My GSoC blog

Start of the week The task I've been assigned this week is to write tests for the airML module and start writing a blog to showcase my progress in GSoC. Why? Testing is really important because it helps us to find out the defects and errors that were made during the development phases.  How? Using python library 'unittest' I wrote test functions for all the functions that I have implemented in the previous week. It was a simple task and by following the documentation of python ( https://docs.python.org/3/library/unittest.html ), I was able to implement it. Also, I referred to Tensorflow testing best practices.  Next Task I was asked to create documentation following the TensorFlow code style guide.  Why? Documentation helps the users and other developers to understand the functionalities of the implemented application. And it will be easy for others who come after me to understand and improve the system. How? Refactored the documentation in the README

Start of the week The task given for this week is to implement a python application for machine learning trained models. For that, I used KBox( https://github.com/AKSW/KBox ) as the base. I created an interface between KBox and airML ( https://github.com/AKSW/airML ) including useful functions for my GSoC project.  Why? That is very useful for reproducibility research, it allows other users to reuse models without having to re-train them. For now, I have trained the monument_300 dataset in NSPM . How? Instead of reimplementing everything from KBox, I implemented useful functions that allow me to share, locate and install trained models. These following functions are implemented by me: list(kns=False) Description:List all available models(kns=False) or list all KNS services(kns=True). Args: kns:'boolean',defines whether to list only the KNS services or not Returns: None Throws: OSError install(modelID, format=None, version=None) Description:Install