Migrates compute_dp_sgd_privacy to print new privacy statement from compute_dp_sgd_privacy_lib.

PiperOrigin-RevId: 520147633

Author: galenmandrew

tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy.py

@@ -31,26 +31,48 @@
 from absl import app
 from absl import flags
 
-from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy
+from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy_statement
 
-FLAGS = flags.FLAGS
 
-flags.DEFINE_integer('N', None, 'Total number of examples')
-flags.DEFINE_integer('batch_size', None, 'Batch size')
-flags.DEFINE_float('noise_multiplier', None, 'Noise multiplier for DP-SGD')
-flags.DEFINE_float('epochs', None, 'Number of epochs (may be fractional)')
-flags.DEFINE_float('delta', 1e-6, 'Target delta')
+_NUM_EXAMPLES = flags.DEFINE_integer('N', None, 'Total number of examples.')
+_BATCH_SIZE = flags.DEFINE_integer('batch_size', None, 'Batch size.')
+_NOISE_MULTIPLIER = flags.DEFINE_float(
+    'noise_multiplier', None, 'Noise multiplier for DP-SGD.'
+)
+_NUM_EPOCHS = flags.DEFINE_float(
+    'epochs', None, 'Number of epochs (may be fractional).'
+)
+_DELTA = flags.DEFINE_float('delta', 1e-6, 'Target delta.')
+_USED_MICROBATCHING = flags.DEFINE_bool(
+    'used_microbatching',
+    True,
+    'Whether microbatching was used (with microbatch size greater than one).',
+)
+_MAX_EXAMPLES_PER_USER = flags.DEFINE_integer(
+    'max_examples_per_user',
+    None,
+    (
+        'Maximum number of examples per user, applicable. Used to compute a'
+        ' user-level DP guarantee.'
+    ),
+)
+
+flags.mark_flags_as_required(['N', 'batch_size', 'noise_multiplier', 'epochs'])
 
 
 def main(argv):
   del argv  # argv is not used.
 
-  assert FLAGS.N is not None, 'Flag N is missing.'
-  assert FLAGS.batch_size is not None, 'Flag batch_size is missing.'
-  assert FLAGS.noise_multiplier is not None, 'Flag noise_multiplier is missing.'
-  assert FLAGS.epochs is not None, 'Flag epochs is missing.'
-  compute_dp_sgd_privacy(FLAGS.N, FLAGS.batch_size, FLAGS.noise_multiplier,
-                         FLAGS.epochs, FLAGS.delta)
+  statement = compute_dp_sgd_privacy_statement(
+      _NUM_EXAMPLES.value,
+      _BATCH_SIZE.value,
+      _NUM_EPOCHS.value,
+      _NOISE_MULTIPLIER.value,
+      _DELTA.value,
+      _USED_MICROBATCHING.value,
+      _MAX_EXAMPLES_PER_USER.value,
+  )
+  print(statement)
 
 
 if __name__ == '__main__':