kneed 0.7.0

Knee-point detection in Python

kneed

Knee-point detection in Python

This repository is an attempt to implement the kneedle algorithm, published here. Given a set of x and y values, kneed will return the knee point of the function. The knee point is the point of maximum curvature.

Table of contents

• Installation
• Usage
  • Input Data
  • Find Knee
  • Visualize
• Documentation
• Contributing
• Citation

Installation

kneed has been tested with Python 3.5, 3.6, 3.7 and 3.8.

anaconda

$ conda install -c conda-forge kneed

pip

$ pip install kneed

Clone from GitHub

$ git clone https://github.com/arvkevi/kneed.git
$ python setup.py install

Usage

These steps introduce how to use kneed by reproducing Figure 2 from the manuscript.

Input Data

The DataGenerator class is only included as a utility to generate sample datasets.

Note: x and y must be equal length arrays.

from kneed import DataGenerator, KneeLocator

x, y = DataGenerator.figure2()

print([round(i, 3) for i in x])
print([round(i, 3) for i in y])

[0.0, 0.111, 0.222, 0.333, 0.444, 0.556, 0.667, 0.778, 0.889, 1.0]
[-5.0, 0.263, 1.897, 2.692, 3.163, 3.475, 3.696, 3.861, 3.989, 4.091]

Find Knee

The knee (or elbow) point is calculated simply by instantiating the KneeLocator class with x, y and the appropriate curve and direction.
Here, kneedle.knee and/or kneedle.elbow store the point of maximum curvature.

kneedle = KneeLocator(x, y, S=1.0, curve="concave", direction="increasing")

print(round(kneedle.knee, 3))
0.222

print(round(kneedle.elbow, 3))
0.222

The knee point returned is a value along the x axis. The y value at the knee can be identified:

print(round(kneedle.knee_y, 3))
1.897

Visualize

The KneeLocator class also has two plotting functions for quick visualizations. Note that all (x, y) are transformed for the normalized plots

# Normalized data, normalized knee, and normalized distance curve.
kneedle.plot_knee_normalized()

# Raw data and knee.
kneedle.plot_knee()

Documentation

Documentation of the parameters and a full API reference can be found here.

Contributing

Contributions are welcome, please refer to CONTRIBUTING to learn more about how to contribute.

Citation

Finding a “Kneedle” in a Haystack: Detecting Knee Points in System Behavior Ville Satopa † , Jeannie Albrecht† , David Irwin‡ , and Barath Raghavan§ †Williams College, Williamstown, MA ‡University of Massachusetts Amherst, Amherst, MA § International Computer Science Institute, Berkeley, CA