Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/18315#discussion_r133664633
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/HingeAggregator.scala
---
@@ -0,0 +1,106 @@
+/*
+ * 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.aggregator
+
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg._
+
+/**
+ * HingeAggregator computes the gradient and loss for loss function
("hinge" or
+ * "squared_hinge", as used in binary classification for instances in
sparse or dense
--- End diff --
It seems this only support _hinge_ loss currently. BTW, if we support
_squared hinge_ in the future, what is the best way? Add a param _loss
function_ for ```HingeAggregator``` or just add a new
```SquaredHingeAggregator```? The later way should be more clear, but with more
code.
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