semvec/pypi-packages huggingface dataset:
fselect 1.0.4
Feature Selection for Clustering
License: MIT
Requires Python: >=3.5,<4.0

# Feature Selection for Clustering: fselect

![](https://img.shields.io/github/license/billodalroy/fselect?color=blue&style=flat-square)
![](https://img.shields.io/pypi/v/fselect?color=green&style=flat-square)
[![Downloads](https://pepy.tech/badge/fselect)](https://pepy.tech/project/fselect)

A fast and scalable implementation of A-RANK algorithm as proposed
by Dash, M. and Liu, H. in their paper "Feature Selection for Clustering" for selecting features
from a dataset using an entropy measure using fast python libraries: numpy, pandas and scikit-learn.

## Getting Started  

Install the package:

```bash
pip install fselect
```

Import the main function:

```python
from fselect import rank_features  
```

Prepare a dataframe with normalized continuous features:  

```python  
import pandas as pd

df = pd.DataFrame({
    'feature1': [...],
    'feature2': [...],    
    [...]
})
```

Rank the features:

```python
ranked_df = rank_features(df)  
```

The returned dataframe \`ranked_df\` contains columns: "rank", "feature", "entropy" sorted by entropy.

## Usage

The main parameters:  

- `dataframe: pd.DataFrame` - Input dataframe with continuous normalized features
- `remove_correlated_columns: bool` (optional) - Whether to remove highly correlated columns before ranking
- `correlation_threshold: float` (optional) - Correlation threshold to determine correlated columns (default 0.999)

**Remove correlated columns first**

```python
ranked_df = rank_features(df, remove_correlated_columns=True)  
```

**Custom correlation threshold**   

```python
ranked_df = rank_features(df, remove_correlated_columns=True, correlation_threshold=0.95) 
```

## Algorithm  

The entropy calculation is based on the equations defined in the ARANK paper. It calculates a similarity matrix of the dataframe and computes entropy from the same.
pycounts_cl 0.1.0
524 Assignment 1
License: MIT
Requires Python: >=3.9,<4.0

# pycounts_cl

524 Assignment 1

## Installation

```bash
$ pip install pycounts_cl
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycounts_cl` was created by Chun Li. It is licensed under the terms of the MIT license.

## Credits

`pycounts_cl` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).