mirror of
https://github.com/NicolasBohn/NexQuant.git
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WinstonLiyt
25cd4eec76
* init a scenario for kaggle feature engineering * Added support for loading and storing RAG in Kaggle scenarios. * fix a ci bug * Add RAG after each experiment's feedback. * add a promt * fix a bug * fix a bug * add a readme * refine the code in knowledge loading
104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
from collections import defaultdict
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from typing import Any
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import fire
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from rdagent.app.kaggle.conf import KAGGLE_IMPLEMENT_SETTING
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from rdagent.components.workflow.conf import BasePropSetting
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from rdagent.components.workflow.rd_loop import RDLoop
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from rdagent.core.developer import Developer
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from rdagent.core.exception import ModelEmptyError
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from rdagent.core.proposal import (
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Hypothesis2Experiment,
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HypothesisExperiment2Feedback,
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HypothesisGen,
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Trace,
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)
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from rdagent.core.scenario import Scenario
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from rdagent.core.utils import import_class
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from rdagent.log import rdagent_logger as logger
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from rdagent.scenarios.kaggle.knowledge_management.vector_base import (
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KaggleExperienceBase,
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)
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from rdagent.scenarios.kaggle.proposal.proposal import (
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KG_ACTION_FEATURE_ENGINEERING,
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KG_ACTION_FEATURE_PROCESSING,
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)
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class ModelRDLoop(RDLoop):
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def __init__(self, PROP_SETTING: BasePropSetting):
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with logger.tag("init"):
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scen: Scenario = import_class(PROP_SETTING.scen)(PROP_SETTING.competition)
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logger.log_object(scen, tag="scenario")
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self.vector_base = KaggleExperienceBase()
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if KAGGLE_IMPLEMENT_SETTING.rag_path:
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self.vector_base.load(KAGGLE_IMPLEMENT_SETTING.rag_path)
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self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.hypothesis_gen)(scen)
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logger.log_object(self.hypothesis_gen, tag="hypothesis generator")
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self.hypothesis2experiment: Hypothesis2Experiment = import_class(PROP_SETTING.hypothesis2experiment)()
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logger.log_object(self.hypothesis2experiment, tag="hypothesis2experiment")
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self.feature_coder: Developer = import_class(PROP_SETTING.feature_coder)(scen)
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logger.log_object(self.feature_coder, tag="feature coder")
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self.model_coder: Developer = import_class(PROP_SETTING.model_coder)(scen)
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logger.log_object(self.model_coder, tag="model coder")
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self.feature_runner: Developer = import_class(PROP_SETTING.feature_runner)(scen)
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logger.log_object(self.feature_runner, tag="feature runner")
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self.model_runner: Developer = import_class(PROP_SETTING.model_runner)(scen)
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logger.log_object(self.model_runner, tag="model runner")
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self.summarizer: HypothesisExperiment2Feedback = import_class(PROP_SETTING.summarizer)(scen)
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logger.log_object(self.summarizer, tag="summarizer")
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self.trace = Trace(scen=scen)
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super(RDLoop, self).__init__()
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def coding(self, prev_out: dict[str, Any]):
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with logger.tag("d"): # develop
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if prev_out["propose"].action in [KG_ACTION_FEATURE_ENGINEERING, KG_ACTION_FEATURE_PROCESSING]:
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exp = self.feature_coder.develop(prev_out["exp_gen"])
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else:
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exp = self.model_coder.develop(prev_out["exp_gen"])
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logger.log_object(exp.sub_workspace_list, tag="coder result")
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return exp
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def running(self, prev_out: dict[str, Any]):
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with logger.tag("ef"): # evaluate and feedback
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if prev_out["propose"].action in [KG_ACTION_FEATURE_ENGINEERING, KG_ACTION_FEATURE_PROCESSING]:
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exp = self.feature_runner.develop(prev_out["coding"])
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else:
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exp = self.model_runner.develop(prev_out["coding"])
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logger.log_object(exp, tag="runner result")
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return exp
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skip_loop_error = (ModelEmptyError,)
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def main(path=None, step_n=None, competition=None):
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"""
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Auto R&D Evolving loop for models in a kaggle{} scenario.
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You can continue running session by
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.. code-block:: bash
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dotenv run -- python rdagent/app/kaggle/loop.py [--competition titanic] $LOG_PATH/__session__/1/0_propose --step_n 1 # `step_n` is a optional paramter
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rdagent kaggle --competition playground-series-s4e8 # You are encouraged to use this one.
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"""
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if competition:
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KAGGLE_IMPLEMENT_SETTING.competition = competition
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if path is None:
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model_loop = ModelRDLoop(KAGGLE_IMPLEMENT_SETTING)
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else:
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model_loop = ModelRDLoop.load(path)
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model_loop.run(step_n=step_n)
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if __name__ == "__main__":
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fire.Fire(main)
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