Dependencies

TALENT relies on the following dependencies to provide a comprehensive machine learning toolbox for tabular data analysis. Ensure that these packages are installed in your environment before using TALENT:

Python Libraries

PyTorch : The deep learning framework used for model development and training in TALENT.
scikit-learn : Provides classical machine learning models and utilities for data preprocessing and evaluation.
pandas : A data manipulation and analysis library for handling tabular data.
numpy : Fundamental package for scientific computing with Python, including support for large, multi-dimensional arrays and matrices.
scipy : A library for scientific and technical computing, used for optimization, integration, and statistics.
tqdm : A library for creating progress bars, used to display the progress of model training and data processing.

Optional Dependencies

Some methods in TALENT require additional dependencies for specific tasks. If you intend to use the following methods, make sure to install these optional packages:

faiss-gpu : Required for TabR to efficiently handle nearest neighbor searches. Install via conda:
conda install faiss-gpu -c pytorch

Installation

To install the necessary dependencies for TALENT, you can use the following commands:

Using pip:

Install the required libraries from the requirements.txt file:
```
pip install -r requirements.txt
```
Using conda:

If you are using conda, you can create a new environment and install the dependencies:
```
conda create -n talent python=3.10
conda activate talent
pip install -r requirements.txt
```

Additional Notes

Ensure that your Python version is compatible with the dependencies listed above. TALENT is tested with Python 3.10 and PyTorch 2.0.1.