kneed 0.2.3

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.

Installation

To install use pip:

 $ pip install kneed                                                                                                                                                                                            

Or clone the repo:

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

Tested with Python 3.5 and 3.6

Usage

This reproduces Figure 2 from the manuscript.

x and y must be equal length arrays.
DataGenerator has functions to generate sample datasets.

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]

Instantiating KneeLocator with x, y and the appropriate curve and direction will find the knee (or elbow) point.
Here, kneedle.knee stores the knee point of the curve.

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

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

# .elbow can also be used to access point of maximum curvature
print(round(kneedle.elbow, 3))
0.222

The KneeLocator class also has some plotting functions for quick visualization of the curve (blue), the distance curve (red) and the knee (dashed line, if present)

kneedle.plot_knee_normalized()

Average Knee from 5000 NoisyGaussians when mu=50 and sigma=10

import numpy as np

knees = []
for i in range(5000):
    x,y = DataGenerator.noisy_gaussian(mu=50, sigma=10, N=1000)
    kneedle = KneeLocator(x, y, curve='concave', direction='increasing')
    knees.append(kneedle.knee)

np.mean(knees)
60.921051806064931

Application

Find the optimal number of clusters (k) to use in k-means clustering. See the tutorial in the notebooks folder, this can be achieved with the direction keyword argument:

KneeLocator(x, y, curve='convex', direction='decreasing')

Contributing

Contributions are welcome, if you have suggestions or would like to make improvements please submit an issue or pull request.

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