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/*
* 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.regression
import org.json4s.{DefaultFormats, JValue}
import org.apache.spark.annotation.Experimental
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.rdd.RDD
/**
* @since 0.8.0
*/
@Experimental
trait RegressionModel extends Serializable {
/**
* Predict values for the given data set using the model trained.
*
* @param testData RDD representing data points to be predicted
* @return 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 Double prediction 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 RegressionModel {
/**
* Helper method for loading GLM regression model metadata.
* @return numFeatures
*/
def getNumFeatures(metadata: JValue): Int = {
implicit val formats = DefaultFormats
(metadata \ "numFeatures").extract[Int]
}
}
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