From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Sat, 25 Jan 2025 22:52:40 +0000 (+0100)
Subject: feat: add n_estimators to HPO
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=d79aa4c5732d73162b50583d3d2fcc152d393072;p=freqai-strategies.git

feat: add n_estimators to HPO

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

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index ecd61af..0a4a8fd 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -98,6 +98,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             params = {
                 **self.model_training_parameters,
                 **{
+                    "n_estimators": hp.get("n_estimators"),
                     "learning_rate": hp.get("learning_rate"),
                     "reg_alpha": hp.get("reg_alpha"),
                     "reg_lambda": hp.get("reg_lambda"),
@@ -214,6 +215,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
 def objective(trial, X, y, weights, X_test, y_test, params):
     study_params = {
         "objective": "rmse",
+        "n_estimators": trial.suggest_int("n_estimators", 100, 1000),
         "learning_rate": trial.suggest_loguniform("learning_rate", 1e-8, 1.0),
         "reg_alpha": trial.suggest_loguniform("reg_alpha", 1e-8, 10.0),
         "reg_lambda": trial.suggest_loguniform("reg_lambda", 1e-8, 10.0),
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 2c34f43..55a1e66 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -98,6 +98,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             params = {
                 **self.model_training_parameters,
                 **{
+                    "n_estimators": hp.get("n_estimators"),
                     "learning_rate": hp.get("learning_rate"),
                     "gamma": hp.get("gamma"),
                     "reg_alpha": hp.get("reg_alpha"),
@@ -216,6 +217,7 @@ def objective(trial, X, y, weights, X_test, y_test, params):
     study_params = {
         "objective": "reg:squarederror",
         "eval_metric": "rmse",
+        "n_estimators": trial.suggest_int("n_estimators", 100, 1000),
         "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, 10.0),