Package for information estimation, independence test, etc.
Project description
general-information-estimation-framework
TODOs
script/独立性检验/statistical_power_test.pyscipt/时延关联检测/main.py中背景值较高, 与代用数据计算结果不吻合- statistital_significance/surrog_indep_test.py中通过随机抽样获得关联值分布
Project Purpose
This project aims for:
- computing higher-order information interactions between different types (discrete & continuous) of variables
- uncovering complex associations and causal relationships in high-dimensional data
Project Structure
|-- general-information-estimation-framework
|-- estimate # information estimation base on KNN, KDE, etc.
|-- __init__.py
|-- setting.py # NOTE var types: "d" for discrete and "c" for continuous
|-- util.py
|-- _univar_encoding.py # encoding one-dimensional variable data
|-- gief # general information estimation
__init__.py
|-- entropy # marginal and conditional entropies
|-- __init__.py
|-- cond_entropy.py
|-- marg_entropy.py
|-- mutual_info
|-- __init__.py
|-- _kraskov.py # KNN estimation proposed by Kraskov et al.
|-- _ross.py # proposed by Ross
|-- mi.py # mutual information estimation
|-- cmi.py # conditional mutual information estimation
|-- statistical_tools
|-- (deprecated) bootstrap_coeff.py # association measure based on bootstrap test
|-- surrog_indep_test.py # association measure and independence test based on surrogate data
|-- time_delayed_association.py # time delayed association detection
|-- script
|-- 独立性检验
|-- indep_test.py # independence test
|-- statistical_power_test.py # statistical power test
|-- 条件独立性检验
|-- cond_indep_test.py # conditional independence test
|-- 时延关联检测
file dependency plot:
flowchart LR
subgraph estimate
subgraph gief
H-G & MI-G & CMI-G
end
subgraph kde
MI-KDE
end
subgraph other
coefficient --> PearsonCorr & SpearmanCorr & DistCorr
end
subgraph quant_based
MI-Classic --> MI-cut & MI-qcut
MI-Darbellay
end
end
subgraph statistical_tools
cal_general_assoc --> surrog_indep_test
surrog_indep_test --> time_delayed_assocication
end
estimate --cal_assoc & cal_cond_assoc--> cal_general_assoc
References
- A. Kraskov, H. Stoegbauer, P. Grassberger: Estimating Mutual Information. Physical Review E, 2003.
- D. Lombardi, S. Pant: A Non-Parametric K-Nearest Neighbor Entropy Estimator. Physical Review E, 2015.
- B. C. Ross: Mutual Information between Discrete and Continuous Data Sets. PLoS One, 2014.
- https://github.com/dizcza/entropy-estimators
- https://github.com/danielhomola/mifs
Project details
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