omnixai.explainers.timeseries.agnostic package
The SHAP explainer for time series tasks. |
omnixai.explainers.timeseries.agnostic.shap module
The SHAP explainer for time series tasks.
- class omnixai.explainers.timeseries.agnostic.shap.ShapTimeseries(training_data, predict_function, mode='anomaly_detection', **kwargs)
Bases:
ExplainerBaseThe SHAP explainer for time series forecasting and anomaly detection. If using this explainer, please cite the original work: https://github.com/slundberg/shap.
- Parameters
training_data (
Timeseries) – The data used to initialize the explainer.predict_function (
Callable) – The prediction function corresponding to the model to explain. The input ofpredict_functionis an Timeseries instance. The output ofpredict_functionis the anomaly score (higher scores imply more anomalous) for anomaly detection or the predicted value for forecasting.mode (
str) – The task type, e.g., anomaly_detection or forecasting.
- explanation_type = 'local'
- alias = ['shap']
- explain(X, **kwargs)
Generates the feature-importance explanations for the input instances.
- Parameters
X (
Timeseries) – An instance of Timeseries representing one input instance or a batch of input instances.kwargs – Additional parameters for shap.KernelExplainer.shap_values, e.g., “nsamples” for the number of times to re-evaluate the model when explaining each prediction.
- Return type
- Returns
The feature-importance explanations for all the input instances.