Merge pull request #109 from dianna-ai/fix_axis_labels_name

Rename axes_labels parameter to axis_labels

Author: loostrum

dianna/methods/lime.py

@@ -20,7 +20,7 @@ def __init__(self,
                  mask_string=None,
                  random_state=None,
                  char_level=False,
-                 axes_labels=None,
+                 axis_labels=None,
                  preprocess_function=None,
                  ):  # pylint: disable=too-many-arguments
         """
@@ -37,7 +37,7 @@ def __init__(self,
             mask_string (str, optional): mask string
             random_state (int or np.RandomState, optional): seed or random state
             char_level (bool, optional): char level
-            axes_labels (dict/list, optional): If a dict, key,value pairs of axis index, name.
+            axis_labels (dict/list, optional): If a dict, key,value pairs of axis index, name.
                                                If a list, the name of each axis where the index
                                                in the list is the axis index
             preprocess_function (callable, optional): Function to preprocess input data with
@@ -62,7 +62,7 @@ def __init__(self,
                                                   )
 
         self.preprocess_function = preprocess_function
-        self.axes_labels = axes_labels if axes_labels is not None else []
+        self.axis_labels = axis_labels if axis_labels is not None else []
 
     def explain_text(self,
                      model_or_function,
@@ -166,7 +166,7 @@ def _prepare_image_data(self, input_data):
         Returns:
             transformed input data, preprocessing function to use with utils.get_function()
         """
-        input_data = utils.to_xarray(input_data, self.axes_labels, LIME.required_labels)
+        input_data = utils.to_xarray(input_data, self.axis_labels, LIME.required_labels)
         # remove batch axis from input data; this is only here for a consistent API
         # but LIME wants data without batch axis
         if not len(input_data['batch']) == 1:

dianna/methods/rise.py

@@ -19,14 +19,14 @@ class RISE:
     required_labels = ('batch', 'channels')
 
     def __init__(self, n_masks=1000, feature_res=8, p_keep=0.5,  # pylint: disable=too-many-arguments
-                 axes_labels=None, preprocess_function=None):
+                 axis_labels=None, preprocess_function=None):
         """RISE initializer.
 
         Args:
             n_masks (int): Number of masks to generate.
             feature_res (int): Resolution of features in masks.
             p_keep (float): Fraction of image to keep in each mask
-            axes_labels (dict/list, optional): If a dict, key,value pairs of axis index, name.
+            axis_labels (dict/list, optional): If a dict, key,value pairs of axis index, name.
                                                If a list, the name of each axis where the index
                                                in the list is the axis index
             preprocess_function (callable, optional): Function to preprocess input data with
@@ -37,7 +37,7 @@ def __init__(self, n_masks=1000, feature_res=8, p_keep=0.5,  # pylint: disable=t
         self.preprocess_function = preprocess_function
         self.masks = None
         self.predictions = None
-        self.axes_labels = axes_labels if axes_labels is not None else []
+        self.axis_labels = axis_labels if axis_labels is not None else []
 
     def explain_text(self, model_or_function, input_text, labels=(0,), batch_size=100):
         """Runs the RISE explainer on text.
@@ -136,7 +136,7 @@ def explain_image(self, model_or_function, input_data, labels=None, batch_size=1
             Explanation heatmap for each class (np.ndarray).
         """
         # convert data to xarray
-        input_data = utils.to_xarray(input_data, self.axes_labels, RISE.required_labels)
+        input_data = utils.to_xarray(input_data, self.axis_labels, RISE.required_labels)
         # batch axis should always be first
         input_data = utils.move_axis(input_data, 'batch', 0)
         input_data, full_preprocess_function = self._prepare_image_data(input_data)

dianna/utils/misc.py

@@ -38,20 +38,20 @@ def get_kwargs_applicable_to_function(function, kwargs):
             if key in function.__code__.co_varnames}
 
 
-def to_xarray(data, axes_labels, required_labels=None):
+def to_xarray(data, axis_labels, required_labels=None):
     """Converts numpy data and axes labels to an xarray object."""
-    if isinstance(axes_labels, dict):
+    if isinstance(axis_labels, dict):
         # key = axis index, value = label
         # not all axes have to be present in the input, but we need to provide
         # a name for each axis
         # first ensure negative indices are converted to positive ones
-        indices = list(axes_labels.keys())
+        indices = list(axis_labels.keys())
         for index in indices:
             if index < 0:
-                axes_labels[data.ndim + index] = axes_labels.pop(index)
-        labels = [axes_labels[index] if index in axes_labels else f'dim_{index}' for index in range(data.ndim)]
+                axis_labels[data.ndim + index] = axis_labels.pop(index)
+        labels = [axis_labels[index] if index in axis_labels else f'dim_{index}' for index in range(data.ndim)]
     else:
-        labels = list(axes_labels)
+        labels = list(axis_labels)
 
     # check if the required labels are present
     if required_labels is not None:

tests/test_common_usage.py

@@ -5,10 +5,10 @@
 
 
 input_data = np.random.random((1, 224, 224, 3))
-axes_labels = {0: 'batch', -1: 'channels'}
+axis_labels = {0: 'batch', -1: 'channels'}
 
 
 def test_common_RISE_pipeline():  # noqa: N802 ignore case
-    heatmap = dianna.explain_image(run_model, input_data, method="RISE", axes_labels=axes_labels)[0]
+    heatmap = dianna.explain_image(run_model, input_data, method="RISE", axis_labels=axis_labels)[0]
     dianna.visualization.plot_image(heatmap, show_plot=False)
     dianna.visualization.plot_image(heatmap, original_data=input_data[0], show_plot=False)

tests/test_lime.py

@@ -17,7 +17,7 @@ def test_lime_function(self):
         labels = ('batch', 'y', 'x', 'channels')
         heatmap_expected = np.load('tests/test_data/heatmap_lime_function.npy')
 
-        explainer = LIME(random_state=42, axes_labels=labels)
+        explainer = LIME(random_state=42, axis_labels=labels)
         heatmap = explainer.explain_image(run_model, input_data, num_samples=100)
 
         assert heatmap[0].shape == input_data[0].shape[:2]
@@ -38,7 +38,7 @@ def preprocess(data):
         labels = ('batch', 'channels', 'y', 'x')
 
         heatmap = dianna.explain_image(model_filename, input_data, method="LIME", preprocess_function=preprocess, random_state=42,
-                                       axes_labels=labels)
+                                       axis_labels=labels)
 
         heatmap_expected = np.load('tests/test_data/heatmap_lime_filename.npy')
         assert heatmap[0].shape == input_data[0, 0].shape

tests/test_rise.py

@@ -16,9 +16,9 @@ def test_rise_function(self):
         """Test if rise runs and outputs the correct shape given some data and a model function."""
         input_data = np.random.random((1, 224, 224, 3))
         # y and x axis labels are not actually mandatory for this test
-        axes_labels = ['batch', 'y', 'x', 'channels']
+        axis_labels = ['batch', 'y', 'x', 'channels']
 
-        heatmaps = dianna.explain_image(run_model, input_data, method="RISE", axes_labels=axes_labels, n_masks=200)
+        heatmaps = dianna.explain_image(run_model, input_data, method="RISE", axis_labels=axis_labels, n_masks=200)
 
         assert heatmaps[0].shape == input_data[0].shape[:2]
 
@@ -27,9 +27,9 @@ def test_rise_filename(self):
         model_filename = 'tests/test_data/mnist_model.onnx'
         input_data = generate_data(batch_size=1).astype(np.float32)
         # y and x axis labels are not actually mandatory for this test
-        axes_labels = ['batch', 'channels', 'y', 'x']
+        axis_labels = ['batch', 'channels', 'y', 'x']
 
-        heatmaps = dianna.explain_image(model_filename, input_data, method="RISE", axes_labels=axes_labels, n_masks=200)
+        heatmaps = dianna.explain_image(model_filename, input_data, method="RISE", axis_labels=axis_labels, n_masks=200)
 
         assert heatmaps[0].shape == input_data[0].shape[1:]
 

tutorials/rise_mnist.ipynb

@@ -155,7 +155,7 @@
     }
    ],
    "source": [
-    "explainer = RISE(n_masks=2000, feature_res=8, p_keep=.8, axes_labels=axis_labels)\n",
+    "explainer = RISE(n_masks=2000, feature_res=8, p_keep=.8, axis_labels=axis_labels)\n",
     "heatmaps = explainer.explain_image(run_model, X_test[[i_instance]])"
    ]
   },