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

import shutil
from pathlib import Path

import pandas as pd

from rdagent.components.runner import CachedRunner
from rdagent.core.exception import CoderError, FactorEmptyError, ModelEmptyError
from rdagent.core.experiment import ASpecificExp, Experiment
from rdagent.core.utils import cache_with_pickle
from rdagent.oai.llm_utils import md5_hash
from rdagent.scenarios.kaggle.experiment.kaggle_experiment import (
    KGFactorExperiment,
    KGModelExperiment,
)


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)
        cached_key_from_exp = CachedRunner.get_cache_key(self, exp)
        return md5_hash(codes + cached_key_from_exp)

    def assign_cached_result(self, exp: Experiment, cached_res: Experiment) -> Experiment:
        exp = CachedRunner.assign_cached_result(self, exp, cached_res)
        if cached_res.experiment_workspace.workspace_path.exists():
            for csv_file in cached_res.experiment_workspace.workspace_path.glob("*.csv"):
                shutil.copy(csv_file, exp.experiment_workspace.workspace_path)
            for py_file in (cached_res.experiment_workspace.workspace_path / "feature").glob("*.py"):
                shutil.copy(py_file, exp.experiment_workspace.workspace_path / "feature")
            for py_file in (cached_res.experiment_workspace.workspace_path / "model").glob("*.py"):
                shutil.copy(py_file, exp.experiment_workspace.workspace_path / "model")
        exp.experiment_workspace.data_description = cached_res.experiment_workspace.data_description
        return exp

    @cache_with_pickle(get_cache_key, CachedRunner.assign_cached_result)
    def init_develop(self, exp: KGFactorExperiment | KGModelExperiment) -> KGFactorExperiment | KGModelExperiment:
        """
        For the initial development, the experiment serves as a benchmark for feature engineering.
        """

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

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

        exp.result = result

        sub_result_score_path = Path(exp.experiment_workspace.workspace_path) / "sub_submission_score.csv"
        if sub_result_score_path.exists():
            sub_submission_df = pd.read_csv(sub_result_score_path)
            exp.sub_results = sub_submission_df.set_index("Model")["score"].to_dict()

        return exp


class KGModelRunner(KGCachedRunner[KGModelExperiment]):
    @cache_with_pickle(KGCachedRunner.get_cache_key, KGCachedRunner.assign_cached_result)
    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.file_dict == {}:
                raise ModelEmptyError("No model is implemented.")
            else:
                model_file_name = f"model/model_{model_type.lower()}.py"
                exp.experiment_workspace.inject_files(**{model_file_name: sub_ws.file_dict["model.py"]})
        else:
            raise ModelEmptyError("No model is implemented.")
        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
        sub_result_score_path = Path(exp.experiment_workspace.workspace_path) / "sub_submission_score.csv"
        if sub_result_score_path.exists():
            sub_submission_df = pd.read_csv(sub_result_score_path)
            exp.sub_results = sub_submission_df.set_index("Model")["score"].to_dict()

        return exp


class KGFactorRunner(KGCachedRunner[KGFactorExperiment]):
    @cache_with_pickle(KGCachedRunner.get_cache_key, KGCachedRunner.assign_cached_result)
    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.file_dict == {}:
                continue
            execued_df = sub_ws.execute()[1]
            if execued_df is None:
                continue
            implemented_factor_count += 1
            target_feature_file_name = f"feature/feature_{current_feature_file_count:05d}.py"
            exp.experiment_workspace.inject_files(**{target_feature_file_name: sub_ws.file_dict["factor.py"]})
            feature_shape = execued_df.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])

        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
        sub_result_score_path = Path(exp.experiment_workspace.workspace_path) / "sub_submission_score.csv"
        if sub_result_score_path.exists():
            sub_submission_df = pd.read_csv(sub_result_score_path)
            exp.sub_results = sub_submission_df.set_index("Model")["score"].to_dict()

        return exp