A short introduction and conclusion to the OpenBikes 2016 Challenge
Jan 26, 2017

During my undergraduate internship in 2015 I started a side project called OpenBikes. The idea was to visualize and analyze bike sharing over multiple cities. Axel Bellec joined me and in 2016 we won a national open data competition. Since then we haven’t pursued anything major, instead we use OpenBikes to try out technologies and to apply concepts we learn at university and on online.

Before the 2016 summer holidays one of my professors, Aurélien Garivier, mentioned that he was considering using our data for a Kaggle-like competition between some statistics curriculums in France. Near the end of the summer I sat down with a group of professors and we decided upon a format for the so-called “Challenge”. The general idea was to provide student teams with historical data on multiple bike stations and ask to do some forecasting which we would then score based on a secret truth. The whole thing lasted from the 5th of October 2016 till the 26th of January 2017 when the best team was crowned.

The challenge was split into two phases. During the first phase, the teams were provided with data spanning from the 1st of April until the 5th of October at 10 AM. The data contained updates on the number of bikes at each station, the geographical position of the stations and the weather in each city. They were asked to forecast the number of bikes at 30 stations for 10 fixed timesteps ranging from the 5th of October at 10 AM until the 9th of October. We picked 10 stations from Toulouse, Lyon and Paris. Each team had access to an account page where they could deposit their submission which were then automatically scored. A public leaderboard was available at the homepage of the website Axel and I built.

During the second part of the challenge, which lasted from the 12th of January 2017 until the 20th of January 2017, the teams were provided with a new dataset containing similar data than the first part, except that Lyon has been swapped out for New-York. The data went from the 1st of April 2016 until the 11th of January 2017. The timesteps to predict were the same - both test sets went from a Wednesday till a Sunday. The teams did not get any feedback when they made a submission during the second part, they were scored based on their last submission, à l’aveugle.

In each part of the challenge the chosen metric to score the students was the mean absolute error between their submissions and the truth. The use of the MAE makes it possible to say things such as “team A was, on average, 3.2 bikes off target”.

## Technical notes

Axel and I had been collecting freely available bike sharing data since October 2015. To do this we put in a place a homemade crawler which would interrogate various APIs and aggregate their data into a single format. We stored the number of bikes at each station at different timesteps with MongoDB. The metadata concerning the of the cities and the bike stations was stored with PostgreSQL. We also exposed an API to be able to use our data in an hypothetical mobile app. We deployed our crawler/API on a 20\$ DigitalOcean server. The glue language was Python. The whole thing is available on GitHub.

To host the challenge I wrote a simple Django application (my first time!) which Axel kindly deployed on the same server as the crawler. The application used a SQLite as a database backend, partly because I wanted to try it out in production but because anything more powerful was unnecessary. Moreover SQLite stores it’s data in *.db file which can easily be transfered for doing some descriptive statistics. Again, the code is available on GitHub.

## Results

The turnout was quite high considering the fact that we didn’t put that much effort into the matter. All in all 8 curriculums and 50 teams took part in the challenge. A total of 947 valid submissions were made, which makes an average of 19 submissions per teams and 7 submissions a day - our server could easily handle that! Of course the rate at which teams submitted wasn’t uniform through time, as can be seen on the following chart.

Philippe Besse suggested looking into the relationship between the number of submissions and the best score per team. The idea was to see if any overfitting had occured, in other words that the best scores were obtained by making many submissions with small adjustments. As can be seen on the following chart, an expected phenomenon arises: teams that have the best scores are usually the ones that have submitted more than others. Interestingly this becomes less obvious the more the teams submit, which basically means that getting a better score becomes harder and harder - this is always the case in data science competitions. To illustrate this phenomenon I fitted an exponential curve to the data.

The final public leaderboard (for the first part that is) was the following.

Team name Curriculum Best score Number of submissions
Dream Team ISAE - SUPAERO 2.7944230769230773 49
Mr Nobody Université de Bordeaux 3.49 14
Oh l'équipe Université de Bordeaux 3.49 45
PrédiX Ecole Polytechnique 3.5402333333332323 47
Louison Bobet StatEco - Toulouse School of Economics 3.61 61
Armstrong GMM - INSA 3.62 35
OpenBikes CMISID - Université Paul Sabatier 3.62930918737076 7
Ravenclaw Université de Bordeaux 3.67 64
LA ROUE ARRIÈRE CMISID - Université Paul Sabatier 3.679264355300685 6
WeLoveTheHail GMM - INSA 3.703333333333333 5
GMMerckx GMM - INSA 3.7266666666666666 20
TEAM_SKY Université de Bordeaux 3.7333333333333334 17
zoomzoom Université de Bordeaux 3.7333333333333334 21
KAMEHAMEHA Université de Bordeaux 3.7333333333333334 21
Tricycles GMM - INSA 3.743333333333333 102
AfricanCommunity CMISID - Université Paul Sabatier 3.7480411895507957 45
Avermaert Université de Bordeaux 3.75 21
ZiZou98 <3 GMM - INSA 3.776666666666667 54
LesCyclosTouristes CMISID - Université Paul Sabatier 3.8066666666666666 33
Ul-Team Université de Bordeaux 3.8619785774274002 6
Les Grosses Données MAPI3 - Université Paul Sabatier 3.866305333958716 6
H2O GMM - INSA 3.8833333333333333 14
RYJA Team CMISID - Université Paul Sabatier 3.9366666666666665 3
NSS Université de Bordeaux 3.9476790826017845 8
Pas le temps de niaiser CMISID - Université Paul Sabatier 3.9486790826017866 1
TheCrazyInsaneFlyingMonkeySpaceInvaders StatEco - Toulouse School of Economics 4.003399297813541 14
Jul saint-Jean-la-Puenta StatEco - Toulouse School of Economics 4.003399297813541 13
Four and one StatEco - Toulouse School of Economics 4.04 24
test CMISID - Université Paul Sabatier 4.088626133087001 38
Pedalo GMM - INSA 4.088626133087581 1
alela Université de Bordeaux 4.173333333333333 2
MoAx GMM - INSA 4.199905182463245 20
Pas de Pau Université de Pau 4.25912069022148 2
Le Gruppetto StatEco - Toulouse School of Economics 4.333333333333333 8
Lolilol Université de Bordeaux 4.338140757878622 1
DataScientist2017 StatEco - Toulouse School of Economics 5.003333333333333 11
SRAM MAPI3 - Université Paul Sabatier 5.10472813815054 1
Outliers MAPI3 - Université Paul Sabatier 5.3133333333333335 28
Jean Didier Vélo ♯♯ MAPI3 - Université Paul Sabatier 5.3133333333333335 5
Velouse CMISID - Université Paul Sabatier 5.343147845575032 12
TEAM NNBJ CMISID - Université Paul Sabatier 5.343147845575032 1
MAPI3 - Université Paul Sabatier 5.49 5
JMEG MAPI3 - Université Paul Sabatier 5.783141628450403 7
player CMISID - Université Paul Sabatier 6.3133333333333335 21
TSE-BigData StatEco - Toulouse School of Economics 6.536927792628606 5
kangou Université de Bordeaux 6.826666666666667 16
On aura votre Pau Supaéro Université de Pau 7.306310702178721 1
Les Pédales StatEco - Toulouse School of Economics 7.803333333333334 1
BIKES FINDERS StatEco - Toulouse School of Economics 8.07 1
Université Bordeaux Enseignants ISAE - SUPAERO 8.59 3
Pedalo GMM - INSA 8.876666666666667 1

Congratulations to team “Dream Team” for winning, by far, the first part of the challenge. The rest of best teams seem to have hit a wall at ~3.6 bikes. This can be seen on the following chart which shows the best score of the 10 best teams along time.

As for the ranking for the second part of the challenge (the blindfolded part), here is the final ranking:

Team name Curriculum Best score
Le Gruppetto StatEco - Toulouse School of Economics 3.2229284855662748
OpenBikes CMISID - Université Paul Sabatier 3.73651042834827
Louison Bobet StatEco - Toulouse School of Economics 3.816666666666667
Mr Nobody Université de Bordeaux 3.94
Oh l'équipe Université de Bordeaux 3.953333333333333
Ravenclaw Université de Bordeaux 4.05
Tricycles GMM - INSA 4.24
Four and one StatEco - Toulouse School of Economics 4.45
PrédiX Ecole Polytechnique 4.523916666666766
WeLoveTheHail GMM - INSA 4.596666666666667
ZiZou98 <3 4.596666666666667
Dream Team ISAE - SUPAERO 4.6402666666666725
GMMerckx GMM - INSA 4.706091376666668
LA ROUE ARRIÈRE CMISID - Université Paul Sabatier 4.711600488605138
Avermaert Université de Bordeaux 4.74
TSE-BigData StatEco - Toulouse School of Economics 4.754570707888592
Pedalo GMM - INSA 4.755557911517161
test CMISID - Université Paul Sabatier 4.755557911517161
Armstrong GMM - INSA 4.87
H2O GMM - INSA 4.93
LesCyclosTouristes CMISID - Université Paul Sabatier 4.963333333333333
AfricanCommunity CMISID - Université Paul Sabatier 4.977632664232967
TheCrazyInsaneFlyingMonkeySpaceInvaders StatEco - Toulouse School of Economics 5.092628340778687
NSS Université de Bordeaux 5.2957133684308015
Ul-Team Université de Bordeaux 5.374689402650746
Les Pédales StatEco - Toulouse School of Economics 5.492804691024131
Les Grosses Données MAPI3 - Université Paul Sabatier 5.62947781887139
Velouse CMISID - Université Paul Sabatier 5.666666666666668
DataScientist2017 StatEco - Toulouse School of Economics 5.756666666666668
JMEG MAPI3 - Université Paul Sabatier 5.981138200799902
RYJA Team CMISID - Université Paul Sabatier 6.026666666666666
BIKES FINDERS StatEco - Toulouse School of Economics 6.076666666666667
TEAM_SKY Université de Bordeaux 6.123333333333332
KAMEHAMEHA Université de Bordeaux 6.123333333333332
zoomzoom Université de Bordeaux 6.123333333333332
MAPI3 - Université Paul Sabatier 6.246666666666667
Outliers MAPI3 - Université Paul Sabatier 7.926666666666668
TEAM NNBJ CMISID - Université Paul Sabatier 8.243333333333334
MoAx GMM - INSA 10.946772841269727

Team “Le Gruppetto” is officially the winner of the challenge! “The Rabbit and the Turtle” anyone? The fact that the second was blindfolded completely reversed the rankings and favored teams with robust methods whilst penalizing overfitters. Whatsmore “only” 39 teams took part in the second part (50 did in the first one); maybe some teams felt that their ranking wouldn’t change, but the fact is that “Le Gruppetto” were 34th before being 1st. It ain’t over till the fat lady sings. The following chart shows the best score per team for both parts of the challenge.

## Who used what?

Every team was asked to submit their code for the second submission. Mostly this was required to make sure no team had cheated by retrieving the data from an API, however this was also the occasion to see what tools the students were using. Here is a brief summary:

• 23 teams used R (mostly xgboost, dplyr, gbm, randomForest, caret)
• 19 teams used random forests
• 15 teams used Python (mostly pandas and sklearn)
• 8 teams used some form of averaging (which isn’t really machine learning), 5 went further and used the averages as features
• 7 teams used gradient boosted trees
• 4 teams used Jupyter with Python
• 3 teams used model stacking (they averaged the outputs of their individual models)
• 3 teams used vanilla linear regression
• 2 teams averaged the number of bikes in the surrounding stations
• 2 teams used randomized decision trees (they did fairly well)
• 2 teams used $$k$$ nearest neighbours
• 1 team used RMarkdown
• 1 team used LASSO regression
• 1 team used a CART
• 1 team used a SARIMA process
• 1 team used a recurrent neural network (me!)
• 1 team used dynamic time warping
• 1 team used principal component analysis

An interesting fact is that out of the 7 teams who used Python, 4 used it for preparing their data but coded their model in R.

The winners of first part used a random forest, dynamic time warping and principal component decomposition - their Python code is quite hard to grasp! As for the winners of the second part, they coded in R and looked at surrounding stations whilst using gradient boosted trees.

## Conclusion

I would like to thank all the students and teachers that participated from the following schools:

Personally I had a great experience organization wise. I had a few panic attacks whilst preparing the CSV files and updating the website, but generally everything went quite smoothly. As a participant my feelings were more mixed; to be totally honest I wasn’t very inclined to participate. It’s difficult to do in front and behind the stage!