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Demo library

Project description

Random Robust Cut Forest - Moody's Analytics


pip install RRCF_Outlier_Detection


  1. Use the RRCTree included in rrcf package as a week learner for creating a forest, incrementing the outlier

    detection power.

  2. Do the code more user-friendly for its fast implementation

  3. Automatize the process of outlier detection through the usage of 3 Sigma analysis


Outlier_Detector ( x, num_trees, num_samples per tree )

  • Trains the RRCForest

  • Parameters:

    x Numpy Array / Data from which we want to detect outliers

    num_trees int / Number of trees that are going to be used a weak learners for the forest

    num_samples per tree int / 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 samples

    rrcf_outlier_detector () Returns a Numpy array with the detected outliers from the input samples


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

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