LoggerInbagRisk: Log the train risk.

In schalkdaniel/compboost: Efficient Component-Wise Boosting Implementation

Log the train risk.

Description

This class logs the train risk for a specific loss function.

Arguments

logger_id	(character(1)) Identifier of the logger.
use_as_stopper	(logical(1)) Boolean to indicate if the logger should also be used as a stopper.
loss	(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile) An initialized S4 loss object (requires to call ⁠Loss*$new(...)⁠). See the respective help page for further information.
eps_for_break	(numeric(1)) This argument becomes active if the loss is also used as stopper. If the relative improvement of the logged inbag risk falls above this boundary the stopper returns TRUE.
patience	(integer(1)) The number of consecutive conditions that must be true to return a stop signal.

Format

S4 object.

Usage

LoggerInbagRisk$new(logger_id, use_as_stopper, loss, eps_for_break, patience)

Details

This logger computes the risk for the training data \mathcal{D} = \{(x^{(i)},\ y^{(i)})\ |\ i \in \{1, \dots, n\}\} and stores it into a vector. The empirical risk \mathcal{R}_\mathrm{emp} for iteration m is calculated by:

\mathcal{R}_\mathrm{emp}^{[m]} = \frac{1}{n}\sum\limits_{i = 1}^n L(y^{(i)}, \hat{f}^{[m]}(x^{(i)}))

Note:

• If m=0 than \hat{f} is just the offset.

• The implementation to calculate \mathcal{R}_\mathrm{emp}^{[m]} is done in two steps:

  1. Calculate vector risk_temp of losses for every observation for given response y^{(i)} and prediction \hat{f}^{[m]}(x^{(i)}).

  2. Average over risk_temp.

  This procedure ensures, that it is possible to e.g. use the AUC or any arbitrary performance measure for risk logging. This gives just one value for risk_temp and therefore the average equals the loss function. If this is just a value (like for the AUC) then the value is returned.

Fields

This class doesn't contain public fields.

Methods

• ⁠$summarizeLogger()⁠: ⁠() -> ()⁠

Examples

# Used loss:
log_bin = LossBinomial$new()

# Define logger:
log_inbag_risk = LoggerInbagRisk$new("inbag", FALSE, log_bin, 0.05, 5)

# Summarize logger:
log_inbag_risk$summarizeLogger()

schalkdaniel/compboost documentation built on April 15, 2023, 9:03 p.m.