Appendix: Architecture of **abess**
===================================

In this page, we briefly introduce our core code of ``abess``, which is summarized in the Figure below. 

.. image:: figure/architecture.png 

The core code of ``abess`` is built with C++ and the figure above shows the
software architecture of ``abess`` and each building block will be described
as follows.

-  The **Data** class accept the (sparse) tabular data from R and Python
   interfaces, and returns a object containing the predictors are
   (optionally) screened or normalized.
-  The **Algorithm** class, as the core class in ``abess``, implements the
   generic splicing procedure for best subset selection with the support
   for :math:`L_2`-regularization for parameters, group-structure
   predictors, and nuisance selection. The concrete algorithms are
   programmed in the subclass of **Algorithm** by rewriting the virtual
   function interfaces of class **Algorithm**. Seven implemented best
   subset selection tasks for supervised learning and unsupervised
   learning are presented in the above Figure. Beyond that, the
   modularized design facilitates users extend the library to various
   machine learning tasks by writing subclass of **Algorithm** class.
-  The **Metric** The serves as a evaluator. It evaluates the
   estimation returned by **Algorithm** by cross validation or
   information criterion like Akaike information criterion and high
   dimensional Bayesian information criterion.
-  Finally, **R or Python interfaces** collects the results from
   **Metric** and **Algorithm**. In R package, S3 methods are programmed
   such that generic functions (like ``print``, ``coef`` and ``plot``) can be
   directly used to attain the best subset selection results, and
   visualize solution paths and tuning parameter curve. In Python
   package, each model in abess is a sub-class of ``scikit-learn``'s
   BaseEstimator class such that users can not only use a familiar API
   to train a model but also seamlessly combine model in ``abess``
   preprocessing, feature transformation, and model selection module
   within ``scikit-learn``.