├── README.md ├── Supplemental Material.pdf ├── data ├── LST1000.csv ├── LST1500.csv ├── LST2000.csv ├── LST250.csv ├── LST2500.csv ├── LST3000.csv ├── LST4000.csv ├── LST500.csv ├── LST5000.csv ├── LST6000.csv ├── LST7000.csv ├── LST750.csv ├── LST8000.csv ├── LST9000.csv ├── PZH_LST1000.csv ├── PZH_LST2000.csv ├── PZH_LST3000.csv ├── PZH_LST4000.csv ├── PZH_LST5000.csv ├── PZH_LST6000.csv ├── h1n1_100.csv ├── h1n1_150.csv ├── h1n1_50.csv ├── ndvi_10.csv ├── ndvi_20.csv ├── ndvi_30.csv ├── ndvi_40.csv ├── ndvi_5.csv ├── ndvi_50.csv ├── sim300.csv ├── sim500.csv └── sim700.csv ├── img └── schematic diagram.png ├── omgd.py ├── omgd.yml ├── requirements.txt ├── sample.py ├── simulation.py ├── test.ipynb └── test.py /README.md: -------------------------------------------------------------------------------- 1 | # Optimal Multivariate-stratification Geographical Detector 2 | ## How to cite 3 | Guo, Y., Wu, Z., Zheng, Z., & Li, X. (2024). An optimal multivariate-stratification geographical detector model for revealing the impact of multi-factor combinations on the dependent variable. GIScience & Remote Sensing, 61(1). https://doi.org/10.1080/15481603.2024.2422941 4 | 5 | ## Basic knowledge of Geodetector 6 | http://geodetector.cn/ 7 | 8 | ## Schematic diagram 9 | ![image](https://github.com/gisgyf/OMGD/blob/main/img/schematic%20diagram.png) 10 | 11 | ## Files description 12 | - ***data*** folder contains all the case data used in the article, stored in csv file format.
13 | - ***omgd.py*** consists of the main functions of the Optimal Multivariate-stratification Geographical Detector (OMGD) model, including computation functions and visualization functions.
14 | - ***sample.py*** is used to sample data at a certain ratio (e.g. 50%).
15 | - ***simulation.py*** offers simulation results discussed in Section 3.2.
16 | - ***test.ipynb*** and ***test.py*** are used to reproduce the results shown in the main text.
17 | - ***Supplemental material.pdf*** is the overview of functions of the OMGD model.
18 | - ***requirements.txt*** and ***omgd.yml*** helps you quickly build Python / anaconda environment, respectivly. 19 | 20 | ## Python (Anaconda) Environment 21 | You can install the dependencies using pip, or download the conda environment(folder) directly and configurate it. 22 | ### Install using pip 23 | Python >= 3.7 & Python < 3.11 24 | > cd ***your folder path***
25 | > pip install wheel==0.41.2
26 | > pip install -r requirements.txt 27 | ### Conda environment 28 | Download anaconda from https://www.anaconda.com/, open anaconda promt (using search bar), change the location to the folder which contains ***'omgd.yml'***, change the prefix in the ***'omgd.yml'*** to the target location, input ***'conda env create -f omgd.yml -n omgd'*** and ***'conda env list'*** to check if the omgd environment is configurated. 29 | 30 | ## How to use 31 | Open ***test.ipynb*** or ***test.py***, run the code to see if it works. 32 | 33 | ## Description of main functions 34 | 35 | ### def scale_detector(path_list: Sequence, Y, factors:Sequence, disc_interval:Sequence, type_factors:Sequence=[], quantile:float=0.8, n_variates=1, random_state=0) 36 | 37 | - scale detector can be used to detect the optimal spatial scale for spatial stratified heterogeneity analysis.
38 | - ***path_list*** is a list includes various files location (different spatial scales), ***Y*** is the dependent variable field name and ***factors*** is a list containing field name of explanatory variables.
39 | - ***disc_interval*** specifys the classification (stratification) number (e.g. [3, 7] indicates the explanatory variables / explanatory variables combinations are classified into 3, 4, 5, 6 and 7 categories iterately to find the optimal classification number.
40 | - ***type_factor*** specifys the fields (name) which is already categorized rather than continuous.
41 | - ***quantile*** defines how many variables are used to calculated the avaergae ***Q*** value (e.g., we have 10 variables / variables combinations, and the quantile is 0.8, then only the top 3 ***Q*** values are used to calculate the avaergae ***Q*** value).
42 | - ***n_variates*** indicates how many variables are used in the analysis.
43 | - the scale detector function returns scale_result (dataframe) and the best_scale (location of the optimal scale data file), the scale_result can be plotted using scale_plot (scale_result, size_list=[], dpi=100, unit='') 44 | 45 | ### def omgd(df:pd.DataFrame, Y, factors:Sequence, n_variates:int, disc_interval:Sequence, type_factors:Sequence=[], random_state=0) 46 | 47 | - one step omgd model, returns a dictionary (omgd_result) contains original dataframe (omgd_result['original']), classification result (omgd_result['classify']), factor detector result (omgd_result['factor']),
48 | - interaction detector result (omgd_result['interaction']), risk detector result (omgd_result['risk']) and ecological detector result (omgd_result['ecological']) 49 | 50 | ### def omgd.factor_detector(df, Y, factors:Sequence) 51 | ### def omgd.interation_detector(df:pd.DataFrame, Y, factors:Sequence) 52 | ### def omgd.risk_detector(df, Y, factors:Sequence) 53 | ### def omgd.ecological_detector(df, Y, factors:Sequence) 54 | 55 | - functions of four basic detectors.
56 | - ***df***: dataframe, includes ***Y*** fields (dependent variable) and ***factors*** fields (explanatory variables).
57 | - omgd.factor_detector returns a dataframe which is sorted according to Geodetector ***Q*** value.
58 | - omgd.interation_detector returns a dataframe which is sorted according to Geodetector ***Q*** value.
59 | - omgd.risk_detector returns a list, which includes the significance test results and the mean values of each strata.
60 | - omgd.ecological_detector returns a dataframe.
61 | - the four baisc detectors can be plotting using ***factor_plot***, ***factor_plot***, ***risk_plot*** and ***ecological_plot***, respectively. (e.g. omgd.factor_plot(factor_result)).
62 | 63 | ### def classify(X, n_clusters, classify_result, colname, random_state=0) 64 | 65 | - function classify is used to automatically classify (discritize) continuous variables or variables combinations into ***n_clusters*** catogory, ***X*** is the values dataframe (explanatory variables), the result is stored in ***classify_result*** and colname is made up of single or multiple fields (explanatory variables) spliced together using '_'.
66 | - the classification result can be plotted using 'classify_plot(original_df:pd.DataFrame, classify_df:pd.DataFrame, dpi=100, nrows=0, ncols=0, unit_list=[])' 67 | 68 | 69 | 70 | -------------------------------------------------------------------------------- /Supplemental Material.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gisgyf/OMGD/d312a52d6fcbec302d2ad200d8459582edd2b202/Supplemental Material.pdf -------------------------------------------------------------------------------- /data/LST3000.csv: -------------------------------------------------------------------------------- 1 | LST,Built,DEM,MNDWI,NDBI,NDVI,Roads 2 | 42.18946135,0.262234534,116.1655569,-0.291674095,-0.16236218,0.771018955,0.001950149 3 | 35.97625783,2.678755409,75.9409124,-0.31639468,-0.194397774,0.756432876,0.002142684 4 | 35.4883555,0.186587591,60.75580959,-0.263169707,-0.222627719,0.762068001,0.002486025 5 | 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43.80427938,0.129166667,1687.063437,-0.062449097,-0.025968206,0.725298418,0.141217119 228 | 41.28616613,0.156666667,1738.324939,-0.159103775,-0.036567555,0.707346753,0.496145916 229 | 45.27638072,0.039722222,1422.249967,-0.338904204,0.109966287,0.594276259,0.0991939 230 | 29.09396537,0,2323.683773,-0.441744112,-0.126356046,0.810638295,0 231 | 32.12208043,0.015555556,1970.994218,-0.441311503,-0.071334142,0.778895347,0.001255127 232 | 35.50318961,0.045277778,1821.864595,-0.01875186,-0.050393465,0.776169254,0.633816789 233 | 33.91249248,0.004722222,1866.418513,0.115503482,-0.060539226,0.796859163,0.343286224 234 | 34.34493467,0.0625,1953.537379,-0.000726068,-0.06522383,0.758310252,0.476027334 235 | 35.06528428,0.051944444,1976.385983,-0.328317149,-0.050097958,0.709373362,0.245809274 236 | 42.86115557,0.003055556,1539.447948,-0.312464317,0.106721345,0.640799563,0.032634667 237 | 32.37659949,0,2053.792274,-0.434114111,-0.112321374,0.817608405,1.31698E-05 238 | 33.28572445,0,1989.682095,-0.344101801,-0.030813067,0.751186519,0.896424591 239 | 33.28817783,0.064166667,2000.870415,-0.212958767,-0.035181634,0.704666125,0.355400908 240 | 34.88493721,0.465,1861.975752,0.221689921,0.001083243,0.669387963,0.897112978 241 | 41.37176095,0.261666667,1696.376074,-0.215574828,0.051584971,0.677965652,0.949691954 242 | 43.30507756,0,1624.350327,-0.312514558,0.120823341,0.577363,1.147205935 243 | 35.80793553,0.133055556,1898.22797,-0.03323096,-0.025737089,0.708233096,1.031680758 244 | 41.18349071,0.062222222,1431.848697,-0.280525968,0.125905486,0.600256736,0.207128805 245 | 38.07804959,0,1623.973576,-0.084324027,0.038726675,0.664245443,0.447297872 246 | -------------------------------------------------------------------------------- /data/ndvi_50.csv: -------------------------------------------------------------------------------- 1 | "NDVIchange","Climatezone","Mining","Tempchange","Precipitation","GDP","Popdensity" 2 | 0.10371,"Bwk","low",0.28593,232.44,12.55,1.26792 3 | 0.02683,"Bwk","low",0.27702,215.61,2.69,0.80951 4 | 0.05612,"Bwk","low",0.23386,223.12,0,3.13873 5 | 0.10837,"Bsk","low",0.2798,424.96,69.48,8.82606 6 | 0.16432,"Bsk","very low",0.34199,454.01,135.44,15.46709 7 | 0.00023,"Bwk","low",0.40529,210.76,0,0.11753 8 | -5e-04,"Bwk","low",0.39631,195.53,3.71,0.0325 9 | 0.00171,"Bwk","low",0.37824,189.68,0.75,0.30539 10 | 0.01605,"Bwk","low",0.32607,206.4,11.89,1.8471 11 | 0.05455,"Bsk","low",0.19958,288.8,28.55,6.11528 12 | 0.0581,"Bsk","low",0.20447,334.57,253.91,14.01556 13 | 0.07261,"Bsk","low",0.25421,380.56,37.62,2.4841 14 | 0.0977,"Bsk","very low",0.40258,416.17,61.59,8.76882 15 | -0.00028,"Bwk","low",0.44307,163.12,0,0.08975 16 | -0.00782,"Bwk","low",0.44877,166.64,0,0.42707 17 | -0.00323,"Bwk","low",0.45345,178.56,9.89,13.57254 18 | 0.09033,"Bsk","low",0.32865,245.58,840.69,16.11005 19 | 0.00862,"Bsk","low",0.26974,244.99,14.6,6.61548 20 | 0.00833,"Bsk","medium",0.20043,282.11,36.45,11.65696 21 | 0.06632,"Bsk","medium",0.22131,333.6,50.82,2.90429 22 | 0.07828,"Bsk","very low",0.4302,384.57,606.28,18.37758 23 | 0.09743,"Bsk","very low",0.67744,430.35,52.56,12.38063 24 | 0.06109,"Bsk","very low",0.13271,214.49,98.5,2.6601 25 | -0.01095,"Bwk","low",0.44453,153.19,0,0.64161 26 | 0.0038,"Bwk","low",0.51468,156.98,0,3.24514 27 | 0.05151,"Bwk","low",0.44476,211.79,37.21,16.95255 28 | 0.0328,"Bwk","very low",0.3824,198.52,417.55,33.06157 29 | 0.04577,"Bsk","medium",0.27598,231.96,59.43,19.34037 30 | 0.05068,"Bsk","medium",0.23292,291.93,359.63,6.9411 31 | 0.06652,"Bsk","very low",0.46327,347.5,19.57,6.4269 32 | 0.11891,"Bsk","very low",0.7277,399.24,187.24,8.40486 33 | 0.15882,"Bsk","very high",0.73092,457.78,253.68,26.22565 34 | 0.02382,"Bwk","very low",-0.00859,192.1,2.51,0.77025 35 | 0.00536,"Bwk","very low",0.03602,150.98,55.48,2.36215 36 | 0.00544,"Bwk","very low",0.07342,142.5,0,2.06587 37 | -0.00925,"Bwk","very low",0.23885,117.26,27.3,0.37325 38 | -0.013,"Bwk","very low",0.29654,111,1.82,0.55065 39 | -0.00258,"Bwk","low",0.41362,132.12,0,0.12233 40 | -0.00603,"Bwk","low",0.53061,131.43,1.07,1.26985 41 | -0.00693,"Bwk","low",0.64297,128.82,103.51,1.7106 42 | 0.00227,"Bwk","low",0.59854,160.08,1.28,6.80794 43 | 0.00975,"Bwk","medium",0.50879,191.68,4474.42,215.69767 44 | 0.03483,"Bsk","medium",0.37975,235.84,0,4.6853 45 | 0.07236,"Bsk","medium",0.34358,267.3,40.63,6.71234 46 | 0.09766,"Bsk","medium",0.38731,321.65,505.35,8.41796 47 | 0.1416,"Bsk","very low",0.53335,382.06,351.27,17.49128 48 | 0.16514,"Bsk","high",0.63288,433.57,6481.39,247.18221 49 | 0.19327,"Dwb","very high",0.62578,476.52,949.97,57.88698 50 | 0.24308,"Dwa","very high",0.62665,468.96,2292.64,71.3371 51 | -0.01681,"Bwk","very low",-0.10436,138.54,1.64,0.15391 52 | -0.01408,"Bwk","very low",-0.06757,128.72,2.8,0.15825 53 | -0.01503,"Bwk","very low",-0.01222,107.74,0.93,0.28461 54 | -0.01438,"Bwk","very low",0.06999,99.4,0.63,1.03442 55 | -0.01239,"Bwk","very low",0.09503,129.43,0.16,0.31828 56 | -0.01062,"Bwk","very low",0.1518,139.33,3.08,0.31874 57 | -0.01663,"Bwk","very low",0.24188,130.28,4.69,0.29039 58 | -0.01102,"Bwk","low",0.2931,146.94,55.27,0.19933 59 | -0.00935,"Bwk","low",0.41843,154.71,0,0.5524 60 | -0.02249,"Bwk","low",0.67429,119.73,0,1.43382 61 | -0.02,"Bwk","low",0.77616,120.32,2.46,0.9926 62 | 0.01962,"Bwk","medium",0.71419,162.64,654.53,43.96936 63 | 0.01534,"Bwk","very low",0.62095,208.65,6.54,4.60726 64 | 0.07069,"Bwk","very low",0.54819,235.51,0.83,3.25296 65 | 0.09524,"Bsk","very low",0.43777,290.83,86.94,9.42442 66 | 0.10385,"Bsk","high",0.49501,358.12,284.68,28.304 67 | 0.11887,"Bsk","high",0.64847,398.34,431.51,26.08772 68 | 0.18688,"Bsk","high",0.71899,429.6,459.29,24.4657 69 | 0.20422,"Dwb","very high",0.72836,447.49,1139.57,57.00245 70 | 0.23313,"Dwb","very low",0.72943,446.09,1484.91,84.57889 71 | 0.23313,"Dwb","very low",0.71354,462.09,150.57,28.96063 72 | -0.02066,"Bwk","very low",-0.19173,118.5,1.3,0.37666 73 | -0.02044,"Bwk","very low",-0.15071,103.49,0.44,0.03922 74 | -0.0179,"Bwk","very low",-0.08666,94.7,0.08,0.01235 75 | -0.01768,"Bwk","very low",-0.01481,85.98,0.02,0 76 | -0.01685,"Bwk","very low",0.04185,90.64,0.39,0.11882 77 | -0.01947,"Bwk","very low",0.09621,102.81,0,0.10819 78 | -0.01873,"Bwk","very low",0.13956,120.36,1.95,0.20921 79 | -0.02104,"Bwk","very low",0.16514,147.89,1.66,0.17069 80 | -0.00765,"Bwk","very low",0.287,140.45,0,0.14935 81 | -0.00032,"Bwk","low",0.44058,146.55,0,0.24343 82 | -0.01201,"Bwk","low",0.61066,164,3.15,0.45495 83 | 0.01106,"Bwk","low",0.82702,140.97,44.27,0.80798 84 | 0.11591,"Bwk","very low",0.92718,135.36,1168.74,38.00145 85 | 0.07872,"Bwk","very low",0.88577,168.59,423.27,18.83133 86 | 0.01553,"Bwk","very low",0.77024,212.43,40.44,2.2475 87 | 0.08947,"Bsk","very low",0.69142,253.44,1527.09,42.01326 88 | 0.18362,"Bsk","very low",0.77647,311.09,440.02,27.28448 89 | 0.24736,"Bsk","very low",0.88964,341.29,19157.75,866.64702 90 | 0.29986,"Bsk","very low",0.86183,393.62,2497.9,215.82922 91 | 0.37633,"Bsk","very low",0.87787,412.18,2060.78,110.58034 92 | 0.35226,"Bsk","very low",0.91221,410.11,5992.27,140.57208 93 | 0.27029,"Dwb","very low",0.85185,440.5,241.51,57.16413 94 | 0.26411,"Bsk","very low",0.89085,428.9,1418.34,54.46767 95 | 0.23353,"Bsk","very low",0.9907,411.91,1325.82,107.85239 96 | 0.24335,"Bsk","very low",0.99289,443.42,360.3,35.28326 97 | -0.0204,"Bwk","very low",-0.1046,71.57,0.04,0.01938 98 | -0.01726,"Bwk","very low",-0.0039,64.61,0,0.01858 99 | -0.01384,"Bwk","very low",0.07347,63.6,0,0.02208 100 | -0.01689,"Bwk","very low",0.09853,79.11,0,0.01436 101 | -0.01718,"Bwk","very low",0.10972,104.86,0,0.09051 102 | -0.01849,"Bwk","very low",0.17449,114.89,0,0.12401 103 | -0.01698,"Bwk","very low",0.26895,119.76,0,0.12401 104 | -0.01479,"Bwk","low",0.38564,130.51,0,0.14759 105 | -0.00927,"Bwk","low",0.57877,129.6,9.22,0.16299 106 | -0.00581,"Bwk","low",0.77702,137.93,4.56,0.90879 107 | 0.00687,"Bwk","very low",0.85364,188.44,11.21,1.4061 108 | 0.16514,"Bwk","very low",1.01093,176.01,1034.48,55.55459 109 | 0.3386,"Bwk","very low",1.10164,161.88,5162.85,234.04933 110 | 0.28838,"Bwk","very low",1.06659,192.74,2132.75,71.67197 111 | 0.32578,"Bsk","very low",1.07166,226.77,525.96,53.40364 112 | 0.14256,"Bsk","very low",1.15046,287.85,308.83,21.55188 113 | 0.14687,"Bsk","very low",1.03052,359.62,854.08,29.90002 114 | 0.18952,"Bsk","very low",0.87835,420.42,890.93,42.94107 115 | 0.20811,"Dwb","very low",0.82799,457.18,428.23,28.99456 116 | 0.25048,"Dwb","very low",0.91181,426.87,13407.73,776.24196 117 | 0.2695,"Bsk","very low",0.96152,405.98,593.84,52.57662 118 | 0.20749,"Dwb","very low",0.94875,414.67,967.56,47.41804 119 | 0.1862,"Bsk","very low",1.08201,370.89,4685.27,191.53082 120 | 0.17832,"Bsk","very low",1.13683,375.03,1143.84,83.23164 121 | -0.01687,"Bwk","very low",-0.36629,108.86,1.33,0.15004 122 | -0.01773,"Bwk","very low",-0.30846,114.3,0.06,0.02312 123 | -0.0096,"Bwk","very low",0.02546,59.32,4.67,0.19133 124 | -0.01603,"Bwk","very low",0.1462,51.98,0,0.02729 125 | -0.01517,"Bwk","very low",0.22583,52.54,0,0.06951 126 | -0.01574,"Bwk","very low",0.22526,70.61,0,0.09611 127 | -0.01589,"Bwk","very low",0.24078,85.74,10.1,0.14507 128 | -0.02113,"Bwk","very low",0.31127,94.12,0,0.09738 129 | -0.00874,"Bwk","very low",0.45113,90.26,0,0.09384 130 | -0.01633,"Bwk","low",0.64184,88.59,0,0.11543 131 | -0.01445,"Bwk","low",0.79346,107.82,0,0.18282 132 | -0.01589,"Bwk","very low",0.99329,118.87,0,1.32805 133 | -0.00058,"Bwk","very low",1.0759,162.93,35.24,0.97104 134 | 0.01414,"Bwk","very low",1.02915,245.74,0.15,6.85408 135 | 0.09038,"Bwk","very low",1.16431,217.79,106.6,15.41163 136 | 0.12437,"Bwk","very low",1.2073,227.96,162.41,19.37508 137 | 0.10759,"Bsk","very low",1.27273,240.16,735.11,25.72422 138 | 0.06554,"Bsk","very low",1.28513,297.35,32.26,11.17454 139 | 0.13384,"Bsk","very low",1.21241,333.26,245.53,11.31966 140 | 0.18395,"Bsk","very low",1.14,346.2,195.77,19.82514 141 | 0.14309,"Bsk","very low",1.08726,368.64,291.2,14.22602 142 | 0.20121,"Bsk","very low",1.10244,360.31,873.59,43.00202 143 | 0.19168,"Dwb","very low",1.0649,374.84,399.27,22.13708 144 | 0.1362,"Dwb","very low",1.11836,353.99,850.81,38.32817 145 | 0.12161,"Bsk","very low",1.19825,337.69,1999.65,75.9303 146 | 0.19874,"Bsk","very low",1.26555,339.31,301.34,53.71673 147 | -0.0222,"Bwk","very low",-0.19871,75.36,0.19,0.01406 148 | -0.02209,"Bwk","very low",-0.12703,84.61,0,0 149 | -0.01787,"Bwk","very low",0.03021,69.04,9.71,0.37315 150 | -0.01397,"Bwk","very low",0.22444,48.49,7.27,0.42877 151 | -0.01431,"Bwk","very low",0.35895,47.46,0,0.05488 152 | -0.02118,"Bwk","very low",0.4469,44.92,0,0.22402 153 | -0.01803,"Bwk","very low",0.45429,49.62,8.07,0.68404 154 | -0.01739,"Bwk","very low",0.4596,58.23,0,1.28567 155 | -0.01798,"Bwk","very low",0.50855,70.1,0,0.0662 156 | -0.01615,"Bwk","low",0.59666,82.06,0,0.07786 157 | -0.01854,"Bwk","low",0.75926,87.43,0,0.06707 158 | -0.02176,"Bwk","very low",1.13752,117.57,0,0.98189 159 | -0.02315,"Bwk","very low",1.27928,142.28,0,0.78482 160 | -0.0137,"Bwk","very low",1.34993,172.5,4.76,0.53044 161 | -0.0034,"Bwk","very low",1.32373,219.99,11.53,0.53143 162 | 0.01283,"Bwk","very low",1.33266,243.14,32.79,3.09105 163 | 0.04951,"Bsk","very low",1.36568,256.68,31.7,2.43165 164 | 0.05831,"Bsk","very low",1.38053,287.42,43.98,1.12042 165 | 0.05683,"Bsk","very low",1.40958,282.77,216.83,9.83419 166 | 0.04572,"Bsk","very low",1.41327,273.28,260.06,13.40717 167 | 0.02971,"Bsk","very low",1.39355,278.22,80.73,4.10342 168 | 0.06512,"Bsk","very low",1.38147,278.5,30.12,7.38473 169 | 0.0189,"Bsk","very low",1.34811,291.1,97.88,10.71574 170 | 0.04828,"Bsk","very low",1.39623,278.04,29.54,11.59147 171 | 0.10063,"Bsk","very low",1.38055,309.86,791.78,38.71184 172 | 0.12176,"Bsk","very low",1.42294,331.77,1542.66,51.67279 173 | 0.23504,"Dwb","very low",1.62388,390.75,863.75,34.36588 174 | 0.32473,"Dwb","very low",1.7058,488.61,1852,83.35584 175 | 0.32455,"Dwa","very low",2.19183,433.14,2353.45,217.26822 176 | -0.01845,"Bwk","very low",-0.13195,86.82,0,0 177 | -0.02135,"Bwk","very low",-0.02824,76.74,0,0 178 | -0.02165,"Bwk","very low",0.11231,62.2,0,0 179 | -0.02077,"Bwk","very low",0.28959,47.33,1.08,0.04717 180 | -0.0205,"Bwk","very low",0.45265,43.34,16.27,0.83572 181 | -0.01576,"Bwk","very low",0.59221,44.26,7.61,1.14901 182 | 0.00565,"Bwk","very low",0.66877,46.9,94.58,1.88931 183 | -0.01869,"Bwk","very low",0.64374,58.55,0,0.41327 184 | -0.01792,"Bwk","very low",0.56562,86.27,0,2.16416 185 | -0.02338,"Bwk","very low",1.5344,151.28,3.69,0.37372 186 | -0.01752,"Bwk","very low",1.56586,182.88,19.76,1.14702 187 | -0.01211,"Bwk","very low",1.58295,205.2,30.16,2.02289 188 | 0.0074,"Bwk","very low",1.63563,210.7,16.09,1.6626 189 | 0.02476,"Bwk","very low",1.66627,218.63,0.12,0.77614 190 | 0.01686,"Bwk","very low",1.7178,210.9,1.35,0.39642 191 | 0.02187,"Bsk","very low",1.71892,208.01,11.66,0.72811 192 | -4e-05,"Bsk","very low",1.71824,204.31,17.84,0.84416 193 | 0.01062,"Bsk","very low",1.66662,218.42,0.25,0.48598 194 | 0.00389,"Bsk","very low",1.6445,226.69,0,2.59714 195 | -0.00309,"Bsk","very low",1.67658,225.65,123.07,3.27301 196 | -0.01418,"Bsk","very low",1.67959,257.96,9.42,4.83495 197 | 0.02612,"Bsk","very low",1.73399,292.16,219.24,8.98708 198 | 0.08263,"Bsk","very low",1.85523,325.1,186.94,10.67953 199 | 0.17211,"Dwb","very low",1.9261,363.57,363.01,18.94066 200 | 0.21748,"Dwb","very low",1.90333,378.05,323.47,22.09991 201 | 0.27568,"Dwb","very low",1.83811,394.12,834.61,24.2432 202 | 0.33498,"Dwb","very low",1.40361,450.76,423.79,51.54844 203 | 0.29168,"Dwa","very low",1.71138,428.64,7151.28,252.21348 204 | 0.32779,"Dwb","very low",2.88908,452.19,378.11,58.99983 205 | -0.02119,"Bwk","very low",0.09843,80.63,1.26,0.10792 206 | -0.02069,"Bwk","very low",0.13921,84.22,3.82,0.12407 207 | -0.01902,"Bwk","very low",0.27738,71.96,3.95,0.20213 208 | -0.01339,"Bwk","very low",0.46792,53.26,0,0 209 | -0.01588,"Bwk","very low",0.62963,47.82,3.81,0.21043 210 | -0.01947,"Bwk","very low",0.7606,48.48,0,0.46336 211 | -0.00361,"Bwk","very low",0.80954,57.97,37.83,0.61706 212 | -0.01458,"Bwk","very low",0.74723,79,0,1.04062 213 | 0.00848,"Bwk","very low",2.00942,166.21,5.3,0.44702 214 | -0.00586,"Bwk","very low",1.96707,171.33,2.43,0.21222 215 | -0.00336,"Bwk","very low",1.92145,184.33,0,8.63122 216 | 0.00272,"Bsk","very low",1.93418,189.21,331.16,7.06228 217 | -0.01481,"Bsk","very low",1.97539,209.85,14.21,0.96538 218 | 0.02178,"Bsk","very low",2.02786,249.62,19.57,1.0389 219 | 0.10512,"Bsk","very low",2.10326,291.13,83.03,2.45327 220 | 0.13292,"Bsk","very low",2.11482,330.61,51.53,3.04005 221 | 0.19494,"Dwb","very low",2.01813,357.25,47.16,6.02872 222 | 0.19958,"Dwb","very low",1.85154,383.82,37.29,3.36978 223 | 0.33939,"Dwc","very low",1.60182,422.26,19.64,4.05798 224 | 0.43556,"Dwc","very low",1.17038,472.75,219.76,11.109 225 | 0.31393,"Dwb","very low",1.00277,432.03,576.53,31.06497 226 | 0.23026,"Bsk","very low",1.41404,393.21,1315.84,188.86612 227 | 0.29087,"Bsk","very low",2.08619,388.2,1267.66,95.83015 228 | 0.25975,"Dwa","very low",2.36743,422.44,1944.07,86.08974 229 | 0.34467,"Dwa","very low",2.14163,433.95,592.11,51.42849 230 | -0.01822,"Bwk","very low",0.27269,68.31,4.15,0.23751 231 | -0.01735,"Bwk","very low",0.35793,61.99,14.2,0.84878 232 | -0.01561,"Bwk","very low",0.47307,54.2,11.32,1.13036 233 | 0.04483,"Bwk","very low",2.16998,172.43,0.86,0.21606 234 | 0.01909,"Bwk","very low",2.19927,157.85,9.62,0.58115 235 | 0.01427,"Bwk","very low",2.16124,160.38,12.66,1.29869 236 | 0.01518,"Bsk","very low",2.12937,175.55,65.83,2.17719 237 | 0.01433,"Bsk","very low",2.15069,194.81,7.36,1.26229 238 | 0.03807,"Bsk","low",2.18881,230.96,13.77,0.89359 239 | 0.07296,"Bsk","low",2.21766,271.3,15.79,0.94652 240 | 0.11865,"Bsk","very low",2.16265,313.61,8.05,1.58983 241 | 0.17289,"Dwb","very low",2.02321,347.47,88.99,2.24743 242 | 0.20168,"Dwb","very low",1.83212,372.62,138.39,6.8832 243 | 0.26744,"Dwb","very low",1.58917,392.96,96.04,4.66105 244 | 0.33248,"Dwb","very low",1.24091,410.45,556.09,36.43349 245 | 0.29437,"Dwb","very low",0.96223,411.71,482.91,23.06267 246 | 0.21213,"Bsk","very low",1.22097,392.48,1253.89,49.56917 247 | 0.12268,"Bsk","very low",1.54892,395.22,237.35,32.84245 248 | 0.18305,"Bsk","very low",1.50392,405.63,380.9,41.23987 249 | 0.23235,"Bsk","very low",1.24029,420.96,1128.7,53.14781 250 | 0.24148,"Dwa","very low",1.43038,435.63,275.99,32.72957 251 | 0.23538,"Dwa","very low",2.07858,435.59,674.54,41.90319 252 | 0.00146,"Bwk","very low",2.34774,151.81,489.02,28.61931 253 | 0.01908,"Bwk","low",2.29991,165.16,7.47,0.80362 254 | 0.00742,"Bsk","low",2.26906,192.24,106.08,2.67667 255 | 0.0357,"Bsk","low",2.28479,227.9,0.07,2.70522 256 | 0.09423,"Bsk","very low",2.2709,270,1.03,2.03815 257 | 0.16675,"Bsk","very low",2.19316,298.44,37.89,1.15465 258 | 0.1998,"Dwb","very low",1.9876,343.77,61.95,4.31719 259 | 0.30311,"Dwb","medium",1.79041,367.7,7.17,1.05316 260 | 0.40753,"Dwb","very low",1.5249,408.9,22.64,2.14668 261 | 0.37502,"Dwb","very low",1.24545,424.19,1070.46,21.80841 262 | 0.23775,"Dwb","very low",1.07556,387.7,811.52,59.3591 263 | 0.19035,"Bsk","very low",1.08795,394.93,151.69,25.93006 264 | 0.16834,"Bsk","very low",1.23772,394.14,192.49,14.94925 265 | 0.15975,"Bsk","very low",1.21765,396.85,229.31,17.47221 266 | 0.19979,"Bsk","very low",1.17802,401.14,1324.08,56.98953 267 | 0.20299,"Bsk","very low",1.39324,406.86,934.07,32.80322 268 | 0.17224,"Dwa","very low",1.9437,420.62,179.51,11.97449 269 | 0.26496,"Dwa","very low",2.77024,443.37,1153.25,49.4946 270 | 0.37019,"Dwa","very low",3.125,464.9,741.26,44.70935 271 | 0.02005,"Bwk","low",2.44667,172.47,1.33,0.42662 272 | 0.0254,"Bsk","low",2.39142,181.2,7.46,0.55668 273 | 0.02592,"Bsk","low",2.40899,190.15,36.55,1.00907 274 | 0.0388,"Bsk","low",2.41463,225.47,0,1.43299 275 | 0.10599,"Bsk","very low",2.33727,272.88,239.13,3.38231 276 | 0.20005,"Bsk","very low",2.16311,315.54,12.06,11.69121 277 | 0.1908,"Bsk","medium",2.00613,321.35,1842.06,79.48333 278 | 0.31091,"Dwb","medium",1.76417,364.61,4.97,1.10129 279 | 0.36921,"Dwb","very low",1.52309,409.11,39.57,2.79193 280 | 0.39691,"Dwb","very low",1.30636,434.24,323.33,17.67795 281 | 0.2896,"Dwb","very low",1.17409,426.64,342.54,22.20014 282 | 0.31581,"Dwb","very low",1.11753,438.31,813.64,39.70057 283 | 0.23489,"Bsk","very low",1.25755,410.94,1173.39,64.31122 284 | 0.21798,"Bsk","very low",1.36207,396.7,1068.7,48.9784 285 | 0.27166,"Bsk","very low",1.47986,386.58,832.3,42.02551 286 | 0.33531,"Bsk","very low",1.62785,385.09,2139.02,102.55897 287 | 0.35076,"Bsk","very low",1.775,394.97,6689.4,333.82875 288 | 0.26229,"Dwa","very low",2.21245,431.7,1088.32,52.94628 289 | 0.34734,"Dwa","very low",2.62859,475.62,1123.73,47.20233 290 | 0.0235,"Bwk","low",2.4705,193.75,0,0.19673 291 | 0.04706,"Bsk","low",2.38034,216.66,0.05,0.42949 292 | 0.07114,"Bsk","low",2.41626,229.07,0.45,0.7799 293 | 0.08177,"Bsk","very low",2.47211,246.18,0,0.60742 294 | 0.13923,"Bsk","very low",2.37246,276.71,9.38,0.66391 295 | 0.13987,"Bsk","very low",2.19027,302.77,7.72,1.31443 296 | 0.16046,"Bsk","medium",2.02121,312.19,59.74,5.55806 297 | 0.24996,"Bsk","high",1.8211,347.48,50.87,1.43259 298 | 0.21962,"Dwb","high",1.64725,387.68,0.85,1.31524 299 | 0.36501,"Dwb","high",1.51362,429.22,259.96,16.17262 300 | 0.42681,"Dwc","very low",1.40234,480.13,85.44,10.58174 301 | 0.37058,"Dwb","very low",1.46758,460.09,488.14,29.72859 302 | 0.3144,"Dwb","very low",1.48924,442.89,567.54,31.1463 303 | 0.17608,"Bsk","very low",1.52732,408.24,168.52,11.27368 304 | 0.23938,"Bsk","medium",1.5501,388.88,204.82,9.15505 305 | 0.24509,"Bsk","very low",1.58659,379.85,357.38,33.0665 306 | 0.31948,"Bsk","very low",1.62782,390,720.03,57.05979 307 | 0.33868,"Dwa","very low",1.83274,423.96,1022.14,64.9284 308 | 0.34227,"Dwa","very low",2.0553,462.6,1686.35,98.26957 309 | 0.07015,"Bsk","low",2.44208,203.65,0.49,0.19964 310 | 0.19225,"Bsk","very low",2.23273,308.6,13.07,0.70582 311 | 0.17209,"Bsk","very low",2.12033,308.13,15.68,1.20172 312 | 0.12265,"Bsk","medium",2.0323,299.31,33.13,0.96288 313 | 0.14912,"Bsk","high",1.89815,333.1,17.04,1.02376 314 | 0.21011,"Dwb","high",1.81978,376.03,0,0.99187 315 | 0.30697,"Dwb","high",1.83063,413.13,11.71,2.53629 316 | 0.37855,"Dwb","high",1.83463,460.58,120.75,3.25823 317 | 0.44784,"Dwc","high",1.78555,496.23,0,10.04312 318 | 0.32727,"Dwb","very low",1.76933,457.21,146.12,6.83158 319 | 0.29742,"Dwb","medium",1.62028,443.47,225.67,19.67379 320 | 0.2966,"Bsk","medium",1.55851,402.88,960.61,47.65558 321 | 0.21738,"Bsk","medium",1.5627,388.5,379.5,17.64013 322 | 0.24232,"Dwc","very low",2.08789,327.34,4.26,0.68306 323 | 0.16064,"Bsk","medium",1.97636,305.72,0,0.6074 324 | 0.13552,"Bsk","medium",1.94616,327.48,600.86,8.45638 325 | 0.21488,"Bsk","medium",2.01763,356.32,0,9.41141 326 | 0.28574,"Dwb","high",2.1579,392.91,11.91,0.89195 327 | 0.35047,"Dwb","high",2.2451,432.28,20.51,2.96089 328 | 0.38077,"Dwb","high",2.11836,476.54,10.34,2.81202 329 | 0.42273,"Dwb","medium",1.88352,497.02,36.9,4.60942 330 | 0.38542,"Dwb","medium",1.71943,474.09,106.97,6.99586 331 | 0.37301,"Dwa","very low",1.62252,433.57,421.02,21.91805 332 | 0.30158,"Dwa","very low",1.6221,409.48,1021.16,53.32618 333 | 0.28526,"Dwb","medium",1.92942,342.33,8.26,0.88597 334 | 0.26942,"Dwb","medium",2.06781,375.04,7.37,0.80573 335 | 0.27468,"Dwb","medium",2.3128,397.49,23.36,0.73837 336 | 0.28777,"Dwb","medium",2.48406,429.93,121.42,5.64894 337 | 0.35872,"Dwb","medium",2.32128,468.03,592.86,20.37378 338 | 0.41293,"Dwb","medium",1.98413,498.37,182.93,28.53852 339 | 0.4273,"Dwb","very low",1.77778,489.35,391.05,7.68303 340 | 0.36627,"Dwa","very low",1.65387,470.09,323.55,24.98391 341 | 0.37377,"Dwa","very low",1.66013,444.24,1557.59,71.52555 342 | 0.36525,"Bsk","medium",1.92179,345.22,10.71,1.10911 343 | 0.30458,"Dwb","medium",2.04656,378.9,0.48,0.49465 344 | 0.34212,"Dwb","medium",2.22142,418.22,8.7,0.57504 345 | 0.37552,"Dwc","medium",2.34312,460.68,31.21,1.36293 346 | 0.41649,"Dwc","medium",2.22684,502.03,22.54,1.62434 347 | 0.46843,"Dwc","very low",1.94186,532.27,29.5,3.25771 348 | 0.44402,"Dwb","very low",1.68768,541.62,291.11,11.12605 349 | 0.40551,"Dwb","very low",1.55554,533.25,263.21,27.0268 350 | 0.39724,"Dwa","very low",1.70774,485.66,79.62,22.92424 351 | 0.36674,"Dwb","medium",2.06614,422.15,0.32,0.054 352 | 0.41932,"Dwc","medium",2.10177,470.31,18.03,1.635 353 | 0.50245,"Dwc","medium",2.01389,539.15,3.39,0.27533 354 | 0.4739,"Dwc","medium",1.7879,581.98,11.5,0.40372 355 | 0.44834,"Dwb","very low",1.55159,589.05,45.21,4.37272 356 | 0.42586,"Dwb","very low",1.43946,570.13,603.97,23.62838 357 | 0.41984,"Dwb","very low",1.52171,536.02,150.55,102.95818 358 | 0.45247,"Dwa","very low",1.62261,508.03,372.81,14.25043 359 | 0.48016,"Dwc","very low",1.60992,631.18,205.38,8.67791 360 | 0.45735,"Dwc","very low",1.4337,631.38,56.81,6.71978 361 | 0.43669,"Dwb","very low",1.32324,614.08,47.62,12.68775 362 | 0.42642,"Dwb","very low",1.42873,566.59,576.59,19.34242 363 | 0.47339,"Dwa","very low",1.54746,526.31,1321.36,77.15998 364 | 0.50021,"Dwc","very low",1.53656,561.87,158.52,1.45303 365 | 0.4735,"Dwc","very low",1.46942,640.4,36.55,10.29718 366 | 0.4533,"Dwc","very low",1.33968,657.29,1.03,3.89334 367 | 0.43806,"Dwb","very low",1.31629,622.2,31.61,2.17113 368 | 0.44051,"Dwb","very low",1.36583,588.88,572.45,36.0475 369 | 0.33346,"Bsk","very low",2.23889,322.78,0,0.88129 370 | 0.31901,"Bsk","very low",2.11764,322.24,0,0.82466 371 | 0.30855,"Bsk","very low",1.96225,333.02,0,0.49777 372 | 0.25459,"Bsk","very low",1.84455,356.6,14.91,1.6092 373 | 0.40321,"Dwb","very low",1.46396,470,1.2,1.49185 374 | 0.45643,"Dwc","very low",1.41494,538.17,0,0.77718 375 | 0.48806,"Dwc","medium",1.32101,622.46,0,0.29759 376 | 0.45288,"Dwc","very low",1.25576,655.34,76.54,3.30976 377 | 0.43809,"Dwc","very low",1.24794,645.78,49.28,0.69891 378 | 0.43717,"Dwb","very low",1.30681,608.35,388.55,20.81663 379 | 0.35885,"Bsk","very low",2.17445,331.1,14.25,1.86581 380 | 0.28993,"Bsk","very low",2.03076,331.75,125.6,1.58518 381 | 0.30304,"Bsk","very low",1.93226,352.68,4.97,2.39931 382 | 0.3052,"Dwb","very low",1.75911,398.5,8.16,2.68475 383 | 0.37552,"Dwb","very low",1.53405,447.59,12.62,1.76081 384 | 0.40676,"Dwc","medium",1.36958,506.22,124.94,1.64109 385 | 0.47007,"Dwc","medium",1.26097,574.2,0,1.34122 386 | 0.46125,"Dwc","medium",1.20806,633.55,112.3,3.59574 387 | 0.43962,"Dwc","very low",1.25194,641.01,0.66,5.75522 388 | 0.42972,"Dwb","very low",1.27598,627.76,360.96,13.57675 389 | 0.46347,"Dwb","very low",1.33326,603.14,1611.61,100.45203 390 | 0.40115,"Bsk","very low",2.10608,351.41,0,3.15378 391 | 0.22841,"Bsk","very low",2.05468,331.28,1.31,1.61346 392 | 0.25684,"Dwb","very low",1.97988,342.45,29.21,1.36874 393 | 0.34679,"Dwb","very low",1.77318,385.7,18.14,1.32457 394 | 0.43666,"Dwb","medium",1.51841,431.07,1.23,1.57375 395 | 0.43172,"Dwb","medium",1.31196,475.14,199.41,11.86357 396 | 0.49248,"Dwc","medium",1.1559,546.34,0,1.40499 397 | 0.4694,"Dwc","very low",1.14716,615.15,34.23,6.97499 398 | 0.44939,"Dwc","very low",1.2815,636.79,127.03,6.6636 399 | 0.42819,"Dwc","very low",1.32522,639.08,21.69,8.19634 400 | 0.44323,"Dwb","very low",1.32672,623.11,294.18,17.93083 401 | 0.5054,"Dwb","very low",1.28519,609.85,660.01,32.44411 402 | 0.47597,"Dwb","very low",1.13204,596.95,834.41,128.82816 403 | 0.41011,"Dwb","very low",1.85825,371.84,0,0.40659 404 | 0.38871,"Dwc","very low",1.8863,362.72,1028.24,24.24201 405 | 0.30945,"Dwb","very low",1.90951,347.14,695.98,46.08962 406 | 0.29984,"Dwb","medium",1.70555,380.87,11.86,2.63393 407 | 0.39131,"Dwb","medium",1.46263,416.7,190.18,9.98788 408 | 0.41786,"Dwb","low",1.23901,454.46,2657.8,146.39814 409 | 0.48876,"Dwc","medium",1.07608,509.93,1702.91,81.8634 410 | 0.48571,"Dwc","very low",1.11462,573.18,160.97,6.48275 411 | 0.46462,"Dwc","very low",1.24795,626.97,5.5,3.6239 412 | 0.43278,"Dwc","very low",1.32715,640.68,3.09,1.87186 413 | 0.42469,"Dwb","very low",1.32863,632.75,45.72,11.09285 414 | 0.46224,"Dwb","very low",1.22684,617.71,295.22,20.07997 415 | 0.45026,"Dwb","very low",1.04202,612.52,295.7,38.15781 416 | 0.42576,"Dwb","medium",1.56744,394.89,6.53,0.55806 417 | 0.49449,"Dwb","medium",1.38358,425.05,0.34,1.25824 418 | 0.49307,"Dwc","medium",1.21165,460.91,36.24,6.68108 419 | 0.51118,"Dwc","medium",1.06915,509.92,14.57,7.36314 420 | 0.47976,"Dwc","very low",1.08579,559.76,255.35,11.20586 421 | 0.45259,"Dwc","very low",1.14662,620.51,0.06,6.14221 422 | 0.44022,"Dwc","very low",1.25225,625.29,0.02,2.67791 423 | 0.42631,"Dwc","very low",1.24928,631.06,22.88,0.97291 424 | 0.44564,"Dwb","very low",1.11677,625.09,149.18,6.07478 425 | 0.45036,"Dwb","very low",0.88229,612.21,286.19,16.67228 426 | 0.49608,"Dwb","medium",1.37262,430.89,9.42,0.73984 427 | 0.52281,"Dwc","very low",1.30001,464.45,292.27,12.15329 428 | 0.49223,"Dwc","medium",1.16294,517.16,23.85,1.75753 429 | 0.46134,"Dwc","very low",1.06517,575.51,145.39,6.39497 430 | 0.4421,"Dwc","very low",1.04214,621.98,1.16,3.88905 431 | 0.43153,"Dwc","very low",1.0644,634.14,0,0.00516 432 | 0.4317,"Dwc","very low",1.05209,622.9,9.77,3.23819 433 | 0.44942,"Dwc","very low",0.91849,613.93,63.11,9.42971 434 | 0.47988,"Dwb","very low",0.71818,603.7,801.88,38.82025 435 | 0.506,"Dwc","very low",1.40643,460.18,4.58,0.96515 436 | 0.49098,"Dwc","very low",1.34551,480.77,41.28,5.22071 437 | 0.47851,"Dwc","very low",1.23297,511.99,9.85,6.40646 438 | 0.45958,"Dwc","very low",1.06441,573.08,211.09,7.85349 439 | 0.44481,"Dwc","very low",0.97221,608.62,0.46,5.74393 440 | 0.43612,"Dwc","very 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as np 3 | from scipy.stats import ncf, t, f 4 | from sklearn.cluster import KMeans, AgglomerativeClustering, SpectralClustering, MiniBatchKMeans, BisectingKMeans 5 | from sklearn.mixture import GaussianMixture 6 | from sklearn.preprocessing import StandardScaler, KBinsDiscretizer 7 | import jenkspy 8 | from itertools import combinations, chain 9 | from typing import Sequence 10 | import matplotlib.pyplot as plt 11 | import matplotlib.markers as mks 12 | from mpl_toolkits.axes_grid1 import make_axes_locatable 13 | from matplotlib.colors import ListedColormap 14 | import seaborn as sns 15 | import math 16 | import warnings 17 | import os 18 | warnings.filterwarnings("ignore") 19 | 20 | 21 | def factor_detector(df, Y, factors:Sequence): 22 | y = df[Y] 23 | # calculate total number N and variance σ² of dependent variable 24 | N = len(y) 25 | sigma2 = np.var(y) 26 | factor_result = pd.DataFrame() 27 | 28 | for factor in factors: 29 | x = df[factor] 30 | # calculate q 31 | q = 1 32 | unique_x = np.unique(x) 33 | L = len(np.unique(x)) 34 | if L == 1: 35 | continue 36 | group_means = [] 37 | for val in unique_x: 38 | yi = y[x == val] 39 | group_means.append(yi.mean()) 40 | ni = len(yi) 41 | sigma_i2 = np.var(yi) 42 | q -= (ni * sigma_i2) / (N * sigma2) 43 | 44 | # calculate p 45 | F = (N - L) / (L - 1) * q / (1 - q) 46 | group_means = np.array(group_means) 47 | λ = (1 / sigma2) * (np.sum(group_means ** 2) - (1 / N) * 48 | np.sum(np.sqrt([len(y[x == val]) for val in unique_x]) * group_means) ** 2) 49 | 50 | p = ncf.sf(F, L - 1, N - L, nc=λ) 51 | 52 | if p < 0.05: 53 | temp = pd.DataFrame({'factor': [factor], 'q': [q], 'p': [p]}) 54 | factor_result = temp if factor_result.empty else pd.concat([factor_result, temp], ignore_index=True) 55 | 56 | if not factor_result.empty: 57 | factor_result = factor_result.sort_values(by='q', ascending=False).reset_index(drop=True) 58 | 59 | return factor_result 60 | 61 | def interation_detector(df:pd.DataFrame, Y, factors:Sequence): 62 | df_copy = df.copy() 63 | # permutation 64 | pair_combinations = list(combinations(df[factors], 2)) 65 | 66 | # interation factors 67 | interation_factors = [] 68 | for combination in pair_combinations: 69 | c1, c2 = combination 70 | interation_factors.append(f'{c1}+{c2}') 71 | df_copy[f'{c1}+{c2}']= df_copy[c1].apply(str) + '_' + df_copy[c2].apply(str) 72 | interation_result = factor_detector(df_copy, Y, interation_factors) 73 | 74 | return interation_result 75 | 76 | def factor_plot(factor_result, dpi=100): 77 | fig, ax = plt.subplots(dpi=dpi) 78 | 79 | factors = factor_result['factor'] 80 | q = factor_result['q'] 81 | 82 | colors = plt.cm.coolwarm_r(np.linspace(0.1, 0.9, len(factors))) 83 | ax.barh(factors, q, align='center', color=colors) 84 | 85 | for i, val in enumerate(q): 86 | ax.text(val, factors[i], f'{val:.4f}', ha='right', va='center') 87 | 88 | ax.invert_yaxis() # labels read top-to-bottom 89 | ax.set_xlabel('Q value') 90 | ax.spines['top'].set_visible(False) 91 | ax.spines['right'].set_visible(False) 92 | ax.spines['left'].set_visible(False) 93 | ax.tick_params(axis='y', length=0) 94 | 95 | plt.tight_layout() 96 | 97 | 98 | def risk_detector(df:pd.DataFrame, Y, factors:Sequence): 99 | risk_result_list = [] 100 | for factor in factors: 101 | # zonal statistics 102 | risk_result = df.groupby(factor)[Y].agg('mean').to_frame().T 103 | 104 | # Create a new DataFrame with the indexes of the column names of the original DataFrame, and all values are NaN 105 | df_new = pd.DataFrame(np.nan, index=risk_result.columns, columns=risk_result.columns) 106 | 107 | # Use the concat function to connect two DataFrames together. 108 | risk_result = pd.concat([risk_result, df_new]) 109 | 110 | # permutation 111 | pair_combinations = list(combinations(set(df[factor]), 2)) 112 | for combination in pair_combinations: 113 | # mean values 114 | mean_z1, mean_z2 = risk_result[combination[0]].values[0], risk_result[combination[1]].values[0] 115 | 116 | # dependent variables and its lengths 117 | y_z1, y_z2 = df[df[factor]==combination[0]][Y], df[df[factor]==combination[1]][Y] 118 | len_z1, len_z2 = len(y_z1), len(y_z2) 119 | if len_z1 <= 1 or len_z2 <= 1: 120 | risk_result.loc[combination[1], combination[0]] = False 121 | continue 122 | 123 | # sample variance 124 | variance_z1, variance_z2 = y_z1.var(ddof=1), y_z2.var(ddof=1) 125 | est_var_z1, est_var_z2 = variance_z1 / len_z1, variance_z2 / len_z2 126 | 127 | # t-statistics 128 | t_statistics = (mean_z1 - mean_z2) / np.sqrt(est_var_z1 + est_var_z2) 129 | # degrees of freedom 130 | if math.pow(est_var_z2, 2) / (len_z2 - 1) == 0: 131 | continue 132 | d_freedom = ((est_var_z1 + est_var_z2) / 133 | (math.pow(est_var_z1, 2) / (len_z1 - 1) + 134 | math.pow(est_var_z2, 2) / (len_z2 - 1))) 135 | # p 136 | p = t.sf(np.abs(t_statistics), d_freedom) 137 | 138 | # t-test 139 | risk_result.loc[combination[1], combination[0]] = True if p < 0.05 else False 140 | 141 | risk_result_list.append(risk_result) 142 | 143 | return risk_result_list 144 | 145 | def risk_plot(risk_result_list, dpi=100, nrows=0, ncols=0, unit=''): 146 | if nrows==0 or ncols==0: 147 | nrows = math.ceil(math.sqrt(len(risk_result_list))) 148 | ncols = nrows - 1 if nrows * (nrows - 1) >= len(risk_result_list) else nrows 149 | 150 | fig, axes = plt.subplots(nrows, ncols, dpi=dpi) 151 | 152 | for i, val in enumerate(risk_result_list): 153 | if ncols == 1 and nrows == 1: 154 | temp_ax = axes 155 | else: 156 | temp_ax = axes[math.floor(i / ncols), i % ncols] 157 | 158 | risk_mean = val.copy().iloc[0] 159 | risk_sig = val.copy().iloc[1:] 160 | risk_sig.fillna(False, inplace=True) 161 | 162 | labels = risk_sig.astype(str) 163 | labels[labels == 'True'] = 'T' 164 | labels[labels == 'False'] = 'F' 165 | 166 | mask = np.triu(np.ones_like(risk_sig, dtype=bool)) 167 | 168 | # check if is all True 169 | risk_sig = risk_sig.astype(int) 170 | total = sum(range(len(risk_sig))) 171 | cmap = ListedColormap(['#87CEEB', '#EE6363']) if risk_sig.sum().sum() < total else ListedColormap(['#EE6363']) 172 | sns.heatmap(risk_sig, mask=mask, annot=labels, fmt='', square=True, cbar=False, cmap=cmap, ax=temp_ax, annot_kws={'color': 'white'}) 173 | # edge lines 174 | temp_ax.patch.set_visible(True) 175 | temp_ax.patch.set_edgecolor('black') 176 | temp_ax.patch.set_linewidth(0.8) 177 | 178 | temp_ax.tick_params(axis='y', labelrotation=0) 179 | temp_ax.set_xlabel('Class') 180 | temp_ax.set_ylabel('Class') 181 | temp_ax.set_xticks([j + 0.5 for j in risk_sig.index], list(risk_sig.index)) 182 | temp_ax.set_yticks([j + 0.5 for j in risk_sig.index], list(risk_sig.index)) 183 | temp_ax.set_title(val.columns.name) 184 | 185 | # Creating additional axes for plotting bar charts 186 | divider = make_axes_locatable(temp_ax) 187 | ax_bar = divider.append_axes("right", size="50%", pad=0) 188 | 189 | # same colors as classify result 190 | colors = plt.cm.rainbow(np.linspace(0, 1, len(risk_mean))) 191 | 192 | ax_bar.barh([j + 0.5 for j in risk_mean.index], risk_mean.values, color=colors, alpha=0.8) 193 | ax_bar.set_ylim(temp_ax.get_ylim()) 194 | ax_bar.set_yticks([j + 0.5 for j in risk_mean.index], list(risk_mean.index)) 195 | ax_bar.set_xticks([]) 196 | ax_bar.set_xlabel(f'{val.index[0]} Mean') if unit == '' else ax_bar.set_xlabel(f'{val.index[0]} Mean ({unit})') 197 | 198 | for i2, val2 in enumerate(risk_mean.values): 199 | ax_bar.text(ax_bar.get_xlim()[0], i2 + 0.5, f'{val2:.2f}', ha='left', va='center') 200 | 201 | # remove the abundant axis 202 | if i < nrows * ncols - 1: 203 | for j in range(i + 1, nrows * ncols): 204 | axes[math.floor(j / ncols), j % ncols].set_axis_off() 205 | 206 | plt.tight_layout() 207 | 208 | 209 | def ecological_detector(df:pd.DataFrame, Y, factors:Sequence): 210 | ecological_result = pd.DataFrame(columns=factors, index=factors) 211 | # permutation 212 | pair_combinations = list(combinations(factors, 2)) 213 | 214 | for combination in pair_combinations: 215 | f1, f2 = combination 216 | 217 | # calculate variance of dependent variable under each independent variable 218 | sum_variance1, sum_variance2 = 0, 0 219 | N1, N2 = len(df[f1].notna()), len(df[f2].notna()) 220 | 221 | for type in set(df[f1]): 222 | sum_variance1 += len(df[df[f1]==type].notna()) * np.var(df[df[f1]==type][Y]) 223 | for type in set(df[f2]): 224 | sum_variance2 += len(df[df[f2]==type].notna()) * np.var(df[df[f2]==type][Y]) 225 | 226 | # F and p 227 | if sum_variance1 > sum_variance2: 228 | F = (N2 * (N1 - 1) * sum_variance1) / (N1 * (N2 - 1) * sum_variance2) 229 | p = f.sf(F, N1 - 1, N2 - 1) 230 | else: 231 | F = (N2 * (N1-1) * sum_variance2) / (N1 * (N2-1) * sum_variance1) 232 | p = f.sf(F, N2 - 1, N1 - 1) 233 | 234 | ecological_result.loc[f2, f1] = True if p < 0.05 else False 235 | 236 | return ecological_result 237 | 238 | def ecological_plot(ecological_result, dpi=100): 239 | df = ecological_result.copy() 240 | df.fillna(False, inplace=True) 241 | 242 | labels = df.astype(str) 243 | labels[labels == 'True'] = 'T' 244 | labels[labels == 'False'] = 'F' 245 | 246 | mask = np.triu(np.ones_like(df, dtype=bool)) 247 | 248 | df = df.astype(int) 249 | total = sum(range(len(df))) 250 | 251 | fig, ax = plt.subplots(dpi=dpi) 252 | cmap = ListedColormap(['#87CEEB', '#EE6363']) if df.sum().sum() < total else ListedColormap(['#EE6363']) 253 | sns.heatmap(df, mask=mask, annot=labels, fmt='', square=True, cbar=False, cmap=cmap, ax=ax, annot_kws={'color': 'white'}) 254 | 255 | # edge lines 256 | ax.patch.set_visible(True) 257 | ax.patch.set_edgecolor('black') 258 | ax.patch.set_linewidth(0.8) 259 | ax.set_xticklabels([i for i in range(len(df))], rotation='horizontal') 260 | ax.set_yticklabels([f'{i}: {val}' for i, val in enumerate(df.index)]) 261 | 262 | plt.tight_layout() 263 | 264 | 265 | def classify(X, n_clusters, classify_result, colname, random_state=0): 266 | if len(colname.split('_')) <= 2: 267 | # equal intervals 268 | uni = KBinsDiscretizer(n_bins=n_clusters, encode='ordinal', strategy='uniform') 269 | uni.fit(X) 270 | uni_disc = uni.transform(X) 271 | uni_disc = uni_disc.reshape(uni_disc.shape[0]).astype(int) 272 | uni_name = f'{colname}uni{n_clusters}' 273 | 274 | # quantile 275 | qt = KBinsDiscretizer(n_bins=n_clusters, encode='ordinal', strategy='quantile') 276 | qt.fit(X) 277 | qt_disc = qt.transform(X) 278 | qt_disc = qt_disc.reshape(qt_disc.shape[0]).astype(int) 279 | qt_name = f'{colname}qt{n_clusters}' 280 | 281 | # Jenks Natural Breaks 282 | jnb = jenkspy.JenksNaturalBreaks(n_clusters) 283 | jnb.fit(X.reshape(X.shape[0])) 284 | jnb_disc = jnb.labels_ 285 | jnb_name = f'{colname}jnb{n_clusters}' 286 | 287 | # geometric interval 288 | gmt_std = (X.max() / X.min()) ** (1 / n_clusters) 289 | gmt_intervals = [X.min() * gmt_std ** k for k in range(n_clusters + 1)] 290 | gmt_disc = np.digitize(X, gmt_intervals) 291 | gmt_disc = list(chain.from_iterable(gmt_disc)) 292 | gmt_name = f'{colname}gmt{n_clusters}' 293 | 294 | # standard deviation 295 | mean, min, max = (X.mean(), X.min(), X.max()) 296 | std = np.std(X) 297 | std_intervals = [min, max] 298 | n = int(n_clusters / 2) 299 | count = 0 300 | for i in range(n): 301 | if i == 0 and n_clusters % 2 == 0: 302 | std_intervals.append(mean) 303 | else: 304 | if n_clusters % 2 != 0: 305 | i += 1 306 | if mean - i * std < min: 307 | std_intervals.append(mean + (i + count) * std) 308 | count += 1 309 | std_intervals.append(mean + (i + count) * std) 310 | elif mean + i * std > max: 311 | std_intervals.append(mean - (i + count) * std) 312 | count += 1 313 | std_intervals.append(mean - (i + count) * std) 314 | else: 315 | std_intervals.extend([mean - i * std, mean + i * std]) 316 | 317 | std_intervals.sort() 318 | std_disc = np.digitize(X, std_intervals) 319 | std_disc = list(chain.from_iterable(std_disc)) 320 | std_name = f'{colname}std{n_clusters}' 321 | 322 | disc_result = pd.DataFrame([uni_disc, qt_disc, jnb_disc, gmt_disc, std_disc]).T 323 | disc_result.columns = [uni_name, qt_name, jnb_name, gmt_name, std_name] 324 | classify_result = pd.concat([classify_result, disc_result], axis=1) 325 | 326 | else: 327 | scaler = StandardScaler() 328 | X = scaler.fit_transform(X) 329 | 330 | # k-means 331 | if len(X) > 10000: 332 | kmeans = KMeans(n_clusters=n_clusters, random_state=random_state, n_init='auto', init='k-means++') 333 | else: 334 | kmeans = MiniBatchKMeans(n_clusters=n_clusters, random_state=random_state, n_init='auto', init='k-means++') 335 | clusters_kms = kmeans.fit_predict(X) 336 | kms_name = f'{colname}kms{n_clusters}' 337 | 338 | # AgglomerativeClustering 339 | agg_clustering = AgglomerativeClustering(n_clusters=n_clusters, linkage='single', metric="euclidean") 340 | agg_clustering.fit(X) 341 | clusters_agg = agg_clustering.labels_ 342 | agg_name = f'{colname}agg{n_clusters}' 343 | 344 | # SpectralClustering 345 | spectral_clustering = SpectralClustering(n_clusters=n_clusters, eigen_solver='amg', random_state=random_state, 346 | affinity='nearest_neighbors', n_jobs=-1, assign_labels='cluster_qr') 347 | spectral_clustering.fit(X) 348 | clusters_spc = spectral_clustering.labels_ 349 | spc_name = f'{colname}spc{n_clusters}' 350 | 351 | # GaussianMixture 352 | gsm = GaussianMixture(n_components=n_clusters, random_state=random_state) 353 | gsm.fit(X) 354 | clusters_gsm = gsm.predict(X) 355 | gsm_name = f'{colname}gsm{n_clusters}' 356 | 357 | # BisectingKMeans 358 | bisecting_kmeans = BisectingKMeans(n_clusters=n_clusters, random_state=random_state, init='k-means++', 359 | bisecting_strategy='largest_cluster') 360 | bisecting_kmeans.fit(X) 361 | clusters_bkm = bisecting_kmeans.labels_ 362 | bkm_name = f'{colname}bkm{n_clusters}' 363 | 364 | # set new columns based on the clustering results 365 | clusters = pd.DataFrame([clusters_kms, clusters_agg, clusters_spc, clusters_gsm, clusters_bkm]).T 366 | clusters.columns = [kms_name, agg_name, spc_name, gsm_name, bkm_name] 367 | classify_result = pd.concat([classify_result, clusters], axis=1) 368 | 369 | return classify_result 370 | 371 | def classify_plot(original_df:pd.DataFrame, classify_df:pd.DataFrame, dpi=100, nrows=0, ncols=0, unit_list=[]): 372 | Y = classify_df.columns[0] 373 | cols2split = classify_df.columns[1:] 374 | final_cols = [[j for j in i.split('_')] for i in classify_df[cols2split]] 375 | nvars = len(final_cols[0]) 376 | unit_dict = {} 377 | if len(unit_list) == len(original_df.columns): 378 | unit_dict = {key: value for key, value in zip(original_df.columns, unit_list)} 379 | 380 | if nrows==0 or ncols==0: 381 | nrows = math.ceil(math.sqrt(len(cols2split))) 382 | ncols = nrows - 1 if nrows * (nrows - 1) >= len(cols2split) else nrows 383 | 384 | # if number of variables >= 2, 3d plot, else 2d plot 385 | if nvars > 4: 386 | return np.nan 387 | elif nvars > 3: 388 | fig, axes = plt.subplots(nrows=nrows, ncols=ncols, subplot_kw={"projection": "3d"}, dpi=dpi) 389 | else: 390 | fig, axes = plt.subplots(nrows=nrows, ncols=ncols, dpi=dpi) 391 | 392 | # plot in subplots 393 | for i in range(nrows): 394 | for j in range(ncols): 395 | plot_index = i*ncols+j 396 | if ncols == 1 and nrows == 1: 397 | temp_ax = axes 398 | else: 399 | temp_ax = axes[math.floor(plot_index / ncols), plot_index % ncols] 400 | if plot_index >= len(final_cols): 401 | temp_ax.set_axis_off() 402 | continue 403 | 404 | x_label = final_cols[plot_index][0] 405 | y_label = Y if nvars <= 2 else final_cols[plot_index][1] 406 | z_label = final_cols[plot_index][2] if nvars > 3 else '' 407 | 408 | if z_label: 409 | scatter = temp_ax.scatter(original_df[x_label], original_df[y_label], original_df[z_label], s=7, 410 | c=classify_df[classify_df.columns[plot_index+1]], cmap='rainbow', alpha=0.8) 411 | temp_ax.set_zlabel(z_label) 412 | else: 413 | scatter = temp_ax.scatter(original_df[x_label], original_df[y_label], s=7, 414 | c=classify_df[classify_df.columns[plot_index + 1]], cmap='rainbow', alpha=0.8) 415 | 416 | temp_ax.set_xlabel(x_label) 417 | temp_ax.set_ylabel(y_label) 418 | if unit_dict != {}: 419 | if unit_dict[x_label] != '': 420 | temp_ax.set_xlabel(f'{x_label} ({unit_dict[x_label]})') 421 | if unit_dict[y_label] != '': 422 | temp_ax.set_ylabel(f'{y_label} ({unit_dict[y_label]})') 423 | 424 | # Creating additional axes for plotting bar charts 425 | divider = make_axes_locatable(temp_ax) 426 | if nvars > 3: 427 | ax_legend = divider.append_axes("left", size="30%", pad=0, axes_class=plt.Axes) 428 | else: 429 | ax_legend = divider.append_axes("right", size="30%", pad=0, axes_class=plt.Axes) 430 | legend = ax_legend.legend(*scatter.legend_elements(), loc='center', framealpha=0, 431 | ncol=math.ceil(classify_df[classify_df.columns[plot_index + 1]].nunique() / 10)) 432 | ax_legend.add_artist(legend) 433 | ax_legend.set_xticks([]) 434 | ax_legend.set_yticks([]) 435 | ax_legend.set_ylabel(classify_df.columns[plot_index+1].split('_')[-1] + '_class') 436 | 437 | plt.tight_layout() 438 | 439 | 440 | def omgd(df:pd.DataFrame, Y, factors:Sequence, n_variates:int, disc_interval:Sequence, type_factors:Sequence=[], random_state=0): 441 | # deal with type factors: 442 | for type_factor in type_factors: 443 | # get unique 444 | unique_items = set(df[type_factor]) 445 | # reclass the type factor to number 446 | mapping_dict = {} 447 | for i, item in enumerate(sorted(unique_items)): 448 | mapping_dict[item] = i 449 | df[type_factor] = [mapping_dict[item] for item in df[type_factor]] 450 | print(f'transform of {type_factor}: \n {mapping_dict}') 451 | 452 | # permutation 453 | pair_combinations = list(combinations(df[factors], n_variates)) 454 | # Predefined classification results 455 | classify_result = df[Y].copy() 456 | factor_result = pd.DataFrame() 457 | 458 | for i in pair_combinations: 459 | # Optimization of each combination except type factors 460 | temp_result = df[Y].copy() 461 | 462 | if len(i) == 1 and i[0] in type_factors: 463 | temp_result = pd.concat([temp_result, df[i[0]]], axis=1) 464 | optimal = factor_detector(temp_result, Y, temp_result.columns[1:]) 465 | classify_result = pd.concat([classify_result, temp_result[temp_result.columns[1:]]], axis=1) 466 | if not optimal.empty: 467 | # transform into dataframe 468 | optimal = optimal.loc[0].to_frame().T 469 | factor_result = optimal if factor_result.empty else pd.concat([factor_result, optimal]) 470 | continue 471 | 472 | X = df[list(i)].values 473 | 474 | basic_name = '' 475 | for k in range(len(i)): 476 | basic_name = list(i)[k] + '_' if basic_name == '' else basic_name + list(i)[k] + '_' 477 | 478 | for j in disc_interval: 479 | temp_result = classify(X, j, temp_result, basic_name, random_state) 480 | 481 | classify_result = pd.concat([classify_result, temp_result[temp_result.columns[1:]]], axis=1) 482 | optimal = factor_detector(temp_result, Y, temp_result.columns[1:]) 483 | if not optimal.empty: 484 | # transform into dataframe 485 | optimal = optimal.loc[0].to_frame().T 486 | factor_result = optimal if factor_result.empty else pd.concat([factor_result, optimal]) 487 | 488 | # result store in dictionary 489 | omgd_result = {} 490 | omgd_result['original'] = df 491 | 492 | classify_result = classify_result[[Y] + list(factor_result['factor'])] 493 | omgd_result['classify'] = classify_result 494 | 495 | factor_result = factor_result.sort_values(by='q', ascending=False).reset_index(drop=True) 496 | omgd_result['factor'] = factor_result 497 | 498 | if n_variates == 1: 499 | interaction_result = interation_detector(classify_result, Y, classify_result.columns[1:]) 500 | omgd_result['interaction'] = interaction_result 501 | 502 | risk_result_list = risk_detector(classify_result, Y, classify_result.columns[1:]) 503 | omgd_result['risk'] = risk_result_list 504 | if n_variates < len(factors): 505 | ecological_result = ecological_detector(classify_result, Y, classify_result.columns[1:]) 506 | omgd_result['ecological'] = ecological_result 507 | 508 | return omgd_result 509 | 510 | def omgd_plot(omgd_result, dpi=100, nrows=0, ncols=0, unit_list:list=[]): 511 | classify_plot(omgd_result['original'], omgd_result['classify'], dpi=dpi, nrows=nrows, ncols=ncols, unit_list=unit_list) 512 | factor_plot(omgd_result['factor'], dpi=dpi) 513 | if 'interaction' in omgd_result.keys(): 514 | factor_plot(omgd_result['interaction'], dpi=dpi) 515 | risk_plot(omgd_result['risk'], dpi=dpi, nrows=nrows, ncols=ncols, unit=unit_list[0] if len(unit_list) > 0 else '') 516 | if 'ecological' in omgd_result.keys(): 517 | ecological_plot(omgd_result['ecological'], dpi=dpi) 518 | 519 | 520 | def scale_detector(path_list: Sequence, Y, factors:Sequence, disc_interval:Sequence, type_factors:Sequence=[], quantile:float=0.8, n_variates=1, random_state=0): 521 | scale_result = pd.DataFrame() 522 | 523 | for path in path_list: 524 | base_name = os.path.basename(path).split('.')[0] 525 | if path.endswith('csv'): 526 | df = pd.read_csv(path) 527 | elif path.endswith('xls') or path.endswith('xlsx'): 528 | df = pd.read_excel(path) 529 | else: 530 | print(f'{path} has invalid file type') 531 | continue 532 | 533 | # deal with type factors: 534 | for type_factor in type_factors: 535 | # get unique 536 | unique_items = set(df[type_factor]) 537 | # reclass the type factor to number 538 | mapping_dict = {} 539 | for i, item in enumerate(sorted(unique_items)): 540 | mapping_dict[item] = i 541 | df[type_factor] = [mapping_dict[item] for item in df[type_factor]] 542 | 543 | # permutation 544 | pair_combinations = list(combinations(df[factors], n_variates)) 545 | # Predefined classification results 546 | factor_result = pd.DataFrame() 547 | 548 | for i in pair_combinations: 549 | # Optimization of each combination except type factors 550 | temp_result = df[Y].copy() 551 | 552 | if len(i) == 1 and i[0] in type_factors: 553 | temp_result = pd.concat([temp_result, df[i[0]]], axis=1) 554 | optimal = factor_detector(temp_result, Y, temp_result.columns[1:]) 555 | if not optimal.empty: 556 | # transform into dataframe 557 | optimal = optimal.loc[0].to_frame().T 558 | factor_result = optimal if factor_result.empty else pd.concat([factor_result, optimal]) 559 | continue 560 | 561 | X = df[list(i)].values 562 | 563 | basic_name = '' 564 | for k in range(len(i)): 565 | basic_name = list(i)[k] + '_' if basic_name == '' else basic_name + list(i)[k] + '_' 566 | 567 | for j in disc_interval: 568 | temp_result = classify(X, j, temp_result, basic_name, random_state) 569 | 570 | optimal = factor_detector(temp_result, Y, temp_result.columns[1:]) 571 | if not optimal.empty: 572 | # transform into dataframe 573 | optimal = optimal.loc[0].to_frame().T 574 | factor_result = optimal if factor_result.empty else pd.concat([factor_result, optimal]) 575 | 576 | factor_result = factor_result[['factor', 'q']].rename(columns={'q': f'{base_name}'}) 577 | factor_result['factor'] = factor_result['factor'].apply(lambda x: x if '_' not in x else '_'.join(x.split('_')[:-1])) 578 | scale_result = factor_result if scale_result.empty else scale_result.merge(factor_result, on='factor', how='outer') 579 | 580 | scale_result = scale_result.set_index('factor') 581 | scale_result = scale_result.apply(pd.to_numeric) 582 | 583 | # Calculate the average of the selected quantile Q values in each column 584 | quantiles = scale_result.quantile(quantile) 585 | evaluate = scale_result.apply(lambda col: col[col >= quantiles[col.name]].mean()) 586 | scale_result.loc[f'{quantile:.0%} quantile'] = evaluate 587 | best_scale = path_list[list(evaluate).index(max(evaluate))] 588 | 589 | return scale_result, best_scale 590 | 591 | def scale_plot(scale_result, size_list=[], dpi=100, unit=''): 592 | # plot the result 593 | x = scale_result.index 594 | colors_list = plt.cm.rainbow(np.linspace(0, 1, len(x))) 595 | markers_list = list(mks.MarkerStyle.markers.keys()) 596 | labels_line = [] 597 | 598 | # plot factors values lines 599 | fig, ax1 = plt.subplots(dpi=dpi) 600 | for i, val in enumerate(x[:-1]): 601 | line = ax1.plot(scale_result.columns, scale_result.loc[val], color=colors_list[i], 602 | marker=markers_list[i], linestyle='--', label=val) 603 | labels_line.extend(line) 604 | 605 | # plot quantile values line 606 | ax2 = ax1.twinx() 607 | quantile_values = scale_result.loc[x[-1]] 608 | final_line = ax2.plot(scale_result.columns, quantile_values, color='black', marker='X', 609 | linewidth=2, markersize=12, label=x[-1]) 610 | labels_line.extend(final_line) 611 | 612 | # add text for quantile values 613 | for i, val in enumerate(quantile_values): 614 | ax2.annotate(f'{val:.4f}', (scale_result.columns[i], quantile_values[i]), 615 | textcoords="offset points", xytext=(0, 10), ha='center') 616 | 617 | # add legend, axis label and show the plot 618 | labels = [l.get_label() for l in labels_line] 619 | plt.legend(labels_line, labels, loc='lower right', framealpha=0, ncol=math.ceil(len(scale_result) / 10)) 620 | if size_list != []: 621 | ax1.set_xticks(scale_result.columns, size_list) 622 | ax1.set_xlabel(f'Size of spatial unit ({unit})') if unit != '' else ax1.set_xlabel('Size of spatial unit') 623 | ax1.set_ylabel('Q value') 624 | ax2.set_ylabel(f'The {labels[-1]} of Q values') 625 | 626 | plt.tight_layout() 627 | -------------------------------------------------------------------------------- /omgd.yml: -------------------------------------------------------------------------------- 1 | name: base 2 | channels: 3 | - conda-forge 4 | - defaults 5 | dependencies: 6 | - archspec=0.2.3=pyhd8ed1ab_0 7 | - asttokens=2.0.5=pyhd3eb1b0_0 8 | - blas=1.0=mkl 9 | - boltons=24.0.0=pyhd8ed1ab_0 10 | - bottleneck=1.3.7=py310h9128911_0 11 | - brotli=1.0.9=ha925a31_2 12 | - brotli-python=1.1.0=py310h00ffb61_1 13 | - bzip2=1.0.8=h2bbff1b_5 14 | - ca-certificates=2024.7.2=haa95532_0 15 | - certifi=2024.7.4=py310haa95532_0 16 | - cffi=1.16.0=py310h8d17308_0 17 | - charset-normalizer=3.3.2=pyhd8ed1ab_0 18 | - colorama=0.4.6=pyhd8ed1ab_0 19 | - comm=0.2.1=py310haa95532_0 20 | - conda=24.4.0=py310haa95532_0 21 | - conda-libmamba-solver=24.1.0=pyhd8ed1ab_0 22 | - conda-package-handling=2.2.0=pyh38be061_0 23 | - conda-package-streaming=0.9.0=pyhd8ed1ab_0 24 | - contourpy=1.2.0=py310h59b6b97_0 25 | - cycler=0.11.0=pyhd3eb1b0_0 26 | - decorator=5.1.1=pyhd3eb1b0_0 27 | - descartes=1.1.0=pyhd3eb1b0_4 28 | - distro=1.9.0=pyhd8ed1ab_0 29 | - et_xmlfile=1.1.0=py310haa95532_0 30 | - exceptiongroup=1.2.0=py310haa95532_0 31 | - executing=0.8.3=pyhd3eb1b0_0 32 | - fmt=10.2.1=h181d51b_0 33 | - fonttools=4.51.0=py310h2bbff1b_0 34 | - freetype=2.12.1=ha860e81_0 35 | - glib=2.78.4=hd77b12b_0 36 | - glib-tools=2.78.4=hd77b12b_0 37 | - gst-plugins-base=1.18.5=h9e645db_0 38 | - gstreamer=1.18.5=hd78058f_0 39 | - icc_rt=2022.1.0=h6049295_2 40 | - icu=58.2=ha925a31_3 41 | - idna=3.7=pyhd8ed1ab_0 42 | - intel-openmp=2023.1.0=h59b6b97_46320 43 | - ipython=8.20.0=py310haa95532_0 44 | - ipython_genutils=0.2.0=pyhd3eb1b0_1 45 | - ipywidgets=8.1.2=py310haa95532_0 46 | - jedi=0.18.1=py310haa95532_1 47 | - joblib=1.4.0=py310haa95532_0 48 | - jpeg=9e=h2bbff1b_1 49 | - jsonpatch=1.33=pyhd8ed1ab_0 50 | - jsonpointer=2.4=py310h5588dad_3 51 | - jupyterlab_widgets=3.0.10=py310haa95532_0 52 | - kiwisolver=1.4.4=py310hd77b12b_0 53 | - krb5=1.21.2=heb0366b_0 54 | - lerc=3.0=hd77b12b_0 55 | - libarchive=3.7.2=h313118b_1 56 | - libclang=12.0.0=default_h627e005_2 57 | - libcurl=8.7.1=hd5e4a3a_0 58 | - libdeflate=1.17=h2bbff1b_1 59 | - libffi=3.4.4=hd77b12b_0 60 | - libglib=2.78.4=ha17d25a_0 61 | - libiconv=1.17=hcfcfb64_2 62 | - libmamba=1.5.8=h3f09ed1_0 63 | - libmambapy=1.5.8=py310h04f2035_0 64 | - libogg=1.3.5=h2bbff1b_1 65 | - libpng=1.6.39=h8cc25b3_0 66 | - libsolv=0.7.28=h12be248_2 67 | - libssh2=1.11.0=h7dfc565_0 68 | - libtiff=4.5.1=hd77b12b_0 69 | - libvorbis=1.3.7=he774522_0 70 | - libwebp-base=1.3.2=h2bbff1b_0 71 | - libxml2=2.12.6=hc3477c8_2 72 | - libzlib=1.2.13=hcfcfb64_5 73 | - lz4-c=1.9.4=hcfcfb64_0 74 | - lzo=2.10=hcfcfb64_1001 75 | - mamba=1.5.8=py310hd9d798f_0 76 | - matplotlib=3.7.3=py310h5588dad_0 77 | - matplotlib-base=3.7.3=py310hc9baf74_0 78 | - matplotlib-inline=0.1.6=py310haa95532_0 79 | - menuinst=2.0.2=py310h00ffb61_0 80 | - mkl=2023.1.0=h6b88ed4_46358 81 | - mkl-service=2.4.0=py310h2bbff1b_1 82 | - mkl_fft=1.3.8=py310h2bbff1b_0 83 | - mkl_random=1.2.4=py310h59b6b97_0 84 | - numexpr=2.8.7=py310h2cd9be0_0 85 | - numpy=1.26.4=py310h055cbcc_0 86 | - numpy-base=1.26.4=py310h65a83cf_0 87 | - openjpeg=2.4.0=h4fc8c34_0 88 | - openpyxl=3.0.10=py310h2bbff1b_0 89 | - openssl=3.2.1=hcfcfb64_1 90 | - packaging=24.0=pyhd8ed1ab_0 91 | - palettable=3.3.0=pyhd3eb1b0_0 92 | - pandas=2.1.4=py310h4ed8f06_0 93 | - parso=0.8.3=pyhd3eb1b0_0 94 | - pcre2=10.42=h0ff8eda_1 95 | - pillow=10.2.0=py310h2bbff1b_0 96 | - pip=23.3.1=py310haa95532_0 97 | - platformdirs=4.2.1=pyhd8ed1ab_0 98 | - pluggy=1.5.0=pyhd8ed1ab_0 99 | - ply=3.11=py310haa95532_0 100 | - prompt-toolkit=3.0.43=py310haa95532_0 101 | - prompt_toolkit=3.0.43=hd3eb1b0_0 102 | - pure_eval=0.2.2=pyhd3eb1b0_0 103 | - pyamg=4.2.3=py310h415e7bb_0 104 | - pybind11-abi=4=hd8ed1ab_3 105 | - pycosat=0.6.6=py310h8d17308_0 106 | - pycparser=2.22=pyhd8ed1ab_0 107 | - pygments=2.15.1=py310haa95532_1 108 | - pyparsing=3.0.9=py310haa95532_0 109 | - pyqt=5.15.10=py310hd77b12b_0 110 | - pyqt5-sip=12.13.0=py310h2bbff1b_0 111 | - pysal=2.0.0=py_0 112 | - pysocks=1.7.1=pyh0701188_6 113 | - python=3.10.14=he1021f5_0 114 | - python-dateutil=2.8.2=pyhd3eb1b0_0 115 | - python-tzdata=2023.3=pyhd3eb1b0_0 116 | - python_abi=3.10=2_cp310 117 | - pytz=2024.1=py310haa95532_0 118 | - qt-main=5.15.2=he8e5bd7_7 119 | - reproc=14.2.4.post0=hcfcfb64_1 120 | - reproc-cpp=14.2.4.post0=h63175ca_1 121 | - requests=2.31.0=pyhd8ed1ab_0 122 | - ruamel.yaml=0.18.6=py310h8d17308_0 123 | - ruamel.yaml.clib=0.2.8=py310h8d17308_0 124 | - scikit-learn=1.3.0=py310h4ed8f06_1 125 | - seaborn=0.12.2=py310haa95532_0 126 | - setuptools=68.2.2=py310haa95532_0 127 | - sip=6.7.12=py310hd77b12b_0 128 | - six=1.16.0=pyhd3eb1b0_1 129 | - sqlite=3.41.2=h2bbff1b_0 130 | - stack_data=0.2.0=pyhd3eb1b0_0 131 | - tbb=2021.8.0=h59b6b97_0 132 | - threadpoolctl=2.2.0=pyh0d69192_0 133 | - tk=8.6.12=h2bbff1b_0 134 | - tomli=2.0.1=py310haa95532_0 135 | - tornado=6.3.3=py310h2bbff1b_0 136 | - tqdm=4.66.2=pyhd8ed1ab_0 137 | - traitlets=5.7.1=py310haa95532_0 138 | - truststore=0.8.0=pyhd8ed1ab_0 139 | - tzdata=2024a=h04d1e81_0 140 | - ucrt=10.0.22621.0=h57928b3_0 141 | - unicodedata2=15.1.0=py310h2bbff1b_0 142 | - urllib3=2.2.1=pyhd8ed1ab_0 143 | - vc=14.2=h21ff451_1 144 | - vc14_runtime=14.38.33130=h82b7239_18 145 | - vs2015_runtime=14.38.33130=hcb4865c_18 146 | - wcwidth=0.2.5=pyhd3eb1b0_0 147 | - wheel=0.41.2=py310haa95532_0 148 | - widgetsnbextension=4.0.10=py310haa95532_0 149 | - win_inet_pton=1.1.0=pyhd8ed1ab_6 150 | - xz=5.4.6=h8cc25b3_0 151 | - yaml-cpp=0.8.0=h63175ca_0 152 | - zlib=1.2.13=hcfcfb64_5 153 | - zstandard=0.22.0=py310h0009e47_0 154 | - zstd=1.5.5=h12be248_0 155 | - pip: 156 | - ipympl==0.9.4 157 | - jenkspy==0.4.0 158 | - scipy==1.12.0 159 | prefix: D:\Anaconda\envs\omgd 160 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | contourpy==1.3.0 2 | cycler==0.12.1 3 | fonttools==4.54.1 4 | jenkspy==0.4.0 5 | joblib==1.4.2 6 | kiwisolver==1.4.7 7 | matplotlib==3.7.3 8 | numpy==1.26.4 9 | packaging==24.2 10 | pandas==2.1.4 11 | pillow==11.0.0 12 | pyamg==4.2.3 13 | pyparsing==3.2.0 14 | python-dateutil==2.9.0.post0 15 | pytz==2024.2 16 | scikit-learn==1.3.0 17 | scipy==1.12.0 18 | seaborn==0.12.2 19 | six==1.16.0 20 | threadpoolctl==3.5.0 21 | tzdata==2024.2 22 | -------------------------------------------------------------------------------- /sample.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import omgd 3 | 4 | data = pd.read_csv('data/LST2000.csv') 5 | Y = data.columns[0] 6 | X = data.columns[1:] 7 | discitv = range(3, 8) 8 | n_variates = 1 9 | type_factors = [] 10 | 11 | # sampling of 25%, 50% and 75% of data 12 | data25 = data.sample(frac=0.25, random_state=0).reset_index() 13 | data50 = data.sample(frac=0.50, random_state=0).reset_index() 14 | data75 = data.sample(frac=0.75, random_state=0).reset_index() 15 | 16 | 17 | omgd25 = omgd.omgd(data25, Y=Y, factors=X, n_variates=n_variates, disc_interval=discitv, type_factors=type_factors) 18 | print('omgd25\n', omgd25['factor']) 19 | omgd50 = omgd.omgd(data50, Y=Y, factors=X, n_variates=n_variates, disc_interval=discitv, type_factors=type_factors) 20 | print('omgd50\n', omgd50['factor']) 21 | omgd75 = omgd.omgd(data75, Y=Y, factors=X, n_variates=n_variates, disc_interval=discitv, type_factors=type_factors) 22 | print('omgd75\n', omgd75['factor']) 23 | omgd100 = omgd.omgd(data, Y=Y, factors=X, n_variates=n_variates, disc_interval=discitv, type_factors=type_factors) 24 | print('omgd100\n', omgd100['factor']) -------------------------------------------------------------------------------- /simulation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy import stats 3 | import matplotlib.pyplot as plt 4 | import pandas as pd 5 | import omgd 6 | 7 | # set random seed for reproducibility 8 | np.random.seed(0) 9 | 10 | # define a function to generate and standardize data 11 | def generate_data(size, distribution): 12 | if distribution == 'normal': 13 | data = np.random.normal(loc=0, scale=1, size=size) 14 | elif distribution == 't': 15 | data = np.random.standard_t(df=30, size=size) 16 | elif distribution == 'uniform': 17 | data = np.random.uniform(low=0.0, high=1.0, size=size) 18 | elif distribution == 'gamma': 19 | data = np.random.gamma(2, 2, size) 20 | 21 | # 标准化数据 22 | return (data - data.mean()) / data.std() * 10 23 | 24 | # generate data 25 | sample_sizes = [300, 500, 700] 26 | distributions = ['normal', 't', 'uniform', 'gamma'] 27 | data_sets = {size: {dist: generate_data(size, dist) for dist in distributions} 28 | for size in sample_sizes} 29 | 30 | # create graph and subplots 31 | fig, axes = plt.subplots(3, 4, figsize=(15, 10)) 32 | 33 | for i, size in enumerate(sample_sizes): 34 | for j, (dist, title) in enumerate(zip(distributions, 35 | ['Normal', 'T', 'Uniform', 'Gamma'])): 36 | axes[i, j].hist(data_sets[size][dist], bins=20, edgecolor='black') 37 | axes[i, j].set_title(f'{title} (n={size})') 38 | axes[i, j].set_xlabel('Value') 39 | axes[i, j].set_ylabel('Frequency') 40 | 41 | 42 | # print statistical information 43 | for size in sample_sizes: 44 | print(f"\nSample size: {size}") 45 | for dist, name in zip(distributions, ['Normal', 'T', 'Uniform', 'Gamma']): 46 | data = data_sets[size][dist] 47 | print(f"{name} distribution:") 48 | print(f" Mean: {data.mean():.4f}") 49 | print(f" Standard deviation: {data.std():.4f}") 50 | print(f" Skewness: {stats.skew(data):.4f}") 51 | 52 | # Create sorted DataFrames 53 | dataframes = {} 54 | for size in sample_sizes: 55 | df_data = {} 56 | for dist in distributions: 57 | df_data[dist] = np.sort(data_sets[size][dist]) 58 | dataframes[size] = pd.DataFrame(df_data) 59 | 60 | # Create scatter plots comparing normal distribution with others 61 | fig, axes = plt.subplots(3, 3, figsize=(10, 10)) 62 | 63 | for i, size in enumerate(sample_sizes): 64 | normal_data = dataframes[size]['normal'] 65 | for j, dist in enumerate(['t', 'uniform', 'gamma']): 66 | dist_data = dataframes[size][dist] 67 | axes[i, j].scatter(normal_data, dist_data) 68 | axes[i, j].set_title(f'Normal & {dist.capitalize()} (n={size})') 69 | axes[i, j].set_xlabel('Normal') 70 | axes[i, j].set_ylabel(dist.capitalize()) 71 | 72 | plt.tight_layout() 73 | plt.show() 74 | 75 | # Print the first few rows of each DataFrame 76 | for size, df in dataframes.items(): 77 | print(f"\nDataFrame for sample size {size}:") 78 | # print(df.head()) 79 | df.to_csv(f'data/sim{size}.csv', index=False) 80 | 81 | omgd_result = omgd.omgd(df, Y=df.columns[0], factors=df.columns[1:], n_variates=1, disc_interval=range(3, 8)) 82 | print(omgd_result['factor']) -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import omgd 3 | import matplotlib.pyplot as plt 4 | 5 | 6 | ## parameters 7 | 8 | # file with different spatial units 9 | path_list = ['data/LST250.csv', 'data/LST500.csv', 'data/LST750.csv', 'data/LST1000.csv','data/LST1500.csv', 'data/LST2000.csv'] 10 | # dependent variable 11 | Y = 'LST' 12 | # explainatory variables 13 | factors = ['Built', 'DEM', 'MNDWI', 'NDBI', 'NDVI', 'Roads'] 14 | # explainatory variables which is not continuous variables 15 | type_factors = [] 16 | # class number interval 17 | disc_interval = range(3, 8) 18 | # quantile of the Q value 19 | quantile = 0.8 20 | # unit of dependent variables and independent variables (for plotting) 21 | unit_list=['°C', '%', 'm', '', '', '', ''] 22 | # number of explanatory variables used in the calculation 23 | n_variates = 2 24 | 25 | ## scale detector 26 | scale_result, best_scale = omgd.scale_detector(path_list, Y, factors, disc_interval, type_factors, quantile=quantile, 27 | n_variates=n_variates, random_state=0) 28 | omgd.scale_plot(scale_result, size_list=[250, 500, 750, 1000, 1500, 2000], dpi=200, unit='m') 29 | plt.show() 30 | 31 | 32 | ## one step OMGD model 33 | 34 | omgd_result = omgd.omgd(pd.read_csv(best_scale), Y=Y, factors=factors, n_variates=n_variates, disc_interval=disc_interval) 35 | print(omgd_result['classify']) 36 | # omgd_result['classify'].to_csv('LST_classify.csv') 37 | print(omgd_result['factor']) 38 | omgd.omgd_plot(omgd_result, unit_list=unit_list) 39 | plt.show() 40 | 41 | 42 | # path_list = ['data/ndvi_5.csv', 'data/ndvi_10.csv', 'data/ndvi_20.csv', 'data/ndvi_30.csv', 'data/ndvi_40.csv', 'data/ndvi_50.csv'] 43 | # Y = 'NDVIchange' 44 | # X = ['Climatezone', 'Mining', 'Tempchange', 'Precipitation', 'GDP', 'Popdensity'] 45 | # type_factors = ['Climatezone', 'Mining'] 46 | # discitv = range(3, 8) 47 | 48 | # path_list = ['data/h1n1_50.csv', 'data/h1n1_100.csv', 'data/h1n1_150.csv'] 49 | # Y = 'H1N1' 50 | # X = ['temp', 'prec', 'humi', 'popd', 'gdpd', 'rdds', 'sensepop', 'urbanpop', 'medicost', 'Georegion'] 51 | # type_factors = ['Georegion'] 52 | # discitv = range(3, 8) 53 | 54 | # n_variates = 6 55 | # 56 | # scale_result, best_scale = omgd.scale_detector(path_list, Y, X, discitv, type_factors, quantile=0.8, n_variates=n_variates) 57 | # omgd.scale_plot(scale_result, size_list=[5, 10, 20, 30, 40, 50], dpi=200, unit='km') 58 | # plt.show() 59 | 60 | # df = pd.read_csv('data/ndvi_40.csv') 61 | # omgd_result = omgd.omgd(df, Y=Y, factors=X, n_variates=n_variates, disc_interval=discitv, type_factors=type_factors) 62 | # omgd.factor_plot(omgd_result['factor'][:10]) 63 | # 64 | # plt.show() 65 | 66 | 67 | # path_list = ['data/PZH_LST1000.csv', 'data/PZH_LST2000.csv', 'data/PZH_LST3000.csv', 68 | # 'data/PZH_LST4000.csv', 'data/PZH_LST5000.csv', 'data/PZH_LST6000.csv'] 69 | # data = pd.read_csv('data/PZH_LST5000.csv') 70 | # Y = data.columns[0] 71 | # X = data.columns[1:] 72 | # discitv = range(3, 8) 73 | # n_variates = 6 74 | # # 75 | # # # # scale detector 76 | # # # scale_result, best_scale = omgd.scale_detector(path_list, Y, X, discitv, quantile=0.8, n_variates=n_variates) 77 | # # # omgd.scale_plot(scale_result, size_list=[1, 2, 3, 4, 5, 6], dpi=200, unit='km') 78 | # # # plt.show() 79 | # # 80 | # df = pd.read_csv('data/PZH_LST5000.csv') 81 | # omgd_result = omgd.omgd(df, Y=Y, factors=X, n_variates=n_variates, disc_interval=discitv, type_factors=[]) 82 | # omgd.factor_plot(omgd_result['factor'][:10]) 83 | # 84 | # plt.show() --------------------------------------------------------------------------------