Add the validation score and training time for create_function in XGBoost

evadb/binder/function_expression_binder.py

@@ -112,7 +112,7 @@ def bind_func_expr(binder: StatementBinder, node: FunctionExpression):
             if string_comparison_case_insensitive(node.name, "CHATGPT"):
                 # if the user didn't provide any API_KEY, check if we have one in the catalog
                 if "OPENAI_API_KEY" not in properties.keys():
-                    OpenAI_key = binder._catalog().get_configuration_catalog_value(
+                    openai_key = binder._catalog().get_configuration_catalog_value(
                         "OPENAI_API_KEY"
                     )
                     properties["openai_api_key"] = openai_key

evadb/executor/create_function_executor.py

@@ -259,12 +259,16 @@ def handle_xgboost_function(self):
 
         impl_path = Path(f"{self.function_dir}/xgboost.py").absolute().as_posix()
         io_list = self._resolve_function_io(None)
+        best_score = model.best_loss
+        train_time = model.best_config_train_time
         return (
             self.node.name,
             impl_path,
             self.node.function_type,
             io_list,
             self.node.metadata,
+            best_score,
+            train_time,
         )
 
     def handle_ultralytics_function(self):
@@ -586,6 +590,8 @@ def exec(self, *args, **kwargs):
         )
 
         overwrite = False
+        best_score = False
+        train_time = False
         # check catalog if it already has this function entry
         if self.catalog().get_function_catalog_entry_by_name(self.node.name):
             if self.node.if_not_exists:
@@ -648,6 +654,8 @@ def exec(self, *args, **kwargs):
                 function_type,
                 io_list,
                 metadata,
+                best_score,
+                train_time,
             ) = self.handle_xgboost_function()
         elif string_comparison_case_insensitive(self.node.function_type, "Forecasting"):
             (
@@ -674,7 +682,18 @@ def exec(self, *args, **kwargs):
             msg = f"Function {self.node.name} overwritten."
         else:
             msg = f"Function {self.node.name} added to the database."
-        yield Batch(pd.DataFrame([msg]))
+        if best_score and train_time:
+            yield Batch(
+                pd.DataFrame(
+                    [
+                        msg,
+                        "Validation Score: " + str(best_score),
+                        "Training time: " + str(train_time),
+                    ]
+                )
+            )
+        else:
+            yield Batch(pd.DataFrame([msg]))
 
     def _try_initializing_function(
         self, impl_path: str, function_args: Dict = {}

test/integration_tests/long/test_model_train.py

@@ -138,7 +138,9 @@ def test_xgboost_regression(self):
             METRIC 'r2'
             TASK 'regression';
         """
-        execute_query_fetch_all(self.evadb, create_predict_function)
+        result = execute_query_fetch_all(self.evadb, create_predict_function)
+        self.assertEqual(len(result.columns), 1)
+        self.assertEqual(len(result), 3)
 
         predict_query = """
             SELECT PredictRentXgboost(number_of_rooms, number_of_bathrooms, days_on_market, rental_price) FROM HomeRentals LIMIT 10;
@@ -158,7 +160,9 @@ def test_xgboost_classification(self):
             METRIC 'accuracy'
             TASK 'classification';
         """
-        execute_query_fetch_all(self.evadb, create_predict_function)
+        result = execute_query_fetch_all(self.evadb, create_predict_function)
+        self.assertEqual(len(result.columns), 1)
+        self.assertEqual(len(result), 3)
 
         predict_query = """
             SELECT PredictEmployeeXgboost(payment_tier, age, gender, experience_in_current_domain, leave_or_not) FROM Employee LIMIT 10;

test/integration_tests/short/test_select_executor.py

@@ -108,7 +108,7 @@ def test_should_raise_binder_error_on_non_existent_column(self):
         with self.assertRaises(BinderError) as ctx:
             execute_query_fetch_all(self.evadb, select_query)
         self.assertEqual(
-            "Cannnot find column b1. Did you mean a1? The feasible columns are ['_row_id', 'a0', 'a1', 'a2'].",
+            "Cannot find column b1. Did you mean a1? The feasible columns are ['_row_id', 'a0', 'a1', 'a2'].",
             str(ctx.exception),
         )