spark-bart
A pure scala/spark implementation of the BART(bayes additive regressions trees model of Chipman et al and related model, like BACT(bayes additive classification tree), BCF(bayes causal forest) etc
example
import org.apache.spark.ml.linalg.Vectors
import math.{Pi, sin}
import util.Random
import org.apache.spark.ml.regression.{BayesAdditiveTreeRegressionModel, BayesAdditiveTreeRegressor}
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
import org.apache.spark.ml.evaluation.RegressionEvaluator
val sampleCnt = (1e3).toInt
val p = 10
// Friedman’s test function with a category
val df = spark.createDataFrame(
Range(0, sampleCnt).map{i =>
val x = Array.tabulate(p){i =>
if(i == 9) Random.nextInt(5).toDouble else Random.nextDouble
}
val features = Vectors.dense(x)
val label = (10 * math.sin(math.Pi * x(0) * x(1)) +
20 * math.pow(x(2) - 0.5, 2) +
10 * x(3) + 5 * x(4) + Random.nextGaussian)
(features, label)
}).toDF("features", "label")
val Array(trainingData, testData) = df.randomSplit(Array(0.7, 0.3))
val BART = new BayesAdditiveTreeRegressor().
setNumBurn(1000).
setNumSim(100).
setParallelChainCnt(4).
setNumThin(2).
setCategoryFeatureIndexes(Array(9)).
setCategoryFeatureArity(Array(5))
val bartModel = BART.fit(trainingData)
val bartPred = bartModel.transform(testData)
val evaluator = new RegressionEvaluator().
setLabelCol("label").
setPredictionCol("prediction").
setMetricName("rmse")
val bartRMSE = evaluator.evaluate(bartPred)
println(s"bart: Root Mean Squared Error (RMSE) on test data = $bartRMSE")
//bart: Root Mean Squared Error (RMSE) on test data = 1.83
val GBT = new GBTRegressor()
val GBTModel = GBT.fit(trainingData)
val gbtPred = GBTModel.transform(testData)
val gbtRMSE = evaluator.evaluate(gbtPred)
println(s"gbt: Root Mean Squared Error (RMSE) on test data = $gbtRMSE")
//gbt: Root Mean Squared Error (RMSE) on test data = 2.31
bartModel.asInstanceOf[BayesAdditiveTreeRegressionModel].
write.overwrite.save("bart-model")