放弃原来的rewiring rate(实际上是breaking rate)
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wjsjwr
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f36f3ae836
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570c4d0c01
75
br_plot.py
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75
br_plot.py
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import numpy as np
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from matplotlib import pyplot as plt
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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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from sys import argv
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blue = '#0984e3'
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red = '#d63031'
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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 p2(e, rw, postfix, show=True, showline=True):
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# p2散点图
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fig = plt.figure(figsize=(4, 3))
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ax = fig.gca()
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if showline:
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fp1,residuals,rank,sv,rcond = sp.polyfit(e, rw, 1, full=True)
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print("残差：",residuals)
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print('Model parameter:',fp1)
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print("Other parameters: rank=%s, sv=%s, rcond=%s"%(str(rank), str(sv), str(rcond)))
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f1 = sp.poly1d(fp1)
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print("error= %f" % error(f1, e, rw))
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fx = sp.linspace(np.min(e), np.max(e), 2)
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plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
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plt.scatter(e, rw, color='white', edgecolors=blue, linewidths=2, zorder=101)
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ax.set_xlabel(r'$TtD$', family='sans-serif', size=20)
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ax.set_ylabel(r'Breaking Rate', family='sans-serif', size=20)
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# ax.set_xlim(0, 1440)
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# ax.set_xticks(sp.linspace(int(min(e)*0.9), int(max(e)*1.1), 4))
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ax.tick_params(labelsize=14)
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ax.set_ylim(0, 0.6)
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ax.set_yticks(sp.linspace(0, 0.6, 5))
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plt.tight_layout()
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if show:
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plt.show()
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else:
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plt.savefig("graph/eid_co_sca_%s.eps" % postfix)
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# 皮尔逊相关系数
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print("pearson: %f, p-value: %f" % pearsonr(e, rw))
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def p2c(e,c,m,s):
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p2(e,c,m,s,False)
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def plot(mode, show):
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rw = np.loadtxt("outputs/BR_%s.csv" % mode, delimiter=',')
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e = np.loadtxt("outputs/EID_%s.csv" % mode, delimiter=',')
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p2(e[:-1], rw[1:], mode, show)
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# if mode == 'SURVIVE':
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# p2(e[:-1], rw[1:], mode, show)
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# else:
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# p2c(e[:-1], rw[1:], mode, show)
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if __name__ == '__main__':
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# eid_plot c/s 1/2 t/f
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mode = 'CLASSIC' if argv[1] == 'c' else 'SURVIVE'
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show = argv[2] == 't'
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plot(mode, show)
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"""
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SURVIVE
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残差： [ 0.06081364]
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Model parameter: [ -3.54739130e-04 4.41956696e-01]
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Other parameters: rank=2, sv=[ 1.40523958 0.15906514], rcond=3.10862446895e-15
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error= 0.060814
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pearson: -0.837958, p-value: 0.000183
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CLASSIC
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pearson: 0.384519, p-value: 0.174626
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"""
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27
breaking_rate.py
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breaking_rate.py
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import csv
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import numpy as np
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from island.match import Match
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from island.matches import Matches
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def calc_and_save(mode):
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matches = Matches.from_profile(mode)
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ans = np.zeros(15)
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div = np.zeros(15)
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for m in matches.data:
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maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
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for i in range(1, maxr):
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rows = m.query('action', 'done').where(lambda x: x['rno']==i).raw_data
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br = 0
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for r in rows:
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if m.query('action', 'done').where(lambda x: x['rno']==i+1 and ((x['a']==r['a'] and x['b']==r['b']) or (x['a']==r['b'] and x['b']==r['a']))).count() == 0:
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br += 1
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ans[i] += br
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div[i] += len(rows)
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div[np.where(div==0)[0]] = 1
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ans /= div
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with open("outputs/BR_%s.csv"%mode, 'w') as f:
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csv.writer(f).writerow(ans)
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if __name__ == '__main__':
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calc_and_save('CLASSIC')
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calc_and_save('SURVIVE')
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1
outputs/BR_CLASSIC.csv
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outputs/BR_CLASSIC.csv
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0.0,0.268817204301,0.194871794872,0.161764705882,0.144278606965,0.147058823529,0.127962085308,0.148325358852,0.0873786407767,0.111111111111,0.0903225806452,0.120967741935,0.102803738318,0.0833333333333,0.0
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outputs/BR_SURVIVE.csv
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outputs/BR_SURVIVE.csv
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0.0,0.378205128205,0.40625,0.237442922374,0.167539267016,0.121951219512,0.148648648649,0.0625,0.0166666666667,0.0508474576271,0.0779220779221,0.0740740740741,0.25,0.9,0.25
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rewiring_rate.py
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rewiring_rate.py
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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 p1(x, rewires, tau, postfix, show):
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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, rewires, color=green, linewidth=3)
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ax.set_ylim(0, 0.5)
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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(2, 15)
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ax.set_xlabel("Rounds")
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ax.set_ylabel("Rewiring Rate", color=green)
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ax.tick_params(axis='y', labelcolor=green)
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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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if show:
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plt.show()
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else:
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plt.savefig("graph/tau_p_rewire_plot_%s.eps" % postfix)
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def p2c(tau, rewires, show):
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# # p2散点图
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fig = plt.figure(figsize=(4, 3))
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ax = fig.gca()
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plt.scatter(tau, rewires, color=green, linewidths=2, zorder=100)
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ax.set_xlabel('$\\tau_{f}$', family='sans-serif', size=20)
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ax.set_ylabel('Rewiring Rate', size=20)
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ax.set_xlim(0, 1440)
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ax.set_ylim(0, 0.6)
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ax.set_xticks(sp.linspace(0, 1440, 5))
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ax.tick_params(labelsize=14)
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ax.set_ylim(0, 0.6)
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ax.set_yticks(sp.linspace(0, 0.6, 5))
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plt.tight_layout()
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if show:
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plt.show()
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else:
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plt.savefig("graph/tau_f_rewire_sca_c.eps")
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# 皮尔逊相关系数
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print("pearson: %f, p-value: %f" % pearsonr(tau, rewires))
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def p2(tau, rewires, postfix, show):
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# # p2散点图
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fig = plt.figure(figsize=(4, 3))
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ax = fig.gca()
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fp1,residuals,rank,sv,rcond = sp.polyfit(tau, rewires, 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, tau, rewires))
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print("Other parameters: rank=%s, sv=%s, rcond=%s" % (str(rank), str(sv), str(rcond)))
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# fx = sp.linspace(0,max(tau2),1000)
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fx = sp.linspace(0,1440,2)
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plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
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plt.scatter(tau, rewires, color=green, linewidths=2, zorder=100)
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# plt.scatter(tau_r, coopr_r, color='white', edgecolors=green, linewidths=2, zorder=101)
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ax.set_xlabel('$\\tau_{f}$', family='sans-serif', size=20)
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ax.set_ylabel('Rewiring Rate', size=20)
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ax.set_xlim(0, 1440)
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ax.set_ylim(0, 0.6)
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ax.set_xticks(sp.linspace(0, 1440, 5))
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ax.tick_params(labelsize=14)
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ax.set_ylim(0, 0.6)
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ax.set_yticks(sp.linspace(0, 0.6, 5))
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plt.tight_layout()
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if show:
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plt.show()
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else:
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plt.savefig("graph/tau_f_rewire_sca_%s.eps" % postfix)
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# 皮尔逊相关系数
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print("pearson: %f, p-value: %f" % pearsonr(tau, rewires))
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if __name__ == '__main__':
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mode = 'CLASSIC'
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matches = Matches.from_profile_expr(lambda r: mode in r)
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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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rewires = []
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x = np.arange(2, max_round+1)
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mwRe = {} # Match-wise frequency of rewiring
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mwTau = {} # Match-wise Tau
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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(1, max_round):
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re = []
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for j in range(len(matches.data)):
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rewire = 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 r in rows:
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if matches.data[j].query('action', 'done').where(lambda x: x['rno']==i and ((x['a']==r['a'] and x['b']==r['b']) or (x['a']==r['b'] and x['b']==r['a']))).count() == 0:
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rewire += 1
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if rows:
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re.append(float(rewire) / float(len(rows)*2))
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mwRe["%s-%d"%(j,i)] = re[-1]
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if re:
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rewires.append(np.average(re))
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for i in range(1, max_round):
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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 = matches.data[j].get_tr(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 and trs[l] > 0:
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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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green = '#00b894'
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red = '#d63031'
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# p1折线图
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# p1(x, rewires, tau, mode, True)
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p2c(tau, rewires, False)
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# p2(tau[:12], rewires[:12], mode, False)
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'''
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classic
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残差： [ 0.05873797]
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Model parameter: [ 9.81549075e-04 -9.87729952e-01]
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error= 0.058738
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Other parameters: rank=2, sv=[ 1.41291267 0.06064473], rcond=3.10862446895e-15
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pearson: -0.507660, p-value: 0.063859
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survive
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残差： [ 0.00291864]
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Model parameter: [ 0.0001647 -0.02264606]
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error= 0.002919
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Other parameters: rank=2, sv=[ 1.33444456 0.46824962], rcond=2.6645352591e-15
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pearson: 0.947474, p-value: 0.000003
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'''
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