semvec/pypi-packages huggingface dataset:
EvoCluster 1.0.6
An Open-Source Nature-Inspired Optimization Clustering Framework in Python
Keywords: python,first package

EvoCluster is an open source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired meta heuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and customizable implementation of the metaheuristic based clustering algorithms which canbe utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. 

- Web Page: http://evo-ml.com/evocluster/
- Paper: https://link.springer.com/chapter/10.1007/978-3-030-43722-0_2
- Extended Paper: https://link.springer.com/article/10.1007/s42979-021-00511-0
- Source code: https://github.com/RaneemQaddoura/EvoCluster/

Features
- Ten nature-inspired metaheuristic optimizers are implemented (SSA, PSO, GA, BAT, FFA, GWO, WOA, MVO, MFO, and CS).
- Five objective functions (SSE, TWCV, SC, DB, and DI).
- Thirty datasets obtained from Scikit learn, UCI, School of Computing at University of Eastern Finland, ELKI, KEEL, and Naftali Harris Blog
- Twelve evaluation measures (SSE, Purity, Entropy, HS, CS, VM, AMI, ARI, Fmeasure, TWCV, SC, Accuracy, DI, DB, and Standard Diviation)
- More than twenty distance measures
- Ten different ways for detecting the k value
- The implimentation uses the fast array manipulation using [NumPy] (http://www.numpy.org/).
- Matrix support using [SciPy's] (https://www.scipy.org/) package.
- Simple and efficient tools for prediction using [sklearn] (https://scikit-learn.org/stable/)
- File data analysis and manipulation tool using [pandas] (https://pandas.pydata.org/)
- Plot interactive visualizations using [matplotlib] (https://matplotlib.org/)
- More optimizers, objective functions, adatasets, and evaluation measures are coming soon.
async-spotify 0.4.4
An async spotify api client
Requires Python: >=3.6
Keywords: Spotify,Async,API,Wrapper,AioHttp

# AsyncSpotify

![Unit Tests](https://github.com/HuiiBuh/AsyncSpotify/workflows/Unit%20Tests/badge.svg)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/65dd7dbb2b4b4efcb3fc365f2d3f1684)](https://app.codacy.com/manual/nhaderer1/AsyncSpotify?utm_source=github.com&utm_medium=referral&utm_content=HuiiBuh/AsyncSpotify&utm_campaign=Badge_Grade_Dashboard)
[![codecov](https://codecov.io/gh/HuiiBuh/AsyncSpotify/branch/master/graph/badge.svg?token=0oC3x1chKb)](https://codecov.io/gh/HuiiBuh/AsyncSpotify)

## Documentation

The full documentation can be viewed [here](https://huiibuh.github.io/AsyncSpotify/).

## Why should you use this library

+ 100% Code coverage
+ Completely type annotated
+ Completely async
+ Scales up to (theoretically) unlimited requests per second (tested with 1000 simultaneous requests which lead to a 429)
+ Customize the timeout, maximal simultaneous request
+ Throws custom errors, so you can catch different errors easily
      + Token expiration
      + Rate limit violation
      + An invalid album id
+ Gets rid of the client credential workflow if you provide spotify cookies
    + Good for automated testing
+ Offers a hook which gets called if the token expires, so you can automatically update the token

## Installation

You need at least python3.6 to install the package

```bash
pip install async-spotify
```

## Example

For more in depth examples take a look [here](https://huiibuh.github.io/AsyncSpotify/EXAMPLES/) or visit the documentation.

```python
from async_spotify import SpotifyApiClient
from async_spotify.authentification import SpotifyAuthorisationToken
from async_spotify.authentification.authorization_flows import AuthorizationCodeFlow
# Create a auth_code_flow object and load the auth_code_flow from env variables
auth_flow = AuthorizationCodeFlow()
auth_flow.load_from_env()

# Create a new Api client and pass the auth_code_flow
api_client = SpotifyApiClient(auth_flow, hold_authentication=True)

# Get the auth token with your code
code: str = "Your Spotify Code"
auth_token: SpotifyAuthorisationToken = await api_client.get_auth_token_with_code(code)

# Create a new client
await api_client.create_new_client(request_limit=1500)

# Start making queries with the internally saved token
album_tracks: dict = await api_client.albums.get_tracks('03dlqdFWY9gwJxGl3AREVy')

# If you pass a valid auth_token this auth_token will be used for making the requests
album_tracks: dict = await api_client.albums.get_tracks('03dlqdFWY9gwJxGl3AREVy', auth_token)

# Every argument mentioned by the Spotify API can be passed as kwarg. The client will figure out if your provided kwarg
# should be added to the request body, or the url
await api_client.player.play(context_uri="spotify:album:5ht7ItJgpBH7W6vJ5BqpPr", device_id="whatever_id")
#                                 ^                                                 ^
#                                URL                                               BODY
```

## Tests

See [here](STUFF.md).