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Code for reproducing results presented in "Unifying pairwise interactions in complex dynamics"

DOI

Readme and code in this repo were compiled by @olivercliff (originally published in another repository) with additions by @anniegbryant.

This repository provides python and R code (and data) to reproduce figures presented in the paper, "Unifying pairwise interactions in complex dynamics".

We provide both precomputed .csv files, with which to recreate the figures, as well as scripts to generate these .csv files from scratch.

Download pyspi and create an environment

First, download pyspi and create a conda environment to install the package as per the documentation. In a linux environment, this involves the following steps from a terminal (in your desired directory):

git clone [email protected]:DynamicsAndNeuralSystems/pyspi.git
cd pyspi
conda create -n pyspi python=3.9
conda activate pyspi

NOTE: If using the legacy pynats branch, please create the conda environment with python=3.6.7.

You will also need to download and install octave; follow the instructions here.

Switch to the pynats branch and install

The pynats branch was retroactively added to the pyspi repository as legacy code that was used to generate the results from the main paper. If you would like to replicate the results from the paper as closely as possible, we would recommend checking out the pynats branch, which is also available as a release. However, given this work was not computed via a container, such as docker, the results may vary slightly from those originally reported.

In the pyspi folder and environment that were created above, checkout and install the pynats branch (this will take a while):

git checkout pynats
pip install .

Figure 2: Hierarchical clustering for SPI performance on 1053 MTS dataset

Because of the large amount of processing required to generate this figure from scratch, we have provided pre-computed CSV files (in the data directory) from which you can easily regenerate the components of Figure 2 using the Generate_Figure2_Visuals.ipynb notebook.

In order to re-compute the CSV files from the raw MTS data, we have provided the following script: process_mts_database.py, which computes all SPIs for each of the 1053 MTS datasets in the database.

Once this script has been executed, re-run the Generate_Figure2_Visuals.ipynb notebook, pointing to the location of the new CSV files to regenerate the figures.

NOTE: The script process_mts_database.py was computed on a cluster, where each dataset was evaluated separately. If running this script locally, it will likely take several months to complete (and so we would advise using the pyspi distribute workflow to recompute all SPIs on all 1053 datasets, if necessary).

Figure 3: Classification case study with three example datasets

This repository also includes all the code you need to replicate our classification case study, separated into four parts:

  1. Downloading the data
  2. Computing SPIs with pyspi*
  3. Fitting linear SVM classifiers*
  4. Visualizing results as in the manuscript

*The code is structured such that these steps are run as distributed jobs on a high-performance computing (HPC) cluster given the size of the datasets. If you do not have access to such a cluster, the code can be adapted to run locally -- however, note that it can take a very long time, depending on your computer specs.

NOTE: dataset_processing/parse_datasets_for_pyspi.R and dataset_processing/parse_datasets_for_pyspi.R assume you have pyspi-distribute installed, and will need to be modified accordingly if not.

If you would like to skip ahead to visualizing pre-computed results, you may download pre-computed .feather files from zenodo. The statistics underlying the visualizations can also be found in the following files:

  • data/All_combined_SPI_stats_for_Fig3_histogram.csv
  • data/Individual_SPI_stats_for_Fig3_histogram.csv
  • data/SPI_stats_for_Fig3_violin_plots.csv

Step 1: Downloading the data

All data included in our classification analysis is freely available to the public from the following sources:

  1. Smartwatch activity dataset: here
  2. EEG state dataset: here
  3. fMRI film dataset: here

We have included an R script to automatically download and parse the data from these scripts for you, located at dataset_processing/parse_datasets_for_pyspi.R. Make sure to update line 12 to the path where you have cloned pyspi-distribute if you did so. You might also wish to change the path to where you want the data to be downloaded and processed; the default is the data/ folder in this repository, but you can modify this on line 11.

This script will automatically download and extract the data for the three datasets, combine the time-series data into feather files, and output individual samples' multivariate time series (MTS) matrices as NumPy binary files (.npy). This is the file format supplied to pyspi-distribute via the sample.yaml file that the script automatically generates.

If this step runs successfully, you should have the following three folders within your data/ folder in your cloned version of this repo:

  1. BasicMotions/ (smartwatch activity dataset)
  2. SelfRegulationSCP1/ (EEG state dataset)
  3. Rest_vs_Film_fMRI/ (fMRI film dataset)

Within each of these folders, you should have the following subfolders:

  1. arff_files/ OR time_series_files/
  2. numpy_files/
  3. processed_data/

The numpy_files/ subfolder should include one .npy file for each sample in the given MTS and one sample.yaml configuration file for pyspi-distribute.

Step 2: Running pyspi-distribute on a cluster

Once your data is downloaded and prepped from Step 1, you can run pyspi-distribute on the cluster. If you wish to use the script provided in this repo (call_run_pyspi_distribute.sh), note that you should modify lines 3-18 to supply your data path on the cluster, the name of your conda environment where pyspi is installed, and PBS job specifications such as memory and walltime to request. Some sensible defaults for resource requests are provided as a starting point.

After updating those lines, you can run call_run_pyspi_distribute.sh from your terminal on an HPC cluster with the following:

bash dataset_processing/call_run_pyspi_distribute.sh

Note that by default, this script submits all PBS jobs to be queued and scheduled using qsub at once; if you prefer to submit pyspi jobs one case study dataset at a time, you can update line 25 to comment out individual datasets.

Once all of the MTS have been processed with pyspi-distribute, you can combine the outputs from individual jobs with dataset_processing/merge_psypi_data.py. If you have your data stored somewhere other than within the data/ folder in this repository, you should update the paths on lines 137, 158, and 179 accordingly. You can run this python script with the following:

python3 dataset_processing/merge_pyspi_data.py

For the classification case study in our paper, we examined the 237 SPIs that were available at the time of manuscript drafting (which can be found in data/SPI_info.csv). Of these, for each of the three classification datasets, we filtered out any SPIs that yielded constant values (i.e., returned the same value for every pair in the MTS across all samples) and/or could not be computed for all samples, presumably due to numerical issues. These quality control steps are applied in dataset_processing/prep_datasets_for_classification.py, which can be run with the following:

python3 dataset_processing/prep_datasets_for_classification.py

This will prepare DataFrames in .feather files to fit linear SVMs and classify groups as implemented in our paper.

Step 3: Fitting linear SVM classifiers

To evaluate how well each SPI could separate between classes in the three datasets, we fit linear support vector machine (SVM) classifiers using the sklearn.svm.SVC() function with default parameters for each SPI. For the smartwatch activity and EEG state datasets, we performed stratified resampling 30 times with the same proportions applied in Ruiz et al. (2021). For the film fMRI dataset, since there were 29 individuals in the dataset, we performed leave-one-individual-out cross-validation (LOOCV) such that for each of 29 iterations, the SVM was trained on the resting and film-viewing fMRI scans from 28 individuals and evaluated on an unseen test set of the left-out individual's rest and film-viewing fMRI scans. In each case, we fit null models by shuffling the class labels and fitting classifiers to the shuffled data 100x per SPI to yield 100 null accuracy values for SPI; we opted to pool null values together across SPIs to create a joint null distribution to evaluate statistical significance. We also evaluated the performance of all SPIs combined into one SVM classifier for problem with the same resampling approaches.

This classification step can be reproduced by running dataset_processing/run_call_classify.sh. Here, you should modify arguments in lines 3-9 to specify where you have the data from this project stored, your conda environment, and parameters for your qsub job submission such as walltime and memory to request.

Once you have modified dataset_processing/run_call_classify.sh as appropriate, you can run the script as follows:

bash classification/run_call_classify.sh

This bash script submits the PBS script dataset_processing/call_classify.pbs for each of the three datasets to be processed in parallel as three separate PBS jobs. Each job will run classification/classify.py on the given dataset, fitting SVMs with resampling to each SPI separately as well as to all SPIs together. For each dataset, this will output four .feather files to the data directory you specify in line 3 of classification/run_call_classify.sh:

  1. {dataset_ID}_main_SPI_wise_acc.feather: The classification accuracy for each resample for SVMs fit to each SPI individually
  2. {dataset_ID}_null_SPI_wise_acc.feather: The null classification accuracy for each resample for SVMs fit to each SPI individually
  3. {dataset_ID}_main_full_acc.feather: The classification accuracy for each resample with all SPIs combined into one SVM
  4. {dataset_ID}_null_full_acc.feather: The null classification accuracy for each resample for all SPIs combined into one SVM

Step 4: Visualizing results as in the manuscript

All result visualizations in the classification case study analysis were generated using R. To replicate these figures, you can run the code chunks in the R Markdown Generate_Figure3_Visuals.Rmd. The output from this Markdown document is included as a static HTML docoment that you can view, Generate_Figure3_Visuals.html.

Note that the script does not automatically save output figures, so users may manually save visualizations using the ggsave() function.