from grand import IndividualAnomalyInductive
if __name__ == '__main__':
# Get data from one unit (vehicle)
dataset = load_artificial_toy(0) #load_vehicles()
unit1_train = [x for dt, x in dataset.stream_unit(1)
] # we use unit number 1 for training
# Create an instance of IndividualAnomalyInductive
indev = IndividualAnomalyInductive(
w_martingale=15, # Window size for computing the deviation level
non_conformity=
"median", # Strangeness measure: "median" or "knn" or "lof"
k=50, # Used if non_conformity is "knn"
dev_threshold=.6) # Threshold on the deviation level
# Fit the IndividualAnomalyInductive detector to unit1_train
indev.fit(unit1_train)
# At each time step dt, a data-point x comes from the stream of unit number 0
for dt, x in dataset.stream_unit(0):
devContext = indev.predict(dt, x)
st, pv, dev, isdev = devContext.strangeness, devContext.pvalue, devContext.deviation, devContext.is_deviating
print("Time: {} ==> strangeness: {}, p-value: {}, deviation: {} ({})".
format(dt, st, pv, dev, "high" if isdev else "low"))
# Plot p-values and deviation level over time
indev.plot_deviations()
from grand import IndividualAnomalyInductive
if __name__ == '__main__':
# Get data from one unit (vehicle)
dataset = load_artificial(0) #load_vehicles()
unit1_train = [x for dt, x in dataset.stream_unit(1)
] # we use unit number 1 for training
# Create an instance of IndividualAnomalyInductive
indev = IndividualAnomalyInductive(
w_martingale=15, # Window size for computing the deviation level
non_conformity=
"median", # Strangeness measure: "median" or "knn" or "lof"
k=50, # Used if non_conformity is "knn"
dev_threshold=.6) # Threshold on the deviation level
# Fit the IndividualAnomalyInductive detector to unit1_train
indev.fit(unit1_train)
# At each time step dt, a data-point x comes from the stream of unit number 0
for dt, x in dataset.stream_unit(0):
devContext = indev.predict(dt, x)
st, pv, dev, isdev = devContext.strangeness, devContext.pvalue, devContext.deviation, devContext.is_deviating
print("Time: {} ==> strangeness: {}, p-value: {}, deviation: {} ({})".
format(dt, st, pv, dev, "high" if isdev else "low"))
# Plot p-values and deviation level over time
indev.plot_deviations(figsize=(10, 5))