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vote_sim.py
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vote_sim.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.image as mpimg
from matplotlib.text import TextPath
from mpl_toolkits.mplot3d import Axes3D
#from matplotlib import cm
import utils
# Types of problems to handle
# https://www.rangevoting.org/AssetBC.html
# https://bs.google.com/forum/#!topic/electionscience/Rk4ZGf-s-s8
centerists = False
#centerists = True
#maximize = False
maximize = True
minimize = True
#Number of winners
W = 5
#the maximum possible score is 5
K = 5
voter_groups = []
#Three parties
mean_red = [2, -2]
cov_red = [[5, -2], [-2, 2]] # diagonal covariance
pos_red = np.random.multivariate_normal(mean_red, cov_red, 5000)
df_red = pd.DataFrame.from_records(pos_red, columns = ['x','y'])
df_red['colour'] = 'red'
voter_groups.append(df_red)
mean_green = [0, 0]
cov_green = [[0.5, 0], [0, 0.5]] # diagonal covariance
pos_green = np.random.multivariate_normal(mean_green, cov_green, 5000)
df_green = pd.DataFrame.from_records(pos_green, columns = ['x','y'])
df_green['colour'] = 'green'
voter_groups.append(df_green)
mean_blue = [2, 2]
cov_blue = [[2, 2], [2, 5]] # diagonal covariance
pos_blue = np.random.multivariate_normal(mean_blue, cov_blue, 5000)
df_blue = pd.DataFrame.from_records(pos_blue, columns = ['x','y'])
df_blue['colour'] = 'blue'
voter_groups.append(df_blue)
#
# candidates = [['A',0,0],
# ['Z',0,2.5],
# ['R1',-1*np.sqrt(3), 1],
# ['R2',-2.5*np.sqrt(3), 2.5],
# ['R3',-4*np.sqrt(3), 4],
# ['G1',0, -2],
# ['G2',0, -5],
# ['G3',0, -8],
# ['B1',1*np.sqrt(3), 1],
# ['B2',2.5*np.sqrt(3),2.5],
# ['B3',4*np.sqrt(3), 4]]
# #4 parties
# mean_red = [-1.5, 1.5]
# cov_red = [[1, 0], [0, 1]] # diagonal covariance
# pos_red = np.random.multivariate_normal(mean_red, cov_red, 4000)
# df_red = pd.DataFrame.from_records(pos_red, columns = ['x','y'])
# df_red['colour'] = 'red'
# voter_groups.append(df_red)
# mean_green = [-1.5, -1.5]
# cov_green = [[1, 0], [0, 1]] # diagonal covariance
# pos_green = np.random.multivariate_normal(mean_green, cov_green, 1500)
# df_green = pd.DataFrame.from_records(pos_green, columns = ['x','y'])
# df_green['colour'] = 'green'
# voter_groups.append(df_green)
# mean_blue = [1.5, 1.5]
# cov_blue = [[1, 0], [0, 1]] # diagonal covariance
# pos_blue = np.random.multivariate_normal(mean_blue, cov_blue, 2000)
# df_blue = pd.DataFrame.from_records(pos_blue, columns = ['x','y'])
# df_blue['colour'] = 'blue'
# voter_groups.append(df_blue)
# mean_yellow = [1.5, -1.5]
# cov_yellow = [[1, 0], [0, 1]] # diagonal covariance
# pos_yellow = np.random.multivariate_normal(mean_yellow, cov_yellow, 2500)
# df_yellow = pd.DataFrame.from_records(pos_yellow, columns = ['x','y'])
# df_yellow['colour'] = 'yellow'
# voter_groups.append(df_yellow)
# candidates = [
# ['A',0,0],
# ['Z1',0,0.5],
# ['Z2',0,1.5],
# ['Z3',0,2.5],
# ['R1',-0.5, 0.5],
# ['R2',-1.5, 1.5],
# ['R3',-2.5, 2.5],
# ['G1',-0.5, -0.5],
# ['G2',-1.5, -1.5],
# ['G3',-2.5, -2.5],
# ['B1',0.5, 0.5],
# ['B2',1.5, 1.5],
# ['B3',2.5, 2.5],
# ['Y1',0.5, -0.5],
# ['Y2',1.5, -1.5],
# ['Y3',2.5, -2.5]
# ]
candidates = [
['A',2.5,-2.5],
['B',0, 0],
['C',2.5, 2.5]
]
df_can = pd.DataFrame.from_records(candidates, columns = ['Name','x','y'] )
fig = plt.figure(figsize=(20,10))
fig.suptitle('Political Simulation')
#image
try:
ax1 = fig.add_subplot(1, 2, 1)
img=mpimg.imread('Political Compass.jpg')
ax1.imshow(img)
ax1.axis('off')
except:
print('image missing')
# Scatter plot
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(df_red['x'],df_red['y'],".",label = 'Red', color='r')
ax2.plot(df_green['x'],df_green['y'],".",label = 'Green', color='g')
ax2.plot(df_blue['x'],df_blue['y'],".",label = 'Blue', color='b')
#ax2.plot(df_yellow['x'],df_yellow['y'],".",label = 'Yellow', color='y')
#Candidates
for c in candidates:
ax2.plot(c[1], c[2],marker=TextPath((0,0), c[0]),markersize=20, color='k')
ax2.set_xlim(-10, 10)
ax2.set_ylim(-10, 10)
ax2.set_title('Political Compass')
ax2.set_xlabel('Planned Economy <-- Economics --> Free Market')
ax2.set_ylabel('Liberal <-- Government --> Authoritarian')
lgd2 = ax2.legend(loc=1)
fig.savefig("Simulated_Spectrum", dpi=300)
if centerists:
mean_center = [0,0]
cov_center = [[5, 0], [0, 5]] # diagonal covariance
pos_center = np.random.multivariate_normal(mean_center, cov_center, 3500)
df_center = pd.DataFrame.from_records(pos_center, columns = ['x','y'])
df_center['colour'] = 'center'
voter_groups.append(df_center)
df_voters = pd.concat(voter_groups,ignore_index=True)
#Number of voters
V = df_voters.shape[0]
#Make 3d plot of df_voters
fig2 = plt.figure(figsize=(20,10))
fig2.suptitle('Voter Density')
#histogram
axa = fig2.add_subplot(121, projection='3d')
hist, xedges, yedges = np.histogram2d(df_voters['x'], df_voters['y'], bins=40, range=[[-10, 10], [-10, 10]])
X, Y = np.meshgrid(xedges[:-1] + 0.125, yedges[:-1] + 0.125, indexing="ij")
xpos = X.ravel()
ypos = Y.ravel()
zpos = 0
# Construct arrays with the dimensions for the bars.
dx = dy = 0.25 * np.ones_like(zpos)
dz = hist.ravel()
axa.bar3d(xpos, ypos, zpos, dx, dy, dz, zsort='average')
axa.set_xlabel('Economic')
axa.set_ylabel('Government')
axa.set_zlabel('Voter Count')
axa.view_init(35, 240)
#surface
axb = fig2.add_subplot(122, projection='3d')
surf = axb.plot_surface(X, Y, hist, cmap="gist_rainbow", linewidth=0, antialiased=False)
surf.set_edgecolors(surf.to_rgba(surf._A))
#surf.set_facecolors("white")
#cset = axb.contour(X, Y, hist, zdir='z', offset=-100, cmap=cm.coolwarm)
#cset = axb.contour(X, Y, hist, zdir='x', offset=-40, cmap=cm.coolwarm)
#cset = axb.contour(X, Y, hist, zdir='y', offset=40, cmap=cm.coolwarm)
axb.set_xlabel('Economic')
axb.set_ylabel('Government')
axb.set_zlabel('Voter Count')
axb.view_init(35, 240)
fig2.colorbar(ax = axb, mappable = surf, shrink=0.5, aspect=5)
fig2.savefig("3D_Population", dpi=300)
#Get distances then scores
distance = pd.DataFrame()
S = pd.DataFrame()
for c in candidates:
distance[c[0]] = df_voters[['x', 'y']].sub(np.array([c[1], c[2]])).pow(2).sum(1).pow(0.5)
S[c[0]] = round(np.clip(K - 2.0*distance[c[0]], 0.0, K))
#rowwise max set to 5
if maximize:
columns = distance.idxmin('columns')
for index in S.index:
S.loc[index,columns[index]] = 5
#rowwise min set to 0
if minimize:
columns = distance.idxmax('columns')
for index in S.index:
S.loc[index,columns[index]] = 0
S_temp = S.copy()
S_temp['colour'] = df_voters['colour']
print(S_temp.groupby('colour').mean())
#
winners = {}
metrics = {}
#SSS with scaling
utilitarian_scale_score_winners = utils.get_winners(S_in=S.copy(),Selection = 'Utilitarian',Reweight = 'Scale Score', K=K, W=W)
winners['utilitarian_scale_score_winners'] = utilitarian_scale_score_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = utilitarian_scale_score_winners, method = 'utilitarian_scale_score', K=K)
#SSS with capping
utilitarian_cap_score_winners = utils.get_winners(S_in=S.copy(),Selection = 'Utilitarian',Reweight = 'Cap Score', K=K, W=W)
winners['utilitarian_cap_score_winners'] = utilitarian_cap_score_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = utilitarian_cap_score_winners, method = 'utilitarian_cap_score', K=K)
utilitarian_webster_winners = utils.get_winners(S_in=S.copy(),Selection = 'Utilitarian',Reweight = 'Webster', K=K, W=W)
winners['utilitarian_webster_winners'] = utilitarian_webster_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = utilitarian_webster_winners, method = 'utilitarian_webster', K=K)
utilitarian_jefferson_winners = utils.get_winners(S_in=S.copy(),Selection = 'Utilitarian',Reweight = 'Jefferson', K=K, W=W)
winners['utilitarian_jefferson_winners'] = utilitarian_jefferson_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = utilitarian_jefferson_winners, method = 'utilitarian_jefferson', K=K)
utilitarian_allocate_winners = utils.get_winners(S_in=S.copy(),Selection = 'Utilitarian',Reweight = 'Allocate', K=K, W=W)
winners['utilitarian_allocate_winners'] = utilitarian_allocate_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = utilitarian_allocate_winners, method = 'utilitarian_allocate', K=K)
utilitarian_allocate_current_winners = utils.get_winners(S_in=S.copy(),Selection = 'Utilitarian',Reweight = 'Allocate Current', K=K, W=W)
winners['utilitarian_allocate_current_winners'] = utilitarian_allocate_current_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = utilitarian_allocate_current_winners, method = 'utilitarian_allocate_current', K=K)
hare_ballots_allocate_winners = utils.get_winners(S_in=S.copy(),Selection = 'Hare_Ballots',Reweight = 'Allocate', K=K, W=W)
winners['hare_ballots_allocate_winners'] = hare_ballots_allocate_winners
metrics = utils.get_metrics(S_in=S.copy(), metrics =metrics, winner_list = hare_ballots_allocate_winners, method = 'hare_ballots_allocate', K=K)
print(pd.DataFrame.from_dict(winners).T)
results = pd.DataFrame.from_dict(metrics)
for col in results.columns:
print('---------------------------')
print(results[col])
print(' ')
plt.show()