From 3655cba1ddadc33b23e83a6dfd54b94f0fa5433c Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Mon, 10 Feb 2025 23:49:04 +0100 Subject: [PATCH] fix(qav3): revert min/max prediction smoothing MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit ))# --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 4 ++-- .../user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index fa7b436..fc06813 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -239,7 +239,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): return eval_set, eval_weights -def min_max_pred( +def __min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int ): local_pred_df = pd.DataFrame() @@ -258,7 +258,7 @@ def min_max_pred( return min_pred, max_pred -def __min_max_pred( +def min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int ): pred_df_sorted = pd.DataFrame() diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index bfe6062..9f89034 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -239,7 +239,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): return eval_set, eval_weights -def min_max_pred( +def __min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int ): local_pred_df = pd.DataFrame() @@ -258,7 +258,7 @@ def min_max_pred( return min_pred, max_pred -def __min_max_pred( +def min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int ): pred_df_sorted = pd.DataFrame() -- 2.43.0