mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-31 17:27:42 +00:00
Tim
6f9843a7df
* custom data * fix: simplify competition check and log local description file * no sample data * feat: add test evaluation module with error handling support * fix: update eval path to use eval_sub_dir and add valid_check TODO * refactor: add MLETestEval check to conditionally run grading steps * avoid blank stdout * valid in testeval * rename test.csv to avoid conflict * Support Disabling sample submission * refactoring * fix: remove DS_KAGGLE_DATA and update prompt instructions * add try for grade * ignore submission * fix: remove tee from eval command and warn about pipeline exit code detection * optional to use raw description * support old data * add execution result to stdout * add metric to raw description * custom data explain * add debug_path * rst update --------- Co-authored-by: Young <afe.young@gmail.com>
80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
from pydantic_settings import SettingsConfigDict
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from rdagent.core.conf import ExtendedBaseSettings
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class KaggleBasePropSetting(ExtendedBaseSettings):
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model_config = SettingsConfigDict(env_prefix="KG_", protected_namespaces=())
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# 1) overriding the default
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scen: str = "rdagent.scenarios.kaggle.experiment.scenario.KGScenario"
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"""Scenario class for data mining model"""
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hypothesis_gen: str = "rdagent.scenarios.kaggle.proposal.proposal.KGHypothesisGen"
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"""Hypothesis generation class"""
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hypothesis2experiment: str = "rdagent.scenarios.kaggle.proposal.proposal.KGHypothesis2Experiment"
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"""Hypothesis to experiment class"""
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feature_coder: str = "rdagent.scenarios.kaggle.developer.coder.KGFactorCoSTEER"
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"""Feature Coder class"""
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model_feature_selection_coder: str = "rdagent.scenarios.kaggle.developer.coder.KGModelFeatureSelectionCoder"
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"""Model Feature Selection Coder class"""
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model_coder: str = "rdagent.scenarios.kaggle.developer.coder.KGModelCoSTEER"
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"""Model Coder class"""
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feature_runner: str = "rdagent.scenarios.kaggle.developer.runner.KGFactorRunner"
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"""Feature Runner class"""
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model_runner: str = "rdagent.scenarios.kaggle.developer.runner.KGModelRunner"
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"""Model Runner class"""
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summarizer: str = "rdagent.scenarios.kaggle.developer.feedback.KGExperiment2Feedback"
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"""Summarizer class"""
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evolving_n: int = 10
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"""Number of evolutions"""
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competition: str = ""
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"""Kaggle competition name, e.g., 'sf-crime'"""
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template_path: str = "rdagent/scenarios/kaggle/experiment/templates"
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"""Kaggle competition base templates path"""
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local_data_path: str = ""
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"""Folder storing Kaggle competition data"""
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# Evaluation on Test related
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if_using_mle_data: bool = False
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auto_submit: bool = False
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"""Automatically upload and submit each experiment result to Kaggle platform"""
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# Conditionally set the knowledge_base based on the use of graph RAG
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knowledge_base: str = ""
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"""Knowledge base class, uses 'KGKnowledgeGraph' when advanced graph-based RAG is enabled, otherwise empty."""
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if_action_choosing_based_on_UCB: bool = False
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"""Enable decision mechanism based on UCB algorithm"""
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domain_knowledge_path: str = "/data/userdata/share/kaggle/domain_knowledge"
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"""Folder storing domain knowledge files in .case format"""
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knowledge_base_path: str = "kg_graph.pkl"
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"""Advanced version of graph-based RAG"""
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rag_path: str = "git_ignore_folder/kaggle_vector_base.pkl"
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"""Base version of vector-based RAG"""
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if_using_vector_rag: bool = False
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"""Enable basic vector-based RAG"""
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if_using_graph_rag: bool = False
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"""Enable advanced graph-based RAG"""
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mini_case: bool = False
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"""Enable mini-case study for experiments"""
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KAGGLE_IMPLEMENT_SETTING = KaggleBasePropSetting()
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