Fix formatting with pre-commit

- Changed single quotes to double quotes for consistency
- Removed trailing whitespace
- Fixed line spacing

Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

flaml/automl/automl.py

@@ -829,7 +829,7 @@ class AutoML(BaseEstimator):
 
         # Only flatten if not multi-target regression
         if isinstance(y_pred, np.ndarray) and y_pred.ndim > 1:
-            is_multi_target = getattr(self._state, 'is_multi_target', False)
+            is_multi_target = getattr(self._state, "is_multi_target", False)
             if not is_multi_target:
                 y_pred = y_pred.flatten()
         if self._label_transformer:
@@ -2495,7 +2495,7 @@ class AutoML(BaseEstimator):
         self._state.error_metric = error_metric
 
         is_spark_dataframe = isinstance(X_train, psDataFrame) or isinstance(dataframe, psDataFrame)
-        is_multi_target = getattr(self._state, 'is_multi_target', False)
+        is_multi_target = getattr(self._state, "is_multi_target", False)
         estimator_list = task.default_estimator_list(estimator_list, is_spark_dataframe, is_multi_target)
 
         if is_spark_dataframe and self._use_spark:

flaml/automl/data.py

@@ -373,14 +373,14 @@ class DataTransformer:
                 datetime_columns,
             )
             self._drop = drop
-        
+
         # Check if y is multi-target (DataFrame or 2D array with multiple targets)
         is_multi_target = False
         if isinstance(y, DataFrame) and y.shape[1] > 1:
             is_multi_target = True
         elif isinstance(y, np.ndarray) and y.ndim == 2 and y.shape[1] > 1:
             is_multi_target = True
-        
+
         # Skip label encoding for multi-target regression
         if is_multi_target and task.is_regression():
             self.label_transformer = None

flaml/automl/model.py

@@ -2112,7 +2112,7 @@ class CatBoostEstimator(BaseEstimator):
             cat_features = list(X_train.select_dtypes(include="category").columns)
         else:
             cat_features = []
-        
+
         # Detect multi-target regression and set appropriate loss function
         is_multi_target = False
         if self._task.is_regression():
@@ -2120,10 +2120,10 @@ class CatBoostEstimator(BaseEstimator):
                 is_multi_target = True
             elif isinstance(y_train, DataFrame) and y_train.shape[1] > 1:
                 is_multi_target = True
-        
+
         if is_multi_target and "loss_function" not in self.params:
             self.params["loss_function"] = "MultiRMSE"
-        
+
         use_best_model = kwargs.get("use_best_model", True)
         n = max(int(len(y_train) * 0.9), len(y_train) - 1000) if use_best_model else len(y_train)
         X_tr, y_tr = X_train[:n], y_train[:n]

flaml/automl/task/generic_task.py

@@ -231,7 +231,7 @@ class GenericTask(Task):
             elif isinstance(automl._y_train_all, pd.DataFrame):
                 is_multi_target = True
                 n_targets = automl._y_train_all.shape[1]
-        
+
         state.is_multi_target = is_multi_target
         state.n_targets = n_targets
 
@@ -1287,7 +1287,9 @@ class GenericTask(Task):
         pred_time /= n
         return val_loss, metric, train_time, pred_time
 
-    def default_estimator_list(self, estimator_list: List[str], is_spark_dataframe: bool = False, is_multi_target: bool = False) -> List[str]:
+    def default_estimator_list(
+        self, estimator_list: List[str], is_spark_dataframe: bool = False, is_multi_target: bool = False
+    ) -> List[str]:
         if "auto" != estimator_list:
             n_estimators = len(estimator_list)
             if is_spark_dataframe:
@@ -1316,7 +1318,7 @@ class GenericTask(Task):
                         "Non-spark dataframes only support estimator names not ending with `_spark`. Non-supported "
                         "estimators are removed."
                     )
-            
+
             # Filter out unsupported estimators for multi-target regression
             if is_multi_target and self.is_regression():
                 # List of estimators that support multi-target regression natively
@@ -1382,7 +1384,7 @@ class GenericTask(Task):
             for est in estimator_list
             if (est.endswith("_spark") if is_spark_dataframe else not est.endswith("_spark"))
         ]
-        
+
         # Filter for multi-target regression support
         if is_multi_target and self.is_regression():
             # List of estimators that support multi-target regression natively
@@ -1393,7 +1395,7 @@ class GenericTask(Task):
                     "Multi-target regression only supports estimators: xgboost, xgb_limitdepth, catboost. "
                     "No supported estimator is available."
                 )
-        
+
         return estimator_list
 
     def default_metric(self, metric: str) -> str:

flaml/automl/task/time_series_task.py

@@ -458,7 +458,9 @@ class TimeSeriesTask(Task):
         pred_time /= n
         return val_loss, metric, train_time, pred_time
 
-    def default_estimator_list(self, estimator_list: List[str], is_spark_dataframe: bool, is_multi_target: bool = False) -> List[str]:
+    def default_estimator_list(
+        self, estimator_list: List[str], is_spark_dataframe: bool, is_multi_target: bool = False
+    ) -> List[str]:
         assert not is_spark_dataframe, "Spark is not yet supported for time series"
 
         # TODO: why not do this if/then in the calling function?

test/automl/test_multi_target.py

@@ -16,9 +16,7 @@ class TestMultiTargetRegression(unittest.TestCase):
     def setUp(self):
         """Create multi-target regression datasets for testing."""
         # Create synthetic multi-target regression data
-        self.X, self.y = make_regression(
-            n_samples=200, n_features=10, n_targets=3, random_state=42, noise=0.1
-        )
+        self.X, self.y = make_regression(n_samples=200, n_features=10, n_targets=3, random_state=42, noise=0.1)
         self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
             self.X, self.y, test_size=0.2, random_state=42
         )