From addb6292d4d0f248945b971e6a78431a4e8f1f40 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Thu, 6 Feb 2025 20:30:38 +0100 Subject: [PATCH] refactor(qav3): improve log messages MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 6 +++--- .../freqaimodels/XGBoostRegressorQuickAdapterV35.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index befe599..1f3ec75 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -90,9 +90,9 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): self.__optuna_hp = study.best_params # log params for key, value in self.__optuna_hp.items(): - logger.info(f"Optuna hyperopt {key:>20s} : {value}") + logger.info(f"Optuna hyperopt | {key:>20s} : {value}") logger.info( - f"Optuna hyperopt {'best objective value':>20s} : {study.best_value}" + f"Optuna hyperopt | {'best objective value':>20s} : {study.best_value}" ) train_window = self.__optuna_hp.get("train_period_candles") @@ -138,7 +138,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): ) if candle_diff < 0: logger.warning( - f"Fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go" + f"{pair} fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go" ) warmed_up = False diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index d59c289..edda07e 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -90,9 +90,9 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): self.__optuna_hp = study.best_params # log params for key, value in self.__optuna_hp.items(): - logger.info(f"Optuna hyperopt {key:>20s} : {value}") + logger.info(f"Optuna hyperopt | {key:>20s} : {value}") logger.info( - f"Optuna hyperopt {'best objective value':>20s} : {study.best_value}" + f"Optuna hyperopt | {'best objective value':>20s} : {study.best_value}" ) train_window = self.__optuna_hp.get("train_period_candles") @@ -138,7 +138,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): ) if candle_diff < 0: logger.warning( - f"Fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go" + f"{pair} fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go" ) warmed_up = False -- 2.43.0