add simplebaggingregressor

imodels/util/ensemble.py

@@ -72,6 +72,41 @@ def predict(self, X):
         return predictions
 
 
+class SimpleBaggingRegressor:
+    def __init__(self, estimator, n_estimators=10, random_state=None):
+        self.estimator = estimator
+        self.n_estimators = n_estimators
+        self.random_state = random_state
+
+    def fit(self, X, y):
+        np.random.seed(self.random_state)
+        self.estimators_ = []
+
+        rng = np.random.default_rng(self.random_state)
+        for _ in range(self.n_estimators):
+            # Simple bootstrap sampling
+            # sample_indices = np.random.choice(
+            # range(X.shape[0]), size=X.shape[0], replace=True)
+            sample_indices = rng.choice(
+                range(X.shape[0]), size=X.shape[0], replace=True)
+            X_sample = X[sample_indices]
+            y_sample = y[sample_indices]
+
+            # Fit a base estimator
+            # estimator = DecisionTreeRegressor()
+            estimator = clone(self.estimator)
+            estimator.fit(X_sample, y_sample)
+            self.estimators_.append(estimator)
+
+    def predict(self, X):
+        # Collect predictions from each base estimator
+        predictions = np.array([estimator.predict(X)
+                               for estimator in self.estimators_])
+
+        # Aggregate predictions
+        return np.mean(predictions, axis=0)
+
+
 if __name__ == '__main__':
     X, y, feature_names = imodels.get_clean_dataset('california_housing')
     X_train, X_test, y_train, y_test = train_test_split(
@@ -83,7 +118,9 @@ def predict(self, X):
     # estimator = DecisionTreeRegressor(max_depth=3)
     estimator = imodels.algebraic.gam_multitask.MultiTaskGAMRegressor()
     for n_estimators in [1, 3, 5]:
-        residual_boosting_regressor = ResidualBoostingRegressor(
+        # residual_boosting_regressor = ResidualBoostingRegressor(
+        # estimator=estimator, n_estimators=n_estimators)
+        residual_boosting_regressor = SimpleBaggingRegressor(
             estimator=estimator, n_estimators=n_estimators)
         residual_boosting_regressor.fit(X_train, y_train)