Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17583#discussion_r132058898
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/FuncTransformer.scala ---
@@ -0,0 +1,141 @@
+/*
+ * 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.ml.feature
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream,
ObjectInputStream, ObjectOutputStream}
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.feature.FuncTransformer.FuncTransformerWriter
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.expressions.UserDefinedFunction
+import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * :: Experimental ::
+ * FuncTransformer helps create a custom feature transformer easily for
DataFrame, such like
+ * conditional conversion(if...else...), type conversion, array indexing
and many string ops.
+ * Note that FuncTransformer supports serialization via Scala
ObjectOutputStream and may not
+ * guarantee save/load compatibility between different Scala version.
+ * @param func a custom UserDefinedFunction to map from inputCol to
outputCol e.g.
+ * udf { (i: Double) => i + 1 }. Only udf with one input is
supported for now.
+ */
+@Experimental
+@Since("2.3.0")
+class FuncTransformer @Since("2.3.0") (
+ @Since("2.3.0") override val uid: String,
+ @Since("2.3.0") val func: UserDefinedFunction
+ ) extends Transformer with HasInputCol with HasOutputCol with
DefaultParamsWritable {
+
+ @Since("2.3.0")
+ def this(func: UserDefinedFunction) =
this(Identifiable.randomUID("FuncTransformer"), func)
+
+ setDefault(inputCol -> "input", outputCol -> "output")
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ @Since("2.3.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ transformSchema(dataset.schema, logging = true)
+ dataset.withColumn($(outputCol), func(col($(inputCol))))
+ }
+
+ @Since("2.3.0")
+ override def transformSchema(schema: StructType): StructType = {
+ func.inputTypes match {
+ case Some(funcInputType) =>
+ require(funcInputType.length == 1, "FuncTransformer only supports
udf with one input")
+ val dataType = schema($(inputCol)).dataType
+ require(dataType == funcInputType.head, s"data type mismatch: udf
input type" +
+ s" ${funcInputType.head}; inputCol ${$(inputCol)} data type
$dataType ")
+ case None =>
+ val dataType = schema($(inputCol)).dataType
+ require(dataType.isInstanceOf[StructType], s"When func input types
is None," +
+ s" FuncTransformer only supports StructType. ${$(inputCol)} is
$dataType")
+ }
+ val outputFields = schema.fields :+ StructField($(outputCol),
func.dataType, false)
+ StructType(outputFields)
+ }
+
+ @Since("2.3.0")
+ override def copy(extra: ParamMap): FuncTransformer = {
+ val copied = new FuncTransformer(uid, func)
+ copyValues(copied, extra)
+ }
+
+ @Since("2.3.0")
+ override def write: MLWriter = new FuncTransformerWriter(this)
+}
+
+/**
+ * :: Experimental ::
+ * Companion object for FuncTransformer with save and load function.
+ */
+@Experimental
+@Since("2.3.0")
+object FuncTransformer extends DefaultParamsReadable[FuncTransformer] {
+
+ private[FuncTransformer]
+ class FuncTransformerWriter(instance: FuncTransformer) extends MLWriter {
+
+ private case class Data(func: Array[Byte])
+
+ override protected def saveImpl(path: String): Unit = {
+ DefaultParamsWriter.saveMetadata(instance, path, sc)
+ val bo = new ByteArrayOutputStream()
+ new ObjectOutputStream(bo).writeObject(instance.func)
--- End diff --
cc @liancheng @cloud-fan Any suggestions/potential risks about saving udf ?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
