From e3a8818406268253fd81862ecdd6f5912aef27a0 Mon Sep 17 00:00:00 2001 From: Lukasz Misiuda Date: Wed, 31 Jan 2018 14:51:20 -0800 Subject: [PATCH] Add FM and LDA to the documentation. Update version for docs to match current and updates in README. --- README.rst | 6 +++--- doc/conf.py | 2 +- doc/factorization_machines.rst | 22 ++++++++++++++++++++++ doc/index.rst | 5 ++++- doc/lda.rst | 22 ++++++++++++++++++++++ 5 files changed, 52 insertions(+), 5 deletions(-) create mode 100644 doc/factorization_machines.rst create mode 100644 doc/lda.rst diff --git a/README.rst b/README.rst index e06dea8b3c..50d1509cf7 100644 --- a/README.rst +++ b/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 ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1428,11 +1428,11 @@ 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 diff --git a/doc/conf.py b/doc/conf.py index 83105e3ab7..3675148d67 100644 --- a/doc/conf.py +++ b/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 diff --git a/doc/factorization_machines.rst b/doc/factorization_machines.rst new file mode 100644 index 0000000000..4427f0cd35 --- /dev/null +++ b/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: diff --git a/doc/index.rst b/doc/index.rst index 968cdb1024..1a840f6257 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -38,7 +38,7 @@ 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 @@ -46,3 +46,6 @@ Amazon provides implementations of some common machine learning algortithms opti kmeans pca linear_learner + sagemaker.amazon.amazon_estimator + factorization_machines + lda diff --git a/doc/lda.rst b/doc/lda.rst new file mode 100644 index 0000000000..f6b965e577 --- /dev/null +++ b/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: