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Mark Aron Szulyovszky
1856fcad22
* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference() * fix(WalkForward): use Dataframes to call Transformation.fit_transform() * feat(WalkForward): restored option for models to recieve unscaled data * fix(Transformations): output DataFrame as expected * fix(Tests): missing new property
13 lines
560 B
Python
13 lines
560 B
Python
from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
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from transformations.sklearn import SKLearnTransformation
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from utils.types import ScalerTypes
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def get_scaler(type: ScalerTypes) -> SKLearnTransformation:
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if type == 'normalize':
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return SKLearnTransformation(Normalizer())
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elif type == 'minmax':
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return SKLearnTransformation(MinMaxScaler(feature_range= (-1, 1)))
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elif type == 'standardize':
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return SKLearnTransformation(StandardScaler())
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else:
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raise Exception("Scaler type not supported") |