mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-05 19:17:43 +00:00
Xu Yang
be2c19307e
* Implemented model.py - Need to run within the RDAgent folder (relevant path) - Each time copy a template & insert code & run qlib & store result back to experiment * Create model.py * Create conf.yaml This is the sample conf.yaml to be copied each time. This has gone several times of iteration and is now working for both tabular and Time-Series data. * Create read_exp.py This is to read the results within Qlib * Create ReadMe.md * Update model.py * Create test_model.py A testing file that separates model code generation and running&feedback section. * move the template folder * help xisen finish the model runner * help xisen fix improve model feedback generation * delete debug file * rename readme.md --------- Co-authored-by: Xisen Wang <118058822+Xisen-Wang@users.noreply.github.com>
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
import shutil
|
|
import uuid
|
|
from pathlib import Path
|
|
|
|
import pandas as pd
|
|
|
|
from rdagent.components.coder.model_coder.model import (
|
|
ModelExperiment,
|
|
ModelImplementation,
|
|
)
|
|
from rdagent.components.runner import CachedRunner
|
|
from rdagent.components.runner.conf import RUNNER_SETTINGS
|
|
from rdagent.core.log import RDAgentLog
|
|
from rdagent.core.task_generator import TaskGenerator
|
|
from rdagent.utils.env import QTDockerEnv
|
|
|
|
|
|
class QlibModelRunner(CachedRunner[ModelImplementation]):
|
|
"""
|
|
Docker run
|
|
Everything in a folder
|
|
- config.yaml
|
|
- Pytorch `model.py`
|
|
- results in `mlflow`
|
|
|
|
https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_nn.py
|
|
- pt_model_uri: hard-code `model.py:Net` in the config
|
|
- let LLM modify model.py
|
|
"""
|
|
|
|
def generate(self, exp: ModelExperiment) -> ModelExperiment:
|
|
|
|
if RUNNER_SETTINGS.runner_cache_result:
|
|
cache_hit, result = self.get_cache_result(exp)
|
|
if cache_hit:
|
|
exp.result = result
|
|
return exp
|
|
TEMPLATE_PATH = Path(__file__).parent / "model_template" # Can be updated
|
|
|
|
# To set the experiment level workspace and prepare the workspaces use the first task as the target task
|
|
exp.exp_ws = ModelImplementation(target_task=exp.sub_tasks[0])
|
|
exp.exp_ws.prepare()
|
|
|
|
# to copy_template_to_workspace
|
|
for file_path in TEMPLATE_PATH.iterdir():
|
|
shutil.copyfile(file_path, exp.exp_ws.workspace_path / file_path.name)
|
|
|
|
# to replace & inject code
|
|
exp.exp_ws.inject_code(**{"model.py": exp.sub_implementations[0].code_dict["model.py"]})
|
|
|
|
env_to_use = {}
|
|
|
|
if exp.sub_tasks[0].model_type == "TimeSeries":
|
|
env_to_use = {"dataset_cls": "TSDatasetH", "step_len": 20, "num_timesteps": 20}
|
|
elif exp.sub_tasks[0].model_type == "Tabular":
|
|
env_to_use = {"dataset_cls": "DatasetH"}
|
|
# to execute
|
|
qtde = QTDockerEnv()
|
|
|
|
# Preparing the Docker environment
|
|
qtde.prepare()
|
|
|
|
# Run the Docker container with the specified entry
|
|
|
|
execute_log = qtde.run(
|
|
local_path=exp.exp_ws.workspace_path, entry="qrun conf.yaml", env={"PYTHONPATH": "./", **env_to_use}
|
|
)
|
|
|
|
# Run the experiment analysis code
|
|
execute_log = qtde.run(local_path=exp.exp_ws.workspace_path, entry="python read_exp_res.py")
|
|
|
|
csv_path = exp.exp_ws.workspace_path / "qlib_res.csv"
|
|
|
|
if not csv_path.exists():
|
|
RDAgentLog().error(f"File {csv_path} does not exist.")
|
|
return None
|
|
|
|
result = pd.read_csv(csv_path, index_col=0).iloc[:,0]
|
|
|
|
exp.result = result
|
|
if RUNNER_SETTINGS.runner_cache_result:
|
|
self.dump_cache_result(exp, result)
|
|
|
|
return exp
|