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fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)

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* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction

* fix(Evaluate): print results

* fix(Evaluate): make sure we have numerical stability in returns

* fix(Inference): only output and print stats in training mode

* fix(Evaluate): don't add miniscule amount to result
This commit is contained in:
Mark Aron Szulyovszky
2022-02-01 13:09:00 +01:00
committed by GitHub
parent 3eb3ea94e3
commit f85ee6bb9c
11 changed files with 61 additions and 46 deletions
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training/train_model.py
+13 -11
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@@ -17,11 +17,11 @@ def train_models(
from_index: Optional[pd.Timestamp],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
level: str,
print_results: bool,
output_stats: bool,
transformations_over_time: TransformationsOverTime,
models_over_time: Optional[list[ModelOverTime]]
) -> list[TrainingOutcome]:
return [train_model(ticker_to_predict, X, y, forward_returns, model, expanding_window, sliding_window_size, retrain_every, from_index, no_of_classes, level, print_results, transformations_over_time, models_over_time[index] if models_over_time else None) for index, model in enumerate(models)]
return [train_model(ticker_to_predict, X, y, forward_returns, model, expanding_window, sliding_window_size, retrain_every, from_index, no_of_classes, level, output_stats, transformations_over_time, models_over_time[index] if models_over_time else None) for index, model in enumerate(models)]
def train_model(
@@ -36,7 +36,7 @@ def train_model(
from_index: Optional[pd.Timestamp],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
level: str,
print_results: bool,
output_stats: bool,
transformations_over_time: TransformationsOverTime,
model_over_time: Optional[ModelOverTime]
) -> TrainingOutcome:
@@ -73,13 +73,15 @@ def train_model(
)
assert len(predictions) == len(y)
stats = evaluate_predictions(
forward_returns = forward_returns,
y_pred = predictions,
y_true = y,
no_of_classes=no_of_classes,
print_results = print_results,
discretize=True
)
if output_stats:
stats = evaluate_predictions(
forward_returns = forward_returns,
y_pred = predictions,
y_true = y,
no_of_classes=no_of_classes,
discretize=True
)
else:
stats = None
return TrainingOutcome(model_id, predictions, probabilities, stats, model_over_time)