91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
import json
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from matplotlib import pyplot as plt
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from matplotlib import patches as pch
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from island.match import Match
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from island.matches import Matches
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import numpy as np
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from scipy.stats import pearsonr
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class Solution:
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def __init__(self, mode):
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self.mode = mode
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self.matches = Matches.from_profile_expr(lambda r: mode in r)
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def food_loss(self):
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survivals = {}
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with open('survivals.json', 'r') as f:
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survivals = json.load(f)
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fl = [[],[]]
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payoff = {
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'C': {
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'C': 2.6,
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'D': 0
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},
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'D': {
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'C': 4.5,
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'D': 0.5
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}
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}
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for m in self.matches.data:
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info = m.query('game', 'created').select('info').first()['info']
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game_end_at = int(info['game_end_at'])
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for p in m.query('player', 'join').raw_data:
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pid = p['pid']
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for i in range(2, game_end_at):
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if pid not in survivals[m.name][str(i)]: break
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c, t = 0, 0
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for r in m.query('action', 'done').where(lambda x: x['rno'] == i - 1 and (x['a'] == pid or x['b'] == pid)).raw_data:
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if r['act_a' if r['a'] == pid else 'act_b'] == 'C':
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c += 1
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t += 1
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is_coop = 0 if c * 2 >= t else 1
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f = -3.0 if self.mode == 'SURVIVE' else 0
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for r in m.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == pid or x['b'] == pid)).raw_data:
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if r['a'] == pid:
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f += payoff[r['act_a']][r['act_b']] * r['tr'] / 1440.0
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else:
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f += payoff[r['act_b']][r['act_a']] * r['tr'] / 1440.0
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fl[is_coop].append(-f)
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print(len(fl[0]), len(fl[1]))
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print(sum(fl[0]) / len(fl[0]), sum(fl[1]) / len(fl[1]))
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return fl
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if __name__ == '__main__':
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sc = Solution('CLASSIC')
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fc = sc.food_loss()
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ss = Solution('SURVIVE')
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fs = ss.food_loss()
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blue = '#0984e3'
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red = '#d63031'
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green = '#00b894'
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c = [blue, red]
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labels = ['Dissipative', 'Classic']
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fig = plt.figure(figsize=(8, 6))
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ax = fig.gca()
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index = np.arange(2)
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widths = 0.3
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vp1 = ax.violinplot(fs, positions=index-widths/2, widths=widths, showmeans=True, showmedians=True)
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vp2 = ax.violinplot(fc, positions=index+widths/2, widths=widths, showmeans=True, showmedians=True)
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hdl1 = pch.Patch(facecolor=vp1['bodies'][0].get_facecolor()[0])
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hdl2 = pch.Patch(facecolor=vp2['bodies'][0].get_facecolor()[0])
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vp1['cmeans'].set_edgecolor(green)
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vp1['cmeans'].set_linestyle(':')
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vp2['cmeans'].set_edgecolor(green)
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vp2['cmeans'].set_linestyle(':')
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ax.set_ylabel('Food Loss')
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ax.yaxis.grid(True, linestyle='--')
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# ax.set_title('Scores by group and gender')
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ax.set_xticks(index)
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ax.set_xticklabels(['C', 'D'])
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ax.set_ylim(-6, 4)
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ax.set_xlabel("Previous Move")
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ax.legend([hdl1, hdl2], labels)
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fig.tight_layout()
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# plt.show()
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plt.savefig("graph/FoodLoss.svg")
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