Module spatial_inequality.main

"""
Example Python script to minimize the Spatial Inequality Index for all states in
the US. All configuration parameters are set as per our publication.

Paper:
    Fair Partitioning of Public Resources: Redrawing DistrictBoundary to
    Minimize Spatial Inequality in SchoolFunding (online at
    `https://nunomota.github.io/assets/papers/www2021.pdf`).
"""
import os
import json
import numpy as np
import pandas as pd

from pathlib import Path

from core.greedy_algorithm import *
from auxiliary.data_handler import load_data

if __name__ == "__main__":
    # Load data
    aug_school_info, school_assignment = load_data()

    # Initialize greedy parameters
    GREEDY_PARAMS = {
        "min_schools_per_district": 1,
        "max_schools_per_district": 500
    }
    # Initialize EarlyStopper params
    EARLY_STOPPER_PARAMS = {
        "early_stopper_it": 1000,
        "early_stopper_tol": 0.1
    }

    # Checkpoint variables
    SAVE_METRICS = True
    N_RUNS_PER_STATE = 20

    # All existing states
    ALL_STATES = [
        'Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',
        'Colorado', 'Connecticut', 'Delaware',
        'Florida', 'Georgia', 'Idaho', 'Illinois', 'Indiana',
        'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland',
        'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana',
        'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico',
        'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma',
        'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina',
        'South Dakota', 'Texas', 'Utah', 'Vermont', 'Virginia',
        'Washington', 'West Virginia', 'Wisconsin', 'Wyoming'
    ]

    overall_metrics = {}
    if os.path.isfile("./metrics.json"):
        # Load previous metrics from file
        with open("./metrics.json", "r") as file:
            overall_metrics = json.load(file)
    else:
        # Run and save new metrics
        for state in ALL_STATES:
            # Get average run for algorithm
            mean, std, metrics = get_expectable_run_for_state(
                state,
                aug_school_info,
                school_assignment,
                N_RUNS_PER_STATE,
                GREEDY_PARAMS,
                EARLY_STOPPER_PARAMS
            )
            metrics = metrics.as_dict()
            # Store results
            overall_metrics[state] = {
                "mean_inequality": mean,
                "std_inequality": std,
                "metrics": metrics
            }

        # Save metrics in file
        if SAVE_METRICS is True:
            with open("./metrics.json", "w") as file:
                json.dump(overall_metrics, file)