blacklist filtering moved before the cleanup phase in order to have case sensitive regex #485
|
@ -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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||||
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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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||||
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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 {
|
||||
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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||||
}
|
||||
}
|
||||
|
||||
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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|
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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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||||
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||||
// create fields for blacklist
|
||||
val filtered = conf.getPace.getModel.asScala.flatMap(fdef => {
|
||||
if (conf.blacklists().containsKey(fdef.getName)) {
|
||||
val fdef_filtered = fdef.clone()
|
||||
fdef_filtered.setName(fdef.getName + "_filtered")
|
||||
Seq(fdef, fdef_filtered)
|
||||
}
|
||||
else {
|
||||
Seq(fdef)
|
||||
}
|
||||
})
|
||||
|
||||
// Construct a Spark StructType representing the schema of the model
|
||||
(Seq(identifier) ++ conf.getPace.getModel.asScala)
|
||||
(Seq(identifier) ++ filtered)
|
||||
.foldLeft(
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new StructType()
|
||||
)((resType, fieldDef) => {
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|
@ -44,7 +56,6 @@ case class SparkModel(conf: DedupConfig) {
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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))
|
||||
df
|
||||
.map(r => rowFromJson(r))(SparkCompatUtils.encoderFor(schema))
|
||||
}
|
||||
|
||||
def rowFromJson(json: String): Row = {
|
||||
|
@ -64,9 +76,11 @@ case class SparkModel(conf: DedupConfig) {
|
|||
|
||||
schema.fieldNames.zipWithIndex.foldLeft(values) {
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case ((res, (fname, index))) =>
|
||||
val fdef = conf.getPace.getModelMap.get(fname)
|
||||
|
||||
val fdef = conf.getPace.getModelMap.get(fname.split("_filtered")(0))
|
||||
|
||||
if (fdef != null) {
|
||||
if (!fname.contains("_filtered")) { //process fields with no blacklist
|
||||
res(index) = fdef.getType match {
|
||||
case Type.String | Type.Int =>
|
||||
MapDocumentUtil.truncateValue(
|
||||
|
@ -99,6 +113,26 @@ case class SparkModel(conf: DedupConfig) {
|
|||
case Type.DoubleArray =>
|
||||
MapDocumentUtil.getJPathArray(fdef.getPath, json)
|
||||
}
|
||||
}
|
||||
else { //process fields with blacklist
|
||||
val blacklist: Predicate[String] = conf.blacklists().get(fdef.getName)
|
||||
|
||||
res(index) = fdef.getType match {
|
||||
case Type.List | Type.JSON =>
|
||||
MapDocumentUtil.truncateList(
|
||||
MapDocumentUtil.getJPathList(fdef.getPath, documentContext, fdef.getType),
|
||||
fdef.getSize
|
||||
).asScala.filter((v: String) => !blacklist.test(v))
|
||||
|
||||
case _ =>
|
||||
val value: String = MapDocumentUtil.truncateValue(
|
||||
MapDocumentUtil.getJPathString(fdef.getPath, documentContext),
|
||||
fdef.getLength
|
||||
)
|
||||
if (blacklist.test(value)) "" else value
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
val filter = fdef.getFilter
|
||||
|
||||
|
@ -125,13 +159,12 @@ case class SparkModel(conf: DedupConfig) {
|
|||
}
|
||||
|
||||
if (StringUtils.isNotBlank(fdef.getInfer)) {
|
||||
val inferFrom : String = if (StringUtils.isNotBlank(fdef.getInferenceFrom)) fdef.getInferenceFrom else fdef.getPath
|
||||
val inferFrom: String = if (StringUtils.isNotBlank(fdef.getInferenceFrom)) fdef.getInferenceFrom else fdef.getPath
|
||||
res(index) = res(index) match {
|
||||
case x: Seq[String] => x.map(inference(_, MapDocumentUtil.getJPathString(inferFrom, documentContext), fdef.getInfer))
|
||||
case _ => inference(res(index).toString, MapDocumentUtil.getJPathString(inferFrom, documentContext), fdef.getInfer)
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
res
|
||||
|
@ -139,6 +172,7 @@ case class SparkModel(conf: DedupConfig) {
|
|||
}
|
||||
|
||||
new GenericRowWithSchema(values, schema)
|
||||
|
||||
}
|
||||
|
||||
def clean(value: String, cleantype: String) : String = {
|
||||
|
|
|
@ -227,4 +227,17 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
|
|||
System.out.println(cf.apply(conf, Lists.newArrayList(s)));
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testNumAuthorsTitleSuffixPrefixChain() {
|
||||
|
||||
final ClusteringFunction cf = new NumAuthorsTitleSuffixPrefixChain(params);
|
||||
params.put("mod", 10);
|
||||
|
||||
final String title = "PARP-2 Regulates SIRT1 Expression and Whole-Body Energy Expenditure";
|
||||
final String num_authors = "10";
|
||||
System.out.println("title = " + title);
|
||||
System.out.println("num_authors = " + num_authors);
|
||||
System.out.println(cf.apply(conf, Lists.newArrayList(num_authors, title)));
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
@ -327,4 +327,16 @@ public class ComparatorTest extends AbstractPaceTest {
|
|||
|
||||
}
|
||||
|
||||
@Test
|
||||
public void titleVersionMatchTest() {
|
||||
|
||||
TitleVersionMatch titleVersionMatch = new TitleVersionMatch(params);
|
||||
|
||||
double result = titleVersionMatch
|
||||
.compare(
|
||||
"parp 2 regulates sirt 1 expression and whole body energy expenditure",
|
||||
"parp 2 regulates sirt 1 expression and whole body energy expenditure", conf);
|
||||
assertEquals(1.0, result);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
@ -91,7 +91,6 @@ public class SparkBlockStats extends AbstractSparkAction {
|
|||
.read()
|
||||
.textFile(DedupUtility.createEntityPath(graphBasePath, subEntity))
|
||||
.transform(deduper.model().parseJsonDataset())
|
||||
.transform(deduper.filterAndCleanup())
|
||||
.transform(deduper.generateClustersWithCollect())
|
||||
.filter(functions.size(new Column("block")).geq(1));
|
||||
|
||||
|
|
|
@ -96,7 +96,7 @@
|
|||
"aggregation": "MAX",
|
||||
"positive": "layer4",
|
||||
"negative": "NO_MATCH",
|
||||
"undefined": "MATCH",
|
||||
"undefined": "layer4",
|
||||
"ignoreUndefined": "true"
|
||||
},
|
||||
"layer4": {
|
||||
|
|
Loading…
Reference in New Issue