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Learning hyperparameters of Bayesian models by matching moments of prior predictive distribution.

Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami; Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching. Journal of Machine Learning Research. 24(67):1−51, 2023.

@article{JMLR:v24:21-0623,
  author  = {Eliezer de Souza da Silva and Tomasz Kuśmierczyk and Marcelo Hartmann and Arto Klami},
  title   = {Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching},
  journal = {Journal of Machine Learning Research},
  year    = {2023},
  volume  = {24},
  number  = {67},
  pages   = {1--51},
  url     = {http://jmlr.org/papers/v24/21-0623.html}
}

Pre-installation Requirements

The code was tested using Python 3.7.4 from Anaconda 2019.10. with TensorFlow 2.1 and TensorFlow Probability 0.9.0. It uses numpy, pandas, seaborn, and matplotlib.

Data

The hetrec-lastfm dataset along with train-test split can be found in the directory named data.

Code illustrating gradient-based optimization

The code illustrating gradient-based optimization can be found in the directory named gradient_optimization.

Main files

  • pmf_sgd_optimization.ipynb - Jupter Notebook illustrating how priors matching requested values of prior predictive expectation and/or variance can be found for Poisson Matrix Factorization (PMF) model using SGD.
  • hpf_sgd_optimization.ipynb - Jupter Notebook illustrating how priors matching requested values of prior predictive expectation and/or variance can be found for Hierarchical Poisson Matrix Factorization (HPF) model using SGD.
  • pmf_estimators_analysis.ipynb - Jupter Notebook illustrating bias and variance of the estimators used in pmf_sgd_optimization.ipynb for PMF model.
  • pmf_surface_visualizations.ipynb - Jupter Notebook illustrating 1D and 2D projections of optimization space for the problem of matching Poisson Matrix Factorization (PMF) prior predicitve distribution variance (minimization of the discrepancy=(Variance-100)^2 ). We consider two parametrizations: abcd vs mu-sgima.

Sampling code

  • pmf_model.py - Methods calculating E[Y] and E[Y^2] (and therefore also Var[Y]) over prior predictive distribution for Poisson Matrix Factorization.
  • hpf_model.py - Methods calculating E[Y] and E[Y^2] (and therefore also Var[Y]) over prior predictive distribution for Hierarchical Poisson Matrix Factorization.

Additional files

  • aux.py, aux_plt.py, boplotting/* - Auxiliary functions for tensor processing and plotting.

Experiments illustrating convergance of gradient-based optimization

The code can be found in gradient_optimization_experiments. It contains two subfolders PMF_Convergence and HPF_Convergence.

Visualization of PMF posterior quality using PSIS-LOO

The code computing PSIS-LOO on the test subset of fitted PMF can be found in posterior_visualization. The scripts pmf_precompute_objectives_posterior.py and python pmf_precompute_objectives_posterior2.py precompute certain set of configurations specified inside those files and write to respectively pmf_precompute_objectives_posterior.py.csv and pmf_precompute_objectives_posterior2.py.csv. The outputs can be then previewed with VISUALIZATION.ipynb and VISUALIZATION2.ipynb. VISUALIZATION_K.ipynb plots PSIS-LOO on test subset for various K with a,b,c,d set to prior optimal values.

Comparison of Bayesian optimization of PSIS-LOO

The code can be found in bo_optimization. To run the experiment use: RUN_EXPERIMENT_BO.sh. It requires RoBO - a Robust Bayesian Optimization framework (https://github.com/automl/RoBO) to be preinstalled. Results can be displayed using the Jupter Notebook VISUALIZATION_BO.ipynb.

Sensitivity to Model Mismatch

The code can be found in sensitivity analysis folder. To visualize the experiment results open the jupyter-notebook sensitivity_analysis.ipynb. To re-run the experiment run the python scripts - python poisson_prior_exp_negbin_async.py: experiment sampling from a Negative Binomial - python poisson_prior_exp_binomial_async.py: experiment sampling from a PMF but with a probability of randomly zeroing each of the entries of the matrix. Both experiments will generate csv files with the results. The files with a suffix final_*.csv can be analyzed in the sensitivity_analysis.ipynb notebook simply by adding new cells, keeping the same code from previous cells and just adjusting the file name that is loaded.