update documentation and test

CHANGES.txt

@@ -3,4 +3,6 @@ v<0.0.1>, <07/15/2018> -- Add basic functionalities and examples.
 v<0.0.1>, <07/15/2018> -- Add clusterer ensemble.
 v<0.0.2>, <07/16/2018> -- Hot fixes.
 v<0.0.3>, <07/16/2018> -- Add documentation.
-v<0.0.3>, <07/17/2018> -- Add Travis-ci integration.
+v<0.0.3>, <07/17/2018> -- Add Travis-ci integration.
+v<0.0.4>, <07/17/2018> -- Update unit test and clustering algorithms.
+v<0.0.4>, <07/17/2018> -- Update documentation.

README.rst

@@ -58,20 +58,17 @@ combo: A Python Toolbox for Combination Tasks in Machine Learning
 -----
 
 
-**combo** is a Python combination toolbox for
-fusing/aggregating/selecting ML models and scores,
-under **supervised**, **unsupervised**, and **semi-supervised** scenarios. It
-consists methods for various tasks, including **classification**,
-**clustering**, **anomaly detection**, and **raw score combination**.
+**combo** is a Python toolbox for combining or aggregating ML models and
+scores for various tasks, including **classification**, **clustering**,
+**anomaly detection**, and **raw score**. It has been widely used in data
+science competitions and real-world tasks, such as Kaggle.
 
-Model and score combination is an important subtask in
+Model and score combination can be regarded as a subtask of
 `ensemble learning <https://en.wikipedia.org/wiki/Ensemble_learning>`_,
 but is often beyond the scope of ensemble learning. For instance,
-averaging the results of a single classifier with multiple runs is deemed as
-a good way of eliminating the randomness for a better stability.
-Model combination has been widely used in data science competitions and
-real-world tasks, such as Kaggle. See figure below for some popular combination
-approaches.
+averaging the results of multiple runs of a ML model is deemed as
+a reliable way of eliminating the randomness for better stability. See
+figure below for some popular combination approaches.
 
 .. image:: https://raw.githubusercontent.com/yzhao062/combo/master/docs/figs/framework_demo.png
    :target: https://raw.githubusercontent.com/yzhao062/combo/master/docs/figs/framework_demo.png
@@ -82,7 +79,7 @@ combo is featured for:
 
 * **Unified APIs, detailed documentation, and interactive examples** across various algorithms.
 * **Advanced models**, including dynamic classifier/ensemble selection and LSCP.
-* **Rich applications** for classification, clustering, anomaly detection, and raw score combination.
+* **Broad applications** for classification, clustering, anomaly detection, and raw score.
 * **Comprehensive coverage** for supervised, unsupervised, and semi-supervised scenarios.
 * **Optimized performance with JIT and parallelization** when possible, using `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_.
 

combo/version.py

@@ -20,4 +20,4 @@
 # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
-__version__ = '0.0.3'  # pragma: no cover
+__version__ = '0.0.4'  # pragma: no cover

docs/index.rst

@@ -63,20 +63,17 @@ Welcome to combo's documentation!
 -----
 
 
-**combo** is a Python combination toolbox for
-fusing/aggregating/selecting ML models and scores,
-under **supervised**, **unsupervised**, and **semi-supervised** scenarios. It
-consists methods for various tasks, including **classification**,
-**clustering**, **anomaly detection**, and **raw score combination**.
+**combo** is a Python toolbox for combining or aggregating ML models and
+scores for various tasks, including **classification**, **clustering**,
+**anomaly detection**, and **raw score**. It has been widely used in data
+science competitions and real-world tasks, such as Kaggle.
 
-Model and score combination is an important subtask in
+Model and score combination can be regarded as a subtask of
 `ensemble learning <https://en.wikipedia.org/wiki/Ensemble_learning>`_,
 but is often beyond the scope of ensemble learning. For instance,
-averaging the results of a single classifier with multiple runs is deemed as
-a good way of eliminating the randomness for a better stability.
-Model combination has been widely used in data science competitions and
-real-world tasks, such as Kaggle. See figure below for some popular combination
-approaches.
+averaging the results of multiple runs of a ML model is deemed as
+a reliable way of eliminating the randomness for better stability. See
+figure below for some popular combination approaches.
 
 .. image:: https://raw.githubusercontent.com/yzhao062/combo/master/docs/figs/framework_demo.png
    :target: https://raw.githubusercontent.com/yzhao062/combo/master/docs/figs/framework_demo.png
@@ -87,7 +84,7 @@ combo is featured for:
 
 * **Unified APIs, detailed documentation, and interactive examples** across various algorithms.
 * **Advanced models**, including dynamic classifier/ensemble selection and LSCP.
-* **Rich applications** for classification, clustering, anomaly detection, and raw score combination.
+* **Broad applications** for classification, clustering, anomaly detection, and raw score.
 * **Comprehensive coverage** for supervised, unsupervised, and semi-supervised scenarios.
 * **Optimized performance with JIT and parallelization** when possible, using `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_.