refactor(qav3): utils function namespace alignment

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/ReforceXY/user_data/freqaimodels/ReforceXY.py b/ReforceXY/user_data/freqaimodels/ReforceXY.py
index 0c315f0e4dd37182e4bd1b72367ac9e7940a7658..147dccdb32446ec1ad8fb8eac927d6a123f72b2e 100644 (file)
--- a/ReforceXY/user_data/freqaimodels/ReforceXY.py
+++ b/ReforceXY/user_data/freqaimodels/ReforceXY.py
@@ -43,7 +43,6 @@ from freqtrade.freqai.RL.BaseReinforcementLearningModel import (
 from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
 from freqtrade.strategy import timeframe_to_minutes
 
-
 matplotlib.use("Agg")
 warnings.filterwarnings("ignore", category=UserWarning)
 warnings.filterwarnings("ignore", category=FutureWarning)

diff --git a/ReforceXY/user_data/strategies/RLAgentStrategy.py b/ReforceXY/user_data/strategies/RLAgentStrategy.py
index fa14346aa221d4d79df53387db4e175f82c07f8a..6eb3c9dc052452336629804c9d586fec7a438662 100644 (file)
--- a/ReforceXY/user_data/strategies/RLAgentStrategy.py
+++ b/ReforceXY/user_data/strategies/RLAgentStrategy.py
@@ -7,7 +7,6 @@ from pandas import DataFrame
 
 from freqtrade.strategy import IStrategy
 
-
 logger = logging.getLogger(__name__)
 
 ACTION_COLUMN = "&-action"

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index 934765e209f0032f0a7f0dd514c5b9f26a0317a1..98e7b013d33a93c1345ce1f4842c0a22239fac0f 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -21,7 +21,7 @@ from Utils import (
     calculate_min_extrema,
     calculate_n_extrema,
     fit_regressor,
-    get_callbacks,
+    get_optuna_callbacks,
     get_optuna_study_model_parameters,
     largest_divisor,
     round_to_nearest_int,
@@ -1279,7 +1279,7 @@ def train_objective(
         eval_set=[(X_test, y_test)],
         eval_weights=[test_weights],
         model_training_parameters=model_training_parameters,
-        callbacks=get_callbacks(trial, regressor),
+        callbacks=get_optuna_callbacks(trial, regressor),
     )
     y_pred = model.predict(X_test)
 
@@ -1314,7 +1314,7 @@ def hp_objective(
         eval_set=[(X_test, y_test)],
         eval_weights=[test_weights],
         model_training_parameters=model_training_parameters,
-        callbacks=get_callbacks(trial, regressor),
+        callbacks=get_optuna_callbacks(trial, regressor),
     )
     y_pred = model.predict(X_test)
 

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index 6eb70c27213f0469ed4dc982e4846ff3f7e8a505..6b8d5f4d5e7ca9c00327598abbf6fd70946c28fe 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -13,7 +13,6 @@ import talib.abstract as ta
 
 from technical import qtpylib
 
-
 T = TypeVar("T", pd.Series, float)
 
 
@@ -631,7 +630,7 @@ def zigzag(
 regressors = {"xgboost", "lightgbm"}
 
 
-def get_callbacks(trial: optuna.trial.Trial, regressor: str) -> list[Callable]:
+def get_optuna_callbacks(trial: optuna.trial.Trial, regressor: str) -> list[Callable]:
     if regressor == "xgboost":
         callbacks = [
             optuna.integration.XGBoostPruningCallback(trial, "validation_0-rmse")