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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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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)