Added three new keys on hypothesis reasoning

rdagent/components/proposal/prompts.yaml

@@ -14,7 +14,7 @@ hypothesis_gen:
     {{ hypothesis_and_feedback }}
     To help you generate new {{targets}}, we have prepared the following information for you:
     {{ RAG }}
-    Please generate the new hypothesis based on the information above.
+    Please generate the new hypothesis based on the information above. Also generate the relevant keys for the reasoning and the distilled knowledge that follows. 
 
 hypothesis2experiment:
   system_prompt: |-

rdagent/core/proposal.py

@@ -25,15 +25,21 @@ class Hypothesis:
     - Belief
     """
 
-    def __init__(self, hypothesis: str, reason: str, concise_reason: str) -> None:
+    def __init__(self, hypothesis: str, reason: str, concise_reason: str, concise_observation: str, concise_justification: str, concise_knowledge: str) -> None:
         self.hypothesis: str = hypothesis
         self.reason: str = reason
         self.concise_reason: str = concise_reason
+        self.concise_observation: str = concise_observation
+        self.concise_justification: str = concise_justification 
+        self.concise_knowledge: str = concise_knowledge
 
     def __str__(self) -> str:
         return f"""Hypothesis: {self.hypothesis}
                 Reason: {self.reason}
                 Concise Reason & Knowledge: {self.concise_reason}
+                Concise Observation: {self.concise_observation}
+                Concise Justification: {self.concise_justification}
+                Concise Knowledge: {self.concise_knowledge}
                 """
 
     # source: data_ana | model_nan = None

rdagent/scenarios/qlib/prompts.yaml

@@ -13,8 +13,11 @@ hypothesis_output_format: |-
   The output should follow JSON format. The schema is as follows:
   {
     "hypothesis": "The new hypothesis generated based on the information provided.", Note that this should focus on model architecture, not training process or feature engineering or anything else
-    "reason": "The reason why you generate this hypothesis.",
-    "concise_reason": Two line summary. First line focuses on the a concise justification for the change. 2nd Line learns from first line and previous experiences (hypothesis & experiments & code & feedbacks) to generalise a knowledge statement (use tend to/because/if/generally/etc. ). 
+    "reason": "The reason why you generate this hypothesis. It should be comprehensive and logical. It should cover the other keys below and extend them.",
+    "concise_reason": Two line summary. First line focuses on the a concise justification for the change. 2nd Line learns from first line and previous experiences (hypothesis & experiments & code & feedbacks) to generalise a knowledge statement (use tend to/because/if/generally/etc. ). This is the summary of the three keys below. 
+    "concise_observation": One line summary. It focuses on the observation of the given scenario, data characteristics, or previous experiences (failures & succeses).
+    "concise_justification": One line summary. It focuses on the justification for the change in new hypothesis and the route of exploration supporting the growth of the hypothesis, based on the observation. 
+    "concise_knowledge": One line summary. It focuses on a transferable knowledege that comes with the new hypothesis. Use conditional grammar. eg. "If...., ..; When..., .; and etc"
   }
 
 model_hypothesis_specification: |-

rdagent/scenarios/qlib/proposal/model_proposal.py

@@ -43,6 +43,9 @@ def convert_response(self, response: str) -> ModelHypothesis:
             hypothesis=response_dict["hypothesis"],
             reason=response_dict["reason"],
             concise_reason=response_dict["concise_reason"],
+            concise_observation=response_dict["concise_observation"],
+            concise_justification=response_dict["concise_justification"],
+            concise_knowledge=response_dict["concise_knowledge"],
         )
         return hypothesis