NexQuant/rdagent/scenarios/kaggle/developer/runner.py

import json
import pickle
import shutil
from pathlib import Path

from rdagent.components.runner import CachedRunner
from rdagent.components.runner.conf import RUNNER_SETTINGS
from rdagent.core.exception import CoderError, FactorEmptyError, ModelEmptyError
from rdagent.core.experiment import ASpecificExp
from rdagent.core.prompts import Prompts
from rdagent.oai.llm_utils import md5_hash
from rdagent.scenarios.kaggle.experiment.kaggle_experiment import (
    KGFactorExperiment,
    KGModelExperiment,
)

prompt_dict = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")


class KGCachedRunner(CachedRunner[ASpecificExp]):
    def get_cache_key(self, exp: ASpecificExp) -> str:
        codes = []
        for f in sorted((exp.experiment_workspace.workspace_path / "feature").glob("*.py"), key=lambda x: x.name):
            codes.append(f.read_text())
        for f in sorted((exp.experiment_workspace.workspace_path / "model").glob("*.py"), key=lambda x: x.name):
            codes.append(f.read_text())
        codes = "\n".join(codes)
        return md5_hash(codes)

    def init_develop(self, exp: KGFactorExperiment | KGModelExperiment) -> KGFactorExperiment | KGModelExperiment:
        """
        For the initial development, the experiment serves as a benchmark for feature engineering.
        """
        if RUNNER_SETTINGS.cache_result:
            cache_hit, result = self.get_cache_result(exp)
            if cache_hit:
                exp.result = result
                return exp

        env_to_use = {"PYTHONPATH": "./"}

        result = exp.experiment_workspace.execute(run_env=env_to_use)

        exp.result = result

        if RUNNER_SETTINGS.cache_result:
            self.dump_cache_result(exp, result)

        return exp


class KGModelRunner(KGCachedRunner[KGModelExperiment]):
    def develop(self, exp: KGModelExperiment) -> KGModelExperiment:
        if exp.based_experiments and exp.based_experiments[-1].result is None:
            exp.based_experiments[-1] = self.init_develop(exp.based_experiments[-1])

        sub_ws = exp.sub_workspace_list[0]
        if sub_ws is not None:
            # TODO: There's a possibility of generating a hybrid model (lightgbm + xgboost), which results in having two items in the model_type list.
            model_type = sub_ws.target_task.model_type

            if sub_ws.code_dict == {}:
                raise ModelEmptyError("No model is implemented.")
            else:
                model_file_name = f"model/model_{model_type.lower()}.py"
                exp.experiment_workspace.inject_code(**{model_file_name: sub_ws.code_dict["model.py"]})

        if RUNNER_SETTINGS.cache_result:
            cache_hit, result = self.get_cache_result(exp)
            if cache_hit:
                exp.result = result
                return exp

        env_to_use = {"PYTHONPATH": "./"}

        result = exp.experiment_workspace.execute(run_env=env_to_use)

        if result is None:
            raise CoderError("No result is returned from the experiment workspace")

        exp.result = result
        if RUNNER_SETTINGS.cache_result:
            self.dump_cache_result(exp, result)

        return exp


class KGFactorRunner(KGCachedRunner[KGFactorExperiment]):
    def develop(self, exp: KGFactorExperiment) -> KGFactorExperiment:
        current_feature_file_count = len(list(exp.experiment_workspace.workspace_path.glob("feature/feature*.py")))
        implemented_factor_count = 0
        for sub_ws in exp.sub_workspace_list:
            if sub_ws.code_dict == {}:
                continue
            implemented_factor_count += 1
            target_feature_file_name = f"feature/feature_{current_feature_file_count:05d}.py"
            exp.experiment_workspace.inject_code(**{target_feature_file_name: sub_ws.code_dict["factor.py"]})
            feature_shape = sub_ws.execute()[1].shape[-1]
            exp.experiment_workspace.data_description.append((sub_ws.target_task.get_task_information(), feature_shape))
            current_feature_file_count += 1
        if implemented_factor_count == 0:
            raise FactorEmptyError("No factor is implemented")

        # initial template result
        if exp.based_experiments and exp.based_experiments[-1].result is None:
            exp.based_experiments[-1] = self.init_develop(exp.based_experiments[-1])

        if RUNNER_SETTINGS.cache_result:
            cache_hit, result = self.get_cache_result(exp)
            if cache_hit:
                exp.result = result
                return exp

        env_to_use = {"PYTHONPATH": "./"}

        result = exp.experiment_workspace.execute(run_env=env_to_use)

        if result is None:
            raise CoderError("No result is returned from the experiment workspace")

        exp.result = result

        if RUNNER_SETTINGS.cache_result:
            self.dump_cache_result(exp, result)

        return exp