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Merge pull request #4 from georgianpartners/bolton

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Ensuring pylint is 10/10
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Christopher Choquette Choo 2019-07-18 15:07:52 -04:00 committed by GitHub
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5 changed files with 105 additions and 350 deletions
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6
privacy/bolton/__init__.py
View file

@ -17,9 +17,9 @@ from distutils.version import LooseVersion
import tensorflow as tf
if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
raise ImportError("Please upgrade your version of tensorflow from: {0} "
"to at least 2.0.0 to use privacy/bolton".format(
LooseVersion(tf.__version__)))
raise ImportError("Please upgrade your version "
"of tensorflow from: {0} to at least 2.0.0 to "
"use privacy/bolton".format(LooseVersion(tf.__version__)))
if hasattr(sys, 'skip_tf_privacy_import'): # Useful for standalone scripts.
pass
else:

282
privacy/bolton/losses.py
View file

@ -160,11 +160,11 @@ class StrongConvexHuber(losses.Loss, StrongConvexMixin):
one = tf.constant(1, dtype=self.dtype)
four = tf.constant(4, dtype=self.dtype)
if z > one + h:
if z > one + h: # pylint: disable=no-else-return
return _ops.convert_to_tensor_v2(0, dtype=self.dtype)
elif tf.math.abs(one - z) <= h:
return one / (four * h) * tf.math.pow(one + h - z, 2)
return one - z # elif: z < one - h
return one - z
def radius(self):
"""See super class."""
@ -300,281 +300,3 @@ class StrongConvexBinaryCrossentropy(
set to half the 0.5 * reg_lambda.
"""
return L1L2(l2=self.reg_lambda/2)
# class StrongConvexSparseCategoricalCrossentropy(
# losses.CategoricalCrossentropy,
# StrongConvexMixin
# ):
# """
# Strong Convex version of CategoricalCrossentropy loss using l2 weight
# regularization.
# """
#
# def __init__(self,
# reg_lambda: float,
# C: float,
# radius_constant: float,
# from_logits: bool = True,
# label_smoothing: float = 0,
# reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
# name: str = 'binarycrossentropy',
# dtype=tf.float32):
# """
# Args:
# reg_lambda: Weight regularization constant
# C: Penalty parameter C of the loss term
# radius_constant: constant defining the length of the radius
# reduction: reduction type to use. See super class
# label_smoothing: amount of smoothing to perform on labels
# relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
# name: Name of the loss instance
# dtype: tf datatype to use for tensor conversions.
# """
# if reg_lambda <= 0:
# raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
# if C <= 0:
# raise ValueError('c: {0}, should be >= 0'.format(C))
# if radius_constant <= 0:
# raise ValueError('radius_constant: {0}, should be >= 0'.format(
# radius_constant
# ))
#
# self.C = C
# self.dtype = dtype
# self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
# super(StrongConvexSparseCategoricalCrossentropy, self).__init__(
# reduction=reduction,
# name=name,
# from_logits=from_logits,
# label_smoothing=label_smoothing,
# )
# self.radius_constant = radius_constant
#
# def call(self, y_true, y_pred):
# """Compute loss
#
# Args:
# y_true: Ground truth values.
# y_pred: The predicted values.
#
# Returns:
# Loss values per sample.
# """
# loss = super()
# loss = loss * self.C
# return loss
#
# def radius(self):
# """See super class.
# """
# return self.radius_constant / self.reg_lambda
#
# def gamma(self):
# """See super class.
# """
# return self.reg_lambda
#
# def beta(self, class_weight):
# """See super class.
# """
# max_class_weight = self.max_class_weight(class_weight, self.dtype)
# return self.C * max_class_weight + self.reg_lambda
#
# def lipchitz_constant(self, class_weight):
# """See super class.
# """
# max_class_weight = self.max_class_weight(class_weight, self.dtype)
# return self.C * max_class_weight + self.reg_lambda * self.radius()
#
# def kernel_regularizer(self):
# """
# l2 loss using reg_lambda as the l2 term (as desired). Required for
# this loss function to be strongly convex.
# :return:
# """
# return L1L2(l2=self.reg_lambda)
#
# class StrongConvexSparseCategoricalCrossentropy(
# losses.SparseCategoricalCrossentropy,
# StrongConvexMixin
# ):
# """
# Strong Convex version of SparseCategoricalCrossentropy loss using l2 weight
# regularization.
# """
#
# def __init__(self,
# reg_lambda: float,
# C: float,
# radius_constant: float,
# from_logits: bool = True,
# label_smoothing: float = 0,
# reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
# name: str = 'binarycrossentropy',
# dtype=tf.float32):
# """
# Args:
# reg_lambda: Weight regularization constant
# C: Penalty parameter C of the loss term
# radius_constant: constant defining the length of the radius
# reduction: reduction type to use. See super class
# label_smoothing: amount of smoothing to perform on labels
# relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
# name: Name of the loss instance
# dtype: tf datatype to use for tensor conversions.
# """
# if reg_lambda <= 0:
# raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
# if C <= 0:
# raise ValueError('c: {0}, should be >= 0'.format(C))
# if radius_constant <= 0:
# raise ValueError('radius_constant: {0}, should be >= 0'.format(
# radius_constant
# ))
#
# self.C = C
# self.dtype = dtype
# self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
# super(StrongConvexHuber, self).__init__(reduction=reduction,
# name=name,
# from_logits=from_logits,
# label_smoothing=label_smoothing,
# )
# self.radius_constant = radius_constant
#
# def call(self, y_true, y_pred):
# """Compute loss
#
# Args:
# y_true: Ground truth values.
# y_pred: The predicted values.
#
# Returns:
# Loss values per sample.
# """
# loss = super()
# loss = loss * self.C
# return loss
#
# def radius(self):
# """See super class.
# """
# return self.radius_constant / self.reg_lambda
#
# def gamma(self):
# """See super class.
# """
# return self.reg_lambda
#
# def beta(self, class_weight):
# """See super class.
# """
# max_class_weight = self.max_class_weight(class_weight, self.dtype)
# return self.C * max_class_weight + self.reg_lambda
#
# def lipchitz_constant(self, class_weight):
# """See super class.
# """
# max_class_weight = self.max_class_weight(class_weight, self.dtype)
# return self.C * max_class_weight + self.reg_lambda * self.radius()
#
# def kernel_regularizer(self):
# """
# l2 loss using reg_lambda as the l2 term (as desired). Required for
# this loss function to be strongly convex.
# :return:
# """
# return L1L2(l2=self.reg_lambda)
#
#
# class StrongConvexCategoricalCrossentropy(
# losses.CategoricalCrossentropy,
# StrongConvexMixin
# ):
# """
# Strong Convex version of CategoricalCrossentropy loss using l2 weight
# regularization.
# """
#
# def __init__(self,
# reg_lambda: float,
# C: float,
# radius_constant: float,
# from_logits: bool = True,
# label_smoothing: float = 0,
# reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
# name: str = 'binarycrossentropy',
# dtype=tf.float32):
# """
# Args:
# reg_lambda: Weight regularization constant
# C: Penalty parameter C of the loss term
# radius_constant: constant defining the length of the radius
# reduction: reduction type to use. See super class
# label_smoothing: amount of smoothing to perform on labels
# relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
# name: Name of the loss instance
# dtype: tf datatype to use for tensor conversions.
# """
# if reg_lambda <= 0:
# raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
# if C <= 0:
# raise ValueError('c: {0}, should be >= 0'.format(C))
# if radius_constant <= 0:
# raise ValueError('radius_constant: {0}, should be >= 0'.format(
# radius_constant
# ))
#
# self.C = C
# self.dtype = dtype
# self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
# super(StrongConvexHuber, self).__init__(reduction=reduction,
# name=name,
# from_logits=from_logits,
# label_smoothing=label_smoothing,
# )
# self.radius_constant = radius_constant
#
# def call(self, y_true, y_pred):
# """Compute loss
#
# Args:
# y_true: Ground truth values.
# y_pred: The predicted values.
#
# Returns:
# Loss values per sample.
# """
# loss = super()
# loss = loss * self.C
# return loss
#
# def radius(self):
# """See super class.
# """
# return self.radius_constant / self.reg_lambda
#
# def gamma(self):
# """See super class.
# """
# return self.reg_lambda
#
# def beta(self, class_weight):
# """See super class.
# """
# max_class_weight = self.max_class_weight(class_weight, self.dtype)
# return self.C * max_class_weight + self.reg_lambda
#
# def lipchitz_constant(self, class_weight):
# """See super class.
# """
# max_class_weight = self.max_class_weight(class_weight, self.dtype)
# return self.C * max_class_weight + self.reg_lambda * self.radius()
#
# def kernel_regularizer(self):
# """
# l2 loss using reg_lambda as the l2 term (as desired). Required for
# this loss function to be strongly convex.
# :return:
# """
# return L1L2(l2=self.reg_lambda)

2
privacy/bolton/models.py
View file

@ -24,7 +24,7 @@ from privacy.bolton.losses import StrongConvexMixin
from privacy.bolton.optimizers import Bolton
class BoltonModel(Model):
class BoltonModel(Model): # pylint: disable=abstract-method
"""Bolton episilon-delta differential privacy model.
The privacy guarantees are dependent on the noise that is sampled. Please

2
privacy/bolton/optimizers_test.py
View file

@ -32,7 +32,7 @@ from privacy.bolton.losses import StrongConvexMixin
from privacy.bolton import optimizers as opt
class TestModel(Model):
class TestModel(Model): # pylint: disable=abstract-method
"""Bolton episilon-delta model.
Uses 4 key steps to achieve privacy guarantees:
1. Adds noise to weights after training (output perturbation).

151
tutorials/bolton_tutorial.py
View file

@ -1,13 +1,29 @@
# 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.
"""Tutorial for bolton module, the model and the optimizer."""
import sys
sys.path.append('..')
import tensorflow as tf
from privacy.bolton import losses
from privacy.bolton import models
"""First, we will create a binary classification dataset with a single output
dimension. The samples for each label are repeated data points at different
points in space."""
import tensorflow as tf # pylint: disable=wrong-import-position
from privacy.bolton import losses # pylint: disable=wrong-import-position
from privacy.bolton import models # pylint: disable=wrong-import-position
# -------
# First, we will create a binary classification dataset with a single output
# dimension. The samples for each label are repeated data points at different
# points in space.
# -------
# Parameters for dataset
n_samples = 10
input_dim = 2
@ -22,42 +38,50 @@ print(x.shape, y.shape)
generator = tf.data.Dataset.from_tensor_slices((x, y))
generator = generator.batch(10)
generator = generator.shuffle(10)
"""First, we will explore using the pre - built BoltonModel, which is a thin
wrapper around a Keras Model using a single - layer neural network.
It automatically uses the Bolton Optimizer which encompasses all the logic
required for the Bolton Differential Privacy method."""
# -------
# First, we will explore using the pre - built BoltonModel, which is a thin
# wrapper around a Keras Model using a single - layer neural network.
# It automatically uses the Bolton Optimizer which encompasses all the logic
# required for the Bolton Differential Privacy method.
# -------
bolt = models.BoltonModel(n_outputs) # tell the model how many outputs we have.
"""Now, we will pick our optimizer and Strongly Convex Loss function. The loss
must extend from StrongConvexMixin and implement the associated methods.Some
existing loss functions are pre - implemented in bolton.loss"""
# -------
# Now, we will pick our optimizer and Strongly Convex Loss function. The loss
# must extend from StrongConvexMixin and implement the associated methods.Some
# existing loss functions are pre - implemented in bolton.loss
# -------
optimizer = tf.optimizers.SGD()
reg_lambda = 1
C = 1
radius_constant = 1
loss = losses.StrongConvexBinaryCrossentropy(reg_lambda, C, radius_constant)
"""For simplicity, we pick all parameters of the StrongConvexBinaryCrossentropy
to be 1; these are all tunable and their impact can be read in losses.
StrongConvexBinaryCrossentropy.We then compile the model with the chosen
optimizer and loss, which will automatically wrap the chosen optimizer with the
Bolton Optimizer, ensuring the required components function as required for
privacy guarantees."""
# -------
# For simplicity, we pick all parameters of the StrongConvexBinaryCrossentropy
# to be 1; these are all tunable and their impact can be read in losses.
# StrongConvexBinaryCrossentropy.We then compile the model with the chosen
# optimizer and loss, which will automatically wrap the chosen optimizer with the
# Bolton Optimizer, ensuring the required components function as required for
# privacy guarantees.
# -------
bolt.compile(optimizer, loss)
"""To fit the model, the optimizer will require additional information about
the dataset and model.These parameters are:
1. the class_weights used
2. the number of samples in the dataset
3. the batch size which the model will try to infer, if possible. If not, you
will be required to pass these explicitly to the fit method.
As well, there are two privacy parameters than can be altered:
1. epsilon, a float
2. noise_distribution, a valid string indicating the distriution to use (must be
implemented)
The BoltonModel offers a helper method,.calculate_class_weight to aid in
class_weight calculation."""
# -------
# To fit the model, the optimizer will require additional information about
# the dataset and model.These parameters are:
# 1. the class_weights used
# 2. the number of samples in the dataset
# 3. the batch size which the model will try to infer, if possible. If not, you
# will be required to pass these explicitly to the fit method.
#
# As well, there are two privacy parameters than can be altered:
# 1. epsilon, a float
# 2. noise_distribution, a valid string indicating the distriution to use (must be
# implemented)
#
# The BoltonModel offers a helper method,.calculate_class_weight to aid in
# class_weight calculation.
# required parameters
class_weight = None # default, use .calculate_class_weight to specify other values
# -------
class_weight = None # default, use .calculate_class_weight for other values
batch_size = None # default, if it cannot be inferred, specify this
n_samples = None # default, if it cannot be iferred, specify this
# privacy parameters
@ -72,13 +96,15 @@ bolt.fit(x,
n_samples=n_samples,
noise_distribution=noise_distribution,
epochs=2)
"""We may also train a generator object, or try different optimizers and loss
functions. Below, we will see that we must pass the number of samples as the fit
method is unable to infer it for a generator."""
# -------
# We may also train a generator object, or try different optimizers and loss
# functions. Below, we will see that we must pass the number of samples as the
# fit method is unable to infer it for a generator.
# -------
optimizer2 = tf.optimizers.Adam()
bolt.compile(optimizer2, loss)
# required parameters
class_weight = None # default, use .calculate_class_weight to specify other values
class_weight = None # default, use .calculate_class_weight for other values
batch_size = None # default, if it cannot be inferred, specify this
n_samples = None # default, if it cannot be iferred, specify this
# privacy parameters
@ -95,7 +121,9 @@ try:
)
except ValueError as e:
print(e)
"""And now, re running with the parameter set."""
# -------
# And now, re running with the parameter set.
# -------
n_samples = 20
bolt.fit(generator,
epsilon=epsilon,
@ -105,42 +133,47 @@ bolt.fit(generator,
noise_distribution=noise_distribution,
verbose=0
)
"""You don't have to use the bolton model to use the Bolton method.
There are only a few requirements:
1. make sure any requirements from the loss are implemented in the model.
2. instantiate the optimizer and use it as a context around your fit operation.
"""
from privacy.bolton.optimizers import Bolton
"""Here, we create our own model and setup the Bolton optimizer."""
class TestModel(tf.keras.Model):
def __init__(self, reg_layer, n_outputs=1):
# -------
# You don't have to use the bolton model to use the Bolton method.
# There are only a few requirements:
# 1. make sure any requirements from the loss are implemented in the model.
# 2. instantiate the optimizer and use it as a context around the fit operation.
# -------
# -------------------- Part 2, using the Optimizer
from privacy.bolton.optimizers import Bolton # pylint: disable=wrong-import-position
# -------
# Here, we create our own model and setup the Bolton optimizer.
# -------
class TestModel(tf.keras.Model): # pylint: disable=abstract-method
def __init__(self, reg_layer, number_of_outputs=1):
super(TestModel, self).__init__(name='test')
self.output_layer = tf.keras.layers.Dense(n_outputs,
self.output_layer = tf.keras.layers.Dense(number_of_outputs,
kernel_regularizer=reg_layer
)
def call(self, inputs):
def call(self, inputs): # pylint: disable=arguments-differ
return self.output_layer(inputs)
optimizer = tf.optimizers.SGD()
loss = losses.StrongConvexBinaryCrossentropy(reg_lambda, C, radius_constant)
optimizer = Bolton(optimizer, loss)
"""Now, we instantiate our model and check for 1. Since our loss requires L2
regularization over the kernel, we will pass it to the model."""
# -------
# Now, we instantiate our model and check for 1. Since our loss requires L2
# regularization over the kernel, we will pass it to the model.
# -------
n_outputs = 1 # parameter for model and optimizer context.
test_model = TestModel(loss.kernel_regularizer(), n_outputs)
test_model.compile(optimizer, loss)
"""We comply with 2., and use the Bolton Optimizer as a context around the fit
method."""
# -------
# We comply with 2., and use the Bolton Optimizer as a context around the fit
# method.
# -------
# parameters for context
noise_distribution = 'laplace'
epsilon = 2
class_weights = 1 # Previously, the fit method auto-detected the class_weights.
# Here, we need to pass the class_weights explicitly. 1 is the equivalent of None.
# Here, we need to pass the class_weights explicitly. 1 is the same as None.
n_samples = 20
batch_size = 5