Merge pull request #19 from lukapecnik/badges_readme

badges and readme update

README.md

@@ -1,5 +1,15 @@
 # NiaAML
 
+[![PyPI Version](https://img.shields.io/pypi/v/niaaml.svg)](https://pypi.python.org/pypi/niaaml)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/niaaml.svg)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/niaaml.svg)
+[![GitHub license](https://img.shields.io/github/license/lukapecnik/niaaml.svg)](https://github.com/lukapecnik/niaaml/blob/master/LICENSE)
+
+![GitHub commit activity](https://img.shields.io/github/commit-activity/w/lukapecnik/niaaml.svg)
+[![Average time to resolve an issue](http://isitmaintained.com/badge/resolution/lukapecnik/niaaml.svg)](http://isitmaintained.com/project/lukapecnik/niaaml "Average time to resolve an issue")
+[![Percentage of issues still open](http://isitmaintained.com/badge/open/lukapecnik/niaaml.svg)](http://isitmaintained.com/project/lukapecnik/niaaml "Percentage of issues still open")
+![GitHub contributors](https://img.shields.io/github/contributors/lukapecnik/niaaml.svg)
+
 NiaAML is an automated machine learning Python framework based on nature-inspired algorithms for optimization. The name comes from the automated machine learning method of the same name [[1]](#1). Its goal is to efficiently compose the best possible classification pipeline for the given task using components on the input. The components are divided into three groups: feature seletion algorithms, feature transformation algorithms and classifiers. The framework uses nature-inspired algorithms for optimization to choose the best set of components for the classification pipeline on the output and optimize their parameters. We use <a href="https://github.com/NiaOrg/NiaPy">NiaPy framework</a> for the optimization process which is a popular Python collection of nature-inspired algorithms. The NiaAML framework is easy to use and customize or expand to suit your needs.
 
 The NiaAML framework allows you not only to run full pipeline optimization, but also separate implemented components such as classifiers, feature selection algorithms, etc. It currently supports only numeric features on the input. **However, we are planning to add support for categorical features too.** See the [examples](examples) for more information.
@@ -12,6 +22,12 @@ Install NiaAML with pip:
 pip install niaaml
 ```
 
+In case you would like to try out the latest pre-release version of the framework, install it using:
+
+```sh
+pip install niaaml --pre
+```
+
 ## Components
 
 In the following sections you can see a list of currently implemented components divided into groups: classifiers, feature selection algorithms and feature transformation algorithms. At the end you can also see a list of currently implemented fitness functions for the optimization process. All of the components are passed into the optimization process using their class names. Let's say we want to choose between Adaptive Boosting, Bagging and Multi Layer Perceptron classifiers, Select K Best and Select Percentile feature selection algorithms and Normalizer as the feature transformation algorithm (may not be selected during the optimization process).

README.rst

@@ -26,6 +26,12 @@ Install NiaAML with pip:
 
     pip install niaaml
 
+In case you would like to try out the latest pre-release version of the framework, install it using:
+
+.. code:: sh
+
+    pip install niaaml --pre
+
 Usage
 -----