def test_maximum_generator_newerincremental_with_signature_distance(self):
rg = RandomGenerator(relative_matching=1)
signature = ParentChildByNameTopologySignature()
alg = IncrementalDistanceAlgorithm(signature=signature)
alg.prototypes = [rg.prototype]
decorator = DistanceDecorator(normalized=True)
decorator.algorithm = alg
decorator.start_tree()
for event in rg:
decorator.add_event(event)
decorator.finish_tree()
self.assertEqual(decorator.data()[-1][-1][-1][-1], 0)
def test_creation(self):
decorator = DistanceDecorator(normalized=False)
self.assertEqual(decorator._name, "distances")
self.assertEqual([], decorator.data())
self.assertEqual({"distances": []}, decorator.descriptive_data())
decorator = DistanceDecorator(normalized=True)
self.assertEqual(decorator._name, "normalized_distances")
self.assertEqual([], decorator.data())
self.assertEqual({"normalized_distances": []}, decorator.descriptive_data())
def test_maximum_generator_incremental_distance(self):
rg = RandomGenerator(relative_matching=1)
alg = IncrementalDistanceAlgorithm()
alg.prototypes = [rg.prototype]
decorator = DistanceDecorator(normalized=True)
decorator.algorithm = alg
decorator.start_tree()
for event in rg:
distance = decorator.add_event(event)[0]
decorator.finish_tree()
self.assertEqual(distance[0], [0])
def test_default_generator_incremental_distance(self):
rg = RandomGenerator(seed=1234)
alg = IncrementalDistanceAlgorithm()
alg.prototypes = [rg.prototype]
vector_decorator = DistanceDecorator()
vector_decorator._algorithm = alg
vector_decorator.start_tree()
for event in rg:
distance = vector_decorator.add_event(event)[0]
vector_decorator.finish_tree()
self.assertEqual(distance[0], [110])
def test_normalized_results(self):
decorator = DistanceDecorator(normalized=True)
algorithm = IncrementalDistanceAlgorithm(
signature=EnsembleSignature(
signatures=[ParentChildByNameTopologySignature(),
ParentChildOrderByNameTopologySignature()])
)
algorithm.prototypes = [simple_prototype(), simple_monitoring_tree()]
decorator.wrap_algorithm(algorithm)
algorithm.start_tree()
self.assertEqual([[[[], []], [[], []]]], decorator.data())
for event in Event.from_tree(simple_monitoring_tree(), supported={ProcessStartEvent: True}):
algorithm.add_event(event)
algorithm.finish_tree()
self.assertEqual([[
[ # ParentChildByNameTopologySignature
[2 / 3, 1 / 3, 1 / 3, 0.0], # simple_prototype
[2 / 3, 1 / 3, 1 / 3, 0.0] # simple_monitoring_tree
],
[ # ParentChildOrderByNameTopologySignature
[4 / 5, 3 / 5, 3 / 5, 2 / 5],
[2 / 3, 1 / 3, 1 / 3, 0.0]
]]], decorator.data())
def test_two_prototypes(self):
decorator = DistanceDecorator(normalized=False)
algorithm = IncrementalDistanceAlgorithm()
algorithm.prototypes = [simple_prototype(), simple_monitoring_tree()]
decorator.wrap_algorithm(algorithm)
algorithm.start_tree()
self.assertEqual([[[[], []]]], decorator.data())
for event in Event.from_tree(simple_monitoring_tree(), supported={ProcessStartEvent: True}):
algorithm.add_event(event)
algorithm.finish_tree()
self.assertEqual([[[[2, 1, 1, 0], [2, 1, 1, 0]]]], decorator.data())
def test_ensemble(self):
decorator = DistanceDecorator(normalized=False)
algorithm = IncrementalDistanceAlgorithm(
signature=EnsembleSignature(
signatures=[ParentChildByNameTopologySignature(),
ParentChildOrderByNameTopologySignature()]))
algorithm.prototypes = [simple_prototype()]
decorator.wrap_algorithm(algorithm)
algorithm.start_tree()
self.assertEqual([[[[]], [[]]]], decorator.data())
for event in Event.from_tree(simple_monitoring_tree(), supported={ProcessStartEvent: True}):
algorithm.add_event(event)
algorithm.finish_tree()
self.assertEqual([[[[2, 1, 1, 0]], [[4, 3, 3, 2]]]], decorator.data())
def test_bigger_tree(self):
rg = RandomGenerator(prototype_node_count=100,
tree_node_count=200,
relative_matching=.9,
relative_repetition=.5,
seed=1234)
signature = ParentChildByNameTopologySignature()
alg = IncrementalDistanceAlgorithm(signature=signature)
alg.prototypes = [rg.prototype]
decorator = DistanceDecorator(normalized=True)
decorator.wrap_algorithm(alg)
decorator.start_tree()
for event in rg:
decorator.add_event(event)
decorator.finish_tree()
self.assertAlmostEqual(decorator.data()[-1][-1][-1][-1], 0.185, 2)
def from_name(name):
if "anomaly" in name:
from assess.decorators.anomalydecorator import AnomalyDecorator
# FIXME: how to pass parameter?
return AnomalyDecorator()
elif "normalized_ensembles" in name:
from assess.decorators.ensemblematrixdecorator import \
EnsembleMatrixDecorator
return EnsembleMatrixDecorator(normalized=True)
elif "normalized_ensembledistances" in name:
from assess.decorators.ensembledistancedecorator import \
EnsembleDistanceDecorator
return EnsembleDistanceDecorator(normalized=True)
elif "ensembledistances" in name:
from assess.decorators.ensembledistancedecorator import \
EnsembleDistanceDecorator
return EnsembleDistanceDecorator(normalized=False)
elif "ensembleanomaly" in name:
from assess.decorators.ensembleanomalydecorator import \
EnsembleAnomalyDecorator
return EnsembleAnomalyDecorator()
elif "compression" in name:
from assess.decorators.compressionfactordecorator import \
CompressionFactorDecorator
return CompressionFactorDecorator()
elif "data" in name:
from assess.decorators.datadecorator import DataDecorator
return DataDecorator()
elif "normalized_ensembles" in name:
from assess.decorators.ensemblematrixdecorator import \
EnsembleMatrixDecorator
return EnsembleMatrixDecorator(normalized=True)
elif "ensembles" in name:
from assess.decorators.ensemblematrixdecorator import \
EnsembleMatrixDecorator
return EnsembleMatrixDecorator(normalized=False)
elif "normalized_distances" in name:
from assess.decorators.distancedecorator import \
DistanceDecorator
return DistanceDecorator(normalized=True)
elif "distances" in name:
from assess.decorators.distancedecorator import \
DistanceDecorator
return DistanceDecorator(normalized=False)
elif "normalized_matrix" in name:
from assess.decorators.distancematrixdecorator import \
DistanceMatrixDecorator
return DistanceMatrixDecorator(normalized=True)
elif "matrix" in name:
from assess.decorators.distancematrixdecorator import \
DistanceMatrixDecorator
return DistanceMatrixDecorator(normalized=False)
elif "accumulated_distance_performance" in name:
from assess.decorators.distanceperformancedecorator import \
DistancePerformanceDecorator
return DistancePerformanceDecorator(accumulated=True)
elif "distance_performance" in name:
from assess.decorators.distanceperformancedecorator import \
DistancePerformanceDecorator
return DistancePerformanceDecorator(accumulated=False)
elif "accumulated_performance" in name:
from assess.decorators.performancedecorator import \
PerformanceDecorator
return PerformanceDecorator(accumulated=True)
elif "performance" in name:
from assess.decorators.performancedecorator import \
PerformanceDecorator
return PerformanceDecorator(accumulated=False)
elif "accumulated_signature_performance" in name:
from assess.decorators.signatureperformancedecorator import \
SignaturePerformanceDecorator
return SignaturePerformanceDecorator(accumulated=True)
elif "signature_performance" in name:
from assess.decorators.signatureperformancedecorator import \
SignaturePerformanceDecorator
return SignaturePerformanceDecorator(accumulated=False)
elif "signature" in name:
from assess.decorators.signaturedecorator import \
SignatureDecorator
return SignatureDecorator()
elif "ensembles" in name:
from assess.decorators.ensemblematrixdecorator import \
EnsembleMatrixDecorator
return EnsembleMatrixDecorator(normalized=False)