Avoid too many return statements within this function.

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        return result

Avoid too many return statements within this function.

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        return lambda x: x <= value  # type: ignore

Avoid too many return statements within this function.

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            return

Avoid too many return statements within this function.

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            return x <= value

Avoid too many return statements within this function.

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        return lambda x: x > value  # type: ignore

Avoid too many return statements within this function.

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                return result

Avoid too many return statements within this function.

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        return Predicate(Relation._and, left=left, right=right, component=left._component), i

Avoid too many return statements within this function.

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            return PreprocessedColumn.apply(list_format)

Avoid too many return statements within this function.

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        return False

Avoid too many return statements within this function.

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            return x != value

Avoid too many return statements within this function.

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            return not self._right(metric or x)  # type: ignore

Method resolve has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  @Override
  public HashMap<String, Metric<?>> resolve(ColumnSchema schema) {
    HashMap<String, Metric<?>> resolvedMetrics = new HashMap<>();

    if (DataTypes.Integral.includes(schema.getType())) {

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Function is_k_item_relevant has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def is_k_item_relevant(self, row: pd.core.series.Series, k: int) -> int:
        if self.convert_non_numeric:
            return 1 if row[self.prediction_column][k - 1] in row[self.target_column] else 0
        else:
            index_ki = row[self.prediction_column].index(k)

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Function register_dataset_udf has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def register_dataset_udf(
    col_names: List[str],
    udf_name: Optional[str] = None,
    metrics: Optional[List[MetricSpec]] = None,
    namespace: Optional[str] = None,

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Method merge has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  @Override
  public T merge(T lhs, T rhs) throws UnsupportedError {
    if (lhs instanceof Double) {
      Double result = lhs.doubleValue() + rhs.doubleValue();
      return (T) result;

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Function truncate_time_ms has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def truncate_time_ms(t: int, granularity: TimeGranularity) -> int:
    dt = datetime.fromtimestamp(t / 1000, tz=tz.tzutc()).replace(second=0, microsecond=0)

    if granularity == TimeGranularity.Minute:
        trunc = dt

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Function get_result_set has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def get_result_set(self, dataset_timestamp: Optional[datetime]) -> SegmentedResultSet:
        segmented_profiles: Dict[str, Dict[Segment, DatasetProfile]] = dict()
        for segment_key in self._cache:
            segments = segmented_profiles.get(segment_key.parent_id)
            if segments is None:

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Function sum_gains has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def sum_gains(self, row: pd.core.series.Series, k: int) -> int:
        if self.convert_non_numeric:
            return sum(
                [1 if pred_val in row[self.target_column] else 0 for pred_val in row[self.prediction_column][:k]]
            )

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Function _drop_non_output_columns has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def _drop_non_output_columns(result: SegmentedResultSet, keep_columns: Set[str]) -> SegmentedResultSet:
    for partition in result._segments.values():
        for segment in partition.values():
            for column in {column for column in segment._columns.keys() if column not in keep_columns}:
                segment._columns.pop(column)

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Function process_batch has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def process_batch(self, batch: List[LoggerMessage], batch_type: Type[LoggerMessage]) -> None:
        if batch_type == TrackMessage:
            self._process_track_messages(cast(List[TrackMessage], batch))
        elif batch_type == FlushMessage:
            self._process_flush_messages(cast(List[FlushMessage], batch))

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