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Refactoring bolton package to bolt_on only in code usages.

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Christopher Choquette Choo 2019-07-31 10:52:41 -04:00
parent 223f2cc640
commit c0bd19365b
10 changed files with 23 additions and 23 deletions
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10
privacy/__init__.py
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@ -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.bolton.optimizers import BoltOn
from privacy.bolton.losses import StrongConvexMixin
from privacy.bolton.losses import StrongConvexBinaryCrossentropy
from privacy.bolton.losses import StrongConvexHuber
from privacy.bolt_on.models import BoltOnModel
from privacy.bolt_on.optimizers import BoltOn
from privacy.bolt_on.losses import StrongConvexMixin
from privacy.bolt_on.losses import StrongConvexBinaryCrossentropy
from privacy.bolt_on.losses import StrongConvexHuber

0
privacy/bolton/README.md → privacy/bolt_on/README.md
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8
privacy/bolton/__init__.py → privacy/bolt_on/__init__.py
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@ -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.bolton.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.bolton.losses import StrongConvexBinaryCrossentropy # pylint: disable=g-import-not-at-top
from privacy.bolt_on.models import BoltOnModel # pylint: disable=g-import-not-at-top
from privacy.bolt_on.optimizers import BoltOn # pylint: disable=g-import-not-at-top
from privacy.bolt_on.losses import StrongConvexHuber # pylint: disable=g-import-not-at-top
from privacy.bolt_on.losses import StrongConvexBinaryCrossentropy # pylint: disable=g-import-not-at-top

0
privacy/bolton/losses.py → privacy/bolt_on/losses.py
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6
privacy/bolton/losses_test.py → privacy/bolt_on/losses_test.py
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@ -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.bolton.losses import StrongConvexHuber
from privacy.bolton.losses import StrongConvexMixin
from privacy.bolt_on.losses import StrongConvexBinaryCrossentropy
from privacy.bolt_on.losses import StrongConvexHuber
from privacy.bolt_on.losses import StrongConvexMixin
@contextmanager

4
privacy/bolton/models.py → privacy/bolt_on/models.py
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@ -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.bolton.optimizers import BoltOn
from privacy.bolt_on.losses import StrongConvexMixin
from privacy.bolt_on.optimizers import BoltOn
class BoltOnModel(Model): # pylint: disable=abstract-method

6
privacy/bolton/models_test.py → privacy/bolt_on/models_test.py
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@ -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.bolton.losses import StrongConvexMixin
from privacy.bolton.optimizers import BoltOn
from privacy.bolt_on import models
from privacy.bolt_on.losses import StrongConvexMixin
from privacy.bolt_on.optimizers import BoltOn
class TestLoss(losses.Loss, StrongConvexMixin):

2
privacy/bolton/optimizers.py → privacy/bolt_on/optimizers.py
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@ -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

4
privacy/bolton/optimizers_test.py → privacy/bolt_on/optimizers_test.py
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@ -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.bolton.losses import StrongConvexMixin
from privacy.bolt_on import optimizers as opt
from privacy.bolt_on.losses import StrongConvexMixin
class TestModel(Model): # pylint: disable=abstract-method

6
tutorials/bolton_tutorial.py
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@ -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.bolton import models # pylint: disable=wrong-import-position
from privacy.bolton.optimizers import BoltOn # pylint: disable=wrong-import-position
from privacy.bolt_on import losses # pylint: disable=wrong-import-position
from privacy.bolt_on import models # 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