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...penstef-models/src/openstef_models/transforms/postprocessing/isotonic_quantile_calibrator.py
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I have checked it and it looks great actually.
Maybe indeed only a few nitpicks:
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Is that we should avoid calibration if the data is too small for some reason. Just to cover all the bases.
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100 points limit may be too strict since it picks 100 nearest points. It can also be that it's too big and thus picks irrelevant samples for quantile calculation. So adaptive sampling is probably a good default for a reason. We should probably keep it. <10 is probably too small though.
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Because we have a fit function, it's easy to recalibrate on a recent forecast, so that's pretty nice.
Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
* feature: add Selector transform * add ForecastInputDataset testcases * add selected_features to presets * add doctest Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
…ne bug (OpenSTEF#787) Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
…lating. (OpenSTEF#790) Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
…hether there is sufficient data. Signed-off-by: Fleur Petit <fleur.petit@alliander.com>
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…uantile Signed-off-by: Egor Dmitriev <egor.dmitriev@alliander.com>
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Summary
This PR implements a postprocessing calibration method for quantile estimation using sk-learn's IsotonicRegression.
Changes
IsotonicCalibratorto postprocessing transforms.IsotonicCalibratorfunctionality on different data with different settings.IsotonicCalibratoron observed vs expected quantilesTesting