eval.groovy

import org.grouplens.lenskit.knn.item.*
import org.grouplens.lenskit.transform.normalize.*
import org.grouplens.lenskit.eval.metrics.topn.*;


trainTest {
    dataset crossfold("ml-100k") {
        source csvfile("ml-100k/u.data"){ // relative (or absolute) path to the dataset. In the current format, it assumes that you have a folder named "ml-100k" containing the "u.data" file 
            delimiter "\t"
            domain {
                minimum 1.0
                maximum 5.0
                precision 1.0
            }
        }
    }

    algorithm("PersMean") {
        bind ItemScorer to UserMeanItemScorer
        bind (UserMeanBaseline, ItemScorer) to ItemMeanRatingItemScorer
    }


    metric RMSEPredictMetric

    metric topNnDCG {
                   listSize 10
                   candidates ItemSelectors.allItems()
                   exclude ItemSelectors.trainingItems()
          }

    output "eval-results.csv"
}