[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

sdgx/data_models/metadata.py

@@ -5,7 +5,7 @@
 from collections.abc import Iterable
 from itertools import chain
 from pathlib import Path
-from typing import Any, Dict, List, Set, Literal, Union
+from typing import Any, Dict, List, Literal, Set, Union
 
 import pandas as pd
 from pydantic import BaseModel, Field, field_validator

sdgx/models/components/optimize/ndarray_loader.py

@@ -37,7 +37,8 @@ def __init__(self, cache_root: str | Path = DEFAULT_CACHE_ROOT, save_to_file=Tru
     def get_auto_save(raw_data) -> NDArrayLoader:
         save_to_file = True
         if isinstance(raw_data, pd.DataFrame) or (
-                isinstance(raw_data, DataLoader) and isinstance(raw_data.data_connector, DataFrameConnector)
+            isinstance(raw_data, DataLoader)
+            and isinstance(raw_data.data_connector, DataFrameConnector)
         ):
             save_to_file = False
         return NDArrayLoader(save_to_file=save_to_file)

sdgx/models/components/optimize/sdv_ctgan/data_transformer.py

@@ -16,7 +16,9 @@
     ClusterBasedNormalizer,
     OneHotEncoder,
 )
-from sdgx.models.components.sdv_rdt.transformers.categorical import NormalizedLabelEncoder
+from sdgx.models.components.sdv_rdt.transformers.categorical import (
+    NormalizedLabelEncoder,
+)
 from sdgx.utils import logger
 
 SpanInfo = namedtuple("SpanInfo", ["dim", "activation_fn"])
@@ -126,11 +128,12 @@ def fit(self, data_loader: DataLoader, discrete_columns=()):
                 #  or column_name in self.metadata.label_columns
                 logger.debug(f"Fitting discrete column {column_name}...")
                 encoder_type = None
-                if self.metadata.categorical_encoder and column_name in self.metadata.categorical_encoder:
+                if (
+                    self.metadata.categorical_encoder
+                    and column_name in self.metadata.categorical_encoder
+                ):
                     encoder_type = self.metadata.categorical_encoder[column_name]
-                column_transform_info = self._fit_discrete(
-                    data_loader[[column_name]], encoder_type
-                )
+                column_transform_info = self._fit_discrete(data_loader[[column_name]], encoder_type)
             else:
                 logger.debug(f"Fitting continuous column {column_name}...")
                 column_transform_info = self._fit_continuous(data_loader[[column_name]])

sdgx/models/ml/single_table/ctgan.py

@@ -442,7 +442,7 @@ def save(self, save_dir: str | Path):
         return SDVBaseSynthesizer.save(self, save_dir / self.MODEL_SAVE_NAME)
 
     @classmethod
-    def load(cls, save_dir: str | Path, device: str=None) -> "CTGANSynthesizerModel":
+    def load(cls, save_dir: str | Path, device: str = None) -> "CTGANSynthesizerModel":
         return SDVBaseSynthesizer.load(save_dir / cls.MODEL_SAVE_NAME, device)
 
     @staticmethod