# Simulations to identify the relationship structure among multiple datasets using Independent Vector Analysis
This package contains the code for reproducing the simulations of our paper:
Isabell Lehmann, Tanuj Hasija, Ben Gabrielson, M. A. B. S. Akhonda, Vince D. Calhoun, Tülay Adali,
**Identifying the Relationship Structure among Multiple Datasets Using Independent Vector Analysis: Application to Multi-task fMRI Data**, *submitted in 2023*
## Installing this Package
The only pre-requisite is to have **Python 3** (version >=3.11) installed. This package can be
installed (optionally in a virtual environment) with:
git clone https://github.com/SSTGroup/relationship_structure_identification
cd relationship_structure_identification
pip install -e .
Required third-party packages will automatically be installed.
## Generating Simulations and Results
The simulated data is generated with:
cd relationship_structure_identification
python simulations.py @../simulations/simulation_parameters.txt
After running the code, the folder *relationship_structure_identification/simulations* will contain the generated .npy files,
consisting of the true data and the estimated results for IVA-L-SOS, the bootstrap and the clustering, for each Monte-Carlo run.
Then, the performance metrics are calcuted with (from the relationship_structure_identification folder):
python performance_metrics.py @../simulations/performance_parameters.txt
The .npy files containing the performace metrics will also be saved in the *simulations* folder.
## Visualizing Results
After having calculated the performance metrics, the boxplots can be generated by running the
notebook.
The *metrics_rhoxx.npy* files must be in the *simulations* folder.
## Changing Parameters
By changing the scenario in *parameters.txt*, the simulations for different values of 'rho' are generated.
The other parameters are set to the values according to the simulations in our paper.
The default values can be changed by adding the parameters to the *simulation_parameters.txt* or
*performance_parameters.txt* files.
## Contact
In case of questions, suggestions, problems etc. please send an email to isabell.lehmann@sst.upb.de,
or open an issue here on Github.
## Citing
If you use this code in an academic paper, please cite [1]
@article{Lehmann2023,
title = {Identifying the Relationship Structure among Multiple Datasets Using Independent Vector Analysis: Application to Multi-task fMRI Data},
author = {Lehmann, Isabell and Hasija, Tanuj and Gabrielson, Ben and Akhonda, M. A. B. S. and Calhoun, Vince D. and Adali, T{\"u}lay},
booktitle={tba},
pages={tba},
year={2023}
}
[1] Isabell Lehmann, Tanuj Hasija, et al.,
**Identifying the Relationship Structure among Multiple Datasets Using Independent Vector Analysis: Application to Multi-task fMRI Data**,
*submitted in 2023*.
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"description": "# Simulations to identify the relationship structure among multiple datasets using Independent Vector Analysis\r\n\r\nThis package contains the code for reproducing the simulations of our paper:\r\n\r\nIsabell Lehmann, Tanuj Hasija, Ben Gabrielson, M. A. B. S. Akhonda, Vince D. Calhoun, T\u00c3\u00bclay Adali,\r\n**Identifying the Relationship Structure among Multiple Datasets Using Independent Vector Analysis: Application to Multi-task fMRI Data**, *submitted in 2023*\r\n\r\n\r\n## Installing this Package\r\n\r\nThe only pre-requisite is to have **Python 3** (version >=3.11) installed. This package can be\r\ninstalled (optionally in a virtual environment) with:\r\n\r\n git clone https://github.com/SSTGroup/relationship_structure_identification\r\n cd relationship_structure_identification\r\n pip install -e .\r\n\r\nRequired third-party packages will automatically be installed.\r\n\r\n\r\n## Generating Simulations and Results\r\n\r\nThe simulated data is generated with:\r\n\r\n cd relationship_structure_identification\r\n python simulations.py @../simulations/simulation_parameters.txt\r\n\r\nAfter running the code, the folder *relationship_structure_identification/simulations* will contain the generated .npy files,\r\nconsisting of the true data and the estimated results for IVA-L-SOS, the bootstrap and the clustering, for each Monte-Carlo run.\r\n\r\nThen, the performance metrics are calcuted with (from the relationship_structure_identification folder):\r\n\r\n python performance_metrics.py @../simulations/performance_parameters.txt\r\n\r\nThe .npy files containing the performace metrics will also be saved in the *simulations* folder.\r\n\r\n\r\n## Visualizing Results\r\n\r\nAfter having calculated the performance metrics, the boxplots can be generated by running the\r\nnotebook.\r\nThe *metrics_rhoxx.npy* files must be in the *simulations* folder. \r\n\r\n\r\n## Changing Parameters\r\n\r\nBy changing the scenario in *parameters.txt*, the simulations for different values of 'rho' are generated.\r\nThe other parameters are set to the values according to the simulations in our paper.\r\nThe default values can be changed by adding the parameters to the *simulation_parameters.txt* or\r\n*performance_parameters.txt* files.\r\n\r\n\r\n## Contact\r\n\r\nIn case of questions, suggestions, problems etc. please send an email to isabell.lehmann@sst.upb.de,\r\nor open an issue here on Github.\r\n\r\n\r\n## Citing\r\n\r\nIf you use this code in an academic paper, please cite [1]\r\n\r\n @article{Lehmann2023,\r\n title = {Identifying the Relationship Structure among Multiple Datasets Using Independent Vector Analysis: Application to Multi-task fMRI Data},\r\n author = {Lehmann, Isabell and Hasija, Tanuj and Gabrielson, Ben and Akhonda, M. A. B. S. and Calhoun, Vince D. and Adali, T{\\\"u}lay},\r\n booktitle={tba},\r\n pages={tba},\r\n year={2023}\r\n } \r\n\r\n[1] Isabell Lehmann, Tanuj Hasija, et al.,\r\n**Identifying the Relationship Structure among Multiple Datasets Using Independent Vector Analysis: Application to Multi-task fMRI Data**,\r\n*submitted in 2023*.\r\n\r\n\r\n\r\n",
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