mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-01 17:37:43 +00:00
xuangu-fang
e71d8f6c3c
* rebase selection code * bug-free run: checkpoint selection and dynamic EDA loading * add prototypes of various selectors, to imp. and test later * fix EDA write bug * imp SOTA-Jump policy * fix small bug * allow to set different selector by .env * add always-win selector * add init length for AlwaysWinCKPSelector * add back_jump selector * auto lint * add sota_exp_to_submit attribute; change the name of ckp_selector and sota-selector * fix bug * auto lint * working on auto sota selector * add subtrace counter * fix bug, remove unuse selector * add auto sota selector * auto lint * fix bug * fix small logic bug * add logging * add inject_diverse feat * auto lint * capable to None-select * feat: add hypothesis_gen config and ExpGen2TraceAndMerge functionality * refactor: use dynamic import for experiment generator instantiation * feat: add BestValidSelector for improved SOTA experiment selection * runnable twin-trace version * fix logic error of trace-merge * auto lint * use import_class to set selector, * auto-lint --------- Co-authored-by: Young <afe.young@gmail.com>
90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
from typing import Literal
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from pydantic_settings import SettingsConfigDict
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from rdagent.app.kaggle.conf import KaggleBasePropSetting
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class DataScienceBasePropSetting(KaggleBasePropSetting):
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# TODO: Kaggle Setting should be the subclass of DataScience
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model_config = SettingsConfigDict(env_prefix="DS_", protected_namespaces=())
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# Main components
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## Scen
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scen: str = "rdagent.scenarios.data_science.scen.KaggleScen"
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"""Scenario class for data mining model"""
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hypothesis_gen: str = "rdagent.scenarios.data_science.proposal.exp_gen.DSExpGen"
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"""Hypothesis generation class"""
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## Workflow Related
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consecutive_errors: int = 5
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## Coding Related
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coding_fail_reanalyze_threshold: int = 3
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debug_timeout: int = 600
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"""The timeout limit for running on debugging data"""
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full_timeout: int = 3600
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"""The timeout limit for running on full data"""
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### specific feature
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#### enable specification
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spec_enabled: bool = True
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#### proposal related
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proposal_version: str = "v1"
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coder_on_whole_pipeline: bool = False
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max_trace_hist: int = 3
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coder_max_loop: int = 10
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runner_max_loop: int = 1
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rule_base_eval: bool = False
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sample_data: bool = True
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use_raw_description: bool = False
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#### model dump
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enable_model_dump: bool = False
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enable_doc_dev: bool = False
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model_dump_check_level: Literal["medium", "high"] = "medium"
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### selector related
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#### checkpoint selector related
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# selector_name: str = "latest"
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selector_name: str = "rdagent.scenarios.data_science.proposal.exp_gen.ckp_select.LatestCKPSelector"
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"""The name of the selector to use"""
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sota_count_window: int = 5
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"""The number of trials to consider for SOTA count"""
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sota_count_threshold: int = 1
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"""The threshold for SOTA count"""
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#### SOTA experiment selector related
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sota_exp_selector_name: str = "rdagent.scenarios.data_science.proposal.exp_gen.sota_exp_select.GlobalSOTASelector"
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"""The name of the SOTA experiment selector to use"""
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### knowledge base
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enable_knowledge_base: bool = False
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knowledge_base_version: str = "v1"
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knowledge_base_path: str | None = None
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idea_pool_json_path: str | None = None
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### archive log folder after each loop
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enable_log_archive: bool = True
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log_archive_path: str | None = None
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log_archive_temp_path: str | None = (
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None # This is to store the mid tar file since writing the tar file is preferred in local storage then copy to target storage
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)
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#### Evaluation on Test related
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eval_sub_dir: str = "eval" # TODO: fixme, this is not a good name
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"""We'll use f"{DS_RD_SETTING.local_data_path}/{DS_RD_SETTING.eval_sub_dir}/{competition}"
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to find the scriipt to evaluate the submission on test"""
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### inject diverse
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enable_inject_diverse: bool = False
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DS_RD_SETTING = DataScienceBasePropSetting()
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