Demo library
Project description
Random Robust Cut Forest - Moody's Analytics
Installation
pip install RRCF_Outlier_Detection
Objectives
-
Use the RRCTree included in
rrcf
package as a week learner for creating a forest, incrementing the outlierdetection power.
-
Do the code more user-friendly for its fast implementation
-
Automatize the process of outlier detection through the usage of 3 Sigma analysis
Functions
Outlier_Detector ( x, num_trees, num_samples per tree )
-
Trains the RRCForest
-
Parameters:
x
Numpy Array / Data from which we want to detect outliersnum_trees
int / Number of trees that are going to be used a weak learners for the forestnum_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 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
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