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https://github.com/arvkevi/kneed
Knee point detection in Python :chart_with_upwards_trend:
https://github.com/arvkevi/kneed
data-analysis data-science elbow-method knee-point python scientific-computing systems
Last synced: 28 days ago
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Knee point detection in Python :chart_with_upwards_trend:
- Host: GitHub
- URL: https://github.com/arvkevi/kneed
- Owner: arvkevi
- License: bsd-3-clause
- Created: 2017-12-11T01:39:14.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2023-08-21T14:36:34.000Z (10 months ago)
- Last Synced: 2024-01-15T15:04:19.663Z (5 months ago)
- Topics: data-analysis, data-science, elbow-method, knee-point, python, scientific-computing, systems
- Language: Python
- Homepage: https://kneed.readthedocs.io
- Size: 12.2 MB
- Stars: 662
- Watchers: 11
- Forks: 73
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Lists
- awesome-resources - Kneed, kneedle algorithm, Python
README
# kneed
Knee-point detection in Python
[](https://pepy.tech/project/kneed) [](https://pepy.tech/project/kneed)  [](https://share.streamlit.io/arvkevi/ikneed/main/ikneed.py) [](https://codecov.io/gh/arvkevi/kneed)[](https://zenodo.org/badge/latestdoi/113799037)
This repository is an attempt to implement the kneedle algorithm, published [here](https://www1.icsi.berkeley.edu/~barath/papers/kneedle-simplex11.pdf). 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](#installation)
- [Usage](#usage)
- [Input Data](#input-data)
- [Find Knee](#find-knee)
- [Visualize](#visualize)
- [Documentation](#documentation)
- [Interactive](#interactive)
- [Contributing](#contributing)
- [Citation](#citation)
## Installation
`kneed` has been tested with Python 3.7, 3.8, 3.9, and 3.10.
**anaconda**
```bash
$ conda install -c conda-forge kneed
```
**pip**
```bash
$ pip install kneed # To install only knee-detection algorithm
$ pip install kneed[plot] # To also install plotting functions for quick visualizations
```
**Clone from GitHub**
```bash
$ git clone https://github.com/arvkevi/kneed.git && cd kneed
$ pip install -e .
```
## 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.
```python
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.
```python
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:
```python
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**
```python
# Normalized data, normalized knee, and normalized distance curve.
kneedle.plot_knee_normalized()
```
```python
# Raw data and knee.
kneedle.plot_knee()
```
## Documentation
Documentation of the parameters and a full API reference can be found [here](https://kneed.readthedocs.io/).
## Interactive
An interactive streamlit app was developed to help users explore the effect of tuning the parameters.
There are two sites where you can test out kneed by copy-pasting your own data:
1. https://share.streamlit.io/arvkevi/ikneed/main/ikneed.py
2. https://ikneed.herokuapp.com/
You can also run your own version -- head over to the [source code for ikneed](https://github.com/arvkevi/ikneed).
## Contributing
Contributions are welcome, please refer to [CONTRIBUTING](https://github.com/arvkevi/kneed/blob/main/CONTRIBUTING.md)
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