org.apache.spark.mllib.classification.ClassificationModel.scala Maven / Gradle / Ivy

/*
 * 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.mllib.classification

import org.json4s.{DefaultFormats, JValue}

import org.apache.spark.annotation.Since
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.rdd.RDD

/**
 * Represents a classification model that predicts to which of a set of categories an example
 * belongs. The categories are represented by double values: 0.0, 1.0, 2.0, etc.
 */
@Since("0.8.0")
trait ClassificationModel extends Serializable {
  /**
   * Predict values for the given data set using the model trained.
   *
   * @param testData RDD representing data points to be predicted
   * @return an RDD[Double] where each entry contains the corresponding prediction
   */
  @Since("1.0.0")
  def predict(testData: RDD[Vector]): RDD[Double]

  /**
   * Predict values for a single data point using the model trained.
   *
   * @param testData array representing a single data point
   * @return predicted category from the trained model
   */
  @Since("1.0.0")
  def predict(testData: Vector): Double

  /**
   * Predict values for examples stored in a JavaRDD.
   * @param testData JavaRDD representing data points to be predicted
   * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
   */
  @Since("1.0.0")
  def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
    predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
}

private[mllib] object ClassificationModel {

  /**
   * Helper method for loading GLM classification model metadata.
   * @return (numFeatures, numClasses)
   */
  def getNumFeaturesClasses(metadata: JValue): (Int, Int) = {
    implicit val formats = DefaultFormats
    ((metadata \ "numFeatures").extract[Int], (metadata \ "numClasses").extract[Int])
  }
}