def __init__(self, concept_id=0, seed=None, noise=0):
self.cf = concept_id
self.seed = seed
stream = SineGenerator(classification_function=concept_id,
random_state=seed)
stream.prepare_for_use()
super().__init__(stream)
def main():
# start agent network server
agentNetwork = AgentNetwork()
# init agents
gen_agent = agentNetwork.add_agent(agentType=DataStreamAgent)
trainer_agent = agentNetwork.add_agent(agentType=Trainer)
predictor_agent = agentNetwork.add_agent(agentType=Predictor)
evaluator_agent = agentNetwork.add_agent(agentType=Evaluator)
monitor_agent_1 = agentNetwork.add_agent(agentType=MonitorAgent)
monitor_agent_2 = agentNetwork.add_agent(agentType=MonitorAgent)
gen_agent.init_parameters(stream=SineGenerator(), pretrain_size=1000,
batch_size=1)
trainer_agent.init_parameters(ml_model=HoeffdingTree())
# connect agents : We can connect multiple agents to any particular agent
# However the agent needs to implement handling multiple input types
agentNetwork.bind_agents(gen_agent, trainer_agent)
agentNetwork.bind_agents(gen_agent, predictor_agent)
agentNetwork.bind_agents(trainer_agent, predictor_agent)
agentNetwork.bind_agents(predictor_agent, evaluator_agent)
agentNetwork.bind_agents(evaluator_agent, monitor_agent_1)
agentNetwork.bind_agents(predictor_agent, monitor_agent_2)
# set all agents states to "Running"
agentNetwork.set_running_state()
# allow for shutting down the network after execution
return agentNetwork
self.send_output(res)
if __name__ == '__main__':
# start agent network server
agentNetwork = AgentNetwork()
# init agents
gen_agent = agentNetwork.add_agent(agentType=DataStreamAgent)
trainer_agent = agentNetwork.add_agent(agentType=Trainer)
predictor_agent = agentNetwork.add_agent(agentType=Predictor)
evaluator_agent = agentNetwork.add_agent(agentType=Evaluator)
monitor_agent_1 = agentNetwork.add_agent(agentType=MonitorAgent)
monitor_agent_2 = agentNetwork.add_agent(agentType=MonitorAgent)
gen_agent.init_parameters(stream=SineGenerator(),
pretrain_size=1000,
batch_size=1)
trainer_agent.init_parameters(ml_model=HoeffdingTree())
# connect agents : We can connect multiple agents to any particular agent
# However the agent needs to implement handling multiple input types
agentNetwork.bind_agents(gen_agent, trainer_agent)
agentNetwork.bind_agents(gen_agent, predictor_agent)
agentNetwork.bind_agents(trainer_agent, predictor_agent)
agentNetwork.bind_agents(predictor_agent, evaluator_agent)
agentNetwork.bind_agents(evaluator_agent, monitor_agent_1)
agentNetwork.bind_agents(predictor_agent, monitor_agent_2)
# set all agents states to "Running"
agentNetwork.set_running_state()
def prepare_for_use(self):
if self.generator in ['sea', 'sine']:
self.concepts = [v for v in range(0, 4)]
elif self.generator in ['stagger']:
self.concepts = [v for v in range(0, 3)]
elif self.generator in ['mixed']:
self.concepts = [v for v in range(0, 2)]
elif self.generator in ['led']:
self.concepts = [v for v in range(0, 7)]
elif self.generator in ['tree']:
self.concepts = [2, 3, 4, 5, 6, 7, 8, 9, 10]
if self.concept_shift_step > 0:
for concept in self.all_concepts:
stream = AGRAWALGenerator(classification_function=concept,
random_state=self.random_state,
balance_classes=False,
perturbation=0.05)
stream.prepare_for_use()
self.streams.append(stream)
else:
for concept in self.concepts:
if self.generator == 'agrawal':
stream = AGRAWALGenerator(classification_function=concept,
random_state=self.random_state,
balance_classes=False,
perturbation=0.05)
elif self.generator == 'sea':
stream = SEAGenerator(classification_function=concept,
random_state=self.random_state,
balance_classes=False,
noise_percentage=0.05)
elif self.generator == 'sine':
stream = SineGenerator(classification_function=concept,
random_state=self.random_state,
balance_classes=False,
has_noise=False)
elif self.generator == 'stagger':
stream = STAGGERGenerator(classification_function=concept,
random_state=self.random_state,
balance_classes=False)
elif self.generator == 'mixed':
stream = MIXEDGenerator(classification_function=concept,
random_state=self.random_state,
balance_classes=False)
elif self.generator == 'led':
stream = LEDGeneratorDrift(random_state=self.random_state,
has_noise=True,
n_drift_features=concept)
elif self.generator == 'tree':
stream = RandomTreeGenerator(tree_random_state=concept,
sample_random_state=concept,
max_tree_depth=concept+2,
min_leaf_depth=concept,
n_classes=2)
else:
print(f"unknown stream generator {self.generator}")
exit()
stream.prepare_for_use()
self.streams.append(stream)
self.cur_stream = self.streams[0]
self.drift_stream = self.streams[1]
stream = self.cur_stream
self.n_samples = stream.n_samples
self.n_targets = stream.n_targets
self.n_features = stream.n_features
self.n_num_features = stream.n_num_features
self.n_cat_features = stream.n_cat_features
self.n_classes = stream.n_classes
self.cat_features_idx = stream.cat_features_idx
self.feature_names = stream.feature_names
self.target_names = stream.target_names
self.target_values = stream.target_values
self.n_targets = stream.n_targets
self.name = 'drifting' + stream.name
print(f"len: {len(self.concepts)}")
self.concept_probs = \
self.__get_poisson_probs(len(self.concepts), self.lam)
"""CD at 2000 and then every 1000"""
# MIXED
stream = ReoccuringDriftStream(stream=MIXEDGenerator(classification_function=0), drift_stream=MIXEDGenerator(classification_function=1), width=1, alpha=90, pause=1000, position=2000)
stream.name = 'MIXED Abrupt Reoccuring'
streams.append(stream)
# SEA
stream = ReoccuringDriftStream(stream=SEAGenerator(classification_function=0), drift_stream=SEAGenerator(classification_function=1), width=1, alpha=90, pause=1000, position=2000)
stream.name = 'SEA Abrupt Reoccuring'
streams.append(stream)
# SINE
stream = ReoccuringDriftStream(stream=SineGenerator(classification_function=0), drift_stream=SineGenerator(classification_function=1), width=1, alpha=90, pause=1000, position=2000)
stream.name = 'Sine Abrupt Reoccuring'
streams.append(stream)
# LED
stream = ReoccuringDriftStream(stream=LEDGeneratorDrift(noise_percentage=0.2, has_noise=True, n_drift_features=5), drift_stream=LEDGeneratorDrift(noise_percentage=0, has_noise=True, n_drift_features=10), width=1, alpha=90, pause=1000, position=2000)
stream.name = 'LED Abrupt Reoccuring'
streams.append(stream)
# TODO: lets calc conf matrix over all streams
def main():
overall_kswin_tp = overall_kswin_tn = overall_kswin_fp = overall_kswin_fn = 0