diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala | 11 | ||||
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala | 6 |
2 files changed, 14 insertions, 3 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index 965ce3d6f2..bc8154692e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -609,9 +609,14 @@ class LogisticRegression @Since("1.2.0") ( Friedman, et al. "Regularization Paths for Generalized Linear Models via Coordinate Descent," https://core.ac.uk/download/files/153/6287975.pdf */ - val denseValues = denseCoefficientMatrix.values - val coefficientMean = denseValues.sum / denseValues.length - denseCoefficientMatrix.update(_ - coefficientMean) + val centers = Array.fill(numFeatures)(0.0) + denseCoefficientMatrix.foreachActive { case (i, j, v) => + centers(j) += v + } + centers.transform(_ / numCoefficientSets) + denseCoefficientMatrix.foreachActive { case (i, j, v) => + denseCoefficientMatrix.update(i, j, v - centers(j)) + } } // center the intercepts when using multinomial algorithm diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index c858b9bbfc..83f575e838 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -1139,6 +1139,9 @@ class LogisticRegressionSuite 0.10095851, -0.85897154, 0.08392798, 0.07904499), isTransposed = true) val interceptsR = Vectors.dense(-2.10320093, 0.3394473, 1.76375361) + model1.coefficientMatrix.colIter.foreach(v => assert(v.toArray.sum ~== 0.0 absTol eps)) + model2.coefficientMatrix.colIter.foreach(v => assert(v.toArray.sum ~== 0.0 absTol eps)) + assert(model1.coefficientMatrix ~== coefficientsR relTol 0.05) assert(model1.coefficientMatrix.toArray.sum ~== 0.0 absTol eps) assert(model1.interceptVector ~== interceptsR relTol 0.05) @@ -1204,6 +1207,9 @@ class LogisticRegressionSuite -0.3180040, 0.9679074, -0.2252219, -0.4319914, 0.2452411, -0.6046524, 0.1050710, 0.1180180), isTransposed = true) + model1.coefficientMatrix.colIter.foreach(v => assert(v.toArray.sum ~== 0.0 absTol eps)) + model2.coefficientMatrix.colIter.foreach(v => assert(v.toArray.sum ~== 0.0 absTol eps)) + assert(model1.coefficientMatrix ~== coefficientsR relTol 0.05) assert(model1.coefficientMatrix.toArray.sum ~== 0.0 absTol eps) assert(model1.interceptVector.toArray === Array.fill(3)(0.0)) |