scala.googleapis.bigquery.RankingMetrics.scala Maven / Gradle / Ivy

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package googleapis.bigquery

import io.circe._
import io.circe.syntax._

final case class RankingMetrics(
    /** Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
      */
    averageRank: Option[Double] = None,
    /** Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
      */
    meanAveragePrecision: Option[Double] = None,
    /** Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
      */
    meanSquaredError: Option[Double] = None,
    /** A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.
      */
    normalizedDiscountedCumulativeGain: Option[Double] = None,
)
object RankingMetrics {
  implicit val encoder: Encoder[RankingMetrics] = Encoder.instance { x =>
    Json.obj(
      "averageRank" := x.averageRank,
      "meanAveragePrecision" := x.meanAveragePrecision,
      "meanSquaredError" := x.meanSquaredError,
      "normalizedDiscountedCumulativeGain" := x.normalizedDiscountedCumulativeGain,
    )
  }
  implicit val decoder: Decoder[RankingMetrics] = Decoder.instance { c =>
    for {
      v0 <- c.get[Option[Double]]("averageRank")
      v1 <- c.get[Option[Double]]("meanAveragePrecision")
      v2 <- c.get[Option[Double]]("meanSquaredError")
      v3 <- c.get[Option[Double]]("normalizedDiscountedCumulativeGain")
    } yield RankingMetrics(v0, v1, v2, v3)
  }
}