From 3ad97c3d04ab5655edae68723fd0365703ba59c8 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Fri, 7 Mar 2025 13:45:04 +0100 Subject: [PATCH] refactor(qav3): refine type definition MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 8 ++++---- .../freqaimodels/XGBoostRegressorQuickAdapterV35.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index 3a81851..6e30d73 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -270,7 +270,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int, - ) -> tuple[float, float]: + ) -> tuple[pd.Series, pd.Series]: prediction_thresholds_smoothing = self.freqai_info.get( "prediction_thresholds_smoothing", "mean" ) @@ -489,7 +489,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int, - ) -> tuple[float, float]: + ) -> tuple[pd.Series, pd.Series]: pred_df_sorted = ( pred_df.select_dtypes(exclude=["object"]) .copy() @@ -507,7 +507,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): def mean_min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int -) -> tuple[float, float]: +) -> tuple[pd.Series, pd.Series]: pred_df_sorted = ( pred_df.select_dtypes(exclude=["object"]) .copy() @@ -524,7 +524,7 @@ def mean_min_max_pred( def median_min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int -) -> tuple[float, float]: +) -> tuple[pd.Series, pd.Series]: pred_df_sorted = ( pred_df.select_dtypes(exclude=["object"]) .copy() diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index 9957a2c..5330cc9 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -271,7 +271,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int, - ) -> tuple[float, float]: + ) -> tuple[pd.Series, pd.Series]: prediction_thresholds_smoothing = self.freqai_info.get( "prediction_thresholds_smoothing", "mean" ) @@ -490,7 +490,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int, - ) -> tuple[float, float]: + ) -> tuple[pd.Series, pd.Series]: pred_df_sorted = ( pred_df.select_dtypes(exclude=["object"]) .copy() @@ -508,7 +508,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): def mean_min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int -) -> tuple[float, float]: +) -> tuple[pd.Series, pd.Series]: pred_df_sorted = ( pred_df.select_dtypes(exclude=["object"]) .copy() @@ -525,7 +525,7 @@ def mean_min_max_pred( def median_min_max_pred( pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int -) -> tuple[float, float]: +) -> tuple[pd.Series, pd.Series]: pred_df_sorted = ( pred_df.select_dtypes(exclude=["object"]) .copy() -- 2.43.0