Precision and NDCG are NaN

[abdollahpouri]

Hello,
I am trying to use the code snippet you provided in your Github page to run the recommendation algorithm in my program. Here is my code:

public class MyRecommender {
    public static String CONFIG_FILE =
            "conf/librec.properties";

    public static void main(String[] args) throws Exception {

        // recommender configuration
        Configuration conf = new Configuration();
        String confFilePath = CONFIG_FILE;
        Properties prop = new Properties();
        prop.load(new FileInputStream(confFilePath));
        for (String name : prop.stringPropertyNames()) {
            conf.set(name, prop.getProperty(name));
        }

        // build data model
        DataModel dataModel = new TextDataModel(conf);
        dataModel.buildDataModel();

        // set recommendation context
        RecommenderContext context = new RecommenderContext(conf, dataModel);
        RecommenderSimilarity similarity = new PCCSimilarity();
        similarity.buildSimilarityMatrix(dataModel);
        context.setSimilarity(similarity);

        // training
        Recommender recommender = new UserKNNRecommender();
        recommender.recommend(context);

        // evaluation
        RecommenderEvaluator maeEvaluator = new MAEEvaluator();
        RecommenderEvaluator precisionEvaluator = new PrecisionEvaluator();
        RecommenderEvaluator normalizedDCGEvaluator = new NormalizedDCGEvaluator();


        double precision=recommender.evaluate(precisionEvaluator);
        //double mae=recommender.evaluate(maeEvaluator);
        double ndcg=recommender.evaluate(normalizedDCGEvaluator);
       // System.out.println("MAE: "+ mae );
        System.out.println("Precision: "+ precision );
        System.out.println("NDCG: "+ ndcg );

        // recommendation results
        List recommendedItemList = recommender.getRecommendedList();
        RecommendedFilter filter = new GenericRecommendedFilter();
        recommendedItemList = filter.filter(recommendedItemList);
    }
}

And here is my librec.properties file:

# set data directory
dfs.data.dir=data
# set result directory
# recommender result will output in this folder
dfs.result.dir=result


data.model.splitter=testset
data.input.path=ml-100k
data.testset.path=ml-100k/u1.test


rec.recommender.isranking=true
rec.recommender.ranking.topn=10



# convertor
# load data and splitting data 
# into two (or three) set
# setting dataset name
# setting dataset format(UIR, UIRT)
data.column.format=UIR
# setting method of split data
# value can be ratio, loocv, given, KCV
#data.splitter.cv.number=5
# using rating to split dataset
data.splitter.ratio=rating
# filmtrust dataset is saved by text
# text, arff is accepted
data.model.format=text
# the ratio of trainset
# this value should in (0,1)
data.splitter.trainset.ratio=0.8

# Detailed configuration of loocv, given, KCV 
# is written in User Guide 

# set the random seed for reproducing the results (split data, init parameters and other methods using random)
# default is set 1l
# if do not set ,just use System.currentTimeMillis() as the seed and could not reproduce the results.
rec.random.seed=1

# binarize threshold mainly used in ranking
# -1.0 - maxRate, binarize rate into -1.0 and 1.0
# binThold = -1.0， do nothing
# binThold = value, rating > value is changed to 1.0 other is 0.0, mainly used in ranking
# for PGM 0.0 maybe a better choose
data.convert.binarize.threshold=-1.0

# evaluation the result or not
rec.eval.enable=true

# specifies evaluators
# rec.eval.classes=auc,precision,recall...
# if rec.eval.class is blank 
# every evaluator will be calculated
# rec.eval.classes=auc,precision,recall

# evaluator value set is written in User Guide
# if this algorithm is ranking only true or false




#can use user,item,social similarity, default value is user, maximum values:user,item,social
#rec.recommender.similarities=user

rec.similarity.class=pcc
rec.neighbors.knn.number=50
rec.recommender.class=userknn
rec.recommender.similarities=user
rec.filter.class=generic
rec.similarity.shrinkage=10

But both precision and ncdg are NaN.

[SunYatong]

Ranking evaluator requires an additional "topN" configuration.

        // evaluation
        RecommenderEvaluator precisionEvaluator = new PrecisionEvaluator();
        RecommenderEvaluator normalizedDCGEvaluator = new NormalizedDCGEvaluator();
        precisionEvaluator.setTopN(10);
        normalizedDCGEvaluator.setTopN(10);