[DOC] tutorial/workshop for pydata Seattle 2023

[fkiraly]

We will need to prepare a tutorial/workshop for pydata Seattle 2023 - 90min incl Q/A, modus is live presentation (but can be prepared from notebooks or dev IDE)

To collect thoughts.

My thoughts:

• the "obvious" content focuses on a step-by-step guide to create a package using skbase, perhaps an "easy" use case and an "advanced" one.
• we probably want 15min intro on the general interface, i.e., a 101 of the sklearn/sktime-like interface that skbase follows, before we get into implementation details.
• the patterns are pretty clean imo, so perhaps we can also show sktime (assuming the refactor is released by then), and maybe another package

[fkiraly]

draft content sketch

Part 1 – the sklearn-like interface – 20-30min

Exposition – what are the key features? 1 jupyter notebook Using sktime as an example?

“objects” constructor get_params/set_params basic tags configs mention repr, pretty-printing

composition – simple show get_params/set_params for composition composition – heterogeneous, pipelines show get_params/set_params again

“estimators” Fitting, is_fitted Get_fitted_params Show atomic, composition simple, composition pipeline

Lookup all_objects aka sktime all_estimators all_tags

Testing Get_test_params Create_test_instance Create_test_instances_and_names Check_estimator

Part 2 – creating your own sklearn-like with skbase

Showcase simple mock package – 5min Quick walkthrough on usage, parallel to part 1

Show codebase, check through the below – 5min Step-by-step instructions – 20min Import of BaseObject as parent Special methods in child package Tags in child package Configs in child package Testing (superficial)

2a search/retrieval – 5min All_objects interface

2b estimators – 5min Fit method Get_fitted_params

2c heterogeneous estimators – 10min Heterogeneous mixins Example composite

2d testing – 5min Importing BaseObject, BaseEstimator tests Extending tests

3 wrapup, summary, invite to contribute – 5min

[fkiraly]

done! here: https://github.com/sktime/sktime-tutorial-pydata-seattle-2023