| package io.prediction.algorithms.mahout.itemrec.knnuserbased |
| |
| import org.specs2.mutable._ |
| import com.github.nscala_time.time.Imports._ |
| import scala.io.Source |
| import java.io.File |
| import java.io.FileWriter |
| import java.io.BufferedWriter |
| |
| import io.prediction.algorithms.mahout.itemrec.MahoutJob |
| import io.prediction.algorithms.mahout.itemrec.TestUtils |
| |
| class KNNUserBasedJobSpec extends Specification { |
| |
| val ratingsCSV = List( |
| "1,1,3", |
| "4,1,5", |
| "1,2,3", |
| "3,2,2", |
| "4,2,4", |
| "1,3,5", |
| "2,3,1", |
| "3,3,2", |
| "2,4,2", |
| "3,4,3", |
| "4,4,2" |
| ) |
| |
| val itemsIndexTSV = List( |
| s"1\ti1\tt1,t2\t12345000", |
| s"2\ti2\tt1\t12346000", |
| s"3\ti3\tt2,t3\t12346100", |
| s"4\ti4\tt3\t12347100" |
| ) |
| |
| val appid = 25 |
| val engineid = 31 |
| val algoid = 32 |
| |
| val jobName = |
| "io.prediction.algorithms.mahout.itemrec.knnuserbased.KNNUserBasedJob" |
| |
| "KNNUserBasedJob with unseenOnly=false" should { |
| val testDir = "/tmp/pio_test/KNNUserBasedJob/unseenOnlyfalse/" |
| val inputFile = s"${testDir}ratings.csv" |
| val itemsFile = s"${testDir}itemsIndex.tsv" |
| val outputFile = s"${testDir}predicted.tsv" |
| |
| val testDirFile = new File(testDir) |
| testDirFile.mkdirs() |
| |
| val jobArgs = Map( |
| "input" -> inputFile, |
| "itemsFile" -> itemsFile, |
| "output" -> outputFile, |
| "appid" -> appid, |
| "engineid" -> engineid, |
| "algoid" -> algoid, |
| "booleanData" -> false, |
| "numRecommendations" -> 5, |
| "nearestN" -> 10, |
| "userSimilarity" -> "LogLikelihoodSimilarity", |
| "weighted" -> false, |
| "minSimilarity" -> 5e-324, |
| "samplingRate" -> 1, |
| "unseenOnly" -> false, |
| "recommendationTime" -> 0 |
| ) |
| |
| TestUtils.writeToFile(ratingsCSV, inputFile) |
| TestUtils.writeToFile(itemsIndexTSV, itemsFile) |
| |
| val predictedExpected = List( |
| "1\t[2:3.0,4:2.2805154,3:1.3898838]", |
| "2\t[1:4.0,3:3.5,2:3.0,4:2.5]", |
| "3\t[1:4.0,2:3.5,3:2.559535,4:2.0]", |
| "4\t[2:2.5,3:2.402577,4:2.3898838]" |
| ) |
| |
| MahoutJob.main(Array(jobName) ++ TestUtils.argMapToArray(jobArgs)) |
| |
| "generate prediction output correctly" in { |
| val predicted = Source.fromFile(outputFile) |
| .getLines().toList |
| |
| predicted must containTheSameElementsAs(predictedExpected) |
| } |
| |
| } |
| |
| "KNNUserBasedJob with unseenOnly=false and subset itemsIndex" should { |
| val testDir = "/tmp/pio_test/KNNUserBasedJob/unseenOnlyfalseSubSetItemsIndex/" |
| val inputFile = s"${testDir}ratings.csv" |
| val itemsFile = s"${testDir}itemsIndex.tsv" |
| val outputFile = s"${testDir}predicted.tsv" |
| |
| val itemsIndexTSV = List( |
| s"2\ti2\tt1\t12346000", |
| s"4\ti4\tt3\t12347100" |
| ) |
| |
| val testDirFile = new File(testDir) |
| testDirFile.mkdirs() |
| |
| val jobArgs = Map( |
| "input" -> inputFile, |
| "itemsFile" -> itemsFile, |
| "output" -> outputFile, |
| "appid" -> appid, |
| "engineid" -> engineid, |
| "algoid" -> algoid, |
| "booleanData" -> false, |
| "numRecommendations" -> 5, |
| "nearestN" -> 10, |
| "userSimilarity" -> "LogLikelihoodSimilarity", |
| "weighted" -> false, |
| "minSimilarity" -> 5e-324, |
| "samplingRate" -> 1, |
| "unseenOnly" -> false, |
| "recommendationTime" -> 0 |
| ) |
| |
| TestUtils.writeToFile(ratingsCSV, inputFile) |
| TestUtils.writeToFile(itemsIndexTSV, itemsFile) |
| |
| val predictedExpected = List( |
| "1\t[2:3.0,4:2.2805154]", |
| "2\t[2:3.0,4:2.5]", |
| "3\t[2:3.5,4:2.0]", |
| "4\t[2:2.5,4:2.3898838]" |
| ) |
| |
| MahoutJob.main(Array(jobName) ++ TestUtils.argMapToArray(jobArgs)) |
| |
| "generate prediction output correctly" in { |
| val predicted = Source.fromFile(outputFile) |
| .getLines().toList |
| |
| predicted must containTheSameElementsAs(predictedExpected) |
| } |
| |
| } |
| |
| "KNNUserBasedJob with unseenOnly=true" should { |
| val testDir = "/tmp/pio_test/KNNUserBasedJob/unseenOnlytrue/" |
| val inputFile = s"${testDir}ratings.csv" |
| val itemsFile = s"${testDir}itemsIndex.tsv" |
| val outputFile = s"${testDir}predicted.tsv" |
| |
| val testDirFile = new File(testDir) |
| testDirFile.mkdirs() |
| |
| val jobArgs = Map( |
| "input" -> inputFile, |
| "itemsFile" -> itemsFile, |
| "output" -> outputFile, |
| "appid" -> appid, |
| "engineid" -> engineid, |
| "algoid" -> algoid, |
| "booleanData" -> false, |
| "numRecommendations" -> 5, |
| "nearestN" -> 10, |
| "userSimilarity" -> "LogLikelihoodSimilarity", |
| "weighted" -> false, |
| "minSimilarity" -> 5e-324, |
| "samplingRate" -> 1, |
| "unseenOnly" -> true, |
| "recommendationTime" -> 0 |
| ) |
| |
| TestUtils.writeToFile(ratingsCSV, inputFile) |
| TestUtils.writeToFile(itemsIndexTSV, itemsFile) |
| |
| val predictedExpected = List( |
| "1\t[4:2.2805154]", |
| "2\t[1:4.0,2:3.0]", |
| "3\t[1:4.0]", |
| "4\t[3:2.402577]" |
| ) |
| |
| MahoutJob.main(Array(jobName) ++ TestUtils.argMapToArray(jobArgs)) |
| |
| "generate prediction output correctly" in { |
| val predicted = Source.fromFile(outputFile) |
| .getLines().toList |
| |
| predicted must containTheSameElementsAs(predictedExpected) |
| } |
| } |
| |
| "KNNUserBasedJob with unseenOnly=true and seenFile" should { |
| val testDir = "/tmp/pio_test/KNNUserBasedJob/unseenOnlytrueSeenFile/" |
| val inputFile = s"${testDir}ratings.csv" |
| val itemsFile = s"${testDir}itemsIndex.tsv" |
| val outputFile = s"${testDir}predicted.tsv" |
| val seenFile = s"${testDir}seen.csv" |
| |
| val testDirFile = new File(testDir) |
| testDirFile.mkdirs() |
| |
| val seenCSV = List( |
| "1,1", |
| "4,1", |
| "1,2", |
| "2,1", |
| "3,1" |
| ) |
| |
| val jobArgs = Map( |
| "input" -> inputFile, |
| "itemsFile" -> itemsFile, |
| "output" -> outputFile, |
| "appid" -> appid, |
| "engineid" -> engineid, |
| "algoid" -> algoid, |
| "booleanData" -> false, |
| "numRecommendations" -> 5, |
| "nearestN" -> 10, |
| "userSimilarity" -> "LogLikelihoodSimilarity", |
| "weighted" -> false, |
| "minSimilarity" -> 5e-324, |
| "samplingRate" -> 1, |
| "unseenOnly" -> true, |
| "seenFile" -> seenFile, |
| "recommendationTime" -> 0 |
| ) |
| |
| TestUtils.writeToFile(ratingsCSV, inputFile) |
| TestUtils.writeToFile(itemsIndexTSV, itemsFile) |
| TestUtils.writeToFile(seenCSV, seenFile) |
| |
| val predictedExpected = List( |
| "1\t[4:2.2805154,3:1.3898838]", |
| "2\t[3:3.5,2:3.0,4:2.5]", |
| "3\t[2:3.5,3:2.559535,4:2.0]", |
| "4\t[2:2.5,3:2.402577,4:2.3898838]" |
| ) |
| |
| MahoutJob.main(Array(jobName) ++ TestUtils.argMapToArray(jobArgs)) |
| |
| "generate prediction output correctly" in { |
| val predicted = Source.fromFile(outputFile) |
| .getLines().toList |
| |
| predicted must containTheSameElementsAs(predictedExpected) |
| } |
| } |
| |
| "KNNUserBasedJob with unseenOnly=true and seenFile and subset itemsIndex" should { |
| val testDir = "/tmp/pio_test/KNNUserBasedJob/unseenOnlytrueSeenFileSubSetItemsIndex/" |
| val inputFile = s"${testDir}ratings.csv" |
| val itemsFile = s"${testDir}itemsIndex.tsv" |
| val outputFile = s"${testDir}predicted.tsv" |
| val seenFile = s"${testDir}seen.csv" |
| |
| val testDirFile = new File(testDir) |
| testDirFile.mkdirs() |
| |
| val itemsIndexTSV = List( |
| s"1\ti1\tt1,t2\t12345000", |
| s"2\ti2\tt1\t12346000", |
| s"4\ti4\tt3\t12347100" |
| ) |
| |
| val seenCSV = List( |
| "1,1", |
| "4,1", |
| "1,2", |
| "2,1", |
| "3,1" |
| ) |
| |
| val jobArgs = Map( |
| "input" -> inputFile, |
| "itemsFile" -> itemsFile, |
| "output" -> outputFile, |
| "appid" -> appid, |
| "engineid" -> engineid, |
| "algoid" -> algoid, |
| "booleanData" -> false, |
| "numRecommendations" -> 5, |
| "nearestN" -> 10, |
| "userSimilarity" -> "LogLikelihoodSimilarity", |
| "weighted" -> false, |
| "minSimilarity" -> 5e-324, |
| "samplingRate" -> 1, |
| "unseenOnly" -> true, |
| "seenFile" -> seenFile, |
| "recommendationTime" -> 0 |
| ) |
| |
| TestUtils.writeToFile(ratingsCSV, inputFile) |
| TestUtils.writeToFile(itemsIndexTSV, itemsFile) |
| TestUtils.writeToFile(seenCSV, seenFile) |
| |
| val predictedExpected = List( |
| "1\t[4:2.2805154]", |
| "2\t[2:3.0,4:2.5]", |
| "3\t[2:3.5,4:2.0]", |
| "4\t[2:2.5,4:2.3898838]" |
| ) |
| |
| MahoutJob.main(Array(jobName) ++ TestUtils.argMapToArray(jobArgs)) |
| |
| "generate prediction output correctly" in { |
| val predicted = Source.fromFile(outputFile) |
| .getLines().toList |
| |
| predicted must containTheSameElementsAs(predictedExpected) |
| } |
| } |
| |
| "KNNUserBasedJob with unseenOnly=true and empty seenFile" should { |
| val testDir = "/tmp/pio_test/KNNUserBasedJob/unseenOnlytrueEmptySeenFile/" |
| val inputFile = s"${testDir}ratings.csv" |
| val itemsFile = s"${testDir}itemsIndex.tsv" |
| val outputFile = s"${testDir}predicted.tsv" |
| val seenFile = s"${testDir}seen.csv" |
| |
| val testDirFile = new File(testDir) |
| testDirFile.mkdirs() |
| |
| val seenCSV = List() |
| |
| val jobArgs = Map( |
| "input" -> inputFile, |
| "itemsFile" -> itemsFile, |
| "output" -> outputFile, |
| "appid" -> appid, |
| "engineid" -> engineid, |
| "algoid" -> algoid, |
| "booleanData" -> false, |
| "numRecommendations" -> 5, |
| "nearestN" -> 10, |
| "userSimilarity" -> "LogLikelihoodSimilarity", |
| "weighted" -> false, |
| "minSimilarity" -> 5e-324, |
| "samplingRate" -> 1, |
| "unseenOnly" -> true, |
| "seenFile" -> seenFile, |
| "recommendationTime" -> 0 |
| ) |
| |
| TestUtils.writeToFile(ratingsCSV, inputFile) |
| TestUtils.writeToFile(itemsIndexTSV, itemsFile) |
| TestUtils.writeToFile(seenCSV, seenFile) |
| |
| val predictedExpected = List( |
| "1\t[2:3.0,4:2.2805154,3:1.3898838]", |
| "2\t[1:4.0,3:3.5,2:3.0,4:2.5]", |
| "3\t[1:4.0,2:3.5,3:2.559535,4:2.0]", |
| "4\t[2:2.5,3:2.402577,4:2.3898838]" |
| ) |
| |
| MahoutJob.main(Array(jobName) ++ TestUtils.argMapToArray(jobArgs)) |
| |
| "generate prediction output correctly" in { |
| val predicted = Source.fromFile(outputFile) |
| .getLines().toList |
| |
| predicted must containTheSameElementsAs(predictedExpected) |
| } |
| } |
| |
| } |
