[ENG] fix Lag for numpy int
#2832
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fkiraly
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Aug 10, 2022
Prototype rework of the direct reducer, to showcase how a larger refactor could work and solicit feedback. Reworks the direct reducer in order to: * allow concurrent use of exogeneous data `X`, up to and including the setting where only `X` at the same time is used to predict `y` * support `pandas` input to `sklearn` estimators and associated features, e.g., inspecting fitted model parameters with feature names * factor out the complexity of lagging and windowing to the transformer `Lag`, and of imputing to the transformer `Imputer`. The code logic specific to the redicuer * support for time stamp-like lags, through `Lag` Depends on fix #2832 since `ForecastingHorizon` yields numpy integers which apparently break the `Lag` transformer.
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For strange reasons,
Lagerrors out if the integers in an integer lag specification arenumpyintegers, as opposed to python native integers.This is relevant when attempting to pass integers from a
ForecastingHorizontoLag, which is the natural thing to do in reduction.The fix is added to
Lag, which now also can deal withnumpyintegers.