PEP8

deepcell_toolbox/metrics.py

@@ -63,8 +63,6 @@
 from deepcell_toolbox.compute_overlap import compute_overlap  # pylint: disable=E0401
 from deepcell_toolbox.compute_overlap_3D import compute_overlap_3D
 
-# TODO: store the object/pixel metrics on Metrics better
-# TODO: clean up print functions
 
 class Detection(object):  # pylint: disable=useless-object-inheritance
     """Object to hold relevant information about a given detection."""
@@ -92,7 +90,7 @@ def __eq__(self, other):
     def __hash__(self):
         """Custom hasher, allow Detections to be hashable."""
         return tuple((self.true_index, self.pred_index)).__hash__()
-    
+
     def __repr__(self):
         return 'Detection({}, {})'.format(self.true_index, self.pred_index)
 
@@ -178,10 +176,10 @@ def __init__(self, y_true, y_pred):
         if not np.issubdtype(y_pred.dtype, np.integer):
             warnings.warn('Casting y_pred from {} to int'.format(y_pred.dtype))
             y_pred = y_pred.astype('int32')
-        
+
         self.y_true = y_true
         self.y_pred = y_pred
-    
+
     def to_dict(self):
         return dict()
 
@@ -256,7 +254,7 @@ def precision(self):
         except ZeroDivisionError:
             _precision = 0
         return _precision
-    
+
     @property
     def f1(self):
         _recall = self.recall
@@ -365,7 +363,7 @@ def __init__(self,
             raise ValueError('Expected dimensions for y_true (2D data) are 2 '
                              '(x, y) and 3 (x, y, chan). '
                              'Got ndim: {}'.format(y_true.ndim))
-        
+
         elif is_3d and y_true.ndim != 3:
             raise ValueError('Expected dimensions for y_true (3D data) is 3.'
                              'Requires format is: (z, x, y)'
@@ -501,7 +499,7 @@ def _get_modified_iou(self, force_event_links):
             force_event_links (:obj:`bool'): Whether to modify IOU values of
                 large objects if they have been split or merged by
                 a small object.
-        
+
         Returns:
             np.array: The modified IoU matrix.
         """
@@ -520,8 +518,10 @@ def _get_modified_iou(self, force_event_links):
             pred_mask = self.y_pred == pred_label
 
             # fraction of true cell that is contained within pred cell, vice versa
-            true_in_pred = np.count_nonzero(self.y_true[pred_mask] == true_label) / np.sum(true_mask)
-            pred_in_true = np.count_nonzero(self.y_pred[true_mask] == pred_label) / np.sum(pred_mask)
+            true_in_pred = np.count_nonzero(
+                self.y_true[pred_mask] == true_label) / np.sum(true_mask)
+            pred_in_true = np.count_nonzero(
+                self.y_pred[true_mask] == pred_label) / np.sum(pred_mask)
 
             iou_val = self.iou[true_idx, pred_idx]
             max_val = np.max([true_in_pred, pred_in_true])
@@ -981,7 +981,7 @@ def calc_pixel_stats(self, y_true, y_pred, axis=-1):
         Args:
             y_true (numpy.array): Ground truth annotations after transform
             y_pred (numpy.array): Model predictions without labeling
-        
+
         Returns:
             list: list of dictionaries with each stat being a key.
 
@@ -1079,7 +1079,7 @@ def calc_object_stats(self, y_true, y_pred, progbar=True):
 
         all_object_metrics = []  # store all calculated metrics
         is_batch_relabeled = False  # used to warn if batches were relabeled
-        
+
         for i in tqdm(range(y_true.shape[0]), disable=not progbar):
             # check if labels aren't sequential, raise warning on first occurence if so
             true_batch, pred_batch = y_true[i], y_pred[i]
@@ -1136,7 +1136,7 @@ def print_object_report(self, object_metrics):
         }
         for k in errors:
             errors[k] = int(object_metrics[k].sum())
-        
+
         bad_detections = [
             'gained_det_from_split',
             'missed_det_from_merge',

deepcell_toolbox/metrics_test.py

@@ -295,7 +295,7 @@ def test_init(self):
         true_idx, pred_idx = [1, 2], [2, 3]
         detection = metrics.Detection(true_idx, pred_idx)
         assert detection.is_catastrophe
-    
+
     def test_hash(self):
         # test that Detections get hashed appropriately
         detection_set = set()
@@ -306,7 +306,7 @@ def test_hash(self):
         assert d2 is not d1
         # should be in the set since d2 == d1
         assert d2 in detection_set
-    
+
     def test_eq(self):
         # test Detection equality comparisons
         detection_set = set()
@@ -317,12 +317,13 @@ def test_eq(self):
         assert d2 is not d1
         assert d2 == d1
 
-        assert d1 != None
+        assert d1 is not None
 
         print(d1)  # test that __repr__ is called
 
 
 class TestPixelMetrics():
+
     def test_init(self):
         y_true, _ = _sample1(10, 10, 30, 30, True)
 
@@ -332,10 +333,10 @@ def test_init(self):
         # Test mismatched input size
         with pytest.raises(ValueError):
             metrics.PixelMetrics(y_true, y_true[0])
-        
+
         # using float dtype warns but still works
         o = metrics.PixelMetrics(y_true.astype('float'), y_true.astype('float'))
-    
+
     def test_y_true_equals_y_pred(self):
         y_true, _ = _sample1(10, 10, 30, 30, True)
         y_pred = y_true.copy()
@@ -360,7 +361,7 @@ def test_y_pred_empty(self):
         assert o.precision == 0
         assert o.f1 == 0
         assert o.jaccard == 0
-    
+
     def test_y_true_empty(self):
         y_pred, _ = _sample1(10, 10, 30, 30, True)
 
@@ -398,11 +399,11 @@ def test_init(self):
         y_true = np.zeros(shape=(10, 15, 15, 10))  # too many dimensions
         with pytest.raises(ValueError):
             metrics.ObjectMetrics(y_true, y_true, is_3d=True)
-        
+
         # Test mismatched input size
         with pytest.raises(ValueError):
             metrics.ObjectMetrics(y_true, y_true[0])
-        
+
         # using float dtype warns but still works
         o = metrics.PixelMetrics(y_true.astype('float'), y_true.astype('float'))
 
@@ -446,7 +447,7 @@ def test_y_pred_empty(self):
         assert o.precision == 0
         assert o.f1 == 0
         assert o.jaccard == 0
-    
+
     def test_y_true_empty(self):
         y_pred, _ = _sample1(10, 10, 30, 30, True)
 
@@ -620,4 +621,4 @@ def test_split_stack():
     with pytest.raises(ValueError):
         metrics.split_stack(arr, False, 11, 0, 10, 1)
     with pytest.raises(ValueError):
-        metrics.split_stack(arr, False, 10, 0, 11, 1)
+        metrics.split_stack(arr, False, 10, 0, 11, 1)