* General model wrappers and methods supporting multi-label classifiers
* Support for pytorch multi-label binary classifier
* New model output types + single implementation of score method that supports multiple output types.
* Anonymization with pytorch multi-output binary model
* Support for multi-label binary models in minimizer.
* Support for multi-label logits/probabilities
---------
Signed-off-by: abigailt <abigailt@il.ibm.com>
* Support 1-hot encoded features in anonymization (#72)
* Fix anonymization adult notebook + new notebook to demonstrate anonymization on 1-hot encoded data
* Minimizer: No default encoder, if none provided data is supplied to the model as is. Fix data type of representative values. Fix and add more tests.
Signed-off-by: abigailt <abigailt@il.ibm.com>
* Update requirements
* Update incompatible scipy version
* Reduce runtime of dataset assessment tests
* ncp is now a class that contains 3 values: fit_score, transform_score and generalizations_score so that it doesn't matter in what order the different methods are called, all calculated ncp scores are stored.
Generalizations can now be applied either from tree cells or from global generalizations struct depending on the value of generalize_using_transform. Representative values can also be computed from global generalizations.
Removing a feature from the generalization can also be applied in either mode.
* Compute generalizations with test data when possible (for computing better representatives).
* Externalize common test code to methods.
* Remove tensorflow dependency if not using keras model
* Remove xgboost dependency if not using xgboost model
* Documentation updates
Signed-off-by: abigailt <abigailt@il.ibm.com>
