mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from .rfe import RFETransformation
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from .pca import PCATransformation
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from .sklearn import SKLearnTransformation
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from typing import Literal, Optional
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from models.model_map import default_feature_selector_classification
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from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
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ScalerTypes = Literal["normalize", "minmax", "standardize"]
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def get_rfe(n_feature_to_select: int) -> Optional[RFETransformation]:
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if n_feature_to_select > 0:
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return RFETransformation(
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n_feature_to_select=n_feature_to_select,
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model=default_feature_selector_classification,
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)
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else:
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return None
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def get_pca(
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ratio_components_to_keep: float, sliding_window_size: int
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) -> Optional[PCATransformation]:
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if ratio_components_to_keep > 0:
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return PCATransformation(
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ratio_components_to_keep=ratio_components_to_keep,
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sliding_window_size=sliding_window_size,
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)
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else:
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return None
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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")
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