stranger is a framework for unsupervised anomalies detection that simplifies the user experience because the one does not need to be concerned with the many packages and functions that are required. It acts as a wrapper around existing packages (“a la Caret”) and provides in a clean and uniform toolkit for evaluation explaination reporting routines. Hence the name
stranger taht stands for “Simple Toolkit in R for Anomalies Get Explain and Report”.
stranger provides wrapper around several packages that contain anomaly detection routines. One approach is called a
weird. Currently implemented methods (weirds) can be obtained by using
weird_list function. Underlying methods deal with: Angle-based Outlier Factor, autoencode, isolation Forest, kmeans (), k-Nearest Neighbour, Local Outlier Factor, Mahalanobis distance, Semi-robust principal components > distances, randomforest outlier metric.
Obviously, to be able to exploit
stranger, user will need to have various packages installed – those ones containing computational routines.
stranger, user has at disposal an analysis workflow.
Main functions associated with proposed analysis workflow deal with:
crazyfy: treating missing values, factors/charaters variables (methods usually require numeric values), deduplicate data (but keeping a matching table to restore all records), scaling (important!)…
strange(using one weird) or
stranger(using many weirds at once).
In addition, those steps lead to objects having a specific S3 class and some visualization is possible thanks to dedicated
We did write some vignette to accompany you in the discovery of anomalies using
stranger. We recommend to read vignettes in the following order:
strangerfunction, the possibility to
merge, stack (aggregate) methods and normalize metics with
singularizeand also derive your own anomalies based on manual filtering.
stranger is not currently available on CRAN. Install it from github:
# install.packages(devtools) devtools::install_github("welovedatascience/stranger")
get_infomethods for every class