431 lines
13 KiB
Python
431 lines
13 KiB
Python
# Copyright 2019, The TensorFlow Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Unit testing for losses."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from contextlib import contextmanager
|
|
from io import StringIO
|
|
import sys
|
|
from absl.testing import parameterized
|
|
import tensorflow as tf
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.keras import keras_parameterized
|
|
from tensorflow.python.keras.regularizers import L1L2
|
|
from privacy.bolton.losses import StrongConvexBinaryCrossentropy
|
|
from privacy.bolton.losses import StrongConvexHuber
|
|
from privacy.bolton.losses import StrongConvexMixin
|
|
|
|
|
|
@contextmanager
|
|
def captured_output():
|
|
"""Capture std_out and std_err within context."""
|
|
new_out, new_err = StringIO(), StringIO()
|
|
old_out, old_err = sys.stdout, sys.stderr
|
|
try:
|
|
sys.stdout, sys.stderr = new_out, new_err
|
|
yield sys.stdout, sys.stderr
|
|
finally:
|
|
sys.stdout, sys.stderr = old_out, old_err
|
|
|
|
|
|
class StrongConvexMixinTests(keras_parameterized.TestCase):
|
|
"""Tests for the StrongConvexMixin."""
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'beta not implemented',
|
|
'fn': 'beta',
|
|
'args': [1]},
|
|
{'testcase_name': 'gamma not implemented',
|
|
'fn': 'gamma',
|
|
'args': []},
|
|
{'testcase_name': 'lipchitz not implemented',
|
|
'fn': 'lipchitz_constant',
|
|
'args': [1]},
|
|
{'testcase_name': 'radius not implemented',
|
|
'fn': 'radius',
|
|
'args': []},
|
|
])
|
|
|
|
def test_not_implemented(self, fn, args):
|
|
"""Test that the given fn's are not implemented on the mixin.
|
|
|
|
Args:
|
|
fn: fn on Mixin to test
|
|
args: arguments to fn of Mixin
|
|
"""
|
|
with self.assertRaises(NotImplementedError):
|
|
loss = StrongConvexMixin()
|
|
getattr(loss, fn, None)(*args)
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'radius not implemented',
|
|
'fn': 'kernel_regularizer',
|
|
'args': []},
|
|
])
|
|
def test_return_none(self, fn, args):
|
|
"""Test that fn of Mixin returns None.
|
|
|
|
Args:
|
|
fn: fn of Mixin to test
|
|
args: arguments to fn of Mixin
|
|
"""
|
|
loss = StrongConvexMixin()
|
|
ret = getattr(loss, fn, None)(*args)
|
|
self.assertEqual(ret, None)
|
|
|
|
|
|
class BinaryCrossesntropyTests(keras_parameterized.TestCase):
|
|
"""tests for BinaryCrossesntropy StrongConvex loss."""
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'normal',
|
|
'reg_lambda': 1,
|
|
'C': 1,
|
|
'radius_constant': 1
|
|
}, # pylint: disable=invalid-name
|
|
])
|
|
def test_init_params(self, reg_lambda, C, radius_constant):
|
|
"""Test initialization for given arguments.
|
|
|
|
Args:
|
|
reg_lambda: initialization value for reg_lambda arg
|
|
C: initialization value for C arg
|
|
radius_constant: initialization value for radius_constant arg
|
|
"""
|
|
# test valid domains for each variable
|
|
loss = StrongConvexBinaryCrossentropy(reg_lambda, C, radius_constant)
|
|
self.assertIsInstance(loss, StrongConvexBinaryCrossentropy)
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'negative c',
|
|
'reg_lambda': 1,
|
|
'C': -1,
|
|
'radius_constant': 1
|
|
},
|
|
{'testcase_name': 'negative radius',
|
|
'reg_lambda': 1,
|
|
'C': 1,
|
|
'radius_constant': -1
|
|
},
|
|
{'testcase_name': 'negative lambda',
|
|
'reg_lambda': -1,
|
|
'C': 1,
|
|
'radius_constant': 1
|
|
}, # pylint: disable=invalid-name
|
|
])
|
|
def test_bad_init_params(self, reg_lambda, C, radius_constant):
|
|
"""Test invalid domain for given params. Should return ValueError.
|
|
|
|
Args:
|
|
reg_lambda: initialization value for reg_lambda arg
|
|
C: initialization value for C arg
|
|
radius_constant: initialization value for radius_constant arg
|
|
"""
|
|
# test valid domains for each variable
|
|
with self.assertRaises(ValueError):
|
|
StrongConvexBinaryCrossentropy(reg_lambda, C, radius_constant)
|
|
|
|
@test_util.run_all_in_graph_and_eager_modes
|
|
@parameterized.named_parameters([
|
|
# [] for compatibility with tensorflow loss calculation
|
|
{'testcase_name': 'both positive',
|
|
'logits': [10000],
|
|
'y_true': [1],
|
|
'result': 0,
|
|
},
|
|
{'testcase_name': 'positive gradient negative logits',
|
|
'logits': [-10000],
|
|
'y_true': [1],
|
|
'result': 10000,
|
|
},
|
|
{'testcase_name': 'positivee gradient positive logits',
|
|
'logits': [10000],
|
|
'y_true': [0],
|
|
'result': 10000,
|
|
},
|
|
{'testcase_name': 'both negative',
|
|
'logits': [-10000],
|
|
'y_true': [0],
|
|
'result': 0
|
|
},
|
|
])
|
|
def test_calculation(self, logits, y_true, result):
|
|
"""Test the call method to ensure it returns the correct value.
|
|
|
|
Args:
|
|
logits: unscaled output of model
|
|
y_true: label
|
|
result: correct loss calculation value
|
|
"""
|
|
logits = tf.Variable(logits, False, dtype=tf.float32)
|
|
y_true = tf.Variable(y_true, False, dtype=tf.float32)
|
|
loss = StrongConvexBinaryCrossentropy(0.00001, 1, 1)
|
|
loss = loss(y_true, logits)
|
|
self.assertEqual(loss.numpy(), result)
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'beta',
|
|
'init_args': [1, 1, 1],
|
|
'fn': 'beta',
|
|
'args': [1],
|
|
'result': tf.constant(2, dtype=tf.float32)
|
|
},
|
|
{'testcase_name': 'gamma',
|
|
'fn': 'gamma',
|
|
'init_args': [1, 1, 1],
|
|
'args': [],
|
|
'result': tf.constant(1, dtype=tf.float32),
|
|
},
|
|
{'testcase_name': 'lipchitz constant',
|
|
'fn': 'lipchitz_constant',
|
|
'init_args': [1, 1, 1],
|
|
'args': [1],
|
|
'result': tf.constant(2, dtype=tf.float32),
|
|
},
|
|
{'testcase_name': 'kernel regularizer',
|
|
'fn': 'kernel_regularizer',
|
|
'init_args': [1, 1, 1],
|
|
'args': [],
|
|
'result': L1L2(l2=0.5),
|
|
},
|
|
])
|
|
def test_fns(self, init_args, fn, args, result):
|
|
"""Test that fn of BinaryCrossentropy loss returns the correct result.
|
|
|
|
Args:
|
|
init_args: init values for loss instance
|
|
fn: the fn to test
|
|
args: the arguments to above function
|
|
result: the correct result from the fn
|
|
"""
|
|
loss = StrongConvexBinaryCrossentropy(*init_args)
|
|
expected = getattr(loss, fn, lambda: 'fn not found')(*args)
|
|
if hasattr(expected, 'numpy') and hasattr(result, 'numpy'): # both tensor
|
|
expected = expected.numpy()
|
|
result = result.numpy()
|
|
if hasattr(expected, 'l2') and hasattr(result, 'l2'): # both l2 regularizer
|
|
expected = expected.l2
|
|
result = result.l2
|
|
self.assertEqual(expected, result)
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'label_smoothing',
|
|
'init_args': [1, 1, 1, True, 0.1],
|
|
'fn': None,
|
|
'args': None,
|
|
'print_res': 'The impact of label smoothing on privacy is unknown.'
|
|
},
|
|
])
|
|
def test_prints(self, init_args, fn, args, print_res):
|
|
"""Test logger warning from StrongConvexBinaryCrossentropy.
|
|
|
|
Args:
|
|
init_args: arguments to init the object with.
|
|
fn: function to test
|
|
args: arguments to above function
|
|
print_res: print result that should have been printed.
|
|
"""
|
|
with captured_output() as (out, err): # pylint: disable=unused-variable
|
|
loss = StrongConvexBinaryCrossentropy(*init_args)
|
|
if fn is not None:
|
|
getattr(loss, fn, lambda *arguments: print('error'))(*args)
|
|
self.assertRegexMatch(err.getvalue().strip(), [print_res])
|
|
|
|
|
|
class HuberTests(keras_parameterized.TestCase):
|
|
"""tests for BinaryCrossesntropy StrongConvex loss."""
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'normal',
|
|
'reg_lambda': 1,
|
|
'c': 1,
|
|
'radius_constant': 1,
|
|
'delta': 1,
|
|
},
|
|
])
|
|
def test_init_params(self, reg_lambda, c, radius_constant, delta):
|
|
"""Test initialization for given arguments.
|
|
|
|
Args:
|
|
reg_lambda: initialization value for reg_lambda arg
|
|
c: initialization value for C arg
|
|
radius_constant: initialization value for radius_constant arg
|
|
delta: the delta parameter for the huber loss
|
|
"""
|
|
# test valid domains for each variable
|
|
loss = StrongConvexHuber(reg_lambda, c, radius_constant, delta)
|
|
self.assertIsInstance(loss, StrongConvexHuber)
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'negative c',
|
|
'reg_lambda': 1,
|
|
'c': -1,
|
|
'radius_constant': 1,
|
|
'delta': 1
|
|
},
|
|
{'testcase_name': 'negative radius',
|
|
'reg_lambda': 1,
|
|
'c': 1,
|
|
'radius_constant': -1,
|
|
'delta': 1
|
|
},
|
|
{'testcase_name': 'negative lambda',
|
|
'reg_lambda': -1,
|
|
'c': 1,
|
|
'radius_constant': 1,
|
|
'delta': 1
|
|
},
|
|
{'testcase_name': 'negative delta',
|
|
'reg_lambda': 1,
|
|
'c': 1,
|
|
'radius_constant': 1,
|
|
'delta': -1
|
|
},
|
|
])
|
|
def test_bad_init_params(self, reg_lambda, c, radius_constant, delta):
|
|
"""Test invalid domain for given params. Should return ValueError.
|
|
|
|
Args:
|
|
reg_lambda: initialization value for reg_lambda arg
|
|
c: initialization value for C arg
|
|
radius_constant: initialization value for radius_constant arg
|
|
delta: the delta parameter for the huber loss
|
|
"""
|
|
# test valid domains for each variable
|
|
with self.assertRaises(ValueError):
|
|
StrongConvexHuber(reg_lambda, c, radius_constant, delta)
|
|
|
|
# test the bounds and test varied delta's
|
|
@test_util.run_all_in_graph_and_eager_modes
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'delta=1,y_true=1 z>1+h decision boundary',
|
|
'logits': 2.1,
|
|
'y_true': 1,
|
|
'delta': 1,
|
|
'result': 0,
|
|
},
|
|
{'testcase_name': 'delta=1,y_true=1 z<1+h decision boundary',
|
|
'logits': 1.9,
|
|
'y_true': 1,
|
|
'delta': 1,
|
|
'result': 0.01*0.25,
|
|
},
|
|
{'testcase_name': 'delta=1,y_true=1 1-z< h decision boundary',
|
|
'logits': 0.1,
|
|
'y_true': 1,
|
|
'delta': 1,
|
|
'result': 1.9**2 * 0.25,
|
|
},
|
|
{'testcase_name': 'delta=1,y_true=1 z < 1-h decision boundary',
|
|
'logits': -0.1,
|
|
'y_true': 1,
|
|
'delta': 1,
|
|
'result': 1.1,
|
|
},
|
|
{'testcase_name': 'delta=2,y_true=1 z>1+h decision boundary',
|
|
'logits': 3.1,
|
|
'y_true': 1,
|
|
'delta': 2,
|
|
'result': 0,
|
|
},
|
|
{'testcase_name': 'delta=2,y_true=1 z<1+h decision boundary',
|
|
'logits': 2.9,
|
|
'y_true': 1,
|
|
'delta': 2,
|
|
'result': 0.01*0.125,
|
|
},
|
|
{'testcase_name': 'delta=2,y_true=1 1-z < h decision boundary',
|
|
'logits': 1.1,
|
|
'y_true': 1,
|
|
'delta': 2,
|
|
'result': 1.9**2 * 0.125,
|
|
},
|
|
{'testcase_name': 'delta=2,y_true=1 z < 1-h decision boundary',
|
|
'logits': -1.1,
|
|
'y_true': 1,
|
|
'delta': 2,
|
|
'result': 2.1,
|
|
},
|
|
{'testcase_name': 'delta=1,y_true=-1 z>1+h decision boundary',
|
|
'logits': -2.1,
|
|
'y_true': -1,
|
|
'delta': 1,
|
|
'result': 0,
|
|
},
|
|
])
|
|
def test_calculation(self, logits, y_true, delta, result):
|
|
"""Test the call method to ensure it returns the correct value.
|
|
|
|
Args:
|
|
logits: unscaled output of model
|
|
y_true: label
|
|
delta:
|
|
result: correct loss calculation value
|
|
"""
|
|
logits = tf.Variable(logits, False, dtype=tf.float32)
|
|
y_true = tf.Variable(y_true, False, dtype=tf.float32)
|
|
loss = StrongConvexHuber(0.00001, 1, 1, delta)
|
|
loss = loss(y_true, logits)
|
|
self.assertAllClose(loss.numpy(), result)
|
|
|
|
@parameterized.named_parameters([
|
|
{'testcase_name': 'beta',
|
|
'init_args': [1, 1, 1, 1],
|
|
'fn': 'beta',
|
|
'args': [1],
|
|
'result': tf.Variable(1.5, dtype=tf.float32)
|
|
},
|
|
{'testcase_name': 'gamma',
|
|
'fn': 'gamma',
|
|
'init_args': [1, 1, 1, 1],
|
|
'args': [],
|
|
'result': tf.Variable(1, dtype=tf.float32),
|
|
},
|
|
{'testcase_name': 'lipchitz constant',
|
|
'fn': 'lipchitz_constant',
|
|
'init_args': [1, 1, 1, 1],
|
|
'args': [1],
|
|
'result': tf.Variable(2, dtype=tf.float32),
|
|
},
|
|
{'testcase_name': 'kernel regularizer',
|
|
'fn': 'kernel_regularizer',
|
|
'init_args': [1, 1, 1, 1],
|
|
'args': [],
|
|
'result': L1L2(l2=0.5),
|
|
},
|
|
])
|
|
def test_fns(self, init_args, fn, args, result):
|
|
"""Test that fn of BinaryCrossentropy loss returns the correct result.
|
|
|
|
Args:
|
|
init_args: init values for loss instance
|
|
fn: the fn to test
|
|
args: the arguments to above function
|
|
result: the correct result from the fn
|
|
"""
|
|
loss = StrongConvexHuber(*init_args)
|
|
expected = getattr(loss, fn, lambda: 'fn not found')(*args)
|
|
if hasattr(expected, 'numpy') and hasattr(result, 'numpy'): # both tensor
|
|
expected = expected.numpy()
|
|
result = result.numpy()
|
|
if hasattr(expected, 'l2') and hasattr(result, 'l2'): # both l2 regularizer
|
|
expected = expected.l2
|
|
result = result.l2
|
|
self.assertEqual(expected, result)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|