forked from D-Net/dnet-hadoop
next step of MAG conversion implemented
This commit is contained in:
parent
934ad570e0
commit
b771d67e9d
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@ -21,11 +21,15 @@ object DoiBoostMappingUtil {
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def generateDataInfo(): DataInfo = {
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generateDataInfo("0.9")
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}
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def generateDataInfo(trust:String): DataInfo = {
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val di = new DataInfo
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di.setDeletedbyinference(false)
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di.setInferred(false)
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di.setInvisible(false)
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di.setTrust("0.9")
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di.setTrust(trust)
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di.setProvenanceaction(createQualifier("sysimport:actionset", "dnet:provenanceActions"))
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di
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}
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@ -140,6 +140,15 @@ case object Crossref2Oaf {
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result.setRelevantdate(List(createdDate, postedDate, acceptedDate, publishedOnlineDate, publishedPrintDate).filter(p => p != null).asJava)
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//Mapping Subject
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val subjectList:List[String] = (json \ "subject").extractOrElse[List[String]](List())
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if (subjectList.nonEmpty) {
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result.setSubject(subjectList.map(s=> createSP(s, "keywords", "dnet:subject_classification_typologies")).asJava)
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}
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//Mapping AUthor
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val authorList: List[mappingAuthor] = (json \ "author").extractOrElse[List[mappingAuthor]](List())
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result.setAuthor(authorList.map(a => generateAuhtor(a.given.orNull, a.family, a.ORCID.orNull)).asJava)
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@ -40,6 +40,9 @@ case class MagPaperUrl(PaperId: Long, SourceType: Option[Int], SourceUrl: Option
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case class MagUrl(PaperId: Long, instances: List[String])
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case class MagSubject(FieldOfStudyId:Long, DisplayName:String, MainType:Option[String], Score:Float){}
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case class MagFieldOfStudy(PaperId:Long, subjects:List[MagSubject]) {}
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case class MagJournal(JournalId: Long, Rank: Option[Int], NormalizedName: Option[String], DisplayName: Option[String], Issn: Option[String], Publisher: Option[String], Webpage: Option[String], PaperCount: Option[Long], CitationCount: Option[Long], CreatedDate: Option[java.sql.Timestamp]) {}
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@ -135,6 +138,8 @@ case object ConversionUtil {
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j.setIssnPrinted(journal.Issn.get)
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pub.setJournal(j)
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}
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pub.setCollectedfrom(List(createMAGCollectedFrom()).asJava)
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pub.setDataInfo(generateDataInfo())
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pub
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}
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@ -1,20 +1,18 @@
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package eu.dnetlib.doiboost.mag
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.schema.oaf.Publication
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import eu.dnetlib.doiboost.DoiBoostMappingUtil.asField
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import eu.dnetlib.dhp.schema.oaf.{Publication, StructuredProperty}
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import eu.dnetlib.doiboost.DoiBoostMappingUtil
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import eu.dnetlib.doiboost.DoiBoostMappingUtil.{asField, createSP}
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import org.apache.commons.io.IOUtils
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import org.apache.spark.SparkConf
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import org.apache.spark.rdd.RDD
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import org.apache.spark.sql.{Dataset, Encoder, Encoders, SaveMode, SparkSession}
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import org.slf4j.{Logger, LoggerFactory}
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import org.apache.spark.sql.functions._
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import scala.collection.JavaConverters._
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object SparkPreProcessMAG {
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def main(args: Array[String]): Unit = {
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val logger: Logger = LoggerFactory.getLogger(getClass)
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@ -31,110 +29,138 @@ object SparkPreProcessMAG {
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val sourcePath = parser.get("sourcePath")
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import spark.implicits._
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implicit val mapEncoderPubs: Encoder[Publication] = org.apache.spark.sql.Encoders.kryo[Publication]
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implicit val tupleForJoinEncoder = Encoders.tuple(Encoders.STRING, mapEncoderPubs)
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implicit val tupleForJoinEncoder: Encoder[(String, Publication)] = Encoders.tuple(Encoders.STRING, mapEncoderPubs)
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// logger.info("Phase 1) make uninque DOI in Papers:")
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// val d: Dataset[MagPapers] = spark.read.load(s"${parser.get("sourcePath")}/Papers").as[MagPapers]
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//
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//
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// // Filtering Papers with DOI, and since for the same DOI we have multiple version of item with different PapersId we get the last one
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// val result: RDD[MagPapers] = d.where(col("Doi").isNotNull).rdd.map { p: MagPapers => Tuple2(p.Doi, p) }.reduceByKey { case (p1: MagPapers, p2: MagPapers) =>
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// var r = if (p1 == null) p2 else p1
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// if (p1 != null && p2 != null) {
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// if (p1.CreatedDate != null && p2.CreatedDate != null) {
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// if (p1.CreatedDate.before(p2.CreatedDate))
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// r = p1
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// else
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// r = p2
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// } else {
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// r = if (p1.CreatedDate == null) p2 else p1
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// }
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// }
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// r
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// }.map(_._2)
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//
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// val distinctPaper: Dataset[MagPapers] = spark.createDataset(result)
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// distinctPaper.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/Papers_distinct")
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// logger.info(s"Total number of element: ${result.count()}")
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//
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// logger.info("Phase 3) Group Author by PaperId")
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// val authors = spark.read.load(s"$sourcePath/Authors").as[MagAuthor]
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//
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// val affiliation = spark.read.load(s"$sourcePath/Affiliations").as[MagAffiliation]
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//
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// val paperAuthorAffiliation = spark.read.load(s"$sourcePath/PaperAuthorAffiliations").as[MagPaperAuthorAffiliation]
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//
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//
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// paperAuthorAffiliation.joinWith(authors, paperAuthorAffiliation("AuthorId").equalTo(authors("AuthorId")))
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// .map { case (a: MagPaperAuthorAffiliation, b: MagAuthor) => (a.AffiliationId, MagPaperAuthorDenormalized(a.PaperId, b, null)) }
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// .joinWith(affiliation, affiliation("AffiliationId").equalTo(col("_1")), "left")
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// .map(s => {
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// val mpa = s._1._2
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// val af = s._2
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// if (af != null) {
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// MagPaperAuthorDenormalized(mpa.PaperId, mpa.author, af.DisplayName)
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// } else
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// mpa
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// }).groupBy("PaperId").agg(collect_list(struct($"author", $"affiliation")).as("authors"))
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// .write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_1_paper_authors")
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//
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// logger.info("Phase 4) create First Version of publication Entity with Paper Journal and Authors")
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//
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// val journals = spark.read.load(s"$sourcePath/Journals").as[MagJournal]
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//
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// val papers = spark.read.load((s"${parser.get("targetPath")}/Papers_distinct")).as[MagPapers]
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//
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// val paperWithAuthors = spark.read.load(s"${parser.get("targetPath")}/merge_step_1_paper_authors").as[MagPaperWithAuthorList]
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//
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// val firstJoin = papers.joinWith(journals, papers("JournalId").equalTo(journals("JournalId")), "left")
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// firstJoin.joinWith(paperWithAuthors, firstJoin("_1.PaperId").equalTo(paperWithAuthors("PaperId")), "left")
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// .map { a: ((MagPapers, MagJournal), MagPaperWithAuthorList) => ConversionUtil.createOAFFromJournalAuthorPaper(a) }.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_2")
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//
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//
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// var magPubs: Dataset[(String, Publication)] = spark.read.load(s"${parser.get("targetPath")}/merge_step_2").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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//
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// val paperUrlDataset = spark.read.load(s"$sourcePath/PaperUrls").as[MagPaperUrl].groupBy("PaperId").agg(collect_list(struct("sourceUrl")).as("instances")).as[MagUrl]
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//
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//
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// logger.info("Phase 5) enrich publication with URL and Instances")
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//
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// magPubs.joinWith(paperUrlDataset, col("_1").equalTo(paperUrlDataset("PaperId")), "left")
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// .map { a: ((String, Publication), MagUrl) => ConversionUtil.addInstances((a._1._2, a._2)) }
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// .write.mode(SaveMode.Overwrite)
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// .save(s"${parser.get("targetPath")}/merge_step_3")
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//
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//
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// logger.info("Phase 6) Enrich Publication with description")
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// val pa = spark.read.load(s"${parser.get("sourcePath")}/PaperAbstractsInvertedIndex").as[MagPaperAbstract]
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// pa.map(ConversionUtil.transformPaperAbstract).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/PaperAbstract")
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//
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// val paperAbstract = spark.read.load((s"${parser.get("targetPath")}/PaperAbstract")).as[MagPaperAbstract]
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//
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//
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// magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_3").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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//
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// magPubs.joinWith(paperAbstract, col("_1").equalTo(paperAbstract("PaperId")), "left").map(p => {
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// val pub = p._1._2
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// val abst = p._2
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// if (abst != null) {
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// pub.setDescription(List(asField(abst.IndexedAbstract)).asJava)
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// }
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// pub
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// }
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// ).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_4")
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//
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logger.info("Phase 7) Enrich Publication with FieldOfStudy")
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val magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_4").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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val fos = spark.read.load(s"$sourcePath/FieldsOfStudy").select($"FieldOfStudyId".alias("fos"), $"DisplayName", $"MainType")
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val pfos = spark.read.load(s"$sourcePath/PaperFieldsOfStudy")
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val paperField = pfos.joinWith(fos, fos("fos").equalTo(pfos("FieldOfStudyId")))
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.select($"_1.FieldOfStudyId", $"_2.DisplayName", $"_2.MainType", $"_1.PaperId", $"_1.Score")
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.groupBy($"PaperId").agg(collect_list(struct($"FieldOfStudyId", $"DisplayName", $"MainType", $"Score")).as("subjects"))
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.as[MagFieldOfStudy]
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logger.info("Phase 1) make uninque DOI in Papers:")
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val d: Dataset[MagPapers] = spark.read.load(s"${parser.get("sourcePath")}/Papers").as[MagPapers]
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// Filtering Papers with DOI, and since for the same DOI we have multiple version of item with different PapersId we get the last one
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val result: RDD[MagPapers] = d.where(col("Doi").isNotNull).rdd.map { p: MagPapers => Tuple2(p.Doi, p) }.reduceByKey { case (p1: MagPapers, p2: MagPapers) =>
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var r = if (p1 == null) p2 else p1
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if (p1 != null && p2 != null) {
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if (p1.CreatedDate != null && p2.CreatedDate != null) {
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if (p1.CreatedDate.before(p2.CreatedDate))
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r = p1
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else
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r = p2
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} else {
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r = if (p1.CreatedDate == null) p2 else p1
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magPubs.joinWith(paperField, col("_1").equalTo(paperField("PaperId")), "left").
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map(item => {
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val publication = item._1._2
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val fieldOfStudy = item._2
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if (fieldOfStudy != null && fieldOfStudy.subjects != null && fieldOfStudy.subjects.nonEmpty) {
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val p: List[StructuredProperty] = fieldOfStudy.subjects.flatMap(s => {
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val s1 = createSP(s.DisplayName, "keywords", "dnet:subject_classification_typologies")
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val di = DoiBoostMappingUtil.generateDataInfo(s.Score.toString)
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var resList: List[StructuredProperty] = List(s1)
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if (s.MainType.isDefined) {
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val maintp = s.MainType.get
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val s2 = createSP(s.MainType.get, "keywords", "dnet:subject_classification_typologies")
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s2.setDataInfo(di)
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resList = resList ::: List(s2)
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if (maintp.contains(".")) {
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val s3 = createSP(maintp.split("\\.").head, "keywords", "dnet:subject_classification_typologies")
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s3.setDataInfo(di)
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resList = resList ::: List(s3)
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}
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}
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resList
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})
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publication.setSubject(p.asJava)
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}
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}
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r
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}.map(_._2)
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val distinctPaper: Dataset[MagPapers] = spark.createDataset(result)
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distinctPaper.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/Papers_distinct")
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logger.info(s"Total number of element: ${result.count()}")
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logger.info("Phase 3) Group Author by PaperId")
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val authors = spark.read.load(s"$sourcePath/Authors").as[MagAuthor]
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val affiliation =spark.read.load(s"$sourcePath/Affiliations").as[MagAffiliation]
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val paperAuthorAffiliation =spark.read.load(s"$sourcePath/PaperAuthorAffiliations").as[MagPaperAuthorAffiliation]
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paperAuthorAffiliation.joinWith(authors, paperAuthorAffiliation("AuthorId").equalTo(authors("AuthorId")))
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.map{case (a:MagPaperAuthorAffiliation,b:MagAuthor )=> (a.AffiliationId,MagPaperAuthorDenormalized(a.PaperId, b, null)) }
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.joinWith(affiliation, affiliation("AffiliationId").equalTo(col("_1")), "left")
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.map(s => {
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val mpa = s._1._2
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val af = s._2
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if (af!= null) {
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MagPaperAuthorDenormalized(mpa.PaperId, mpa.author, af.DisplayName)
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} else
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mpa
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}).groupBy("PaperId").agg(collect_list(struct($"author", $"affiliation")).as("authors"))
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.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_1_paper_authors")
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logger.info("Phase 4) create First Version of publication Entity with Paper Journal and Authors")
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val journals = spark.read.load(s"$sourcePath/Journals").as[MagJournal]
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val papers =spark.read.load((s"${parser.get("targetPath")}/Papers_distinct")).as[MagPapers]
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val paperWithAuthors = spark.read.load(s"${parser.get("targetPath")}/merge_step_1_paper_authors").as[MagPaperWithAuthorList]
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val firstJoin =papers.joinWith(journals, papers("JournalId").equalTo(journals("JournalId")),"left")
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firstJoin.joinWith(paperWithAuthors, firstJoin("_1.PaperId").equalTo(paperWithAuthors("PaperId")), "left")
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.map { a: ((MagPapers, MagJournal), MagPaperWithAuthorList) => ConversionUtil.createOAFFromJournalAuthorPaper(a) }.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_2")
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var magPubs:Dataset[(String,Publication)] = spark.read.load(s"${parser.get("targetPath")}/merge_step_2").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String,Publication)]
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val paperUrlDataset = spark.read.load(s"$sourcePath/PaperUrls").as[MagPaperUrl].groupBy("PaperId").agg(collect_list(struct("sourceUrl")).as("instances")).as[MagUrl]
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logger.info("Phase 5) enrich publication with URL and Instances")
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magPubs.joinWith(paperUrlDataset, col("_1").equalTo(paperUrlDataset("PaperId")), "left")
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.map{a:((String,Publication), MagUrl) => ConversionUtil.addInstances((a._1._2, a._2))}
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.write.mode(SaveMode.Overwrite)
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.save(s"${parser.get("targetPath")}/merge_step_3")
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logger.info("Phase 6) Enrich Publication with description")
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val pa = spark.read.load(s"${parser.get("sourcePath")}/PaperAbstractsInvertedIndex").as[MagPaperAbstract]
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pa.map(ConversionUtil.transformPaperAbstract).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/PaperAbstract")
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val paperAbstract =spark.read.load((s"${parser.get("targetPath")}/PaperAbstract")).as[MagPaperAbstract]
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magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_3").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String,Publication)]
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magPubs.joinWith(paperAbstract,col("_1").equalTo(paperAbstract("PaperId")), "left").map(p=>
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{
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val pub = p._1._2
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val abst = p._2
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if (abst!= null) {
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pub.setDescription(List(asField(abst.IndexedAbstract)).asJava)
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}
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pub
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}
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).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_4")
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publication
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}).map{s:Publication => s}(Encoders.bean(classOf[Publication])).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/mag_publication")
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}
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}
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@ -22,7 +22,7 @@
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</property>
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</parameters>
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<start to="ResetWorkingPath"/>
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<start to="PreprocessMag"/>
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<kill name="Kill">
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@ -24,47 +24,21 @@ class MAGMappingTest {
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val mapper = new ObjectMapper()
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@Test
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def testMAGCSV(): Unit = {
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// SparkPreProcessMAG.main("-m local[*] -s /data/doiboost/mag/datasets -t /data/doiboost/mag/datasets/preprocess".split(" "))
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val sparkConf: SparkConf = new SparkConf
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val spark: SparkSession = SparkSession.builder()
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.config(sparkConf)
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.appName(getClass.getSimpleName)
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.master("local[*]")
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.getOrCreate()
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import spark.implicits._
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def testSplitter():Unit = {
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val s = "sports.team"
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implicit val mapEncoderPubs: Encoder[Publication] = org.apache.spark.sql.Encoders.kryo[Publication]
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implicit val longBarEncoder = Encoders.tuple(Encoders.STRING, mapEncoderPubs)
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val sourcePath = "/data/doiboost/mag/input"
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mapper.getSerializationConfig.enable(SerializationConfig.Feature.INDENT_OUTPUT)
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val magOAF = spark.read.load("$sourcePath/merge_step_4").as[Publication]
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println(magOAF.first().getOriginalId)
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magOAF.map(k => (ConversionUtil.extractMagIdentifier(k.getOriginalId.asScala),k)).as[(String,Publication)].show()
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println((ConversionUtil.extractMagIdentifier(magOAF.first().getOriginalId.asScala)))
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val magIDRegex: Regex = "^[0-9]+$".r
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||||
println(magIDRegex.findFirstMatchIn("suca").isDefined)
|
||||
if (s.contains(".")) {
|
||||
println(s.split("\\.")head)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
@Test
|
||||
def buildInvertedIndexTest(): Unit = {
|
||||
val json_input = Source.fromInputStream(getClass.getResourceAsStream("invertedIndex.json")).mkString
|
||||
|
|
|
|||
Loading…
Reference in New Issue