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fix(Reporting): use weighted average (with no_of_samples as weights) and only report level-1 OR level-2 model performance (#91)

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* fix(Reporting): use weighted average (with no_of_samples as weights) and only report level-1 OR level-2 model performance

* chore(Config): updated sweep config

* fix(Reporting): missing import

* fix(Evaluation): get_first_valid_return_index can deal with zero valid indexes

* fix(Training): increase threshold for skipping assets

* fix(DataLoader): target asset should be always the first column
This commit is contained in:
Mark Aron Szulyovszky
2021-12-26 12:15:11 +01:00
committed by GitHub
parent fc4e59a7d2
commit a9b05dbd42
6 changed files with 41 additions and 19 deletions
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training/training.py
+1 -1
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@@ -57,7 +57,7 @@ def run_single_asset_trainig(
method = method,
no_of_classes=no_of_classes
)
column_name = ticker_to_predict + "_" + model_name + "_" + str(level)
column_name = ticker_to_predict + "_" + model_name + "_lvl" + str(level)
results[column_name] = result
# column names for model outputs should be different, so we can differentiate between original data and model predictions later, where necessary
predictions["model_" + column_name] = preds