Correct load_class bugs in tabsurvey models

.gitignore

@@ -165,3 +165,14 @@ out/
 
 website/_site
 website/.jekyll-cache
+
+#uv
+uv.lock
+
+# Huggingface
+HF_TOKEN
+
+# Notebooks
+notebooks/
+
+# Data

.python-version

@@ -1 +1 @@
-3.8
+3.12

README.md

@@ -1,23 +1,21 @@
 # TabularBench
 
-TabularBench: Adversarial robustness benchmark for tabular data
+TabularBench: Adversarial Robustness Benchmark for Tabular Data
 
-**Leaderboard**: [https://serval-uni-lu.github.io/tabularbench/](https://serval-uni-lu.github.io/tabularbench/)
-
-**Documentation**: [https://serval-uni-lu.github.io/tabularbench/doc](https://serval-uni-lu.github.io/tabularbench/doc)
+This is a fork from the [original benchmark repository](https://github.com/serval-uni-lu/tabularbench).
+This version has been updated to work with `python 3.12` and the latest versions of the dependencies.
+It has also been refactored to be more lightweight, and thus the original leaderbord and documentation have been removed.
+To access the original leaderboard and documentation, please refer to the original repository.
 
 **Research papers**:
 
 - Benchmark: [TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases](https://arxiv.org/abs/2408.07579)
 - CAA attack: [Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data](https://arxiv.org/abs/2406.00775)
 - CAPGD attack: [Towards Adaptive Attacks on Constrained Tabular Machine Learning](https://openreview.net/forum?id=DnvYdmR9OB)
-- MOEVA attack: [A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space](https://arxiv.org/abs/2112.01156)
 
 **How to cite**:
 
-Would you like to reference the CAA attack?
-
-Then consider citing our paper, to appear in NeurIPS 2024 (spotlight):
+To reference the *CAA attack*, consider citing the following paper:
 
 ```bibtex
 @misc{simonetto2024caa,
@@ -29,9 +27,7 @@ Then consider citing our paper, to appear in NeurIPS 2024 (spotlight):
 }
 ```
 
-Would you like to reference the benchmark, the leaderboard or the model zoo?
-
-Then consider citing our paper, to appear in NeurIPS 2024 Datasets and Benchmarks:
+To reference the benchmark, consider citing the following paper:
 
 ```bibtex
 @misc{simonetto2024tabularbench,
@@ -43,128 +39,25 @@ Then consider citing our paper, to appear in NeurIPS 2024 Datasets and Benchmark
 }
 ```
 
-## Installation
-
-### Using Docker (recommended)
-
-1. Clone the repository
-
-2. Build the Docker image
-
-    ```bash
-    ./tasks/docker_build.sh
-    ```
-
-3. Run the Docker container
-
-    ```bash
-    ./tasks/run_benchmark.sh
-    ```
-
-Note: The `./tasks/run_benchmark.sh` script mounts the current directory to the `/workspace` directory in the Docker container.
-This allows you to edit the code on your host machine and run the code in the Docker container without rebuilding.
-
-### Using Pip
 
-We recommend using Python 3.8.10.
-
-1. Install the package from PyPI
-
-    ```bash
-    pip install tabularbench
-    ```
-
-### With Pyenv and Poetry
-
-1. Clone the repository
-
-2. Create a virtual environment using [Pyenv](https://github.com/pyenv/pyenv) with Python 3.8.10.
-
-3. Install the dependencies using [Poetry](https://python-poetry.org/).
-
- ```bash
-    poetry install
- ```
+## Installation
 
-### Using conda
+### UV
 
 1. Clone the repository
 
-2. Create a virtual environment using [Conda](https://docs.anaconda.com/free/miniconda/) with Python 3.8.10.
-
-    ```bash
-    conda create -n tabularbench python=3.8.10
-    ```
-
-3. Activate the conda environment.
-
-    ```bash
-    conda activate tabularbench
-    ```
+``bash
+git clone https://github.com/serval-uni-lu/tabularbench-cf.git
+``
 
-4. Install the dependencies using Pip.
-
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-## How to use
-
-### Run the benchmark
-
-You can run the benchmark with the following command:
-
-```bash
-python -m tasks.run_benchmark
-```
-
-or with Docker:
+2. Navigate to the project directory
 
 ```bash
-docker_run_benchmark
-```
-
-### Using the API
-
-You can also use the API to run the benchmark. See `tasks/run_benchmark.py` for an example.
-
-```python
-clean_acc, robust_acc = benchmark(
-    dataset="URL",
-    model="STG_Default",
-    distance="L2",
-    constraints=True,
-)
+cd tabularbench-cf
 ```
 
-### Retrain the models
-
-We provide the models and parameters used in the paper.
-You can retrain the models with the following command:
+3. Install the dependencies using [UV](https://uv.io/).
 
 ```bash
-python -m tasks.train_model
-```
-
-Edit the `tasks/train_model.py` file to change the model, dataset, and training method.
-
-## Data availability
-
-Datasets, pretrained models, and synthetic data are publicly available [here](https://uniluxembourg-my.sharepoint.com/:f:/g/personal/thibault_simonetto_uni_lu/EvkG4BI0EqJFu436biA2C_sBpkEKTTjA5PgZU_Z9jwNNSA?e=62a4Dm).
-The folder structure on the Shared folder should be followed locally to ensure the code runs correctly.
-
-> [!NOTE]
-> We are transitioning to Hugging Face for data storage. The model's data is now available on Huggin Face [here](https://huggingface.co/serval-uni-lu/tabularbench/tree/main).
-
-**Datasets**: Datasets are downloaded automatically in `data/datasets` when used.
-
-**Models** (HuggingFace): Models are now downloaded automatically as needed when running the benchmark. Only the required model for a specific setting will be downloaded. Pretrained models remain available in the `data/models` folder on OneDrive.
-
-**Model parameters**: Optimal parameters (from hyperparameters search) are required to train models and are in `data/model_parameters`.
-
-**Synthetic data**: The synthetic data generated by GANs is available in the folder `data/synthetic`.
-
-## Naming
-
-For technical reasons, the names of datasets, models, and training methods are different from the paper.
-The mapping can be found in [docs/naming.md](docs/naming.md).
+uv sync
+```