Adding changes to load models with complex objects

src/main/scala/com/johnsnowlabs/nlp/ParamsAndFeaturesReadable.scala

@@ -16,8 +16,14 @@
 
 package com.johnsnowlabs.nlp
 
+import com.johnsnowlabs.nlp.LegacyMetadataSupport.ParamsReflection
+import org.apache.hadoop.fs.Path
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.param.Params
 import org.apache.spark.ml.util.{DefaultParamsReadable, MLReader}
 import org.apache.spark.sql.SparkSession
+import org.json4s.jackson.JsonMethods.{compact, parse, render}
+import org.json4s.{DefaultFormats, JNothing, JNull, JObject, JValue}
 
 import scala.collection.mutable.ArrayBuffer
 import scala.util.{Failure, Success, Try}
@@ -29,7 +35,15 @@ class FeaturesReader[T <: HasFeatures](
 
   override def load(path: String): T = {
 
-    val instance = baseReader.load(path)
+    val instance =
+      try {
+        // Let Spark's own loader handle modern bundles.
+        baseReader.load(path)
+      } catch {
+        case e: NoSuchElementException if isMissingParamError(e) =>
+          // Reconstruct legacy models that referenced params removed in newer releases.
+          loadWithLegacyParams(path)
+      }
 
     for (feature <- instance.features) {
       val value = feature.deserialize(sparkSession, path, feature.name)
@@ -40,6 +54,59 @@ class FeaturesReader[T <: HasFeatures](
 
     instance
   }
+
+  private def isMissingParamError(e: NoSuchElementException): Boolean = {
+    val msg = Option(e.getMessage).getOrElse("")
+    msg.contains("Param")
+  }
+
+  private def loadWithLegacyParams(path: String): T = {
+    val metadata = LegacyMetadataSupport.load(path, sparkSession)
+    val cls = Class.forName(metadata.className)
+    val ctor = cls.getConstructor(classOf[String])
+    val instance = ctor.newInstance(metadata.uid).asInstanceOf[Params]
+    setParamsIgnoringUnknown(instance, metadata)
+    instance.asInstanceOf[T]
+  }
+
+  private def setParamsIgnoringUnknown(
+      instance: Params,
+      metadata: LegacyMetadataSupport.Metadata): Unit = {
+    // Replay active params; skip mismatches so legacy bundles still come back.
+    assignParams(instance, metadata.params, isDefault = false, metadata)
+
+    val hasDefaultSection = metadata.defaultParams != JNothing && metadata.defaultParams != JNull
+    if (hasDefaultSection) {
+      // If the metadata carried defaults, restore only those that still exists.
+      assignParams(instance, metadata.defaultParams, isDefault = true, metadata)
+    }
+  }
+
+  private def assignParams(
+      instance: Params,
+      jsonParams: JValue,
+      isDefault: Boolean,
+      metadata: LegacyMetadataSupport.Metadata): Unit = {
+    jsonParams match {
+      case JObject(pairs) =>
+        pairs.foreach { case (paramName, jsonValue) =>
+          if (instance.hasParam(paramName)) {
+            val param = instance.getParam(paramName)
+            val value = param.jsonDecode(compact(render(jsonValue)))
+            if (isDefault) {
+              // Spark keeps setDefault protected; call it via reflection to restore legacy defaults.
+              ParamsReflection.setDefault(instance, param, value)
+            } else {
+              instance.set(param, value)
+            }
+          }
+        }
+      case JNothing | JNull =>
+      case other =>
+        throw new IllegalArgumentException(
+          s"Cannot recognize JSON metadata when loading legacy params for ${metadata.className}: $other")
+    }
+  }
 }
 
 trait ParamsAndFeaturesReadable[T <: HasFeatures] extends DefaultParamsReadable[T] {
@@ -137,3 +204,79 @@ trait ParamsAndFeaturesFallbackReadable[T <: HasFeatures] extends ParamsAndFeatu
 
   override def read: MLReader[T] = new FeaturesFallbackReader(super.read, onRead, fallbackLoad)
 }
+
+// Minimal metadata parser + helper utilities for replaying legacy params.
+protected object LegacyMetadataSupport {
+
+  object ParamsReflection {
+    private val setDefaultMethod = {
+      val maybeMethod = classOf[Params].getDeclaredMethods.find { method =>
+        method.getName == "setDefault" && method.getParameterCount == 2
+      }
+
+      maybeMethod match {
+        case Some(method) =>
+          method.setAccessible(true)
+          method
+        case None =>
+          throw new NoSuchMethodException("Params.setDefault(Param, value) not found via reflection")
+      }
+    }
+
+    def setDefault[T](
+        params: Params,
+        param: org.apache.spark.ml.param.Param[T],
+        value: T): Unit = {
+      setDefaultMethod.invoke(params, param, toAnyRef(value))
+    }
+
+    // Mirror JVM boxing rules so reflection can call the protected method safely.
+    private def toAnyRef(value: Any): AnyRef = {
+      if (value == null) {
+        null
+      } else {
+        value match {
+          case v: AnyRef => v
+          case v: Boolean => java.lang.Boolean.valueOf(v)
+          case v: Byte => java.lang.Byte.valueOf(v)
+          case v: Short => java.lang.Short.valueOf(v)
+          case v: Int => java.lang.Integer.valueOf(v)
+          case v: Long => java.lang.Long.valueOf(v)
+          case v: Float => java.lang.Float.valueOf(v)
+          case v: Double => java.lang.Double.valueOf(v)
+          case v: Char => java.lang.Character.valueOf(v)
+          case other =>
+            throw new IllegalArgumentException(
+              s"Unsupported default value type ${other.getClass}")
+        }
+      }
+    }
+  }
+
+  case class Metadata(
+      className: String,
+      uid: String,
+      sparkVersion: String,
+      params: JValue,
+      defaultParams: JValue,
+      metadataJson: String)
+
+  def load(path: String, spark: SparkSession): Metadata = {
+    val metadataPath = new Path(path, "metadata").toString
+    val metadataStr = spark.sparkContext.textFile(metadataPath, 1).first()
+    parseMetadata(metadataStr)
+  }
+
+  private def parseMetadata(metadataStr: String): Metadata = {
+    val metadata = parse(metadataStr)
+    implicit val format: DefaultFormats.type = DefaultFormats
+
+    val className = (metadata \ "class").extract[String]
+    val uid = (metadata \ "uid").extract[String]
+    val sparkVersion = (metadata \ "sparkVersion").extractOpt[String].getOrElse("0.0")
+    val params = metadata \ "paramMap"
+    val defaultParams = metadata \ "defaultParamMap"
+
+    Metadata(className, uid, sparkVersion, params, defaultParams, metadataStr)
+  }
+}

src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerTest.scala

@@ -21,7 +21,7 @@ import com.johnsnowlabs.nlp.annotators.SparkSessionTest
 import com.johnsnowlabs.nlp.annotators.er.EntityRulerFixture._
 import com.johnsnowlabs.nlp.base.LightPipeline
 import com.johnsnowlabs.nlp.util.io.{ExternalResource, ReadAs}
-import com.johnsnowlabs.tags.FastTest
+import com.johnsnowlabs.tags.{FastTest, SlowTest}
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.scalatest.flatspec.AnyFlatSpec
 
@@ -850,4 +850,25 @@ class EntityRulerTest extends AnyFlatSpec with SparkSessionTest {
     entityRulerPipeline
   }
 
+  it should "serialize EntityRulerModel" taggedAs SlowTest in {
+    //Should be run with Java 8 and Scala 2.12
+    val entityRuler = new EntityRulerApproach()
+      .setInputCols("document", "token")
+      .setOutputCol("entities")
+      .setPatternsResource("src/test/resources/entity-ruler/keywords_only.json", ReadAs.TEXT)
+    val entityRulerModel = entityRuler.fit(emptyDataSet)
+
+    entityRulerModel.write.overwrite().save("./tmp_entity_ruler_model_java8_scala2_12")
+  }
+
+  it should "deserialize EntityRulerModel" taggedAs SlowTest in {
+    val textDataSet = Seq(text1).toDS.toDF("text")
+    val loadedEntityRulerModel = EntityRulerModel.load("./tmp_entity_ruler_model_java8_scala2_12")
+
+    val pipeline =
+      new Pipeline().setStages(Array(documentAssembler, tokenizer, loadedEntityRulerModel))
+    val resultDf = pipeline.fit(emptyDataSet).transform(textDataSet)
+    resultDf.select("entities").show(truncate = false)
+  }
+
 }