Function extract_textual_features has a Cognitive Complexity of 79 (exceeds 5 allowed). Consider refactoring. Open
def extract_textual_features(
candidates: Union[Candidate, List[Candidate]],
) -> Iterator[Tuple[int, str, int]]:
"""Extract textual features.
- Read upRead up
Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"
Further reading
Function _get_window_features has a Cognitive Complexity of 46 (exceeds 5 allowed). Consider refactoring. Open
def _get_window_features(
context: Dict[str, Any],
idxs: List[int],
window: int = settings["featurization"]["textual"]["window_feature"]["size"],
combinations: bool = settings["featurization"]["textual"]["window_feature"][- Read upRead up
Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"
Further reading
Cyclomatic complexity is too high in function extract_textual_features. (26) Open
def extract_textual_features(
candidates: Union[Candidate, List[Candidate]],
) -> Iterator[Tuple[int, str, int]]:
"""Extract textual features.
- Read upRead up
- Exclude checks
Cyclomatic Complexity
Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.
Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:
| Construct | Effect on CC | Reasoning |
|---|---|---|
| if | +1 | An if statement is a single decision. |
| elif | +1 | The elif statement adds another decision. |
| else | +0 | The else statement does not cause a new decision. The decision is at the if. |
| for | +1 | There is a decision at the start of the loop. |
| while | +1 | There is a decision at the while statement. |
| except | +1 | Each except branch adds a new conditional path of execution. |
| finally | +0 | The finally block is unconditionally executed. |
| with | +1 | The with statement roughly corresponds to a try/except block (see PEP 343 for details). |
| assert | +1 | The assert statement internally roughly equals a conditional statement. |
| Comprehension | +1 | A list/set/dict comprehension of generator expression is equivalent to a for loop. |
| Boolean Operator | +1 | Every boolean operator (and, or) adds a decision point. |
Cyclomatic complexity is too high in function _get_window_features. (19) Open
def _get_window_features(
context: Dict[str, Any],
idxs: List[int],
window: int = settings["featurization"]["textual"]["window_feature"]["size"],
combinations: bool = settings["featurization"]["textual"]["window_feature"][- Read upRead up
- Exclude checks
Cyclomatic Complexity
Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.
Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:
| Construct | Effect on CC | Reasoning |
|---|---|---|
| if | +1 | An if statement is a single decision. |
| elif | +1 | The elif statement adds another decision. |
| else | +0 | The else statement does not cause a new decision. The decision is at the if. |
| for | +1 | There is a decision at the start of the loop. |
| while | +1 | There is a decision at the while statement. |
| except | +1 | Each except branch adds a new conditional path of execution. |
| finally | +0 | The finally block is unconditionally executed. |
| with | +1 | The with statement roughly corresponds to a try/except block (see PEP 343 for details). |
| assert | +1 | The assert statement internally roughly equals a conditional statement. |
| Comprehension | +1 | A list/set/dict comprehension of generator expression is equivalent to a for loop. |
| Boolean Operator | +1 | Every boolean operator (and, or) adds a decision point. |
Cyclomatic complexity is too high in function _get_word_feats. (8) Open
def _get_word_feats(span: SpanMention) -> Iterator[str]:
attrib = "words"
if span.stable_id not in unary_word_feats:
unary_word_feats[span.stable_id] = set()- Read upRead up
- Exclude checks
Cyclomatic Complexity
Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.
Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:
| Construct | Effect on CC | Reasoning |
|---|---|---|
| if | +1 | An if statement is a single decision. |
| elif | +1 | The elif statement adds another decision. |
| else | +0 | The else statement does not cause a new decision. The decision is at the if. |
| for | +1 | There is a decision at the start of the loop. |
| while | +1 | There is a decision at the while statement. |
| except | +1 | Each except branch adds a new conditional path of execution. |
| finally | +0 | The finally block is unconditionally executed. |
| with | +1 | The with statement roughly corresponds to a try/except block (see PEP 343 for details). |
| assert | +1 | The assert statement internally roughly equals a conditional statement. |
| Comprehension | +1 | A list/set/dict comprehension of generator expression is equivalent to a for loop. |
| Boolean Operator | +1 | Every boolean operator (and, or) adds a decision point. |
Avoid deeply nested control flow statements. Open
if not to_add:
to_add = "None"
new_pos_tags.append(to_add)Avoid deeply nested control flow statements. Open
for f in unary_tdl_feats[span.stable_id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
for f in _get_word_feats(span):Avoid deeply nested control flow statements. Open
if span.stable_id not in unary_tdl_feats:
unary_tdl_feats[span.stable_id] = set()
for f in get_tdl_feats(xmltree.root, sidxs):
unary_tdl_feats[span.stable_id].add(f)
for f in unary_tdl_feats[span.stable_id]:Avoid deeply nested control flow statements. Open
for f in _get_ddlib_feats(span, get_as_dict(span.sentence), sidxs):
yield candidate.id, f"DDL_{f}", DEF_VALUE
# Add TreeDLib entity features
if span.stable_id not in unary_tdl_feats:Avoid deeply nested control flow statements. Open
if candidate.id not in multinary_tdl_feats:
multinary_tdl_feats[candidate.id] = set()
for f in get_tdl_feats(xmltree.root, s_idxs):
multinary_tdl_feats[candidate.id].add(f)
for f in multinary_tdl_feats[candidate.id]:Avoid deeply nested control flow statements. Open
for span, sent, s_idx, i in zip(
spans, sents, s_idxs, range(len(spans))
):
for f in _get_ddlib_feats(span, sent, s_idx):Avoid deeply nested control flow statements. Open
for f in multinary_tdl_feats[candidate.id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
for i, span in enumerate(spans):Function _get_window_features has 5 arguments (exceeds 4 allowed). Consider refactoring. Open
def _get_window_features(Function _get_ddlib_feats has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring. Open
def _get_ddlib_feats(
span: SpanMention, context: Dict[str, Any], idxs: List[int]
) -> Iterator[str]:
"""Minimalist port of generic mention features from ddlib."""
if span.stable_id not in unary_ddlib_feats:- Read upRead up
Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"