dfr_kswin {heimdall}

KSWIN method

Description

Kolmogorov-Smirnov Windowing method for concept drift detection doi:10.1016/j.neucom.2019.11.111.

Usage

dfr_kswin(
  target_feat,
  window_size = 100,
  stat_size = 30,
  alpha = 0.005,
  data = NULL
)

Arguments

target_feat

Feature to be monitored.

window_size

Size of the sliding window (must be > 2*stat_size)

stat_size

Size of the statistic window

alpha

Probability for the test statistic of the Kolmogorov-Smirnov-Test The alpha parameter is very sensitive, therefore should be set below 0.01.

data

Already collected data to avoid cold start.

Value

dfr_kswin object

Examples

library(daltoolbox)
library(heimdall)

# This example uses a dist-based drift detector with a synthetic dataset.

data(st_drift_examples)
data <- st_drift_examples$univariate
data$event <- NULL

model <- dfr_kswin(target_feat='serie')

detection <- NULL
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, data.frame(idx=i, event=output$drift, type=type))
}

detection[detection$type == 'drift',]