Switch to 2024 black style (#657)

eland/dataframe.py

@@ -956,8 +956,10 @@ class DataFrame(NDFrame):
         elif verbose is False:  # specifically set to False, not nesc None
             _non_verbose_repr()
         else:
-            _non_verbose_repr() if exceeds_info_cols else _verbose_repr(
+            (
-                number_of_columns
+                _non_verbose_repr()
+                if exceeds_info_cols
+                else _verbose_repr(number_of_columns)
             )
 
         # pandas 0.25.1 uses get_dtype_counts() here. This

eland/field_mappings.py

@@ -443,9 +443,9 @@ class FieldMappings:
                 try:
                     series = df.loc[df.es_field_name == es_field_name_keyword]
                     if not series.empty and series.is_aggregatable.squeeze():
-                        row_as_dict[
+                        row_as_dict["aggregatable_es_field_name"] = (
-                            "aggregatable_es_field_name"
+                            es_field_name_keyword
-                        ] = es_field_name_keyword
+                        )
                     else:
                         row_as_dict["aggregatable_es_field_name"] = None
                 except KeyError:

eland/ml/exporters/_sklearn_deserializers.py

@@ -169,9 +169,11 @@ class TargetMeanEncoder(FunctionTransformer):
         def func(column):
             return np.array(
                 [
-                    target_map[str(category)]
+                    (
-                    if category in target_map
+                        target_map[str(category)]
-                    else fallback_value
+                        if category in target_map
+                        else fallback_value
+                    )
                     for category in column
                 ]
             ).reshape(-1, 1)
@@ -197,9 +199,11 @@ class FrequencyEncoder(FunctionTransformer):
         def func(column):
             return np.array(
                 [
-                    frequency_map[str(category)]
+                    (
-                    if category in frequency_map
+                        frequency_map[str(category)]
-                    else fallback_value
+                        if category in frequency_map
+                        else fallback_value
+                    )
                     for category in column
                 ]
             ).reshape(-1, 1)

eland/ml/pytorch/traceable_model.py

@@ -50,12 +50,10 @@ class TraceableModel(ABC):
         return self._trace()
 
     @abstractmethod
-    def sample_output(self) -> torch.Tensor:
+    def sample_output(self) -> torch.Tensor: ...
-        ...
 
     @abstractmethod
-    def _trace(self) -> TracedModelTypes:
+    def _trace(self) -> TracedModelTypes: ...
-        ...
 
     def classification_labels(self) -> Optional[List[str]]:
         return None

eland/ml/pytorch/transformers.py

@@ -496,8 +496,7 @@ class _TransformerTraceableModel(TraceableModel):
         )
 
     @abstractmethod
-    def _prepare_inputs(self) -> transformers.BatchEncoding:
+    def _prepare_inputs(self) -> transformers.BatchEncoding: ...
-        ...
 
 
 class _TraceableClassificationModel(_TransformerTraceableModel, ABC):

eland/ml/transformers/lightgbm.py

@@ -97,9 +97,11 @@ class LGBMForestTransformer(ModelTransformer):
         return TreeNode(
             node_idx=node_id,
             leaf_value=[float(tree_node_json_obj["leaf_value"])],
-            number_samples=int(tree_node_json_obj["leaf_count"])
+            number_samples=(
-            if "leaf_count" in tree_node_json_obj
+                int(tree_node_json_obj["leaf_count"])
-            else None,
+                if "leaf_count" in tree_node_json_obj
+                else None
+            ),
         )
 
     def build_tree(self, tree_id: int, tree_json_obj: Dict[str, Any]) -> Tree:
@@ -235,9 +237,11 @@ class LGBMClassifierTransformer(LGBMForestTransformer):
         return TreeNode(
             node_idx=node_id,
             leaf_value=leaf_val,
-            number_samples=int(tree_node_json_obj["leaf_count"])
+            number_samples=(
-            if "leaf_count" in tree_node_json_obj
+                int(tree_node_json_obj["leaf_count"])
-            else None,
+                if "leaf_count" in tree_node_json_obj
+                else None
+            ),
         )
 
     def check_model_booster(self) -> None:

eland/operations.py

@@ -1156,9 +1156,11 @@ class Operations:
         # piggy-back on that single aggregation.
         if extended_stats_calls >= 2:
             es_aggs = [
-                ("extended_stats", es_agg)
+                (
-                if es_agg in extended_stats_es_aggs
+                    ("extended_stats", es_agg)
-                else es_agg
+                    if es_agg in extended_stats_es_aggs
+                    else es_agg
+                )
                 for es_agg in es_aggs
             ]
 

noxfile.py

@@ -75,7 +75,7 @@ def lint(session):
     session.run("python", "utils/license-headers.py", "check", *SOURCE_FILES)
     session.run("black", "--check", "--target-version=py38", *SOURCE_FILES)
     session.run("isort", "--check", "--profile=black", *SOURCE_FILES)
-    session.run("flake8", "--ignore=E501,W503,E402,E712,E203", *SOURCE_FILES)
+    session.run("flake8", "--extend-ignore=E203,E402,E501,E704,E712", *SOURCE_FILES)
 
     # TODO: When all files are typed we can change this to .run("mypy", "--strict", "eland/")
     session.log("mypy --show-error-codes --strict eland/")

tests/dataframe/test_iterrows_itertuples_pytest.py

@@ -54,9 +54,13 @@ class TestDataFrameIterrowsItertuples(TestData):
             # Shim which uses pytest.approx() for floating point values inside tuples.
             assert len(left) == len(right)
             assert all(
-                (lt == rt)  # Not floats? Use ==
+                (
-                if not isinstance(lt, float) and not isinstance(rt, float)
+                    # Not floats? Use ==
-                else (lt == pytest.approx(rt))  # If both are floats use pytest.approx()
+                    (lt == rt)
+                    if not isinstance(lt, float) and not isinstance(rt, float)
+                    # If both are floats use pytest.approx()
+                    else (lt == pytest.approx(rt))
+                )
                 for lt, rt in zip(left, right)
             )