Metadata-Version: 2.4
Name: pyscreeningfs
Version: 0.1.0
Summary: This is a Python implementation by the authors of the paper 'Online Feature Screening for Data Streams With Concept Drift' from Dr. Mingyuan Wang and Dr. Adrian Barbu. Contain various feature selection methods.
Home-page: https://github.com/tbonewmy/Online-Feature-Screening-for-Datastream-with-Sparsity-Concept-Drifting
Author: Mingyuan Wang
Author-email: bruce.wmy.research@gmail.com
License: Apache-2.0
Keywords: feature selection,feature screening,variable screening,online learning,online feature selection,concept drift,data drift,machine learning,artificial intelligence,statistics
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: Microsoft :: Windows
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=2.2.4
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Online-Feature-Screening-for-Datastream-with-Sparsity-Concept-Drifting

This is a Python implementation by the authors of the paper **"Online Feature Screening for Data Streams With Concept Drift"** from Dr. Mingyuan Wang and Dr. Adrian Barbu.\
Please cite this paper if you use or build on our method. [doi.org/10.1109/TKDE.2022.3232752](https://doi.org/10.1109/TKDE.2022.3232752)

## Installation

### Prerequisites

* `Python` 3.10 or newer
* `pip`
* `numpy` 2.2.4 or newer

### Note
Although the package is designed OS independent, it was only tested on Windows. You might need to use methods listed below other than `pip install pyscreeningfs`.
   \
   \
**For users installing from source (e.g., if no pre-built wheels are available for your system):**
You will need a C++ compiler compatible with your Python installation:
* **Windows:** Microsoft Visual C++ Build Tools (part of Visual Studio, or standalone).
* **Linux:** `gcc` and `g++` (usually included or easily installed via your package manager, e.g., `sudo apt-get install build-essential`).
* **macOS:** Xcode Command Line Tools (install with `xcode-select --install`).

### Install via git clone
1. Clone repository
``` bash
git clone https://github.com/yourusername/repo_name.git
```
2. Navigate into the cloned repository directory
```
cd repo_name 
```
3. Install
```
pip install .
```

### Install via download
1. Download the repository
2. Unpack to your own folder your_folder/repo_name
3. Navigate into the unpacked repository directory
``` bash
cd repo_name  
```
4. Install
``` bash
pip install .
```
### Install via pip (Currently unavailable)

If pre-built wheels are available for your system on PyPI (coming soon!), you can install directly:
```
pip install pyscreeningfs
```

## Data
For .svm sparse data, visit [https://www.sysnet.ucsd.edu/projects/url/](https://www.sysnet.ucsd.edu/projects/url/) \
Download and put into `data/url_svmlight/`

## Demo
For a demo, see testing.py in the root directory.
