Finish implementation of custom indices names.

PiperOrigin-RevId: 545440374

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/codelabs/example.py

@@ -20,6 +20,7 @@ import os
 import tempfile
 
 from absl import app
+from absl import flags
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -31,6 +32,15 @@ from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import
 from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import plotting
 from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import privacy_report
 
+_CUSTOM_SLICES = flags.DEFINE_boolean(
+    "custom_slices",
+    default=False,
+    help="If true, custom slices are used.",
+)
+
+
+TRAIN_SET_SIZE = TEST_SET_SIZE = 1000
+
 
 def generate_random_cluster(center, scale, num_points):
   return np.random.normal(size=(num_points, len(center))) * scale + center
@@ -104,9 +114,11 @@ def main(unused_argv):
   # the generated clusters. More noise makes the classification harder.
   noise_scale = 2
   training_features, training_labels = generate_features_and_labels(
-      samples_per_cluster=250, scale=noise_scale)
+      samples_per_cluster=TRAIN_SET_SIZE // 4, scale=noise_scale
+  )
   test_features, test_labels = generate_features_and_labels(
-      samples_per_cluster=250, scale=noise_scale)
+      samples_per_cluster=TEST_SET_SIZE // 4, scale=noise_scale
+  )
 
   num_clusters = int(round(np.max(training_labels))) + 1
 
@@ -143,6 +155,21 @@ def main(unused_argv):
           epoch_num=num_epochs_per_round * (i + 1),
           model_variant_label=model_name)
 
+      if _CUSTOM_SLICES.value:
+        custom_train_indices = np.array([i % 2 for i in range(TRAIN_SET_SIZE)])
+        custom_test_indices = np.array(
+            [(i + 1) % 2 for i in range(TEST_SET_SIZE)]
+        )
+        slicing_spec = data_structures.SlicingSpec(
+            all_custom_train_indices=[custom_train_indices],
+            all_custom_test_indices=[custom_test_indices],
+            custom_slices_names={0: "name0", 1: "name1"},
+        )
+      else:
+        slicing_spec = data_structures.SlicingSpec(
+            entire_dataset=True, by_class=True
+        )
+
       attack_results = mia.run_attacks(
           data_structures.AttackInputData(
               labels_train=training_labels,
@@ -150,7 +177,7 @@ def main(unused_argv):
               probs_train=training_pred,
               probs_test=test_pred,
           ),
-          data_structures.SlicingSpec(entire_dataset=True, by_class=True),
+          slicing_spec,
           attack_types=(
               data_structures.AttackType.THRESHOLD_ATTACK,
               data_structures.AttackType.LOGISTIC_REGRESSION,

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/data_structures.py

@@ -67,9 +67,15 @@ class SingleSliceSpec:
       return 'Loss percentiles: %d-%d' % self.value
 
     if self.feature == SlicingFeature.CUSTOM:
-      custom_train_indices, custom_test_indices, group_value = self.value
+      custom_train_indices, custom_test_indices, slice_value, slice_name = (
-      return (f'Custom indices: train = {custom_train_indices}, '
+          self.value
-              f'test = {custom_test_indices}, group_value = {group_value}')
+      )
+      if slice_name is not None:
+        return f'Custom indices: slice_name = {slice_name}'
+      return (
+          f'Custom indices: train = {custom_train_indices}, '
+          f'test = {custom_test_indices}, group_value = {slice_value}'
+      )
 
     return '%s=%s' % (self.feature.name, self.value)
 

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/data_structures_test.py

@@ -44,8 +44,11 @@ class SingleSliceSpecTest(parameterized.TestCase):
       (SlicingFeature.CLASS, 2, 'CLASS=2'),
       (SlicingFeature.PERCENTILE, (10, 20), 'Loss percentiles: 10-20'),
       (SlicingFeature.CORRECTLY_CLASSIFIED, True, 'CORRECTLY_CLASSIFIED=True'),
-      (SlicingFeature.CUSTOM, (np.array([1]), np.array([2, 1]), 1),
+      (
-       'Custom indices: train = [1], test = [2 1], group_value = 1'),
+          SlicingFeature.CUSTOM,
+          (np.array([1]), np.array([2, 1]), 1, None),
+          'Custom indices: train = [1], test = [2 1], group_value = 1',
+      ),
   )
   def testStr(self, feature, value, expected_str):
     self.assertEqual(str(SingleSliceSpec(feature, value)), expected_str)

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/dataset_slicing.py

@@ -264,13 +264,18 @@ def get_single_slice_specs(
             f"Too many groups ({groups.size}) for slicing by custom indices. "
             f"Should be no more than {_MAX_NUM_OF_SLICES}.")
       for g in groups:
+        group_name = None
         if slicing_spec.custom_slices_names is not None:
           if g not in slicing_spec.custom_slices_names:
             raise ValueError(f"Custom slice={g} is not in custom_slices_names")
-          group_id = slicing_spec.custom_slices_names[g]
+          group_name = slicing_spec.custom_slices_names[g]
-        else:
+
-          group_id = (custom_train_indices, custom_test_indices, g)
+        result.append(
-        result.append(SingleSliceSpec(SlicingFeature.CUSTOM, group_id))
+            SingleSliceSpec(
+                SlicingFeature.CUSTOM,
+                (custom_train_indices, custom_test_indices, g, group_name),
+            )
+        )
   return result
 
 
@@ -294,7 +299,8 @@ def get_slice(
         data, slice_spec.value, return_slice_indices
     )
   elif slice_spec.feature == SlicingFeature.CUSTOM:
-    custom_train_indices, custom_test_indices, group_value = slice_spec.value
+    custom_train_indices, custom_test_indices, group_value, _ = slice_spec.value
+
     data_slice = _slice_by_custom_indices(
         data,
         custom_train_indices,

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/dataset_slicing_test.py

@@ -109,8 +109,10 @@ class SingleSliceSpecsTest(parameterized.TestCase):
         all_custom_train_indices=[custom_train_indices],
         all_custom_test_indices=[custom_test_indices])
     expected = [self.ENTIRE_DATASET_SLICE] + [
-        SingleSliceSpec(SlicingFeature.CUSTOM,
+        SingleSliceSpec(
-                        (custom_train_indices, custom_test_indices, g))
+            SlicingFeature.CUSTOM,
+            (custom_train_indices, custom_test_indices, g, None),
+        )
         for g in expected_groups
     ]
     output = get_single_slice_specs(input_data)
@@ -138,11 +140,49 @@ class SingleSliceSpecsTest(parameterized.TestCase):
     for custom_train_indices, custom_test_indices, eg in zip(
         all_custom_train_indices, all_custom_test_indices,
         expected_group_values):
-      expected.extend([
+      expected.extend(
-          SingleSliceSpec(SlicingFeature.CUSTOM,
+          [
-                          (custom_train_indices, custom_test_indices, g))
+              SingleSliceSpec(
-          for g in eg
+                  SlicingFeature.CUSTOM,
-      ])
+                  (custom_train_indices, custom_test_indices, g, None),
+              )
+              for g in eg
+          ]
+      )
+    output = get_single_slice_specs(input_data)
+    self.assertTrue(_are_lists_equal(output, expected))
+
+  def test_slicing_by_custom_indices_slice_name(self):
+    all_custom_train_indices = [
+        np.array([1, 2, 1, 2]),
+    ]
+    all_custom_test_indices = [
+        np.array([2, 2, 1, 2]),
+    ]
+    custom_slices_names = {1: 'slice1', 2: 'slice2'}
+    expected_group_values = [[1, 2]]
+
+    input_data = SlicingSpec(
+        all_custom_train_indices=all_custom_train_indices,
+        all_custom_test_indices=all_custom_test_indices,
+        custom_slices_names=custom_slices_names,
+    )
+    expected = [self.ENTIRE_DATASET_SLICE]
+    for custom_train_indices, custom_test_indices, eg in zip(
+        all_custom_train_indices, all_custom_test_indices, expected_group_values
+    ):
+      for g in eg:
+        expected.append(
+            SingleSliceSpec(
+                SlicingFeature.CUSTOM,
+                (
+                    custom_train_indices,
+                    custom_test_indices,
+                    g,
+                    custom_slices_names[g],
+                ),
+            )
+        )
     output = get_single_slice_specs(input_data)
     self.assertTrue(_are_lists_equal(output, expected))
 
@@ -298,7 +338,9 @@ class GetSliceTest(parameterized.TestCase):
     custom_train_indices = np.array([2, 2, 100, 4])
     custom_test_indices = np.array([100, 2, 2, 2])
     custom_slice = SingleSliceSpec(
-        SlicingFeature.CUSTOM, (custom_train_indices, custom_test_indices, 2))
+        SlicingFeature.CUSTOM,
+        (custom_train_indices, custom_test_indices, 2, None),
+    )
     output = get_slice(self.input_data, custom_slice)
     np.testing.assert_array_equal(output.logits_train,
                                   np.array([[0, 1, 0], [2, 0, 3]]))
@@ -325,7 +367,9 @@ class GetSliceTest(parameterized.TestCase):
   def test_slice_by_custom_indices_wrong_size(self, custom_train_indices,
                                               custom_test_indices):
     custom_slice = SingleSliceSpec(
-        SlicingFeature.CUSTOM, (custom_train_indices, custom_test_indices, 2))
+        SlicingFeature.CUSTOM,
+        (custom_train_indices, custom_test_indices, 2, None),
+    )
     self.assertRaises(ValueError, get_slice, self.input_data, custom_slice)
 
 
@@ -420,7 +464,9 @@ class GetSliceTestForMultilabelData(absltest.TestCase):
     custom_train_indices = np.array([2, 2, 100, 4])
     custom_test_indices = np.array([100, 2, 2, 2])
     custom_slice = SingleSliceSpec(
-        SlicingFeature.CUSTOM, (custom_train_indices, custom_test_indices, 2))
+        SlicingFeature.CUSTOM,
+        (custom_train_indices, custom_test_indices, 2, 'slice_name'),
+    )
     output = get_slice(self.input_data, custom_slice)
     # Check logits.
     with self.subTest(msg='Check logits'):