Does Darts provide methods for unsupervised anomaly detection models?

[ETTAN93]

Based on the darts documentation on anomaly models, it seems like the 2 available ones - filtering anomaly model and forecasting anomaly model both require the model to be initially fitted to a series without anomalies, i.e. a supervised anomaly detection model.

Is my understanding correct? Does Darts offer any unsupervised models for anomaly detection?

[madtoinou]

Hi @ETTAN93,

At the moment, Darts does not offer any unsupervised models for anomaly detection but it can be added to the roadmap, especially if contributors propose architectures and open PRs.

[ETTAN93]

@madtoinou thanks for that.

Another thing to clarify, I used eval_accuracy in darts as part of the quantile detector class and compared it to the results I got from sklearn's recall_score.

I passed the same y_test and y_pred series to both:

from sklearn.metrics import recall_score
qd_recall = qd.eval_accuracy(y_test_series, qd_y_pred_series, metric='recall')
sklearn_recall = recall_score(y_test_series.pd_series(), qd_y_pred_series.pd_series())

For some reason, I am getting the inverse of values from both, i.e. when I sum the two recall scores, I end up with 1.0. In this particular case, qd_recall from darts returns me 0.9946808510638298 whereas the recall_score from sklearn returns me 0.005319148936170213.

Am I passing in the wrong parameters to the darts function? As far as I understand, the anomaly_score parameter should be the y_pred_series from the model? what does the window parameter do?

The same also happens when I evaluate the accuracy score. The two scores that are returned sums up to 1.

from sklearn.metrics import accuracy_score
qd_accuracy= qd.eval_accuracy(y_test_series, qd_y_pred_series, metric='accuracy')
sklearn_accuracy = accuracy_score(y_test_series.pd_series(), qd_y_pred_series.pd_series())

[dennisbader]

Hi @ETTAN93, QuantileDetector.eval_accuracy() expects the predicted scores from the Scorer and not the output of QuantileDetector.detect().

The following should work:

# darts
qd = QuantileDetector(high_quantile=0.5)
anom_pred = qd.fit_detect(scores_pred)
qd_recall = qd.eval_accuracy(anom_true, scores_pred, metric="recall")

# sklearn
sl_recall = recall_score(
    anom_true.slice_intersect(anom_pred).pd_series(), 
    anom_pred.slice_intersect(anom_true).pd_series()
)

print(qd_recall, sl_recall)

outputs: (0.6923, 0.6923)

You could also use eval_accuracy_from_binary_prediction() fromdarts.ad.utilsto compute the recall on the output of theQuantileDetector`.

Note also that in 1-2 weeks we'll release the new Darts version with the refactored anomaly detection module (including an example notebook). So the API will change slightly (see the changes and PR here).

[ETTAN93]

@dennisbader how is the scores_preddefined?

[dennisbader]

It can be any numeric non-binary input series. The detector converts non-binary to binary. In the example above it was the output from KMeansScorer.score(). But you can also use it on other series as shown below:

from sklearn.metrics import recall_score

from darts import TimeSeries
from darts.ad import QuantileDetector
from darts.datasets import AirPassengersDataset

series = AirPassengersDataset().load()

# flag values above 400 as anomalies
anom_true = TimeSeries.from_dataframe(
    series.pd_dataframe() > 400
)

# darts
qd = QuantileDetector(high_quantile=0.95)
anom_pred = qd.fit_detect(series)
qd_recall = qd.eval_accuracy(anom_true, series, metric="recall")

# sklearn
sl_recall = recall_score(
    anom_true.slice_intersect(anom_pred).pd_series(),
    anom_pred.slice_intersect(anom_true).pd_series()
)

print(qd_recall, sl_recall)

gives (0.2857, 0.2857)