ProfileDifference: Profile Difference Calculation for Change Detection

In datadriftR: Concept Drift Detection Methods for Stream Data

Profile Difference Calculation for Change Detection

Description

Implements the calculation of profile differences using various methods such as PDI, L2, and L2 derivative. The class provides methods for setting profiles and calculating the differences.

Details

The class supports multiple methods for calculating profile differences, including the Profile Disparity Index (PDI) using gold or simple derivative methods, and L2 norm and L2 derivative calculations. It allows for customization of various parameters such as embedding dimensions, derivative orders, and thresholds.

Public fields

method

The method used for profile difference calculation.

deriv

The method used for derivative calculation.

gold_spline

Boolean indicating if cubic spline should be used in gold method.

gold_embedding

Embedding dimension for gold method.

nderiv

Order of the derivative for simple method.

gold_spline_threshold

Threshold for cubic spline in gold method.

epsilon

Small value to avoid numerical issues.

profile1

The first profile.

profile2

The second profile.

Methods

Public methods

• ProfileDifference$new()

• ProfileDifference$reset()

• ProfileDifference$set_profiles()

• ProfileDifference$calculate_difference()

• ProfileDifference$clone()

---

Method new()

Initializes the ProfileDifference class.

Usage

ProfileDifference$new(
  method = "pdi",
  deriv = "gold",
  gold_spline = TRUE,
  gold_embedding = 4,
  nderiv = 4,
  gold_spline_threshold = 0.01,
  epsilon = NULL
)

Arguments

method

The method used for profile difference calculation.

deriv

The method used for derivative calculation.

gold_spline

Boolean indicating if cubic spline should be used in gold method.

gold_embedding

Embedding dimension for gold method.

nderiv

Order of the derivative for simple method.

gold_spline_threshold

Threshold for cubic spline in gold method.

epsilon

Small value to avoid numerical issues.

---

Method reset()

Resets the internal state of the detector.

Usage

ProfileDifference$reset()

---

Method set_profiles()

Sets the profiles for comparison.

Usage

ProfileDifference$set_profiles(profile1, profile2)

Arguments

profile1

The first profile.

profile2

The second profile.

---

Method calculate_difference()

Calculates the difference between the profiles.

Usage

ProfileDifference$calculate_difference()

Returns

A list containing the method details and the calculated distance.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ProfileDifference$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Kobylińska, K., Krzyziński, M., Machowicz, R., Adamek, M., & Biecek, P. (2023). Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data. arXiv e-prints, arXiv-2308.

Examples

set.seed(123)  # Setting a seed for reproducibility
profile1 <- list(x = 1:100, y = sin(1:100))
profile2 <- list(x = 1:100, y = sin(1:100) + rnorm(100, 0, 0.1))
pd <- ProfileDifference$new(method = "pdi", deriv = "gold")
pd$set_profiles(profile1, profile2)
result <- pd$calculate_difference()
message(result)

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