Enhanced UX (easier integration with scanpy)
Added a decorator to force transform_output="pandas"
when calling scBoolSeq().fit(X)
/ scBoolSeq().transform(X)
/ scBoolSeq().binarize(X)
.
This means that scBoolSeq can now be effortlessly integrated in scRNA-seq pipelines currently using scanpy
, without using with sklearn.config_context(transform_output="pandas")
.
Parallel processing is now performed using sklearn.model_selection.KFold(n_workers, shuffle=False).split(df)
, rather than numpy.array_split(df)
which would raise a FutureWarning, and won't be fixed as explained in numpy issue #24889.