multi channel support in kaggle scenario (#337)

rdagent/scenarios/kaggle/experiment/prompts.yaml

@@ -10,6 +10,7 @@ kg_description_template:
       "Target Description": "A description of the target variable to be predicted",
       "Competition Features": "Two-line description of the overall features involved within the competition as background."
       "Submission Specifications": "The submission specification & sample submission csv descriptions for the model to output."
+      "Submission channel number to each sample": "The number of channels in the output for each sample, e.g., 1 for regression, N for N class classification with probabilities, etc. A Integer. If not specified, it is 1."
     }
     Since these might be very similar column names in data like one_hot_encoded columns, you can use some regex to group them together.
 
@@ -314,12 +315,15 @@ kg_feature_simulator: |-
 
 kg_model_output_format: |-
   For feature related tasks, the output should be a pandas DataFrame with the new features. The columns should be the new features, and the rows should correspond to the number of samples in the input DataFrame.
-  For model related tasks:
-  1. the output should be an np.ndarray with the appropriate number of predictions & the appropriate values within each prediction 
-  2. the output should be a 2D array with dimensions corresponding to the number of predictions and the number of things to output. Eg, if 4 predictions, each prediction needs to predict 3 probabilities, then (4,3). Or (8, 1) if there are 8 predictions but each prediction is only one value.
-  3. please reference the competition's submission requirement and align with that. 
-  Submission Requirements here:\n: {{submission_specifications}}
+  For model related tasks, the output should be an np.ndarray with the appropriate number of predictions.
+  {% if channel == 1 %}
+  For each sample, the output should be a single value (e.g., (8, 1) if there are 8 samples).
+  {% else %}
+  For each sample, the output should be multiple values with {{ channel }} numbers (e.g., (8, {{ channel }}) if there are 8 samples).
+  {% endif %}
   
 kg_model_simulator: |-
   The models will be trained on the competition dataset and evaluated on their ability to predict the target. Metrics like accuracy and AUC-ROC is used to evaluate the model performance. 
   Model performance will be iteratively improved based on feedback from evaluation results.
+  Your output should follow some requirements to submit to the competition:
+  {{ submission_specifications }}

rdagent/scenarios/kaggle/experiment/scenario.py

@@ -32,16 +32,15 @@ class KGScenario(Scenario):
         self.target_description = None
         self.competition_features = None
         self.submission_specifications = None
+        self.model_output_channel = None
         self._analysis_competition_description()
+        self.if_action_choosing_based_on_UCB = KAGGLE_IMPLEMENT_SETTING.if_action_choosing_based_on_UCB
 
-        # Move these assignments after _analysis_competition_description
         self._output_format = self.output_format
         self._interface = self.interface
         self._simulator = self.simulator
         self._background = self.background
 
-        self.if_action_choosing_based_on_UCB = KAGGLE_IMPLEMENT_SETTING.if_action_choosing_based_on_UCB
-
     def _analysis_competition_description(self):
         sys_prompt = (
             Environment(undefined=StrictUndefined)
@@ -64,25 +63,15 @@ class KGScenario(Scenario):
             json_mode=True,
         )
 
-        try:
-            response_json_analysis = json.loads(response_analysis)
-            self.competition_type = response_json_analysis.get("Competition Type", "No type provided")
-            self.competition_description = response_json_analysis.get(
-                "Competition Description", "No description provided"
-            )
-            self.target_description = response_json_analysis.get("Target Description", "No target provided")
-            self.competition_features = response_json_analysis.get("Competition Features", "No features provided")
-            self.submission_specifications = response_json_analysis.get(
-                "Submission Specifications", "No submission requirements provided"
-            )
-        except json.JSONDecodeError:
-            print(f"Failed to parse JSON response: {response_analysis}")
-            # Set default values if JSON parsing fails
-            self.competition_type = "Unknown"
-            self.competition_description = "No description available"
-            self.target_description = "No target available"
-            self.competition_features = "No features available"
-            self.submission_specifications = "No submission requirements available"
+        response_json_analysis = json.loads(response_analysis)
+        self.competition_type = response_json_analysis.get("Competition Type", "No type provided")
+        self.competition_description = response_json_analysis.get("Competition Description", "No description provided")
+        self.target_description = response_json_analysis.get("Target Description", "No target provided")
+        self.competition_features = response_json_analysis.get("Competition Features", "No features provided")
+        self.submission_specifications = response_json_analysis.get(
+            "Submission Specifications", "No submission requirements provided"
+        )
+        self.model_output_channel = response_json_analysis.get("Submission channel number to each sample", 1)
 
     def get_competition_full_desc(self) -> str:
         return f"""Competition Type: {self.competition_type}
@@ -155,7 +144,7 @@ class KGScenario(Scenario):
         return (
             Environment(undefined=StrictUndefined)
             .from_string(prompt_dict["kg_model_output_format"])
-            .render(submission_specifications=self.submission_specifications)
+            .render(channel=self.model_output_channel)
         )
 
     @property
@@ -168,10 +157,15 @@ The model code should follow the interface:
 
     @property
     def simulator(self) -> str:
+        kg_model_simulator = (
+            Environment(undefined=StrictUndefined)
+            .from_string(prompt_dict["kg_model_simulator"])
+            .render(submission_specifications=self.submission_specifications)
+        )
         return f"""The feature code should follow the simulator:
 {prompt_dict['kg_feature_simulator']}
 The model code should follow the simulator:
-{prompt_dict['kg_model_simulator']}
+{kg_model_simulator}
 """
 
     @property