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Distributed Fast Histogram Algorithm (#4011)

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* add back train method but mark as deprecated

* add back train method but mark as deprecated

* add back train method but mark as deprecated

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* init

* allow hist algo

* more changes

* temp

* update

* remove hist sync

* udpate rabit

* change hist size

* change the histogram

* update kfactor

* sync per node stats

* temp

* update

* final

* code clean

* update rabit

* more cleanup

* fix errors

* fix failed tests

* enforce c++11

* fix lint issue

* broadcast subsampled feature correctly

* revert some changes

* fix lint issue

* enable monotone and interaction constraints

* don't specify default for monotone and interactions

* update docs
This commit is contained in:
Nan Zhu
2019-02-05 05:12:53 -08:00
committed by GitHub
parent 8905df4a18
commit ae3bb9c2d5
16 changed files with 169 additions and 88 deletions
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1
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala
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@@ -31,7 +31,6 @@ object SparkTraining {
println("Usage: program input_path")
sys.exit(1)
}
val spark = SparkSession.builder().getOrCreate()
val inputPath = args(0)
val schema = new StructType(Array(

6
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
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@@ -263,8 +263,10 @@ object XGBoost extends Serializable {
validateSparkSslConf(sparkContext)
if (params.contains("tree_method")) {
require(params("tree_method") != "hist", "xgboost4j-spark does not support fast histogram" +
" for now")
require(params("tree_method") == "hist" ||
params("tree_method") == "approx" ||
params("tree_method") == "auto", "xgboost4j-spark only supports tree_method as 'hist'," +
" 'approx' and 'auto'")
}
if (params.contains("train_test_ratio")) {
logger.warn("train_test_ratio is deprecated since XGBoost 0.82, we recommend to explicitly" +

33
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala
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@@ -50,10 +50,21 @@ private[spark] trait BoosterParams extends Params {
* overfitting. [default=6] range: [1, Int.MaxValue]
*/
final val maxDepth = new IntParam(this, "maxDepth", "maximum depth of a tree, increase this " +
"value will make model more complex/likely to be overfitting.", (value: Int) => value >= 1)
"value will make model more complex/likely to be overfitting.", (value: Int) => value >= 0)
final def getMaxDepth: Int = $(maxDepth)
/**
* Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.
*/
final val maxLeaves = new IntParam(this, "maxLeaves",
"Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.",
(value: Int) => value >= 0)
final def getMaxLeaves: Int = $(maxDepth)
/**
* minimum sum of instance weight(hessian) needed in a child. If the tree partition step results
* in a leaf node with the sum of instance weight less than min_child_weight, then the building
@@ -147,7 +158,9 @@ private[spark] trait BoosterParams extends Params {
* growth policy for fast histogram algorithm
*/
final val growPolicy = new Param[String](this, "growPolicy",
"growth policy for fast histogram algorithm",
"Controls a way new nodes are added to the tree. Currently supported only if" +
" tree_method is set to hist. Choices: depthwise, lossguide. depthwise: split at nodes" +
" closest to the root. lossguide: split at nodes with highest loss change.",
(value: String) => BoosterParams.supportedGrowthPolicies.contains(value))
final def getGrowPolicy: String = $(growPolicy)
@@ -242,6 +255,22 @@ private[spark] trait BoosterParams extends Params {
final def getTreeLimit: Int = $(treeLimit)
final val monotoneConstraints = new Param[String](this, name = "monotoneConstraints",
doc = "a list in length of number of features, 1 indicate monotonic increasing, - 1 means " +
"decreasing, 0 means no constraint. If it is shorter than number of features, 0 will be " +
"padded ")
final def getMonotoneConstraints: String = $(monotoneConstraints)
final val interactionConstraints = new Param[String](this,
name = "interactionConstraints",
doc = "Constraints for interaction representing permitted interactions. The constraints" +
" must be specified in the form of a nest list, e.g. [[0, 1], [2, 3, 4]]," +
" where each inner list is a group of indices of features that are allowed to interact" +
" with each other. See tutorial for more information")
final def getInteractionConstraints: String = $(interactionConstraints)
setDefault(eta -> 0.3, gamma -> 0, maxDepth -> 6,
minChildWeight -> 1, maxDeltaStep -> 0,
growPolicy -> "depthwise", maxBins -> 16,

5
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala
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@@ -231,10 +231,11 @@ private[spark] trait ParamMapFuncs extends Params {
def XGBoostToMLlibParams(xgboostParams: Map[String, Any]): Unit = {
for ((paramName, paramValue) <- xgboostParams) {
if ((paramName == "booster" && paramValue != "gbtree") ||
(paramName == "updater" && paramValue != "grow_histmaker,prune")) {
(paramName == "updater" && (paramValue != "grow_histmaker,prune" ||
paramValue != "hist"))) {
throw new IllegalArgumentException(s"you specified $paramName as $paramValue," +
s" XGBoost-Spark only supports gbtree as booster type" +
" and grow_histmaker,prune as the updater type")
" and grow_histmaker,prune or hist as the updater type")
}
val name = CaseFormat.LOWER_UNDERSCORE.to(CaseFormat.LOWER_CAMEL, paramName)
params.find(_.name == name) match {

62
jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala
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@@ -18,18 +18,21 @@ package ml.dmlc.xgboost4j.scala.spark
import java.nio.file.Files
import java.util.concurrent.LinkedBlockingDeque
import ml.dmlc.xgboost4j.java.Rabit
import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
import ml.dmlc.xgboost4j.scala.DMatrix
import ml.dmlc.xgboost4j.scala.rabit.RabitTracker
import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.TaskContext
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql._
import org.scalatest.FunSuite
import scala.util.Random
import ml.dmlc.xgboost4j.java.Rabit
class XGBoostGeneralSuite extends FunSuite with PerTest {
test("test Rabit allreduce to validate Scala-implemented Rabit tracker") {
@@ -108,66 +111,89 @@ class XGBoostGeneralSuite extends FunSuite with PerTest {
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
ignore("test with fast histo depthwise") {
test("test with fast histo with monotone_constraints") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "6", "silent" -> "1",
val paramMap = Map("eta" -> "1",
"max_depth" -> "6", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "depthwise",
"eval_metric" -> "error", "num_round" -> 5, "num_workers" -> math.min(numWorkers, 2))
// TODO: histogram algorithm seems to be very very sensitive to worker number
"num_round" -> 5, "num_workers" -> numWorkers, "monotone_constraints" -> "(1, 0)")
val model = new XGBoostClassifier(paramMap).fit(training)
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
ignore("test with fast histo lossguide") {
test("test with fast histo with interaction_constraints") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1",
"max_depth" -> "6", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "depthwise",
"num_round" -> 5, "num_workers" -> numWorkers, "interaction_constraints" -> "[[1,2],[2,3,4]]")
val model = new XGBoostClassifier(paramMap).fit(training)
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
test("test with fast histo depthwise") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1",
"max_depth" -> "6", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "depthwise",
"num_round" -> 5, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
test("test with fast histo lossguide") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "lossguide",
"max_leaves" -> "8", "eval_metric" -> "error", "num_round" -> 5,
"num_workers" -> math.min(numWorkers, 2))
"max_leaves" -> "8", "num_round" -> 5,
"num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)
}
ignore("test with fast histo lossguide with max bin") {
test("test with fast histo lossguide with max bin") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "0",
"objective" -> "binary:logistic", "tree_method" -> "hist",
"grow_policy" -> "lossguide", "max_leaves" -> "8", "max_bin" -> "16",
"eval_metric" -> "error", "num_round" -> 5, "num_workers" -> math.min(numWorkers, 2))
"eval_metric" -> "error", "num_round" -> 5, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)
}
ignore("test with fast histo depthwidth with max depth") {
test("test with fast histo depthwidth with max depth") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "0",
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "6", "silent" -> "0",
"objective" -> "binary:logistic", "tree_method" -> "hist",
"grow_policy" -> "depthwise", "max_leaves" -> "8", "max_depth" -> "2",
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> math.min(numWorkers, 2))
"grow_policy" -> "depthwise", "max_depth" -> "2",
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)
}
ignore("test with fast histo depthwidth with max depth and max bin") {
test("test with fast histo depthwidth with max depth and max bin") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "0",
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "6", "silent" -> "0",
"objective" -> "binary:logistic", "tree_method" -> "hist",
"grow_policy" -> "depthwise", "max_depth" -> "2", "max_bin" -> "2",
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> math.min(numWorkers, 2))
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)

5
jvm-packages/xgboost4j/src/test/java/ml/dmlc/xgboost4j/java/BoosterImplTest.java
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@@ -382,11 +382,12 @@ public class BoosterImplTest {
metrics, null, null, 0);
for (int i = 0; i < metrics.length; i++)
for (int j = 1; j < metrics[i].length; j++) {
TestCase.assertTrue(metrics[i][j] >= metrics[i][j - 1]);
TestCase.assertTrue(metrics[i][j] >= metrics[i][j - 1] ||
Math.abs(metrics[i][j] - metrics[i][j - 1]) < 0.1);
}
for (int i = 0; i < metrics.length; i++)
for (int j = 0; j < metrics[i].length; j++) {
TestCase.assertTrue(metrics[i][j] >= threshold);
TestCase.assertTrue(metrics[i][j] >= threshold);
}
booster.dispose();
}