Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/17094#discussion_r118476253
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/loss/DifferentiableRegularization.scala
---
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.optim.loss
+
+import breeze.optimize.DiffFunction
+
+/**
+ * A Breeze diff function which represents a cost function for
differentiable regularization
+ * of parameters. e.g. L2 regularization: 1 / 2 regParam * beta dot beta
+ *
+ * @tparam T The type of the coefficients being regularized.
+ */
+private[ml] trait DifferentiableRegularization[T] extends DiffFunction[T] {
+
+ def regParam: Double
+
+}
+
+/**
+ * A Breeze diff function for computing the L2 regularized loss and
gradient of an array of
+ * coefficients.
+ *
+ * @param regParam The magnitude of the regularization.
+ * @param shouldApply A function (Int => Boolean) indicating whether a
given index should have
+ * regularization applied to it.
+ * @param featuresStd Option indicating whether the regularization should
be scaled by the standard
+ * deviation of the features.
+ */
+private[ml] class L2Regularization(
+ val regParam: Double,
+ shouldApply: Int => Boolean,
+ featuresStd: Option[Array[Double]]) extends
DifferentiableRegularization[Array[Double]] {
+
+ override def calculate(coefficients: Array[Double]): (Double,
Array[Double]) = {
+ var sum = 0.0
+ val gradient = new Array[Double](coefficients.length)
+ coefficients.indices.filter(shouldApply).foreach { j =>
+ featuresStd match {
+ case Some(std) =>
+ if (std(j) != 0.0) {
+ val temp = coefficients(j) / (std(j) * std(j))
+ sum += coefficients(j) * temp
+ gradient(j) = regParam * temp
+ } else {
+ 0.0
+ }
+ case None =>
+ sum += coefficients(j) * coefficients(j)
--- End diff --
Not sure if this is performance critical but in a few blocks like this an
array index is dereferenced many times and could be saved off, if it mattered,
to optimize a bit
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