├── train_frame.csv ├── train_space_frame.csv ├── README.md ├── time_train ├── time_2.csv ├── time_6.csv ├── time_4.csv ├── time_9.csv ├── time_1.csv ├── time_11.csv ├── time_12.csv ├── time_8.csv ├── time_7.csv ├── time_10.csv ├── time_5.csv ├── time_3.csv ├── only_AQI.csv ├── train_3day_onlyAQI.csv └── origin_train_only6AQI_2.csv ├── AQI_label.py ├── space_train ├── space_2.csv ├── space_4.csv ├── space_9.csv ├── space_11.csv ├── space_6.csv ├── space_3.csv ├── space_1.csv ├── space_10.csv ├── space_12.csv ├── space_5.csv ├── space_8.csv └── space_7.csv ├── space_AQI_label.py ├── origin_6AQI_label.py ├── only_6AQI.py └── improved_PSO_train_NN.py /train_frame.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | ,70,,,,, 3 | -------------------------------------------------------------------------------- /train_space_frame.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 0,0,0,0,0,0,0,0,0,0,0,0,0 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PSO-train-BP-NN 2 | This project use PSO (Particle Swarm Optimization) to train the single layer NN structure. 3 | 4 | If you only want to see the training process and result, 'improved_PSO_train_NN.py' can run directly. 5 | 6 | 'NN_train_AQI.py' is the traditional NN set up by keras, which can compare with the result of 'improved_PSO_train_NN.py'. 7 | 8 | When the 'improved_PSO_train_NN.py' run, the console will represent the number of epoch, the error of val data, the error of test data, and cost time per epoch. 9 | 10 | The data is air quality index download from Environmental Protection Administration Executive Yuan, R.O.C. (Taiwan). (https://data.epa.gov.tw/dataset/aqx_p_13) 11 | 12 | 'space' and 'time' is original data. 13 | 14 | 'space_train' and 'time_train' is the data after preprocessing. 15 | 16 | 'AQI_label.py', 'only_6AQI.py', 'space_AQI_label.py',and 'origin_6AQI_label.py' is the preprocessing of data. 17 | 18 | The process divided the data into three kinds. 19 | 20 | 1.Predict the AQI at different times in the same place. 21 | 22 | 2.Predict the AQI at different places in the same time. 23 | 24 | 3.Predict AQI from 6 kinds of air quality values. 25 | 26 | 27 | -------------------------------------------------------------------------------- /time_train/time_2.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/2/1,70,0.47666666666666674,24.75,15.291666666666666,0.8291666666666666,49.6482683982684 3 | 2020/2/2,70,0.5208333333333334,18.083333333333332,9.541666666666666,1.6916666666666667,30.979437229437227 4 | 2020/2/3,70,0.7899999999999999,16.347826086956523,10.26086956521739,1.9304347826086956,33.31451157538114 5 | 2020/2/4,70,0.96875,15.666666666666666,10.375,1.6000000000000003,33.685064935064936 6 | 2020/2/5,70,0.5675,18.666666666666668,10.208333333333334,0.9291666666666667,33.1439393939394 7 | 2020/2/6,70,0.5982608695652174,15.608695652173912,9.0,1.8130434782608698,29.22077922077922 8 | 2020/2/7,70,0.8291304347826087,18.25,7.541666666666667,1.0608695652173912,24.485930735930737 9 | 2020/2/8,70,0.5791666666666667,22.583333333333332,10.75,0.8833333333333333,34.9025974025974 10 | 2020/2/9,70,0.43875000000000003,25.375,16.375,2.1333333333333333,53.26851851851852 11 | 2020/2/10,70,0.43041666666666667,31.541666666666668,17.833333333333332,1.0791666666666666,57.04938271604938 12 | 2020/2/11,70,1.1570833333333332,33.041666666666664,20.208333333333332,1.7291666666666667,63.20679012345679 13 | 2020/2/12,70,1.4920833333333334,39.166666666666664,25.416666666666668,2.0666666666666664,76.70987654320989 14 | 2020/2/13,70,0.9695833333333334,26.82608695652174,17.125,1.9958333333333336,55.21296296296296 15 | 2020/2/14,70,0.9554166666666667,27.869565217391305,18.434782608695652,1.6041666666666667,58.608695652173914 16 | 2020/2/15,70,1.0429166666666667,20.958333333333332,13.208333333333334,1.6916666666666667,42.884199134199136 17 | 2020/2/16,70,0.47750000000000004,11.25,6.833333333333333,3.8625000000000003,22.186147186147185 18 | 2020/2/17,70,0.50125,32.333333333333336,13.916666666666666,1.2727272727272727,45.183982683982684 19 | 2020/2/18,70,0.3408695652173913,22.73913043478261,10.608695652173912,1.173913043478261,34.4438170525127 20 | 2020/2/19,70,0.5583333333333332,20.791666666666668,9.041666666666666,1.2833333333333332,29.356060606060606 21 | 2020/2/20,70,0.6766666666666667,18.458333333333332,10.166666666666666,1.875,33.00865800865801 22 | 2020/2/21,70,0.9904166666666665,32.75,18.291666666666668,1.1500000000000001,58.23765432098766 23 | 2020/2/22,70,0.595,37.583333333333336,25.333333333333332,0.7666666666666666,76.49382716049382 24 | 2020/2/23,70,0.3666666666666667,51.0,20.434782608695652,0.9608695652173914,63.7938808373591 25 | 2020/2/24,70,0.8075000000000001,19.708333333333332,11.291666666666666,0.9791666666666666,36.66125541125541 26 | 2020/2/25,70,1.2170833333333333,39.041666666666664,23.75,2.7999999999999994,72.38888888888889 27 | 2020/2/26,70,0.8112499999999999,30.416666666666668,19.041666666666668,1.95,60.1820987654321 28 | 2020/2/27,70,0.46208333333333335,20.869565217391305,11.909090909090908,0.5391304347826086,38.66587957497048 29 | 2020/2/28,70,0.9491666666666667,46.125,22.875,2.1791666666666667,70.12037037037038 30 | 2020/2/29,70,1.025,29.25,19.833333333333332,2.595833333333333,62.23456790123457 31 | -------------------------------------------------------------------------------- /time_train/time_6.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/6/1,70,0.8395652173913044,16.375,8.333333333333334,2.1391304347826083,27.056277056277057 3 | 2020/6/2,70,0.9914285714285714,21.59090909090909,11.347826086956522,1.2333333333333332,36.843591191417275 4 | 2020/6/3,70,0.6191666666666666,20.08695652173913,9.08695652173913,2.591304347826087,29.503105590062113 5 | 2020/6/4,70,0.5995833333333334,22.458333333333332,10.333333333333334,1.6333333333333335,33.54978354978355 6 | 2020/6/5,70,0.68375,22.583333333333332,11.333333333333334,1.0625,36.7965367965368 7 | 2020/6/6,70,0.5225,19.125,8.083333333333334,2.125,26.244588744588746 8 | 2020/6/7,70,0.6625,14.583333333333334,7.541666666666667,1.7333333333333332,24.485930735930737 9 | 2020/6/8,70,0.8283333333333333,16.708333333333332,6.833333333333333,0.9791666666666666,22.186147186147185 10 | 2020/6/9,70,0.821304347826087,16.043478260869566,7.086956521739131,1.7782608695652176,23.00959909655562 11 | 2020/6/10,70,0.8033333333333333,19.166666666666668,10.333333333333334,2.225,33.54978354978355 12 | 2020/6/11,70,0.93,29.708333333333332,15.416666666666666,2.4,50.05411255411255 13 | 2020/6/12,70,0.8604166666666666,26.916666666666668,14.083333333333334,1.05,45.72510822510823 14 | 2020/6/13,70,0.6629166666666667,25.5,12.708333333333334,2.129166666666667,41.260822510822514 15 | 2020/6/14,70,0.7033333333333333,21.625,12.666666666666666,1.5083333333333335,41.125541125541126 16 | 2020/6/15,70,0.5854166666666667,20.208333333333332,6.333333333333333,1.1791666666666667,20.562770562770563 17 | 2020/6/16,70,0.5333333333333333,17.916666666666668,7.958333333333333,1.7874999999999999,25.83874458874459 18 | 2020/6/17,70,0.5933333333333334,22.083333333333332,9.833333333333334,1.3499999999999996,31.926406926406926 19 | 2020/6/18,70,0.5012500000000001,19.291666666666668,9.375,1.6375,30.43831168831169 20 | 2020/6/19,70,0.5875,21.565217391304348,10.304347826086957,2.1869565217391305,33.45567476002259 21 | 2020/6/20,70,0.6033333333333334,20.708333333333332,9.875,2.5,32.061688311688314 22 | 2020/6/21,70,0.4595833333333334,20.391304347826086,10.73913043478261,2.3000000000000003,34.86730660643704 23 | 2020/6/22,70,0.5004166666666666,18.875,8.041666666666666,2.829166666666667,26.10930735930736 24 | 2020/6/23,70,0.5913636363636364,19.52173913043478,8.91304347826087,1.0181818181818183,28.93845285149633 25 | 2020/6/24,70,0.6479166666666667,21.208333333333332,9.75,2.1708333333333334,31.655844155844157 26 | 2020/6/25,70,0.4529166666666667,21.125,10.0,1.7541666666666664,32.467532467532465 27 | 2020/6/26,70,0.45458333333333334,17.75,8.791666666666666,1.6791666666666665,28.54437229437229 28 | 2020/6/27,70,0.4795833333333333,20.208333333333332,10.208333333333334,2.433333333333333,33.1439393939394 29 | 2020/6/28,70,0.48499999999999993,16.625,8.75,9.416666666666666,28.40909090909091 30 | 2020/6/29,70,0.5770833333333334,19.043478260869566,10.08695652173913,1.4772727272727273,32.749858836815356 31 | 2020/6/30,70,0.59,20.681818181818183,9.863636363636363,1.1636363636363636,32.02479338842975 32 | -------------------------------------------------------------------------------- /time_train/time_4.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/4/1,70,0.5258333333333334,16.26086956521739,7.173913043478261,0.39545454545454545,23.29192546583851 3 | 2020/4/2,70,0.4116666666666667,16.916666666666668,8.208333333333334,1.5916666666666668,26.650432900432904 4 | 2020/4/3,70,0.4608333333333334,29.458333333333332,14.0,1.75,45.45454545454545 5 | 2020/4/4,70,0.4145833333333333,26.083333333333332,13.333333333333334,1.4363636363636365,43.29004329004329 6 | 2020/4/5,70,0.35125,34.958333333333336,16.0,1.9333333333333336,52.2962962962963 7 | 2020/4/6,70,0.43826086956521737,26.458333333333332,10.541666666666666,1.8666666666666665,34.226190476190474 8 | 2020/4/7,70,0.57,16.5,9.333333333333334,2.120833333333333,30.303030303030305 9 | 2020/4/8,70,0.5604166666666667,26.166666666666668,11.166666666666666,2.4583333333333335,36.25541125541125 10 | 2020/4/9,70,0.5776190476190476,28.75,14.789473684210526,1.6714285714285715,48.01777170198223 11 | 2020/4/10,70,0.4708695652173913,24.5,12.26086956521739,1.4173913043478261,39.808018068887634 12 | 2020/4/11,70,0.9416666666666668,31.333333333333332,19.708333333333332,2.9958333333333336,61.910493827160494 13 | 2020/4/12,70,0.6833333333333332,26.916666666666668,13.916666666666666,1.3624999999999998,45.183982683982684 14 | 2020/4/13,70,0.7527272727272728,41.13636363636363,23.869565217391305,3.490476190476191,72.69887278582931 15 | 2020/4/14,70,0.5408695652173913,41.59090909090909,24.5,1.05,74.33333333333334 16 | 2020/4/15,70,0.9495833333333333,43.75,27.875,1.8166666666666667,83.08333333333334 17 | 2020/4/16,70,0.8636363636363636,36.73913043478261,22.08695652173913,2.4363636363636365,68.07729468599034 18 | 2020/4/17,70,1.0395833333333333,38.5,22.541666666666668,2.1416666666666666,69.25617283950618 19 | 2020/4/18,70,0.8241666666666667,39.375,23.375,1.695833333333333,71.41666666666667 20 | 2020/4/19,70,0.9441666666666667,34.208333333333336,23.958333333333332,1.8916666666666668,72.92901234567901 21 | 2020/4/20,70,0.7008333333333333,27.59090909090909,17.142857142857142,1.7750000000000001,55.25925925925926 22 | 2020/4/21,70,0.9520833333333334,17.666666666666668,9.291666666666666,1.7375,30.167748917748916 23 | 2020/4/22,70,0.4170833333333334,6.666666666666667,3.0833333333333335,1.2666666666666666,10.01082251082251 24 | 2020/4/23,70,0.39083333333333337,17.0,12.375,1.0583333333333333,40.17857142857143 25 | 2020/4/24,70,0.3895652173913044,14.73913043478261,9.173913043478262,1.6565217391304348,29.785431959345004 26 | 2020/4/25,70,0.8308333333333332,19.291666666666668,9.5,1.2904761904761906,30.844155844155843 27 | 2020/4/26,70,0.81875,19.285714285714285,12.791666666666666,2.295833333333333,41.531385281385276 28 | 2020/4/27,70,0.5369565217391304,17.59090909090909,8.217391304347826,2.3956521739130436,26.679841897233203 29 | 2020/4/28,70,0.385,34.125,12.541666666666666,0.725,40.71969696969697 30 | 2020/4/29,70,0.42208333333333337,34.833333333333336,15.833333333333334,0.8041666666666667,51.864197530864196 31 | 2020/4/30,70,0.8325000000000001,36.75,20.708333333333332,1.6375,64.50308641975309 32 | -------------------------------------------------------------------------------- /time_train/time_9.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/9/1,70,0.7000000000000001,22.0,15.333333333333334,3.1130434782608694,49.78354978354979 3 | 2020/9/2,70,0.6529166666666666,29.541666666666668,18.208333333333332,1.9541666666666668,58.0216049382716 4 | 2020/9/3,70,0.80625,23.791666666666668,16.208333333333332,1.8458333333333332,52.83641975308642 5 | 2020/9/4,70,0.5233333333333333,24.5,15.333333333333334,1.1500000000000001,49.78354978354979 6 | 2020/9/5,70,0.6741666666666667,27.916666666666668,15.625,2.4181818181818184,51.324074074074076 7 | 2020/9/6,70,0.5695833333333334,30.708333333333332,17.958333333333332,0.1789473684210526,57.373456790123456 8 | 2020/9/7,70,0.5979166666666667,35.36363636363637,20.227272727272727,2.0842105263157897,63.255892255892256 9 | 2020/9/8,70,0.7941666666666668,41.18181818181818,26.818181818181817,2.3304347826086955,80.34343434343434 10 | 2020/9/9,70,0.8904545454545455,35.72727272727273,21.818181818181817,0.7666666666666667,67.38047138047138 11 | 2020/9/10,70,0.9420833333333333,32.708333333333336,20.5,1.5521739130434784,63.96296296296296 12 | 2020/9/11,70,0.8566666666666668,30.5,17.916666666666668,2.3521739130434782,57.26543209876544 13 | 2020/9/12,70,0.6370833333333333,,11.625,1.179166666666667,37.743506493506494 14 | 2020/9/13,70,0.46125,,5.208333333333333,0.4166666666666667,16.91017316017316 15 | 2020/9/14,70,0.5247619047619048,,4.869565217391305,2.1904761904761907,15.810276679841898 16 | 2020/9/15,70,0.7200000000000001,24.384615384615383,10.833333333333334,1.9869565217391305,35.17316017316018 17 | 2020/9/16,70,0.73375,22.583333333333332,13.541666666666666,1.9333333333333333,43.96645021645021 18 | 2020/9/17,70,0.8133333333333334,26.333333333333332,14.916666666666666,2.404166666666667,48.43073593073593 19 | 2020/9/18,70,0.7070833333333333,27.208333333333332,14.333333333333334,0.9190476190476191,46.53679653679654 20 | 2020/9/19,70,0.5595833333333333,18.375,8.958333333333334,1.535294117647059,29.085497835497836 21 | 2020/9/20,70,0.44708333333333333,12.916666666666666,6.625,2.158333333333333,21.50974025974026 22 | 2020/9/21,70,0.515,27.708333333333332,10.583333333333334,1.4750000000000003,34.36147186147186 23 | 2020/9/22,70,0.4483333333333333,26.61904761904762,10.136363636363637,2.775,32.91027154663519 24 | 2020/9/23,70,0.45083333333333336,23.875,9.666666666666666,2.7133333333333334,31.385281385281385 25 | 2020/9/24,70,0.5870833333333333,15.166666666666666,6.291666666666667,2.5083333333333333,20.42748917748918 26 | 2020/9/25,70,0.73,24.666666666666668,13.958333333333334,2.2529411764705882,45.31926406926407 27 | 2020/9/26,70,0.5045833333333333,20.541666666666668,10.375,2.7583333333333333,33.685064935064936 28 | 2020/9/27,70,0.39749999999999996,9.5,5.75,2.0173913043478264,18.66883116883117 29 | 2020/9/28,70,0.5979166666666665,10.375,5.0,3.6608695652173906,16.233766233766232 30 | 2020/9/29,70,0.57875,21.958333333333332,8.166666666666666,3.022727272727273,26.515151515151512 31 | 2020/9/30,70,0.7560869565217392,19.904761904761905,10.869565217391305,2.073913043478261,35.29079616036138 32 | -------------------------------------------------------------------------------- /time_train/time_1.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/1/1,70,0.3591666666666667,14.75,5.5,0.8041666666666667,17.857142857142858 3 | 2020/1/2,70,0.4895833333333333,17.458333333333332,6.333333333333333,0.3826086956521739,20.562770562770563 4 | 2020/1/3,70,0.7329166666666667,15.416666666666666,8.291666666666666,0.8916666666666667,26.92099567099567 5 | 2020/1/4,70,0.6304166666666667,16.625,8.875,1.3708333333333333,28.814935064935064 6 | 2020/1/5,70,0.3875,13.083333333333334,7.916666666666667,1.4541666666666668,25.703463203463205 7 | 2020/1/6,70,1.4256521739130434,46.166666666666664,28.083333333333332,2.0652173913043477,83.62345679012346 8 | 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2020/10/13,70,0.37458333333333327,13.5,8.083333333333334,0.625,26.244588744588746 15 | 2020/10/14,70,0.45541666666666664,31.625,12.333333333333334,2.0,40.04329004329004 16 | 2020/10/15,70,0.43086956521739134,31.26086956521739,12.136363636363637,1.9473684210526316,39.403778040141674 17 | 2020/10/16,70,0.47208333333333324,30.5,13.166666666666666,1.7458333333333336,42.74891774891775 18 | 2020/10/17,70,0.4454166666666666,25.75,9.708333333333334,0.9249999999999999,31.520562770562773 19 | 2020/10/18,70,0.38916666666666666,24.0,12.0,2.4875000000000003,38.96103896103896 20 | 2020/10/19,70,0.48,28.833333333333332,13.541666666666666,3.6958333333333333,43.96645021645021 21 | 2020/10/20,70,0.46208333333333335,28.782608695652176,13.26086956521739,2.795833333333333,43.05477131564088 22 | 2020/10/21,70,0.45166666666666666,12.583333333333334,6.458333333333333,2.0541666666666667,20.968614718614717 23 | 2020/10/22,70,0.66375,9.666666666666666,5.958333333333333,1.5625,19.345238095238095 24 | 2020/10/23,70,0.61625,57.875,21.666666666666668,1.0666666666666667,66.98765432098766 25 | 2020/10/24,70,0.31,47.333333333333336,13.875,1.5333333333333332,45.048701298701296 26 | 2020/10/25,70,0.2808333333333333,21.708333333333332,7.0,0.4791666666666667,22.727272727272727 27 | 2020/10/26,70,0.3582608695652174,20.875,8.083333333333334,1.9826086956521736,26.244588744588746 28 | 2020/10/27,70,0.3825,24.041666666666668,11.5,1.7695652173913046,37.33766233766234 29 | 2020/10/28,70,0.37749999999999995,26.208333333333332,14.708333333333334,2.1708333333333334,47.75432900432901 30 | 2020/10/29,70,0.4617391304347826,27.82608695652174,13.217391304347826,2.859090909090909,42.913608130999435 31 | 2020/10/30,70,0.43541666666666673,38.958333333333336,14.25,2.9458333333333333,46.26623376623377 32 | 2020/10/31,70,0.3545833333333333,27.5,10.791666666666666,1.5041666666666667,35.03787878787879 33 | -------------------------------------------------------------------------------- /time_train/time_5.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/5/1,70,0.97625,42.0,25.75,2.004166666666667,77.57407407407408 3 | 2020/5/2,70,0.9,40.0,24.666666666666668,3.6,74.76543209876544 4 | 2020/5/3,70,0.6829166666666667,26.916666666666668,17.208333333333332,2.125,55.42901234567901 5 | 2020/5/4,70,1.0365217391304349,32.541666666666664,16.666666666666668,1.908695652173913,54.0246913580247 6 | 2020/5/5,70,0.7725,37.25,23.541666666666668,1.3833333333333335,71.84876543209877 7 | 2020/5/6,70,0.7545833333333333,24.666666666666668,12.125,1.991666666666667,39.366883116883116 8 | 2020/5/7,70,0.7566666666666667,19.625,11.916666666666666,2.358333333333333,38.69047619047619 9 | 2020/5/8,70,0.8091666666666667,20.434782608695652,11.826086956521738,1.1772727272727272,38.396386222473176 10 | 2020/5/9,70,0.6216666666666667,19.166666666666668,10.916666666666666,2.2041666666666666,35.44372294372294 11 | 2020/5/10,70,0.6074999999999999,21.130434782608695,14.041666666666666,1.4958333333333333,45.589826839826834 12 | 2020/5/11,70,0.5820833333333333,18.166666666666668,13.695652173913043,2.05,44.466403162055336 13 | 2020/5/12,70,0.5775,21.375,14.791666666666666,2.2041666666666666,48.02489177489177 14 | 2020/5/13,70,0.55375,44.21739130434783,27.041666666666668,1.3791666666666667,80.92283950617285 15 | 2020/5/14,70,0.8504166666666665,34.0,23.125,1.0999999999999999,70.76851851851852 16 | 2020/5/15,70,1.0256521739130435,34.65217391304348,23.217391304347824,1.190909090909091,71.00805152979066 17 | 2020/5/16,70,0.6979166666666666,23.166666666666668,13.958333333333334,2.254166666666667,45.31926406926407 18 | 2020/5/17,70,0.5341666666666666,15.125,6.875,1.6083333333333332,22.321428571428573 19 | 2020/5/18,70,0.7758333333333333,17.875,9.173913043478262,2.891666666666667,29.785431959345004 20 | 2020/5/19,70,0.8420833333333334,21.666666666666668,12.541666666666666,0.7086956521739131,40.71969696969697 21 | 2020/5/20,70,0.6283333333333333,19.125,11.041666666666666,0.691304347826087,35.849567099567096 22 | 2020/5/21,70,0.7516666666666666,13.173913043478262,5.478260869565218,2.4434782608695653,17.786561264822137 23 | 2020/5/22,70,0.96125,8.083333333333334,4.916666666666667,1.95,15.963203463203463 24 | 2020/5/23,70,0.5141666666666667,6.916666666666667,4.708333333333333,1.3666666666666665,15.286796536796535 25 | 2020/5/24,70,0.4000000000000001,15.666666666666666,7.166666666666667,2.0416666666666665,23.26839826839827 26 | 2020/5/25,70,0.6754166666666667,22.291666666666668,10.916666666666666,1.6958333333333335,35.44372294372294 27 | 2020/5/26,70,0.8552173913043477,19.333333333333332,9.375,2.4173913043478263,30.43831168831169 28 | 2020/5/27,70,0.45875,12.541666666666666,5.0,2.529166666666667,16.233766233766232 29 | 2020/5/28,70,0.6243478260869565,7.956521739130435,4.409090909090909,2.2217391304347824,14.315230224321134 30 | 2020/5/29,70,1.1056521739130434,14.91304347826087,8.956521739130435,2.2347826086956526,29.07961603613778 31 | 2020/5/30,70,0.8937500000000002,21.291666666666668,11.208333333333334,1.6624999999999999,36.39069264069264 32 | 2020/5/31,70,0.555,15.666666666666666,7.458333333333333,1.7708333333333333,24.215367965367964 33 | -------------------------------------------------------------------------------- /time_train/time_3.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,SiteId,CO,PM10,PM25,SO2,AQI 2 | 2020/3/1,70,0.7212500000000001,23.958333333333332,16.833333333333332,0.5541666666666667,54.456790123456784 3 | 2020/3/2,70,0.4283333333333333,19.833333333333332,9.0,1.4124999999999999,29.22077922077922 4 | 2020/3/3,70,0.5730434782608697,23.391304347826086,11.478260869565217,1.7590909090909093,37.267080745341616 5 | 2020/3/4,70,0.5366666666666667,11.25,7.166666666666667,1.2208333333333334,23.26839826839827 6 | 2020/3/5,70,0.43500000000000005,20.541666666666668,13.375,0.7041666666666666,43.425324675324674 7 | 2020/3/6,70,0.7491666666666666,18.625,10.416666666666666,2.1041666666666665,33.82034632034632 8 | 2020/3/7,70,1.08625,34.875,21.833333333333332,1.1125,67.41975308641975 9 | 2020/3/8,70,0.48291666666666666,16.166666666666668,9.0,0.9916666666666667,29.22077922077922 10 | 2020/3/9,70,0.9179166666666666,38.75,24.5,2.841666666666667,74.33333333333334 11 | 2020/3/10,70,0.6178260869565217,14.826086956521738,9.08695652173913,1.2521739130434781,29.503105590062113 12 | 2020/3/11,70,0.46166666666666667,41.416666666666664,19.125,0.5208333333333334,60.39814814814815 13 | 2020/3/12,70,1.3787500000000001,25.083333333333332,16.041666666666668,2.4875000000000003,52.40432098765432 14 | 2020/3/13,70,1.1865217391304348,17.08695652173913,11.347826086956522,1.735,36.843591191417275 15 | 2020/3/14,70,0.51625,26.75,14.416666666666666,1.6291666666666664,46.807359307359306 16 | 2020/3/15,70,0.40625,49.041666666666664,17.166666666666668,1.5041666666666664,55.320987654320994 17 | 2020/3/16,70,0.41958333333333336,39.458333333333336,14.708333333333334,3.2875,47.75432900432901 18 | 2020/3/17,70,0.8979166666666666,46.416666666666664,22.208333333333332,1.4000000000000001,68.39197530864197 19 | 2020/3/18,70,1.2866666666666666,49.541666666666664,32.166666666666664,3.483333333333334,94.20987654320987 20 | 2020/3/19,70,1.3366666666666667,36.416666666666664,28.75,2.733333333333333,85.35185185185185 21 | 2020/3/20,70,0.8468181818181819,37.43478260869565,24.652173913043477,0.6833333333333333,74.72785829307568 22 | 2020/3/21,70,1.0929166666666668,43.0,24.041666666666668,2.470833333333333,73.14506172839506 23 | 2020/3/22,70,0.86,39.708333333333336,25.333333333333332,0.8958333333333334,76.49382716049382 24 | 2020/3/23,70,0.4995833333333333,39.833333333333336,20.041666666666668,2.5375,62.7746913580247 25 | 2020/3/24,70,0.44875,31.041666666666668,12.25,1.1791666666666665,39.77272727272727 26 | 2020/3/25,70,1.1141666666666665,35.08695652173913,21.08695652173913,2.766666666666667,65.48470209339774 27 | 2020/3/26,70,1.0204761904761905,29.47826086956522,19.636363636363637,2.195,61.72390572390573 28 | 2020/3/27,70,0.9404166666666667,35.166666666666664,23.041666666666668,1.4333333333333333,70.55246913580248 29 | 2020/3/28,70,0.5145833333333334,9.041666666666666,5.333333333333333,1.7041666666666664,17.316017316017316 30 | 2020/3/29,70,0.34708333333333335,18.666666666666668,6.75,1.9041666666666668,21.915584415584416 31 | 2020/3/30,70,0.8800000000000001,18.208333333333332,10.041666666666666,2.795652173913044,32.60281385281385 32 | 2020/3/31,70,0.810952380952381,17.565217391304348,7.260869565217392,3.645454545454545,23.5742518351214 33 | -------------------------------------------------------------------------------- /AQI_label.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Jan 7 14:08:32 2021 4 | 5 | @author: ray 6 | """ 7 | 8 | import numpy as np 9 | import pandas as pd 10 | result_all = pd.DataFrame() 11 | for month_num in range(1,13): 12 | df_data = pd.read_csv('./time/time_'+ str(month_num) +'.csv', index_col= False) 13 | new_frame = pd.read_csv('train_frame.csv') 14 | # df_data_drop = df_data[(True-df_data.isin(['x']))] 15 | result = pd.DataFrame() 16 | 17 | days = int(df_data.shape[0]/11) 18 | # for num, col in enumerate(df_data.iloc[:,6]): 19 | data_temp = pd.DataFrame() 20 | AQI_result = np.zeros(shape=[6, days]) 21 | 22 | for day_num in range(days): 23 | data_temp = df_data.iloc[day_num*11:(day_num*11)+11, :].reset_index() 24 | ItemId = data_temp["ItemId"] 25 | for Id_num in range(11): 26 | if(ItemId[Id_num]==3): 27 | O3 = data_temp.iloc[Id_num, 8:].astype(float) 28 | mean_O3 = O3.mean() 29 | if(mean_O3 < 125): 30 | AQI_O3 = 0 31 | if(mean_O3 >= 125 and mean_O3 <= 164): 32 | AQI_O3 = (mean_O3-125)*(49/39)+101 33 | if(mean_O3 > 164): 34 | AQI_O3 = (mean_O3-165)*(49/39)+151 35 | 36 | if(ItemId[Id_num]==33): 37 | PM25 = data_temp.iloc[Id_num,8:].astype(float) 38 | mean_PM25 = PM25.mean() 39 | if(mean_PM25 < 15.5): 40 | AQI_PM25 = (mean_PM25)*(50/15.4) 41 | if(mean_PM25 >=15.5 and mean_PM25 <35.4): 42 | AQI_PM25 = (mean_PM25-15.5)*(49/18.9)+51 43 | if(mean_PM25 >=35.4 and mean_PM25 <54.4): 44 | AQI_PM25 = (mean_PM25-35.5)*(49/18.9)+101 45 | if(mean_PM25 >=54.4): 46 | AQI_PM25 = (mean_PM25-54.5)*(49/95.9)+151 47 | 48 | if(ItemId[Id_num]==4): 49 | PM10 = data_temp.iloc[Id_num, 8:].astype(float) 50 | mean_PM10 = PM10.mean() 51 | if(mean_PM10 < 54): 52 | AQI_PM10 = (mean_PM10)*(50/54) 53 | if(mean_PM10 >=54 and mean_PM10 <125): 54 | AQI_PM10 = (mean_PM25-54)*(49/70)+51 55 | if(mean_PM10 >=125 and mean_PM10 <254): 56 | AQI_PM10 = (mean_PM25-125)*(49/128)+101 57 | if(mean_PM10 >=254): 58 | AQI_PM10 = (mean_PM25-254)*(49/99)+151 59 | 60 | if(ItemId[Id_num]==2): 61 | CO = data_temp.iloc[Id_num, 8:].astype(float) 62 | mean_CO = CO.mean() 63 | if(mean_CO < 4.4): 64 | AQI_CO = (mean_CO)*(50/4.4) 65 | if(mean_CO >=4.4 and mean_CO <9.4): 66 | AQI_CO = (mean_CO-4.4)*(49/4.9)+51 67 | if(mean_CO >=9.4 and mean_CO <12.4): 68 | AQI_CO = (mean_CO-9.4)*(49/2.9)+101 69 | if(mean_CO >=12.4): 70 | AQI_CO = (mean_CO-12.4)*(49/2.9)+151 71 | 72 | if(ItemId[Id_num]==1): 73 | SO2 = data_temp.iloc[Id_num, 8:].astype(float) 74 | mean_SO2 = SO2.mean() 75 | if(mean_SO2 < 35): 76 | AQI_SO2 = (mean_SO2)*(50/35) 77 | if(mean_SO2 >=35 and mean_SO2 <75): 78 | AQI_SO2 = (mean_SO2-35)*(49/39)+51 79 | if(mean_SO2 >=75 and mean_SO2 <185): 80 | AQI_SO2 = (mean_SO2-75)*(49/109)+101 81 | if(mean_SO2 >=185): 82 | AQI_SO2 = (mean_SO2-185)*(49/118)+151 83 | 84 | if(ItemId[Id_num]==144): 85 | wind_direct = data_temp.iloc[Id_num, 8:].astype(float) 86 | mean_wind_direct = wind_direct.mean() 87 | 88 | if(ItemId[Id_num]==14): 89 | temper = data_temp.iloc[Id_num, 8:].astype(float) 90 | mean_temper = temper.mean() 91 | 92 | if(ItemId[Id_num]==23): 93 | rain = data_temp.iloc[Id_num, 8:].astype(float) 94 | mean_rain = rain.sum() 95 | 96 | if(ItemId[Id_num]==38): 97 | humidity = data_temp.iloc[Id_num, 8:].astype(float) 98 | mean_humidity = humidity.mean() 99 | 100 | if(ItemId[Id_num]==144): 101 | wind_speed = data_temp.iloc[Id_num, 8:].astype(float) 102 | mean_wind_speed = wind_speed.mean() 103 | 104 | temp_AQI = np.max([AQI_CO, AQI_O3, AQI_PM10, AQI_PM25, AQI_SO2]) 105 | data_temp_result = np.array([mean_wind_direct, mean_temper, mean_rain, mean_humidity, mean_wind_speed, temp_AQI]) 106 | AQI_result[:, day_num] = data_temp_result.T 107 | new_frame.iloc[0, 2:8] = data_temp_result 108 | new_frame.iloc[0, 0] = data_temp.iloc[0, 7] 109 | result = pd.concat([result, new_frame], axis=0) 110 | 111 | result.to_csv('./time_train/time_'+ str(month_num) +'.csv', index = False) 112 | result_all = pd.concat([result_all, result], axis=0) 113 | 114 | result_all.to_csv('./time_train/train_all.csv', index = False) 115 | 116 | frame_3day = pd.read_csv('train_frame_3day.csv') 117 | result_train = pd.DataFrame() 118 | 119 | for train_num in range(363): 120 | # col1 = pd.DataFrame(result_all.iloc[train_num, 2:8]).T 121 | # col2 = pd.DataFrame(result_all.iloc[train_num+1, 2:8]).T 122 | # col3 = pd.DataFrame(result_all.iloc[train_num+2, 2:8]).T 123 | # label = pd.DataFrame(result_all.iloc[train_num+3, 7]) 124 | 125 | # result_train_temp = pd.concat([col1, col2, col3, label], axis=1) 126 | # result_train = pd.concat([result_train, result_train_temp], axis=0) 127 | 128 | frame_3day.iloc[0, :6] = result_all.iloc[train_num, 2:8].values 129 | frame_3day.iloc[0, 6:12] = result_all.iloc[train_num + 1, 2:8].values 130 | frame_3day.iloc[0, 12:18]= result_all.iloc[train_num + 2, 2:8].values 131 | frame_3day.iloc[0, 18] = result_all.iloc[train_num + 3, 7] 132 | result_train = pd.concat([result_train, frame_3day], axis=0) 133 | 134 | result_train.to_csv('./time_train/train_3day.csv', index = False) 135 | 136 | 137 | 138 | -------------------------------------------------------------------------------- /space_train/space_2.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/2/1,69.7962962962963,68.17592592592592,68.71604938271605,81.57098765432099,102.72839506172839,101.43209876543209,91.40123456790124,52.94444444444444,57.69753086419753,57.91358024691358,57.26543209876544,49.6482683982684 3 | 2020/2/2,45.183982683982684,52.2962962962963,67.41975308641975,78.43827160493828,98.6388888888889,116.3395061728395,91.29320987654322,28.40909090909091,36.52597402597402,31.114718614718615,46.53679653679654,30.979437229437227 4 | 2020/2/3,46.40151515151515,43.560606060606055,46.942640692640694,48.29545454545455,66.98765432098766,69.47222222222223,73.36111111111111,20.42748917748918,24.756493506493506,22.186147186147185,48.566017316017316,31.926406926406926 5 | 2020/2/4,33.82034632034632,38.14935064935065,41.531385281385276,45.72510822510823,68.5,63.20679012345679,75.52160493827161,24.350649350649352,18.3982683982684,12.581168831168831,36.52597402597402,33.685064935064936 6 | 2020/2/5,36.93181818181818,29.22077922077922,22.591991341991342,25.974025974025974,48.836580086580085,56.293209876543216,78.33024691358025,22.727272727272727,15.963203463203463,22.727272727272727,35.03787878787879,33.1439393939394 7 | 2020/2/6,38.01406926406926,35.714285714285715,52.40432098765432,45.99567099567099,68.39197530864197,66.87962962962963,72.60493827160494,18.533549783549784,17.045454545454547,28.54437229437229,36.93181818181818,28.003246753246753 8 | 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-------------------------------------------------------------------------------- /space_train/space_6.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/6/1,24.350649350649352,19.886363636363637,19.074675324675326,13.425925925925926,19.345238095238095,14.158950617283951,14.069264069264069,17.045454545454547,23.403679653679653,23.809523809523807,22.186147186147185,27.056277056277057 3 | 2020/6/2,15.692640692640692,20.42748917748918,23.538961038961038,12.461419753086421,15.827922077922079,11.574074074074074,16.774891774891774,17.992424242424242,17.992424242424242,19.886363636363637,23.538961038961038,35.30844155844156 4 | 2020/6/3,20.156926406926406,17.72186147186147,15.963203463203463,11.535493827160494,17.4512987012987,12.577160493827162,11.959876543209877,14.204545454545455,25.297619047619047,28.814935064935064,21.780303030303028,28.273809523809526 5 | 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2020/6/8,20.698051948051948,11.498917748917748,12.191358024691358,7.600308641975309,15.54783950617284,14.737654320987653,17.59259259259259,16.504329004329005,13.001543209876543,12.581168831168831,21.780303030303028,22.186147186147185 10 | 2020/6/9,22.862554112554115,11.228354978354979,12.581168831168831,12.152777777777779,22.0508658008658,20.29220779220779,24.485930735930737,10.416666666666668,16.165123456790123,14.660493827160494,25.162337662337663,22.0508658008658 11 | 2020/6/10,29.356060606060606,21.23917748917749,27.597402597402596,16.233766233766232,25.703463203463205,16.36904761904762,19.074675324675326,10.687229437229437,16.233766233766232,19.20995670995671,30.844155844155843,33.54978354978355 12 | 2020/6/11,31.655844155844157,21.780303030303028,28.138528138528137,21.915584415584416,27.191558441558442,16.774891774891774,16.743827160493826,13.257575757575756,16.049382716049383,35.44372294372294,39.908008658008654,50.05411255411255 13 | 2020/6/12,25.568181818181817,23.538961038961038,21.23917748917749,21.645021645021647,29.897186147186147,26.515151515151512,29.356060606060606,16.91017316017316,15.74074074074074,25.162337662337663,39.77272727272727,45.72510822510823 14 | 2020/6/13,35.17316017316018,33.955627705627705,34.09090909090909,24.350649350649352,45.99567099567099,51.324074074074076,30.787037037037038,20.562770562770563,18.942901234567902,22.067901234567902,36.66125541125541,41.260822510822514 15 | 2020/6/14,28.138528138528137,26.244588744588746,16.504329004329005,16.78240740740741,24.891774891774894,19.483024691358025,13.657407407407408,16.504329004329005,12.692901234567902,29.491341991341994,34.63203463203463,41.125541125541126 16 | 2020/6/15,22.0508658008658,17.992424242424242,13.96604938271605,13.773148148148149,16.233766233766232,12.885802469135802,16.820987654320987,11.228354978354979,14.853395061728396,24.485930735930737,21.915584415584416,20.562770562770563 17 | 2020/6/16,19.48051948051948,15.151515151515152,16.628086419753085,15.74074074074074,16.91017316017316,11.882716049382717,17.631172839506174,13.798701298701298,18.804112554112557,25.703463203463205,19.20995670995671,25.83874458874459 18 | 2020/6/17,24.08008658008658,18.3982683982684,17.045454545454547,14.660493827160494,17.18073593073593,10.570987654320987,13.933982683982684,10.281385281385282,23.944805194805195,30.303030303030305,25.568181818181817,31.926406926406926 19 | 2020/6/18,26.37987012987013,24.62121212121212,35.714285714285715,17.72186147186147,19.48051948051948,11.728395061728394,12.31060606060606,12.987012987012987,23.133116883116884,34.226190476190474,31.25,30.43831168831169 20 | 2020/6/19,26.785714285714285,22.862554112554115,23.674242424242426,21.645021645021647,21.374458874458874,13.695987654320987,16.91017316017316,10.551948051948052,21.374458874458874,30.70887445887446,29.22077922077922,32.061688311688314 21 | 2020/6/20,33.1439393939394,28.814935064935064,27.326839826839823,19.074675324675326,22.0508658008658,15.422077922077921,17.4512987012987,12.581168831168831,24.08008658008658,30.57359307359307,33.82034632034632,32.061688311688314 22 | 2020/6/21,38.01406926406926,37.202380952380956,24.891774891774894,30.43831168831169,25.432900432900432,16.098484848484848,28.95021645021645,20.562770562770563,27.597402597402596,42.74891774891775,48.43073593073593,33.41450216450216 23 | 2020/6/22,23.674242424242426,15.016233766233766,15.827922077922079,13.233024691358024,15.557359307359308,11.458333333333334,14.467592592592593,13.663419913419911,31.114718614718615,29.356060606060606,24.350649350649352,26.10930735930736 24 | 2020/6/23,26.92099567099567,18.939393939393938,17.206790123456788,15.4320987654321,14.197530864197532,11.419753086419753,13.88888888888889,14.61038961038961,32.87337662337662,28.54437229437229,25.432900432900432,27.73268398268398 25 | 2020/6/24,23.538961038961038,19.886363636363637,23.538961038961038,17.901234567901234,16.504329004329005,13.503086419753087,14.544753086419753,15.422077922077921,29.626623376623375,32.332251082251084,29.491341991341994,31.655844155844157 26 | 2020/6/25,20.562770562770563,16.63961038961039,16.774891774891774,14.00462962962963,17.316017316017316,10.686728395061728,11.574074074074074,14.61038961038961,28.40909090909091,35.57900432900433,24.485930735930737,32.467532467532465 27 | 2020/6/26,21.374458874458874,18.26298701298701,12.987012987012987,10.956790123456791,20.29220779220779,13.348765432098764,16.91017316017316,19.20995670995671,23.538961038961038,33.1439393939394,26.37987012987013,28.54437229437229 28 | 2020/6/27,25.568181818181817,21.23917748917749,19.615800865800868,20.562770562770563,19.75108225108225,15.8179012345679,16.975308641975307,10.687229437229437,20.60185185185185,25.432900432900432,28.138528138528137,33.1439393939394 29 | 2020/6/28,22.591991341991342,19.074675324675326,16.098484848484848,17.857142857142858,16.36904761904762,13.695987654320987,11.363636363636363,12.04004329004329,15.277777777777779,26.515151515151512,22.456709956709958,28.40909090909091 30 | 2020/6/29,24.891774891774894,25.568181818181817,23.133116883116884,19.48051948051948,14.745670995670997,11.612654320987653,4.6682098765432105,12.31060606060606,21.334876543209877,23.674242424242426,28.679653679653683,31.385281385281385 31 | 2020/6/30,19.20995670995671,20.156926406926406,16.280864197530864,14.544753086419753,13.933982683982684,11.419753086419753,10.532407407407408,12.175324675324676,22.183641975308642,24.891774891774894,25.297619047619047,29.356060606060606 32 | -------------------------------------------------------------------------------- /space_AQI_label.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jan 11 09:50:11 2021 4 | 5 | @author: ray 6 | """ 7 | 8 | import numpy as np 9 | import pandas as pd 10 | result_all = pd.DataFrame() 11 | for month_num in range(1,13): 12 | df_data = pd.read_csv('./space/space_'+ str(month_num) +'.csv', index_col= False) 13 | new_frame = pd.read_csv('train_space_frame.csv') 14 | # df_data_drop = df_data[(True-df_data.isin(['x']))] 15 | result = pd.DataFrame() 16 | 17 | days = int(df_data.shape[0]/132) 18 | # for num, col in enumerate(df_data.iloc[:,6]): 19 | data_temp = pd.DataFrame() 20 | # AQI_result = np.zeros(shape=[5, days]) 21 | 22 | for day_num in range(days): 23 | 24 | data_temp = df_data.iloc[day_num * 132:(day_num * 132)+132, :].reset_index() 25 | space_ItemId = data_temp["ItemId"] 26 | 27 | for space_num in range(12): 28 | ItemId = space_ItemId[space_num * 11 : (space_num * 11)+11] 29 | data_space_temp = data_temp.iloc[space_num * 11 : (space_num * 11)+11].reset_index() 30 | for Id_num in range(11): 31 | # print(ItemId[Id_num]) 32 | if(ItemId.iloc[Id_num]==3): 33 | # print('ok') 34 | AQI_O3 = 0 35 | mean_O3 = 0 36 | O3 = np.float64(data_space_temp.iloc[Id_num, 9:]) 37 | mean_O3 = O3.mean() 38 | if(mean_O3 < 125): 39 | AQI_O3 = 0 40 | if(mean_O3 >= 125 and mean_O3 <= 164): 41 | AQI_O3 = (mean_O3-125)*(49/39)+101 42 | if(mean_O3 > 164): 43 | AQI_O3 = (mean_O3-165)*(49/39)+151 44 | # print('AQI_O3', AQI_O3) 45 | 46 | if(ItemId.iloc[Id_num]==33): 47 | AQI_PM25 = 0 48 | mean_PM25 = 0 49 | PM25 = np.float64(data_space_temp.iloc[Id_num, 9:]) 50 | mean_PM25 = PM25.mean() 51 | if(mean_PM25 < 15.5): 52 | AQI_PM25 = (mean_PM25)*(50/15.4) 53 | if(mean_PM25 >=15.5 and mean_PM25 <35.4): 54 | AQI_PM25 = (mean_PM25-15.5)*(49/18.9)+51 55 | if(mean_PM25 >=35.4 and mean_PM25 <54.4): 56 | AQI_PM25 = (mean_PM25-35.5)*(49/18.9)+101 57 | if(mean_PM25 >=54.4): 58 | AQI_PM25 = (mean_PM25-54.5)*(49/95.9)+151 59 | # print('AQI_PM25', AQI_PM25) 60 | 61 | if(ItemId.iloc[Id_num]==4): 62 | AQI_PM10 = 0 63 | mean_PM10 = 0 64 | PM10 = np.float64(data_space_temp.iloc[Id_num, 9:]) 65 | mean_PM10 = PM10.mean() 66 | if(mean_PM10 < 54): 67 | AQI_PM10 = (mean_PM10)*(50/54) 68 | if(mean_PM10 >=54 and mean_PM10 <125): 69 | AQI_PM10 = (mean_PM25-54)*(49/70)+51 70 | if(mean_PM10 >=125 and mean_PM10 <254): 71 | AQI_PM10 = (mean_PM25-125)*(49/128)+101 72 | if(mean_PM10 >=254): 73 | AQI_PM10 = (mean_PM25-254)*(49/99)+151 74 | # print('AQI_PM10', AQI_PM10, 'mean_PM10', mean_PM10) 75 | 76 | if(ItemId.iloc[Id_num]==2): 77 | AQI_CO = 0 78 | mean_CO = 0 79 | CO = np.float64(data_space_temp.iloc[Id_num, 9:]) 80 | mean_CO = CO.mean() 81 | if(mean_CO < 4.4): 82 | AQI_CO = (mean_CO)*(50/4.4) 83 | if(mean_CO >=4.4 and mean_CO <9.4): 84 | AQI_CO = (mean_CO-4.4)*(49/4.9)+51 85 | if(mean_CO >=9.4 and mean_CO <12.4): 86 | AQI_CO = (mean_CO-9.4)*(49/2.9)+101 87 | if(mean_CO >=12.4): 88 | AQI_CO = (mean_CO-12.4)*(49/2.9)+151 89 | # print('AQI_CO', AQI_CO, 'mean CO', mean_CO) 90 | 91 | if(ItemId.iloc[Id_num]==1): 92 | AQI_SO2 = 0 93 | mean_SO2 = 0 94 | SO2 = np.float64(data_space_temp.iloc[Id_num, 9:]) 95 | # print(SO2) 96 | mean_SO2 = SO2.mean() 97 | if(mean_SO2 < 35): 98 | AQI_SO2 = (mean_SO2)*(50/35) 99 | if(mean_SO2 >=35 and mean_SO2 <75): 100 | AQI_SO2 = (mean_SO2-35)*(49/39)+51 101 | if(mean_SO2 >=75 and mean_SO2 <185): 102 | AQI_SO2 = (mean_SO2-75)*(49/109)+101 103 | if(mean_SO2 >=185): 104 | AQI_SO2 = (mean_SO2-185)*(49/118)+151 105 | # print('AQI_SO2', AQI_SO2) 106 | 107 | AQI_temp = np.max([AQI_CO, AQI_O3, AQI_PM10, AQI_PM25, AQI_SO2]) 108 | new_frame.iloc[0, (space_num + 1)] = AQI_temp 109 | # print('day num: ',day_num, 'space_num ',space_num) 110 | # print('new_frame ',new_frame.iloc[0, (space_num + 1)]) 111 | # print('--------------------------------------------') 112 | new_frame.iloc[0, 0] = data_space_temp.iloc[0, 8] 113 | 114 | result = pd.concat([result, new_frame], axis=0) 115 | new_frame.iloc[0,:] = 0 116 | result.to_csv('./space_train/space_'+ str(month_num) +'.csv', index = False) 117 | result_all = pd.concat([result_all, result], axis=0) 118 | 119 | result_all.to_csv('./space_train/train_space_AQI.csv', index = False) 120 | 121 | # frame_3day = pd.read_csv('train_frame_3day.csv') 122 | # result_train = pd.DataFrame() 123 | 124 | # for train_num in range(363): 125 | # # col1 = pd.DataFrame(result_all.iloc[train_num, 2:8]).T 126 | # # col2 = pd.DataFrame(result_all.iloc[train_num+1, 2:8]).T 127 | # # col3 = pd.DataFrame(result_all.iloc[train_num+2, 2:8]).T 128 | # # label = pd.DataFrame(result_all.iloc[train_num+3, 7]) 129 | 130 | # # result_train_temp = pd.concat([col1, col2, col3, label], axis=1) 131 | # # result_train = pd.concat([result_train, result_train_temp], axis=0) 132 | 133 | # frame_3day.iloc[0, :6] = result_all.iloc[train_num, 2:8].values 134 | # frame_3day.iloc[0, 6:12] = result_all.iloc[train_num + 1, 2:8].values 135 | # frame_3day.iloc[0, 12:18]= result_all.iloc[train_num + 2, 2:8].values 136 | # frame_3day.iloc[0, 18] = result_all.iloc[train_num + 3, 7] 137 | # result_train = pd.concat([result_train, frame_3day], axis=0) 138 | 139 | # result_train.to_csv('./time_train/train_3day.csv', index = False) -------------------------------------------------------------------------------- /space_train/space_3.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/3/1,61.370370370370374,60.29012345679012,66.66358024691358,74.44135802469137,99.28703703703704,97.12654320987654,61.0462962962963,21.103896103896105,34.09090909090909,40.71969696969697,24.485930735930737,54.456790123456784 3 | 2020/3/2,32.467532467532465,28.003246753246753,24.08008658008658,33.82034632034632,57.157407407407405,67.95987654320989,97.01851851851853,18.26298701298701,20.29220779220779,19.75108225108225,33.82034632034632,29.22077922077922 4 | 2020/3/3,48.701298701298704,51.108024691358025,53.48456790123456,47.61904761904761,64.71913580246914,66.77160493827161,74.65740740740742,14.069264069264069,22.727272727272727,22.456709956709958,44.913419913419915,35.714285714285715 5 | 2020/3/4,33.1439393939394,47.21320346320346,51.54012345679013,46.13095238095238,69.47222222222223,74.54938271604938,88.5925925925926,17.857142857142858,18.939393939393938,18.127705627705627,33.685064935064936,23.26839826839827 6 | 2020/3/5,35.44372294372294,31.520562770562773,20.833333333333336,26.37987012987013,47.21320346320346,47.077922077922075,69.68827160493828,11.498917748917748,14.88095238095238,21.374458874458874,42.74891774891775,43.425324675324674 7 | 2020/3/6,45.048701298701296,45.183982683982684,64.28703703703704,56.18518518518518,86.75617283950618,87.40432098765433,76.92592592592592,15.151515151515152,13.528138528138529,24.350649350649352,60.29012345679012,33.82034632034632 8 | 2020/3/7,67.52777777777779,57.80555555555556,62.7746913580247,67.52777777777779,99.28703703703704,92.4814814814815,80.59876543209877,19.886363636363637,25.432900432900432,32.87337662337662,77.03395061728395,67.41975308641975 9 | 2020/3/8,63.53086419753086,71.95679012345678,65.4753086419753,80.59876543209877,115.0432098765432,118.82407407407408,74.00925925925927,19.75108225108225,31.926406926406926,39.77272727272727,62.45061728395062,29.22077922077922 10 | 2020/3/9,61.4783950617284,57.157407407407405,65.36728395061729,58.0216049382716,67.63580246913581,72.71296296296296,54.99691358024692,21.780303030303028,36.39069264069264,35.44372294372294,72.49691358024693,74.33333333333334 11 | 2020/3/10,31.79112554112554,25.162337662337663,25.83874458874459,27.191558441558442,34.76731601731602,35.849567099567096,51.432098765432094,26.10930735930736,16.504329004329005,26.515151515151512,32.467532467532465,28.273809523809526 12 | 2020/3/11,58.23765432098766,62.126543209876544,59.858024691358025,61.802469135802475,73.57716049382717,70.76851851851852,65.9074074074074,21.374458874458874,36.25541125541125,55.53703703703704,60.29012345679012,60.39814814814815 13 | 2020/3/12,84.59567901234568,72.06481481481481,107.1574074074074,89.34876543209877,127.57407407407408,120.98456790123458,87.94444444444446,30.167748917748916,44.50757575757576,34.63203463203463,62.23456790123457,52.40432098765432 14 | 2020/3/13,41.396103896103895,59.101851851851855,82.43518518518519,63.42283950617284,112.23456790123457,125.62962962962963,83.2993827160494,38.5551948051948,27.86796536796537,32.73809523809524,43.01948051948052,35.30844155844156 15 | 2020/3/14,51.21604938271605,44.50757575757576,32.196969696969695,40.58441558441558,51.54012345679013,58.12962962962963,72.82098765432099,14.88095238095238,25.568181818181817,32.73809523809524,54.99691358024692,46.807359307359306 16 | 2020/3/15,61.910493827160494,64.71913580246914,66.23148148148148,69.04012345679013,81.13888888888889,80.70679012345678,66.23148148148148,36.12012987012987,56.50925925925926,58.885802469135804,62.882716049382715,55.320987654320994 17 | 2020/3/16,52.83641975308642,64.61111111111111,81.35493827160494,81.35493827160494,78.00617283950618,71.5246913580247,68.5,53.48456790123456,46.807359307359306,48.97186147186147,59.425925925925924,47.75432900432901 18 | 2020/3/17,79.4104938271605,79.08641975308642,84.91975308641975,77.35802469135803,78.5462962962963,80.59876543209877,75.08950617283952,43.154761904761905,46.40151515151515,49.51298701298701,78.00617283950618,68.39197530864197 19 | 2020/3/18,85.13580246913581,80.38271604938272,88.26851851851853,99.61111111111111,113.42283950617283,103.1604938271605,91.07716049382717,47.61904761904761,43.560606060606055,58.4537037037037,106.40123456790124,94.20987654320987 20 | 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2020/10/19,38.41991341991342,29.356060606060606,22.591991341991342,35.44372294372294,55.861111111111114,61.802469135802475,64.39506172839506,18.66883116883117,17.4512987012987,21.23917748917749,42.884199134199136,43.96645021645021 21 | 2020/10/20,37.743506493506494,34.226190476190474,37.60822510822511,47.48376623376623,63.31481481481482,69.25617283950618,72.28086419753086,30.032467532467532,32.061688311688314,27.597402597402596,40.71969696969697,41.260822510822514 22 | 2020/10/21,22.0508658008658,29.356060606060606,45.99567099567099,52.620370370370374,69.7962962962963,78.33024691358025,86.21604938271605,11.228354978354979,16.098484848484848,9.604978354978355,26.37987012987013,20.968614718614717 23 | 2020/10/22,20.562770562770563,21.374458874458874,19.074675324675326,23.674242424242426,39.120370370370374,45.589826839826834,71.20061728395062,10.822510822510823,16.550925925925927,8.116883116883116,28.138528138528137,19.345238095238095 24 | 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2020/10/27,23.538961038961038,43.69588744588745,59.75,66.66358024691358,82.65123456790124,85.24382716049382,77.89814814814815,23.538961038961038,33.54978354978355,33.82034632034632,46.807359307359306,37.33766233766234 29 | 2020/10/28,35.714285714285715,52.08024691358025,62.882716049382715,68.0679012345679,95.1820987654321,101.98765432098766,89.02469135802471,23.403679653679653,35.30844155844156,40.04329004329004,58.561728395061735,47.75432900432901 30 | 2020/10/29,37.06709956709957,44.77813852813853,48.701298701298704,54.78086419753086,82.00308641975309,91.40123456790124,84.48765432098767,27.73268398268398,37.743506493506494,37.743506493506494,49.37770562770563,41.125541125541126 31 | 2020/10/30,42.34307359307359,48.701298701298704,43.69588744588745,51.432098765432094,66.44753086419753,74.87345679012346,88.26851851851853,34.76731601731602,43.01948051948052,46.13095238095238,53.48456790123456,46.26623376623377 32 | 2020/10/31,25.617283950617285,37.60822510822511,42.34307359307359,54.0246913580247,69.90432098765433,78.43827160493828,77.6820987654321,31.25,38.41991341991342,33.82034632034632,41.242283950617285,35.03787878787879 33 | -------------------------------------------------------------------------------- /space_train/space_12.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/12/1,25.703463203463205,15.692640692640692,25.027056277056275,35.849567099567096,57.04938271604938,65.15123456790124,78.9783950617284,17.18073593073593,16.774891774891774,21.645021645021647,44.23701298701299,33.1439393939394 3 | 2020/12/2,25.432900432900432,20.968614718614717,30.032467532467532,41.531385281385276,51.432098765432094,63.63888888888889,75.52160493827161,26.244588744588746,19.074675324675326,19.345238095238095,40.04329004329004,30.032467532467532 4 | 2020/12/3,26.515151515151512,20.156926406926406,29.491341991341994,31.25,33.27922077922078,38.96103896103896,44.23701298701299,19.20995670995671,17.58658008658009,9.74025974025974,42.34307359307359,27.326839826839823 5 | 2020/12/4,18.533549783549784,11.904761904761903,23.403679653679653,35.44372294372294,38.96103896103896,46.26623376623377,51.97222222222222,16.504329004329005,13.528138528138529,6.628787878787878,35.57900432900433,22.862554112554115 6 | 2020/12/5,15.286796536796535,7.575757575757576,24.485930735930737,32.332251082251084,47.21320346320346,56.293209876543216,62.7746913580247,12.175324675324676,10.957792207792208,7.711038961038961,27.326839826839823,25.432900432900432 7 | 2020/12/6,8.526234567901236,7.711038961038961,44.913419913419915,35.30844155844156,61.802469135802475,63.20679012345679,74.22530864197532,12.851731601731602,14.745670995670997,7.44047619047619,21.50974025974026,14.475108225108224 8 | 2020/12/7,14.204545454545455,15.827922077922079,29.085497835497836,69.68827160493828,61.58641975308642,62.66666666666667,71.74074074074075,10.822510822510823,16.504329004329005,6.4935064935064934,26.92099567099567,14.204545454545455 9 | 2020/12/8,7.44047619047619,15.286796536796535,30.844155844155843,42.20779220779221,54.99691358024692,45.048701298701296,71.63271604938271,10.416666666666668,11.769480519480519,8.658008658008658,25.703463203463205,13.798701298701298 10 | 2020/12/9,10.957792207792208,20.968614718614717,43.83116883116883,34.496753246753244,47.75432900432901,61.15432098765432,72.49691358024693,14.745670995670997,14.339826839826841,8.522727272727273,23.944805194805195,15.692640692640692 11 | 2020/12/10,21.50974025974026,38.41991341991342,46.672077922077925,42.74891774891775,58.99382716049382,69.58024691358025,75.84567901234568,9.334415584415584,11.228354978354979,7.034632034632034,31.655844155844157,19.345238095238095 12 | 2020/12/11,25.703463203463205,40.4491341991342,39.908008658008654,49.37770562770563,77.14197530864197,86.21604938271605,116.3395061728395,10.687229437229437,16.774891774891774,8.793290043290044,31.520562770562773,16.91017316017316 13 | 2020/12/12,62.882716049382715,67.52777777777779,48.97186147186147,68.39197530864197,88.05246913580248,101.10802469135803,115.15123456790124,12.445887445887447,32.061688311688314,41.260822510822514,75.41358024691358,72.49691358024693 14 | 2020/12/13,93.8858024691358,106.07716049382717,83.7314814814815,112.3425925925926,146.4783950617284,143.99382716049385,128.43827160493828,22.727272727272727,37.743506493506494,51.0,95.39814814814815,78.76234567901236 15 | 2020/12/14,29.491341991341994,48.97186147186147,44.37229437229437,66.3395061728395,103.26851851851852,124.87345679012347,142.0493827160494,21.645021645021647,21.780303030303028,16.504329004329005,34.09090909090909,28.273809523809526 16 | 2020/12/15,12.31060606060606,18.939393939393938,27.73268398268398,35.44372294372294,44.37229437229437,58.885802469135804,63.53086419753086,7.846320346320346,9.875541125541124,10.822510822510823,27.462121212121215,18.3982683982684 17 | 2020/12/16,11.228354978354979,15.827922077922079,26.244588744588746,32.467532467532465,48.43073593073593,56.50925925925926,70.01234567901234,8.116883116883116,6.764069264069264,9.334415584415584,29.22077922077922,18.127705627705627 18 | 2020/12/17,11.769480519480519,17.18073593073593,33.82034632034632,39.231601731601735,51.97222222222222,58.34567901234568,65.04320987654322,9.875541125541124,13.392857142857142,11.228354978354979,25.83874458874459,19.345238095238095 19 | 2020/12/18,17.992424242424242,26.10930735930736,51.108024691358025,41.80194805194805,69.47222222222223,75.62962962962963,79.08641975308642,9.469696969696969,20.156926406926406,8.387445887445887,32.467532467532465,25.568181818181817 20 | 2020/12/19,21.780303030303028,19.074675324675326,30.303030303030305,32.73809523809524,39.908008658008654,49.91883116883117,60.1820987654321,14.204545454545455,17.72186147186147,9.74025974025974,32.467532467532465,24.485930735930737 21 | 2020/12/20,12.716450216450216,17.58658008658009,18.127705627705627,30.70887445887446,42.07251082251082,53.80864197530864,60.93827160493827,10.146103896103895,12.04004329004329,10.01082251082251,26.10930735930736,22.186147186147185 22 | 2020/12/21,11.498917748917748,8.522727272727273,28.95021645021645,36.7965367965368,58.885802469135804,64.71913580246914,73.68518518518519,7.846320346320346,17.18073593073593,11.228354978354979,29.897186147186147,22.186147186147185 23 | 2020/12/22,16.098484848484848,23.674242424242426,48.29545454545455,40.17857142857143,50.18939393939394,65.69135802469137,66.44753086419753,7.44047619047619,12.445887445887447,12.31060606060606,31.655844155844157,29.626623376623375 24 | 2020/12/23,25.027056277056275,30.979437229437227,47.75432900432901,38.41991341991342,65.25925925925927,78.5462962962963,90.96913580246914,7.981601731601732,12.445887445887447,21.374458874458874,36.39069264069264,43.01948051948052 25 | 2020/12/24,61.26234567901234,64.07098765432099,55.75308641975308,61.69444444444444,62.882716049382715,73.25308641975309,79.51851851851852,12.175324675324676,27.191558441558442,31.520562770562773,80.16666666666667,72.06481481481481 26 | 2020/12/25,49.91883116883117,57.48148148148148,56.07716049382716,71.84876543209877,92.37345679012347,103.7006172839506,127.46604938271606,30.167748917748916,34.226190476190474,43.29004329004329,68.5,58.99382716049382 27 | 2020/12/26,43.01948051948052,51.648148148148145,52.83641975308642,62.126543209876544,84.70370370370371,94.75,80.92283950617285,23.809523809523807,23.26839826839827,28.54437229437229,51.75617283950617,41.80194805194805 28 | 2020/12/27,58.885802469135804,61.802469135802475,42.884199134199136,64.71913580246914,87.08024691358025,84.05555555555557,77.25,22.862554112554115,25.297619047619047,36.12012987012987,62.7746913580247,49.24242424242424 29 | 2020/12/28,38.5551948051948,46.807359307359306,48.97186147186147,57.589506172839506,66.66358024691358,75.73765432098766,73.68518518518519,23.944805194805195,40.71969696969697,37.202380952380956,49.107142857142854,52.2962962962963 30 | 2020/12/29,71.74074074074075,83.83950617283952,96.91049382716051,94.10185185185186,125.95370370370371,133.7314814814815,125.95370370370371,17.857142857142858,35.984848484848484,34.63203463203463,76.92592592592592,62.7746913580247 31 | 2020/12/30,47.077922077922075,48.02489177489177,42.07251082251082,54.56481481481482,58.561728395061735,70.55246913580248,76.92592592592592,26.244588744588746,35.714285714285715,32.60281385281385,60.1820987654321,55.10493827160494 32 | 2020/12/31,49.24242424242424,51.0,54.132716049382715,62.45061728395062,65.25925925925927,70.44444444444444,75.30555555555556,30.70887445887446,46.53679653679654,51.21604938271605,61.910493827160494,55.21296296296296 33 | -------------------------------------------------------------------------------- /origin_6AQI_label.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jan 11 09:50:11 2021 4 | 5 | @author: ray 6 | """ 7 | 8 | import numpy as np 9 | import pandas as pd 10 | result_all = pd.DataFrame() 11 | for month_num in range(1,13): 12 | df_data = pd.read_csv('./time/time_'+ str(month_num) +'.csv', index_col= False) 13 | new_frame = pd.read_csv('train_frame.csv') 14 | # df_data_drop = df_data[(True-df_data.isin(['x']))] 15 | result = pd.DataFrame() 16 | 17 | days = int(df_data.shape[0]/11) 18 | # for num, col in enumerate(df_data.iloc[:,6]): 19 | data_temp = pd.DataFrame() 20 | AQI_result = np.zeros(shape=[5, days]) 21 | 22 | for day_num in range(days): 23 | data_temp = df_data.iloc[day_num*11:(day_num*11)+11, :].reset_index() 24 | ItemId = data_temp["ItemId"] 25 | for Id_num in range(11): 26 | if(ItemId[Id_num]==3): 27 | AQI_O3 = 0 28 | mean_O3 = 0 29 | O3 = data_temp.iloc[Id_num, 8:].astype(float) 30 | mean_O3 = O3.mean() 31 | if(mean_O3 < 125): 32 | AQI_O3 = 0 33 | if(mean_O3 >= 125 and mean_O3 <= 164): 34 | AQI_O3 = (mean_O3-125)*(49/39)+101 35 | if(mean_O3 > 164): 36 | AQI_O3 = (mean_O3-165)*(49/39)+151 37 | # print('AQI_O3', AQI_O3) 38 | 39 | if(ItemId[Id_num]==33): 40 | AQI_PM25 = 0 41 | mean_PM25 = 0 42 | PM25 = data_temp.iloc[Id_num,8:].astype(float) 43 | mean_PM25 = PM25.mean() 44 | if(mean_PM25 < 15.5): 45 | AQI_PM25 = (mean_PM25)*(50/15.4) 46 | if(mean_PM25 >=15.5 and mean_PM25 <35.4): 47 | AQI_PM25 = (mean_PM25-15.5)*(49/18.9)+51 48 | if(mean_PM25 >=35.4 and mean_PM25 <54.4): 49 | AQI_PM25 = (mean_PM25-35.5)*(49/18.9)+101 50 | if(mean_PM25 >=54.4): 51 | AQI_PM25 = (mean_PM25-54.5)*(49/95.9)+151 52 | # print('AQI_PM25', AQI_PM25) 53 | 54 | if(ItemId[Id_num]==4): 55 | AQI_PM10 = 0 56 | mean_PM10 = 0 57 | PM10 = data_temp.iloc[Id_num, 8:].astype(float) 58 | mean_PM10 = PM10.mean() 59 | if(mean_PM10 < 54): 60 | AQI_PM10 = (mean_PM10)*(50/54) 61 | if(mean_PM10 >=54 and mean_PM10 <125): 62 | AQI_PM10 = (mean_PM25-54)*(49/70)+51 63 | if(mean_PM10 >=125 and mean_PM10 <254): 64 | AQI_PM10 = (mean_PM25-125)*(49/128)+101 65 | if(mean_PM10 >=254): 66 | AQI_PM10 = (mean_PM25-254)*(49/99)+151 67 | # print('AQI_PM10', AQI_PM10) 68 | 69 | if(ItemId[Id_num]==2): 70 | AQI_CO = 0 71 | mean_CO = 0 72 | CO = data_temp.iloc[Id_num, 8:].astype(float) 73 | mean_CO = CO.mean() 74 | if(mean_CO < 4.4): 75 | AQI_CO = (mean_CO)*(50/4.4) 76 | if(mean_CO >=4.4 and mean_CO <9.4): 77 | AQI_CO = (mean_CO-4.4)*(49/4.9)+51 78 | if(mean_CO >=9.4 and mean_CO <12.4): 79 | AQI_CO = (mean_CO-9.4)*(49/2.9)+101 80 | if(mean_CO >=12.4): 81 | AQI_CO = (mean_CO-12.4)*(49/2.9)+151 82 | # print('AQI_CO', AQI_CO) 83 | 84 | if(ItemId[Id_num]==1): 85 | AQI_SO2 = 0 86 | mean_SO2 = 0 87 | SO2 = data_temp.iloc[Id_num, 8:].astype(float) 88 | mean_SO2 = SO2.mean() 89 | if(mean_SO2 < 35): 90 | AQI_SO2 = (mean_SO2)*(50/35) 91 | if(mean_SO2 >=35 and mean_SO2 <75): 92 | AQI_SO2 = (mean_SO2-35)*(49/39)+51 93 | if(mean_SO2 >=75 and mean_SO2 <185): 94 | AQI_SO2 = (mean_SO2-75)*(49/109)+101 95 | if(mean_SO2 >=185): 96 | AQI_SO2 = (mean_SO2-185)*(49/118)+151 97 | # print('AQI_SO2', AQI_SO2) 98 | 99 | # if(ItemId[Id_num]==144): 100 | # wind_direct = data_temp.iloc[Id_num, 8:].astype(float) 101 | # mean_wind_direct = wind_direct.mean() 102 | 103 | # if(ItemId[Id_num]==14): 104 | # temper = data_temp.iloc[Id_num, 8:].astype(float) 105 | # mean_temper = temper.mean() 106 | 107 | # if(ItemId[Id_num]==23): 108 | # rain = data_temp.iloc[Id_num, 8:].astype(float) 109 | # mean_rain = rain.sum() 110 | 111 | # if(ItemId[Id_num]==38): 112 | # humidity = data_temp.iloc[Id_num, 8:].astype(float) 113 | # mean_humidity = humidity.mean() 114 | 115 | # if(ItemId[Id_num]==144): 116 | # wind_speed = data_temp.iloc[Id_num, 8:].astype(float) 117 | # mean_wind_speed = wind_speed.mean() 118 | 119 | temp_AQI = np.max([AQI_CO, AQI_O3, AQI_PM10, AQI_PM25, AQI_SO2]) 120 | data_temp_result = np.array([mean_CO, mean_PM10, mean_PM25, mean_SO2, temp_AQI]) 121 | AQI_result[:, day_num] = data_temp_result.T 122 | new_frame.iloc[0, 2:7] = data_temp_result 123 | new_frame.iloc[0, 0] = data_temp.iloc[0, 7] 124 | result = pd.concat([result, new_frame], axis=0) 125 | 126 | result.to_csv('./time_train/time_'+ str(month_num) +'.csv', index = False) 127 | result_all = pd.concat([result_all, result], axis=0) 128 | 129 | result_all.to_csv('./time_train/origin_train_only6AQI.csv', index = False) 130 | 131 | # frame_3day = pd.read_csv('train_frame_3day.csv') 132 | # result_train = pd.DataFrame() 133 | 134 | # for train_num in range(363): 135 | # # col1 = pd.DataFrame(result_all.iloc[train_num, 2:8]).T 136 | # # col2 = pd.DataFrame(result_all.iloc[train_num+1, 2:8]).T 137 | # # col3 = pd.DataFrame(result_all.iloc[train_num+2, 2:8]).T 138 | # # label = pd.DataFrame(result_all.iloc[train_num+3, 7]) 139 | 140 | # # result_train_temp = pd.concat([col1, col2, col3, label], axis=1) 141 | # # result_train = pd.concat([result_train, result_train_temp], axis=0) 142 | 143 | # frame_3day.iloc[0, :6] = result_all.iloc[train_num, 2:8].values 144 | # frame_3day.iloc[0, 6:12] = result_all.iloc[train_num + 1, 2:8].values 145 | # frame_3day.iloc[0, 12:18]= result_all.iloc[train_num + 2, 2:8].values 146 | # frame_3day.iloc[0, 18] = result_all.iloc[train_num + 3, 7] 147 | # result_train = pd.concat([result_train, frame_3day], axis=0) 148 | 149 | # result_train.to_csv('./time_train/train_3day.csv', index = False) -------------------------------------------------------------------------------- /space_train/space_5.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/5/1,91.83333333333334,85.24382716049382,71.63271604938271,89.56481481481482,82.32716049382717,71.41666666666667,68.39197530864197,26.244588744588746,33.00865800865801,43.69588744588745,91.83333333333334,77.57407407407408 3 | 2020/5/2,77.35802469135803,66.66358024691358,57.04938271604938,70.01234567901234,64.17901234567901,38.82575757575758,37.878787878787875,19.615800865800868,19.48051948051948,37.06709956709957,78.65432098765433,74.76543209876544 4 | 2020/5/3,42.20779220779221,35.30844155844156,42.20779220779221,33.54978354978355,39.63744588744589,24.485930735930737,25.974025974025974,26.650432900432904,23.403679653679653,46.53679653679654,52.40432098765432,55.42901234567901 5 | 2020/5/4,40.99025974025974,37.06709956709957,42.20779220779221,31.385281385281385,39.09632034632035,31.79112554112554,36.66125541125541,40.31385281385281,37.60822510822511,51.432098765432094,49.6482683982684,54.0246913580247 6 | 2020/5/5,52.18827160493827,49.107142857142854,54.132716049382715,34.63203463203463,45.72510822510823,42.74891774891775,45.45454545454545,35.57900432900433,37.878787878787875,43.01948051948052,61.4783950617284,71.84876543209877 7 | 2020/5/6,38.14935064935065,29.356060606060606,28.679653679653683,26.37987012987013,27.73268398268398,22.321428571428573,28.95021645021645,25.974025974025974,36.7965367965368,45.45454545454545,37.06709956709957,39.366883116883116 8 | 2020/5/7,24.215367965367964,32.60281385281385,35.30844155844156,33.00865800865801,29.22077922077922,21.50974025974026,32.467532467532465,25.027056277056275,23.403679653679653,21.103896103896105,34.63203463203463,38.69047619047619 9 | 2020/5/8,38.82575757575758,35.984848484848484,42.47835497835498,28.54437229437229,32.60281385281385,21.103896103896105,30.70887445887446,28.95021645021645,17.857142857142858,30.032467532467532,43.69588744588745,36.7965367965368 10 | 2020/5/9,28.679653679653683,22.727272727272727,22.862554112554115,20.29220779220779,25.432900432900432,16.233766233766232,28.138528138528137,21.374458874458874,23.133116883116884,30.70887445887446,28.003246753246753,35.44372294372294 11 | 2020/5/10,34.496753246753244,25.703463203463205,24.215367965367964,17.4512987012987,25.297619047619047,13.392857142857142,21.780303030303028,17.72186147186147,31.25,30.979437229437227,37.878787878787875,45.589826839826834 12 | 2020/5/11,58.0216049382716,62.7746913580247,54.78086419753086,46.13095238095238,58.23765432098766,55.861111111111114,47.077922077922075,27.597402597402596,34.63203463203463,27.86796536796537,39.63744588744589,42.61363636363636 13 | 2020/5/12,53.48456790123456,57.69753086419753,60.39814814814815,56.07716049382716,87.62037037037038,78.33024691358025,67.09567901234567,42.07251082251082,36.39069264069264,28.814935064935064,53.48456790123456,48.02489177489177 14 | 2020/5/13,69.90432098765433,66.23148148148148,63.31481481481482,70.01234567901234,92.9135802469136,73.36111111111111,73.25308641975309,56.401234567901234,69.3641975308642,72.82098765432099,69.25617283950618,80.92283950617285 15 | 2020/5/14,76.70987654320989,77.79012345679013,65.36728395061729,76.27777777777779,67.41975308641975,49.91883116883117,65.04320987654322,62.01851851851852,65.4753086419753,71.84876543209877,84.48765432098767,70.76851851851852 16 | 2020/5/15,53.160493827160494,47.21320346320346,46.26623376623377,52.7283950617284,60.50617283950618,52.18827160493827,57.589506172839506,21.50974025974026,32.467532467532465,45.99567099567099,49.91883116883117,68.5 17 | 2020/5/16,35.44372294372294,33.41450216450216,37.60822510822511,38.41991341991342,53.160493827160494,51.75617283950617,31.655844155844157,14.61038961038961,14.88095238095238,26.10930735930736,38.5551948051948,45.31926406926407 18 | 2020/5/17,16.280864197530864,16.504329004329005,14.069264069264069,15.827922077922079,23.809523809523807,16.280864197530864,21.780303030303028,10.146103896103895,13.040123456790123,14.313271604938272,17.72186147186147,22.321428571428573 19 | 2020/5/18,27.056277056277057,21.374458874458874,22.186147186147185,22.0508658008658,23.674242424242426,17.72186147186147,20.156926406926406,17.045454545454547,15.93364197530864,19.345238095238095,26.10930735930736,28.54437229437229 20 | 2020/5/19,23.403679653679653,21.780303030303028,13.663419913419911,14.61038961038961,19.48051948051948,16.91017316017316,29.085497835497836,18.804112554112557,26.10930735930736,24.350649350649352,40.71969696969697,40.71969696969697 21 | 2020/5/20,32.73809523809524,27.326839826839823,34.226190476190474,14.339826839826841,23.809523809523807,17.72186147186147,26.244588744588746,13.798701298701298,21.50974025974026,26.244588744588746,45.589826839826834,35.849567099567096 22 | 2020/5/21,31.79112554112554,28.138528138528137,31.114718614718615,18.804112554112557,16.628086419753085,15.58641975308642,20.29220779220779,11.363636363636363,17.4512987012987,8.834876543209877,33.27922077922078,17.045454545454547 23 | 2020/5/22,10.281385281385282,21.374458874458874,10.957792207792208,8.522727272727273,13.257575757575756,6.057098765432099,13.798701298701298,12.987012987012987,20.562770562770563,7.214506172839506,20.02164502164502,15.963203463203463 24 | 2020/5/23,14.339826839826841,17.72186147186147,14.745670995670997,19.615800865800868,57.589506172839506,52.18827160493827,44.37229437229437,10.687229437229437,11.535493827160494,8.179012345679013,15.557359307359308,15.286796536796535 25 | 2020/5/24,30.032467532467532,31.114718614718615,26.515151515151512,24.08008658008658,59.641975308641975,54.348765432098766,48.16017316017316,7.575757575757576,8.522727272727273,14.204545454545455,25.83874458874459,23.26839826839827 26 | 2020/5/25,38.96103896103896,31.79112554112554,41.260822510822514,38.5551948051948,44.50757575757576,29.22077922077922,28.003246753246753,12.04004329004329,12.5,15.827922077922079,39.231601731601735,35.44372294372294 27 | 2020/5/26,28.40909090909091,24.215367965367964,23.133116883116884,22.321428571428573,25.568181818181817,16.774891774891774,21.780303030303028,13.933982683982684,17.045454545454547,15.557359307359308,30.844155844155843,30.43831168831169 28 | 2020/5/27,16.63961038961039,25.83874458874459,13.850308641975309,19.074675324675326,19.345238095238095,11.766975308641976,30.57359307359307,14.475108225108224,17.316017316017316,15.151515151515152,19.345238095238095,16.233766233766232 29 | 2020/5/28,28.679653679653683,30.43831168831169,36.39069264069264,36.12012987012987,33.27922077922078,23.944805194805195,23.133116883116884,17.4512987012987,7.575757575757576,7.523148148148148,28.40909090909091,13.122294372294373 30 | 2020/5/29,39.09632034632035,48.701298701298704,42.74891774891775,30.70887445887446,24.08008658008658,25.297619047619047,29.491341991341994,15.827922077922079,17.58658008658009,9.606481481481481,43.69588744588745,27.86796536796537 31 | 2020/5/30,25.297619047619047,18.939393939393938,18.66883116883117,13.88888888888889,20.949074074074073,18.364197530864196,18.3982683982684,15.286796536796535,15.8179012345679,20.02164502164502,29.22077922077922,36.39069264069264 32 | 2020/5/31,18.3982683982684,11.093073593073592,16.774891774891774,14.506172839506172,14.351851851851851,13.88888888888889,16.473765432098766,13.663419913419911,26.92099567099567,25.703463203463205,14.069264069264069,24.215367965367964 33 | -------------------------------------------------------------------------------- /only_6AQI.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sun Jan 10 11:51:33 2021 4 | 5 | @author: ray 6 | """ 7 | 8 | import numpy as np 9 | import pandas as pd 10 | result_all = pd.DataFrame() 11 | for month_num in range(1,13): 12 | df_data = pd.read_csv('./time/time_'+ str(month_num) +'.csv', index_col= False) 13 | new_frame = pd.read_csv('train_frame.csv') 14 | # df_data_drop = df_data[(True-df_data.isin(['x']))] 15 | result = pd.DataFrame() 16 | 17 | days = int(df_data.shape[0]/11) 18 | # for num, col in enumerate(df_data.iloc[:,6]): 19 | data_temp = pd.DataFrame() 20 | AQI_result = np.zeros(shape=[5, days]) 21 | 22 | for day_num in range(days): 23 | data_temp = df_data.iloc[day_num*11:(day_num*11)+11, :].reset_index() 24 | ItemId = data_temp["ItemId"] 25 | for Id_num in range(11): 26 | if(ItemId[Id_num]==3): 27 | AQI_O3 = 0 28 | mean_O3 = 0 29 | O3 = data_temp.iloc[Id_num, 8:].astype(float) 30 | mean_O3 = O3.mean() 31 | if(mean_O3 < 125): 32 | AQI_O3 = 0 33 | if(mean_O3 >= 125 and mean_O3 <= 164): 34 | AQI_O3 = (mean_O3-125)*(49/39)+101 35 | if(mean_O3 > 164): 36 | AQI_O3 = (mean_O3-165)*(49/39)+151 37 | # print('AQI_O3', AQI_O3) 38 | 39 | if(ItemId[Id_num]==33): 40 | AQI_PM25 = 0 41 | mean_PM25 = 0 42 | PM25 = data_temp.iloc[Id_num,8:].astype(float) 43 | mean_PM25 = PM25.mean() 44 | if(mean_PM25 < 15.5): 45 | AQI_PM25 = (mean_PM25)*(50/15.4) 46 | if(mean_PM25 >=15.5 and mean_PM25 <35.4): 47 | AQI_PM25 = (mean_PM25-15.5)*(49/18.9)+51 48 | if(mean_PM25 >=35.4 and mean_PM25 <54.4): 49 | AQI_PM25 = (mean_PM25-35.5)*(49/18.9)+101 50 | if(mean_PM25 >=54.4): 51 | AQI_PM25 = (mean_PM25-54.5)*(49/95.9)+151 52 | # print('AQI_PM25', AQI_PM25) 53 | 54 | if(ItemId[Id_num]==4): 55 | AQI_PM10 = 0 56 | mean_PM10 = 0 57 | PM10 = data_temp.iloc[Id_num, 8:].astype(float) 58 | mean_PM10 = PM10.mean() 59 | if(mean_PM10 < 54): 60 | AQI_PM10 = (mean_PM10)*(50/54) 61 | if(mean_PM10 >=54 and mean_PM10 <125): 62 | AQI_PM10 = (mean_PM25-54)*(49/70)+51 63 | if(mean_PM10 >=125 and mean_PM10 <254): 64 | AQI_PM10 = (mean_PM25-125)*(49/128)+101 65 | if(mean_PM10 >=254): 66 | AQI_PM10 = (mean_PM25-254)*(49/99)+151 67 | # print('AQI_PM10', AQI_PM10) 68 | 69 | if(ItemId[Id_num]==2): 70 | AQI_CO = 0 71 | mean_CO = 0 72 | CO = data_temp.iloc[Id_num, 8:].astype(float) 73 | mean_CO = CO.mean() 74 | if(mean_CO < 4.4): 75 | AQI_CO = (mean_CO)*(50/4.4) 76 | if(mean_CO >=4.4 and mean_CO <9.4): 77 | AQI_CO = (mean_CO-4.4)*(49/4.9)+51 78 | if(mean_CO >=9.4 and mean_CO <12.4): 79 | AQI_CO = (mean_CO-9.4)*(49/2.9)+101 80 | if(mean_CO >=12.4): 81 | AQI_CO = (mean_CO-12.4)*(49/2.9)+151 82 | # print('AQI_CO', AQI_CO) 83 | 84 | if(ItemId[Id_num]==1): 85 | AQI_SO2 = 0 86 | mean_SO2 = 0 87 | SO2 = data_temp.iloc[Id_num, 8:].astype(float) 88 | mean_SO2 = SO2.mean() 89 | if(mean_SO2 < 35): 90 | AQI_SO2 = (mean_SO2)*(50/35) 91 | if(mean_SO2 >=35 and mean_SO2 <75): 92 | AQI_SO2 = (mean_SO2-35)*(49/39)+51 93 | if(mean_SO2 >=75 and mean_SO2 <185): 94 | AQI_SO2 = (mean_SO2-75)*(49/109)+101 95 | if(mean_SO2 >=185): 96 | AQI_SO2 = (mean_SO2-185)*(49/118)+151 97 | # print('AQI_SO2', AQI_SO2) 98 | 99 | # if(ItemId[Id_num]==144): 100 | # wind_direct = data_temp.iloc[Id_num, 8:].astype(float) 101 | # mean_wind_direct = wind_direct.mean() 102 | 103 | # if(ItemId[Id_num]==14): 104 | # temper = data_temp.iloc[Id_num, 8:].astype(float) 105 | # mean_temper = temper.mean() 106 | 107 | # if(ItemId[Id_num]==23): 108 | # rain = data_temp.iloc[Id_num, 8:].astype(float) 109 | # mean_rain = rain.sum() 110 | 111 | # if(ItemId[Id_num]==38): 112 | # humidity = data_temp.iloc[Id_num, 8:].astype(float) 113 | # mean_humidity = humidity.mean() 114 | 115 | # if(ItemId[Id_num]==144): 116 | # wind_speed = data_temp.iloc[Id_num, 8:].astype(float) 117 | # mean_wind_speed = wind_speed.mean() 118 | 119 | temp_AQI = np.max([AQI_CO, AQI_O3, AQI_PM10, AQI_PM25, AQI_SO2]) 120 | # print(temp_AQI) 121 | data_temp_result = np.array([AQI_CO, AQI_PM10, AQI_PM25, AQI_SO2, temp_AQI]) 122 | AQI_result[:, day_num] = data_temp_result 123 | new_frame.iloc[0, 2:7] = data_temp_result 124 | new_frame.iloc[0, 0] = data_temp.iloc[0, 7] 125 | result = pd.concat([result, new_frame], axis=0) 126 | 127 | result.to_csv('./time_train/time_'+ str(month_num) +'.csv', index = False) 128 | result_all = pd.concat([result_all, result], axis=0) 129 | 130 | result_all.to_csv('./time_train/train_only6AQI_3.csv', index = False) 131 | 132 | # frame_3day = pd.read_csv('train_frame_3day.csv') 133 | # result_train = pd.DataFrame() 134 | 135 | # for train_num in range(363): 136 | # # col1 = pd.DataFrame(result_all.iloc[train_num, 2:8]).T 137 | # # col2 = pd.DataFrame(result_all.iloc[train_num+1, 2:8]).T 138 | # # col3 = pd.DataFrame(result_all.iloc[train_num+2, 2:8]).T 139 | # # label = pd.DataFrame(result_all.iloc[train_num+3, 7]) 140 | 141 | # # result_train_temp = pd.concat([col1, col2, col3, label], axis=1) 142 | # # result_train = pd.concat([result_train, result_train_temp], axis=0) 143 | 144 | # frame_3day.iloc[0, :6] = result_all.iloc[train_num, 2:8].values 145 | # frame_3day.iloc[0, 6:12] = result_all.iloc[train_num + 1, 2:8].values 146 | # frame_3day.iloc[0, 12:18]= result_all.iloc[train_num + 2, 2:8].values 147 | # frame_3day.iloc[0, 18] = result_all.iloc[train_num + 3, 7] 148 | # result_train = pd.concat([result_train, frame_3day], axis=0) 149 | 150 | # result_train.to_csv('./time_train/train_3day.csv', index = False) -------------------------------------------------------------------------------- /space_train/space_8.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/8/1,87.40432098765433,59.533950617283956,41.260822510822514,42.47835497835498,35.17316017316018,30.979437229437227,45.048701298701296,10.281385281385282,15.625,15.625,48.43073593073593,7.407407407407407 3 | 2020/8/2,30.57359307359307,29.491341991341994,27.191558441558442,28.40909090909091,26.92099567099567,25.162337662337663,33.1439393939394,11.363636363636363,16.098484848484848,12.31060606060606,30.70887445887446,12.31060606060606 4 | 2020/8/3,21.645021645021647,17.992424242424242,16.63961038961039,20.02164502164502,14.544753086419753,13.122294372294373,10.146103896103895,14.339826839826841,24.350649350649352,10.551948051948052,20.698051948051948,13.663419913419911 5 | 2020/8/4,18.804112554112557,12.445887445887447,12.037037037037036,12.31060606060606,10.416666666666666,8.371913580246913,10.956790123456791,14.339826839826841,16.975308641975307,20.42748917748918,24.62121212121212,33.685064935064936 6 | 2020/8/5,28.95021645021645,15.151515151515152,15.70216049382716,17.992424242424242,19.886363636363637,20.02164502164502,22.569444444444443,12.175324675324676,16.098484848484848,13.663419913419911,34.496753246753244,24.485930735930737 7 | 2020/8/6,23.674242424242426,17.58658008658009,15.54783950617284,22.186147186147185,21.75925925925926,26.10930735930736,25.11574074074074,27.462121212121215,61.0462962962963,48.02489177489177,19.753086419753085,34.09090909090909 8 | 2020/8/7,35.44372294372294,34.36147186147186,31.114718614718615,36.66125541125541,45.31926406926407,48.566017316017316,23.919753086419753,11.265432098765432,42.47835497835498,34.496753246753244,24.074074074074073,38.41991341991342 9 | 2020/8/8,37.878787878787875,37.06709956709957,32.467532467532465,34.09090909090909,42.20779220779221,51.21604938271605,34.226190476190474,7.291666666666667,13.933982683982684,14.88095238095238,24.421296296296298,34.226190476190474 10 | 2020/8/9,32.467532467532465,28.814935064935064,25.974025974025974,28.138528138528137,38.41991341991342,36.12012987012987,30.844155844155843,14.429012345679013,21.103896103896105,23.403679653679653,16.319444444444443,24.891774891774894 11 | 2020/8/10,42.74891774891775,42.74891774891775,41.66666666666667,39.63744588744589,34.9025974025974,34.9025974025974,31.25,12.345679012345679,15.046296296296296,24.215367965367964,22.260802469135804,26.785714285714285 12 | 2020/8/11,29.76190476190476,25.83874458874459,19.345238095238095,18.804112554112557,17.47685185185185,14.544753086419753,15.856481481481481,16.087962962962962,18.209876543209877,20.968614718614717,39.5021645021645,26.244588744588746 13 | 2020/8/12,33.41450216450216,28.40909090909091,21.374458874458874,18.939393939393938,16.435185185185187,14.930555555555555,19.345238095238095,9.604978354978355,21.720679012345677,19.345238095238095,25.162337662337663,30.032467532467532 14 | 2020/8/13,24.62121212121212,19.75108225108225,16.36904761904762,15.422077922077921,19.521604938271604,18.939393939393938,23.133116883116884,9.1991341991342,13.001543209876543,15.286796536796535,42.07251082251082,33.54978354978355 15 | 2020/8/14,23.26839826839827,19.074675324675326,14.00462962962963,17.4512987012987,17.316017316017316,20.156926406926406,23.944805194805195,10.01082251082251,12.152777777777779,10.822510822510823,35.03787878787879,17.18073593073593 16 | 2020/8/15,29.085497835497836,24.756493506493506,19.074675324675326,24.215367965367964,18.939393939393938,22.862554112554115,23.944805194805195,14.61038961038961,20.698051948051948,23.809523809523807,36.39069264069264,26.92099567099567 17 | 2020/8/16,30.844155844155843,27.326839826839823,23.26839826839827,28.003246753246753,22.321428571428573,33.82034632034632,35.849567099567096,57.589506172839506,45.048701298701296,46.13095238095238,36.66125541125541,34.36147186147186 18 | 2020/8/17,53.592592592592595,29.22077922077922,27.006172839506174,47.34848484848485,25.192901234567902,43.560606060606055,54.0246913580247,16.63961038961039,33.41450216450216,21.645021645021647,32.467532467532465,6.764069264069264 19 | 2020/8/18,60.29012345679012,29.320987654320987,29.66820987654321,61.26234567901234,27.700617283950617,58.77777777777778,62.23456790123457,6.790123456790123,12.808641975308642,14.583333333333334,33.82034632034632,4.74537037037037 20 | 2020/8/19,57.04938271604938,28.279320987654323,22.878086419753085,55.21296296296296,24.074074074074073,29.76190476190476,35.44372294372294,55.861111111111114,46.13095238095238,53.70061728395062,63.74691358024692,44.1017316017316 21 | 2020/8/20,85.89197530864197,38.61882716049383,35.648148148148145,63.63888888888889,26.041666666666668,38.41991341991342,46.26623376623377,63.42283950617284,69.47222222222223,37.191358024691354,80.16666666666667,29.085497835497836 22 | 2020/8/21,75.52160493827161,35.68672839506173,38.348765432098766,69.58024691358025,46.942640692640694,59.75,59.641975308641975,25.703463203463205,25.83874458874459,18.094135802469136,44.23701298701299,11.381172839506172 23 | 2020/8/22,17.72186147186147,38.01406926406926,51.432098765432094,49.51298701298701,60.50617283950618,54.456790123456784,35.44372294372294,26.515151515151512,8.757716049382717,7.330246913580247,16.91017316017316,7.677469135802468 24 | 2020/8/23,26.650432900432904,16.63961038961039,20.42748917748918,20.833333333333336,22.727272727272727,22.321428571428573,15.963203463203463,17.4512987012987,18.325617283950617,7.677469135802468,24.756493506493506,9.413580246913579 25 | 2020/8/24,34.36147186147186,29.491341991341994,30.844155844155843,26.515151515151512,25.432900432900432,25.162337662337663,12.581168831168831,12.987012987012987,20.02164502164502,18.595679012345677,41.531385281385276,19.09722222222222 26 | 2020/8/25,38.01406926406926,29.626623376623375,35.30844155844156,30.979437229437227,27.597402597402596,33.00865800865801,31.926406926406926,14.745670995670997,22.862554112554115,27.597402597402596,26.10930735930736,40.04329004329004 27 | 2020/8/26,34.63203463203463,24.08008658008658,23.944805194805195,24.485930735930737,21.374458874458874,18.939393939393938,18.66883116883117,12.987012987012987,29.085497835497836,36.39069264069264,31.655844155844157,33.82034632034632 28 | 2020/8/27,10.84104938271605,14.204545454545455,17.18073593073593,15.422077922077921,10.570987654320987,8.252164502164502,21.23917748917749,24.215367965367964,28.54437229437229,23.809523809523807,27.73268398268398,25.568181818181817 29 | 2020/8/28,14.699074074074074,22.456709956709958,20.833333333333336,17.992424242424242,20.42748917748918,16.233766233766232,25.568181818181817,21.103896103896105,26.37987012987013,16.319444444444443,32.467532467532465,25.703463203463205 30 | 2020/8/29,19.984567901234566,33.685064935064936,38.5551948051948,44.1017316017316,34.226190476190474,19.615800865800868,23.809523809523807,18.939393939393938,23.538961038961038,25.703463203463205,48.566017316017316,25.027056277056275 31 | 2020/8/30,15.93364197530864,32.332251082251084,53.916666666666664,47.88961038961039,44.642857142857146,25.703463203463205,29.626623376623375,15.557359307359308,13.663419913419911,17.4512987012987,36.93181818181818,14.745670995670997 32 | 2020/8/31,14.043209876543209,25.297619047619047,46.53679653679654,42.61363636363636,41.66666666666667,35.714285714285715,30.032467532467532,19.886363636363637,21.374458874458874,23.674242424242426,42.34307359307359,30.167748917748916 33 | -------------------------------------------------------------------------------- /space_train/space_7.csv: -------------------------------------------------------------------------------- 1 | MonitorDate,ID_24,ID_26,ID_28,ID_33,ID_37,ID_42,ID_53,ID_61,ID_63,ID_65,ID_68,ID_70 2 | 2020/7/1,18.66883116883117,14.88095238095238,14.197530864197532,17.399691358024693,13.933982683982684,13.734567901234568,14.930555555555555,18.3982683982684,23.26839826839827,29.897186147186147,20.156926406926406,27.73268398268398 3 | 2020/7/2,26.650432900432904,20.562770562770563,15.046296296296296,15.200617283950619,14.339826839826841,13.040123456790123,21.374458874458874,19.886363636363637,20.562770562770563,21.915584415584416,24.350649350649352,25.432900432900432 4 | 2020/7/3,27.597402597402596,21.103896103896105,16.504329004329005,15.354938271604937,18.533549783549784,16.473765432098766,20.42748917748918,10.957792207792208,18.788580246913583,17.4512987012987,25.162337662337663,32.87337662337662 5 | 2020/7/4,30.70887445887446,23.26839826839827,32.061688311688314,17.72186147186147,25.162337662337663,16.242283950617285,23.403679653679653,12.31060606060606,16.78240740740741,30.303030303030305,35.44372294372294,35.44372294372294 6 | 2020/7/5,37.60822510822511,30.032467532467532,30.979437229437227,25.974025974025974,24.891774891774894,20.698051948051948,28.679653679653683,8.252164502164502,15.856481481481481,30.844155844155843,32.73809523809524,31.79112554112554 7 | 2020/7/6,27.73268398268398,23.674242424242426,22.862554112554115,24.485930735930737,20.698051948051948,17.857142857142858,22.456709956709958,12.445887445887447,18.51851851851852,28.679653679653683,30.70887445887446,37.878787878787875 8 | 2020/7/7,16.36904761904762,18.26298701298701,15.692640692640692,21.103896103896105,14.969135802469136,12.581168831168831,12.268518518518519,11.093073593073592,21.23917748917749,29.085497835497836,20.156926406926406,34.09090909090909 9 | 2020/7/8,21.103896103896105,14.88095238095238,14.853395061728396,17.316017316017316,15.084876543209878,12.61574074074074,15.557359307359308,10.687229437229437,23.133116883116884,28.54437229437229,24.350649350649352,27.597402597402596 10 | 2020/7/9,23.674242424242426,22.862554112554115,14.351851851851851,15.016233766233766,20.949074074074073,12.229938271604938,17.857142857142858,10.551948051948052,24.08008658008658,28.138528138528137,23.538961038961038,27.056277056277057 11 | 2020/7/10,23.26839826839827,18.939393939393938,15.470679012345679,19.615800865800868,17.554012345679013,14.506172839506172,17.18073593073593,13.798701298701298,28.138528138528137,34.36147186147186,24.350649350649352,31.655844155844157 12 | 2020/7/11,30.57359307359307,22.997835497835496,23.538961038961038,21.374458874458874,18.127705627705627,15.046296296296296,16.63961038961039,12.04004329004329,33.54978354978355,33.82034632034632,29.897186147186147,33.1439393939394 13 | 2020/7/12,29.491341991341994,22.727272727272727,20.698051948051948,22.456709956709958,23.538961038961038,13.663419913419911,19.345238095238095,21.915584415584416,26.37987012987013,26.785714285714285,35.44372294372294,30.303030303030305 14 | 2020/7/13,34.496753246753244,33.27922077922078,30.844155844155843,28.54437229437229,27.191558441558442,15.557359307359308,19.886363636363637,16.504329004329005,20.562770562770563,21.103896103896105,36.93181818181818,39.5021645021645 15 | 2020/7/14,38.01406926406926,35.714285714285715,34.76731601731602,29.491341991341994,25.027056277056275,17.992424242424242,22.727272727272727,11.363636363636363,12.851731601731602,21.23917748917749,52.2962962962963,32.73809523809524 16 | 2020/7/15,39.77272727272727,32.87337662337662,25.432900432900432,28.40909090909091,23.133116883116884,15.393518518518519,15.692640692640692,11.363636363636363,15.963203463203463,23.403679653679653,47.21320346320346,41.66666666666667 17 | 2020/7/16,20.968614718614717,17.045454545454547,11.228354978354979,18.533549783549784,17.316017316017316,10.378086419753087,17.992424242424242,12.04004329004329,26.10930735930736,26.515151515151512,20.02164502164502,26.37987012987013 18 | 2020/7/17,17.857142857142858,13.257575757575756,12.581168831168831,14.88095238095238,13.657407407407408,11.226851851851851,15.286796536796535,7.981601731601732,21.645021645021647,23.944805194805195,19.615800865800868,21.50974025974026 19 | 2020/7/18,22.321428571428573,17.045454545454547,21.103896103896105,17.18073593073593,13.122294372294373,9.876543209876543,13.618827160493828,15.151515151515152,24.215367965367964,25.974025974025974,22.456709956709958,18.939393939393938 20 | 2020/7/19,30.844155844155843,26.92099567099567,19.75108225108225,22.0508658008658,16.63961038961039,12.962962962962964,23.403679653679653,9.063852813852813,20.156926406926406,28.40909090909091,35.03787878787879,35.849567099567096 21 | 2020/7/20,31.79112554112554,24.485930735930737,15.963203463203463,18.66883116883117,13.773148148148149,13.425925925925926,16.319444444444443,8.793290043290044,16.63961038961039,21.23917748917749,32.196969696969695,28.40909090909091 22 | 2020/7/21,27.326839826839823,17.045454545454547,17.72186147186147,14.745670995670997,18.804112554112557,13.233024691358024,24.350649350649352,8.252164502164502,15.016233766233766,16.098484848484848,25.703463203463205,30.70887445887446 23 | 2020/7/22,20.156926406926406,19.345238095238095,18.3982683982684,21.780303030303028,29.356060606060606,20.02164502164502,27.326839826839823,9.875541125541124,12.31060606060606,15.151515151515152,27.462121212121215,35.17316017316018 24 | 2020/7/23,30.303030303030305,30.57359307359307,37.47294372294372,29.897186147186147,29.897186147186147,19.20995670995671,41.125541125541126,7.575757575757576,11.769480519480519,20.156926406926406,38.01406926406926,29.22077922077922 25 | 2020/7/24,23.674242424242426,22.727272727272727,19.48051948051948,17.857142857142858,15.66358024691358,14.969135802469136,22.456709956709958,8.928571428571429,10.918209876543209,19.886363636363637,27.326839826839823,30.43831168831169 26 | 2020/7/25,24.08008658008658,23.26839826839827,23.809523809523807,17.045454545454547,19.521604938271604,14.351851851851851,13.001543209876543,9.74025974025974,16.098484848484848,22.0508658008658,28.40909090909091,23.809523809523807 27 | 2020/7/26,24.891774891774894,20.698051948051948,16.36904761904762,15.422077922077921,16.78240740740741,13.348765432098764,20.02164502164502,7.169913419913421,14.745670995670997,26.244588744588746,27.73268398268398,25.162337662337663 28 | 2020/7/27,26.10930735930736,17.045454545454547,16.504329004329005,12.716450216450216,14.204545454545455,13.233024691358024,12.716450216450216,9.604978354978355,12.268518518518519,19.75108225108225,26.10930735930736,23.674242424242426 29 | 2020/7/28,24.62121212121212,22.591991341991342,22.0508658008658,11.634199134199134,15.8179012345679,11.998456790123457,15.557359307359308,10.01082251082251,11.921296296296296,13.503086419753087,31.25,15.692640692640692 30 | 2020/7/29,36.52597402597402,26.10930735930736,22.997835497835496,23.674242424242426,25.297619047619047,16.435185185185187,27.326839826839823,10.416666666666668,11.265432098765432,11.072530864197532,27.326839826839823,9.25925925925926 31 | 2020/7/30,38.82575757575758,41.66666666666667,60.39814814814815,52.2962962962963,28.003246753246753,21.6820987654321,43.96645021645021,11.228354978354979,17.316017316017316,11.921296296296296,20.968614718614717,6.764069264069264 32 | 2020/7/31,83.83950617283952,79.73456790123457,55.75308641975308,60.50617283950618,46.672077922077925,27.083333333333332,70.33641975308642,10.416666666666668,14.467592592592593,16.975308641975307,47.61904761904761,6.222943722943723 33 | -------------------------------------------------------------------------------- /improved_PSO_train_NN.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Fri Jan 8 10:47:08 2021 4 | 5 | @author: ray 6 | """ 7 | import time 8 | import random 9 | import matplotlib.pyplot as plt 10 | import pandas as pd 11 | import numpy as np 12 | from sklearn.model_selection import train_test_split 13 | np.seterr(divide='ignore', invalid='ignore') 14 | 15 | 16 | input_data = pd.read_csv('./space_train/train_space_AQI_2.csv') 17 | 18 | shape_num = input_data.shape 19 | X_row = input_data.iloc[:, :shape_num[1]-1] 20 | Y_row = input_data.iloc[:, shape_num[1]-1] 21 | 22 | train_x, val_test_x, train_y, val_test_y = train_test_split(X_row, Y_row, test_size = 0.3, shuffle = True) 23 | val_x, test_x, val_y, test_y = train_test_split(val_test_x, val_test_y, test_size = 0.5, shuffle = True) 24 | 25 | def relu(x): 26 | if x < 0: return 0 27 | else: return x 28 | 29 | def sigmoid(x): 30 | return 1. / (1 + np.exp(-x)) 31 | 32 | def tanh(x): 33 | return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x)) 34 | 35 | def plot_fit(val_output, val_y, epoch_num): 36 | 37 | plt.figure(figsize=(8.8,4)) 38 | plt.plot(val_y) 39 | plt.plot(val_output) 40 | 41 | plt.title('Predict Result of epoch:' + str(epoch_num)) 42 | plt.ylabel('value') 43 | plt.xlabel('number') 44 | plt.legend(['val_y', 'val_output'], loc='best') 45 | 46 | plt.show() 47 | 48 | def NN(X, train_x, train_y, input_kernel, hid_kernel, output_kernel, epoch_num): 49 | fit_w = X[:input_kernel*hid_kernel] 50 | fit_wbias = X[input_kernel*hid_kernel: ((input_kernel*hid_kernel)+hid_kernel)] 51 | fit_v = X[((input_kernel*hid_kernel)+hid_kernel): (((input_kernel*hid_kernel)+hid_kernel)+hid_kernel)] 52 | fit_vbias = X[(((input_kernel*hid_kernel)+hid_kernel)+hid_kernel): ((((input_kernel*hid_kernel)+hid_kernel)+hid_kernel)+output_kernel)] 53 | 54 | data_result = np.zeros(train_x.shape[0]) 55 | 56 | for input_num in range(train_x.shape[0]): 57 | input_x = np.zeros(input_kernel) 58 | for input_for_hid_num in range(hid_kernel): 59 | if(input_for_hid_num == 0): 60 | input_x = train_x.iloc[input_num].T 61 | else: 62 | input_x = np.hstack([input_x, train_x.iloc[input_num].T]) 63 | 64 | hid_temp = fit_w * input_x 65 | hid_result = np.zeros(hid_kernel) 66 | for hid_num in range(hid_kernel): 67 | hid_result[hid_num] = relu(np.sum(hid_temp[hid_num * input_kernel : (hid_num * input_kernel) + input_kernel]) + fit_wbias[hid_num]) 68 | 69 | 70 | output_temp = fit_v * hid_result 71 | data_result[input_num] = np.sum(output_temp + fit_vbias) 72 | 73 | val_y = np.zeros(train_y.shape) 74 | val_y[:] = train_y[:] 75 | plot_fit(data_result, val_y, epoch_num) 76 | 77 | 78 | def fitfunction(X, train_x, train_y, input_kernel, hid_kernel, output_kernel): 79 | 80 | ################ calculate NN structure ####################### 81 | fit_w = X[:input_kernel*hid_kernel] 82 | fit_wbias = X[input_kernel*hid_kernel: ((input_kernel*hid_kernel)+hid_kernel)] 83 | fit_v = X[((input_kernel*hid_kernel)+hid_kernel): (((input_kernel*hid_kernel)+hid_kernel)+hid_kernel)] 84 | fit_vbias = X[(((input_kernel*hid_kernel)+hid_kernel)+hid_kernel): ((((input_kernel*hid_kernel)+hid_kernel)+hid_kernel)+output_kernel)] 85 | 86 | data_result = np.zeros(train_x.shape[0]) 87 | 88 | for input_num in range(train_x.shape[0]): 89 | input_x = np.zeros(input_kernel) 90 | for input_for_hid_num in range(hid_kernel): 91 | if(input_for_hid_num == 0): 92 | input_x = train_x.iloc[input_num].T 93 | else: 94 | input_x = np.hstack([input_x, train_x.iloc[input_num].T]) 95 | 96 | hid_temp = fit_w * input_x 97 | hid_result = np.zeros(hid_kernel) 98 | for hid_num in range(hid_kernel): 99 | hid_result[hid_num] = relu(np.sum(hid_temp[hid_num*input_kernel : (hid_num*input_kernel) + input_kernel]) + fit_wbias[hid_num]) 100 | 101 | 102 | output_temp = fit_v * hid_result 103 | data_result[input_num] = np.sum(output_temp + fit_vbias) 104 | 105 | ############## calculate fittness ###################### 106 | val_y = np.zeros(train_y.shape) 107 | val_y[:] = train_y[:] 108 | 109 | 110 | fittness = np.mean(np.abs(val_y-data_result.flatten())/val_y) 111 | 112 | return fittness 113 | 114 | 115 | 116 | # PSO 參數 117 | wmin = 0.02 118 | wmax = 1 #20 119 | c1min = 0.05 120 | c1max = 1.2 # 500 121 | c2min = 0.05 122 | c2max = 1.2 #500 123 | 124 | 125 | input_layer_kernel = shape_num[1]-1 # number of input layer kernel 126 | hidden_layer_kernel = 6 # number of hidden layer kernel 127 | output_layer_kernel = 1 # number of output layer kernel 128 | 129 | 130 | dim = input_layer_kernel*hidden_layer_kernel + hidden_layer_kernel + hidden_layer_kernel*output_layer_kernel + output_layer_kernel #維度 131 | p = dim * 3 #粒子數量 132 | iteration = 30 #迭代次數 133 | X = np.zeros((p, dim)) #粒子位置 134 | V = np.zeros((p, dim)) #粒子速度 135 | pbest = np.zeros((p, dim, iteration+1)) #個體最佳解 136 | gbest = np.zeros(dim) #群體最佳解 137 | pbest_fit = np.zeros((p, iteration+1)) #個體最佳適應值 138 | gbest_fit = 10**12 #群體最佳適應值 139 | 140 | #初始化群體 141 | def init_Population(p, dim, X, V, pbest_fit, gbest_fit, gbest, pbest, input_num, hidden_num, output_num): 142 | 143 | for i in range(p): 144 | for j in range(dim): 145 | V[i,j] = random.uniform(-0.85, 0.85) 146 | X[i,j] = random.uniform(-0.85, 0.85) 147 | 148 | pbest[i, :, 0] = X[i, :] 149 | # print('particle num: '+str(i)) 150 | pbest_fit[i, 0] = fitfunction(X[i, :], train_x, train_y, input_num, hidden_num, output_num) 151 | # print(pbest_fit) 152 | if(pbest_fit[i, 0] < gbest_fit): 153 | gbest_fit = pbest_fit[i, 0] 154 | gbest = pbest[i, :, 0] 155 | print('update gbest, gbest: ', gbest_fit) 156 | 157 | return p, dim, X, V, pbest_fit, gbest_fit, gbest, pbest 158 | 159 | 160 | def iterator(wmax, wmin, c1max, c1min, c2max, c2min, p, dim, iteration, X, V, pbest, gbest, pbest_fit, gbest_fit, input_num, hidden_num, output_num): 161 | 162 | particle_bound = 2 163 | 164 | fitness = np.zeros(iteration+1) 165 | fitness[0]= gbest_fit 166 | for tt in range(iteration): 167 | t_start = time.time() 168 | 169 | 170 | 171 | w = wmin + (iteration-tt)/iteration*(wmax-wmin) 172 | c1 = c1min + (iteration-tt)/iteration*(c1max-c1min) 173 | c2 = c2max + (iteration-tt)/iteration*(c2min-c2max) 174 | 175 | 176 | 177 | 178 | for i in range(p): 179 | #速度及位置更新 180 | # print('particle_num: ', i) 181 | rand1 = random.random() 182 | rand2 = random.random() 183 | if (tt != 0): 184 | V[i] = w*V[i] + c1*rand1*(pbest[i,:,tt+1] - X[i, :]) + c2*rand2*(gbest - X[i, :]) 185 | X[i, :] = X[i, :] + V[i] 186 | 187 | for col_num in range(dim): 188 | if(X[i, col_num] < -particle_bound or X[i, col_num] > particle_bound): 189 | X[i, col_num] = random.uniform(-particle_bound, particle_bound) 190 | 191 | 192 | # for i in range(p): 193 | temp = fitfunction(X[i], train_x, train_y, input_num, hidden_num, output_num) 194 | if(temp < pbest_fit[i, tt]): #更新個體最佳及個體最佳適應值 195 | if(X[i, 0] >= -particle_bound and X[i, 0] <= particle_bound): 196 | pbest[i,:,tt+1] = X[i, :] 197 | pbest_fit[i, tt+1] = fitfunction(pbest[i,:,tt+1], train_x, train_y, input_num, hidden_num, output_num) 198 | else: 199 | pbest_fit[i, tt+1] = pbest_fit[i, tt] 200 | pbest[i, :, tt+1] = pbest[i,:,tt] 201 | #### mutation ##### 202 | mutation_rate = random.uniform(0,1) 203 | if(mutation_rate < 0.05): 204 | X[i, :] = random.uniform(-0.85, 0.85) 205 | 206 | if(pbest_fit[i, tt+1] < gbest_fit): #更新群體最佳及群體最佳適應值 207 | gbest = pbest[i, :, tt+1] 208 | gbest_fit = pbest_fit[i, tt+1] 209 | 210 | 211 | 212 | 213 | 214 | fitness[tt+1] = gbest_fit 215 | NN(gbest, train_x, train_y, input_num, hidden_num, output_num, tt) 216 | t_end = time.time() 217 | duration = t_end - t_start 218 | val_error = fitfunction(gbest, val_x, val_y, input_num, hidden_num, output_num) 219 | 220 | 221 | print('epoch_num: '+ str(tt) + ', gbest: ', gbest_fit, ', cost time：', duration, 'sec ,val_test mape: ', val_error) 222 | 223 | 224 | 225 | 226 | return fitness, gbest, pbest, pbest_fit 227 | #執行 228 | t_start_all = time.time() 229 | p, dim, X, V, pbest_fit, gbest_fit, gbest, pbest = init_Population(p, dim, X, V, pbest_fit, gbest_fit, gbest, pbest, input_layer_kernel, hidden_layer_kernel, output_layer_kernel) 230 | fitness, gbest, pbest, pbest_fit = iterator(wmax, wmin, c1max, c1min, c2max, c2min, p, dim, iteration, X, V, pbest, gbest, pbest_fit, gbest_fit, input_layer_kernel, hidden_layer_kernel, output_layer_kernel) 231 | t_end_all = time.time() 232 | duration_all = t_end_all - t_start_all 233 | 234 | 235 | 236 | 237 | ############ plot PSO history ########### 238 | plt.figure(figsize = (12, 12)) 239 | plt.title("fitness") 240 | plt.xlabel("iteration") 241 | plt.ylabel("fitness") 242 | t = np.array([t for t in range(0,iteration+1)]) 243 | fitness = np.array(fitness) 244 | plt.plot(t, fitness) 245 | #plt.ylim(0, 10) 246 | #plt.yscale('log') 247 | plt.show() 248 | 249 | ########### plot test result ############### 250 | NN(gbest, val_x, val_y, input_layer_kernel, hidden_layer_kernel, output_layer_kernel, 30) 251 | error = fitfunction(gbest, val_x, val_y, input_layer_kernel, hidden_layer_kernel, output_layer_kernel) 252 | print('total time: ', duration_all) 253 | print('val_test mape: ', error) 254 | 255 | NN(gbest, test_x, test_y, input_layer_kernel, hidden_layer_kernel, output_layer_kernel, 30) 256 | error = fitfunction(gbest, test_x, test_y, input_layer_kernel, hidden_layer_kernel, output_layer_kernel) 257 | print('total time: ', duration_all) 258 | print('test mape: ', error) 259 | 260 | df_gbest = pd.DataFrame(gbest) 261 | df_gbest.to_csv('PSO_gbest.csv') 262 | 263 | -------------------------------------------------------------------------------- /time_train/only_AQI.csv: -------------------------------------------------------------------------------- 1 | AQI,AQI.1,AQI.2,AQI.3 2 | 0.146558373,0.176800844,0.24787065,28.81493506 3 | 0.176800844,0.24787065,0.26904038,25.7034632 4 | 0.24787065,0.26904038,0.234261538,83.62345679 5 | 0.26904038,0.234261538,0.881669053,77.35802469 6 | 0.234261538,0.881669053,0.811636451,47.00734049 7 | 0.881669053,0.811636451,0.472388123,42.07251082 8 | 0.811636451,0.472388123,0.417228486,41.80194805 9 | 0.472388123,0.417228486,0.414204239,54.56481481 10 | 0.417228486,0.414204239,0.556862677,32.60281385 11 | 0.414204239,0.556862677,0.311379838,48.7012987 12 | 0.556862677,0.311379838,0.491322539,54.7808642 13 | 0.311379838,0.491322539,0.559277595,43.83116883 14 | 0.491322539,0.559277595,0.436886092,41.66666667 15 | 0.559277595,0.436886092,0.412692115,15.01623377 16 | 0.436886092,0.412692115,0.114803779,51.54012346 17 | 0.412692115,0.114803779,0.523053835,58.5617284 18 | 0.114803779,0.523053835,0.601538647,57.91358025 19 | 0.523053835,0.601538647,0.594293895,73.93880837 20 | 0.601538647,0.594293895,0.77341776,89.56481481 21 | 0.594293895,0.77341776,0.948079278,65.04320988 22 | 0.77341776,0.948079278,0.673986166,84.27160494 23 | 0.948079278,0.673986166,0.888913805,24.08008658 24 | 0.673986166,0.888913805,0.216116056,15.28679654 25 | 0.888913805,0.216116056,0.117828026,13.83399209 26 | 0.216116056,0.117828026,0.101589134,24.08008658 27 | 0.117828026,0.101589134,0.216116056,32.1969697 28 | 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