* Squashed commit of wrappers:
Wrapper minimizer
* apply dataset wrapper on minimizer
* apply changes on minimization notebook
* add black_box_access and unlimited_queries params
Dataset wrapper anonymizer
Add features_names to ArrayDataset
and allow providing features names in QI and Cat features not just indexes
update notebooks
categorical features and QI passed by indexes
dataset include feature names and is_pandas param
add pytorch Dataset
Remove redundant code.
Use data wrappers in model wrapper APIs.
add generic dataset components
Create initial version of wrappers for models
* Fix handling of categorical features
commit d53818644e
Author: olasaadi <92303887+olasaadi@users.noreply.github.com>
Date: Mon Mar 7 20:12:55 2022 +0200
Build the dt on all features anon (#23)
* add param to build the DT on all features and not just on QI
* one-hot encoding only for categorical features
commit c47819a031
Author: abigailt <abigailt@il.ibm.com>
Date: Wed Feb 23 19:40:11 2022 +0200
Update docs
commit 7e2ce7fe96
Merge: 7fbd1e4752871d
Author: abigailt <abigailt@il.ibm.com>
Date: Wed Feb 23 19:26:44 2022 +0200
Merge remote-tracking branch 'origin/main' into main
commit 7fbd1e4b90
Author: abigailt <abigailt@il.ibm.com>
Date: Wed Feb 23 19:22:54 2022 +0200
Update version and docs
commit 752871dd0c
Author: olasaadi <92303887+olasaadi@users.noreply.github.com>
Date: Wed Feb 23 14:57:12 2022 +0200
add minimization notebook (#22)
* add german credit notebook to showcase new features (minimize only some features and categorical features)
* add notebook to show minimization data on a regression problem
* add german credit notebook to showcase new features (minimize only some features and categorical features)
* add notebook to show minimization data on a regression problem
* QI updates
* update code to support training ML on QI features
* fix code so features that are not from QI should not be part of generalizations
and add description
* merging two branches, training on QI and on all data
* adding tests and asserts
* support categorical features
* update the documentation and readme
added a test for the case where cells are supplied as a param.
* add big tests (adult test and iris)
and fixed bugs
* update transform to return numpy if original data is numpy
* added nursery test
* break loop if there is an illegal level
* Stop pruning one step before passing accuracy threshold
* adding asserts and fix DecisionTreeClassifier init
* Fix tests
Co-authored-by: abigailt <abigailt@il.ibm.com>
