Un-pandas GroupedTimeSeriesSplit
#605
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FBruzzesi
commented
Dec 29, 2023
| for i in range(self.n_splits): | ||
| yield np.where(groups == i)[0], np.where(groups == i + 1)[0] | ||
| yield group_indices[i], group_indices[i + 1] | ||
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| def _calc_first_and_last_split_index(self, X=None, y=None, groups=None): |
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In a way, the changes in _calc_first_and_last_split_index are the only hard core difference
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Following all the effort put into adopting Narwhals, it would be nice to revamp this as well. I will close the PR and come back to the topic! |
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Description
In #604 there was a tiny hint at this 😂
In this PR I tried to remove the use of pandas from
GroupedTimeSeriesSplitby moving on numpy backend. The only method that still needs a "dataframe"-like object is.summary()(which it's even a nice to have).In a way this is an alternative way to achieve #597 when a dataframe is just a nice abstraction but not mandatory. The only function that has to work to proceed forward and run
.split()method isindexable(from sklearn.utils.validation).