KLDivergence: Kullback-Leibler Divergence (KLD) for Change Detection

In datadriftR: Concept Drift Detection Methods for Stream Data

Kullback-Leibler Divergence (KLD) for Change Detection

Description

Implements the Kullback-Leibler Divergence (KLD) calculation between two probability distributions using histograms. The class can detect drift by comparing the divergence to a predefined threshold.

Details

The Kullback-Leibler Divergence (KLD) is a measure of how one probability distribution diverges from a second, expected probability distribution. This class uses histograms to approximate the distributions and calculates the KLD to detect changes over time. If the divergence exceeds a predefined threshold, it signals a detected drift.

Public fields

epsilon

Value to add to small probabilities to avoid log(0) issues.

base

The base of the logarithm used in KLD calculation.

bins

Number of bins used for the histogram.

drift_level

The threshold for detecting drift.

drift_detected

Boolean indicating if drift has been detected.

p

Initial distribution.

kl_result

The result of the KLD calculation.

Methods

Public methods

• KLDivergence$new()

• KLDivergence$reset()

• KLDivergence$set_initial_distribution()

• KLDivergence$add_distribution()

• KLDivergence$calculate_kld()

• KLDivergence$get_kl_result()

• KLDivergence$is_drift_detected()

• KLDivergence$clone()

---

Method new()

Initializes the KLDivergence class.

Usage

KLDivergence$new(epsilon = 1e-10, base = exp(1), bins = 10, drift_level = 0.2)

Arguments

epsilon

Value to add to small probabilities to avoid log(0) issues.

base

The base of the logarithm used in KLD calculation.

bins

Number of bins used for the histogram.

drift_level

The threshold for detecting drift.

---

Method reset()

Resets the internal state of the detector.

Usage

KLDivergence$reset()

---

Method set_initial_distribution()

Sets the initial distribution.

Usage

KLDivergence$set_initial_distribution(initial_p)

Arguments

initial_p

The initial distribution.

---

Method add_distribution()

Adds a new distribution and calculates the KLD.

Usage

KLDivergence$add_distribution(q)

Arguments

q

The new distribution.

---

Method calculate_kld()

Calculates the KLD between two distributions.

Usage

KLDivergence$calculate_kld(p, q)

Arguments

p

The initial distribution.

q

The new distribution.

Returns

The KLD value.

---

Method get_kl_result()

Returns the current KLD result.

Usage

KLDivergence$get_kl_result()

Returns

The current KLD value.

---

Method is_drift_detected()

Checks if drift has been detected.

Usage

KLDivergence$is_drift_detected()

Returns

TRUE if drift is detected, otherwise FALSE.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KLDivergence$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Kullback, S., and Leibler, R.A. (1951). On Information and Sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.

Examples

set.seed(123)  # Setting a seed for reproducibility
initial_data <- c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)
kld <- KLDivergence$new(bins = 10, drift_level = 0.2)
kld$set_initial_distribution(initial_data)

new_data <- c(0.2, 0.2, 0.3, 0.4, 0.4, 0.5, 0.6, 0.7, 0.7, 0.8)
kld$add_distribution(new_data)

kl_result <- kld$get_kl_result()
message(paste("KL Divergence:", kl_result))

if (kld$is_drift_detected()) {
  message("Drift detected.")
}

datadriftR documentation built on April 3, 2025, 6:13 p.m.