Deborah.DeborahCore.MLSequenceLightGBM

ml_sequence_LightGBM(; 
    model_tag::String,
    X_data::Dict{String, NamedTuple},
    Y_tr_vec::Vector{T},
    Y_bc_vec::Vector{T},
    Y_ul_vec::Vector{T},
    Y_lb_vec::Vector{T},
    tr_conf_arr::Vector{Int},
    bc_conf_arr::Vector{Int},
    ul_conf_arr::Vector{Int},
    partition::DatasetPartitioner.DatasetPartitionInfo,
    X_list::Vector{String},
    paths::PathConfigBuilderDeborah.DeborahPathConfig,
    jobid::Union{Nothing, String}
) -> Tuple{Any, Dict{Symbol, Matrix}} where T<:Real

Train and evaluate a LightGBM model using the JuliaAI/MLJ.jl framework. Generates predicted $Y$ matrices for training, bias correction, unlabeled, and labeled sets.

Keyword Arguments

• model_tag::String : Short model identifier.
• X_data::Dict{String, NamedTuple}: Input feature dictionary. Each key maps to a NamedTuple with vectors for :tr, :bc, :ul, :lb.
• Y_tr_vec::Vector{T} : Target vector for training set.
• Y_bc_vec::Vector{T} : Target vector for bias correction set.
• Y_ul_vec::Vector{T} : Target vector for unlabeled set
• Y_lb_vec::Vector{T} : Target vector for labeled set.
• tr_conf_arr::Vector{Int} : Row-wise config index mapping for training set.
• bc_conf_arr::Vector{Int} : Row-wise config index mapping for bias correction set.
• ul_conf_arr::Vector{Int} : Row-wise config index mapping for unlabeled set.
• partition::DatasetPartitioner.DatasetPartitionInfo : Configuration and counts for dataset partitioning.
• X_list::Vector{String} : Ordered list of feature names to be used.
• paths::PathConfigBuilderDeborah.DeborahPathConfig : Contains path strings for saving result output.
• jobid::Union{Nothing, String} : Optional job tag for logging.

Returns

• Tuple{Any, Dict{Symbol, Matrix}}
  • mach : Trained JuliaAI/MLJ.jl model (machine) using Y_tr_vec.
  • Y_mats : Dictionary mapping:
    • :YP_tr → predicted $Y$ matrix on training set
    • :YP_bc → predicted $Y$ matrix on bias correction set
    • :YP_ul → predicted $Y$ matrix on unlabeled set