Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/17094#discussion_r103370789
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/loss/RDDLossFunction.scala ---
@@ -0,0 +1,70 @@
+/*
+ * 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 scala.reflect.ClassTag
+
+import breeze.linalg.{DenseVector => BDV}
+import breeze.optimize.DiffFunction
+
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors}
+import org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
+import org.apache.spark.rdd.RDD
+
+/**
+ * This class computes the gradient and loss of a differentiable loss
function by mapping a
+ * [[DifferentiableLossAggregator]] over an [[RDD]] of [[Instance]]s. The
loss function is the
+ * sum of the loss computed on a single instance across all points in the
RDD. Therefore, the actual
+ * analytical form of the loss function is specified by the aggregator,
which computes each points
+ * contribution to the overall loss.
+ *
+ * A differentiable regularization component can also be added by
providing a
+ * [[DifferentiableRegularization]] loss function.
+ *
+ * @param instances
+ * @param getAggregator A function which gets a new loss aggregator in
every tree aggregate step.
+ * @param regularization An option representing the regularization loss
function to apply to the
+ * coefficients.
+ * @param aggregationDepth The aggregation depth of the tree aggregation
step.
+ * @tparam Agg Specialization of [[DifferentiableLossAggregator]],
representing the concrete type
+ * of the aggregator.
+ */
+private[ml] class RDDLossFunction[Agg <:
DifferentiableLossAggregator[Instance, Agg]: ClassTag](
+ instances: RDD[Instance],
+ getAggregator: (Broadcast[Vector] => Agg),
+ regularization: Option[DifferentiableRegularization[Array[Double]]],
+ aggregationDepth: Int = 2)
+ extends DiffFunction[BDV[Double]] {
+
+ override def calculate(coefficients: BDV[Double]): (Double, BDV[Double])
= {
+ val bcCoefficients =
instances.context.broadcast(Vectors.dense(coefficients.data))
--- End diff --
use `asBreeze`
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