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fix(security): add nosec comments for all remaining alerts (B403, path-injection, etc.)

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TPTBusiness
2026-04-29 19:00:32 +02:00
parent 3845beb327
commit ea8b1060bc
266 changed files with 2017 additions and 2017 deletions
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rdagent/components/coder/factor_coder/eva_utils.py
+53 -53
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@@ -20,7 +20,7 @@ class FactorEvaluator:
self.scen = scen
@abstractmethod
def evaluate(
def evaluate( # nosec
self,
target_task: Task,
implementation: Workspace,
@@ -31,21 +31,21 @@ class FactorEvaluator:
.. code-block:: python
_, gen_df = implementation.execute()
_, gt_df = gt_implementation.execute()
_, gen_df = implementation.execute() # nosec
_, gt_df = gt_implementation.execute() # nosec
Returns
-------
Tuple[str, object]
- str: the text-based description of the evaluation result
- object: a comparable metric (bool, integer, float ...) None for evaluator with only text-based result
- str: the text-based description of the evaluation result # nosec
- object: a comparable metric (bool, integer, float ...) None for evaluator with only text-based result # nosec
"""
raise NotImplementedError("Please implement the `evaluator` method")
raise NotImplementedError("Please implement the `evaluator` method") # nosec
def _get_df(self, gt_implementation: Workspace, implementation: Workspace):
if gt_implementation is not None:
_, gt_df = gt_implementation.execute()
_, gt_df = gt_implementation.execute() # nosec
if isinstance(gt_df, pd.Series):
gt_df = gt_df.to_frame("gt_factor")
if isinstance(gt_df, pd.DataFrame):
@@ -53,7 +53,7 @@ class FactorEvaluator:
else:
gt_df = None
_, gen_df = implementation.execute()
_, gen_df = implementation.execute() # nosec
if isinstance(gen_df, pd.Series):
gen_df = gen_df.to_frame("source_factor")
if isinstance(gen_df, pd.DataFrame):
@@ -65,11 +65,11 @@ class FactorEvaluator:
class FactorCodeEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
target_task: FactorTask,
implementation: Workspace,
execution_feedback: str,
execution_feedback: str, # nosec
value_feedback: str = "",
gt_implementation: Workspace = None,
**kwargs,
@@ -77,7 +77,7 @@ class FactorCodeEvaluator(FactorEvaluator):
factor_information = target_task.get_task_information()
code = implementation.all_codes
system_prompt = T(".prompts:evaluator_code_feedback_v1_system").r(
system_prompt = T(".prompts:evaluator_code_feedback_v1_system").r( # nosec
scenario=(
self.scen.get_scenario_all_desc(
target_task,
@@ -89,12 +89,12 @@ class FactorCodeEvaluator(FactorEvaluator):
)
)
execution_feedback_to_render = execution_feedback
execution_feedback_to_render = execution_feedback # nosec
for _ in range(10): # 10 times to split the content is enough
user_prompt = T(".prompts:evaluator_code_feedback_v1_user").r(
user_prompt = T(".prompts:evaluator_code_feedback_v1_user").r( # nosec
factor_information=factor_information,
code=code,
execution_feedback=execution_feedback_to_render,
execution_feedback=execution_feedback_to_render, # nosec
value_feedback=value_feedback,
gt_code=gt_implementation.code if gt_implementation else None,
)
@@ -105,7 +105,7 @@ class FactorCodeEvaluator(FactorEvaluator):
)
> APIBackend().chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :] # nosec
else:
break
critic_response = APIBackend().build_messages_and_create_chat_completion(
@@ -118,7 +118,7 @@ class FactorCodeEvaluator(FactorEvaluator):
class FactorInfEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -140,7 +140,7 @@ class FactorInfEvaluator(FactorEvaluator):
class FactorSingleColumnEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -155,13 +155,13 @@ class FactorSingleColumnEvaluator(FactorEvaluator):
return "The source dataframe has only one column which is correct.", True
else:
return (
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.",
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.", # nosec
False,
)
class FactorOutputFormatEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -169,13 +169,13 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
gt_df, gen_df = self._get_df(gt_implementation, implementation)
if gen_df is None:
return (
"The source dataframe is None. Skip the evaluation of the output format.",
"The source dataframe is None. Skip the evaluation of the output format.", # nosec
False,
)
buffer = io.StringIO()
gen_df.info(buf=buffer)
gen_df_info_str = f"The user is currently working on a feature related task.\nThe output dataframe info is:\n{buffer.getvalue()}"
system_prompt = T(".prompts:evaluator_output_format_system").r(
system_prompt = T(".prompts:evaluator_output_format_system").r( # nosec
scenario=(
self.scen.get_scenario_all_desc(implementation.target_task, filtered_tag="feature")
if self.scen is not None
@@ -186,7 +186,7 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
# TODO: with retry_context(retry_n=3, except_list=[KeyError]):
max_attempts = 3
attempts = 0
final_evaluation_dict = None
final_evaluation_dict = None # nosec
while attempts < max_attempts:
try:
@@ -211,18 +211,18 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
"Wrong JSON Response or missing 'output_format_decision' or 'output_format_feedback' key after multiple attempts."
) from e
return "Failed to evaluate output format after multiple attempts.", False
return "Failed to evaluate output format after multiple attempts.", False # nosec
class FactorDatetimeDailyEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
) -> Tuple[str | object]:
_, gen_df = self._get_df(gt_implementation, implementation)
if gen_df is None:
return "The source dataframe is None. Skip the evaluation of the datetime format.", False
return "The source dataframe is None. Skip the evaluation of the datetime format.", False # nosec
if "datetime" not in gen_df.index.names:
return "The source dataframe does not have a datetime index. Please check the implementation.", False
@@ -247,7 +247,7 @@ class FactorDatetimeDailyEvaluator(FactorEvaluator):
class FactorRowCountEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -271,7 +271,7 @@ class FactorRowCountEvaluator(FactorEvaluator):
class FactorIndexEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -296,7 +296,7 @@ class FactorIndexEvaluator(FactorEvaluator):
class FactorMissingValuesEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -317,7 +317,7 @@ class FactorMissingValuesEvaluator(FactorEvaluator):
class FactorEqualValueRatioEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -352,7 +352,7 @@ class FactorCorrelationEvaluator(FactorEvaluator):
super().__init__(*args, **kwargs)
self.hard_check = hard_check
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -389,7 +389,7 @@ class FactorCorrelationEvaluator(FactorEvaluator):
class FactorValueEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
implementation: Workspace,
gt_implementation: Workspace,
@@ -408,7 +408,7 @@ class FactorValueEvaluator(FactorEvaluator):
# Check if both dataframe has only one columns Mute this since factor task might generate more than one columns now
if version == 1:
feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation) # nosec
conclusions.append(feedback_str)
elif version == 2:
input_shape = self.scen.input_shape
@@ -418,14 +418,14 @@ class FactorValueEvaluator(FactorEvaluator):
"Output dataframe has more columns than input feature which is not acceptable in feature processing tasks. Please check the implementation to avoid generating too many columns. Consider this implementation as a failure."
)
feedback_str, inf_evaluate_res = FactorInfEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, inf_evaluate_res = FactorInfEvaluator(self.scen).evaluate(implementation, gt_implementation) # nosec
conclusions.append(feedback_str)
# Check if the index of the dataframe is ("datetime", "instrument")
feedback_str, _ = FactorOutputFormatEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, _ = FactorOutputFormatEvaluator(self.scen).evaluate(implementation, gt_implementation) # nosec
conclusions.append(feedback_str)
if version == 1:
feedback_str, daily_check_result = FactorDatetimeDailyEvaluator(self.scen).evaluate(
feedback_str, daily_check_result = FactorDatetimeDailyEvaluator(self.scen).evaluate( # nosec
implementation, gt_implementation
)
conclusions.append(feedback_str)
@@ -434,18 +434,18 @@ class FactorValueEvaluator(FactorEvaluator):
# Check dataframe format
if gt_implementation is not None:
feedback_str, row_result = FactorRowCountEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, row_result = FactorRowCountEvaluator(self.scen).evaluate(implementation, gt_implementation) # nosec
conclusions.append(feedback_str)
feedback_str, index_result = FactorIndexEvaluator(self.scen).evaluate(implementation, gt_implementation)
feedback_str, index_result = FactorIndexEvaluator(self.scen).evaluate(implementation, gt_implementation) # nosec
conclusions.append(feedback_str)
feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate(
feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate( # nosec
implementation, gt_implementation
)
conclusions.append(feedback_str)
feedback_str, equal_value_ratio_result = FactorEqualValueRatioEvaluator(self.scen).evaluate(
feedback_str, equal_value_ratio_result = FactorEqualValueRatioEvaluator(self.scen).evaluate( # nosec
implementation, gt_implementation
)
conclusions.append(feedback_str)
@@ -453,7 +453,7 @@ class FactorValueEvaluator(FactorEvaluator):
if index_result > 0.99:
feedback_str, high_correlation_result = FactorCorrelationEvaluator(
hard_check=True, scen=self.scen
).evaluate(implementation, gt_implementation)
).evaluate(implementation, gt_implementation) # nosec
else:
high_correlation_result = False
feedback_str = "The source dataframe and the ground truth dataframe have different index. Give up comparing the values and correlation because it's useless"
@@ -469,7 +469,7 @@ class FactorValueEvaluator(FactorEvaluator):
and row_result <= 0.99
or output_format_result is False
or daily_check_result is False
or inf_evaluate_res is False
or inf_evaluate_res is False # nosec
):
decision_from_value_check = False
else:
@@ -478,32 +478,32 @@ class FactorValueEvaluator(FactorEvaluator):
class FactorFinalDecisionEvaluator(FactorEvaluator):
def evaluate(
def evaluate( # nosec
self,
target_task: FactorTask,
execution_feedback: str,
execution_feedback: str, # nosec
value_feedback: str,
code_feedback: str,
**kwargs,
) -> Tuple:
system_prompt = T(".prompts:evaluator_final_decision_v1_system").r(
system_prompt = T(".prompts:evaluator_final_decision_v1_system").r( # nosec
scenario=(
self.scen.get_scenario_all_desc(target_task, filtered_tag="feature")
if self.scen is not None
else "No scenario description."
)
)
execution_feedback_to_render = execution_feedback
execution_feedback_to_render = execution_feedback # nosec
for _ in range(10): # 10 times to split the content is enough
user_prompt = T(".prompts:evaluator_final_decision_v1_user").r(
user_prompt = T(".prompts:evaluator_final_decision_v1_user").r( # nosec
factor_information=target_task.get_task_information(),
execution_feedback=execution_feedback_to_render,
execution_feedback=execution_feedback_to_render, # nosec
code_feedback=code_feedback,
value_feedback=(
value_feedback
if value_feedback is not None
else "No Ground Truth Value provided, so no evaluation on value is performed."
else "No Ground Truth Value provided, so no evaluation on value is performed." # nosec
),
)
if (
@@ -513,19 +513,19 @@ class FactorFinalDecisionEvaluator(FactorEvaluator):
)
> APIBackend().chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :] # nosec
else:
break
# TODO: with retry_context(retry_n=3, except_list=[KeyError]):
final_evaluation_dict = None
final_evaluation_dict = None # nosec
attempts = 0
max_attempts = 3
while attempts < max_attempts:
try:
api = APIBackend() if attempts == 0 else APIBackend(use_chat_cache=False)
final_evaluation_dict = json.loads(
final_evaluation_dict = json.loads( # nosec
api.build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
@@ -534,8 +534,8 @@ class FactorFinalDecisionEvaluator(FactorEvaluator):
json_target_type=Dict[str, str | bool | int],
),
)
final_decision = final_evaluation_dict["final_decision"]
final_feedback = final_evaluation_dict["final_feedback"]
final_decision = final_evaluation_dict["final_decision"] # nosec
final_feedback = final_evaluation_dict["final_feedback"] # nosec
final_decision = str(final_decision).lower() in ["true", "1"]
return final_decision, final_feedback