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[SPARK-25623][SPARK-25624][SPARK-25625][TEST] Reduce test time of LogisticRegressionSuite #22659

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[SPARK-25623][SPARK-25624][SPARK-25625][TEST] Reduce test time of LogisticRegressionSuite #22659

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28 changes: 15 additions & 13 deletions mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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 = {
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand All @@ -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)
Expand Down