mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-29 00:17:44 +00:00
Xu Yang
@@ -0,0 +1,231 @@
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from __future__ import annotations
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import re
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from typing import List
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from pandas.core.api import DataFrame as DataFrame
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from core.evolving_framework import Evaluator as EvolvingEvaluator
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from core.evolving_framework import Feedback, QueriedKnowledge
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from core.log import FinCoLog
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from factor_implementation.evolving.evolvable_subjects import (
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FactorImplementationList,
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)
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from factor_implementation.share_modules.conf import FactorImplementSettings
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from factor_implementation.share_modules.evaluator import (
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Evaluator as FactorImplementationEvaluator,
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)
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from factor_implementation.share_modules.evaluator import (
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FactorImplementationCodeEvaluator,
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FactorImplementationFinalDecisionEvaluator,
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FactorImplementationValueEvaluator,
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)
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from factor_implementation.share_modules.factor import (
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FactorImplementation,
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FactorImplementationTask,
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)
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from core.utils import multiprocessing_wrapper
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class FactorImplementationSingleFeedback:
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"""This class is a feedback to single implementation which is generated from an evaluator."""
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def __init__(
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self,
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execution_feedback: str = None,
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value_generated_flag: bool = False,
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code_feedback: str = None,
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factor_value_feedback: str = None,
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final_decision: bool = None,
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final_feedback: str = None,
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final_decision_based_on_gt: bool = None,
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) -> None:
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self.execution_feedback = execution_feedback
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self.value_generated_flag = value_generated_flag
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self.code_feedback = code_feedback
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self.factor_value_feedback = factor_value_feedback
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self.final_decision = final_decision
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self.final_feedback = final_feedback
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self.final_decision_based_on_gt = final_decision_based_on_gt
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def __str__(self) -> str:
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return f"""------------------Factor Execution Feedback------------------
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{self.execution_feedback}
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------------------Factor Code Feedback------------------
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{self.code_feedback}
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------------------Factor Value Feedback------------------
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{self.factor_value_feedback}
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------------------Factor Final Feedback------------------
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{self.final_feedback}
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------------------Factor Final Decision------------------
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This implementation is {'SUCCESS' if self.final_decision else 'FAIL'}.
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"""
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class FactorImplementationsMultiFeedback(
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Feedback,
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List[FactorImplementationSingleFeedback],
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):
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"""Feedback contains a list, each element is the corresponding feedback for each factor implementation."""
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class FactorImplementationEvaluatorV1(FactorImplementationEvaluator):
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"""This class is the v1 version of evaluator for a single factor implementation.
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It calls several evaluators in share modules to evaluate the factor implementation.
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"""
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def __init__(self) -> None:
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self.code_evaluator = FactorImplementationCodeEvaluator()
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self.value_evaluator = FactorImplementationValueEvaluator()
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self.final_decision_evaluator = FactorImplementationFinalDecisionEvaluator()
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def evaluate(
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self,
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target_task: FactorImplementationTask,
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implementation: FactorImplementation,
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gt_implementation: FactorImplementation = None,
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queried_knowledge: QueriedKnowledge = None,
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**kwargs,
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) -> FactorImplementationSingleFeedback:
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if implementation is None:
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return None
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target_task_information = target_task.get_factor_information()
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if (
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queried_knowledge is not None
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and target_task_information in queried_knowledge.success_task_to_knowledge_dict
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):
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return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
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elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
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return FactorImplementationSingleFeedback(
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execution_feedback="This task has failed too many times, skip implementation.",
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value_generated_flag=False,
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code_feedback="This task has failed too many times, skip code evaluation.",
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factor_value_feedback="This task has failed too many times, skip value evaluation.",
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final_decision=False,
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final_feedback="This task has failed too many times, skip final decision evaluation.",
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final_decision_based_on_gt=False,
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)
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else:
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factor_feedback = FactorImplementationSingleFeedback()
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(
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factor_feedback.execution_feedback,
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source_df,
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) = implementation.execute()
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# Remove the long list of numbers in the feedback
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pattern = r"(?<=\D)(,\s+-?\d+\.\d+){50,}(?=\D)"
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factor_feedback.execution_feedback = re.sub(pattern, ", ", factor_feedback.execution_feedback)
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execution_feedback_lines = [
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line for line in factor_feedback.execution_feedback.split("\n") if "warning" not in line.lower()
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]
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factor_feedback.execution_feedback = "\n".join(execution_feedback_lines)
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if source_df is None:
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factor_feedback.factor_value_feedback = "No factor value generated, skip value evaluation."
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factor_feedback.value_generated_flag = False
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value_decision = None
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else:
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factor_feedback.value_generated_flag = True
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if gt_implementation is not None:
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_, gt_df = gt_implementation.execute(store_result=True)
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else:
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gt_df = None
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try:
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source_df = source_df.sort_index()
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if gt_df is not None:
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gt_df = gt_df.sort_index()
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(
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factor_feedback.factor_value_feedback,
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value_decision,
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) = self.value_evaluator.evaluate(source_df=source_df, gt_df=gt_df)
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except Exception as e:
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FinCoLog().warning("Value evaluation failed with exception: %s", e)
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factor_feedback.factor_value_feedback = "Value evaluation failed."
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value_decision = False
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factor_feedback.final_decision_based_on_gt = gt_implementation is not None
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if value_decision is not None and value_decision is True:
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# To avoid confusion, when value_decision is True, we do not need code feedback
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factor_feedback.code_feedback = "Final decision is True and there are no code critics."
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factor_feedback.final_decision = value_decision
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factor_feedback.final_feedback = "Value evaluation passed, skip final decision evaluation."
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else:
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factor_feedback.code_feedback = self.code_evaluator.evaluate(
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target_task=target_task,
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implementation=implementation,
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execution_feedback=factor_feedback.execution_feedback,
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value_feedback=factor_feedback.factor_value_feedback,
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gt_implementation=gt_implementation,
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)
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(
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factor_feedback.final_decision,
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factor_feedback.final_feedback,
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) = self.final_decision_evaluator.evaluate(
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target_task=target_task,
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execution_feedback=factor_feedback.execution_feedback,
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value_feedback=factor_feedback.factor_value_feedback,
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code_feedback=factor_feedback.code_feedback,
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)
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return factor_feedback
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class FactorImplementationsMultiEvaluator(EvolvingEvaluator):
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def __init__(self, single_evaluator=FactorImplementationEvaluatorV1()) -> None:
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super().__init__()
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self.single_factor_implementation_evaluator = single_evaluator
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def evaluate(
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self,
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evo: FactorImplementationList,
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queried_knowledge: QueriedKnowledge = None,
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**kwargs,
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) -> FactorImplementationsMultiFeedback:
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multi_implementation_feedback = FactorImplementationsMultiFeedback()
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# for index in range(len(evo.target_factor_tasks)):
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# corresponding_implementation = evo.corresponding_implementations[index]
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# corresponding_gt_implementation = (
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# evo.corresponding_gt_implementations[index]
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# if evo.corresponding_gt_implementations is not None
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# else None
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# )
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# multi_implementation_feedback.append(
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# self.single_factor_implementation_evaluator.evaluate(
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# target_task=evo.target_factor_tasks[index],
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# implementation=corresponding_implementation,
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# gt_implementation=corresponding_gt_implementation,
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# queried_knowledge=queried_knowledge,
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# )
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# )
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calls = []
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for index in range(len(evo.target_factor_tasks)):
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corresponding_implementation = evo.corresponding_implementations[index]
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corresponding_gt_implementation = (
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evo.corresponding_gt_implementations[index]
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if evo.corresponding_gt_implementations is not None
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else None
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)
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calls.append(
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(
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self.single_factor_implementation_evaluator.evaluate,
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(
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evo.target_factor_tasks[index],
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corresponding_implementation,
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corresponding_gt_implementation,
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queried_knowledge,
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),
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),
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)
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multi_implementation_feedback = multiprocessing_wrapper(calls, n=FactorImplementSettings().evo_multi_proc_n)
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final_decision = [
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None if single_feedback is None else single_feedback.final_decision
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for single_feedback in multi_implementation_feedback
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]
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print(f"Final decisions: {final_decision} True count: {final_decision.count(True)}")
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return multi_implementation_feedback
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@@ -0,0 +1,34 @@
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from __future__ import annotations
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import pandas as pd
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from core.evolving_framework import EvolvableSubjects
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from core.log import FinCoLog
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from factor_implementation.share_modules.factor import (
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FactorImplementation,
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FactorImplementationTask,
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)
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class FactorImplementationList(EvolvableSubjects):
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"""
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Factors is a list.
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"""
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def __init__(
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self,
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target_factor_tasks: list[FactorImplementationTask],
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corresponding_gt_implementations: list[FactorImplementation] = None,
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):
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super().__init__()
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self.target_factor_tasks = target_factor_tasks
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self.corresponding_implementations: list[FactorImplementation] = []
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if corresponding_gt_implementations is not None and len(
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corresponding_gt_implementations,
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) != len(target_factor_tasks):
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self.corresponding_gt_implementations = None
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FinCoLog.warning(
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"The length of corresponding_gt_implementations is not equal to the length of target_factor_tasks, set corresponding_gt_implementations to None",
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)
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else:
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self.corresponding_gt_implementations = corresponding_gt_implementations
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@@ -0,0 +1,298 @@
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from __future__ import annotations
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import json
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import random
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from abc import abstractmethod
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from copy import deepcopy
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from typing import TYPE_CHECKING
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from jinja2 import Template
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from core.evolving_framework import EvolvingStrategy, QueriedKnowledge
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from oai.llm_utils import APIBackend
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from factor_implementation.share_modules.conf import FactorImplementSettings
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from factor_implementation.share_modules.factor import (
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FactorImplementation,
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FactorImplementationTask,
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FileBasedFactorImplementation,
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)
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from factor_implementation.share_modules.prompt import (
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FactorImplementationPrompts,
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)
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from factor_implementation.share_modules.utils import get_data_folder_intro
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from core.utils import multiprocessing_wrapper
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if TYPE_CHECKING:
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from factor_implementation.evolving.evolvable_subjects import (
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FactorImplementationList,
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)
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from factor_implementation.evolving.knowledge_management import (
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FactorImplementationQueriedKnowledge,
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FactorImplementationQueriedKnowledgeV1,
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)
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class MultiProcessEvolvingStrategy(EvolvingStrategy):
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@abstractmethod
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def implement_one_factor(
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self,
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target_task: FactorImplementationTask,
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queried_knowledge: QueriedKnowledge = None,
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) -> FactorImplementation:
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raise NotImplementedError
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def evolve(
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self,
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*,
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evo: FactorImplementationList,
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queried_knowledge: FactorImplementationQueriedKnowledge | None = None,
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**kwargs,
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) -> FactorImplementationList:
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new_evo = deepcopy(evo)
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new_evo.corresponding_implementations = [None for _ in new_evo.target_factor_tasks]
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to_be_finished_task_index = []
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for index, target_factor_task in enumerate(new_evo.target_factor_tasks):
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target_factor_task_desc = target_factor_task.get_factor_information()
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if target_factor_task_desc in queried_knowledge.success_task_to_knowledge_dict:
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new_evo.corresponding_implementations[index] = queried_knowledge.success_task_to_knowledge_dict[
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target_factor_task_desc
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].implementation
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elif (
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target_factor_task_desc not in queried_knowledge.success_task_to_knowledge_dict
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and target_factor_task_desc not in queried_knowledge.failed_task_info_set
|
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):
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to_be_finished_task_index.append(index)
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if FactorImplementSettings().implementation_factors_per_round < len(to_be_finished_task_index):
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to_be_finished_task_index = random.sample(
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to_be_finished_task_index,
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FactorImplementSettings().implementation_factors_per_round,
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)
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result = multiprocessing_wrapper(
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[
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(self.implement_one_factor, (new_evo.target_factor_tasks[target_index], queried_knowledge))
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for target_index in to_be_finished_task_index
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],
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n=FactorImplementSettings().evo_multi_proc_n,
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)
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for index, target_index in enumerate(to_be_finished_task_index):
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new_evo.corresponding_implementations[target_index] = result[index]
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# for target_index in to_be_finished_task_index:
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# new_evo.corresponding_implementations[target_index] = self.implement_one_factor(
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# new_evo.target_factor_tasks[target_index], queried_knowledge
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# )
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return new_evo
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|
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class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
|
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def implement_one_factor(
|
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self,
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target_task: FactorImplementationTask,
|
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queried_knowledge: FactorImplementationQueriedKnowledgeV1 = None,
|
||||
) -> FactorImplementation:
|
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factor_information_str = target_task.get_factor_information()
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|
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if queried_knowledge is not None and factor_information_str in queried_knowledge.success_task_to_knowledge_dict:
|
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return queried_knowledge.success_task_to_knowledge_dict[factor_information_str].implementation
|
||||
elif queried_knowledge is not None and factor_information_str in queried_knowledge.failed_task_info_set:
|
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return None
|
||||
else:
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||||
queried_similar_successful_knowledge = (
|
||||
queried_knowledge.working_task_to_similar_successful_knowledge_dict[factor_information_str]
|
||||
if queried_knowledge is not None
|
||||
else []
|
||||
)
|
||||
queried_former_failed_knowledge = (
|
||||
queried_knowledge.working_task_to_former_failed_knowledge_dict[factor_information_str]
|
||||
if queried_knowledge is not None
|
||||
else []
|
||||
)
|
||||
|
||||
queried_former_failed_knowledge_to_render = queried_former_failed_knowledge
|
||||
|
||||
system_prompt = Template(
|
||||
FactorImplementationPrompts()["evolving_strategy_factor_implementation_v1_system"],
|
||||
).render(
|
||||
data_info=get_data_folder_intro(),
|
||||
queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
|
||||
)
|
||||
session = APIBackend(use_chat_cache=False).build_chat_session(
|
||||
session_system_prompt=system_prompt,
|
||||
)
|
||||
|
||||
queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge
|
||||
while True:
|
||||
user_prompt = (
|
||||
Template(
|
||||
FactorImplementationPrompts()["evolving_strategy_factor_implementation_v1_user"],
|
||||
)
|
||||
.render(
|
||||
factor_information_str=factor_information_str,
|
||||
queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
|
||||
)
|
||||
.strip("\n")
|
||||
)
|
||||
if (
|
||||
session.build_chat_completion_message_and_calculate_token(
|
||||
user_prompt,
|
||||
)
|
||||
< FactorImplementSettings().chat_token_limit
|
||||
):
|
||||
break
|
||||
elif len(queried_former_failed_knowledge_to_render) > 1:
|
||||
queried_former_failed_knowledge_to_render = queried_former_failed_knowledge_to_render[1:]
|
||||
elif len(queried_similar_successful_knowledge_to_render) > 1:
|
||||
queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge_to_render[1:]
|
||||
# print(
|
||||
# f"length of queried_similar_successful_knowledge_to_render: {len(queried_similar_successful_knowledge_to_render)}, length of queried_former_failed_knowledge_to_render: {len(queried_former_failed_knowledge_to_render)}"
|
||||
# )
|
||||
|
||||
code = json.loads(
|
||||
session.build_chat_completion(
|
||||
user_prompt=user_prompt,
|
||||
json_mode=True,
|
||||
),
|
||||
)["code"]
|
||||
# ast.parse(code)
|
||||
factor_implementation = FileBasedFactorImplementation(
|
||||
target_task,
|
||||
code,
|
||||
)
|
||||
|
||||
return factor_implementation
|
||||
|
||||
|
||||
class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
|
||||
def implement_one_factor(
|
||||
self,
|
||||
target_task: FactorImplementationTask,
|
||||
queried_knowledge,
|
||||
) -> FactorImplementation:
|
||||
error_summary = FactorImplementSettings().v2_error_summary
|
||||
target_factor_task_information = target_task.get_factor_information()
|
||||
|
||||
if (
|
||||
queried_knowledge is not None
|
||||
and target_factor_task_information in queried_knowledge.success_task_to_knowledge_dict
|
||||
):
|
||||
return queried_knowledge.success_task_to_knowledge_dict[target_factor_task_information].implementation
|
||||
elif queried_knowledge is not None and target_factor_task_information in queried_knowledge.failed_task_info_set:
|
||||
return None
|
||||
else:
|
||||
queried_similar_component_knowledge = (
|
||||
queried_knowledge.component_with_success_task[target_factor_task_information]
|
||||
if queried_knowledge is not None
|
||||
else []
|
||||
) # A list, [success task implement knowledge]
|
||||
|
||||
queried_similar_error_knowledge = (
|
||||
queried_knowledge.error_with_success_task[target_factor_task_information]
|
||||
if queried_knowledge is not None
|
||||
else {}
|
||||
) # A dict, {{error_type:[[error_imp_knowledge, success_imp_knowledge],...]},...}
|
||||
|
||||
queried_former_failed_knowledge = (
|
||||
queried_knowledge.former_traces[target_factor_task_information] if queried_knowledge is not None else []
|
||||
)
|
||||
|
||||
queried_former_failed_knowledge_to_render = queried_former_failed_knowledge
|
||||
|
||||
system_prompt = Template(
|
||||
FactorImplementationPrompts()["evolving_strategy_factor_implementation_v1_system"],
|
||||
).render(
|
||||
data_info=get_data_folder_intro(),
|
||||
queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
|
||||
)
|
||||
|
||||
session = APIBackend(use_chat_cache=False).build_chat_session(
|
||||
session_system_prompt=system_prompt,
|
||||
)
|
||||
|
||||
queried_similar_component_knowledge_to_render = queried_similar_component_knowledge
|
||||
queried_similar_error_knowledge_to_render = queried_similar_error_knowledge
|
||||
error_summary_critics = ""
|
||||
while True:
|
||||
if (
|
||||
error_summary
|
||||
and len(queried_similar_error_knowledge_to_render) != 0
|
||||
and len(queried_former_failed_knowledge_to_render) != 0
|
||||
):
|
||||
error_summary_system_prompt = (
|
||||
Template(FactorImplementationPrompts()["evolving_strategy_error_summary_v2_system"])
|
||||
.render(
|
||||
factor_information_str=target_factor_task_information,
|
||||
code_and_feedback=queried_former_failed_knowledge_to_render[
|
||||
-1
|
||||
].get_implementation_and_feedback_str(),
|
||||
)
|
||||
.strip("\n")
|
||||
)
|
||||
session_summary = APIBackend(use_chat_cache=False).build_chat_session(
|
||||
session_system_prompt=error_summary_system_prompt,
|
||||
)
|
||||
while True:
|
||||
error_summary_user_prompt = (
|
||||
Template(FactorImplementationPrompts()["evolving_strategy_error_summary_v2_user"])
|
||||
.render(
|
||||
queried_similar_component_knowledge=queried_similar_component_knowledge_to_render,
|
||||
)
|
||||
.strip("\n")
|
||||
)
|
||||
if (
|
||||
session_summary.build_chat_completion_message_and_calculate_token(error_summary_user_prompt)
|
||||
< FactorImplementSettings().chat_token_limit
|
||||
):
|
||||
break
|
||||
elif len(queried_similar_error_knowledge_to_render) > 0:
|
||||
queried_similar_error_knowledge_to_render = queried_similar_error_knowledge_to_render[:-1]
|
||||
error_summary_critics = session_summary.build_chat_completion(
|
||||
user_prompt=error_summary_user_prompt,
|
||||
json_mode=False,
|
||||
)
|
||||
|
||||
user_prompt = (
|
||||
Template(
|
||||
FactorImplementationPrompts()["evolving_strategy_factor_implementation_v2_user"],
|
||||
)
|
||||
.render(
|
||||
factor_information_str=target_factor_task_information,
|
||||
queried_similar_component_knowledge=queried_similar_component_knowledge_to_render,
|
||||
queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
|
||||
error_summary=error_summary,
|
||||
error_summary_critics=error_summary_critics,
|
||||
)
|
||||
.strip("\n")
|
||||
)
|
||||
if (
|
||||
session.build_chat_completion_message_and_calculate_token(
|
||||
user_prompt,
|
||||
)
|
||||
< FactorImplementSettings().chat_token_limit
|
||||
):
|
||||
break
|
||||
elif len(queried_former_failed_knowledge_to_render) > 1:
|
||||
queried_former_failed_knowledge_to_render = queried_former_failed_knowledge_to_render[1:]
|
||||
elif len(queried_similar_component_knowledge_to_render) > len(
|
||||
queried_similar_error_knowledge_to_render,
|
||||
):
|
||||
queried_similar_component_knowledge_to_render = queried_similar_component_knowledge_to_render[:-1]
|
||||
elif len(queried_similar_error_knowledge_to_render) > 0:
|
||||
queried_similar_error_knowledge_to_render = queried_similar_error_knowledge_to_render[:-1]
|
||||
|
||||
# print(
|
||||
# len(queried_similar_component_knowledge_to_render),
|
||||
# len(queried_similar_error_knowledge_to_render),
|
||||
# len(queried_former_failed_knowledge_to_render),
|
||||
# )
|
||||
|
||||
response = session.build_chat_completion(
|
||||
user_prompt=user_prompt,
|
||||
json_mode=True,
|
||||
)
|
||||
code = json.loads(response)["code"]
|
||||
factor_implementation = FileBasedFactorImplementation(target_task, code)
|
||||
return factor_implementation
|
||||
@@ -0,0 +1,311 @@
|
||||
import json
|
||||
import pickle
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
from fire.core import Fire
|
||||
from tqdm import tqdm
|
||||
|
||||
from core.evolving_framework import EvoAgent, KnowledgeBase
|
||||
from factor_implementation.evolving.evaluators import (
|
||||
FactorImplementationEvaluatorV1,
|
||||
FactorImplementationsMultiEvaluator,
|
||||
)
|
||||
from factor_implementation.evolving.evolvable_subjects import (
|
||||
FactorImplementationList,
|
||||
)
|
||||
from factor_implementation.evolving.evolving_strategy import (
|
||||
FactorEvolvingStrategy,
|
||||
FactorEvolvingStrategyWithGraph,
|
||||
)
|
||||
from factor_implementation.evolving.knowledge_management import (
|
||||
FactorImplementationGraphKnowledgeBase,
|
||||
FactorImplementationGraphRAGStrategy,
|
||||
FactorImplementationKnowledgeBaseV1,
|
||||
FactorImplementationRAGStrategyV1,
|
||||
)
|
||||
from factor_implementation.share_modules.factor import (
|
||||
FactorImplementationTask,
|
||||
FileBasedFactorImplementation,
|
||||
)
|
||||
from core.utils import multiprocessing_wrapper
|
||||
|
||||
ALPHA101_INIT_COMPONENTS = [
|
||||
"1. abs(): absolute value to certain columns",
|
||||
"2. log(): log value to certain columns",
|
||||
"3. sign(): sign value to certain columns",
|
||||
"4. add_two_columns(): add two columns",
|
||||
"5. minus_two_columns(): minus two columns",
|
||||
"6. times_two_columns(): times two columns",
|
||||
"7. divide_two_columns(): divide two columns",
|
||||
"8. add_value_to_columns(): add value to columns",
|
||||
"9. minus_value_to_columns(): minus value to columns",
|
||||
"10. rank(): cross-sectional rank value to columns",
|
||||
"11. delay(): value of each data d days ago",
|
||||
"12. correlation(): time-serial correlation of column_left and column_right for the past d days",
|
||||
"13. covariance(): time-serial covariance of column_left and column_right for the past d days",
|
||||
"14. scale_to_a(): scale the columns to sum(abs(x)) is a",
|
||||
"15. delta(): today’s value of x minus the value of x d days ago",
|
||||
"16. signedpower(): x^a",
|
||||
"17. decay_linear(): weighted moving average over the past d days with linearly decaying weights d, d – 1, …, 1 (rescaled to sum up to 1)",
|
||||
"18. indneutralize(): x cross-sectionally neutralized against groups g (subindustries, industries, sectors, etc.), i.e., x is cross-sectionally demeaned within each group g",
|
||||
"19. ts_min(): time-series min over the past d days, operator min applied across the time-series for the past d days; non-integer number of days d is converted to floor(d)",
|
||||
"20. ts_max(): time-series max over the past d days, operator max applied across the time-series for the past d days; non-integer number of days d is converted to floor(d)",
|
||||
"21. ts_argmax(): which day ts_max(x, d) occurred on",
|
||||
"22. ts_argmin(): which day ts_min(x, d) occurred on",
|
||||
"23. ts_rank(): time-series rank in the past d days",
|
||||
"24. min(): ts_min(x, d)",
|
||||
"25. max(): ts_max(x, d)",
|
||||
"26. sum(): time-series sum over the past d days",
|
||||
"27. product(): time-series product over the past d days",
|
||||
"28. stddev(): moving time-series standard deviation over the past d days",
|
||||
]
|
||||
|
||||
|
||||
class FactorImplementationEvolvingCli:
|
||||
# TODO: we should use polymorphism to load knowledge base, strategies instead of evolving_version
|
||||
# TODO: Can we refactor FactorImplementationEvolvingCli into a learning framework to differentiate our learning paradiagm with other ones by iteratively retrying?
|
||||
def __init__(self, evolving_version=2) -> None:
|
||||
self.evolving_version = evolving_version
|
||||
self.knowledge_base = None
|
||||
self.latest_factor_implementations = None
|
||||
|
||||
def run_evolving_framework(
|
||||
self,
|
||||
factor_implementations: FactorImplementationList,
|
||||
factor_knowledge_base: KnowledgeBase,
|
||||
max_loops: int = 20,
|
||||
with_knowledge: bool = True,
|
||||
with_feedback: bool = True,
|
||||
knowledge_self_gen: bool = True,
|
||||
) -> FactorImplementationList:
|
||||
"""
|
||||
Main target: Implement factors.
|
||||
The system also leverages the former knowledge to help implement the factors. Also, new knowledge might be generated during the implementation to help the following implementation.
|
||||
The gt_code and gt_value in the Factor instance is used to evaluate the implementation, and the feedback is used to generate high-quality knowledge which helps the agent to evolve.
|
||||
"""
|
||||
es = FactorEvolvingStrategyWithGraph() if self.evolving_version == 2 else FactorEvolvingStrategy()
|
||||
rag = (
|
||||
FactorImplementationGraphRAGStrategy(factor_knowledge_base)
|
||||
if self.evolving_version == 2
|
||||
else FactorImplementationRAGStrategyV1(factor_knowledge_base)
|
||||
)
|
||||
factor_evaluator = FactorImplementationsMultiEvaluator(FactorImplementationEvaluatorV1())
|
||||
ea = EvoAgent(es, rag=rag)
|
||||
|
||||
for _ in tqdm(range(max_loops), "Implementing factors"):
|
||||
factor_implementations = ea.step_evolving(
|
||||
factor_implementations,
|
||||
factor_evaluator,
|
||||
with_knowledge=with_knowledge,
|
||||
with_feedback=with_feedback,
|
||||
knowledge_self_gen=knowledge_self_gen,
|
||||
)
|
||||
return factor_implementations
|
||||
|
||||
def load_or_init_knowledge_base(self, former_knowledge_base_path: Path = None, component_init_list: list = []):
|
||||
if former_knowledge_base_path is not None and former_knowledge_base_path.exists():
|
||||
factor_knowledge_base = pickle.load(open(former_knowledge_base_path, "rb"))
|
||||
if self.evolving_version == 1 and not isinstance(
|
||||
factor_knowledge_base, FactorImplementationKnowledgeBaseV1
|
||||
):
|
||||
raise ValueError("The former knowledge base is not compatible with the current version")
|
||||
elif self.evolving_version == 2 and not isinstance(
|
||||
factor_knowledge_base,
|
||||
FactorImplementationGraphKnowledgeBase,
|
||||
):
|
||||
raise ValueError("The former knowledge base is not compatible with the current version")
|
||||
else:
|
||||
factor_knowledge_base = (
|
||||
FactorImplementationGraphKnowledgeBase(
|
||||
init_component_list=component_init_list,
|
||||
)
|
||||
if self.evolving_version == 2
|
||||
else FactorImplementationKnowledgeBaseV1()
|
||||
)
|
||||
return factor_knowledge_base
|
||||
|
||||
def implement_factors(
|
||||
self,
|
||||
factor_implementations: FactorImplementationList,
|
||||
former_knowledge_base_path: Path = None,
|
||||
new_knowledge_base_path: Path = None,
|
||||
component_init_list: list = [],
|
||||
max_loops: int = 20,
|
||||
):
|
||||
factor_knowledge_base = self.load_or_init_knowledge_base(
|
||||
former_knowledge_base_path=former_knowledge_base_path,
|
||||
component_init_list=component_init_list,
|
||||
)
|
||||
|
||||
new_factor_implementations = self.run_evolving_framework(
|
||||
factor_implementations=factor_implementations,
|
||||
factor_knowledge_base=factor_knowledge_base,
|
||||
max_loops=max_loops,
|
||||
with_knowledge=True,
|
||||
with_feedback=True,
|
||||
knowledge_self_gen=True,
|
||||
)
|
||||
if new_knowledge_base_path is not None:
|
||||
pickle.dump(factor_knowledge_base, open(new_knowledge_base_path, "wb"))
|
||||
self.knowledge_base = factor_knowledge_base
|
||||
self.latest_factor_implementations = factor_implementations
|
||||
return new_factor_implementations
|
||||
|
||||
def _read_alpha101_factors(
|
||||
self,
|
||||
alpha101_evo_subs_path: Path = None,
|
||||
alpha101_data_path=Path().cwd() / "git_ignore_folder" / "alpha101_related_files",
|
||||
start_index=0,
|
||||
end_index=32,
|
||||
read_gt_factors=True,
|
||||
) -> FactorImplementationList:
|
||||
"""
|
||||
Read the alpha101 factors from the alpha101_related_files folder
|
||||
"""
|
||||
if alpha101_evo_subs_path is not None and alpha101_evo_subs_path.exists():
|
||||
factor_implementations = pickle.load(open(alpha101_evo_subs_path, "rb"))
|
||||
else:
|
||||
target_factor_plain_list = json.load(
|
||||
open(alpha101_data_path / "target_factor_task_list.json"),
|
||||
)
|
||||
name_to_code = json.load(open(alpha101_data_path / "name_to_code.json"))
|
||||
gt_df = pd.read_hdf(alpha101_data_path / "gt_filtered.h5")
|
||||
|
||||
# First read the target factor task
|
||||
target_factor_tasks = []
|
||||
for factor_list_item in target_factor_plain_list:
|
||||
target_factor_tasks.append(
|
||||
FactorImplementationTask(
|
||||
factor_name=factor_list_item[0],
|
||||
factor_description=factor_list_item[1],
|
||||
factor_formulation=factor_list_item[2],
|
||||
factor_formulation_description=factor_list_item[3],
|
||||
),
|
||||
)
|
||||
|
||||
# Second read the gt factor implementations
|
||||
corresponding_gt_implementations = []
|
||||
for factor_task in target_factor_tasks:
|
||||
name = factor_task.factor_name
|
||||
gt_code = name_to_code[name]
|
||||
gt_value = gt_df.loc(axis=1)[[name]]
|
||||
corresponding_gt_implementations.append(
|
||||
FileBasedFactorImplementation(
|
||||
code=gt_code,
|
||||
executed_factor_value_dataframe=gt_value,
|
||||
target_task=factor_task,
|
||||
),
|
||||
)
|
||||
|
||||
# Finally generate the factor implementations as evolvable subjects
|
||||
factor_implementations = FactorImplementationList(
|
||||
target_factor_tasks=target_factor_tasks,
|
||||
corresponding_gt_implementations=(corresponding_gt_implementations if read_gt_factors else None),
|
||||
)
|
||||
|
||||
factor_implementations.target_factor_tasks = factor_implementations.target_factor_tasks[start_index:end_index]
|
||||
factor_implementations.corresponding_gt_implementations = (
|
||||
factor_implementations.corresponding_gt_implementations[start_index:end_index] if read_gt_factors else None
|
||||
)
|
||||
|
||||
return factor_implementations
|
||||
|
||||
def implement_alpha101(
|
||||
self,
|
||||
max_loops=30,
|
||||
) -> FactorImplementationList:
|
||||
"""
|
||||
Implement the alpha101 factors to gather knowledge TODO: implement the code
|
||||
"""
|
||||
factor_implementations = self._read_alpha101_factors(
|
||||
alpha101_evo_subs_path=Path.cwd() / "alpha101_evo_subs.pkl",
|
||||
start_index=0,
|
||||
end_index=64,
|
||||
read_gt_factors=True,
|
||||
)
|
||||
self.implement_factors(
|
||||
factor_implementations,
|
||||
former_knowledge_base_path=Path.cwd()
|
||||
/ f"alpha101_knowledge_base_v{self.evolving_version}_project_product.pkl",
|
||||
new_knowledge_base_path=Path.cwd()
|
||||
/ f"alpha101_knowledge_base_v{self.evolving_version}_project_product.pkl",
|
||||
component_init_list=ALPHA101_INIT_COMPONENTS,
|
||||
max_loops=100,
|
||||
)
|
||||
|
||||
factor_implementations = self._read_alpha101_factors(
|
||||
alpha101_evo_subs_path=Path.cwd() / "alpha101_evo_subs.pkl",
|
||||
start_index=64,
|
||||
end_index=96,
|
||||
read_gt_factors=False,
|
||||
)
|
||||
final_imp = self.implement_factors(
|
||||
factor_implementations,
|
||||
former_knowledge_base_path=Path.cwd()
|
||||
/ f"alpha101_knowledge_base_v{self.evolving_version}_project_product.pkl",
|
||||
new_knowledge_base_path=Path.cwd()
|
||||
/ f"alpha101_knowledge_base_v{self.evolving_version}_self_evolving_project_product.pkl",
|
||||
component_init_list=ALPHA101_INIT_COMPONENTS,
|
||||
max_loops=10,
|
||||
)
|
||||
final_imp.corresponding_gt_implementations = factor_implementations = self._read_alpha101_factors(
|
||||
alpha101_evo_subs_path=Path.cwd() / "alpha101_evo_subs.pkl",
|
||||
start_index=64,
|
||||
end_index=96,
|
||||
read_gt_factors=True,
|
||||
).corresponding_gt_implementations
|
||||
|
||||
feedbacks = FactorImplementationsMultiEvaluator().evaluate(final_imp)
|
||||
print([feedback.final_decision if feedback is not None else None for feedback in feedbacks].count(True))
|
||||
|
||||
def implement_amc(
|
||||
self, evo_sub_path_str, former_knowledge_base_path_str, implementation_dump_path_str, slice_index
|
||||
):
|
||||
factor_implementations: FactorImplementationList = pickle.load(open(evo_sub_path_str, "rb"))
|
||||
factor_implementations.target_factor_tasks = factor_implementations.target_factor_tasks[
|
||||
slice_index * 16 : slice_index * 16 + 16
|
||||
]
|
||||
if len(factor_implementations.target_factor_tasks) == 0:
|
||||
return
|
||||
if Path(implementation_dump_path_str).exists():
|
||||
return
|
||||
factor_implementations = self.implement_factors(
|
||||
factor_implementations,
|
||||
former_knowledge_base_path=Path(former_knowledge_base_path_str),
|
||||
component_init_list=ALPHA101_INIT_COMPONENTS,
|
||||
max_loops=10,
|
||||
)
|
||||
pickle.dump(factor_implementations, open(implementation_dump_path_str, "wb"))
|
||||
|
||||
def execute_command(self, command, cwd):
|
||||
print(command, cwd)
|
||||
try:
|
||||
subprocess.check_output(
|
||||
command,
|
||||
shell=True,
|
||||
cwd=cwd,
|
||||
)
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(e.output.decode())
|
||||
|
||||
def multi_inference_amc_factors(self, type):
|
||||
slice_count = {"price_volume": 35, "fundamental": 24, "high_frequency": 16}[type]
|
||||
res = multiprocessing_wrapper(
|
||||
[
|
||||
(
|
||||
self.execute_command,
|
||||
(
|
||||
f"python src/scripts/factor_implementation/baselines/evolving/factor_implementation_evolving_cli.py implement_amc ./{type}_factors.pkl ./knowledge_base_v2_with_alpha101_and_10_factors.pkl ./inference_amc_factors_{type}_{slice}.pkl {slice}",
|
||||
Path.cwd(),
|
||||
),
|
||||
)
|
||||
for slice in range(slice_count)
|
||||
],
|
||||
n=2,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
Fire(FactorImplementationEvolvingCli)
|
||||
@@ -0,0 +1,905 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import copy
|
||||
import json
|
||||
import random
|
||||
import re
|
||||
from itertools import combinations
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
from jinja2 import Template
|
||||
|
||||
from core.evolving_framework import (
|
||||
EvolvableSubjects,
|
||||
EvoStep,
|
||||
Knowledge,
|
||||
KnowledgeBase,
|
||||
QueriedKnowledge,
|
||||
RAGStrategy,
|
||||
)
|
||||
from finco.graph import UndirectedGraph, UndirectedNode
|
||||
from oai.llm_utils import APIBackend, calculate_embedding_distance_between_str_list
|
||||
from core.log import FinCoLog
|
||||
from factor_implementation.evolving.evaluators import (
|
||||
FactorImplementationSingleFeedback,
|
||||
)
|
||||
from factor_implementation.share_modules.conf import FactorImplementSettings
|
||||
from factor_implementation.share_modules.factor import (
|
||||
FactorImplementation,
|
||||
FactorImplementationTask,
|
||||
)
|
||||
from factor_implementation.share_modules.prompt import (
|
||||
FactorImplementationPrompts,
|
||||
)
|
||||
|
||||
|
||||
class FactorImplementationKnowledge(Knowledge):
|
||||
def __init__(
|
||||
self,
|
||||
target_task: FactorImplementationTask,
|
||||
implementation: FactorImplementation,
|
||||
feedback: FactorImplementationSingleFeedback,
|
||||
) -> None:
|
||||
"""
|
||||
Initialize a FactorKnowledge object. The FactorKnowledge object is used to store a factor implementation without the ground truth code and value.
|
||||
|
||||
Args:
|
||||
factor (Factor): The factor object associated with the KnowledgeManagement.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
self.target_task = target_task
|
||||
self.implementation = implementation
|
||||
self.feedback = feedback
|
||||
|
||||
def get_implementation_and_feedback_str(self) -> str:
|
||||
return f"""------------------Factor implementation code:------------------
|
||||
{self.implementation.code}
|
||||
------------------Factor implementation feedback:------------------
|
||||
{self.feedback!s}
|
||||
"""
|
||||
|
||||
|
||||
class FactorImplementationQueriedKnowledge(QueriedKnowledge):
|
||||
def __init__(self, success_task_to_knowledge_dict: dict = {}, failed_task_info_set: set = set()) -> None:
|
||||
self.success_task_to_knowledge_dict = success_task_to_knowledge_dict
|
||||
self.failed_task_info_set = failed_task_info_set
|
||||
|
||||
|
||||
class FactorImplementationKnowledgeBaseV1(KnowledgeBase):
|
||||
def __init__(self) -> None:
|
||||
self.implementation_trace: dict[str, FactorImplementationKnowledge] = dict()
|
||||
self.success_task_info_set: set[str] = set()
|
||||
|
||||
self.task_to_embedding = dict()
|
||||
|
||||
def query(self) -> QueriedKnowledge | None:
|
||||
"""
|
||||
Query the knowledge base to get the queried knowledge. So far is handled in RAG strategy.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class FactorImplementationQueriedKnowledgeV1(FactorImplementationQueriedKnowledge):
|
||||
def __init__(self) -> None:
|
||||
self.working_task_to_former_failed_knowledge_dict = dict()
|
||||
self.working_task_to_similar_successful_knowledge_dict = dict()
|
||||
super().__init__()
|
||||
|
||||
|
||||
class FactorImplementationRAGStrategyV1(RAGStrategy):
|
||||
def __init__(self, knowledgebase: FactorImplementationKnowledgeBaseV1) -> None:
|
||||
super().__init__(knowledgebase)
|
||||
self.current_generated_trace_count = 0
|
||||
|
||||
def generate_knowledge(
|
||||
self,
|
||||
evolving_trace: list[EvoStep],
|
||||
*,
|
||||
return_knowledge: bool = False,
|
||||
) -> Knowledge | None:
|
||||
if len(evolving_trace) == self.current_generated_trace_count:
|
||||
return
|
||||
else:
|
||||
for trace_index in range(
|
||||
self.current_generated_trace_count,
|
||||
len(evolving_trace),
|
||||
):
|
||||
evo_step = evolving_trace[trace_index]
|
||||
implementations = evo_step.evolvable_subjects
|
||||
feedback = evo_step.feedback
|
||||
for task_index in range(len(implementations.target_factor_tasks)):
|
||||
target_task = implementations.target_factor_tasks[task_index]
|
||||
target_task_information = target_task.get_factor_information()
|
||||
implementation = implementations.corresponding_implementations[task_index]
|
||||
single_feedback = feedback[task_index]
|
||||
if single_feedback is None:
|
||||
continue
|
||||
single_knowledge = FactorImplementationKnowledge(
|
||||
target_task=target_task,
|
||||
implementation=implementation,
|
||||
feedback=single_feedback,
|
||||
)
|
||||
if target_task_information not in self.knowledgebase.success_task_info_set:
|
||||
self.knowledgebase.implementation_trace.setdefault(
|
||||
target_task_information,
|
||||
[],
|
||||
).append(single_knowledge)
|
||||
|
||||
if single_feedback.final_decision == True:
|
||||
self.knowledgebase.success_task_info_set.add(
|
||||
target_task_information,
|
||||
)
|
||||
self.current_generated_trace_count = len(evolving_trace)
|
||||
|
||||
def query(
|
||||
self,
|
||||
evo: EvolvableSubjects,
|
||||
evolving_trace: list[EvoStep],
|
||||
) -> QueriedKnowledge | None:
|
||||
v1_query_former_trace_limit = FactorImplementSettings().v1_query_former_trace_limit
|
||||
v1_query_similar_success_limit = FactorImplementSettings().v1_query_similar_success_limit
|
||||
fail_task_trial_limit = FactorImplementSettings().fail_task_trial_limit
|
||||
|
||||
queried_knowledge = FactorImplementationQueriedKnowledgeV1()
|
||||
for target_factor_task in evo.target_factor_tasks:
|
||||
target_factor_task_information = target_factor_task.get_factor_information()
|
||||
if target_factor_task_information in self.knowledgebase.success_task_info_set:
|
||||
queried_knowledge.success_task_to_knowledge_dict[target_factor_task_information] = (
|
||||
self.knowledgebase.implementation_trace[target_factor_task_information][-1]
|
||||
)
|
||||
else:
|
||||
if (
|
||||
len(
|
||||
self.knowledgebase.implementation_trace.setdefault(
|
||||
target_factor_task_information,
|
||||
[],
|
||||
),
|
||||
)
|
||||
>= fail_task_trial_limit
|
||||
):
|
||||
queried_knowledge.failed_task_info_set.add(target_factor_task_information)
|
||||
else:
|
||||
queried_knowledge.working_task_to_former_failed_knowledge_dict[target_factor_task_information] = (
|
||||
self.knowledgebase.implementation_trace.setdefault(
|
||||
target_factor_task_information,
|
||||
[],
|
||||
)[-v1_query_former_trace_limit:]
|
||||
)
|
||||
|
||||
knowledge_base_success_task_list = list(
|
||||
self.knowledgebase.success_task_info_set,
|
||||
)
|
||||
similarity = calculate_embedding_distance_between_str_list(
|
||||
[target_factor_task_information],
|
||||
knowledge_base_success_task_list,
|
||||
)[0]
|
||||
similar_indexes = sorted(
|
||||
range(len(similarity)),
|
||||
key=lambda i: similarity[i],
|
||||
reverse=True,
|
||||
)[:v1_query_similar_success_limit]
|
||||
similar_successful_knowledge = [
|
||||
self.knowledgebase.implementation_trace.setdefault(
|
||||
knowledge_base_success_task_list[index],
|
||||
[],
|
||||
)[-1]
|
||||
for index in similar_indexes
|
||||
]
|
||||
queried_knowledge.working_task_to_similar_successful_knowledge_dict[
|
||||
target_factor_task_information
|
||||
] = similar_successful_knowledge
|
||||
return queried_knowledge
|
||||
|
||||
|
||||
class FactorImplementationQueriedGraphKnowledge(FactorImplementationQueriedKnowledge):
|
||||
# Aggregation of knowledge
|
||||
def __init__(
|
||||
self,
|
||||
former_traces: dict = {},
|
||||
component_with_success_task: dict = {},
|
||||
error_with_success_task: dict = {},
|
||||
**kwargs,
|
||||
) -> None:
|
||||
self.former_traces = former_traces
|
||||
self.component_with_success_task = component_with_success_task
|
||||
self.error_with_success_task = error_with_success_task
|
||||
super().__init__(**kwargs)
|
||||
|
||||
|
||||
class FactorImplementationGraphRAGStrategy(RAGStrategy):
|
||||
def __init__(self, knowledgebase: FactorImplementationGraphKnowledgeBase) -> None:
|
||||
super().__init__(knowledgebase)
|
||||
self.current_generated_trace_count = 0
|
||||
self.prompt = FactorImplementationPrompts()
|
||||
|
||||
def generate_knowledge(
|
||||
self,
|
||||
evolving_trace: list[EvoStep],
|
||||
*,
|
||||
return_knowledge: bool = False,
|
||||
) -> Knowledge | None:
|
||||
if len(evolving_trace) == self.current_generated_trace_count:
|
||||
return None
|
||||
|
||||
else:
|
||||
for trace_index in range(self.current_generated_trace_count, len(evolving_trace)):
|
||||
evo_step = evolving_trace[trace_index]
|
||||
implementations = evo_step.evolvable_subjects
|
||||
feedback = evo_step.feedback
|
||||
for task_index in range(len(implementations.target_factor_tasks)):
|
||||
single_feedback = feedback[task_index]
|
||||
target_task = implementations.target_factor_tasks[task_index]
|
||||
target_task_information = target_task.get_factor_information()
|
||||
implementation = implementations.corresponding_implementations[task_index]
|
||||
single_feedback = feedback[task_index]
|
||||
if single_feedback is None:
|
||||
continue
|
||||
single_knowledge = FactorImplementationKnowledge(
|
||||
target_task=target_task,
|
||||
implementation=implementation,
|
||||
feedback=single_feedback,
|
||||
)
|
||||
if (
|
||||
target_task_information not in self.knowledgebase.success_task_to_knowledge_dict
|
||||
and implementation is not None
|
||||
):
|
||||
self.knowledgebase.working_trace_knowledge.setdefault(target_task_information, []).append(
|
||||
single_knowledge,
|
||||
) # save to working trace
|
||||
if single_feedback.final_decision == True:
|
||||
self.knowledgebase.success_task_to_knowledge_dict.setdefault(
|
||||
target_task_information,
|
||||
single_knowledge,
|
||||
)
|
||||
# Do summary for the last step and update the knowledge graph
|
||||
self.knowledgebase.update_success_task(
|
||||
target_task_information,
|
||||
)
|
||||
else:
|
||||
# generate error node and store into knowledge base
|
||||
error_analysis_result = []
|
||||
if not single_feedback.value_generated_flag:
|
||||
error_analysis_result = self.analyze_error(
|
||||
single_feedback.execution_feedback,
|
||||
feedback_type="execution",
|
||||
)
|
||||
else:
|
||||
error_analysis_result = self.analyze_error(
|
||||
single_feedback.factor_value_feedback,
|
||||
feedback_type="value",
|
||||
)
|
||||
self.knowledgebase.working_trace_error_analysis.setdefault(
|
||||
target_task_information,
|
||||
[],
|
||||
).append(
|
||||
error_analysis_result,
|
||||
) # save to working trace error record, for graph update
|
||||
|
||||
self.current_generated_trace_count = len(evolving_trace)
|
||||
return None
|
||||
|
||||
def query(self, evo: EvolvableSubjects, evolving_trace: list[EvoStep]) -> QueriedKnowledge | None:
|
||||
conf_knowledge_sampler = FactorImplementSettings().v2_knowledge_sampler
|
||||
factor_implementation_queried_graph_knowledge = FactorImplementationQueriedGraphKnowledge(
|
||||
success_task_to_knowledge_dict=self.knowledgebase.success_task_to_knowledge_dict,
|
||||
)
|
||||
|
||||
factor_implementation_queried_graph_knowledge = self.former_trace_query(
|
||||
evo,
|
||||
factor_implementation_queried_graph_knowledge,
|
||||
FactorImplementSettings().v2_query_former_trace_limit,
|
||||
)
|
||||
factor_implementation_queried_graph_knowledge = self.component_query(
|
||||
evo,
|
||||
factor_implementation_queried_graph_knowledge,
|
||||
FactorImplementSettings().v2_query_component_limit,
|
||||
knowledge_sampler=conf_knowledge_sampler,
|
||||
)
|
||||
factor_implementation_queried_graph_knowledge = self.error_query(
|
||||
evo,
|
||||
factor_implementation_queried_graph_knowledge,
|
||||
FactorImplementSettings().v2_query_error_limit,
|
||||
knowledge_sampler=conf_knowledge_sampler,
|
||||
)
|
||||
return factor_implementation_queried_graph_knowledge
|
||||
|
||||
def analyze_component(
|
||||
self,
|
||||
target_factor_task_information,
|
||||
) -> list[UndirectedNode]: # Hardcode: certain component nodes
|
||||
all_component_nodes = self.knowledgebase.graph.get_all_nodes_by_label_list(["component"])
|
||||
all_component_content = ""
|
||||
for _, component_node in enumerate(all_component_nodes):
|
||||
all_component_content += f"{component_node.content}, \n"
|
||||
analyze_component_system_prompt = Template(self.prompt["analyze_component_prompt_v1_system"]).render(
|
||||
all_component_content=all_component_content,
|
||||
)
|
||||
|
||||
analyze_component_user_prompt = target_factor_task_information
|
||||
try:
|
||||
component_no_list = json.loads(
|
||||
APIBackend().build_messages_and_create_chat_completion(
|
||||
system_prompt=analyze_component_system_prompt,
|
||||
user_prompt=analyze_component_user_prompt,
|
||||
json_mode=True,
|
||||
),
|
||||
)["component_no_list"]
|
||||
return [all_component_nodes[index - 1] for index in sorted(list(set(component_no_list)))]
|
||||
except:
|
||||
FinCoLog.warning("Error when analyzing components.")
|
||||
analyze_component_user_prompt = "Your response is not a valid component index list."
|
||||
|
||||
return []
|
||||
|
||||
def analyze_error(
|
||||
self,
|
||||
single_feedback,
|
||||
feedback_type="execution",
|
||||
) -> list[
|
||||
UndirectedNode | str
|
||||
]: # Hardcode: Raised errors, existed error nodes + not existed error nodes(here, they are strs)
|
||||
if feedback_type == "execution":
|
||||
match = re.search(
|
||||
r'File "(?P<file>.+)", line (?P<line>\d+), in (?P<function>.+)\n\s+(?P<error_line>.+)\n(?P<error_type>\w+): (?P<error_message>.+)',
|
||||
single_feedback,
|
||||
)
|
||||
if match:
|
||||
error_details = match.groupdict()
|
||||
# last_traceback = f'File "{error_details["file"]}", line {error_details["line"]}, in {error_details["function"]}\n {error_details["error_line"]}'
|
||||
error_type = error_details["error_type"]
|
||||
error_line = error_details["error_line"]
|
||||
error_contents = [f"ErrorType: {error_type}" + "\n" + f"Error line: {error_line}"]
|
||||
else:
|
||||
error_contents = ["Undefined Error"]
|
||||
elif feedback_type == "value": # value check error
|
||||
value_check_types = r"The source dataframe and the ground truth dataframe have different rows count.|The source dataframe and the ground truth dataframe have different index.|Some values differ by more than the tolerance of 1e-6.|No sufficient correlation found when shifting up|Something wrong happens when naming the multi indices of the dataframe."
|
||||
error_contents = re.findall(value_check_types, single_feedback)
|
||||
else:
|
||||
error_contents = ["Undefined Error"]
|
||||
|
||||
all_error_nodes = self.knowledgebase.graph.get_all_nodes_by_label_list(["error"])
|
||||
if not len(all_error_nodes):
|
||||
return error_contents
|
||||
else:
|
||||
error_list = []
|
||||
for error_content in error_contents:
|
||||
for error_node in all_error_nodes:
|
||||
if error_content == error_node.content:
|
||||
error_list.append(error_node)
|
||||
else:
|
||||
error_list.append(error_content)
|
||||
if error_list[-1] in error_list[:-1]:
|
||||
error_list.pop()
|
||||
|
||||
return error_list
|
||||
|
||||
def former_trace_query(
|
||||
self,
|
||||
evo: EvolvableSubjects,
|
||||
factor_implementation_queried_graph_knowledge: FactorImplementationQueriedGraphKnowledge,
|
||||
v2_query_former_trace_limit: int = 5,
|
||||
) -> Union[QueriedKnowledge, set]:
|
||||
"""
|
||||
Query the former trace knowledge of the working trace, and find all the failed task information which tried more than fail_task_trial_limit times
|
||||
"""
|
||||
fail_task_trial_limit = FactorImplementSettings().fail_task_trial_limit
|
||||
|
||||
for target_factor_task in evo.target_factor_tasks:
|
||||
target_factor_task_information = target_factor_task.get_factor_information()
|
||||
if (
|
||||
target_factor_task_information not in self.knowledgebase.success_task_to_knowledge_dict
|
||||
and target_factor_task_information in self.knowledgebase.working_trace_knowledge
|
||||
and len(self.knowledgebase.working_trace_knowledge[target_factor_task_information])
|
||||
>= fail_task_trial_limit
|
||||
):
|
||||
factor_implementation_queried_graph_knowledge.failed_task_info_set.add(target_factor_task_information)
|
||||
|
||||
if (
|
||||
target_factor_task_information not in self.knowledgebase.success_task_to_knowledge_dict
|
||||
and target_factor_task_information
|
||||
not in factor_implementation_queried_graph_knowledge.failed_task_info_set
|
||||
and target_factor_task_information in self.knowledgebase.working_trace_knowledge
|
||||
):
|
||||
former_trace_knowledge = copy.copy(
|
||||
self.knowledgebase.working_trace_knowledge[target_factor_task_information],
|
||||
)
|
||||
# in former trace query we will delete the right trace in the following order:[..., value_generated_flag is True, value_generated_flag is False, ...]
|
||||
# because we think this order means a deterioration of the trial (like a wrong gradient descent)
|
||||
current_index = 1
|
||||
while current_index < len(former_trace_knowledge):
|
||||
if (
|
||||
not former_trace_knowledge[current_index].feedback.value_generated_flag
|
||||
and former_trace_knowledge[current_index - 1].feedback.value_generated_flag
|
||||
):
|
||||
former_trace_knowledge.pop(current_index)
|
||||
else:
|
||||
current_index += 1
|
||||
|
||||
factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information] = (
|
||||
former_trace_knowledge[-v2_query_former_trace_limit:]
|
||||
)
|
||||
else:
|
||||
factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information] = []
|
||||
|
||||
return factor_implementation_queried_graph_knowledge
|
||||
|
||||
def component_query(
|
||||
self,
|
||||
evo: EvolvableSubjects,
|
||||
factor_implementation_queried_graph_knowledge: FactorImplementationQueriedGraphKnowledge,
|
||||
v2_query_component_limit: int = 5,
|
||||
knowledge_sampler: float = 1.0,
|
||||
) -> QueriedKnowledge | None:
|
||||
# queried_component_knowledge = FactorImplementationQueriedGraphComponentKnowledge()
|
||||
for target_factor_task in evo.target_factor_tasks:
|
||||
target_factor_task_information = target_factor_task.get_factor_information()
|
||||
if (
|
||||
target_factor_task_information in self.knowledgebase.success_task_to_knowledge_dict
|
||||
or target_factor_task_information in factor_implementation_queried_graph_knowledge.failed_task_info_set
|
||||
):
|
||||
factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
] = []
|
||||
else:
|
||||
if target_factor_task_information not in self.knowledgebase.task_to_component_nodes:
|
||||
self.knowledgebase.task_to_component_nodes[target_factor_task_information] = self.analyze_component(
|
||||
target_factor_task_information,
|
||||
)
|
||||
|
||||
component_analysis_result = self.knowledgebase.task_to_component_nodes[target_factor_task_information]
|
||||
|
||||
if len(component_analysis_result) > 1:
|
||||
task_des_node_list = self.knowledgebase.graph_query_by_intersection(
|
||||
component_analysis_result,
|
||||
constraint_labels=["task_description"],
|
||||
)
|
||||
single_component_constraint = (v2_query_component_limit // len(component_analysis_result)) + 1
|
||||
else:
|
||||
task_des_node_list = []
|
||||
single_component_constraint = v2_query_component_limit
|
||||
factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
] = []
|
||||
for component_node in component_analysis_result:
|
||||
# Reverse iterate, a trade-off with intersection search
|
||||
count = 0
|
||||
for task_des_node in self.knowledgebase.graph_query_by_node(
|
||||
node=component_node,
|
||||
step=1,
|
||||
constraint_labels=["task_description"],
|
||||
block=True,
|
||||
)[::-1]:
|
||||
if task_des_node not in task_des_node_list:
|
||||
task_des_node_list.append(task_des_node)
|
||||
count += 1
|
||||
if count >= single_component_constraint:
|
||||
break
|
||||
|
||||
for node in task_des_node_list:
|
||||
for searched_node in self.knowledgebase.graph_query_by_node(
|
||||
node=node,
|
||||
step=50,
|
||||
constraint_labels=[
|
||||
"task_success_implement",
|
||||
],
|
||||
block=True,
|
||||
):
|
||||
if searched_node.label == "task_success_implement":
|
||||
target_knowledge = self.knowledgebase.node_to_implementation_knowledge_dict[
|
||||
searched_node.id
|
||||
]
|
||||
if (
|
||||
target_knowledge
|
||||
not in factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
]
|
||||
):
|
||||
factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
].append(target_knowledge)
|
||||
|
||||
# finally add embedding related knowledge
|
||||
knowledge_base_success_task_list = list(self.knowledgebase.success_task_to_knowledge_dict)
|
||||
|
||||
similarity = calculate_embedding_distance_between_str_list(
|
||||
[target_factor_task_information],
|
||||
knowledge_base_success_task_list,
|
||||
)[0]
|
||||
similar_indexes = sorted(
|
||||
range(len(similarity)),
|
||||
key=lambda i: similarity[i],
|
||||
reverse=True,
|
||||
)
|
||||
embedding_similar_successful_knowledge = [
|
||||
self.knowledgebase.success_task_to_knowledge_dict[knowledge_base_success_task_list[index]]
|
||||
for index in similar_indexes
|
||||
]
|
||||
for knowledge in embedding_similar_successful_knowledge:
|
||||
if (
|
||||
knowledge
|
||||
not in factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
]
|
||||
):
|
||||
factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
].append(knowledge)
|
||||
|
||||
if knowledge_sampler > 0:
|
||||
factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
] = [
|
||||
knowledge
|
||||
for knowledge in factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
]
|
||||
if random.uniform(0, 1) <= knowledge_sampler
|
||||
]
|
||||
|
||||
# Make sure no less than half of the knowledge are from GT
|
||||
queried_knowledge_list = factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
]
|
||||
queried_from_gt_knowledge_list = [
|
||||
knowledge
|
||||
for knowledge in queried_knowledge_list
|
||||
if knowledge.feedback is not None and knowledge.feedback.final_decision_based_on_gt == True
|
||||
]
|
||||
queried_without_gt_knowledge_list = [
|
||||
knowledge
|
||||
for knowledge in queried_knowledge_list
|
||||
if knowledge.feedback is not None and knowledge.feedback.final_decision_based_on_gt == False
|
||||
]
|
||||
queried_from_gt_knowledge_count = max(
|
||||
min(v2_query_component_limit // 2, len(queried_from_gt_knowledge_list)),
|
||||
v2_query_component_limit - len(queried_without_gt_knowledge_list),
|
||||
)
|
||||
factor_implementation_queried_graph_knowledge.component_with_success_task[
|
||||
target_factor_task_information
|
||||
] = (
|
||||
queried_from_gt_knowledge_list[:queried_from_gt_knowledge_count]
|
||||
+ queried_without_gt_knowledge_list[: v2_query_component_limit - queried_from_gt_knowledge_count]
|
||||
)
|
||||
|
||||
return factor_implementation_queried_graph_knowledge
|
||||
|
||||
def error_query(
|
||||
self,
|
||||
evo: EvolvableSubjects,
|
||||
factor_implementation_queried_graph_knowledge: FactorImplementationQueriedGraphKnowledge,
|
||||
v2_query_error_limit: int = 5,
|
||||
knowledge_sampler: float = 1.0,
|
||||
) -> QueriedKnowledge | None:
|
||||
# queried_error_knowledge = FactorImplementationQueriedGraphErrorKnowledge()
|
||||
for task_index, target_factor_task in enumerate(evo.target_factor_tasks):
|
||||
target_factor_task_information = target_factor_task.get_factor_information()
|
||||
factor_implementation_queried_graph_knowledge.error_with_success_task[target_factor_task_information] = {}
|
||||
if (
|
||||
target_factor_task_information in self.knowledgebase.success_task_to_knowledge_dict
|
||||
or target_factor_task_information in factor_implementation_queried_graph_knowledge.failed_task_info_set
|
||||
):
|
||||
factor_implementation_queried_graph_knowledge.error_with_success_task[
|
||||
target_factor_task_information
|
||||
] = []
|
||||
else:
|
||||
factor_implementation_queried_graph_knowledge.error_with_success_task[
|
||||
target_factor_task_information
|
||||
] = []
|
||||
if (
|
||||
target_factor_task_information in self.knowledgebase.working_trace_error_analysis
|
||||
and len(self.knowledgebase.working_trace_error_analysis[target_factor_task_information]) > 0
|
||||
and len(factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information])
|
||||
> 0
|
||||
):
|
||||
queried_last_trace = factor_implementation_queried_graph_knowledge.former_traces[
|
||||
target_factor_task_information
|
||||
][-1]
|
||||
target_index = self.knowledgebase.working_trace_knowledge[target_factor_task_information].index(
|
||||
queried_last_trace,
|
||||
)
|
||||
last_knowledge_error_analysis_result = self.knowledgebase.working_trace_error_analysis[
|
||||
target_factor_task_information
|
||||
][target_index]
|
||||
else:
|
||||
last_knowledge_error_analysis_result = []
|
||||
|
||||
error_nodes = []
|
||||
for error_node in last_knowledge_error_analysis_result:
|
||||
if not isinstance(error_node, UndirectedNode):
|
||||
error_node = self.knowledgebase.graph_get_node_by_content(content=error_node)
|
||||
if error_node is None:
|
||||
continue
|
||||
error_nodes.append(error_node)
|
||||
|
||||
if len(error_nodes) > 1:
|
||||
task_trace_node_list = self.knowledgebase.graph_query_by_intersection(
|
||||
error_nodes,
|
||||
constraint_labels=["task_trace"],
|
||||
output_intersection_origin=True,
|
||||
)
|
||||
single_error_constraint = (v2_query_error_limit // len(error_nodes)) + 1
|
||||
else:
|
||||
task_trace_node_list = []
|
||||
single_error_constraint = v2_query_error_limit
|
||||
for error_node in error_nodes:
|
||||
# Reverse iterate, a trade-off with intersection search
|
||||
count = 0
|
||||
for task_trace_node in self.knowledgebase.graph_query_by_node(
|
||||
node=error_node,
|
||||
step=1,
|
||||
constraint_labels=["task_trace"],
|
||||
block=True,
|
||||
)[::-1]:
|
||||
if task_trace_node not in task_trace_node_list:
|
||||
task_trace_node_list.append([[error_node], task_trace_node])
|
||||
count += 1
|
||||
if count >= single_error_constraint:
|
||||
break
|
||||
|
||||
# for error_node in last_knowledge_error_analysis_result:
|
||||
# if not isinstance(error_node, UndirectedNode):
|
||||
# error_node = self.knowledgebase.graph_get_node_by_content(content=error_node)
|
||||
# if error_node is None:
|
||||
# continue
|
||||
# for searched_node in self.knowledgebase.graph_query_by_node(
|
||||
# node=error_node,
|
||||
# step=1,
|
||||
# constraint_labels=["task_trace"],
|
||||
# block=True,
|
||||
# ):
|
||||
# if searched_node not in [node[0] for node in task_trace_node_list]:
|
||||
# task_trace_node_list.append((searched_node, error_node.content))
|
||||
|
||||
same_error_success_knowledge_pair_list = []
|
||||
same_error_success_node_set = set()
|
||||
for error_node_list, trace_node in task_trace_node_list:
|
||||
for searched_trace_success_node in self.knowledgebase.graph_query_by_node(
|
||||
node=trace_node,
|
||||
step=50,
|
||||
constraint_labels=[
|
||||
"task_trace",
|
||||
"task_success_implement",
|
||||
"task_description",
|
||||
],
|
||||
block=True,
|
||||
):
|
||||
if (
|
||||
searched_trace_success_node not in same_error_success_node_set
|
||||
and searched_trace_success_node.label == "task_success_implement"
|
||||
):
|
||||
same_error_success_node_set.add(searched_trace_success_node)
|
||||
|
||||
trace_knowledge = self.knowledgebase.node_to_implementation_knowledge_dict[trace_node.id]
|
||||
success_knowledge = self.knowledgebase.node_to_implementation_knowledge_dict[
|
||||
searched_trace_success_node.id
|
||||
]
|
||||
error_content = ""
|
||||
for index, error_node in enumerate(error_node_list):
|
||||
error_content += f"{index+1}. {error_node.content}; "
|
||||
same_error_success_knowledge_pair_list.append(
|
||||
(
|
||||
error_content,
|
||||
(trace_knowledge, success_knowledge),
|
||||
),
|
||||
)
|
||||
|
||||
if knowledge_sampler > 0:
|
||||
same_error_success_knowledge_pair_list = [
|
||||
knowledge
|
||||
for knowledge in same_error_success_knowledge_pair_list
|
||||
if random.uniform(0, 1) <= knowledge_sampler
|
||||
]
|
||||
|
||||
same_error_success_knowledge_pair_list = same_error_success_knowledge_pair_list[:v2_query_error_limit]
|
||||
factor_implementation_queried_graph_knowledge.error_with_success_task[
|
||||
target_factor_task_information
|
||||
] = same_error_success_knowledge_pair_list
|
||||
|
||||
return factor_implementation_queried_graph_knowledge
|
||||
|
||||
|
||||
class FactorImplementationGraphKnowledgeBase(KnowledgeBase):
|
||||
def __init__(self, init_component_list=None) -> None:
|
||||
"""
|
||||
Load knowledge, offer brief information of knowledge and common handle interfaces
|
||||
"""
|
||||
self.graph: UndirectedGraph = UndirectedGraph.load(Path.cwd() / "graph.pkl")
|
||||
FinCoLog().info(f"Knowledge Graph loaded, size={self.graph.size()}")
|
||||
|
||||
if init_component_list:
|
||||
for component in init_component_list:
|
||||
exist_node = self.graph.get_node_by_content(content=component)
|
||||
node = exist_node if exist_node else UndirectedNode(content=component, label="component")
|
||||
self.graph.add_nodes(node=node, neighbors=[])
|
||||
|
||||
# A dict containing all working trace until they fail or succeed
|
||||
self.working_trace_knowledge = {}
|
||||
|
||||
# A dict containing error analysis each step aligned with working trace
|
||||
self.working_trace_error_analysis = {}
|
||||
|
||||
# Add already success task
|
||||
self.success_task_to_knowledge_dict = {}
|
||||
|
||||
# key:node_id(for task trace and success implement), value:knowledge instance(aka 'FactorImplementationKnowledge')
|
||||
self.node_to_implementation_knowledge_dict = {}
|
||||
|
||||
# store the task description to component nodes
|
||||
self.task_to_component_nodes = {}
|
||||
|
||||
def get_all_nodes_by_label(self, label: str) -> list[UndirectedNode]:
|
||||
return self.graph.get_all_nodes_by_label(label)
|
||||
|
||||
def update_success_task(
|
||||
self,
|
||||
success_task_info: str,
|
||||
): # Transfer the success tasks' working trace to knowledge storage & graph
|
||||
success_task_trace = self.working_trace_knowledge[success_task_info]
|
||||
success_task_error_analysis_record = (
|
||||
self.working_trace_error_analysis[success_task_info]
|
||||
if success_task_info in self.working_trace_error_analysis
|
||||
else []
|
||||
)
|
||||
task_des_node = UndirectedNode(content=success_task_info, label="task_description")
|
||||
self.graph.add_nodes(
|
||||
node=task_des_node,
|
||||
neighbors=self.task_to_component_nodes[success_task_info],
|
||||
) # 1st version, we assume that all component nodes are given
|
||||
for index, trace_unit in enumerate(success_task_trace): # every unit: single_knowledge
|
||||
neighbor_nodes = [task_des_node]
|
||||
if index != len(success_task_trace) - 1:
|
||||
trace_node = UndirectedNode(
|
||||
content=trace_unit.get_implementation_and_feedback_str(),
|
||||
label="task_trace",
|
||||
)
|
||||
self.node_to_implementation_knowledge_dict[trace_node.id] = trace_unit
|
||||
for node_index, error_node in enumerate(success_task_error_analysis_record[index]):
|
||||
if type(error_node).__name__ == "str":
|
||||
queried_node = self.graph.get_node_by_content(content=error_node)
|
||||
if queried_node is None:
|
||||
new_error_node = UndirectedNode(content=error_node, label="error")
|
||||
self.graph.add_node(node=new_error_node)
|
||||
success_task_error_analysis_record[index][node_index] = new_error_node
|
||||
else:
|
||||
success_task_error_analysis_record[index][node_index] = queried_node
|
||||
neighbor_nodes.extend(success_task_error_analysis_record[index])
|
||||
self.graph.add_nodes(node=trace_node, neighbors=neighbor_nodes)
|
||||
else:
|
||||
success_node = UndirectedNode(
|
||||
content=trace_unit.get_implementation_and_feedback_str(),
|
||||
label="task_success_implement",
|
||||
)
|
||||
self.graph.add_nodes(node=success_node, neighbors=neighbor_nodes)
|
||||
self.node_to_implementation_knowledge_dict[success_node.id] = trace_unit
|
||||
|
||||
def query(self):
|
||||
pass
|
||||
|
||||
def graph_get_node_by_content(self, content: str) -> UndirectedNode:
|
||||
return self.graph.get_node_by_content(content=content)
|
||||
|
||||
def graph_query_by_content(
|
||||
self,
|
||||
content: Union[str, list[str]],
|
||||
topk_k: int = 5,
|
||||
step: int = 1,
|
||||
constraint_labels: list[str] = None,
|
||||
constraint_node: UndirectedNode = None,
|
||||
similarity_threshold: float = 0.0,
|
||||
constraint_distance: float = 0,
|
||||
block: bool = False,
|
||||
) -> list[UndirectedNode]:
|
||||
"""
|
||||
search graph by content similarity and connection relationship, return empty list if nodes' chain without node
|
||||
near to constraint_node
|
||||
|
||||
Parameters
|
||||
----------
|
||||
constraint_distance
|
||||
content
|
||||
topk_k: the upper number of output for each query, if the number of fit nodes is less than topk_k, return all fit nodes's content
|
||||
step
|
||||
constraint_labels
|
||||
constraint_node
|
||||
similarity_threshold
|
||||
block: despite the start node, the search can only flow through the constraint_label type nodes
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
"""
|
||||
|
||||
return self.graph.query_by_content(
|
||||
content=content,
|
||||
topk_k=topk_k,
|
||||
step=step,
|
||||
constraint_labels=constraint_labels,
|
||||
constraint_node=constraint_node,
|
||||
similarity_threshold=similarity_threshold,
|
||||
constraint_distance=constraint_distance,
|
||||
block=block,
|
||||
)
|
||||
|
||||
def graph_query_by_node(
|
||||
self,
|
||||
node: UndirectedNode,
|
||||
step: int = 1,
|
||||
constraint_labels: list[str] = None,
|
||||
constraint_node: UndirectedNode = None,
|
||||
constraint_distance: float = 0,
|
||||
block: bool = False,
|
||||
) -> list[UndirectedNode]:
|
||||
"""
|
||||
search graph by connection, return empty list if nodes' chain without node near to constraint_node
|
||||
Parameters
|
||||
----------
|
||||
node : start node
|
||||
step : the max steps will be searched
|
||||
constraint_labels : the labels of output nodes
|
||||
constraint_node : the node that the output nodes must connect to
|
||||
constraint_distance : the max distance between output nodes and constraint_node
|
||||
block: despite the start node, the search can only flow through the constraint_label type nodes
|
||||
|
||||
Returns
|
||||
-------
|
||||
A list of nodes
|
||||
|
||||
"""
|
||||
nodes = self.graph.query_by_node(
|
||||
node=node,
|
||||
step=step,
|
||||
constraint_labels=constraint_labels,
|
||||
constraint_node=constraint_node,
|
||||
constraint_distance=constraint_distance,
|
||||
block=block,
|
||||
)
|
||||
return nodes
|
||||
|
||||
def graph_query_by_intersection(
|
||||
self,
|
||||
nodes: list[UndirectedNode],
|
||||
steps: int = 1,
|
||||
constraint_labels: list[str] = None,
|
||||
output_intersection_origin: bool = False,
|
||||
) -> list[UndirectedNode] | list[list[list[UndirectedNode], UndirectedNode]]:
|
||||
"""
|
||||
search graph by node intersection, node intersected by a higher frequency has a prior order in the list
|
||||
Parameters
|
||||
----------
|
||||
nodes : node list
|
||||
step : the max steps will be searched
|
||||
constraint_labels : the labels of output nodes
|
||||
output_intersection_origin: output the list that contains the node which form this intersection node
|
||||
|
||||
Returns
|
||||
-------
|
||||
A list of nodes
|
||||
|
||||
"""
|
||||
node_count = len(nodes)
|
||||
assert node_count >= 2, "nodes length must >=2"
|
||||
intersection_node_list = []
|
||||
if output_intersection_origin:
|
||||
origin_list = []
|
||||
for k in range(node_count, 1, -1):
|
||||
possible_combinations = combinations(nodes, k)
|
||||
for possible_combination in possible_combinations:
|
||||
node_list = list(possible_combination)
|
||||
intersection_node_list.extend(
|
||||
self.graph.get_nodes_intersection(node_list, steps=steps, constraint_labels=constraint_labels)
|
||||
)
|
||||
if output_intersection_origin:
|
||||
for _ in range(len(intersection_node_list)):
|
||||
origin_list.append(node_list)
|
||||
intersection_node_list_sort_by_freq = []
|
||||
for index, node in enumerate(intersection_node_list):
|
||||
if node not in intersection_node_list_sort_by_freq:
|
||||
if output_intersection_origin:
|
||||
intersection_node_list_sort_by_freq.append([origin_list[index], node])
|
||||
else:
|
||||
intersection_node_list_sort_by_freq.append(node)
|
||||
|
||||
return intersection_node_list_sort_by_freq
|
||||
@@ -0,0 +1,42 @@
|
||||
from pathlib import Path
|
||||
|
||||
from finco.conf import FincoSettings
|
||||
|
||||
|
||||
class FactorImplementSettings(FincoSettings):
|
||||
file_based_execution_data_folder: str = str(
|
||||
(Path().cwd() / "git_ignore_folder" / "factor_implementation_source_data").absolute(),
|
||||
)
|
||||
file_based_execution_workspace: str = str(
|
||||
(Path().cwd() / "git_ignore_folder" / "factor_implementation_workspace").absolute(),
|
||||
)
|
||||
implementation_execution_cache_location: str = str(
|
||||
(Path().cwd() / "git_ignore_folder" / "factor_implementation_execution_cache.pkl").absolute(),
|
||||
)
|
||||
enable_execution_cache: bool = True # whether to enable the execution cache
|
||||
|
||||
# TODO: the factor implement specific settings should not appear in this settings
|
||||
# Evolving should have a method specific settings
|
||||
# evolving related config
|
||||
fail_task_trial_limit: int = 20
|
||||
|
||||
v1_query_former_trace_limit: int = 5
|
||||
v1_query_similar_success_limit: int = 5
|
||||
|
||||
v2_query_component_limit: int = 1
|
||||
v2_query_error_limit: int = 1
|
||||
v2_query_former_trace_limit: int = 1
|
||||
v2_error_summary: bool = False
|
||||
v2_knowledge_sampler: float = 1.0
|
||||
|
||||
chat_token_limit: int = (
|
||||
100000 # 100000 is the maximum limit of gpt4, which might increase in the future version of gpt
|
||||
)
|
||||
|
||||
implementation_factors_per_round: int = 100 # how many factors to choose for each round of evolving
|
||||
evo_multi_proc_n: int = 16 # how many processes to use for evolving (including eval & generation)
|
||||
|
||||
file_based_execution_timeout: int = 120 # seconds for each factor implementation execution
|
||||
|
||||
|
||||
FIS = FactorImplementSettings()
|
||||
@@ -0,0 +1,535 @@
|
||||
import json
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Tuple
|
||||
|
||||
import pandas as pd
|
||||
from jinja2 import Template
|
||||
|
||||
from oai.llm_utils import APIBackend
|
||||
from finco.log import FinCoLog
|
||||
from factor_implementation.share_modules.conf import FactorImplementSettings
|
||||
from factor_implementation.share_modules.factor import (
|
||||
FactorImplementation,
|
||||
FactorImplementationTask,
|
||||
)
|
||||
from factor_implementation.share_modules.prompt import (
|
||||
FactorImplementationPrompts,
|
||||
)
|
||||
|
||||
|
||||
class Evaluator(ABC):
|
||||
@abstractmethod
|
||||
def evaluate(
|
||||
self,
|
||||
target_task: FactorImplementationTask,
|
||||
implementation: FactorImplementation,
|
||||
gt_implementation: FactorImplementation,
|
||||
**kwargs,
|
||||
):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class FactorImplementationCodeEvaluator(Evaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
target_task: FactorImplementationTask,
|
||||
implementation: FactorImplementation,
|
||||
execution_feedback: str,
|
||||
factor_value_feedback: str = "",
|
||||
gt_implementation: FactorImplementation = None,
|
||||
**kwargs,
|
||||
):
|
||||
factor_information = target_task.get_factor_information()
|
||||
code = implementation.code
|
||||
|
||||
system_prompt = FactorImplementationPrompts()["evaluator_code_feedback_v1_system"]
|
||||
|
||||
execution_feedback_to_render = execution_feedback
|
||||
user_prompt = Template(
|
||||
FactorImplementationPrompts()["evaluator_code_feedback_v1_user"],
|
||||
).render(
|
||||
factor_information=factor_information,
|
||||
code=code,
|
||||
execution_feedback=execution_feedback_to_render,
|
||||
factor_value_feedback=factor_value_feedback,
|
||||
gt_code=gt_implementation.code if gt_implementation else None,
|
||||
)
|
||||
while (
|
||||
APIBackend().build_messages_and_calculate_token(
|
||||
user_prompt=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
former_messages=[],
|
||||
)
|
||||
> FactorImplementSettings().chat_token_limit
|
||||
):
|
||||
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
|
||||
user_prompt = Template(
|
||||
FactorImplementationPrompts()["evaluator_code_feedback_v1_user"],
|
||||
).render(
|
||||
factor_information=factor_information,
|
||||
code=code,
|
||||
execution_feedback=execution_feedback_to_render,
|
||||
factor_value_feedback=factor_value_feedback,
|
||||
gt_code=gt_implementation.code if gt_implementation else None,
|
||||
)
|
||||
critic_response = APIBackend().build_messages_and_create_chat_completion(
|
||||
user_prompt=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
json_mode=False,
|
||||
)
|
||||
|
||||
# critic_response = json.loads(critic_response)
|
||||
return critic_response
|
||||
|
||||
|
||||
class FactorImplementationEvaluator(Evaluator):
|
||||
# TODO:
|
||||
# I think we should have unified interface for all evaluates, for examples.
|
||||
# So we should adjust the interface of other factors
|
||||
@abstractmethod
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
"""You can get the dataframe by
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
_, gt_df = gt.execute()
|
||||
_, gen_df = gen.execute()
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[str, object]
|
||||
- str: the text-based description of the evaluation result
|
||||
- object: a comparable metric (bool, integer, float ...)
|
||||
|
||||
"""
|
||||
raise NotImplementedError("Please implement the `evaluator` method")
|
||||
|
||||
def _get_df(self, gt: FactorImplementation, gen: FactorImplementation):
|
||||
_, gt_df = gt.execute()
|
||||
_, gen_df = gen.execute()
|
||||
if isinstance(gen_df, pd.Series):
|
||||
gen_df = gen_df.to_frame("source_factor")
|
||||
if isinstance(gt_df, pd.Series):
|
||||
gt_df = gt_df.to_frame("gt_factor")
|
||||
return gt_df, gen_df
|
||||
|
||||
|
||||
# NOTE: the following evaluators are splited from FactorImplementationValueEvaluator
|
||||
|
||||
|
||||
class FactorImplementationSingleColumnEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
|
||||
if len(gen_df.columns) == 1 and len(gt_df.columns) == 1:
|
||||
return "Both dataframes have only one column.", True
|
||||
elif len(gen_df.columns) != 1:
|
||||
gen_df = gen_df.iloc(axis=1)[
|
||||
[
|
||||
0,
|
||||
]
|
||||
]
|
||||
return (
|
||||
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.",
|
||||
False,
|
||||
)
|
||||
return "", False
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationIndexFormatEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
idx_name_right = gen_df.index.names == ("datetime", "instrument")
|
||||
if idx_name_right:
|
||||
return (
|
||||
'The index of the dataframe is ("datetime", "instrument") and align with the predefined format.',
|
||||
True,
|
||||
)
|
||||
else:
|
||||
return (
|
||||
'The index of the dataframe is not ("datetime", "instrument"). Please check the implementation.',
|
||||
False,
|
||||
)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationRowCountEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
|
||||
if gen_df.shape[0] == gt_df.shape[0]:
|
||||
return "Both dataframes have the same rows count.", True
|
||||
else:
|
||||
return (
|
||||
f"The source dataframe and the ground truth dataframe have different rows count. The source dataframe has {gen_df.shape[0]} rows, while the ground truth dataframe has {gt_df.shape[0]} rows. Please check the implementation.",
|
||||
False,
|
||||
)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationIndexEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
|
||||
if gen_df.index.equals(gt_df.index):
|
||||
return "Both dataframes have the same index.", True
|
||||
else:
|
||||
return (
|
||||
"The source dataframe and the ground truth dataframe have different index. Please check the implementation.",
|
||||
False,
|
||||
)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationMissingValuesEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
|
||||
if gen_df.isna().sum().sum() == gt_df.isna().sum().sum():
|
||||
return "Both dataframes have the same missing values.", True
|
||||
else:
|
||||
return (
|
||||
f"The dataframes do not have the same missing values. The source dataframe has {gen_df.isna().sum().sum()} missing values, while the ground truth dataframe has {gt_df.isna().sum().sum()} missing values. Please check the implementation.",
|
||||
False,
|
||||
)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationValuesEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
|
||||
try:
|
||||
close_values = gen_df.sub(gt_df).abs().lt(1e-6)
|
||||
result_int = close_values.astype(int)
|
||||
pos_num = result_int.sum().sum()
|
||||
acc_rate = pos_num / close_values.size
|
||||
except:
|
||||
close_values = gen_df
|
||||
if close_values.all().iloc[0]:
|
||||
return (
|
||||
"All values in the dataframes are equal within the tolerance of 1e-6.",
|
||||
acc_rate,
|
||||
)
|
||||
else:
|
||||
return (
|
||||
"Some values differ by more than the tolerance of 1e-6. Check for rounding errors or differences in the calculation methods.",
|
||||
acc_rate,
|
||||
)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationCorrelationEvaluator(FactorImplementationEvaluator):
|
||||
def __init__(self, hard_check: bool) -> None:
|
||||
self.hard_check = hard_check
|
||||
|
||||
def evaluate(
|
||||
self,
|
||||
gt: FactorImplementation,
|
||||
gen: FactorImplementation,
|
||||
) -> Tuple[str, object]:
|
||||
gt_df, gen_df = self._get_df(gt, gen)
|
||||
|
||||
concat_df = pd.concat([gen_df, gt_df], axis=1)
|
||||
concat_df.columns = ["source", "gt"]
|
||||
ic = concat_df.groupby("datetime").apply(lambda df: df["source"].corr(df["gt"])).dropna().mean()
|
||||
ric = (
|
||||
concat_df.groupby("datetime")
|
||||
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
|
||||
.dropna()
|
||||
.mean()
|
||||
)
|
||||
|
||||
if self.hard_check:
|
||||
if ic > 0.99 and ric > 0.99:
|
||||
return (
|
||||
f"The dataframes are highly correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}.",
|
||||
True,
|
||||
)
|
||||
else:
|
||||
return (
|
||||
f"The dataframes are not sufficiently high correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent.",
|
||||
False,
|
||||
)
|
||||
else:
|
||||
return f"The ic is ({ic:.6f}) and the rankic is ({ric:.6f}).", ic
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationValEvaluator(FactorImplementationEvaluator):
|
||||
def evaluate(self, gt: FactorImplementation, gen: FactorImplementation):
|
||||
_, gt_df = gt.execute()
|
||||
_, gen_df = gen.execute()
|
||||
# FIXME: refactor the two classes
|
||||
fiv = FactorImplementationValueEvaluator()
|
||||
return fiv.evaluate(source_df=gen_df, gt_df=gt_df)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__class__.__name__
|
||||
|
||||
|
||||
class FactorImplementationValueEvaluator(Evaluator):
|
||||
# TODO: let's discuss the about the interface of the evaluator
|
||||
def evaluate(
|
||||
self,
|
||||
source_df: pd.DataFrame,
|
||||
gt_df: pd.DataFrame,
|
||||
**kwargs,
|
||||
) -> Tuple:
|
||||
conclusions = []
|
||||
|
||||
if isinstance(source_df, pd.Series):
|
||||
source_df = source_df.to_frame("source_factor")
|
||||
conclusions.append(
|
||||
"The source dataframe is a series, better convert it to a dataframe.",
|
||||
)
|
||||
if gt_df is not None and isinstance(gt_df, pd.Series):
|
||||
gt_df = gt_df.to_frame("gt_factor")
|
||||
conclusions.append(
|
||||
"The ground truth dataframe is a series, convert it to a dataframe.",
|
||||
)
|
||||
|
||||
# Check if both dataframe has only one columns
|
||||
if len(source_df.columns) == 1:
|
||||
conclusions.append("The source dataframe has only one column which is correct.")
|
||||
else:
|
||||
conclusions.append(
|
||||
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.",
|
||||
)
|
||||
source_df = source_df.iloc(axis=1)[
|
||||
[
|
||||
0,
|
||||
]
|
||||
]
|
||||
|
||||
if list(source_df.index.names) != ["datetime", "instrument"]:
|
||||
conclusions.append(
|
||||
rf"The index of the dataframe is not (\"datetime\", \"instrument\"), instead is {source_df.index.names}. Please check the implementation.",
|
||||
)
|
||||
else:
|
||||
conclusions.append(
|
||||
'The index of the dataframe is ("datetime", "instrument") and align with the predefined format.',
|
||||
)
|
||||
|
||||
# Check if both dataframe have the same rows count
|
||||
if gt_df is not None:
|
||||
if source_df.shape[0] == gt_df.shape[0]:
|
||||
conclusions.append("Both dataframes have the same rows count.")
|
||||
same_row_count_result = True
|
||||
else:
|
||||
conclusions.append(
|
||||
f"The source dataframe and the ground truth dataframe have different rows count. The source dataframe has {source_df.shape[0]} rows, while the ground truth dataframe has {gt_df.shape[0]} rows. Please check the implementation.",
|
||||
)
|
||||
same_row_count_result = False
|
||||
|
||||
# Check whether both dataframe has the same index
|
||||
if source_df.index.equals(gt_df.index):
|
||||
conclusions.append("Both dataframes have the same index.")
|
||||
same_index_result = True
|
||||
else:
|
||||
conclusions.append(
|
||||
"The source dataframe and the ground truth dataframe have different index. Please check the implementation.",
|
||||
)
|
||||
same_index_result = False
|
||||
|
||||
# Check for the same missing values (NaN)
|
||||
if source_df.isna().sum().sum() == gt_df.isna().sum().sum():
|
||||
conclusions.append("Both dataframes have the same missing values.")
|
||||
same_missing_values_result = True
|
||||
else:
|
||||
conclusions.append(
|
||||
f"The dataframes do not have the same missing values. The source dataframe has {source_df.isna().sum().sum()} missing values, while the ground truth dataframe has {gt_df.isna().sum().sum()} missing values. Please check the implementation.",
|
||||
)
|
||||
same_missing_values_result = False
|
||||
|
||||
# Check if the values are the same within a small tolerance
|
||||
if not same_index_result:
|
||||
conclusions.append(
|
||||
"The source dataframe and the ground truth dataframe have different index. Give up comparing the values and correlation because it's useless",
|
||||
)
|
||||
same_values_result = False
|
||||
high_correlation_result = False
|
||||
else:
|
||||
close_values = source_df.sub(gt_df).abs().lt(1e-6)
|
||||
if close_values.all().iloc[0]:
|
||||
conclusions.append(
|
||||
"All values in the dataframes are equal within the tolerance of 1e-6.",
|
||||
)
|
||||
same_values_result = True
|
||||
else:
|
||||
conclusions.append(
|
||||
"Some values differ by more than the tolerance of 1e-6. Check for rounding errors or differences in the calculation methods.",
|
||||
)
|
||||
same_values_result = False
|
||||
|
||||
# Check the ic and rankic between the two dataframes
|
||||
concat_df = pd.concat([source_df, gt_df], axis=1)
|
||||
concat_df.columns = ["source", "gt"]
|
||||
try:
|
||||
ic = concat_df.groupby("datetime").apply(lambda df: df["source"].corr(df["gt"])).dropna().mean()
|
||||
ric = (
|
||||
concat_df.groupby("datetime")
|
||||
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
|
||||
.dropna()
|
||||
.mean()
|
||||
)
|
||||
|
||||
if ic > 0.99 and ric > 0.99:
|
||||
conclusions.append(
|
||||
f"The dataframes are highly correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}.",
|
||||
)
|
||||
high_correlation_result = True
|
||||
else:
|
||||
conclusions.append(
|
||||
f"The dataframes are not sufficiently high correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent.",
|
||||
)
|
||||
high_correlation_result = False
|
||||
|
||||
# Check for shifted alignments only in the "datetime" index
|
||||
max_shift_days = 2
|
||||
for shift in range(-max_shift_days, max_shift_days + 1):
|
||||
if shift == 0:
|
||||
continue # Skip the case where there is no shift
|
||||
|
||||
shifted_source_df = source_df.groupby(level="instrument").shift(shift)
|
||||
concat_df = pd.concat([shifted_source_df, gt_df], axis=1)
|
||||
concat_df.columns = ["source", "gt"]
|
||||
shifted_ric = (
|
||||
concat_df.groupby("datetime")
|
||||
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
|
||||
.dropna()
|
||||
.mean()
|
||||
)
|
||||
if shifted_ric > 0.99:
|
||||
conclusions.append(
|
||||
f"The dataframes are highly correlated with a shift of {max_shift_days} days in the 'date' index. Shifted rankic: {shifted_ric:.6f}.",
|
||||
)
|
||||
break
|
||||
else:
|
||||
conclusions.append(
|
||||
f"No sufficient correlation found when shifting up to {max_shift_days} days in the 'date' index. Investigate the factors that might be causing discrepancies.",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
FinCoLog().warning(f"Error occurred when calculating the correlation: {str(e)}")
|
||||
conclusions.append(
|
||||
f"Some error occurred when calculating the correlation. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent. Error: {e}",
|
||||
)
|
||||
high_correlation_result = False
|
||||
|
||||
# Combine all conclusions into a single string
|
||||
conclusion_str = "\n".join(conclusions)
|
||||
|
||||
final_result = (same_values_result or high_correlation_result) if gt_df is not None else False
|
||||
return conclusion_str, final_result
|
||||
|
||||
|
||||
# TODO:
|
||||
def shorten_prompt(tpl: str, render_kwargs: dict, shorten_key: str, max_trail: int = 10) -> str:
|
||||
"""When the prompt is too long. We have to shorten it.
|
||||
But we should not truncate the prompt directly, so we should find the key we want to shorten and then shorten it.
|
||||
"""
|
||||
# TODO: this should replace most of code in
|
||||
# - FactorImplementationFinalDecisionEvaluator.evaluate
|
||||
# - FactorImplementationCodeEvaluator.evaluate
|
||||
|
||||
|
||||
class FactorImplementationFinalDecisionEvaluator(Evaluator):
|
||||
def evaluate(
|
||||
self,
|
||||
target_task: FactorImplementationTask,
|
||||
execution_feedback: str,
|
||||
value_feedback: str,
|
||||
code_feedback: str,
|
||||
**kwargs,
|
||||
) -> Tuple:
|
||||
system_prompt = FactorImplementationPrompts()["evaluator_final_decision_v1_system"]
|
||||
execution_feedback_to_render = execution_feedback
|
||||
user_prompt = Template(
|
||||
FactorImplementationPrompts()["evaluator_final_decision_v1_user"],
|
||||
).render(
|
||||
factor_information=target_task.get_factor_information(),
|
||||
execution_feedback=execution_feedback_to_render,
|
||||
code_feedback=code_feedback,
|
||||
factor_value_feedback=(
|
||||
value_feedback
|
||||
if value_feedback is not None
|
||||
else "No Ground Truth Value provided, so no evaluation on value is performed."
|
||||
),
|
||||
)
|
||||
while (
|
||||
APIBackend().build_messages_and_calculate_token(
|
||||
user_prompt=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
former_messages=[],
|
||||
)
|
||||
> FactorImplementSettings().chat_token_limit
|
||||
):
|
||||
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
|
||||
user_prompt = Template(
|
||||
FactorImplementationPrompts()["evaluator_final_decision_v1_user"],
|
||||
).render(
|
||||
factor_information=target_task.get_factor_information(),
|
||||
execution_feedback=execution_feedback_to_render,
|
||||
code_feedback=code_feedback,
|
||||
factor_value_feedback=(
|
||||
value_feedback
|
||||
if value_feedback is not None
|
||||
else "No Ground Truth Value provided, so no evaluation on value is performed."
|
||||
),
|
||||
)
|
||||
|
||||
final_evaluation_dict = json.loads(
|
||||
APIBackend().build_messages_and_create_chat_completion(
|
||||
user_prompt=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
json_mode=True,
|
||||
),
|
||||
)
|
||||
return (
|
||||
final_evaluation_dict["final_decision"],
|
||||
final_evaluation_dict["final_feedback"],
|
||||
)
|
||||
@@ -0,0 +1,26 @@
|
||||
class ImplementRunException(Exception):
|
||||
"""
|
||||
Exceptions raised when Implementing and running code.
|
||||
- start: FactorImplementationTask => FactorGenerator
|
||||
- end: Get dataframe after execution
|
||||
|
||||
The more detailed evaluation in dataframe values are managed by the evaluator.
|
||||
"""
|
||||
|
||||
|
||||
class CodeFormatException(ImplementRunException):
|
||||
"""
|
||||
The generated code is not found due format error.
|
||||
"""
|
||||
|
||||
|
||||
class RuntimeErrorException(ImplementRunException):
|
||||
"""
|
||||
The generated code fail to execute the script.
|
||||
"""
|
||||
|
||||
|
||||
class NoOutputException(ImplementRunException):
|
||||
"""
|
||||
The code fail to generate output file.
|
||||
"""
|
||||
@@ -0,0 +1,221 @@
|
||||
import pickle
|
||||
import subprocess
|
||||
import uuid
|
||||
from abc import ABC, abstractmethod
|
||||
from pathlib import Path
|
||||
from typing import Tuple, Union
|
||||
|
||||
import pandas as pd
|
||||
from filelock import FileLock
|
||||
|
||||
from oai.llm_utils import md5_hash
|
||||
from finco.log import FinCoLog
|
||||
from factor_implementation.share_modules.conf import FactorImplementSettings
|
||||
from factor_implementation.share_modules.exception import (
|
||||
CodeFormatException,
|
||||
NoOutputException,
|
||||
RuntimeErrorException,
|
||||
)
|
||||
|
||||
|
||||
class FactorImplementationTask:
|
||||
# TODO: remove the factor_ prefix may be better
|
||||
def __init__(
|
||||
self,
|
||||
factor_name,
|
||||
factor_description,
|
||||
factor_formulation,
|
||||
factor_formulation_description,
|
||||
variables: dict = {},
|
||||
) -> None:
|
||||
self.factor_name = factor_name
|
||||
self.factor_description = factor_description
|
||||
self.factor_formulation = factor_formulation
|
||||
self.factor_formulation_description = factor_formulation_description
|
||||
# TODO: check variables a good candidate
|
||||
self.variables = variables
|
||||
|
||||
def get_factor_information(self):
|
||||
return f"""factor_name: {self.factor_name}
|
||||
factor_description: {self.factor_description}
|
||||
factor_formulation: {self.factor_formulation}
|
||||
factor_formulation_description: {self.factor_formulation_description}"""
|
||||
|
||||
@staticmethod
|
||||
def from_dict(dict):
|
||||
return FactorImplementationTask(**dict)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"<{self.__class__.__name__}[{self.factor_name}]>"
|
||||
|
||||
|
||||
class FactorImplementation(ABC):
|
||||
def __init__(self, target_task: FactorImplementationTask) -> None:
|
||||
self.target_task = target_task
|
||||
|
||||
@abstractmethod
|
||||
def execute(self, *args, **kwargs) -> Tuple[str, pd.DataFrame]:
|
||||
raise NotImplementedError("__call__ method is not implemented.")
|
||||
|
||||
|
||||
class FileBasedFactorImplementation(FactorImplementation):
|
||||
"""
|
||||
This class is used to implement a factor by writing the code to a file.
|
||||
Input data and output factor value are also written to files.
|
||||
"""
|
||||
|
||||
# TODO: (Xiao) think raising errors may get better information for processing
|
||||
FB_FROM_CACHE = "The factor value has been executed and stored in the instance variable."
|
||||
FB_EXEC_SUCCESS = "Execution succeeded without error."
|
||||
FB_CODE_NOT_SET = "code is not set."
|
||||
FB_EXECUTION_SUCCEEDED = "Execution succeeded without error."
|
||||
FB_OUTPUT_FILE_NOT_FOUND = "\nExpected output file not found."
|
||||
FB_OUTPUT_FILE_FOUND = "\nExpected output file found."
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
target_task: FactorImplementationTask,
|
||||
code,
|
||||
executed_factor_value_dataframe=None,
|
||||
raise_exception=False,
|
||||
) -> None:
|
||||
super().__init__(target_task)
|
||||
self.code = code
|
||||
self.executed_factor_value_dataframe = executed_factor_value_dataframe
|
||||
self.logger = FinCoLog()
|
||||
self.raise_exception = raise_exception
|
||||
self.workspace_path = Path(
|
||||
FactorImplementSettings().file_based_execution_workspace,
|
||||
) / str(uuid.uuid4())
|
||||
|
||||
@staticmethod
|
||||
def link_data_to_workspace(data_path: Path, workspace_path: Path):
|
||||
data_path = Path(data_path)
|
||||
workspace_path = Path(workspace_path)
|
||||
for data_file_path in data_path.iterdir():
|
||||
workspace_data_file_path = workspace_path / data_file_path.name
|
||||
if workspace_data_file_path.exists():
|
||||
workspace_data_file_path.unlink()
|
||||
subprocess.run(
|
||||
["ln", "-s", data_file_path, workspace_data_file_path],
|
||||
check=False,
|
||||
)
|
||||
|
||||
def execute(self, store_result: bool = False) -> Tuple[str, pd.DataFrame]:
|
||||
"""
|
||||
execute the implementation and get the factor value by the following steps:
|
||||
1. make the directory in workspace path
|
||||
2. write the code to the file in the workspace path
|
||||
3. link all the source data to the workspace path folder
|
||||
4. execute the code
|
||||
5. read the factor value from the output file in the workspace path folder
|
||||
returns the execution feedback as a string and the factor value as a pandas dataframe
|
||||
|
||||
parameters:
|
||||
store_result: if True, store the factor value in the instance variable, this feature is to be used in the gt implementation to avoid multiple execution on the same gt implementation
|
||||
"""
|
||||
if self.code is None:
|
||||
if self.raise_exception:
|
||||
raise CodeFormatException(self.FB_CODE_NOT_SET)
|
||||
else:
|
||||
# TODO: to make the interface compatible with previous code. I kept the original behavior.
|
||||
raise ValueError(self.FB_CODE_NOT_SET)
|
||||
with FileLock(self.workspace_path / "execution.lock"):
|
||||
(Path.cwd() / "git_ignore_folder" / "factor_implementation_execution_cache").mkdir(
|
||||
exist_ok=True, parents=True
|
||||
)
|
||||
if FactorImplementSettings().enable_execution_cache:
|
||||
# NOTE: cache the result for the same code
|
||||
target_file_name = md5_hash(self.code)
|
||||
cache_file_path = (
|
||||
Path.cwd()
|
||||
/ "git_ignore_folder"
|
||||
/ "factor_implementation_execution_cache"
|
||||
/ f"{target_file_name}.pkl"
|
||||
)
|
||||
if cache_file_path.exists() and not self.raise_exception:
|
||||
cached_res = pickle.load(open(cache_file_path, "rb"))
|
||||
if store_result and cached_res[1] is not None:
|
||||
self.executed_factor_value_dataframe = cached_res[1]
|
||||
return cached_res
|
||||
|
||||
if self.executed_factor_value_dataframe is not None:
|
||||
return self.FB_FROM_CACHE, self.executed_factor_value_dataframe
|
||||
|
||||
source_data_path = Path(
|
||||
FactorImplementSettings().file_based_execution_data_folder,
|
||||
)
|
||||
self.workspace_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
code_path = self.workspace_path / f"{self.target_task.factor_name}.py"
|
||||
code_path.write_text(self.code)
|
||||
|
||||
self.link_data_to_workspace(source_data_path, self.workspace_path)
|
||||
|
||||
execution_feedback = self.FB_EXECUTION_SUCCEEDED
|
||||
try:
|
||||
subprocess.check_output(
|
||||
f"python {code_path}",
|
||||
shell=True,
|
||||
cwd=self.workspace_path,
|
||||
stderr=subprocess.STDOUT,
|
||||
timeout=FactorImplementSettings().file_based_execution_timeout,
|
||||
)
|
||||
except subprocess.CalledProcessError as e:
|
||||
import site
|
||||
|
||||
execution_feedback = (
|
||||
e.output.decode()
|
||||
.replace(str(code_path.parent.absolute()), r"/path/to")
|
||||
.replace(str(site.getsitepackages()[0]), r"/path/to/site-packages")
|
||||
)
|
||||
if len(execution_feedback) > 2000:
|
||||
execution_feedback = (
|
||||
execution_feedback[:1000] + "....hidden long error message...." + execution_feedback[-1000:]
|
||||
)
|
||||
if self.raise_exception:
|
||||
raise RuntimeErrorException(execution_feedback)
|
||||
except subprocess.TimeoutExpired:
|
||||
execution_feedback += f"Execution timeout error and the timeout is set to {FactorImplementSettings().file_based_execution_timeout} seconds."
|
||||
if self.raise_exception:
|
||||
raise RuntimeErrorException(execution_feedback)
|
||||
|
||||
workspace_output_file_path = self.workspace_path / "result.h5"
|
||||
if not workspace_output_file_path.exists():
|
||||
execution_feedback += self.FB_OUTPUT_FILE_NOT_FOUND
|
||||
executed_factor_value_dataframe = None
|
||||
if self.raise_exception:
|
||||
raise NoOutputException(execution_feedback)
|
||||
else:
|
||||
try:
|
||||
executed_factor_value_dataframe = pd.read_hdf(workspace_output_file_path)
|
||||
execution_feedback += self.FB_OUTPUT_FILE_FOUND
|
||||
except Exception as e:
|
||||
execution_feedback += f"Error found when reading hdf file: {e}"[:1000]
|
||||
executed_factor_value_dataframe = None
|
||||
|
||||
if store_result and executed_factor_value_dataframe is not None:
|
||||
self.executed_factor_value_dataframe = executed_factor_value_dataframe
|
||||
|
||||
if FactorImplementSettings().enable_execution_cache:
|
||||
pickle.dump(
|
||||
(execution_feedback, executed_factor_value_dataframe),
|
||||
open(cache_file_path, "wb"),
|
||||
)
|
||||
return execution_feedback, executed_factor_value_dataframe
|
||||
|
||||
def __str__(self) -> str:
|
||||
# NOTE:
|
||||
# If the code cache works, the workspace will be None.
|
||||
return f"File Factor[{self.target_task.factor_name}]: {self.workspace_path}"
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return self.__str__()
|
||||
|
||||
@staticmethod
|
||||
def from_folder(task: FactorImplementationTask, path: Union[str, Path], **kwargs):
|
||||
path = Path(path)
|
||||
factor_path = (path / task.factor_name).with_suffix(".py")
|
||||
with factor_path.open("r") as f:
|
||||
code = f.read()
|
||||
return FileBasedFactorImplementation(task, code=code, **kwargs)
|
||||
@@ -0,0 +1,31 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List
|
||||
|
||||
from factor_implementation.share_modules.factor import (
|
||||
FactorImplementation,
|
||||
FactorImplementationTask,
|
||||
)
|
||||
|
||||
|
||||
class FactorGenerator(ABC):
|
||||
"""
|
||||
Because implementing factors will help each other in the process of implementation, we use the interface `List[FactorImplementationTask] -> List[FactorImplementation]` instead of single factor .
|
||||
"""
|
||||
|
||||
def __init__(self, target_task_l: List[FactorImplementationTask]) -> None:
|
||||
self.target_task_l = target_task_l
|
||||
|
||||
@abstractmethod
|
||||
def generate(self, *args, **kwargs) -> List[FactorImplementation]:
|
||||
raise NotImplementedError("generate method is not implemented.")
|
||||
|
||||
def collect_feedback(self, feedback_obj_l: List[object]):
|
||||
"""
|
||||
When online evaluation.
|
||||
The preivous feedbacks will be collected to support advanced factor generator
|
||||
|
||||
Parameters
|
||||
----------
|
||||
feedback_obj_l : List[object]
|
||||
|
||||
"""
|
||||
@@ -0,0 +1,23 @@
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
import yaml
|
||||
|
||||
from finco.utils import SingletonBaseClass
|
||||
|
||||
|
||||
class FactorImplementationPrompts(Dict, SingletonBaseClass):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
prompt_yaml_path = Path(__file__).parent / "prompts.yaml"
|
||||
|
||||
prompt_yaml_dict = yaml.load(
|
||||
open(
|
||||
prompt_yaml_path,
|
||||
encoding="utf8",
|
||||
),
|
||||
Loader=yaml.FullLoader,
|
||||
)
|
||||
|
||||
for key, value in prompt_yaml_dict.items():
|
||||
self[key] = value
|
||||
@@ -0,0 +1,196 @@
|
||||
evaluator_code_feedback_v1_system: |-
|
||||
Your job is to give critic to user's code. User's code is expected to implement some factors in quant investment. The code contains reading data from a HDF5(H5) file, calculate the factor to each instrument on each datetime, and save the result pandas dataframe to a HDF5(H5) file.
|
||||
|
||||
User will firstly provide you the information of the factor, which includes the name of the factor, description of the factor, the formulation of the factor and the description of the formulation. You can check whether user's code is align with the factor.
|
||||
|
||||
The user will provide the source python code and the execution error message if execution failed.
|
||||
The user might provide you the ground truth code for you to provide the critic. You should not leak the ground truth code to the user in any form but you can use it to provide the critic.
|
||||
|
||||
User has also compared the factor values calculated by the user's code and the ground truth code. The user will provide you some analyze result comparing two output. You may find some error in the code which caused the difference between the two output.
|
||||
|
||||
If the ground truth code is provided, your critic should only consider checking whether the user's code is align with the ground truth code since the ground truth is definitely correct.
|
||||
If the ground truth code is not provided, your critic should consider checking whether the user's code is reasonable and correct.
|
||||
|
||||
You should provide the suggestion to each of your critic to help the user improve the code. Please response the critic in the following format. Here is an example structure for the output:
|
||||
critic 1: The critic message to critic 1
|
||||
critic 2: The critic message to critic 2
|
||||
evaluator_code_feedback_v1_user: |-
|
||||
--------------Factor information:---------------
|
||||
{{ factor_information }}
|
||||
--------------Python code:---------------
|
||||
{{ code }}
|
||||
--------------Execution feedback:---------------
|
||||
{{ execution_feedback }}
|
||||
{% if factor_value_feedback is not none %}
|
||||
--------------Factor value feedback:---------------
|
||||
{{ factor_value_feedback }}
|
||||
{% endif %}
|
||||
{% if gt_code is not none %}
|
||||
--------------Ground truth Python code:---------------
|
||||
{{ gt_code }}
|
||||
{% endif %}
|
||||
evolving_strategy_factor_implementation_v1_system: |-
|
||||
The user is trying to implement some factors in quant investment, and you are the one to help write the python code.
|
||||
|
||||
{{ data_info }}
|
||||
|
||||
The user will provide you a formulation of the factor, which contains some function calls and some operators. You need to implement the function calls and operators in python. Your code is expected to align the formulation in any form which means The user needs to get the exact factor values with your code as expected.
|
||||
|
||||
Your code should contain the following part: the import part, the function part, and the main part. You should write a main function name: "calculate_{function_name}" and call this function in "if __name__ == __main__" part. Don't write any try-except block in your code. The user will catch the exception message and provide the feedback to you.
|
||||
|
||||
User will write your code into a python file and execute the file directly with "python {your_file_name}.py". You should calculate the factor values and save the result into a HDF5(H5) file named "result.h5" in the same directory as your python file. The result file is a HDF5(H5) file containing a pandas dataframe. The index of the dataframe is the "datetime" and "instrument", and the single column name is the factor name,and the value is the factor value. The result file should be saved in the same directory as your python file.
|
||||
|
||||
To help you write the correct code, the user might provide multiple information that helps you write the correct code:
|
||||
1. The user might provide you the correct code to similar factors. Your should learn from these code to write the correct code.
|
||||
2. The user might provide you the failed former code and the corresponding feedback to the code. The feedback contains to the execution, the code and the factor value. You should analyze the feedback and try to correct the latest code.
|
||||
3. The user might provide you the suggestion to the latest fail code and some similar fail to correct pairs. Each pair contains the fail code with similar error and the corresponding corrected version code. You should learn from these suggestion to write the correct code.
|
||||
|
||||
Your must write your code based on your former lastest attempt below which consists of your former code and code feedback, you should read the former attempt carefully and must not modify the right part of your former code.
|
||||
{% if queried_former_failed_knowledge|length != 0 %}
|
||||
--------------Your former latest attempt:---------------
|
||||
{% for former_failed_knowledge in queried_former_failed_knowledge %}
|
||||
=====Code to implementation {{ loop.index }}=====
|
||||
{{ former_failed_knowledge.implementation.code }}
|
||||
=====Feedback to implementation {{ loop.index }}=====
|
||||
{{ former_failed_knowledge.feedback }}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
A typical format of `result.h5` may be like following:
|
||||
datetime instrument
|
||||
2020-01-02 SZ000001 -0.001796
|
||||
SZ000166 0.005780
|
||||
SZ000686 0.004228
|
||||
SZ000712 0.001298
|
||||
SZ000728 0.005330
|
||||
...
|
||||
2021-12-31 SZ000750 0.000000
|
||||
SZ000776 0.002459
|
||||
|
||||
Please response the code in the following json format. Here is an example structure for the JSON output:
|
||||
{
|
||||
"code": "The Python code as a string."
|
||||
}
|
||||
|
||||
evolving_strategy_factor_implementation_v1_user: |-
|
||||
--------------Target factor information:---------------
|
||||
{{ factor_information_str }}
|
||||
|
||||
{% if queried_similar_successful_knowledge|length != 0 %}
|
||||
--------------Correct code to similar factors:---------------
|
||||
{% for similar_successful_knowledge in queried_similar_successful_knowledge %}
|
||||
=====Factor {{loop.index}}:=====
|
||||
{{ similar_successful_knowledge.target_task.get_factor_information() }}
|
||||
=====Code:=====
|
||||
{{ similar_successful_knowledge.implementation.code }}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
{% if queried_former_failed_knowledge|length != 0 %}
|
||||
--------------Former failed code:---------------
|
||||
{% for former_failed_knowledge in queried_former_failed_knowledge %}
|
||||
=====Code to implementation {{ loop.index }}=====
|
||||
{{ former_failed_knowledge.implementation.code }}
|
||||
=====Feedback to implementation {{ loop.index }}=====
|
||||
{{ former_failed_knowledge.feedback }}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
evaluator_final_decision_v1_system: |-
|
||||
User is trying to implement some factor in quant investment and has finished a version of implementation. User has finished evaluation and got some feedback from the evaluator.
|
||||
The evaluator run the code and get the factor value dataframe and provide several feedback regarding user's code and code output. You should analyze the feedback and considering the factor description to give a final decision about the evaluation result. The final decision concludes whether the factor is implemented correctly and if not, detail feedback containing reason and suggestion if the final decision is False.
|
||||
|
||||
The implementation final decision is considered in the following logic:
|
||||
1. If the value and the ground truth value are exactly the same under a small tolerance, the implementation is considered correct.
|
||||
2. If the value and the ground truth value have a high correlation on ic or rank ic, the implementation is considered correct.
|
||||
3. If no ground truth value is not provided, the implementation is considered correct if the code execution is successful and the code feedback is reasonable.
|
||||
|
||||
Please response the critic in the json format. Here is an example structure for the JSON output, please strictly follow the format:
|
||||
{
|
||||
"final_decision": True,
|
||||
"final_feedback": "The final feedback message",
|
||||
}
|
||||
|
||||
evaluator_final_decision_v1_user: |-
|
||||
--------------Factor information:---------------
|
||||
{{ factor_information }}
|
||||
--------------Execution feedback:---------------
|
||||
{{ execution_feedback }}
|
||||
--------------Code feedback:---------------
|
||||
{{ code_feedback }}
|
||||
--------------Factor value feedback:---------------
|
||||
{{ factor_value_feedback }}
|
||||
|
||||
|
||||
analyze_component_prompt_v1_system: |-
|
||||
User is getting a new task that might consist of the components below (given in component_index: component_description):
|
||||
{{all_component_content}}
|
||||
|
||||
You should find out what components does the new task have, and put their indices in a list.
|
||||
Please response the critic in the json format. Here is an example structure for the JSON output, please strictly follow the format:
|
||||
{
|
||||
"component_no_list": the list containing indices of components.
|
||||
}
|
||||
|
||||
|
||||
|
||||
evolving_strategy_factor_implementation_v2_user: |-
|
||||
--------------Target factor information:---------------
|
||||
{{ factor_information_str }}
|
||||
|
||||
{% if queried_similar_error_knowledge|length != 0 %}
|
||||
{% if not error_summary %}
|
||||
Recall your last failure, your implementation met some errors.
|
||||
When doing other tasks, you met some similar errors but you finally solve them. Here are some examples:
|
||||
{% for error_content, similar_error_knowledge in queried_similar_error_knowledge %}
|
||||
--------------Factor information to similar error ({{error_content}}):---------------
|
||||
{{ similar_error_knowledge[0].target_task.get_factor_information() }}
|
||||
=====Code with similar error ({{error_content}}):=====
|
||||
{{ similar_error_knowledge[0].implementation.code }}
|
||||
=====Success code to former code with similar error ({{error_content}}):=====
|
||||
{{ similar_error_knowledge[1].implementation.code }}
|
||||
{% endfor %}
|
||||
{% else %}
|
||||
Recall your last failure, your implementation met some errors.
|
||||
After reviewing some similar errors and their solutions, here are some suggestions for you to correct your code:
|
||||
{{error_summary_critics}}
|
||||
{% endif %}
|
||||
{% endif %}
|
||||
{% if queried_similar_component_knowledge|length != 0 %}
|
||||
Here are some success implements of similar component tasks, take them as references:
|
||||
--------------Correct code to similar factors:---------------
|
||||
{% for similar_component_knowledge in queried_similar_component_knowledge %}
|
||||
=====Factor {{loop.index}}:=====
|
||||
{{ similar_component_knowledge.target_task.get_factor_information() }}
|
||||
=====Code:=====
|
||||
{{ similar_component_knowledge.implementation.code }}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
|
||||
evolving_strategy_error_summary_v2_system: |-
|
||||
You are doing the following task:
|
||||
{{factor_information_str}}
|
||||
|
||||
You have written some code but it meets errors like the following:
|
||||
{{code_and_feedback}}
|
||||
|
||||
The user has found some tasks that met similar errors, and their final correct solutions.
|
||||
Please refer to these similar errors and their solutions, provide some clear, short and accurate critics that might help you solve the issues in your code.
|
||||
|
||||
Please response the critic in the following format. Here is an example structure for the output:
|
||||
critic 1: The critic message to critic 1
|
||||
critic 2: The critic message to critic 2
|
||||
|
||||
evolving_strategy_error_summary_v2_user: |-
|
||||
{% if queried_similar_error_knowledge|length != 0 %}
|
||||
{% for error_content, similar_error_knowledge in queried_similar_error_knowledge %}
|
||||
--------------Factor information to similar error ({{error_content}}):---------------
|
||||
{{ similar_error_knowledge[0].target_task.get_factor_information() }}
|
||||
=====Code with similar error ({{error_content}}):=====
|
||||
{{ similar_error_knowledge[0].implementation.code }}
|
||||
=====Success code to former code with similar error ({{error_content}}):=====
|
||||
{{ similar_error_knowledge[1].implementation.code }}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
@@ -0,0 +1,51 @@
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# render it with jinja
|
||||
from jinja2 import Template
|
||||
|
||||
from factor_implementation.share_modules.conf import FIS
|
||||
|
||||
TPL = """
|
||||
{{file_name}}
|
||||
```{{type_desc}}
|
||||
{{content}}
|
||||
````
|
||||
"""
|
||||
# Create a Jinja template from the string
|
||||
JJ_TPL = Template(TPL)
|
||||
|
||||
|
||||
def get_data_folder_intro():
|
||||
"""Direclty get the info of the data folder.
|
||||
It is for preparing prompting message.
|
||||
"""
|
||||
content_l = []
|
||||
for p in Path(FIS.file_based_execution_data_folder).iterdir():
|
||||
if p.name.endswith(".h5"):
|
||||
df = pd.read_hdf(p)
|
||||
# get df.head() as string with full width
|
||||
pd.set_option("display.max_columns", None) # or 1000
|
||||
pd.set_option("display.max_rows", None) # or 1000
|
||||
pd.set_option("display.max_colwidth", None) # or 199
|
||||
rendered = JJ_TPL.render(
|
||||
file_name=p.name,
|
||||
type_desc="generated by `pd.read_hdf(filename).head()`",
|
||||
content=df.head().to_string(),
|
||||
)
|
||||
content_l.append(rendered)
|
||||
elif p.name.endswith(".md"):
|
||||
with open(p) as f:
|
||||
content = f.read()
|
||||
rendered = JJ_TPL.render(
|
||||
file_name=p.name,
|
||||
type_desc="markdown",
|
||||
content=content,
|
||||
)
|
||||
content_l.append(rendered)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"file type {p.name} is not supported. Please implement its description function.",
|
||||
)
|
||||
return "\n ----------------- file spliter -------------\n".join(content_l)
|
||||
Reference in New Issue
Block a user