DPMMSubClusters.jl

This is the code repository for the Julia package (with an optional Python wrapper) that corresponds to our paper, Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia, which was presented at CCGrid2019 High Performance Computing Maching Learning workshop (HPML).

Note that due to improvements in the code we have made since the time of the pulication of the paper, this package is now faster than what we reported there. This is a CPU-based package, which supports multiple cores and multiple machines. However, please also check out its even-faster GPU counterpart.

Docs are now available.

Requirements

This package was developed and tested on Julia 1.0.3, prior versions will not work. The following dependencies are required:

• Distributed
• DistributedArrays
• Distributions
• JLD2
• LinearAlgebra
• NPZ
• Random
• SpecialFunctions
• StatsBase

Installation

Use Julia's package manager: (v1.0) pkg> add DPMMSubClusters

Usage

This package is aimed for distributed parallel computing, while working with no workers is possible. Adding more workers, distributed across different machines, are encouraged for increased performance.

It is recommended to use BLAS.set_num_threads(1), when working with larger datasets increasing the amount of workers will do the trick, BLAS multi threading might disturb the multiprocessing, resulting in slower inference.

For all the workers to recognize the package, you must start with @everywhere using DPMMSubClusters. If you require to set the seed (using the seed kwarg), add @everywhere using Random as well.

The package currently contains priors for handling Multinomial or Gaussian mixture models.

While being very verstile in the setting and configuration, there are 2 modes which you can work with, either the Basic, which will use mostly predefined configuration, and will take the data as an argument, or Advanced use, which allows more configuration, loading data from file, and saving the model, or running from a saved checkpoint.

Basic

In order to run in the basic mode, use the function:

labels, clusters, weights = fit(all_data::AbstractArray{Float32,2},local_hyper_params::distribution_hyper_params,α_param::Float32;
        iters::Int64 = 100, init_clusters::Int64 = 1,seed = nothing, verbose = true, save_model = false, burnout = 20, gt = nothing)

Or, if opting for the default Gaussian weak prior:

labels, clusters, weights = fit(all_data::AbstractArray{Float32,2},α_param::Float32;
        iters::Int64 = 100, init_clusters::Int64 = 1,seed = nothing, verbose = true, save_model = false,burnout = 20, gt = nothing)

* note that while we dispatch on Float32, other numbers will work as well, and will be cast if needed.

Args and Kwargs:

• all_data - The data, should be DxN.
• local_hyper_params - The prior you plan to use, can be either Multinomial, or NIW (example below on how to create one)
• α_param - Concetration parameter
• iters - Number of iterations
• seed - Random seed, can also be set seperatly. note that if seting seperatly you must set it on all workers.
• verbose - Printing status on every iteration.
• save_model - If true, will save a checkpoint every 25 iterations, note that if you opt for saving, I recommend the advanced mode.
• burnout - How many iteration before allowing clusters to split/merge, reducing this number will result in faster inference, but with higher variance between the different runs.
• gt - Ground Truth, if supplied will perform NMI and VI tests on every iteration.

Return values:

fit will return the following:

labels, cluster_params, weights, iteration_time_history, nmi_score_history,likelihood_history, cluster_count_history

Note that weights does not sum up to 1, but to 1 minus the weight of the non-instanisated components.

Examples: 2d Gaussian with plotting. Image Segmentation.

Reducing the burnout will increase the speed and reduce stability, increasing the variance in the results.

When supplied with gt kwarg, it will perform NMI and Variation of Information analysis on each iteration.

The return values for the fit methods is:

labels, clusters, weights, iteration_time, nmi_history, likelihood_history, cluster_count_history

Advanced

In this mode you are required to supply a params file, example for one is the file global_params.jl. It includes all the configurable params. Running it is as simple as:

dp = dp_parallel(model_params::String; verbose = true, save_model = true, burnout = 5, gt = nothing)

Will return:

dp, iteration_time_history , nmi_score_history, liklihood_history, cluster_count_history

The returned value dp is a data structure:

mutable struct dp_parallel_sampling
    model_hyperparams::model_hyper_params
    group::local_group
end

In which contains the local_group, another structure:

mutable struct local_group
    model_hyperparams::model_hyper_params
    points::AbstractArray{Float64,2}
    labels::AbstractArray{Int64,1}
    labels_subcluster::AbstractArray{Int64,1}
    local_clusters::Vector{local_cluster}
    weights::Vector{Float64}
end

Note that for data loading the package use NPZ , which utilize python numpy files. Thus the data files must be pythonic, and be of the shape NxD.

Example of running from a params file, including saving and loading, with a multinomial prior.

Additional Functions

Additional function exposed to the user include:

• run_model_from_checkpoint(file_name) : Used to restart a saved run, file_name must point to a valid checkpoint file created during a run of the model. Note that the params files used for running the model initialy must still be available and in the same location, this is true for the data as well.
• calculate_posterior(model) : Calculate the posterior of a model, returned from dp_parallel.
• generate_gaussian_data(N::Int64, D::Int64, K::Int64): Randomly generates gaussian data, N points, of dimension D from K clusters. return value is points, labels, cluster_means, cluster_covariance.
• generate_mnmm_data(N::Int64, D::Int64, K::Int64, trials::Int64): Similar to above, just for multinomial data, the return value is points, labels, clusters

Misc

For any questions: dinari@post.bgu.ac.il

Contributions, feature requests, suggestion etc.. are welcomed.

If you use this code for your work, please cite the following:

@inproceedings{dinari2019distributed,
  title={Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia},
  author={Dinari, Or and Yu, Angel and Freifeld, Oren and Fisher III, John W},
  booktitle={2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)},
  pages={518--525},
  year={2019}
}