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refactor(Naming): use new convention, added Ensemble model parameter back, support multiple Meta-Labeling models (#132)

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* refactor(Naming): use `primary_models` & `meta_labeling_models`

* refactor(Naming): using primary * meta_labeling across config and in pipeline

* feat(Pipeline): added back Ensemble models

* fix(Pipeline): compiler error

* fix(Config): typo

* chore(Pipeline): removed unused averaging step

* revert the changes in discretizing

* chore(Pipeline): remove sharpe improvement logging

* fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame

* fix(Pipeline): discard unnecessary ensemble_probabilities

* fix(Pipeline): fixes regarding various meta-labeling ensemble bugs

* fix(Reporting): use the new naming convention

* fix(Reporting): use the right variable

* feat(Sweep): new sweep for ensemble models

* fix(Sweep): config reference

* fix(Config): simplified dev config

* fix(Models): use the faster LR model

* fix(Models): use LGBM in the meta-labeling model for speed

* fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
This commit is contained in:
Mark Aron Szulyovszky
2022-01-09 17:21:06 +01:00
committed by GitHub
parent 22b3167cb9
commit b1c04afb13
20 changed files with 202 additions and 239 deletions
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feature_selection/feature_selection.py
+13 -13
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@@ -5,20 +5,20 @@ from models.base import Model
from models.sklearn import SKLearnModel
from utils.scaler import get_scaler
from utils.types import ScalerTypes
from utils.hashing import hash_df, hash_series
from diskcache import Cache
cache = Cache(".cachedir/feature_selection")
# from utils.hashing import hash_df, hash_series
# from diskcache import Cache
# cache = Cache(".cachedir/feature_selection")
def select_features(**kwargs) -> pd.DataFrame:
hashed = kwargs['data_config_hash'] + kwargs['model'].get_name() + str(kwargs['n_features_to_select']) + kwargs['backup_model'].get_name() + kwargs['scaling'] + str(kwargs['dynamic_feature_selection'])
if hashed in cache:
return cache.get(hashed)
else:
return_value = __select_features(**kwargs)
cache[hashed] = return_value
return return_value
# def select_features(**kwargs) -> pd.DataFrame:
# hashed = kwargs['data_config_hash'] + kwargs['model'].get_name() + str(kwargs['n_features_to_select']) + kwargs['backup_model'].get_name() + kwargs['scaling']
# if hashed in cache:
# return cache.get(hashed)
# else:
# return_value = __select_features(**kwargs)
# cache[hashed] = return_value
# return return_value
def __select_features(X: pd.DataFrame, y: pd.Series, model: Model, n_features_to_select: int, backup_model: SKLearnModel, scaling: ScalerTypes, dynamic_feature_selection: bool, data_config_hash: str) -> pd.DataFrame:
def select_features(X: pd.DataFrame, y: pd.Series, model: Model, n_features_to_select: int, backup_model: SKLearnModel, scaling: ScalerTypes) -> pd.DataFrame:
''' Select features using RFECV, returns a pd.DataFrame (X) with only the selected features.'''
if model.model_type != 'ml': return X
@@ -34,7 +34,7 @@ def __select_features(X: pd.DataFrame, y: pd.Series, model: Model, n_features_to
feat_selector_model = backup_model.model
# selector = RFECV(feat_selector_model, cv = cv, step=5, min_features_to_select=min_features_to_select)
step = 0.05 if dynamic_feature_selection else 5
step = 0.05
selector = RFE(feat_selector_model, n_features_to_select= n_features_to_select, step=step)
selector = selector.fit(X_scaled, y)
print("Kept %d features out of %d" % (selector.n_features_, X_scaled.shape[1]))