hvanhovell commented on code in PR #48791: URL: https://github.com/apache/spark/pull/48791#discussion_r1876933726
########## sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLUtils.scala: ########## @@ -0,0 +1,293 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.connect.ml + +import java.util.ServiceLoader + +import scala.collection.immutable.HashSet +import scala.jdk.CollectionConverters.{IterableHasAsScala, MapHasAsScala} + +import org.apache.commons.lang3.reflect.MethodUtils.{invokeMethod, invokeStaticMethod} + +import org.apache.spark.connect.proto +import org.apache.spark.ml.{Estimator, Transformer} +import org.apache.spark.ml.linalg.{Matrices, Matrix, Vector, Vectors} +import org.apache.spark.ml.param.Params +import org.apache.spark.ml.util.MLWritable +import org.apache.spark.sql.{DataFrame, Dataset} +import org.apache.spark.sql.connect.common.LiteralValueProtoConverter +import org.apache.spark.sql.connect.planner.SparkConnectPlanner +import org.apache.spark.sql.connect.service.SessionHolder +import org.apache.spark.util.Utils + +private[ml] object MLUtils { + + private lazy val estimators: Map[String, Class[_]] = { + val loader = Utils.getContextOrSparkClassLoader + val serviceLoader = ServiceLoader.load(classOf[Estimator[_]], loader) + val providers = serviceLoader.asScala.toList + providers.map(est => est.getClass.getName -> est.getClass).toMap + } + + private lazy val transformers: Map[String, Class[_]] = { + val loader = Utils.getContextOrSparkClassLoader + val serviceLoader = ServiceLoader.load(classOf[Transformer], loader) + val providers = serviceLoader.asScala.toList + providers.map(est => est.getClass.getName -> est.getClass).toMap + } + + def deserializeVector(vector: proto.Vector): Vector = { + if (vector.hasDense) { + val values = vector.getDense.getValueList.asScala.map(_.toDouble).toArray + Vectors.dense(values) + } else { + val size = vector.getSparse.getSize + val indices = vector.getSparse.getIndexList.asScala.map(_.toInt).toArray + val values = vector.getSparse.getValueList.asScala.map(_.toDouble).toArray + Vectors.sparse(size, indices, values) + } + } + + def deserializeMatrix(matrix: proto.Matrix): Matrix = { + if (matrix.hasDense) { + val values = matrix.getDense.getValueList.asScala.map(_.toDouble).toArray + Matrices.dense(matrix.getDense.getNumRows, matrix.getDense.getNumCols, values) + } else { + val sparse = matrix.getSparse + val colPtrs = sparse.getColptrList.asScala.map(_.toInt).toArray + val rowIndices = sparse.getRowIndexList.asScala.map(_.toInt).toArray + val values = sparse.getValueList.asScala.map(_.toDouble).toArray + Matrices.sparse(sparse.getNumRows, sparse.getNumCols, colPtrs, rowIndices, values) + } + } + + def setInstanceParams(instance: Params, params: proto.MlParams): Unit = { + params.getParamsMap.asScala.foreach { case (name, paramProto) => + val p = instance.getParam(name) + val value = if (paramProto.hasLiteral) { + convertParamValue( + p.paramValueClassTag.runtimeClass, + LiteralValueProtoConverter.toCatalystValue(paramProto.getLiteral)) + } else if (paramProto.hasVector) { + deserializeVector(paramProto.getVector) + } else if (paramProto.hasMatrix) { + deserializeMatrix(paramProto.getMatrix) + } else { + throw MlUnsupportedException(s"Unsupported parameter type for ${name}") + } + instance.set(p, value) + } + } + + private def convertArray(paramType: Class[_], array: Array[_]): Array[_] = { + if (paramType == classOf[Byte]) { + array.map(_.asInstanceOf[Byte]) + } else if (paramType == classOf[Short]) { + array.map(_.asInstanceOf[Short]) + } else if (paramType == classOf[Int]) { + array.map(_.asInstanceOf[Int]) + } else if (paramType == classOf[Long]) { + array.map(_.asInstanceOf[Long]) + } else if (paramType == classOf[Float]) { + array.map(_.asInstanceOf[Float]) + } else if (paramType == classOf[Double]) { + array.map(_.asInstanceOf[Double]) + } else if (paramType == classOf[String]) { + array.map(_.asInstanceOf[String]) + } else { + array + } + } + + private def convertParamValue(paramType: Class[_], value: Any): Any = { + // Some cases the param type might be mismatched with the value type. + // Because in python side we only have int / float type for numeric params. + // e.g.: + // param type is Int but client sends a Long type. + // param type is Long but client sends a Int type. + // param type is Float but client sends a Double type. + // param type is Array[Int] but client sends a Array[Long] type. + // param type is Array[Float] but client sends a Array[Double] type. + // param type is Array[Array[Int]] but client sends a Array[Array[Long]] type. + // param type is Array[Array[Float]] but client sends a Array[Array[Double]] type. + if (paramType == classOf[Byte]) { + value.asInstanceOf[java.lang.Number].byteValue() + } else if (paramType == classOf[Short]) { + value.asInstanceOf[java.lang.Number].shortValue() + } else if (paramType == classOf[Int]) { + value.asInstanceOf[java.lang.Number].intValue() + } else if (paramType == classOf[Long]) { + value.asInstanceOf[java.lang.Number].longValue() + } else if (paramType == classOf[Float]) { + value.asInstanceOf[java.lang.Number].floatValue() + } else if (paramType == classOf[Double]) { + value.asInstanceOf[java.lang.Number].doubleValue() + } else if (paramType.isArray) { + val compType = paramType.getComponentType + val array = value.asInstanceOf[Array[_]].map { e => + convertParamValue(compType, e) + } + convertArray(compType, array) + } else { + value + } + } + + def parseRelationProto(relation: proto.Relation, sessionHolder: SessionHolder): DataFrame = { + val planner = new SparkConnectPlanner(sessionHolder) + val plan = planner.transformRelation(relation) + Dataset.ofRows(sessionHolder.session, plan) + } + + /** + * Get the Estimator instance according to the proto information + * + * @param operator + * MlOperator information + * @param params + * The optional parameters of the estimator + * @return + * the estimator + */ + def getEstimator(operator: proto.MlOperator, params: Option[proto.MlParams]): Estimator[_] = { + // TODO support plugin + // Get the estimator according to the operator name + val name = operator.getName + if (estimators.isEmpty || !estimators.contains(name)) { + throw MlUnsupportedException(s"Unsupported Estimator, found ${name}") + } + val uid = operator.getUid + val estimator: Estimator[_] = estimators(name) + .getConstructor(classOf[String]) + .newInstance(uid) + .asInstanceOf[Estimator[_]] + + // Set parameters for the estimator + params.foreach(p => MLUtils.setInstanceParams(estimator, p)) + estimator + } + + def load(className: String, path: String): Object = { + // scalastyle:off classforname + val clazz = Class.forName(className) Review Comment: Use `SparkClassUtils.classForName(..)`. Also if this is Scala, you can also resolve the Module class and call load on it. Other question, why are you not using the registry for this? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
