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https://github.com/NicolasBohn/NexQuant.git
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Linlang
ae2aa6e9b4
* fix_ruff_error1 * fix_ruff_error * fix ruff error * fix ruff error * pass model.py * rename exception class * rename exception class * rename func name generate_feedback * remove prepare args * optimize code * optimize code * fix code error
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import shutil
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import uuid
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from pathlib import Path
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import pandas as pd
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from rdagent.components.coder.model_coder.model import ModelExperiment, ModelFBWorkspace
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from rdagent.components.runner import CachedRunner
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from rdagent.components.runner.conf import RUNNER_SETTINGS
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from rdagent.core.developer import Developer
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from rdagent.core.exception import ModelEmptyError
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from rdagent.log import rdagent_logger as logger
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from rdagent.scenarios.qlib.experiment.model_experiment import QlibModelExperiment
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from rdagent.utils.env import QTDockerEnv
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class QlibModelRunner(CachedRunner[QlibModelExperiment]):
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"""
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Docker run
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Everything in a folder
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- config.yaml
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- Pytorch `model.py`
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- results in `mlflow`
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https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_nn.py
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- pt_model_uri: hard-code `model.py:Net` in the config
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- let LLM modify model.py
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"""
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def develop(self, exp: QlibModelExperiment) -> QlibModelExperiment:
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if RUNNER_SETTINGS.cache_result:
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cache_hit, result = self.get_cache_result(exp)
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if cache_hit:
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exp.result = result
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return exp
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if exp.sub_workspace_list[0].code_dict.get("model.py") is None:
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raise ModelEmptyError("model.py is empty")
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# to replace & inject code
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exp.experiment_workspace.inject_code(**{"model.py": exp.sub_workspace_list[0].code_dict["model.py"]})
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env_to_use = {"PYTHONPATH": "./"}
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if exp.sub_tasks[0].model_type == "TimeSeries":
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env_to_use.update({"dataset_cls": "TSDatasetH", "step_len": 20, "num_timesteps": 20})
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elif exp.sub_tasks[0].model_type == "Tabular":
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env_to_use.update({"dataset_cls": "DatasetH"})
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result = exp.experiment_workspace.execute(qlib_config_name="conf.yaml", run_env=env_to_use)
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exp.result = result
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if RUNNER_SETTINGS.cache_result:
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self.dump_cache_result(exp, result)
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return exp
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