Weka StringToWordVector attributes omitted - weka

Question

I´m working with Weka. My problem is, that some of the attributes are omitted after using StringToWordVector. So here is my code:
This is the ARFF file before using any filter:
#relation QueryResult
#attribute class {Qualität,Bord,Kite,Harness}
#attribute text {evo,foil,end,fin,edg}
#data
Qualität,evo
Bord,foil
Kite,end
Harness,fin
Qualität,edg
Here is my java code:
Instances train = new Instances(loadInstancesForWeka("root","",sqlCommand));
train.setClassIndex(train.numAttributes() - 2);
System.out.println(train);
NominalToString filter1 = new NominalToString();
filter1.setInputFormat(train);
train = Filter.useFilter(train, filter1);
System.out.println("\nSelect nach NominaltoString \n"+train);
//filter
StringToWordVector filter = new StringToWordVector();
filter.setInputFormat(train);
train = Filter.useFilter(train, filter);
After using the Vector it looks like this:
#relation 'QueryResult-weka.filters.unsupervised.attribute.NominalToString-Clast-weka.filters.unsupervised.attribute.StringToWordVector-R2-W1000-prune-rate-1.0-N0-stemmerweka.core.stemmers.NullStemmer-stopwords-handlerweka.core.stopwords.Null-M1-tokenizerweka.core.tokenizers.WordTokenizer -delimiters \" \\r\\n\\t.,;:\\\'\\\"()?!\"'
#attribute class {Qualität,Bord,Kite,Harness}
#attribute edg numeric
#attribute evo numeric
#attribute foil numeric
#attribute end numeric
#attribute fin numeric
#data
{2 1}
{0 Bord,3 1}
{0 Kite,4 1}
{0 Harness,5 1}
{1 1}
So why are the attributes "foil,end,fin" omitted? Thank you for your help.

Answer 1

There aren't any attributes omitted from your output. The output is in sparse ARFF format:
Sparse ARFF files are very similar to ARFF files, but data with value
0 are not explicitly represented. ...
Each instance is surrounded by
curly braces, and the format for each entry is:
[index] [space] [value] where index is the attribute index (starting from 0).
So for the third instance in your example,
{0 Kite,4 1}
means that attribute 0 for this instance is Kite, attribute 4 (i.e. 'end') is 1, and the other attributes are 0.
It makes sense for StringToWordVector to produce sparse output because it creates a lot of new attributes, most of which will be 0 for each instance. If you need the non-sparse version you can use weka.filters.unsupervised.instance.SparseToNonSparse.