This repository contains the python code for our ICLR 2020 paper: Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality.
- Python 3.7
- CUDA 10.1
- PyTorch 1.0.1
- NumPy, SciPy
- python main.py --dataset lorenz --dsid 1 --model sru --n 10 --T 250 --F 10 --nepochs 2000 --mu1 0.021544 --mu2 0.031623 --mu3 0 --lr 0.001 --joblog crossval/logs/test.npz
Model: eSRU_2LF (Economy SRU with two-layer MLP as feedback's second stage) Dataset: VAR, T=250, F=10, dataset id = 1
- python main.py --dataset var --dsid 1 --model eSRU_2LF --n 10 --T 500 --F 30 --nepochs 2000 --mu1 0.021544 --mu2 0.031623 --mu3 0.464159 --lr 0.001 --joblog crossval/logs/test.npz
Model: eSRU_2LF (Economy SRU with two-layer MLP as feedback's second stage) Dataset: NetSim (BOLD signals), T=200, dataset id = 1
- python main.py --dataset netsim --dsid 1 --model eSRU_2LF --n 15 --T 200 --F 0 --nepochs 2000 --mu1 0.021544 --mu2 0.031623 --mu3 0.464159 --lr 0.001 --joblog crossval/logs/test.npz
Model: eSRU_1LF (Economy SRU with single layer MLP as feedback's second stage) Dataset: Dream-3 (Yeast1), T=966
- python main.py --dataset gene --dsid 1 --model eSRU_1LF --n 100 --T 966 --F 0 --nepochs 2000 --mu1 0.021544 --mu2 0.031623 --mu3 0.464159 --lr 0.001 --joblog crossval/logs/test.npz
| Argument | Type | Description |
|---|---|---|
| dataset | string | lorenz/var/netsim/gene |
| dsid | int | dataset id (Range: 1-5) |
| model | string | sru (Standard SRU) |
| n | int | No. of timeseries/Nodes in the Granger causal graph (n = 10 for lorenz/var, n = 15 for netsim, n = 100 for gene) |
| T | int | Length of input timeseries (T = 250/500/1000 for lorenz, T =500/1000 for var, T = 200 for netsim, T = 966 for gene) |
| F | int | External forcing in lorenz model/ percentage sparsity in var model (F = 10/40 for lorenz, F = 30 for var, F = 0 for gene/netsim) |
| nepochs | int | No. of training epochs |
| mu1 | float | Bias for ridge regularization of all unregularized weights in the model |
| mu2 | float | Bias for block sparse regularization of input layer weights |
| mu3 | float | Bias for group sparse regularization of output feature layer weights in Economy SRU |
| lr | float | Learning rate for BPTT step in the proximal gradient descent algorithm |
- The adjacency matrix of the estimated Granger causal graph is printed as the output once the run is completed.
- A summary of model configuration and final results is stored in 'test.npz' located at: SRU_for_GCI_standalone\crossval\logs
- The Dream-3 datasets for the gene interaction network inference experiments are taken from "http://dreamchallenges.org/project-list/dream3-2008/".
- The BOLD-FMRI (NetSim) timeseries data used for the brain connectome inference experiments are taken from "https://www.fmrib.ox.ac.uk/datasets/netsim/index.html"