feat: add RD-Agent-Quant scenario (#838)

* fix model input shape bug and costeer_model bug * fix a bug * fix a bug in docker result extraction * a system-level optimization * add a filter of stdout * update * add stdout to model * model training_hyperparameters update * quant scenario * update some quant settings * llm choose action * Thompson Sampling Bandit for action choosing * refine both scens * add trace messages for quant scen * fix some bugs * fix some bugs * update * update * update * fix * fix * fix * update for merge * fix ci * fix some bugs * fix ci * fix ci * fix ci * fix ci * refactor * default qlib4rdagent local env downloading * fix ci * fix ci * fix a bug * fix ci * fix: align all prompts on template (#908) * use template to render all prompts * fix CI --------- Co-authored-by: Xu Yang <xuyang1@microsoft.com> * add fin_quant in cli * fix a bug * fix ci * fix some bugs * refactor * remove the columns in hypothesis if no value generated in this column * fix a bug * fix ci * fix conda env * add qlib gitignore * remove existed qlib folder & install torch in qlib conda * fix workspace ui in feedback * align model config in coder and runner in docker or conda * fix CI * fix CI --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com> Co-authored-by: Xu Yang <xuyang1@microsoft.com>
2026-08-02 01:47:43 +00:00 · 2025-05-29 16:16:51 +08:00
parent b0e88c7375
commit d1019cb568
64 changed files with 2427 additions and 1025 deletions
@@ -1,20 +1,16 @@
 import io
 import json
 from abc import abstractmethod
-from pathlib import Path
 from typing import Dict, Tuple

 import pandas as pd
-from jinja2 import Environment, StrictUndefined

 from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
 from rdagent.components.coder.factor_coder.factor import FactorTask
 from rdagent.core.experiment import Task, Workspace
-from rdagent.core.prompts import Prompts
 from rdagent.oai.llm_conf import LLM_SETTINGS
 from rdagent.oai.llm_utils import APIBackend
-
-evaluate_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
+from rdagent.utils.agent.tpl import T


 class FactorEvaluator:
@@ -81,36 +77,26 @@ class FactorCodeEvaluator(FactorEvaluator):
        factor_information = target_task.get_task_information()
        code = implementation.all_codes

-        system_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(evaluate_prompts["evaluator_code_feedback_v1_system"])
-            .render(
-                scenario=(
-                    self.scen.get_scenario_all_desc(
-                        target_task,
-                        filtered_tag="feature",
-                        simple_background=FACTOR_COSTEER_SETTINGS.simple_background,
-                    )
-                    if self.scen is not None
-                    else "No scenario description."
+        system_prompt = T(".prompts:evaluator_code_feedback_v1_system").r(
+            scenario=(
+                self.scen.get_scenario_all_desc(
+                    target_task,
+                    filtered_tag="feature",
+                    simple_background=FACTOR_COSTEER_SETTINGS.simple_background,
                )
+                if self.scen is not None
+                else "No scenario description."
            )
        )

        execution_feedback_to_render = execution_feedback
        for _ in range(10):  # 10 times to split the content is enough
-            user_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    evaluate_prompts["evaluator_code_feedback_v1_user"],
-                )
-                .render(
-                    factor_information=factor_information,
-                    code=code,
-                    execution_feedback=execution_feedback_to_render,
-                    value_feedback=value_feedback,
-                    gt_code=gt_implementation.code if gt_implementation else None,
-                )
+            user_prompt = T(".prompts:evaluator_code_feedback_v1_user").r(
+                factor_information=factor_information,
+                code=code,
+                execution_feedback=execution_feedback_to_render,
+                value_feedback=value_feedback,
+                gt_code=gt_implementation.code if gt_implementation else None,
            )
            if (
                APIBackend().build_messages_and_calculate_token(
@@ -189,17 +175,11 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
        buffer = io.StringIO()
        gen_df.info(buf=buffer)
        gen_df_info_str = f"The user is currently working on a feature related task.\nThe output dataframe info is:\n{buffer.getvalue()}"
-        system_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(
-                evaluate_prompts["evaluator_output_format_system"],
-            )
-            .render(
-                scenario=(
-                    self.scen.get_scenario_all_desc(implementation.target_task, filtered_tag="feature")
-                    if self.scen is not None
-                    else "No scenario description."
-                )
+        system_prompt = T(".prompts:evaluator_output_format_system").r(
+            scenario=(
+                self.scen.get_scenario_all_desc(implementation.target_task, filtered_tag="feature")
+                if self.scen is not None
+                else "No scenario description."
            )
        )

@@ -504,35 +484,25 @@ class FactorFinalDecisionEvaluator(FactorEvaluator):
        code_feedback: str,
        **kwargs,
    ) -> Tuple:
-        system_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(evaluate_prompts["evaluator_final_decision_v1_system"])
-            .render(
-                scenario=(
-                    self.scen.get_scenario_all_desc(target_task, filtered_tag="feature")
-                    if self.scen is not None
-                    else "No scenario description."
-                )
+        system_prompt = T(".prompts:evaluator_final_decision_v1_system").r(
+            scenario=(
+                self.scen.get_scenario_all_desc(target_task, filtered_tag="feature")
+                if self.scen is not None
+                else "No scenario description."
            )
        )
        execution_feedback_to_render = execution_feedback

        for _ in range(10):  # 10 times to split the content is enough
-            user_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    evaluate_prompts["evaluator_final_decision_v1_user"],
-                )
-                .render(
-                    factor_information=target_task.get_task_information(),
-                    execution_feedback=execution_feedback_to_render,
-                    code_feedback=code_feedback,
-                    value_feedback=(
-                        value_feedback
-                        if value_feedback is not None
-                        else "No Ground Truth Value provided, so no evaluation on value is performed."
-                    ),
-                )
+            user_prompt = T(".prompts:evaluator_final_decision_v1_user").r(
+                factor_information=target_task.get_task_information(),
+                execution_feedback=execution_feedback_to_render,
+                code_feedback=code_feedback,
+                value_feedback=(
+                    value_feedback
+                    if value_feedback is not None
+                    else "No Ground Truth Value provided, so no evaluation on value is performed."
+                ),
            )
            if (
                APIBackend().build_messages_and_calculate_token(