refactor: improve a bit training reproducibility

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 10252681ce4b1a521ea333fa5d6cd2687f21fcfc..6289765869126bbb95f925a55e3d1d1415dd7f04 100644 (file)
--- a/ReforceXY/user_data/freqaimodels/ReforceXY.py
+++ b/ReforceXY/user_data/freqaimodels/ReforceXY.py
@@ -261,6 +261,9 @@ class ReforceXY(BaseReinforcementLearningModel):
 
         model_params: Dict[str, Any] = copy.deepcopy(self.model_training_parameters)
 
+        if model_params.get("seed") is None:
+            model_params["seed"] = 42
+
         if self.lr_schedule:
             lr = model_params.get("learning_rate", 0.0003)
             if isinstance(lr, (int, float)):

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index 68354f40e23d96c9b71c2f756d7661428cef4602..0208770c83a96b028aed9c272fc4d12d9698dc0a 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -290,7 +290,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
         y_test = data_dictionary.get("test_labels")
         test_weights = data_dictionary.get("test_weights")
 
-        model_training_parameters = self.model_training_parameters
+        model_training_parameters = copy.deepcopy(self.model_training_parameters)
 
         start_time = time.time()
         if self._optuna_hyperopt:

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index 740028584fc06b0b8ae1c6b02453773051af0bca..69101e39ae5c458ae0ed41854551bf9efbb98152 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -743,6 +743,9 @@ def fit_regressor(
     init_model: Any = None,
     callbacks: Optional[list[Callable]] = None,
 ) -> Any:
+    if model_training_parameters.get("seed") is None:
+        model_training_parameters["seed"] = 1
+
     if regressor == "xgboost":
         from xgboost import XGBRegressor