mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-09 17:11:05 +00:00
Mark Aron Szulyovszky
cc70d3f907
* feat(Selection): added prototype feature selection python script * feat(Utils): added some helpers for the future from Advances in Financial ML book * feat(Selection): added RFECV * feat(Selection): added configurable feature selection step into pipeline * feat(Config): added level_1 & level_2 default config, PCA before feature selection process starts * feat(Selection): added backup feature selector models if current one can't output feature importance, removed unnecessary array for level-2 models * fix(Training): deal with zero first value coming out of static models * feat(Sweep): added feature selection sweep * fix(Sweep): config problem * fix(Sweep): config * chore(Utils): removed unnecessary purged k-fold crossval class * feat(Config): added dimensionality_reduction as a separate flag * fix(Sweep): config updated * fix(Sweep): sweep name * chore(Config): updated level_2 config to the best performing configuation
50 lines
2.4 KiB
Python
50 lines
2.4 KiB
Python
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
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from utils.typing import FeatureExtractorConfig
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from utils.helpers import flatten
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__presets = dict(
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debug_future_lookahead = [('debug_future', feature_debug_future_lookahead, [1])],
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single_mom = [('mom', feature_mom, [30])],
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single_vol = [('vol', feature_vol, [30])],
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mom = [('mom', feature_mom, [10, 20, 30, 60, 90])],
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vol = [('vol', feature_vol, [10, 20, 30, 60])],
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lags_up_to_5 = [('lag', feature_lag, [1,2,3,4,5])],
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lags_up_to_10 = [('lag', feature_lag, [1,2,3,4,5,6,7,8,9,10])],
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date_all = [
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('day_of_week', feature_day_of_week, [0]),
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('day_of_month', feature_day_of_month, [0]),
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('month', feature_month, [0])],
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date_days = [
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('day_of_week', feature_day_of_week, [0]),
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('day_of_month', feature_day_of_month, [0]),
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],
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roc = [('roc', feature_ROC, [10, 30])],
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rsi = [('rsi', feature_ROC, [10, 30, 100])],
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stod = [('stod', feature_STOD, [10, 30, 200])],
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stok = [('stok', feature_STOK, [10, 30, 200])],
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)
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presets = __presets | dict(
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level_1 = __presets["mom"] + __presets["vol"],
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level_2 = __presets["mom"] + __presets["vol"] + __presets["roc"] + __presets["rsi"] + __presets["stod"] + __presets["stok"],
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)
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def preprocess_feature_extractors_config(data_dict: dict) -> dict:
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data_dict = data_dict.copy()
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keys = ['own_features', 'other_features']
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for key in keys:
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preset_names = data_dict[key]
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data_dict[key] = flatten([presets[preset_name] for preset_name in preset_names])
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return data_dict
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# Use this if ever we want to create an independent boolean for each featureextractor
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# def preprocess_feature_extractors_config(data_dict: dict) -> dict:
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# prefixes = ['own_features', 'other_features']
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# features_dict = dict()
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# for prefix in prefixes:
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# features_to_include = [key.replace(prefix + "_", "") for key, value in data_dict.items() if key.startswith(prefix) and value == True]
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# features_dict[prefix] = flatten([presets[feature_name] for feature_name in features_to_include])
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# data_dict = {k: v for k, v in data_dict.items() if not (k.startswith(prefixes[0]) or k.startswith(prefixes[1]))}
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# return (data_dict | features_dict) |