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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
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config/presets.py
+82 -85
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@@ -1,104 +1,101 @@
from .types import RawConfig, Config
def get_dev_config() -> RawConfig:
classification_models = ["LogisticRegression_two_class"]
return RawConfig(
directional_models_meta = False,
dimensionality_reduction = False,
n_features_to_select = 30,
expanding_window_base = False,
expanding_window_meta = False,
sliding_window_size_base = 380,
sliding_window_size_meta = 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'],
directional_models = classification_models,
meta_models = [],
event_filter = 'none',
labeling = 'two_class',
forecasting_horizon = 100,
directional_models_meta=False,
dimensionality_reduction=False,
n_features_to_select=30,
expanding_window_base=False,
expanding_window_meta=False,
sliding_window_size_base=380,
sliding_window_size_meta=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"],
directional_models=classification_models,
meta_models=[],
event_filter="none",
labeling="two_class",
forecasting_horizon=100,
)
def get_default_ensemble_config() -> RawConfig:
classification_models = ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
meta_models = ['LogisticRegression_two_class', 'LGBM']
classification_models = [
"LogisticRegression_two_class",
"LDA",
"NB",
"RFC",
"XGB_two_class",
"LGBM",
"StaticMom",
]
meta_models = ["LogisticRegression_two_class", "LGBM"]
return RawConfig(
directional_models_meta = True,
dimensionality_reduction = False,
n_features_to_select = 30,
expanding_window_base = False,
expanding_window_meta = True,
sliding_window_size_base = 380,
sliding_window_size_meta = 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'],
directional_models = classification_models,
meta_models = meta_models,
event_filter = 'cusum_vol',
labeling = 'two_class',
forecasting_horizon = 100,
directional_models_meta=True,
dimensionality_reduction=False,
n_features_to_select=30,
expanding_window_base=False,
expanding_window_meta=True,
sliding_window_size_base=380,
sliding_window_size_meta=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"],
directional_models=classification_models,
meta_models=meta_models,
event_filter="cusum_vol",
labeling="two_class",
forecasting_horizon=100,
)
def get_lightweight_ensemble_config() -> RawConfig:
classification_models = ['LogisticRegression_two_class', 'LGBM']
meta_models = ['LogisticRegression_two_class', 'LGBM']
classification_models = ["LogisticRegression_two_class", "LGBM"]
meta_models = ["LogisticRegression_two_class", "LGBM"]
return RawConfig(
directional_models_meta = True,
dimensionality_reduction = True,
n_features_to_select = 30,
expanding_window_base = True,
expanding_window_meta = True,
sliding_window_size_base = 3800,
sliding_window_size_meta = 2400,
retrain_every = 1000,
scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
assets = ['fivemin_crypto'],
target_asset = 'BTC_USD',
other_assets = [],
exogenous_data = [],
load_non_target_asset= False,
own_features = ['level_1'],
other_features = [],
exogenous_features = [],
directional_models = classification_models,
meta_models = meta_models,
event_filter = 'cusum_fixed',
labeling = 'two_class',
forecasting_horizon = 50,
directional_models_meta=True,
dimensionality_reduction=True,
n_features_to_select=30,
expanding_window_base=True,
expanding_window_meta=True,
sliding_window_size_base=3800,
sliding_window_size_meta=2400,
retrain_every=1000,
scaler="minmax", # 'normalize' 'minmax' 'standardize'
assets=["fivemin_crypto"],
target_asset="BTC_USD",
other_assets=[],
exogenous_data=[],
load_non_target_asset=False,
own_features=["level_1"],
other_features=[],
exogenous_features=[],
directional_models=classification_models,
meta_models=meta_models,
event_filter="cusum_fixed",
labeling="two_class",
forecasting_horizon=50,
)