Orange Blog

By: AJDA, Apr 23, 2018

Installing Add-ons Works Again

Dear Orange users, Some of you might have an issue installing add-ons with the following issue popping up: xmlrpc.client.Fault: <Fault -32601: 'server error; requested method not found'> This is the result of the migration to a new infrastructure at PyPi, which provides the installation of add-ons. Our team has rallied to adjust the add-on installer so it works with the new and improved service. In order to make the add-on installer work (again), please download the latest version of Orange (3.


By: AJDA, Mar 28, 2018

Orange with Spectroscopy Add-on Workshop

We have just concluded our enhanced Introduction to Data Science workshop, which included several workflows for spectroscopy analysis. Spectroscopy add-on is intended for the analysis of spectral data and it is just as fun as our other add-ons (if not more!). We will prove it with a simple classification workflow. First, install Spectroscopy add-on from Options - Add-ons menu in Orange. Restart Orange for the add-on to appear. Great, you are ready for some spectral analysis!


By: AJDA, Feb 2, 2018

Image Analytics Workshop at AIUCD 2018

This week, Primož and I flew to the south of Italy to hold a workshop on Image Analytics through Data Mining at AIUCD 2018 conference. The workshop was intended to familiarize digital humanities researchers with options that visual programming environments offer for image analysis. In about 5 hours we discussed image embedding, clustering, finding closest neighbors and classification of images. While it is often a challenge to explain complex concepts in such a short time, it is much easier when working with Orange.


By: THOCEVAR, Dec 23, 2017

Speeding Up Network Visualization

The Orange3 Network add-on contains a convenient Network Explorer widget for network visualization. Orange uses an iterative force-directed method (a variation of the Fruchterman-Reingold Algorithm) to layout the nodes on the 2D plane. The goal of force-directed methods is to draw connected nodes close to each other as if the edges that connect the nodes were acting as springs. We also don’t want all nodes crowded in a single point, but would rather have them spaced evenly.


By: ASTARIC, Oct 13, 2017

Diving Into Car Registration Data

Last week, we presented Orange at the Festival of Open Data, a mini-conference organized by the Slovenian government, dedicated to the promotion of transparent access to government data. In a 10 minute presentation, we showed how Orange can be used to visualize and explore what kinds of vehicles were registered for the first time in Slovenia in 2017. The original dataset is available at theOPSI portal and it consists of 73 files, one for each month since January 2012.


By: BLAZ, Apr 25, 2017

Outliers in Traffic Signs

Say I am given a collection of images of traffic signs, and would like to find which signs stick out. That is, which traffic signs look substantially different from the others. I would assume that the traffic signs are not equally important and that some were designed to be noted before the others. I have assembled a small set of regulatory and warning traffic signs and stored the references to their images in a traffic-signs-w.


By: AJDA, Apr 3, 2017

Image Analytics: Clustering

Data does not always come in a nice tabular form. It can also be a collection of text, audio recordings, video materials or even images. However, computers can only work with numbers, so for any data mining, we need to transform such unstructured data into a vector representation. For retrieving numbers from unstructured data, Orange can use deep network embedders. We have just started to include various embedders in Orange, and for now, they are available for text and images.


By: AJDA, Jan 23, 2017

Preparing Scraped Data

One of the key questions of every data analysis is how to get the data and put it in the right form(at). In this post I’ll show you how to easily get the data from the web and transfer it to a file Orange can read. Related: Creating a new data table in Orange through Python First, we’ll have to do some scripting. We’ll use a couple of Python libraries - urllib.


By: AJDA, Sep 23, 2016

Text Mining: version 0.2.0

Orange3-Text has just recently been polished, updated and enhanced! Our GSoC student Alexey has helped us greatly to achieve another milestone in Orange development and release the latest 0.2.0 version of our text mining add-on. The new release, which is already available on PyPi, includes Wikipedia and SimHash widgets and a rehaul of Bag of Words, Topic Modeling and Corpus Viewer. Wikipedia widget allows retrieving sources from Wikipedia API and can handle multiple queries.


By: PRIMOZGODEC, Aug 25, 2016

Visualizing Gradient Descent

This is a guest blog from the Google Summer of Code project. Gradient Descent was implemented as a part of my Google Summer of Code project and it is available in the Orange3-Educational add-on. It simulates gradient descent for either Logistic or Linear regression, depending on the type of the input data. Gradient descent is iterative approach to optimize model parameters that minimize the cost function. In machine learning, the cost function corresponds to prediction error when the model is used on the training data set.