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From harsha2010 <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-7015][MLLib][WIP] Multiclass to Binary ...
Date Wed, 06 May 2015 14:29:43 GMT
Github user harsha2010 commented on a diff in the pull request:

    https://github.com/apache/spark/pull/5830#discussion_r29763179
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/reduction/OneVsRestClassifier.scala
---
    @@ -0,0 +1,175 @@
    +/*
    + * 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.attribute.BinaryAttribute
    +import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams}
    +import org.apache.spark.ml.param.Param
    +import org.apache.spark.ml.util.{MetadataUtils, 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 [[OneVsRestClassifier]].
    + */
    +private[ml] trait OneVsRestParams extends ClassifierParams {
    +
    +  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 baseClassifier: Param[ClassifierType]  =
    +    new Param(this, "baseClassifier", "base binary classifier/regressor ")
    +
    +  /** @group getParam */
    +  def getBaseClassifier: ClassifierType = getOrDefault(baseClassifier)
    +
    +}
    +
    +/**
    + *
    + * @param parent
    + * @param baseClassificationModels the binary classification models for reduction.
    + */
    +@AlphaComponent
    +private[ml] class OneVsRestModel(
    +    override val parent: OneVsRestClassifier,
    +    val baseClassificationModels: Array[Model[_]])
    +  extends Model[OneVsRestModel] with OneVsRestParams {
    +
    +  /**
    +   * 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
    +    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, $(predictionCol), DoubleType)
    +    sqlCtx.createDataFrame(results, outputSchema)
    +  }
    +
    +  @DeveloperApi
    +  protected def featuresDataType: DataType = new VectorUDT
    +
    +  override def transformSchema(schema: StructType): StructType = {
    +    validateAndTransformSchema(schema, fitting = false, featuresDataType)
    +  }
    +
    +}
    +
    +/**
    + * :: Experimental ::
    + *
    + * Reduction of Multiclass Classification to Binary Classification.
    + * Performs reduction using one against all strategy.
    + * For a multiclass classification with k classes, train k models (one per class).
    + * Each example is scored against all k models and the model with highest score
    + * is picked to label the example.
    + *
    + */
    +class OneVsRestClassifier extends Estimator[OneVsRestModel]
    +  with OneVsRestParams {
    +
    +  @DeveloperApi
    +  protected def featuresDataType: DataType = new VectorUDT
    +
    +  /** @group setParam */
    +  def setBaseClassifier(value: ClassifierType): this.type = set(baseClassifier, value)
    +
    +  override def fit(dataset: DataFrame): OneVsRestModel = {
    +
    +    // determine number of classes either from metadata if provided, or via computation.
    +    val labelSchema = dataset.schema($(labelCol))
    +    val computeNumClasses: () => Int = () => {
    +      dataset.select($(labelCol)).distinct.count().toInt
    --- End diff --
    
    The distinct signature in DF is without parentheses
    
    /**
       * Returns a new [[DataFrame]] that contains only the unique rows from this [[DataFrame]].
       * @group dfops
       */
      override def distinct: DataFrame = Distinct(logicalPlan)


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