mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
e0a24fb46f
* ignore result csv file * fix app scripts * rename taskgenerator to developer and generate to develop * fix a config bug in coder * fix a small bug in factor coder evaluators * remove a single logger in factor coder evaluators * fix a small bug in model coder main.py * rename Implementation to Workspace * move the prepare the inject_code into FBWorkspace to align all the behavior * fix a small bug in model feedback * remove debug lines for multi processing and simplify evaluators multi proc * add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging * make hypothesisgen a abc class * use Qlib***Experiment * fix a small bug * rename Imp to Ws * rename sub_implementations to sub_workspace_list * fix a bug in feedback not presented as content in prompts * move proposal pys to proposal folder * reformat the folder * align factor and model qlib workspace and use template to handle the workspace * add a filter to evoagent to filter out false evo * align multi_proc_n into RDAGENT seeting * handle when runner gets empty experiment * fix logger merge remaining problems * fix black and isort automatically
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
import json
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from pathlib import Path
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from typing import Dict
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
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FactorEvolvingItem,
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)
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from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
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from rdagent.core.conf import RD_AGENT_SETTINGS
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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from rdagent.log import rdagent_logger as logger
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from rdagent.oai.llm_utils import APIBackend
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scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
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import random
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to_be_finished_task_index = random.sample(
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to_be_finished_task_index,
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implementation_factors_per_round,
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)
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logger.info(f"The random selection is: {to_be_finished_task_index}")
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return to_be_finished_task_index
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def LLMSelect(
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to_be_finished_task_index,
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implementation_factors_per_round,
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evo: FactorEvolvingItem,
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former_trace: Dict,
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scen: Scenario,
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):
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tasks = []
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for i in to_be_finished_task_index:
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# find corresponding former trace for each task
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target_factor_task_information = evo.sub_tasks[i].get_task_information()
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if target_factor_task_information in former_trace:
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tasks.append((i, evo.sub_tasks[i], former_trace[target_factor_task_information]))
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(
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scheduler_prompts["select_implementable_factor_system"],
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)
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.render(
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scenario=scen.get_scenario_all_desc(),
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)
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)
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for _ in range(10): # max attempt to reduce the length of user_prompt
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(
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scheduler_prompts["select_implementable_factor_user"],
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)
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.render(
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factor_num=implementation_factors_per_round,
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sub_tasks=tasks,
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)
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)
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if (
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APIBackend().build_messages_and_calculate_token(
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user_prompt=user_prompt,
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system_prompt=system_prompt,
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)
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< RD_AGENT_SETTINGS.chat_token_limit
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):
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break
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response = APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_prompt,
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system_prompt=system_prompt,
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json_mode=True,
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)
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try:
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selection = json.loads(response)["selected_factor"]
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if not isinstance(selection, list):
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return to_be_finished_task_index
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selection_index = [x for x in selection if isinstance(x, int)]
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except:
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return to_be_finished_task_index
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return selection_index
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