add decision time

calc_participants.py

@@ -6,12 +6,17 @@ from island.matches import Matches
 
 result = 0
 count = 0
+ma,mi=0,1000
 
 # ms = Matches.from_profile('CCCN')
 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)
+    l = len(m.query('player', 'join').where(lambda x: 'bot' not in x or x['bot'] == False).select('pid').raw_data)
+    ma = max([ma,l])
+    mi = min([mi,l])
+    result += l
     count += 1
+print(ma,mi)
 
 print(result)
 print("participants: %d, matches: %d, avg: %f" % (result, count, result / count))

count_decision_time.py

@@ -0,0 +1,88 @@
+"""
+计算决策时间以及最终博弈对象
+A:平均决策时间是用户每轮的总操作时间/最后建立博弈关系的数量
+A:最大博弈对手数量是45秒/平均决策时间
+Q:那平均决策时间就是从一轮开始，到这一轮中最后一个操作完成之间的间隔对吗
+A:对的
+Q:具体比方说A在10秒的时候发出了请求，对面B5秒确认，那么A的决策时长是10秒还是15秒，B的决策时长是5秒还是15秒呢？
+A:A是10秒，B是15秒
+"""
+import csv
+import json
+import numpy as np
+from island.matches import Matches
+
+# type
+R_START = 0
+A_SEND = 1
+A_RECEIVE = 2
+A_REPLY = 3
+A_REPLY_RECEIVE = 5
+A_COUNT = 4
+
+"""
+k -> "game:player:round"
+v -> [(type,value)]
+"""
+kvstore = {}
+
+ms = Matches.from_profile_expr(lambda r: True)
+for m in ms.data:
+    info = m.query('game', 'created').select('info').first()['info']
+    game_end_at = int(info['game_end_at'])
+    last_ts = int(info['next_start']) + 45
+    round_start_ts = m.query('game', 'sunrise').orderby('created_at').raw_data
+    for rno in range(1, game_end_at+1):
+        for r in m.query('action', None).where(lambda x: x['rno'] == rno).orderby('created_at').raw_data:
+            if r['act'] == 'request':
+                key = "%s:%d:%d" % (m.name, r['from'], rno)
+                if key not in kvstore:
+                    kvstore[key] = [(R_START, last_ts)]
+                kvstore[key].append((A_SEND, r['created_at']))
+                key = "%s:%d:%d" % (m.name, r['to'], rno)
+                if key not in kvstore:
+                    kvstore[key] = [(R_START, last_ts)]
+                kvstore[key].append((A_RECEIVE, r['created_at']))
+            elif r['act'] == 'approve' or r['act'] == 'deny':
+                kvstore["%s:%d:%d" % (m.name, r['from'], rno)].append((A_REPLY, r['created_at']))
+                kvstore["%s:%d:%d" % (m.name, r['to'], rno)].append((A_REPLY_RECEIVE, r['created_at']))
+            elif r['act'] == 'done':
+                kvstore["%s:%d:%d" % (m.name, r['a'], rno)].append((A_COUNT, r['created_at']))
+                kvstore["%s:%d:%d" % (m.name, r['b'], rno)].append((A_COUNT, r['created_at']))
+        if rno-1 >= len(round_start_ts):
+            break
+        else:
+            last_ts = round_start_ts[rno-1]['created_at']
+
+result = []
+total_decision_time = 0
+for rk, r in kvstore.items():
+    dt = 0
+    cur = 0
+    cnt = 0
+    for k,v in r:
+        if k == R_START:
+            dt = v
+        elif k == A_SEND:
+            cur = v
+        elif k == A_REPLY:
+            cur = v
+        elif k == A_COUNT:
+            cnt += 1
+    if cur == 0:
+        continue
+    else:
+        if cnt <= 1:
+            dt = cur - dt
+        else:
+            dt = (cur - dt) / cnt
+        if dt * (cnt if cnt > 0 else 1) > 45:
+            print("???%s => %f" % (rk, dt))
+            continue
+    total_decision_time += dt
+    result.append([dt, cnt])
+
+print("Total decision: %d, time: %d, avg: %f" % (len(result), total_decision_time, total_decision_time / len(result)))
+#Total decision: 9625, time: 136406, avg: 14.172055
+with open('outputs/decision.csv', 'w') as f:
+    csv.writer(f).writerows(result)

count_neighbors.py

@@ -0,0 +1,18 @@
+# from G254
+import json
+from pathlib import Path
+from island.match import Match
+from island.matches import Matches
+
+count = 0
+players = set()
+
+# ms = Matches.from_profile('CCCN')
+ms = Matches.from_profile_expr(lambda r: True)
+for m in ms.data:
+    for r in m.query('neighbor', 'create').raw_data:
+        count += 2
+        players.add(r['a'])
+        players.add(r['b'])
+
+print("participants: %d, degree: %d, avg: %f" % (len(players), count, count / len(players)))

decision_time.py

@@ -0,0 +1,47 @@
+import csv
+from math import floor
+import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib import colors
+from matplotlib import cm
+
+data = np.loadtxt('outputs/decision.csv', delimiter=',')
+# print(data)
+print(data.shape)
+ndata = np.zeros(shape=(6, 45))
+for i in range(data.shape[0]):
+    ans = floor(data[i, 0]) - 1
+    if ans < 0:
+        ans = 0
+    elif ans >= 45:
+        ans = 44
+    ndata[int(data[i, 1]),  ans] += 1
+
+print(ndata[4,11])
+
+
+for i in range(ndata.shape[0]):
+    for j in range(ndata.shape[1]):
+        if ndata[i,j] == 0:
+            ndata[i,j] = np.nan
+
+# ndata = np.log(ndata+1)
+# ndata += 1
+fig = plt.figure()
+ax = fig.gca()
+# ax.set_xlabel()
+ax.set_facecolor('white')
+ax.set_xticks(np.arange(0,45,11))
+colors = cm.get_cmap('OrRd')
+colors.set_bad('white')
+ax.set_xticklabels(np.arange(1, 46, 11))
+dct = ax.imshow(ndata, cmap=colors, origin='lower')#, norm=colors.LogNorm())
+
+ax.plot(np.arange(7, 45), np.floor(45 / np.arange(8, 46)),
+        marker='x', color='black', markersize=3, linestyle='none', markeredgewidth=0.5)
+ax.set_xlabel("Decision time")
+ax.set_ylabel("Number of opponents")
+fig.colorbar(dct, ax=ax, orientation='horizontal', aspect=40)
+
+# plt.show()
+plt.savefig('graph/decision_time.eps')

island/match.py

@@ -22,14 +22,15 @@ class Match:
         """
         start a query
         eg. match.query('game', 'created')
+        if cat or act is `None', the corresponding field matches anything
         :param cat: category
-        "param act: action
+        :param act: action
         """
         if self.dtype == 'mid':
             raise RuntimeError("Query on intermediate result.")
         result = []
         for entry in self.raw_data:
-            if entry['cat'] == cat and entry['act'] == act:
+            if (cat == None or entry['cat'] == cat) and (act == None or entry['act'] == act):
                 result.append(entry)
         return Match(result, dtype='mid')
 

plot_cluster_life.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
         for k in sorted(map(int, raw.keys())):
             idx.append(k)
             data.append(np.array(raw[str(k)]))
-    fig = plt.figure(figsize=(3,3))
+    fig = plt.figure(figsize=(4.5,3))
     ax = fig.gca()
     bp = ax.boxplot(data, labels=idx)#, boxprops=dict(facecolor='lightblue'))
     ax.plot(np.arange(1, len(idx)+1), [np.mean(r) for r in data], '^-', color='green', linewidth=1, alpha=0.8)
@@ -34,4 +34,5 @@ if __name__ == "__main__":
         boxPolygon = Polygon(boxCoords, facecolor='lightblue')
         ax.add_patch(boxPolygon)
     fig.tight_layout()
-    plt.show()
+    # plt.show()
+    plt.savefig("graph/cluster_%d_life.eps" % cb)