Multivariate detector#
xStream Detector#
from streamad.util import StreamGenerator, MultivariateDS, plot
from streamad.model import xStreamDetector
ds = MultivariateDS()
stream = StreamGenerator(ds.data)
model = xStreamDetector(depth=10)
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Rshash Detector#
from streamad.util import StreamGenerator, MultivariateDS, plot
from streamad.model import RShashDetector
ds = MultivariateDS()
stream = StreamGenerator(ds.data)
model = RShashDetector(components_num=10)
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Half Space Tree Detector#
from streamad.util import StreamGenerator, MultivariateDS, plot
from streamad.model import HSTreeDetector
ds = MultivariateDS()
stream = StreamGenerator(ds.data)
model = HSTreeDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
LODA Detector#
from streamad.util import StreamGenerator, MultivariateDS, plot
from streamad.model import LodaDetector
ds = MultivariateDS()
stream = StreamGenerator(ds.data)
model = LodaDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
One Class SVM Detector#
from streamad.util import StreamGenerator, MultivariateDS, plot
from streamad.model import OCSVMDetector
ds = MultivariateDS()
stream = StreamGenerator(ds.data)
model = OCSVMDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
RRCF Detector#
from streamad.util import StreamGenerator, MultivariateDS, plot
from streamad.model import RrcfDetector
ds = MultivariateDS()
stream = StreamGenerator(ds.data)
model = RrcfDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)