From 2312192573fcaea997da085638675bef34eef30b Mon Sep 17 00:00:00 2001
From: Liwei Song <liweisongpku@gmail.com>
Date: Mon, 14 Dec 2020 15:02:56 -0500
Subject: [PATCH] update test code

---
 .../membership_inference_attack_test.py       | 98 -------------------
 1 file changed, 98 deletions(-)

diff --git a/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py b/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py
index fd4db2b..be3092a 100644
--- a/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py
+++ b/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py
@@ -19,7 +19,6 @@ import numpy as np
 from tensorflow_privacy.privacy.membership_inference_attack import membership_inference_attack as mia
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackType
-from tensorflow_privacy.privacy.membership_inference_attack.data_structures import Seq2SeqAttackInputData
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleSliceSpec
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingFeature
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec
@@ -35,67 +34,6 @@ def get_test_input(n_train, n_test):
       labels_test=np.array([i % 5 for i in range(n_test)]))
 
 
-def get_seq2seq_test_input(n_train,
-                           n_test,
-                           max_seq_in_batch,
-                           max_tokens_in_sequence,
-                           vocab_size,
-                           seed=None):
-  """Returns example inputs for attacks on seq2seq models."""
-  if seed is not None:
-    np.random.seed(seed=seed)
-
-  logits_train, labels_train = [], []
-  for _ 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, labels_test = [], []
-  for _ 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)
-    logits_test.append(batch_logits)
-    labels_test.append(batch_labels)
-
-  return Seq2SeqAttackInputData(
-      logits_train=iter(logits_train),
-      logits_test=iter(logits_test),
-      labels_train=iter(labels_train),
-      labels_test=iter(labels_test),
-      vocab_size=vocab_size,
-      train_size=n_train,
-      test_size=n_test)
-
-
-def _get_batch_logits_and_labels(num_sequences, max_tokens_in_sequence,
-                                 vocab_size):
-  num_tokens_in_sequence = np.random.choice(max_tokens_in_sequence,
-                                            num_sequences) + 1
-  batch_logits, batch_labels = [], []
-  for num_tokens in num_tokens_in_sequence:
-    logits, labels = _get_sequence_logits_and_labels(num_tokens, vocab_size)
-    batch_logits.append(logits)
-    batch_labels.append(labels)
-  return np.array(
-      batch_logits, dtype=object), np.array(
-          batch_labels, dtype=object)
-
-
-def _get_sequence_logits_and_labels(num_tokens, vocab_size):
-  sequence_logits = []
-  for _ in range(num_tokens):
-    token_logits = np.random.random(vocab_size)
-    token_logits /= token_logits.sum()
-    sequence_logits.append(token_logits)
-  sequence_labels = np.random.choice(vocab_size, num_tokens)
-  return np.array(
-      sequence_logits, dtype=np.float32), np.array(
-          sequence_labels, dtype=np.float32)
-
 
 class RunAttacksTest(absltest.TestCase):
 
@@ -160,42 +98,6 @@ class RunAttacksTest(absltest.TestCase):
     # If accuracy is already present, simply return it.
     self.assertIsNone(mia._get_accuracy(None, labels))
 
-  def test_run_seq2seq_attack_size(self):
-    result = mia.run_seq2seq_attack(
-        get_seq2seq_test_input(
-            n_train=10,
-            n_test=5,
-            max_seq_in_batch=3,
-            max_tokens_in_sequence=5,
-            vocab_size=2))
-
-    self.assertLen(result.single_attack_results, 1)
-
-  def test_run_seq2seq_attack_trained_sets_attack_type(self):
-    result = mia.run_seq2seq_attack(
-        get_seq2seq_test_input(
-            n_train=10,
-            n_test=5,
-            max_seq_in_batch=3,
-            max_tokens_in_sequence=5,
-            vocab_size=2))
-    seq2seq_result = list(result.single_attack_results)[0]
-    self.assertEqual(seq2seq_result.attack_type, AttackType.LOGISTIC_REGRESSION)
-
-  def test_run_seq2seq_attack_calculates_correct_auc(self):
-    result = mia.run_seq2seq_attack(
-        get_seq2seq_test_input(
-            n_train=20,
-            n_test=10,
-            max_seq_in_batch=3,
-            max_tokens_in_sequence=5,
-            vocab_size=3,
-            seed=12345),
-        balance_attacker_training=False)
-    seq2seq_result = list(result.single_attack_results)[0]
-    np.testing.assert_almost_equal(
-        seq2seq_result.roc_curve.get_auc(), 0.63, decimal=2)
-
   def test_run_compute_privacy_risk_score_correct_score(self):
     result = mia._compute_privacy_risk_score(
         AttackInputData(