Refactoring bolton package to bolt_on only in code usages.

privacy/__init__.py

@@ -42,8 +42,8 @@ else:
   from privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
   from privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
 
-  from privacy.bolton.models import BoltOnModel
+  from privacy.bolt_on.models import BoltOnModel
-  from privacy.bolton.optimizers import BoltOn
+  from privacy.bolt_on.optimizers import BoltOn
-  from privacy.bolton.losses import StrongConvexMixin
+  from privacy.bolt_on.losses import StrongConvexMixin
-  from privacy.bolton.losses import StrongConvexBinaryCrossentropy
+  from privacy.bolt_on.losses import StrongConvexBinaryCrossentropy
-  from privacy.bolton.losses import StrongConvexHuber
+  from privacy.bolt_on.losses import StrongConvexHuber

privacy/bolt_on/__init__.py

@@ -23,7 +23,7 @@ if LooseVersion(tf.__version__) < LooseVersion("2.0.0"):
 if hasattr(sys, "skip_tf_privacy_import"):  # Useful for standalone scripts.
   pass
 else:
-  from privacy.bolton.models import BoltOnModel  # pylint: disable=g-import-not-at-top
+  from privacy.bolt_on.models import BoltOnModel  # pylint: disable=g-import-not-at-top
-  from privacy.bolton.optimizers import BoltOn  # pylint: disable=g-import-not-at-top
+  from privacy.bolt_on.optimizers import BoltOn  # pylint: disable=g-import-not-at-top
-  from privacy.bolton.losses import StrongConvexHuber  # pylint: disable=g-import-not-at-top
+  from privacy.bolt_on.losses import StrongConvexHuber  # pylint: disable=g-import-not-at-top
-  from privacy.bolton.losses import StrongConvexBinaryCrossentropy  # pylint: disable=g-import-not-at-top
+  from privacy.bolt_on.losses import StrongConvexBinaryCrossentropy  # pylint: disable=g-import-not-at-top

privacy/bolt_on/losses_test.py

@@ -25,9 +25,9 @@ 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.bolt_on.losses import StrongConvexBinaryCrossentropy
-from privacy.bolton.losses import StrongConvexHuber
+from privacy.bolt_on.losses import StrongConvexHuber
-from privacy.bolton.losses import StrongConvexMixin
+from privacy.bolt_on.losses import StrongConvexMixin
 
 
 @contextmanager

privacy/bolt_on/models.py

@@ -20,8 +20,8 @@ import tensorflow as tf
 from tensorflow.python.framework import ops as _ops
 from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.models import Model
-from privacy.bolton.losses import StrongConvexMixin
+from privacy.bolt_on.losses import StrongConvexMixin
-from privacy.bolton.optimizers import BoltOn
+from privacy.bolt_on.optimizers import BoltOn
 
 
 class BoltOnModel(Model):  # pylint: disable=abstract-method

privacy/bolt_on/models_test.py

@@ -24,9 +24,9 @@ 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.bolt_on import models
-from privacy.bolton.losses import StrongConvexMixin
+from privacy.bolt_on.losses import StrongConvexMixin
-from privacy.bolton.optimizers import BoltOn
+from privacy.bolt_on.optimizers import BoltOn
 
 
 class TestLoss(losses.Loss, StrongConvexMixin):

privacy/bolt_on/optimizers.py

@@ -20,7 +20,7 @@ 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 privacy.bolton.losses import StrongConvexMixin
+from privacy.bolt_on.losses import StrongConvexMixin
 
 _accepted_distributions = ['laplace']  # implemented distributions for noising
 

privacy/bolt_on/optimizers_test.py

@@ -28,8 +28,8 @@ from tensorflow.python.keras.models import Model
 from tensorflow.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
 from tensorflow.python.keras.regularizers import L1L2
 from tensorflow.python.platform import test
-from privacy.bolton import optimizers as opt
+from privacy.bolt_on import optimizers as opt
-from privacy.bolton.losses import StrongConvexMixin
+from privacy.bolt_on.losses import StrongConvexMixin
 
 
 class TestModel(Model):  # pylint: disable=abstract-method

tutorials/bolton_tutorial.py

@@ -16,9 +16,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import tensorflow as tf  # pylint: disable=wrong-import-position
-from privacy.bolton import losses  # pylint: disable=wrong-import-position
+from privacy.bolt_on import losses  # pylint: disable=wrong-import-position
-from privacy.bolton import models  # pylint: disable=wrong-import-position
+from privacy.bolt_on import models  # pylint: disable=wrong-import-position
-from privacy.bolton.optimizers import BoltOn  # pylint: disable=wrong-import-position
+from privacy.bolt_on.optimizers import BoltOn  # 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