PClean

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)

Using PClean

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.

Differences from the paper

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.

svvvs/PClean

PClean

Using PClean

Differences from the paper