Scala Evaluator类代码示例

本文整理汇总了Scala中org.apache.spark.ml.evaluation.Evaluator类的典型用法代码示例。如果您正苦于以下问题：Scala Evaluator类的具体用法？Scala Evaluator怎么用？Scala Evaluator使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

在下文中一共展示了Evaluator类的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Scala代码示例。

示例1: LogisticRegression

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.classification

import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator

import org.apache.spark.ml.evaluation.{Evaluator, MulticlassClassificationEvaluator}
import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer}
import org.apache.spark.ml
import org.apache.spark.ml.linalg.Vectors


object LogisticRegression extends BenchmarkAlgorithm
  with TestFromTraining with TrainingSetFromTransformer with ScoringWithEvaluator {

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    DataGenerator.generateContinuousFeatures(
      ctx.sqlContext,
      numExamples,
      ctx.seed(),
      numPartitions,
      numFeatures)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    val rng = ctx.newGenerator()
    val coefficients =
      Vectors.dense(Array.fill[Double](ctx.params.numFeatures)(2 * rng.nextDouble() - 1))
    // Small intercept to prevent some skew in the data.
    val intercept = 0.01 * (2 * rng.nextDouble - 1)
    ModelBuilder.newLogisticRegressionModel(coefficients, intercept)
  }

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    new ml.classification.LogisticRegression()
      .setTol(tol)
      .setMaxIter(maxIter)
      .setRegParam(regParam)
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new MulticlassClassificationEvaluator()
}

开发者ID:summerDG，项目名称:spark-sql-perf，代码行数:46，代码来源:LogisticRegression.scala

示例2: TreeOrForestClassification

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.classification

import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer, TreeUtils}
import org.apache.spark.ml.classification.DecisionTreeClassifier
import org.apache.spark.ml.evaluation.{Evaluator, MulticlassClassificationEvaluator}
import org.apache.spark.sql.DataFrame

import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator


abstract class TreeOrForestClassification extends BenchmarkAlgorithm
  with TestFromTraining with TrainingSetFromTransformer with ScoringWithEvaluator {

  import TreeOrForestClassification.getFeatureArity

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    val featureArity: Array[Int] = getFeatureArity(ctx)
    val data: DataFrame = DataGenerator.generateMixedFeatures(ctx.sqlContext, numExamples,
      ctx.seed(), numPartitions, featureArity)
    TreeUtils.setMetadata(data, "features", featureArity)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    ModelBuilder.newDecisionTreeClassificationModel(ctx.params.depth, ctx.params.numClasses,
      getFeatureArity(ctx), ctx.seed())
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new MulticlassClassificationEvaluator()
}

object DecisionTreeClassification extends TreeOrForestClassification {

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    new DecisionTreeClassifier()
      .setMaxDepth(depth)
      .setSeed(ctx.seed())
  }
}

object TreeOrForestClassification {

  
  def getFeatureArity(ctx: MLBenchContext): Array[Int] = {
    val numFeatures = ctx.params.numFeatures
    val fourthFeatures = numFeatures / 4
    Array.fill[Int](fourthFeatures)(2) ++ // low-arity categorical
      Array.fill[Int](fourthFeatures)(20) ++ // high-arity categorical
      Array.fill[Int](numFeatures - 2 * fourthFeatures)(0) // continuous
  }
}

开发者ID:summerDG，项目名称:spark-sql-perf，代码行数:56，代码来源:DecisionTreeClassification.scala

示例3: GBTClassification

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.classification

import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer, TreeUtils}
import org.apache.spark.ml.classification.GBTClassifier
import org.apache.spark.ml.evaluation.{Evaluator, MulticlassClassificationEvaluator}
import org.apache.spark.sql._

import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator


object GBTClassification extends BenchmarkAlgorithm
  with TestFromTraining with TrainingSetFromTransformer with ScoringWithEvaluator {

  import TreeOrForestClassification.getFeatureArity

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    val featureArity: Array[Int] = getFeatureArity(ctx)
    val data: DataFrame = DataGenerator.generateMixedFeatures(ctx.sqlContext, numExamples,
      ctx.seed(), numPartitions, featureArity)
    TreeUtils.setMetadata(data, "features", featureArity)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    import ctx.params._
    // We add +1 to the depth to make it more likely that many iterations of boosting are needed
    // to model the true tree.
    ModelBuilder.newDecisionTreeClassificationModel(depth + 1, numClasses, getFeatureArity(ctx),
      ctx.seed())
  }

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    // TODO: subsamplingRate, featureSubsetStrategy
    // TODO: cacheNodeIds, checkpoint?
    new GBTClassifier()
      .setMaxDepth(depth)
      .setMaxIter(maxIter)
      .setSeed(ctx.seed())
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new MulticlassClassificationEvaluator()
}

开发者ID:summerDG，项目名称:spark-sql-perf，代码行数:47，代码来源:GBTClassification.scala

示例4: GLMRegression

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.regression

import org.apache.spark.ml.evaluation.{Evaluator, RegressionEvaluator}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.regression.GeneralizedLinearRegression
import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer}

import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator


object GLMRegression extends BenchmarkAlgorithm with TestFromTraining with
  TrainingSetFromTransformer with ScoringWithEvaluator {

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    DataGenerator.generateContinuousFeatures(
      ctx.sqlContext,
      numExamples,
      ctx.seed(),
      numPartitions,
      numFeatures)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    import ctx.params._
    val rng = ctx.newGenerator()
    val coefficients =
      Vectors.dense(Array.fill[Double](ctx.params.numFeatures)(2 * rng.nextDouble() - 1))
    // Small intercept to prevent some skew in the data.
    val intercept = 0.01 * (2 * rng.nextDouble - 1)
    val m = ModelBuilder.newGLR(coefficients, intercept)
    m.set(m.link, link.get)
    m.set(m.family, family.get)
    m
  }

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    new GeneralizedLinearRegression()
      .setLink(link)
      .setFamily(family)
      .setRegParam(regParam)
      .setMaxIter(maxIter)
      .setTol(tol)
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new RegressionEvaluator()
}

开发者ID:summerDG，项目名称:spark-sql-perf，代码行数:52，代码来源:GLMRegression.scala

示例5: LinearRegression

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.regression

import org.apache.spark.ml
import org.apache.spark.ml.evaluation.{Evaluator, RegressionEvaluator}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer}

import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator


object LinearRegression extends BenchmarkAlgorithm with TestFromTraining with
  TrainingSetFromTransformer with ScoringWithEvaluator {

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    DataGenerator.generateContinuousFeatures(
      ctx.sqlContext,
      numExamples,
      ctx.seed(),
      numPartitions,
      numFeatures)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    val rng = ctx.newGenerator()
    val coefficients =
      Vectors.dense(Array.fill[Double](ctx.params.numFeatures)(2 * rng.nextDouble() - 1))
    // Small intercept to prevent some skew in the data.
    val intercept = 0.01 * (2 * rng.nextDouble - 1)
    ModelBuilder.newLinearRegressionModel(coefficients, intercept)
  }

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    new ml.regression.LinearRegression()
      .setSolver("l-bfgs")
      .setRegParam(regParam)
      .setMaxIter(maxIter)
      .setTol(tol)
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new RegressionEvaluator()
}

开发者ID:summerDG，项目名称:spark-sql-perf，代码行数:47，代码来源:LinearRegression.scala

示例6: ALS

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.recommendation

import org.apache.spark.ml
import org.apache.spark.ml.evaluation.{RegressionEvaluator, Evaluator}
import org.apache.spark.ml.{Transformer, Estimator}
import org.apache.spark.sql._

import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator
import com.databricks.spark.sql.perf.mllib.{ScoringWithEvaluator, BenchmarkAlgorithm, MLBenchContext}

object ALS extends BenchmarkAlgorithm with ScoringWithEvaluator {

  override def trainingDataSet(ctx: MLBenchContext): DataFrame = {
    import ctx.params._
    DataGenerator.generateRatings(
      ctx.sqlContext,
      numUsers,
      numItems,
      numExamples,
      numTestExamples,
      implicitPrefs = false,
      numPartitions,
      ctx.seed())._1
  }

  override def testDataSet(ctx: MLBenchContext): DataFrame = {
    import ctx.params._
    DataGenerator.generateRatings(
      ctx.sqlContext,
      numUsers,
      numItems,
      numExamples,
      numTestExamples,
      implicitPrefs = false,
      numPartitions,
      ctx.seed())._2
  }

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    new ml.recommendation.ALS()
      .setSeed(ctx.seed())
      .setRegParam(regParam)
      .setNumBlocks(numPartitions)
      .setRank(rank)
      .setMaxIter(maxIter)
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator = {
    new RegressionEvaluator().setLabelCol("rating")
  }
}

开发者ID:summerDG，项目名称:spark-sql-perf，代码行数:54，代码来源:ALS.scala

示例7: TreeOrForestClassification

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.classification

import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer, TreeUtils}
import org.apache.spark.ml.classification.DecisionTreeClassifier
import org.apache.spark.ml.evaluation.{Evaluator, MulticlassClassificationEvaluator}
import org.apache.spark.sql.DataFrame

import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator


abstract class TreeOrForestClassification extends BenchmarkAlgorithm
  with TestFromTraining with TrainingSetFromTransformer with ScoringWithEvaluator {

  import TreeOrForestClassification.getFeatureArity

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    val featureArity: Array[Int] = getFeatureArity(ctx)
    val data: DataFrame = DataGenerator.generateMixedFeatures(ctx.sqlContext, numExamples,
      ctx.seed(), numPartitions, featureArity)
    TreeUtils.setMetadata(data, "label", numClasses, "features", featureArity)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    ModelBuilder.newDecisionTreeClassificationModel(ctx.params.depth, ctx.params.numClasses,
      getFeatureArity(ctx), ctx.seed())
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new MulticlassClassificationEvaluator()
}

object DecisionTreeClassification extends TreeOrForestClassification {

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    new DecisionTreeClassifier()
      .setMaxDepth(depth)
      .setSeed(ctx.seed())
  }
}

object TreeOrForestClassification {

  
  def getFeatureArity(ctx: MLBenchContext): Array[Int] = {
    val numFeatures = ctx.params.numFeatures
    val fourthFeatures = numFeatures / 4
    Array.fill[Int](fourthFeatures)(2) ++ // low-arity categorical
      Array.fill[Int](fourthFeatures)(20) ++ // high-arity categorical
      Array.fill[Int](numFeatures - 2 * fourthFeatures)(0) // continuous
  }
}

开发者ID:sparkonpower，项目名称:spark-sql-perf-spark2.0.0，代码行数:56，代码来源:DecisionTreeClassification.scala

示例8: GBTClassification

//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.classification

import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer, TreeUtils}
import org.apache.spark.ml.classification.GBTClassifier
import org.apache.spark.ml.evaluation.{Evaluator, MulticlassClassificationEvaluator}
import org.apache.spark.sql._

import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator


object GBTClassification extends BenchmarkAlgorithm
  with TestFromTraining with TrainingSetFromTransformer with ScoringWithEvaluator {

  import TreeOrForestClassification.getFeatureArity

  override protected def initialData(ctx: MLBenchContext) = {
    import ctx.params._
    val featureArity: Array[Int] = getFeatureArity(ctx)
    val data: DataFrame = DataGenerator.generateMixedFeatures(ctx.sqlContext, numExamples,
      ctx.seed(), numPartitions, featureArity)
    TreeUtils.setMetadata(data, "label", numClasses, "features", featureArity)
  }

  override protected def trueModel(ctx: MLBenchContext): Transformer = {
    import ctx.params._
    // We add +1 to the depth to make it more likely that many iterations of boosting are needed
    // to model the true tree.
    ModelBuilder.newDecisionTreeClassificationModel(depth + 1, numClasses, getFeatureArity(ctx),
      ctx.seed())
  }

  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
    import ctx.params._
    // TODO: subsamplingRate, featureSubsetStrategy
    // TODO: cacheNodeIds, checkpoint?
    new GBTClassifier()
      .setMaxDepth(depth)
      .setMaxIter(maxIter)
      .setSeed(ctx.seed())
  }

  override protected def evaluator(ctx: MLBenchContext): Evaluator =
    new MulticlassClassificationEvaluator()
}

开发者ID:sparkonpower，项目名称:spark-sql-perf-spark2.0.0，代码行数:47，代码来源:GBTClassification.scala

注：本文中的org.apache.spark.ml.evaluation.Evaluator类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。