Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10743#discussion_r50032693
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -276,113 +276,123 @@ class LogisticRegression @Since("1.2.0") (
val numClasses = histogram.length
val numFeatures = summarizer.mean.size
- if (numInvalid != 0) {
- val msg = s"Classification labels should be in {0 to ${numClasses -
1} " +
- s"Found $numInvalid invalid labels."
- logError(msg)
- throw new SparkException(msg)
- }
-
- if (numClasses > 2) {
- val msg = s"Currently, LogisticRegression with ElasticNet in ML
package only supports " +
- s"binary classification. Found $numClasses in the input dataset."
- logError(msg)
- throw new SparkException(msg)
- }
+ val (coefficients, intercept, objectiveHistory) = {
+ if (numInvalid != 0) {
+ val msg = s"Classification labels should be in {0 to ${numClasses
- 1} " +
+ s"Found $numInvalid invalid labels."
+ logError(msg)
+ throw new SparkException(msg)
+ }
- val featuresMean = summarizer.mean.toArray
- val featuresStd = summarizer.variance.toArray.map(math.sqrt)
+ if (numClasses > 2) {
+ val msg = s"Currently, LogisticRegression with ElasticNet in ML
package only supports " +
+ s"binary classification. Found $numClasses in the input dataset."
+ logError(msg)
+ throw new SparkException(msg)
+ } else if ($(fitIntercept) && numClasses == 2 && histogram(0) ==
0.0) {
+ logWarning(s"All labels are one and fitIntercept=true, so the
coefficients will be " +
+ s"zeros and the intercept will be positive infinity; as a
result, " +
+ s"training is not needed.")
+ (Vectors.sparse(numFeatures, Seq()), Double.PositiveInfinity,
Array.empty[Double])
+ } else if ($(fitIntercept) && numClasses == 1) {
+ logWarning(s"All labels are one and fitIntercept=true, so the
coefficients will be " +
+ s"zeros and the intercept will be negative infinity; as a
result, " +
+ s"training is not needed.")
+ (Vectors.sparse(numFeatures, Seq()), Double.NegativeInfinity,
Array.empty[Double])
+ } else {
+ val featuresMean = summarizer.mean.toArray
+ val featuresStd = summarizer.variance.toArray.map(math.sqrt)
- val regParamL1 = $(elasticNetParam) * $(regParam)
- val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
+ val regParamL1 = $(elasticNetParam) * $(regParam)
+ val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
- val costFun = new LogisticCostFun(instances, numClasses,
$(fitIntercept), $(standardization),
- featuresStd, featuresMean, regParamL2)
+ val costFun = new LogisticCostFun(instances, numClasses,
$(fitIntercept),
+ $(standardization), featuresStd, featuresMean, regParamL2)
- val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) {
- new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
- } else {
- def regParamL1Fun = (index: Int) => {
- // Remove the L1 penalization on the intercept
- if (index == numFeatures) {
- 0.0
+ val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) ==
0.0) {
+ new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
} else {
- if ($(standardization)) {
- regParamL1
- } else {
- // If `standardization` is false, we still standardize the data
- // to improve the rate of convergence; as a result, we have to
- // perform this reverse standardization by penalizing each
component
- // differently to get effectively the same objective function
when
- // the training dataset is not standardized.
- if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index)
else 0.0
+ def regParamL1Fun = (index: Int) => {
+ // Remove the L1 penalization on the intercept
+ if (index == numFeatures) {
+ 0.0
+ } else {
+ if ($(standardization)) {
+ regParamL1
+ } else {
+ // If `standardization` is false, we still standardize the
data
+ // to improve the rate of convergence; as a result, we
have to
+ // perform this reverse standardization by penalizing each
component
+ // differently to get effectively the same objective
function when
+ // the training dataset is not standardized.
+ if (featuresStd(index) != 0.0) regParamL1 /
featuresStd(index) else 0.0
+ }
+ }
}
+ new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun,
$(tol))
}
- }
- new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun,
$(tol))
- }
-
- val initialCoefficientsWithIntercept =
- Vectors.zeros(if ($(fitIntercept)) numFeatures + 1 else numFeatures)
-
- if ($(fitIntercept)) {
- /*
- For binary logistic regression, when we initialize the
coefficients as zeros,
- it will converge faster if we initialize the intercept such that
- it follows the distribution of the labels.
-
- {{{
- P(0) = 1 / (1 + \exp(b)), and
- P(1) = \exp(b) / (1 + \exp(b))
- }}}, hence
- {{{
- b = \log{P(1) / P(0)} = \log{count_1 / count_0}
- }}}
- */
- initialCoefficientsWithIntercept.toArray(numFeatures)
- = math.log(histogram(1) / histogram(0))
- }
- val states = optimizer.iterations(new CachedDiffFunction(costFun),
- initialCoefficientsWithIntercept.toBreeze.toDenseVector)
+ val initialCoefficientsWithIntercept =
+ Vectors.zeros(if ($(fitIntercept)) numFeatures + 1 else
numFeatures)
+
+ if ($(fitIntercept)) {
+ /*
--- End diff --
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