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more fixes

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npapernot 2019-07-25 16:13:32 +00:00
parent 8e6bcf9b4a
commit 8974a95b9a
8 changed files with 114 additions and 107 deletions
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2
privacy/bolton/__init__.py
View file

@ -16,7 +16,7 @@ import sys
from distutils.version import LooseVersion
import tensorflow as tf
if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
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__)))

10
privacy/bolton/losses.py
View file

@ -102,11 +102,11 @@ class StrongConvexHuber(losses.Loss, StrongConvexMixin):
"""Strong Convex version of Huber loss using l2 weight regularization."""
def __init__(self,
reg_lambda: float,
C: float,
radius_constant: float,
delta: float,
reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
reg_lambda,
C,
radius_constant,
delta,
reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
dtype=tf.float32):
"""Constructor.

9
privacy/bolton/losses_test.py
View file

@ -261,8 +261,9 @@ class HuberTests(keras_parameterized.TestCase):
Args:
reg_lambda: initialization value for reg_lambda arg
C: initialization value for C 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)
@ -295,11 +296,11 @@ class HuberTests(keras_parameterized.TestCase):
},
])
def test_bad_init_params(self, reg_lambda, c, radius_constant, delta):
"""Test invalid domain for given params. Should return ValueError
"""Test invalid domain for given params. Should return ValueError.
Args:
reg_lambda: initialization value for reg_lambda arg
C: initialization value for C arg
c: initialization value for C arg
radius_constant: initialization value for radius_constant arg
delta: the delta parameter for the huber loss
"""
@ -406,7 +407,7 @@ class HuberTests(keras_parameterized.TestCase):
},
])
def test_fns(self, init_args, fn, args, result):
"""Test that fn of BinaryCrossentropy loss returns the correct result
"""Test that fn of BinaryCrossentropy loss returns the correct result.
Args:
init_args: init values for loss instance

51
privacy/bolton/models.py
View file

@ -86,10 +86,12 @@ class BoltonModel(Model): # pylint: disable=abstract-method
**kwargs): # pylint: disable=arguments-differ
"""See super class. Default optimizer used in Bolton method is SGD.
Missing args.
"""
if not isinstance(loss, StrongConvexMixin):
raise ValueError("loss function must be a Strongly Convex and therefore "
"extend the StrongConvexMixin.")
raise ValueError('loss function must be a Strongly Convex and therefore '
'extend the StrongConvexMixin.')
if not self._layers_instantiated: # compile may be called multiple times
# for instance, if the input/outputs are not defined until fit.
self.output_layer = tf.keras.layers.Dense(
@ -150,7 +152,7 @@ class BoltonModel(Model): # pylint: disable=abstract-method
data_size = n_samples
elif hasattr(x, 'shape'):
data_size = x.shape[0]
elif hasattr(x, "__len__"):
elif hasattr(x, '__len__'):
data_size = len(x)
else:
data_size = None
@ -187,10 +189,12 @@ class BoltonModel(Model): # pylint: disable=abstract-method
n_samples=None,
steps_per_epoch=None,
**kwargs): # pylint: disable=arguments-differ
"""
This method is the same as fit except for when the passed dataset
is a generator. See super method and fit for more details.
Args:
"""Fit with a generator..
This method is the same as fit except for when the passed dataset
is a generator. See super method and fit for more details.
Args:
n_samples: number of individual samples in x
noise_distribution: the distribution to get noise from.
epsilon: privacy parameter, which trades off utility and privacy. See
@ -206,7 +210,7 @@ class BoltonModel(Model): # pylint: disable=abstract-method
data_size = n_samples
elif hasattr(generator, 'shape'):
data_size = generator.shape[0]
elif hasattr(generator, "__len__"):
elif hasattr(generator, '__len__'):
data_size = len(generator)
else:
data_size = None
@ -232,13 +236,14 @@ class BoltonModel(Model): # pylint: disable=abstract-method
num_classes=None):
"""Calculates class weighting to be used in training.
Args:
Args:
class_weights: str specifying type, array giving weights, or None.
class_counts: If class_weights is not None, then an array of
the number of samples for each class
num_classes: If class_weights is not None, then the number of
classes.
Returns: class_weights as 1D tensor, to be passed to model's fit method.
Returns:
class_weights as 1D tensor, to be passed to model's fit method.
"""
# Value checking
class_keys = ['balanced']
@ -246,14 +251,14 @@ class BoltonModel(Model): # pylint: disable=abstract-method
if isinstance(class_weights, str):
is_string = True
if class_weights not in class_keys:
raise ValueError("Detected string class_weights with "
"value: {0}, which is not one of {1}."
"Please select a valid class_weight type"
"or pass an array".format(class_weights,
raise ValueError('Detected string class_weights with '
'value: {0}, which is not one of {1}.'
'Please select a valid class_weight type'
'or pass an array'.format(class_weights,
class_keys))
if class_counts is None:
raise ValueError("Class counts must be provided if using "
"class_weights=%s" % class_weights)
raise ValueError('Class counts must be provided if using '
'class_weights=%s' % class_weights)
class_counts_shape = tf.Variable(class_counts,
trainable=False,
dtype=self._dtype).shape
@ -261,12 +266,12 @@ class BoltonModel(Model): # pylint: disable=abstract-method
raise ValueError('class counts must be a 1D array.'
'Detected: {0}'.format(class_counts_shape))
if num_classes is None:
raise ValueError("num_classes must be provided if using "
"class_weights=%s" % class_weights)
raise ValueError('num_classes must be provided if using '
'class_weights=%s' % class_weights)
elif class_weights is not None:
if num_classes is None:
raise ValueError("You must pass a value for num_classes if "
"creating an array of class_weights")
raise ValueError('You must pass a value for num_classes if '
'creating an array of class_weights')
# performing class weight calculation
if class_weights is None:
class_weights = 1
@ -280,11 +285,11 @@ class BoltonModel(Model): # pylint: disable=abstract-method
else:
class_weights = _ops.convert_to_tensor_v2(class_weights)
if len(class_weights.shape) != 1:
raise ValueError("Detected class_weights shape: {0} instead of "
"1D array".format(class_weights.shape))
raise ValueError('Detected class_weights shape: {0} instead of '
'1D array'.format(class_weights.shape))
if class_weights.shape[0] != num_classes:
raise ValueError(
"Detected array length: {0} instead of: {1}".format(
'Detected array length: {0} instead of: {1}'.format(
class_weights.shape[0],
num_classes))
return class_weights

88
privacy/bolton/models_test.py
View file

@ -17,17 +17,16 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
from tensorflow.python.keras import losses
from tensorflow.python.framework import ops as _ops
from tensorflow.python.keras.regularizers import L1L2
from absl.testing import parameterized
import tensorflow as tf
from tensorflow.python.framework import ops as _ops
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import losses
from tensorflow.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
from tensorflow.python.keras.regularizers import L1L2
from privacy.bolton import models
from privacy.bolton.optimizers import Bolton
from privacy.bolton.losses import StrongConvexMixin
from privacy.bolton.optimizers import Bolton
class TestLoss(losses.Loss, StrongConvexMixin):
@ -41,9 +40,11 @@ class TestLoss(losses.Loss, StrongConvexMixin):
def radius(self):
"""Radius, R, of the hypothesis space W.
W is a convex set that forms the hypothesis space.
Returns: radius
Returns:
radius
"""
return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
@ -69,7 +70,8 @@ class TestLoss(losses.Loss, StrongConvexMixin):
Args:
class_weight: class weights used
Returns: L
Returns:
L
"""
return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
@ -81,11 +83,10 @@ class TestLoss(losses.Loss, StrongConvexMixin):
)
def max_class_weight(self, class_weight):
"""the maximum weighting in class weights (max value) as a scalar tensor
"""the maximum weighting in class weights (max value) as a scalar tensor.
Args:
class_weight: class weights used
dtype: the data type for tensor conversions.
Returns:
maximum class weighting as tensor scalar
@ -104,7 +105,7 @@ class TestLoss(losses.Loss, StrongConvexMixin):
class TestOptimizer(OptimizerV2):
"""Test optimizer used for testing Bolton model"""
"""Test optimizer used for testing Bolton model."""
def __init__(self):
super(TestOptimizer, self).__init__('test')
@ -152,7 +153,7 @@ class InitTests(keras_parameterized.TestCase):
},
])
def test_bad_init_params(self, n_outputs):
"""test bad initializations of BoltonModel that should raise errors
"""test bad initializations of BoltonModel that should raise errors.
Args:
n_outputs: number of output neurons
@ -174,12 +175,12 @@ class InitTests(keras_parameterized.TestCase):
},
])
def test_compile(self, n_outputs, loss, optimizer):
"""test compilation of BoltonModel
"""test compilation of BoltonModel.
Args:
n_outputs: number of output neurons
loss: instantiated TestLoss instance
optimizer: instanced TestOptimizer instance
n_outputs: number of output neurons
loss: instantiated TestLoss instance
optimizer: instanced TestOptimizer instance
"""
# test compilation of valid tf.optimizer and tf.loss
with self.cached_session():
@ -200,12 +201,12 @@ class InitTests(keras_parameterized.TestCase):
}
])
def test_bad_compile(self, n_outputs, loss, optimizer):
"""test bad compilations of BoltonModel that should raise errors
"""test bad compilations of BoltonModel that should raise errors.
Args:
n_outputs: number of output neurons
loss: instantiated TestLoss instance
optimizer: instanced TestOptimizer instance
n_outputs: number of output neurons
loss: instantiated TestLoss instance
optimizer: instanced TestOptimizer instance
"""
# test compilaton of invalid tf.optimizer and non instantiated loss.
with self.cached_session():
@ -215,17 +216,18 @@ class InitTests(keras_parameterized.TestCase):
def _cat_dataset(n_samples, input_dim, n_classes, generator=False):
"""
Creates a categorically encoded dataset (y is categorical).
returns the specified dataset either as a static array or as a generator.
Will have evenly split samples across each output class.
Each output class will be a different point in the input space.
"""Creates a categorically encoded dataset.
Creates a categorically encoded dataset (y is categorical).
returns the specified dataset either as a static array or as a generator.
Will have evenly split samples across each output class.
Each output class will be a different point in the input space.
Args:
n_samples: number of rows
input_dim: input dimensionality
n_classes: output dimensionality
generator: False for array, True for generator
n_samples: number of rows
input_dim: input dimensionality
n_classes: output dimensionality
generator: False for array, True for generator
Returns:
X as (n_samples, input_dim), Y as (n_samples, n_outputs)
"""
@ -246,6 +248,7 @@ def _cat_dataset(n_samples, input_dim, n_classes, generator=False):
return dataset
return x_set, y_set
def _do_fit(n_samples,
input_dim,
n_outputs,
@ -301,7 +304,7 @@ def _do_fit(n_samples,
class FitTests(keras_parameterized.TestCase):
"""Test cases for keras model fitting"""
"""Test cases for keras model fitting."""
# @test_util.run_all_in_graph_and_eager_modes
@parameterized.named_parameters([
@ -323,7 +326,7 @@ class FitTests(keras_parameterized.TestCase):
},
])
def test_fit(self, generator, reset_n_samples):
"""Tests fitting of BoltonModel
"""Tests fitting of BoltonModel.
Args:
generator: True for generator test, False for iterator test.
@ -355,7 +358,7 @@ class FitTests(keras_parameterized.TestCase):
},
])
def test_fit_gen(self, generator):
"""Tests the fit_generator method of BoltonModel
"""Tests the fit_generator method of BoltonModel.
Args:
generator: True to test with a generator dataset
@ -392,7 +395,7 @@ class FitTests(keras_parameterized.TestCase):
},
])
def test_bad_fit(self, generator, reset_n_samples, distribution):
"""Tests fitting with invalid parameters, which should raise an error
"""Tests fitting with invalid parameters, which should raise an error.
Args:
generator: True to test with generator, False is iterator
@ -442,9 +445,8 @@ class FitTests(keras_parameterized.TestCase):
class_weights,
class_counts,
num_classes,
result
):
"""Tests the BOltonModel calculate_class_weights method
result):
"""Tests the BOltonModel calculate_class_weights method.
Args:
class_weights: the class_weights to use
@ -496,26 +498,28 @@ class FitTests(keras_parameterized.TestCase):
'class_weights': [[1], [1]],
'class_counts': None,
'num_classes': 2,
'err_msg': "Detected class_weights shape"},
'err_msg': 'Detected class_weights shape'},
{'testcase_name': 'class counts array, wrong number classes',
'class_weights': [1, 1, 1],
'class_counts': None,
'num_classes': 2,
'err_msg': 'Detected array length:'},
])
def test_class_errors(self,
class_weights,
class_counts,
num_classes,
err_msg):
"""Tests the BOltonModel calculate_class_weights method with invalid params
which should raise the expected errors.
"""Tests the BOltonModel calculate_class_weights method.
This test passes invalid params which should raise the expected errors.
Args:
class_weights: the class_weights to use
class_counts: count of number of samples for each class
num_classes: number of outputs neurons
result: expected result
err_msg:
"""
clf = models.BoltonModel(1, 1)
with self.assertRaisesRegexp(ValueError, err_msg): # pylint: disable=deprecated-method

4
privacy/bolton/optimizers.py
View file

@ -108,8 +108,8 @@ class Bolton(optimizer_v2.OptimizerV2):
Descent-based Analytics by Xi Wu et. al.
"""
def __init__(self, # pylint: disable=super-init-not-called
optimizer: optimizer_v2.OptimizerV2,
loss: StrongConvexMixin,
optimizer,
loss,
dtype=tf.float32,
):
"""Constructor.

55
privacy/bolton/optimizers_test.py
View file

@ -263,12 +263,7 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
def test_project(self, r, shape, n_out, init_value, result):
"""test that a fn of Bolton optimizer is working as expected.
Args:
fn: method of Optimizer to test
args: args to optimizer fn
result: the expected result
test_attr: None if the fn returns the test result. Otherwise, this is
the attribute of Bolton to check against result with.
Missing args:
"""
tf.random.set_seed(1)
@ -455,12 +450,14 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
'args': [1, 1]},
])
def test_not_reroute_fn(self, fn, args):
"""Test that a fn that should not be rerouted to the internal optimizer is
in face not rerouted.
"""Test function is not rerouted.
Args:
fn: fn to test
args: arguments to that fn
Test that a fn that should not be rerouted to the internal optimizer is
in fact not rerouted.
Args:
fn: fn to test
args: arguments to that fn
"""
@tf.function
def test_run(fn, args):
@ -492,12 +489,13 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
'attr': '_iterations'}
])
def test_reroute_attr(self, attr):
""" test that attribute of internal optimizer is correctly rerouted to
the internal optimizer
"""Test a function is rerouted.
Args:
attr: attribute to test
result: result after checking attribute
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer.
Args:
attr: attribute to test
"""
loss = TestLoss(1, 1, 1)
internal_optimizer = TestOptimizer()
@ -510,12 +508,13 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
'attr': '_not_valid'}
])
def test_attribute_error(self, attr):
"""Test that attribute of internal optimizer is correctly rerouted to
the internal optimizer
"""Test rerouting of attributes.
Args:
attr: attribute to test
result: result after checking attribute
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer
Args:
attr: attribute to test
"""
loss = TestLoss(1, 1, 1)
internal_optimizer = TestOptimizer()
@ -537,9 +536,7 @@ class SchedulerTest(keras_parameterized.TestCase):
""" test that attribute of internal optimizer is correctly rerouted to
the internal optimizer
Args:
attr: attribute to test
result: result after checking attribute
Missing args
"""
scheduler = opt.GammaBetaDecreasingStep()
with self.assertRaisesRegexp(Exception, err_msg): # pylint: disable=deprecated-method
@ -557,12 +554,12 @@ class SchedulerTest(keras_parameterized.TestCase):
'res': 0.333333333},
])
def test_call(self, step, res):
""" test that attribute of internal optimizer is correctly rerouted to
the internal optimizer
"""Test call.
Args:
attr: attribute to test
result: result after checking attribute
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer
Missing Args:
"""
beta = _ops.convert_to_tensor_v2(2, dtype=tf.float32)
gamma = _ops.convert_to_tensor_v2(1, dtype=tf.float32)

2
tutorials/bolton_tutorial.py
View file

@ -116,7 +116,7 @@ try:
noise_distribution=noise_distribution,
verbose=0)
except ValueError as e:
print(e)
print e
# -------
# And now, re running with the parameter set.
# -------