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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-4964) FlinkML - Add StringIndexer
Date Thu, 03 Nov 2016 17:01:58 GMT

    [ https://issues.apache.org/jira/browse/FLINK-4964?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15633464#comment-15633464
] 

ASF GitHub Bot commented on FLINK-4964:
---------------------------------------

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

    https://github.com/apache/flink/pull/2740#discussion_r86391230
  
    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/preprocessing/StringIndexer.scala
---
    @@ -0,0 +1,163 @@
    +/*
    + * 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.flink.ml.preprocessing
    +
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.common.{Parameter, ParameterMap}
    +import org.apache.flink.ml.pipeline.{FitOperation, TransformDataSetOperation, Transformer}
    +import org.apache.flink.ml.preprocessing.StringIndexer.HandleInvalid
    +
    +import scala.collection.immutable.Seq
    +
    +/**
    +  * StringIndexer maps a DataSet[String] to a DataSet[(String,Int)] where each label
is
    +  * associated with an index.The indices are in [0,numLabels) and are ordered by label
    +  * frequencies. The most frequent label has index 0.
    +  *
    +  * @example
    +  * {{{
    +  *                val trainingDS: DataSet[String] = env.fromCollection(data)
    +  *                val transformer = StringIndexer().setHandleInvalid("skip")
    +  *
    +  *                transformer.fit(trainingDS)
    +  *                val transformedDS = transformer.transform(trainingDS)
    +  * }}}
    +  *
    +  *
    +  * You can manage unseen labels using HandleInvalid parameter. If HandleInvalid is
    +  * set to "skip" (see example),then each line containing an unseen label is skipped.
    +  * Otherwise an exception is raised.
    +  *
    +  * =Parameters=
    +  *
    +  * -[[HandleInvalid]]: Define how to handle unseen labels: by default is "skip"
    +  *
    +  *
    +  */
    +class StringIndexer extends Transformer[StringIndexer] {
    +
    +  private[preprocessing] var metricsOption: Option[Map[String, Int]] = None
    +
    +
    +  /**
    +    * Set the value to handle unseen labels
    +    * @param value set to "skip" if you want to filter line with unseen labels
    +    * @return StringIndexer instance with HandleInvalid value
    +    */
    +  def setHandleInvalid(value: String): this.type ={
    +    parameters.add( HandleInvalid, value )
    +    this
    +  }
    +
    +}
    +
    +object StringIndexer {
    +
    +  case object HandleInvalid extends Parameter[String] {
    +    val defaultValue: Option[String] = Some( "skip" )
    +  }
    +
    +  // ==================================== Factory methods ========================================
    +
    +  def apply(): StringIndexer ={
    +    new StringIndexer( )
    +  }
    +
    +  // ====================================== Operations ===========================================
    +
    +  /**
    +    *  Trains [[StringIndexer]] by learning the count of each labels in the input DataSet.
    +    *
    +    * @return [[FitOperation]] training the [[StringIndexer]] on string labels
    +    */
    +  implicit def fitStringIndexer ={
    +    new FitOperation[StringIndexer, String] {
    +      def fit(instance: StringIndexer, fitParameters: ParameterMap,
    +        input: DataSet[String]): Unit = {
    +        val metrics = extractIndices( input )
    +        instance.metricsOption = Some( metrics )
    +      }
    +    }
    +  }
    +
    +  /**
    +    * Count the frequency of each label, sort them in a decreasing order and assign an
index
    +    *
    +    * @param input input Dataset containing labels
    +    * @return a map that returns for each label (key) its index (value)
    +    */
    +  private def extractIndices(input: DataSet[String]): Map[String, Int] ={
    +
    +    implicit val resultTypeInformation = createTypeInformation[(String, Int)]
    +
    +    val mapper = input
    +      .map( s => (s, 1) )
    +      .groupBy( 0 )
    +      .reduce( (a, b) => (a._1, a._2 + b._2) )
    +      .collect( )
    --- End diff --
    
    `collect()` returns all results (via an accumulator) to the calling program. I'm not seeing
why this would be necessary.


> FlinkML - Add StringIndexer
> ---------------------------
>
>                 Key: FLINK-4964
>                 URL: https://issues.apache.org/jira/browse/FLINK-4964
>             Project: Flink
>          Issue Type: New Feature
>            Reporter: Thomas FOURNIER
>            Priority: Minor
>
> Add StringIndexer as described here:
> http://spark.apache.org/docs/latest/ml-features.html#stringindexer
> This will be added in package preprocessing of FlinkML



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