Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5830#discussion_r30092217
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/reduction/OneVsRest.scala ---
@@ -0,0 +1,194 @@
+/*
+ * 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 java.util.UUID
+
+import scala.language.existentials
+
+import org.apache.spark.annotation.{AlphaComponent, Experimental}
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.BinaryAttribute
+import org.apache.spark.ml.classification.{ClassificationModel, Classifier}
+import org.apache.spark.ml.param.Param
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.mllib.linalg.Vector
+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 [[OneVsRest]].
+ */
+private[ml] trait OneVsRestParams extends PredictorParams {
+
+ type ClassifierType = Classifier[F, E, M] forSome {
+ type F ;
+ type M <: ClassificationModel[F,M];
+ type E <: Classifier[F, E,M]
+ }
+
+ /**
+ * param for the base classifier that we reduce multiclass
classification into.
+ * @group param
+ */
+ val classifier: Param[ClassifierType] =
+ new Param(this, "classifier", "base binary classifier ")
+
+ /** @group getParam */
+ def getClassifier: ClassifierType = $(classifier)
+
+}
+
+/**
+ * Model produced by [[OneVsRest]].
+ *
+ * @param parent
+ * @param models the binary classification models for reduction.
+ */
+@AlphaComponent
+class OneVsRestModel(
+ override val parent: OneVsRest,
+ val models: Array[_ <: ClassificationModel[_,_]])
+ extends Model[OneVsRestModel] with OneVsRestParams {
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = false,
getClassifier.featuresDataType)
+ }
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ // Check schema
+ transformSchema(dataset.schema, logging = true)
+
+ // determine the input columns: these need to be passed through
+ val origCols = dataset.schema.map(f => col(f.name))
+
+ // add an accumulator column to store predictions of all the models
+ val accColName = "mbc$acc" + UUID.randomUUID().toString
+ val init: () => Map[Int, Double] = () => {Map()}
+ val mapType = MapType(IntegerType, DoubleType, false)
+ val newDataset = dataset.withColumn(accColName,callUDF(init, mapType))
+
+ // persist if underlying dataset is not persistent.
+ val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ if (handlePersistence) {
+ newDataset.persist(StorageLevel.MEMORY_AND_DISK)
+ }
+
+ // update the accumulator column with the result of prediction of
models
+ val aggregatedDataset =
models.zipWithIndex.foldLeft[DataFrame](newDataset) {
+ case (df, (model, index)) => {
+ val rawPredictionCol = model.getRawPredictionCol
+ val columns = origCols ++ List(col(rawPredictionCol),
col(accColName))
+
+ // add temporary column to store intermediate scores and update
+ val tmpColName = "mbc$tmp" + UUID.randomUUID().toString
+ val update: (Map[Int, Double], Vector) => Map[Int, Double] =
+ (predictions: Map[Int, Double], prediction: Vector) => {
+ predictions + ((index, prediction(1)))
+ }
+ val updateUdf = callUDF(update, mapType, col(accColName),
col(rawPredictionCol))
+ val transformedDataset = model.transform(df).select(columns:_*)
+ val updatedDataset = transformedDataset.withColumn(tmpColName,
updateUdf)
+ val newColumns = origCols ++ List(col(tmpColName))
+
+ // switch out the intermediate column with the accumulator column
+ updatedDataset.select(newColumns:_*).withColumnRenamed(tmpColName,
accColName)
+ }
+ }
+
+ if (handlePersistence) {
+ newDataset.unpersist()
+ }
+
+ // output the index of the classifier with highest confidence as
prediction
+ val label: Map[Int, Double] => Double = (predictions: Map[Int,
Double]) => {
+ predictions.maxBy(_._2)._1.toDouble
+ }
+ aggregatedDataset.withColumn($(predictionCol), callUDF(label,
DoubleType, col(accColName)))
+ }
+}
+
+/**
+ * :: 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.
+ *
+ */
+@Experimental
+class OneVsRest extends Estimator[OneVsRestModel] with OneVsRestParams {
+
+ /** @group setParam */
+ // TODO: Find a better way to do this. Existential Types don't work with
Java API so cast needed.
+ def setClassifier(value: Classifier[_,_,_]): this.type = {
+ set(classifier, value.asInstanceOf[ClassifierType])
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = true,
getClassifier.featuresDataType)
+ }
+
+ override def fit(dataset: DataFrame): OneVsRestModel = {
+ // determine number of classes either from metadata if provided, or
via computation.
+ val labelSchema = dataset.schema($(labelCol))
+ val computeNumClasses: () => Int = () => {
+ val Row(maxLabelIndex: Double) = dataset.agg(max($(labelCol))).head()
+ // classes are assumed to be numbered from 0,...,maxLabelIndex
+ maxLabelIndex.toInt + 1
+ }
+ val numClasses =
MetadataUtils.getNumClasses(labelSchema).fold(computeNumClasses())(identity)
+
+ val multiclassLabeled = dataset.select($(labelCol), $(featuresCol))
+
+ // persist if underlying dataset is not persistent.
+ val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ if (handlePersistence) {
+ multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK)
+ }
+
+ // create k columns, one for each binary classifier.
+ val models = Range(0, numClasses).par.map { index =>
+
+ val label: Double => Double = (label: Double) => {
+ if (label.toInt == index) 1.0 else 0.0
+ }
+
+ // generate new label metadata for the binary problem.
+ // TODO: use when ... otherwise after SPARK-7321 is merged
+ val labelUDF = callUDF(label, DoubleType, col($(labelCol)))
+ val newLabelMeta =
BinaryAttribute.defaultAttr.withName("label").toMetadata()
+ val skipFeatures: Any => Boolean = (name: Any) =>
name.toString.equals(featuresCol.name)
--- End diff --
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