def test_accuracy_stream(self):
stream = SEAGenerator(random_state=42)
stream.prepare_for_use()
clf = ARSLVQ(sigma=0.5,
prototypes_per_class=2,
batch_size=5,
decay_rate=0.999)
evaluator = EvaluatePrequential(show_plot=False,
max_samples=20000,
batch_size=5)
evaluator.evaluate(stream, clf, model_names=['ARSLVQ'])
measurements = np.asarray(evaluator.get_measurements()[0])[0]
self.assertTrue(
measurements.get_accuracy() >= 0.84,
msg='Accuracy was {} but has to be greater than 0.84'.format(
measurements.get_accuracy()))
self.assertTrue(
measurements.get_kappa() >= 0.68,
msg='Kappa was {} but has to be greater than 0.68'.format(
measurements.get_kappa()))
def test_accuracy_stream(self):
stream = ConceptDriftStream(stream=SEAGenerator(random_state=112,
noise_percentage=0.1),
drift_stream=SEAGenerator(
random_state=112,
classification_function=1,
noise_percentage=0.1),
random_state=None,
position=20000,
width=50000)
stream.prepare_for_use()
clf = GLVQ(prototypes_per_class=6,
beta=2,
C=None,
decay_rate=0.9,
gradient_descent="SGD")
evaluator = EvaluatePrequential(pretrain_size=1,
show_plot=False,
max_samples=20000,
batch_size=1)
evaluator.evaluate(stream, clf, model_names=['GLVQ'])
measurements = np.asarray(evaluator.get_measurements()[0])[0]
self.assertTrue(
measurements.get_accuracy() >= 0.93,
msg='Accuracy was {} but has to be greater than 0.93'.format(
measurements.get_accuracy()))
self.assertTrue(
measurements.get_kappa() >= 0.84,
msg='Kappa was {} but has to be greater than 0.84'.format(
measurements.get_kappa()))
def test_stream(self):
stream = SEAGenerator(classification_function = 2, random_state = 112, balance_classes = False, noise_percentage = 0.28)
stream.prepare_for_use()
evaluator = EvaluatePrequential(show_plot=False,max_samples=5000,
restart_stream=True,batch_size=10,metrics=['kappa', 'kappa_m', 'accuracy'])
evaluator.evaluate(stream=stream, model=RRSLVQ(prototypes_per_class=4,sigma=10))
measurements = np.asarray(evaluator.get_measurements()[0])[0]
self.assertIsNotNone(eval)
self.assertTrue(measurements.get_accuracy() >= 0.5,
msg='Accuracy was {} but has to be greater than 0.5'.
format(measurements.get_accuracy()))
def test_reoccuring(self):
s1 = MIXEDGenerator(classification_function = 1, random_state= 112, balance_classes = False)
s2 = MIXEDGenerator(classification_function = 0, random_state= 112, balance_classes = False)
stream = ReoccuringDriftStream(stream=s1,
drift_stream=s2,
random_state=None,
alpha=90.0, # angle of change grade 0 - 90
position=2000,
width=500)
stream.prepare_for_use()
evaluator = EvaluatePrequential(show_plot=False,batch_size=10,
max_samples=1000,
metrics=['accuracy', 'kappa_t', 'kappa_m', 'kappa'],
output_file=None)
eval = evaluator.evaluate(stream=stream, model=OzaBaggingAdwin(base_estimator=KNN()))
measurements = np.asarray(evaluator.get_measurements()[0])[0]
self.assertIsNotNone(eval)
self.assertTrue(measurements.get_accuracy() >= 0.6,
msg='Accuracy was {} but has to be greater than 0.6'.
format(measurements.get_accuracy()))
def test_accuracy_stream(self):
stream = SEAGenerator(random_state=42)
stream.prepare_for_use()
clf = GRLVQ(prototypes_per_class=2, regularization=5.0, beta=2, C=None)
evaluator = EvaluatePrequential(pretrain_size=1,
show_plot=False,
max_samples=20000,
batch_size=1)
evaluator.evaluate(stream, clf, model_names=['GRLVQ'])
measurements = np.asarray(evaluator.get_measurements()[0])[0]
self.assertTrue(
measurements.get_accuracy() >= 0.7,
msg='Accuracy was {} but has to be greater than 0.7'.format(
measurements.get_accuracy()))
self.assertTrue(
measurements.get_kappa() >= 0.3,
msg='Kappa was {} but has to be greater than 0.3'.format(
measurements.get_kappa()))
from skmultiflow.data.sea_generator import SEAGenerator
from rslvq import RSLVQ
from adaptive_rslvqs_batch import RSLVQ as BARSLVQ
from skmultiflow.evaluation.evaluate_prequential import EvaluatePrequential
from arslvq import RSLVQ as ARSLVQ
import numpy as np
from rslvq_stream import RSLVQ as MasterRSLVQ
stream = SEAGenerator()
stream.prepare_for_use()
clf = [
RSLVQ(batch_size=5),
BARSLVQ(gradient_descent='Adadelta',
batch_size=5,
decay_rate=0.9,
sigma=1.0),
RSLVQ(),
MasterRSLVQ(gradient_descent='Adadelta', decay_rate=0.999, sigma=1.0)
]
evaluator = EvaluatePrequential(max_samples=100000,
batch_size=5,
show_plot=True)
evaluator.evaluate(stream=stream,
model=clf,
model_names=['BRSLVQ', 'BARSLVQ', 'RSLVQ', 'MRSLVQ'])
measurements = np.asarray(evaluator.get_measurements()[0])[0]