randam.choicesのKeyErrorの解決方法について

大学の卒業研究でマルチエージェントシミュレーションを用いて学級モデルを構築しています。
生徒は想像している人間関係、他の生徒への好感度、クラス内での影響力の３つの設定を持っています。
シミュレーションの流れは、
会話相手（1人）の選択→話題対象の選択→好感度の更新（2種類）→関係の構築
→影響力の更新→想像している人間関係の更新
以上の流れで行います

これを再現するために次のコードを組みました

python
1import numpy as np
2import random 
3
4a = 0.4 
5b = 0.4
6c = 0.2
7v1 = 0.3
8v2 = 0.3
9v3 = 0.6
10w1 = 0.2
11w2 = 0.2
12w3 = 0.6
13ap = 0.3
14rm = 0.25
15q = 0.3
16issues =10
17
18
19def sign(x,y):
20  if x*y>0:
21    sign = 1
22  if x*y == 0:
23    sign = 0
24  if x*y<0:
25    sign = -1
26  return sign
27
28def gij(x,y,z):
29  change = v1*(sign(x,y)*(abs(x*y)**0.5)-z)
30  return change
31
32
33class Agent:
34  def __init__(self,agent_id):
35    self.id = agent_id
36    self.talk = []
37    self.topic = []
38    self.f = np.random.uniform(low=0.0, high=0.1, size=None) #影響力
39    self.s = [0] #影響力更新で使用
40    self.frind_list = [] #友人リンク
41    self.hi_friend_list = [] #被友人リンク
42    self.exclusion_list = [] #排斥リンク
43    self.hi_exclusion_list = [] #被排斥リンク
44    self.bullying_list = [] #いじめリンク
45    self.hi_bullying_list = [] #被いじめリンク
46    self.lp = {} #自分からuへの好感度
47    self.lc = {} #実際の好感度
48    self.lagent = {} #自分が思うuからの好感度
49    self.lpi = {}  #自分が思うuからjへの好感度
50    self.lx = {}  #自分の話題xへの好感度
51    self.lxagent = {}  #自分が思うuの話題xへの好感度
52
53  def First_set(self,agents): #初期設定
54    issues = 10
55    for agent in agents:
56      kari = {}
57      if agent is self:
58        continue
59      likability = np.random.uniform(low=-0.1, high=0.1, size=None)
60      self.lp[agent.id] = likability
61      self.lc[agent.id] = likability
62      like = np.random.uniform(low=-0.1, high=0.1, size=None)
63      self.lagent[agent.id] = like
64      for t in agents:
65        if t is agent:
66          continue
67        interested = np.random.uniform(low=-0.1, high=0.1, size=None)
68        kari[t.id] = interested
69      self.lpi[agent.id] = kari
70
71    for issue in range(issues):
72      instinct = np.random.uniform(low=-0.1, high=0.1, size=None)
73      self.lx[issue] = instinct
74      kari = {}
75      for agent in agents:
76        if issue is agent:
77          continue
78        instinct = np.random.uniform(low=-0.1, high=0.1, size=None)
79        kari[agent.id] = instinct
80      self.lxagent[issue] = kari
81
82  def decide_talk_man(self,agents): #会話相手の選択
83    agents_except_me = {}
84    weights_for_me = []
85    z = 0
86    for agent in agents:
87      if agent is self:
88        continue
89      h = a*self.lp[agent.id]+b*self.lagent[agent.id]*(1-(self.f-agent.f))+c*(self.f-agent.f)
90      h = (h+0.8)/1.6 #正規化
91      z += h
92    for agent in agents:
93      if agent is self:
94        continue
95      h = a*self.lp[agent.id]+b*self.lagent[agent.id]*(1-(self.f-agent.f))+c*(self.f-agent.f)
96      h = (h+0.8)/1.6 #正規化
97      weight = h / z
98      agents_except_me[agent.id] = agent.id
99      weights_for_me.append(weight)
100    self.talk = random.choices(agents_except_me, weights=weights_for_me)
101    print(self.talk)
102    return self.talk
103
104  def decide_topic(self,agents,talk): #話題対象の選択
105    talks = self.talk[0]
106    topic_except_me = {}
107    weights_for_me = []
108    zz = 0
109
110    for agent in agents:
111      if agent is self:
112        continue
113      if agent.id == talks:
114        for age in agents:
115          talk_f = agent.f
116          if age is agent or age is self:
117            continue
118          zz += abs(self.lp[age.id]*self.f+self.lpi[agent.id][age.id]*talk_f)
119
120    for issue in range(issues):
121      for agent in agents:
122        if agent is self:
123          continue
124        if agent.id ==talks:
125          zz += abs(self.lx[issue]*self.f+self.lxagent[issue][agent.id]*talk_f)
126
127    for agent in agents:
128      if agent is self:
129        continue
130      if agent.id == talks:
131        for age in agents:
132          if age is agent or age is self:
133            continue
134          r = abs(self.lp[age.id]*self.f+self.lpi[agent.id][age.id]*talk_f)/zz
135          topic_except_me[age.id] = age.id
136          weights_for_me.append(r)
137
138    for issue in range(issues): 
139      for agent in agents:
140        if agent is self:
141          continue
142        if agent.id ==talks:
143          r = abs(self.lx[issue]*self.f+self.lxagent[issue][agent.id]*talk_f)/zz
144          issue += 31
145          topic_except_me[issue] = issue
146          weights_for_me.append(r)
147
148    self.topic = random.choices(topic_except_me, weights=weights_for_me)
149    print(self.topic)
150    return self.topic
151    
152  def update_like1(self,agents,talk,topic): #好感度更新１
153    talks = talk[0]
154    topics = topic[0] 
155    if topics <= 30:
156      x = self.lp[talks]
157      y = self.lp[topics]
158      z = self.lpi[talks][topics]
159    else:
160      topics -= 31
161      x = self.lx[topics]
162      y = self.lp[talks]
163      z = self.lxagent[topics][talks]
164    l = x*y*z
165    if l>=0:
166      dlu1 = gij(y,z,x)
167      dlx1 = gij(x,z,y)
168    else:
169      if x >= y:
170        dlu1 = gij(x,z,0)
171        dlx1 = 0
172      else:
173        dlu1 = 0
174        dlx1 = gij(y,z,0)
175    for agent in agents:
176      if agent.id is talks:
177        r = w1*self.lp[talks]+w2*self.lagent[talks]*(1-(self.f-agent.f))+w3*(self.f-agent.f)
178        r = (r+0.8)/1.6
179        break      
180    weights_for_stra = {r,1-r}
181    kinds_of_stra = ["mini","same"]
182    stra = random.choices(kinds_of_stra, weights=weights_for_stra)
183    if stra is "mini":
184      dlu2 = v2*(self.lagent[talks]-self.lp[talks])
185    else:
186      dlu2 = v3*sign(1,(self.lagent[talks]))*(abs(self.lagent[talks]+self.lp[talks]))
187    
188    dlu = dlu1+dlu2
189    dlx = dlx1
190    return dlu,dlx
191
192  def update_like2(self,talk,topic,dlu,dlx): #好感度更新2
193    talks = self.talk[0]
194    topics = self.topic[0] 
195    self.lp[talks] = self.lp[talks]+dlu
196    self.lc[talks] = self.lp[talks]
197    if topics <= 30:
198      self.lp[topics] += dlx
199
200      if self.lp[topics] > 1.0:
201        self.lp[topics] = 1.0
202      elif self.lp[topics] < -1.0:
203        self.lp[topics] = -1.0
204    else:
205      topics -= 31
206      self.lx[topics] += dlx
207
208      if self.lx[topics] > 1.0:
209        self.lx[topics] = 1.0
210      elif self.lx[topics] < -1.0:
211        self.lx[topics] = -1.0
212    
213  
214  def link(self,agents): #関係の構築
215    for agent in agents:
216      if agent is self:
217        continue
218      if self.lc[agent.id] >= 0.12:
219        self.frind_list.append(agent.id)
220        agent.hi_friend_list.append(self.id)
221      elif self.lc[agent.id] <= -0.04:
222        self.exclusion_list.append(agent.id)
223        agent.hi_exclusion_list.append(self.id)
224      else:
225        pass
226
227
228  def update_f(self,turn): #影響力の更新
229    self.s.append(len(self.hi_friend_list)-len(self.hi_exclusion_list))
230    dr = rm*(self.s[turn+1]-self.s[turn])
231    self.f += dr
232
233    if  self.f > 1.0:
234      self.f = 1.0
235    elif  self.f < 0:
236      self.f = 0
237
238
239  def updete_Pnet(self,agents): #自分が想像している人間関係の更新
240    for agent in agents:
241      for age in agents:
242        if agent is age or self is agent:
243          continue
244        E = np.random.uniform(low=-0.3, high=0.3, size=None)
245        dlp = ap*(agent.lc[age.id]-self.lpi[agent.id][age.id])+E
246        self.lpi[agent.id][age.id] += dlp
247
248        if  self.lpi[agent.id][age.id] > 1.0:
249           self.lpi[agent.id][age.id] = 1.0
250        elif  self.lpi[agent.id][age.id] < -1.0:
251           self.lpi[agent.id][age.id] = -1.0
252
253
254class Simulation:
255  def __init__(self):
256    self.agents = []
257
258  def generate_agents(self, agents_num):
259    self.agents = [Agent(agent_id) for agent_id in range(agents_num)]
260    for agent in self.agents:
261      agent.First_set(self.agents)
262
263  def play_the_game(self,episode):
264    num_agents = 31
265    turns = 3000
266    self.generate_agents(num_agents) 
267    for turn in range(turns):
268      print(turn,"ターン目")
269      for agent in self.agents:
270        print(agent)
271        self.talk = agent.decide_talk_man(self.agents)
272        self.topic = agent.decide_topic(self.agents,self.talk)
273        dlu,dlx = agent.update_like1(self.agents,self.talk,self.topic)
274        agent.update_like2(self.talk,self.topic,dlu,dlx)
275        agent.link(self.agents)
276        agent.update_f(turn)
277        agent.updete_Pnet(self.agents)
278
279    
280
281
282simulation = Simulation()
283simulation.play_the_game(31)
284for agent in simulation.agents:
285    print("Likabilities for Agent", agent.id, ":")
286    for agent_id in agent.lc:
287      print("    agent", agent_id, ":", agent.lc[agent_id])
288    for issue in range(issues):
289      print("    issue", issue, ":", agent.lx[issue])
290    print("    frind", ":", agent.frlist)
291    print()
292

これを実行したところ動かすことはできるように見えるののですが、
self.talk = random.choices(agents_except_me, weights=weights_for_me)
self.topic = random.choices(topic_except_me, weights=weights_for_me)
この二つの箇所で毎回違う種類のエラーがでます。
そのエラーはKeyError: 13というようにKeyError: （数字）がでます。
このエラーを出さないようにするためにはどのようにコードを修正すればいいでしょうか？

can110

2021/10/07 07:41

提示コードをそのまま実行すると「AttributeError: 'Agent' object has no attribute 'hi_frind_list'」エラーが発生します。コードは正しいでしょうか？

HGK

2021/10/07 09:03

スペルミスをしていました

行動規範の内容に同意します

回答1件

ベストアンサー

原因はrandom.choicesの選択対象として辞書を渡しているからです。
たとえばランダム選択の結果2番目の値を返そうとしますが、キー値が2である要素がない場合に提示エラーが発生します。
対応策としては、以下のように欲しいもの（キーか値か両方か）をリスト化して渡せばよいです。
参考：Getting key error from random.choice()

Python
1import random
2
3d = {1:2, 3:4, 5:6}
4w = [0.1,0.2,9999.9] # 2番目の要素が選択されるように重みづけ
5#print(random.choices(d, weights=w)) # KeyError: 2
6
7print(random.choices(list(d.keys()), weights=w))  # キー[5]
8print(random.choices(list(d.values()), weights=w))# 値  [6]
9print(random.choices(list(d.items()), weights=w)) # キーと値 [(5, 6)]