Last week Blaž, Marko and I held a week long introductory Data Mining and Machine Learning course at the Ljubljana Doctoral Summer School 2018. We got a room full of dedicated students and we embarked on a journey through standard and advanced machine learning techniques, all presented of course in Orange. We have covered a wide array of topics, from different clustering techniques (hierarchical clustering, k-means) to predictive models (logistic regression, naive Bayes, decision trees, random forests), regression and regularization, projections, text mining and image analytics.
Definitely the biggest crowd-pleaser was the Geo add-on in combination with the HDI data set. First, we got the HDI data from Datasets. A quick glimpse into a data table to check the output. We have information on some key performance indicators gathered by the United Nations for 188 countries. Now we would like to know which countries are similar based on the reported indicators. We will use Distances with Euclidean distance and use Ward linkage in Hierarchical Clustering.
We got our results in a dendrogram. Interestingly, the United States seems similar to Cuba. Let us select this cluster and inspect what the most significant feature for this cluster. We will use the Data output of Hierarchical Clustering which append a column indicating whether the data instances was selected or not. Then we will use Box Plot, group by Selected and check Order by relevance. It seems like these countries have the longest life expectancy at age 59. Go ahead and inspect other clusters by yourself!
Of course, when we are talking about countries one naturally wants to see them on a map! That is easy. We will use the Geo add-on. First, we need to convert all the country names to geographical coordinates. We will do this with Geocoding, where we will encode column Country to latitude and longitude. Remember to use the same output as before, that is Data to Data.
Now, let us display these countries on a map with Choropleth widget. Beautiful. It is so easy to explore country data, when you see it on a map. You can try coloring also by HDI or any other feature.
The final workflow:
We always try to keep our workshops fresh and interesting and visualizations are the best way to achieve this. Till the next workshop!
This week we held our first Girls Go Data Mining workshop. The workshop brought together curious women and intuitively introduced them to essential data mining and machine learning concepts. Of course, we used Orange to explore visualizations, build predictive models, perform clustering and dive into text analysis. The workshop was supported by NumFocus through their small development grant initiative and we hope to repeat it next year with even more ladies attending!
In two days, we covered many topics. On day one, we got to know Orange and the concept of visual programming, where the user construct analytical workflow by stacking visual components. Then we got to know several useful visualizations, such as box plot, scatter plot, distributions, and mosaic display, which give us an initial overview of the data and the potentially interesting patterns. Finally, we got our hands dirty with predictive modeling. We learnt about decision trees, logistic regression, and naive Bayes classifiers, and observed the models in tree viewer and nomogram. It is great having interpretable models and we had great fun exploring what is in the model!
On the second day, we tried to uncover groups in our data with clustering. First, we tried hierarchical clustering and explored the discovered clusters with box plot. Then we also tried k-means and learnt, why this method is better than hierarchical clustering. In the final part, we talked about the methods for text mining, how to do preprocessing, construct a bag of words and perform the machine learning on corpora. We used both clustering and classification and tried to find interesting information about Grimm tales.
One thing that always comes up as really useful in our workshops is Orange’s ability to output different types of data. For example, in Hierarchical Clustering, we can select the similarity cutoff at the top and output clusters. Our data table will have an additional column Cluster, with cluster labels for each data instance.
We can explore clusters by connecting a Box Plot to Hierarchical Clustering, selecting Cluster in Subgroups and using Order by relevance option. This sorts the variables in Box Plot by how well they separate between clusters or, in other words, what is typical of each cluster.
We used zoo.tab and made the cutoff at three clusters. It looks like the first cluster gives milk. Could these be a cluster of mammals?
Indeed it is!
Another option is to select a specific cluster in the dendrogram. Then, we have to rewire the connection between Hierarchical Clustering and Box Plot by setting it to Data. Data option will output the entire data set, with an extra column showing whether the data instance was selected or not. In our case, there would be a Yes if the instance is in the selected cluster and No if it is not.
Then we can use Box Plot to observe what is particular for our selected cluster.
It looks like animals from our selected cluster have feathers. Probably, this is a cluster of birds. We can check this with the same procedure as above.
In summary, most Orange visualizations have two outputs – Selected Data and Data. Selected Data will output a subset of data instances selected in the visualization (or selected clusters in the case of hierarchical clustering), while Data will output the entire data table with a column defining whether a data instance was selected or not. This is very useful if we want to inspect what is typical of an interesting group in our data, inspect clusters or even manually define groups.
Overall, this was another interesting workshop and we hope to continue our fruitful partnership with NumFocus and keep offering free educational events for beginners and experts alike!
Today we have finished a series of workshops for the Ministry of Public Affairs. This was a year-long cooperation and we had many students asking many different questions. There was however one that we talked about a lot. If I have a survey, how do I get it into Orange?
We are using EnKlik Anketa service, which is a great Slovenian product offering a wide array of options for the creation of surveys. We have created one such simple survey to use as a test. I am now inside EnKlik Anketa online service and I can see my survey has been successfully filled out.
Now I have to create a public link to my survey in order to access the data in Orange. I have to click on an icon in the top right part and select ‘Public link’.
A new window opens, where I select ‘Add new public link’. This will generate a public connection to my survey results. But be careful, the type of the connection needs to be Data, not Analysis! Orange can’t read already analyzed data, it needs raw data from Data pane.
Now, all I have to do is open Orange, place EnKlik Anketa widget from the Prototypes add-on onto the canvas, enter the public link into the ‘Public link URL’ fields and press Enter. If your data has loaded successfully, the widget will display available variables and information in the Info pane.
From here on you can continue your analysis just like you would with any other data source!
Last week Marko and I visited the land of the midnight sun – Norway! We held a two-day workshop on spectroscopy data analysis in Orange at the Norwegian University of Life Sciences. The students from BioSpec lab were yet again incredible and we really dug deep into Orange.
One thing we did was see how to join data from two different sources. It would often happen that you have measurements in one file and the labels in the other. Or in our case, we wanted to add images to our zoo.tab data. First, find the zoo.tab in the File widget under Browse documentation datasets. Observe the data in the Data Table.
This data contains 101 animal described with 16 different features (hair, aquatic, eggs, etc.), a name and a type. Now we will manually create the second table in Excel. The first column will contain the names of the animals as they appear in the original file. The second column will contain links to images of animals. Open your favorite browser and find a couple of images corresponding to selected animals. Then add links to images below the image column. Just like that:
Remember, you need a three-row header to define the column that contains images. Under the image column add string in the second and type=image in the third row. This will tell Orange where to look for images. Now, we can check our animals in Image Viewer.
Finally, it is time to bring in the images to the existing zoo data set. Connect the original File to Merge Data. Then add the second file with animal images to Merge Data. The default merging method will take the first data input as original data and the second data as extra data. The column to match by is defined in the widget. In our case, it is the name column. This means Orange will look at the first name column and find matching instances in the second name column.
A quick look at the merged data shows us an additional image column that we appended to the original file.
This is the final workflow. Merge Data now contains a single data table on the output and you can continue your analysis from there.
Find out more about spectroscopy for Orange on our YouTube channel or contribute to the project on Github.
Janez and I have recently returned from a two-week stay in Moscow, Russian Federation, where we were teaching data mining to MA students of Applied Statistics. This is a new Master’s course that attracts the best students from different backgrounds and teaches them statistical methods for work in the industry.
It was a real pleasure working at HSE. The students were proactive by asking questions and really challenged us to do our best.
One of the things we did was compute minimum cost of misclassifications. The story goes like this. Sara is a doctor and has data on 303 patients with heart disease (Orange’s heart-disease.tab data set). She used some classifiers and now has to decide how many patients to send for further tests. Naive Bayes classifier, for example, returned probabilities of a patient being sick (column Naive Bayes 1). For each threshold in probabilites, she will compute how many false positives (patients declared sick when healthy) and how many false negatives (patients declared healthy when sick) a classifiers returns. Each mistake is associated with a cost. Now she wants to find out, how many patients to send for tests (what probability threshold to choose) so that her cost is the lowest.
First, import all the libraries we will need:
import matplotlib.pyplot as plt
import numpy as np
from Orange.data import Table
from Orange.classification import NaiveBayesLearner, TreeLearner
from Orange.evaluation import CrossValidation
Then load heart disease data (and print a sample).
heart = Table("heart_disease")
Now, train classifiers and select probabilities of Naive Bayes for a patient being sick.
scores = CrossValidation(heart, [NaiveBayesLearner(), TreeLearner()])
#take probabilites of class 1 (sick) of NaiveBayesLearner
p1 = scores.probabilities[:, 1]
#take actual class values
y = scores.actual
#cost of false positive (patient classified as sick when healthy)
fp_cost = 500
#cost of false negative (patient classified as healthy when sick)
fn_cost = 800
Set counts, where we declare 0 patients being sick (threshold >1).
fp = 0
#start with threshold above 1 (no one is sick)
fn = np.sum(y)
For each threshold, compute the cost associated with each type of mistake.
ps = 
costs = 
#compute costs of classifying i patients as sick
for i in np.argsort(p1)[::-1]:
if y[i] == 0:
fp += 1
fn -= 1
costs.append(fp * fp_cost + fn * fn_cost)
In the end, we get a list of probability thresholds and associated costs. Now let us find the minimum cost and its probability of a patient being sick.
costs = np.array(costs)
#find probability of a patient being sick at lowest cost
This means the threshold that minimizes our cost for a given classifier is 0.620655. Sara would send all the patients with a probability of being sick higher or equal than 0.620655 for further tests.
At the end, we can also plot the cost to patients sent curve.
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!
Use Datasets widget and load Collagen spectroscopy data. This data contains cells measured with FTIR and annotated with the major chemical compound at the imaged part of a cell. A quick glance in a Data Table will give us an idea how the data looks like. Seems like a very standard spectral data set.
Now we want to determine, whether we can classify cells by type based on their spectral profiles. First, connect Datasets to Test & Score. We will use 10-fold cross-validation to score the performance of our model. Next, we will add Logistic Regression to model the data. One final thing. Spectral data often needs some preprocessing. Let us perform a simple preprocessing step by applying Cut (keep) filter and retaining only the wave numbers between 1500 and 1800. When we connect it to Test & Score, we need to keep in mind to connect the Preprocessor output of Preprocess Spectra.
Let us see how well our model performs. Not bad. A 0.99 AUC score. Seems like it is almost perfect. But is it really so?
Confusion Matrix gives us a detailed picture. Our model fails almost exclusively on DNA cell type. Interesting.
We will select the misclassified DNA cells and feed them to Spectra to inspect what went wrong. Instead of coloring by type, we will color by prediction from Logistic Regression. Can you find out why these spectra were classified incorrectly?
This is one of the simplest examples with spectral data. It is basically the same procedure as with standard data – data is fed as data, learner (LR) as learner and preprocessor as preprocessor directly to Test & Score to avoid overfitting. Play around with Spectroscopy add-on and let us know what you think! 🙂
HHMI | Janelia is one of the prettiest researcher campuses I have ever visited. Located in Ashburn, VA, about 20 minutes from Washington Dulles airport, it is conveniently located yet, in a way, secluded from the buzz of the capital. We adored the guest house with a view of the lake, tasty Janelia-style breakfast (hash-browns with two eggs and sausage, plus a bagel with cream cheese) in the on-campus pub, beautifully-designed interiors to foster collaborations and interactions, and late-evening discussions in the in-house pub.
All these thanks to the invitation of Andrew Lemire, a manager of a shared high-throughput genomics resource, and Dr. Vilas Menon, a mathematician specializing in quantitative genomics. With Andy and Vilas, we have been collaborating in the past few months on trying to devise a simple and intuitive tool for analysis of single-cell gene expression data. Single cell high-throughput technology is one of the latest approaches that allow us to see what is happening within a single cell, and it does that by simultaneously scanning through potentially thousands of cells. That generates loads of data, and apparently, we have been trying to fit Orange for single-cell data analysis task.
Namely, in the past half a year, we have been perfecting an add-on for Orange with components for single-cell analysis. This endeavor became so vital that we have even designed a new installation of Orange, called scOrange. With everything still in prototype stage, we had enough courage to present the tool at Janelia, first through a seminar, and the next day within a five-hour lecture that I gave together with Martin Strazar, a PhD student and bioinformatics expert from my lab. Many labs are embarking on single cell technology at Janelia, and by the crowd that gathered at both events, it looks like that everyone was there.
Orange, or rather, scOrange, worked as expected, and hands-on workshop was smooth, despite testing the software on some rather large data sets. Our Orange add-on for single-cell analytics is still in early stage of development, but already has some advanced features like biomarker discovery and tools for characterization of cell clusters that may help in revealing hidden relations between genes and phenotypes. Thanks to Andy and Vilas, Janelia proved an excellent proving ground for scOrange, and we are looking forward to our next hands-on single-cell analytics workshop in Houston.
On Monday we finished the second part of the workshop for the Statistical Office of Republic of Slovenia. The crowd was tough – these guys knew their numbers and asked many challenging questions. And we loved it!
One thing we discussed was how to properly test your model. Ok, we know never to test on the same data you’ve built your model with, but even training and testing on separate data is sometimes not enough. Say I’ve tested Naive Bayes, Logistic Regression and Tree. Sure, I can select the one that gives the best performance, but we could potentially (over)fit our model, too.
To account for this, we would normally split the data to 3 parts:
training data for building a model
validation data for testing which parameters and which model to use
test data for estmating the accurracy of the model
Let us try this in Orange. Load heart-disease.tab data set from Browse documentation data sets in File widget. We have 303 patients diagnosed with blood vessel narrowing (1) or diagnosed as healthy (0).
Now, we will split the data into two parts, 85% of data for training and 15% for testing. We will send the first 85% onwards to build a model.
We will use Naive Bayes, Logistic Regression and Tree, but you can try other models, too. This is also a place and time to try different parameters. Now we will send the models to Test & Score. We used cross-validation and discovered Logistic Regression scores the highest AUC. Say this is the model and parameters we want to go with.
Now it is time to bring in our test data (the remaining 15%) for testing. Connect Data Sampler to Test & Score once again and set the connection Remaining Data – Test Data.
Test & Score will warn us we have test data present, but unused. Select Test on test data option and observe the results. These are now the proper scores for our models.
Seems like LogReg still performs well. Such procedure would normally be useful when testing a lot of models with different parameters (say +100), which you would not normally do in Orange. But it’s good to know how to do the scoring properly. Now we’re off to report on the results in Nature… 😉
We’ve been having a blast with recent Orange workshops. While Blaž was getting tanned in India, Anže and I went to the charming Liverpool to hold a session for business school professors on how to teach business with Orange.
Obviously, when we say teach business, we mean how to do data mining for business, say predict churn or employee attrition, segment customers, find which items to recommend in an online store and track brand sentiment with text analysis.
For this purpose, we have made some updates to our Associate add-on and added a new data set to Data Sets widget which can be used for customer segmentation and discovering which item groups are frequently bought together. Like this:
We load the Online Retail data set.
Since we have transactions in rows and items in columns, we have to transpose the data table in order to compute distances between items (rows). We could also simply ask Distances widget to compute distances between columns instead of rows. Then we send the transposed data table to Distances and compute cosine distance between items (cosine distance will only tell us, which items are purchased together, disregarding the amount of items purchased).
Finally, we observe the discovered clusters in Hierarchical Clustering. Seems like mugs and decorative signs are frequently bought together. Why so? Select the group in Hierarchical Clustering and observe the cluster in a Data Table. Consider this an exercise in data exploration. 🙂
The second workshop was our standard Introduction to Data Mining for Ministry of Public Affairs.
This group, similar to the one from India, was a pack of curious individuals who asked many interesting questions and were not shy to challenge us. How does a Tree know which attribute to split by? Is Tree better than Naive Bayes? Or is perhaps Logistic Regression better? How do we know which model works best? And finally, what is the mean of sauerkraut and beans? It has to be jota!
Workshops are always fun, when you have a curious set of individuals who demand answers! 🙂
Two days ago we held another Introduction to Data Mining workshop at our faculty. This time the target audience was a group of public sector professionals and our challenge was finding the right data set to explain key data mining concepts. Iris is fun, but not everyone is a biologist, right? Fortunately, we found this really nice data set with ballot counts from the Slovenian National Assembly (thanks to Parlameter).
The data contains ballot counts, statistics, and description for 84 members of the parliament (MPs). First, we inspected the data in a Data Table. Each MP is described with 14 meta features and has 18 ballot counts recorded.
We have some numerical features, which means we can also inspect the data in Scatter Plot. We will plot MPs’ attendance vs. the number of their initiatives. Quite interesting! There is a big group of MPs who regularly attend the sessions, but rarely propose changes. Could this be the coalition?
The next question that springs to our mind is – can we discover interesting voting patterns from our data? Let us see. We first explored the data in Hierarchical Clustering. Looks like there are some nice clusters in our data! The blue cluster is the coalition, red the SDS party and green the rest (both from the opposition).
But it is hard to inspect so many data instances in a dendrogram. For example, we have no idea how similar are the voting records of Eva Irgl and Alenka Bratušek. Surely, there must be a better way to explore similarities and perhaps verify that voting patterns exist at even a party-level… Let us try MDS. MDS transforms multidimensional data into a 2D projection so that similar data instances lie close to each other.
Ah, this is nice! We even colored data points by the party. MDS beautifully shows the coalition (blue dots) and the opposition (all other colors). Even parties are clustered together. But there are some outliers. Let us inspect Matej Tonin, who is quite far away from his orange group. Seems like he was missing at the last two sessions and did not vote. Hence his voting is treated differently.
It is always great to inspect discovered groups and outliers. This way an expert can interpret the clusters and also explain, what outliers mean. Sometimes it is simply a matter of data (missing values), but sometimes we could find shifting alliances. Perhaps an outlier could be an MP about to switch to another party.
You can have fun with these data, too. Let us know if you discover something interesting!