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aseimel
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#!/usr/bin/env julia
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#############################################################################
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## 02_data_loading.jl
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## Load and preprocess data for latent trait model
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## Loads three datasets: text_data (manifesto + PolDem), expert dimension-specific, expert general L-R
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##
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## Supports both:
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## - 4D model (V10): type_high/type_low columns for bipolar bridges
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## - 2D model (V1): dim_idx + direction columns for direct estimation
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#############################################################################
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using DataFrames, CSV, CategoricalArrays, Statistics
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#############################################################################
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## UNION MAPPING: Individual party estimates via mean-constituent model
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## Loads data/union_mapping.csv and builds lookup structures
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#############################################################################
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"""
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load_union_mapping(project_root::String)
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Load union_mapping.csv and build lookup dictionaries.
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Returns (union_to_constituents, constituent_to_union) dicts.
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If file is missing or empty, returns empty dicts (backwards compatible).
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"""
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function load_union_mapping(project_root::String=".")
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mapping_file = joinpath(project_root, "data", "union_mapping.csv")
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union_to_constituents = Dict{Int, Vector{Int}}()
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constituent_to_union = Dict{Int, Int}()
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if !isfile(mapping_file)
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println(" No union_mapping.csv found - running without union decomposition")
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return union_to_constituents, constituent_to_union
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end
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df = CSV.read(mapping_file, DataFrame)
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if nrow(df) == 0
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println(" union_mapping.csv is empty - running without union decomposition")
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return union_to_constituents, constituent_to_union
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end
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for row in eachrow(df)
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union_id = row.manifesto_pf_id
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expert_id = row.expert_pf_id
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if !haskey(union_to_constituents, union_id)
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union_to_constituents[union_id] = Int[]
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end
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if !(expert_id in union_to_constituents[union_id])
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push!(union_to_constituents[union_id], expert_id)
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end
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constituent_to_union[expert_id] = union_id
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end
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println(" Union mapping loaded: $(length(union_to_constituents)) unions, $(length(constituent_to_union)) constituents")
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return union_to_constituents, constituent_to_union
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end
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#############################################################################
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## SEGMENT-BASED INDEXING CONFIGURATION
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## Split parties at gaps > MAX_GAP years to avoid flat posteriors
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#############################################################################
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const MAX_GAP = 7 # Maximum years between observations within a segment
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const MIN_OBS = 2 # Minimum observations per segment (drop segments with fewer)
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#############################################################################
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## 2D MODEL MAPPING CONFIGURATION
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## Maps type_high/type_low pairs to dim_idx + direction
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#############################################################################
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const TYPE_TO_DIM_DIRECTION = Dict(
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# Economic dimension: pro_market = right (+1), pro_welfare = left (-1)
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("pro_market", "pro_welfare") => (dim_idx=1, direction=1), # Right
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("pro_welfare", "pro_market") => (dim_idx=1, direction=-1), # Left
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# Cultural dimension: traditional = TAN (+1), cosmopolitan = GAL (-1)
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("traditional", "cosmopolitan") => (dim_idx=2, direction=1), # TAN
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("cosmopolitan", "traditional") => (dim_idx=2, direction=-1) # GAL
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)
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# Expert dimension mapping (lrecon -> economic, galtan/cultural -> galtan)
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const EXPERT_VAR_TO_DIM = Dict(
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"lrecon_ches" => 1,
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"lrecon_vparty" => 1,
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"welf_vparty" => 1,
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"lrecon_gps" => 1,
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"lrecon_poppa" => 1,
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"galtan_ches" => 2,
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"libcon_gps" => 2,
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"immig_vparty" => 2,
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"lgbt_vparty" => 2,
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"culsup_vparty" => 2,
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"relig_vparty" => 2,
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"gender_vparty" => 2
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)
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function load_and_preprocess_4dim_data(start_year=1950; data_dir::String=".")
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println("Loading 4D latent trait data files...")
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println("Start year filter: $start_year")
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data_dir != "." && println("Data directory: $data_dir")
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# Load union mapping (check data_dir first, fall back to project root)
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println("\nLoading union mapping...")
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union_mapping_dir = isfile(joinpath(data_dir, "data", "union_mapping.csv")) ? data_dir : "."
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union_to_constituents, constituent_to_union = load_union_mapping(union_mapping_dir)
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# Load the three datasets
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text_data_raw = CSV.read(joinpath(data_dir, "text_data.csv"), DataFrame)
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expert_raw = CSV.read(joinpath(data_dir, "expert.csv"), DataFrame)
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lr_data_raw = CSV.read(joinpath(data_dir, "lr_data.csv"), DataFrame)
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# Filter to start year BEFORE calculating year0
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text_data_raw = text_data_raw[text_data_raw.year .>= start_year, :]
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expert_raw = expert_raw[expert_raw.year .>= start_year, :]
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lr_data_raw = lr_data_raw[lr_data_raw.year .>= start_year, :]
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println("Data filtered to $start_year onwards:")
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println(" Text data (manifesto + PolDem): $(nrow(text_data_raw)) observations")
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println(" Expert: $(nrow(expert_raw)) observations")
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println(" L-R data: $(nrow(lr_data_raw)) observations")
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# Define base year for relative time indexing
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year0 = Int(minimum([minimum(text_data_raw.year), minimum(expert_raw.year), minimum(lr_data_raw.year)])) - 1
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println("Base year set to: $year0")
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# Create type mapping for the four dimensions
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type_map = Dict(
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"pro_market" => 1,
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"pro_welfare" => 2,
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"cosmopolitan" => 3,
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"traditional" => 4
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)
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println("Type mapping: pro_market=1, pro_welfare=2, cosmopolitan=3, traditional=4")
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# Process text data (manifesto + PolDem media)
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text_data = copy(text_data_raw)
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text_data = text_data[text_data.year .> year0, :]
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# Add type indices for text items (V4/V10: bipolar bridge structure)
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if !("type_high" in names(text_data)) || !("type_low" in names(text_data))
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error("Text data must contain 'type_high' and 'type_low' columns with values: pro_market, pro_welfare, cosmopolitan, traditional")
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end
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text_data.type_high_idx = [type_map[t] for t in text_data.type_high]
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text_data.type_low_idx = [type_map[t] for t in text_data.type_low]
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# V1 (2D model): Add dim_idx and direction columns
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# Maps type_high/type_low to single dimension + direction
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dim_idx_man = Int[]
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direction_man = Int[]
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for row in eachrow(text_data)
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key = (row.type_high, row.type_low)
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if haskey(TYPE_TO_DIM_DIRECTION, key)
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mapping = TYPE_TO_DIM_DIRECTION[key]
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push!(dim_idx_man, mapping.dim_idx)
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push!(direction_man, mapping.direction)
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else
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# Unknown mapping - this should not happen with valid data
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error("Unknown type_high/type_low pair: $(row.type_high) / $(row.type_low)")
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end
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end
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text_data.dim_idx_man = dim_idx_man
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text_data.direction_man = direction_man
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# Standard processing
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text_data.country = categorical(text_data.country)
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text_data.party = categorical(text_data.party)
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text_data.var = categorical(text_data.var)
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text_data.Year = Int.(text_data.year) .- year0
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sort!(text_data, [:country, :party, :year, :var])
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println("Text data processed: $(nrow(text_data)) observations with bipolar bridge structure")
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# Process expert dimension-specific data (bipolar items like lrecon_ches, galtan_ches)
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expert_dim_vars = ["lrecon_ches", "galtan_ches", "lrecon_vparty", "welf_vparty",
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"lrecon_gps", "libcon_gps", "lrecon_poppa",
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"immig_vparty", "lgbt_vparty", "culsup_vparty", "relig_vparty", "gender_vparty"]
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expert_dim = expert_raw[in.(expert_raw.var, Ref(expert_dim_vars)), :]
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expert_dim = expert_dim[(expert_dim.year .> year0) .& (expert_dim.val .>= 0) .& (expert_dim.val .<= 1), :]
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# V5: Load integer observations, scale sizes, and expert counts for beta-binomial likelihood
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expert_dim.val_int = Int.(expert_dim.val_int)
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expert_dim.n_scale = Int.(expert_dim.n_scale)
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expert_dim.n_experts = Int.(expert_dim.n_experts)
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# Add type mappings for dimension-specific expert data
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if !("type_low" in names(expert_dim)) || !("type_high" in names(expert_dim))
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error("Expert data must contain 'type_low' and 'type_high' columns")
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end
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expert_dim.type_high_idx = [type_map[t] for t in expert_dim.type_high]
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expert_dim.type_low_idx = [type_map[t] for t in expert_dim.type_low]
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# V1 (2D model): Add dim_idx for expert dimension data
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dim_idx_exp = Int[]
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for row in eachrow(expert_dim)
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var_name = string(row.var)
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if haskey(EXPERT_VAR_TO_DIM, var_name)
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push!(dim_idx_exp, EXPERT_VAR_TO_DIM[var_name])
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else
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# Fallback: infer from type_high/type_low
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key = (row.type_high, row.type_low)
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if haskey(TYPE_TO_DIM_DIRECTION, key)
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push!(dim_idx_exp, TYPE_TO_DIM_DIRECTION[key].dim_idx)
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else
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error("Unknown expert variable: $var_name with type pair $(row.type_high) / $(row.type_low)")
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end
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end
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end
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expert_dim.dim_idx_exp = dim_idx_exp
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expert_dim.country = categorical(expert_dim.country)
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expert_dim.party = categorical(expert_dim.party)
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expert_dim.var = categorical(expert_dim.var)
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expert_dim.Year = Int.(expert_dim.year) .- year0
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sort!(expert_dim, [:country, :party, :year, :var])
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println("Expert dimension-specific data processed: $(nrow(expert_dim)) observations")
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# Process expert general L-R data (cross-dimensional anchoring)
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lr_vars = ["lr_ches", "lr_poppa", "lr_morgan"] # General left-right items
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expert_lr = lr_data_raw[in.(lr_data_raw.var, Ref(lr_vars)), :]
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expert_lr = expert_lr[(expert_lr.year .> year0) .& (expert_lr.val .>= 0) .& (expert_lr.val .<= 1), :]
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# V5: Load integer observations, scale sizes, and expert counts for beta-binomial likelihood
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expert_lr.val_int = Int.(expert_lr.val_int)
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expert_lr.n_scale = Int.(expert_lr.n_scale)
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expert_lr.n_experts = Int.(expert_lr.n_experts)
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expert_lr.country = categorical(expert_lr.country)
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expert_lr.party = categorical(expert_lr.party)
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expert_lr.var = categorical(expert_lr.var)
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expert_lr.Year = Int.(expert_lr.year) .- year0
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sort!(expert_lr, [:country, :party, :year, :var])
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println("Expert general L-R data processed: $(nrow(expert_lr)) observations")
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# Validate data integrity
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println("\nData validation:")
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# Check text data dimension pair distribution (V4: bipolar bridges)
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type_pair_counts = combine(groupby(text_data, [:type_high, :type_low]), nrow => :count)
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for row in eachrow(type_pair_counts)
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println(" $(row.type_high) ↔ $(row.type_low): $(row.count) text data observations")
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end
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# Check expert dimension-specific type pairs
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type_pair_counts = combine(groupby(expert_dim, [:type_high, :type_low]), nrow => :count)
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for row in eachrow(type_pair_counts)
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println(" $(row.type_high) - $(row.type_low): $(row.count) expert dimension-specific observations")
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end
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# Check general L-R items
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lr_var_counts = combine(groupby(expert_lr, :var), nrow => :count)
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for row in eachrow(lr_var_counts)
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println(" $(row.var): $(row.count) general L-R observations")
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end
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# Check overlapping parties across datasets
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text_data_parties = Set(text_data.party)
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expert_dim_parties = Set(expert_dim.party)
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expert_lr_parties = Set(expert_lr.party)
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all_parties = union(text_data_parties, expert_dim_parties, expert_lr_parties)
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println("\nParty coverage:")
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println(" Total unique parties: $(length(all_parties))")
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println(" In text data: $(length(text_data_parties))")
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println(" In expert dimension-specific: $(length(expert_dim_parties))")
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println(" In expert general L-R: $(length(expert_lr_parties))")
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println(" In all three datasets: $(length(intersect(text_data_parties, expert_dim_parties, expert_lr_parties)))")
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return text_data, expert_dim, expert_lr, year0, union_to_constituents, constituent_to_union
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end
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# Execute if run directly
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if abspath(PROGRAM_FILE) == @__FILE__
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text_data, expert_dim, expert_lr, year0, u2c, c2u = load_and_preprocess_4dim_data()
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println("4D data loading test completed successfully")
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end
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