Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/filippobovo/robustats
Robustats is a Python library for high-performance computation of robust statistical estimators.
https://github.com/filippobovo/robustats
c fast high-performance medcouple mode numpy python-library python3 robust-estimators robust-statistics weighted-median
Last synced: 6 days ago
JSON representation
Robustats is a Python library for high-performance computation of robust statistical estimators.
- Host: GitHub
- URL: https://github.com/filippobovo/robustats
- Owner: FilippoBovo
- License: mit
- Created: 2019-08-03T16:46:56.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-20T15:48:41.000Z (7 months ago)
- Last Synced: 2024-04-14T13:10:21.174Z (6 months ago)
- Topics: c, fast, high-performance, medcouple, mode, numpy, python-library, python3, robust-estimators, robust-statistics, weighted-median
- Language: C
- Homepage:
- Size: 48.8 KB
- Stars: 48
- Watchers: 2
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Robustats
Robustats is a Python library for high-performance computation of robust statistical estimators.
The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats).
Estimators implemented in the library:
- **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\]
- **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7]
- **Mode** (temporal complexity: `O(n * log(n))`) [8]
## How to Install
This library requires Python 3.
You can install the library using Pip.
```shell
pip install robustats
```
You can also install the library directly from GitHub using the following command.
```shell
pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats'
```
Otherwise, you may clone the repository, and install and test the Robustats package in the following way.
```shell
git clone https://github.com/FilippoBovo/robustats.git
cd robustats
pip install -e .
python -m unittest
```
## How to Use
This is an example of how to use the Robustats library in Python.
```python
import numpy as np
import robustats
# Weighted Median
x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9])
weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8])
weighted_median = robustats.weighted_median(x, weights)
print("The weighted median is {}".format(weighted_median))
# Output: The weighted median is 2.1
# Medcouple
x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14])
medcouple = robustats.medcouple(x)
print("The medcouple is {}".format(medcouple))
# Output: The medcouple is 0.7749999999999999
# Mode
x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.])
mode = robustats.mode(x)
print("The mode is {}".format(mode))
# Output: The mode is 3.0
```
## How to Contribute
If you wish to contribute to this library, please follow the patterns and style of the rest of the code.
Moreover, install the Git hooks.
```shell
git config core.hooksPath .githooks
```
Tips:
- In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886).
- Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops.
## References
\[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false).
\[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf).
\[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median).
\[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632)
\[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013)
\[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/)
\[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple)
\[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011)