mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-09 21:10:56 +00:00
chore: remove redundant tag in old scenarios (#917)
* 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
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XianBW
@@ -17,12 +17,11 @@ class FactorRDLoop(RDLoop):
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skip_loop_error = (FactorEmptyError,)
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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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exp = self.runner.develop(prev_out["coding"])
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if exp is None:
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logger.error(f"Factor extraction failed.")
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raise FactorEmptyError("Factor extraction failed.")
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logger.log_object(exp, tag="runner result")
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exp = self.runner.develop(prev_out["coding"])
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if exp is None:
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logger.error(f"Factor extraction failed.")
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raise FactorEmptyError("Factor extraction failed.")
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logger.log_object(exp, tag="runner result")
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return exp
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@@ -112,27 +112,26 @@ class FactorReportLoop(FactorRDLoop, metaclass=LoopMeta):
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self.steps = ["propose_hypo_exp", "propose", "direct_exp_gen", "coding", "running", "feedback"]
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def propose_hypo_exp(self, prev_out: dict[str, Any]):
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with logger.tag("r"):
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while True:
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if FACTOR_FROM_REPORT_PROP_SETTING.is_report_limit_enabled and self.valid_pdf_file_count > 15:
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break
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report_file_path = self.judge_pdf_data_items[self.pdf_file_index]
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logger.info(f"Processing number {self.pdf_file_index} report: {report_file_path}")
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self.pdf_file_index += 1
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exp, hypothesis = extract_hypothesis_and_exp_from_reports(str(report_file_path))
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if exp is None:
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continue
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self.valid_pdf_file_count += 1
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exp.based_experiments = [QlibFactorExperiment(sub_tasks=[], hypothesis=hypothesis)] + [
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t[0] for t in self.trace.hist if t[1]
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]
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exp.sub_workspace_list = exp.sub_workspace_list[: FACTOR_FROM_REPORT_PROP_SETTING.max_factors_per_exp]
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exp.sub_tasks = exp.sub_tasks[: FACTOR_FROM_REPORT_PROP_SETTING.max_factors_per_exp]
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logger.log_object(hypothesis, tag="hypothesis generation")
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logger.log_object(exp.sub_tasks, tag="experiment generation")
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self.current_loop_hypothesis = hypothesis
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self.current_loop_exp = exp
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return None
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while True:
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if FACTOR_FROM_REPORT_PROP_SETTING.is_report_limit_enabled and self.valid_pdf_file_count > 15:
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break
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report_file_path = self.judge_pdf_data_items[self.pdf_file_index]
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logger.info(f"Processing number {self.pdf_file_index} report: {report_file_path}")
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self.pdf_file_index += 1
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exp, hypothesis = extract_hypothesis_and_exp_from_reports(str(report_file_path))
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if exp is None:
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continue
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self.valid_pdf_file_count += 1
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exp.based_experiments = [QlibFactorExperiment(sub_tasks=[], hypothesis=hypothesis)] + [
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t[0] for t in self.trace.hist if t[1]
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]
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exp.sub_workspace_list = exp.sub_workspace_list[: FACTOR_FROM_REPORT_PROP_SETTING.max_factors_per_exp]
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exp.sub_tasks = exp.sub_tasks[: FACTOR_FROM_REPORT_PROP_SETTING.max_factors_per_exp]
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logger.log_object(hypothesis, tag="hypothesis generation")
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logger.log_object(exp.sub_tasks, tag="experiment generation")
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self.current_loop_hypothesis = hypothesis
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self.current_loop_exp = exp
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return None
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def propose(self, prev_out: dict[str, Any]):
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return self.current_loop_hypothesis
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@@ -141,9 +140,8 @@ class FactorReportLoop(FactorRDLoop, metaclass=LoopMeta):
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return {"propose": self.current_loop_hypothesis, "exp_gen": self.current_loop_exp}
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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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exp = self.coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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logger.log_object(exp.sub_workspace_list, tag="coder result")
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exp = self.coder.develop(prev_out["direct_exp_gen"]["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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@@ -31,70 +31,66 @@ class QuantRDLoop(RDLoop):
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)
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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)()
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logger.log_object(scen, tag="scenario")
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scen: Scenario = import_class(PROP_SETTING.scen)()
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logger.log_object(scen, tag="scenario")
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self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.quant_hypothesis_gen)(scen)
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logger.log_object(self.hypothesis_gen, tag="quant hypothesis generator")
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self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.quant_hypothesis_gen)(scen)
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logger.log_object(self.hypothesis_gen, tag="quant hypothesis generator")
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self.factor_hypothesis2experiment: Hypothesis2Experiment = import_class(
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PROP_SETTING.factor_hypothesis2experiment
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)()
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logger.log_object(self.factor_hypothesis2experiment, tag="factor hypothesis2experiment")
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self.model_hypothesis2experiment: Hypothesis2Experiment = import_class(
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PROP_SETTING.model_hypothesis2experiment
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)()
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logger.log_object(self.model_hypothesis2experiment, tag="model hypothesis2experiment")
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self.factor_hypothesis2experiment: Hypothesis2Experiment = import_class(
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PROP_SETTING.factor_hypothesis2experiment
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)()
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logger.log_object(self.factor_hypothesis2experiment, tag="factor hypothesis2experiment")
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self.model_hypothesis2experiment: Hypothesis2Experiment = import_class(
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PROP_SETTING.model_hypothesis2experiment
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)()
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logger.log_object(self.model_hypothesis2experiment, tag="model hypothesis2experiment")
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self.factor_coder: Developer = import_class(PROP_SETTING.factor_coder)(scen)
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logger.log_object(self.factor_coder, tag="factor 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.factor_coder: Developer = import_class(PROP_SETTING.factor_coder)(scen)
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logger.log_object(self.factor_coder, tag="factor 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.factor_runner: Developer = import_class(PROP_SETTING.factor_runner)(scen)
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logger.log_object(self.factor_runner, tag="factor 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.factor_runner: Developer = import_class(PROP_SETTING.factor_runner)(scen)
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logger.log_object(self.factor_runner, tag="factor 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.factor_summarizer: Experiment2Feedback = import_class(PROP_SETTING.factor_summarizer)(scen)
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logger.log_object(self.factor_summarizer, tag="factor summarizer")
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self.model_summarizer: Experiment2Feedback = import_class(PROP_SETTING.model_summarizer)(scen)
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logger.log_object(self.model_summarizer, tag="model summarizer")
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self.factor_summarizer: Experiment2Feedback = import_class(PROP_SETTING.factor_summarizer)(scen)
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logger.log_object(self.factor_summarizer, tag="factor summarizer")
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self.model_summarizer: Experiment2Feedback = import_class(PROP_SETTING.model_summarizer)(scen)
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logger.log_object(self.model_summarizer, tag="model summarizer")
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self.trace = QuantTrace(scen=scen)
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super(RDLoop, self).__init__()
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self.trace = QuantTrace(scen=scen)
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super(RDLoop, self).__init__()
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def direct_exp_gen(self, prev_out: dict[str, Any]):
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with logger.tag("r"): # research
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hypo = self._propose()
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assert hypo.action in ["factor", "model"]
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if hypo.action == "factor":
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exp = self.factor_hypothesis2experiment.convert(hypo, self.trace)
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else:
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exp = self.model_hypothesis2experiment.convert(hypo, self.trace)
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logger.log_object(exp.sub_tasks, tag="experiment generation")
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hypo = self._propose()
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assert hypo.action in ["factor", "model"]
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if hypo.action == "factor":
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exp = self.factor_hypothesis2experiment.convert(hypo, self.trace)
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else:
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exp = self.model_hypothesis2experiment.convert(hypo, self.trace)
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logger.log_object(exp.sub_tasks, tag="experiment generation")
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return {"propose": hypo, "exp_gen": exp}
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def coding(self, prev_out: dict[str, Any]):
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with logger.tag("d"): # development
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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exp = self.factor_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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exp = self.model_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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logger.log_object(exp, tag="coder result")
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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exp = self.factor_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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exp = self.model_coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
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logger.log_object(exp, 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"):
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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exp = self.factor_runner.develop(prev_out["coding"])
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if exp is None:
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logger.error(f"Factor extraction failed.")
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raise FactorEmptyError("Factor extraction failed.")
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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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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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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exp = self.factor_runner.develop(prev_out["coding"])
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if exp is None:
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logger.error(f"Factor extraction failed.")
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raise FactorEmptyError("Factor extraction failed.")
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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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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def feedback(self, prev_out: dict[str, Any]):
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@@ -107,16 +103,14 @@ class QuantRDLoop(RDLoop):
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reason="",
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decision=False,
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)
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with logger.tag("ef"): # evaluate and feedback
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logger.log_object(feedback, tag="feedback")
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logger.log_object(feedback, tag="feedback")
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self.trace.hist.append((prev_out["direct_exp_gen"]["exp_gen"], feedback))
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else:
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if prev_out["direct_exp_gen"]["propose"].action == "factor":
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feedback = self.factor_summarizer.generate_feedback(prev_out["running"], self.trace)
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elif prev_out["direct_exp_gen"]["propose"].action == "model":
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feedback = self.model_summarizer.generate_feedback(prev_out["running"], self.trace)
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with logger.tag("ef"):
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logger.log_object(feedback, tag="feedback")
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logger.log_object(feedback, tag="feedback")
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self.trace.hist.append((prev_out["running"], feedback))
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