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We already have some end-to-end examples/tutorials. These show how to accomplish a high level goal with this library ("how do I create a projection of my dataset using KPCovR").
The standard tool to add such example in Python is called doctests: one write input code as if there was a python prompt (>>>); and the corresponding output. Then, there are tools to run the input code and check that output match. Check out the corresponding module in python standard library: https://docs.python.org/3/library/doctest.html
The first task for this issue would be to make sure doctests are part of the test suite checked on CI
We already have some end-to-end examples/tutorials. These show how to accomplish a high level goal with this library ("how do I create a projection of my dataset using KPCovR").
It would be nice to also have "inline" examples inside the reference documentation, showing how to use each function separately ("how do I call FPS.fit with warm_start=True"). For a good example of how this would look like, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA.transform.
The standard tool to add such example in Python is called doctests: one write input code as if there was a python prompt (
>>>
); and the corresponding output. Then, there are tools to run the input code and check that output match. Check out the corresponding module in python standard library: https://docs.python.org/3/library/doctest.htmlThe first task for this issue would be to make sure doctests are part of the test suite checked on CI
We already have some classes with such examples (https://scikit-cosmo.readthedocs.io/en/latest/preprocessing.html#skcosmo.preprocessing.flexible_scaler.KernelNormalizer), but I think all classes should come with such a small example.
The second task for this issue would be to add doctests/examples to all classes in skcosmo.
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