Architectural goals

[nphilou]

• More 'sklearn'-like API sklearn-like interface #29
• Numpy array support Numpy support #31

Proposal A
Decorators to convert into DataFrame

Proposal B
array-like transformers (personal preference)

Issues
Should we return np.array or pd.DataFrame with np.array as input ?
See scikit-learn/scikit-learn#5523 (comment)

Comments and links:
SL/DataFrame current support: https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html

SL is more likely to (better) support DataFrame in the future: https://scikit-learn.org/dev/roadmap.html
SL sample properties future support: scikit-learn/scikit-learn#4497
SL/Pandas mapping: https://github.com/scikit-learn-contrib/sklearn-pandas (not maintained)

Cesium is not fit_transform compliant: cesium-ml/cesium#243
Prophet don't use np.array at all and isn't sklearn API compliant (but still use fit/transform model). My suggestion would be to have something close to Prophet to allow people using both libs in the same project as smoothly as possible.

Scikit-learn transformers useful links:
https://scikit-learn.org/dev/developers/develop.html
https://github.com/scikit-learn-contrib/project-template/blob/master/skltemplate/_template.py#L136

To be discussed:

• Transformers name
• Transformers category
• TDA features management Change fit_transform in TDA features #28

[nphilou]

• Refactor causality_tests tests

Modules to make (more) sklearn compatible

• causality_tests
• experimental
• feature_creation
• models