feat: add learning_rate to HPO

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

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index d2e5b7586dff8359ea7e1c4b95e80c3e8f849c41..02c1d9bf6493bf1066a47f7e5d83703b5c10c59b 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -98,7 +98,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             params = {
                 **self.model_training_parameters,
                 **{
-                    # "learning_rate": hp.get("learning_rate"),
+                    "learning_rate": hp.get("learning_rate"),
                     # "gamma": hp.get("gamma"),
                     # "reg_alpha": hp.get("reg_alpha"),
                     # "reg_lambda": hp.get("reg_lambda"),
@@ -216,7 +216,7 @@ def objective(trial, X, y, weights, X_test, y_test, params):
     study_params = {
         "objective": "reg:squarederror",
         "eval_metric": "rmse",
-        # "learning_rate": trial.suggest_loguniform("learning_rate", 1e-8, 1.0),
+        "learning_rate": trial.suggest_loguniform("learning_rate", 1e-8, 1.0),
         # "gamma": trial.suggest_loguniform("gamma", 1e-8, 1.0),
         # "reg_alpha": trial.suggest_loguniform("reg_alpha", 1e-8, 1.0),
         # "reg_lambda": trial.suggest_loguniform("reg_lambda", 1e-8, 1.0),