[REVIEW] Adding cuml.experimental to the Docs (#2942)

* Adding cuml.experimental to the docs

* Adding PR to CHANGELOG

* Docstring + licensing improvements

* Incorporating feedback from PR code review

* Reverting the @check_cupy8 decorator and moving from classes to __init__

Co-authored-by: viclafargue <viclafargue@nvidia.com>

CHANGELOG.md

@@ -51,6 +51,7 @@
 - PR #2914: Add tests for XGBoost multi-class models in FIL
 - PR #2930: Pin libfaiss to <=1.6.3
 - PR #2928: Updating Estimators Derived from Base for Consistency
+- PR #2942: Adding `cuml.experimental` to the Docs
 
 ## Bug Fixes
 - PR #2885: Changing test target for NVTX wrapper test

docs/source/api.rst

@@ -476,5 +476,26 @@ Dask Base Classes and Mixins
 .. autoclass:: cuml.dask.common.base.DelayedInverseTransformMixin
    :members:
 
+Experimental
+============
+
+.. warning:: The `cuml.experimental` module contains features that are still
+    under development. It is not recommended to depend on features in this
+    module as they may change in future releases.
+
+.. note:: Due to the nature of this module, it is not imported by default by
+    the root `cuml` package. Each `experimental` submodule must be imported
+    separately.
+
+Decomposition
+-------------
 .. autoclass:: cuml.experimental.decomposition.IncrementalPCA
-   :members:
+   :members:
+
+Preprocessing
+-------------
+.. automodule:: cuml.experimental.preprocessing
+   :members: Binarizer, KBinsDiscretizer, MaxAbsScaler, MinMaxScaler,
+      Normalizer, RobustScaler, SimpleImputer, StandardScaler,
+      add_dummy_feature, binarize, minmax_scale, normalize,
+      PolynomialFeatures, robust_scale, scale

python/cuml/_thirdparty/sklearn/README.md

@@ -1,6 +1,6 @@
 # GPU accelerated Scikit-Learn preprocessing
 
-This directory contains code originating from the Scikit-Learn library. The Scikit-Learn license applies accordingly (see `/thirdparty/LICENSES/LICENSE.scikit_learn`).
+This directory contains code originating from the Scikit-Learn library. The Scikit-Learn license applies accordingly (see `/thirdparty/LICENSES/LICENSE.scikit_learn`). Original authors mentioned in the code do not endorse or promote this production.
 
 This work is dedicated to providing GPU accelerated tools for preprocessing. The Scikit-Learn code is slightly modified to make it possible to take common inputs used throughout cuML such as Numpy and Cupy arrays, Pandas and cuDF dataframes and compute the results on GPU.
 

python/cuml/_thirdparty/sklearn/exceptions.py

@@ -1,3 +1,8 @@
+# This code originates from the Scikit-Learn library,
+# it was since modified to allow GPU acceleration.
+# This code is under BSD 3 clause license.
+
+
 """
 The :mod:`sklearn.exceptions` module includes all custom warnings and error
 classes used across scikit-learn.

python/cuml/_thirdparty/sklearn/preprocessing/__init__.py

@@ -1,3 +1,8 @@
+# This code originates from the Scikit-Learn library,
+# it was since modified to allow GPU acceleration.
+# This code is under BSD 3 clause license.
+
+
 """
 The :mod:`sklearn.preprocessing` module includes scaling, centering,
 normalization, binarization methods.