放弃原来的rewiring rate(实际上是breaking rate)

br_plot.py

@@ -0,0 +1,75 @@
+import numpy as np
+from matplotlib import pyplot as plt
+import scipy as sp
+from scipy.stats import pearsonr
+from matplotlib import markers
+from sys import argv
+blue = '#0984e3'
+red = '#d63031'
+
+def error(f,x,y):
+    return sp.sum((f(x)-y)**2)
+
+def p2(e, rw, postfix, show=True, showline=True):
+    # p2散点图
+    fig = plt.figure(figsize=(4, 3))
+    ax = fig.gca()
+    if showline:
+        fp1,residuals,rank,sv,rcond = sp.polyfit(e, rw, 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, e, rw))
+        fx = sp.linspace(np.min(e), np.max(e), 2)
+        plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
+
+    plt.scatter(e, rw, color='white', edgecolors=blue, linewidths=2, zorder=101)
+    ax.set_xlabel(r'$TtD$', family='sans-serif', size=20)
+    ax.set_ylabel(r'Breaking Rate', family='sans-serif', size=20)
+    # ax.set_xlim(0, 1440)
+    # ax.set_xticks(sp.linspace(int(min(e)*0.9), int(max(e)*1.1), 4))
+    ax.tick_params(labelsize=14)
+    ax.set_ylim(0, 0.6)
+    ax.set_yticks(sp.linspace(0, 0.6, 5))
+    plt.tight_layout()
+    if show:
+        plt.show()
+    else:
+        plt.savefig("graph/eid_co_sca_%s.eps" % postfix)
+
+    # 皮尔逊相关系数
+    print("pearson: %f, p-value: %f" % pearsonr(e, rw))
+
+def p2c(e,c,m,s):
+    p2(e,c,m,s,False)
+
+def plot(mode, show):
+    rw = np.loadtxt("outputs/BR_%s.csv" % mode, delimiter=',')
+    e = np.loadtxt("outputs/EID_%s.csv" % mode, delimiter=',')
+
+    p2(e[:-1], rw[1:], mode, show)
+
+    # if mode == 'SURVIVE':
+    #     p2(e[:-1], rw[1:], mode, show)
+    # else:
+    #     p2c(e[:-1], rw[1:], mode, show)
+
+if __name__ == '__main__':
+    # eid_plot c/s 1/2 t/f
+    mode = 'CLASSIC' if argv[1] == 'c' else 'SURVIVE'
+    show = argv[2] == 't'
+    plot(mode, show)
+
+    """
+SURVIVE
+残差： [ 0.06081364]
+Model parameter: [ -3.54739130e-04   4.41956696e-01]
+Other parameters: rank=2, sv=[ 1.40523958  0.15906514], rcond=3.10862446895e-15
+error= 0.060814
+pearson: -0.837958, p-value: 0.000183
+
+
+CLASSIC
+pearson: 0.384519, p-value: 0.174626
+    """

breaking_rate.py

@@ -0,0 +1,27 @@
+import csv
+import numpy as np
+from island.match import Match
+from island.matches import Matches
+
+def calc_and_save(mode):
+    matches = Matches.from_profile(mode)
+    ans = np.zeros(15)
+    div = np.zeros(15)
+    for m in matches.data:
+        maxr = int(m.query('game', 'created').first()['info']['game_end_at'])
+        for i in range(1, maxr):
+            rows = m.query('action', 'done').where(lambda x: x['rno']==i).raw_data
+            br = 0
+            for r in rows:
+                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:
+                    br += 1
+            ans[i] += br
+            div[i] += len(rows)
+    div[np.where(div==0)[0]] = 1
+    ans /= div
+    with open("outputs/BR_%s.csv"%mode, 'w') as f:
+        csv.writer(f).writerow(ans)   
+
+if __name__ == '__main__':
+    calc_and_save('CLASSIC')
+    calc_and_save('SURVIVE')

outputs/BR_CLASSIC.csv

@@ -0,0 +1 @@
+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

outputs/BR_SURVIVE.csv

@@ -0,0 +1 @@
+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

outputs/EID_CLASSIC.csv

@@ -1 +1 @@
-108.837209302,-33.488372093,-66.976744186,8.37209302326,-64.1860465116,-78.1395348837,-61.3953488372,-29.3023255814,-118.604651163,-127.741935484,-198.445229682,-110.769230769,-176.115107914,-564.705882353,0.0
+108.837209302,-33.488372093,-66.976744186,8.37209302326,-64.1860465116,-78.1395348837,-61.3953488372,-29.3023255814,-118.604651163,-127.741935484,-198.445229682,-110.769230769,-176.115107914,-564.705882353,0.0

outputs/EID_SURVIVE.csv

@@ -1 +1 @@
--737.628803245,-800.8735363,-1231.79775281,-1322.91139241,-1377.45454545,-1382.1686747,-1373.72384937,-1433.71179039,-1426.54205607,-1408.92086331,-1440.0,-1440.0,-600.0,-1440.0,0.0
+-737.628803245,-800.8735363,-1231.79775281,-1322.91139241,-1377.45454545,-1382.1686747,-1373.72384937,-1433.71179039,-1426.54205607,-1408.92086331,-1440.0,-1440.0,-600.0,-1440.0,0.0

rewiring_rate.py

@@ -1,196 +0,0 @@
-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 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(2, 15)
-    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)
-
-    plt.tight_layout()
-    if show:
-        plt.show()
-    else:
-        plt.savefig("graph/tau_p_rewire_plot_%s.eps" % postfix)
-
-
-def p2c(tau, rewires, show):
-    # # p2散点图
-    fig = plt.figure(figsize=(4, 3))
-    ax = fig.gca()
-
-    plt.scatter(tau, rewires, color=green, linewidths=2, zorder=100)
-    ax.set_xlabel('$\\tau_{f}$', family='sans-serif', size=20)
-    ax.set_ylabel('Rewiring Rate', size=20)
-    ax.set_xlim(0, 1440)
-    ax.set_ylim(0, 0.6)
-    ax.set_xticks(sp.linspace(0, 1440, 5))
-    ax.tick_params(labelsize=14)
-    ax.set_ylim(0, 0.6)
-    ax.set_yticks(sp.linspace(0, 0.6, 5))
-    plt.tight_layout()
-    if show:
-        plt.show()
-    else:
-        plt.savefig("graph/tau_f_rewire_sca_c.eps")
-
-    # 皮尔逊相关系数
-    print("pearson: %f, p-value: %f" % pearsonr(tau, rewires))
-
-def p2(tau, rewires, postfix, show):
-    # # p2散点图
-    fig = plt.figure(figsize=(4, 3))
-    ax = fig.gca()
-    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('$\\tau_{f}$', family='sans-serif', size=20)
-    ax.set_ylabel('Rewiring Rate', size=20)
-    ax.set_xlim(0, 1440)
-    ax.set_ylim(0, 0.6)
-    ax.set_xticks(sp.linspace(0, 1440, 5))
-    ax.tick_params(labelsize=14)
-    ax.set_ylim(0, 0.6)
-    ax.set_yticks(sp.linspace(0, 0.6, 5))
-    plt.tight_layout()
-    if show:
-        plt.show()
-    else:
-        plt.savefig("graph/tau_f_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.json', 'r') as f:
-        survivals = json.load(f)
-
-    neighbors = {}
-    rewires = []
-    x = np.arange(2, max_round+1)
-    mwRe = {} # Match-wise frequency of rewiring
-    mwTau = {} # Match-wise Tau
-    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(1, max_round):
-        re = []
-        for j in range(len(matches.data)):
-            rewire = 0
-            rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
-            for r in rows:
-                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:
-                    rewire += 1
-
-            if rows:
-                re.append(float(rewire) / float(len(rows)*2))
-                mwRe["%s-%d"%(j,i)] = re[-1]
-        if re:
-            rewires.append(np.average(re))
-
-
-    for i in range(1, max_round):
-        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 = matches.data[j].get_tr(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 and trs[l] > 0:
-                                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)
-
-
-    green = '#00b894'
-    red = '#d63031'
-
-    # p1折线图
-    # p1(x, rewires, tau, mode, True)
-    p2c(tau, rewires, False)
-    # p2(tau[:12], rewires[:12], 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: -0.507660, p-value: 0.063859
-
-survive
-残差： [ 0.00291864]
-Model parameter: [ 0.0001647  -0.02264606]
-error= 0.002919
-Other parameters: rank=2, sv=[ 1.33444456  0.46824962], rcond=2.6645352591e-15
-pearson: 0.947474, p-value: 0.000003
-'''