def create_graph():
"""Create a graph which prints hello for each even number x in the input stream,
using a conditional RuleBasedModel node and a HelloPrinter h1.Action."""
graph = h1.Graph()
graph.start()\
.add(h1.Decision(RuleBasedModel(), result_field="predictions"))\
.add(yes=HelloPrinter(), no=h1.NoOp())
graph.end()
return graph
def create_autocyber_graph():
graph = h1.Graph()
graph.start()\
.add(WindowGenerator())\
.add(h1.Decision(MsgFreqEventDetectorModel().load(), decision_field="WindowInAttack"))\
.add(yes=GradientBoostingMsgClassifierModel().load(),
no=NoOp())
graph.end()
return graph
m.train(data)
m.stats
# Don't run automatically this easily overwite latest version in AHT's computer,
# I need to use correct version in the tutorial notebooks
# m.persist()
from AutomotiveCybersecurity.graph import WindowGenerator
df = pd.read_csv(data["test_attack_files"][0])
df.columns = [
'Timestamp', 'Label', 'CarSpeed', 'SteeringAngle', 'YawRate', 'Gx', 'Gy'
]
graph = h1.Graph()
graph.start()\
.add(WindowGenerator())\
.add(m)
graph.end()
results = graph.predict({"df": df})
results["event_detection_results"]
from AutomotiveCybersecurity.models.gradient_boosting_msg_classifier import GradientBoostingMsgClassifierModel
m2 = GradientBoostingMsgClassifierModel()
data = m2.load_data(20)
prepared_data = m2.prep_data(data)
m2.train(prepared_data)