diff --git a/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala b/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala index 25f0ff381..1b1c850ba 100644 --- a/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala +++ b/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala @@ -70,7 +70,7 @@ case object Crossref2Oaf { "reference-book" -> "0002 Book", "monograph" -> "0002 Book", "journal-article" -> "0001 Article", - "dissertation" -> "0006 Doctoral thesis", + "dissertation" -> "0044 Thesis", "other" -> "0038 Other literature type", "peer-review" -> "0015 Review", "proceedings" -> "0004 Conference object", @@ -206,11 +206,16 @@ case object Crossref2Oaf { else { instance.setDateofacceptance(asField(createdDate.getValue)) } - val s: String = (json \ "URL").extract[String] - val links: List[String] = ((for {JString(url) <- json \ "link" \ "URL"} yield url) ::: List(s)).filter(p => p != null).distinct - if (links.nonEmpty) { - instance.setUrl(links.asJava) - } + val s: List[String] = List("https://doi.org/" + doi) +// val links: List[String] = ((for {JString(url) <- json \ "link" \ "URL"} yield url) ::: List(s)).filter(p => p != null && p.toLowerCase().contains(doi.toLowerCase())).distinct +// if (links.nonEmpty) { +// instance.setUrl(links.asJava) +// } + if(s.nonEmpty) + { + instance.setUrl(s.asJava) + } + result.setInstance(List(instance).asJava) //IMPORTANT diff --git a/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/mag/SparkProcessMAG.scala b/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/mag/SparkProcessMAG.scala index ecb389af8..016279787 100644 --- a/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/mag/SparkProcessMAG.scala +++ b/dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/mag/SparkProcessMAG.scala @@ -111,26 +111,9 @@ object SparkProcessMAG { .map(item => ConversionUtil.updatePubsWithConferenceInfo(item)) .write .mode(SaveMode.Overwrite) - .save(s"$workingPath/merge_step_2_conference") - - - magPubs= spark.read.load(s"$workingPath/merge_step_2_conference").as[Publication] - .map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)] - - val paperUrlDataset = spark.read.load(s"$sourcePath/PaperUrls").as[MagPaperUrl].groupBy("PaperId").agg(collect_list(struct("sourceUrl")).as("instances")).as[MagUrl] - - - logger.info("Phase 5) enrich publication with URL and Instances") - magPubs.joinWith(paperUrlDataset, col("_1").equalTo(paperUrlDataset("PaperId")), "left") - .map { a: ((String, Publication), MagUrl) => ConversionUtil.addInstances((a._1._2, a._2)) } - .write.mode(SaveMode.Overwrite) .save(s"$workingPath/merge_step_3") -// logger.info("Phase 6) Enrich Publication with description") -// val pa = spark.read.load(s"${parser.get("sourcePath")}/PaperAbstractsInvertedIndex").as[MagPaperAbstract] -// pa.map(ConversionUtil.transformPaperAbstract).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/PaperAbstract") - val paperAbstract = spark.read.load((s"$workingPath/PaperAbstract")).as[MagPaperAbstract] @@ -162,12 +145,14 @@ object SparkProcessMAG { .write.mode(SaveMode.Overwrite) .save(s"$workingPath/mag_publication") + spark.read.load(s"$workingPath/mag_publication").as[Publication] + .filter(p => p.getId == null) + .groupByKey(p => p.getId) + .reduceGroups((a:Publication, b:Publication) => ConversionUtil.mergePublication(a,b)) + .map(_._2) + .write.mode(SaveMode.Overwrite).save(s"$targetPath/magPublication") - val s:RDD[Publication] = spark.read.load(s"$workingPath/mag_publication").as[Publication] - .map(p=>Tuple2(p.getId, p)).rdd.reduceByKey((a:Publication, b:Publication) => ConversionUtil.mergePublication(a,b)) - .map(_._2) - spark.createDataset(s).as[Publication].write.mode(SaveMode.Overwrite).save(s"$targetPath/magPublication") } } diff --git a/dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/doiboost/crossref/CrossrefMappingTest.scala b/dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/doiboost/crossref/CrossrefMappingTest.scala index 75fb3f787..5ef92cfa4 100644 --- a/dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/doiboost/crossref/CrossrefMappingTest.scala +++ b/dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/doiboost/crossref/CrossrefMappingTest.scala @@ -612,4 +612,26 @@ class CrossrefMappingTest { } + @Test + def testMultipleURLs() :Unit = { + val json = Source.fromInputStream(getClass.getResourceAsStream("multiple_urls.json")).mkString + + + assertNotNull(json) + assertFalse(json.isEmpty); + + val resultList: List[Oaf] = Crossref2Oaf.convert(json) + + assertTrue(resultList.nonEmpty) + + + val item : Result = resultList.filter(p => p.isInstanceOf[Result]).head.asInstanceOf[Result] + + assertEquals(1, item.getInstance().size()) + assertEquals(1, item.getInstance().get(0).getUrl().size()) + assertEquals("https://doi.org/10.1016/j.jas.2019.105013", item.getInstance().get(0).getUrl().get(0)) + //println(mapper.writeValueAsString(item)) + + } + } diff --git a/dhp-workflows/dhp-doiboost/src/test/resources/eu/dnetlib/doiboost/crossref/multiple_urls.json b/dhp-workflows/dhp-doiboost/src/test/resources/eu/dnetlib/doiboost/crossref/multiple_urls.json new file mode 100644 index 000000000..5f90feac4 --- /dev/null +++ b/dhp-workflows/dhp-doiboost/src/test/resources/eu/dnetlib/doiboost/crossref/multiple_urls.json @@ -0,0 +1,614 @@ + +{ +"indexed": { +"date-parts": [ +[ +2021, +10, +31 +] +], +"date-time": "2021-10-31T15:48:01Z", +"timestamp": 1635695281393 +}, +"reference-count": 39, +"publisher": "Elsevier BV", +"license": [ +{ +"start": { +"date-parts": [ +[ +2019, +12, +1 +] +], +"date-time": "2019-12-01T00:00:00Z", +"timestamp": 1575158400000 +}, +"content-version": "tdm", +"delay-in-days": 0, +"URL": "https://www.elsevier.com/tdm/userlicense/1.0/" +}, +{ +"start": { +"date-parts": [ +[ +2019, +9, +13 +] +], +"date-time": "2019-09-13T00:00:00Z", +"timestamp": 1568332800000 +}, +"content-version": "vor", +"delay-in-days": 0, +"URL": "http://creativecommons.org/licenses/by/4.0/" +} +], +"funder": [ +{ +"DOI": "10.13039/100001182", +"name": "INSTAP", +"doi-asserted-by": "publisher" +}, +{ +"DOI": "10.13039/100014440", +"name": "Ministry of Science, Innovation and Universities", +"doi-asserted-by": "publisher", +"award": [ +"RYC-2016-19637" +] +}, +{ +"DOI": "10.13039/100010661", +"name": "European Union’s Horizon 2020", +"doi-asserted-by": "publisher", +"award": [ +"746446" +] +} +], +"content-domain": { +"domain": [ +"elsevier.com", +"sciencedirect.com" +], +"crossmark-restriction": true +}, +"short-container-title": [ +"Journal of Archaeological Science" +], +"published-print": { +"date-parts": [ +[ +2019, +12 +] +] +}, +"DOI": "10.1016/j.jas.2019.105013", +"type": "journal-article", +"created": { +"date-parts": [ +[ +2019, +9, +25 +] +], +"date-time": "2019-09-25T20:05:08Z", +"timestamp": 1569441908000 +}, +"page": "105013", +"update-policy": "http://dx.doi.org/10.1016/elsevier_cm_policy", +"source": "Crossref", +"is-referenced-by-count": 21, +"title": [ +"A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery" +], +"prefix": "10.1016", +"volume": "112", +"author": [ +{ +"given": "H.A.", +"family": "Orengo", +"sequence": "first", +"affiliation": [ + +] +}, +{ +"given": "A.", +"family": "Garcia-Molsosa", +"sequence": "additional", +"affiliation": [ + +] +} +], +"member": "78", +"reference": [ +{ +"key": "10.1016/j.jas.2019.105013_bib1", +"doi-asserted-by": "crossref", +"first-page": "85", +"DOI": "10.1080/17538947.2016.1250829", +"article-title": "Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications", +"volume": "10", +"author": "Agapiou", +"year": "2017", +"journal-title": "Int. 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The potential of small UAV's (drones) for site detection and heritage management in archaeological survey projects: a case study from Le Pianelle in the Tappino Valley, Molise (Italy)", +"volume": "22", +"author": "Stek", +"year": "2016", +"journal-title": "J. Cult. Herit." +}, +{ +"key": "10.1016/j.jas.2019.105013_bib37", +"series-title": "Side-by-Side Survey. Comparative Regional Studies in the Mediterranean World", +"first-page": "65", +"article-title": "Side-by-side and back to front: exploring intra-regional latitudinal and longitudinal comparability in survey data. Three case studies from Metaponto, southern Italy", +"author": "Thomson", +"year": "2004" +}, +{ +"key": "10.1016/j.jas.2019.105013_bib38", +"series-title": "Digital Discovery. Exploring New Frontiers in Human Heritage. Computer Applications and Quantitative Methods in Archaeology", +"article-title": "Computer vision and machine learning for archaeology", +"author": "van der Maaten", +"year": "2007" +}, +{ +"key": "10.1016/j.jas.2019.105013_bib39", +"doi-asserted-by": "crossref", +"first-page": "1114", +"DOI": "10.1111/j.1475-4754.2012.00667.x", +"article-title": "Computer vision-based orthophoto mapping of complex archaeological sites: the ancient quarry of Pitaranha (Portugal-Spain)", +"volume": "54", +"author": "Verhoeven", +"year": "2012", +"journal-title": "Archaeometry" +}, +{ +"key": "10.1016/j.jas.2019.105013_bib40", +"series-title": "A Guide for Salvage Archeology", +"author": "Wendorf", +"year": "1962" +} +], +"container-title": [ +"Journal of Archaeological Science" +], +"original-title": [ + +], +"language": "en", +"link": [ +{ +"URL": "https://api.elsevier.com/content/article/PII:S0305440319301001?httpAccept=text/xml", +"content-type": "text/xml", +"content-version": "vor", +"intended-application": "text-mining" +}, +{ +"URL": "https://api.elsevier.com/content/article/PII:S0305440319301001?httpAccept=text/plain", +"content-type": "text/plain", +"content-version": "vor", +"intended-application": "text-mining" +} +], +"deposited": { +"date-parts": [ +[ +2019, +11, +25 +] +], +"date-time": "2019-11-25T06:46:34Z", +"timestamp": 1574664394000 +}, +"score": 1, +"subtitle": [ + +], +"short-title": [ + +], +"issued": { +"date-parts": [ +[ +2019, +12 +] +] +}, +"references-count": 39, +"alternative-id": [ +"S0305440319301001" +], +"URL": "http://dx.doi.org/10.1016/j.jas.2019.105013", +"relation": { + +}, +"ISSN": [ +"0305-4403" +], +"issn-type": [ +{ +"value": "0305-4403", +"type": "print" +} +], +"subject": [ +"Archaeology", +"Archaeology" +], +"published": { +"date-parts": [ +[ +2019, +12 +] +] +}, +"assertion": [ +{ +"value": "Elsevier", +"name": "publisher", +"label": "This article is maintained by" +}, +{ +"value": "A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery", +"name": "articletitle", +"label": "Article Title" +}, +{ +"value": "Journal of Archaeological Science", +"name": "journaltitle", +"label": "Journal Title" +}, +{ +"value": "https://doi.org/10.1016/j.jas.2019.105013", +"name": "articlelink", +"label": "CrossRef DOI link to publisher maintained version" +}, +{ +"value": "article", +"name": "content_type", +"label": "Content Type" +}, +{ +"value": "© 2019 The Authors. Published by Elsevier Ltd.", +"name": "copyright", +"label": "Copyright" +} +], +"article-number": "105013" +} diff --git a/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveEntities.scala b/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveEntities.scala new file mode 100644 index 000000000..afd195ed0 --- /dev/null +++ b/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveEntities.scala @@ -0,0 +1,107 @@ +package eu.dnetlib.dhp.oa.graph.resolution + +import com.fasterxml.jackson.databind.ObjectMapper +import eu.dnetlib.dhp.application.ArgumentApplicationParser +import eu.dnetlib.dhp.common.HdfsSupport +import eu.dnetlib.dhp.schema.common.EntityType +import eu.dnetlib.dhp.schema.oaf.{OtherResearchProduct, Publication, Result, Software, Dataset => OafDataset} +import org.apache.commons.io.IOUtils +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.spark.SparkConf +import org.apache.spark.sql._ +import org.slf4j.{Logger, LoggerFactory} + +object SparkResolveEntities { + + val mapper = new ObjectMapper() + val entities = List(EntityType.dataset,EntityType.publication, EntityType.software, EntityType.otherresearchproduct) + + def main(args: Array[String]): Unit = { + val log: Logger = LoggerFactory.getLogger(getClass) + val conf: SparkConf = new SparkConf() + val parser = new ArgumentApplicationParser(IOUtils.toString(getClass.getResourceAsStream("/eu/dnetlib/dhp/oa/graph/resolution/resolve_params.json"))) + parser.parseArgument(args) + val spark: SparkSession = + SparkSession + .builder() + .config(conf) + .appName(getClass.getSimpleName) + .master(parser.get("master")).getOrCreate() + + + val graphBasePath = parser.get("graphBasePath") + log.info(s"graphBasePath -> $graphBasePath") + val workingPath = parser.get("workingPath") + log.info(s"workingPath -> $workingPath") + val unresolvedPath = parser.get("unresolvedPath") + log.info(s"unresolvedPath -> $unresolvedPath") + + val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration) + fs.mkdirs(new Path(workingPath)) + + resolveEntities(spark, workingPath, unresolvedPath) + generateResolvedEntities(spark, workingPath, graphBasePath) + + // TO BE conservative we keep the original entities in the working dir + // and save the resolved entities on the graphBasePath + //In future these lines of code should be removed + entities.foreach { + e => + fs.rename(new Path(s"$graphBasePath/$e"), new Path(s"$workingPath/${e}_old")) + fs.rename(new Path(s"$workingPath/resolvedGraph/$e"), new Path(s"$graphBasePath/$e")) + } + +} + + +def resolveEntities(spark: SparkSession, workingPath: String, unresolvedPath: String) = { + implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result]) + import spark.implicits._ + + val rPid: Dataset[(String, String)] = spark.read.load(s"$workingPath/relationResolvedPid").as[(String, String)] + val up: Dataset[(String, Result)] = spark.read.text(unresolvedPath).as[String].map(s => mapper.readValue(s, classOf[Result])).map(r => (r.getId, r))(Encoders.tuple(Encoders.STRING, resEncoder)) + + rPid.joinWith(up, rPid("_2").equalTo(up("_1")), "inner").map { + r => + val result = r._2._2 + val dnetId = r._1._1 + result.setId(dnetId) + result + }.write.mode(SaveMode.Overwrite).save(s"$workingPath/resolvedEntities") + } + + + def deserializeObject(input:String, entity:EntityType ) :Result = { + + entity match { + case EntityType.publication => mapper.readValue(input, classOf[Publication]) + case EntityType.dataset => mapper.readValue(input, classOf[OafDataset]) + case EntityType.software=> mapper.readValue(input, classOf[Software]) + case EntityType.otherresearchproduct=> mapper.readValue(input, classOf[OtherResearchProduct]) + } + } + + def generateResolvedEntities(spark:SparkSession, workingPath: String, graphBasePath:String) = { + + implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result]) + import spark.implicits._ + + val re:Dataset[Result] = spark.read.load(s"$workingPath/resolvedEntities").as[Result] + entities.foreach { + e => + + spark.read.text(s"$graphBasePath/$e").as[String] + .map(s => deserializeObject(s, e)) + .union(re) + .groupByKey(_.getId) + .reduceGroups { + (x, y) => + x.mergeFrom(y) + x + }.map(_._2) + .filter(r => r.getClass.getSimpleName.toLowerCase != "result") + .map(r => mapper.writeValueAsString(r))(Encoders.STRING) + .write.mode(SaveMode.Overwrite).option("compression", "gzip").text(s"$workingPath/resolvedGraph/$e") + } + } +} diff --git a/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveRelation.scala b/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveRelation.scala index db93bc43f..cd517dd5e 100644 --- a/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveRelation.scala +++ b/dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/resolution/SparkResolveRelation.scala @@ -96,6 +96,21 @@ object SparkResolveRelation { .text(s"$graphBasePath/relation") } + def extractInstanceCF(input: String): List[(String, String)] = { + implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats + lazy val json: json4s.JValue = parse(input) + val result: List[(String, String)] = for { + JObject(iObj) <- json \ "instance" + JField("collectedfrom", JObject(cf)) <- iObj + JField("instancetype", JObject(instancetype)) <- iObj + JField("value", JString(collectedFrom)) <- cf + JField("classname", JString(classname)) <- instancetype + } yield (classname, collectedFrom) + + result + + } + def extractPidsFromRecord(input: String): (String, List[(String, String)]) = { implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats @@ -108,14 +123,7 @@ object SparkResolveRelation { JField("classid", JString(pidType)) <- qualifier } yield (pidValue, pidType) - val alternateIds: List[(String, String)] = for { - JObject(pids) <- json \\ "alternateIdentifier" - JField("value", JString(pidValue)) <- pids - JField("qualifier", JObject(qualifier)) <- pids - JField("classid", JString(pidType)) <- qualifier - } yield (pidValue, pidType) - - (id, result ::: alternateIds) + (id, result) } @@ -128,7 +136,7 @@ object SparkResolveRelation { source != null } - private def extractPidResolvedTableFromJsonRDD(spark: SparkSession, graphPath: String, workingPath: String) = { + def extractPidResolvedTableFromJsonRDD(spark: SparkSession, graphPath: String, workingPath: String) = { import spark.implicits._ val d: RDD[(String, String)] = spark.sparkContext.textFile(s"$graphPath/*") diff --git a/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/oozie_app/workflow.xml b/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/oozie_app/workflow.xml index 52f0e6e9d..4773fc87c 100644 --- a/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/oozie_app/workflow.xml +++ b/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/oozie_app/workflow.xml @@ -4,6 +4,10 @@ graphBasePath the path of the graph + + unresolvedPath + the path of the unresolved Entities + @@ -36,5 +40,33 @@ + + + + yarn + cluster + Resolve Relations in raw graph + eu.dnetlib.dhp.oa.graph.resolution.SparkResolveEntities + dhp-graph-mapper-${projectVersion}.jar + + --executor-memory=${sparkExecutorMemory} + --executor-cores=${sparkExecutorCores} + --driver-memory=${sparkDriverMemory} + --conf spark.extraListeners=${spark2ExtraListeners} + --conf spark.sql.shuffle.partitions=10000 + --conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners} + --conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress} + --conf spark.eventLog.dir=${nameNode}${spark2EventLogDir} + + --masteryarn + --graphBasePath${graphBasePath} + --unresolvedPath${unresolvedPath} + --workingPath${workingDir} + + + + + + \ No newline at end of file diff --git a/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/resolve_entities_params.json b/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/resolve_entities_params.json new file mode 100644 index 000000000..f38cc1291 --- /dev/null +++ b/dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/resolution/resolve_entities_params.json @@ -0,0 +1,6 @@ +[ + {"paramName":"mt", "paramLongName":"master", "paramDescription": "should be local or yarn", "paramRequired": true}, + {"paramName":"w", "paramLongName":"workingPath", "paramDescription": "the source Path", "paramRequired": true}, + {"paramName":"u", "paramLongName":"unresolvedPath", "paramDescription": "the source Path", "paramRequired": true}, + {"paramName":"g", "paramLongName":"graphBasePath", "paramDescription": "the path of the raw graph", "paramRequired": true} +] \ No newline at end of file diff --git a/dhp-workflows/dhp-graph-mapper/src/test/java/eu/dnetlib/dhp/oa/graph/resolution/ResolveEntitiesTest.scala b/dhp-workflows/dhp-graph-mapper/src/test/java/eu/dnetlib/dhp/oa/graph/resolution/ResolveEntitiesTest.scala new file mode 100644 index 000000000..9a142d3c0 --- /dev/null +++ b/dhp-workflows/dhp-graph-mapper/src/test/java/eu/dnetlib/dhp/oa/graph/resolution/ResolveEntitiesTest.scala @@ -0,0 +1,190 @@ +package eu.dnetlib.dhp.oa.graph.resolution + + +import com.fasterxml.jackson.databind.ObjectMapper +import eu.dnetlib.dhp.schema.common.EntityType +import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils +import eu.dnetlib.dhp.schema.oaf.{Result, StructuredProperty} +import org.apache.commons.io.FileUtils +import org.apache.spark.SparkConf +import org.apache.spark.sql._ +import org.junit.jupiter.api.Assertions._ +import org.junit.jupiter.api.TestInstance.Lifecycle +import org.junit.jupiter.api.{AfterAll, BeforeAll, Test, TestInstance} + +import java.nio.file.{Files, Path} +import scala.collection.JavaConverters._ +import scala.io.Source + +@TestInstance(Lifecycle.PER_CLASS) +class ResolveEntitiesTest extends Serializable { + + var workingDir:Path = null + + val FAKE_TITLE = "FAKETITLE" + val FAKE_SUBJECT = "FAKESUBJECT" + + var sparkSession:Option[SparkSession] = None + + + @BeforeAll + def setUp() :Unit = { + workingDir = Files.createTempDirectory(getClass.getSimpleName) + + val conf = new SparkConf() + sparkSession = Some(SparkSession + .builder() + .config(conf) + .appName(getClass.getSimpleName) + .master("local[*]").getOrCreate()) + populateDatasets(sparkSession.get) + generateUpdates(sparkSession.get) + + } + + + @AfterAll + def tearDown():Unit = { + FileUtils.deleteDirectory(workingDir.toFile) + sparkSession.get.stop() + + + } + + + + + def generateUpdates(spark:SparkSession):Unit = { + val template = Source.fromInputStream(this.getClass.getResourceAsStream("updates")).mkString + + + val pids:List[String] = template.lines.map{id => + val r = new Result + r.setId(id.toLowerCase.trim) + r.setSubject(List(OafMapperUtils.structuredProperty(FAKE_SUBJECT, OafMapperUtils.qualifier("fos","fosCS", "fossSchema", "fossiFIgo"), null)).asJava) + r.setTitle(List(OafMapperUtils.structuredProperty(FAKE_TITLE, OafMapperUtils.qualifier("fos","fosCS", "fossSchema", "fossiFIgo"), null)).asJava) + r + }.map{r => + val mapper = new ObjectMapper() + + mapper.writeValueAsString(r)}.toList + + + val sc =spark.sparkContext + + println(sc.parallelize(pids).count()) + + spark.createDataset(sc.parallelize(pids))(Encoders.STRING).write.mode(SaveMode.Overwrite).option("compression", "gzip").text(s"$workingDir/updates") + + + + + + import spark.implicits._ + implicit val resEncoder: Encoder[Result] = Encoders.bean(classOf[Result]) + val ds = spark.read.text(s"$workingDir/updates").as[String].map{s => val mapper = new ObjectMapper() + mapper.readValue(s, classOf[Result])}.collect() + + + + + assertEquals(4, ds.length) + ds.foreach{r => assertNotNull(r.getSubject)} + ds.foreach{r => assertEquals(1,r.getSubject.size())} + ds.foreach{r => assertNotNull(r.getTitle)} + ds.foreach{r => assertEquals(1,r.getTitle.size())} + + + + ds.flatMap(r => r.getTitle.asScala.map(t => t.getValue)).foreach(t => assertEquals(FAKE_TITLE,t)) + ds.flatMap(r => r.getSubject.asScala.map(t => t.getValue)).foreach(t => assertEquals(FAKE_SUBJECT,t)) + + println("generated Updates") + } + + + def populateDatasets(spark:SparkSession):Unit = { + import spark.implicits._ + val entities =SparkResolveEntities.entities + + entities.foreach{ + e => + val template = Source.fromInputStream(this.getClass.getResourceAsStream(s"$e")).mkString + spark.createDataset(spark.sparkContext.parallelize(template.lines.toList)).as[String].write.option("compression", "gzip").text(s"$workingDir/graph/$e") + println(s"Created Dataset $e") + } + SparkResolveRelation.extractPidResolvedTableFromJsonRDD(spark, s"$workingDir/graph", s"$workingDir/work") + + } + + + @Test + def testResolution():Unit = { + val spark:SparkSession = sparkSession.get + implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result]) + SparkResolveEntities.resolveEntities(spark,s"$workingDir/work", s"$workingDir/updates" ) + + val ds = spark.read.load(s"$workingDir/work/resolvedEntities").as[Result] + + assertEquals(3, ds.count()) + + ds.collect().foreach{ + r => + assertTrue(r.getId.startsWith("50")) + } + } + + + + + private def structuredPContainsValue(l:java.util.List[StructuredProperty], exptectedValue:String):Boolean = { + l.asScala.exists(p =>p.getValue!= null && p.getValue.equalsIgnoreCase(exptectedValue)) + } + + @Test + def testUpdate():Unit = { + val spark:SparkSession = sparkSession.get + import spark.implicits._ + implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result]) + val m = new ObjectMapper() + SparkResolveEntities.resolveEntities(spark,s"$workingDir/work", s"$workingDir/updates" ) + SparkResolveEntities.generateResolvedEntities(spark,s"$workingDir/work",s"$workingDir/graph" ) + + + + + val pubDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/publication").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.publication)) + val t = pubDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count() + + + + val datDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/dataset").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.dataset)) + val td = datDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count() + + + val softDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/software").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.software)) + val ts = softDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count() + + + val orpDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/otherresearchproduct").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.otherresearchproduct)) + val to = orpDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count() + + + assertEquals(0, t) + assertEquals(2, td) + assertEquals(1, ts) + assertEquals(0, to) + + } + + + + + + + + + + + +} diff --git a/dhp-workflows/dhp-graph-mapper/src/test/resources/eu/dnetlib/dhp/oa/graph/resolution/dataset b/dhp-workflows/dhp-graph-mapper/src/test/resources/eu/dnetlib/dhp/oa/graph/resolution/dataset new file mode 100644 index 000000000..05c875148 --- /dev/null +++ b/dhp-workflows/dhp-graph-mapper/src/test/resources/eu/dnetlib/dhp/oa/graph/resolution/dataset @@ -0,0 +1,3 @@ +{"author":[{"affiliation":[],"fullname":"Greenough, B","name":"B","pid":[],"rank":1,"surname":"Greenough"}],"bestaccessright":{"classid":"UNKNOWN","classname":"not available","schemeid":"dnet:access_modes","schemename":"dnet:access_modes"},"collectedfrom":[{"key":"10|openaire____::c6df70599aa984f16ee52b4b86d2e89f","value":"DANS (Data Archiving and Networked 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Učinak nativnog propolisa pokazao ovisio je o dozi i to tako da je u manjoj dozi djelovao antioksidacijski (zaštitno), a u većoj dozi prooksidacijski (štetno). Oksidacijski status nakon obradaa nativnim propolisom nije pokazao tkivnu, ali je pokazao doznu ovisnost. Antioksidacijski status pokazao je tkivnu i doznu ovisnost ; u mozgu nije pokazao promjenu, dok je najveća promjena zabilježena u plućima i u manjoj mjeri u jetri. Promjena aktivnosti antioksidacijskih enzima u jetri nije korelirala s količinom njihove mRNA, dok je u plućima promjena u aktivnosti istih uglavnom bila proporcionalna promjeni u mRNA. Nije zapažena značajna promjena u ekpresiji proteina za sve ispitivane antioksidacijske enzime, niti obzirom na dozu, niti obzirom na ispitivano tkivo. Analizom DNA čipova utvrđeno je da propolis u dozi od 100 mg kg-1 u jetri ima zaštitni učinak, dok je u dozi od 300 mg kg-1 taj učinak prooksidacijski. U plućima je propolis u obje doze imao zaštitni učinak, dok u mozgu nije imao učinka na ispitivane gene. U hiperbaričkim uvjetima propolis je smanjio oksidacijski stres u sva tri ispitivana tkiva u dozi od 100 mg kg-1. U plućima je taj učinak propolis ostvario povećanjem aktivnosti antioksidacijskih enzima (MnSOD, KAT). Antioksidacijski učinak propolisa porijeklom iz Hrvatske do sada nije istraživan u in vivo sistemima. Isto tako po prvi puta se ispituje nativni propolis (tehnologijska inovacija) za razliku od do sada ispitivanih vodenih ili alkoholnih ekstrakata. Rezultati ove studije također pokazuju da sposobnost zaštitnog odgovora u plućima na oksidacijski stres koji postoji u novorođenih, ali se gubi u odraslih jedinki, može biti obnovljena uporabom nativnog propolisa tijekom hiperoksije. Ta spoznaja može pomoći u zaštiti od hiperoksije u populaciji odraslih tijekom terapije kisikom."}],"externalReference":[],"extraInfo":[],"format":[],"fulltext":[],"id":"50|57a035e5b1ae::15786c1f0f2930ea867a234fbcb8ef08","instance":[{"accessright":{"classid":"OPEN","classname":"Open Access","schemeid":"dnet:access_modes","schemename":"dnet:access_modes"},"alternateIdentifier":[],"collectedfrom":{"key":"10|openaire____::345c9d171ef3c5d706d08041d506428c","value":"Croatian Scientific Bibliography - CROSBI"},"dateofacceptance":{"dataInfo":{"deletedbyinference":false,"inferred":false,"invisible":false,"provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"Harvested","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"},"trust":"0.9"},"value":"2006-01-01"},"hostedby":{"key":"10|openaire____::345c9d171ef3c5d706d08041d506428c","value":"Croatian Scientific Bibliography - CROSBI"},"instancetype":{"classid":"doctoral thesis","classname":"doctoral 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