def demo():
""" _test_pipeline
This demo demonstrates the Pipeline structure seemingly working as a
learner, while being passed as parameter to an EvaluatePrequential
object.
"""
# # Setup the stream
# stream = FileStream("../data/datasets/covtype.csv", -1, 1)
# stream.prepare_for_use()
# # If used for Hoeffding Trees then need to pass indices for Nominal attributes
# Test with RandomTreeGenerator
# stream = RandomTreeGenerator(n_classes=2, n_numerical_attributes=5)
# stream.prepare_for_use()
# Test with WaveformGenerator
stream = WaveformGenerator()
stream.prepare_for_use()
# Setup the classifier
#classifier = PerceptronMask()
#classifier = NaiveBayes()
#classifier = PassiveAggressiveClassifier()
classifier = HoeffdingTreeClassifier()
# Setup the pipeline
pipe = Pipeline([('Hoeffding Tree', classifier)])
# Setup the evaluator
evaluator = EvaluatePrequential(show_plot=True, pretrain_size=1000, max_samples=100000)
# Evaluate
evaluator.evaluate(stream=stream, model=pipe)
def demo(output_file=None, instances=40000):
""" _test_comparison_holdout
This demo will test a holdout evaluation task when more than one learner is
evaluated, which makes it a comparison task.
Parameters
----------
output_file: string, optional
If passed this parameter indicates the output file name. If left blank,
no output file will be generated.
instances: int (Default: 40000)
The evaluation's maximum number of instances.
"""
# Setup the File Stream
# stream = FileStream("../data/datasets/covtype.csv", -1, 1)
stream = WaveformGenerator()
stream.prepare_for_use()
# Setup the classifier
clf_one = HoeffdingTree()
# clf_two = KNNAdwin(n_neighbors=8, max_window_size=2000)
# classifier = PassiveAggressiveClassifier()
# classifier = SGDRegressor()
# classifier = PerceptronMask()
# Setup the pipeline
classifier = [clf_one]
# Setup the evaluator
evaluator = EvaluateHoldout(test_size=500,
dynamic_test_set=True,
max_samples=instances,
batch_size=1,
n_wait=5000,
max_time=1000,
output_file=output_file,
show_plot=True,
metrics=['kappa'])
# Evaluate
evaluator.evaluate(stream=stream, model=classifier)
def demo(output_file=None, instances=40000):
""" _test_holdout
This demo runs a holdout evaluation task with one learner. The default
stream is a WaveformGenerator. The default learner is a SGDClassifier,
which is inserted into a Pipeline structure. All the default values can
be changing by uncommenting/commenting the code below.
Parameters
----------
output_file: string
The name of the csv output file
instances: int
The evaluation's max number of instances
"""
# Setup the File Stream
# stream = FileStream("../data/datasets/covtype.csv", -1, 1)
stream = WaveformGenerator()
stream.prepare_for_use()
# Setup the classifier
classifier = SGDClassifier()
# classifier = PassiveAggressiveClassifier()
# classifier = SGDRegressor()
# classifier = PerceptronMask()
# Setup the pipeline
pipe = Pipeline([('Classifier', classifier)])
# Setup the evaluator
evaluator = EvaluateHoldout(test_size=2000,
dynamic_test_set=True,
max_samples=instances,
batch_size=1,
n_wait=15000,
max_time=1000,
output_file=output_file,
show_plot=True,
metrics=['kappa', 'kappa_t', 'performance'])
# Evaluate
evaluator.evaluate(stream=stream, model=pipe)
def demo(output_file=None, instances=40000):
""" _test_prequential_bagging
This demo shows the evaluation process of a LeverageBaggingClassifier,
initialized with different base estimators.
Parameters
----------
output_file: string
The name of the csv output file
instances: int
The evaluation's max number of instances
"""
# Setup the File Stream
# stream = FileStream("../data/datasets/sea_big.csv", -1, 1)
#stream = SEAGenerator(classification_function=2, noise_percentage=0.0)
#stream.prepare_for_use()
stream = WaveformGenerator()
stream.prepare_for_use()
# Setup the classifier
#classifier = OzaBaggingADWINClassifier(base_estimator=KNNClassifier(n_neighbors=8, max_window_size=2000,
# leaf_size=30))
#classifier = LeverageBaggingClassifier(base_estimator=KNNClassifier(n_neighbors=8, max_window_size=2000,
# leaf_size=30),
# n_estimators=1)
pipe = LeverageBaggingClassifier(base_estimator=HoeffdingTreeClassifier(),
n_estimators=2)
# Setup the pipeline
#pipe = Pipeline([('Classifier', classifier)])
# Setup the evaluator
evaluator = EvaluatePrequential(pretrain_size=2000,
max_samples=instances,
output_file=output_file,
show_plot=False)
# Evaluate
evaluator.evaluate(stream=stream, model=pipe)
def main():
global agentNetwork
# start agent network
agentNetwork = AgentNetwork()
# add agents
data_stream_agent_1 = agentNetwork.add_agent(agentType=DataStreamAgent)
ml_agent_hoeffdingTree = agentNetwork.add_agent(agentType=ML_Model)
ml_agent_neuralNets = agentNetwork.add_agent(agentType=ML_Model)
monitor_agent_1 = agentNetwork.add_agent(agentType=MonitorAgent)
# init parameters
data_stream_agent_1.init_parameters(stream=WaveformGenerator(),
pretrain_size=1000,
batch_size=100)
ml_agent_hoeffdingTree.init_parameters(ml_model=HoeffdingTreeClassifier())
ml_agent_neuralNets.init_parameters(ml_model=NaiveBayes())
# connect agents
agentNetwork.bind_agents(data_stream_agent_1, ml_agent_hoeffdingTree)
agentNetwork.bind_agents(data_stream_agent_1, ml_agent_neuralNets)
agentNetwork.bind_agents(ml_agent_hoeffdingTree, monitor_agent_1)
agentNetwork.bind_agents(ml_agent_neuralNets, monitor_agent_1)
agentNetwork.set_running_state()
# allow for shutting down the network after execution
return agentNetwork
from skmultiflow.trees.hoeffding_tree import HoeffdingTree
from hoeffopttree import HoeffdingOptTree
from skmultiflow.evaluation.evaluate_prequential import EvaluatePrequential
from skmultiflow.data.file_stream import FileStream
from skmultiflow.data import LEDGenerator, RandomTreeGenerator, RandomRBFGenerator, WaveformGenerator
import matplotlib as plt
plt.interactive(True)
#dataset = "elec"
dataset = "covtype"
#stream = FileStream("./data/"+dataset+".csv", n_targets=1, target_idx=-1)
#stream = LEDGenerator()
#stream = RandomTreeGenerator()
#stream = RandomRBFGenerator()
stream = WaveformGenerator(has_noise=False)
stream.prepare_for_use()
h = [
HoeffdingOptTree(),
# HoeffdingTree()
]
evaluator = EvaluatePrequential(pretrain_size=1000,
max_samples=20000,
show_plot=True,
metrics=['accuracy'],
output_file='result_' + dataset + '.csv',
batch_size=1)
# 4. Run
evaluator.evaluate(stream=stream, model=h, model_names=["HOT"])
'''import pandas as pd
from skmultiflow.data import WaveformGenerator
from skmultiflow.trees import HoeffdingTree
from skmultiflow.evaluation import EvaluatePrequential
import scutds
# 1. Create a stream
stream = WaveformGenerator()
stream.prepare_for_use()
# 2. Instantiate the HoeffdingTree classifier
sd = scutds.ScutDS()
# 3. Setup the evaluator
evaluator = EvaluatePrequential(show_plot=False,
pretrain_size=1000,
batch_size = 1000,
max_samples=10000)
# 4. Run evaluation
evaluator.evaluate(stream=stream, model=sd)
from skmultiflow.data import WaveformGenerator
from skmultiflow.trees.hoeffding_tree import HoeffdingTreeClassifier
from skmultiflow.evaluation.evaluate_prequential import EvaluatePrequential
# Create a stream
stream = WaveformGenerator()
stream.prepare_for_use() # Not required for v0.5.0+
# Instantiate the HoeffdingTreeClassifier
ht = HoeffdingTreeClassifier()
# Setup the evaluator
evaluator = EvaluatePrequential(show_plot=False,
pretrain_size=200,
max_samples=20000)
# Run evaluation
evaluator.evaluate(stream=stream, model=ht)
num_accurate = [1 if y == True else 0 for y in res]
accuracy = np.sum(num_accurate) / len(num_accurate) * 100
return accuracy
if __name__ == '__main__':
#start agent network
agentNetwork = AgentNetwork()
#add agents
data_stream_agent_1 = agentNetwork.add_agent(agentType=DataStreamAgent)
ml_agent_hoeffdingTree = agentNetwork.add_agent(agentType=ML_Model)
ml_agent_neuralNets = agentNetwork.add_agent(agentType=ML_Model)
monitor_agent_1 = agentNetwork.add_agent(agentType=MonitorAgent)
#init parameters
data_stream_agent_1.init_parameters(stream=WaveformGenerator(),
pretrain_size=1000,
batch_size=100)
ml_agent_hoeffdingTree.init_parameters(ml_model=HoeffdingTree())
ml_agent_neuralNets.init_parameters(ml_model=NaiveBayes())
#connect agents
agentNetwork.bind_agents(data_stream_agent_1, ml_agent_hoeffdingTree)
agentNetwork.bind_agents(data_stream_agent_1, ml_agent_neuralNets)
agentNetwork.bind_agents(ml_agent_hoeffdingTree, monitor_agent_1)
agentNetwork.bind_agents(ml_agent_neuralNets, monitor_agent_1)
agentNetwork.set_running_state()