mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
1cd0119589
* fix(FeatureExtractor): apply log to transform some series to normality * feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features * feat(FeatureExtractors): added standard scaling for exogenous data * feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection * fix(Config): sweep config * feat(Models): output probability, store it * feat(Core): added caching to select_features() and load_data() * fix(Dependencies): added diskcache * fix(Training): error when creating results DF * feat(Models): added xgboost, fixed tests * refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching * fix(Tests): new syntax * fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow * fix(Model): XGBoost config * feat(Cache): add run_clear_cache script * fix(Pipeline) accidentally re-instatiating all_predictions for each asset
13 lines
471 B
Python
13 lines
471 B
Python
from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
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from typing import Optional, Union
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from utils.types import ScalerTypes
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def get_scaler(type: ScalerTypes) -> Optional[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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return None |