Changes for pylint.

privacy/bolton/losses.py

@@ -317,283 +317,3 @@ class StrongConvexBinaryCrossentropy(
     :return:
     """
     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)
-

privacy/bolton/models_test.py

@@ -53,7 +53,7 @@ class TestLoss(losses.Loss, StrongConvexMixin):
     """
     return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
 
-  def beta(self, class_weight):
+  def beta(self, class_weight):  # pylint: disable=unused-argument
     """Beta smoothess
 
     Args:
@@ -64,7 +64,7 @@ class TestLoss(losses.Loss, StrongConvexMixin):
     """
     return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
 
-  def lipchitz_constant(self, class_weight):
+  def lipchitz_constant(self, class_weight):  # pylint: disable=unused-argument
     """ L lipchitz continuous
 
     Args:

privacy/bolton/optimizers.py

@@ -20,7 +20,6 @@ from __future__ import print_function
 import tensorflow as tf
 from tensorflow.python.keras.optimizer_v2 import optimizer_v2
 from tensorflow.python.ops import math_ops
-from tensorflow.python import ops as _ops
 from privacy.bolton.losses import StrongConvexMixin
 
 _accepted_distributions = ['laplace']  # implemented distributions for noising

privacy/bolton/optimizers_test.py

@@ -25,7 +25,6 @@ from tensorflow.python.keras.regularizers import L1L2
 from tensorflow.python.keras.initializers import constant
 from tensorflow.python.keras import losses
 from tensorflow.python.keras.models import Model
-from tensorflow.python.framework import ops as _ops
 from tensorflow.python.framework import test_util
 from tensorflow.python import ops as _ops
 from absl.testing import parameterized
@@ -33,7 +32,6 @@ from privacy.bolton.losses import StrongConvexMixin
 from privacy.bolton import optimizers as opt
 
 
-
 class TestModel(Model):
   """
   Bolton episilon-delta model
@@ -69,18 +67,6 @@ class TestModel(Model):
     )
 
 
-  # def call(self, inputs):
-  #   """Forward pass of network
-  #
-  #   Args:
-  #       inputs: inputs to neural network
-  #
-  #   Returns:
-  #
-  #   """
-  #   return self.output_layer(inputs)
-
-
 class TestLoss(losses.Loss, StrongConvexMixin):
   """Test loss function for testing Bolton model"""
   def __init__(self, reg_lambda, C, radius_constant, name='test'):
@@ -105,7 +91,7 @@ class TestLoss(losses.Loss, StrongConvexMixin):
     """
     return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
 
-  def beta(self, class_weight):
+  def beta(self, class_weight):  # pylint: disable=unused-argument
     """Beta smoothess
 
     Args:
@@ -116,7 +102,7 @@ class TestLoss(losses.Loss, StrongConvexMixin):
     """
     return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
 
-  def lipchitz_constant(self, class_weight):
+  def lipchitz_constant(self, class_weight):  # pylint: disable=unused-argument
     """ L lipchitz continuous
 
     Args:
@@ -217,7 +203,7 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
     model.layers[0].kernel = \
       model.layers[0].kernel_initializer((model.layer_input_shape[0],
                                           model.n_outputs))
-    bolton._is_init = True
+    bolton._is_init = True  # pylint: disable=protected-access
     bolton.layers = model.layers
     bolton.epsilon = 2
     bolton.noise_distribution = 'laplace'
@@ -279,7 +265,7 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
       model.layers[0].kernel = \
         model.layers[0].kernel_initializer((model.layer_input_shape[0],
                                             model.n_outputs))
-      bolton._is_init = True
+      bolton._is_init = True  # pylint: disable=protected-access
       bolton.layers = model.layers
       bolton.epsilon = 2
       bolton.noise_distribution = 'laplace'
@@ -431,7 +417,7 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
     model.layers[0].kernel = \
       model.layers[0].kernel_initializer((model.layer_input_shape[0],
                                           model.n_outputs))
-    bolton._is_init = True
+    bolton._is_init = True  # pylint: disable=protected-access
     bolton.layers = model.layers
     bolton.epsilon = 2
     bolton.noise_distribution = 'laplace'
@@ -467,7 +453,7 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
       model.layers[0].kernel = \
         model.layers[0].kernel_initializer((model.layer_input_shape[0],
                                             model.n_outputs))
-      bolton._is_init = True
+      bolton._is_init = True  # pylint: disable=protected-access
       bolton.noise_distribution = 'laplace'
       bolton.epsilon = 1
       bolton.layers = model.layers