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Code for our paper "Learning a compressed sensing measurement matrix via gradient unrolling".
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README.md

Toy example

Under the folder toy_example, we provide a jupyter notebook jan22_toy_example.ipynb that works through the training and evaluation of our autoencoder (as well as other baseline algorithms) for the synthetic1 dataset. We highly recommend interested readers to take a look before diving deep into our code.

Overview

The source code contains four parts:

  1. Core
    • model.py
    • utils.py
    • datasets.py
    • baselines.py
  2. Code for each dataset
    • synthetic_main.py
    • synthetic_powerlaw_main.py
    • amazon_main.py
    • amazon_parallel_l1.py
    • rcv1_main.py
    • rcv1_parallel_l1.py
  3. Scripts for reproducing our results
    • scripts/synthetic1.sh
    • scripts/synthetic2.sh
    • scripts/amazon.sh
    • scripts/rcv1.sh
    • scripts/synthetic_powerlaw.sh
  4. Code and scripts for one of the baselines Simple AE + l1-min
    • synthetic_simpleAE.py
    • amazon_simpleAE.py
    • rcv1_simpleAE.py
    • scripts are under simpleAE_scripts/

Run

To reproduce our experimental results, first run chmod +x scripts/*.sh to make the scripts executable. After that, run the given scripts:

  • $ ./scripts/synthetic1.sh
  • $ ./scripts/synthetic2.sh
  • $ ./scripts/amazon.sh
  • $ ./scripts/rcv1.sh
  • $ ./scripts/synthetic_powerlaw.sh

Note:

  1. The results are stored in a python dictionary which is then saved under the folder ckpts/. They can be used to reproduce the figures shown in our paper.
  2. Before running amazon.sh, download train.csv from this kaggle competition and specify its location via --data_dir.
  3. The RCV1 dataset will be fetched automatically using the sklearn.datasets.fetch_rcv1 function.
  4. To reproduce results of one of the baselines Simple AE + l1-min, run scripts under the folder simpleAE_scripts/.
  5. For high-dimensional vectors, solving l1-min using Gurobi takes a long time on a single CPU. To speed up, we solve l1_min in parallel on a multi-core machine. In amazon_main.py and rcv1_main.py, performance evaluation is performed on a small set of the test samples (while training is still done using the complete training set). After training the autoencoder, we use a multi-core machine and solve l1_min in parallel on the complete test set using amazon_parallel_l1.py and rcv1_parallel_l1.py. Depending on your multi-core machine, solving l1_min in parallel on the complete test set may still take a long time, I would recommend running amazon_parallel_l1.py and rcv1_parallel_l1.py first with a small subset (by setting a small number for the parameters num_core and batch in the python file).

Environment

Here is our software environment.

  1. Python 2.7.12
    • numpy 1.13.3
    • sklearn 0.19.1
    • scipy 1.0.0
    • joblib 0.10.0
  2. Tensorflow r1.4
  3. Gurobi 7.5.1
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