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vikumn/eval_final.groovy

Last active Aug 29, 2015
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import org.grouplens.lenskit.knn.item.*
import org.grouplens.lenskit.baseline.*
import org.grouplens.lenskit.transform.normalize.*
import org.grouplens.lenskit.eval.metrics.topn.*;
import org.grouplens.lenskit.ItemScorer
import org.grouplens.lenskit.baseline.ItemMeanRatingItemScorer
import org.grouplens.lenskit.core.Transient
import org.grouplens.lenskit.data.dao.EventDAO
import org.grouplens.lenskit.data.dao.UserDAO
import org.grouplens.lenskit.eval.data.traintest.QueryData
import org.grouplens.lenskit.eval.metrics.predict.*
import org.grouplens.lenskit.external.ExternalProcessItemScorerBuilder
import javax.inject.Inject
import javax.inject.Provider
/**
* Shim class to run item-mean.py to build an ItemScorer.
*/
class ExternalItemMeanScorerBuilder implements Provider<ItemScorer>{
EventDAO eventDAO
UserDAO userDAO
@Inject
public ExternalItemMeanScorerBuilder(@Transient EventDAO events,
@Transient @QueryData UserDAO users) {
eventDAO = events
userDAO = users
}
@Override
ItemScorer get() {
def wrk = new File("external-scratch")
wrk.mkdirs()
def builder = new ExternalProcessItemScorerBuilder()
// Note: don't use file names because it will interact badly with crossfolding
return builder.setWorkingDir(wrk)
.setExecutable("python") //can be "R", "matlab", "ruby" etc
.addArgument("../item_mean.py") //relative (or absolute) location of sample recommender
.addArgument("--for-users")
.addRatingFileArgument(eventDAO)
.addUserFileArgument(userDAO)
.build()
}
}
trainTest {
dataset crossfold("ml-100k") {
source csvfile("ml-100k/u.data") { //relative (or absolute) path to the dataset
delimiter "\t"
domain {
minimum 1.0
maximum 5.0
precision 1.0
}
}
}
algorithm("PersMean") {
bind ItemScorer to UserMeanItemScorer
bind (UserMeanBaseline, ItemScorer) to ItemMeanRatingItemScorer
}
algorithm("ExternalAlgorithm") {
bind ItemScorer toProvider ExternalItemMeanScorerBuilder
}
metric RMSEPredictMetric
metric topNnDCG {
listSize 10
candidates ItemSelectors.allItems()
exclude ItemSelectors.trainingItems()
}
output "eval-results.csv"
}
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