[jira] [Comment Edited] (SPARK-13857) Feature parity for ALS ML with MLLIB

["Danilo Ascione (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15822021#comment-15822021 ] 

Danilo Ascione edited comment on SPARK-13857 at 1/17/17 4:12 PM:
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I have a pipeline similar to [~abudd2014]'s one. I have implemented a dataframe api based RankingEvaluator that takes care of getting the top K recommendations at the evaluation phase of the pipeline, and it can be used in model selection pipeline (Cross-Validation). 
Sample usage code:
{code}
val als = new ALS() //input dataframe (userId, itemId, clicked)
      .setUserCol("userId")
      .setItemCol("itemId")
      .setRatingCol("clicked")
      .setImplicitPrefs(true)

val paramGrid = new ParamGridBuilder()
    .addGrid(als.regParam, Array(0.01,0.1))
    .addGrid(als.alpha, Array(40.0, 1.0))
    .build()

val evaluator = new RankingEvaluator()
    .setMetricName("mpr") //Mean Percentile Rank
    .setLabelCol("itemId")
    .setPredictionCol("prediction")
    .setQueryCol("userId")
    .setK(5) //Top K
 
val cv = new CrossValidator()
  .setEstimator(als)
  .setEvaluator(evaluator)
  .setEstimatorParamMaps(paramGrid)
  .setNumFolds(3)

val crossValidatorModel = cv.fit(inputDF)

// Print the average metrics per ParamGrid entry
val avgMetricsParamGrid = crossValidatorModel.avgMetrics

// Combine with paramGrid to see how they affect the overall metrics
val combined = paramGrid.zip(avgMetricsParamGrid)
{code}

Then the resulting "bestModel" from cross validation model is used to generate the top K recommendations in batches.

RankingEvaluator code is here [https://github.com/apache/spark/pull/16618/files#diff-0345c4cb1878d3bb0d84297202fdc95f]

I would appreciate any feedback. Thanks!



was (Author: danilo.ascione):
I have a pipeline similar to [~abudd2014]'s one. I have implemented a dataframe api based RankingEvaluator that takes care of getting the top K recommendations at the evaluation phase of the pipeline, and it can be used in model selection pipeline (Cross-Validation). 
Sample usage code:
{code}
val als = new ALS() //input dataframe (userId, itemId, clicked)
      .setUserCol("userId")
      .setItemCol("itemId")
      .setRatingCol("clicked")
      .setImplicitPrefs(true)

val paramGrid = new ParamGridBuilder()
    .addGrid(als.regParam, Array(0.01,0.1))
    .addGrid(als.alpha, Array(40.0, 1.0))
    .build()

val evaluator = new RankingEvaluator()
    .setMetricName("mpr") //Mean Percentile Rank
    .setLabelCol("itemId")
    .setPredictionCol("prediction")
    .setQueryCol("userId")
    .setK(5) //Top K
 
val cv = new CrossValidator()
  .setEstimator(als)
  .setEvaluator(evaluator)
  .setEstimatorParamMaps(paramGrid)
  .setNumFolds(3)

val crossValidatorModel = cv.fit(inputDF)

// Print the average metrics per ParamGrid entry
val avgMetricsParamGrid = crossValidatorModel.avgMetrics

// Combine with paramGrid to see how they affect the overall metrics
val combined = paramGrid.zip(avgMetricsParamGrid)
{code}

Then the resulting "bestModel" from cross validation model is used to generate the top K recommendations in batches.

RankingEvaluator code is here [https://github.com/daniloascione/spark/commit/c93ab86d35984e9f70a3b4f543fb88f5541333f0]

I would appreciate any feedback. Thanks!


> Feature parity for ALS ML with MLLIB
> ------------------------------------
>
>                 Key: SPARK-13857
>                 URL: https://issues.apache.org/jira/browse/SPARK-13857
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Nick Pentreath
>            Assignee: Nick Pentreath
>
> Currently {{mllib.recommendation.MatrixFactorizationModel}} has methods {{recommendProducts/recommendUsers}} for recommending top K to a given user / item, as well as {{recommendProductsForUsers/recommendUsersForProducts}} to recommend top K across all users/items.
> Additionally, SPARK-10802 is for adding the ability to do {{recommendProductsForUsers}} for a subset of users (or vice versa).
> Look at exposing or porting (as appropriate) these methods to ALS in ML. 
> Investigate if efficiency can be improved at the same time (see SPARK-11968).



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