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https://github.com/lucacappelletti94/dictances
Distances and divergences between distributions implemented in the best way I found in python.
https://github.com/lucacappelletti94/dictances
Last synced: 7 days ago
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Distances and divergences between distributions implemented in the best way I found in python.
- Host: GitHub
- URL: https://github.com/lucacappelletti94/dictances
- Owner: LucaCappelletti94
- License: mit
- Created: 2018-05-08T12:52:15.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-06T19:20:08.000Z (4 months ago)
- Last Synced: 2024-10-03T12:23:15.703Z (about 1 month ago)
- Language: Python
- Size: 210 KB
- Stars: 44
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Dictances
[](https://pypi.org/project/dictances/)
[](https://pepy.tech/project/dictances)
[](https://github.com/LucaCappelletti94/dictances/blob/master/LICENSE)
[](https://www.python.org/downloads/)
[](https://github.com/LucaCappelletti94/dictances/actions/)
Distances and divergences between discrete distributions described as dictionaries implemented in Python.
These are meant as fast solutions to compute distances and divergences between discrete distributions, especially when the two distributions contain a significant amount of events with nil probability which are not described in the dictionaries.
Find the complete list just below.
## How do I install this package?
As usual, just download it using pip:
```shell
pip install dictances
```
## Available metrics
A number of distances and divergences are available:
| Distances | Methods |
| ----------------------------------------------------------------------------------------------------------- | ------------------------------------------- |
| [Bhattacharyya distance](https://en.wikipedia.org/wiki/Bhattacharyya_distance) | `bhattacharyya` |
| [Bhattacharyya coefficient](https://en.wikipedia.org/wiki/Bhattacharyya_distance#Bhattacharyya_coefficient) | `bhattacharyya_coefficient` |
| [Canberra distance](https://en.wikipedia.org/wiki/Canberra_distance) | `canberra` |
| [Chebyshev distance](https://en.wikipedia.org/wiki/Chebyshev_distance) | `chebyshev` |
| [Chi Square distance](https://en.wikipedia.org/wiki/Chi-squared_test) | `chi_square` |
| [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) | `cosine` |
| [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance) | `euclidean` |
| [Hamming distance](https://en.wikipedia.org/wiki/Hamming_distance) | `hamming` |
| [Jensen-Shannon divergence](https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence) | `jensen_shannon` |
| [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) | `kullback_leibler` |
| [Mean absolute error](https://en.wikipedia.org/wiki/Mean_absolute_error) | `mae` |
| [Taxicab geometry](https://en.wikipedia.org/wiki/Taxicab_geometry) | `manhattan`, `cityblock`, `total_variation` |
| [Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) | `minkowsky` |
| [Mean squared error](https://en.wikipedia.org/wiki/Mean_squared_error) | `mse` |
| [Pearson's distance](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#Pearson's_distance) | `pearson` |
| [Squared deviations from the mean](https://en.wikipedia.org/wiki/Squared_deviations_from_the_mean) | `squared_variation` |
## Usage example with points
Suppose you have a point described by `my_first_dictionary` and another one described by `my_second_dictionary`:
```python
from dictances import cosine
my_first_dictionary = {
"a": 56,
"b": 34,
"c": 89
}
my_second_dictionary = {
"a": 21,
"d": 51,
"e": 74
}
cosine(my_first_dictionary, my_second_dictionary)
#>>> 0.8847005261889619
```
## Usage example with distributions
Suppose you have a point described by `my_first_dictionary` and another one described by `my_second_dictionary`:
```python
from dictances import bhattacharyya, bhattacharyya_coefficient
a = {
"event_1": 0.4,
"event_2": 0.1,
"event_3": 0.2,
"event_4": 0.3,
}
b = {
"event_1": 0.1,
"event_2": 0.2,
"event_5": 0.2,
"event_9": 0.5,
}
bhattacharyya_coefficient(a, b)
#>>> 0.3414213562373095
bhattacharyya(a, b)
#>>> 1.07463791569453
```
## Handling nested dictionaries
If you need to compute the distance between two nested dictionaries you can use [`deflate_dict`](https://github.com/LucaCappelletti94/deflate_dict) as follows:
```python
from dictances import cosine
from deflate_dict import deflate
my_first_dictionary = {
"a": 8,
"b": {
"c": 3,
"d": 6
}
}
my_second_dictionary = {
"b": {
"c": 8,
"d": 1
},
"y": 3,
}
cosine(deflate(my_first_dictionary), deflate(my_second_dictionary))
```
## License
This software is distributed under the MIT License. See the [LICENSE](LICENSE) file for more information.
## How to contribute
If you want to contribute to the project and make it better, your help is very welcome. If you find a bug, please raise an issue with all the minimal code necessary to reproduce it. If you have a feature request, please open an adequately detailed issue to discuss it before starting to work on it in a pull request.