mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
Xu Yang
1d9b4cd2ec
* use CoSTEER as component name * rename factorimplementation to avoid confusion * rename modelimplementation * align benchmark and evolving evaluators * add scenario to evaluator init function * rename all factorimplementationknowledge in CoSTEER * remove all scenario related information in component * remove useless code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
import json
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from pathlib import Path
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from typing import List, Tuple
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.factor_coder.factor import FactorExperiment, FactorTask
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from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
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from rdagent.components.proposal.factor_proposal import (
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FactorHypothesis,
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FactorHypothesis2Experiment,
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FactorHypothesisGen,
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)
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from rdagent.core.prompts import Prompts
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from rdagent.core.proposal import HypothesisSet, Scenario, Trace
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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QlibFactorHypothesis = FactorHypothesis
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class QlibFactorHypothesisGen(FactorHypothesisGen):
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def __init__(self, scen: Scenario) -> Tuple[dict, bool]:
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super().__init__(scen)
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def prepare_context(self, trace: Trace) -> None:
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hypothesis_feedback = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis_and_feedback"])
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.render(trace=trace)
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)
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context_dict = {
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"hypothesis_and_feedback": hypothesis_feedback,
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"RAG": ...,
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"hypothesis_output_format": prompt_dict["hypothesis_output_format"],
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}
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return context_dict, True
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def convert_response(self, response: str) -> FactorHypothesis:
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response_dict = json.loads(response)
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hypothesis = QlibFactorHypothesis(hypothesis=response_dict["hypothesis"], reason=response_dict["reason"])
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return hypothesis
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class QlibFactorHypothesis2Experiment(FactorHypothesis2Experiment):
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def prepare_context(self, hs: HypothesisSet) -> Tuple[dict | bool]:
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scenario = hs.trace.scen.get_scenario_all_desc()
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experiment_output_format = prompt_dict["experiment_output_format"]
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hypothesis_and_feedback = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis_and_feedback"])
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.render(trace=hs.trace)
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)
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experiment_list: List[FactorExperiment] = [t[1] for t in hs.trace.hist]
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factor_list = []
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for experiment in experiment_list:
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factor_list.extend(experiment.sub_tasks)
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return {
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"scenario": scenario,
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"hypothesis_and_feedback": hypothesis_and_feedback,
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"experiment_output_format": experiment_output_format,
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"factor_list": factor_list,
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"RAG": ...,
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}, True
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def convert_response(self, response: str) -> FactorExperiment:
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response_dict = json.loads(response)
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tasks = []
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for factor_name in response_dict:
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description = response_dict[factor_name]["description"]
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formulation = response_dict[factor_name]["formulation"]
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variables = response_dict[factor_name]["variables"]
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tasks.append(FactorTask(factor_name, description, formulation, variables))
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return FactorExperiment(tasks)
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