Code for the paper RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk. This repository contains the implementation of RiskLoc and all baseline multi-dimensional root cause localization methods as well as code to generate synthetic data as described in the paper.
Implemented algorithms: RiskLoc, AutoRoot, Squeeze, HotSpot, and Adtributor (normal and recursive).
- pandas
- numpy
- scipy
- kneed (for squeeze)
- loguru (for squeeze)
To run, use the run.py file. There are a couple of options, either to use a single file or to run all files in a directory (including all subdirectories).
Example of running a single file using RiskLoc in debug mode:
python run.py riskloc --run-path /data/B0/B_cuboid_layer_1_n_ele_1/1450653900.csv --debug
Example of running all files in a particular setting for a dataset (setting derived to True):
python run.py riskloc --run-path /data/D/B_cuboid_layer_3_n_ele_3 --derived
Example of running all files in a dataset:
python run.py riskloc --run-path /data/B0
Example of running all datasets with 20 threads:
python run.py riskloc --n-threads 20
Changing riskloc to any of the supported algorithms will run those instead, see below.
The supported algorithms are:
$ python run.py --help
usage: run.py [-h] {riskloc,autoroot,squeeze,old squeeze,hotspot,r_adtributor,adtributor} ...
RiskLoc
positional arguments: {riskloc,autoroot,squeeze,old squeeze,hotspot,r_adtributor,adtributor}
algorithm specific help
riskloc riskloc help
autoroot autoroot help
squeeze squeeze help
hotspot autoroot help
r_adtributor r_adtributor help
adtributor adtributor help
optional arguments:
-h, --help show this help message and exit
The code for Squeeze is adapted from the recently released code from the original publication: https://github.com/NetManAIOps/Squeeze.
To see the algorithm-specific arguments run: python run.py 'algorithm' --help. For example, for RiskLoc:
$ python run.py riskloc --help
usage: run.py riskloc [-h] [--data-root DATA_ROOT] [--run-path RUN_PATH] [--derived [DERIVED]] [--n-threads N_THREADS] [--output-suffix OUTPUT_SUFFIX] [--debug [DEBUG]] [--risk-threshold RISK_THRESHOLD] [--ep-prop-threshold EP_PROP_THRESHOLD]
optional arguments:
-h, --help show this help message and exit
--data-root DATA_ROOT root directory for all datasets (default ./data/)
--run-path RUN_PATH directory or file to be run;
if a directory, any subdirectories will be considered as well;
must contain data-path as a prefix
--derived [DERIVED] derived dataset (defaults to True for the D dataset and False for others)
--n-threads N_THREADS number of threads to run
--output-suffix OUTPUT_SUFFIX suffix for output file
--debug [DEBUG] debug mode
--risk-threshold RISK_THRESHOLD risk threshold
--pep-threshold PEP_THRESHOLD proportional explanatory power threshold
--prune-elements [PRUNE_ELEMENTS] use element pruning (True/False)
The risk-threshold and pep-threshold arguments are specific for the RiskLoc while the rest are shared by all algorithms. To see the algorithm-specific arguments for other algorithms simply run them with the --help flag or check the code in run.py.
The semi-synthetic datasets can be downloaded from: https://github.com/NetManAIOps/Squeeze. To run these, place them within the data/ directory and name them: A, B0, B1, B2, B3, B4, and D, respectively.
The three synthetic datasets used in the paper can be generated using generate_dataset.py as follows.
S dataset:
python generate_dataset.py --num 1000 --dataset-name S --seed 121
L dataset:
python generate_dataset.py --num 1000 --dataset-name L --seed 122 --dims 10 24 10 15 --noise-level 0.0 0.1 --anomaly-severity 0.5 1.0 --anomaly-deviation 0.0 0.0 --num-anomaly 1 5 --num-anomaly-elements 1 1 --only-last-layer
H dataset:
python generate_dataset.py --num 100 --dataset-name H --seed 123 --dims 10 5 250 20 8 12
In addition, new, interesting datasets can be created using generate_dataset.py for extended empirical verification and research purposes. Supported input arguments can be found at the beginning of the generate_dataset.py file or using the --help flag.
@article{riskloc,
title={RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk},
author={Kalander, Marcus},
journal={arXiv preprint arXiv:2205.10004},
year={2022}
}