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https://github.com/NicolasBohn/NexQuant.git
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Linlang
ae2aa6e9b4
* fix_ruff_error1 * fix_ruff_error * fix ruff error * fix ruff error * pass model.py * rename exception class * rename exception class * rename func name generate_feedback * remove prepare args * optimize code * optimize code * fix code error
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
"""
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TODO: Model Structure RD-Loop
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TODO: move the following code to a new class: Model_RD_Agent
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"""
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# import_from
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from rdagent.app.qlib_rd_loop.conf import PROP_SETTING
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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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scen: Scenario = import_class(PROP_SETTING.model_scen)()
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hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.model_hypothesis_gen)(scen)
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hypothesis2experiment: Hypothesis2Experiment = import_class(PROP_SETTING.model_hypothesis2experiment)()
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qlib_model_coder: Developer = import_class(PROP_SETTING.model_coder)(scen)
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qlib_model_runner: Developer = import_class(PROP_SETTING.model_runner)(scen)
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qlib_model_summarizer: HypothesisExperiment2Feedback = import_class(PROP_SETTING.model_summarizer)(scen)
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trace = Trace(scen=scen)
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with logger.tag("model.loop"):
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for _ in range(PROP_SETTING.evolving_n):
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try:
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with logger.tag("r"): # research
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hypothesis = hypothesis_gen.gen(trace)
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logger.log_object(hypothesis, tag="hypothesis generation")
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exp = hypothesis2experiment.convert(hypothesis, trace)
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logger.log_object(exp.sub_tasks, tag="experiment generation")
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with logger.tag("d"): # develop
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exp = qlib_model_coder.develop(exp)
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logger.log_object(exp.sub_workspace_list, tag="model coder result")
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with logger.tag("ef"): # evaluate and feedback
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exp = qlib_model_runner.develop(exp)
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logger.log_object(exp, tag="model runner result")
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feedback = qlib_model_summarizer.generate_feedback(exp, hypothesis, trace)
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logger.log_object(feedback, tag="feedback")
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trace.hist.append((hypothesis, exp, feedback))
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except ModelEmptyError as e:
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logger.warning(e)
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continue
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