Refactoring bolton to bolt_on from package refactor for comments + strings.

privacy/bolt_on/__init__.py

@@ -19,7 +19,7 @@ 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__)))
+                    "use privacy/bolt_on".format(LooseVersion(tf.__version__)))
 if hasattr(sys, "skip_tf_privacy_import"):  # Useful for standalone scripts.
   pass
 else:

privacy/bolt_on/losses.py

@@ -11,7 +11,7 @@
 # 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.
-"""Loss functions for bolton method."""
+"""Loss functions for BoltOn method."""
 
 from __future__ import absolute_import
 from __future__ import division

privacy/bolt_on/models.py

@@ -11,7 +11,7 @@
 # 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.
-"""BoltOn model for bolton method of differentially private ML."""
+"""BoltOn model for Bolt-on method of differentially private ML."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -134,7 +134,7 @@ class BoltOnModel(Model):  # pylint: disable=abstract-method
                     whose dim == n_classes.
       n_samples: the number of individual samples in x.
       epsilon: privacy parameter, which trades off between utility an privacy.
-                See the bolton paper for more description.
+                See the bolt-on paper for more description.
       noise_distribution: the distribution to pull noise from.
       steps_per_epoch:
       **kwargs: kwargs to keras Model.fit. See super.

privacy/bolt_on/optimizers.py

@@ -11,7 +11,7 @@
 # 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.
-"""BoltOn Optimizer for bolton method."""
+"""BoltOn Optimizer for Bolt-on method."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -91,7 +91,7 @@ class BoltOn(optimizer_v2.OptimizerV2):
 
   BoltOn optimizer wraps another tf optimizer to be used
   as the visible optimizer to the tf model. No matter the optimizer
-  passed, "BoltOn" enables the bolton model to control the learning rate
+  passed, "BoltOn" enables the bolt-on model to control the learning rate
   based on the strongly convex loss.
 
   To use the BoltOn method, you must:
@@ -100,7 +100,7 @@ class BoltOn(optimizer_v2.OptimizerV2):
 
   This can be accomplished by the following:
   optimizer = tf.optimizers.SGD()
-  loss = privacy.bolton.losses.StrongConvexBinaryCrossentropy()
+  loss = privacy.bolt_on.losses.StrongConvexBinaryCrossentropy()
   bolton = BoltOn(optimizer, loss)
   with bolton(*args) as _:
     model.fit()

tutorials/bolton_tutorial.py

@@ -11,7 +11,7 @@
 # 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."""
+"""Tutorial for bolt_on module, the model and the optimizer."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -48,7 +48,7 @@ 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
+# existing loss functions are pre - implemented in bolt_on.loss
 # -------
 optimizer = tf.optimizers.SGD()
 reg_lambda = 1
@@ -132,7 +132,7 @@ bolt.fit(generator,
          noise_distribution=noise_distribution,
          verbose=0)
 # -------
-# You don't have to use the bolton model to use the BoltOn method.
+# 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.