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feat: trace merging (#836)

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* feat: runnalbe -- add exp_gen_cls param, get_leaves and merge exp gen functionalities

* fix: remove unused scenario_desc and update YAML task labels

* feat: override selection and update merge task description

* lint

* lint

* lint

* lint

* lint

* fix: log competition setting to enable mle_summary

* fix name error
This commit is contained in:
you-n-g
2025-04-29 09:30:45 +08:00
committed by GitHub
parent 45931b1012
commit a73f2dbc12
11 changed files with 126 additions and 15 deletions
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rdagent/app/data_science/loop.py
+12
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@@ -166,6 +166,7 @@ class DataScienceRDLoop(RDLoop):
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")
if DS_RD_SETTING.enable_knowledge_base and DS_RD_SETTING.knowledge_base_version == "v1":
logger.log_object(self.trace.knowledge_base, tag="knowledge_base")
self.trace.knowledge_base.dump()
@@ -228,6 +229,7 @@ class DataScienceRDLoop(RDLoop):
replace_timer: bool = True,
) -> "LoopBase":
session = super().load(path, output_path, do_truncate, replace_timer)
logger.log_object(DS_RD_SETTING.competition, tag="competition") # NOTE: necessary to make mle_summary work.
if DS_RD_SETTING.enable_knowledge_base and DS_RD_SETTING.knowledge_base_version == "v1":
session.trace.knowledge_base = DSKnowledgeBase(
path=DS_RD_SETTING.knowledge_base_path, idea_pool_json_path=DS_RD_SETTING.idea_pool_json_path
@@ -257,6 +259,7 @@ def main(
do_truncate=True,
timeout=None,
replace_timer=True,
exp_gen_cls: str | None = None,
):
"""
@@ -275,6 +278,10 @@ def main(
competition :
do_truncate :
If set to True, the logger will truncate the future log messages by calling `logger.storage.truncate`.
replace_timer :
If session is loaded, should we replace the timer with session.timer
exp_gen_cls :
When we have different stages, we can replace the exp_gen with the new proposal
Auto R&D Evolving loop for models in a Kaggle scenario.
@@ -300,6 +307,11 @@ def main(
kaggle_loop = DataScienceRDLoop(DS_RD_SETTING)
else:
kaggle_loop = DataScienceRDLoop.load(path, output_path, do_truncate, replace_timer)
# replace exp_gen if we have new class
if exp_gen_cls is not None:
kaggle_loop.exp_gen = import_class(exp_gen_cls)(kaggle_loop.exp_gen.scen)
kaggle_loop.run(step_n=step_n, loop_n=loop_n, all_duration=timeout)