zhengruifeng commented on a change in pull request #31985:
URL: https://github.com/apache/spark/pull/31985#discussion_r602997003
##########
File path:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
##########
@@ -1863,21 +1899,125 @@ class LogisticRegressionSuite extends MLTest with
DefaultReadWriteTest {
0.0, 0.0, 0.0, 0.09064661,
-0.1144333, 0.3204703, -0.1621061, -0.2308192,
0.0, -0.4832131, 0.0, 0.0), isTransposed = true)
- val interceptsRStd = Vectors.dense(-0.72638218, -0.01737265, 0.74375484)
+ val interceptsRStd = Vectors.dense(-0.69265374, -0.2260274, 0.9186811)
val coefficientsR = new DenseMatrix(3, 4, Array(
0.0, 0.0, 0.01641412, 0.03570376,
-0.05110822, 0.0, -0.21595670, -0.16162836,
0.0, 0.0, 0.0, 0.0), isTransposed = true)
val interceptsR = Vectors.dense(-0.44707756, 0.75180900, -0.3047314)
- assert(model1.coefficientMatrix ~== coefficientsRStd absTol 0.05)
- assert(model1.interceptVector ~== interceptsRStd relTol 0.1)
+ assert(model1.coefficientMatrix ~== coefficientsRStd absTol 1e-3)
+ assert(model1.interceptVector ~== interceptsRStd relTol 1e-3)
assert(model1.interceptVector.toArray.sum ~== 0.0 absTol eps)
- assert(model2.coefficientMatrix ~== coefficientsR absTol 0.02)
- assert(model2.interceptVector ~== interceptsR relTol 0.1)
+ assert(model2.coefficientMatrix ~== coefficientsR absTol 1e-3)
+ assert(model2.interceptVector ~== interceptsR relTol 1e-3)
assert(model2.interceptVector.toArray.sum ~== 0.0 absTol eps)
}
+ test("SPARK-34860: multinomial logistic regression with intercept, with
small var") {
Review comment:
master does not pass this newly add testsuite:
```
// scalastyle:off println
println("R")
println(interceptsR)
println(coefficientsR)
println()
println("model1")
println(model1.interceptVector)
println(model1.coefficientMatrix)
println()
println("model2")
println(model2.interceptVector)
println(model2.coefficientMatrix)
println()
println("R2")
println(interceptsR2)
println(coefficientsR2)
println()
println("model3")
println(model3.interceptVector)
println(model3.coefficientMatrix)
// scalastyle:on println
```
this PR:
```
R
[2.91748298,-17.510746,14.59326301]
0.21755977 0.01647541 0.16507778 -0.1401668
-0.24436 0.7564655 -0.2955698 1.3262009
0.02680026 -0.77294095 0.13049206 -1.18603411
model1
[2.933958199942738,-17.543164024163175,14.609205824220437]
0.21812136899052606 0.015486127035160564 0.16560717317181253
-0.14189621394905397
-0.2454895541210769 0.7584152697648037 -0.2966285999752721
1.3296192946128171
0.027368185130550855 -0.7739013967999642 0.13102142680345957
-1.187723080663763
model2
[2.933958199942738,-17.543164024163175,14.609205824220437]
0.21812136899052606 0.015486127035160564 0.16560717317181253
-0.14189621394905397
-0.2454895541210769 0.7584152697648037 -0.2966285999752721
1.3296192946128171
0.027368185130550855 -0.7739013967999642 0.13102142680345957
-1.187723080663763
R2
[1.751626027,-3.9297124987,2.178086472]
0.019970169 0.079611293 0.003959452 0.110024399
-4.788494E-4 0.0010097453 -5.832701E-4 0.0
-0.01936999 -0.080851149 -0.003319687 -0.112435972
model3
[1.7516587309368687,-3.9297178332916585,2.1780591023547897]
0.019968543900064605 0.07960456424549685 0.0039592584764418055
0.11002491382872195
-4.7805989516075794E-4 0.0010124410611496804 -5.830912612961964E-4 0.0
-0.01936890596857533 -0.08084716280475213 -0.0033195486718121834
-0.1124344396230352
```
master:
```
R
[2.91748298,-17.510746,14.59326301]
0.21755977 0.01647541 0.16507778 -0.1401668
-0.24436 0.7564655 -0.2955698 1.3262009
0.02680026 -0.77294095 0.13049206 -1.18603411
model1
[3.2289115796175536,-3.8874667667006286,0.6585551870830749]
0.21614280080869921 0.010853354751576538 0.16526956599746928
-0.16826299113708829
-0.24226138413980347 0.766137782321547 -0.2961105375461299
-0.01353727702893284
0.02611858333110428 -0.7769911370731234 0.13084097154866067
0.18180026816602116
model2
[3.2289115795385817,-3.8874667667014213,0.65855518716284]
0.216142800347921 0.01085335149421333 0.1652695665789533
-0.16826299025797364
-0.24226138429694594 0.7661377826486023 -0.2961105377075671
-0.013537276769415511
0.026118583949024932 -0.7769911341428156 0.13084097112861381
0.18180026702738916
R2
[1.751626027,-3.9297124987,2.178086472]
0.019970169 0.079611293 0.003959452 0.110024399
-4.788494E-4 0.0010097453 -5.832701E-4 0.0
-0.01936999 -0.080851149 -0.003319687 -0.112435972
model3
[3.2372615840468177,-3.887368655600576,0.6501070715537581]
0.019839138236381705 0.0794011365650966 0.0039488069038510374
-0.028039490169532715
-4.7766310601985774E-4 0.001015028177410154 -5.837314941386912E-4
0.003248397753479403
-0.019272077893351464 -0.0806696209827808 -0.0033080309178124827
0.04470991419683469
```
We can see that new impl generate solution much more close to GLMNET.
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