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* fix isort & black & toml-sort & sphinx error

* fix ci error

* fix ci error

* add comments

* Update Makefile

* change sphinx build command

* add auto-lint

* add black args

* format with black

* Auto Linting document

* fix ci error

---------

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: Young <afe.young@gmail.com>
This commit is contained in:
Linlang
2024-07-26 12:12:16 +08:00
committed by GitHub
parent 45b7a169fe
commit c7cfd397ca
56 changed files with 604 additions and 475 deletions
Download Patch File Download Diff File Expand all files Collapse all files
rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
+10 -4
View File
@@ -356,7 +356,9 @@ class FactorValueEvaluator(FactorEvaluator):
# Check if both dataframe have the same rows count
if gt_implementation is not None:
feedback_str, single_column_result = FactorRowCountEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, single_column_result = FactorRowCountEvaluator(self.scen).evaluate(
implementation, gt_implementation
)
conclusions.append(feedback_str)
feedback_str, same_index_result = FactorIndexEvaluator(self.scen).evaluate(
@@ -364,7 +366,9 @@ class FactorValueEvaluator(FactorEvaluator):
)
conclusions.append(feedback_str)
feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate(
implementation, gt_implementation
)
conclusions.append(feedback_str)
feedback_str, equal_value_ratio_result = FactorEqualValueCountEvaluator(self.scen).evaluate(
@@ -464,8 +468,10 @@ class FactorFinalDecisionEvaluator(Evaluator):
except KeyError as e:
attempts += 1
if attempts >= max_attempts:
raise KeyError("Response from API is missing 'final_decision' or 'final_feedback' key after multiple attempts.") from e
raise KeyError(
"Response from API is missing 'final_decision' or 'final_feedback' key after multiple attempts."
) from e
return None, None
rdagent/components/coder/factor_coder/CoSTEER/evolving_strategy.py
+6 -2
View File
@@ -68,8 +68,12 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
# if the number of factors to be implemented is larger than the limit, we need to select some of them
if FACTOR_IMPLEMENT_SETTINGS.select_ratio < 1:
# if the number of loops is equal to the select_loop, we need to select some of them
implementation_factors_per_round = round(FACTOR_IMPLEMENT_SETTINGS.select_ratio * len(to_be_finished_task_index) + 0.5) # ceilling
implementation_factors_per_round = min(implementation_factors_per_round, len(to_be_finished_task_index)) # but not exceed the total number of tasks
implementation_factors_per_round = round(
FACTOR_IMPLEMENT_SETTINGS.select_ratio * len(to_be_finished_task_index) + 0.5
) # ceilling
implementation_factors_per_round = min(
implementation_factors_per_round, len(to_be_finished_task_index)
) # but not exceed the total number of tasks
if FACTOR_IMPLEMENT_SETTINGS.select_method == "random":
to_be_finished_task_index = RandomSelect(
rdagent/components/coder/factor_coder/factor.py
+1 -5
View File
@@ -10,11 +10,7 @@ import pandas as pd
from filelock import FileLock
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.core.exception import (
CodeFormatError,
NoOutputError,
CustomRuntimeError,
)
from rdagent.core.exception import CodeFormatError, CustomRuntimeError, NoOutputError
from rdagent.core.experiment import Experiment, FBWorkspace, Task
from rdagent.log import rdagent_logger as logger
from rdagent.oai.llm_utils import md5_hash