Downloading and Installing LibSVM
- To run LibSVM, you will want Python. If you don't have it installed, you can download and install it from . Either current stable version should work.
- You will also need to download and install LibSVM itself, which you can do by downloading the zip file from . Windows users should copy the libsvm.dll file from the /windows directory into their C:\Windows\System32 directory. Mac/Linux users should be able to simply cd into the libsvm directory and run
then add the libsvm directory to your PATH.
- If you can, install [gnuplot]. If you have trouble with this (as I did), you can use the modified files with the gnuplot dependency stripped out. Simply replace tools/easy.py with Machine Learning/easy.py and tools/grid.py with Machine Learning/grid.py
 Converting the Data
As with most (if not all) data problems, choosing and formatting the data is the most time-consuming step but also one of the most important.
One approach for reducing the data is to take a subset; you can use Thomas' perl script to take a sample of some number of the training set and test set, by choosing a random subset of the students and only including lines which include them. You can use the perl script sample_training.pl to do this, by running:
perl sample_training.pl -numitems 100 ~/kdd/algebra_2008_2009_train.txt
(assuming your data is located in ~/kdd)
For SVM, ultimately we need to format the data in two files: a training file and a test file. Each of these will have a numeric class and several numeric predictors. The general format is as follows:
<class> 1:<value> 2:<value> 3:<value> ...
with an entry (1:, 2:, 3:,...) for each numeric predictor. For example,
0 1:0 2:0 3:0 4:0 5:0 6:1 7:0 8:0 9:0 10:0 11:0 12:0 13:0 14:0 15:0 16:0 17:0 18:0
Thomas created a perl script to take a training set and convert it (and the corresponding test set) into the correct format by using "correct on first attempt" as the output class and converting student and problem id into a series of binary flag variables (one for each student and problem, indicating whether this class regards this student or this problem). However, this results in a fairly obscene number of predictor variables, even on a stripped-down dataset. So there is almost certainly a better way. But if you don't have one, you can download this script and run
perl convert_features.pl ~/kdd/algebra_2008_2009_train.txt_sample_100_random_students.csv
Assuming your data files are in ~/kdd, this will generate output files ~/kdd/algebra_2008_2009_train.txt_sample_10_random_students.csv_converted.txt and ~/kdd/algebra_2008_2009_train.txt_sample_10_random_students.csv_converted.t in the appropriate format.
 Running SVM on the Data
- cd into your libsvm installation's tools directory and run the following command (assuming your training and test files are in ~/kdd and named appropriately):
python easy.py ~/kdd/algebra_2008_2009_train.txt_sample_10_random_students.csv_converted.txt ~/kdd/algebra_2008_2009_train.txt_sample_10_random_students.csv_converted.t | tee output.txt
- If you have many predictor variables, this will take a long time. Prohibitively long, probably.
- This will automatically scale your training and test data and iteratively search over the parameter space for penalty parameter c and kernel parameters, using cross-validation in order to find the best fit for the training data.
- It will generate an output for each item in the test set with a 0/1 classification. Apparently there is a way to get libSVM to output real values between 0 and 1 (depending on confidence), but we haven't yet investigated doing this.