mirror of
https://github.com/gsi-upm/soil
synced 2024-11-24 11:52:29 +00:00
105 lines
4.4 KiB
Python
105 lines
4.4 KiB
Python
import random
|
|
from ..BaseBehaviour import *
|
|
|
|
|
|
class BigMarketModel(BaseBehaviour):
|
|
"""
|
|
Settings:
|
|
Names:
|
|
enterprises [Array]
|
|
|
|
tweet_probability_enterprises [Array]
|
|
Users:
|
|
tweet_probability_users
|
|
|
|
tweet_relevant_probability
|
|
|
|
tweet_probability_about [Array]
|
|
|
|
sentiment_about [Array]
|
|
"""
|
|
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
self.enterprises = environment.environment_params['enterprises']
|
|
self.type = ""
|
|
self.number_of_enterprises = len(environment.environment_params['enterprises'])
|
|
|
|
if self.id < self.number_of_enterprises: # Enterprises
|
|
self.state['id'] = self.id
|
|
self.type = "Enterprise"
|
|
self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
|
|
else: # normal users
|
|
self.state['id'] = self.number_of_enterprises
|
|
self.type = "User"
|
|
self.tweet_probability = environment.environment_params['tweet_probability_users']
|
|
self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
|
|
self.tweet_probability_about = environment.environment_params['tweet_probability_about'] # List
|
|
self.sentiment_about = environment.environment_params['sentiment_about'] # List
|
|
|
|
def step(self, now):
|
|
|
|
if self.id < self.number_of_enterprises: # Enterprise
|
|
self.enterpriseBehaviour()
|
|
else: # Usuario
|
|
self.userBehaviour()
|
|
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
|
|
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
|
|
|
|
super().step(now)
|
|
|
|
def enterpriseBehaviour(self):
|
|
|
|
if random.random()< self.tweet_probability: # Tweets
|
|
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbour users
|
|
for x in aware_neighbors:
|
|
if random.uniform(0,10) < 5:
|
|
x.sentiment_about[self.id] += 0.1 # Increments for enterprise
|
|
else:
|
|
x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise
|
|
|
|
# Establecemos limites
|
|
if x.sentiment_about[self.id] > 1:
|
|
x.sentiment_about[self.id] = 1
|
|
if x.sentiment_about[self.id]< -1:
|
|
x.sentiment_about[self.id] = -1
|
|
|
|
x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
|
|
|
|
def userBehaviour(self):
|
|
|
|
if random.random() < self.tweet_probability: # Tweets
|
|
if random.random() < self.tweet_relevant_probability: # Tweets something relevant
|
|
# Tweet probability per enterprise
|
|
for i in range(self.number_of_enterprises):
|
|
random_num = random.random()
|
|
if random_num < self.tweet_probability_about[i]:
|
|
# The condition is fulfilled, sentiments are evaluated towards that enterprise
|
|
if self.sentiment_about[i] < 0:
|
|
# NEGATIVO
|
|
self.userTweets("negative",i)
|
|
elif self.sentiment_about[i] == 0:
|
|
# NEUTRO
|
|
pass
|
|
else:
|
|
# POSITIVO
|
|
self.userTweets("positive",i)
|
|
|
|
def userTweets(self,sentiment,enterprise):
|
|
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbours users
|
|
for x in aware_neighbors:
|
|
if sentiment == "positive":
|
|
x.sentiment_about[enterprise] +=0.003
|
|
elif sentiment == "negative":
|
|
x.sentiment_about[enterprise] -=0.003
|
|
else:
|
|
pass
|
|
|
|
# Establecemos limites
|
|
if x.sentiment_about[enterprise] > 1:
|
|
x.sentiment_about[enterprise] = 1
|
|
if x.sentiment_about[enterprise] < -1:
|
|
x.sentiment_about[enterprise] = -1
|
|
|
|
x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
|