Apply suggestions from code review

Co-authored-by: nabenabe0928 <[email protected]>

Author: ravinkohli

autoPyTorch/datasets/tabular_dataset.py

@@ -89,7 +89,7 @@ def __init__(self,
                          seed=seed, train_transforms=train_transforms,
                          dataset_name=dataset_name,
                          val_transforms=val_transforms)
-        self.issigned = bool(np.any(X.data if self.issparse else X < 0))
+        self.issigned = bool(np.any((X.data if self.issparse else X) < 0))
         if self.output_type is not None:
             if STRING_TO_OUTPUT_TYPES[self.output_type] in CLASSIFICATION_OUTPUTS:
                 self.task_type = TASK_TYPES_TO_STRING[TABULAR_CLASSIFICATION]

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/feature_preprocessing/ExtraTreesPreprocessorClassification.py

@@ -21,7 +21,7 @@
 
 class ExtraTreesPreprocessorClassification(autoPyTorchFeaturePreprocessingComponent):
     """
-    Selects features based on importance weights calculated using extra trees
+    Select features based on importance weights calculated using extra trees
     """
     def __init__(self, bootstrap: bool = True, n_estimators: int = 10,
                  criterion: str = "gini", max_features: float = 0.5,