PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning

Warning: This is a rapidly evolving research prototype.

PClean was created at the MIT Probabilistic Computing Project.

If you use PClean in your research, please cite the our 2021 AISTATS paper:

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. Lew, A. K.; Agrawal, M.; Sontag, D.; and Mansinghka, V. K. (2021, March). In International Conference on Artificial Intelligence and Statistics (pp. 1927-1935). PMLR. (pdf)

To use PClean, create a Julia file with the following structure:

using PClean using DataFrames: DataFrame import CSV # Load data data = CSV.File(filepath) |> DataFrame # Define PClean model PClean.@model MyModel begin @class ClassName1 begin ... end ... @class ClassNameN begin ... end end # Align column names of CSV with variables in the model. # Format is ColumnName CleanVariable DirtyVariable, or, if # there is no corruption for a certain variable, one can omit # the DirtyVariable. query = @query MyModel.ClassNameN [ HospitalName hosp.name observed_hosp_name Condition metric.condition.desc observed_condition ... ] # Configure observed dataset observations = [ObservedDataset(query, data)] # Configuration config = PClean.InferenceConfig(1, 2; use_mh_instead_of_pg=true) # SMC initialization state = initialize_trace(observations, config) # Rejuvenation sweeps run_inference!(state, config) # Evaluate accuracy, if ground truth is available ground_truth = CSV.File(filepath) |> CSV.DataFrame results = evaluate_accuracy(data, ground_truth, state, query) # Can print results.f1, results.precision, results.accuracy, etc. println(results) # Even without ground truth, can save the entire latent database to CSV files: PClean.save_results(dir, dataset_name, state, observations)

Then, from this directory, run the Julia file.

JULIA_PROJECT=. julia my_file.jl 

To learn to write a PClean model, see our paper, but note the surface syntax changes described below.

As a DSL embedded into Julia, our implementation of the PClean language has some differences, in terms of surface syntax, from the stand-alone syntax presented in our paper:

(1) Instead of latent class C ... end, we write @class C begin ... end.

(2) Instead of subproblem begin ... end, inference hints are given using ordinary Julia begin ... end blocks.

(3) Instead of parameter x ~ d(...), we use @learned x :: D{...}. The set of distributions D for parameters is somewhat restricted.

(4) Instead of x ~ d(...) preferring E, we write x ~ d(..., E).

(5) Instead of observe x as y, ... from C, write @query ModelName.C [x y; ...]. Clauses of the form x z y are also allowed, and tell PClean that the model variable C.z represents a clean version of x, whose observed (dirty) version is modeled as C.y. This is used when automatically reconstructing a clean, flat dataset.

The names of built-in distributions may also be different, e.g. AddTypos instead of typos, and ProportionsParameter instead of dirichlet.