mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
64 lines
1.9 KiB
Python
64 lines
1.9 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 (
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MinMaxScaler,
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Normalizer,
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StandardScaler,
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RobustScaler,
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PowerTransformer,
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QuantileTransformer,
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)
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ScalerTypes = Literal[
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"normalize", "minmax", "standardize", "robust", "box-cox", "quantile"
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]
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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, initial_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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initial_window_size=initial_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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elif type == "robust":
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return SKLearnTransformation(
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RobustScaler(with_centering=False, quantile_range=(0.10, 0.90))
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)
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elif type == "box-cox":
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return SKLearnTransformation(
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PowerTransformer(method="box-cox", standardize=True)
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)
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elif type == "quantile":
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return SKLearnTransformation(
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QuantileTransformer(n_quantiles=100, output_distribution="normal")
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)
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else:
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raise Exception("Scaler type not supported")
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