mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-20 06:18:08 +00:00
feat(Events): added EventFilter, EventLabeller (#186)
This commit is contained in:
@@ -0,0 +1,109 @@
|
||||
from .types import RawConfig, Config
|
||||
|
||||
def get_dev_config() -> RawConfig:
|
||||
|
||||
regression_models = ["Lasso"]
|
||||
classification_models = ["LogisticRegression_two_class"]
|
||||
|
||||
return RawConfig(
|
||||
primary_models_meta_labeling = False,
|
||||
dimensionality_reduction = False,
|
||||
n_features_to_select = 30,
|
||||
expanding_window_base = False,
|
||||
expanding_window_meta_labeling = False,
|
||||
sliding_window_size_base = 380,
|
||||
sliding_window_size_meta_labeling = 1,
|
||||
retrain_every = 20,
|
||||
scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
|
||||
|
||||
assets = ['daily_only_btc'],
|
||||
target_asset = 'BTC_USD',
|
||||
other_assets = [],
|
||||
exogenous_data = [],
|
||||
load_non_target_asset= True,
|
||||
own_features = ['level_2', 'date_days'],
|
||||
other_features = ['single_mom'],
|
||||
exogenous_features = ['z_score'],
|
||||
|
||||
primary_models = classification_models,
|
||||
meta_labeling_models = [],
|
||||
ensemble_model = None,
|
||||
|
||||
event_filter = 'none',
|
||||
labeling = 'two_class'
|
||||
)
|
||||
|
||||
|
||||
def get_default_ensemble_config() -> RawConfig:
|
||||
|
||||
regression_models = ["Lasso", "KNN", "RFR"]
|
||||
classification_models = ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
|
||||
meta_labeling_models = ['LogisticRegression_two_class', 'LGBM']
|
||||
ensemble_model = 'Average'
|
||||
|
||||
return RawConfig(
|
||||
primary_models_meta_labeling = True,
|
||||
dimensionality_reduction = False,
|
||||
n_features_to_select = 30,
|
||||
expanding_window_base = False,
|
||||
expanding_window_meta_labeling = True,
|
||||
sliding_window_size_base = 380,
|
||||
sliding_window_size_meta_labeling = 240,
|
||||
retrain_every = 10,
|
||||
scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
|
||||
|
||||
assets = ['daily_crypto'],
|
||||
target_asset = 'BTC_USD',
|
||||
other_assets = ['daily_etf'],
|
||||
exogenous_data = ['daily_glassnode'],
|
||||
load_non_target_asset= True,
|
||||
own_features = ['level_2', 'date_days', 'lags_up_to_5'],
|
||||
other_features = ['level_2', 'lags_up_to_5'],
|
||||
exogenous_features = ['z_score'],
|
||||
|
||||
primary_models = classification_models,
|
||||
meta_labeling_models = meta_labeling_models,
|
||||
ensemble_model = ensemble_model,
|
||||
|
||||
event_filter = 'cusum_vol',
|
||||
labeling = 'two_class'
|
||||
)
|
||||
|
||||
|
||||
|
||||
def get_lightweight_ensemble_config() -> RawConfig:
|
||||
|
||||
regression_models = ["Lasso", "KNN"]
|
||||
classification_models = ['LogisticRegression_two_class', 'SVC']
|
||||
meta_labeling_models = ['LogisticRegression_two_class', 'LGBM']
|
||||
ensemble_model = 'Average'
|
||||
|
||||
return RawConfig(
|
||||
primary_models_meta_labeling = True,
|
||||
dimensionality_reduction = True,
|
||||
n_features_to_select = 30,
|
||||
expanding_window_base = False,
|
||||
expanding_window_meta_labeling = True,
|
||||
sliding_window_size_base = 380,
|
||||
sliding_window_size_meta_labeling = 240,
|
||||
retrain_every = 40,
|
||||
scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
|
||||
|
||||
assets = ['daily_crypto_lightweight'],
|
||||
target_asset = 'BCH_USD',
|
||||
other_assets = ['daily_etf'],
|
||||
exogenous_data = ['daily_glassnode'],
|
||||
load_non_target_asset= True,
|
||||
own_features = ['level_2' ],
|
||||
other_features = ['level_2'],
|
||||
exogenous_features = ['z_score'],
|
||||
|
||||
primary_models = classification_models,
|
||||
meta_labeling_models = meta_labeling_models,
|
||||
ensemble_model = ensemble_model,
|
||||
|
||||
event_filter = 'none',
|
||||
labeling = 'two_class'
|
||||
)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user