rdaensemble 1.9.0

Redistricting ensembles

rdaensemble

Redistricting ensembles

Installation

To clone the repository:

$ git clone https://github.com/rdatools/rdaensemble
$ cd rdaensemble

To run the scripts, install the dependencies:

pip install -r requirements.txt

To install the package in another project:

$ pip install rdaensemble

Usage

To generate an ensemble of plans, use one of the *_ensemble.py scripts:

• rmfrst_ensemble.py for Random Maps from Random Spanning Trees (RMfRST)
• rmfrsp_ensemble.py for Random Maps from Random Starting Points (RMfRSP)
• recom_ensemble.py for ReCom

There are example calls in each file. Note: The resulting ensemble JSON files can be quite large--bigger than GitHub's 100 MB file size limit-- so we recommend that you write them to a directory which is not under source control.

To score the plans in an ensemble, use the score_ensemble.py script.

The other scripts are specific to our "trade-offs in redistricting" project and are not generally useful.

Notes

The inputs for generating & scoring ensembles are:

from rdascore import load_data, load_shapes, load_graph, load_metadata

data: Dict[str, Dict[str, int | str]] = load_data(data_path)
shapes: Dict[str, Any] = load_shapes(shapes_path)
graph: Dict[str, List[str]] = load_graph(graph_path)
metadata: Dict[str, Any] = load_metadata(state_code, data_path)

The precinct data, shapes, and graphs are all available in the companion repository rdatools/rdabase in the data directory by state. They are named NC_2020_data.csv, NC_2020_shapes_simplified.json, and NC_2020_graph.json, for example.

Theoretically, these inputs can come from any source, but for simplicity, reproducibility, and apples-to-apples comparisons, it's best to use the input files in rdabase.

Ensembles are saved as JSON files. A file contains metadata about the ensemble, including the method used to generate it, and then a plans key with a list of plans:

plans: List[Dict[str, str | float | Dict[str, int | str]]]

Each plan item has a name (str), an optional weight (float), and a plan (Dict[str, int | str]]) which represents the assignments as geoid: district_id key: value pairs.

Scores for the plans in an ensemble are saved as a CSV file, with one row per plan and one column per metric. The metrics are the same as those produced by rdatools/rdascore, except they also include the energy of the plan. The metric names are descriptive.

When a scores CSV file is produced, a companion JSON file with metadata about the scoring is also generated.