Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/21081#discussion_r183556811
--- Diff: mllib/src/main/scala/org/apache/spark/ml/util/DatasetUtils.scala
---
@@ -0,0 +1,54 @@
+/*
+ * 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.util
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.linalg.{Vectors, VectorUDT}
+import org.apache.spark.sql.{Column, Dataset}
+import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{ArrayType, DoubleType, FloatType}
+
+
+private[spark] object DatasetUtils {
+
+ /**
+ * preprocessing the input feature column to Vector
--- End diff --
This is a bit unclear. How about: "Cast a column in a Dataset to a Vector
type."
Also, this isn't specific to features, so please clarify that below.
Finally, the key thing to document is the list of supported input types, so
I'd add that.
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