Fix nits

tensorflow_privacy/privacy/membership_inference_attack/data_structures.py

@@ -374,10 +374,10 @@ def _append_array_shape(arr: np.array, arr_name: str, result):
     result.append(' %s with shape: %s,' % (arr_name, arr.shape))
 
 
-def _is_generator(gen, gen_name):
+def _is_iterator(obj, obj_name):
-  """Checks whether gen is a generator."""
+  """Checks whether obj is a generator."""
-  if gen is not None and not isinstance(gen, Iterator):
+  if obj is not None and not isinstance(obj, Iterator):
-    raise ValueError('%s should be a generator.' % gen_name)
+    raise ValueError('%s should be a generator.' % obj_name)
 
 
 @dataclass
@@ -393,7 +393,7 @@ class Seq2SeqAttackInputData:
   labels_train: Iterator[np.ndarray] = None
   labels_test: Iterator[np.ndarray] = None
 
-  # Denotes size of the target sequence vocabulary.
+  # Size of the target sequence vocabulary.
   vocab_size: int = None
 
   # Train, test size = number of batches in training, test set.
@@ -431,10 +431,10 @@ class Seq2SeqAttackInputData:
     if self.test_size is not None and not int:
       raise ValueError('test_size should be of integer type')
 
-    _is_generator(self.logits_train, 'logits_train')
+    _is_iterator(self.logits_train, 'logits_train')
-    _is_generator(self.logits_test, 'logits_test')
+    _is_iterator(self.logits_test, 'logits_test')
-    _is_generator(self.labels_train, 'labels_train')
+    _is_iterator(self.labels_train, 'labels_train')
-    _is_generator(self.labels_test, 'labels_test')
+    _is_iterator(self.labels_test, 'labels_test')
 
   def __str__(self):
     """Return the shapes of variables that are not None."""

tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py

@@ -36,20 +36,18 @@ def get_test_input(n_train, n_test):
 
 
 def get_seq2seq_test_input(n_train, n_test, max_seq_in_batch, max_tokens_in_sequence, vocab_size, seed=None):
-  """Get example inputs for attacks on seq2seq models."""
+  """Returns example inputs for attacks on seq2seq models."""
   if seed is not None:
     np.random.seed(seed=seed)
 
-  logits_train = []
+  logits_train, labels_train = [], []
-  labels_train = []
   for i in range(n_train):
     num_sequences = np.random.choice(max_seq_in_batch, 1)[0] + 1
     batch_logits, batch_labels = _get_batch_logits_and_labels(num_sequences, max_tokens_in_sequence, vocab_size)
     logits_train.append(batch_logits)
     labels_train.append(batch_labels)
 
-  logits_test = []
+  logits_test, labels_test = [], []
-  labels_test = []
   for i in range(n_test):
     num_sequences = np.random.choice(max_seq_in_batch, 1)[0] + 1
     batch_logits, batch_labels = _get_batch_logits_and_labels(num_sequences, max_tokens_in_sequence, vocab_size)

tensorflow_privacy/privacy/membership_inference_attack/models.py

@@ -23,6 +23,7 @@ from sklearn import linear_model
 from sklearn import model_selection
 from sklearn import neighbors
 from sklearn import neural_network
+from typing import Iterator, List
 
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import Seq2SeqAttackInputData
@@ -134,8 +135,8 @@ def create_seq2seq_attacker_data(attack_input_data: Seq2SeqAttackInputData,
   attack_input_test = _get_average_ranks(attack_input_data.logits_test, attack_input_data.labels_test)
 
   if balance:
-    min_size = min(attack_input_data.train_size,
+    min_size = min(len(attack_input_train),
-                   attack_input_data.test_size)
+                   len(attack_input_test))
     attack_input_train = _sample_multidimensional_array(attack_input_train,
                                                         min_size)
     attack_input_test = _sample_multidimensional_array(attack_input_test,
@@ -159,7 +160,8 @@ def create_seq2seq_attacker_data(attack_input_data: Seq2SeqAttackInputData,
                       is_training_labels_test)
 
 
-def _get_average_ranks(logits, labels):
+def _get_average_ranks(logits: Iterator[np.ndarray],
+                       labels: Iterator[np.ndarray]) -> np.ndarray:
   """Returns the average rank of tokens in a batch of sequences.
 
   Args:
@@ -180,7 +182,8 @@ def _get_average_ranks(logits, labels):
   return np.array(ranks)
 
 
-def _get_ranks_for_sequence(logits, labels):
+def _get_ranks_for_sequence(logits: np.ndarray,
+                            labels: np.ndarray) -> List:
   """Returns ranks for a sequence.
 
   Args:
@@ -190,11 +193,10 @@ def _get_ranks_for_sequence(logits, labels):
   Returns:
     An array of ranks for tokens in the sequence, dim = (num_tokens, 1).
   """
-  scores = -logits
+  sequence_ranks = []
-  all_ranks = np.empty_like(scores)
+  for logit, label in zip(logits, labels.astype(int)):
-  for i, s in enumerate(scores):
+    rank = rankdata(-logit, method='min')[label] - 1.0
-    all_ranks[i] = rankdata(s, method='min') - 1
+    sequence_ranks.append(rank)
-  sequence_ranks = all_ranks[np.arange(len(all_ranks)), labels.astype(int)].tolist()
 
   return sequence_ranks