Random Robust Cut Forest - Moody's Analytics
Installation
pip install RRCF_Outlier_Detection
Objectives
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Use the RRCTree included in
rrcfpackage as a week learner for creating a forest, incrementing the outlierdetection power.
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Do the code more user-friendly for its fast implementation
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Automatize the process of outlier detection through the usage of 3 Sigma analysis
Functions
Outlier_Detector ( x, num_trees, num_samples per tree )
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Trains the RRCForest
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Parameters:
xNumpy Array / Data from which we want to detect outliersnum_treesint / Number of trees that are going to be used a weak learners for the forestnum_samples per treeint / Number of samples per tree. this parameter is recommended to be established as (1 / Estimated Proportion of Outliers) -
Attributes:
rrcf_outlier_score ()Returns a Pandas' series with the CoDist scores for all input samplesrrcf_outlier_detector ()Returns a Numpy array with the detected outliers from the input samples
Citing
M. Bartos, A. Mullapudi, & S. Troutman, rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams, in: Journal of Open Source Software, The Open Journal, Volume 4, Number 35. 2019
1 maintainer
Metadata
1 maintainer
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