We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project.

翻译：我们提出了coniferest，一个用Python编写的开源通用主动异常检测框架。本文描述了该软件包的设计与实现算法。目前，该框架通过Isolation forest算法支持静态离群值检测分析。此外，Active Anomaly Discovery（AAD）和Pineforest算法可用于处理主动异常检测问题。我们在系列合成数据集上评估了算法与软件包性能。同时，我们介绍了在SNAD项目中将该软件包应用于实际天文数据以执行主动异常检测任务的若干成功案例。