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update test code

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Liwei Song 2020-12-14 15:02:56 -05:00
parent 3f40b8c465
commit 2312192573
1 changed files with 0 additions and 98 deletions
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tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py
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@ -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(