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feat: filter feature which is high correlation to former implemented features (#145)

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* filter feature which is high correlation to former implemented features

* use multiprocessing to calculate IC and some minor fix
This commit is contained in:
Xu Yang
2024-08-02 14:41:17 +08:00
committed by GitHub
parent 7d279721b4
commit 060f569720
11 changed files with 276 additions and 12 deletions
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rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
-1
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@@ -611,7 +611,6 @@ class FactorEvaluatorForCoder(FactorEvaluator):
value_feedback=factor_feedback.factor_value_feedback,
code_feedback=factor_feedback.code_feedback,
)
logger.info(factor_feedback.final_decision)
return factor_feedback
rdagent/components/coder/factor_coder/config.py
+1 -1
View File
@@ -44,7 +44,7 @@ class FactorImplementSettings(BaseSettings):
max_loop: int = 10
knowledge_base_path: Union[str, None] = None
new_knowledge_base_path: Union[str, None] = None
new_knowledge_base_path: Union[str, None] = knowledge_base_path
python_bin: str = "python"
rdagent/components/coder/factor_coder/factor.py
-1
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@@ -70,7 +70,6 @@ class FactorFBWorkspace(FBWorkspace):
) -> None:
super().__init__(*args, **kwargs)
self.executed_factor_value_dataframe = executed_factor_value_dataframe
self.logger = logger
self.raise_exception = raise_exception
@staticmethod