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Playing the Kaggle Two Sigma Challenge – Part 4

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Introduction

The Kaggle Two Sigma Financial Modeling Challenge ran from December 1, 2016 through March 1, 2017. In previous blog posts I introduced the challenge, covered what I did in December then what I did in January. In this posting I’ll continue on with what I did in February. This consisted of refining my work from before, finding ways to refine the methods I was using and getting more done during the Kaggle VM runs.

The source code for these articles is located here. The file use2.py is the code I used to train offline. You can see how I comment/uncomment code to try different things. The file multimodelmultitime.py shows how to use these results for 3 regression models and 1 random forest model. The offline file use2.py uses the datafile train.h5 which is obtained from the Kaggle competition, I can’t redistribute this, but you can get it from Kaggle by acknowledging the terms of use.

Training Offline

Usually training was the slowest part of running these solution. It was quite hard to setup a solution with ensemble averaging when you only had time to train one algorithm. Within the Kaggle community there are a number of people that religiously rely on gradient boosting for their solutions and gradient boosting has provided the key components in previous winning solutions. Unfortunately in this competition it was very hard to get gradient boosting to converge within the runtime provided. Some of the participants took to training gradient boosting offline locally on their computers and then taking the trained model and inserting it into the source code to run in the Kaggle VM. This was quite painful since the trained model is a binary Python object. So they pickled it to a string and then output the string as an ascii representation of the hex digits that they could cut and paste into the Kaggle source code. The problem here was that the Kaggle source file is limited to 1meg in size, so it limited the size of the model they could use. However a number of people got this to work.

I thought about this and realized that for linear regression, this was much easier. In linear regression the model only requires the coefficient array which is the size of the number of variables and the intercept. So generating these and cut/pasting them into the Kaggle solution is quite easy. I was a bit worried that the final test data would have different training data, which would cause this method to fail, but in the end it turned out to be ok. A few people questioned whether this was against the rules of the competition, but no one could quote an exact rule to prevent it, just that you might need to provide the code that produced the numbers. Kaggle never gave a definitive answer to this question when asked.

Bigger Ensembles

With all this in mind, I trained my regression models offline. Some algorithms are quite slow so this opened up quite a few possibilities. I basically ran through all the regression algorithms in scikit-learn and then used a collection of them that gave the best scores individually. Scikit-learn has a lot of regression algorithms and many of them didn’t perform very well. The best results I got were for Lasso, ElasticNet (with L1 ratios bigger than 0.4) and Orthogonal Matching Pursuit. Generally I found the algorithms that eliminated a lot of variables (setting their coefficients to zero) worked the best. I was a bit surprised that Ridge regression worked quite badly for me (more on that next time). I also tried some adding some polynomial components using the scikit-learn PolynomialFeatures function, but I couldn’t find anything useful here.

I trained these models using cross-validation (ie the CV versions of the functions). Cross-validation divides the data up and does various training/testing on different folds to find the best results. To some degree this avoids overfitting and provides more robustness to bad data.

Further I ran these regressions on two views of the data, one on my last data/current data on a bunch of columns and the other on the whole dataset but just for the current time stamp. Once doing this for one regression, adding more regressions didn’t seem to slow down processing much and the overall time I was using wasn’t much. So I had enough processing time leftover to add an ExtraTreesRegressor which was trained during the runs.

It took quite a few submissions to figure out a good balance of solutions. Perhaps with more time a better optimum could have been obtained, but hard time limits are often good.

RANSAC

A number of people in the competition with more of a data background spent quite a bit of time cleaning the data which seemed quite noisy with quite a few bad outliers. I wasn’t really keen on this and really wanted my ML algorithms to do this for me. This is when I discovered the the scikit-learn functions for dealing with outliers and modeling errors. The one I found useful was RANSAC (RANdom SAmple Consensus). I thought this was quite a clever algorithm to use subsets of the data to figure out the outliers (by how far they were from various prediction) and to find a good subset of the data without outliers to train on. You pass a linear model into RANSAC to use for estimating and then you can get the coefficients out at the end to use. The downside is that running RANSAC is very slow and to get good results it would take me about 8 hours to train a single linear model.

The good news here is that using RANSAC rather than cross-validation, I improved my score quite a bit and as a result ended up in about 70th place before the competition ended. You can pass the cross-validation version of the function into RANSAC to perhaps get even better results, but I found this too slow (ie to was still running after a day or two).

Summary

This wraps up what I did in February and basically the RANSAC version of my best Ensemble is what I submitted as my final result for the competition. Next time I’ll discuss the final results of the competition and how I did on the final test dataset.

Written by smist08

March 7, 2017 at 9:25 pm