def messageSelectionMethod(self, currentTimeStep, user, currentTopics, **conf):
if self.lastObservedTimeStep != currentTimeStep:
self._updateTopicProbabilities(currentTimeStep, currentTopics, **conf)
message = None
if GeneralMethods.trueWith(user.messagingProbability):
if GeneralMethods.trueWith(user.newTopicProbability):
topic = Topic(len(currentTopics))
currentTopics.append(topic)
message = user.generateMessage(currentTimeStep, topic)
else:
if GeneralMethods.trueWith(user.probabilityOfPickingPopularTopic):
if user.topicClass != None:
topicIndex = GeneralMethods.weightedChoice(
[i[1] for i in self.topicProbabilities[user.topicClass]]
)
topic = self.topicProbabilities[user.topicClass][topicIndex][0]
message = user.generateMessage(currentTimeStep, topic)
if not GeneralMethods.trueWith(topic.stickiness):
message = None
else:
topicIndex = GeneralMethods.weightedChoice([i[1] for i in self.topTopics])
topic = self.topTopics[topicIndex][0]
message = user.generateMessage(currentTimeStep, topic)
else:
if user.topicClass != None:
message = user.generateMessage(
currentTimeStep, random.choice(self.topicProbabilities[user.topicClass])[0]
)
else:
topicIndex = GeneralMethods.weightedChoice([i[1] for i in self.allTopics])
topic = self.allTopics[topicIndex][0]
message = user.generateMessage(currentTimeStep, topic)
return message
def hedging_get_best_model(mf_model_id_to_cumulative_losses):
if not mf_model_id_to_cumulative_losses: return random.sample(LIST_OF_MODELS, 1)[0]
else:
total_weight = float(sum(mf_model_id_to_cumulative_losses.values()))
for model in mf_model_id_to_cumulative_losses.keys(): mf_model_id_to_cumulative_losses[model]/=total_weight
id_and_model_id_and_cumulative_losses = [(id, model_id, cumulative_loss)
for id, (model_id, cumulative_loss) in
enumerate(mf_model_id_to_cumulative_losses.iteritems())
]
selected_id = GeneralMethods.weightedChoice(zip(*id_and_model_id_and_cumulative_losses)[2])
return filter(lambda (id, model, _): id==selected_id, id_and_model_id_and_cumulative_losses)[0][1]
