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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
c611481eb6
* refactor(WalkForward): separate train / test functions (draft) to potentially help with inference later * fix(Training): use the new separate train / test functions * feat(Training): return and pass in scalers that are necessary for inference * fix(Project): runtime errors * fix(WalkForward): use the correct `train_from` value * fix(Tests): for new walk_forward functions() * refactor(WalkForward): rename `walk_forward_test()` to `walk_forward_inference()`
13 lines
484 B
Python
13 lines
484 B
Python
from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
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from typing import Union
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from utils.types import ScalerTypes
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def get_scaler(type: ScalerTypes) -> Union[MinMaxScaler, Normalizer, StandardScaler]:
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if type == 'normalize':
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return Normalizer()
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elif type == 'minmax':
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return MinMaxScaler(feature_range= (-1, 1))
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elif type == 'standardize':
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return StandardScaler()
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else:
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raise Exception("Scaler type not supported") |