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feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105)

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* 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
This commit is contained in:
Mark Aron Szulyovszky
2022-01-04 11:44:35 +01:00
committed by GitHub
parent 867269df2b
commit 1cd0119589
27 changed files with 324 additions and 206 deletions
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feature_extractors/feature_extractor_presets.py
+4 -2
View File
@@ -1,7 +1,7 @@
from feature_extractors.feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_vol, feature_day_of_month, feature_day_of_week, feature_month, feature_debug_future_lookahead
from feature_extractors.feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_standard_scaling, feature_vol, feature_day_of_month, feature_day_of_week, feature_month, feature_debug_future_lookahead
from utils.types import FeatureExtractorConfig
from utils.helpers import flatten
from feature_extractors.fractional_differentiation import feature_fractional_differentiation
from feature_extractors.fractional_differentiation import feature_fractional_differentiation, feature_fractional_differentiation_log
__presets = dict(
debug_future_lookahead = [('debug_future', feature_debug_future_lookahead, [1])],
@@ -24,6 +24,8 @@ __presets = dict(
stod = [('stod', feature_STOD, [10, 30, 200])],
stok = [('stok', feature_STOK, [10, 30, 200])],
fracdiff = [('fracdiff', feature_fractional_differentiation, [10, 30])],
fracdiff_log = [('fracdiff_log', feature_fractional_differentiation_log, [10, 30])],
standard_scaling = [('standard_scaling', feature_standard_scaling, [0])],
)
presets = __presets | dict(