Orange Blog
By: BIOLAB, Jan 9, 2012
Multi-label classification (and Multi-target prediction) in Orange
The last summer, student Wencan Luo participated in Google Summer of Code to implement Multi-label Classification in Orange. He provided a framework, implemented a few algorithms and some prototype widgets. His work has been “hidden” in our repositories for too long; finally, we have merged part of his code into Orange (widgets are not there yet …) and added a more general support for multi-target prediction. You can load multi-label tab-delimited data (e.
By: BIOLAB, Oct 26, 2011
GSoC Mentor Summit
On 22th and 23th October 2011 there was Google Summer of Code Mentor Summit in Mountain View, California. Google Summer of Code is Google’s program for encouraging students to work on open-source projects during their summer break. Because this year Orange participated in this program too, we decided to participate also in this summit and get to know other mentors, other open-source projects and organizations, exchange our experiences, learn something new, and improve our connections and collaborations with others.
By: BIOLAB, Sep 3, 2011
GSoC Review: Visualizations with Qt
During the course of this summer, I created a new plotting library for Orange plot, replacing the use of PyQwt. I can say that I have succesfully completed my project, but the library (and especially the visualization widgets) could still use some more work. The new library supports a similar interface, so little change is needed to convert individual widgets, but it also has several advantages over the old implementation:
By: BIOLAB, Sep 2, 2011
GSoC Review: Multi-label Classification Implementation
Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label l from a set of disjoint labels L, |L| > 1. If |L| = 2, then the learning problem is called a binary classification problem, while if |L| > 2, then it is called a multi-class classification problem (Tsoumakas & Katakis, 2007). Multi-label classification methods are increasingly used by many applications, such as textual data classification, protein function classification, music categorization and semantic scene classification.
By: BIOLAB, Sep 1, 2011
GSoC Review: MF - Matrix Factorization Techniques for Data Mining
MF - Matrix Factorization Techniques for Data Mining is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface to matrix factorization algorithms and methods. The MF works with numpy dense matrices and scipy sparse matrices (where this is possible to save on space).
By: BIOLAB, Jul 20, 2011
Orange GSoC: Multi-label Classification Implementation
Multi-label classification is one of the three projects of Google Summer Code 2011 for Orange. The main goal is to extend the Orange to support multi-label, including dataset support, two basic multi-label classifications-problem-transformation methods & algorithm adaptation methods, evaluation measures, GUI support, documentation, testing, and so on. My name is Wencan Luo, from China. I’m very happy to work with my mentor Matija. Until now, we have finished a framework for multi-label support for Orange.
By: BIOLAB, Jun 30, 2011
Orange GSoC: Visualizations with Qt
Hello, my name is Miha Čančula and this summer I’m working on Orange as part of Google’s Summer of Code program, mentored by Miha Štajdohar. My task is to replace the current visualization framework based on Qwt with a custom library, depending only on Qt. This library will better support Orange’s very specific visualizations and will replace the unmaintained PyQwt. I have a lot of experience with Qt and its graphics classes, both in C++ and Python, but I’m relatively now to Orange.
By: BIOLAB, Jun 24, 2011
Orange GSoC: MF Techniques for Data Mining
I am one of three students who are working on GSoC projects for Orange this year. The objective of the project Matrix Factorization Techniques for Data Mining is to provide the Orange community with a unified and efficient interface to matrix factorization algorithms and methods. For that purpose I have been developing a library which will include a number of published factorization algorithms and initialization methods and will facilitate combinations of these to produce new strategies.
By: BIOLAB, Apr 25, 2011
Accepted GSoC 2011 students announced
Accepted proposals/projects for Google Summer of Code 2011 have been announced. We got 3 students which will this year work on Orange: Marinka Žitnik: Matrix Factorization Techniques for Data Mining Miha Čančula: 2D visualization using PyQt Wencan Luo: Multi-label classification Congrats to all accepted students. We are looking forward working with you. And for all other students: please apply again next year. Your proposals were good, but we just could not accept everybody.
By: BIOLAB, Apr 8, 2011
Student application period for GSoC 2011 has ended
Student application period for Google Summer of Code 2011 has ended. We got 47 proposals from students all around the world. Now it is time for us to evaluate them and select the best proposals and the best students to work this year on Orange.