSamples, numTrainingExamples, regressionFactory, rng, WIDTH_CONSTANT

Constructor Summary

Constructors

Constructor	Description
LIMEText(SplittableRandom rng, org.tribuo.Model<Label> innerModel, org.tribuo.SparseTrainer<Regressor> explanationTrainer, int numSamples, TextFeatureExtractor<Label> extractor, Tokenizer tokenizer)	Constructs a LIME explainer for a model which uses text data.

Method Summary

Modifier and Type	Method	Description
LIMEExplanation	explain(String inputText)	Converts the supplied text into an Example, and generates an explanation of the contained Model's prediction.
protected String	nameFeature(String name, int idx)	Generate the feature name by combining the word and index.
protected List<org.tribuo.Example<Regressor>>	sampleData(String inputText, List<Token> tokens)	Samples a new dataset from the input text.

Methods inherited from class org.tribuo.classification.explanations.lime.LIMEBase

explain, explainWithSamples, kernelDist, measureDistance, samplePoint, trainExplainer, transformOutput

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

LIMEText

public LIMEText(SplittableRandom rng,
 org.tribuo.Model<Label> innerModel,
 org.tribuo.SparseTrainer<Regressor> explanationTrainer,
 int numSamples,
 TextFeatureExtractor<Label> extractor,
 Tokenizer tokenizer)

Constructs a LIME explainer for a model which uses text data.

Parameters:

rng - The rng to use for sampling.

innerModel - The model to explain.

explanationTrainer - The sparse trainer to use to generate explanations.

numSamples - The number of samples to generate for each explanation.

extractor - The TextFeatureExtractor used to generate text features from a string.

tokenizer - The tokenizer used to tokenize the examples.

Method Details

explain

public LIMEExplanation explain(String inputText)

Description copied from interface: TextExplainer

Converts the supplied text into an Example, and generates an explanation of the contained Model's prediction.

Specified by:

explain in interface TextExplainer<Regressor>

Parameters:

inputText - The text to explain.

Returns:

An explanation of the prediction on this input text.

nameFeature

protected String nameFeature(String name,
 int idx)

Generate the feature name by combining the word and index.

Parameters:

name - The word.

idx - The index.

Returns:

A string representing both of the inputs.

sampleData

protected List<org.tribuo.Example<Regressor>> sampleData(String inputText,
 List<Token> tokens)

Samples a new dataset from the input text. Uses the tokenized representation, removes words by blanking them out. Only removes words to generate a new sentence, and does not generate the empty sentence.

Parameters:

inputText - The input text.

tokens - The tokenized representation of the input text.

Returns:

A list of samples from the input text.