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.


---
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 infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---

Reply via email to