Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1897#discussion_r16099150
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
---
@@ -137,11 +154,45 @@ abstract class GeneralizedLinearAlgorithm[M <:
GeneralizedLinearModel]
throw new SparkException("Input validation failed.")
}
+ /**
+ * Scaling to minimize the condition number:
+ *
+ * During the optimization process, the convergence (rate) depends on
the condition number of
+ * the training dataset. Scaling the variables often reduces this
condition number, thus
+ * improving the convergence rate dramatically. Without reducing the
condition number,
+ * some training datasets mixing the columns with different scales may
not be able to converge.
+ *
+ * GLMNET and LIBSVM packages perform the scaling to reduce the
condition number, and return
+ * the weights in the original scale.
+ * See page 9 in
http://cran.r-project.org/web/packages/glmnet/glmnet.pdf
+ *
+ * Here, if useFeatureScaling is enabled, we will standardize the
training features by dividing
+ * the variance of each column (without subtracting the mean), and
train the model in the
+ * scaled space. Then we transform the coefficients from the scaled
space to the original scale
+ * as GLMNET and LIBSVM do.
+ *
+ * Currently, it's only enabled in LogisticRegressionWithLBFGS
+ */
+ val scaler = if (useFeatureScaling) {
+ (new StandardScaler).fit(input.map(x => x.features))
+ } else {
+ null
+ }
+
// Prepend an extra variable consisting of all 1.0's for the intercept.
val data = if (addIntercept) {
- input.map(labeledPoint => (labeledPoint.label,
appendBias(labeledPoint.features)))
+ if(useFeatureScaling) {
+ input.map(labeledPoint =>
+ (labeledPoint.label,
appendBias(scaler.transform(labeledPoint.features))))
+ } else {
+ input.map(labeledPoint => (labeledPoint.label,
appendBias(labeledPoint.features)))
+ }
} else {
- input.map(labeledPoint => (labeledPoint.label,
labeledPoint.features))
+ if (useFeatureScaling) {
+ input.map(labeledPoint => (labeledPoint.label,
scaler.transform(labeledPoint.features)))
+ } else {
+ input.map(labeledPoint => (labeledPoint.label,
labeledPoint.features))
--- End diff --
should use `input` itself
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
