jan rathfelder
10/09/2024, 8:07 PMsk_boxcox = PowerTransformer(method="box-cox", standardize=False)
boxcox_global = GlobalSklearnTransformer(sk_boxcox)
scaler = MinMaxScaler(feature_range=(0, 1))
minmax_global = GlobalSklearnTransformer(scaler)
target_transforms = [minmax_global]
target_transforms=target_transforms
jan rathfelder
10/09/2024, 8:10 PMJosé Morales
10/09/2024, 8:14 PMjan rathfelder
10/09/2024, 8:16 PMjan rathfelder
10/09/2024, 8:28 PMJosé Morales
10/09/2024, 8:33 PMmlf = your_load_fn(path)
assert mlf.ts.target_transforms is not None
if that assert fails then the target transformations weren't saved and thus the results will be the raw predictions from the modeljan rathfelder
10/09/2024, 8:34 PMjan rathfelder
10/09/2024, 8:37 PMJosé Morales
10/09/2024, 8:43 PMfitted_tfm = loaded_model.ts.target_transforms[0].transformer_
fitted_tfm.data_min_, fitted_tfm.data_max_
jan rathfelder
10/09/2024, 8:53 PM