dfr_ddm {heimdall}

Adapted Drift Detection Method (DDM) method

Description

DDM is a concept change detection method based on the PAC learning model premise, that the learner’s error rate will decrease as the number of analysed samples increase, as long as the data distribution is stationary. doi:10.1007/978-3-540-28645-5_29.

Usage

dfr_ddm(min_instances = 30, warning_level = 2, out_control_level = 3)

Arguments

min_instances

The minimum number of instances before detecting change

warning_level

Necessary level for warning zone (2 standard deviation)

out_control_level

Necessary level for a positive drift detection

Value

dfr_ddm object

Examples

library(daltoolbox)
library(heimdall)

# This example uses an error-based drift detector with a synthetic 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_ddm()

detection <- NULL
output <- list(obj=model, drift=FALSE)
for (i in 1:length(data$prediction)){
 output <- update_state(output$obj, data$prediction[i])
 if (output$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',]