diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala | 29 |
1 files changed, 15 insertions, 14 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 17de215b97..2b7145362a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -205,7 +205,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] throw new SparkException("Input validation failed.") } - /** + /* * Scaling columns to unit variance as a heuristic to reduce the condition number: * * During the optimization process, the convergence (rate) depends on the condition number of @@ -225,26 +225,27 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] * Currently, it's only enabled in LogisticRegressionWithLBFGS */ val scaler = if (useFeatureScaling) { - (new StandardScaler(withStd = true, withMean = false)).fit(input.map(x => x.features)) + new StandardScaler(withStd = true, withMean = false).fit(input.map(_.features)) } else { null } // Prepend an extra variable consisting of all 1.0's for the intercept. - val data = if (addIntercept) { - if (useFeatureScaling) { - input.map(labeledPoint => - (labeledPoint.label, appendBias(scaler.transform(labeledPoint.features)))) - } else { - input.map(labeledPoint => (labeledPoint.label, appendBias(labeledPoint.features))) - } - } else { - if (useFeatureScaling) { - input.map(labeledPoint => (labeledPoint.label, scaler.transform(labeledPoint.features))) + // TODO: Apply feature scaling to the weight vector instead of input data. + val data = + if (addIntercept) { + if (useFeatureScaling) { + input.map(lp => (lp.label, appendBias(scaler.transform(lp.features)))).cache() + } else { + input.map(lp => (lp.label, appendBias(lp.features))).cache() + } } else { - input.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) + if (useFeatureScaling) { + input.map(lp => (lp.label, scaler.transform(lp.features))).cache() + } else { + input.map(lp => (lp.label, lp.features)) + } } - } /** * TODO: For better convergence, in logistic regression, the intercepts should be computed |