blacklist filtering moved before the cleanup phase in order to have case sensitive regex
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@ -38,7 +38,7 @@ public class NumAuthorsTitleSuffixPrefixChain extends AbstractClusteringFunction
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@Override
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protected Collection<String> doApply(Config conf, String s) {
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return suffixPrefixChain(cleanup(s), param("mod"));
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return suffixPrefixChain(cleanup(s), paramOrDefault("mod", 10));
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}
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private Collection<String> suffixPrefixChain(String s, int mod) {
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@ -54,6 +54,22 @@ public class FieldDef implements Serializable {
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public FieldDef() {
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}
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public FieldDef clone() {
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FieldDef fieldDef = new FieldDef();
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fieldDef.setName(this.name);
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fieldDef.setPath(this.path);
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fieldDef.setType(this.type);
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fieldDef.setOverrideMatch(this.overrideMatch);
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fieldDef.setSize(this.size);
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fieldDef.setLength(this.length);
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fieldDef.setFilter(this.filter);
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fieldDef.setSorted(this.sorted);
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fieldDef.setClean(this.clean);
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fieldDef.setInfer(this.infer);
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fieldDef.setInferenceFrom(this.inferenceFrom);
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return fieldDef;
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}
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public String getInferenceFrom() {
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return inferenceFrom;
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}
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@ -19,48 +19,10 @@ case class SparkDeduper(conf: DedupConfig) extends Serializable {
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val model: SparkModel = SparkModel(conf)
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val dedup: (Dataset[Row] => Dataset[Row]) = df => {
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df.transform(filterAndCleanup)
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.transform(generateClustersWithCollect)
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df.transform(generateClustersWithCollect)
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.transform(processBlocks)
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}
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val filterAndCleanup: (Dataset[Row] => Dataset[Row]) = df => {
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val df_with_filters = conf.getPace.getModel.asScala.foldLeft(df)((res, fdef) => {
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if (conf.blacklists.containsKey(fdef.getName)) {
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res.withColumn(
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fdef.getName + "_filtered",
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filterColumnUDF(fdef).apply(new Column(fdef.getName))
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)
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} else {
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res
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}
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})
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df_with_filters
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}
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def filterColumnUDF(fdef: FieldDef): UserDefinedFunction = {
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val blacklist: Predicate[String] = conf.blacklists().get(fdef.getName)
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if (blacklist == null) {
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throw new IllegalArgumentException("Column: " + fdef.getName + " does not have any filter")
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} else {
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fdef.getType match {
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case Type.List | Type.JSON =>
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udf[Array[String], Array[String]](values => {
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values.filter((v: String) => !blacklist.test(v))
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})
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case _ =>
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udf[String, String](v => {
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if (blacklist.test(v)) ""
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else v
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})
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}
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}
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}
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val generateClustersWithCollect: (Dataset[Row] => Dataset[Row]) = df_with_filters => {
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var df_with_clustering_keys: Dataset[Row] = null
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@ -5,12 +5,12 @@ import eu.dnetlib.pace.common.AbstractPaceFunctions
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import eu.dnetlib.pace.config.{DedupConfig, Type}
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import eu.dnetlib.pace.util.{MapDocumentUtil, SparkCompatUtils}
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import org.apache.commons.lang3.StringUtils
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import org.apache.spark.sql.catalyst.encoders.RowEncoder
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import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
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import org.apache.spark.sql.types.{DataTypes, Metadata, StructField, StructType}
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import org.apache.spark.sql.{Dataset, Row}
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import java.util.Locale
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import java.util.function.Predicate
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import java.util.regex.Pattern
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import scala.collection.JavaConverters._
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@ -29,8 +29,20 @@ case class SparkModel(conf: DedupConfig) {
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identifier.setName(identifierFieldName)
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identifier.setType(Type.String)
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// create fields for blacklist
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val filtered = conf.getPace.getModel.asScala.flatMap(fdef => {
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if (conf.blacklists().containsKey(fdef.getName)) {
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val fdef_filtered = fdef.clone()
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fdef_filtered.setName(fdef.getName + "_filtered")
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Seq(fdef, fdef_filtered)
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}
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else {
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Seq(fdef)
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}
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})
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// Construct a Spark StructType representing the schema of the model
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(Seq(identifier) ++ conf.getPace.getModel.asScala)
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(Seq(identifier) ++ filtered)
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.foldLeft(
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new StructType()
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)((resType, fieldDef) => {
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@ -44,7 +56,6 @@ case class SparkModel(conf: DedupConfig) {
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})
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})
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}
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val identityFieldPosition: Int = schema.fieldIndex(identifierFieldName)
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@ -52,7 +63,8 @@ case class SparkModel(conf: DedupConfig) {
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val orderingFieldPosition: Int = schema.fieldIndex(orderingFieldName)
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val parseJsonDataset: (Dataset[String] => Dataset[Row]) = df => {
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df.map(r => rowFromJson(r))(SparkCompatUtils.encoderFor(schema))
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df
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.map(r => rowFromJson(r))(SparkCompatUtils.encoderFor(schema))
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}
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def rowFromJson(json: String): Row = {
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@ -64,9 +76,11 @@ case class SparkModel(conf: DedupConfig) {
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schema.fieldNames.zipWithIndex.foldLeft(values) {
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case ((res, (fname, index))) =>
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val fdef = conf.getPace.getModelMap.get(fname)
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val fdef = conf.getPace.getModelMap.get(fname.split("_filtered")(0))
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if (fdef != null) {
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if (!fname.contains("_filtered")) { //process fields with no blacklist
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res(index) = fdef.getType match {
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case Type.String | Type.Int =>
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MapDocumentUtil.truncateValue(
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@ -99,6 +113,26 @@ case class SparkModel(conf: DedupConfig) {
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case Type.DoubleArray =>
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MapDocumentUtil.getJPathArray(fdef.getPath, json)
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}
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}
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else { //process fields with blacklist
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val blacklist: Predicate[String] = conf.blacklists().get(fdef.getName)
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res(index) = fdef.getType match {
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case Type.List | Type.JSON =>
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MapDocumentUtil.truncateList(
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MapDocumentUtil.getJPathList(fdef.getPath, documentContext, fdef.getType),
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fdef.getSize
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).asScala.filter((v: String) => !blacklist.test(v))
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case _ =>
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val value: String = MapDocumentUtil.truncateValue(
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MapDocumentUtil.getJPathString(fdef.getPath, documentContext),
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fdef.getLength
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)
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if (blacklist.test(value)) "" else value
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}
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}
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val filter = fdef.getFilter
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@ -125,13 +159,12 @@ case class SparkModel(conf: DedupConfig) {
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}
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if (StringUtils.isNotBlank(fdef.getInfer)) {
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val inferFrom : String = if (StringUtils.isNotBlank(fdef.getInferenceFrom)) fdef.getInferenceFrom else fdef.getPath
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val inferFrom: String = if (StringUtils.isNotBlank(fdef.getInferenceFrom)) fdef.getInferenceFrom else fdef.getPath
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res(index) = res(index) match {
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case x: Seq[String] => x.map(inference(_, MapDocumentUtil.getJPathString(inferFrom, documentContext), fdef.getInfer))
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case _ => inference(res(index).toString, MapDocumentUtil.getJPathString(inferFrom, documentContext), fdef.getInfer)
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}
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}
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}
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res
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@ -139,6 +172,7 @@ case class SparkModel(conf: DedupConfig) {
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}
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new GenericRowWithSchema(values, schema)
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}
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def clean(value: String, cleantype: String) : String = {
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@ -227,4 +227,17 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
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System.out.println(cf.apply(conf, Lists.newArrayList(s)));
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}
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@Test
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public void testNumAuthorsTitleSuffixPrefixChain() {
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final ClusteringFunction cf = new NumAuthorsTitleSuffixPrefixChain(params);
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params.put("mod", 10);
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final String title = "PARP-2 Regulates SIRT1 Expression and Whole-Body Energy Expenditure";
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final String num_authors = "10";
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System.out.println("title = " + title);
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System.out.println("num_authors = " + num_authors);
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System.out.println(cf.apply(conf, Lists.newArrayList(num_authors, title)));
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}
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}
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@ -327,4 +327,16 @@ public class ComparatorTest extends AbstractPaceTest {
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}
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@Test
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public void titleVersionMatchTest() {
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TitleVersionMatch titleVersionMatch = new TitleVersionMatch(params);
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double result = titleVersionMatch
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.compare(
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"parp 2 regulates sirt 1 expression and whole body energy expenditure",
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"parp 2 regulates sirt 1 expression and whole body energy expenditure", conf);
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assertEquals(1.0, result);
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}
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}
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@ -91,7 +91,6 @@ public class SparkBlockStats extends AbstractSparkAction {
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.read()
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.textFile(DedupUtility.createEntityPath(graphBasePath, subEntity))
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.transform(deduper.model().parseJsonDataset())
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.transform(deduper.filterAndCleanup())
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.transform(deduper.generateClustersWithCollect())
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.filter(functions.size(new Column("block")).geq(1));
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@ -96,7 +96,7 @@
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"aggregation": "MAX",
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"positive": "layer4",
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"negative": "NO_MATCH",
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"undefined": "MATCH",
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"undefined": "layer4",
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"ignoreUndefined": "true"
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},
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"layer4": {
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