Add FM and LDA to the documentation. (#66)

README.rst

@@ -39,7 +39,7 @@ 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-1.0.0.tar.gz
+    pip install dist/sagemaker-1.0.3.tar.gz
 
 Supported Python versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you
 
 The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
 
-SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
+SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.
 
 Definition and usage
 ~~~~~~~~~~~~~~~~~~~~
-Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
+Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:
 
 - ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
 - ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components

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 = '1.0'
+version = '1.0.3'
 project = u'sagemaker'
 
 # Add any Sphinx extension module names here, as strings. They can be extensions

doc/factorization_machines.rst

@@ -0,0 +1,22 @@
+FactorizationMachines
+-------------------------
+
+The Amazon SageMaker Factorization Machines algorithm.
+
+.. autoclass:: sagemaker.FactorizationMachines
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+    :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value
+
+
+.. autoclass:: sagemaker.FactorizationMachinesModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.FactorizationMachinesPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/index.rst

@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker
 
 SageMaker First-Party Algorithms
 --------------------------------
-Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
+Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.
 
 .. toctree::
     :maxdepth: 2
 
     kmeans
     pca
     linear_learner
+    sagemaker.amazon.amazon_estimator
+    factorization_machines
+    lda

doc/lda.rst

@@ -0,0 +1,22 @@
+LDA
+--------------------
+
+The Amazon SageMaker LDA algorithm.
+
+.. autoclass:: sagemaker.LDA
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+    :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol
+
+
+.. autoclass:: sagemaker.LDAModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.LDAPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance: