Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5830#discussion_r29702850
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/reduction/Multiclass2Binary.scala ---
@@ -0,0 +1,209 @@
+/*
+ * 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.reduction
+
+import scala.language.existentials
+
+import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.classification.{ClassificationModel,
Classifier, ClassifierParams}
+import org.apache.spark.ml.param.{IntParam, Param, ParamMap}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Params for [[Multiclass2Binary]].
+ */
+private[ml] trait Multiclass2BinaryParams extends ClassifierParams {
+
+ type ClassifierType = Classifier[F, E, M] forSome {
+ type F ;
+ type M <: ClassificationModel[F,M];
+ type E <: Classifier[F, E,M]
+ }
+
+ /**
+ * param for prediction column name
+ * @group param
+ */
+ val idCol: Param[String] =
+ new Param(this, "idCol", "id column name")
+
+ setDefault(idCol, "id")
+
+ /**
+ * param for base classifier index column name
+ * @group param
+ */
+ val indexCol: Param[String] =
+ new Param(this, "indexCol", "classifier index column name")
+
+ setDefault(indexCol, "index")
+
+ /**
+ * param for the base classifier that we reduce multiclass
classification into.
+ * @group param
+ */
+ val baseClassifier: Param[ClassifierType] =
+ new Param(this, "baseClassifier", "base binary classifier/regressor ")
+
+ /** @group getParam */
+ def getBaseClassifier: ClassifierType = getOrDefault(baseClassifier)
+
+ /**
+ * param for number of classes.
+ * @group param
+ */
+ val k: IntParam = new IntParam(this, "k", "number of classes")
+
+ /** @group getParam */
+ def getK(): Int = getOrDefault(k)
+
+}
+
+/**
+ *
+ * @param parent
+ * @param baseClassificationModels the binary classification models for
reduction.
+ */
+@AlphaComponent
+private[ml] class Multiclass2BinaryModel(
+ override val parent: Multiclass2Binary,
+ val baseClassificationModels: Seq[Model[_]])
+ extends Model[Multiclass2BinaryModel] with Multiclass2BinaryParams {
+
+ /**
+ * Transforms the dataset with provided parameter map as additional
parameters.
+ * @param dataset input dataset
+ * @return transformed dataset
+ */
+ override def transform(dataset: DataFrame): DataFrame = {
+ // Check schema
+ val parentSchema = dataset.schema
+ transformSchema(parentSchema, logging = true)
+ val sqlCtx = dataset.sqlContext
+
+ // score each model on every data point and pick the model with
highest score
+ // TODO: Add randomization when there are ties.
+ val predictions = baseClassificationModels.zipWithIndex.par.map { case
(model, index) =>
+ val output = model.transform(dataset)
+ output.select($(rawPredictionCol)).map { case Row(p: Vector) =>
List((index, p(1))) }
+ }.reduce[RDD[List[(Int, Double)]]] { case (x, y) =>
+ x.zip(y).map { case ((a, b)) =>
+ a ++ b
+ }
+ }.
+ map(_.maxBy(_._2))
+
+ // ensure that we pass through columns that are part of the original
dataset.
+ val results = dataset.select(col("*")).rdd.zip(predictions).map { case
((row, (label, _))) =>
+ Row.fromSeq(row.toSeq ++ List(label.toDouble))
+ }
+
+ // the output schema should retain all input fields and add prediction
column.
+ val outputSchema = SchemaUtils.appendColumn(parentSchema,
$(predictionCol), DoubleType)
+ sqlCtx.createDataFrame(results, outputSchema)
+ }
+
+ @DeveloperApi
+ protected def featuresDataType: DataType = new VectorUDT
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = false, featuresDataType)
+ }
+
+}
+
+/**
+ * :: Experimental ::
+ *
+ * Reduction of Multiclass Classification to Binary Classification.
+ * Performs reduction using one against all strategy.
+ * For a multiclass classification with k classes, train k models (one per
class).
+ * Each example is scored against all k models and the model with highest
score
+ * is picked to label the example.
+ *
+ */
+class Multiclass2Binary extends Estimator[Multiclass2BinaryModel]
+ with Multiclass2BinaryParams {
+
+ @DeveloperApi
+ protected def featuresDataType: DataType = new VectorUDT
+
+ /** @group setParam */
+ def setBaseClassifier(value: ClassifierType): this.type =
set(baseClassifier, value)
+
+ /** @group setParam */
+ def setNumClasses(value: Int): this.type = set(k, value)
+
+ override def fit(dataset: DataFrame): Multiclass2BinaryModel = {
+
+ val numClasses = $(k)
+ val multiclassLabeled = dataset.select($(labelCol), $(featuresCol))
+
+ // persist if underlying dataset is not persistent
+ val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ if (handlePersistence) {
+ multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK)
+ }
+
+ // create k columns, one for each binary classifier.
+ val models = Range(0, numClasses).par.map { index =>
+ val labelColName = "mc2b$" + index
+ val label: Double => Double = (label: Double) => {
+ if (label.toInt == index) 1.0 else 0.0
+ }
+ val labelUDF = callUDF(label, DoubleType, col($(labelCol)))
+ val trainingDataset = multiclassLabeled.withColumn(labelColName,
labelUDF)
+ val classifier = newClassifier(extractParamMap(), labelColName)
+ classifier.fit(trainingDataset)
--- End diff --
`classifier.fit(trainingDataset, classifer.labelCol -> labelColName)`
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