Online Anomaly Detection

PySAD provides methods for online/sequential anomaly detection, i.e. anomaly detection on streaming data, where the model updates itself as a new instance arrives.

Resource-Efficient

Streaming methods efficiently handle the limited memory and processing time requirements of the data streams so that they can be used in near real-time. The methods can only store an instance or a small window of recent instances.

Complete

PySAD contains stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, probability calibrators and more. In addition to streaming models, PySAD also provides integrations for batch anomaly detectors of the PyOD so that they can be used in the streaming setting.

Comprehensive

PySAD serves models that are specifically designed for both univariate and multivariate data. Furthermore, one can experiment via PySAD in supervised, semi-supervised and unsupervised setting.

User Friendly

Users with any experience level can easily use PySAD. One can easily design experiments and combine the tools in the framework. Moreover, the existing methods in PySAD are easy to extend.

Free and Open Source Software (FOSS)

PySAD is distributed under BSD License 2.0 and favors FOSS principles.

Quick Start

Here’s a simple example showing how to use PySAD for anomaly detection on streaming data:

# Import modules.
from pysad.evaluation import AUROCMetric
from pysad.models import LODA
from pysad.utils import Data


model = LODA()  # Init model
metric = AUROCMetric()  # Init area under receiver-operating- characteristics curve metric
streaming_data = Data().get_iterator("arrhythmia.mat")  # Get data streamer.

for x, y_true in streaming_data:  # Stream data.
    anomaly_score = model.fit_score_partial(x)  # Fit the instance to model and score the instance.

    metric.update(y_true, anomaly_score)  # Update the AUROC metric.

# Output the resulting AUROCMetric.
print(f"Area under ROC metric is {metric.get()}.")