Package 'heimdall' reference manual

Title:	Drift Adaptable Models
Description:	By analyzing streaming datasets, it is possible to observe significant changes in the data distribution or models' accuracy during their prediction (concept drift). The goal of 'heimdall' is to measure when concept drift occurs. The package makes available several state-of-the-art methods. It also tackles how to adapt models in a nonstationary context. Some concept drifts methods are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.
Authors:	Lucas Tavares [aut], Leonardo Carvalho [aut], Diego Carvalho [aut], Esther Pacitti [aut], Fabio Porto [aut], Eduardo Ogasawara [aut, ths, cre] , Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) [cph]
Maintainer:	Eduardo Ogasawara <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.717
Built:	2024-11-11 02:22:39 UTC
Source:	https://github.com/cefet-rj-dal/heimdall

ADWIN method

Description

Adaptive Windowing method for concept drift detection doi:10.1137/1.9781611972771.42.

Usage

dfr_adwin(target_feat = NULL, delta = 2e-05)
dfr_adwin(target_feat = NULL, delta = 2e-05)

Arguments

`target_feat`	Feature to be monitored.
`delta`	The significance parameter for the ADWIN algorithm.

Value

dfr_adwin object

Examples

#Use the same example of dfr_cumsum changing the constructor to:
#model <- dfr_adwin(target_feat='serie')
#Use the same example of dfr_cumsum changing the constructor to:
#model <- dfr_adwin(target_feat='serie')

Autoencoder-Based Drift Detection method

Description

Autoencoder-Based method for concept drift detection doi:0.1109/ICDMW58026.2022.00109.

Usage

dfr_aedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)
dfr_aedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)

Arguments

`features`	Features to be monitored
`input_size`	Input size
`encoding_size`	Encoding Size
`batch_size`	Batch Size for batch learning
`num_epochs`	Number of Epochs for training
`learning_rate`	Learning Rate
`window_size`	Size of the most recent data to be used
`monitoring_step`	The number of rows that the drifter waits to be is updated
`criteria`	The method to be used to check if there is a drift. May be mann_whitney (default) or kolmogorov_smirnov

Value

dfr_aedd object

Convolutional Autoencoder-Based Drift Detection method

Description

Convolutional Autoencoder-Based method for concept drift detection doi:0.1109/ICDMW58026.2022.00109.

Usage

dfr_caedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)
dfr_caedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)

Arguments

`features`	Features to be monitored
`input_size`	Input size
`encoding_size`	Encoding Size
`batch_size`	Batch Size for batch learning
`num_epochs`	Number of Epochs for training
`learning_rate`	Learning Rate
`window_size`	Size of the most recent data to be used
`monitoring_step`	The number of rows that the drifter waits to be is updated
`criteria`	The method to be used to check if there is a drift. May be mann_whitney (default) or kolmogorov_smirnov

Value

dfr_caedd object

Cumulative Sum for Concept Drift Detection (CUMSUM) method

Description

The cumulative sum (CUSUM) is a sequential analysis technique used for change detection.

Usage

dfr_cusum(lambda = 100)
dfr_cusum(lambda = 100)

Arguments

lambda

Necessary level for warning zone (2 standard deviation)

Value

dfr_cusum 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_cusum()

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',]
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_cusum()

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',]

Denoising Autoencoder-Based Drift Detection method

Description

Denoising Autoencoder-Based method for concept drift detection doi:0.1109/ICDMW58026.2022.00109.

Usage

dfr_daedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)
dfr_daedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)

Arguments

`features`	Features to be monitored
`input_size`	Input size
`encoding_size`	Encoding Size
`batch_size`	Batch Size for batch learning
`num_epochs`	Number of Epochs for training
`learning_rate`	Learning Rate
`window_size`	Size of the most recent data to be used
`monitoring_step`	The number of rows that the drifter waits to be is updated
`criteria`	The method to be used to check if there is a drift. May be mann_whitney (default) or kolmogorov_smirnov

Value

dfr_aedd object

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)
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',]
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',]

Adapted EWMA for Concept Drift Detection (ECDD) method

Description

ECDD is a concept change detection method that uses an exponentially weighted moving average (EWMA) chart to monitor the misclassification rate of an streaming classifier.

Usage

dfr_ecdd(lambda = 0.2, min_run_instances = 30, average_run_length = 100)
dfr_ecdd(lambda = 0.2, min_run_instances = 30, average_run_length = 100)

Arguments

`lambda`	The minimum number of instances before detecting change
`min_run_instances`	Necessary level for warning zone (2 standard deviation)
`average_run_length`	Necessary level for a positive drift detection

Value

dfr_ecdd 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_ecdd()

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',]
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_ecdd()

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',]

Adapted Early Drift Detection Method (EDDM) method

Description

EDDM (Early Drift Detection Method) aims to improve the detection rate of gradual concept drift in DDM, while keeping a good performance against abrupt concept drift. doi:2747577a61c70bc3874380130615e15aff76339e

Usage

dfr_eddm(
  min_instances = 30,
  min_num_errors = 30,
  warning_level = 0.95,
  out_control_level = 0.9
)
dfr_eddm(
  min_instances = 30,
  min_num_errors = 30,
  warning_level = 0.95,
  out_control_level = 0.9
)

Arguments

`min_instances`	The minimum number of instances before detecting change
`min_num_errors`	The minimum number of errors before detecting change
`warning_level`	Necessary level for warning zone
`out_control_level`	Necessary level for a positive drift detection

Value

dfr_eddm 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_eddm()

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',]
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_eddm()

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',]

Adapted Hoeffding Drift Detection Method (HDDM) method

Description

is a drift detection method based on the Hoeffding’s inequality. HDDM_A uses the average as estimator. doi:10.1109/TKDE.2014.2345382.

Usage

dfr_hddm(
  drift_confidence = 0.001,
  warning_confidence = 0.005,
  two_side_option = TRUE
)
dfr_hddm(
  drift_confidence = 0.001,
  warning_confidence = 0.005,
  two_side_option = TRUE
)

Arguments

`drift_confidence`	Confidence to the drift
`warning_confidence`	Confidence to the warning
`two_side_option`	Option to monitor error increments and decrements (two-sided) or only increments (one-sided)

Value

dfr_hddm 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_hddm()

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',]
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_hddm()

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',]

Inactive dummy detector

Description

Implements Inactive Dummy Detector

Usage

dfr_inactive()
dfr_inactive()

Value

Drifter object

Examples

# See ?hcd_ddm for an example of DDM drift detector
# See ?hcd_ddm for an example of DDM drift detector

KL Distance method

Description

Kullback Leibler Windowing method for concept drift detection.

Usage

dfr_kldist(target_feat = NULL, window_size = 100, p_th = 0.05, data = NULL)
dfr_kldist(target_feat = NULL, window_size = 100, p_th = 0.05, data = NULL)

Arguments

`target_feat`	Feature to be monitored.
`window_size`	Size of the sliding window (must be > 2*stat_size)
`p_th`	Probability theshold for the test statistic of the Kullback Leibler distance.
`data`	Already collected data to avoid cold start.

Value

dfr_kldist 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_kldist(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',]
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_kldist(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',]

KSWIN method

Description

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

Usage

dfr_kswin(
  target_feat = NULL,
  window_size = 1500,
  stat_size = 500,
  alpha = 1e-07,
  data = NULL
)
dfr_kswin(
  target_feat = NULL,
  window_size = 1500,
  stat_size = 500,
  alpha = 1e-07,
  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',]
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',]

Mean Comparison Distance method

Description

Mean Comparison statistical method for concept drift detection.

Usage

dfr_mcdd(target_feat = NULL, alpha = 1e-08, window_size = 1500)
dfr_mcdd(target_feat = NULL, alpha = 1e-08, window_size = 1500)

Arguments

`target_feat`	Feature to be monitored
`alpha`	Probability theshold for all test statistics
`window_size`	Size of the sliding window

Value

dfr_mcdd 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_mcdd(target_feat='depart_visibility')

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',]
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_mcdd(target_feat='depart_visibility')

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',]

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

Passive dummy detector

Description

Implements Passive Dummy Detector

Usage

dfr_passive()
dfr_passive()

Value

Drifter object

Examples

# See ?hcd_ddm for an example of DDM drift detector
# See ?hcd_ddm for an example of DDM drift detector

Stacked Autoencoder-Based Drift Detection method

Description

Stacked Autoencoder-Based method for concept drift detection doi:0.1109/ICDMW58026.2022.00109.

Usage

dfr_saedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)
dfr_saedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)

Arguments

`features`	Features to be monitored
`input_size`	Input size
`encoding_size`	Encoding Size
`batch_size`	Batch Size for batch learning
`num_epochs`	Number of Epochs for training
`learning_rate`	Learning Rate
`window_size`	Size of the most recent data to be used
`monitoring_step`	The number of rows that the drifter waits to be is updated
`criteria`	The method to be used to check if there is a drift. May be mann_whitney (default) or kolmogorov_smirnov

Value

dfr_saedd object

Variational Autoencoder-Based Drift Detection method

Description

Variational Autoencoder-Based method for concept drift detection doi:0.1109/ICDMW58026.2022.00109.

Usage

dfr_vaedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)
dfr_vaedd(
  features,
  input_size,
  encoding_size,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney"
)

Arguments

`features`	Features to be monitored
`input_size`	Input size
`encoding_size`	Encoding Size
`batch_size`	Batch Size for batch learning
`num_epochs`	Number of Epochs for training
`learning_rate`	Learning Rate
`window_size`	Size of the most recent data to be used
`monitoring_step`	The number of rows that the drifter waits to be is updated
`criteria`	The method to be used to check if there is a drift. May be mann_whitney (default) or kolmogorov_smirnov

Value

dfr_vaedd object

Distribution Based Drifter sub-class

Description

Implements Distribution Based drift detectors

Usage

dist_based(target_feat)
dist_based(target_feat)

Arguments

target_feat

Feature to be monitored.

Value

Drifter object

Drifter

Description

Ancestor class for drift detection

Usage

drifter()
drifter()

Value

Drifter object

Examples

# See ?dd_ddm for an example of DDM drift detector
# See ?dd_ddm for an example of DDM drift detector

Error Based Drifter sub-class

Description

Implements Error Based drift detectors

Usage

error_based()
error_based()

Value

Drifter object

Examples

# See ?hcd_ddm for an example of DDM drift detector
# See ?hcd_ddm for an example of DDM drift detector

Process Batch

Description

Process Batch

Usage

## S3 method for class 'drifter'
fit(obj, data, prediction, ...)
## S3 method for class 'drifter'
fit(obj, data, prediction, ...)

Arguments

`obj`	Drifter object
`data`	data batch in data frame format
`prediction`	prediction batch as vector format
`...`	opitional arguments

Value

updated Drifter object

Metric

Description

Ancestor class for metric calculation

Usage

metric()
metric()

Value

Metric object

Examples

# See ?metric for an example of DDM drift detector
# See ?metric for an example of DDM drift detector

Accuracy Calculator

Description

Class for accuracy calculation

Usage

mt_accuracy()
mt_accuracy()

Value

Metric object

Examples

# See ?mt_accuracy for an example of Accuracy Calculator
# See ?mt_accuracy for an example of Accuracy Calculator

FScore Calculator

Description

Class for FScore calculation

Usage

mt_fscore(f = 1)
mt_fscore(f = 1)

Arguments

`f`	The F parameter for the F-Score metric

Value

Metric object

Examples

# See ?mt_precision for an example of FScore Calculator
# See ?mt_precision for an example of FScore Calculator

Precision Calculator

Description

Class for precision calculation

Usage

mt_precision()
mt_precision()

Value

Metric object

Examples

# See ?mt_precision for an example of Precision Calculator
# See ?mt_precision for an example of Precision Calculator

Recall Calculator

Description

Class for recall calculation

Usage

mt_recall()
mt_recall()

Value

Metric object

Examples

# See ?mt_recall for an example of Recall Calculator
# See ?mt_recall for an example of Recall Calculator

Multi Criteria Drifter sub-class

Description

Implements Multi Criteria drift detectors

Usage

multi_criteria()
multi_criteria()

Value

Drifter object

Multivariate Distribution Based Drifter sub-class

Description

Implements Multivariate Distribution Based drift detectors

Usage

mv_dist_based(features)
mv_dist_based(features)

Arguments

features

Features to be monitored.

Value

Drifter object

Reset State

Description

Reset Drifter State

Usage

reset_state(obj)
reset_state(obj)

Arguments

obj

Drifter object

Value

updated Drifter object

Examples

# See ?hcd_ddm for an example of DDM drift detector
# See ?hcd_ddm for an example of DDM drift detector

Synthetic time series for concept drift detection

Description

A list of multivariate time series for drift detection

example1: a bivariate dataset with one multivariate concept drift example

Usage

data(st_drift_examples)
data(st_drift_examples)

Format

A list of time series.

Source

Stealthy package

References

Stealthy package

Examples

data(st_drift_examples)
dataset <- st_drift_examples$example1
data(st_drift_examples)
dataset <- st_drift_examples$example1

Stealthy

Description

Ancestor class for drift adaptive models

Usage

stealthy(
  model,
  drift_method,
  th = 0.5,
  target_uni_drifter = FALSE,
  verbose = FALSE
)
stealthy(
  model,
  drift_method,
  th = 0.5,
  target_uni_drifter = FALSE,
  verbose = FALSE
)

Arguments

`model`	The algorithm object to be used for predictions
`drift_method`	The algorithm object to detect drifts
`th`	The threshold to be used with classification algorithms
`target_uni_drifter`	Passes the prediction target to the drifts as the target feat when the drifter is univariate and dist_based.
`verbose`	if TRUE shows drift messages

Value

Stealthy object

Examples

# See ?dd_ddm for an example of DDM drift detector
# See ?dd_ddm for an example of DDM drift detector

Update State

Description

Update Drifter State

Usage

update_state(obj, value)
update_state(obj, value)

Arguments

`obj`	Drifter object
`value`	a value that represents a processed batch

Value

updated Drifter object

Examples

# See ?hcd_ddm for an example of DDM drift detector
# See ?hcd_ddm for an example of DDM drift detector

Package 'heimdall'

Help Index

ADWIN method

Description

Usage

Arguments

Value

Examples

Autoencoder-Based Drift Detection method

Description

Usage

Arguments

Value

Convolutional Autoencoder-Based Drift Detection method

Description

Usage

Arguments

Value

Cumulative Sum for Concept Drift Detection (CUMSUM) method

Description

Usage

Arguments

Value

Examples

Denoising Autoencoder-Based Drift Detection method

Description

Usage

Arguments

Value

Adapted Drift Detection Method (DDM) method

Description

Usage

Arguments

Value

Examples

Adapted EWMA for Concept Drift Detection (ECDD) method

Description

Usage

Arguments

Value

Examples

Adapted Early Drift Detection Method (EDDM) method

Description

Usage

Arguments

Value

Examples

Adapted Hoeffding Drift Detection Method (HDDM) method

Description

Usage

Arguments

Value

Examples

Inactive dummy detector

Description

Usage

Value

Examples

KL Distance method

Description

Usage

Arguments

Value

Examples

KSWIN method

Description

Usage

Arguments

Value

Examples

Mean Comparison Distance method

Description

Usage

Arguments

Value

Examples

Adapted Page Hinkley method

Description

Usage

Arguments