Function get_election_data

data/utils.py:14–42

Download and process the Election dataset used in CorrelationGNN (https://arxiv.org/abs/2002.08274) Parameters: norm_x = logical; should features be normalized norm_y = logical; should outcome be normalized coords_as_feats = logical; should lat/lon coordinates be added as featu

(norm_x=True, norm_y=True, coords_as_feats=False)

from functools import reduce

def get_election_data(norm_x=True, norm_y=True, coords_as_feats=False):
    '''
    Download and process the Election dataset used in CorrelationGNN (https://arxiv.org/abs/2002.08274)

    Parameters:
    norm_x = logical; should features be normalized
    norm_y = logical; should outcome be normalized
    coords_as_feats = logical; should lat/lon coordinates be added as features

    Return:
    coords = spatial coordinates (lon/lat)
    x = features at location (excluding outcome variable)
    y = outcome variable
    '''
    path_to_data = './data/election'

    c = torch.load(path_to_data + '/c.pt')
    x = torch.load(path_to_data + '/x.pt')
    y = torch.load(path_to_data + '/y.pt')

    if norm_y==True:
        y = ((y - y.min()) / (y.max() - y.min()))
    if norm_x==True:
        for i in range(x.shape[1]):
            x[:,i] = ((x[:,i] - x[:,i].min()) / (x[:,i].max() - x[:,i].min()))
    if coords_as_feats:
            x = torch.cat((x,c),1)

    return c, x, y

def get_cali_housing_data(norm_x=True, norm_y=True, coords_as_feats=False, add_coord_noise=True):
    '''