update documentation

README.rst

@@ -70,15 +70,16 @@ combo: A Python Toolbox for Machine Learning Model Combination
 
 **combo** is a comprehensive Python toolbox for combining machine
 learning (ML) models and scores for various tasks, including **classification**,
-**clustering**, **anomaly detection**, and **raw score**.
+**clustering**, and **anomaly detection**.
 
-Model combination has been widely used in data science competitions and
-real-world tasks, such as Kaggle. It can be considered as a subtask of
+**Model combination** has been widely used in data science competitions and
+real-world tasks, such as Kaggle [#Bell2007Lessons]_.
+It can be considered 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 multiple runs of a ML model is deemed as
 a reliable way of eliminating the randomness. See
-figure below for some popular combination approaches.
+figure below for basic 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
@@ -172,9 +173,9 @@ API Cheatsheet & Reference
 Full API Reference: (https://pycombo.readthedocs.io/en/latest/api.html). API cheatsheet for most of the models:
 
 
-* **fit(X)**\ : Fit an estimator.
+* **fit(X)**\ : Fit estimator. y is optional for unsupervised methods.
 * **predict(X)**\ : Predict on a particular sample once the estimator is fitted.
-* **predict_proba(X)**\ : Predict the probability of a sample belonging to each class. Only applicable for classification tasks.
+* **predict_proba(X)**\ : Predict the probability of a sample belonging to each class once the estimator is fitted.
 
 
 ----
@@ -411,6 +412,8 @@ Reference
 
 .. [#Aggarwal2015Theoretical] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles. *ACM SIGKDD Explorations Newsletter*, 17(1), pp.24-47.
 
+.. [#Bell2007Lessons] Bell, R.M. and Koren, Y., 2007. Lessons from the Netflix prize challenge. *SIGKDD Explorations*, 9(2), pp.75-79.
+
 .. [#Gorman2016Kaggle] Gorman, B. (2016). A Kaggler's Guide to Model Stacking in Practice. [online] The Official Blog of Kaggle.com. Available at: http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice [Accessed 26 Jul. 2019].
 
 .. [#Ko2008From] Ko, A.H., Sabourin, R. and Britto Jr, A.S., 2008. From dynamic classifier selection to dynamic ensemble selection. *Pattern recognition*, 41(5), pp.1718-1731.

docs/api_cc.rst

@@ -0,0 +1,24 @@
+API CheatSheet
+==============
+
+The following APIs are applicable for most models for easy use.
+
+* :func:`pyod.models.base.BaseAggregator.fit`: Fit estimator. y is optional for unsupervised methods.
+* :func:`pyod.models.base.BaseAggregator.predict`: Predict on a particular sample once the estimator is fitted.
+* :func:`pyod.models.base.BaseAggregator.predict_proba`: Predict the probability of a sample belonging to each class once the estimator is fitted.
+* :func:`pyod.models.base.BaseAggregator.get_params`: Get the parameters of the model.
+* :func:`pyod.models.base.BaseAggregator.set_params`: Set the parameters of the model.
+* Each base estimator can be accessed by calling `clf[i]` where i is the estimator index.
+
+
+See base class definition below:
+
+combo.models.base module
+------------------------
+
+.. automodule:: combo.models.base
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+

docs/index.rst

@@ -75,15 +75,16 @@ Welcome to combo's documentation!
 
 **combo** is a comprehensive Python toolbox for combining machine
 learning (ML) models and scores for various tasks, including **classification**,
-**clustering**, **anomaly detection**, and **raw score**.
+**clustering**, and **anomaly detection**.
 
-Model combination has been widely used in data science competitions and
-real-world tasks, such as Kaggle. It can be considered as a subtask of
+**Model combination** has been widely used in data science competitions and
+real-world tasks, such as Kaggle :cite:`a-bell2007lessons`.
+It can be considered 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 multiple runs of a ML model is deemed as
 a reliable way of eliminating the randomness. See
-figure below for some popular combination approaches.
+figure below for basic 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
@@ -127,9 +128,9 @@ API Cheatsheet & Reference
 Full API Reference: (https://pycombo.readthedocs.io/en/latest/api.html). API cheatsheet for most of the models:
 
 
-* **fit(X)**\ : Fit an estimator.
+* **fit(X)**\ : Fit estimator. y is optional for unsupervised methods.
 * **predict(X)**\ : Predict on a particular sample once the estimator is fitted.
-* **predict_proba(X)**\ : Predict the probability of a sample belonging to each class. Only applicable for classification tasks.
+* **predict_proba(X)**\ : Predict the probability of a sample belonging to each class once the estimator is fitted.
 
 
 ----
@@ -218,8 +219,10 @@ for suggestions and ideas.
    :hidden:
    :caption: Documentation
 
+   api_cc
    api
 
+
 .. toctree::
    :maxdepth: 2
    :hidden:

docs/zreferences.bib

@@ -67,4 +67,15 @@ @misc{gorman2016kaggle
   title = {A Kaggler's Guide to Model Stacking in Practice},
   year = 2016,
   howpublished = {Available at \url{http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice}},
+}
+
+@article{bell2007lessons,
+  title={Lessons from the Netflix prize challenge.},
+  author={Bell, Robert M and Koren, Yehuda},
+  journal={SIGKDD Explorations},
+  volume={9},
+  number={2},
+  pages={75--79},
+  year={2007},
+  publisher={Citeseer}
 }