dice_ml package¶
Subpackages¶
- dice_ml.data_interfaces package
- dice_ml.explainer_interfaces package
- Submodules
- dice_ml.explainer_interfaces.dice_pytorch module
- dice_ml.explainer_interfaces.dice_tensorflow1 module
- dice_ml.explainer_interfaces.dice_tensorflow2 module
- dice_ml.explainer_interfaces.explainer_base module
- dice_ml.explainer_interfaces.feasible_base_vae module
- dice_ml.explainer_interfaces.feasible_model_approx module
- Module contents
- dice_ml.model_interfaces package
- dice_ml.utils package
Submodules¶
dice_ml.data module¶
Module pointing to different implementations of Data class
DiCE requires only few parameters about the data such as the range of continuous features and the levels of categorical features. Hence, DiCE can be used for a private data whose meta data are only available (such as the feature names and range/levels of different features) by specifying appropriate parameters.
dice_ml.dice module¶
Module pointing to different implementations of DiCE based on different frameworks such as Tensorflow or PyTorch.
dice_ml.diverse_counterfactuals module¶
-
class
dice_ml.diverse_counterfactuals.
CounterfactualExamples
(data_interface, test_instance, test_pred, final_cfs, final_cfs_preds, final_cfs_sparse=None, cfs_preds_sparse=None, posthoc_sparsity_param=0, desired_class='opposite')[source]¶ Bases:
object
A class to store and visualize the resulting counterfactual explanations.
dice_ml.model module¶
Module pointing to different implementations of Model class
The implementations contain methods to access the output or gradients of ML models trained based on different frameworks such as Tensorflow or PyTorch.