loo on features

xgboost/custom_xgboost.py

@@ -1,39 +1,39 @@
-import xgboost as xgb
-import numpy as np
-
-class CustomXGBoostGPU:
-    def __init__(self, X_train, X_test, y_train, y_test):
-        self.X_train = X_train.astype(np.float32)
-        self.X_test = X_test.astype(np.float32)
-        self.y_train = y_train.astype(np.float32)
-        self.y_test = y_test.astype(np.float32)
-        self.model = None
-        self.params = None  # Will be set during training
-
-    def train(self, **xgb_params):
-        params = {
-            'tree_method': 'hist',
-            'device': 'cuda',
-            'objective': 'reg:squarederror',
-            'eval_metric': 'rmse',
-            'verbosity': 1,
-        }
-        params.update(xgb_params)
-        self.params = params  # Store params for later access
-        dtrain = xgb.DMatrix(self.X_train, label=self.y_train)
-        dtest = xgb.DMatrix(self.X_test, label=self.y_test)
-        evals = [(dtrain, 'train'), (dtest, 'eval')]
-        self.model = xgb.train(params, dtrain, num_boost_round=100, evals=evals, early_stopping_rounds=10)
-        return self.model
-
-    def predict(self, X):
-        if self.model is None:
-            raise ValueError('Model not trained yet.')
-        dmatrix = xgb.DMatrix(X.astype(np.float32))
-        return self.model.predict(dmatrix)
-
-    def save_model(self, file_path):
-        """Save the trained XGBoost model to the specified file path."""
-        if self.model is None:
-            raise ValueError('Model not trained yet.')
-        self.model.save_model(file_path)
+import xgboost as xgb
+import numpy as np
+
+class CustomXGBoostGPU:
+    def __init__(self, X_train, X_test, y_train, y_test):
+        self.X_train = X_train.astype(np.float32)
+        self.X_test = X_test.astype(np.float32)
+        self.y_train = y_train.astype(np.float32)
+        self.y_test = y_test.astype(np.float32)
+        self.model = None
+        self.params = None  # Will be set during training
+
+    def train(self, **xgb_params):
+        params = {
+            'tree_method': 'hist',
+            'device': 'cuda',
+            'objective': 'reg:squarederror',
+            'eval_metric': 'rmse',
+            'verbosity': 1,
+        }
+        params.update(xgb_params)
+        self.params = params  # Store params for later access
+        dtrain = xgb.DMatrix(self.X_train, label=self.y_train)
+        dtest = xgb.DMatrix(self.X_test, label=self.y_test)
+        evals = [(dtrain, 'train'), (dtest, 'eval')]
+        self.model = xgb.train(params, dtrain, num_boost_round=100, evals=evals, early_stopping_rounds=10)
+        return self.model
+
+    def predict(self, X):
+        if self.model is None:
+            raise ValueError('Model not trained yet.')
+        dmatrix = xgb.DMatrix(X.astype(np.float32))
+        return self.model.predict(dmatrix)
+
+    def save_model(self, file_path):
+        """Save the trained XGBoost model to the specified file path."""
+        if self.model is None:
+            raise ValueError('Model not trained yet.')
+        self.model.save_model(file_path)