Silhouette plot is such a nice method for visually assessing cluster quality and the degree of cluster membership that we simply couldn’t wait to get it into Orange3. And now we did.
What this visualization displays is the average distance between instances within the cluster and instances in the nearest cluster. For a given data instance, the silhouette close to 1 indicates that the data instance is close to the center of the cluster. Instances with silhouette scores close to 0 are on the border between two clusters. Overall, the quality of the clustering could be assessed by the average silhouette scores of the data instances. But here, we are more interested in the individual silhouettes and their visualization in the silhouette plot.
Using the good old iris data set, we are going to assess the silhouettes for each of the data instances. In k-means we set the number of clusters to 3 and send the data to Silhouette plot. Good clusters should include instances with higher silhouette scores. But we’re doing the opposite. In Orange, we are selecting instances with scores close to 0 from the silhouette plot and pass them to other widgets for exploration. No surprise, they are at the periphery of two clusters. This is so perfectly demonstrated in the scatter plot.
Let’s do something wild now. We’ll use the silhouette on a class attribute of Iris (no clustering here, just using the original class values from the data set). Here is our hypothesis: the data instances with low silhouette values are also those that will be misclassified by some learning algorithm. Say, by a random forest.
We will use ten-fold cross validation in Test&Score, send the evaluation results to confusion matrix and select misclassified instances in the widget. Then we will explore the inclusion of these misclassifications in the set of low-silhouette instances in the Venn diagram. The agreement (i.e. the intersection in Venn) between the two techniques is quite high.
Finally, we can observe these instances in the Scatter Plot. Classifiers indeed have problems with borderline data instances. Our hypothesis was correct.
Silhouette plot is yet another one of the great visualizations that can help you with data analysis or with understanding certain machine learning concepts. What did we say? Fruitful and fun!
Holiday season is upon us and even the Orange team is in a festive mood. This is why we made a Color widget!
This fascinating artsy widget will allow you to play with your data set in a new and exciting way. No more dull visualizations and default color schemes! Set your own colors the way YOU want it to! Care for some magical cyan-to-magenta? Or do you prefer a more festive red-to-green? How about several shades of gray? Color widget is your go-to stop for all things color (did you notice it’s our only widget with a colorful icon?). 🙂
Coloring works with most visualization widgets, such as scatter plot, distributions, box plot, mosaic display and linear projection. Set the colors for discrete values and gradients for continuous values in this widget, and the same palletes will be used in all downstream widgets. As a bonus, the Color widget also allows you to edit the names of variables and values.
Remember – the (blue) sky is the limit.
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:
- Animations: When using a single curve to show all data points, data changes only move the points instead of replacing them. These moves are now animated, as are color and size changes.
- Multithreading: All position calculations are done in separate threads, so the interface remains responsive even when an long operation is running in the background.
- Speed: I removed several occurances of needlessly clearing and repopulating the graph.
- Simplicity: Because it was written with Orange in mind, the new library has functions that match Orange’s data structures. This leads to simpler code in widgets using the library, and less operations in Python.
- Appearance: The plot can use the system palette, or a custom color theme. In general, I think it looks much nicer that Qwt-based plots.
- Documentation: There is an extensive API documentation (will soon be available at Orange 2.5 documentation), as well as two widget examples.
However, there are also disadvantages to using the new library. They are not many, and I’ve been trying to keep them as few and as small as possible, but there still are some.
- Line rendering: For some reason, whenever lines are rendered on the plot, the whole widget starts acting very slow. The effect is even more noticeable when zooming. As far as I can tell, this happens in Qt’s drawing libraries, so there is not much I can do about it.
- Axis labels: With a large number of long axis labels, the formatting gets rather ugly. This is a minor inconvenience, but it does make the plots look unprofessional.
Fortunately, I have little school obligations this september, so I think I will be able to work on Orange some more, at least until school starts. I have already added gesture support and some minor improvements since the end of the program.
Finally, I’d like to take this opportunity to thank the Orange team, especially my mentor Miha, for accepting me and helping me throughout the summer. It’s been an interesting project, and I’ll be happy to continue working with the same software and the same team.