Merge pull request #22 from etlundquist/docs

Docs

README.md

@@ -2,13 +2,16 @@
 
 [![PyPI version](https://badge.fury.io/py/rankfm.svg)](https://badge.fury.io/py/rankfm)
 [![CircleCI](https://circleci.com/gh/etlundquist/rankfm.svg?style=shield)](https://circleci.com/gh/etlundquist/rankfm)
+[![Documentation Status](https://readthedocs.org/projects/rankfm/badge/?version=latest)](https://rankfm.readthedocs.io/en/latest/?badge=latest)
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 
 RankFM is a python implementation of the general Factorization Machines model class described in [Rendle 2010](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) adapted for collaborative filtering recommendation/ranking problems with implicit feedback user-item interaction data. It uses [Bayesian Personalized Ranking (BPR)](https://arxiv.org/pdf/1205.2618.pdf) and a variant of [Weighted Approximate-Rank Pairwise (WARP)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.587.3946&rep=rep1&type=pdf) loss to learn model weights via Stochastic Gradient Descent (SGD). It can (optionally) incorporate individual training sample weights and/or user/item auxiliary features to augment the main interaction data for model training.
 
-The core training/prediction/recommendation methods are written in [Cython](https://cython.org/). This makes it possible to scale to millions of users, items, and interactions. Designed for ease-of-use, RankFM accepts both `pd.DataFrame` and `np.ndarray` inputs. You do not have to convert your data to `scipy.sparse` matrices or re-map user/item identifiers to matrix indexes prior to use - RankFM internally maps all user/item identifiers to zero-based integer indexes, but always converts its outputs back to the original user/item identifiers from your data, which can be arbitrary (non-zero-based, non-consecutive) integers or even strings.
+The core (training, prediction, recommendation) methods are written in [Cython](https://cython.org/), making it possible to scale to millions of users, items, and interactions. Designed for ease-of-use, RankFM accepts both `pd.DataFrame` and `np.ndarray` inputs - you do not have to convert your data to `scipy.sparse` matrices or re-map user/item identifiers to matrix indexes prior to use. RankFM internally maps all user/item identifiers to zero-based integer indexes, but always converts its outputs back to the original user/item identifiers from your data, which can be arbitrary (non-zero-based, non-consecutive) integers or even strings.
 
-In addition to the familiar `fit()`, `predict()`, `recommend()` methods, RankFM includes additional utilities `similiar_users()` and `similar_items()` to find the most similar users/items to a given user/item based on latent factor space embeddings. A number of popular recommendation/ranking evaluation metric functions have been included in the separate `evaluation` module to streamline model tuning and validation. See the **Quickstart** section below to get started, and the `/examples` folder for more in-depth jupyter notebook walkthroughs with several popular open-source data sets.
+In addition to the familiar `fit()`, `predict()`, `recommend()` methods, RankFM includes additional utilities `similiar_users()` and `similar_items()` to find the most similar users/items to a given user/item based on latent factor space embeddings. A number of popular recommendation/ranking evaluation metric functions have been included in the separate `evaluation` module to streamline model tuning and validation.
+
+See the **Quickstart** section below to get started and the `/examples` folder for more in-depth jupyter notebook walkthroughs using several popular open-source data sets. For comprehensive documentation on the main model class and the evaluation methods see the [Online Documentation](https://rankfm.readthedocs.io/en/latest/).
 
 This package is currently under active development and should not yet be considered fully stable. The core functionality is in place and working, but has not yet been rigorously tested against a wide variety of real-world data sets, modeling objectives, edge cases, user errors, etc. If you do find a problem or have suggestions for improvement please let me know!
 

docs/source/conf.py

@@ -16,6 +16,10 @@
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
 
+# import dependencies
+# -------------------
+
+import sphinx_rtd_theme
 
 # -- Project information -----------------------------------------------------
 
@@ -42,6 +46,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
+    'sphinx_rtd_theme'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -77,7 +82,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+html_theme = 'sphinx_rtd_theme'
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -176,4 +181,4 @@
 epub_exclude_files = ['search.html']
 
 
-# -- Extension configuration -------------------------------------------------
+# -- Extension configuration -------------------------------------------------