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Several update on the repo (see desc) (#76)

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* ignore result csv file

* fix app scripts

* rename taskgenerator to developer and generate to develop

* fix a config bug in coder

* fix a small bug in factor coder evaluators

* remove a single logger in factor coder evaluators

* fix a small bug in model coder main.py

* rename Implementation to Workspace

* move the prepare the inject_code into FBWorkspace to align all the behavior

* fix a small bug in model feedback

* remove debug lines for multi processing and simplify evaluators multi proc

* add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging

* make hypothesisgen a abc class

* use Qlib***Experiment

* fix a small bug

* rename Imp to Ws

* rename sub_implementations to sub_workspace_list

* fix a bug in feedback not presented as content in prompts

* move proposal pys to proposal folder

* reformat the folder

* align factor and model qlib workspace and use template to handle the workspace

* add a filter to evoagent to filter out false evo

* align multi_proc_n into RDAGENT seeting

* handle when runner gets empty experiment

* fix logger merge remaining problems

* fix black and isort automatically
This commit is contained in:
Xu Yang
2024-07-17 15:00:13 +08:00
committed by GitHub
parent eee2b3c56a
commit e0a24fb46f
76 changed files with 804 additions and 702 deletions
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rdagent/components/coder/factor_coder/CoSTEER/evolving_strategy.py
+22 -38
View File
@@ -16,14 +16,11 @@ from rdagent.components.coder.factor_coder.CoSTEER.scheduler import (
LLMSelect,
RandomSelect,
)
from rdagent.components.coder.factor_coder.factor import (
FactorTask,
FileBasedFactorImplementation,
)
from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
from rdagent.core.conf import RD_AGENT_SETTINGS
from rdagent.core.evolving_framework import EvolvingStrategy, QueriedKnowledge
from rdagent.core.experiment import Implementation
from rdagent.core.experiment import Workspace
from rdagent.core.prompts import Prompts
from rdagent.core.utils import multiprocessing_wrapper
from rdagent.oai.llm_utils import APIBackend
@@ -43,7 +40,7 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
self,
target_task: FactorTask,
queried_knowledge: QueriedKnowledge = None,
) -> Implementation:
) -> Workspace:
raise NotImplementedError
def evolve(
@@ -53,15 +50,12 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
queried_knowledge: FactorQueriedKnowledge | None = None,
**kwargs,
) -> FactorEvolvingItem:
self.num_loop += 1
new_evo = deepcopy(evo)
# 1.找出需要evolve的factor
to_be_finished_task_index = []
for index, target_factor_task in enumerate(new_evo.sub_tasks):
for index, target_factor_task in enumerate(evo.sub_tasks):
target_factor_task_desc = target_factor_task.get_task_information()
if target_factor_task_desc in queried_knowledge.success_task_to_knowledge_dict:
new_evo.sub_implementations[index] = queried_knowledge.success_task_to_knowledge_dict[
evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
target_factor_task_desc
].implementation
elif (
@@ -87,30 +81,27 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
to_be_finished_task_index = LLMSelect(
to_be_finished_task_index,
implementation_factors_per_round,
new_evo,
evo,
queried_knowledge.former_traces,
self.scen,
)
result = multiprocessing_wrapper(
[
(self.implement_one_factor, (new_evo.sub_tasks[target_index], queried_knowledge))
(self.implement_one_factor, (evo.sub_tasks[target_index], queried_knowledge))
for target_index in to_be_finished_task_index
],
n=FACTOR_IMPLEMENT_SETTINGS.evo_multi_proc_n,
n=RD_AGENT_SETTINGS.multi_proc_n,
)
for index, target_index in enumerate(to_be_finished_task_index):
new_evo.sub_implementations[target_index] = result[index]
if evo.sub_workspace_list[target_index] is None:
evo.sub_workspace_list[target_index] = FactorFBWorkspace(target_task=evo.sub_tasks[target_index])
evo.sub_workspace_list[target_index].inject_code(**{"factor.py": result[index]})
# for target_index in to_be_finished_task_index:
# new_evo.sub_implementations[target_index] = self.implement_one_factor(
# new_evo.sub_tasks[target_index], queried_knowledge
# )
evo.corresponding_selection = to_be_finished_task_index
new_evo.corresponding_selection = to_be_finished_task_index
return new_evo
return evo
class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
@@ -118,7 +109,7 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
self,
target_task: FactorTask,
queried_knowledge: FactorQueriedKnowledgeV1 = None,
) -> Implementation:
) -> str:
factor_information_str = target_task.get_task_information()
if queried_knowledge is not None and factor_information_str in queried_knowledge.success_task_to_knowledge_dict:
@@ -149,7 +140,7 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
)
)
session = APIBackend(use_chat_cache=False).build_chat_session(
session = APIBackend(use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache).build_chat_session(
session_system_prompt=system_prompt,
)
@@ -185,14 +176,8 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
json_mode=True,
),
)["code"]
# ast.parse(code)
factor_implementation = FileBasedFactorImplementation(
target_task,
)
factor_implementation.prepare()
factor_implementation.inject_code(**{"factor.py": code})
return factor_implementation
return code
class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
@@ -205,7 +190,7 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
self,
target_task: FactorTask,
queried_knowledge,
) -> Implementation:
) -> str:
error_summary = FACTOR_IMPLEMENT_SETTINGS.v2_error_summary
# 1. 提取因子的背景信息
target_factor_task_information = target_task.get_task_information()
@@ -249,7 +234,7 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
)
)
session = APIBackend(use_chat_cache=False).build_chat_session(
session = APIBackend(use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache).build_chat_session(
session_system_prompt=system_prompt,
)
@@ -276,7 +261,9 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
)
.strip("\n")
)
session_summary = APIBackend(use_chat_cache=False).build_chat_session(
session_summary = APIBackend(
use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache
).build_chat_session(
session_system_prompt=error_summary_system_prompt,
)
for _ in range(10): # max attempt to reduce the length of error_summary_user_prompt
@@ -335,7 +322,4 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
json_mode=True,
)
code = json.loads(response)["code"]
factor_implementation = FileBasedFactorImplementation(target_task)
factor_implementation.prepare()
factor_implementation.inject_code(**{"factor.py": code})
return factor_implementation
return code