DDM {datadriftR}

DDM (Drift Detection Method)

Description

Implements the Drift Detection Method (DDM), used for detecting concept drift in data streams by analyzing the performance of online learners. The method monitors changes in the error rate of a learner, signaling potential concept drift.

Details

DDM is designed to be simple yet effective for detecting concept drift by monitoring the error rate of any online classifier. The method is particularly sensitive to increases in the error rate, which is typically a strong indicator of concept drift.

Public fields

min_instances

Minimum number of instances required before drift detection begins.

warning_level

Multiplier for the standard deviation to set the warning threshold.

out_control_level

Multiplier for the standard deviation to set the out-of-control threshold.

sample_count

Counter for the number of samples processed.

miss_prob

Current estimated probability of misclassification.

miss_std

Current estimated standard deviation of misclassification probability.

miss_prob_sd_min

Minimum recorded value of misclassification probability plus its standard deviation.

miss_prob_min

Minimum recorded misclassification probability.

miss_sd_min

Minimum recorded standard deviation.

estimation

Current estimation of misclassification probability.

change_detected

Boolean indicating if a drift has been detected.

warning_detected

Boolean indicating if a warning level has been reached.

delay

Delay since the last relevant sample.

Methods

Public methods

• DDM$new()

• DDM$reset()

• DDM$add_element()

• DDM$detected_change()

• DDM$clone()

Method new()

Initializes the DDM detector with specific parameters.

Usage

DDM$new(min_num_instances = 30, warning_level = 2, out_control_level = 3)

Arguments

min_num_instances

Minimum number of samples required before starting drift detection.

warning_level

Threshold multiplier for setting a warning level.

out_control_level

Threshold multiplier for setting the out-of-control level.

Method reset()

Resets the internal state of the DDM detector.

Usage

DDM$reset()

Method add_element()

Adds a new prediction error value to the model, updates the calculation of the misclassification probability and its standard deviation, and checks for warnings or drifts based on updated statistics.

Usage

DDM$add_element(prediction)

Arguments

prediction

The new data point (prediction error) to be added to the model.

Method detected_change()

Returns a boolean indicating whether a drift has been detected based on the monitored statistics.

Usage

DDM$detected_change()

Method clone()

The objects of this class are cloneable with this method.

Usage

DDM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295

Implementation: https://github.com/scikit-multiflow/scikit-multiflow/blob/a7e316d1cc79988a6df40da35312e00f6c4eabb2/src/skmultiflow/drift_detection/ddm.py

Examples

set.seed(123)  # Setting a seed for reproducibility
data_part1 <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(0.7, 0.3))

# Introduce a change in data distribution
data_part2 <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(0.3, 0.7))

# Combine the two parts
data_stream <- c(data_part1, data_part2)
ddm <- DDM$new()
# Iterate through the data stream
for (i in seq_along(data_stream)) {
  ddm$add_element(data_stream[i])
  if (ddm$change_detected) {
    message(paste("Drift detected!", i))
  } else if (ddm$warning_detected) {
    # message(paste("Warning detected at position:", i))
  }
}