def test_client_update_works_as_expected(self):
max_num_heavy_hitters = tf.constant(10)
max_user_contribution = tf.constant(10)
possible_prefix_extensions = tf.constant(['a', 'b', 'c', 'd', 'e'],
dtype=tf.string)
discovered_prefixes = tf.constant(['a', 'b', 'c', 'd', 'e'],
dtype=tf.string)
round_num = tf.constant(1)
num_sub_rounds = tf.constant(1)
sample_data = tf.data.Dataset.from_tensor_slices(
['a', '', 'abc', 'bac', 'abb', 'aaa', 'acc', 'hi'])
client_output = triehh_tf.client_update(sample_data,
discovered_prefixes,
possible_prefix_extensions,
round_num, num_sub_rounds,
max_num_heavy_hitters,
max_user_contribution)
expected_client_votes = tf.constant(
[[1, 2, 1, 0, 0], [1, 0, 0, 0, 0], [0, 0, 0, 0, 0], [
0, 0, 0, 0, 0
], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
dtype=tf.int32)
self.assertAllEqual(client_output.client_votes, expected_client_votes)
def test_client_update_works_as_expected(self):
max_num_prefixes = tf.constant(10)
max_user_contribution = tf.constant(10)
possible_prefix_extensions = tf.constant(
['a', 'b', 'c', 'd', 'e', triehh_tf.DEFAULT_TERMINATOR],
dtype=tf.string)
discovered_prefixes = tf.constant(['a', 'b', 'c', 'd', 'e'],
dtype=tf.string)
round_num = tf.constant(1)
num_sub_rounds = tf.constant(1)
sample_data = tf.data.Dataset.from_tensor_slices(
['a', '', 'abc', 'bac', 'abb', 'aaa', 'acc', 'hi'])
client_output = triehh_tf.client_update(
sample_data, discovered_prefixes, possible_prefix_extensions,
round_num, num_sub_rounds, max_num_prefixes, max_user_contribution)
# Each string is attached with triehh_tf.DEFAULT_TERMINATOR before the
# client votes, so 'a$' get a vote here.
expected_client_votes = tf.constant(
[[1, 2, 1, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
dtype=tf.int32)
self.assertAllEqual(client_output.client_votes, expected_client_votes)
def test_client_update_works_on_empty_local_datasets(self):
max_num_prefixes = tf.constant(10)
max_user_contribution = tf.constant(10)
possible_prefix_extensions = tf.constant(
['a', 'b', 'c', 'd', 'e', triehh_tf.DEFAULT_TERMINATOR],
dtype=tf.string)
discovered_prefixes = tf.constant(['a', 'b', 'c', 'd', 'e'],
dtype=tf.string)
round_num = tf.constant(1)
num_sub_rounds = tf.constant(1)
# Force an empty dataset that yields tf.string. Using `from_tensor_slices`
# defaults to yielding tf.int32 values.
sample_data = tf.data.Dataset.from_generator(
generator=lambda: iter(()), output_types=tf.string, output_shapes=())
client_output = triehh_tf.client_update(sample_data, discovered_prefixes,
possible_prefix_extensions,
round_num, num_sub_rounds,
max_num_prefixes,
max_user_contribution)
expected_client_votes = tf.constant(
[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
dtype=tf.int32)
self.assertAllEqual(client_output.client_votes, expected_client_votes)
def test_client_update_works_on_empty_local_datasets(self):
max_num_prefixes = tf.constant(10)
max_user_contribution = tf.constant(10)
possible_prefix_extensions = tf.constant(
['a', 'b', 'c', 'd', 'e', triehh_tf.DEFAULT_TERMINATOR],
dtype=tf.string)
discovered_prefixes = tf.constant(['a', 'b', 'c', 'd', 'e'],
dtype=tf.string)
round_num = tf.constant(1)
num_sub_rounds = tf.constant(1)
sample_data = tf.data.Dataset.from_tensor_slices([])
client_output = triehh_tf.client_update(
sample_data, discovered_prefixes, possible_prefix_extensions,
round_num, num_sub_rounds, max_num_prefixes, max_user_contribution)
expected_client_votes = tf.constant(
[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
dtype=tf.int32)
self.assertAllEqual(client_output.client_votes, expected_client_votes)
def client_update_fn(tf_dataset, discovered_prefixes, round_num):
return client_update(tf_dataset, discovered_prefixes,
tf.constant(possible_prefix_extensions),
round_num, num_sub_rounds, max_num_prefixes,
max_user_contribution)
def client_update_fn(tf_dataset, discovered_prefixes, round_num):
return client_update(tf_dataset, discovered_prefixes,
tf.constant(possible_prefix_extensions),
round_num, num_sub_rounds, max_num_prefixes,
max_user_contribution,
tf.constant(default_terminator, dtype=tf.string))
