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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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run_pipeline.py
+1 -1
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@@ -69,7 +69,7 @@ def __run_training(config: Config) -> PipelineOutcome:
bet_sizing_outcomes = [bet_sizing_with_meta_models(X, outcome.predictions, y, forward_returns, config.meta_models, config, 'meta', None, None, None) for outcome in directional_training_outcome.training]
# 4. Ensemble weights
ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes)
ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes, config.mode == 'training')
# 5. (Optional) Additional bet sizing on top of the ensembled weights
ensemble_bet_sizing_outcome = bet_sizing_with_meta_models(X, ensemble_outcome.weights, y, forward_returns, config.meta_models, config, 'ensemble', None, None, None) if len(config.meta_models) > 0 else None