java.lang.NullPointerException

[shabir1]

data sample:
Document1 label1 forest=3.4 tree=5 wood=2.85 hammer=1 colour=1 leaf=1.5
Document2 label2 forest=10 tree=5 wood=2.75 hammer=1 colour=4 leaf=1

String lineRegex = "^(\S*)[\s,](\S)[\s,](.)$";
String dataRegex = "[\p{L}([0-9]*\.[0-9]+|[0-9]+)_\=]+";
ArrayList pipeList = new ArrayList();
pipeList.add(new Target2Label());
pipeList.add( new Input2CharSequence() );
pipeList.add( new CharSequence2TokenSequence(Pattern.compile(dataRegex)) );
pipeList.add( new TokenSequenceParseFeatureString(true,true,"=") );
pipeList.add( new PrintInputAndTarget());
InstanceList instances = new InstanceList (new SerialPipes(pipeList));
Reader fileReader = new InputStreamReader(new FileInputStream(new File(dataPath)),
"UTF-8");
instances.addThruPipe(new CsvIterator (fileReader, Pattern.compile(lineRegex),
3, 2, 1));

ClassifierTrainer trainClassify = new NaiveBayesTrainer();
trainClassify.train(instances);

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name: 1419
target: +adwapq-50k
input: TokenSequence [CapitalGain=0.0 span[0..15], education=5 feature(education)=5.0 span[16..27], occupation=0 span[28..40], race=0 span[41..47], sex=1 feature(sex)=1.0 span[48..53], capitalLoss=0.0 span[54..69], HoursPerWeek=40.0 feature(HoursPerWeek)=40.0 span[70..87], fnlwgt=115070.0 feature(fnlwgt)=115070.0 span[88..103], MaritalStatus=0 span[104..119], NativeCountry=0 span[120..135], workclass=2 feature(workclass)=2.0 span[136..147], relationship=0 span[148..162], age=47.0 feature(age)=47.0 span[163..171], EducationNum=10.0 feature(EducationNum)=10.0 span[172..189]]
Token#0:CapitalGain=0.0 span[0..15]
Token#1:education=5 feature(education)=5.0 span[16..27]
Token#2:occupation=0 span[28..40]
Token#3:race=0 span[41..47]
Token#4:sex=1 feature(sex)=1.0 span[48..53]
Token#5:capitalLoss=0.0 span[54..69]
Token#6:HoursPerWeek=40.0 feature(HoursPerWeek)=40.0 span[70..87]
Token#7:fnlwgt=115070.0 feature(fnlwgt)=115070.0 span[88..103]
Token#8:MaritalStatus=0 span[104..119]
Token#9:NativeCountry=0 span[120..135]
Token#10:workclass=2 feature(workclass)=2.0 span[136..147]
Token#11:relationship=0 span[148..162]
Token#12:age=47.0 feature(age)=47.0 span[163..171]
Token#13:EducationNum=10.0 feature(EducationNum)=10.0 span[172..189]

name: 1420
target: +adwapq-50k
input: TokenSequence [CapitalGain=0.0 span[0..15], education=5 feature(education)=5.0 span[16..27], occupation=11 feature(occupation)=11.0 span[28..41], race=0 span[42..48], sex=0 span[49..54], capitalLoss=0.0 span[55..70], HoursPerWeek=50.0 feature(HoursPerWeek)=50.0 span[71..88], fnlwgt=172582.0 feature(fnlwgt)=172582.0 span[89..104], MaritalStatus=0 span[105..120], NativeCountry=0 span[121..136], workclass=5 feature(workclass)=5.0 span[137..148], relationship=3 feature(relationship)=3.0 span[149..163], age=19.0 feature(age)=19.0 span[164..172], EducationNum=10.0 feature(EducationNum)=10.0 span[173..190]]
Token#0:CapitalGain=0.0 span[0..15]
Token#1:education=5 feature(education)=5.0 span[16..27]
Token#2:occupation=11 feature(occupation)=11.0 span[28..41]
Token#3:race=0 span[42..48]
Token#4:sex=0 span[49..54]
Token#5:capitalLoss=0.0 span[55..70]
Token#6:HoursPerWeek=50.0 feature(HoursPerWeek)=50.0 span[71..88]
Token#7:fnlwgt=172582.0 feature(fnlwgt)=172582.0 span[89..104]
Token#8:MaritalStatus=0 span[105..120]
Token#9:NativeCountry=0 span[121..136]
Token#10:workclass=5 feature(workclass)=5.0 span[137..148]
Token#11:relationship=3 feature(relationship)=3.0 span[149..163]
Token#12:age=19.0 feature(age)=19.0 span[164..172]
Token#13:EducationNum=10.0 feature(EducationNum)=10.0 span[173..190]

java.lang.NullPointerException
at cc.mallet.types.Multinomial$Estimator.setAlphabet(Multinomial.java:308)
at cc.mallet.classify.NaiveBayesTrainer.setup(NaiveBayesTrainer.java:251)
at cc.mallet.classify.NaiveBayesTrainer.trainIncremental(NaiveBayesTrainer.java:200)
at cc.mallet.classify.NaiveBayesTrainer.train(NaiveBayesTrainer.java:193)
at cc.mallet.classify.NaiveBayesTrainer.train(NaiveBayesTrainer.java:59)