emmmm

.gitignore

@@ -3,4 +3,5 @@ graph
 wos-data-new
 wos-data-casual
 *.svg
-__pycache__
+__pycache__
+.vscode/

.vscode/settings.json

@@ -1,3 +1,3 @@
 {
-    "python.pythonPath": "/Users/wjsjwr/.pyenv/versions/anaconda3-5.0.1/bin/python"
+    "python.pythonPath": "C:\\Users\\wjs\\Anaconda3\\python.exe"
 }

coopr_per_round.py

@@ -2,14 +2,15 @@ import json
 from matplotlib import pyplot as plt
 from island.match import Match
 from island.matches import Matches
+import numpy as np
 
 matches = Matches('wos-data-new')
-max_round = 17
+max_round = 15
 
 coopr = []
 yerr_min = []
 yerr_max = []
-x = []
+x = np.arange(1, max_round+1)
 bx = []
 
 survivals = {}
@@ -36,10 +37,11 @@ for i in range(max_round):
         yerr_min.append(coopr[-1] - min(co))
         yerr_max.append(max(co) - coopr[-1])
         print("%f, %f, %f"%(yerr_min[-1], yerr_max[-1], coopr[-1]))
-        x.append(i+1)
+
 
 plt.figure()
 # plt.errorbar(x, coopr, yerr=[yerr_min, yerr_max], fmt='o', capsize=4)
-plt.boxplot(bx, showmeans=True, meanline=True)
-# plt.show()
-plt.savefig('graph/co_per_round.png')
+# plt.boxplot(bx, showmeans=True, meanline=True)
+plt.plot(x, coopr)
+plt.show()
+# plt.savefig('graph/co_per_round.png')

interactions_per_round.py

@@ -5,7 +5,7 @@ from island.matches import Matches
 import numpy as np
 
 matches = Matches('wos-data-new')
-max_round = 15
+max_round = 14
 
 coopr = []
 yerr_min = []
@@ -54,11 +54,11 @@ ax = fig.gca()
 ax.stackplot(x+1, y, labels=labels, colors=colors)
 ax.set_xticks(x+1)
 ax.set_xticklabels(x+1)
-ax.set_xlim(1,15)
+ax.set_xlim(1,max_round)
 ax.set_ylim(0, 1)
 ax.set_xlabel('Round')
 ax.set_ylabel('Fraction of k')
 ax.legend()
 plt.tight_layout()
-# plt.show()
-plt.savefig('graph/interaction_per_round.eps')
+plt.show()
+# plt.savefig('graph/interaction_per_round.eps')

k_and_new_partner.py

@@ -0,0 +1,113 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+import numpy as np
+from scipy.stats import pearsonr
+
+
+matches = Matches('wos-data-new')
+
+k = np.arange(2, 11)
+succ = np.zeros(9)
+total = np.zeros(9)
+
+survivals = {}
+with open('survivals.json', 'r') as f:
+    survivals = json.load(f)
+
+neighbors = {}
+for i in range(len(matches.data)):
+    m = matches.data[i]
+    n = {}
+    for r in m.query('neighbor', 'create').raw_data:
+        if r['a'] in n:
+            n[r['a']].append(r['b'])
+        else:
+            n[r['a']] = [r['b']]
+
+        if r['b'] in n:
+            n[r['b']].append(r['a'])
+        else:
+            n[r['b']] = [r['a']]    
+    neighbors[matches.names[i]] = n
+
+for m_i in range(len(matches.data)):
+    m = matches.data[m_i]
+    info = m.query('game', 'created').select('info').first()['info']
+    conf = json.loads(info['config'])
+    game_end_at = int(info['game_end_at'])
+
+    for p in m.query('player', 'join').raw_data:
+        pid = p['pid']
+        for i in range(2, game_end_at):
+            neighborhood = []
+            if pid not in neighbors[matches.names[m_i]]:
+                break
+            for j in neighbors[matches.names[m_i]][pid]:
+                if j in survivals[matches.names[m_i]][str(i-1)]:
+                    neighborhood.append(j)
+            if len(neighborhood) < 2:
+                break
+            previous_round_partner = []
+            for r in m.query('action', 'done').where(lambda x: x['rno']==i-1 and (x['a']==pid or x['b']==pid)).raw_data:
+                if r['a'] == pid:
+                    previous_round_partner.append(r['b'])
+                else:
+                    previous_round_partner.append(r['a'])
+            new_partner_request = 0
+            for r in m.query('action', 'request').where(lambda x: x['rno']==i and (x['from']==pid or x['to']==pid)).raw_data:
+                if r['from'] == pid:
+                    if r['to'] not in previous_round_partner:
+                        new_partner_request += 1
+                else:
+                    if r['from'] not in previous_round_partner:
+                        new_partner_request += 1
+            if new_partner_request == 0:
+                continue
+            new_partner_succ = 0
+            for r in m.query('action', 'done').where(lambda x: x['rno']==i and (x['a']==pid or x['b']==pid)).raw_data:
+                if r['a'] == pid:
+                    if r['b'] not in previous_round_partner:
+                        new_partner_succ += 1
+                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
+
+
+red = '#d63031'
+fig = plt.figure(figsize=(6.4, 4))
+ax = fig.gca()
+bar_width = 0.35
+opacity = 1
+error_config = {'ecolor': '0.3', 'capsize': 4}
+rects1 = ax.bar(k, succ, bar_width,
+                alpha=opacity, color='#00b894',
+                # yerr=c_req_suc_std, error_kw=error_config,
+                label='Success')
+rects3 = ax.bar(k + bar_width, total, bar_width,
+                alpha=opacity, color='#fdcb6e',
+                # yerr=d_req_suc_mean, error_kw=error_config,
+                label='Requests')
+
+ax.set_xlabel('k')
+ax.set_ylabel('Count')
+# ax.set_title('Scores by group and gender')
+ax.set_xticks(k + bar_width / 2)
+ax.set_xticklabels(k)
+ax.legend()
+ax2 = ax.twinx()
+ax2.plot(k, succ/total,linewidth=2,color=red, ls='--')
+ax2.set_ylabel("Frequency of new partners", family='sans-serif', color=red)
+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')
+
+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))

tau_p_co.py

@@ -0,0 +1,206 @@
+import json
+from matplotlib import pyplot as plt
+from island.match import Match
+from island.matches import Matches
+import numpy as np
+import scipy as sp
+from scipy.stats import pearsonr
+from matplotlib import markers
+
+def error(f,x,y):
+    return sp.sum((f(x)-y)**2)
+
+def get_tr(m, i, target, nb, sv):
+    """
+    # 计算tau_p的方式是统计周围邻居剩余的tr
+    # 本函数计算第i轮，所有有消耗tr的player，最后剩余的tr
+    # 由于是计算剩余tr，所以不能计算和target之间的交互
+    # nb是target的邻居
+    """
+    trs = {}
+    req = m.query('action', 'request').where(lambda x: x['rno'] == i+1)
+    app = m.query('action', 'approve').where(lambda x: x['rno'] == i+1).raw_data
+    can = m.query('action', 'cancel').where(lambda x: x['rno'] == i+1).raw_data
+    den = m.query('action', 'deny').where(lambda x: x['rno'] == i+1).raw_data
+
+    fr = []
+    for r in m.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == target or x['b'] == target)).raw_data:
+        if r['a'] == target:
+            fr.append(r['b'])
+        elif r['b'] == target:
+            fr.append(r['a']) 
+
+    truenb = []
+    for r in nb:
+        if r not in fr and r in sv:
+            truenb.append(r)
+            trs[r] = 1440
+
+    for r in req.raw_data:
+        if r['from'] in truenb and r['to'] != target :
+            if r['from'] not in trs:
+                trs[r['from']] = 1440
+            trs[r['from']] -= r['tr']
+
+    for r in app:
+        if r['from'] in truenb and r['to'] != target:
+            if r['from'] not in trs:
+                trs[r['from']] = 1440
+            trs[r['from']] -= r['tr']
+
+    for r in can:
+        if r['from'] in trs and r['to'] != target:
+            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']
+
+    for r in den:
+        if r['to'] in trs and r['from'] != target:
+            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']
+
+    return trs
+
+
+matches = Matches('wos-data-new')
+max_round = 15
+
+survivals = {}
+with open('survivals.json', 'r') as f:
+    survivals = json.load(f)
+
+neighbors = {}
+coopr = []
+x = np.arange(1, max_round)
+mwCo = {} # Match-wise frequency of cooperation
+mwTau = {} # Match-wise Tau
+bx = []
+tau = []
+
+for i in range(len(matches.data)):
+    m = matches.data[i]
+    n = {}
+    for r in m.query('neighbor', 'create').raw_data:
+        if r['a'] in n:
+            n[r['a']].append(r['b'])
+        else:
+            n[r['a']] = [r['b']]
+
+        if r['b'] in n:
+            n[r['b']].append(r['a'])
+        else:
+            n[r['b']] = [r['a']]    
+    neighbors[matches.names[i]] = n
+
+for i in range(max_round):
+    co = []
+    for j in range(len(matches.data)):
+        coop = 0
+        rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
+        for row in rows:
+            if row['act_a'] == 'C':
+                coop += 1
+            if row['act_b'] == 'C':
+                coop += 1
+
+        if rows:
+            co.append(float(coop) / float(len(rows)*2))
+            mwCo["%s-%d"%(j,i)] = co[-1]
+    bx.append(co)
+    if co:
+        coopr.append(np.average(co))
+
+
+for i in range(max_round-1):
+    tp = []
+    for j in range(len(matches.data)):
+        if i == 0:
+            for r in matches.data[j].query('player', 'join').raw_data:
+                t = 0
+                k = r['pid']
+                if k not in neighbors[matches.names[j]]:
+                    print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
+                else:
+                    t = 1440 * len(neighbors[matches.names[j]][k])
+                tp.append(t if t < 1440 else 1440)
+                mwTau["%s-%d"%(j,i)] = tp[-1]
+        else:
+            if str(i) not in survivals[matches.names[j]]:
+                continue
+            for k in survivals[matches.names[j]][str(i)]:
+                t = 0
+                if k not in neighbors[matches.names[j]]:
+                    print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
+                else:
+                    trs = get_tr(matches.data[j], i, k, neighbors[matches.names[j]][k], survivals[matches.names[j]][str(i)])
+                    for l in neighbors[matches.names[j]][k]:
+                        if l in trs:
+                            t += trs[l]
+                tp.append(t if t < 1440 else 1440)
+                mwTau["%s-%d"%(j,i)] = tp[-1]
+
+    if tp:
+        tau.append(np.average(tp))
+    else:
+        tau.append(0)
+
+
+blue = '#0984e3'
+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)
+
+# plt.tight_layout()
+# # plt.show()
+# plt.savefig('graph/tau_p_co_plot.eps')
+
+tau2 = []
+coopr2 = []
+tau_r = []
+coopr_r = []
+for i in range(len(tau)):
+    if tau[i] <= 720:
+        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)
+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))