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feat(Inference): Models are collected and structured. (#120)

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* feat: Added collection of models into a dictionary.

* feat: Models are now saved in a structured way into a dictionary.

* Rename run_model_test.py to run_model_dev.py

* fix(Pipeline): missing variable statement

Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
This commit is contained in:
Daniel Szemerey
2022-01-07 15:33:50 +01:00
committed by GitHub
co-authored by Mark Aron Szulyovszky
parent c8211e90ff
commit 78a7fe028e
6 changed files with 36 additions and 20 deletions
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training/meta_labeling.py
+3 -3
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@@ -15,7 +15,7 @@ def run_meta_labeling_training(
data_config: dict,
model_config: dict,
training_config: dict
) -> tuple[pd.Series, pd.Series, pd.DataFrame]:
) -> tuple[pd.Series, pd.Series, pd.DataFrame, dict]:
discretize = discretize_threeway_threshold(0.33)
discretized_predictions = input_predictions.apply(discretize)
@@ -29,7 +29,7 @@ def run_meta_labeling_training(
meta_X = pd.concat([meta_selected_features_X, input_predictions, discretized_predictions], axis = 1)
_, meta_preds, meta_probabilities = run_single_asset_trainig(
_, meta_preds, meta_probabilities, all_models_single_asset = run_single_asset_trainig(
ticker_to_predict = "prediction_correct",
original_X = meta_X,
X = meta_X,
@@ -59,4 +59,4 @@ def run_meta_labeling_training(
)
meta_result.rename("model_" + target_asset + "_meta_lvl" + str(2), inplace=True)
return meta_result, avg_predictions_with_sizing, meta_probabilities
return meta_result, avg_predictions_with_sizing, meta_probabilities, all_models_single_asset