Fix inaccurate linear learner hyper-parameter and sagemaker version in doc and readme

CHANGELOG.rst

@@ -2,30 +2,36 @@
 CHANGELOG
 =========
 
+1.2.2
+=====
+
+* bug-fix: Estimators: fix valid range of hyper-parameter 'loss' in linear learner
+
 1.2.1
-========
+=====
+
 * bug-fix: Change Local Mode to use a sagemaker-local docker network
 
 1.2.0
-========
+=====
 
 * feature: Add Support for Local Mode
 * feature: Estimators: add support for TensorFlow 1.6.0
 * feature: Estimators: add support for MXNet 1.1.0
 * feature: Frameworks: Use more idiomatic ECR repository naming scheme
 
 1.1.3
-========
+=====
 
 * bug-fix: TensorFlow: Display updated data correctly for TensorBoard launched from ``run_tensorboard_locally=True``
 * feature: Tests: create configurable ``sagemaker_session`` pytest fixture for all integration tests
-* bug-fix: AmazonEstimators: fix inaccurate hyper-parameters in kmeans, pca and linear learner
-* feature: Add new hyperparameters for linear learner.
+* bug-fix: Estimators: fix inaccurate hyper-parameters in kmeans, pca and linear learner
+* feature: Estimators: Add new hyperparameters for linear learner.
 
 1.1.2
 =====
 
-* bug-fix: AmazonEstimators: do not call create bucket if data location is provided
+* bug-fix: Estimators: do not call create bucket if data location is provided
 
 1.1.1
 =====

README.rst

@@ -47,7 +47,7 @@ You can install from source by cloning this repository and issuing a pip install
 
     git clone https://github.com/aws/sagemaker-python-sdk.git
     python setup.py sdist
-    pip install dist/sagemaker-1.1.3.tar.gz
+    pip install dist/sagemaker-1.2.2.tar.gz
 
 Supported Python versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~

doc/conf.py

@@ -18,7 +18,7 @@ def __getattr__(cls, name):
                 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
-version = '1.1.3'
+version = '1.2.2'
 project = u'sagemaker'
 
 # Add any Sphinx extension module names here, as strings. They can be extensions

setup.py

@@ -11,7 +11,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.2.1",
+      version="1.2.2",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

src/sagemaker/amazon/linear_learner.py

@@ -44,13 +44,14 @@ class LinearLearner(AmazonAlgorithmEstimatorBase):
     init_sigma = hp('init_sigma', gt(0), 'A float greater-than 0', float)
     init_bias = hp('init_bias', (), 'A number', float)
     optimizer = hp('optimizer', isin('sgd', 'adam', 'auto'), 'One of "sgd", "adam" or "auto', str)
-    loss = hp('loss', isin('logistic', 'squared_loss', 'absolute_loss', 'auto'),
+    loss = hp('loss', isin('logistic', 'squared_loss', 'absolute_loss', 'hinge_loss', 'eps_insensitive_squared_loss',
+                           'eps_insensitive_absolute_loss', 'quantile_loss', 'huber_loss', 'auto'),
               '"logistic", "squared_loss", "absolute_loss", "hinge_loss", "eps_insensitive_squared_loss", '
               '"eps_insensitive_absolute_loss", "quantile_loss", "huber_loss" or "auto"', str)
     wd = hp('wd', ge(0), 'A float greater-than or equal to 0', float)
     l1 = hp('l1', ge(0), 'A float greater-than or equal to 0', float)
     momentum = hp('momentum', (ge(0), lt(1)), 'A float in [0,1)', float)
-    learning_rate = hp('learning_rate', gt(0), 'A float greater-than or equal to 0', float)
+    learning_rate = hp('learning_rate', gt(0), 'A float greater-than 0', float)
     beta_1 = hp('beta_1', (ge(0), lt(1)), 'A float in [0,1)', float)
     beta_2 = hp('beta_2', (ge(0), lt(1)), 'A float in [0,1)', float)
     bias_lr_mult = hp('bias_lr_mult', gt(0), 'A float greater-than 0', float)