206 lines
6.2 KiB
Python
206 lines
6.2 KiB
Python
import json
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from matplotlib import pyplot as plt
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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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import scipy as sp
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from scipy.stats import pearsonr
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from matplotlib import markers
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def error(f,x,y):
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return sp.sum((f(x)-y)**2)
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def get_tr(m, i, target, nb, sv):
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"""
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# 计算tau_p的方式是统计周围邻居剩余的tr
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# 本函数计算第i轮，所有有消耗tr的player，最后剩余的tr
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# 由于是计算剩余tr，所以不能计算和target之间的交互
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# nb是target的邻居
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"""
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trs = {}
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req = m.query('action', 'request').where(lambda x: x['rno'] == i+1)
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app = m.query('action', 'approve').where(lambda x: x['rno'] == i+1).raw_data
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can = m.query('action', 'cancel').where(lambda x: x['rno'] == i+1).raw_data
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den = m.query('action', 'deny').where(lambda x: x['rno'] == i+1).raw_data
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fr = []
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for r in m.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == target or x['b'] == target)).raw_data:
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if r['a'] == target:
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fr.append(r['b'])
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elif r['b'] == target:
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fr.append(r['a'])
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truenb = []
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for r in nb:
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if r not in fr and r in sv:
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truenb.append(r)
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trs[r] = 1440
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for r in req.raw_data:
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if r['from'] in truenb and r['to'] != target :
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if r['from'] not in trs:
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trs[r['from']] = 1440
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trs[r['from']] -= r['tr']
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for r in app:
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if r['from'] in truenb and r['to'] != target:
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if r['from'] not in trs:
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trs[r['from']] = 1440
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trs[r['from']] -= r['tr']
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for r in can:
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if r['from'] in trs and r['to'] != target:
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trs[r['from']] += req.where(lambda x: x['from'] == r['from'] and x['to'] == r['to'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr']
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for r in den:
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if r['to'] in trs and r['from'] != target:
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trs[r['to']] += req.where(lambda x: x['from'] == r['to'] and x['to'] == r['from'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr']
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return trs
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matches = Matches('wos-data-new')
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max_round = 15
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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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neighbors = {}
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coopr = []
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x = np.arange(1, max_round)
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mwCo = {} # Match-wise frequency of cooperation
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mwTau = {} # Match-wise Tau
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bx = []
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tau = []
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for i in range(len(matches.data)):
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m = matches.data[i]
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n = {}
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for r in m.query('neighbor', 'create').raw_data:
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if r['a'] in n:
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n[r['a']].append(r['b'])
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else:
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n[r['a']] = [r['b']]
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if r['b'] in n:
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n[r['b']].append(r['a'])
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else:
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n[r['b']] = [r['a']]
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neighbors[matches.names[i]] = n
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for i in range(max_round):
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co = []
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for j in range(len(matches.data)):
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coop = 0
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rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
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for row in rows:
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if row['act_a'] == 'C':
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coop += 1
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if row['act_b'] == 'C':
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coop += 1
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if rows:
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co.append(float(coop) / float(len(rows)*2))
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mwCo["%s-%d"%(j,i)] = co[-1]
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bx.append(co)
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if co:
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coopr.append(np.average(co))
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for i in range(max_round-1):
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tp = []
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for j in range(len(matches.data)):
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if i == 0:
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for r in matches.data[j].query('player', 'join').raw_data:
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t = 0
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k = r['pid']
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if k not in neighbors[matches.names[j]]:
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print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
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else:
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t = 1440 * len(neighbors[matches.names[j]][k])
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tp.append(t if t < 1440 else 1440)
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mwTau["%s-%d"%(j,i)] = tp[-1]
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else:
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if str(i) not in survivals[matches.names[j]]:
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continue
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for k in survivals[matches.names[j]][str(i)]:
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t = 0
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if k not in neighbors[matches.names[j]]:
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print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
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else:
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trs = get_tr(matches.data[j], i, k, neighbors[matches.names[j]][k], survivals[matches.names[j]][str(i)])
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for l in neighbors[matches.names[j]][k]:
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if l in trs:
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t += trs[l]
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tp.append(t if t < 1440 else 1440)
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mwTau["%s-%d"%(j,i)] = tp[-1]
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if tp:
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tau.append(np.average(tp))
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else:
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tau.append(0)
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blue = '#0984e3'
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red = '#d63031'
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# p1折线图
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# fig = plt.figure(figsize=(6.4, 3.6))
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# ax = fig.gca()
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# ax.plot(x, coopr, color=blue, linewidth=3)
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# ax.set_ylim(0.5, 1)
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# ax2 = ax.twinx()
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# ax2.plot(x, tau, color=red, linewidth=3)
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# ax2.set_ylim(0,1440)
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# ax.set_xlim(1,14)
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# ax.set_xlabel("Rounds")
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# ax.set_ylabel("Frequency of Cooperation", color=blue)
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# ax.tick_params(axis='y', labelcolor=blue)
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# ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
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# ax2.tick_params(axis='y', labelcolor=red)
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# plt.tight_layout()
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# # plt.show()
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# plt.savefig('graph/tau_p_co_plot.eps')
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tau2 = []
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coopr2 = []
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tau_r = []
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coopr_r = []
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for i in range(len(tau)):
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if tau[i] <= 720:
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tau2.append(tau[i])
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coopr2.append(coopr[i])
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else:
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tau_r.append(tau[i])
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coopr_r.append(coopr[i])
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# p2散点图
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fig = plt.figure(figsize=(6.4, 3.6))
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ax = fig.gca()
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# ax.set_ylim(0.5, 1)
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fp1,residuals,rank,sv,rcond = sp.polyfit(tau2, coopr2, 1, full=True)
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print("残差：",residuals)
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print('Model parameter:',fp1)
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f1 = sp.poly1d(fp1)
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print("error= %f" % error(f1, tau2, coopr2))
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# fx = sp.linspace(0,max(tau2),1000)
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fx = sp.linspace(0,720,2)
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plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
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plt.scatter(tau2, coopr2, color=blue, linewidths=2, zorder=100)
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plt.scatter(tau_r, coopr_r, color='white', edgecolors=blue, linewidths=2, zorder=101)
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ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
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ax.set_ylabel('Frequency of Cooperation')
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ax.set_xlim(0, 1440)
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ax.set_xticks(sp.linspace(0, 1440, 13))
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ax.set_ylim(0.5, 1)
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plt.tight_layout()
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plt.show()
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# plt.savefig('graph/tau_p_co_sca.eps')
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# 皮尔逊相关系数
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print("pearson: %f, p-value: %f" % pearsonr(tau2, coopr2)) |