org.apache.spark.mllib.optimization.SimpleUpdater Scala Examples

The following examples show how to use org.apache.spark.mllib.optimization.SimpleUpdater. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
Example 1
Source File: LinearRegression.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.optimization.{L1Updater, SimpleUpdater, SquaredL2Updater}
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.util.MLUtils

spark-examples-*.jar \
          |  data/mllib/sample_linear_regression_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"LinearRegression with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    val splits = examples.randomSplit(Array(0.8, 0.2))
    val training = splits(0).cache()
    val test = splits(1).cache()

    val numTraining = training.count()
    val numTest = test.count()
    println(s"Training: $numTraining, test: $numTest.")

    examples.unpersist(blocking = false)

    val updater = params.regType match {
      case NONE => new SimpleUpdater()
      case L1 => new L1Updater()
      case L2 => new SquaredL2Updater()
    }

    val algorithm = new LinearRegressionWithSGD()
    algorithm.optimizer
      .setNumIterations(params.numIterations)
      .setStepSize(params.stepSize)
      .setUpdater(updater)
      .setRegParam(params.regParam)

    val model = algorithm.run(training)

    val prediction = model.predict(test.map(_.features))
    val predictionAndLabel = prediction.zip(test.map(_.label))

    val loss = predictionAndLabel.map { case (p, l) =>
      val err = p - l
      err * err
    }.reduce(_ + _)
    val rmse = math.sqrt(loss / numTest)

    println(s"Test RMSE = $rmse.")

    sc.stop()
  }
}
// scalastyle:on println
Example 2
Source File: LinearRegression.scala    From iolap   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1Updater}

spark-examples-*.jar \
          |  data/mllib/sample_linear_regression_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams).map { params =>
      run(params)
    } getOrElse {
      sys.exit(1)
    }
  }

  def run(params: Params) {
    val conf = new SparkConf().setAppName(s"LinearRegression with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    val splits = examples.randomSplit(Array(0.8, 0.2))
    val training = splits(0).cache()
    val test = splits(1).cache()

    val numTraining = training.count()
    val numTest = test.count()
    println(s"Training: $numTraining, test: $numTest.")

    examples.unpersist(blocking = false)

    val updater = params.regType match {
      case NONE => new SimpleUpdater()
      case L1 => new L1Updater()
      case L2 => new SquaredL2Updater()
    }

    val algorithm = new LinearRegressionWithSGD()
    algorithm.optimizer
      .setNumIterations(params.numIterations)
      .setStepSize(params.stepSize)
      .setUpdater(updater)
      .setRegParam(params.regParam)

    val model = algorithm.run(training)

    val prediction = model.predict(test.map(_.features))
    val predictionAndLabel = prediction.zip(test.map(_.label))

    val loss = predictionAndLabel.map { case (p, l) =>
      val err = p - l
      err * err
    }.reduce(_ + _)
    val rmse = math.sqrt(loss / numTest)

    println(s"Test RMSE = $rmse.")

    sc.stop()
  }
}
Example 3
Source File: LinearRegression.scala    From BigDatalog   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1Updater}

spark-examples-*.jar \
          |  data/mllib/sample_linear_regression_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams).map { params =>
      run(params)
    } getOrElse {
      sys.exit(1)
    }
  }

  def run(params: Params) {
    val conf = new SparkConf().setAppName(s"LinearRegression with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    val splits = examples.randomSplit(Array(0.8, 0.2))
    val training = splits(0).cache()
    val test = splits(1).cache()

    val numTraining = training.count()
    val numTest = test.count()
    println(s"Training: $numTraining, test: $numTest.")

    examples.unpersist(blocking = false)

    val updater = params.regType match {
      case NONE => new SimpleUpdater()
      case L1 => new L1Updater()
      case L2 => new SquaredL2Updater()
    }

    val algorithm = new LinearRegressionWithSGD()
    algorithm.optimizer
      .setNumIterations(params.numIterations)
      .setStepSize(params.stepSize)
      .setUpdater(updater)
      .setRegParam(params.regParam)

    val model = algorithm.run(training)

    val prediction = model.predict(test.map(_.features))
    val predictionAndLabel = prediction.zip(test.map(_.label))

    val loss = predictionAndLabel.map { case (p, l) =>
      val err = p - l
      err * err
    }.reduce(_ + _)
    val rmse = math.sqrt(loss / numTest)

    println(s"Test RMSE = $rmse.")

    sc.stop()
  }
}
// scalastyle:on println