diff --git a/rdagent/components/proposal/model_proposal.py b/rdagent/components/proposal/model_proposal.py index 57696296..31f1711b 100644 --- a/rdagent/components/proposal/model_proposal.py +++ b/rdagent/components/proposal/model_proposal.py @@ -36,7 +36,7 @@ class ModelHypothesisGen(HypothesisGen): Environment(undefined=StrictUndefined) .from_string(prompt_dict["hypothesis_gen"]["system_prompt"]) .render( - targets="factors", + targets="model", scenario=self.scen.get_scenario_all_desc(), hypothesis_output_format=context_dict["hypothesis_output_format"], ) @@ -45,7 +45,7 @@ class ModelHypothesisGen(HypothesisGen): Environment(undefined=StrictUndefined) .from_string(prompt_dict["hypothesis_gen"]["user_prompt"]) .render( - targets="factors", + targets="model", hypothesis_and_feedback=context_dict["hypothesis_and_feedback"], RAG=context_dict["RAG"], ) @@ -74,7 +74,7 @@ class ModelHypothesis2Experiment(Hypothesis2Experiment[ModelExperiment]): Environment(undefined=StrictUndefined) .from_string(prompt_dict["hypothesis2experiment"]["system_prompt"]) .render( - targets="factors", + targets="model", scenario=trace.scen.get_scenario_all_desc(), experiment_output_format=context["experiment_output_format"], ) @@ -83,7 +83,7 @@ class ModelHypothesis2Experiment(Hypothesis2Experiment[ModelExperiment]): Environment(undefined=StrictUndefined) .from_string(prompt_dict["hypothesis2experiment"]["user_prompt"]) .render( - targets="factors", + targets="model", target_hypothesis=context["target_hypothesis"], hypothesis_and_feedback=context["hypothesis_and_feedback"], target_list=context["target_list"], diff --git a/rdagent/core/proposal.py b/rdagent/core/proposal.py index 30370d21..03d74cce 100644 --- a/rdagent/core/proposal.py +++ b/rdagent/core/proposal.py @@ -54,6 +54,15 @@ class Trace(Generic[ASpecificScen]): self.scen: ASpecificScen = scen self.hist: list[Tuple[Hypothesis, Experiment, HypothesisFeedback]] = [] + def get_last_experiment_info(self) -> Tuple[Hypothesis, ASpecificTask, Any]: + """Access the last experiment result, sub-task, and the corresponding hypothesis.""" + # TODO: The return value does not align with the signature. + if not self.hist: + return None + last_hypothesis, last_experiment, _ = self.hist[-1] + last_task = last_experiment.sub_tasks[-1] + last_result = last_experiment.result + return last_hypothesis, last_task, last_result class HypothesisGen: def __init__(self, scen: Scenario): diff --git a/rdagent/scenarios/qlib/prompts.yaml b/rdagent/scenarios/qlib/prompts.yaml index af79c378..f917b220 100644 --- a/rdagent/scenarios/qlib/prompts.yaml +++ b/rdagent/scenarios/qlib/prompts.yaml @@ -44,7 +44,7 @@ model_experiment_output_format: |- "variable or function name 1": "description of variable or function 1", "variable or function name 2": "description of variable or function 2" } - "model_type": "type of model 1, Tabular or TimesSeries" + "model_type": "type of model 1, Tabular or TimesSeries" # Should be one of "Tabular" or "TimeSeries" } # Don't add ellipsis (...) or any filler text that might cause JSON parsing errors here! } diff --git a/rdagent/scenarios/qlib/task_generator/feedback.py b/rdagent/scenarios/qlib/task_generator/feedback.py index 3ad83cd4..4629f2cc 100644 --- a/rdagent/scenarios/qlib/task_generator/feedback.py +++ b/rdagent/scenarios/qlib/task_generator/feedback.py @@ -2,10 +2,8 @@ # Implement to feedback. import json -import pickle from pathlib import Path -import pandas as pd from jinja2 import Environment, StrictUndefined from rdagent.core.experiment import Experiment @@ -18,16 +16,12 @@ from rdagent.core.proposal import ( Trace, ) from rdagent.oai.llm_utils import APIBackend -from rdagent.utils.env import QTDockerEnv feedback_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml") DIRNAME = Path(__file__).absolute().resolve().parent logger = RDAgentLog() -class QlibModelHypothesisExperiment2Feedback(HypothesisExperiment2Feedback): ... - - class QlibFactorHypothesisExperiment2Feedback(HypothesisExperiment2Feedback): def generateFeedback(self, exp: Experiment, hypothesis: Hypothesis, trace: Trace) -> HypothesisFeedback: """ @@ -102,3 +96,82 @@ class QlibFactorHypothesisExperiment2Feedback(HypothesisExperiment2Feedback): ) return hypothesis_feedback + + +class QlibModelHypothesisExperiment2Feedback(HypothesisExperiment2Feedback): + """Generated feedbacks on the hypothesis from **Executed** Implementations of different tasks & their comparisons with previous performances""" + + def generateFeedback(self, exp: Experiment, hypothesis: Hypothesis, trace: Trace) -> HypothesisFeedback: + """ + The `ti` should be executed and the results should be included, as well as the comparison between previous results (done by LLM). + For example: `mlflow` of Qlib will be included. + """ + + # Define the system prompt for hypothesis feedback + sys_prompt_hypothesis = ( + "You are a professional result analysis assistant. You will receive a result and a hypothesis. " + "Your task is to provide feedback on how well the result supports or refutes the hypothesis by judging from the observation of performance increase or decrease. " + "Please provide detailed and constructive feedback. " + "Example JSON Structure for Result Analysis: " + '{"Observations": "Your overall observations here", "Feedback for Hypothesis": "Observations related to the hypothesis", ' + '"New Hypothesis": "Put your new hypothesis here.", "Reasoning": "Provide reasoning for the hypothesis here.", ' + '"Decision": "True or False"}' + ) + + # Define the user prompt for hypothesis feedback + context = trace.scen + last_experiment_info = trace.get_last_experiment_info() + + if last_experiment_info: + last_hypothesis, last_task, last_result = last_experiment_info + last_info_str = f"Last Round Information:\nHypothesis: {last_hypothesis.hypothesis}\nTask: {last_task}\nResult: {last_result}\n" + else: + last_info_str = "This is the first round. No previous information available." + + usr_prompt_hypothesis = f""" + We are in an experiment of finding hypothesis and validating or rejecting them so that in the end we have a powerful model generated. + Here are the context: {context}. + {last_info_str} + + Now let's come to this round. You will receive the result and you will evaluate if the performance increases or decreases. + Hypothesis: {hypothesis.hypothesis}\n + Relevant Reasoning: {hypothesis.reason}\n + Result: {exp.result}\n + + Compare and observe. Which result has a better return and lower risk? If the performance increases， the hypothesis should be considered positive (working). + Hence, with the hypotheses, relevant reasoning, and results in mind (comparison), provide detailed and constructive feedback and suggest a new hypothesis. + """ + + try: + # Call the APIBackend to generate the response for hypothesis feedback + response_hypothesis = APIBackend().build_messages_and_create_chat_completion( + user_prompt=usr_prompt_hypothesis, + system_prompt=sys_prompt_hypothesis, + json_mode=True, + ) + + # Parse the JSON response to extract the feedback + response_json_hypothesis = json.loads(response_hypothesis) + hypothesis_feedback = HypothesisFeedback( + observations=response_json_hypothesis.get("Observations", "No observations provided"), + hypothesis_evaluation=response_json_hypothesis.get("Feedback for Hypothesis", "No feedback provided"), + new_hypothesis=response_json_hypothesis.get("New Hypothesis", "No new hypothesis provided"), + reason=response_json_hypothesis.get("Reasoning", "No reasoning provided"), + decision=response_json_hypothesis.get("Decision", "false").lower() == "true", + ) + + return hypothesis_feedback + + except json.JSONDecodeError as e: + # TODO: (Xiao) I think raising a specific type of ERROR to make caller know sth bad has happened would be more reasonable + print("Error parsing JSON response from LLM for hypothesis feedback:", e) + except Exception as e: + print("An unexpected error occurred while generating hypothesis feedback:", e) + + return HypothesisFeedback( + observations="No observations", + hypothesis_evaluation="No feedback", + new_hypothesis="No new hypothesis", + reason="No reasoning", + decision=False, + )