mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
WinstonLiyt
96424bd1ff
* fuse all code into one commit * remove container auto * change remove method * add kaggle env start * change kaggle api * change structure * add crawler * add requirements * refeact the code * delete mistaken codes * merge docker settings and crawler * add chrome install README for crawler usage * Connect scen with Kaggle to download data * Reformat some files to pass CI. * fix some ci errors * fix a ci error * fix a ci error --------- Co-authored-by: Bowen Xian <xianbowen@outlook.com>
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
from collections import defaultdict
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import fire
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from rdagent.app.kaggle.conf import PROP_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.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.utils import import_class
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from rdagent.log import rdagent_logger as logger
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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.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.coder: Developer = import_class(PROP_SETTING.coder)(scen)
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logger.log_object(self.coder, tag="coder")
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self.runner: Developer = import_class(PROP_SETTING.runner)(scen)
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logger.log_object(self.runner, tag="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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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:: python
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dotenv run -- python rdagent/app/kaggle/model.py [--competition titanic] $LOG_PATH/__session__/1/0_propose --step_n 1 # `step_n` is a optional paramter
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"""
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if competition:
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PROP_SETTING.competition = competition
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if path is None:
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model_loop = ModelRDLoop(PROP_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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