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Description:
gbfs 0.2.1
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.
Table of contents
Our Contributions
Installation
Usage
GB-AFS
Initialization
Feature-Selection
Visualization
GB-BC-FS
Status
Documentation
Contribution
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
$ pip install gbfs
Clone from GitHub
$ 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:
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:
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:
gbafs.plot_feature_space()
GB-BC-FS
Status
Currently in development.
Documentation
For more information on available commands and usage, refer to the documentation.
Contribution
Contributions to gbfs are welcome! If you encounter any issues or have suggestions for improvements, please open an issue.
Citation
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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