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Code Issues Packages Projects Releases Wiki Activity

refactor(Types): added nested types for Reporting (#162)

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This commit is contained in:
Mark Aron Szulyovszky
2022-01-13 09:21:07 +01:00
committed by GitHub
parent 1856fcad22
commit 3c2a0d4247
8 changed files with 73 additions and 61 deletions
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training/training_steps.py
+24 -6
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@@ -4,11 +4,20 @@ from operator import itemgetter
from training.primary_model import train_primary_model
from training.meta_labeling import train_meta_labeling_model
from utils.encapsulation import Reporting, Asset, Single_Model, Training_Step
from reporting.types import Reporting
def primary_step(X: pd.DataFrame, y:pd.Series, original_X:pd.DataFrame, X_pca:pd.DataFrame, asset:list, target_returns:pd.Series, configs: dict, reporting: Reporting) -> tuple[Training_Step, pd.DataFrame]:
training_step = Training_Step(level='primary')
def primary_step(
X: pd.DataFrame,
y:pd.Series,
original_X:pd.DataFrame,
X_pca:pd.DataFrame,
asset:list,
target_returns:pd.Series,
configs: dict,
reporting: Reporting
) -> tuple[Reporting.Training_Step, pd.DataFrame]:
training_step = Reporting.Training_Step(level='primary')
model_config, training_config, data_config = itemgetter('model_config', 'training_config', 'data_config')(configs)
# 3. Train Primary models
@@ -60,8 +69,18 @@ def primary_step(X: pd.DataFrame, y:pd.Series, original_X:pd.DataFrame, X_pca:pd
return training_step, current_predictions
def secondary_step(X:pd.DataFrame, y:pd.Series, original_X:pd.DataFrame, X_pca:pd.DataFrame, current_predictions:pd.DataFrame, asset:list, target_returns:pd.Series, configs: dict, reporting: Reporting) -> Training_Step:
training_step = Training_Step(level='secondary')
def secondary_step(
X:pd.DataFrame,
y:pd.Series,
original_X:pd.DataFrame,
X_pca:pd.DataFrame,
current_predictions:pd.DataFrame,
asset:list,
target_returns:pd.Series,
configs: dict,
reporting: Reporting
) -> Reporting.Training_Step:
training_step = Reporting.Training_Step(level='secondary')
model_config, training_config, data_config = itemgetter('model_config', 'training_config', 'data_config')(configs)
# 5. Ensemble primary model predictions (If Ensemble model is present)
@@ -90,7 +109,6 @@ def secondary_step(X:pd.DataFrame, y:pd.Series, original_X:pd.DataFrame, X_pca:p
reporting.all_predictions = pd.concat([reporting.all_predictions, ensemble_predictions], axis=1)
if len(model_config['meta_labeling_models']) > 0:
# 3. Train a Meta-labeling model on the averaged level-1 model predictions