## GB-FS
**gbfs** is a comprehensive repository dedicated to advancing Graph-Based Feature Selection methodologies in machine learning. Our project houses two significant contributions to the field: GB-AFS and GB-BC-FS, each developed to address the intricate challenges of feature selection with graph-based solutions.
[](https://pepy.tech/project/gbfs) [](https://pepy.tech/project/gbfs)
[](https://github.com/davidlevinwork/gbfs/actions/workflows/ci.yml)
[](https://github.com/davidlevinwork/gbfs/actions/workflows/tests.yml)
## Table of contents
- [Our Contributions](#our-contributions)
- [Installation](#installation)
- [Usage](#usage)
- [GB-AFS](#GB-AFS)
- [Initialization](#Initialization)
- [Feature-Selection](#Feature-Selection)
- [Visualization](#visualization)
- [GB-BC-FS](#GB-BC-FS)
- [Status](#status)
- [Documentation](#documentation)
- [Contribution](#contribution)
- [Citation](#citation)
## Our Contributions
- **GB-AFS (Graph-Based Automatic Feature Selection)**: A method that automates the process of feature selection for multi-class classification tasks, ensuring the minimal yet most effective set of features is utilized for model training.
- **GB-BC-FS (Graph-Based Budget-Constrained Feature Selection)**: Currently in development, this method seeks to enhance feature selection by integrating budget constraints, ensuring the cost of each feature is considered.
## Installation
`gbfs` has been tested with Python 3.10.
**pip**
```bash
$ pip install gbfs
```
**Clone from GitHub**
```bash
$ git clone https://github.com/davidlevinwork/gbfs.git && cd gbfs
$ poetry install
$ poetry shell
```
## Usage
### GB-AFS
#### Initialization
To begin working with GB-AFS, the first step is to initialize the GB-AFS object:
``` py bash
from gbfs import GBAFS
gbafs = GBAFS(
dataset_path="path/to/your/dataset.csv",
separability_metric="your_separability_metric",
dim_reducer_model="your_dimensionality_reduction_method",
label_column="class",
)
```
#### Feature-Selection
After initializing the GB-AFS object, you can move forward with the process of selecting features:
``` py bash
selected_features = gbafs.select_features()
print("Selected Feature Indices:", selected_features)
```
#### Visualization
GB-AFS also incorporates a technique for visualizing the chosen features within the feature space, offering insights into their distribution and how distinct they are:
``` py bash
gbafs.plot_feature_space()
```
### GB-BC-FS
#### Status
Currently in development.
## Documentation
For more information on available commands and usage, refer to the [documentation](https://davidlevinwork.github.io/gbfs/).
## Contribution
Contributions to `gbfs` are welcome! If you encounter any issues or have suggestions for improvements, please open an issue.
## Citation
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"description": "## GB-FS\n\n**gbfs** is a comprehensive repository dedicated to advancing Graph-Based Feature Selection methodologies in machine learning. Our project houses two significant contributions to the field: GB-AFS and GB-BC-FS, each developed to address the intricate challenges of feature selection with graph-based solutions.\n\n[](https://pepy.tech/project/gbfs) [](https://pepy.tech/project/gbfs)\n[](https://github.com/davidlevinwork/gbfs/actions/workflows/ci.yml)\n[](https://github.com/davidlevinwork/gbfs/actions/workflows/tests.yml)\n\n\n## Table of contents\n- [Our Contributions](#our-contributions)\n- [Installation](#installation)\n- [Usage](#usage)\n - [GB-AFS](#GB-AFS)\n - [Initialization](#Initialization)\n - [Feature-Selection](#Feature-Selection)\n - [Visualization](#visualization)\n - [GB-BC-FS](#GB-BC-FS)\n - [Status](#status)\n- [Documentation](#documentation)\n- [Contribution](#contribution)\n- [Citation](#citation)\n\n## Our Contributions\n\n- **GB-AFS (Graph-Based Automatic Feature Selection)**: A method that automates the process of feature selection for multi-class classification tasks, ensuring the minimal yet most effective set of features is utilized for model training.\n \n- **GB-BC-FS (Graph-Based Budget-Constrained Feature Selection)**: Currently in development, this method seeks to enhance feature selection by integrating budget constraints, ensuring the cost of each feature is considered.\n\n## Installation\n\n`gbfs` has been tested with Python 3.10.\n\n**pip**\n```bash\n$ pip install gbfs \n```\n\n**Clone from GitHub**\n```bash\n$ git clone https://github.com/davidlevinwork/gbfs.git && cd gbfs\n$ poetry install\n$ poetry shell\n```\n\n## Usage\n\n### GB-AFS\n\n#### Initialization\n\nTo begin working with GB-AFS, the first step is to initialize the GB-AFS object:\n\n``` py bash\nfrom gbfs import GBAFS\n\ngbafs = GBAFS(\n dataset_path=\"path/to/your/dataset.csv\",\n separability_metric=\"your_separability_metric\",\n dim_reducer_model=\"your_dimensionality_reduction_method\",\n label_column=\"class\",\n)\n```\n\n#### Feature-Selection\nAfter initializing the GB-AFS object, you can move forward with the process of selecting features:\n\n``` py bash\nselected_features = gbafs.select_features()\n\nprint(\"Selected Feature Indices:\", selected_features)\n```\n\n#### Visualization\nGB-AFS also incorporates a technique for visualizing the chosen features within the feature space, offering insights into their distribution and how distinct they are:\n\n``` py bash\ngbafs.plot_feature_space()\n```\n\n### GB-BC-FS\n\n#### Status\nCurrently in development.\n\n## Documentation\nFor more information on available commands and usage, refer to the [documentation](https://davidlevinwork.github.io/gbfs/).\n\n## Contribution\nContributions to `gbfs` are welcome! If you encounter any issues or have suggestions for improvements, please open an issue.\n\n## Citation",
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