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From srowen <>
Subject [GitHub] spark pull request: [SPARK-4588] ML Attributes
Date Sat, 07 Mar 2015 00:03:56 GMT
Github user srowen commented on a diff in the pull request:
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala ---
    @@ -0,0 +1,234 @@
    + * 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
    + *
    + *
    + *
    + * 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.
    + */
    +import scala.collection.mutable.ArrayBuffer
    +import org.apache.spark.mllib.linalg.VectorUDT
    +import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField}
    + * Attributes that describe a vector ML column.
    + *
    + * @param name name of the attribute group (the ML column name)
    + * @param numAttributes optional number of attributes. At most one of `numAttributes`
and `attrs`
    + *                      can be defined.
    + * @param attrs optional array of attributes. Attribute will be copied with their corresponding
    + *              indices in the array.
    + */
    +class AttributeGroup private (
    --- End diff --
    Does that effectively 'flatten' the schema, you mean? so if I have two numeric columns,
and one vector-valued columns with 3 features inside, then the assembler makes a representation
of 5 features? If that's right, then yes now I get what you mean by this. So, there is nothing
here that represents the original schema with its nesting, since that would never be how it
is presented to an algorithm, right?

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