Github user srowen commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3637#discussion_r21562163
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/LabeledPoint.scala ---
    @@ -0,0 +1,52 @@
    +/*
    + * 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
    +
    +import scala.beans.BeanInfo
    +
    +import org.apache.spark.annotation.AlphaComponent
    +import org.apache.spark.mllib.linalg.Vector
    +
    +/**
    + * :: AlphaComponent ::
    + * Class that represents an instance (data point) for prediction tasks.
    + *
    + * @param label Label to predict
    + * @param features List of features describing this instance
    + * @param weight Instance weight
    + */
    +@AlphaComponent
    +@BeanInfo
    +case class LabeledPoint(label: Double, features: Vector, weight: Double) {
    --- End diff --
    
    All good points. I think there's a "pro" for optimizing storage but that's 
a bit secondary.
    
    I don't think a caller has to 'translate' 0/1 labels to categorical. These 
can be labels called 0 and 1. Given schema information, all of this is stuff 
frameworks can do for you. Is there really a case where the user doesn't know 
schema types, suggests a type, and lets the framework override it?
    
    So, let's say my feature takes on "foo", "bar", "baz" as values. Doesn't 
the caller always have to translate to/from numbers? no big deal but is that 
simpler? I think the schema abstraction is going to help with this, I imagine.
    
    Anyway, not really strongly arguing with you here, just completing the 
discussion. An array of doubles is kind of raw but certainly does the job.


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---

---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]

Reply via email to