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Structure Learning with GOLEM

This repository contains an implementation of the structure learning method described in "On the Role of Sparsity and DAG Constraints for Learning Linear DAGs".

If you find it useful, please consider citing:

@inproceedings{Ng2020role,
  author = {Ng, Ignavier and Ghassami, AmirEmad and Zhang, Kun},
  title = {{On the Role of Sparsity and DAG Constraints for Learning Linear DAGs}},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2020},
}

TL;DR

We formulate a likelihood-based score function with soft sparsity and DAG constraints for learning linear DAGs, which guarantees learning a DAG equivalent to the ground truth DAG, under mild assumption. This leads to an unconstrained optimization problem that can be solved via gradient-based optimization method.

Requirements

Python 3.6+ is required. To install the requirements:

pip install -r requirements.txt

(Optional) For GPU support, install tensorflow-gpu==1.15.0 (with CUDA and cuDNN), e.g., through conda:

conda install tensorflow-gpu==1.15.0

Running GOLEM-EV

The hyperparameters for GOLEM-EV are:

  • equal_variances=True
  • lambda_1=2e-2
  • lambda_2=5.0
  • num_iter=1e+5
  • learning_rate=1e-3.

To run GOLEM-EV:

# Ground truth: 20-node ER2 graph
# Data: Linear DAG model with Gaussian-NV noise
python src/main.py  --seed 1 \
                    --d 20 \
                    --graph_type ER \
                    --degree 4 \
                    --noise_type gaussian_nv \
                    --equal_variances \
                    --lambda_1 2e-2 \
                    --lambda_2 5.0 \
                    --checkpoint_iter 5000

Each run creates a directory based on current datetime to save the training outputs, e.g., output/2020-12-01_12-11-50-562.

Running GOLEM-NV

The hyperparameters for GOLEM-NV are:

  • equal_variances=False
  • lambda_1=2e-3
  • lambda_2=5.0
  • num_iter=1e+5
  • learning_rate=1e-3.

The optimization problem of GOLEM-NV is susceptible to local solutions, so we have to initialize it with the solution returned by GOLEM-EV. The hyperparameters of GOLEM-EV are described in the previous section.

There are two ways to initializate the optimization problem:

(1) Set init to True. By default, the code will load the estimated solution of the latest experiment (based on datetime) in the output directory.
(Please make sure the latest experiment indeed corresponds to GOLEM-EV with same dataset configurations.)

# Ground truth: 20-node ER2 graph
# Data: Linear DAG model with Gaussian-NV noise
python src/main.py  --seed 1 \
                    --d 20 \
                    --graph_type ER \
                    --degree 4 \
                    --noise_type gaussian_nv \
                    --non_equal_variances \
                    --init \
                    --lambda_1 2e-3 \
                    --lambda_2 5.0 \
                    --checkpoint_iter 5000

(2) Set init to True and manually set init_path to the path of estimated solution (.npy file) by GOLEM-EV.

# Ground truth: 20-node ER2 graph
# Data: Linear DAG model with Gaussian-NV noise
python src/main.py  --seed 1 \
                    --d 20 \
                    --graph_type ER \
                    --degree 4 \
                    --noise_type gaussian_nv \
                    --non_equal_variances \
                    --init \
                    --init_path <PATH_TO_NUMPY_MATRIX> \
                    --lambda_1 2e-3 \
                    --lambda_2 5.0 \
                    --checkpoint_iter 5000

Each run creates a directory based on current datetime to save the training outputs, e.g., output/2020-12-01_12-11-50-562.

Examples

  • See golem.py for a minimal usage of GOLEM.

  • See GOLEM-EV.ipynb and GOLEM-NV.ipynb for a demo.

  • Example of solution returned by GOLEM-EV:

    • Ground truth: 20-node ER2 graph
    • Data: Linear DAG model with Gaussian-EV noise.
    example

Acknowledgments

  • The code to generate the synthetic data and compute the metrics (e.g., SHD, TPR) is based on NOTEARS.
  • We are grateful to the authors of the baseline methods for releasing their code.

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On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

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