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

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package googleapis.bigquery

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

final case class AggregateClassificationMetrics(
    /** Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
      */
    precision: Option[Double] = None,
    /** Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
      */
    rocAuc: Option[Double] = None,
    /** Logarithmic Loss. For multiclass this is a macro-averaged metric.
      */
    logLoss: Option[Double] = None,
    /** The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
      */
    f1Score: Option[Double] = None,
    /** Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
      */
    accuracy: Option[Double] = None,
    /** Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      */
    threshold: Option[Double] = None,
    /** Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
      */
    recall: Option[Double] = None,
)
object AggregateClassificationMetrics {
  implicit val encoder: Encoder[
    AggregateClassificationMetrics
  ] = Encoder.instance { x =>
    Json.obj(
      "precision" := x.precision,
      "rocAuc" := x.rocAuc,
      "logLoss" := x.logLoss,
      "f1Score" := x.f1Score,
      "accuracy" := x.accuracy,
      "threshold" := x.threshold,
      "recall" := x.recall,
    )
  }
  implicit val decoder: Decoder[
    AggregateClassificationMetrics
  ] = Decoder.instance { c =>
    for {
      v0 <- c.get[Option[Double]]("precision")
      v1 <- c.get[Option[Double]]("rocAuc")
      v2 <- c.get[Option[Double]]("logLoss")
      v3 <- c.get[Option[Double]]("f1Score")
      v4 <- c.get[Option[Double]]("accuracy")
      v5 <- c.get[Option[Double]]("threshold")
      v6 <- c.get[Option[Double]]("recall")
    } yield AggregateClassificationMetrics(v0, v1, v2, v3, v4, v5, v6)
  }
}