How to reproduce our results

1. Create a Virtual Environment

Option 1: Conda virtual environment built using the environment.yml file.

To create a conda virtual environment with the yml file, run the following command in the terminal

conda env create -f environment.yml

Option 2: Create a virtual environment and install the required packages using the requirements.txt file.

Create a python virtual environment (we use Python 3.9.18), then install the requirements by running the following command

pip3 install -r requirements. txt

2. Generate the Synthetic Data

As the size of the synthetic data is too large to be added to the GitHub, it has to be generated using the create_synthetic_data.py script. The generated data is then stored in the synthetic_data directory.

3. (Optional) Train the MLP model

This can be done by running the train_mlp.py script. You have to provide the directory in which the input data is stored (the matrices F, H and S) as well as the model's name as follows:

python3 train_mlp.py <path_to_input_data> <model_name>

Example usage:

python3 train_mlp.py synthetic_data my_model

4. Test the MLP model

To do this, simply run the model_test.ipynb notebook. Be sure to modify the weights and layer configurations accordingly if you tried training the model with different parameters than the default ones provided in the python script. Make sure to use a Jupyter Kernel that has all the requirements specified to build the virtual environment. To obtain an excel file with the metrics used to assess the quality of the model, you can run the compute_local_metrics.py script.