omnixai.explainers.nlp.agnostic package

`lime`	The LIME explainer for text classification.
`shap`	The SHAP explainer for text classification.
`l2x`	The L2X explainer for NLP tasks.

omnixai.explainers.nlp.agnostic.lime module

The LIME explainer for text classification.

class omnixai.explainers.nlp.agnostic.lime.LimeText(predict_function, mode='classification', **kwargs)

Bases: ExplainerBase

The LIME explainer for text classification. If using this explainer, please cite the original work: https://github.com/marcotcr/lime. This explainer only supports text classification tasks.

Parameters

predict_function (Callable) – The prediction function corresponding to the machine learning model to explain. When the task is classification, the outputs of the predict_function are the class probabilities.
mode (str) – The task type can be classification only.
kwargs – Additional parameters for lime_text.LimeTextExplainer. Please refer to the doc of lime_text.LimeTextExplainer.

explanation_type = 'local'

alias = ['lime']

explain(X, y=None, **kwargs)

Generates the word/token-importance explanations for the input instances.

Parameters

X (Text) – A batch of input instances.
y – A batch of labels to explain. For classification, the top predicted label of each input instance will be explained when y = None.
kwargs – Additional parameters for LimeTextExplainer.explain_instance.

Return type

WordImportance

Returns

The explanations for all the input instances.

omnixai.explainers.nlp.agnostic.shap module

The SHAP explainer for text classification.

class omnixai.explainers.nlp.agnostic.shap.ShapText(model, mode='classification', **kwargs)

Bases: ExplainerBase

The SHAP explainer for text classification. If using this explainer, please cite the original work: https://github.com/slundberg/shap. This explainer only supports TextClassificationPipeline in the transformer library.

Parameters

model (Callable) – The model with type transformers.pipelines.text_classification.TextClassificationPipeline.
mode (str) – The task type can be classification only.
kwargs – Additional parameters for shap.Explainer. Please refer to the doc of shap.Explainer.

explanation_type = 'local'

alias = ['shap']

explain(X, y=None, **kwargs)

Generates the word/token-importance explanations for the input instances.

Parameters

X (Text) – A batch of input instances.
y – A batch of labels to explain. For classification, the top predicted label of each input instance will be explained when y = None.
kwargs – Additional parameters for shap.Explainer.

Return type

WordImportance

Returns

The explanations for all the instances.

omnixai.explainers.nlp.agnostic.l2x module

The L2X explainer for NLP tasks.

class omnixai.explainers.nlp.agnostic.l2x.DefaultSelectionModel(explainer, **kwargs)

Bases: _DefaultModelBase

The default selection model in L2X for text data. It consists of five 1D convolution layers and one linear output layer.

Parameters

explainer – A L2XText explainer.
kwargs – Additional parameters, e.g., hidden_size – the hidden layer size, and kernel_size – the kernel size in 1D convolution.

forward(inputs, masks)

Parameters

inputs – The input IDs.
masks – The input masks.

training: bool

class omnixai.explainers.nlp.agnostic.l2x.DefaultPredictionModel(explainer, **kwargs)

Bases: _DefaultModelBase

The default prediction model in L2X for text data. It consists of three 1D convolution layers and one linear output layer by default.

Parameters

explainer – A L2XText explainer.
kwargs – Additional parameters, e.g., hidden_size – the hidden layer size, and kernel_sizes – the kernel sizes in the 1D convolution layers.

forward(inputs, masks, weights)

Parameters

inputs – The input IDs.
masks – The input masks.
weights – The weights generated via Gumbel-Softmax sampling.

training: bool

class omnixai.explainers.nlp.agnostic.l2x.L2XText(training_data, predict_function, mode='classification', tau=0.5, k=10, selection_model=None, prediction_model=None, loss_function=None, optimizer=None, learning_rate=0.001, batch_size=None, num_epochs=20, **kwargs)

Bases: ExplainerBase

The LIME explainer for text data. If using this explainer, please cite the original work: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation, Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan, https://arxiv.org/abs/1802.07814.

Parameters

training_data (Text) – The data used to train the explainer. training_data should be the training dataset for training the machine learning model.
predict_function (Callable) – The prediction function corresponding to the model to explain. When the model is for classification, the outputs of the predict_function are the class probabilities. When the model is for regression, the outputs of the predict_function are the estimated values.
mode (str) – The task type, e.g., classification or regression.
tau (float) – Parameter tau in Gumbel-Softmax.
k (int) – The maximum number of the selected features in L2X.
selection_model – A pytorch model class for estimating P(S|X) in L2X. If selection_model = None, a default model DefaultSelectionModel will be used.
prediction_model – A pytorch model class for estimating Q(X_S) in L2X. If selection_model = None, a default model DefaultPredictionModel will be used.
loss_function (Optional[Callable]) – The loss function for the task, e.g., nn.CrossEntropyLoss() for classification.
optimizer – The optimizer class for training the explainer, e.g., torch.optim.AdamW.
learning_rate (float) – The learning rate for training the explainer.
batch_size (Optional[int]) – The batch size for training the explainer. If batch_size is None, batch_size will be picked from [32, 64, 128, 256] based on the sample size.
num_epochs (int) – The number of epochs for training the explainer.
kwargs – Additional parameters, e.g., parameters for selection_model and prediction_model.

explanation_type = 'local'

alias = ['l2x', 'L2X']

explain(X, **kwargs)

Generates the explanations for the input instances. For classification, it explains the top predicted label for each input instance.

Parameters: X (Text) – A batch of input instances.
Return type: WordImportance
Returns: The explanations for all the input instances.

save(directory, filename=None, **kwargs)

Saves the initialized explainer.

Parameters

directory (str) – The folder for the dumped explainer.
filename (Optional[str]) – The filename (the explainer class name if it is None).