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NexQuant/rdagent/app/data_science/conf.py
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Python

from typing import Literal
from pydantic_settings import SettingsConfigDict
from rdagent.app.kaggle.conf import KaggleBasePropSetting
class DataScienceBasePropSetting(KaggleBasePropSetting):
model_config = SettingsConfigDict(env_prefix="DS_", protected_namespaces=())
# Main components
## Scen
scen: str = "rdagent.scenarios.data_science.scen.KaggleScen"
"""Scenario class for data mining model"""
## Workflow Related
consecutive_errors: int = 5
debug_timeout: int = 600
"""The timeout limit for running on debugging data"""
full_timeout: int = 3600
"""The timeout limit for running on full data"""
### specific feature
#### enable specification
spec_enabled: bool = True
### proposal related
proposal_version: str = "v1"
coder_on_whole_pipeline: bool = False
max_trace_hist: int = 3
coder_max_loop: int = 10
runner_max_loop: int = 1
rule_base_eval: bool = False
### model dump
enable_model_dump: bool = False
enable_doc_dev: bool = False
model_dump_check_level: Literal["medium", "high"] = "medium"
### knowledge base
enable_knowledge_base: bool = False
knowledge_base_version: str = "v1"
knowledge_base_path: str | None = None
idea_pool_json_path: str | None = None
### archive log folder after each loop
enable_log_archive: bool = True
log_archive_path: str | None = None
log_archive_temp_path: str | None = (
None # This is to store the mid tar file since writing the tar file is preferred in local storage then copy to target storage
)
DS_RD_SETTING = DataScienceBasePropSetting()