fidelity: issue 151

deel-ai · Dec 13, 2023 · 21092bf · 21092bf
1 parent 68a36a4
commit 21092bf
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Showing 2 changed files with 4 additions and 6 deletions.
diff --git a/tests/metrics/test_common.py b/tests/metrics/test_common.py
@@ -112,7 +112,7 @@ def test_data_types_shapes():
     explanation_metrics = {
         Deletion: {"steps": 3},
         Insertion: {"steps": 3},
-        MuFidelity: {"nb_samples": 3},
+        MuFidelity: {"nb_samples": 3, "grid_size": None, "subset_percent": 0.9},
     }
 
     for data_type, input_shape in data_types_input_shapes.items():

diff --git a/xplique/metrics/fidelity.py b/xplique/metrics/fidelity.py
@@ -87,8 +87,6 @@ def __init__(self,
 
         # if unspecified use the original equation (pixel-wise modification)
         self.grid_size = grid_size or self.inputs.shape[1]
-        # cardinal of subset (|S| in the equation)
-        self.subset_size = int(self.grid_size ** 2 * self.subset_percent)
 
         self.base_predictions = self.batch_inference_function(self.model, self.inputs,
                                                               self.targets, self.batch_size)
@@ -198,14 +196,14 @@ def _perturb_samples(self,
         if len(inputs.shape) == 2:  # tabular data, grid size is ignored
             # prepare the random masks
             subset_masks = tf.random.uniform((nb_perturbations, inputs.shape[1]), 0, 1, tf.float32)
-            subset_masks = tf.argsort(subset_masks, axis=-1) > self.subset_size
+            subset_masks = subset_masks > self.subset_percent
             subset_masks = tf.cast(subset_masks, tf.float32)
 
         elif len(inputs.shape) == 3:  # time series
             # prepare the random masks
             subset_masks = tf.random.uniform((nb_perturbations, self.grid_size * inputs.shape[2]),
                                              minval=0, maxval=1, dtype=tf.float32)
-            subset_masks = tf.argsort(subset_masks, axis=-1) > self.subset_size
+            subset_masks = subset_masks > self.subset_percent
 
             # and interpolate them if needed
             subset_masks = tf.reshape(tf.cast(subset_masks, tf.float32),
@@ -217,7 +215,7 @@ def _perturb_samples(self,
             # prepare the random masks
             subset_masks = tf.random.uniform(shape=(nb_perturbations, self.grid_size ** 2),
                                              minval=0, maxval=1, dtype=tf.float32)
-            subset_masks = tf.argsort(subset_masks, axis=-1) > self.subset_size
+            subset_masks = subset_masks > self.subset_percent
 
             # and interpolate them if needed
             subset_masks = tf.reshape(tf.cast(subset_masks, tf.float32),