Merge pull request #437 from EducationalTestingService/update-documen…

…tation

Update documentation

CONTRIBUTING.md

@@ -114,5 +114,10 @@ documentation without the example gallery. The resulting HTML files will
 be placed in _build/html/ and are viewable in a web browser. See the
 README file in the doc/ directory for more information.
 
-For building the documentation, you will need [sphinx](http://sphinx.pocoo.org/).
+For building the documentation, you will need [sphinx](http://sphinx.pocoo.org/) as well as the readthedocs sphinx theme. To install both, just run:
+
+      conda install sphinx sphinx_rtd_theme
+
+in your existing conda environment.
+
 

doc/getting_started.rst

@@ -11,7 +11,7 @@ or via ``conda`` (only for Python 3.6)::
     conda install -c defaults -c conda-forge -c desilinguist python=3.6 skll
 
 It can also be downloaded directly from
-`GitHub <http://github.com/EducationalTestingService/skll>`_.
+`GitHub <https://github.com/EducationalTestingService/skll>`_.
 
 
 License

doc/index.rst

@@ -30,6 +30,7 @@ Documentation
    run_experiment
    utilities
    api
+   internal
 
 
 Indices and tables

doc/internal.rst

@@ -0,0 +1,7 @@
+Internal Documentation
+======================
+
+.. toctree::
+   :maxdepth: 4
+
+   internal/release

doc/internal/release.rst

@@ -0,0 +1,42 @@
+Release Process
+===============
+
+This document is only meant for the project administrators, not users and developers.
+
+1. Create a release branch on GitHub.
+
+2. In the release branch:
+
+   a. update the version numbers in ``version.py``.
+
+   b. update the conda recipe.
+
+   c. update the documentation with any new features or details about changes.
+
+   d. run ``make linkcheck`` on the documentation and fix any redirected/broken links.
+
+   e. update the README.
+
+3. Build the new conda package locally on your mac using the following command::
+
+    conda build -c defaults -c conda-forge --python=3.6 skll
+
+4. Convert the package for both linux and windows::
+
+    conda convert -p win-64 -p linux-64 <mac package tarball>
+
+5. Upload all packages to anaconda.org using ``anaconda upload``.
+
+6. Upload source package to PyPI using ``python setup.py sdist upload``.
+
+7. Draft a release on GitHub.
+
+8. Make a pull request with the release branch to be merged into ``master`` and request code review.
+
+9. Once the build for the PR passes and the reviewers approve, merge the release branch into ``master``.
+
+10. Make sure that the RTFD build for ``master`` passes.
+
+11. Tag the latest commit in ``master`` with the appropriate release tag and publish the release on GitHub.
+
+12. Send an email around at ETS announcing the release and the changes.

doc/tutorial.rst

@@ -21,15 +21,15 @@ Titanic Example
 ---------------
 
 Let's see how we can apply the basic workflow above to a simple example using
-the `Titantic: Machine Learning from Disaster <http://www.kaggle.com/c/titanic-gettingStarted/>`__
-data from `Kaggle <http://www.kaggle.com>`__.
+the `Titantic: Machine Learning from Disaster <https://www.kaggle.com/c/titanic/>`__
+data from `Kaggle <https://www.kaggle.com>`__.
 
 Get your data into the correct format
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 The first step to getting the Titanic data is logging into Kaggle and
-downloading `train.csv <http://www.kaggle.com/c/titanic-gettingStarted/download/train.csv>`__
-and `test.csv <http://www.kaggle.com/c/titanic-gettingStarted/download/test.csv>`__.
+downloading `train.csv <https://www.kaggle.com/c/titanic-gettingStarted/download/train.csv>`__
+and `test.csv <https://www.kaggle.com/c/titanic-gettingStarted/download/test.csv>`__.
 Once you have those files, you'll also want to grab the
 `examples folder <https://github.com/EducationalTestingService/skll/tree/master/examples>`__
 on our GitHub page and put ``train.csv`` and ``test.csv`` in ``examples``.
@@ -48,7 +48,7 @@ For this tutorial, we will refer to an "experiment" as having a single data set
 split into training and testing portions.  As part of each
 experiment, we can train and test several models, either simultaneously or
 sequentially, depending whether we're using
-`GridMap <https://github.com/EducationalTestingService/gridmap>`__ or not.
+`GridMap <https://pypi.org/project/gridmap/>`__ or not.
 This will be described in more detail later on, when we are ready to run our
 experiment.
 
@@ -87,9 +87,9 @@ instances IDs for each example.
 
 The :ref:`Tuning` section defines how we want our model to be tuned.  Setting
 :ref:`grid_search <grid_search>` to ``True`` here employs scikit-learn's
-`GridSearchCV <http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV>`_
+`GridSearchCV <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV>`_
 class, which is an implementation of the
-`standard, brute-force approach to hyperparameter optimization <http://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search>`_.
+`standard, brute-force approach to hyperparameter optimization <https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search>`_.
 
 :ref:`objectives <objectives>` refers to the desired objective functions; here,
 ``accuracy`` will optimize for overall accuracy.  You can see a list of all the

skll/experiments.py

@@ -77,7 +77,7 @@ class NumpyTypeEncoder(json.JSONEncoder):
     be serialized by the json module, so we must convert them to int objects.
 
     A related issue where this was adapted from:
-    http://stackoverflow.com/questions/11561932/why-does-json-dumpslistnp-arange5-fail-while-json-dumpsnp-arange5-tolis
+    https://stackoverflow.com/questions/11561932/why-does-json-dumpslistnp-arange5-fail-while-json-dumpsnp-arange5-tolis
     """
 
     def default(self, obj):