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Update several DPQuery tests to TF v2.

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PiperOrigin-RevId: 468763153
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Steve Chien 2022-08-19 12:40:10 -07:00 committed by A. Unique TensorFlower
parent 7fe491f7a4
commit fd64be5b5b
4 changed files with 169 additions and 210 deletions
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141
tensorflow_privacy/privacy/dp_query/distributed_skellam_query_test.py
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@ -23,80 +23,68 @@ import tensorflow_probability as tfp
class DistributedSkellamQueryTest(tf.test.TestCase, parameterized.TestCase): class DistributedSkellamQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_skellam_sum_no_noise(self): def test_skellam_sum_no_noise(self):
with self.cached_session() as sess: record1 = tf.constant([2, 0], dtype=tf.int32)
record1 = tf.constant([2, 0], dtype=tf.int32) record2 = tf.constant([-1, 1], dtype=tf.int32)
record2 = tf.constant([-1, 1], dtype=tf.int32)
query = distributed_skellam_query.DistributedSkellamSumQuery( query = distributed_skellam_query.DistributedSkellamSumQuery(
l1_norm_bound=10, l2_norm_bound=10, local_stddev=0.0) l1_norm_bound=10, l2_norm_bound=10, local_stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [1, 1]
expected = [1, 1] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_skellam_multiple_shapes(self): def test_skellam_multiple_shapes(self):
with self.cached_session() as sess: tensor1 = tf.constant([2, 0], dtype=tf.int32)
tensor1 = tf.constant([2, 0], dtype=tf.int32) tensor2 = tf.constant([-1, 1, 3], dtype=tf.int32)
tensor2 = tf.constant([-1, 1, 3], dtype=tf.int32) record = [tensor1, tensor2]
record = [tensor1, tensor2]
query = distributed_skellam_query.DistributedSkellamSumQuery( query = distributed_skellam_query.DistributedSkellamSumQuery(
l1_norm_bound=10, l2_norm_bound=10, local_stddev=0.0) l1_norm_bound=10, l2_norm_bound=10, local_stddev=0.0)
query_result, _ = test_utils.run_query(query, [record, record]) query_result, _ = test_utils.run_query(query, [record, record])
result = sess.run(query_result) expected = [2 * tensor1, 2 * tensor2]
expected = [2 * tensor1, 2 * tensor2] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_skellam_raise_type_exception(self): def test_skellam_raise_type_exception(self):
with self.cached_session() as sess, self.assertRaises(TypeError): with self.assertRaises(TypeError):
record1 = tf.constant([2, 0], dtype=tf.float32) record1 = tf.constant([2, 0], dtype=tf.float32)
record2 = tf.constant([-1, 1], dtype=tf.float32) record2 = tf.constant([-1, 1], dtype=tf.float32)
query = distributed_skellam_query.DistributedSkellamSumQuery( query = distributed_skellam_query.DistributedSkellamSumQuery(
l1_norm_bound=10, l2_norm_bound=10, local_stddev=0.0) l1_norm_bound=10, l2_norm_bound=10, local_stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2]) test_utils.run_query(query, [record1, record2])
sess.run(query_result)
def test_skellam_raise_l1_norm_exception(self): def test_skellam_raise_l1_norm_exception(self):
with self.cached_session() as sess, self.assertRaises( with self.assertRaises(tf.errors.InvalidArgumentError):
tf.errors.InvalidArgumentError):
record1 = tf.constant([1, 2], dtype=tf.int32) record1 = tf.constant([1, 2], dtype=tf.int32)
record2 = tf.constant([3, 4], dtype=tf.int32) record2 = tf.constant([3, 4], dtype=tf.int32)
query = distributed_skellam_query.DistributedSkellamSumQuery( query = distributed_skellam_query.DistributedSkellamSumQuery(
l1_norm_bound=1, l2_norm_bound=100, local_stddev=0.0) l1_norm_bound=1, l2_norm_bound=100, local_stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2]) test_utils.run_query(query, [record1, record2])
sess.run(query_result)
def test_skellam_raise_l2_norm_exception(self): def test_skellam_raise_l2_norm_exception(self):
with self.cached_session() as sess, self.assertRaises( with self.assertRaises(tf.errors.InvalidArgumentError):
tf.errors.InvalidArgumentError):
record1 = tf.constant([1, 2], dtype=tf.int32) record1 = tf.constant([1, 2], dtype=tf.int32)
record2 = tf.constant([3, 4], dtype=tf.int32) record2 = tf.constant([3, 4], dtype=tf.int32)
query = distributed_skellam_query.DistributedSkellamSumQuery( query = distributed_skellam_query.DistributedSkellamSumQuery(
l1_norm_bound=10, l2_norm_bound=4, local_stddev=0.0) l1_norm_bound=10, l2_norm_bound=4, local_stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2]) test_utils.run_query(query, [record1, record2])
sess.run(query_result)
def test_skellam_sum_with_noise(self): def test_skellam_sum_with_noise(self):
"""Use only one record to test std.""" """Use only one record to test std."""
with self.cached_session() as sess: record = tf.constant([1], dtype=tf.int32)
record = tf.constant([1], dtype=tf.int32) local_stddev = 1.0
local_stddev = 1.0
query = distributed_skellam_query.DistributedSkellamSumQuery( query = distributed_skellam_query.DistributedSkellamSumQuery(
l1_norm_bound=10.0, l2_norm_bound=10, local_stddev=local_stddev) l1_norm_bound=10.0, l2_norm_bound=10, local_stddev=local_stddev)
noised_sums = []
for _ in range(1000):
query_result, _ = test_utils.run_query(query, [record]) query_result, _ = test_utils.run_query(query, [record])
noised_sums.append(query_result)
noised_sums = [] result_stddev = np.std(noised_sums)
for _ in range(1000): self.assertNear(result_stddev, local_stddev, 0.1)
noised_sums.append(sess.run(query_result))
result_stddev = np.std(noised_sums)
self.assertNear(result_stddev, local_stddev, 0.1)
def test_compare_centralized_distributed_skellam(self): def test_compare_centralized_distributed_skellam(self):
"""Compare the percentiles of distributed and centralized Skellam. """Compare the percentiles of distributed and centralized Skellam.
@ -108,45 +96,44 @@ class DistributedSkellamQueryTest(tf.test.TestCase, parameterized.TestCase):
Both results are evaluated to match percentiles (25, 50, 75). Both results are evaluated to match percentiles (25, 50, 75).
""" """
with self.cached_session() as sess: num_trials = 10000
num_trials = 10000 num_users = 100
num_users = 100 record = tf.zeros([num_trials], dtype=tf.int32)
record = tf.zeros([num_trials], dtype=tf.int32) local_stddev = 1.0
local_stddev = 1.0 query = distributed_skellam_query.DistributedSkellamSumQuery(
query = distributed_skellam_query.DistributedSkellamSumQuery( l1_norm_bound=10.0, l2_norm_bound=10, local_stddev=local_stddev)
l1_norm_bound=10.0, l2_norm_bound=10, local_stddev=local_stddev) distributed_noised = tf.zeros([num_trials], dtype=tf.int32)
for _ in range(num_users):
query_result, _ = test_utils.run_query(query, [record]) query_result, _ = test_utils.run_query(query, [record])
distributed_noised = tf.zeros([num_trials], dtype=tf.int32) distributed_noised += query_result
for _ in range(num_users):
distributed_noised += sess.run(query_result)
def add_noise(v, stddev): def add_noise(v, stddev):
lam = stddev**2 / 2 lam = stddev**2 / 2
noise_poisson1 = tf.random.poisson( noise_poisson1 = tf.random.poisson(
lam=lam, shape=tf.shape(v), dtype=v.dtype) lam=lam, shape=tf.shape(v), dtype=v.dtype)
noise_poisson2 = tf.random.poisson( noise_poisson2 = tf.random.poisson(
lam=lam, shape=tf.shape(v), dtype=v.dtype) lam=lam, shape=tf.shape(v), dtype=v.dtype)
res = v + (noise_poisson1 - noise_poisson2) res = v + (noise_poisson1 - noise_poisson2)
return res return res
record_centralized = tf.zeros([num_trials], dtype=tf.int32) record_centralized = tf.zeros([num_trials], dtype=tf.int32)
centralized_noised = sess.run( centralized_noised = add_noise(record_centralized,
add_noise(record_centralized, local_stddev * np.sqrt(num_users))) local_stddev * np.sqrt(num_users))
tolerance = 5 tolerance = 5
self.assertAllClose( self.assertAllClose(
tfp.stats.percentile(distributed_noised, 50.0), tfp.stats.percentile(distributed_noised, 50.0),
tfp.stats.percentile(centralized_noised, 50.0), tfp.stats.percentile(centralized_noised, 50.0),
atol=tolerance) atol=tolerance)
self.assertAllClose( self.assertAllClose(
tfp.stats.percentile(distributed_noised, 75.0), tfp.stats.percentile(distributed_noised, 75.0),
tfp.stats.percentile(centralized_noised, 75.0), tfp.stats.percentile(centralized_noised, 75.0),
atol=tolerance) atol=tolerance)
self.assertAllClose( self.assertAllClose(
tfp.stats.percentile(distributed_noised, 25.0), tfp.stats.percentile(distributed_noised, 25.0),
tfp.stats.percentile(centralized_noised, 25.0), tfp.stats.percentile(centralized_noised, 25.0),
atol=tolerance) atol=tolerance)
def test_skellam_average_no_noise(self): def test_skellam_average_no_noise(self):
with self.cached_session() as sess: with self.cached_session() as sess:

82
tensorflow_privacy/privacy/dp_query/gaussian_query_test.py
View file

@ -22,64 +22,53 @@ from tensorflow_privacy.privacy.dp_query import test_utils
class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase): class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_gaussian_sum_no_clip_no_noise(self): def test_gaussian_sum_no_clip_no_noise(self):
with self.cached_session() as sess: record1 = tf.constant([2.0, 0.0])
record1 = tf.constant([2.0, 0.0]) record2 = tf.constant([-1.0, 1.0])
record2 = tf.constant([-1.0, 1.0])
query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0) query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [1.0, 1.0]
expected = [1.0, 1.0] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_gaussian_sum_with_clip_no_noise(self): def test_gaussian_sum_with_clip_no_noise(self):
with self.cached_session() as sess: record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0].
record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0]. record2 = tf.constant([4.0, -3.0]) # Not clipped.
record2 = tf.constant([4.0, -3.0]) # Not clipped.
query = gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=0.0) query = gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [1.0, 1.0]
expected = [1.0, 1.0] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_gaussian_sum_with_changing_clip_no_noise(self): def test_gaussian_sum_with_changing_clip_no_noise(self):
with self.cached_session() as sess: record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0].
record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0]. record2 = tf.constant([4.0, -3.0]) # Not clipped.
record2 = tf.constant([4.0, -3.0]) # Not clipped.
l2_norm_clip = tf.Variable(5.0) l2_norm_clip = tf.Variable(5.0)
l2_norm_clip_placeholder = tf.compat.v1.placeholder(tf.float32) query = gaussian_query.GaussianSumQuery(
assign_l2_norm_clip = tf.compat.v1.assign(l2_norm_clip, l2_norm_clip=l2_norm_clip, stddev=0.0)
l2_norm_clip_placeholder) query_result, _ = test_utils.run_query(query, [record1, record2])
query = gaussian_query.GaussianSumQuery(
l2_norm_clip=l2_norm_clip, stddev=0.0)
query_result, _ = test_utils.run_query(query, [record1, record2])
self.evaluate(tf.compat.v1.global_variables_initializer()) expected = [1.0, 1.0]
result = sess.run(query_result) self.assertAllClose(query_result, expected)
expected = [1.0, 1.0]
self.assertAllClose(result, expected)
sess.run(assign_l2_norm_clip, {l2_norm_clip_placeholder: 0.0}) l2_norm_clip.assign(0.0)
result = sess.run(query_result) query_result, _ = test_utils.run_query(query, [record1, record2])
expected = [0.0, 0.0] expected = [0.0, 0.0]
self.assertAllClose(result, expected) self.assertAllClose(query_result, expected)
def test_gaussian_sum_with_noise(self): def test_gaussian_sum_with_noise(self):
with self.cached_session() as sess: record1, record2 = 2.71828, 3.14159
record1, record2 = 2.71828, 3.14159 stddev = 1.0
stddev = 1.0
query = gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=stddev) query = gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=stddev)
noised_sums = []
for _ in range(1000):
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
noised_sums.append(query_result)
noised_sums = [] result_stddev = np.std(noised_sums)
for _ in range(1000): self.assertNear(result_stddev, stddev, 0.1)
noised_sums.append(sess.run(query_result))
result_stddev = np.std(noised_sums)
self.assertNear(result_stddev, stddev, 0.1)
def test_gaussian_sum_merge(self): def test_gaussian_sum_merge(self):
records1 = [tf.constant([2.0, 0.0]), tf.constant([-1.0, 1.0])] records1 = [tf.constant([2.0, 0.0]), tf.constant([-1.0, 1.0])]
@ -100,11 +89,8 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
merged = gaussian_query.GaussianSumQuery(10.0, 1.0).merge_sample_states( merged = gaussian_query.GaussianSumQuery(10.0, 1.0).merge_sample_states(
sample_state_1, sample_state_2) sample_state_1, sample_state_2)
with self.cached_session() as sess:
result = sess.run(merged)
expected = [3.0, 10.0] expected = [3.0, 10.0]
self.assertAllClose(result, expected) self.assertAllClose(merged, expected)
@parameterized.named_parameters( @parameterized.named_parameters(
('type_mismatch', [1.0], (1.0,), TypeError), ('type_mismatch', [1.0], (1.0,), TypeError),

110
tensorflow_privacy/privacy/dp_query/nested_query_test.py
View file

@ -29,85 +29,77 @@ _basic_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
class NestedQueryTest(tf.test.TestCase, parameterized.TestCase): class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_nested_gaussian_sum_no_clip_no_noise(self): def test_nested_gaussian_sum_no_clip_no_noise(self):
with self.cached_session() as sess: query1 = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query1 = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0) query2 = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query2 = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query = nested_query.NestedSumQuery([query1, query2]) query = nested_query.NestedSumQuery([query1, query2])
record1 = [1.0, [2.0, 3.0]] record1 = [1.0, [2.0, 3.0]]
record2 = [4.0, [3.0, 2.0]] record2 = [4.0, [3.0, 2.0]]
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [5.0, [5.0, 5.0]]
expected = [5.0, [5.0, 5.0]] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_nested_gaussian_average_with_clip_no_noise(self): def test_nested_gaussian_average_with_clip_no_noise(self):
with self.cached_session() as sess: query1 = normalized_query.NormalizedQuery(
query1 = normalized_query.NormalizedQuery( gaussian_query.GaussianSumQuery(l2_norm_clip=4.0, stddev=0.0),
gaussian_query.GaussianSumQuery(l2_norm_clip=4.0, stddev=0.0), denominator=5.0)
denominator=5.0) query2 = normalized_query.NormalizedQuery(
query2 = normalized_query.NormalizedQuery( gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=0.0),
gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=0.0), denominator=5.0)
denominator=5.0)
query = nested_query.NestedSumQuery([query1, query2]) query = nested_query.NestedSumQuery([query1, query2])
record1 = [1.0, [12.0, 9.0]] # Clipped to [1.0, [4.0, 3.0]] record1 = [1.0, [12.0, 9.0]] # Clipped to [1.0, [4.0, 3.0]]
record2 = [5.0, [1.0, 2.0]] # Clipped to [4.0, [1.0, 2.0]] record2 = [5.0, [1.0, 2.0]] # Clipped to [4.0, [1.0, 2.0]]
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [1.0, [1.0, 1.0]]
expected = [1.0, [1.0, 1.0]] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_complex_nested_query(self): def test_complex_nested_query(self):
with self.cached_session() as sess: query_ab = gaussian_query.GaussianSumQuery(l2_norm_clip=1.0, stddev=0.0)
query_ab = gaussian_query.GaussianSumQuery(l2_norm_clip=1.0, stddev=0.0) query_c = normalized_query.NormalizedQuery(
query_c = normalized_query.NormalizedQuery( gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0),
gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0), denominator=2.0)
denominator=2.0) query_d = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query_d = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query = nested_query.NestedSumQuery( query = nested_query.NestedSumQuery(
[query_ab, { [query_ab, {
'c': query_c, 'c': query_c,
'd': [query_d] 'd': [query_d]
}]) }])
record1 = [{'a': 0.0, 'b': 2.71828}, {'c': (-4.0, 6.0), 'd': [-4.0]}] record1 = [{'a': 0.0, 'b': 2.71828}, {'c': (-4.0, 6.0), 'd': [-4.0]}]
record2 = [{'a': 3.14159, 'b': 0.0}, {'c': (6.0, -4.0), 'd': [5.0]}] record2 = [{'a': 3.14159, 'b': 0.0}, {'c': (6.0, -4.0), 'd': [5.0]}]
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [{'a': 1.0, 'b': 1.0}, {'c': (1.0, 1.0), 'd': [1.0]}]
expected = [{'a': 1.0, 'b': 1.0}, {'c': (1.0, 1.0), 'd': [1.0]}] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_nested_query_with_noise(self): def test_nested_query_with_noise(self):
with self.cached_session() as sess: stddev = 2.71828
stddev = 2.71828 denominator = 3.14159
denominator = 3.14159
query1 = gaussian_query.GaussianSumQuery(l2_norm_clip=1.5, stddev=stddev) query1 = gaussian_query.GaussianSumQuery(l2_norm_clip=1.5, stddev=stddev)
query2 = normalized_query.NormalizedQuery( query2 = normalized_query.NormalizedQuery(
gaussian_query.GaussianSumQuery(l2_norm_clip=0.5, stddev=stddev), gaussian_query.GaussianSumQuery(l2_norm_clip=0.5, stddev=stddev),
denominator=denominator) denominator=denominator)
query = nested_query.NestedSumQuery((query1, query2)) query = nested_query.NestedSumQuery((query1, query2))
record1 = (3.0, [2.0, 1.5]) record1 = (3.0, [2.0, 1.5])
record2 = (0.0, [-1.0, -3.5]) record2 = (0.0, [-1.0, -3.5])
noised_averages = []
for _ in range(1000):
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
noised_averages.append(tf.nest.flatten(query_result))
noised_averages = [] result_stddev = np.std(noised_averages, 0)
for _ in range(1000): avg_stddev = stddev / denominator
noised_averages.append(tf.nest.flatten(sess.run(query_result))) expected_stddev = [stddev, avg_stddev, avg_stddev]
self.assertArrayNear(result_stddev, expected_stddev, 0.1)
result_stddev = np.std(noised_averages, 0)
avg_stddev = stddev / denominator
expected_stddev = [stddev, avg_stddev, avg_stddev]
self.assertArrayNear(result_stddev, expected_stddev, 0.1)
@parameterized.named_parameters( @parameterized.named_parameters(
('too_many_queries', [_basic_query, _basic_query], [1.0], ValueError), ('too_many_queries', [_basic_query, _basic_query], [1.0], ValueError),

46
tensorflow_privacy/privacy/dp_query/no_privacy_query_test.py
View file

@ -21,40 +21,34 @@ from tensorflow_privacy.privacy.dp_query import test_utils
class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase): class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_sum(self): def test_sum(self):
with self.cached_session() as sess: record1 = tf.constant([2.0, 0.0])
record1 = tf.constant([2.0, 0.0]) record2 = tf.constant([-1.0, 1.0])
record2 = tf.constant([-1.0, 1.0])
query = no_privacy_query.NoPrivacySumQuery() query = no_privacy_query.NoPrivacySumQuery()
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [1.0, 1.0]
expected = [1.0, 1.0] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_no_privacy_average(self): def test_no_privacy_average(self):
with self.cached_session() as sess: record1 = tf.constant([5.0, 0.0])
record1 = tf.constant([5.0, 0.0]) record2 = tf.constant([-1.0, 2.0])
record2 = tf.constant([-1.0, 2.0])
query = no_privacy_query.NoPrivacyAverageQuery() query = no_privacy_query.NoPrivacyAverageQuery()
query_result, _ = test_utils.run_query(query, [record1, record2]) query_result, _ = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result) expected = [2.0, 1.0]
expected = [2.0, 1.0] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
def test_no_privacy_weighted_average(self): def test_no_privacy_weighted_average(self):
with self.cached_session() as sess: record1 = tf.constant([4.0, 0.0])
record1 = tf.constant([4.0, 0.0]) record2 = tf.constant([-1.0, 1.0])
record2 = tf.constant([-1.0, 1.0])
weights = [1, 3] weights = [1, 3]
query = no_privacy_query.NoPrivacyAverageQuery() query = no_privacy_query.NoPrivacyAverageQuery()
query_result, _ = test_utils.run_query( query_result, _ = test_utils.run_query(
query, [record1, record2], weights=weights) query, [record1, record2], weights=weights)
result = sess.run(query_result) expected = [0.25, 0.75]
expected = [0.25, 0.75] self.assertAllClose(query_result, expected)
self.assertAllClose(result, expected)
@parameterized.named_parameters( @parameterized.named_parameters(
('type_mismatch', [1.0], (1.0,), TypeError), ('type_mismatch', [1.0], (1.0,), TypeError),