Error with Inference Labeled LDA/pLDA [Topic Modelling Toolbox]
===

I have been trying to get through with the code for inference from trained labeled LDA model and pLDA using TMT toolbox(stanford nlp group).
I have gone through the examples provided in the following links: 
http://nlp.stanford.edu/software/tmt/tmt-0.3/ 
http://nlp.stanford.edu/software/tmt/tmt-0.4/

Here is the code I'm trying for labeled LDA inference

    val modelPath = file("llda-cvb0-59ea15c7-31-61406081-75faccf7");

    val model = LoadCVB0LabeledLDA(modelPath);`

    val source = CSVFile("pubmed-oa-subset.csv") ~> IDColumn(1);

    val text = {
      source ~>                              // read from the source file
      Column(4) ~>                           // select column containing text
      TokenizeWith(model.tokenizer.get)      //tokenize with model's tokenizer
     }

     val labels = {
      source ~>                              // read from the source file
      Column(2) ~>                           // take column two, the year
      TokenizeWith(WhitespaceTokenizer())     
     }

     val outputPath = file(modelPath, source.meta[java.io.File].getName.replaceAll(".csv",""));

     val dataset = LabeledLDADataset(text,labels,model.termIndex,model.topicIndex);

     val perDocTopicDistributions =  InferCVB0LabeledLDADocumentTopicDistributions(model, dataset);

     val perDocTermTopicDistributions =EstimateLabeledLDAPerWordTopicDistributions(model, dataset, perDocTopicDistributions);

     TSVFile(outputPath+"-word-topic-distributions.tsv").write({
      for ((terms,(dId,dists)) <- text.iterator zip perDocTermTopicDistributions.iterator) yield {
        require(terms.id == dId);
        (terms.id,
         for ((term,dist) <- (terms.value zip dists)) yield {
           term + " " + dist.activeIterator.map({
             case (topic,prob) => model.topicIndex.get.get(topic) + ":" + prob
           }).mkString(" ");
         });
      }
    });


Error

` found   : scalanlp.collection.LazyIterable[(String, Array[Double])]`
` required: Iterable[(String, scalala.collection.sparse.SparseArray[Double])]`
`  EstimateLabeledLDAPerWordTopicDistributions(model, dataset, perDocTopicDistributions);`


I understand it's a type mismatch error. But I don't know how to resolve this for scala.
Basically I don't understand how should I extract the 
1. per doc topic distribution
2. per doc label distribution  after the output of the infer command.

Please help.
Same  in the case of pLDA.
I reach the  inference  command and after that clueless what to do with it.