def test_create_tf_dataset_from_all_clients(self):
client_data = hdf5_client_data.HDF5ClientData(
TransformingClientDataTest.test_data_filepath)
num_transformed_clients = 9
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform, num_transformed_clients)
expansion_factor = num_transformed_clients // len(TEST_DATA)
tf_dataset = transformed_client_data.create_tf_dataset_from_all_clients(
)
self.assertIsInstance(tf_dataset, tf.data.Dataset)
expected_examples = []
for expected_data in six.itervalues(TEST_DATA):
for index in range(expansion_factor):
for i in range(len(expected_data['x'])):
example = {
k: v[i]
for k, v in six.iteritems(expected_data)
}
example['x'] += 10 * index
expected_examples.append(example)
for actual in tf_dataset:
expected = expected_examples.pop(0)
actual = tf.contrib.framework.nest.map_structure(
lambda t: t.numpy(), actual)
self.assertCountEqual(actual, expected)
self.assertEmpty(expected_examples)
def infinite_emnist(emnist_client_data, num_pseudo_clients):
"""Converts a Federated EMNIST dataset into an Infinite Federated EMNIST set.
Infinite Federated EMNIST expands each writer from the EMNIST dataset into
some number of pseudo-clients each of whose characters are the same but apply
a fixed random affine transformation to the original user's characters. The
distribution over affine transformation is approximately equivalent to the one
described at https://www.cs.toronto.edu/~tijmen/affNIST/. It applies the
following transformations in this order:
1. A random rotation chosen uniformly between -20 and 20 degrees.
2. A random shearing adding between -0.2 to 0.2 of the x coordinate to the
y coordinate (after centering).
3. A random scaling between 0.8 and 1.25 (sampled log uniformly).
4. A random translation between -5 and 5 pixels in both the x and y axes.
Args:
emnist_client_data: The `tff.simulation.ClientData` to convert.
num_pseudo_clients: How many pseudo-clients to generate for each real
client. Each pseudo-client is formed by applying a given random affine
transformation to the characters written by a given real user. The first
pseudo-client for a given user applies the identity transformation, so the
original users are always included.
Returns:
An expanded `tff.simulation.ClientData`.
"""
num_client_ids = len(emnist_client_data.client_ids)
return transforming_client_data.TransformingClientData(
raw_client_data=emnist_client_data,
make_transform_fn=_make_transform_fn,
num_transformed_clients=(num_client_ids * num_pseudo_clients))
def test_create_tf_dataset_for_client(self):
client_data = hdf5_client_data.HDF5ClientData(
TransformingClientDataTest.test_data_filepath)
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform, 9)
for client_id in transformed_client_data.client_ids:
tf_dataset = transformed_client_data.create_tf_dataset_for_client(
client_id)
self.assertIsInstance(tf_dataset, tf.data.Dataset)
pattern = r'^(.*)_(\d*)$'
match = re.search(pattern, client_id)
client = match.group(1)
index = int(match.group(2))
for i, actual in enumerate(tf_dataset):
expected = {
k: v[i]
for k, v in six.iteritems(TEST_DATA[client])
}
expected['x'] = expected['x'] + 10 * index
self.assertCountEqual(actual, expected)
for k, v in six.iteritems(actual):
self.assertAllEqual(v.numpy(), expected[k])
def test_create_tf_dataset_from_all_clients(self):
client_data = hdf5_client_data.HDF5ClientData(
TransformingClientDataTest.test_data_filepath)
num_transformed_clients = 9
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform_cons, num_transformed_clients)
expansion_factor = num_transformed_clients // len(TEST_DATA)
tf_dataset = transformed_client_data.create_tf_dataset_from_all_clients(
)
self.assertIsInstance(tf_dataset, tf.data.Dataset)
expected_examples = []
for expected_data in TEST_DATA.values():
for index in range(expansion_factor):
for i in range(len(expected_data['x'])):
example = {
k: v[i].copy()
for k, v in expected_data.items()
}
example['x'] += 10 * index
expected_examples.append(example)
for actual in tf_dataset:
actual = self.evaluate(actual)
expected = expected_examples.pop(0)
self.assertCountEqual(actual, expected)
self.assertEmpty(expected_examples)
def test_default_num_transformed_clients(self):
client_data = from_tensor_slices_client_data.FromTensorSlicesClientData(
TEST_DATA)
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform_cons)
client_ids = transformed_client_data.client_ids
self.assertLen(client_ids, len(TEST_DATA))
self.assertFalse(transformed_client_data._has_pseudo_clients)
def test_client_ids_property(self):
client_data = hdf5_client_data.HDF5ClientData(
TransformingClientDataTest.test_data_filepath)
expansion_factor = 2.5
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, lambda: 0, expansion_factor)
self.assertLen(transformed_client_data.client_ids,
int(len(TEST_DATA) * expansion_factor))
def test_client_ids_property(self):
client_data = from_tensor_slices_client_data.FromTensorSlicesClientData(
TEST_DATA)
num_transformed_clients = 7
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform_cons, num_transformed_clients)
client_ids = transformed_client_data.client_ids
self.assertLen(client_ids, num_transformed_clients)
for client_id in client_ids:
self.assertIsInstance(client_id, str)
self.assertListEqual(client_ids, sorted(client_ids))
self.assertTrue(transformed_client_data._has_pseudo_clients)
def test_client_ids_property(self):
client_data = from_tensor_slices_client_data.FromTensorSlicesClientData(
TEST_DATA)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
transforming_client_data.TransformingClientData(
client_data, _test_transform_cons)
self.assertNotEmpty(w)
self.assertEqual(w[0].category, DeprecationWarning)
self.assertRegex(
str(w[0].message),
'tff.simulation.TransformingClientData is deprecated')
def test_client_ids_property(self):
client_data = from_tensor_slices_client_data.FromTensorSlicesClientData(
TEST_DATA)
num_transformed_clients = 7
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform_cons, num_transformed_clients)
client_ids = transformed_client_data.client_ids
# Check length of client_ids.
self.assertLen(client_ids, 7)
# Check that they are all strings.
for client_id in client_ids:
self.assertIsInstance(client_id, str)
# Check ids are sorted.
self.assertListEqual(client_ids, sorted(client_ids))
def test_fail_on_bad_client_id(self):
client_data = from_tensor_slices_client_data.FromTensorSlicesClientData(
TEST_DATA)
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform_cons, 7)
# The following three should be valid.
transformed_client_data.create_tf_dataset_for_client('CLIENT A_1')
transformed_client_data.create_tf_dataset_for_client('CLIENT B_1')
transformed_client_data.create_tf_dataset_for_client('CLIENT A_2')
# This should not be valid: no corresponding client.
with self.assertRaisesRegex(
ValueError, 'client_id must be a valid string from client_ids.'):
transformed_client_data.create_tf_dataset_for_client('CLIENT D_0')
# This should not be valid: index out of range.
with self.assertRaisesRegex(
ValueError, 'client_id must be a valid string from client_ids.'):
transformed_client_data.create_tf_dataset_for_client('CLIENT B_2')
def test_client_ids_property(self):
client_data = hdf5_client_data.HDF5ClientData(
TransformingClientDataTest.test_data_filepath)
num_transformed_clients = 7
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform, num_transformed_clients)
client_ids = transformed_client_data.client_ids
# Check length of client_ids.
self.assertLen(client_ids, 7)
# Check that they are all strings.
for client_id in client_ids:
self.assertIsInstance(client_id, str)
# Check ids are sorted.
self.assertListEqual(client_ids, sorted(client_ids))
def test_create_tf_dataset_for_client(self):
client_data = from_tensor_slices_client_data.FromTensorSlicesClientData(
TEST_DATA)
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform_cons, 9)
for client_id in transformed_client_data.client_ids:
tf_dataset = transformed_client_data.create_tf_dataset_for_client(
client_id)
self.assertIsInstance(tf_dataset, tf.data.Dataset)
pattern = r'^(.*)_(\d*)$'
match = re.search(pattern, client_id)
client = match.group(1)
index = int(match.group(2))
for i, actual in enumerate(tf_dataset):
actual = self.evaluate(actual)
expected = {k: v[i].copy() for k, v in TEST_DATA[client].items()}
expected['x'] += 10 * index
self.assertCountEqual(actual, expected)
for k, v in actual.items():
self.assertAllEqual(v, expected[k])
def test_fail_on_bad_client_id(self):
client_data = hdf5_client_data.HDF5ClientData(
TransformingClientDataTest.test_data_filepath)
transformed_client_data = transforming_client_data.TransformingClientData(
client_data, _test_transform, 7)
# The following three should be valid.
transformed_client_data.create_tf_dataset_for_client('CLIENT A_1')
transformed_client_data.create_tf_dataset_for_client('CLIENT B_1')
transformed_client_data.create_tf_dataset_for_client('CLIENT A_2')
# This should not be valid: no corresponding client.
with self.assertRaisesRegex(
ValueError,
'client_id must be a valid string from client_ids.'):
transformed_client_data.create_tf_dataset_for_client('CLIENT D_0')
# This should not be valid: index out of range.
with self.assertRaisesRegex(
ValueError,
'client_id must be a valid string from client_ids.'):
transformed_client_data.create_tf_dataset_for_client('CLIENT B_2')