GerryChain.jl

aka

motivation

neato: julia "feels like python, runs like C"

SHAPEFILE_PATH = "./PA_VTD.json"
POPULATION_COL = "TOT_POP"
ASSIGNMENT_COL = "538GOP_PL"

# Initialize graph and partition
graph = BaseGraph(SHAPEFILE_PATH, POPULATION_COL, ASSIGNMENT_COL)
partition = Partition(graph, ASSIGNMENT_COL)

# Define parameters of chain (number of steps and population constraint)
pop_constraint = PopulationConstraint(graph, POPULATION_COL, 0.02)
num_steps = 10

# Initialize Election of interest
election = Election("SEN10", ["SEN10D", "SEN10R"], graph.num_dists)
# Define election-related metrics and scores
election_metrics = [
    vote_count("count_d", election, "SEN10D"),
    efficiency_gap("efficiency_gap", election, "SEN10D"),
    seats_won("seats_won", election, "SEN10D"),
    mean_median("mean_median", election, "SEN10D")
]
scores = [
        DistrictAggregate("presd", "PRES12D"),
        ElectionTracker(election, election_metrics)
]

# Run the chain
println("Running 10-step ReCom chain...")
chain_data = recom_chain(graph, partition, pop_constraint, num_steps, scores)

# Get values of all scores at the 10th state of the chain 
score_dict = get_scores_at_step(chain_data, 10)
# Get all vote counts for each state of the chain
vote_counts_arr = get_score_values(chain_data, "count_d")

running a chain in 34 lines of code!

where is our progress now?

Most of core GerryChain is re-implemented in Julia! 🎊
Some new features! ✨
Package uploaded to Julia registry! 📦
- ```
using Pkg; Pkg.add("GerryChain")
```

some differences w/ gerrychain...

"updaters" are now "scores"
- Tallies ➡ DistrictAggregate scores
- introduce district-level vs. plan-level scores
  - saves time + memory
acceptance functions must return a float in [0, 1] representing probability of acceptance rather than True or False
- forces user to think about what acceptance functions are for, reducing confusion w/ constraints!
ReCom is first class in GerryChain.jl
- Python GerryChain very much built on the notion of "flips"