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Add tests for rank generation

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amad-person 2020-11-06 16:48:44 +08:00
parent 4db54d9485
commit 2355e13f44
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tensorflow_privacy/privacy/membership_inference_attack/models_test.py
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@ -19,6 +19,7 @@ import numpy as np
from tensorflow_privacy.privacy.membership_inference_attack import models
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import Seq2SeqAttackInputData
class TrainedAttackerTest(absltest.TestCase):
@ -55,6 +56,45 @@ class TrainedAttackerTest(absltest.TestCase):
expected = feature[:2] not in attack_input.logits_train
self.assertEqual(attacker_data.is_training_labels_train[i], expected)
def test_create_seq2seq_attacker_data_logits_and_labels(self):
attack_input = Seq2SeqAttackInputData(
logits_train=iter([np.array([np.array([[0.1, 0.1, 0.8], [0.7, 0.3, 0]], dtype=np.float32),
np.array([[0.4, 0.5, 0.1]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.25, 0.6, 0.15], [1, 0, 0]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.9, 0, 0.1], [0.25, 0.5, 0.25]], dtype=np.float32),
np.array([[0, 1, 0], [0.2, 0.1, 0.7]], dtype=np.float32)],
dtype=object)]),
logits_test=iter([np.array([np.array([[0.25, 0.4, 0.35], [0.2, 0.4, 0.4]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.3, 0.3, 0.4], [0.4, 0.4, 0.2]], dtype=np.float32),
np.array([[0.3, 0.35, 0.35]], dtype=np.float32)],
dtype=object)]),
labels_train=iter([np.array([np.array([2, 0], dtype=np.float32),
np.array([1], dtype=np.float32)],
dtype=object),
np.array([np.array([1, 0], dtype=np.float32)],
dtype=object),
np.array([np.array([0, 1], dtype=np.float32),
np.array([1, 2], dtype=np.float32)],
dtype=object)]),
labels_test=iter([np.array([np.array([2, 1], dtype=np.float32)]),
np.array([np.array([2, 0], dtype=np.float32),
np.array([1], dtype=np.float32)],
dtype=object)]),
vocab_size=3,
train_size=3,
test_size=2
)
attacker_data = models.create_seq2seq_attacker_data(
attack_input, 0.25, balance=False)
self.assertLen(attacker_data.features_train, 3)
self.assertLen(attacker_data.features_test, 2)
for i, feature in enumerate(attacker_data.features_train):
self.assertLen(feature, 1) # each feature has one average rank
def test_balanced_create_attacker_data_loss_and_logits(self):
attack_input = AttackInputData(
logits_train=np.array([[1, 2], [5, 6], [8, 9]]),
@ -70,6 +110,47 @@ class TrainedAttackerTest(absltest.TestCase):
expected = feature[:2] not in attack_input.logits_train
self.assertEqual(attacker_data.is_training_labels_train[i], expected)
def test_balanced_create_seq2seq_attacker_data_logits_and_labels(self):
attack_input = Seq2SeqAttackInputData(
logits_train=iter([np.array([np.array([[0.1, 0.1, 0.8], [0.7, 0.3, 0]], dtype=np.float32),
np.array([[0.4, 0.5, 0.1]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.25, 0.6, 0.15], [1, 0, 0]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.9, 0, 0.1], [0.25, 0.5, 0.25]], dtype=np.float32),
np.array([[0, 1, 0], [0.2, 0.1, 0.7]], dtype=np.float32)],
dtype=object)]),
logits_test=iter([np.array([np.array([[0.25, 0.4, 0.35], [0.2, 0.4, 0.4]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.3, 0.3, 0.4], [0.4, 0.4, 0.2]], dtype=np.float32),
np.array([[0.3, 0.35, 0.35]], dtype=np.float32)],
dtype=object),
np.array([np.array([[0.25, 0.4, 0.35], [0.2, 0.4, 0.4]], dtype=np.float32)],
dtype=object)]),
labels_train=iter([np.array([np.array([2, 0], dtype=np.float32),
np.array([1], dtype=np.float32)],
dtype=object),
np.array([np.array([1, 0], dtype=np.float32)],
dtype=object),
np.array([np.array([0, 1], dtype=np.float32),
np.array([1, 2], dtype=np.float32)],
dtype=object)]),
labels_test=iter([np.array([np.array([2, 1], dtype=np.float32)]),
np.array([np.array([2, 0], dtype=np.float32),
np.array([1], dtype=np.float32)],
dtype=object),
np.array([np.array([2, 1], dtype=np.float32)])]),
vocab_size=3,
train_size=3,
test_size=3
)
attacker_data = models.create_seq2seq_attacker_data(
attack_input, 0.33, balance=True)
self.assertLen(attacker_data.features_train, 4)
self.assertLen(attacker_data.features_test, 2)
for i, feature in enumerate(attacker_data.features_train):
self.assertLen(feature, 1) # each feature has one average rank
if __name__ == '__main__':
absltest.main()