Scripts for our paper 'Factor Augmented Sparse Throughput Deep Neural Networks for High Dimensional Regression'

This repo contains scripts of simulation and real data analysis for our paper 'Factor Augmented Sparse Throughput Deep Neural Networks for High Dimensional Regression'. This README file provides instructions to reproduce the result in the numerical studies section of our paper.

Environment and Setup

We use python==3.9, pytorch with version 1.12.1 and numpy with version 1.21.5. Other dependencies can be found in environment.yml.

To create folders to restore the experiments settings, use the following command in FAST-NN/ directory.

mkdir logs

Exp 1: Performance of FAR-NN Estimator

To run a single experiment with data dimension p and random seed s

python far_exp.py --p $p --seed $s --exp_id 1

To reproduce Fig 3 (a), we first replicated the experiment 200 times for each $p$ and save the logs

mkdir logs/exp1-old
bash scripts/exp1.sh

Then plot Fig 3 (a) using the command

cd visualize
python exp1.py

To get the plot in Fig 3 (d), we use the command

python far_vis.py --p 1000

Exp 2: Comparison with Dropout

We need to reuse the logs in Exp 1

cp -r logs/exp1-old logs/exp2-0

To replicate the experiment

mkdir logs/exp2-0.3
mkdir logs/exp2-0.5
mkdir logs/exp2-0.6
mkdir logs/exp2-0.7
mkdir logs/exp2-0.8
mkdir logs/exp2-0.9
bash scripts/exp2-z3.sh
bash scripts/exp2-z5.sh
bash scripts/exp2-z6.sh
bash scripts/exp2-z7.sh
bash scripts/exp2-z8.sh
bash scripts/exp2-z9.sh

Visualize the result (Fig 3 (b))

cd visualize
python exp2.py

Exp 3: When $n_1$ is large enough?

To replicate the experiment

mkdir logs/exp3
bash scripts/exp3.sh

Visualize the result (Fig 3 (c))

cd visualize
python exp3.py

Exp 4

For the result in Fig 4 (a),

mkdir logs/exp4-hcm0-m200 
bash scripts/exp4-1.sh
cd visualize
python exp4-1.py

To reproduce the result in Fig 4 (b), we use the following command

mkdir logs/exp4-hcm3-m200 
bash scripts/exp4-2.sh
cd visualize
python exp4-2.py

To plot an visualize as Fig 5, one should use the following command

python fast_exp.py --hcm_id 0 --p 1000 --visualize_mat True --seed 5
python fast_exp.py --hcm_id 3 --p 1000 --visualize_mat True --seed 5

and the plot will be saved as FAST-NN/a.pdf.

Real Data Application

The following commands are used to reproduce the results in Table 1 of Supplemental Material.

python fredmd_cross.py --idx $id

id is 88 for TB6SMFFM, 87 for TB3SMFFM, and 28 for UEMP15T26.