new fig

break_tie.py

@@ -3,7 +3,7 @@ from matplotlib import pyplot as plt
 from island.match import Match
 from island.matches import Matches
 
-mode = 'SURVIVE'
+mode = 'CLASSIC'
 matches = Matches.from_profile_expr(lambda r: mode in r)
 
 labels = ['Stay Connected', 'Break Tie']
@@ -40,11 +40,12 @@ print(percents)
 _all = sum(percents) / 100
 percents[0] /= _all
 percents[1] /= _all
+print(percents)
 
 
 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/break_tie_%s_%s.eps"%(op, mode))
+plt.show()
+# plt.savefig("graph/break_tie_%s_%s.eps"%(op, mode))

break_tie_bar.py

@@ -0,0 +1,44 @@
+from scipy.stats import chi2_contingency as chi2
+import numpy as np
+from matplotlib import pyplot as plt
+
+'''
+survive
+'''
+break_tie = [13.717872086072923, 86.26126126126125]
+stay_connected = [86.28212791392707, 13.738738738738737]
+'''
+classic
+'''
+# break_tie = [8.331150117893634, 42.07858048162231]
+# stay_connected = [91.66884988210636, 57.9214195183777]
+
+mode = 'SURVIVE'
+
+fig = plt.figure(figsize=(3, 3))
+ax = fig.gca()
+
+index = np.arange(2)
+bar_width = 0.35
+
+opacity = 1
+error_config = {'ecolor': '0.3'}
+
+rects1 = ax.bar(index, break_tie, bar_width,
+                alpha=opacity, color='#fdcb6e',
+                label='Break Tie')
+
+rects2 = ax.bar(index + bar_width, stay_connected, bar_width,
+                alpha=opacity, color='#00b894',
+                label='Stay Connected')
+
+
+ax.set_ylabel('Frequency of Behavior')
+ax.set_title('Behavior after Moves')
+ax.set_xticks(index + bar_width / 2)
+ax.set_xticklabels(('C', 'D'))
+ax.set_ylim(0,100)
+fig.legend(loc='lower center')
+fig.tight_layout()
+# plt.show()
+plt.savefig("graph/break_tie_bar_%s.eps" % (mode))

break_tie_chi2.py

@@ -9,7 +9,7 @@ print("%f, %e, %f" % (chi, p, dof))
 survive:
 chi = 1189.53, p = 1.149752e-260
 
-survive:
+classic:
 chi = 611.59, p = 5.031232e-135
 
 '''

food_loss.py

@@ -0,0 +1,75 @@
+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
+
+
+class Solution:
+    def __init__(self, mode):
+        self.mode = mode
+        self.matches = Matches.from_profile_expr(lambda r: mode in r)
+
+    def food_loss(self):
+        fl = [[],[]]
+        payoff = {
+            'C': {
+                'C': 2.6,
+                'D': 0
+            },
+            'D': {
+                'C': 4.5,
+                'D': 0.5
+            }
+        }
+        for m in self.matches.data:
+            info = m.query('game', 'created').select('info').first()['info']
+            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):
+                    c, t = 0, 0
+                    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['act_a' if r['a'] == pid else 'act_b'] == 'C':
+                            c += 1
+                        t += 1
+                    is_coop = 0 if c * 2 >= t else 1
+                    f = -3.0
+                    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:
+                            f += payoff[r['act_a']][r['act_b']] * r['tr'] / 1440.0
+                        else:
+                            f += payoff[r['act_b']][r['act_a']] * r['tr'] / 1440.0
+                    fl[is_coop].append(f)
+        return fl
+
+
+if __name__ == '__main__':
+    mode = 'SURVIVE'
+    s = Solution(mode)
+    f = s.food_loss()
+    blue = '#0984e3'
+    red = '#d63031'
+    c = [blue, red]
+    labels = ['C', 'D']
+    fig = plt.figure(figsize=(3, 3))
+    ax = fig.gca()
+    error_config = {'ecolor': '0.3', 'capsize': 4}
+    bplot = ax.boxplot(f,
+                        vert=True,  # vertical box alignment
+                        patch_artist=True,  # fill with color
+                        notch=True,  # notch shape
+                        labels=labels)  # will be used to label x-ticks
+
+    for patch, color in zip(bplot['boxes'], c):
+        patch.set_facecolor(color)
+    ax.set_ylabel('Frequency')
+    # ax.set_title('Scores by group and gender')
+
+    ax.legend()
+
+    fig.tight_layout()
+    plt.show()
+    # plt.savefig("graph/new_partner_bar_%s.eps" % mode)

new_partner_bar.py

@@ -0,0 +1,109 @@
+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
+
+class Solution:
+    def __init__(self, mode):
+        self.mode = mode
+        self.matches = Matches.from_profile_expr(lambda r: mode in r)
+        self.survivals = {}
+        with open('survivals.json', 'r') as f:
+            self.survivals = json.load(f)
+        self.neighbors = {}
+        self.read_neighbor()
+
+    def read_neighbor(self):
+        for i, m in enumerate(self.matches.data):
+            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']]
+            self.neighbors[self.matches.names[i]] = n
+
+    def new_partner_after(self):
+        re_after = [0] * 2 #[c,d]
+        su_after = [0] * 2
+        for m in self.matches.data:
+            info = m.query('game', 'created').select('info').first()['info']
+            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):
+                    previous_round_partner = []
+                    c,t = 0,0
+                    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'])
+                            if r['act_a'] == 'C':
+                                c += 1
+                        else:
+                            previous_round_partner.append(r['a'])
+                            if r['act_b'] == 'C':
+                                c += 1
+                        t += 1
+                    is_coop = 0 if c * 2 >= t else 1
+
+                    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
+                    re_after[is_coop] += new_partner_request                        
+                    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
+                    su_after[is_coop] += new_partner_succ
+        for i, _ in enumerate(su_after):
+            su_after[i] /= re_after[i]
+        return su_after
+
+
+if __name__ == '__main__':
+    mode = 'CLASSIC'
+    s = Solution(mode)
+    su_after = s.new_partner_after()
+    index = np.arange(2)
+    
+    blue = '#0984e3'
+    red = '#d63031'
+
+    fig = plt.figure(figsize=(3, 3))
+    ax = fig.gca()
+    bar_width = 0.5
+    opacity = 1
+    error_config = {'ecolor': '0.3', 'capsize': 4}
+    rects1 = ax.bar(index, su_after, bar_width,
+                    color=red, label='New Patner')
+
+    ax.set_ylabel('Frequency')
+    # ax.set_title('Scores by group and gender')
+    ax.set_xticks(index)
+    ax.set_xticklabels(['C', 'D'])
+    ax.set_ylim(0,1)
+    ax.set_xlim(-0.5,1.5)
+    ax.legend()
+
+    fig.tight_layout()
+    # plt.show()
+    plt.savefig("graph/new_partner_bar_%s.eps" % mode)