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Causal discovery for time series

R 3.93% Python 24.23% MATLAB 3.35% C++ 0.30% C 0.01% HTML 32.78% M 0.01% Jupyter Notebook 35.24% Makefile 0.01% Batchfile 0.01% Shell 0.01% Roff 0.01% Cython 0.13%

causal_discovery_for_time_series's Introduction

causal_discovery_for_time_series

Package to test causal discovery algorithm on simulated and real data

THIS SOURCE CODE IS SUPPLIED AS IS WITHOUT WAR RANTY OF ANY KIND AND ITS AUTHOR AND THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH JAIR AND JAIRS PUBLISHERS AND DISTRIBUTORS DISCLAIM ANY AND ALL WARRANTIES INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTIES OR NON INFRINGEMENT THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE AND NEITHER THE AUTHOR NOR JAIR NOR JAIRS PUBLISHERS AND DISTRIBUTORS WILL BE LIABLE FOR DAM AGES OF ANY KIND RESULTING FROM ITS USE Without limiting the generality of the foregoing neither the author nor JAIR nor JAIR's publishers and distributors warrant that the Source Code will be errorfree will operate without interruption or will meet the needs of the user

Methods

Some algorithms are imported from other langauges such as R and Java

Test

To test algorithms on simulated data run:

python3 test_simulated_data.py method structure n_samples num_processor verbose

  • method: causal dicovery algorithms, choose from [GrangerPW, GrangerMV, TCDF, PCMCICMIknn, PCMCIParCorr, oCSE, PCTMI, tsFCI, VarLiNGAM, TiMINO, Dynotears]
  • structure: causal structure, choose from [fork, v_structure, diamond, 7ts2h]
  • n_samples: number of timestamps
  • num_processor: number of processors

Example: python3 test_fmri.py "NBCB" "fork" 1000 1 1

To test algorithms on fmri data run:

python3 test_simulated_data.py method num_processor verbose

Example: python3 test_fmri.py "NBCB" 1 1

causal_discovery_for_time_series's People

Stargazers

 avatar janis avatar DCO avatar Kexin avatar Yasuo Kabe avatar  avatar Omkar Pote avatar  avatar Alex avatar Nikolas Kougioulis avatar H.L. ZUO avatar  avatar Ce Guo avatar  avatar Suryansh Manocha avatar  avatar  avatar Xin Mu avatar Shubham Pachori avatar  avatar Ruoxin Xiong avatar Gian Marco Paldino avatar  avatar Yuxiao Cheng avatar jumpzjl avatar Egawati Panjei avatar Chanhui Lee avatar Linna Wang avatar  avatar Youngdong avatar nhfmaster avatar Dred avatar Ali Izadi avatar Adel avatar  avatar xixnin luo  avatar Huiyang Yi avatar Élise Zhang avatar  avatar DICP_Zhou avatar  avatar Xinyue Wang avatar Peiwen Li avatar  avatar  avatar  avatar Tianzhu Zhang avatar Serafina Di Gioia avatar Fei avatar ChesseBoo avatar  avatar Srikar Mutnuri avatar  avatar Hong Nguyen avatar Xiaojing Zhang avatar Xiaoyu Fang avatar  avatar  avatar  avatar Steven Mortier avatar  avatar Kishan Joshi avatar aqpyc avatar  avatar Manmeet Singh avatar E-Tiger Studio avatar Ployplearn Ravivanpong avatar return-sleep avatar  avatar Uzma Hasan avatar Keegan Hu avatar  avatar  avatar Kaipei Ding avatar  avatar  avatar  avatar

Watchers

Charles Assaad avatar

causal_discovery_for_time_series's Issues

Why are so many dependencies missing?

1、ModuleNotFoundError: No module named 'baselines.scripts_python.python_packages.TsKIKO.tskiko_mv'
2、ModuleNotFoundError: No module named 'baselines.scripts_python.python_packages.CITMI.tigramite.tigramite'

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