KNNDetector

KNNDetector(k=5,
            metric=Euclidean,
            algorithm=:kdtree,
            leafsize=10,
            reorder=true,
            reduction=:maximum)

Calculate the anomaly score of an instance based on the distance to its k-nearest neighbors.

Parameters

k::Integer

Number of neighbors (must be greater than 0).

metric::Metric

This is one of the Metric types defined in the Distances.jl package. It is possible to define your own metrics by creating new types that are subtypes of Metric.

algorithm::Symbol

One of (:kdtree, :balltree). In a kdtree, points are recursively split into groups using hyper-planes. Therefore a KDTree only works with axis aligned metrics which are: Euclidean, Chebyshev, Minkowski and Cityblock. A brutetree linearly searches all points in a brute force fashion and works with any Metric. A balltree recursively splits points into groups bounded by hyper-spheres and works with any Metric.

static::Union{Bool, Symbol}

One of (true, false, :auto). Whether the input data for fitting and transform should be statically or dynamically allocated. If true, the data is statically allocated. If false, the data is dynamically allocated. If :auto, the data is dynamically allocated if the product of all dimensions except the last is greater than 100.

leafsize::Int

Determines at what number of points to stop splitting the tree further. There is a trade-off between traversing the tree and having to evaluate the metric function for increasing number of points.

reorder::Bool

While building the tree this will put points close in distance close in memory since this helps with cache locality. In this case, a copy of the original data will be made so that the original data is left unmodified. This can have a significant impact on performance and is by default set to true.

parallel::Bool

Parallelize score and predict using all threads available. The number of threads can be set with the JULIA_NUM_THREADS environment variable. Note: fit is not parallel.

reduction::Symbol

One of (:maximum, :median, :mean). (reduction=:maximum) was proposed by [1]. Angiulli et al. [2] proposed sum to reduce the distances, but mean has been implemented for numerical stability.

Examples

using OutlierDetection: KNNDetector, fit, transform
detector = KNNDetector()
X = rand(10, 100)
model, result = fit(detector, X; verbosity=0)
test_scores = transform(detector, model, X)

References

[1] Ramaswamy, Sridhar; Rastogi, Rajeev; Shim, Kyuseok (2000): Efficient Algorithms for Mining Outliers from Large Data Sets.

[2] Angiulli, Fabrizio; Pizzuti, Clara (2002): Fast Outlier Detection in High Dimensional Spaces.