mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Linlang
32a29a7479
* feat: parameterize cache paths with USER to avoid conflicts * guide for missing training_hyperparameters * guidance for KeyError: 'concise_reason' * fixed three bugs in the test * fix general_model task bug * fixed some bugs in the med_model scenario * delete comments * format with black * fix mypy error * fix ruff error * fix isort error * sync code * revert cache_path code * revert cache_path code * delete data mining scenario * fix factor report loop * fix LiteLLMAPIBackend log_llm_chat_content setting * refine fin factor report scenario * remove unused LogColors * fix UI * remove medical scenario docs * change **kaggle** to **data_science** * remove default dataset_path in create_debug_data * remove KAGGLE_SETTINGS in kaggle_crawler * limit litellm versions * reformat with black * change README * fix_data_science_docs * make hypothesis observations string * Hiding old versions of kaggle docs * hidding kaggle agent docs --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: yuanteli <1957922024@qq.com>
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
"""
|
|
Model workflow with session control
|
|
It is from `rdagent/app/qlib_rd_loop/model.py` and try to replace `rdagent/app/qlib_rd_loop/RDAgent.py`
|
|
"""
|
|
|
|
from typing import Any
|
|
|
|
from rdagent.components.workflow.conf import BasePropSetting
|
|
from rdagent.core.developer import Developer
|
|
from rdagent.core.proposal import (
|
|
Experiment2Feedback,
|
|
Hypothesis,
|
|
Hypothesis2Experiment,
|
|
HypothesisFeedback,
|
|
HypothesisGen,
|
|
Trace,
|
|
)
|
|
from rdagent.core.scenario import Scenario
|
|
from rdagent.core.utils import import_class
|
|
from rdagent.log import rdagent_logger as logger
|
|
from rdagent.utils.workflow import LoopBase, LoopMeta
|
|
|
|
|
|
class RDLoop(LoopBase, metaclass=LoopMeta):
|
|
|
|
def __init__(self, PROP_SETTING: BasePropSetting):
|
|
scen: Scenario = import_class(PROP_SETTING.scen)()
|
|
logger.log_object(scen, tag="scenario")
|
|
|
|
self.hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.hypothesis_gen)(scen)
|
|
logger.log_object(self.hypothesis_gen, tag="hypothesis generator")
|
|
|
|
self.hypothesis2experiment: Hypothesis2Experiment = import_class(PROP_SETTING.hypothesis2experiment)()
|
|
logger.log_object(self.hypothesis2experiment, tag="hypothesis2experiment")
|
|
|
|
self.coder: Developer = import_class(PROP_SETTING.coder)(scen)
|
|
logger.log_object(self.coder, tag="coder")
|
|
self.runner: Developer = import_class(PROP_SETTING.runner)(scen)
|
|
logger.log_object(self.runner, tag="runner")
|
|
|
|
self.summarizer: Experiment2Feedback = import_class(PROP_SETTING.summarizer)(scen)
|
|
logger.log_object(self.summarizer, tag="summarizer")
|
|
self.trace = Trace(scen=scen)
|
|
super().__init__()
|
|
|
|
# excluded steps
|
|
def _propose(self):
|
|
hypothesis = self.hypothesis_gen.gen(self.trace)
|
|
logger.log_object(hypothesis, tag="hypothesis generation")
|
|
return hypothesis
|
|
|
|
def _exp_gen(self, hypothesis: Hypothesis):
|
|
exp = self.hypothesis2experiment.convert(hypothesis, self.trace)
|
|
logger.log_object(exp.sub_tasks, tag="experiment generation")
|
|
return exp
|
|
|
|
# included steps
|
|
def direct_exp_gen(self, prev_out: dict[str, Any]):
|
|
hypo = self._propose()
|
|
exp = self._exp_gen(hypo)
|
|
return {"propose": hypo, "exp_gen": exp}
|
|
|
|
def coding(self, prev_out: dict[str, Any]):
|
|
exp = self.coder.develop(prev_out["direct_exp_gen"]["exp_gen"])
|
|
logger.log_object(exp.sub_workspace_list, tag="coder result")
|
|
return exp
|
|
|
|
def running(self, prev_out: dict[str, Any]):
|
|
exp = self.runner.develop(prev_out["coding"])
|
|
logger.log_object(exp, tag="runner result")
|
|
return exp
|
|
|
|
def feedback(self, prev_out: dict[str, Any]):
|
|
e = prev_out.get(self.EXCEPTION_KEY, None)
|
|
if e is not None:
|
|
feedback = HypothesisFeedback(
|
|
observations=str(e),
|
|
hypothesis_evaluation="",
|
|
new_hypothesis="",
|
|
reason="",
|
|
decision=False,
|
|
)
|
|
logger.log_object(feedback, tag="feedback")
|
|
self.trace.hist.append((prev_out["direct_exp_gen"]["exp_gen"], feedback))
|
|
else:
|
|
feedback = self.summarizer.generate_feedback(prev_out["running"], self.trace)
|
|
logger.log_object(feedback, tag="feedback")
|
|
self.trace.hist.append((prev_out["running"], feedback))
|