dfr_page_hinkley {heimdall}

Adapted Page Hinkley method

Description

Change-point detection method works by computing the observed values and their mean up to the current moment doi:10.2307/2333009.

Usage

dfr_page_hinkley(
  target_feat,
  min_instances = 30,
  delta = 0.005,
  threshold = 50,
  alpha = 1 - 1e-04
)

Arguments

target_feat

Feature to be monitored.

min_instances

The minimum number of instances before detecting change

delta

The delta factor for the Page Hinkley test

threshold

The change detection threshold (lambda)

alpha

The forgetting factor, used to weight the observed value and the mean

Value

dfr_page_hinkley object

Examples

library(daltoolbox)
library(heimdall)

# This example assumes a model residual where 1 is an error and 0 is a correct prediction.

data(st_drift_examples)
data <- st_drift_examples$univariate
data$event <- NULL
data$prediction <- st_drift_examples$univariate$serie > 4


model <- dfr_page_hinkley(target_feat='serie')

detection <- c()
output <- list(obj=model, drift=FALSE)
for (i in 1:length(data$serie)){
 output <- update_state(output$obj, data$serie[i])
 if (output$drift){
   type <- 'drift'
   output$obj <- reset_state(output$obj)
 }else{
   type <- ''
 }
 detection <- rbind(detection, list(idx=i, event=output$drift, type=type))
}

detection <- as.data.frame(detection)
detection[detection$type == 'drift',]