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Success does not require understanding
I took part in the second Data Science London Hackathon last weekend (also my second hackathon) and it was a very different experience compared to the first hackathon. Once again Carlos and his team really looked after us.
- The data was released 24 hours before the competition started and even though I had spent less than half an hour looking at it, at the start of the competition I did not feel under any time pressure; those 24 hours allowed me to get used to the data and come up with some useful looking ideas.
- The instructions for the first competition had suggested that people form teams of 3-5 and there was a lot of networking happening before the start time. There was no such suggestion this time and as I networked around looking for people to work with I was surprised by the number of people who wanted to work alone; Jonny and Kannappan were the only members from my previous team (the Outliers) who had entered this event, with Kannappan wanting to work alone and Jonny joining me to create a two person team.
- There was less community spirit this time, possible reasons include a lot more single person teams sitting in the corner doing their own thing, fewer people attending (it is the middle of the holiday season), fewer people staying over until the Sunday (perhaps single person teams got disheartened and left or the extra 24 hours of data access meant that teams ran out of ideas/commitment after 36 hours) or me being reduced to a single person team (Jonny had to leave at 20:00) meant I paid more attention to what was happening on the floor.
The problem was to predict what ratings different people would give to various music artists. We were given data involving 50 artists and 48,645 users (artists and users were anonymous) in five files (one contained the training dataset and another the test dataset).
A quick analysis of the data showed that while there were several thousand rows of data per artist there were only half a dozen rows per person, a very sparse dataset.
The most frequent technique I heard mentioned during my initial conversations with attendees was machine learning. In my line of work I am constantly trying to understand what is going on (the purpose of this understanding is to control and make things better) and consider anybody who uses machine learning as being clueless, dim witted or just plain lazy; the problem with machine learning is that it gives answers without explanations (ok decision trees do provide some insights). This insistence on understanding turned out to be my major mistake, the competition metric is based on correctness of answers and not on how well a competitor understands the problem domain. I had a brief conversation with a senior executive from EMI (who supplied the dataset and provided some of the sponsorship money) who showed up on Sunday morning and he had no problem with machine learning providing answers and not explanations.
Having been overly ambitious last time team Outliers went for extreme simplicity and started out with the linear model glm(Rating ~ AGE + GENDER...) being built for each artist (i.e., 50 models). For a small amount of work we got a score of just over 21 and a place of around 70th on the leader board, now we just needed to include information along the lines of “people who like Artist X also like Artist Y”. Unfortunately the only other member of my team (who did not share my view of machine learning and knew something about it) had a prior appointment and had to leave me consuming lots of cpu time on a wild goose chase that required me to have understanding.
The advantages of being in a team include getting feedback from other members (e.g., why are you wasting your time doing that, look how much better this approach is) and having access to different skill sets (e.g., knowing what magic pixie dust values to use for the optional parameters to machine learning routines). It was Sunday morning before I abandoned the ‘understanding’ approach and started thrashing around using various machine learning techniques, which told me that people demographics (e.g., age and gender) were not particularly good predictors compared to other data but did did not reduce my score to the 13-14 range that could be seen on the leader board’s top 20.
Realizing that seven hours was not enough time to learn how to drive R’s machine learning packages well enough to get me into the top ten, I switched tack and spent a lot more time wandering around chatting to people; those whose score was worse than mine were generally willing to chat. Some had gotten completely bogged down in data cleaning and figuring out how to handle missing data (a subject rarely covered in books but of huge importance in real life), I was surprised to find one team doing most of their coding in SQL (my suggestion to only consider Age+Gender improved their score from 35 to 22), I mocked the people using Clojure (people using a Lisp derived language think they have discovered the one true way and will suffer from self doubt if they are not mocked regularly). Afterwards it struck me that well over 50% of attendees were not British (based on their accents), was this yet another indicator of how far British Universities had dumbed down mathematics teaching that natives did not feel up to the challenge (well done to the Bristol undergraduate who turned up) or were the most gung-ho technical folk in London those who had traveled here to work from abroad?
The London winner was Dell Zhang, the only other person sitting at the table I was on (he sat opposite me throughout the competition), who worked quietly away for the whole 24 hours and seemed permanently unimpressed by the score he was achieving; he described his technique as “brute force random forest using Python (the source will be made available on the Data Science website).
Reading through posts made by competitors after the event was as interesting as last time. Factorization Machines seems to be the hot new technique for making predictions based on very sparse data and the libFM is the software I needed to know about last weekend (no R package providing an interface to this C++ code available yet).
Incompetence borne of excessive cleverness
I have just got back from the 24 hour Data Science Global Hackathon; I was an on-site participant at Hub Westminster in London (thanks to Carlos and his team for doing such a great job looking after us all {around 50 turned up from the 100 who registered; the percentage was similar in other cities around the world}). Participants had to be registered by 11:00 UTC, self form into 3-5 person teams ready for the start at 12:00 UTC and finish 24 hours later. The world-wide event had been organized by our London hosts who told us they expected the winning team to come from those in the room; Team Outliers (Wang, Jonny, Kannappan, Bob, Simon, yours truely and Fran for the afternoon) started in an optimistic mood.
At 12:00 an air-quality training dataset + test points was made available and teams given the opportunity to submit eight predictions in each of the two 12 hour time periods. The on-line submissions were evaluated by Kaggle (one of the sponsors, along with EMC) to produce a mean estimated error that was used to rank teams.
The day before the event I had seen a press release saying that the task would involve air-quality and a quick trawl of the Internet threw up just the R package I needed, OpenAir; I also read a couple of Wikipedia articles on air pollution.
Team Outliers individually spent the first hour becoming familiar with the data and then had a get-together to discuss ideas. Since I had a marker pen, was sitting next to a white-board and was the only person with some gray hair I attempted to manage the herding of the data science cats and later went on to plot the pollution monitor sites on Google Maps as well as producing some visually impressive wind Roses (these did not contribute anything towards producing a better solution but if we had had a client they could have been used to give the impression we were doing something useful).
People had various ideas about the techniques to use for building the best model and how the measurements present in the training set might be used to predict air quality (the training data had names such as target_N_S, where N and S were small integer values denoting the kind of pollution and the site where the measurement was made). The training set included measurements of wind speed/direction data and hours of sunlight, and a couple of members wanted to investigate if these would make good predictors. Team Outliers had people looking at all the fancy stuff you find in textbooks, e.g., ARIMA for time series, svm for machine learning, and I was looking at getting the data into the form needed by OpenAir.
There were all sorts of problems with the data, just like real life, e.g., missing values (lots of them), some kinds of quality (i.e., pollutants) were only measured at one or two sites and fuzzy values such as the ‘most common month’ (what ever that might be). Some people were looking at how best to overcome this data quality problem.
20:30 arrived, and some great food was laid out for dinner, no actual predictions yet, but they would be arriving real soon now. A couple of hours later my data formatting project crashed and burned (being a 32-bit system, R got upset about my request to create a vector needing 5.2 Gig; no chance of using the R-based OpenAir package which needed data in a format different from the one we were given it in). Fifteen minutes to midnight I decided that we either used the eight submissions permitted in the first 12 hours or lost them, and wrote a dozen lines of R that built a linear model using one predictor variable (which I knew from some earlier plots was far from linear, but the coding was trivial, and the lm function would take no time to build separate models for each of the 30 odd response variables). I submitted the predictions, and we appeared on the score board at number 65 out of 112. Being better than 47 other teams was a bit of a surprise.
Panic over, we realised that the 12 hours ended at 12:00 UTC which was 01:00 BST (British Summer time) and we had another hour. Wang made a couple of submissions that improved our score, and at around 02:00 I went to grab a few hours sleep.
I was back online at 06:00 to find that team Outliers had slipped to 95th place as the Melbourne and San Francisco teams had improved during their daytime. More good food for breakfast at 08:30.
Jonny drew attention to the fact that the mean absolute error in our team’s current score was almost twice as great as that of the sample solution provided with the data. We had long ago dismissed this solution as being too simplistic (it was effectively a database solution in that it calculated the mean value of the various pollutants in the training set at various chunkID and hour points which were used as keys for ‘looking up’ prediction values required by the test dataset). Maybe team members ought to focus their attention on tweaking this very simple approach rather than our ‘cleverer’ approaches.
I suggested modifying the sample solution to use the median rather than the mean (less susceptible to outliers), this boosted our ranking back into the 1960s. Jonny and Simon tried using a rolling mean, no improvement; Wang tried other variations, no improvement.
Team Outliers finished the Hackathon in equal 61st, along with 22 other teams, out of 114 submissions.
What did we do wrong? Mistakes include:
- trying to do too much for people with our various skill levels in the time available. For instance, I don’t regularly reformat large data tables or try to calculate the error in machine learning models and while I can easily knock out the code I still have to sit down and think about what needs to be written, something that somebody who does this sort of thing regularly would just know how to do; other team members seemed very familiar with the theory but were not used to churning out code quickly.
- spending too much time studying all the various kinds of measurements available in the training set, many of which were not available in the test dataset. We should have started off ignoring measurements in the training set that were not available in the test set, perhaps looking to using these later if time permitted.
Members of team Outliers enjoyed themselves but were a little crestfallen that our clever stuff was not as good as such a crude, but insightful, approach. Most of us used R, a few made use of awk, Python, spreadsheets and Unix shell.
Our hosts are looking to run more data science hackathons this year, in particular one related to the music industry in a few months time. If you are interested in taking part, keep an eye on their website.
Update (later the next day)
At least one team achieved some good results using ARIMA. Fran had started building an ARIMA model, had to leave and nobody else picked it up; I should have been paying more attention to ensure that ideas did not disappear when people left.
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