from pathlib import Path from typing import Any import fire from rdagent.app.data_science.conf import DS_RD_SETTING from rdagent.components.coder.data_science.ensemble import EnsembleCoSTEER from rdagent.components.coder.data_science.ensemble.exp import EnsembleTask from rdagent.components.coder.data_science.feature import FeatureCoSTEER from rdagent.components.coder.data_science.feature.exp import FeatureTask from rdagent.components.coder.data_science.model import ModelCoSTEER from rdagent.components.coder.data_science.model.exp import ModelTask from rdagent.components.coder.data_science.raw_data_loader import DataLoaderCoSTEER from rdagent.components.coder.data_science.raw_data_loader.exp import DataLoaderTask from rdagent.components.coder.data_science.workflow import WorkflowCoSTEER from rdagent.components.coder.data_science.workflow.exp import WorkflowTask from rdagent.components.workflow.conf import BasePropSetting from rdagent.components.workflow.rd_loop import RDLoop from rdagent.core.exception import CoderError, RunnerError from rdagent.core.proposal import ExperimentFeedback from rdagent.core.scenario import Scenario from rdagent.core.utils import import_class from rdagent.log import rdagent_logger as logger from rdagent.scenarios.data_science.dev.feedback import DSExperiment2Feedback from rdagent.scenarios.data_science.dev.runner import DSCoSTEERRunner from rdagent.scenarios.data_science.experiment.experiment import DSExperiment from rdagent.scenarios.data_science.proposal.exp_gen import DSExpGen, DSTrace from rdagent.scenarios.kaggle.kaggle_crawler import download_data class DataScienceRDLoop(RDLoop): skip_loop_error = (CoderError, RunnerError) def __init__(self, PROP_SETTING: BasePropSetting): logger.log_object(PROP_SETTING.competition, tag="competition") scen: Scenario = import_class(PROP_SETTING.scen)(PROP_SETTING.competition) ### shared components in the workflow # TODO: check if knowledge_base = ( import_class(PROP_SETTING.knowledge_base)(PROP_SETTING.knowledge_base_path, scen) if PROP_SETTING.knowledge_base != "" else None ) # 1) task generation from scratch # self.scratch_gen: tuple[HypothesisGen, Hypothesis2Experiment] = DummyHypothesisGen(scen), # 2) task generation from a complete solution # self.exp_gen: ExpGen = import_class(PROP_SETTING.exp_gen)(scen) self.exp_gen = DSExpGen(scen) self.data_loader_coder = DataLoaderCoSTEER(scen) self.feature_coder = FeatureCoSTEER(scen) self.model_coder = ModelCoSTEER(scen) self.ensemble_coder = EnsembleCoSTEER(scen) self.workflow_coder = WorkflowCoSTEER(scen) self.runner = DSCoSTEERRunner(scen) # self.summarizer: Experiment2Feedback = import_class(PROP_SETTING.summarizer)(scen) # logger.log_object(self.summarizer, tag="summarizer") # self.trace = KGTrace(scen=scen, knowledge_base=knowledge_base) self.trace = DSTrace(scen=scen) self.summarizer = DSExperiment2Feedback(scen) super(RDLoop, self).__init__() def direct_exp_gen(self, prev_out: dict[str, Any]): exp = self.exp_gen.gen(self.trace) logger.log_object(exp) # FIXME: this is for LLM debug webapp, remove this when the debugging is done. logger.log_object(exp, tag="debug_exp_gen") return exp def coding(self, prev_out: dict[str, Any]): exp = prev_out["direct_exp_gen"] for tasks in exp.pending_tasks_list: exp.sub_tasks = tasks if isinstance(exp.sub_tasks[0], DataLoaderTask): exp = self.data_loader_coder.develop(exp) elif isinstance(exp.sub_tasks[0], FeatureTask): exp = self.feature_coder.develop(exp) elif isinstance(exp.sub_tasks[0], ModelTask): exp = self.model_coder.develop(exp) elif isinstance(exp.sub_tasks[0], EnsembleTask): exp = self.ensemble_coder.develop(exp) elif isinstance(exp.sub_tasks[0], WorkflowTask): exp = self.workflow_coder.develop(exp) else: raise NotImplementedError(f"Unsupported component in DataScienceRDLoop: {exp.hypothesis.component}") exp.sub_tasks = [] logger.log_object(exp) return exp def running(self, prev_out: dict[str, Any]): exp: DSExperiment = prev_out["coding"] if exp.next_component_required() is None: new_exp = self.runner.develop(exp) logger.log_object(new_exp) return new_exp else: return exp def feedback(self, prev_out: dict[str, Any]) -> ExperimentFeedback: exp: DSExperiment = prev_out["running"] if exp.next_component_required() is None: feedback = self.summarizer.generate_feedback(exp, self.trace) else: feedback = ExperimentFeedback( reason=f"{exp.hypothesis.component} is completed.", decision=True, ) logger.log_object(feedback) return feedback def record(self, prev_out: dict[str, Any]): e = prev_out.get(self.EXCEPTION_KEY, None) if e is None: self.trace.hist.append((prev_out["running"], prev_out["feedback"])) else: self.trace.hist.append( ( prev_out["direct_exp_gen"] if isinstance(e, CoderError) else prev_out["coding"], ExperimentFeedback.from_exception(e), ) ) if self.trace.sota_experiment() is None and len(self.trace.hist) >= DS_RD_SETTING.consecutive_errors: trace_exp_next_component_list = [ type(exp.pending_tasks_list[0][0]) for exp, _ in self.trace.hist[-DS_RD_SETTING.consecutive_errors :] ] last_successful_exp = self.trace.last_successful_exp() if ( last_successful_exp not in [exp for exp, _ in self.trace.hist[-DS_RD_SETTING.consecutive_errors :]] and len(set(trace_exp_next_component_list)) == 1 ): logger.error("Consecutive errors reached the limit. Dumping trace.") logger.log_object(self.trace, tag="trace before restart") self.trace = DSTrace(scen=self.trace.scen, knowledge_base=self.trace.knowledge_base) logger.log_object(self.trace, tag="trace") logger.log_object(self.trace.sota_experiment(), tag="SOTA experiment") def main(path=None, step_n=None, loop_n=None, competition="bms-molecular-translation"): """ Parameters ---------- path : path like `$LOG_PATH/__session__/1/0_propose`. It indicates that we restore the state that after finish the step 0 in loop1 step_n : How many steps to run; if None, it will run forever until error or KeyboardInterrupt loop_n : How many loops to run; if None, it will run forever until error or KeyboardInterrupt - if current loop is incomplete, it will be counted as the first loop for completion. - if both step_n and loop_n are provided, the process will stop as soon as either condition is met. competition : Auto R&D Evolving loop for models in a Kaggle scenario. You can continue running session by .. code-block:: bash dotenv run -- python rdagent/app/data_science/loop.py [--competition titanic] $LOG_PATH/__session__/1/0_propose --step_n 1 # `step_n` is a optional parameter rdagent kaggle --competition playground-series-s4e8 # You are encouraged to use this one. """ if competition is not None: DS_RD_SETTING.competition = competition if DS_RD_SETTING.competition: if DS_RD_SETTING.scen.endswith("KaggleScen"): download_data(competition=DS_RD_SETTING.competition, settings=DS_RD_SETTING) else: if not Path(f"{DS_RD_SETTING.local_data_path}/{competition}").exists(): logger.error(f"Please prepare data for competition {competition} first.") return else: logger.error("Please specify competition name.") if path is None: kaggle_loop = DataScienceRDLoop(DS_RD_SETTING) else: kaggle_loop = DataScienceRDLoop.load(path) kaggle_loop.run(step_n=step_n, loop_n=loop_n) if __name__ == "__main__": fire.Fire(main)