A PredictionIO engine is instantiated by a set of parameters, these parameters determines which algorithm is used as well as the parameter for the algorithm. It naturally raises a question of how to choose the best set of parameters. The evaluation module streamlines the process of tuning the engine to the best parameter set and deploy it.

Evaluation Quick Start

We assume you have run the Recommendation Quick Start will skip the data collection / import instructions.

Edit the AppName

Edit MyRecommendation/src/main/scala/Evaluation.scala to specify the appName you used to import the data.

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object ParamsList extends EngineParamsGenerator {
  private[this] val baseEP = EngineParams(
    dataSourceParams = DataSourceParams(
      appName = "MyApp1",
      ...
      )
  ...
}

Build and run the evaluation

To run evaluation, the command pio eval is used. It takes two mandatory parameter, 1. the Evaluation object, it tells PredictionIO the engine and metric we use for the evaluation; and 2. the EngineParamsGenerator, it contains a list of engine params to test against. The following command kickstarts the evaluation workflow for the classification template (replace "org.template" with your package).

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$ pio build
...
$ pio eval org.template.RecommendationEvaluation \
    org.template.EngineParamsList

You will see the following output:

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...
[INFO 2015-03-31 00:31:53,934] [CoreWorkflow$] runEvaluation started
...
[INFO 2015-03-31 00:35:56,782] [CoreWorkflow$] Updating evaluation instance with result: MetricEvaluatorResult:
  # engine params evaluated: 3
Optimal Engine Params:
  {
  "dataSourceParams":{
    "":{
      "appName":"MyApp1",
      "evalParams":{
        "kFold":5,
        "queryNum":10
      }
    }
  },
  "preparatorParams":{
    "":{

    }
  },
  "algorithmParamsList":[
    {
      "als":{
        "rank":10,
        "numIterations":40,
        "lambda":0.01,
        "seed":3
      }
    }
  ],
  "servingParams":{
    "":{

    }
  }
}
Metrics:
  Precision@K (k=10, threshold=4.0): 0.15205820105820103
  PositiveCount (threshold=4.0): 5.753333333333333
  Precision@K (k=10, threshold=2.0): 0.1542777777777778
  PositiveCount (threshold=2.0): 6.833333333333333
  Precision@K (k=10, threshold=1.0): 0.15068518518518517
  PositiveCount (threshold=1.0): 10.006666666666666
[INFO 2015-03-31 00:36:01,516] [CoreWorkflow$] runEvaluation completed

The console prints out the evaluation meric score of each engine params, and finally pretty print the optimal engine params. Amongs the 3 engine params we evaluate, the best Prediction@k has a score of ~0.1521.

The Evaluation Design

We assume you have read the Tuning and Evaluation section. We will cover the evaluation aspects which are specific to the recommendation engine.

In recommendation evaluation, the raw data is a sequence of known ratings. A rating has 3 components: user, item, and a score. We use the $k-fold$ method for evaluation, the raw data is sliced into a sequence of (training, validation) data tuple.

In the validation data, we construct a query for each user, and get a list of recommended items from the engine. It is vastly different from the classification tutorial, where there is a one-to-one corresponding between the training data point and the validation data point. In this evaluation, our unit of evaluation is user, we evaluate the quality of an engine using the known rating of a user.

Key assumptions

There are multiple assumptions we have to make when we evaluate a recommendation engine:

  • Definition of 'good'. We want to quantify if the engine is able to recommend items which the user likes, we need to define what is meant by 'good'. In this examle, we have two kinds of events: 'rate' and 'buy'. The 'rate' event is associated with a rating value which ranges between 1 to 4, and the 'buy' event is mapped to a rating of 4. When we implement the metric, we have to specify a rating threshold, only the rating above the threshold is considered 'good'.

  • The absense of complete rating. It is extremely unlikely that the training data contains rating for all user-item tuples. In contrast, of a system containing 1000 items, a user may only have rated 20 of them, leaving 980 items unrated. There is no way for us to certainly tell if the user likes an unrated product. When we examine the evaluation result, it is important for us to keep in mind that the final metric is only an approximation of the actual result.

  • Recommendation affects user behavior. Suppose you are a e-commerce company and would like to use the recommendation engine to personalize the landing page, the item you show in the langing page directly impacts what the user is going to purchase. This is different from weather prediction, whatever the weather forecast engine predicts, tomorrow's weather won't be affected. Therefore, when we conduct offline evaluation for recommendation engines, it is possible that the final user behavior is dramatically different from the evaluation result. However, in the evaluation, for simplicity, we have to assume that user behavior is homogenous.

Evaluation Data Generation

Actual Result

In MyRecommendation/src/main/scala/Engine.scala, we define the ActualResult which represents the user rating for validation. It stores the list of ratings in the validation set for a user.

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case class ActualResult(
  ratings: Array[Rating]
)

Implement Data Generate Method in DataSource

In MyRecommendation/src/main/scala/DataSource.scala, the method readEval method reads, and selects, data from datastore and returns a sequence of (training, validation) data.

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case class DataSourceEvalParams(kFold: Int, queryNum: Int)

case class DataSourceParams(
  appName: String,
  evalParams: Option[DataSourceEvalParams]) extends Params

class DataSource(val dsp: DataSourceParams)
  extends PDataSource[TrainingData,
      EmptyEvaluationInfo, Query, ActualResult] {

  @transient lazy val logger = Logger[this.type]

  def getRatings(sc: SparkContext): RDD[Rating] = {

    val eventsRDD: RDD[Event] = PEventStore.find(
      appName = dsp.appName,
      entityType = Some("user"),
      eventNames = Some(List("rate", "buy")), // read "rate" and "buy" event
      // targetEntityType is optional field of an event.
      targetEntityType = Some(Some("item")))(sc)

    val ratingsRDD: RDD[Rating] = eventsRDD.map { event =>
      val rating = try {
        val ratingValue: Double = event.event match {
          case "rate" => event.properties.get[Double]("rating")
          case "buy" => 4.0 // map buy event to rating value of 4
          case _ => throw new Exception(s"Unexpected event ${event} is read.")
        }
        // entityId and targetEntityId is String
        Rating(event.entityId,
          event.targetEntityId.get,
          ratingValue)
      } catch {
        case e: Exception => {
          logger.error(s"Cannot convert ${event} to Rating. Exception: ${e}.")
          throw e
        }
      }
      rating
    }.cache()

    ratingsRDD
  }

  ...

  override
  def readEval(sc: SparkContext)
  : Seq[(TrainingData, EmptyEvaluationInfo, RDD[(Query, ActualResult)])] = {
    require(!dsp.evalParams.isEmpty, "Must specify evalParams")
    val evalParams = dsp.evalParams.get

    val kFold = evalParams.kFold
    val ratings: RDD[(Rating, Long)] = getRatings(sc).zipWithUniqueId

    (0 until kFold).map { idx => {
      val trainingRatings = ratings.filter(_._2 % kFold != idx).map(_._1)
      val testingRatings = ratings.filter(_._2 % kFold == idx).map(_._1)

      val testingUsers: RDD[(String, Iterable[Rating])] = testingRatings.groupBy(_.user)

      (new TrainingData(trainingRatings),
        new EmptyEvaluationInfo(),
        testingUsers.map {
          case (user, ratings) => (Query(user, evalParams.queryNum), ActualResult(ratings.toArray))
        }
      )
    }}
  }
}

The evaluation data generate is controlled by two parameters in the DataSourceEvalParams. The first parameter kFold is the number of fold we use for evaluation, the second parameter queryNum is used for query construction.

The getRating method is factored out from the readTraining method as they both serve the same function of reading from data source and perform a user-item action into a rating (lines 22 - 40).

The readEval method is a k-fold evaluation implementation. We annotate each rating in the raw data by an index (line 54), then in each fold, the rating goes to either the training or testing set based on the modulus value. We group ratings by user, and one query is constructed for each user (line 60).

Evaluation Metrics

In the evaluation and tuning tutorial, we use Metric to compute the quality of an engine variant. However, in actual use cases like recommendation, as we have made many assumptions in our model, using a single metric may lead to a biased evaluation. We will discuss using multiple Metrics to generate a comprehensive evaluation, to generate a more global view of the engine.

Precision@K

Precision@K measures the portion of relevant items amongst the first k items. Recommendation engine usually wants to make sure the top few items recommended are appealing to the user. Think about Google search, we usually give up after looking at the first and second result pages.

Precision@K Parameters

There are two questions associated with it.

  1. How do we define relevant?
  2. What is a good value of k?

Before we answer these questions, we need to understand what constitute a good metric. It is like exams, if everyone get full scores, the exam fails its goal to determine what the candidates don't know; if everyone fails, the exam fails its goal to determine what the candidates know. A good metric should be able to distinguish the good from the bad.

A way to define relevant is to use the notion of rating threshold. If the user rating for an item is higher than a certain threshold, we say it is relevant. However, without looking at the data, it is hard to pick a reasonable threshold. We can set the threshold be as high as the maximum rating of 4.0, but it may severely limit the relevant set size, and the precision scores will be close to zero or undefined (precision is undefined if there is no relevant data). On the other hand, we can set the threshold be as low as the minimum rating, but it makes the precision metric uninformative as well since all scores will be close to 1. Similar argument applies to picking a good value of k too.

A method to choose a good parameter is not to choose one, but instead test out a whole sprectrum of parameters. If an engine variant is good, it should robustly perform well across different metric parameters. The evaluation module supports multiple metrics. The following code snippets demonstrates a sample usage.

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object ComprehensiveRecommendationEvaluation extends Evaluation {
  val ratingThresholds = Seq(0.0, 2.0, 4.0)
  val ks = Seq(1, 3, 10)

  engineEvaluator = (
    RecommendationEngine(),
    MetricEvaluator(
      metric = PrecisionAtK(k = 3, ratingThreshold = 2.0),
      otherMetrics = (
        (for (r <- ratingThresholds) yield PositiveCount(ratingThreshold = r)) ++
        (for (r <- ratingThresholds; k <- ks) yield PrecisionAtK(k = k, ratingThreshold = r))
      )))
}

We have two types of Metrics.

  • PositiveCount is a helper metrics that returns the average number of positive samples for a specific rating threshold, therefore we get some idea about the demographic of the data. If PositiveCount is too low or too high for certain threshold, we know that it should not be used. We have three thresholds (line 2), and three instances of PositiveCount metric are instantiated (line 10), one for each threshold.

  • Precision@K is the actual metrics we use. We have two lists of parameters (lines 2 to 3): ratingThreshold defines what rating is good, and k defines how many items we evaluate in the PredictedResult. We generate a list of all combinations (line 11).

These metrics are specified as otherMetrics (lines 9 to 11), they will be calculated and generated on the evaluation UI.

To run this evaluation, you can:

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$ pio eval org.template.ComprehensiveRecommendationEvaluation \
  org.template.EngineParamsList