Package for information estimation, independence test, causal structure mining, etc.
Project description
general-information-estimation-framework (GIEF)
Package Info
https://pypi.org/search/?q=giefstat
Project Purpose
This project aims to lay a basis for:
- computing higher-order information interactions between different types (discrete & continuous) of variables
- uncovering complex associations and causal relationships in high-dimensional, nonlinear and nonstationary data
Project Structure
|-- giefstat
| |
| |-- __init__.py
| |-- setting.py # 项目设置
| |-- util.py # 通用工具
| |
| |-- coefficient # 基于KNN和KDE等方法的数据信息计算和关联估计方法
| | |-- __init__.py
| | |
| | |-- corr_coeff # 常见相关系数
| | | |-- __init__.py
| | | |-- coeff.py # Pearson系数、Spearman系数和距离相关系数
| | |
| | |-- mi_gief # 通用信息估计
| | | |-- __init__.py
| | | |-- entropy # 信息熵
| | | | |-- __init__.py
| | | | |-- cond_entropy.py # 条件熵估计
| | | | |-- marg_entropy.py # 边际熵估计
| | | |-- mutual_info
| | | |-- __init__.py
| | | |-- _kraskov.py # 由Kraskov等提出的K近邻互信息估计
| | | |-- _ross.py # 由Ross等提出的互信息估计
| | | |-- mi.py # 互信息估计
| | | |-- cmi.py # 条件互信息估计
| | |
| | |-- mi_kde # 基于KDE的边际熵和互信息估计
| | | |-- __init__.py
| | | |-- kde.py
| | |
| | |-- mic # 最大信息系数
| | | |-- __init__.py
| | | |-- _mic_rmic.py # MIC和RMIC计算
| | | |-- mi_cmi.py # 基于MIC和RMIC的互信息和条件互信息估计
| | | |-- rgsr.pickle # RMIC中用于修正MIC下界的回归模型
| | |
| | |-- mi_model
| | | |-- __init__.py
| | | |-- mi_cmi.py # 基于机器学习预测模型的关联和条件关联系数估计
| | |
| | |-- mi_quant
| | |-- __init__.py
| | |-- _quant_darbellay.py # Darbellay数据离散化方法
| | |-- mi_classic.py # 基于经典等距和等频离散化的互信息估计
| | |-- mi_darbellay.py # 基于Darbellay离散化的互信息估计
| |
| |-- independence_test
| | |-- __init__.py
| | |-- surrog_indep_test.py # 基于Bootstrap的关联度量和独立性检验
| |
| |-- time_series # 时序关联和因果挖掘
| | |-- __init__.py
| | |-- util.py # 序列符号化、时延峰解析等工具
| | |-- td_assoc_analysis.py # 成对时延关联分析
| | |-- transfer_entropy.py # 成对时延传递熵检验
| | |-- partial_transfer_entropy.py # 成对时延偏传递熵检验
|
|-- test # 对应方法的单元测试和应用案例
| |-- coefficient
| | |-- corr_coeff
| | | |-- test.py # unittest
| | |-- mi_gief
| | | |-- test.py # unittest
| | |-- mi_kde
| | | |-- test.py # unittest
| | |-- mi_model
| | |-- |-- test.py # unittest
| | |-- mi_quant
| | |-- |-- test.py # unittest
| | |-- mic
| | |-- |-- test.py # unittest
| |
| |-- independence_test
| |-- |-- test_surrog_indep_test.py # 案例测试
| |
| |-- time_series
| |-- |-- test_td_assoc_analysis.py # 案例测试
| |-- |-- test_transfer_entropy.py # 案例测试
Notes
- 根据FGD测试结果, 离散变量可被stdize_values处理后视为连续变量, 代入MI-GIEF中进行计算
- stdize_values在对连续变量处理过程时加入了噪音并归一化
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
Todos
- 紧凑时序因果挖掘
- 基于贝叶斯网络的独立性检验
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