Package ai.djl.training.loss

Class QuantileL1Loss

java.lang.Object
ai.djl.training.evaluator.Evaluator
ai.djl.training.loss.Loss
ai.djl.training.loss.QuantileL1Loss
public class QuantileL1Loss extends Loss
QuantileL1Loss calculates the Weighted Quantile Loss between labels and predictions. It is useful in regression problems to target the best-fit line at a particular quantile. E.g., to target the P90, instantiate new QuantileL1Loss("P90", 0.90). Basically, what this loss function does is to focus on a certain percentile of the data. E.g. q=0.5 is the original default case of regression, meaning the best-fit line lies in the center. When q=0.9, the best-fit line will lie above the center. By differentiating the loss function, the optimal solution will yield the result that, for some special cases like those where \partial forecast / \partial w are uniform, exactly 0.9 of total data points will lie below the best-fit line. 
  def quantile_loss(target, forecast, q):
      return 2 * np.sum(np.abs((forecast - target) * ((target <= forecast) - q)))

Reference: ...

  • Constructor Details

    • QuantileL1Loss

      public QuantileL1Loss(float quantile)
      Computes QuantileL1Loss for regression problem.
      Parameters:
      quantile - the quantile position of the data to focus on

    • QuantileL1Loss

      public QuantileL1Loss(String name, float quantile)
      Computes QuantileL1Loss for regression problem.
      Parameters:
      name - the name of the loss function, default "QuantileL1Loss"
      quantile - the quantile position of the data to focus on

  • Method Details

    • evaluate

      public NDArray evaluate(NDList labels, NDList predictions)
      Calculates the evaluation between the labels and the predictions.
      Specified by:
      evaluate in class Evaluator
      Parameters:
      labels - the correct values
      predictions - the predicted values
      Returns:
      the evaluation result