new version

.gitignore

@@ -1,7 +1,8 @@
 .DS_Store
 graph
 wos-data*
+!wos-data-complete
 user*
 *.svg
 __pycache__
-.vscode/
+.vscode

break_tie.py

@@ -3,7 +3,8 @@ from matplotlib import pyplot as plt
 from island.match import Match
 from island.matches import Matches
 
-matches = Matches('wos-data-new')
+mode = 'SURVIVE'
+matches = Matches.from_profile_expr(lambda r: mode in r)
 
 labels = ['Stay Connected', 'Break Tie']
 percents = [0.0, 0.0]
@@ -16,7 +17,7 @@ for m in matches.data:
 
     for row in m.query('action', 'done').where(lambda x: x['act_a'] == op or x['act_b'] == op).raw_data:
         if row['rno'] == game_end_at:
-            print(row)
+            # print(row)
             continue
         if row['act_a'] == op:
             a = row['a']
@@ -46,4 +47,4 @@ plt.pie(percents, labels=labels, autopct="%1.2f%%", pctdistance=1.1, labeldistan
 plt.legend()
 plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
 # plt.show()
-plt.savefig("graph/break_tie_%s.eps"%op)
+plt.savefig("graph/break_tie_%s_%s.eps"%(op, mode))

break_tie_chi2.py

@@ -1,6 +1,15 @@
 from scipy.stats import chi2_contingency as chi2
 import numpy as np
 
-obs = np.array([[219,113],[661,25]])
+obs = np.array([[2887.0, 459.0], [61.0, 383.0]])
 chi,p,dof,expected = chi2(obs)
-print("%f, %f, %f" % (chi, p, dof))
+print("%f, %e, %f" % (chi, p, dof))
+
+'''
+survive:
+chi = 1189.53, p = 1.149752e-260
+
+survive:
+chi = 611.59, p = 5.031232e-135
+
+'''

calc_dnc_players.py

@@ -4,7 +4,7 @@ from island.match import Match
 from island.matches import Matches
 import numpy as np
 
-matches = Matches('wos-data-compete')
+matches = Matches.from_profile_expr(lambda r: True)
 max_round = 15
 
 total_players = 0

calc_participants.py

@@ -8,18 +8,19 @@ result = 0
 count = 0
 
 # ms = Matches.from_profile('CCCN')
-ms = Matches.from_profile_expr(lambda r: 'LAB' in r and 'SURVIVE' in r and 'COMM' in r)
+ms = Matches.from_profile_expr(lambda r: True)
 for m in ms.data:
     result += len(m.query('player', 'join').where(lambda x: 'bot' not in x or x['bot'] == False).select('pid').raw_data)
     count += 1
 
 print(result)
-print("avg:", result / count)
+print("participants: %d, matches: %d, avg: %f" % (result, count, result / count))
 # 146 users
 # casual: 324
 # new(254-354, 全国复杂网络大会): 205
 # new-2(375-421, 无交流): 320
 # new-3(426-440, 有交流): 203
 # new-4(443-472, 经典模式，无交流): 230
-# new-5(474-，经典模式，有交流): 159
-# total: 1117
+# new-5(474-，经典模式，有交流): 286
+# total: 1244, 81, 15.35
+# lab: 1039, 69, 15.05

calc_survivals.py

@@ -4,11 +4,11 @@ from island.match import Match
 
 result = {}
 
-for file in Path('wos-data-compete').iterdir():
-    p = Path(file)
+for f in Path('wos-data-compete').iterdir():
+    p = Path(f)
     if p.suffix == '.json':
         name = p.stem
-        m = Match.read_from_json(str(file))
+        m = Match.read_from_json(str(f))
         info = m.query('game', 'created').select('info').first()['info']
         conf = json.loads(info['config'])
         game_end_at = int(info['game_end_at'])

coopr_per_round.py

@@ -4,7 +4,8 @@ from island.match import Match
 from island.matches import Matches
 import numpy as np
 
-matches = Matches('wos-data-new-3')
+
+matches = Matches.from_profile_expr(lambda r: 'CLASSIC' in r)
 max_round = 15
 
 coopr = []
@@ -14,7 +15,7 @@ x = np.arange(1, max_round+1)
 bx = []
 
 survivals = {}
-with open('winner.json','r') as f:
+with open('survivals.json','r') as f:
     survivals = json.load(f)
 
 for i in range(max_round):
@@ -46,4 +47,4 @@ plt.figure()
 # plt.boxplot(bx, showmeans=True, meanline=True)
 plt.plot(x, coopr)
 plt.show()
-# plt.savefig('graph/co_per_round.png')
+# plt.savefig('graph/co_per_round.png')

fail_reason.py

@@ -3,11 +3,12 @@ from matplotlib import pyplot as plt
 from island.match import Match
 from island.matches import Matches
 
-matches = Matches('wos-data-new')
+mode = 'SURVIVE'
+matches = Matches.from_profile_expr(lambda r: mode in r)
 
 percents = [0.0, 0.0]
 
-op = 'C'
+op = 'D'
 
 def get_reason(m, i, target):
     r = m.query('action', 'request').where(lambda x: x['rno'] == i+1 and x['from'] == target).raw_data
@@ -52,11 +53,11 @@ percents[0] /= _all
 percents[1] /= _all
 
 
-labels = ['Insufficient Temporal Resource', 'Sufficient Temporal Resource']
+labels = ['Insufficient Time Resource', 'Sufficient Time Resource']
 
 plt.figure()
 plt.pie(percents, labels=labels, autopct="%1.2f%%", pctdistance=1.1, labeldistance=2,startangle=90, colors=['#00b894', '#fdcb6e'])
 plt.legend()
 plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
 # plt.show()
-plt.savefig("graph/fail_reason_%s.eps"%op)
+plt.savefig("graph/fail_reason_%s_%s.eps"%(op, mode))

fail_reason_chi2.py

@@ -1,6 +1,14 @@
 from scipy.stats import chi2_contingency as chi2
 import numpy as np
 
-obs = np.array([[159,90],[53,24]])
+obs = np.array([[203.0, 49.0], [279.0, 78.0]])
 chi,p,dof,expected = chi2(obs)
-print("%f, %f, %f" % (chi, p, dof))
+print("%f, %e, %f" % (chi, p, dof))
+
+'''
+classic
+chi = 0.381964, p = 0.536554
+
+survive
+chi = 23.490576, p = 1.255272e-06
+'''

k_and_new_partner.py

@@ -5,15 +5,15 @@ from island.matches import Matches
 import numpy as np
 from scipy.stats import pearsonr
 
+mode = 'CLASSIC'
+matches = Matches.from_profile_expr(lambda r: mode in r)
 
-matches = Matches('wos-data-new-2')
-
-k = np.arange(2, 11)
-succ = np.zeros(9)
-total = np.zeros(9)
+k = np.arange(5, 11)
+succ = np.zeros(10)
+total = np.zeros(10)
 
 survivals = {}
-with open('survivals-2.json', 'r') as f:
+with open('survivals.json', 'r') as f:
     survivals = json.load(f)
 
 neighbors = {}
@@ -73,10 +73,23 @@ for m_i in range(len(matches.data)):
                 else:
                     if r['a'] not in previous_round_partner:
                         new_partner_succ += 1
-            succ[len(neighborhood)-2] += new_partner_succ
-            total[len(neighborhood)-2] += new_partner_request
+            try:
+                succ[len(neighborhood) - 2] += new_partner_succ
+                total[len(neighborhood) - 2] += new_partner_request
+            except:
+                print("N!: %d" % len(neighborhood))
 
 
+
+print(succ,total)
+# for classic
+succ = succ[4:]
+total = total[4:]
+
+# for survival
+# succ = succ[:-1]
+# total = total[:-1]
+
 red = '#d63031'
 fig = plt.figure(figsize=(6.4, 4))
 ax = fig.gca()
@@ -105,9 +118,9 @@ ax2.tick_params(axis='y', labelcolor=red)
 ax2.set_ylim(0,1)
 fig.tight_layout()
 # plt.show()
-# plt.savefig('graph/k_and_new_partner.eps')
+plt.savefig("graph/k_and_new_partner_%s.eps" % mode)
 
 print("[succ vs k]pearson: %f, p-value: %f" % pearsonr(succ, k))
 print("[total vs k]pearson: %f, p-value: %f" % pearsonr(total, k))
 print("[rate vs k]pearson: %f, p-value: %f" % pearsonr(succ/total, k))
-print(np.average(succ/total))
+print(np.average(succ/total))

profile.csv

@@ -73,3 +73,9 @@ wos-data-compete/G480.json,,LAB,,COMM,,CLASSIC
 wos-data-compete/G481.json,,LAB,,COMM,,CLASSIC
 wos-data-compete/G482.json,,LAB,,COMM,,CLASSIC
 wos-data-compete/G483.json,,LAB,,COMM,,CLASSIC
+wos-data-compete/G484.json,,LAB,,COMM,,CLASSIC
+wos-data-compete/G485.json,,LAB,,COMM,,CLASSIC
+wos-data-compete/G487.json,,LAB,,COMM,,CLASSIC
+wos-data-compete/G488.json,,LAB,,COMM,,CLASSIC
+wos-data-compete/G490.json,,LAB,,COMM,,CLASSIC
+wos-data-compete/G491.json,,LAB,,COMM,,CLASSIC

rewiring_rate.py

@@ -10,13 +10,66 @@ from matplotlib import markers
 def error(f,x,y):
     return sp.sum((f(x)-y)**2)
 
-if __name__ == '__main__':
+def p1(x, rewires, tau, postfix, show):
+    fig = plt.figure(figsize=(6.4, 3.6))
+    ax = fig.gca()
+    ax.plot(x, rewires, color=green, linewidth=3)
+    ax.set_ylim(0, 0.5)
+    ax2 = ax.twinx()
+    ax2.plot(x, tau, color=red, linewidth=3)
+    ax2.set_ylim(0, 1440)
+    ax.set_xlim(1, 14)
+    ax.set_xlabel("Rounds")
+    ax.set_ylabel("Rewiring Rate", color=green)
+    ax.tick_params(axis='y', labelcolor=green)
+    ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
+    ax2.tick_params(axis='y', labelcolor=red)
 
-    matches = Matches('wos-data-new-2')
+    plt.tight_layout()
+    if show:
+        plt.show()
+    else:
+        plt.savefig("graph/tau_p_rewire_plot_%s.eps" % postfix)
+
+def p2(tau, rewires, postfix, show):
+    # # p2散点图
+    fig = plt.figure(figsize=(6.4, 3.6))
+    ax = fig.gca()
+    # ax.set_ylim(0.5, 1)
+    fp1,residuals,rank,sv,rcond = sp.polyfit(tau, rewires, 1, full=True)
+    print("残差：",residuals)
+    print('Model parameter:',fp1)
+    f1 = sp.poly1d(fp1)
+    print("error= %f" % error(f1, tau, rewires))
+    print("Other parameters: rank=%s, sv=%s, rcond=%s" % (str(rank), str(sv), str(rcond)))
+    # fx = sp.linspace(0,max(tau2),1000)
+    fx = sp.linspace(0,1440,2)
+
+    plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
+    plt.scatter(tau, rewires, color=green, linewidths=2, zorder=100)
+    # plt.scatter(tau_r, coopr_r, color='white', edgecolors=green, linewidths=2, zorder=101)
+    ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
+    ax.set_ylabel('Rewiring Rate')
+    ax.set_xlim(0, 1440)
+    ax.set_xticks(sp.linspace(0, 1440, 13))
+    ax.set_ylim(0, 0.6)
+    plt.tight_layout()
+    if show:
+        plt.show()
+    else:
+        plt.savefig("graph/tau_p_rewire_sca_%s.eps" % postfix)
+
+    # 皮尔逊相关系数
+    print("pearson: %f, p-value: %f" % pearsonr(tau, rewires))
+
+
+if __name__ == '__main__':
+    mode = 'CLASSIC'
+    matches = Matches.from_profile_expr(lambda r: mode in r)
     max_round = 15
 
     survivals = {}
-    with open('survivals-2.json', 'r') as f:
+    with open('survivals.json', 'r') as f:
         survivals = json.load(f)
 
     neighbors = {}
@@ -95,49 +148,21 @@ if __name__ == '__main__':
     red = '#d63031'
 
     # p1折线图
-    fig = plt.figure(figsize=(6.4, 3.6))
-    ax = fig.gca()
-    ax.plot(x, rewires, color=green, linewidth=3)
-    ax.set_ylim(0, 0.5)
-    ax2 = ax.twinx()
-    ax2.plot(x, tau, color=red, linewidth=3)
-    ax2.set_ylim(0,1440)
-    ax.set_xlim(1,14)
-    ax.set_xlabel("Rounds")
-    ax.set_ylabel("Rewiring Rate", color=green)
-    ax.tick_params(axis='y', labelcolor=green)
-    ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
-    ax2.tick_params(axis='y', labelcolor=red)
+    # p1(x, rewires, tau, mode, False)
+    p2(tau, rewires, mode, False)
+   
+'''
+classic
+残差： [ 0.05873797]
+Model parameter: [  9.81549075e-04  -9.87729952e-01]
+error= 0.058738
+Other parameters: rank=2, sv=[ 1.41291267  0.06064473], rcond=3.10862446895e-15
+pearson: 0.823000, p-value: 0.000300
 
-    plt.tight_layout()
-    plt.show()
-    # plt.savefig('graph/tau_p_rewire_plot.eps')
-
-    # # p2散点图
-    # fig = plt.figure(figsize=(6.4, 3.6))
-    # ax = fig.gca()
-    # # ax.set_ylim(0.5, 1)
-    # fp1,residuals,rank,sv,rcond = sp.polyfit(tau, rewires, 1, full=True)
-    # print("残差：",residuals)
-    # print('Model parameter:',fp1)
-    # f1 = sp.poly1d(fp1)
-    # print("error= %f" % error(f1, tau, rewires))
-    # # fx = sp.linspace(0,max(tau2),1000)
-    # fx = sp.linspace(0,1440,2)
-
-    # plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
-    # plt.scatter(tau, rewires, color=green, linewidths=2, zorder=100)
-    # # plt.scatter(tau_r, coopr_r, color='white', edgecolors=green, linewidths=2, zorder=101)
-    # ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
-    # ax.set_ylabel('Rewiring Rate')
-    # ax.set_xlim(0, 1440)
-    # ax.set_xticks(sp.linspace(0, 1440, 13))
-    # ax.set_ylim(0, 0.6)
-    # plt.tight_layout()
-    # plt.show()
-    # plt.savefig('graph/tau_p_rewire_sca.eps')
-
-
-
-    # # 皮尔逊相关系数
-    print("pearson: %f, p-value: %f" % pearsonr(tau, rewires))
+survive
+残差： [ 0.05788203]
+Model parameter: [ 0.00033232 -0.06283898]
+error= 0.057882
+Other parameters: rank=2, sv=[ 1.3284034   0.48512309], rcond=3.10862446895e-15
+pearson: 0.893237, p-value: 0.000017
+'''

tau_p_co.py

@@ -7,16 +7,83 @@ import scipy as sp
 from scipy.stats import pearsonr
 from matplotlib import markers
 
+'''
+计算tau_p和合作频率之间的关系
+'''
+
 def error(f,x,y):
     return sp.sum((f(x)-y)**2)
 
+def p1(x, coopr, tau, postfix, show=True):
+    fig = plt.figure(figsize=(6.4, 3.6))
+    ax = fig.gca()
+    ax.plot(x, coopr, color=blue, linewidth=3)
+    ax.set_ylim(0.5, 1)
+    ax2 = ax.twinx()
+    ax2.plot(x, tau, color=red, linewidth=3)
+    ax2.set_ylim(0, 1440)
+    ax.set_xlim(1, 14)
+    ax.set_xlabel("Rounds")
+    ax.set_ylabel("Frequency of Cooperation", color=blue)
+    ax.tick_params(axis='y', labelcolor=blue)
+    ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
+    ax2.tick_params(axis='y', labelcolor=red)
+
+    plt.tight_layout()
+    if show:
+        plt.show()
+    else:
+        plt.savefig("graph/tau_p_co_plot_%s.eps" % postfix)
+
+def p2(tau, coopr, limited, postfix, show=True):
+    tau2 = []
+    coopr2 = []
+    tau_r = []
+    coopr_r = []
+    for i in range(len(tau)):
+        if tau[i] <= limited:
+            tau2.append(tau[i])
+            coopr2.append(coopr[i])
+        else:
+            tau_r.append(tau[i])
+            coopr_r.append(coopr[i])
+
+    # p2散点图
+    fig = plt.figure(figsize=(6.4, 3.6))
+    ax = fig.gca()
+    # ax.set_ylim(0.5, 1)
+    fp1,residuals,rank,sv,rcond = sp.polyfit(tau2, coopr2, 1, full=True)
+    print("残差：",residuals)
+    print('Model parameter:',fp1)
+    print("Other parameters: rank=%s, sv=%s, rcond=%s"%(str(rank), str(sv), str(rcond)))
+    f1 = sp.poly1d(fp1)
+    print("error= %f" % error(f1, tau2, coopr2))
+    fx = sp.linspace(0, limited, 2)
+
+    plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
+    plt.scatter(tau2, coopr2, color=blue, linewidths=2, zorder=100)
+    plt.scatter(tau_r, coopr_r, color='white', edgecolors=blue, linewidths=2, zorder=101)
+    ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
+    ax.set_ylabel('Frequency of Cooperation')
+    ax.set_xlim(0, 1440)
+    ax.set_xticks(sp.linspace(0, 1440, 13))
+    ax.set_ylim(0.5, 1)
+    plt.tight_layout()
+    if show:
+        plt.show()
+    else:
+        plt.savefig("graph/tau_p_co_sca_%s.eps" % postfix)
+
+    # 皮尔逊相关系数
+    print("pearson: %f, p-value: %f" % pearsonr(tau2, coopr2))
+
 if __name__ == '__main__':
   
-    matches = Matches('wos-data-new-3')
+    matches = Matches.from_profile_expr(lambda r: 'SURVIVE' in r)
     max_round = 15
 
     survivals = {}
-    with open('survivals-3.json', 'r') as f:
+    with open('survivals.json', 'r') as f:
         survivals = json.load(f)
 
     neighbors = {}
@@ -101,65 +168,23 @@ if __name__ == '__main__':
     red = '#d63031'
 
     # p1折线图
-    # fig = plt.figure(figsize=(6.4, 3.6))
-    # ax = fig.gca()
-    # ax.plot(x, coopr, color=blue, linewidth=3)
-    # ax.set_ylim(0.5, 1)
-    # ax2 = ax.twinx()
-    # ax2.plot(x, tau, color=red, linewidth=3)
-    # ax2.set_ylim(0,1440)
-    # ax.set_xlim(1,14)
-    # ax.set_xlabel("Rounds")
-    # ax.set_ylabel("Frequency of Cooperation", color=blue)
-    # ax.tick_params(axis='y', labelcolor=blue)
-    # ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
-    # ax2.tick_params(axis='y', labelcolor=red)
+    # p1(x, coopr, tau, 'survive', False)
 
-    # plt.tight_layout()
-    # plt.show()
-    # # plt.savefig('graph/tau_p_co_plot.eps')
+    p2(tau[:12], coopr[:12], 720, 'survive', False)
 
-    tau2 = []
-    coopr2 = []
-    tau_r = []
-    coopr_r = []
-    tau3 = []
-    coopr3 = []
-    for i in range(len(tau)):
-        if tau[i] <= 720:
-            tau2.append(tau[i])
-            coopr2.append(coopr[i])
-            if tau[i] - 316 > 0.00001:
-                tau3.append(tau[i])
-                coopr3.append(coopr[i])
 
-        else:
-            tau_r.append(tau[i])
-            coopr_r.append(coopr[i])
+    '''
+    classic
+    残差： [ 0.00317365]
+    Model parameter: [ -1.24291981e-04   9.70766132e-01]
+    Other parameters: rank=2, sv=[ 1.41276801  0.06392615], rcond=2.6645352591e-15
+    error= 0.003174
+    pearson: -0.607866, p-value: 0.036010
 
-    # p2散点图
-    fig = plt.figure(figsize=(6.4, 3.6))
-    ax = fig.gca()
-    # ax.set_ylim(0.5, 1)
-    fp1,residuals,rank,sv,rcond = sp.polyfit(tau2, coopr2, 1, full=True)
-    print("残差：",residuals)
-    print('Model parameter:',fp1)
-    f1 = sp.poly1d(fp1)
-    print("error= %f" % error(f1, tau2, coopr2))
-    # fx = sp.linspace(0,max(tau2),1000)
-    fx = sp.linspace(0,720,2)
-
-    plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
-    plt.scatter(tau2, coopr2, color=blue, linewidths=2, zorder=100)
-    plt.scatter(tau_r, coopr_r, color='white', edgecolors=blue, linewidths=2, zorder=101)
-    ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
-    ax.set_ylabel('Frequency of Cooperation')
-    ax.set_xlim(0, 1440)
-    ax.set_xticks(sp.linspace(0, 1440, 13))
-    ax.set_ylim(0.5, 1)
-    plt.tight_layout()
-    plt.show()
-    # plt.savefig('graph/tau_p_co_sca.eps')
-
-    # 皮尔逊相关系数
-    print("pearson: %f, p-value: %f" % pearsonr(tau2, coopr2))
+    survive
+    残差： [ 0.00548837]
+    Model parameter: [ -2.71223484e-04   1.00851422e+00]
+    Other parameters: rank=2, sv=[ 1.36321571  0.37635479], rcond=1.99840144433e-15
+    error= 0.005488
+    pearson: -0.829102, p-value: 0.005723
+    '''