fix a small bug in model runner which might cause error when model is the first try (#309)

rdagent/scenarios/kaggle/developer/runner.py

@@ -41,9 +41,60 @@ class KGCachedRunner(CachedRunner[ASpecificExp]):
         codes = "\n".join(codes)
         return md5_hash(codes)
 
+    def init_develop(self, exp: KGFactorExperiment | KGModelExperiment) -> KGFactorExperiment | KGModelExperiment:
+        """
+        For the initial development, the experiment serves as a benchmark for feature engineering.
+        """
+        self.build_from_SOTA(exp)
+        if RUNNER_SETTINGS.cache_result:
+            cache_hit, result = self.get_cache_result(exp)
+            if cache_hit:
+                exp.result = result
+                return exp
+
+        env_to_use = {"PYTHONPATH": "./"}
+
+        result = exp.experiment_workspace.execute(run_env=env_to_use)
+
+        exp.result = result
+        sub_task = FactorTask(
+            factor_name="original features", factor_description="here is the original features", factor_formulation=""
+        )
+
+        org_data_path = (
+            Path(FACTOR_IMPLEMENT_SETTINGS.data_folder) / KAGGLE_IMPLEMENT_SETTING.competition / "X_valid.pkl"
+        )
+        with open(org_data_path, "rb") as f:
+            org_data = pickle.load(f)
+        feature_shape = org_data.shape[-1]
+        exp.experiment_workspace.data_description.append((sub_task.get_task_information(), feature_shape))
+
+        sub_model_1_description = (
+            self.extract_model_task_from_code(
+                (exp.experiment_workspace.workspace_path / "model" / "model_randomforest.py").read_text()
+            )
+            + f"""code: { (exp.experiment_workspace.workspace_path / "model" / "model_randomforest.py").read_text()}"""
+        )
+        sub_model_2_description = (
+            self.extract_model_task_from_code(
+                (exp.experiment_workspace.workspace_path / "model" / "model_xgboost.py").read_text()
+            )
+            + f"""code: { (exp.experiment_workspace.workspace_path / "model" / "model_xgboost.py").read_text()}"""
+        )
+
+        exp.experiment_workspace.model_description["XGBoost"] = sub_model_1_description
+        exp.experiment_workspace.model_description["RandomForest"] = sub_model_2_description
+
+        if RUNNER_SETTINGS.cache_result:
+            self.dump_cache_result(exp, result)
+
+        return exp
+
 
 class KGModelRunner(KGCachedRunner[KGModelExperiment]):
     def develop(self, exp: KGModelExperiment) -> KGModelExperiment:
+        if exp.based_experiments and exp.based_experiments[-1].result is None:
+            exp.based_experiments[-1] = self.init_develop(exp.based_experiments[-1])
         self.build_from_SOTA(exp)
 
         sub_ws = exp.sub_workspace_list[0]
@@ -118,55 +169,6 @@ class KGFactorRunner(KGCachedRunner[KGFactorExperiment]):
 
         return task_desc
 
-    def init_develop(self, exp: KGFactorExperiment) -> KGFactorExperiment:
-        """
-        For the initial development, the experiment serves as a benchmark for feature engineering.
-        """
-        self.build_from_SOTA(exp)
-        if RUNNER_SETTINGS.cache_result:
-            cache_hit, result = self.get_cache_result(exp)
-            if cache_hit:
-                exp.result = result
-                return exp
-
-        env_to_use = {"PYTHONPATH": "./"}
-
-        result = exp.experiment_workspace.execute(run_env=env_to_use)
-
-        exp.result = result
-        sub_task = FactorTask(
-            factor_name="original features", factor_description="here is the original features", factor_formulation=""
-        )
-
-        org_data_path = (
-            Path(FACTOR_IMPLEMENT_SETTINGS.data_folder) / KAGGLE_IMPLEMENT_SETTING.competition / "X_valid.pkl"
-        )
-        with open(org_data_path, "rb") as f:
-            org_data = pickle.load(f)
-        feature_shape = org_data.shape[-1]
-        exp.experiment_workspace.data_description.append((sub_task.get_task_information(), feature_shape))
-
-        sub_model_1_description = (
-            self.extract_model_task_from_code(
-                (exp.experiment_workspace.workspace_path / "model" / "model_randomforest.py").read_text()
-            )
-            + f"""code: { (exp.experiment_workspace.workspace_path / "model" / "model_randomforest.py").read_text()}"""
-        )
-        sub_model_2_description = (
-            self.extract_model_task_from_code(
-                (exp.experiment_workspace.workspace_path / "model" / "model_xgboost.py").read_text()
-            )
-            + f"""code: { (exp.experiment_workspace.workspace_path / "model" / "model_xgboost.py").read_text()}"""
-        )
-
-        exp.experiment_workspace.model_description["XGBoost"] = sub_model_1_description
-        exp.experiment_workspace.model_description["RandomForest"] = sub_model_2_description
-
-        if RUNNER_SETTINGS.cache_result:
-            self.dump_cache_result(exp, result)
-
-        return exp
-
     def develop(self, exp: KGFactorExperiment) -> KGFactorExperiment:
         if exp.based_experiments and exp.based_experiments[-1].result is None:
             exp.based_experiments[-1] = self.init_develop(exp.based_experiments[-1])

rdagent/scenarios/kaggle/proposal/proposal.py

@@ -334,7 +334,7 @@ class KGHypothesis2Experiment(ModelHypothesis2Experiment):
             )
         )
         exp = KGModelExperiment(tasks)
-        exp.based_experiments = [t[1] for t in trace.hist if t[2]]
+        exp.based_experiments = [KGModelExperiment(sub_tasks=[])] + [t[1] for t in trace.hist if t[2]]
         return exp
 
     def convert_response(self, response: str, trace: Trace) -> ModelExperiment: