formatting

Signed-off-by: abigailt <abigailt@il.ibm.com>

apt/minimization/minimizer.py

@@ -878,7 +878,6 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
                         rows[feature] = generalizations['range_representatives'][feature][r_index]
         return original_data_generalized
 
-
     def _generalize_from_tree(self, original_data, prepared_data, level_nodes, cells, cells_by_id):
         mapping_to_cells = self._map_to_cells(prepared_data, level_nodes, cells_by_id)
         all_indexes = []
@@ -1199,6 +1198,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
             else:
                 ranges[feature] = list(set(ranges[feature]))
                 ranges[feature].sort()
+                prev_value = 0
                 for index, value in enumerate(ranges[feature]):
                     if index == 0:
                         # for first range, use min value

apt/utils/datasets/datasets.py

@@ -20,7 +20,7 @@ from scipy.sparse import csr_matrix
 logger = logging.getLogger(__name__)
 
 
-INPUT_DATA_ARRAY_TYPE = Union[np.ndarray, pd.DataFrame, List, Tensor]
+INPUT_DATA_ARRAY_TYPE = Union[np.ndarray, pd.DataFrame, List, Tensor, csr_matrix]
 OUTPUT_DATA_ARRAY_TYPE = np.ndarray
 DATA_PANDAS_NUMPY_TYPE = Union[np.ndarray, pd.DataFrame]
 
@@ -30,15 +30,15 @@ def array2numpy(arr: INPUT_DATA_ARRAY_TYPE) -> OUTPUT_DATA_ARRAY_TYPE:
     """
     converts from INPUT_DATA_ARRAY_TYPE to numpy array
     """
-    if type(arr) == np.ndarray:
+    if isinstance(arr, np.ndarray):
         return arr
-    if type(arr) == pd.DataFrame or type(arr) == pd.Series:
+    if isinstance(arr, pd.DataFrame) or isinstance(arr, pd.Series):
         return arr.to_numpy()
     if isinstance(arr, list):
         return np.array(arr)
-    if type(arr) == Tensor:
+    if isinstance(arr, Tensor):
         return arr.detach().cpu().numpy()
-    if type(arr) == csr_matrix:
+    if isinstance(arr, csr_matrix):
         return arr.toarray()
 
     raise ValueError("Non supported type: ", type(arr).__name__)
@@ -48,15 +48,15 @@ def array2torch_tensor(arr: INPUT_DATA_ARRAY_TYPE) -> Tensor:
     """
     converts from INPUT_DATA_ARRAY_TYPE to torch tensor array
     """
-    if type(arr) == np.ndarray:
+    if isinstance(arr, np.ndarray):
         return torch.from_numpy(arr)
-    if type(arr) == pd.DataFrame or type(arr) == pd.Series:
+    if isinstance(arr, pd.DataFrame) or isinstance(arr, pd.Series):
         return torch.from_numpy(arr.to_numpy())
     if isinstance(arr, list):
         return torch.tensor(arr)
-    if type(arr) == Tensor:
+    if isinstance(arr, Tensor):
         return arr
-    if type(arr) == csr_matrix:
+    if isinstance(arr, csr_matrix):
         return torch.from_numpy(arr.toarray())
 
     raise ValueError("Non supported type: ", type(arr).__name__)
@@ -222,7 +222,7 @@ class ArrayDataset(Dataset):
 
     def __init__(self, x: INPUT_DATA_ARRAY_TYPE, y: Optional[INPUT_DATA_ARRAY_TYPE] = None,
                  features_names: Optional[list] = None, **kwargs):
-        self.is_pandas = self.is_pandas = type(x) == pd.DataFrame or type(x) == pd.Series
+        self.is_pandas = self.is_pandas = isinstance(x, pd.DataFrame) or isinstance(x, pd.Series)
 
         self.features_names = features_names
         self._y = array2numpy(y) if y is not None else None
@@ -330,7 +330,7 @@ class PytorchData(Dataset):
         self._y = array2torch_tensor(y) if y is not None else None
         self._x = array2torch_tensor(x)
 
-        self.is_pandas = type(x) == pd.DataFrame or type(x) == pd.Series
+        self.is_pandas = isinstance(x, pd.DataFrame) or isinstance(x, pd.Series)
 
         if self.is_pandas:
             self.features_names = x.columns

apt/utils/models/model.py

@@ -43,7 +43,7 @@ def get_nb_classes(y: OUTPUT_DATA_ARRAY_TYPE) -> int:
     if y is None:
         return 0
 
-    if type(y) != np.ndarray:
+    if not isinstance(y, np.ndarray):
         raise ValueError("Input should be numpy array")
 
     if is_one_hot(y):
@@ -339,8 +339,8 @@ class BlackboxClassifierPredictions(BlackboxClassifier):
             y_test_pred = check_and_transform_label_format(y_test_pred, nb_classes=self._nb_classes)
 
         if x_train_pred is not None and y_train_pred is not None and x_test_pred is not None and y_test_pred is not None:
-            if type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray \
-               or type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray:
+            if not isinstance(y_train_pred, np.ndarray) or not isinstance(y_test_pred, np.ndarray) \
+               or not isinstance(y_train_pred, np.ndarray) or not isinstance(y_test_pred, np.ndarray):
                 raise NotImplementedError("X/Y Data should be numpy array")
             x_pred = np.vstack((x_train_pred, x_test_pred))
             y_pred = np.vstack((y_train_pred, y_test_pred))

tests/test_minimizer.py

@@ -246,6 +246,7 @@ def create_encoder(numeric_features, categorical_features, x):
 
     return preprocessor, encoded
 
+
 def test_minimizer_params_not_transform(get_cells):
     # Assume two features, age and height, and boolean label
     cells, features, x, y = get_cells
@@ -385,7 +386,7 @@ def test_minimizer_fit_not_transform(get_data_two_features):
 
     gen.fit(dataset=train_dataset, generalize_using_transform=False)
     gener = gen.generalizations
-    expected_generalizations = {'ranges': {'age': [], 'height':[157.0]}, 'categories': {}, 'untouched': []}
+    expected_generalizations = {'ranges': {'age': [], 'height': [157.0]}, 'categories': {}, 'untouched': []}
 
     compare_generalizations(gener, expected_generalizations)