On behavior of lbjava when dealing with real valued features.

##Introduction

There was speculations about lbjava failing when using real-valued features.

Thanks to @Slash0BZ (Ben Zhou) we did a comprehensive experiments, running a few example problems, with different number of real-valued features, across different algorithms, and different number of iterations.

NewsGroup Experiments

Methodology

The data I used was from http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz

I downloaded and unzipped the data and used the script https://github.com/Slash0BZ/Cogcomp-Utils/blob/master/LBJava/20news_data_parser.py to move randomly selected 90% of the files (in each tag) to a training data path and the rest of them to a testing data path.

The parsed data can be read and used directly through https://github.com/Slash0BZ/lbjava/blob/master/lbjava-examples/src/main/java/edu/illinois/cs/cogcomp/lbjava/examples/DocumentReader.java

Then I first tested the original NewsGroupClassifier.lbj defined as https://github.com/Slash0BZ/lbjava/blob/master/lbjava-examples/src/main/lbj/NewsGroupClassifier.lbj

Then I added a constant real feature for each of the examples, an example is

real[] RealFeatureConstant(Document d) <- { int k = 3; sense k; }

int here is fine because lbjava parser will transform the data type to double

Then I tried multiple real features that are randomly generated by Gaussian distribution. The code I used was

import java.util.Random;
real[] GaussianRealFeatures(Document d) <- {
    List words = d.getWords();
    Random ran = new Random();
    for (int i = 0; i < words.size() - 1; i++)
        sense ran.nextGaussian() * 10;
}

I used bash scripts to run test on several algorithms.

Result Tables

NewsGroup (table for single real feature)

Condition\Algorithm	SparseAveragedPerceptron	SparseWinnow	PassiveAggresive	SparseConfidenceWeighted	BinaryMIRA
1 round w/o real features	48.916	92.597	19.038		33.739
1 round w/ real features	47.753	92.491	23.268		32.364
10 rounds w/o real features	82.390	91.539	24.802		76.891
10 rounds w/ real features	82.126	91.529	12.427		75.939
50 rounds w/o real features	84.823	91.592	14.120		77.208
50 rounds w/ real features	85.299	91.433	19.566		76.891
100 rounds w/o real features	85.828	91.433	12.956		76.574
100 rounds w real features	84.770	91.486	15.442		61.026

NewsGroup (table for the same amount of Gaussian random real features as discrete ones)

Condition\Algorithm	SparseAveragedPerceptron	SparseWinnow	PassiveAggresive	BinaryMIRA
1 round w/o real features	51.454	92.597	12.057	33.739
1 round w/ real features	17.980	6.081	14.913	14.225
10 rounds w/o real features	82.813	91.539	22.369	76.891
10 rounds w/ real features	52.829		42.517	45.743
50 rounds w/o real features	84.294	91.592	21.100	77.208
50 rounds w/ real features	75.727		67.054	75.198
100 rounds w/o real features	85.506	91.433	17.768	76.574
100 rounds w real features	77.631		74.828	74.194

###Problems

1. SparseConfidenceWeighted encountered a problem where training takes too long on my server. I had to kill the process after waiting for a long time.

2. SparseWinnow will through a NullPointerException during the testing (testDiscrete()) process after multiple real features are added, if the training rounds are larger than 1.

Badges Experiments

Methodology

Similar to the NewsGroup examples above, the constant real features are the same.

The multiple real features are added through

real[] RealFeatures3(String line) <- {
    for(int i = 0; i < line.length(); i++){
        int k = 3;
        sense k;
    }
}

The random multiple real features are added through

real[] GaussianRealFeatures(String line) <- {
    for (int i = 0; i < line.length(); i++){
        Random ran = new Random();
        sense ran.nextGaussian() * 10;
    }
}

###Result Tables

Badges (table for single real feature)

Condition\Algorithm	SparsePerceptron	SparseWinnow	NaiveBayes
1 round w/o real features	100.0	95.745	100.0
1 round w/ real features	100.0	95.745	100.0
10 rounds w/o real features	100.0	100.0	100.0
10 rounds w/ real features	100.0	100.0	100.0
50 rounds w/o real features	100.0	100.0	100.0
50 rounds w/ real features	100.0	100.0	100.0
100 rounds w/o real features	100.0	100.0	100.0
100 rounds w real features	100.0	100.0	100.0

Badges (table for same amount of constant real features as discrete features)

Condition\Algorithm	SparsePerceptron	SparseWinnow	NaiveBayes
1 round w/o real features	100.0	95.745	100.0
1 round w/ real features	74.468	100.0	100.0
10 rounds w/o real features	100.0	100.0	100.0
10 rounds w/ real features	78.723	100.0	100.0
50 rounds w/o real features	100.0	100.0	100.0
50 rounds w/ real features	100.0	100.0	100.0
100 rounds w/o real features	100.0	100.0	100.0
100 rounds w real features	100.0	100.0	100.0

Badges (table for same amount of of random Gaussian real features as discrete features)

Condition\Algorithm	SparsePerceptron	SparseWinnow	NaiveBayes
1 round w/o real features	100.0	95.745	100.0
1 round w/ real features	55.319	56.383	100.0
10 rounds w/o real features	100.0	100.0	100.0
10 rounds w/ real features	62.766	100.0	100.0
50 rounds w/o real features	100.0	100.0	100.0
50 rounds w/ real features	74.468	87.234	100.0
100 rounds w/o real features	100.0	100.0	100.0
100 rounds w real features	86.170	100.0	100.0

Conclusions

The conclusion made here is that, as more number of real-valued features are added, more training iterations are need to train the system, and there is no clear issues with real-valued features.