Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/3099#discussion_r20104636
--- Diff: mllib/src/main/scala/org/apache/spark/ml/Transformer.scala ---
@@ -0,0 +1,116 @@
+/*
+ * 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
+
+import scala.annotation.varargs
+import scala.reflect.runtime.universe.TypeTag
+
+import org.apache.spark.Logging
+import org.apache.spark.ml.param._
+import org.apache.spark.sql.SchemaRDD
+import org.apache.spark.sql.api.java.JavaSchemaRDD
+import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.catalyst.analysis.Star
+import org.apache.spark.sql.catalyst.dsl._
+import org.apache.spark.sql.catalyst.types._
+
+/**
+ * Abstract class for transformers that transform one dataset into another.
+ */
+abstract class Transformer extends PipelineStage with Params {
+
+ /**
+ * Transforms the dataset with optional parameters
+ * @param dataset input dataset
+ * @param paramPairs optional list of param pairs, overwrite embedded
params
+ * @return transformed dataset
+ */
+ @varargs
+ def transform(dataset: SchemaRDD, paramPairs: ParamPair[_]*): SchemaRDD
= {
+ val map = new ParamMap()
+ paramPairs.foreach(map.put(_))
+ transform(dataset, map)
+ }
+
+ /**
+ * Transforms the dataset with provided parameter map.
+ * @param dataset input dataset
+ * @param paramMap parameters
+ * @return transformed dataset
+ */
+ def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD
+
+ // Java-friendly versions of transform.
+
+ @varargs
+ def transform(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*):
JavaSchemaRDD = {
+ transform(dataset.schemaRDD, paramPairs: _*).toJavaSchemaRDD
+ }
+
+ def transform(dataset: JavaSchemaRDD, paramMap: ParamMap): JavaSchemaRDD
= {
+ transform(dataset.schemaRDD, paramMap).toJavaSchemaRDD
+ }
+}
+
+/**
+ * Abstract class for transformers that take one input column, apply
transformation, and output the
+ * result as a new column.
+ */
+abstract class UnaryTransformer[IN, OUT: TypeTag, T <:
UnaryTransformer[IN, OUT, T]]
+ extends Transformer with HasInputCol with HasOutputCol with Logging {
+
+ def setInputCol(value: String): T = { set(inputCol, value);
this.asInstanceOf[T] }
+ def setOutputCol(value: String): T = { set(outputCol, value);
this.asInstanceOf[T] }
+
+ /**
+ * Creates the transform function using the given param map. The input
param map already takes
+ * account of the embedded param map. So the param values should be
determined solely by the input
+ * param map.
+ */
+ protected def createTransformFunc(paramMap: ParamMap): IN => OUT
+
+ /**
+ * Validates the input type. Throw an exception if it is invalid.
+ */
+ protected def validateInputType(inputType: DataType): Unit = {}
--- End diff --
Actually I guess this whole class is Java-unfriendly because it takes a
TypeTag for OUT. I guess we need another one in Java.
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