Quality Benchmarking

class ratspy.QualityBenchmarking

Class to perform quality benchmarking of augmenters

This module provides functionality to evaluate and compare the quality of different data augmentation techniques.

Currently, it includes using the Dynamic Time Warping (DTW) algorithm to measure the similarity between original and augmented time series data.

Methods

compute_dtw(a, b)

Implementation of Dynamic Time Warping (DTW) algorithm.

static compute_dtw(a, b)

Implementation of Dynamic Time Warping (DTW) algorithm.

This function computes the DTW distance between two sequences and returns the distance along with the optimal path.

# Arguments

• a - First sequence as a list[float].

• b - Second sequence as a list[float].

# Returns

A tuple containing the DTW distance (float) and a list of tuples representing the optimal path as pairs of indices (int, int).