mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-01 01:27:42 +00:00
Haoran Pan
45f022cd32
* concat sota and current result * fix the bug in current_exp's score vs sota's score * fix ci --------- Co-authored-by: yuanteli <1957922024@qq.com>
89 lines
3.8 KiB
Python
89 lines
3.8 KiB
Python
import json
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import pandas as pd
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from rdagent.components.knowledge_management.graph import UndirectedNode
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from rdagent.core.experiment import Experiment
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from rdagent.core.prompts import Prompts
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from rdagent.core.proposal import (
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Experiment2Feedback,
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ExperimentFeedback,
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HypothesisFeedback,
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)
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from rdagent.log import rdagent_logger as logger
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from rdagent.oai.llm_utils import APIBackend
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from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
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from rdagent.scenarios.data_science.proposal.exp_gen import DSTrace
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from rdagent.utils import convert2bool, remove_path_info_from_str
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from rdagent.utils.agent.tpl import T
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from rdagent.utils.repo.diff import generate_diff_from_dict
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class DSExperiment2Feedback(Experiment2Feedback):
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def generate_feedback(self, exp: DSExperiment, trace: DSTrace) -> ExperimentFeedback:
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# 用哪些信息来生成feedback
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# 1. pending_tasks_list[0][0] 任务的描述
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# 2. hypothesis 任务的假设
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# 3. 相对sota_exp的改动
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# 4. result 任务的结果
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# 5. sota_exp.result 之前最好的结果
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sota_exp = trace.sota_experiment()
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sota_desc = T("scenarios.data_science.share:describe.exp").r(
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exp=sota_exp, heading="SOTA of previous exploration of the scenario"
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)
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# Get feedback description using shared template
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feedback_desc = T("scenarios.data_science.share:describe.feedback").r(
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exp_and_feedback=(trace.hist[-1] if trace.hist else None), heading="Previous Trial Feedback"
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)
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# TODO:
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# - Should we choose between the diff from last experiment or last sota ?
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# Retrieve the last experiment from the history
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last_exp = trace.hist[-1][0] if trace.hist else None
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if last_exp and last_exp.experiment_workspace and exp.experiment_workspace:
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# Generate a diff between the two workspaces
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last_exp_files = last_exp.experiment_workspace.file_dict
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current_exp_files = exp.experiment_workspace.file_dict
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diff_edition = generate_diff_from_dict(last_exp_files, current_exp_files)
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else:
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diff_edition = []
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# assumption:
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# The feedback should focus on experiment **improving**.
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# Assume that all the the sota exp is based on the previous sota experiment
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cur_vs_sota_score = None
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if sota_exp:
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cur_score = pd.DataFrame(exp.result).loc["ensemble"].iloc[0]
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sota_score = pd.DataFrame(sota_exp.result).loc["ensemble"].iloc[0]
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cur_vs_sota_score = (
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f"The current score is {cur_score}, while the SOTA score is {sota_score}. "
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f"{'In this competition, higher is better.' if self.scen.metric_direction else 'In this competition, lower is better.'}"
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)
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system_prompt = T(".prompts:exp_feedback.system").r(scenario=self.scen.get_scenario_all_desc())
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user_prompt = T(".prompts:exp_feedback.user").r(
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sota_desc=sota_desc,
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cur_exp=exp,
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diff_edition=diff_edition,
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feedback_desc=feedback_desc,
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cur_vs_sota_score=cur_vs_sota_score,
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)
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resp_dict = json.loads(
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APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_prompt,
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system_prompt=system_prompt,
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json_mode=True,
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)
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)
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return HypothesisFeedback(
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observations=resp_dict.get("Observations", "No observations provided"),
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hypothesis_evaluation=resp_dict.get("Feedback for Hypothesis", "No feedback provided"),
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new_hypothesis=resp_dict.get("New Hypothesis", "No new hypothesis provided"),
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reason=resp_dict.get("Reasoning", "No reasoning provided"),
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decision=convert2bool(resp_dict.get("Replace Best Result", "no")),
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)
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