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Posted to commits@spark.apache.org by sr...@apache.org on 2018/10/09 00:07:10 UTC
spark git commit: [SPARK-25623][SPARK-25624][SPARK-25625][TEST]
Reduce test time of LogisticRegressionSuite
Repository: spark
Updated Branches:
refs/heads/master f3fed2823 -> a4b14a9cf
[SPARK-25623][SPARK-25624][SPARK-25625][TEST] Reduce test time of LogisticRegressionSuite
...with intercept with L1 regularization
## What changes were proposed in this pull request?
In the test, "multinomial logistic regression with intercept with L1 regularization" in the "LogisticRegressionSuite", taking more than a minute due to training of 2 logistic regression model.
However after analysing the training cost over iteration, we can reduce the computation time by 50%.
Training cost vs iteration for model 1
So, model1 is converging after iteration 150.
Training cost vs iteration for model 2
After around 100 iteration, model2 is converging.
So, if we give maximum iteration for model1 and model2 as 175 and 125 respectively, we can reduce the computation time by half.
## How was this patch tested?
Computation time in local setup :
Before change:
~53 sec
After change:
~26 sec
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes #22659 from shahidki31/SPARK-25623.
Authored-by: Shahid <sh...@gmail.com>
Signed-off-by: Sean Owen <se...@databricks.com>
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/a4b14a9c
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/a4b14a9c
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/a4b14a9c
Branch: refs/heads/master
Commit: a4b14a9cf828572829ad74743e68a06eb376ba28
Parents: f3fed28
Author: Shahid <sh...@gmail.com>
Authored: Mon Oct 8 19:07:05 2018 -0500
Committer: Sean Owen <se...@databricks.com>
Committed: Mon Oct 8 19:07:05 2018 -0500
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.../LogisticRegressionSuite.scala | 28 +++++++++++---------
1 file changed, 15 insertions(+), 13 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/a4b14a9c/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
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diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 75c2aeb..84c10e2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -79,7 +79,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
generateMultinomialLogisticInput(coefficients, xMean, xVariance,
addIntercept = true, nPoints, seed)
- sc.parallelize(testData, 4).toDF().withColumn("weight", rand(seed))
+ val df = sc.parallelize(testData, 4).toDF().withColumn("weight", rand(seed))
+ df.cache()
+ df
}
multinomialDataset = {
@@ -1130,9 +1132,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
}
test("binary logistic regression with intercept with ElasticNet regularization") {
- val trainer1 = (new LogisticRegression).setFitIntercept(true).setMaxIter(200)
+ val trainer1 = (new LogisticRegression).setFitIntercept(true).setMaxIter(120)
.setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true).setWeightCol("weight")
- val trainer2 = (new LogisticRegression).setFitIntercept(true)
+ val trainer2 = (new LogisticRegression).setFitIntercept(true).setMaxIter(30)
.setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false).setWeightCol("weight")
val model1 = trainer1.fit(binaryDataset)
@@ -1174,7 +1176,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
val coefficientsR = Vectors.dense(0.0, 0.0, -0.1846038, -0.0559614)
val interceptR = 0.5024256
- assert(model1.intercept ~== interceptRStd relTol 6E-3)
+ assert(model1.intercept ~== interceptRStd relTol 6E-2)
assert(model1.coefficients ~== coefficientsRStd absTol 5E-3)
assert(model2.intercept ~== interceptR relTol 6E-3)
assert(model2.coefficients ~= coefficientsR absTol 1E-3)
@@ -1677,10 +1679,10 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
// use tighter constraints because OWL-QN solver takes longer to converge
val trainer1 = (new LogisticRegression).setFitIntercept(true)
.setElasticNetParam(1.0).setRegParam(0.05).setStandardization(true)
- .setMaxIter(300).setTol(1e-10).setWeightCol("weight")
+ .setMaxIter(160).setTol(1e-10).setWeightCol("weight")
val trainer2 = (new LogisticRegression).setFitIntercept(true)
.setElasticNetParam(1.0).setRegParam(0.05).setStandardization(false)
- .setMaxIter(300).setTol(1e-10).setWeightCol("weight")
+ .setMaxIter(110).setTol(1e-10).setWeightCol("weight")
val model1 = trainer1.fit(multinomialDataset)
val model2 = trainer2.fit(multinomialDataset)
@@ -1767,7 +1769,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
0.0, 0.0, 0.0, 0.0), isTransposed = true)
val interceptsR = Vectors.dense(-0.44215290, 0.76308326, -0.3209304)
- assert(model1.coefficientMatrix ~== coefficientsRStd absTol 0.02)
+ assert(model1.coefficientMatrix ~== coefficientsRStd absTol 0.05)
assert(model1.interceptVector ~== interceptsRStd relTol 0.1)
assert(model1.interceptVector.toArray.sum ~== 0.0 absTol eps)
assert(model2.coefficientMatrix ~== coefficientsR absTol 0.02)
@@ -2145,10 +2147,10 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
test("multinomial logistic regression with intercept with elasticnet regularization") {
val trainer1 = (new LogisticRegression).setFitIntercept(true).setWeightCol("weight")
.setElasticNetParam(0.5).setRegParam(0.1).setStandardization(true)
- .setMaxIter(300).setTol(1e-10)
+ .setMaxIter(220).setTol(1e-10)
val trainer2 = (new LogisticRegression).setFitIntercept(true).setWeightCol("weight")
.setElasticNetParam(0.5).setRegParam(0.1).setStandardization(false)
- .setMaxIter(300).setTol(1e-10)
+ .setMaxIter(90).setTol(1e-10)
val model1 = trainer1.fit(multinomialDataset)
val model2 = trainer2.fit(multinomialDataset)
@@ -2234,8 +2236,8 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
0.0, 0.0, 0.0, 0.0), isTransposed = true)
val interceptsR = Vectors.dense(-0.38857157, 0.62492165, -0.2363501)
- assert(model1.coefficientMatrix ~== coefficientsRStd absTol 0.01)
- assert(model1.interceptVector ~== interceptsRStd absTol 0.01)
+ assert(model1.coefficientMatrix ~== coefficientsRStd absTol 0.05)
+ assert(model1.interceptVector ~== interceptsRStd absTol 0.1)
assert(model1.interceptVector.toArray.sum ~== 0.0 absTol eps)
assert(model2.coefficientMatrix ~== coefficientsR absTol 0.01)
assert(model2.interceptVector ~== interceptsR absTol 0.01)
@@ -2245,10 +2247,10 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
test("multinomial logistic regression without intercept with elasticnet regularization") {
val trainer1 = (new LogisticRegression).setFitIntercept(false).setWeightCol("weight")
.setElasticNetParam(0.5).setRegParam(0.1).setStandardization(true)
- .setMaxIter(300).setTol(1e-10)
+ .setMaxIter(75).setTol(1e-10)
val trainer2 = (new LogisticRegression).setFitIntercept(false).setWeightCol("weight")
.setElasticNetParam(0.5).setRegParam(0.1).setStandardization(false)
- .setMaxIter(300).setTol(1e-10)
+ .setMaxIter(50).setTol(1e-10)
val model1 = trainer1.fit(multinomialDataset)
val model2 = trainer2.fit(multinomialDataset)
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