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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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training/directional_training.py
+43 -35
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@@ -5,7 +5,7 @@ from training.train_model import train_model
from training.walk_forward import walk_forward_process_transformations
from typing import Optional
from config.types import Config
from config.types import Config
from models.base import Model
from models.model_map import default_feature_selector_classification
@@ -13,53 +13,61 @@ from transformations.scaler import get_scaler
from transformations.rfe import RFETransformation
from transformations.pca import PCATransformation
def train_directional_model(
X: pd.DataFrame,
y: pd.Series,
forward_returns: pd.Series,
config: Config,
model: Model,
from_index: Optional[pd.Timestamp],
preloaded_training_step: Optional[DirectionalTrainingOutcome] = None,
) -> DirectionalTrainingOutcome:
X: pd.DataFrame,
y: pd.Series,
forward_returns: pd.Series,
config: Config,
model: Model,
from_index: Optional[pd.Timestamp],
preloaded_training_step: Optional[DirectionalTrainingOutcome] = None,
) -> DirectionalTrainingOutcome:
if preloaded_training_step is None:
print("Preprocess transformations")
transformations_over_time = walk_forward_process_transformations(
X = X,
y = y,
forward_returns = forward_returns,
expanding_window = config.expanding_window_base,
window_size = config.sliding_window_size_base,
retrain_every = config.retrain_every,
from_index = from_index,
transformations= [
X=X,
y=y,
forward_returns=forward_returns,
expanding_window=config.expanding_window_base,
window_size=config.sliding_window_size_base,
retrain_every=config.retrain_every,
from_index=from_index,
transformations=[
get_scaler(config.scaler),
PCATransformation(ratio_components_to_keep=0.5, sliding_window_size=config.sliding_window_size_base),
RFETransformation(n_feature_to_select=40, model=default_feature_selector_classification)
PCATransformation(
ratio_components_to_keep=0.5,
sliding_window_size=config.sliding_window_size_base,
),
RFETransformation(
n_feature_to_select=40,
model=default_feature_selector_classification,
),
],
)
else:
transformations_over_time = preloaded_training_step.transformations
training_outcome = train_model(
ticker_to_predict = config.target_asset[1],
X = X,
y = y,
forward_returns = forward_returns,
model = model,
expanding_window = config.expanding_window_base,
sliding_window_size = config.sliding_window_size_base,
retrain_every = config.retrain_every,
from_index = from_index,
no_of_classes = config.no_of_classes,
level = 'primary',
output_stats= config.mode == 'training',
transformations_over_time = transformations_over_time,
model_over_time = preloaded_training_step.training.model_over_time if preloaded_training_step else None
ticker_to_predict=config.target_asset[1],
X=X,
y=y,
forward_returns=forward_returns,
model=model,
expanding_window=config.expanding_window_base,
sliding_window_size=config.sliding_window_size_base,
retrain_every=config.retrain_every,
from_index=from_index,
no_of_classes=config.no_of_classes,
level="primary",
output_stats=config.mode == "training",
transformations_over_time=transformations_over_time,
model_over_time=preloaded_training_step.training.model_over_time
if preloaded_training_step
else None,
)
if config.mode == 'training':
if config.mode == "training":
print(training_outcome.stats)
return DirectionalTrainingOutcome(training_outcome, transformations_over_time)