Can We Download Orange Faster?

One day Blaž and Janez came to us and started complaining how slow Orange download is in the US. Since they hold a large course at Baylor College of Medicine every year, this causes some frustration.

Related: Introduction to Data Mining Course in Houston

But we have the data and we’ve promptly tried to confirm their complaints by analyzing them… well, in Orange!

First, let us observe the data. We have 4887 recorded download sessions with one meta feature reporting on the country of the download and four features with time, size, speed in bytes and speed in gigabytes of the download.

Data of Orange download statistics. We get reports on the country of download, the size and the time of the download. We have constructed speed and size in gigabytes ourselves with simple formulae.

 

Now let us check the validity of Blaž’s and Janez’s complaint. We will use orange3-geo add-on for plotting geolocated data. For any geoplotting, we need coordinates – latitude and longitude. To retrieve them automatically, we will use Geocoding widget.

We instruct Geocoding to retrieve coordinates from our Country feature. Identifier type tells the widget in what format the region name appears.

 

We told the widget to use the ISO-compliant country code from Country attribute and encode it into coordinates. If we check the new data in a Data Table, we see our data is enhanced with new features.

Enhanced data table. Besides latitude and longitude, Geocoding can also append country-level data (economy, continent, region…).

 

Now that we have coordinates, we can plot these data regionally – in Choropleth widget! This widget plots data on three levels – country, state/region and county/municipality. Levels correspond to the administrative division of each country.

Choropleth widget offers 3 aggregation levels. We chose country (e.g. administrative level 0), but with a more detailed data one could also plot by state/county/municipality. Administrative levels are different for each country (e.g. Bundesländer for Germany, states for the US, provinces for Canada…).

 

In the plot above, we have simply displayed the amount of people (Count) that downloaded Orange in the past couple of months. Seems like we indeed have most users in the US, so it might make sense to solve installation issues for this region first.

Now let us check the speed of the download – it is really so slow in the US? If we take the mean, we can see that Slovenia is far ahead of the rest as far as download speed is concerned. No wonder – we are downloading via the local network. Scandinavia, Central Europe and a part of the Balkans seem to do quite ok as well.

Aggregation by mean.

 

But mean sometimes doesn’t show the right picture – it is sensitive to outliers, which would be the case of Slovenia here. Let us try median instead. Looks like 50% of American download at speed lower than 1.5MB/s. Quite average, but it could be better.

Aggregation by median.

 

And the longest time someone was prepared to wait for the download? Over 3 hours. Kudos, mate! We appreciate it! 🙌

This simple workflow is all it took to do our analysis.

 

So how is your download speed for Orange compared to other things you are downloading? Better, worse? We’re keen to hear it! 👂

Text Analysis Workshop at Digital Humanities 2017

How do you explain text mining in 3 hours? Is it even possible? Can someone be ready to build predictive models and perform clustering in a single afternoon?

It seems so, especially when Orange is involved.

Yesterday, on August 7, we held a 3-hour workshop on text mining and text analysis for a large crowd of esteemed researchers at Digital Humanities 2017 in Montreal, Canada. Surely, after 3 hours everyone was exhausted, both the audience and the lecturers. But at the same time, everyone was also excited. The audience about the possibilities Orange offers for their future projects and the lecturers about the fantastic participants who even during the workshop were already experimenting with their own data.

The biggest challenge was presenting the inner workings of algorithms to a predominantly non-computer science crowd. Luckily, we had Tree Viewer and Nomogram to help us explain Classification Tree and Logistic Regression! Everything is much easier with vizualizations.

 

Classification Tree splits first by the word ‘came’, since it results in the purest split. Next it splits by ‘strange’. Since we still don’t have pure nodes, it continues to ‘bench’, which gives a satisfying result. Trees are easy to explain, but can quickly overfit the data.

 

Logistic Regression transforms word counts to points. The sum of points directly corresponds to class probability. Here, if you see 29 foxes in a text, you get a high probability of Animal Tales. If you don’t see any, then you get a high probability of the opposite class.

 

At the end, we were experimenting with explorative data analysis, where we had Hierarchical Clustering, Corpus Viewer, Image Viewer and Geo Map opened at the same time. This is how a researcher can interactively explore the dendrogram, read the documents from selected clusters, observe the corresponding images and locate them on a map.

Hierarchical Clustering, Image Viewer, Geo Map and Corpus Viewer opened at the same time create an interactive data browser.

 

The workshop was a nice kick-off to an exciting week full of interesting lectures and presentations at Digital Humanities 2017 conference. So much to learn and see!

 

 

Miniconda Installer

Orange has a new friend! It’s Miniconda, Anaconda’s little sister.

 

For a long time, the idea was to utilize the friendly nature of Miniconda to install Orange dependencies, which often misbehaved on some platforms. Miniconda provides Orange with Python 3.6 and conda installer, which is then used to handle everything Orange needs for proper functioning. So sssssss-mooth!

Miniconda Installer

Please know that our Miniconda installer is in a beta state, but we are inviting adventurous testers to try it and report any bugs they find to our issue tracker [there won’t be any of course! 😉 ].

 

Happy testing! 🐍|🍊

 

 

Orange in Pavia, Italy

These days, we (Blaz Zupan and Marinka Zitnik, with full background support of entire Bioinformatics Lab) are running a three-day course on Data Mining in Python. Riccardo Bellazzi, a professor at University of Pavia, a world-renown researcher in biomedical informatics, and most of all, a great friend, has invited us to run the elective course for Pavia’s grad students. The enrollment was, he says, overwhelming, as with over 50 students this is by far the best attended grad course at Pavia’s faculty of engineering in the past years.

We have opted for the hands-on course and a running it as a workshop. The lectures include a new, development version of Orange 3, and mix it with numpy, scikit-learn, matplotlib, networkx and bunch of other libraries. Course themes are classification, clustering, data projection and network analysis.

pavia-group

pavia-rail

pavia-class

Workshops at Baylor College of Medicine

On May 22nd and May 23rd, we (Blaz Zupan and Janez Demsar, assisted by Marinka Zitnik and Balaji Santhanam) have given two hands-on workshops called Data Mining without Programming at Baylor College of Medicine in Houston, Texas.

Actually, there was a lot of programming, but no Python or alike. The workshop was designed for biomedical students and Baylor’s faculty members. We have presented a visual programming approach for development of data mining workflows for interactive data exploration. A three-hour workshop consisted of 15 data mining lessons on visual data exploration, classification, clustering, network analysis, and gene expression analytics. Each lesson focused on a particular data analysis task that the attendees solved with Orange.

The two workshops were organized by Baylor’s Computational and Integrative Biomedical Research Center. Over two days, the event was attended by a large audience of 120 attendees.

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