org.apache.spark.ml.regression.GBTRegressor Scala Examples

The following examples show how to use org.apache.spark.ml.regression.GBTRegressor. 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: GBTRegressionParitySpec.scala    From mleap   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.ml.parity.regression

import org.apache.spark.ml.parity.SparkParityBase
import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
import org.apache.spark.ml.regression.GBTRegressor
import org.apache.spark.ml.{Pipeline, Transformer}
import org.apache.spark.sql._


class GBTRegressionParitySpec extends SparkParityBase {
  override val dataset: DataFrame = baseDataset.select("fico_score_group_fnl", "dti", "loan_amount")
  override val sparkTransformer: Transformer = new Pipeline().setStages(Array(new StringIndexer().
    setInputCol("fico_score_group_fnl").
    setOutputCol("fico_index"),
    new VectorAssembler().
      setInputCols(Array("fico_index", "dti")).
      setOutputCol("features"),
    new GBTRegressor().
      setFeaturesCol("features").
      setLabelCol("loan_amount").
      setPredictionCol("prediction"))).fit(dataset)

  override val unserializedParams = Set("stringOrderType", "labelCol", "seed")
}
Example 2
Source File: OpGBTRegressor.scala    From TransmogrifAI   with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
package com.salesforce.op.stages.impl.regression

import com.salesforce.op.UID
import com.salesforce.op.features.types.{OPVector, Prediction, RealNN}
import com.salesforce.op.stages.impl.CheckIsResponseValues
import com.salesforce.op.stages.sparkwrappers.specific.{OpPredictionModel, OpPredictorWrapper}
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor, OpGBTRegressorParams}

import scala.reflect.runtime.universe.TypeTag


class OpGBTRegressionModel
(
  sparkModel: GBTRegressionModel,
  uid: String = UID[OpGBTRegressionModel],
  operationName: String = classOf[GBTRegressor].getSimpleName
)(
  implicit tti1: TypeTag[RealNN],
  tti2: TypeTag[OPVector],
  tto: TypeTag[Prediction],
  ttov: TypeTag[Prediction#Value]
) extends OpPredictionModel[GBTRegressionModel](
  sparkModel = sparkModel, uid = uid, operationName = operationName
)
Example 3
Source File: OpGBTRegressorTest.scala    From TransmogrifAI   with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
package com.salesforce.op.stages.impl.regression

import com.salesforce.op.features.types._
import com.salesforce.op.stages.impl.PredictionEquality
import com.salesforce.op.stages.sparkwrappers.specific.{OpPredictorWrapper, OpPredictorWrapperModel}
import com.salesforce.op.test._
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
import org.junit.runner.RunWith
import org.scalatest.junit.JUnitRunner

@RunWith(classOf[JUnitRunner])
class OpGBTRegressorTest extends OpEstimatorSpec[Prediction, OpPredictorWrapperModel[GBTRegressionModel],
  OpPredictorWrapper[GBTRegressor, GBTRegressionModel]] with PredictionEquality {

  override def specName: String = Spec[OpGBTRegressor]

  val (inputData, rawLabel, features) = TestFeatureBuilder(
    Seq[(RealNN, OPVector)](
      (10.0.toRealNN, Vectors.dense(1.0, 4.3, 1.3).toOPVector),
      (20.0.toRealNN, Vectors.dense(2.0, 0.3, 0.1).toOPVector),
      (30.0.toRealNN, Vectors.dense(3.0, 3.9, 4.3).toOPVector),
      (40.0.toRealNN, Vectors.dense(4.0, 1.3, 0.9).toOPVector),
      (50.0.toRealNN, Vectors.dense(5.0, 4.7, 1.3).toOPVector)
    )
  )
  val label = rawLabel.copy(isResponse = true)
  val estimator = new OpGBTRegressor().setInput(label, features)

  val expectedResult = Seq(
    Prediction(10.0),
    Prediction(20.0),
    Prediction(30.0),
    Prediction(40.0),
    Prediction(50.0)
  )

  it should "allow the user to set the desired spark parameters" in {
    estimator
      .setMaxIter(10)
      .setMaxDepth(6)
      .setMaxBins(2)
      .setMinInstancesPerNode(2)
      .setMinInfoGain(0.1)
    estimator.fit(inputData)

    estimator.predictor.getMaxIter shouldBe 10
    estimator.predictor.getMaxDepth shouldBe 6
    estimator.predictor.getMaxBins shouldBe 2
    estimator.predictor.getMinInstancesPerNode shouldBe 2
    estimator.predictor.getMinInfoGain shouldBe 0.1
  }
}
Example 4
Source File: GradientBoostedTreeRegressorExample.scala    From spark1.52   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.VectorIndexer
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
// $example off$
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.{SQLContext, DataFrame}

    predictions.select("prediction", "label", "features").show(5)

    // Select (prediction, true label) and compute test error.
    val evaluator = new RegressionEvaluator()
      .setLabelCol("label")//标签列名
      //预测结果列名
      .setPredictionCol("prediction")
       //rmse均方根误差说明样本的离散程度
      .setMetricName("rmse")
    val rmse = evaluator.evaluate(predictions)
     //rmse均方根误差说明样本的离散程度
    println("Root Mean Squared Error (RMSE) on test data = " + rmse)

    val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
    println("Learned regression GBT model:\n" + gbtModel.toDebugString)
    // $example off$

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