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chore: remove redundant tag in old scenarios (#917)

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* remove state.times in old ui

* remove "r" tag

* remove "d" tag

* remove "ef" tag

* remove "init" tag

* fix CI

* remove old tag in app UI

* fix bugs

* fix CI

* some updates

* filter tags
This commit is contained in:
XianBW
2025-06-13 17:49:58 +08:00
committed by GitHub
parent 77e9f186d5
commit 5a3a2bf2be
7 changed files with 238 additions and 290 deletions
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rdagent/app/qlib_rd_loop/quant.py
+48 -54
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@@ -31,70 +31,66 @@ class QuantRDLoop(RDLoop):
)
def __init__(self, PROP_SETTING: BasePropSetting):
with logger.tag("init"):
scen: Scenario = import_class(PROP_SETTING.scen)()
logger.log_object(scen, tag="scenario")
scen: Scenario = import_class(PROP_SETTING.scen)()
logger.log_object(scen, tag="scenario")
self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.quant_hypothesis_gen)(scen)
logger.log_object(self.hypothesis_gen, tag="quant hypothesis generator")
self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.quant_hypothesis_gen)(scen)
logger.log_object(self.hypothesis_gen, tag="quant hypothesis generator")
self.factor_hypothesis2experiment: Hypothesis2Experiment = import_class(
PROP_SETTING.factor_hypothesis2experiment
)()
logger.log_object(self.factor_hypothesis2experiment, tag="factor hypothesis2experiment")
self.model_hypothesis2experiment: Hypothesis2Experiment = import_class(
PROP_SETTING.model_hypothesis2experiment
)()
logger.log_object(self.model_hypothesis2experiment, tag="model hypothesis2experiment")
self.factor_hypothesis2experiment: Hypothesis2Experiment = import_class(
PROP_SETTING.factor_hypothesis2experiment
)()
logger.log_object(self.factor_hypothesis2experiment, tag="factor hypothesis2experiment")
self.model_hypothesis2experiment: Hypothesis2Experiment = import_class(
PROP_SETTING.model_hypothesis2experiment
)()
logger.log_object(self.model_hypothesis2experiment, tag="model hypothesis2experiment")
self.factor_coder: Developer = import_class(PROP_SETTING.factor_coder)(scen)
logger.log_object(self.factor_coder, tag="factor coder")
self.model_coder: Developer = import_class(PROP_SETTING.model_coder)(scen)
logger.log_object(self.model_coder, tag="model coder")
self.factor_coder: Developer = import_class(PROP_SETTING.factor_coder)(scen)
logger.log_object(self.factor_coder, tag="factor coder")
self.model_coder: Developer = import_class(PROP_SETTING.model_coder)(scen)
logger.log_object(self.model_coder, tag="model coder")
self.factor_runner: Developer = import_class(PROP_SETTING.factor_runner)(scen)
logger.log_object(self.factor_runner, tag="factor runner")
self.model_runner: Developer = import_class(PROP_SETTING.model_runner)(scen)
logger.log_object(self.model_runner, tag="model runner")
self.factor_runner: Developer = import_class(PROP_SETTING.factor_runner)(scen)
logger.log_object(self.factor_runner, tag="factor runner")
self.model_runner: Developer = import_class(PROP_SETTING.model_runner)(scen)
logger.log_object(self.model_runner, tag="model runner")
self.factor_summarizer: Experiment2Feedback = import_class(PROP_SETTING.factor_summarizer)(scen)
logger.log_object(self.factor_summarizer, tag="factor summarizer")
self.model_summarizer: Experiment2Feedback = import_class(PROP_SETTING.model_summarizer)(scen)
logger.log_object(self.model_summarizer, tag="model summarizer")
self.factor_summarizer: Experiment2Feedback = import_class(PROP_SETTING.factor_summarizer)(scen)
logger.log_object(self.factor_summarizer, tag="factor summarizer")
self.model_summarizer: Experiment2Feedback = import_class(PROP_SETTING.model_summarizer)(scen)
logger.log_object(self.model_summarizer, tag="model summarizer")
self.trace = QuantTrace(scen=scen)
super(RDLoop, self).__init__()
self.trace = QuantTrace(scen=scen)
super(RDLoop, self).__init__()
def direct_exp_gen(self, prev_out: dict[str, Any]):
with logger.tag("r"): # research
hypo = self._propose()
assert hypo.action in ["factor", "model"]
if hypo.action == "factor":
exp = self.factor_hypothesis2experiment.convert(hypo, self.trace)
else:
exp = self.model_hypothesis2experiment.convert(hypo, self.trace)
logger.log_object(exp.sub_tasks, tag="experiment generation")
hypo = self._propose()
assert hypo.action in ["factor", "model"]
if hypo.action == "factor":
exp = self.factor_hypothesis2experiment.convert(hypo, self.trace)
else:
exp = self.model_hypothesis2experiment.convert(hypo, self.trace)
logger.log_object(exp.sub_tasks, tag="experiment generation")
return {"propose": hypo, "exp_gen": exp}
def coding(self, prev_out: dict[str, Any]):
with logger.tag("d"): # development
if prev_out["direct_exp_gen"]["propose"].action == "factor":
exp = self.factor_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
elif prev_out["direct_exp_gen"]["propose"].action == "model":
exp = self.model_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
logger.log_object(exp, tag="coder result")
if prev_out["direct_exp_gen"]["propose"].action == "factor":
exp = self.factor_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
elif prev_out["direct_exp_gen"]["propose"].action == "model":
exp = self.model_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
logger.log_object(exp, tag="coder result")
return exp
def running(self, prev_out: dict[str, Any]):
with logger.tag("ef"):
if prev_out["direct_exp_gen"]["propose"].action == "factor":
exp = self.factor_runner.develop(prev_out["coding"])
if exp is None:
logger.error(f"Factor extraction failed.")
raise FactorEmptyError("Factor extraction failed.")
elif prev_out["direct_exp_gen"]["propose"].action == "model":
exp = self.model_runner.develop(prev_out["coding"])
logger.log_object(exp, tag="runner result")
if prev_out["direct_exp_gen"]["propose"].action == "factor":
exp = self.factor_runner.develop(prev_out["coding"])
if exp is None:
logger.error(f"Factor extraction failed.")
raise FactorEmptyError("Factor extraction failed.")
elif prev_out["direct_exp_gen"]["propose"].action == "model":
exp = self.model_runner.develop(prev_out["coding"])
logger.log_object(exp, tag="runner result")
return exp
def feedback(self, prev_out: dict[str, Any]):
@@ -107,16 +103,14 @@ class QuantRDLoop(RDLoop):
reason="",
decision=False,
)
with logger.tag("ef"): # evaluate and feedback
logger.log_object(feedback, tag="feedback")
logger.log_object(feedback, tag="feedback")
self.trace.hist.append((prev_out["direct_exp_gen"]["exp_gen"], feedback))
else:
if prev_out["direct_exp_gen"]["propose"].action == "factor":
feedback = self.factor_summarizer.generate_feedback(prev_out["running"], self.trace)
elif prev_out["direct_exp_gen"]["propose"].action == "model":
feedback = self.model_summarizer.generate_feedback(prev_out["running"], self.trace)
with logger.tag("ef"):
logger.log_object(feedback, tag="feedback")
logger.log_object(feedback, tag="feedback")
self.trace.hist.append((prev_out["running"], feedback))