# Ensemble of anomaly scores#

Due to the diversity of different time series data, and the uncertain generalization of different detectors. This function can help you to ensemble anomaly scores from different models.

For different detectors, the anomaly score can be distributed in different ranges. Thus, we need to call calibrator which can normalize the scores into [0,1] before this ensemble module.

## Weight Ensemble#

This ensemble method is based on weighting the anomaly scores. It requires a list of weights of each model to initialize and calculate the weighted anomaly score with strict scores order.

from streamad.util import StreamGenerator, UnivariateDS, plot

ds = UnivariateDS()
stream = StreamGenerator(ds.data)
knn_detector=KNNDetector()
spot_detector=SpotDetector()
knn_calibrator = ZScoreCalibrator(sigma=3)
spot_calibrator = ZScoreCalibrator(sigma=3)
ensemble = WeightEnsemble(ensemble_weights=[0.6, 0.4])

scores = []

for x in stream.iter_item():
# We first score the anomalies using different detectors
knn_score = knn_detector.fit_score(x)
spot_score = spot_detector.fit_score(x)

# Then we calibrate the scores into normalized [0,1]
knn_normalized_score = knn_calibrator.normalize(knn_score)
spot_normalized_score = spot_calibrator.normalize(spot_score)

# Finally we ensemble the scores
score = ensemble.ensemble([knn_normalized_score, spot_normalized_score])
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)


## Vote Ensemble#

This ensemble method is based on votes. It requires a threshold to determine the anomalies for each detector, and report the overall anomalies with a principle of majority votes.

from streamad.util import StreamGenerator, UnivariateDS, plot

ds = UnivariateDS()
stream = StreamGenerator(ds.data)
knn_detector=KNNDetector()
spot_detector=SpotDetector()
knn_calibrator = ZScoreCalibrator(sigma=2)
spot_calibrator = ZScoreCalibrator(sigma=2)
ensemble = VoteEnsemble(threshold=0.8)

scores = []

for x in stream.iter_item():
# We first score the anomalies using different detectors
knn_score = knn_detector.fit_score(x)
spot_score = spot_detector.fit_score(x)

# Then we calibrate the scores into normalized [0,1]
knn_normalized_score = knn_calibrator.normalize(knn_score)
spot_normalized_score = spot_calibrator.normalize(spot_score)

# Finally we ensemble the scores
score = ensemble.ensemble([knn_normalized_score, spot_normalized_score])
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)