mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-04 18:57:44 +00:00
Yuante Li
1726caa414
* fix * refine the code * aligned data with MLE Kaggle competition dataset * fix ci error * fix ci
108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
import json
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import pickle
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import shutil
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from pathlib import Path
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from rdagent.components.runner import CachedRunner
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from rdagent.core.exception import CoderError, FactorEmptyError, ModelEmptyError
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from rdagent.core.experiment import ASpecificExp
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from rdagent.core.prompts import Prompts
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from rdagent.core.utils import cache_with_pickle
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from rdagent.oai.llm_utils import md5_hash
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from rdagent.scenarios.kaggle.experiment.kaggle_experiment import (
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KGFactorExperiment,
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KGModelExperiment,
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)
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prompt_dict = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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class KGCachedRunner(CachedRunner[ASpecificExp]):
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def get_cache_key(self, exp: ASpecificExp) -> str:
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codes = []
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for f in sorted((exp.experiment_workspace.workspace_path / "feature").glob("*.py"), key=lambda x: x.name):
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codes.append(f.read_text())
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for f in sorted((exp.experiment_workspace.workspace_path / "model").glob("*.py"), key=lambda x: x.name):
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codes.append(f.read_text())
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codes = "\n".join(codes)
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if exp.sub_workspace_list is not None:
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for i in range(len(exp.sub_workspace_list)):
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if exp.sub_workspace_list[i] is not None:
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codes += str(exp.sub_workspace_list[i].code_dict.values())
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return md5_hash(codes)
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@cache_with_pickle(get_cache_key, CachedRunner.assign_cached_result)
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def init_develop(self, exp: KGFactorExperiment | KGModelExperiment) -> KGFactorExperiment | KGModelExperiment:
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"""
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For the initial development, the experiment serves as a benchmark for feature engineering.
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"""
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env_to_use = {"PYTHONPATH": "./"}
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result = exp.experiment_workspace.execute(run_env=env_to_use)
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exp.result = result
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return exp
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class KGModelRunner(KGCachedRunner[KGModelExperiment]):
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@cache_with_pickle(KGCachedRunner.get_cache_key, KGCachedRunner.assign_cached_result)
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def develop(self, exp: KGModelExperiment) -> KGModelExperiment:
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if exp.based_experiments and exp.based_experiments[-1].result is None:
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exp.based_experiments[-1] = self.init_develop(exp.based_experiments[-1])
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sub_ws = exp.sub_workspace_list[0]
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if sub_ws is not None:
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# TODO: There's a possibility of generating a hybrid model (lightgbm + xgboost), which results in having two items in the model_type list.
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model_type = sub_ws.target_task.model_type
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if sub_ws.code_dict == {}:
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raise ModelEmptyError("No model is implemented.")
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else:
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model_file_name = f"model/model_{model_type.lower()}.py"
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exp.experiment_workspace.inject_code(**{model_file_name: sub_ws.code_dict["model.py"]})
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env_to_use = {"PYTHONPATH": "./"}
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result = exp.experiment_workspace.execute(run_env=env_to_use)
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if result is None:
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raise CoderError("No result is returned from the experiment workspace")
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exp.result = result
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return exp
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class KGFactorRunner(KGCachedRunner[KGFactorExperiment]):
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@cache_with_pickle(KGCachedRunner.get_cache_key, KGCachedRunner.assign_cached_result)
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def develop(self, exp: KGFactorExperiment) -> KGFactorExperiment:
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current_feature_file_count = len(list(exp.experiment_workspace.workspace_path.glob("feature/feature*.py")))
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implemented_factor_count = 0
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for sub_ws in exp.sub_workspace_list:
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if sub_ws.code_dict == {}:
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continue
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implemented_factor_count += 1
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target_feature_file_name = f"feature/feature_{current_feature_file_count:05d}.py"
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exp.experiment_workspace.inject_code(**{target_feature_file_name: sub_ws.code_dict["factor.py"]})
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feature_shape = sub_ws.execute()[1].shape[-1]
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exp.experiment_workspace.data_description.append((sub_ws.target_task.get_task_information(), feature_shape))
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current_feature_file_count += 1
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if implemented_factor_count == 0:
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raise FactorEmptyError("No factor is implemented")
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# initial template result
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if exp.based_experiments and exp.based_experiments[-1].result is None:
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exp.based_experiments[-1] = self.init_develop(exp.based_experiments[-1])
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env_to_use = {"PYTHONPATH": "./"}
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result = exp.experiment_workspace.execute(run_env=env_to_use)
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if result is None:
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raise CoderError("No result is returned from the experiment workspace")
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exp.result = result
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return exp
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