Metadata-Version: 2.1
Name: giefstat
Version: 0.0.7
Summary: Package for information estimation, independence test, etc.
Home-page: https://github.com/Ulti-Dreisteine/general-information-estimation-framework
Author: Dreisteine
Author-email: dreisteine@163.com
License: MIT
Description: ### general-information-estimation-framework (GIEF)
        
        ### Project Purpose
        
        This project aims for:
        1. computing higher-order information interactions between different types (discrete & continuous) of variables
        2. uncovering complex associations and causal relationships in high-dimensional data
        
        ### Project Structure
        
        ```
            |-- giefstat
            |   |-- __init__.py
            |   |-- setting.py          # 项目设置
            |   |-- util.py             # 通用工具
            |   |
            |   |-- estimate            # 基于KNN和KDE等方法的数据信息计算和关联估计方法
            |   |   |-- __init__.py
            |   |   |
            |   |   |-- correlation_coeff   # 常见相关系数
            |   |   |   |-- __init__.py
            |   |   |   |-- coefficient.py
            |   |   |
            |   |   |-- 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
            |   |   |
            |   |   |-- kde
            |   |   |   |-- __init__.py
            |   |   |   |-- kde.py  # 基于KDE的边际熵和互信息估计
            |   |   |
            |   |   |-- mic
            |   |   |   |-- __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
            |   |   |
            |   |   |-- model_based
            |   |   |   |-- __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
            |   |   |
            |   |   |-- quant_based
            |   |       |-- __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
            |   |   
            |   |-- statistical_tools
            |       |-- __init__.py
            |       |-- surrog_indep_test.py                # 基于代用数据的关联度量和独立性检验
            |       |-- time_delayed_association.py         # 时延关联检测
            |
            |-- script
                |-- 独立性检验
                |   |-- indep_test.py               # 独立性检验测试
                |   |-- statistical_power_test.py   # 统计效能测试
                |
                |-- 条件独立性检验
                |   |-- cond_indep_test.py          # 条件独立性检验测试
                |
                |-- 时延关联检测
                    |-- td_assoc_test.py            # 时延关联检测测试
        
        ```
        
        file dependency plot：
        
        ```mermaid
        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
        
        1. A. Kraskov, H. Stoegbauer, P. Grassberger: Estimating Mutual Information. Physical Review E, 2003.
        2. D. Lombardi, S. Pant: A Non-Parametric K-Nearest Neighbor Entropy Estimator. Physical Review E, 2015.
        3. B. C. Ross: Mutual Information between Discrete and Continuous Data Sets. PLoS One, 2014.
        4. https://github.com/dizcza/entropy-estimators
        5. https://github.com/danielhomola/mifs
        
        ### TODOs
        
        1. ~~script/独立性检验/statistical_power_test.py~~
        2. ~~scipt/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~~
        3. statistital_significance/surrog_indep_test.py中通过随机抽样获得关联值分布
        4. ~~继续estimate中mic及剩下的部分的封装测试~~
        5. 一致性测试
        6. 时效性测试
        
Keywords: python,information estimation
Platform: UNKNOWN
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
