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fix: remove refine decision & bug fix (#1031)

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* remove refine decision & bug fix

* code simpler

* fix CI

---------

Co-authored-by: Xu Yang <xuyang1@microsoft.com>
This commit is contained in:
Xu Yang
2025-07-08 16:34:25 +08:00
committed by GitHub
parent 717f9b9e53
commit af67910000
5 changed files with 9 additions and 116 deletions
Download Patch File Download Diff File Expand all files Collapse all files
rdagent/core/proposal.py
-4
View File
@@ -57,12 +57,10 @@ class ExperimentFeedback(Feedback):
*,
code_change_summary: str | None = None,
decision: bool,
refine_decision: bool = False,
eda_improvement: str | None = None,
exception: Exception | None = None,
) -> None:
self.decision = decision
self.refine_decision = refine_decision
self.eda_improvement = eda_improvement
self.reason = reason
# Exception is not None means failing to generate runnable experiments due to exception.
@@ -100,13 +98,11 @@ class HypothesisFeedback(ExperimentFeedback):
*,
code_change_summary: str | None = None,
decision: bool,
refine_decision: bool = False,
eda_improvement: str | None = None,
) -> None:
super().__init__(
reason,
decision=decision,
refine_decision=refine_decision,
code_change_summary=code_change_summary,
eda_improvement=eda_improvement,
)
rdagent/scenarios/data_science/dev/feedback.py
-1
View File
@@ -102,7 +102,6 @@ class DSExperiment2Feedback(Experiment2Feedback):
if evaluation_not_aligned
else convert2bool(dict_get_with_warning(resp_dict, "Replace Best Result", "no"))
),
refine_decision=convert2bool(dict_get_with_warning(resp_dict, "Refine Decision", "no")),
eda_improvement=dict_get_with_warning(resp_dict, "EDA Improvement", "no"), # EDA improvement suggestion
)
rdagent/scenarios/data_science/proposal/exp_gen/base.py
+1 -1
View File
@@ -226,7 +226,7 @@ class DSTrace(Trace[DataScienceScen, KnowledgeBase]):
# the sota exp should be accepted decision and all required components are completed.
if ef.decision:
return exp, ef
return None, None
return None
def sota_experiment(
self,
rdagent/scenarios/data_science/proposal/exp_gen/proposal.py
+8 -5
View File
@@ -21,7 +21,6 @@ from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
from rdagent.scenarios.data_science.proposal.exp_gen.base import DSHypothesis, DSTrace
from rdagent.scenarios.data_science.proposal.exp_gen.draft import DSDraftExpGen
from rdagent.scenarios.data_science.proposal.exp_gen.idea_pool import DSIdea
from rdagent.scenarios.data_science.proposal.exp_gen.refine import DSRefineExpGen
from rdagent.utils.agent.tpl import T
from rdagent.utils.repo.diff import generate_diff_from_dict
from rdagent.utils.workflow import wait_retry
@@ -721,7 +720,7 @@ class DSProposalV2ExpGen(ExpGen):
hypotheses: list[DSHypothesis],
pipeline: bool,
failed_exp_feedback_list_desc: str,
sota_exp_fb: ExperimentFeedback | None = None,
fb_to_sota_exp: ExperimentFeedback | None = None,
) -> DSExperiment:
if pipeline:
component_info = get_component("Pipeline")
@@ -740,7 +739,7 @@ class DSProposalV2ExpGen(ExpGen):
sota_exp_desc=sota_exp_desc,
hypotheses=hypotheses,
failed_exp_and_feedback_list_desc=failed_exp_feedback_list_desc,
eda_improvement=sota_exp_fb.eda_improvement if sota_exp_fb else None,
eda_improvement=fb_to_sota_exp.eda_improvement if fb_to_sota_exp else None,
)
response = APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
@@ -824,7 +823,11 @@ class DSProposalV2ExpGen(ExpGen):
]
)
sota_exp, sota_exp_fb = trace.sota_experiment_fb()
if sota_exp_fb := trace.sota_experiment_fb() is None:
sota_exp, fb_to_sota_exp = None, None
else:
sota_exp, fb_to_sota_exp = sota_exp_fb
if not isinstance(sota_exp, DSExperiment):
eda_output = None
else:
@@ -922,5 +925,5 @@ class DSProposalV2ExpGen(ExpGen):
),
pipeline=pipeline,
failed_exp_feedback_list_desc=failed_exp_feedback_list_desc,
sota_exp_fb=sota_exp_fb,
fb_to_sota_exp=fb_to_sota_exp,
)
rdagent/scenarios/data_science/proposal/exp_gen/refine.py
-105
View File
@@ -1,105 +0,0 @@
import json
from typing import Any, Dict, List, Optional, Tuple
from rdagent.app.data_science.conf import DS_RD_SETTING
from rdagent.core.proposal import ExpGen
from rdagent.log import rdagent_logger as logger
from rdagent.oai.llm_utils import APIBackend
from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
from rdagent.scenarios.data_science.proposal.exp_gen.base import DSHypothesis, DSTrace
from rdagent.scenarios.data_science.proposal.exp_gen.utils import (
CodingSketch,
get_component,
)
from rdagent.utils.agent.tpl import T
class DSRefineExpGen(ExpGen):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.support_function_calling = APIBackend().support_function_calling()
def task_gen(
self,
component_desc: str,
scenario_desc: str,
sota_exp_desc: str,
sota_exp: DSExperiment,
hypothesis: DSHypothesis,
exp_and_feedback_list_desc: str,
) -> DSExperiment:
component_info = get_component("Pipeline")
data_folder_info = self.scen.processed_data_folder_description
sys_prompt = T(".prompts_refine:task_gen.system").r(
task_output_format=component_info["task_output_format"] if not self.support_function_calling else None,
component_desc=component_desc,
)
user_prompt = T(".prompts_refine:task_gen.user").r(
scenario_desc=scenario_desc,
data_folder_info=data_folder_info,
sota_exp_desc=sota_exp_desc,
hypothesis=hypothesis,
exp_and_feedback_list_desc=exp_and_feedback_list_desc,
)
response = APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=sys_prompt,
response_format=CodingSketch if self.support_function_calling else {"type": "json_object"},
json_target_type=Dict[str, str | Dict[str, str]] if not self.support_function_calling else None,
)
task_dict = json.loads(response)
task_design = (
task_dict.get("task_design", {}) if not self.support_function_calling else task_dict.get("sketch", {})
)
logger.info(f"Task design:\n{task_design}")
task_name = hypothesis.component
description = (
task_design
if isinstance(task_design, str)
else task_design.get("description", f"{component_info['target_name']} description not provided")
)
task_class = component_info["task_class"]
task = task_class(
name=task_name,
description=description,
)
exp = DSExperiment(pending_tasks_list=[[task]], hypothesis=hypothesis)
if sota_exp is not None:
exp.experiment_workspace.inject_code_from_file_dict(sota_exp.experiment_workspace)
return exp
def gen(self, trace: DSTrace) -> DSExperiment:
# Step 0: Prepare
pipeline = DS_RD_SETTING.coder_on_whole_pipeline
component_desc = T("scenarios.data_science.share:component_description_in_pipeline").r()
sota_exp, sota_fb = trace.sota_experiment_fb()
if not isinstance(sota_exp, DSExperiment):
eda_output = None
else:
eda_output = sota_exp.experiment_workspace.file_dict.get("EDA.md", None)
scenario_desc = self.scen.get_scenario_all_desc(trace=trace, eda_output=eda_output)
sota_exp_desc = T("scenarios.data_science.share:describe.exp").r(
exp=sota_exp, heading="Best of previous exploration of the scenario"
)
exp_feedback_list_desc = T("scenarios.data_science.share:describe.trace").r(
exp_and_feedback_list=trace.experiment_and_feedback_list_after_init(return_type="all"),
type="all",
pipeline=pipeline,
)
# Step 1: Generate a Pseudo Hypothesis for Refinement
hypothesis = DSHypothesis(
component="Model",
hypothesis="The current pipeline is note effective in terms of efficiency or hyperparameters. Refinement or Adjustment should be made.",
reason=sota_fb.reason,
)
# Step 2: Design Task to Refine SOTA
return self.task_gen(
component_desc=component_desc,
scenario_desc=scenario_desc,
sota_exp_desc=sota_exp_desc,
sota_exp=sota_exp,
hypothesis=hypothesis,
exp_and_feedback_list_desc=exp_feedback_list_desc,
)