Public APIs

Base.read(file::AbstractString, ::Type{C}) where C <: ProbCircuit

Reads circuit from file; uses extension to detect format type, for example ".psdd" for PSDDs.

Base.write(file::AbstractString, circuit::ProbCircuit)

Writes circuit to file; uses file name extention to detect file format.

Base.write(files::Tuple{AbstractString,AbstractString}, circuit::StructProbCircuit)

Saves circuit and vtree to file.

Learn structure of a single structured decomposable circuit

learn_strudel(train_x; num_mix = 5, init_maxiter = 10, em_maxiter=20)

Learn a mixture of circuits See "Strudel: Learning Structured-Decomposable Probabilistic Circuits. arxiv.org/abs/2007.09331

estimate_parameters!(pc::ProbCircuit, data; pseudocount::Float64, entropy_reg::Float64 = 0.0)

Maximum likelihood estimation of a ProbCircuit's parameters given data

estimate_parameters!(spc::SharedProbCircuit, data; pseudocount::Float64, entropy_reg::Float64 = 0.0)

Maximum likelihood estimation of a SharedProbCircuit's parameters given data.

bagging support: If spc is a SharedProbCircuit and data is an array of DataFrames with the same number of "components", learn each circuit with its corresponding dataset.

estimate_parameters_em!(pc::ProbCircuit, data; pseudocount::Float64, entropy_reg::Float64 = 0.0, exp_update_factor::Float64 = 0.0, update_per_batch::Bool = false)

One epoch of Expectation maximization (EM) parameter learning for circuits. Useful when having missing data or non-deterministic circuits.

• entropy_reg: Entropy Regularization
• update_per_batch: Whether to update circuit paramters per batch (using the ParamBitCircuit's paramters)

estimate_parameters_em_multi_epochs!(circuit::ProbCircuit, train_data)

Runs multiple epochs of EM for circuit. It will run on CPU/GPU based on where train_data is.

Arguments:

• circuit:
• train_data: training data. If want gpu, move to gpu before calling this to_gpu(train_data).

Keyword arguments:

• valid_data=nothing: validation data, should be same device as train_data
• test_data=nothing: test data, data, should be same device as train_data
• entropy_reg::Float64 = 0.0: Entropy regularization
• exp_update_factor_start::Float64 = 0.1:
• exp_update_factor_end::Float64 = 0.9:
• em_warmup_iters=100 : number of EM Warm up iterations
• em_finetune_iters=100: number of EM fine tune iterations
• verbose=false: verbose or not
• verbose_log_rate=1: how often to print info
• save_path=nothing: path of the file to save the partially trained circuit
• save_rate=20: how often to save the circuit to file

Learning from data a structured-decomposable circuit with several structure learning algorithms

marginal(root::ProbCircuit, data::Union{Real,Missing}...)
marginal(root::ProbCircuit, data::Union{Vector{Union{Bool,Missing}},CuVector{UInt8}})
marginal(circuit::ProbCircuit, data::DataFrame)
marginal(circuit::ParamBitCircuit, data::DataFrame)::AbstractVector
marginal(circuit::SharedProbCircuit, data::DataFrame, weights::Union{AbstractArray, Nothing}; component_idx)

Evaluate marginals of the circuit bottom-up for given input(s).

Missing values should be denoted by missing in the data.

Outputs $\log{p(x^o)}$ for each data point.

marginal(circuit::SharedProbCircuit, data::DataFrame, weights::Union{AbstractArray, Nothing} = nothing; component_idx = 0)::AbstractVector

Computes marginals for one circuit in a SharedProbCircuit.

• component_idx: index of the SharedProbCircuit component to comptue marginals on.

max_a_posteriori(root::ProbCircuit, data::Union{Bool,Missing}...)
max_a_posteriori(root::ProbCircuit, data::Union{Vector{<:Union{Bool,Missing}},CuVector{UInt8}})
max_a_posteriori(circuit::ProbCircuit, data::DataFrame)
max_a_posteriori(pbc::ParamBitCircuit, data; Float=Float32)

Evaluate maximum a-posteriori state of the circuit for given input(s).

Outputs the states, and the corresponding probabilities (in log domain).

sample(pc::ProbCircuit, num_samples)
sample(pc::ProbCircuit, num_samples, evidences)

Sample states from the probabilistic circuit distribution. Also can do conditional sampling if evidence is given (any subset of features).

Compile a psdd circuit from clt and vtree