omnixai.explainers.nlp.counterfactual package
The NLP counterfactual explainer based on the Polyjuice model. |
omnixai.explainers.nlp.counterfactual.polyjuice module
The NLP counterfactual explainer based on the Polyjuice model.
- class omnixai.explainers.nlp.counterfactual.polyjuice.Polyjuice(predict_function, mode='classification', **kwargs)
Bases:
ExplainerBaseThe counterfactual explainer for NLP tasks. The method implemented here is based on the model developed by Wu et al., please cite the work: https://github.com/tongshuangwu/polyjuice if using this explainer.
- Parameters
predict_function (
Callable) – The prediction function corresponding to the machine learning model to explain. When the task is classification, the outputs of thepredict_functionare the class probabilities. When the task is qa (question-answering), the outputs are the generated answers.mode (
str) – The task type, e.g., classification pr qa (question-answering).kwargs – Additional parameters, e.g., model_path and cuda.
- explanation_type = 'local'
- alias = ['polyjuice']
- explain(X, max_number_examples=5, **kwargs)
Generates the counterfactual explanations for the input instances.
- Parameters
X (
Text) – A batch of input instances. For question-answering tasks, each instance inXhas format [context] [SEP] [question], i.e., concatenating the context and question with seperator [SEP].max_number_examples (
int) – The maximum number of the generated counterfactual examples for each input instance.kwargs – Additional parameters for polyjuice.Polyjuice, e.g., “ce_type” - the perturb type (“perturb” or “blank”).
- Return type
- Returns
The explanations for all the input instances.