├── README └── src ├── config.py ├── config.pyc ├── first_result.zip ├── first_result ├── 60.csv ├── 61.csv ├── 62.csv ├── 63.csv ├── gbdt_20161020_10.csv ├── gridRF_20161019_4.csv ├── nn_20161025_23.csv ├── rf_20161017_1.csv ├── rf_20161018_2.csv ├── rf_20161019_3.csv ├── rf_20161019_4.csv ├── rf_20161020_5.csv ├── rf_20161020_6.csv ├── rf_20161020_7.csv ├── rf_20161020_8.csv ├── rf_20161020_9.csv ├── rf_20161021_10.csv ├── rf_20161021_11.csv ├── rf_20161021_9.csv ├── rf_20161022_12.csv ├── rf_20161023_13.csv ├── rf_20161023_14.csv ├── rf_20161023_15.csv ├── rf_20161024_16.csv ├── rf_20161024_17.csv ├── rf_20161024_18.csv ├── rf_20161024_19.csv ├── rf_20161025_20.csv ├── rf_20161025_21.csv ├── rf_2016102622.csv ├── rf_20161026_22.csv ├── rf_20161026_23.csv ├── rf_20161026_233.csv ├── rf_20161026_24.csv ├── rf_20161027_25.csv ├── rf_20161027_26.csv ├── rf_20161027_27.csv ├── rf_20161027_28.csv ├── rf_20161028_28.csv ├── rf_20161028_30.csv ├── rf_20161031_32.csv ├── rf_20161031_33.csv ├── rf_20161031_34.csv ├── rf_20161101_35.csv ├── rf_20161101_36.csv ├── rf_20161103_37.csv ├── rf_20161104_39.csv ├── rf_20161104_40.csv ├── rf_20161104_41.csv ├── rf_20161105_42.csv ├── rf_20161105_43.csv ├── rf_20161105_44.csv ├── rf_20161106_45.csv ├── rf_20161106_46.csv ├── rf_20161106_47.csv ├── rf_20161106_48.csv ├── rf_20161107_49.csv ├── rf_20161107_50.csv ├── rf_20161108_51.csv ├── rf_20161108_52.csv ├── rf_20161108_53.csv ├── rf_20161109_54.csv ├── rf_20161110_55.csv ├── rf_20161110_56.csv ├── rf_20161110_57.csv ├── rf_20161111_58.csv ├── rf_20161111_59.csv ├── rf_20161113_62.csv ├── rf_20161113_63.csv ├── rf_20161113_64.csv ├── rf_20161113_65.csv ├── rf_20161114_66.csv ├── rf_20161114_67.csv ├── rf_20161114_68.csv ├── rf_20161114_69.csv ├── rf_last_65.csv └── twolayrf_20161019_5.csv ├── getmf_input.py ├── location.py ├── location.pyc ├── log ├── info ├── output.txt ├── run.log └── svd.log ├── lr.py ├── preprocess.py ├── result ├── 20161206_ensemble.csv ├── Fuck_kaixiaohaode2.csv ├── Fuck_kaixiaohaode5.csv ├── best.csv ├── fusai_clf_rf_1204.csv ├── fusai_rf_20161120_1.csv ├── fusai_rf_20161202_6.csv ├── fusai_type_rf_20161210_2.csv ├── fusai_userWithlocation_rf_20161204_8.csv ├── fusai_user_rf_20161203_7.csv ├── fusai_user_rf_20161204_8.csv ├── fusai_user_rf_20161204_9.csv ├── fusai_user_rf_20161205_10.csv ├── fusai_user_rf_20161205_11.csv ├── fusai_user_rf_20161206_12.csv ├── fusai_user_rf_20161207_13.csv ├── fusai_user_rf_20161208_14.csv ├── fusai_user_rf_20161208_15.csv ├── fusai_user_rf_20161209_15.csv ├── fusai_user_rf_20161210_16.csv ├── fusai_user_rf_20161211_17.csv ├── fusai_user_rf_20161212_18.csv ├── fusai_user_rf_20161213_19.csv ├── fusai_user_rf_20161214_22.csv ├── fusai_user_rf_20161214_23.csv ├── fusai_user_rf_20161215_24.csv ├── fusai_user_rf_20161215_25.unsubmited.csv ├── fusai_user_rf_20161216_26.csv ├── fusai_user_rf_20161216_27.csv ├── fusai_user_rf_20161216_28.csv ├── fusai_user_xgb_20161214_21.csv ├── location_rf_20161130_4.csv ├── location_rf_20161204_9.csv ├── result.txt ├── result.txt_2 ├── s.csv ├── second.csv ├── success.csv ├── type.csv ├── type_rf.csv ├── type_rf_20161128_1.csv ├── type_rf_20161128_2.csv ├── type_rf_20161129_3.csv ├── type_rf_20161201_4.csv ├── type_rf_20161206_5.csv ├── type_rf_20161210_2.csv ├── type_rf_20161212_6.csv ├── user_rf_20161130_5.csv └── user_rf_20161201_6.csv ├── rf.py ├── rf_date.py ├── rf_location.py ├── rf_user.py ├── rf_userWithlocation.py ├── run.sh ├── svd.py ├── svdfeature ├── TEST_NEW_SVM.txt ├── TRAIN_NEW_SVM.txt ├── binaryClassification.conf ├── clean.sh ├── generateDict.py ├── generateSVMFormat.py ├── postpreprocess.py ├── recommond.csv ├── run.sh ├── shopid_key.txt ├── true_pred.txt └── uid_key.txt ├── type_rf.py ├── type_rf.pyc ├── type_xgb.py ├── utils.py ├── utils.pyc └── weights ├── neigh.m ├── neigh.m_01.npy ├── neigh.m_02.npy ├── neigh.m_03.npy ├── neigh.m_04.npy ├── neigh.m_05.npy ├── neigh.m_06.npy └── neigh.m_07.npy /README: -------------------------------------------------------------------------------- 1 | 2016 CCF 大数据与计算智能比赛 2 | 北京联通 《依据用户轨迹的商户精准营销》 3 | 4 | -------------------------------------------------------------------------------- /src/config.py: -------------------------------------------------------------------------------- 1 | PRO_PATH = '/volume1/xuguanggen/competition/unicom' 2 | 3 | SHOP_PROFILE = PRO_PATH+'/new/SHOP_PROFILE_NEW.csv' 4 | USER_PROFILE = PRO_PATH+'/new/USER_PROFILE_NEW.csv' 5 | TRAIN = PRO_PATH + '/new/TRAIN_NEW.csv' 6 | TEST = PRO_PATH + '/new/TEST_NEW.csv' 7 | MYTEST = PRO_PATH +'/new/MYTEST.csv' 8 | USER_TRACE = PRO_PATH + '/new/USER_TRACE_NEW.csv' 9 | 10 | 11 | NUM_NEAREST_SHOPS = 15 12 | NUM_NEAREST_USERS = 8 13 | DESTINATION_NUM_NEAREST_SHOPS = 4 14 | 15 | #### num_user_class * num_shop_class ##### 16 | import numpy as np 17 | NEIGH_CLASS_SIMILARITY = np.array( 18 | [[0.047619047619,0.00952380952381,0.790476190476,0.0761904761905,0.00952380952381,0.0857142857143,0.00952380952381,0.00952380952381,0.00952380952381,0.00952380952381], 19 | [0.972286374134,0.000461893764434,0.000461893764434,0.010623556582,0.000461893764434,0.00923787528868,0.0013856812933,0.0013856812933,0.000923787528868,0.00415704387991], 20 | [0,0,0,0,0,0,0,0,0,0], 21 | [0.00340136054422,0.00340136054422,0.316326530612,0.00340136054422,0.00680272108844,0.544217687075,0.0102040816327,0.0884353741497,0.0204081632653,0.0136054421769], 22 | [0.320062785388,0.000142694063927,0.0162671232877,0.000142694063927,0.0142694063927,0.288242009132,0.000570776255708,0.358732876712,0.00128424657534,0.000570776255708], 23 | [0.327817993795,0.00103412616339,0.047569803516,0.00103412616339,0.0403309203723,0.245087900724,0.00310237849018,0.154084798345,0.182006204757,0.00103412616339], 24 | [0.469522908501,0.00216245438573,0.000135153399108,0.000135153399108,0.000135153399108,0.52426003514,0.000135153399108,0.00189214758751,0.000135153399108,0.00216245438573], 25 | [0.335078534031,0.0183246073298,0.124345549738,0.00130890052356,0.0785340314136,0.393979057592,0.00130890052356,0.00130890052356,0.0471204188482,0.00261780104712], 26 | [0.036676403765,0.000324569944823,0.000324569944823,0.000324569944823,0.000324569944823,0.932164881532,0.000324569944823,0.000324569944823,0.0230444660824,0.00811424862058] 27 | ]) 28 | 29 | 30 | 31 | NEIGH_INCOME_SIMILARITY=np.array([[0.465414567109,0.00732936326157,0.114521300962,0.00137425561154,0.00137425561154,0.0861200183234,0.00137425561154,0.316536875859,0.00320659642694,0.00274851122309], 32 | [0.451404564836,0.000752445447705,0.011161274141,0.00175570604465,0.0107223476298,0.391585151743,0.00050163029847,0.124153498871,0.00664660145473,0.00131677953348], 33 | [0.0809917355372,0.000826446280992,0.000275482093664,0.000275482093664,0.00716253443526,0.842975206612,0.000275482093664,0.00909090909091,0.0506887052342,0.00826446280992]]) 34 | 35 | NEIGH_ENTERTAINMENT_SIMILARITY=np.array([[0.549155437464,0.000379578667679,0.0137597267034,0.000474473334599,0.00294173467451,0.379009299677,0.000284684000759,0.0441260201177,0.0081609413551,0.00170810400455], 36 | [0.293811610077,0.00547645125958,0.052026286966,0.00492880613363,0.0117743702081,0.289430449069,0.000821467688938,0.332420591457,0.00711938663746,0.00219058050383], 37 | [0.0708706971362,0.000289268151577,0.0271912062482,0.000289268151577,0.000578536303153,0.870407868094,0.000289268151577,0.000289268151577,0.0219843795198,0.00896731269887], 38 | [0.265306122449,0.0204081632653,0.00291545189504,0.0262390670554,0.0291545189504,0.215743440233,0.00583090379009,0.233236151603,0.204081632653,0.00291545189504], 39 | [0.0337078651685,0.00561797752809,0.00561797752809,0.00561797752809,0.0786516853933,0.483146067416,0.0168539325843,0.168539325843,0.219101123596,0.00561797752809], 40 | [0.360381861575,0.000298329355609,0.000298329355609,0.000298329355609,0.0101431980907,0.354415274463,0.000894988066826,0.273568019093,0.000596658711217,0.000298329355609], 41 | [0.390151515152,0.00378787878788,0.00378787878788,0.00378787878788,0.265151515152,0.337121212121,0.00378787878788,0.00378787878788,0.00757575757576,0.00378787878788] 42 | ]) 43 | 44 | NEIGH_BABY_SIMILARITY=np.array([[0.191307839004,0.000100371374084,0.0160594198535,0.00200742748168,0.0197731606946,0.535983137609,0.00100371374084,0.201947204657,0.0288065843621,0.00311151259661], 45 | [0.560013571974,0.00245992026465,0.0227330562389,0.000933073203834,0.000169649673424,0.352192722029,8.48248367122e-05,0.0586139621681,0.00076342353041,0.0021206209178] 46 | ]) 47 | 48 | 49 | 50 | 51 | 52 | List_Features = [ 53 | 'NEIGHSHOP_ID', 54 | 'NEIGHSHOP_LONLAT', 55 | 'NEIGHSHOP_DISTANCE', 56 | 'NEIGHSHOP_CLASS', 57 | #'NEIGH_CLASS_SIMILARITY', 58 | #'NEIGH_INCOME_SIMILARITY', 59 | #'NEIGH_ENTERTAINMENT_SIMILARITY', 60 | #'NEIGH_BABY_SIMILARITY', 61 | #'NEIGH_SIMILARITY', 62 | #'DESTINATION_NEIGHSHOP_ID', 63 | 'DESTINATION_NEIGHSHOP_LONLAT', 64 | 'DESTINATION_NEIGHSHOP_DISTANCE', 65 | 'DESTINATION_NEIGHSHOP_CLASS', 66 | 'NEIGHUSER_LONLAT', 67 | 'NEIGHUSER_DISTANCE', 68 | 'NEIGHUSER_CLASS', 69 | 'NEIGHUSER_SHOPID', 70 | 'NEIGHUSE_SHOPTYPE', 71 | 'MF_USERFEATURE', 72 | 'MF_LOCFEATURE', 73 | 'MF_USERFEATURE_SHOP' 74 | #'USER_SVDFEATURE', 75 | #'LOC_SVDFEATURE' 76 | #'MF_USERFEATURE_SHOPTYPE' 77 | #'SHOPTYPE_COUNT' 78 | #'PASSSHOPTYPE_COUNT' 79 | ] 80 | 81 | Common_Features = [ 82 | #'USERID', 83 | 'LON', 84 | 'LAT', 85 | 'INCOME', 86 | 'ENTERTAINMENT', 87 | 'BABY', 88 | 'GENDER', 89 | 'SHOPPING', 90 | 'HOUR', 91 | 'WEEK', 92 | 'DURATION', 93 | 'DESTINATION_LON', 94 | 'DESTINATION_LAT', 95 | #'MOSTSHOPTYPE', 96 | 'PASSDISTANCE', 97 | 'PASSTIME', 98 | 'DURATION_LEVEL', 99 | #'NEIGHSHOP_MOSTTYPE', 100 | #'NEIGHSHOP_MOSTTYPE_COUNT', 101 | 'NEIGHSHOP_CENTER_DISTANCE', 102 | 'DATE', 103 | 'DIRECTION', 104 | 'ISAWAYCENTER', 105 | 'NEIGHUSER_MOST_GOTO', 106 | 'NEIGHUSER_MOST_SHOPTYPE', 107 | 'RECOMMEND_MOST_TYPE', ###with this attribute goto mostshoptype 108 | 'LDA_LOCFEATURE' 109 | ] 110 | 111 | Features =[] 112 | for i in range(1,NUM_NEAREST_SHOPS+1): 113 | Features.append('NEIGHSHOP_ID'+'_'+str(i)) 114 | Features.append('NEIGHSHOP_LON'+'_'+str(i)) 115 | Features.append('NEIGHSHOP_LAT'+'_'+str(i)) 116 | Features.append('NEIGHSHOP_DISTANCE'+'_'+str(i)) 117 | Features.append('NEIGHSHOP_CLASS'+'_'+str(i)) 118 | Features.append('NEIGH_CLASS_SIMILARITY'+'_'+str(i)) 119 | Features.append('NEIGH_INCOME_SIMILARITY'+'_'+str(i)) 120 | Features.append('NEIGH_ENTERTAINMENT_SIMILARITY'+'_'+str(i)) 121 | Features.append('NEIGH_BABY_SIMILARITY'+'_'+str(i)) 122 | Features.append('NEIGH_SIMILARITY'+'_'+str(i)) 123 | 124 | for i in range(1,DESTINATION_NUM_NEAREST_SHOPS+1): 125 | Features.append('DESTINATION_NEIGHSHOP_ID'+'_'+str(i)) 126 | Features.append('DESTINATION_NEIGHSHOP_LON'+'_'+str(i)) 127 | Features.append('DESTINATION_NEIGHSHOP_LAT'+'_'+str(i)) 128 | Features.append('DESTINATION_NEIGHSHOP_DISTANCE'+'_'+str(i)) 129 | Features.append('DESTINATION_NEIGHSHOP_CLASS'+'_'+str(i)) 130 | 131 | 132 | 133 | for i in range(1,NUM_NEAREST_USERS+1): 134 | Features.append('NEIGHUSER_LON_'+str(i)) 135 | Features.append('NEIGHUSER_LAT_'+str(i)) 136 | Features.append('NEIGHUSER_DISTANCE_'+str(i)) 137 | Features.append('NEIGHUSER_CLASS_'+str(i)) 138 | Features.append('NEIGHUSER_SHOPID_'+str(i)) 139 | Features.append('NEIGHUSER_SHOPTYPE_'+str(i)) 140 | 141 | Features = Features + Common_Features 142 | 143 | 144 | CLUSTER_DICT={ 145 | "2 6 0 5":10, # 146 | "2 7 0 8":1, 147 | "2 4 0 6":1, 148 | "3 5 0 8":1, 149 | "2 6 0 6":1, 150 | "3 3 0 9":15, #15 151 | "2 3 1 8":4, 152 | "1 1 1 1":1, 153 | "1 2 1 5":10, 154 | "1 2 1 4":1, # 155 | "1 2 0 5":1, 156 | "1 2 0 6":1, 157 | "2 1 0 5":1, 158 | "2 5 0 5":1, 159 | "2 2 0 5":10, # 160 | "2 2 0 4":1, 161 | "2 2 1 8":1, 162 | "3 1 0 5":1, 163 | "2 1 1 7":23, # 20,23 164 | "3 1 0 6":3, 165 | "2 1 1 2":10, #10 ###### 166 | "2 7 0 5":1, 167 | "2 4 0 2":1, 168 | "2 4 0 5":1, 169 | "3 3 0 2":1, 170 | "2 1 1 6":1, 171 | "2 1 0 2":1, 172 | "1 1 0 2":1, 173 | "1 2 1 7":1, 174 | "1 2 0 4":1, 175 | "1 2 1 8":1, 176 | "2 2 0 1":1, 177 | "2 1 0 6":1, 178 | "3 1 1 7":1, 179 | "2 5 0 6":1, 180 | "2 2 0 6":1, 181 | "2 2 1 2":1, 182 | "1 1 1 7":1, 183 | "1 4 1 7":1, 184 | "1 4 0 4":1 185 | } 186 | -------------------------------------------------------------------------------- /src/config.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuguanggen/2016CCF-unicom/911fc0afa6447bb6ee0140cdba004f4ce7b43328/src/config.pyc -------------------------------------------------------------------------------- /src/first_result.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuguanggen/2016CCF-unicom/911fc0afa6447bb6ee0140cdba004f4ce7b43328/src/first_result.zip -------------------------------------------------------------------------------- /src/first_result/60.csv: -------------------------------------------------------------------------------- 1 | USERID,SHOPID,ARRIVAL_TIME 2 | 410.0,18.0,20140809142715 3 | 2398.0,26.0,20140809140756 4 | 2398.0,7315.0,20140809123808 5 | 2326.0,26.0,20140809163345 6 | 2326.0,9303.0,20140809101028 7 | 2326.0,9332.0,20140809174704 8 | 2428.0,4825.0,20140809141340 9 | 2428.0,3836.0,20140809102735 10 | 1327.0,61.0,20140809141755 11 | 134.0,181.0,20140809151228 12 | 134.0,181.0,20140809180309 13 | 134.0,181.0,20140809143348 14 | 2078.0,181.0,20140809121752 15 | 2078.0,6542.0,20140809174312 16 | 2078.0,6542.0,20140809104130 17 | 2078.0,7231.0,20140809144944 18 | 2078.0,7231.0,20140809155438 19 | 2078.0,7141.0,20140809135233 20 | 2078.0,7141.0,20140809113124 21 | 2776.0,293.0,20140809111527 22 | 2776.0,1019.0,20140809163430 23 | 494.0,3398.0,20140809155306 24 | 494.0,3398.0,20140809132651 25 | 494.0,9900.0,20140809143538 26 | 2245.0,520.0,20140809183822 27 | 2245.0,520.0,20140809172752 28 | 2690.0,520.0,20140809161344 29 | 767.0,9151.0,20140809184943 30 | 767.0,4851.0,20140809174935 31 | 767.0,9194.0,20140809112406 32 | 2655.0,533.0,20140809131206 33 | 2655.0,3771.0,20140809153235 34 | 492.0,553.0,20140809131432 35 | 492.0,3110.0,20140809121607 36 | 492.0,3075.0,20140809153925 37 | 1294.0,549.0,20140809170926 38 | 1278.0,549.0,20140809111206 39 | 763.0,9006.0,20140809154910 40 | 2760.0,8015.0,20140809162913 41 | 1305.0,8015.0,20140809155824 42 | 598.0,9270.0,20140809171243 43 | 545.0,549.0,20140809110819 44 | 545.0,9110.0,20140809120330 45 | 545.0,9147.0,20140809163727 46 | 2609.0,8015.0,20140809121939 47 | 1182.0,9151.0,20140809175307 48 | 1182.0,9290.0,20140809150405 49 | 799.0,782.0,20140809155637 50 | 128.0,936.0,20140809152952 51 | 128.0,1632.0,20140809131156 52 | 2031.0,936.0,20140809101647 53 | 1273.0,1019.0,20140809170226 54 | 1273.0,1019.0,20140809124020 55 | 1273.0,1019.0,20140809164646 56 | 1188.0,1019.0,20140809175613 57 | 1188.0,8860.0,20140809145907 58 | 1188.0,8860.0,20140809150635 59 | 1188.0,8860.0,20140809165450 60 | 1188.0,8860.0,20140809102455 61 | 1381.0,1019.0,20140809174247 62 | 1184.0,8860.0,20140809142119 63 | 1184.0,8066.0,20140809102040 64 | 99.0,8066.0,20140809182117 65 | 2370.0,1019.0,20140809105703 66 | 2370.0,2162.0,20140809135208 67 | 2370.0,2162.0,20140809123500 68 | 2876.0,1217.0,20140809120441 69 | 2614.0,8506.0,20140809144944 70 | 2614.0,8467.0,20140809162625 71 | 781.0,936.0,20140809154721 72 | 255.0,1632.0,20140809111330 73 | 188.0,9588.0,20140809103011 74 | 188.0,1784.0,20140809112204 75 | 188.0,18.0,20140809190446 76 | 2685.0,9553.0,20140809174413 77 | 1839.0,1784.0,20140809170656 78 | 723.0,9588.0,20140809141100 79 | 1733.0,1814.0,20140809175804 80 | 1733.0,1845.0,20140809162151 81 | 558.0,1881.0,20140809115943 82 | 558.0,1881.0,20140809153019 83 | 558.0,1881.0,20140809190851 84 | 1168.0,1881.0,20140809192105 85 | 1996.0,3209.0,20140809165154 86 | 1905.0,3297.0,20140809192814 87 | 1152.0,2184.0,20140809182914 88 | 1044.0,2162.0,20140809124833 89 | 1044.0,2162.0,20140809132735 90 | 2537.0,2162.0,20140809193645 91 | 2537.0,3830.0,20140809141710 92 | 2537.0,3836.0,20140809131412 93 | 565.0,2114.0,20140809154259 94 | 281.0,3073.0,20140809153347 95 | 2580.0,9467.0,20140809104037 96 | 2580.0,3028.0,20140809185614 97 | 1993.0,3148.0,20140809173203 98 | 1993.0,9553.0,20140809101853 99 | 898.0,3293.0,20140809144031 100 | 898.0,3293.0,20140809193736 101 | 687.0,3201.0,20140809173501 102 | 2308.0,3201.0,20140809125958 103 | 2308.0,3201.0,20140809145303 104 | 618.0,1888.0,20140809174903 105 | 473.0,2155.0,20140809133734 106 | 473.0,9788.0,20140809141523 107 | 2412.0,2181.0,20140809103359 108 | 2412.0,3864.0,20140809152830 109 | 2555.0,3295.0,20140809142744 110 | 788.0,3323.0,20140809104248 111 | 176.0,3323.0,20140809121730 112 | 176.0,3323.0,20140809135713 113 | 176.0,3323.0,20140809141745 114 | 176.0,9652.0,20140809152329 115 | 1013.0,3323.0,20140809120909 116 | 2755.0,3323.0,20140809100209 117 | 2755.0,3323.0,20140809112654 118 | 2755.0,3164.0,20140809163651 119 | 110.0,3536.0,20140809173116 120 | 2087.0,3395.0,20140809190149 121 | 363.0,3412.0,20140809101516 122 | 2450.0,9858.0,20140809130025 123 | 2450.0,3536.0,20140809115336 124 | 2195.0,3415.0,20140809172901 125 | 1806.0,3395.0,20140809191502 126 | 1806.0,3416.0,20140809184155 127 | 37.0,3452.0,20140809150309 128 | 755.0,3452.0,20140809124432 129 | 1465.0,3452.0,20140809185225 130 | 1465.0,3452.0,20140809154106 131 | 1465.0,3452.0,20140809143444 132 | 1465.0,3452.0,20140809115327 133 | 2429.0,3427.0,20140809191933 134 | 2434.0,3452.0,20140809122027 135 | 2489.0,3452.0,20140809191220 136 | 2003.0,416.0,20140809182716 137 | 2003.0,9072.0,20140809134555 138 | 2847.0,3536.0,20140809101737 139 | 1068.0,9146.0,20140809132544 140 | 1210.0,3830.0,20140809120534 141 | 1210.0,3830.0,20140809183843 142 | 404.0,9163.0,20140809115203 143 | 404.0,9163.0,20140809140655 144 | 404.0,9163.0,20140809165555 145 | 404.0,9163.0,20140809174920 146 | 1118.0,3763.0,20140809160516 147 | 2591.0,3763.0,20140809122638 148 | 329.0,9139.0,20140809190117 149 | 2728.0,8978.0,20140809195853 150 | 2728.0,9034.0,20140809114747 151 | 1798.0,4978.0,20140809121357 152 | 1798.0,7955.0,20140809155933 153 | 725.0,5025.0,20140809110653 154 | 725.0,3836.0,20140809172018 155 | 1903.0,3885.0,20140809104551 156 | 641.0,7910.0,20140809172329 157 | 2260.0,3826.0,20140809170227 158 | 2071.0,9143.0,20140809165221 159 | 2071.0,3864.0,20140809123553 160 | 1534.0,3836.0,20140809133829 161 | 2699.0,3864.0,20140809112920 162 | 2627.0,3864.0,20140809134511 163 | 2627.0,3864.0,20140809192352 164 | 2352.0,3864.0,20140809115508 165 | 854.0,3836.0,20140809175800 166 | 1175.0,3864.0,20140809102615 167 | 72.0,3864.0,20140809113606 168 | 72.0,3864.0,20140809154243 169 | 72.0,3864.0,20140809173900 170 | 69.0,3864.0,20140809115410 171 | 152.0,7896.0,20140809160955 172 | 152.0,7275.0,20140809195045 173 | 450.0,10382.0,20140809154642 174 | 450.0,10382.0,20140809140512 175 | 450.0,10774.0,20140809104036 176 | 1473.0,4340.0,20140809153733 177 | 926.0,4306.0,20140809195136 178 | 926.0,4306.0,20140809160208 179 | 2901.0,4306.0,20140809193513 180 | 2901.0,4393.0,20140809115813 181 | 2507.0,4337.0,20140809102158 182 | 2507.0,4401.0,20140809151804 183 | 1752.0,4340.0,20140809141038 184 | 2520.0,4340.0,20140809161521 185 | 2163.0,4340.0,20140809110630 186 | 2163.0,4401.0,20140809135410 187 | 2608.0,4340.0,20140809191535 188 | 2530.0,4345.0,20140809114542 189 | 2530.0,4345.0,20140809124104 190 | 1225.0,10431.0,20140809191523 191 | 1225.0,4391.0,20140809160918 192 | 1225.0,4391.0,20140809114827 193 | 427.0,4393.0,20140809165730 194 | 427.0,4426.0,20140809145758 195 | 1242.0,4401.0,20140809175508 196 | 127.0,10406.0,20140809173454 197 | 1464.0,9095.0,20140809195239 198 | 1464.0,9057.0,20140809170235 199 | 433.0,3840.0,20140809174853 200 | 680.0,4626.0,20140809172248 201 | 1498.0,4617.0,20140809180646 202 | 2899.0,4580.0,20140809153258 203 | 1511.0,11019.0,20140809175627 204 | 612.0,11039.0,20140809175412 205 | 1440.0,4631.0,20140809142550 206 | 2476.0,4631.0,20140809143107 207 | 1129.0,4671.0,20140809104252 208 | 1129.0,4671.0,20140809110508 209 | 2059.0,4671.0,20140809162313 210 | 849.0,4535.0,20140809131734 211 | 669.0,4671.0,20140809150432 212 | 597.0,11385.0,20140809125032 213 | 1127.0,11315.0,20140809134909 214 | 49.0,8986.0,20140809155111 215 | 49.0,4848.0,20140809130543 216 | 49.0,9277.0,20140809172919 217 | 2025.0,3864.0,20140809190245 218 | 387.0,9120.0,20140809103748 219 | 2836.0,4962.0,20140809102432 220 | 1102.0,7955.0,20140809144552 221 | 916.0,8978.0,20140809113429 222 | 1834.0,9114.0,20140809182545 223 | 1834.0,9151.0,20140809145427 224 | 1834.0,9332.0,20140809104349 225 | 1778.0,4916.0,20140809103129 226 | 1778.0,3780.0,20140809133210 227 | 1248.0,10485.0,20140809141549 228 | 1248.0,3802.0,20140809153500 229 | 567.0,4916.0,20140809120037 230 | 1948.0,7273.0,20140809153315 231 | 1948.0,9271.0,20140809110145 232 | 1948.0,9271.0,20140809105728 233 | 2264.0,4925.0,20140809111745 234 | 2264.0,9130.0,20140809180940 235 | 2264.0,9151.0,20140809104240 236 | 1387.0,7352.0,20140809172017 237 | 1387.0,7352.0,20140809133756 238 | 1387.0,7352.0,20140809143235 239 | 1387.0,7352.0,20140809154803 240 | 1387.0,7352.0,20140809165532 241 | 1387.0,7352.0,20140809183446 242 | 2411.0,3836.0,20140809122226 243 | 1501.0,4965.0,20140809153621 244 | 1501.0,4965.0,20140809165128 245 | 1501.0,4965.0,20140809123149 246 | 1501.0,4965.0,20140809105945 247 | 804.0,7310.0,20140809131512 248 | 1510.0,7302.0,20140809152655 249 | 1510.0,7955.0,20140809110918 250 | 496.0,5034.0,20140809172000 251 | 496.0,5034.0,20140809151153 252 | 496.0,4943.0,20140809191947 253 | 1311.0,10508.0,20140809100409 254 | 1054.0,26.0,20140809190832 255 | 1739.0,5099.0,20140809150534 256 | 1739.0,5099.0,20140809135551 257 | 1820.0,5099.0,20140809180430 258 | 1808.0,10554.0,20140809191845 259 | 896.0,5486.0,20140809133833 260 | 14.0,10610.0,20140809144803 261 | 14.0,10610.0,20140809164939 262 | 14.0,10610.0,20140809174523 263 | 2062.0,5120.0,20140809160324 264 | 2062.0,5120.0,20140809145032 265 | 2062.0,5486.0,20140809154443 266 | 2062.0,5175.0,20140809192157 267 | 167.0,5175.0,20140809134353 268 | 167.0,5175.0,20140809122419 269 | 167.0,5257.0,20140809181745 270 | 1953.0,5175.0,20140809171337 271 | 1953.0,5175.0,20140809145034 272 | 1953.0,5175.0,20140809112853 273 | 1043.0,5144.0,20140809100349 274 | 1151.0,5257.0,20140809151154 275 | 2857.0,5210.0,20140809105425 276 | 117.0,5486.0,20140809193554 277 | 383.0,10634.0,20140809173943 278 | 383.0,5144.0,20140809153846 279 | 2038.0,5295.0,20140809152943 280 | 2038.0,10576.0,20140809165547 281 | 2564.0,10679.0,20140809140934 282 | 2865.0,61.0,20140809163332 283 | 2222.0,10648.0,20140809183042 284 | 2222.0,10782.0,20140809175232 285 | 2706.0,5311.0,20140809114352 286 | 2706.0,5331.0,20140809180547 287 | 660.0,5331.0,20140809192735 288 | 660.0,5331.0,20140809143446 289 | 756.0,5331.0,20140809164602 290 | 756.0,5331.0,20140809151705 291 | 756.0,5331.0,20140809122745 292 | 2644.0,5517.0,20140809170755 293 | 2644.0,5517.0,20140809161812 294 | 2644.0,5517.0,20140809150915 295 | 2644.0,5517.0,20140809120231 296 | 1082.0,3412.0,20140809110119 297 | 2768.0,5739.0,20140809101408 298 | 2768.0,5764.0,20140809113412 299 | 2768.0,5736.0,20140809123518 300 | 930.0,5739.0,20140809175838 301 | 930.0,5739.0,20140809163709 302 | 930.0,10831.0,20140809184348 303 | 2139.0,5714.0,20140809111352 304 | 690.0,5736.0,20140809135032 305 | 690.0,10879.0,20140809160704 306 | 2465.0,5669.0,20140809103937 307 | 1913.0,5836.0,20140809103707 308 | 484.0,6157.0,20140809170512 309 | 2623.0,6210.0,20140809142339 310 | 1852.0,6270.0,20140809113134 311 | 1852.0,6270.0,20140809173122 312 | 1973.0,6270.0,20140809101014 313 | 2654.0,6270.0,20140809143729 314 | 2108.0,6270.0,20140809104324 315 | 2287.0,6542.0,20140809103153 316 | 2287.0,7215.0,20140809161723 317 | 2131.0,7095.0,20140809100447 318 | 2131.0,7155.0,20140809110314 319 | 2131.0,6484.0,20140809172626 320 | 2579.0,181.0,20140809123506 321 | 1165.0,11902.0,20140809160257 322 | 1165.0,11902.0,20140809185314 323 | 1165.0,11902.0,20140809191150 324 | 1165.0,11902.0,20140809114437 325 | 1165.0,11902.0,20140809145311 326 | 1429.0,7155.0,20140809113941 327 | 1429.0,7231.0,20140809141007 328 | 1619.0,7155.0,20140809175737 329 | 2600.0,6270.0,20140809115543 330 | 2001.0,6738.0,20140809142155 331 | 2484.0,6738.0,20140809153423 332 | 2587.0,6566.0,20140809175801 333 | 915.0,7141.0,20140809125429 334 | 1554.0,7163.0,20140809172139 335 | 1554.0,7163.0,20140809160454 336 | 1554.0,7163.0,20140809130156 337 | 1277.0,7247.0,20140809162249 338 | 1277.0,7163.0,20140809134853 339 | 1277.0,7247.0,20140809145324 340 | 1977.0,7163.0,20140809101938 341 | 2094.0,7068.0,20140809180130 342 | 71.0,7902.0,20140809155454 343 | 204.0,7287.0,20140809150530 344 | 204.0,9151.0,20140809162759 345 | 1469.0,9093.0,20140809143507 346 | 2323.0,9139.0,20140809124305 347 | 806.0,9241.0,20140809134316 348 | 806.0,7310.0,20140809181041 349 | 1199.0,520.0,20140809131413 350 | 1638.0,9332.0,20140809165433 351 | 733.0,7356.0,20140809103139 352 | 1989.0,7356.0,20140809155732 353 | 1989.0,9303.0,20140809192901 354 | 734.0,7360.0,20140809124841 355 | 628.0,5736.0,20140809163157 356 | 628.0,5736.0,20140809194326 357 | 2878.0,5736.0,20140809114525 358 | 2878.0,10831.0,20140809123236 359 | 2878.0,10831.0,20140809195111 360 | 695.0,7483.0,20140809161417 361 | 2656.0,7897.0,20140809143948 362 | 2656.0,3830.0,20140809150657 363 | 1877.0,7287.0,20140809101030 364 | 1877.0,4862.0,20140809161301 365 | 850.0,4340.0,20140809112149 366 | 850.0,8005.0,20140809134910 367 | 656.0,3771.0,20140809123830 368 | 656.0,9114.0,20140809110300 369 | 1218.0,7978.0,20140809155052 370 | 1218.0,7950.0,20140809190443 371 | 1218.0,9130.0,20140809112253 372 | 1218.0,9130.0,20140809145213 373 | 1599.0,7978.0,20140809135707 374 | 1599.0,5002.0,20140809170920 375 | 2334.0,4909.0,20140809165845 376 | 438.0,9256.0,20140809115039 377 | 475.0,7996.0,20140809154836 378 | 475.0,9299.0,20140809125801 379 | 177.0,7996.0,20140809195158 380 | 1991.0,7996.0,20140809122117 381 | 402.0,1019.0,20140809121538 382 | 402.0,8066.0,20140809101831 383 | 1123.0,288.0,20140809182351 384 | 1123.0,288.0,20140809144427 385 | 239.0,8066.0,20140809150112 386 | 341.0,8066.0,20140809112357 387 | 341.0,8066.0,20140809132946 388 | 341.0,8066.0,20140809170523 389 | 341.0,8860.0,20140809165151 390 | 486.0,8066.0,20140809105512 391 | 486.0,8872.0,20140809155020 392 | 1037.0,8066.0,20140809145017 393 | 1037.0,8860.0,20140809103519 394 | 1316.0,8066.0,20140809112716 395 | 1185.0,8066.0,20140809134045 396 | 1734.0,1019.0,20140809160431 397 | 1495.0,8066.0,20140809113703 398 | 2130.0,8066.0,20140809155103 399 | 2130.0,8860.0,20140809191809 400 | 752.0,1632.0,20140809122032 401 | 705.0,8734.0,20140809161043 402 | 2367.0,8860.0,20140809100524 403 | 2367.0,8860.0,20140809114618 404 | 2367.0,8860.0,20140809154446 405 | 2340.0,8860.0,20140809121703 406 | 2340.0,8972.0,20140809182201 407 | 2340.0,8860.0,20140809195636 408 | 2340.0,8972.0,20140809141732 409 | 2340.0,8872.0,20140809103345 410 | 2513.0,8860.0,20140809110423 411 | 1390.0,8860.0,20140809181722 412 | 1451.0,1019.0,20140809181034 413 | 1698.0,8860.0,20140809182453 414 | 1698.0,8860.0,20140809192450 415 | 1835.0,8860.0,20140809121325 416 | 1835.0,8860.0,20140809131332 417 | 1835.0,8860.0,20140809155219 418 | 1835.0,8860.0,20140809164139 419 | 1835.0,8860.0,20140809185216 420 | 1835.0,8860.0,20140809195600 421 | 2492.0,2222.0,20140809151753 422 | 2492.0,2222.0,20140809130549 423 | 1338.0,8860.0,20140809185901 424 | 1006.0,8860.0,20140809102119 425 | 721.0,8872.0,20140809123647 426 | 1708.0,1019.0,20140809163825 427 | 1147.0,8972.0,20140809160018 428 | 1147.0,8972.0,20140809155847 429 | 384.0,9143.0,20140809124912 430 | 285.0,9312.0,20140809114536 431 | 1524.0,9277.0,20140809155659 432 | 1524.0,9277.0,20140809183146 433 | 1524.0,9277.0,20140809163710 434 | 2351.0,4955.0,20140809184419 435 | 2351.0,4919.0,20140809121522 436 | 2351.0,4955.0,20140809150520 437 | 535.0,9096.0,20140809175923 438 | 1812.0,9104.0,20140809171236 439 | 2366.0,9027.0,20140809123307 440 | 439.0,9277.0,20140809130520 441 | 1412.0,9049.0,20140809153854 442 | 1412.0,9049.0,20140809162423 443 | 1412.0,9049.0,20140809195816 444 | 2757.0,9164.0,20140809192836 445 | 459.0,9164.0,20140809190345 446 | 2824.0,7304.0,20140809110324 447 | 778.0,4947.0,20140809181643 448 | 2552.0,9165.0,20140809141732 449 | 1162.0,9120.0,20140809173321 450 | 2217.0,9089.0,20140809180939 451 | 1799.0,9096.0,20140809130047 452 | 1799.0,9096.0,20140809144008 453 | 1869.0,9183.0,20140809182458 454 | 2675.0,9130.0,20140809110236 455 | 2675.0,9130.0,20140809155308 456 | 2604.0,9219.0,20140809125050 457 | 2316.0,9102.0,20140809145947 458 | 1538.0,9102.0,20140809132030 459 | 1538.0,9102.0,20140809155133 460 | 1538.0,9102.0,20140809140116 461 | 1538.0,9102.0,20140809115747 462 | 1475.0,9183.0,20140809120327 463 | 1019.0,9219.0,20140809175650 464 | 857.0,9110.0,20140809114358 465 | 857.0,9110.0,20140809155458 466 | 857.0,9110.0,20140809193609 467 | 857.0,9110.0,20140809170711 468 | 2551.0,7350.0,20140809190602 469 | 15.0,9299.0,20140809164309 470 | 6.0,9299.0,20140809105529 471 | 1968.0,9116.0,20140809144917 472 | 1332.0,9116.0,20140809142544 473 | 1332.0,9116.0,20140809184919 474 | 1332.0,9131.0,20140809104720 475 | 2636.0,520.0,20140809175946 476 | 2636.0,520.0,20140809123722 477 | 1929.0,549.0,20140809150307 478 | 2248.0,9130.0,20140809145201 479 | 2653.0,9130.0,20140809151535 480 | 409.0,9095.0,20140809135511 481 | 409.0,9095.0,20140809154001 482 | 901.0,9089.0,20140809185103 483 | 729.0,9130.0,20140809174042 484 | 883.0,5025.0,20140809183505 485 | 276.0,7310.0,20140809114709 486 | 1924.0,9095.0,20140809100820 487 | 2201.0,9147.0,20140809100832 488 | 2201.0,9147.0,20140809160909 489 | 132.0,10495.0,20140809182014 490 | 986.0,9151.0,20140809163749 491 | 986.0,9151.0,20140809155909 492 | 986.0,9151.0,20140809102112 493 | 1896.0,9151.0,20140809154013 494 | 1279.0,9175.0,20140809124138 495 | 1279.0,9175.0,20140809134520 496 | 1279.0,9175.0,20140809182911 497 | 2145.0,9175.0,20140809113803 498 | 2145.0,9175.0,20140809105045 499 | 2145.0,9175.0,20140809160510 500 | 1790.0,9332.0,20140809132434 501 | 1335.0,9187.0,20140809140857 502 | 1335.0,9187.0,20140809151439 503 | 1581.0,9187.0,20140809135146 504 | 1581.0,9187.0,20140809141835 505 | 670.0,9047.0,20140809193037 506 | 670.0,9047.0,20140809164328 507 | 1944.0,9194.0,20140809171523 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2776.0,1019.0,20140809163430 23 | 494.0,3398.0,20140809155306 24 | 494.0,3398.0,20140809132651 25 | 494.0,9900.0,20140809143538 26 | 2245.0,520.0,20140809183822 27 | 2245.0,520.0,20140809172752 28 | 2690.0,520.0,20140809161344 29 | 767.0,9151.0,20140809184943 30 | 767.0,4851.0,20140809174935 31 | 767.0,9194.0,20140809112406 32 | 2655.0,533.0,20140809131206 33 | 2655.0,3771.0,20140809153235 34 | 492.0,553.0,20140809131432 35 | 492.0,3110.0,20140809121607 36 | 492.0,3075.0,20140809153925 37 | 1294.0,549.0,20140809170926 38 | 1278.0,549.0,20140809111206 39 | 763.0,9006.0,20140809154910 40 | 2760.0,8015.0,20140809162913 41 | 1305.0,8015.0,20140809155824 42 | 598.0,9270.0,20140809171243 43 | 545.0,549.0,20140809110819 44 | 545.0,9110.0,20140809120330 45 | 545.0,9147.0,20140809163727 46 | 2609.0,8015.0,20140809121939 47 | 1182.0,9151.0,20140809175307 48 | 1182.0,9290.0,20140809150405 49 | 799.0,782.0,20140809155637 50 | 128.0,936.0,20140809152952 51 | 128.0,1632.0,20140809131156 52 | 2031.0,936.0,20140809101647 53 | 1273.0,1019.0,20140809170226 54 | 1273.0,1019.0,20140809124020 55 | 1273.0,1019.0,20140809164646 56 | 1188.0,1019.0,20140809175613 57 | 1188.0,8860.0,20140809145907 58 | 1188.0,8860.0,20140809150635 59 | 1188.0,8860.0,20140809165450 60 | 1188.0,8860.0,20140809102455 61 | 1381.0,1019.0,20140809174247 62 | 1184.0,8860.0,20140809142119 63 | 1184.0,8066.0,20140809102040 64 | 99.0,8066.0,20140809182117 65 | 2370.0,1019.0,20140809105703 66 | 2370.0,2162.0,20140809135208 67 | 2370.0,2162.0,20140809123500 68 | 2876.0,1217.0,20140809120441 69 | 2614.0,8506.0,20140809144944 70 | 2614.0,8467.0,20140809162625 71 | 781.0,936.0,20140809154721 72 | 255.0,1632.0,20140809111330 73 | 188.0,9588.0,20140809103011 74 | 188.0,1784.0,20140809112204 75 | 188.0,18.0,20140809190446 76 | 2685.0,9553.0,20140809174413 77 | 1839.0,1784.0,20140809170656 78 | 723.0,9588.0,20140809141100 79 | 1733.0,1814.0,20140809175804 80 | 1733.0,1845.0,20140809162151 81 | 558.0,1881.0,20140809115943 82 | 558.0,1881.0,20140809153019 83 | 558.0,1881.0,20140809190851 84 | 1168.0,1881.0,20140809192105 85 | 1996.0,3209.0,20140809165154 86 | 1905.0,3297.0,20140809192814 87 | 1152.0,2184.0,20140809182914 88 | 1044.0,2162.0,20140809124833 89 | 1044.0,2162.0,20140809132735 90 | 2537.0,2162.0,20140809193645 91 | 2537.0,3830.0,20140809141710 92 | 2537.0,3836.0,20140809131412 93 | 565.0,2114.0,20140809154259 94 | 281.0,3073.0,20140809153347 95 | 2580.0,9467.0,20140809104037 96 | 2580.0,3028.0,20140809185614 97 | 1993.0,3148.0,20140809173203 98 | 1993.0,9553.0,20140809101853 99 | 898.0,3293.0,20140809144031 100 | 898.0,3293.0,20140809193736 101 | 687.0,3201.0,20140809173501 102 | 2308.0,3201.0,20140809125958 103 | 2308.0,3201.0,20140809145303 104 | 618.0,1888.0,20140809174903 105 | 473.0,2155.0,20140809133734 106 | 473.0,9788.0,20140809141523 107 | 2412.0,2181.0,20140809103359 108 | 2412.0,3864.0,20140809152830 109 | 2555.0,3295.0,20140809142744 110 | 788.0,3323.0,20140809104248 111 | 176.0,3323.0,20140809121730 112 | 176.0,3323.0,20140809135713 113 | 176.0,3323.0,20140809141745 114 | 176.0,9652.0,20140809152329 115 | 1013.0,3323.0,20140809120909 116 | 2755.0,3323.0,20140809100209 117 | 2755.0,3323.0,20140809112654 118 | 2755.0,3164.0,20140809163651 119 | 110.0,3536.0,20140809173116 120 | 2087.0,3395.0,20140809190149 121 | 363.0,3412.0,20140809101516 122 | 2450.0,9858.0,20140809130025 123 | 2450.0,3536.0,20140809115336 124 | 2195.0,3415.0,20140809172901 125 | 1806.0,3395.0,20140809191502 126 | 1806.0,3416.0,20140809184155 127 | 37.0,3452.0,20140809150309 128 | 755.0,3452.0,20140809124432 129 | 1465.0,3452.0,20140809185225 130 | 1465.0,3452.0,20140809154106 131 | 1465.0,3452.0,20140809143444 132 | 1465.0,3452.0,20140809115327 133 | 2429.0,3427.0,20140809191933 134 | 2434.0,3452.0,20140809122027 135 | 2489.0,3452.0,20140809191220 136 | 2003.0,416.0,20140809182716 137 | 2003.0,9072.0,20140809134555 138 | 2847.0,3536.0,20140809101737 139 | 1068.0,9146.0,20140809132544 140 | 1210.0,3830.0,20140809120534 141 | 1210.0,3830.0,20140809183843 142 | 404.0,9163.0,20140809115203 143 | 404.0,9163.0,20140809140655 144 | 404.0,9163.0,20140809165555 145 | 404.0,9163.0,20140809174920 146 | 1118.0,3763.0,20140809160516 147 | 2591.0,3763.0,20140809122638 148 | 329.0,9139.0,20140809190117 149 | 2728.0,8978.0,20140809195853 150 | 2728.0,9034.0,20140809114747 151 | 1798.0,4978.0,20140809121357 152 | 1798.0,7955.0,20140809155933 153 | 725.0,5025.0,20140809110653 154 | 725.0,3836.0,20140809172018 155 | 1903.0,3885.0,20140809104551 156 | 641.0,7910.0,20140809172329 157 | 2260.0,3826.0,20140809170227 158 | 2071.0,9143.0,20140809165221 159 | 2071.0,3864.0,20140809123553 160 | 1534.0,3836.0,20140809133829 161 | 2699.0,3864.0,20140809112920 162 | 2627.0,3864.0,20140809134511 163 | 2627.0,3864.0,20140809192352 164 | 2352.0,3864.0,20140809115508 165 | 854.0,3836.0,20140809175800 166 | 1175.0,3864.0,20140809102615 167 | 72.0,3864.0,20140809113606 168 | 72.0,3864.0,20140809154243 169 | 72.0,3864.0,20140809173900 170 | 69.0,3864.0,20140809115410 171 | 152.0,7896.0,20140809160955 172 | 152.0,7275.0,20140809195045 173 | 450.0,10382.0,20140809154642 174 | 450.0,10382.0,20140809140512 175 | 450.0,10774.0,20140809104036 176 | 1473.0,4340.0,20140809153733 177 | 926.0,4306.0,20140809195136 178 | 926.0,4306.0,20140809160208 179 | 2901.0,4306.0,20140809193513 180 | 2901.0,4393.0,20140809115813 181 | 2507.0,4337.0,20140809102158 182 | 2507.0,4401.0,20140809151804 183 | 1752.0,4340.0,20140809141038 184 | 2520.0,4340.0,20140809161521 185 | 2163.0,4340.0,20140809110630 186 | 2163.0,4401.0,20140809135410 187 | 2608.0,4340.0,20140809191535 188 | 2530.0,4345.0,20140809114542 189 | 2530.0,4345.0,20140809124104 190 | 1225.0,10431.0,20140809191523 191 | 1225.0,4391.0,20140809160918 192 | 1225.0,4391.0,20140809114827 193 | 427.0,4393.0,20140809165730 194 | 427.0,4426.0,20140809145758 195 | 1242.0,4401.0,20140809175508 196 | 127.0,10406.0,20140809173454 197 | 1464.0,9095.0,20140809195239 198 | 1464.0,9057.0,20140809170235 199 | 433.0,3840.0,20140809174853 200 | 680.0,4626.0,20140809172248 201 | 1498.0,4617.0,20140809180646 202 | 2899.0,4580.0,20140809153258 203 | 1511.0,11019.0,20140809175627 204 | 612.0,11039.0,20140809175412 205 | 1440.0,4631.0,20140809142550 206 | 2476.0,4631.0,20140809143107 207 | 1129.0,4671.0,20140809104252 208 | 1129.0,4671.0,20140809110508 209 | 2059.0,4671.0,20140809162313 210 | 849.0,4535.0,20140809131734 211 | 669.0,4671.0,20140809150432 212 | 597.0,11385.0,20140809125032 213 | 1127.0,11315.0,20140809134909 214 | 49.0,8986.0,20140809155111 215 | 49.0,4848.0,20140809130543 216 | 49.0,9277.0,20140809172919 217 | 2025.0,3864.0,20140809190245 218 | 387.0,9120.0,20140809103748 219 | 2836.0,4962.0,20140809102432 220 | 1102.0,7955.0,20140809144552 221 | 916.0,8978.0,20140809113429 222 | 1834.0,9114.0,20140809182545 223 | 1834.0,9151.0,20140809145427 224 | 1834.0,9332.0,20140809104349 225 | 1778.0,4916.0,20140809103129 226 | 1778.0,3780.0,20140809133210 227 | 1248.0,10485.0,20140809141549 228 | 1248.0,3802.0,20140809153500 229 | 567.0,4916.0,20140809120037 230 | 1948.0,7273.0,20140809153315 231 | 1948.0,9271.0,20140809110145 232 | 1948.0,9271.0,20140809105728 233 | 2264.0,4925.0,20140809111745 234 | 2264.0,9130.0,20140809180940 235 | 2264.0,9151.0,20140809104240 236 | 1387.0,7352.0,20140809172017 237 | 1387.0,7352.0,20140809133756 238 | 1387.0,7352.0,20140809143235 239 | 1387.0,7352.0,20140809154803 240 | 1387.0,7352.0,20140809165532 241 | 1387.0,7352.0,20140809183446 242 | 2411.0,3836.0,20140809122226 243 | 1501.0,4965.0,20140809153621 244 | 1501.0,4965.0,20140809165128 245 | 1501.0,4965.0,20140809123149 246 | 1501.0,4965.0,20140809105945 247 | 804.0,7310.0,20140809131512 248 | 1510.0,7302.0,20140809152655 249 | 1510.0,7955.0,20140809110918 250 | 496.0,5034.0,20140809172000 251 | 496.0,5034.0,20140809151153 252 | 496.0,4943.0,20140809191947 253 | 1311.0,10508.0,20140809100409 254 | 1054.0,26.0,20140809190832 255 | 1739.0,5099.0,20140809150534 256 | 1739.0,5099.0,20140809135551 257 | 1820.0,5099.0,20140809180430 258 | 1808.0,10554.0,20140809191845 259 | 896.0,5486.0,20140809133833 260 | 14.0,10610.0,20140809144803 261 | 14.0,10610.0,20140809164939 262 | 14.0,10610.0,20140809174523 263 | 2062.0,5120.0,20140809160324 264 | 2062.0,5120.0,20140809145032 265 | 2062.0,5486.0,20140809154443 266 | 2062.0,5175.0,20140809192157 267 | 167.0,5175.0,20140809134353 268 | 167.0,5175.0,20140809122419 269 | 167.0,5257.0,20140809181745 270 | 1953.0,5175.0,20140809171337 271 | 1953.0,5175.0,20140809145034 272 | 1953.0,5175.0,20140809112853 273 | 1043.0,5144.0,20140809100349 274 | 1151.0,5257.0,20140809151154 275 | 2857.0,5210.0,20140809105425 276 | 117.0,5486.0,20140809193554 277 | 383.0,10634.0,20140809173943 278 | 383.0,5144.0,20140809153846 279 | 2038.0,5295.0,20140809152943 280 | 2038.0,10576.0,20140809165547 281 | 2564.0,10679.0,20140809140934 282 | 2865.0,61.0,20140809163332 283 | 2222.0,10648.0,20140809183042 284 | 2222.0,10782.0,20140809175232 285 | 2706.0,5311.0,20140809114352 286 | 2706.0,5331.0,20140809180547 287 | 660.0,5331.0,20140809192735 288 | 660.0,5331.0,20140809143446 289 | 756.0,5331.0,20140809164602 290 | 756.0,5331.0,20140809151705 291 | 756.0,5331.0,20140809122745 292 | 2644.0,5517.0,20140809170755 293 | 2644.0,5517.0,20140809161812 294 | 2644.0,5517.0,20140809150915 295 | 2644.0,5517.0,20140809120231 296 | 1082.0,3412.0,20140809110119 297 | 2768.0,5739.0,20140809101408 298 | 2768.0,5764.0,20140809113412 299 | 2768.0,5736.0,20140809123518 300 | 930.0,5739.0,20140809175838 301 | 930.0,5739.0,20140809163709 302 | 930.0,10831.0,20140809184348 303 | 2139.0,5714.0,20140809111352 304 | 690.0,5736.0,20140809135032 305 | 690.0,10879.0,20140809160704 306 | 2465.0,5669.0,20140809103937 307 | 1913.0,5836.0,20140809103707 308 | 484.0,6157.0,20140809170512 309 | 2623.0,6210.0,20140809142339 310 | 1852.0,6270.0,20140809113134 311 | 1852.0,6270.0,20140809173122 312 | 1973.0,6270.0,20140809101014 313 | 2654.0,6270.0,20140809143729 314 | 2108.0,6270.0,20140809104324 315 | 2287.0,6542.0,20140809103153 316 | 2287.0,7215.0,20140809161723 317 | 2131.0,7095.0,20140809100447 318 | 2131.0,7155.0,20140809110314 319 | 2131.0,6484.0,20140809172626 320 | 2579.0,181.0,20140809123506 321 | 1165.0,11902.0,20140809160257 322 | 1165.0,11902.0,20140809185314 323 | 1165.0,11902.0,20140809191150 324 | 1165.0,11902.0,20140809114437 325 | 1165.0,11902.0,20140809145311 326 | 1429.0,7155.0,20140809113941 327 | 1429.0,7231.0,20140809141007 328 | 1619.0,7155.0,20140809175737 329 | 2600.0,6270.0,20140809115543 330 | 2001.0,6738.0,20140809142155 331 | 2484.0,6738.0,20140809153423 332 | 2587.0,6566.0,20140809175801 333 | 915.0,7141.0,20140809125429 334 | 1554.0,7163.0,20140809172139 335 | 1554.0,7163.0,20140809160454 336 | 1554.0,7163.0,20140809130156 337 | 1277.0,7247.0,20140809162249 338 | 1277.0,7163.0,20140809134853 339 | 1277.0,7247.0,20140809145324 340 | 1977.0,7163.0,20140809101938 341 | 2094.0,7068.0,20140809180130 342 | 71.0,7902.0,20140809155454 343 | 204.0,7287.0,20140809150530 344 | 204.0,9151.0,20140809162759 345 | 1469.0,9093.0,20140809143507 346 | 2323.0,9139.0,20140809124305 347 | 806.0,9241.0,20140809134316 348 | 806.0,7310.0,20140809181041 349 | 1199.0,520.0,20140809131413 350 | 1638.0,9332.0,20140809165433 351 | 733.0,7356.0,20140809103139 352 | 1989.0,7356.0,20140809155732 353 | 1989.0,9303.0,20140809192901 354 | 734.0,7360.0,20140809124841 355 | 628.0,5736.0,20140809163157 356 | 628.0,5736.0,20140809194326 357 | 2878.0,5736.0,20140809114525 358 | 2878.0,10831.0,20140809123236 359 | 2878.0,10831.0,20140809195111 360 | 695.0,7483.0,20140809161417 361 | 2656.0,7897.0,20140809143948 362 | 2656.0,3830.0,20140809150657 363 | 1877.0,7287.0,20140809101030 364 | 1877.0,4862.0,20140809161301 365 | 850.0,4340.0,20140809112149 366 | 850.0,8005.0,20140809134910 367 | 656.0,3771.0,20140809123830 368 | 656.0,9114.0,20140809110300 369 | 1218.0,7978.0,20140809155052 370 | 1218.0,7950.0,20140809190443 371 | 1218.0,9130.0,20140809112253 372 | 1218.0,9130.0,20140809145213 373 | 1599.0,7978.0,20140809135707 374 | 1599.0,5002.0,20140809170920 375 | 2334.0,4909.0,20140809165845 376 | 438.0,9256.0,20140809115039 377 | 475.0,7996.0,20140809154836 378 | 475.0,9299.0,20140809125801 379 | 177.0,7996.0,20140809195158 380 | 1991.0,7996.0,20140809122117 381 | 402.0,1019.0,20140809121538 382 | 402.0,8066.0,20140809101831 383 | 1123.0,288.0,20140809182351 384 | 1123.0,288.0,20140809144427 385 | 239.0,8066.0,20140809150112 386 | 341.0,8066.0,20140809112357 387 | 341.0,8066.0,20140809132946 388 | 341.0,8066.0,20140809170523 389 | 341.0,8860.0,20140809165151 390 | 486.0,8066.0,20140809105512 391 | 486.0,8872.0,20140809155020 392 | 1037.0,8066.0,20140809145017 393 | 1037.0,8860.0,20140809103519 394 | 1316.0,8066.0,20140809112716 395 | 1185.0,8066.0,20140809134045 396 | 1734.0,1019.0,20140809160431 397 | 1495.0,8066.0,20140809113703 398 | 2130.0,8066.0,20140809155103 399 | 2130.0,8860.0,20140809191809 400 | 752.0,1632.0,20140809122032 401 | 705.0,8734.0,20140809161043 402 | 2367.0,8860.0,20140809100524 403 | 2367.0,8860.0,20140809114618 404 | 2367.0,8860.0,20140809154446 405 | 2340.0,8860.0,20140809121703 406 | 2340.0,8972.0,20140809182201 407 | 2340.0,8860.0,20140809195636 408 | 2340.0,8972.0,20140809141732 409 | 2340.0,8872.0,20140809103345 410 | 2513.0,8860.0,20140809110423 411 | 1390.0,8860.0,20140809181722 412 | 1451.0,1019.0,20140809181034 413 | 1698.0,8860.0,20140809182453 414 | 1698.0,8860.0,20140809192450 415 | 1835.0,8860.0,20140809121325 416 | 1835.0,8860.0,20140809131332 417 | 1835.0,8860.0,20140809155219 418 | 1835.0,8860.0,20140809164139 419 | 1835.0,8860.0,20140809185216 420 | 1835.0,8860.0,20140809195600 421 | 2492.0,2222.0,20140809151753 422 | 2492.0,2222.0,20140809130549 423 | 1338.0,8860.0,20140809185901 424 | 1006.0,8860.0,20140809102119 425 | 721.0,8872.0,20140809123647 426 | 1708.0,1019.0,20140809163825 427 | 1147.0,8972.0,20140809160018 428 | 1147.0,8972.0,20140809155847 429 | 384.0,9143.0,20140809124912 430 | 285.0,9312.0,20140809114536 431 | 1524.0,9277.0,20140809155659 432 | 1524.0,9277.0,20140809183146 433 | 1524.0,9277.0,20140809163710 434 | 2351.0,4955.0,20140809184419 435 | 2351.0,4919.0,20140809121522 436 | 2351.0,4955.0,20140809150520 437 | 535.0,9096.0,20140809175923 438 | 1812.0,9104.0,20140809171236 439 | 2366.0,9027.0,20140809123307 440 | 439.0,9277.0,20140809130520 441 | 1412.0,9049.0,20140809153854 442 | 1412.0,9049.0,20140809162423 443 | 1412.0,9049.0,20140809195816 444 | 2757.0,9164.0,20140809192836 445 | 459.0,9164.0,20140809190345 446 | 2824.0,9027.0,20140809110324 447 | 778.0,7304.0,20140809181643 448 | 2552.0,9225.0,20140809141732 449 | 1162.0,9120.0,20140809173321 450 | 2217.0,9089.0,20140809180939 451 | 1799.0,9096.0,20140809130047 452 | 1799.0,9096.0,20140809144008 453 | 1869.0,8992.0,20140809182458 454 | 2675.0,9130.0,20140809110236 455 | 2675.0,9130.0,20140809155308 456 | 2604.0,9219.0,20140809125050 457 | 2316.0,9077.0,20140809145947 458 | 1538.0,9102.0,20140809132030 459 | 1538.0,9102.0,20140809155133 460 | 1538.0,9102.0,20140809140116 461 | 1538.0,9102.0,20140809115747 462 | 1475.0,8992.0,20140809120327 463 | 1019.0,9090.0,20140809175650 464 | 857.0,9110.0,20140809114358 465 | 857.0,9110.0,20140809155458 466 | 857.0,9110.0,20140809193609 467 | 857.0,9110.0,20140809170711 468 | 2551.0,7350.0,20140809190602 469 | 15.0,9299.0,20140809164309 470 | 6.0,9299.0,20140809105529 471 | 1968.0,9116.0,20140809144917 472 | 1332.0,9116.0,20140809142544 473 | 1332.0,9116.0,20140809184919 474 | 1332.0,9131.0,20140809104720 475 | 2636.0,520.0,20140809175946 476 | 2636.0,520.0,20140809123722 477 | 1929.0,549.0,20140809150307 478 | 2248.0,9130.0,20140809145201 479 | 2653.0,9130.0,20140809151535 480 | 409.0,9095.0,20140809135511 481 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-------------------------------------------------------------------------------- /src/first_result/gridRF_20161019_4.csv: -------------------------------------------------------------------------------- 1 | USERID,SHOPID,ARRIVAL_TIME 2 | 410.0, 18.,20140809142715 3 | 2398.0, 7302.,20140809140756 4 | 2398.0, 7302.,20140809123808 5 | 2326.0, 7302.,20140809163345 6 | 2326.0, 7302.,20140809101028 7 | 2326.0, 520.,20140809174704 8 | 2428.0, 520.,20140809141340 9 | 2428.0, 4940.,20140809102735 10 | 1327.0, 10485.,20140809141755 11 | 134.0, 10485.,20140809151228 12 | 134.0, 4978.,20140809180309 13 | 134.0, 4978.,20140809143348 14 | 2078.0, 4916.,20140809121752 15 | 2078.0, 4916.,20140809174312 16 | 2078.0, 4916.,20140809104130 17 | 2078.0, 4916.,20140809144944 18 | 2078.0, 4916.,20140809155438 19 | 2078.0, 4916.,20140809135233 20 | 2078.0, 4916.,20140809113124 21 | 2776.0, 2155.,20140809111527 22 | 2776.0, 1019.,20140809163430 23 | 494.0, 9596.,20140809155306 24 | 494.0, 9596.,20140809132651 25 | 494.0, 1942.,20140809143538 26 | 2245.0, 520.,20140809183822 27 | 2245.0, 520.,20140809172752 28 | 2690.0, 520.,20140809161344 29 | 767.0, 9296.,20140809184943 30 | 767.0, 7302.,20140809174935 31 | 767.0, 4848.,20140809112406 32 | 2655.0, 4916.,20140809131206 33 | 2655.0, 9170.,20140809153235 34 | 492.0, 8005.,20140809131432 35 | 492.0, 3110.,20140809121607 36 | 492.0, 3075.,20140809153925 37 | 1294.0, 4401.,20140809170926 38 | 1278.0, 7273.,20140809111206 39 | 763.0, 9006.,20140809154910 40 | 2760.0, 520.,20140809162913 41 | 1305.0, 520.,20140809155824 42 | 598.0, 9270.,20140809171243 43 | 545.0, 4978.,20140809110819 44 | 545.0, 553.,20140809120330 45 | 545.0, 553.,20140809163727 46 | 2609.0, 4916.,20140809121939 47 | 1182.0, 7312.,20140809175307 48 | 1182.0, 3864.,20140809150405 49 | 799.0, 782.,20140809155637 50 | 128.0, 936.,20140809152952 51 | 128.0, 1632.,20140809131156 52 | 2031.0, 8734.,20140809101647 53 | 1273.0, 1019.,20140809170226 54 | 1273.0, 1019.,20140809124020 55 | 1273.0, 1019.,20140809164646 56 | 1188.0, 1019.,20140809175613 57 | 1188.0, 8860.,20140809145907 58 | 1188.0, 8860.,20140809150635 59 | 1188.0, 8860.,20140809165450 60 | 1188.0, 8860.,20140809102455 61 | 1381.0, 1019.,20140809174247 62 | 1184.0, 8860.,20140809142119 63 | 1184.0, 8066.,20140809102040 64 | 99.0, 8066.,20140809182117 65 | 2370.0, 1019.,20140809105703 66 | 2370.0, 2162.,20140809135208 67 | 2370.0, 2162.,20140809123500 68 | 2876.0, 1217.,20140809120441 69 | 2614.0, 8506.,20140809144944 70 | 2614.0, 8467.,20140809162625 71 | 781.0, 8734.,20140809154721 72 | 255.0, 782.,20140809111330 73 | 188.0, 3148.,20140809103011 74 | 188.0, 1784.,20140809112204 75 | 188.0, 9604.,20140809190446 76 | 2685.0, 3148.,20140809174413 77 | 1839.0, 1784.,20140809170656 78 | 723.0, 1784.,20140809141100 79 | 1733.0, 1814.,20140809175804 80 | 1733.0, 1845.,20140809162151 81 | 558.0, 1668.,20140809115943 82 | 558.0, 1881.,20140809153019 83 | 558.0, 1881.,20140809190851 84 | 1168.0, 1814.,20140809192105 85 | 1996.0, 3323.,20140809165154 86 | 1905.0, 3209.,20140809192814 87 | 1152.0, 2184.,20140809182914 88 | 1044.0, 2162.,20140809124833 89 | 1044.0, 2162.,20140809132735 90 | 2537.0, 2162.,20140809193645 91 | 2537.0, 9143.,20140809141710 92 | 2537.0, 9023.,20140809131412 93 | 565.0, 8972.,20140809154259 94 | 281.0, 3073.,20140809153347 95 | 2580.0, 3028.,20140809104037 96 | 2580.0, 9467.,20140809185614 97 | 1993.0, 3171.,20140809173203 98 | 1993.0, 3171.,20140809101853 99 | 898.0, 3293.,20140809144031 100 | 898.0, 3293.,20140809193736 101 | 687.0, 3201.,20140809173501 102 | 2308.0, 3201.,20140809125958 103 | 2308.0, 3201.,20140809145303 104 | 618.0, 1888.,20140809174903 105 | 473.0, 2181.,20140809133734 106 | 473.0, 9652.,20140809141523 107 | 2412.0, 2181.,20140809103359 108 | 2412.0, 3864.,20140809152830 109 | 2555.0, 9652.,20140809142744 110 | 788.0, 1019.,20140809104248 111 | 176.0, 9596.,20140809121730 112 | 176.0, 3323.,20140809135713 113 | 176.0, 9596.,20140809141745 114 | 176.0, 9652.,20140809152329 115 | 1013.0, 1019.,20140809120909 116 | 2755.0, 9596.,20140809100209 117 | 2755.0, 9596.,20140809112654 118 | 2755.0, 9652.,20140809163651 119 | 110.0, 3293.,20140809173116 120 | 2087.0, 3209.,20140809190149 121 | 363.0, 3323.,20140809101516 122 | 2450.0, 537.,20140809130025 123 | 2450.0, 1910.,20140809115336 124 | 2195.0, 3209.,20140809172901 125 | 1806.0, 3209.,20140809191502 126 | 1806.0, 3209.,20140809184155 127 | 37.0, 3339.,20140809150309 128 | 755.0, 3745.,20140809124432 129 | 1465.0, 3323.,20140809185225 130 | 1465.0, 3323.,20140809154106 131 | 1465.0, 3323.,20140809143444 132 | 1465.0, 3323.,20140809115327 133 | 2429.0, 3745.,20140809191933 134 | 2434.0, 3323.,20140809122027 135 | 2489.0, 1942.,20140809191220 136 | 2003.0, 9652.,20140809182716 137 | 2003.0, 4916.,20140809134555 138 | 2847.0, 1910.,20140809101737 139 | 1068.0, 9165.,20140809132544 140 | 1210.0, 4848.,20140809120534 141 | 1210.0, 4916.,20140809183843 142 | 404.0, 4916.,20140809115203 143 | 404.0, 4916.,20140809140655 144 | 404.0, 10485.,20140809165555 145 | 404.0, 10485.,20140809174920 146 | 1118.0, 10485.,20140809160516 147 | 2591.0, 4916.,20140809122638 148 | 329.0, 4916.,20140809190117 149 | 2728.0, 3780.,20140809195853 150 | 2728.0, 3780.,20140809114747 151 | 1798.0, 4916.,20140809121357 152 | 1798.0, 4916.,20140809155933 153 | 725.0, 3864.,20140809110653 154 | 725.0, 7902.,20140809172018 155 | 1903.0, 9170.,20140809104551 156 | 641.0, 9165.,20140809172329 157 | 2260.0, 3864.,20140809170227 158 | 2071.0, 3864.,20140809165221 159 | 2071.0, 3864.,20140809123553 160 | 1534.0, 3836.,20140809133829 161 | 2699.0, 553.,20140809112920 162 | 2627.0, 3864.,20140809134511 163 | 2627.0, 3864.,20140809192352 164 | 2352.0, 3864.,20140809115508 165 | 854.0, 3836.,20140809175800 166 | 1175.0, 3864.,20140809102615 167 | 72.0, 664.,20140809113606 168 | 72.0, 3864.,20140809154243 169 | 72.0, 3864.,20140809173900 170 | 69.0, 3864.,20140809115410 171 | 152.0, 3864.,20140809160955 172 | 152.0, 3836.,20140809195045 173 | 450.0, 10382.,20140809154642 174 | 450.0, 4233.,20140809140512 175 | 450.0, 4306.,20140809104036 176 | 1473.0, 10406.,20140809153733 177 | 926.0, 4306.,20140809195136 178 | 926.0, 4306.,20140809160208 179 | 2901.0, 4306.,20140809193513 180 | 2901.0, 4393.,20140809115813 181 | 2507.0, 4337.,20140809102158 182 | 2507.0, 4337.,20140809151804 183 | 1752.0, 4340.,20140809141038 184 | 2520.0, 4340.,20140809161521 185 | 2163.0, 4340.,20140809110630 186 | 2163.0, 4401.,20140809135410 187 | 2608.0, 4404.,20140809191535 188 | 2530.0, 1198.,20140809114542 189 | 2530.0, 553.,20140809124104 190 | 1225.0, 4393.,20140809191523 191 | 1225.0, 4393.,20140809160918 192 | 1225.0, 4393.,20140809114827 193 | 427.0, 9291.,20140809165730 194 | 427.0, 4426.,20140809145758 195 | 1242.0, 4401.,20140809175508 196 | 127.0, 10406.,20140809173454 197 | 1464.0, 9170.,20140809195239 198 | 1464.0, 9057.,20140809170235 199 | 433.0, 3840.,20140809174853 200 | 680.0, 4916.,20140809172248 201 | 1498.0, 4401.,20140809180646 202 | 2899.0, 4916.,20140809153258 203 | 1511.0, 4916.,20140809175627 204 | 612.0, 4401.,20140809175412 205 | 1440.0, 4916.,20140809142550 206 | 2476.0, 4916.,20140809143107 207 | 1129.0, 3790.,20140809104252 208 | 1129.0, 4916.,20140809110508 209 | 2059.0, 4916.,20140809162313 210 | 849.0, 4916.,20140809131734 211 | 669.0, 4916.,20140809150432 212 | 597.0, 4916.,20140809125032 213 | 1127.0, 4916.,20140809134909 214 | 49.0, 9291.,20140809155111 215 | 49.0, 520.,20140809130543 216 | 49.0, 9596.,20140809172919 217 | 2025.0, 3864.,20140809190245 218 | 387.0, 4401.,20140809103748 219 | 2836.0, 3864.,20140809102432 220 | 1102.0, 520.,20140809144552 221 | 916.0, 4916.,20140809113429 222 | 1834.0, 9057.,20140809182545 223 | 1834.0, 9151.,20140809145427 224 | 1834.0, 4916.,20140809104349 225 | 1778.0, 4916.,20140809103129 226 | 1778.0, 3780.,20140809133210 227 | 1248.0, 10485.,20140809141549 228 | 1248.0, 10485.,20140809153500 229 | 567.0, 4916.,20140809120037 230 | 1948.0, 8005.,20140809153315 231 | 1948.0, 9081.,20140809110145 232 | 1948.0, 9273.,20140809105728 233 | 2264.0, 4916.,20140809111745 234 | 2264.0, 9151.,20140809180940 235 | 2264.0, 664.,20140809104240 236 | 1387.0, 4916.,20140809172017 237 | 1387.0, 9023.,20140809133756 238 | 1387.0, 9023.,20140809143235 239 | 1387.0, 537.,20140809154803 240 | 1387.0, 9241.,20140809165532 241 | 1387.0, 3864.,20140809183446 242 | 2411.0, 3836.,20140809122226 243 | 1501.0, 4916.,20140809153621 244 | 1501.0, 4916.,20140809165128 245 | 1501.0, 4916.,20140809123149 246 | 1501.0, 4916.,20140809105945 247 | 804.0, 3864.,20140809131512 248 | 1510.0, 7302.,20140809152655 249 | 1510.0, 7302.,20140809110918 250 | 496.0, 7302.,20140809172000 251 | 496.0, 7302.,20140809151153 252 | 496.0, 9121.,20140809191947 253 | 1311.0, 4916.,20140809100409 254 | 1054.0, 4916.,20140809190832 255 | 1739.0, 3780.,20140809150534 256 | 1739.0, 3780.,20140809135551 257 | 1820.0, 3780.,20140809180430 258 | 1808.0, 4306.,20140809191845 259 | 896.0, 4916.,20140809133833 260 | 14.0, 4306.,20140809144803 261 | 14.0, 4306.,20140809164939 262 | 14.0, 4306.,20140809174523 263 | 2062.0, 4916.,20140809160324 264 | 2062.0, 4916.,20140809145032 265 | 2062.0, 4916.,20140809154443 266 | 2062.0, 4916.,20140809192157 267 | 167.0, 4916.,20140809134353 268 | 167.0, 3790.,20140809122419 269 | 167.0, 3790.,20140809181745 270 | 1953.0, 4916.,20140809171337 271 | 1953.0, 4916.,20140809145034 272 | 1953.0, 4916.,20140809112853 273 | 1043.0, 3780.,20140809100349 274 | 1151.0, 3780.,20140809151154 275 | 2857.0, 4916.,20140809105425 276 | 117.0, 4916.,20140809193554 277 | 383.0, 4306.,20140809173943 278 | 383.0, 4306.,20140809153846 279 | 2038.0, 4916.,20140809152943 280 | 2038.0, 4306.,20140809165547 281 | 2564.0, 4916.,20140809140934 282 | 2865.0, 3780.,20140809163332 283 | 2222.0, 4916.,20140809183042 284 | 2222.0, 4916.,20140809175232 285 | 2706.0, 4916.,20140809114352 286 | 2706.0, 3780.,20140809180547 287 | 660.0, 4306.,20140809192735 288 | 660.0, 7302.,20140809143446 289 | 756.0, 3780.,20140809164602 290 | 756.0, 4916.,20140809151705 291 | 756.0, 4916.,20140809122745 292 | 2644.0, 3780.,20140809170755 293 | 2644.0, 4916.,20140809161812 294 | 2644.0, 4916.,20140809150915 295 | 2644.0, 4916.,20140809120231 296 | 1082.0, 1942.,20140809110119 297 | 2768.0, 4393.,20140809101408 298 | 2768.0, 4337.,20140809113412 299 | 2768.0, 4393.,20140809123518 300 | 930.0, 4238.,20140809175838 301 | 930.0, 4238.,20140809163709 302 | 930.0, 1101.,20140809184348 303 | 2139.0, 4393.,20140809111352 304 | 690.0, 4306.,20140809135032 305 | 690.0, 4306.,20140809160704 306 | 2465.0, 4306.,20140809103937 307 | 1913.0, 4393.,20140809103707 308 | 484.0, 3780.,20140809170512 309 | 2623.0, 4306.,20140809142339 310 | 1852.0, 4306.,20140809113134 311 | 1852.0, 4306.,20140809173122 312 | 1973.0, 4916.,20140809101014 313 | 2654.0, 3780.,20140809143729 314 | 2108.0, 3790.,20140809104324 315 | 2287.0, 4916.,20140809103153 316 | 2287.0, 4916.,20140809161723 317 | 2131.0, 4916.,20140809100447 318 | 2131.0, 4916.,20140809110314 319 | 2131.0, 4916.,20140809172626 320 | 2579.0, 4916.,20140809123506 321 | 1165.0, 10485.,20140809160257 322 | 1165.0, 10485.,20140809185314 323 | 1165.0, 4916.,20140809191150 324 | 1165.0, 4916.,20140809114437 325 | 1165.0, 10485.,20140809145311 326 | 1429.0, 4916.,20140809113941 327 | 1429.0, 4916.,20140809141007 328 | 1619.0, 4916.,20140809175737 329 | 2600.0, 4916.,20140809115543 330 | 2001.0, 9081.,20140809142155 331 | 2484.0, 4401.,20140809153423 332 | 2587.0, 4916.,20140809175801 333 | 915.0, 4916.,20140809125429 334 | 1554.0, 3780.,20140809172139 335 | 1554.0, 4916.,20140809160454 336 | 1554.0, 4916.,20140809130156 337 | 1277.0, 10485.,20140809162249 338 | 1277.0, 10485.,20140809134853 339 | 1277.0, 10485.,20140809145324 340 | 1977.0, 4916.,20140809101938 341 | 2094.0, 4916.,20140809180130 342 | 71.0, 7902.,20140809155454 343 | 204.0, 9290.,20140809150530 344 | 204.0, 9151.,20140809162759 345 | 1469.0, 7302.,20140809143507 346 | 2323.0, 7302.,20140809124305 347 | 806.0, 9241.,20140809134316 348 | 806.0, 553.,20140809181041 349 | 1199.0, 9077.,20140809131413 350 | 1638.0, 7302.,20140809165433 351 | 733.0, 4401.,20140809103139 352 | 1989.0, 7302.,20140809155732 353 | 1989.0, 7302.,20140809192901 354 | 734.0, 7360.,20140809124841 355 | 628.0, 7273.,20140809163157 356 | 628.0, 7273.,20140809194326 357 | 2878.0, 4393.,20140809114525 358 | 2878.0, 537.,20140809123236 359 | 2878.0, 7955.,20140809195111 360 | 695.0, 520.,20140809161417 361 | 2656.0, 537.,20140809143948 362 | 2656.0, 520.,20140809150657 363 | 1877.0, 9170.,20140809101030 364 | 1877.0, 7302.,20140809161301 365 | 850.0, 4306.,20140809112149 366 | 850.0, 7310.,20140809134910 367 | 656.0, 4848.,20140809123830 368 | 656.0, 7980.,20140809110300 369 | 1218.0, 3864.,20140809155052 370 | 1218.0, 4916.,20140809190443 371 | 1218.0, 9096.,20140809112253 372 | 1218.0, 3209.,20140809145213 373 | 1599.0, 7302.,20140809135707 374 | 1599.0, 7302.,20140809170920 375 | 2334.0, 7302.,20140809165845 376 | 438.0, 10494.,20140809115039 377 | 475.0, 3745.,20140809154836 378 | 475.0, 9652.,20140809125801 379 | 177.0, 9652.,20140809195158 380 | 1991.0, 9652.,20140809122117 381 | 402.0, 8944.,20140809121538 382 | 402.0, 8860.,20140809101831 383 | 1123.0, 293.,20140809182351 384 | 1123.0, 288.,20140809144427 385 | 239.0, 8860.,20140809150112 386 | 341.0, 8066.,20140809112357 387 | 341.0, 8066.,20140809132946 388 | 341.0, 8066.,20140809170523 389 | 341.0, 8872.,20140809165151 390 | 486.0, 8860.,20140809105512 391 | 486.0, 8872.,20140809155020 392 | 1037.0, 8066.,20140809145017 393 | 1037.0, 8860.,20140809103519 394 | 1316.0, 8066.,20140809112716 395 | 1185.0, 8066.,20140809134045 396 | 1734.0, 8066.,20140809160431 397 | 1495.0, 8066.,20140809113703 398 | 2130.0, 8066.,20140809155103 399 | 2130.0, 8860.,20140809191809 400 | 752.0, 1632.,20140809122032 401 | 705.0, 8697.,20140809161043 402 | 2367.0, 8860.,20140809100524 403 | 2367.0, 8860.,20140809114618 404 | 2367.0, 8860.,20140809154446 405 | 2340.0, 8860.,20140809121703 406 | 2340.0, 8972.,20140809182201 407 | 2340.0, 8860.,20140809195636 408 | 2340.0, 8872.,20140809141732 409 | 2340.0, 8872.,20140809103345 410 | 2513.0, 8860.,20140809110423 411 | 1390.0, 8860.,20140809181722 412 | 1451.0, 8860.,20140809181034 413 | 1698.0, 8860.,20140809182453 414 | 1698.0, 8860.,20140809192450 415 | 1835.0, 8860.,20140809121325 416 | 1835.0, 8860.,20140809131332 417 | 1835.0, 8860.,20140809155219 418 | 1835.0, 8860.,20140809164139 419 | 1835.0, 8860.,20140809185216 420 | 1835.0, 8860.,20140809195600 421 | 2492.0, 8860.,20140809151753 422 | 2492.0, 8972.,20140809130549 423 | 1338.0, 8860.,20140809185901 424 | 1006.0, 8860.,20140809102119 425 | 721.0, 8872.,20140809123647 426 | 1708.0, 8944.,20140809163825 427 | 1147.0, 8972.,20140809160018 428 | 1147.0, 8972.,20140809155847 429 | 384.0, 9235.,20140809124912 430 | 285.0, 537.,20140809114536 431 | 1524.0, 520.,20140809155659 432 | 1524.0, 520.,20140809183146 433 | 1524.0, 9273.,20140809163710 434 | 2351.0, 9006.,20140809184419 435 | 2351.0, 9303.,20140809121522 436 | 2351.0, 9303.,20140809150520 437 | 535.0, 9057.,20140809175923 438 | 1812.0, 9104.,20140809171236 439 | 2366.0, 9023.,20140809123307 440 | 439.0, 520.,20140809130520 441 | 1412.0, 10485.,20140809153854 442 | 1412.0, 10485.,20140809162423 443 | 1412.0, 9121.,20140809195816 444 | 2757.0, 9057.,20140809192836 445 | 459.0, 9151.,20140809190345 446 | 2824.0, 9023.,20140809110324 447 | 778.0, 658.,20140809181643 448 | 2552.0, 9165.,20140809141732 449 | 1162.0, 9006.,20140809173321 450 | 2217.0, 9151.,20140809180939 451 | 1799.0, 9089.,20140809130047 452 | 1799.0, 1888.,20140809144008 453 | 1869.0, 9200.,20140809182458 454 | 2675.0, 3836.,20140809110236 455 | 2675.0, 3836.,20140809155308 456 | 2604.0, 9652.,20140809125050 457 | 2316.0, 9291.,20140809145947 458 | 1538.0, 9291.,20140809132030 459 | 1538.0, 9290.,20140809155133 460 | 1538.0, 9290.,20140809140116 461 | 1538.0, 9170.,20140809115747 462 | 1475.0, 9183.,20140809120327 463 | 1019.0, 9652.,20140809175650 464 | 857.0, 9303.,20140809114358 465 | 857.0, 9006.,20140809155458 466 | 857.0, 9006.,20140809193609 467 | 857.0, 4401.,20140809170711 468 | 2551.0, 9652.,20140809190602 469 | 15.0, 9652.,20140809164309 470 | 6.0, 9652.,20140809105529 471 | 1968.0, 9089.,20140809144917 472 | 1332.0, 9096.,20140809142544 473 | 1332.0, 9096.,20140809184919 474 | 1332.0, 9165.,20140809104720 475 | 2636.0, 9119.,20140809175946 476 | 2636.0, 9164.,20140809123722 477 | 1929.0, 4401.,20140809150307 478 | 2248.0, 3836.,20140809145201 479 | 2653.0, 4439.,20140809151535 480 | 409.0, 4940.,20140809135511 481 | 409.0, 3836.,20140809154001 482 | 901.0, 664.,20140809185103 483 | 729.0, 3836.,20140809174042 484 | 883.0, 4306.,20140809183505 485 | 276.0, 7310.,20140809114709 486 | 1924.0, 658.,20140809100820 487 | 2201.0, 9303.,20140809100832 488 | 2201.0, 9006.,20140809160909 489 | 132.0, 9006.,20140809182014 490 | 986.0, 9151.,20140809163749 491 | 986.0, 9151.,20140809155909 492 | 986.0, 9164.,20140809102112 493 | 1896.0, 9151.,20140809154013 494 | 1279.0, 4306.,20140809124138 495 | 1279.0, 4306.,20140809134520 496 | 1279.0, 9143.,20140809182911 497 | 2145.0, 4306.,20140809113803 498 | 2145.0, 4306.,20140809105045 499 | 2145.0, 9143.,20140809160510 500 | 1790.0, 9006.,20140809132434 501 | 1335.0, 9006.,20140809140857 502 | 1335.0, 9006.,20140809151439 503 | 1581.0, 9303.,20140809135146 504 | 1581.0, 4306.,20140809141835 505 | 670.0, 10490.,20140809193037 506 | 670.0, 9082.,20140809164328 507 | 1944.0, 9143.,20140809171523 508 | 1944.0, 4848.,20140809165010 509 | 2800.0, 10485.,20140809115923 510 | 1097.0, 10485.,20140809184708 511 | 1097.0, 10485.,20140809161013 512 | 2010.0, 7302.,20140809135403 513 | 1589.0, 9170.,20140809181033 514 | 2873.0, 9006.,20140809120130 515 | 899.0, 10635.,20140809135123 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1806.0,416.0,20140809191502 126 | 1806.0,416.0,20140809184155 127 | 37.0,3497.0,20140809150309 128 | 755.0,3452.0,20140809124432 129 | 1465.0,3452.0,20140809185225 130 | 1465.0,3452.0,20140809154106 131 | 1465.0,3452.0,20140809143444 132 | 1465.0,3452.0,20140809115327 133 | 2429.0,3452.0,20140809191933 134 | 2434.0,3452.0,20140809122027 135 | 2489.0,3452.0,20140809191220 136 | 2003.0,7786.0,20140809182716 137 | 2003.0,9280.0,20140809134555 138 | 2847.0,3536.0,20140809101737 139 | 1068.0,9095.0,20140809132544 140 | 1210.0,9095.0,20140809120534 141 | 1210.0,3830.0,20140809183843 142 | 404.0,9163.0,20140809115203 143 | 404.0,533.0,20140809140655 144 | 404.0,533.0,20140809165555 145 | 404.0,9194.0,20140809174920 146 | 1118.0,3763.0,20140809160516 147 | 2591.0,3763.0,20140809122638 148 | 329.0,9139.0,20140809190117 149 | 2728.0,5034.0,20140809195853 150 | 2728.0,9235.0,20140809114747 151 | 1798.0,4916.0,20140809121357 152 | 1798.0,9194.0,20140809155933 153 | 725.0,3864.0,20140809110653 154 | 725.0,9095.0,20140809172018 155 | 1903.0,3885.0,20140809104551 156 | 641.0,3864.0,20140809172329 157 | 2260.0,3826.0,20140809170227 158 | 2071.0,3864.0,20140809165221 159 | 2071.0,3864.0,20140809123553 160 | 1534.0,3836.0,20140809133829 161 | 2699.0,3864.0,20140809112920 162 | 2627.0,3864.0,20140809134511 163 | 2627.0,3864.0,20140809192352 164 | 2352.0,3864.0,20140809115508 165 | 854.0,9095.0,20140809175800 166 | 1175.0,3864.0,20140809102615 167 | 72.0,3864.0,20140809113606 168 | 72.0,3864.0,20140809154243 169 | 72.0,3864.0,20140809173900 170 | 69.0,3864.0,20140809115410 171 | 152.0,3864.0,20140809160955 172 | 152.0,9095.0,20140809195045 173 | 450.0,10382.0,20140809154642 174 | 450.0,10382.0,20140809140512 175 | 450.0,10774.0,20140809104036 176 | 1473.0,4340.0,20140809153733 177 | 926.0,4306.0,20140809195136 178 | 926.0,4306.0,20140809160208 179 | 2901.0,4306.0,20140809193513 180 | 2901.0,4393.0,20140809115813 181 | 2507.0,4337.0,20140809102158 182 | 2507.0,4401.0,20140809151804 183 | 1752.0,4340.0,20140809141038 184 | 2520.0,4340.0,20140809161521 185 | 2163.0,4340.0,20140809110630 186 | 2163.0,4401.0,20140809135410 187 | 2608.0,4340.0,20140809191535 188 | 2530.0,4345.0,20140809114542 189 | 2530.0,4345.0,20140809124104 190 | 1225.0,4391.0,20140809191523 191 | 1225.0,4391.0,20140809160918 192 | 1225.0,4391.0,20140809114827 193 | 427.0,3028.0,20140809165730 194 | 427.0,4426.0,20140809145758 195 | 1242.0,4401.0,20140809175508 196 | 127.0,4340.0,20140809173454 197 | 1464.0,9095.0,20140809195239 198 | 1464.0,9057.0,20140809170235 199 | 433.0,3864.0,20140809174853 200 | 680.0,4486.0,20140809172248 201 | 1498.0,4617.0,20140809180646 202 | 2899.0,4631.0,20140809153258 203 | 1511.0,11019.0,20140809175627 204 | 612.0,11039.0,20140809175412 205 | 1440.0,4631.0,20140809142550 206 | 2476.0,4631.0,20140809143107 207 | 1129.0,4671.0,20140809104252 208 | 1129.0,4671.0,20140809110508 209 | 2059.0,4671.0,20140809162313 210 | 849.0,4535.0,20140809131734 211 | 669.0,4671.0,20140809150432 212 | 597.0,7247.0,20140809125032 213 | 1127.0,11315.0,20140809134909 214 | 49.0,520.0,20140809155111 215 | 49.0,520.0,20140809130543 216 | 49.0,9277.0,20140809172919 217 | 2025.0,3864.0,20140809190245 218 | 387.0,5034.0,20140809103748 219 | 2836.0,4851.0,20140809102432 220 | 1102.0,7955.0,20140809144552 221 | 916.0,4916.0,20140809113429 222 | 1834.0,9095.0,20140809182545 223 | 1834.0,9151.0,20140809145427 224 | 1834.0,9332.0,20140809104349 225 | 1778.0,4916.0,20140809103129 226 | 1778.0,3864.0,20140809133210 227 | 1248.0,4916.0,20140809141549 228 | 1248.0,3763.0,20140809153500 229 | 567.0,4916.0,20140809120037 230 | 1948.0,537.0,20140809153315 231 | 1948.0,9271.0,20140809110145 232 | 1948.0,9271.0,20140809105728 233 | 2264.0,9194.0,20140809111745 234 | 2264.0,9095.0,20140809180940 235 | 2264.0,9151.0,20140809104240 236 | 1387.0,4916.0,20140809172017 237 | 1387.0,9130.0,20140809133756 238 | 1387.0,9130.0,20140809143235 239 | 1387.0,9089.0,20140809154803 240 | 1387.0,9089.0,20140809165532 241 | 1387.0,7352.0,20140809183446 242 | 2411.0,9095.0,20140809122226 243 | 1501.0,9194.0,20140809153621 244 | 1501.0,9194.0,20140809165128 245 | 1501.0,9194.0,20140809123149 246 | 1501.0,9194.0,20140809105945 247 | 804.0,3864.0,20140809131512 248 | 1510.0,9006.0,20140809152655 249 | 1510.0,9194.0,20140809110918 250 | 496.0,9151.0,20140809172000 251 | 496.0,9151.0,20140809151153 252 | 496.0,9194.0,20140809191947 253 | 1311.0,10508.0,20140809100409 254 | 1054.0,26.0,20140809190832 255 | 1739.0,5099.0,20140809150534 256 | 1739.0,5099.0,20140809135551 257 | 1820.0,5099.0,20140809180430 258 | 1808.0,5257.0,20140809191845 259 | 896.0,5129.0,20140809133833 260 | 14.0,10610.0,20140809144803 261 | 14.0,10610.0,20140809164939 262 | 14.0,10610.0,20140809174523 263 | 2062.0,5120.0,20140809160324 264 | 2062.0,5120.0,20140809145032 265 | 2062.0,5129.0,20140809154443 266 | 2062.0,5175.0,20140809192157 267 | 167.0,5175.0,20140809134353 268 | 167.0,5175.0,20140809122419 269 | 167.0,5257.0,20140809181745 270 | 1953.0,5175.0,20140809171337 271 | 1953.0,5175.0,20140809145034 272 | 1953.0,5175.0,20140809112853 273 | 1043.0,5144.0,20140809100349 274 | 1151.0,10610.0,20140809151154 275 | 2857.0,5210.0,20140809105425 276 | 117.0,5236.0,20140809193554 277 | 383.0,10508.0,20140809173943 278 | 383.0,5144.0,20140809153846 279 | 2038.0,10679.0,20140809152943 280 | 2038.0,10576.0,20140809165547 281 | 2564.0,10679.0,20140809140934 282 | 2865.0,61.0,20140809163332 283 | 2222.0,10648.0,20140809183042 284 | 2222.0,10782.0,20140809175232 285 | 2706.0,5311.0,20140809114352 286 | 2706.0,5331.0,20140809180547 287 | 660.0,5331.0,20140809192735 288 | 660.0,5502.0,20140809143446 289 | 756.0,5331.0,20140809164602 290 | 756.0,5331.0,20140809151705 291 | 756.0,5331.0,20140809122745 292 | 2644.0,5517.0,20140809170755 293 | 2644.0,5517.0,20140809161812 294 | 2644.0,5517.0,20140809150915 295 | 2644.0,5517.0,20140809120231 296 | 1082.0,9872.0,20140809110119 297 | 2768.0,5739.0,20140809101408 298 | 2768.0,5736.0,20140809113412 299 | 2768.0,5736.0,20140809123518 300 | 930.0,5654.0,20140809175838 301 | 930.0,5654.0,20140809163709 302 | 930.0,5714.0,20140809184348 303 | 2139.0,5714.0,20140809111352 304 | 690.0,5736.0,20140809135032 305 | 690.0,5736.0,20140809160704 306 | 2465.0,5669.0,20140809103937 307 | 1913.0,1784.0,20140809103707 308 | 484.0,6157.0,20140809170512 309 | 2623.0,6210.0,20140809142339 310 | 1852.0,6472.0,20140809113134 311 | 1852.0,6472.0,20140809173122 312 | 1973.0,6270.0,20140809101014 313 | 2654.0,6270.0,20140809143729 314 | 2108.0,6270.0,20140809104324 315 | 2287.0,6542.0,20140809103153 316 | 2287.0,6542.0,20140809161723 317 | 2131.0,7095.0,20140809100447 318 | 2131.0,7155.0,20140809110314 319 | 2131.0,6542.0,20140809172626 320 | 2579.0,6542.0,20140809123506 321 | 1165.0,11902.0,20140809160257 322 | 1165.0,11902.0,20140809185314 323 | 1165.0,11902.0,20140809191150 324 | 1165.0,11902.0,20140809114437 325 | 1165.0,11902.0,20140809145311 326 | 1429.0,7155.0,20140809113941 327 | 1429.0,7231.0,20140809141007 328 | 1619.0,7155.0,20140809175737 329 | 2600.0,6701.0,20140809115543 330 | 2001.0,6738.0,20140809142155 331 | 2484.0,6738.0,20140809153423 332 | 2587.0,6638.0,20140809175801 333 | 915.0,7141.0,20140809125429 334 | 1554.0,7163.0,20140809172139 335 | 1554.0,7163.0,20140809160454 336 | 1554.0,7163.0,20140809130156 337 | 1277.0,7247.0,20140809162249 338 | 1277.0,7163.0,20140809134853 339 | 1277.0,11902.0,20140809145324 340 | 1977.0,7163.0,20140809101938 341 | 2094.0,6638.0,20140809180130 342 | 71.0,3864.0,20140809155454 343 | 204.0,9095.0,20140809150530 344 | 204.0,9151.0,20140809162759 345 | 1469.0,9095.0,20140809143507 346 | 2323.0,7318.0,20140809124305 347 | 806.0,3864.0,20140809134316 348 | 806.0,3864.0,20140809181041 349 | 1199.0,520.0,20140809131413 350 | 1638.0,9332.0,20140809165433 351 | 733.0,7356.0,20140809103139 352 | 1989.0,7356.0,20140809155732 353 | 1989.0,9303.0,20140809192901 354 | 734.0,7360.0,20140809124841 355 | 628.0,5736.0,20140809163157 356 | 628.0,5736.0,20140809194326 357 | 2878.0,5736.0,20140809114525 358 | 2878.0,10831.0,20140809123236 359 | 2878.0,10831.0,20140809195111 360 | 695.0,7483.0,20140809161417 361 | 2656.0,7897.0,20140809143948 362 | 2656.0,3830.0,20140809150657 363 | 1877.0,9095.0,20140809101030 364 | 1877.0,4862.0,20140809161301 365 | 850.0,4340.0,20140809112149 366 | 850.0,7273.0,20140809134910 367 | 656.0,3771.0,20140809123830 368 | 656.0,9095.0,20140809110300 369 | 1218.0,9259.0,20140809155052 370 | 1218.0,4916.0,20140809190443 371 | 1218.0,9095.0,20140809112253 372 | 1218.0,9095.0,20140809145213 373 | 1599.0,9194.0,20140809135707 374 | 1599.0,9280.0,20140809170920 375 | 2334.0,9271.0,20140809165845 376 | 438.0,7360.0,20140809115039 377 | 475.0,7996.0,20140809154836 378 | 475.0,9095.0,20140809125801 379 | 177.0,7996.0,20140809195158 380 | 1991.0,7996.0,20140809122117 381 | 402.0,1019.0,20140809121538 382 | 402.0,8860.0,20140809101831 383 | 1123.0,288.0,20140809182351 384 | 1123.0,288.0,20140809144427 385 | 239.0,8860.0,20140809150112 386 | 341.0,8066.0,20140809112357 387 | 341.0,8066.0,20140809132946 388 | 341.0,8066.0,20140809170523 389 | 341.0,8872.0,20140809165151 390 | 486.0,8860.0,20140809105512 391 | 486.0,8860.0,20140809155020 392 | 1037.0,8066.0,20140809145017 393 | 1037.0,8860.0,20140809103519 394 | 1316.0,8066.0,20140809112716 395 | 1185.0,8066.0,20140809134045 396 | 1734.0,8066.0,20140809160431 397 | 1495.0,8066.0,20140809113703 398 | 2130.0,8066.0,20140809155103 399 | 2130.0,8860.0,20140809191809 400 | 752.0,1632.0,20140809122032 401 | 705.0,8734.0,20140809161043 402 | 2367.0,8860.0,20140809100524 403 | 2367.0,8860.0,20140809114618 404 | 2367.0,8860.0,20140809154446 405 | 2340.0,8860.0,20140809121703 406 | 2340.0,8860.0,20140809182201 407 | 2340.0,8860.0,20140809195636 408 | 2340.0,8972.0,20140809141732 409 | 2340.0,8860.0,20140809103345 410 | 2513.0,8860.0,20140809110423 411 | 1390.0,8860.0,20140809181722 412 | 1451.0,8860.0,20140809181034 413 | 1698.0,8860.0,20140809182453 414 | 1698.0,8860.0,20140809192450 415 | 1835.0,8860.0,20140809121325 416 | 1835.0,8860.0,20140809131332 417 | 1835.0,8860.0,20140809155219 418 | 1835.0,8860.0,20140809164139 419 | 1835.0,8860.0,20140809185216 420 | 1835.0,8860.0,20140809195600 421 | 2492.0,8860.0,20140809151753 422 | 2492.0,8860.0,20140809130549 423 | 1338.0,8860.0,20140809185901 424 | 1006.0,8860.0,20140809102119 425 | 721.0,8872.0,20140809123647 426 | 1708.0,8944.0,20140809163825 427 | 1147.0,8972.0,20140809160018 428 | 1147.0,8972.0,20140809155847 429 | 384.0,9143.0,20140809124912 430 | 285.0,9312.0,20140809114536 431 | 1524.0,9277.0,20140809155659 432 | 1524.0,9277.0,20140809183146 433 | 1524.0,9277.0,20140809163710 434 | 2351.0,9273.0,20140809184419 435 | 2351.0,9271.0,20140809121522 436 | 2351.0,9271.0,20140809150520 437 | 535.0,9057.0,20140809175923 438 | 1812.0,9095.0,20140809171236 439 | 2366.0,7996.0,20140809123307 440 | 439.0,9277.0,20140809130520 441 | 1412.0,9049.0,20140809153854 442 | 1412.0,9049.0,20140809162423 443 | 1412.0,9049.0,20140809195816 444 | 2757.0,9151.0,20140809192836 445 | 459.0,9151.0,20140809190345 446 | 2824.0,9095.0,20140809110324 447 | 778.0,9095.0,20140809181643 448 | 2552.0,9165.0,20140809141732 449 | 1162.0,9006.0,20140809173321 450 | 2217.0,9095.0,20140809180939 451 | 1799.0,9089.0,20140809130047 452 | 1799.0,1888.0,20140809144008 453 | 1869.0,9095.0,20140809182458 454 | 2675.0,9095.0,20140809110236 455 | 2675.0,9095.0,20140809155308 456 | 2604.0,7996.0,20140809125050 457 | 2316.0,9102.0,20140809145947 458 | 1538.0,9102.0,20140809132030 459 | 1538.0,9102.0,20140809155133 460 | 1538.0,9290.0,20140809140116 461 | 1538.0,9290.0,20140809115747 462 | 1475.0,9095.0,20140809120327 463 | 1019.0,7996.0,20140809175650 464 | 857.0,9110.0,20140809114358 465 | 857.0,9110.0,20140809155458 466 | 857.0,9110.0,20140809193609 467 | 857.0,4345.0,20140809170711 468 | 2551.0,9095.0,20140809190602 469 | 15.0,9095.0,20140809164309 470 | 6.0,9095.0,20140809105529 471 | 1968.0,9095.0,20140809144917 472 | 1332.0,9095.0,20140809142544 473 | 1332.0,9095.0,20140809184919 474 | 1332.0,9165.0,20140809104720 475 | 2636.0,3864.0,20140809175946 476 | 2636.0,9151.0,20140809123722 477 | 1929.0,549.0,20140809150307 478 | 2248.0,9095.0,20140809145201 479 | 2653.0,9130.0,20140809151535 480 | 409.0,9095.0,20140809135511 481 | 409.0,9095.0,20140809154001 482 | 901.0,9095.0,20140809185103 483 | 729.0,9095.0,20140809174042 484 | 883.0,5025.0,20140809183505 485 | 276.0,9151.0,20140809114709 486 | 1924.0,9095.0,20140809100820 487 | 2201.0,9110.0,20140809100832 488 | 2201.0,9124.0,20140809160909 489 | 132.0,10495.0,20140809182014 490 | 986.0,9151.0,20140809163749 491 | 986.0,9151.0,20140809155909 492 | 986.0,3864.0,20140809102112 493 | 1896.0,9151.0,20140809154013 494 | 1279.0,9175.0,20140809124138 495 | 1279.0,9175.0,20140809134520 496 | 1279.0,9175.0,20140809182911 497 | 2145.0,9175.0,20140809113803 498 | 2145.0,9175.0,20140809105045 499 | 2145.0,9332.0,20140809160510 500 | 1790.0,9332.0,20140809132434 501 | 1335.0,9187.0,20140809140857 502 | 1335.0,9187.0,20140809151439 503 | 1581.0,9187.0,20140809135146 504 | 1581.0,9187.0,20140809141835 505 | 670.0,9095.0,20140809193037 506 | 670.0,9095.0,20140809164328 507 | 1944.0,9194.0,20140809171523 508 | 1944.0,9194.0,20140809165010 509 | 2800.0,9072.0,20140809115923 510 | 1097.0,9303.0,20140809184708 511 | 1097.0,9194.0,20140809161013 512 | 2010.0,9006.0,20140809135403 513 | 1589.0,9095.0,20140809181033 514 | 2873.0,9187.0,20140809120130 515 | 899.0,9095.0,20140809135123 516 | 666.0,7352.0,20140809123359 517 | 809.0,26.0,20140809182733 518 | 379.0,9151.0,20140809121527 519 | 1424.0,9151.0,20140809153057 520 | 715.0,9187.0,20140809160415 521 | 745.0,3864.0,20140809192627 522 | 1789.0,9652.0,20140809181450 523 | 289.0,9289.0,20140809165450 524 | 289.0,9289.0,20140809100750 525 | 2534.0,3864.0,20140809124514 526 | 422.0,9318.0,20140809121502 527 | 422.0,9271.0,20140809111301 528 | 422.0,9318.0,20140809152312 529 | 1159.0,4401.0,20140809155217 530 | 1159.0,9095.0,20140809171357 531 | 1017.0,26.0,20140809174847 532 | 137.0,9515.0,20140809164312 533 | 1823.0,3028.0,20140809114622 534 | 2262.0,9553.0,20140809102357 535 | 583.0,9647.0,20140809120056 536 | 583.0,9647.0,20140809144516 537 | 583.0,9647.0,20140809172826 538 | 2107.0,9095.0,20140809123030 539 | 2490.0,9858.0,20140809120320 540 | 2490.0,3452.0,20140809103723 541 | 2617.0,3452.0,20140809103909 542 | 1959.0,3452.0,20140809160646 543 | 2831.0,7786.0,20140809132908 544 | 538.0,10382.0,20140809155453 545 | 538.0,10382.0,20140809122150 546 | 538.0,10382.0,20140809104105 547 | 2210.0,10382.0,20140809154218 548 | 1223.0,4306.0,20140809102347 549 | 1027.0,4306.0,20140809183036 550 | 2247.0,9095.0,20140809114613 551 | 1602.0,9095.0,20140809113714 552 | 2882.0,10495.0,20140809100615 553 | 419.0,9332.0,20140809192909 554 | 1224.0,9095.0,20140809104542 555 | 2035.0,10554.0,20140809181934 556 | 2035.0,10554.0,20140809144930 557 | 1900.0,10576.0,20140809132410 558 | 716.0,9095.0,20140809144351 559 | 2829.0,10576.0,20140809112944 560 | 2829.0,10576.0,20140809174053 561 | 1340.0,9270.0,20140809123730 562 | 1340.0,18.0,20140809111459 563 | 2097.0,10508.0,20140809131356 564 | 1033.0,10610.0,20140809120004 565 | 1095.0,10610.0,20140809125029 566 | 1095.0,10610.0,20140809141205 567 | 1095.0,10610.0,20140809154406 568 | 1095.0,10610.0,20140809161246 569 | 1095.0,10610.0,20140809180810 570 | 2861.0,10610.0,20140809170659 571 | 2582.0,10665.0,20140809132832 572 | 2454.0,10831.0,20140809100149 573 | 992.0,4631.0,20140809143633 574 | 2572.0,11273.0,20140809103104 575 | 166.0,6638.0,20140809174322 576 | 185.0,7163.0,20140809125355 577 | 1980.0,9124.0,20140809182612 578 | 2735.0,4617.0,20140809193608 579 | 2455.0,4488.0,20140809152335 580 | 2129.0,5248.0,20140809142143 581 | 1760.0,4753.0,20140809135536 582 | 1122.0,11327.0,20140809121257 583 | 945.0,11327.0,20140809121849 584 | 945.0,11327.0,20140809110554 585 | 703.0,11315.0,20140809125352 586 | 703.0,11315.0,20140809134750 587 | 703.0,11315.0,20140809192543 588 | 562.0,11327.0,20140809184715 589 | 593.0,11512.0,20140809102751 590 | 1362.0,11512.0,20140809141144 591 | 210.0,11574.0,20140809161640 592 | 2146.0,6210.0,20140809195645 593 | 589.0,11559.0,20140809145545 594 | 952.0,11574.0,20140809165201 595 | 2738.0,11640.0,20140809193629 596 | 2738.0,11640.0,20140809183321 597 | 2169.0,11640.0,20140809152852 598 | 2271.0,6270.0,20140809102629 599 | 2086.0,6472.0,20140809145327 600 | 2086.0,11574.0,20140809135650 601 | 2013.0,6472.0,20140809134635 602 | 2890.0,11825.0,20140809101350 603 | 754.0,11931.0,20140809102545 604 | 1395.0,11931.0,20140809172115 605 | 765.0,11902.0,20140809155743 606 | 765.0,6638.0,20140809103930 607 | 2794.0,6662.0,20140809163949 608 | 313.0,11948.0,20140809154830 609 | 313.0,11948.0,20140809161229 610 | -------------------------------------------------------------------------------- /src/getmf_input.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | TRAIN_PATH='../new/TRAIN_NEW.csv' 4 | 5 | import pandas as pd 6 | 7 | 8 | 9 | if __name__=='__main__': 10 | #df_tr = pd.read_csv('../new/TRAIN_NEW.csv',header=0) 11 | #df_te = pd.read_csv('../new/TEST_NEW.csv',header=0) 12 | 13 | #n = 0 14 | #dic_loc_key = {} 15 | #for i in range(len(df_tr)): 16 | # lon = str(df_tr['LON'][i]) 17 | # lat = str(df_tr['LAT'][i]) 18 | # key = lon+','+lat 19 | # if key not in dic_loc_key.keys(): 20 | # dic_loc_key[key] = n 21 | # n = n+1 22 | # 23 | #for i in range(len(df_te)): 24 | # lon = str(df_te['LON'][i]) 25 | # lat = str(df_te['LAT'][i]) 26 | # key = lon+','+lat 27 | # if key not in dic_loc_key.keys(): 28 | # dic_loc_key[key] = n 29 | # n = n+1 30 | 31 | #f_key = open('../new/loction_key.txt_all','w') 32 | #f_key.write('LON,LAT,LOC_IDX\n') 33 | #for key ,locIdx in dic_loc_key.items(): 34 | # f_key.write(key+','+str(locIdx)+'\n') 35 | #f_key.close() 36 | 37 | #f_out = open('../new/hyk_mf_input.txt_all','w') 38 | #dic_locIdx_time = {} 39 | #for i in range(len(df_tr)): 40 | # uid = str(int(df_tr['USERID'][i])) 41 | # lon = str(df_tr['LON'][i]) 42 | # lat = str(df_tr['LAT'][i]) 43 | # loc_idx = dic_loc_key[lon+','+lat] 44 | # key = uid+','+str(loc_idx) 45 | # if key not in dic_locIdx_time.keys(): 46 | # dic_locIdx_time[key] = 1 47 | # else: 48 | # dic_locIdx_time[key] +=1 49 | 50 | #for i in range(len(df_te)): 51 | # uid = str(int(df_te['USERID'][i])) 52 | # lon = str(df_te['LON'][i]) 53 | # lat = str(df_te['LAT'][i]) 54 | # loc_idx = dic_loc_key[lon+','+lat] 55 | # key = uid+','+str(loc_idx) 56 | # if key not in dic_locIdx_time.keys(): 57 | # dic_locIdx_time[key] = 1 58 | # else: 59 | # dic_locIdx_time[key] +=1 60 | 61 | #for key,times in dic_locIdx_time.items(): 62 | # f_out.write(key+','+str(times)+'\n') 63 | 64 | 65 | ###################################################### 66 | #### LDA input ###################################### 67 | #f_in = open('../new/hyk_mf_input.txt_all','r') 68 | #dic = {} 69 | #for line in f_in: 70 | # data = line.strip().split(',') 71 | # uid = data[0] 72 | # if uid not in dic.keys(): 73 | # loc_list = [] 74 | # loc_list.append(data[1]) 75 | # loc_list.append(data[2]) 76 | # dic[uid] = loc_list 77 | # else: 78 | # loc_list = dic[uid] 79 | # loc_list.append(data[1]) 80 | # loc_list.append(data[2]) 81 | # dic[uid] = loc_list 82 | #f_out = open('../new/lda_input.txt_all','w') 83 | #for key,loc_list in dic.items(): 84 | # string = str(key)+":" 85 | # for v in loc_list: 86 | # string +=v+' ' 87 | # string = string[:-1] 88 | # f_out.write(string+'\n') 89 | 90 | 91 | 92 | ############ mf uid-shopclass ################## 93 | df_tr = pd.read_csv('../new/TRAIN_NEW.csv',header=0) 94 | dic = {} 95 | for i in range(len(df_tr)): 96 | if df_tr['SHOPID'][i] ==0 : 97 | continue 98 | uid = int(df_tr['USERID'][i]) 99 | shopclass = int(df_tr['SHOP_CLASS'][i]) 100 | key = str(uid)+' '+str(shopclass) 101 | if key not in dic.keys(): 102 | dic[key] = 1 103 | else: 104 | dic[key] += 1 105 | 106 | f_out = open('../new/mf_shoptype.txt','w') 107 | for k,v in dic.items(): 108 | f_out.write(k+' '+str(v)+'\n') 109 | 110 | 111 | #if __name__=='__main__': 112 | # df_tr = pd.read_csv(TRAIN_PATH,header=0) 113 | # 114 | # dic = {} 115 | # for i in range(len(df_tr)): 116 | # uid = int(df_tr['USERID'][i]) 117 | # shopid = int(df_tr['SHOPID'][i]) 118 | # if shopid == 0: 119 | # continue 120 | # else: 121 | # key = str(uid)+" "+str(shopid) 122 | # if key not in dic.keys(): 123 | # dic[key] = 1 124 | # else: 125 | # dic[key] += 1 126 | # 127 | # f_out = open('mf_input.txt','w') 128 | # for key,v in dic.items(): 129 | # f_out.write(key+" "+str(v)+'\n') 130 | # f_out.close() 131 | -------------------------------------------------------------------------------- /src/location.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | import os 3 | from time import time 4 | import numpy as np 5 | import pandas as pd 6 | 7 | import cPickle as pickle 8 | from sklearn.cluster import KMeans,MeanShift 9 | 10 | 11 | from sklearn.externals import joblib 12 | from sklearn.ensemble import RandomForestClassifier 13 | from sklearn.neighbors import NearestNeighbors 14 | from sklearn.cross_validation import KFold 15 | from sklearn.cross_validation import cross_val_score 16 | 17 | 18 | from xgboost import XGBClassifier 19 | 20 | 21 | from utils import load_data,save_results 22 | from config import TRAIN,TEST,SHOP_PROFILE 23 | from config import Features 24 | 25 | 26 | 27 | tr_x,tr_y = load_data(TRAIN,True) 28 | te_x = load_data(TEST,False) 29 | 30 | df_tr = pd.read_csv(TRAIN,header=0) 31 | df_te = pd.read_csv(TEST,header=0) 32 | 33 | 34 | 35 | def cluster(tr_lonlat_list,num_clusters,thiskey): 36 | 37 | dict_fileName = r'pkl/dict_'+thiskey+".pkl" 38 | tr_lonlat_list = np.array(tr_lonlat_list) 39 | kmeans = KMeans(n_clusters=num_clusters,n_jobs=-1).fit(tr_lonlat_list) 40 | #mf = MeanShift(bandwidth=0.001,bin_seeding=True,min_bin_freq=5).fit(tr_lonlat_list) 41 | 42 | lonlat_cluster_dict = {} 43 | for i in range(tr_lonlat_list.shape[0]): 44 | key = str(tr_lonlat_list[i][0])+":"+str(tr_lonlat_list[i][1]) 45 | lonlat_cluster_dict[key] = kmeans.labels_[i] 46 | f_w = open(dict_fileName,'w') 47 | pickle.dump(lonlat_cluster_dict,f_w) 48 | return lonlat_cluster_dict 49 | 50 | 51 | def run_cluster_model(thiskey,this_lon,this_lat,sample_te_x,num_clusters): 52 | 53 | dict_fileName = r'pkl/dict_'+thiskey+".pkl" 54 | this_tr_x_fileName = r'pkl/x_'+thiskey+".pkl" 55 | this_tr_y_fileName = r'pkl/y_'+thiskey+'.pkl' 56 | this_tr_lonlat_fileName = r'pkl/tr_lonlat_list_'+thiskey+".pkl" 57 | 58 | lonlat_cluster_dict = {} 59 | this_tr_x = [] 60 | this_tr_y = [] 61 | tr_lonlat_list = [] 62 | 63 | if os.path.exists(dict_fileName): 64 | f_dict = open(dict_fileName,'r') 65 | f_x = open(this_tr_x_fileName,'r') 66 | f_y = open(this_tr_y_fileName,'r') 67 | f_tr_lonlat_list = open(this_tr_lonlat_fileName,'r') 68 | 69 | lonlat_cluster_dict = pickle.load(f_dict) 70 | this_tr_x = pickle.load(f_x) 71 | this_tr_y = pickle.load(f_y) 72 | tr_lonlat_list = pickle.load(f_tr_lonlat_list) 73 | else: 74 | for i in range(tr_x.shape[0]): 75 | if df_tr['SHOPID'][i] == 0: 76 | continue 77 | income = str(df_tr['INCOME'][i]) 78 | enter = str(df_tr['ENTERTAINMENT'][i]) 79 | baby = str(df_tr['BABY'][i]) 80 | shopping = str(df_tr['SHOPPING'][i]) 81 | key = income+" "+enter+" "+baby+" "+shopping 82 | if key == thiskey: 83 | lon = df_tr['LON'][i] 84 | lat = df_tr['LAT'][i] 85 | tr_lonlat_list.append([lon,lat]) 86 | this_tr_x.append(tr_x[i]) 87 | this_tr_y.append(tr_y[i]) 88 | 89 | #### add testset lonlat ######### 90 | 91 | for i in range(te_x.shape[0]): 92 | income = str(df_te['INCOME'][i]) 93 | enter = str(df_te['ENTERTAINMENT'][i]) 94 | baby = str(df_te['BABY'][i]) 95 | shopping = str(df_te['SHOPPING'][i]) 96 | key = income+" "+enter+" "+baby+" "+shopping 97 | if key == thiskey: 98 | lon = df_te['LON'][i] 99 | lat = df_te['LAT'][i] 100 | tr_lonlat_list.append([lon,lat]) 101 | 102 | lonlat_cluster_dict = cluster(tr_lonlat_list,num_clusters,thiskey) 103 | f_x = open(this_tr_x_fileName,'w') 104 | f_y = open(this_tr_y_fileName,'w') 105 | f_lonlat_list = open(this_tr_lonlat_fileName,'w') 106 | pickle.dump(this_tr_x,f_x) 107 | pickle.dump(this_tr_y,f_y) 108 | pickle.dump(tr_lonlat_list,f_lonlat_list) 109 | 110 | 111 | 112 | 113 | 114 | given_cluster_label = lonlat_cluster_dict[str(this_lon)+":"+str(this_lat)] 115 | 116 | sub_this_tr_x = [] 117 | sub_this_tr_y = [] 118 | for i in range(len(this_tr_x)): 119 | lon = tr_lonlat_list[i][0] 120 | lat = tr_lonlat_list[i][1] 121 | key = str(lon)+":"+str(lat) 122 | cluster_label = lonlat_cluster_dict[key] 123 | if given_cluster_label == cluster_label: 124 | sub_this_tr_x.append(this_tr_x[i]) 125 | sub_this_tr_y.append(this_tr_y[i]) 126 | 127 | sub_this_tr_x = np.array(sub_this_tr_x) 128 | sub_this_tr_y = np.array(sub_this_tr_y) 129 | 130 | rf = RandomForestClassifier( 131 | n_estimators = 165, 132 | max_depth = 12, 133 | min_samples_split =2, 134 | bootstrap =True, 135 | warm_start = True, 136 | max_features = 'sqrt', 137 | criterion='entropy', 138 | class_weight = 'balanced', 139 | n_jobs = -1 140 | ).fit(sub_this_tr_x,sub_this_tr_y) 141 | # xgb = XGBClassifier( 142 | # max_depth = 12, 143 | # learning_rate = 0.05, 144 | # n_estimators = 160, 145 | # silent = False, 146 | # objective = 'multi:softmax', 147 | # nthread = -1, 148 | # gamma = 0, 149 | # min_child_weight = 1, 150 | # max_delta_step = 0.7, 151 | # subsample = 1, 152 | # colsample_bytree=1, 153 | # reg_lambda=1, 154 | # base_score=0.1, 155 | # scale_pos_weight=1, 156 | # seed = 1227 157 | # ).fit(sub_this_tr_x,sub_this_tr_y) 158 | this_predict_shopid = rf.predict(sample_te_x) 159 | return this_predict_shopid[0] 160 | 161 | 162 | 163 | def run_simple_model(thiskey,sample_te_x): 164 | rf_fileName = 'pkl/RF_sample_'+thiskey+'.pkl' 165 | if os.path.exists(rf_fileName): 166 | rf = joblib.load(rf_fileName) 167 | this_predict_shopid = rf.predict(sample_te_x) 168 | return this_predict_shopid[0] 169 | else: 170 | sub_this_tr_x = [] 171 | sub_this_tr_y = [] 172 | 173 | for i in range(tr_x.shape[0]): 174 | if df_tr['SHOPID'][i] == 0: 175 | continue 176 | income = str(df_tr['INCOME'][i]) 177 | enter = str(df_tr['ENTERTAINMENT'][i]) 178 | baby = str(df_tr['BABY'][i]) 179 | shopping = str(df_tr['SHOPPING'][i]) 180 | key = income+" "+enter+" "+baby+" "+shopping 181 | if key == thiskey: 182 | sub_this_tr_x.append(tr_x[i]) 183 | sub_this_tr_y.append(tr_y[i]) 184 | 185 | sub_this_tr_x = np.array(sub_this_tr_x) 186 | sub_this_tr_y = np.array(sub_this_tr_y) 187 | rf = RandomForestClassifier( 188 | n_estimators = 12, 189 | min_samples_split =2, 190 | bootstrap =True, 191 | warm_start = True, 192 | max_features = 'sqrt', 193 | criterion='entropy', 194 | class_weight = 'balanced', 195 | n_jobs = -1 196 | ).fit(sub_this_tr_x,sub_this_tr_y) 197 | joblib.dump(rf,rf_fileName) 198 | this_predict_shopid = rf.predict(sample_te_x) 199 | return this_predict_shopid[0] 200 | return 999999 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | #if __name__=='__main__': 209 | # start = time() 210 | # run("1 2 1 5") 211 | # end = time() 212 | # print('Time:\t'+str((end-start)/3600)+' Hours') 213 | -------------------------------------------------------------------------------- /src/location.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuguanggen/2016CCF-unicom/911fc0afa6447bb6ee0140cdba004f4ce7b43328/src/location.pyc -------------------------------------------------------------------------------- /src/log/output.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuguanggen/2016CCF-unicom/911fc0afa6447bb6ee0140cdba004f4ce7b43328/src/log/output.txt -------------------------------------------------------------------------------- /src/log/svd.log: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuguanggen/2016CCF-unicom/911fc0afa6447bb6ee0140cdba004f4ce7b43328/src/log/svd.log -------------------------------------------------------------------------------- /src/lr.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | from time import time 3 | from sklearn.cross_validation import cross_val_score 4 | from sklearn.linear_model import LogisticRegression 5 | import numpy as np 6 | 7 | 8 | def run(): 9 | tr_data = np.loadtxt('../new/TRAIN_LRFORMAT.txt') 10 | te_data = np.loadtxt('../new/TEST_LRFORMAT.txt') 11 | 12 | tr_x = tr_data[:,1:] 13 | tr_y = tr_data[:,0] 14 | te_x = te_data[:,1:] 15 | 16 | lr = LogisticRegression( 17 | solver='liblinear', 18 | multi_class='ovr', 19 | class_weight='balanced', 20 | penalty='l2', 21 | n_jobs=-1) 22 | #te_pred = lr.predict_proba(te_x) 23 | cv = 10 24 | scores = cross_val_score(lr,tr_x,tr_y,cv=cv,scoring='accuracy') 25 | print(str(scores)) 26 | #np.savetxt('result/te_lr.txt',te_pred) 27 | 28 | 29 | if __name__=='__main__': 30 | start = time() 31 | run() 32 | end = time() 33 | print('time :\t'+str((end-start)/3600)+' hours') 34 | -------------------------------------------------------------------------------- /src/rf.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | from time import time 4 | import numpy as np 5 | import pandas as pd 6 | 7 | from sklearn.externals import joblib 8 | from sklearn.ensemble import RandomForestClassifier 9 | from sklearn.cross_validation import KFold 10 | from sklearn.cross_validation import cross_val_score 11 | 12 | from utils import load_data,save_results 13 | from config import TRAIN,TEST 14 | from config import Features 15 | 16 | 17 | Model_Name = 'fusai_rf_20161202_6' 18 | result_csv_path = 'result/'+Model_Name+'.csv' 19 | 20 | 21 | 22 | 23 | def run(): 24 | tr_x ,tr_y = load_data(TRAIN,True) 25 | te_x = load_data(TEST,False) 26 | rf = RandomForestClassifier( 27 | n_estimators = 500, 28 | max_depth = 11, 29 | min_samples_split =2, 30 | bootstrap =True, 31 | warm_start = True, 32 | max_features = 'sqrt', 33 | criterion='entropy', 34 | class_weight = 'balanced', 35 | n_jobs = -1 36 | ) 37 | #rf.fit(tr_x,tr_y) 38 | ##feature_importances = rf.feature_importances_ 39 | ##dic_feature_importances = dict(zip(Features,feature_importances)) 40 | ##dic = sorted(dic_feature_importances.iteritems(),key=lambda d:d[1],reverse=True) 41 | ##print('===========================\n') 42 | ##print('feature_importances:') 43 | ##for i in range(len(dic)): 44 | ## print(dic[i][0]+":\t"+str(dic[i][1])) 45 | #te_pred = rf.predict(te_x) 46 | #save_results(result_csv_path,te_pred) 47 | 48 | #sum_acc = 0 49 | #cv = 10 50 | #kf = KFold(tr_x.shape[0],n_folds = cv,shuffle=True) 51 | #for train,val in kf: 52 | # x_tr,x_val,y_tr,y_val = tr_x[train],tr_x[val],tr_y[train],tr_y[val] 53 | # rf.fit(x_tr,y_tr) 54 | # pred_val = rf.predict(x_val) 55 | # true_count = 0 56 | # for i in range(len(y_val)): 57 | # if y_val[i] == pred_val[i]: 58 | # true_count += 1 59 | # acc = true_count*1.0/len(pred_val) 60 | # sum_acc += acc 61 | # print('acc :'+ str(acc)) 62 | #print('avg acc:'+str(sum_acc/cv)) 63 | cv = 10 64 | scores = cross_val_score(rf,tr_x,tr_y,cv=cv,scoring='f1_weighted') 65 | avg_score = sum(scores)/cv 66 | print(str(scores)) 67 | print('scores:\t'+str(avg_score)) 68 | #while True: 69 | # #rf.fit(tr_x,tr_y) 70 | # scores = cross_val_score(rf,tr_x,tr_y,cv=cv,scoring='f1_weighted') 71 | # avg_score = sum(scores)/cv 72 | # print(str(scores)) 73 | # print('scores:\t'+str(avg_score)) 74 | # if avg_score > 0.6: 75 | # te_pred = rf.predict(te_x) 76 | # save_results(result_csv_path,te_pred) 77 | # break 78 | 79 | #print(str(scores)) 80 | #print(str(sum(scores)/cv)) 81 | ######################################################################################## 82 | 83 | 84 | if __name__=='__main__': 85 | start = time() 86 | run() 87 | end = time() 88 | print('Time:\t'+str((end-start)/3600)+' Hours') 89 | -------------------------------------------------------------------------------- /src/rf_date.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | import sys 3 | import os 4 | from time import time 5 | import numpy as np 6 | import pandas as pd 7 | 8 | 9 | from sklearn.externals import joblib 10 | from sklearn.ensemble import RandomForestClassifier 11 | from sklearn.cross_validation import KFold 12 | from sklearn.cross_validation import cross_val_score 13 | 14 | from utils import load_data,save_results 15 | from config import TRAIN,TEST,SHOP_PROFILE 16 | from config import Features 17 | 18 | result_csv_path = 'result/fusai_type_rf_20161210_2.csv' 19 | 20 | 21 | def getCandidateShopId(df,Idx): 22 | candidate_shopid_list = [] 23 | candidate_neighuser = list(eval(df['NEIGHUSER_SHOPID'][Idx])) 24 | candidate_neighshop = list(eval(df['NEIGHSHOP_ID'][Idx])) 25 | 26 | dic_shopid = {} 27 | for i in range(len(candidate_neighuser)): 28 | if candidate_neighuser[i] != 0: 29 | candidate_shopid_list.append(candidate_neighuser[i]) 30 | dic_shopid[candidate_neighuser[i]] = 1 31 | 32 | for i in range(len(candidate_neighshop)): 33 | cur_shopid = candidate_neighshop[i] 34 | if cur_shopid not in dic_shopid.keys(): 35 | candidate_shopid_list.append(cur_shopid) 36 | return candidate_shopid_list 37 | 38 | 39 | def run(): 40 | tr_x ,tr_y = load_data(TRAIN,True) 41 | df_tr = pd.read_csv(TRAIN,header=0) 42 | te_x = load_data(TEST,False) 43 | df_te = pd.read_csv(TEST,header=0) 44 | df_shop = pd.read_csv(SHOP_PROFILE,header=0) 45 | 46 | dic_tr_x = {} 47 | dic_tr_y = {} 48 | for i in range(tr_x.shape[0]): 49 | cur_shopid = int(df_tr['SHOPID'][i]) 50 | if cur_shopid ==0 : 51 | continue 52 | cur_date = int(df_tr['DATE'][i]) 53 | if cur_date in dic_tr_x.keys(): 54 | sub_tr_x = dic_tr_x[cur_date] 55 | sub_tr_y = dic_tr_y[cur_date] 56 | sub_tr_x.append(tr_x[i]) 57 | sub_tr_y.append(tr_y[i]) 58 | dic_tr_x[cur_date] = sub_tr_x 59 | dic_tr_y[cur_date] = sub_tr_y 60 | else: 61 | sub_tr_x = [] 62 | sub_tr_y = [] 63 | sub_tr_x.append(tr_x[i]) 64 | sub_tr_y.append(tr_y[i]) 65 | dic_tr_x[cur_date] = sub_tr_x 66 | dic_tr_y[cur_date] = sub_tr_y 67 | 68 | dic_rf = {} 69 | for cur_date ,sub_tr_x in dic_tr_x.items(): 70 | sub_tr_x = np.array(sub_tr_x) 71 | sub_tr_y = np.array(dic_tr_y[cur_date]) 72 | rf = RandomForestClassifier( 73 | n_estimators = 200, 74 | max_depth = 11, 75 | min_samples_split =2, 76 | bootstrap =True, 77 | warm_start = True, 78 | max_features = 'sqrt', 79 | criterion='entropy', 80 | class_weight = 'balanced', 81 | n_jobs = -1 82 | ).fit(sub_tr_x,sub_tr_y) 83 | dic_rf[cur_date] = rf 84 | 85 | 86 | 87 | te_pred_list = [] 88 | for i in range(te_x.shape[0]): 89 | duration = df_te['DURATION'][i] 90 | cur_date = df_te['DATE'][i] 91 | if duration <= 15: 92 | te_pred_list.append('') 93 | else: 94 | this_pred_dic = {} 95 | for cur_date,sub_tr_x in dic_tr_x.items(): 96 | rf = dic_rf[cur_date] 97 | this_te_pred = (rf.predict(te_x[i])) 98 | pred_shopid = int(this_te_pred[0]) 99 | if pred_shopid not in this_pred_dic.keys(): 100 | this_pred_dic[pred_shopid] = 1 101 | else: 102 | this_pred_dic[pred_shopid] += 1 103 | sorted_this_pred_dic = sorted(this_pred_dic.iteritems(),key=lambda x:x[1],reverse=True) 104 | 105 | most_shopid = sorted_this_pred_dic[0][0] 106 | te_pred_list.append(most_shopid) 107 | print('te_idx:'+str(i)) 108 | 109 | save_results(result_csv_path,te_pred_list) 110 | 111 | 112 | 113 | if __name__=='__main__': 114 | start = time() 115 | run() 116 | end = time() 117 | print('Time:\t'+str((end-start)/3600)+' Hours') 118 | -------------------------------------------------------------------------------- /src/rf_location.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | from time import time 4 | import numpy as np 5 | import pandas as pd 6 | 7 | from sklearn.cluster import KMeans,MeanShift 8 | 9 | from sklearn.externals import joblib 10 | from sklearn.ensemble import RandomForestClassifier 11 | from sklearn.neighbors import NearestNeighbors 12 | from sklearn.cross_validation import KFold 13 | from sklearn.cross_validation import cross_val_score 14 | 15 | from utils import load_data,save_results 16 | from config import TRAIN,TEST,SHOP_PROFILE 17 | from config import Features 18 | 19 | 20 | 21 | result_csv_path = 'result/location_rf_20161204_9.csv' 22 | NUM_CLUSTERS = 25 23 | 24 | 25 | 26 | def cluster(lonlat_list): 27 | 28 | 29 | #dic_lonlat = {} 30 | #for i in range(len(tr_lonlat_list)): 31 | # lon = tr_lonlat_list[i][0] 32 | # lat = tr_lonlat_list[i][1] 33 | # key = str(lon)+":"+str(lat) 34 | # if key not in dic_lonlat.keys(): 35 | # lonlat_list.append([lon,lat]) 36 | # dic_lonlat[key] = 1 37 | 38 | #for i in range(len(te_lonlat_list)): 39 | # lon = te_lonlat_list[i][0] 40 | # lat = te_lonlat_list[i][1] 41 | # key = str(lon)+":"+str(lat) 42 | # if key not in dic_lonlat.keys(): 43 | # lonlat_list.append([lon,lat]) 44 | # dic_lonlat[key] = 1 45 | 46 | #lonlat_list = np.array(lonlat_list) 47 | 48 | #kmeans = KMeans(n_clusters=NUM_CLUSTERS,n_jobs=-1).fit(lonlat_list) 49 | mf = MeanShift().fit(lonlat_list) 50 | 51 | lonlat_cluster_dict = {} 52 | for i in range(lonlat_list.shape[0]): 53 | key = str(lonlat_list[i][0])+":"+str(lonlat_list[i][1]) 54 | lonlat_cluster_dict[key] = mf.labels_[i] 55 | 56 | 57 | #for i in range(NUM_CLUSTERS): 58 | # count = 0 59 | # for k,v in lonlat_cluster_dict.items(): 60 | # if i == v: 61 | # count += 1 62 | # print('cluster:'+str(i)+'\tcount:'+str(count)) 63 | return lonlat_cluster_dict 64 | 65 | 66 | def run(): 67 | tr_x,tr_y = load_data(TRAIN,True) 68 | te_x = load_data(TEST,False) 69 | 70 | df_tr = pd.read_csv(TRAIN,header=0) 71 | df_te = pd.read_csv(TEST,header=0) 72 | 73 | tr_lonlat_list = np.array(df_tr[['LON','LAT']].values) 74 | te_lonlat_list = np.array(df_te[['LON','LAT']].values) 75 | lonlat_list = np.vstack([tr_lonlat_list,te_lonlat_list]) 76 | print(str(tr_lonlat_list.shape)) 77 | print(str(te_lonlat_list.shape)) 78 | print(str(lonlat_list.shape)) 79 | 80 | lonlat_cluster_dict = cluster(lonlat_list) 81 | 82 | dict_tr_x = {} 83 | dict_tr_y = {} 84 | for cluster_label in range(NUM_CLUSTERS): 85 | sub_tr_x = [] 86 | sub_tr_y = [] 87 | for i in range(tr_x.shape[0]): 88 | lon = df_tr['LON'][i] 89 | lat = df_tr['LAT'][i] 90 | key = str(lon)+":"+str(lat) 91 | c_label = lonlat_cluster_dict[key] 92 | if c_label == cluster_label: 93 | sub_tr_x.append(tr_x[i]) 94 | sub_tr_y.append(tr_y[i]) 95 | sub_tr_x = np.array(sub_tr_x) 96 | sub_tr_y = np.array(sub_tr_y) 97 | dict_tr_x[cluster_label] = sub_tr_x 98 | dict_tr_y[cluster_label] = sub_tr_y 99 | #print('cluster:'+str(cluster_label)+'\tcount:'+str(len(sub_tr_x))) 100 | 101 | rf = RandomForestClassifier( 102 | n_estimators = 150, 103 | max_depth = 11, 104 | min_samples_split =2, 105 | bootstrap =True, 106 | warm_start = True, 107 | max_features = 'sqrt', 108 | criterion='entropy', 109 | class_weight = 'balanced', 110 | n_jobs = -1 111 | ) 112 | dict_rf = {} 113 | for cluster_label,sub_tr_x in dict_tr_x.items(): 114 | sub_tr_x = np.array(sub_tr_x) 115 | sub_tr_y = np.array(dict_tr_y[cluster_label]) 116 | rf = rf.fit(sub_tr_x,sub_tr_y) 117 | dict_rf[cluster_label] = rf 118 | #cv = 10 119 | #scores = cross_val_score(rf,sub_tr_x,sub_tr_y,cv=cv,scoring='f1_weighted') 120 | #avg_score = sum(scores)/cv 121 | #avg_score_list.append(avg_score*(1.0*sub_tr_x.shape[0])/(tr_x.shape[0])) 122 | #print(str(cluster_label)+":"+str(sub_tr_x.shape[0])) 123 | #print(str(scores)) 124 | #print('scores:\t'+str(avg_score)+'\n\n\n') 125 | 126 | #print('max score:\t'+str(max(avg_score_list))) 127 | #print('min score:\t'+str(min(avg_score_list))) 128 | #print('total avg:\t'+str(sum(avg_score_list)/NUM_CLUSTERS)) 129 | 130 | te_pred = [] 131 | for i in range(te_x.shape[0]): 132 | lon = df_te['LON'][i] 133 | lat = df_te['LAT'][i] 134 | key = str(lon)+":"+str(lat) 135 | c_label = lonlat_cluster_dict[key] 136 | cur_rf = dict_rf[cluster_label] 137 | cur_pred = cur_rf.predict(te_x[i]) 138 | te_pred.append(cur_pred[0]) 139 | save_results(result_csv_path,te_pred) 140 | 141 | 142 | if __name__=='__main__': 143 | start = time() 144 | run() 145 | end = time() 146 | print('Time:\t'+str((end-start)/3600)+' Hours') 147 | -------------------------------------------------------------------------------- /src/rf_user.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | from time import time 4 | import numpy as np 5 | import pandas as pd 6 | 7 | from sklearn.cluster import KMeans 8 | 9 | 10 | from sklearn.externals import joblib 11 | from sklearn.ensemble import RandomForestClassifier 12 | from sklearn.cross_validation import KFold 13 | from sklearn.cross_validation import cross_val_score 14 | 15 | from utils import load_data,save_results 16 | from config import TRAIN,TEST,SHOP_PROFILE 17 | from config import Features 18 | from config import CLUSTER_DICT 19 | 20 | from location import run_cluster_model 21 | from location import run_simple_model 22 | 23 | from type_rf import predict_type 24 | 25 | result_csv_path = 'result/fusai_user_rf_20161216_28.csv' 26 | 27 | 28 | 29 | 30 | def run(): 31 | te_x = load_data(TEST,False) 32 | df_te = pd.read_csv(TEST,header=0) 33 | 34 | te_predType_list = predict_type() 35 | te_pred_list = [] 36 | for i in range(te_x.shape[0]): 37 | cur_pred_type = te_predType_list[i] 38 | if cur_pred_type == 0: 39 | te_pred_list.append(0) 40 | continue 41 | income = str(df_te['INCOME'][i]) 42 | entertainment = str(df_te['ENTERTAINMENT'][i]) 43 | baby = str(df_te['BABY'][i]) 44 | shopclass = str(df_te['SHOPPING'][i]) 45 | key = income+" "+entertainment+" "+baby+" "+shopclass 46 | lon = df_te['LON'][i] 47 | lat = df_te['LAT'][i] 48 | num_clusters = CLUSTER_DICT[key] 49 | if num_clusters > 1: 50 | cur_te_pred = run_cluster_model(key,lon,lat,te_x[i],num_clusters) 51 | else: 52 | cur_te_pred = run_simple_model(key,te_x[i]) 53 | te_pred_list.append(cur_te_pred) 54 | print("te_idx:"+str(i)) 55 | 56 | print(str(len(te_pred_list))) 57 | save_results(result_csv_path,te_pred_list) 58 | 59 | 60 | 61 | 62 | #tr_x,tr_y = load_data(TRAIN,True) 63 | #df_tr = pd.read_csv(TRAIN,header=0) 64 | #tr_x_dic = {} 65 | #tr_y_dic = {} 66 | 67 | #tr_lonlat_list = [] 68 | #for i in range(tr_x.shape[0]): 69 | # attr = [ str(df_tr['INCOME'][i]),str(df_tr['ENTERTAINMENT'][i]), str(df_tr['BABY'][i]),str(df_tr['SHOPPING'][i]) ] 70 | # key = " ".join(attr) 71 | # if key not in tr_x_dic.keys(): 72 | # sub_tr_x = [] 73 | # sub_tr_y = [] 74 | # sub_tr_x.append(tr_x[i]) 75 | # sub_tr_y.append(tr_y[i]) 76 | # tr_x_dic[key] = sub_tr_x 77 | # tr_y_dic[key] = sub_tr_y 78 | # else: 79 | # sub_tr_x = tr_x_dic[key] 80 | # sub_tr_y = tr_y_dic[key] 81 | # sub_tr_x.append(tr_x[i]) 82 | # sub_tr_y.append(tr_y[i]) 83 | # tr_x_dic[key] = sub_tr_x 84 | # tr_y_dic[key] = sub_tr_y 85 | # 86 | #print('key:\t'+str(len(tr_x_dic))) 87 | #print('key:\t'+str(len(tr_y_dic))) 88 | 89 | #rf = RandomForestClassifier( 90 | # n_estimators = 150, 91 | # max_depth = 11, 92 | # min_samples_split =2, 93 | # bootstrap =True, 94 | # warm_start = True, 95 | # max_features = 'sqrt', 96 | # criterion='entropy', 97 | # class_weight = 'balanced', 98 | # n_jobs = -1 99 | # ) 100 | #rf_dic = {} 101 | #avg_score = 0 102 | #count = 0 103 | #for key,sub_tr_x in tr_x_dic.items(): 104 | # sub_tr_x = np.array(sub_tr_x) 105 | # sub_tr_y = np.array(tr_y_dic[key]) 106 | # if sub_tr_x.shape[0] <= 100: 107 | # continue 108 | # cv = 10 109 | # print(key +":"+str(sub_tr_x.shape[0])) 110 | # scores = cross_val_score(rf,sub_tr_x,sub_tr_y,cv=10,scoring='f1_weighted') 111 | # print(str(scores)) 112 | # avg_score += sum(scores)/cv 113 | # count += 1 114 | # print('avg scores:\t'+str(sum(scores)/cv)) 115 | # print('\n\n') 116 | #print('total AVG scroe:\t'+str(avg_score/count)) 117 | 118 | 119 | 120 | #print(str(len(te_pred))) 121 | #save_results(result_csv_path,te_pred) 122 | 123 | 124 | if __name__=='__main__': 125 | start = time() 126 | run() 127 | end = time() 128 | print('Time:\t'+str((end-start)/3600)+' Hours') 129 | -------------------------------------------------------------------------------- /src/rf_userWithlocation.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | from time import time 4 | import numpy as np 5 | import pandas as pd 6 | 7 | from sklearn.cluster import KMeans 8 | 9 | 10 | from sklearn.externals import joblib 11 | from sklearn.svm import SVC 12 | from sklearn.ensemble import RandomForestClassifier 13 | from sklearn.neighbors import NearestNeighbors 14 | from sklearn.cross_validation import KFold 15 | from sklearn.cross_validation import cross_val_score 16 | 17 | from utils import load_data,save_results 18 | from config import TRAIN,TEST,SHOP_PROFILE 19 | from config import Features 20 | 21 | 22 | 23 | result_csv_path = 'result/fusai_userWithlocation_rf_20161204_8.csv' 24 | NUM_CLUSTERS = 5 25 | 26 | 27 | def findMostSimilaritySameUserAttr(dict_tr_x,attr_key): 28 | given_cluster_label = attr_key.split(' ')[0] 29 | given_income = attr_key.split(' ')[1] 30 | given_entertainment = attr_key.split(' ')[2] 31 | given_baby = attr_key.split(' ')[3] 32 | given_shopping = attr_key.split(' ')[4] 33 | 34 | similarity_score = {} 35 | max_attr_num = 0 36 | max_attr_count = 0 37 | most_similarity_key = "" 38 | for key,sub_tr_x in dict_tr_x.items(): 39 | cur_cluster_label = key.split(' ')[0] 40 | cur_income = key.split(' ')[1] 41 | cur_entertainment = key.split(' ')[2] 42 | cur_baby = key.split(' ')[3] 43 | cur_shopping = key.split(' ')[4] 44 | if cur_cluster_label != given_cluster_label: 45 | continue 46 | same_attr_num = 0 47 | if cur_income == given_income: 48 | same_attr_num += 1 49 | if cur_entertainment == given_entertainment: 50 | same_attr_num += 1 51 | if cur_baby == given_baby: 52 | same_attr_num += 1 53 | if cur_shopping == given_shopping: 54 | same_attr_num += 1 55 | 56 | if same_attr_num > max_attr_num: 57 | max_attr_num = same_attr_num 58 | max_attr_count = len(sub_tr_x) 59 | most_similarity_key = key 60 | 61 | if same_attr_num == max_attr_num: 62 | if len(sub_tr_x) > max_attr_count: 63 | max_attr_num = same_attr_num 64 | max_attr_count = len(sub_tr_x) 65 | most_similarity_key = key 66 | return most_similarity_key 67 | 68 | 69 | 70 | def cluster(lonlat_list): 71 | 72 | kmeans = KMeans(n_clusters=NUM_CLUSTERS,n_jobs=-1).fit(lonlat_list) 73 | 74 | lonlat_cluster_dict = {} 75 | for i in range(lonlat_list.shape[0]): 76 | key = str(lonlat_list[i][0])+":"+str(lonlat_list[i][1]) 77 | lonlat_cluster_dict[key] = kmeans.labels_[i] 78 | 79 | 80 | #for i in range(NUM_CLUSTERS): 81 | # count = 0 82 | # for k,v in lonlat_cluster_dict.items(): 83 | # if i == v: 84 | # count += 1 85 | # print('cluster:'+str(i)+'\tcount:'+str(count)) 86 | return lonlat_cluster_dict 87 | 88 | 89 | def run(): 90 | tr_x,tr_y = load_data(TRAIN,True) 91 | te_x = load_data(TEST,False) 92 | 93 | df_tr = pd.read_csv(TRAIN,header=0) 94 | df_te = pd.read_csv(TEST,header=0) 95 | 96 | tr_lonlat_list = np.array(df_tr[['LON','LAT']].values) 97 | te_lonlat_list = np.array(df_te[['LON','LAT']].values) 98 | lonlat_list = np.vstack([tr_lonlat_list,te_lonlat_list]) 99 | print(str(tr_lonlat_list.shape)) 100 | print(str(te_lonlat_list.shape)) 101 | print(str(lonlat_list.shape)) 102 | 103 | lonlat_cluster_dict = cluster(lonlat_list) 104 | 105 | dict_tr_x = {} 106 | dict_tr_y = {} 107 | for cluster_label in range(NUM_CLUSTERS): 108 | for i in range(tr_x.shape[0]): 109 | lon = df_tr['LON'][i] 110 | lat = df_tr['LAT'][i] 111 | key = str(lon)+":"+str(lat) 112 | c_label = lonlat_cluster_dict[key] 113 | if c_label == cluster_label: 114 | #attr = " ".join([str(c_label),str(df_tr['ENTERTAINMENT'][i]),str(df_tr['SHOPPING'][i])]) 115 | attr = " ".join([str(c_label),str(df_tr['INCOME'][i]),str(df_tr['ENTERTAINMENT'][i]),str(df_tr['BABY'][i]),str(df_tr['SHOPPING'][i])]) 116 | if attr not in dict_tr_x.keys(): 117 | sub_tr_x = [] 118 | sub_tr_y = [] 119 | sub_tr_x.append(tr_x[i]) 120 | sub_tr_y.append(tr_y[i]) 121 | dict_tr_x[attr] = sub_tr_x 122 | dict_tr_y[attr] = sub_tr_y 123 | else: 124 | sub_tr_x = dict_tr_x[attr] 125 | sub_tr_y = dict_tr_y[attr] 126 | sub_tr_x.append(tr_x[i]) 127 | sub_tr_y.append(tr_y[i]) 128 | dict_tr_x[attr] = sub_tr_x 129 | dict_tr_y[attr] = sub_tr_y 130 | 131 | print('1. begin train.....') 132 | dic_rf = {} 133 | rf = RandomForestClassifier( 134 | n_estimators = 100, 135 | max_depth = 11, 136 | min_samples_split =2, 137 | bootstrap =True, 138 | warm_start = True, 139 | max_features = 'sqrt', 140 | criterion='entropy', 141 | class_weight = 'balanced', 142 | n_jobs = -1 143 | ) 144 | clf_svm = SVC() 145 | total_count = 0 146 | for attr,sub_tr_x in dict_tr_x.items(): 147 | sub_tr_y = dict_tr_y[attr] 148 | total_count += len(sub_tr_x) 149 | print(attr+":\t"+str(len(sub_tr_x))) 150 | rf = rf.fit(np.array(sub_tr_x),np.array(sub_tr_y)) 151 | #clf_svm = clf_svm.fit(np.array(sub_tr_x),np.array(sub_tr_y)) 152 | dic_rf[attr] = rf 153 | print(str(total_count)) 154 | print('rf numbers:\t'+str(len(dic_rf))) 155 | print('2. begin predict.....') 156 | te_pred_list = [] 157 | for i in range(te_x.shape[0]): 158 | lon = df_te['LON'][i] 159 | lat = df_te['LAT'][i] 160 | key = str(lon)+":"+str(lat) 161 | c_label = lonlat_cluster_dict[key] 162 | attr = " ".join([str(c_label),str(df_te['INCOME'][i]),str(df_te['ENTERTAINMENT'][i]),str(df_te['BABY'][i]),str(df_te['SHOPPING'][i])]) 163 | #attr = " ".join([str(c_label),str(df_tr['ENTERTAINMENT'][i]),str(df_tr['SHOPPING'][i])]) 164 | print('te idx:\t'+str(i)+'\t'+attr) 165 | if attr in dict_tr_x.keys(): 166 | sub_tr_x = dict_tr_x[attr] 167 | sub_tr_y = dict_tr_y[attr] 168 | if len(sub_tr_x) == 1: 169 | te_pred_list.append(sub_tr_y[0]) 170 | continue 171 | clf = dic_rf[attr] 172 | te_pred = clf.predict(te_x[i]) 173 | te_pred_list.append(te_pred[0]) 174 | else: 175 | most_similarity_key = findMostSimilaritySameUserAttr(dict_tr_x,attr) 176 | sub_tr_x = dict_tr_x[most_similarity_key] 177 | sub_tr_y = dict_tr_y[most_similarity_key] 178 | if len(sub_tr_x) == 1: 179 | te_pred_list.append(sub_tr_y[0]) 180 | continue 181 | clf = dic_rf[most_similarity_key] 182 | te_pred = clf.predict(te_x[i]) 183 | te_pred_list.append(te_pred[0]) 184 | 185 | save_results(result_csv_path,te_pred_list) 186 | # for i in range(te_x.shape[0]): 187 | # lon = df_te['LON'][i] 188 | # lat = df_te['LAT'][i] 189 | # key = str(lon)+":"+str(lat) 190 | # c_label = lonlat_cluster_dict[key] 191 | # attr = " ".join([str(df_te['INCOME'][i]),str(df_te['ENTERTAINMENT'][i]),str(df_te['BABY'][i]),str(df_te['SHOPPING'][i])]) 192 | # if c_label == cluster_label: 193 | # if attr not in te_attr_dict.keys(): 194 | # te_attr_dict[attr] = 1 195 | # else: 196 | # te_attr_dict[attr] += 1 197 | 198 | # if attr not in tr_attr_dict.keys(): 199 | # IsAllExists = False 200 | # print(str(IsAllExists)) 201 | # print(str(cluster_label)+' Train set...') 202 | # for k,v in tr_attr_dict.items(): 203 | # print(k+":\t"+str(v)) 204 | # print(str(cluster_label)+' Test set...') 205 | # for k,v in te_attr_dict.items(): 206 | # print(k+":\t"+str(v)) 207 | 208 | # print('\n\n\n') 209 | 210 | 211 | if __name__=='__main__': 212 | start = time() 213 | run() 214 | end = time() 215 | print('Time:\t'+str((end-start)/3600)+' Hours') 216 | -------------------------------------------------------------------------------- /src/run.sh: -------------------------------------------------------------------------------- 1 | #! /bin/bash 2 | 3 | #python preprocess.py 4 | python type_rf.py 5 | -------------------------------------------------------------------------------- /src/svd.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | import numpy as np 4 | from scipy.sparse.linalg import svds 5 | from scipy import sparse 6 | 7 | 8 | def vector_to_diagonal(vector): 9 | if (isinstance(vector, np.ndarray) and vector.ndim == 1) or \ 10 | isinstance(vector, list): 11 | length = len(vector) 12 | diag_matrix = np.zeros((length, length)) 13 | np.fill_diagonal(diag_matrix, vector) 14 | return diag_matrix 15 | return None 16 | 17 | 18 | 19 | if __name__=='__main__': 20 | f_in = open('../new/hyk_mf_input.txt_all','r') 21 | 22 | dic = {} 23 | loc_set = set() 24 | for line in f_in: 25 | data = line.strip().split(',') 26 | uid = data[0] 27 | locid = data[1] 28 | times = data[2] 29 | loc_set.add(locid) 30 | if uid not in dic.keys(): 31 | cur_list = [] 32 | cur_list.append(locid) 33 | cur_list.append(times) 34 | dic[uid] = cur_list 35 | else: 36 | cur_list = dic[uid] 37 | cur_list.append(locid) 38 | cur_list.append(times) 39 | dic[uid] = cur_list 40 | 41 | uid_list = [] 42 | mat = np.zeros((len(dic),len(loc_set))) 43 | i = 0 44 | for uid,cur_list in dic.items(): 45 | uid_list.append(uid) 46 | for j in range(len(cur_list)/2): 47 | loc_id = int(cur_list[j*2]) 48 | times = int(cur_list[j*2+1]) 49 | mat[i][loc_id] = times 50 | i += 1 51 | 52 | mat = mat.astype('float') 53 | U,S,V = svds(sparse.csr_matrix(mat),k=15,maxiter=200) 54 | 55 | i = 0 56 | f_out = open('../new/user_svd.txt','w') 57 | f_out.write('USERID,USER_SVDFEATURE\n') 58 | for uid in uid_list: 59 | string = uid+',"[' 60 | for j in range(len(U[i])): 61 | string += str(U[i][j])+',' 62 | string = string[:-1]+']"' 63 | f_out.write(string+'\n') 64 | i = i+1 65 | 66 | f_out.close() 67 | f_out = open('../new/loc_svd.txt','w') 68 | f_out.write('LOC_IDX,LOC_SVDFEATURE\n') 69 | V = np.transpose(V) 70 | for i in range(V.shape[0]): 71 | string = str(i)+',"[' 72 | for j in range(len(V[i])): 73 | string += str(V[i][j])+',' 74 | string = string[:-1]+']"' 75 | f_out.write(string+'\n') 76 | -------------------------------------------------------------------------------- /src/svdfeature/binaryClassification.conf: -------------------------------------------------------------------------------- 1 | # example config for binary classification 2 | 3 | # learning rate for SGD 4 | 5 | base_score = 3 6 | learning_rate = 0.000000001 7 | 8 | # regularization constant for factor usually denote \lambda in CFx papers 9 | wd_item = 0.004 10 | wd_user = 0.004 11 | 12 | # number of each kind of features 13 | num_item = 2078 14 | num_user = 7580 15 | 16 | num_global = 0 17 | 18 | # number of factor 19 | num_factor = 4000 20 | 21 | # translation function for output, 0:linear 2:sigmoid 22 | active_type = 2 23 | 24 | # data for evaluation, binary format, used by svd_feature_infer 25 | test:buffer_feature="TEST_NEW_SVM.txt.buffer" 26 | # buffer for training, binary format, created by make_feature_buffer 27 | buffer_feature = "TRAIN_NEW_SVM.txt.buffer" 28 | # folder to store the model file 29 | model_out_folder="./model" 30 | 31 | input_type = 0 32 | -------------------------------------------------------------------------------- /src/svdfeature/clean.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # clean all the trace of demo 4 | rm -rf pred.txt *.svdpp *.group *.order *.shuffle *.model *.*feature *.*buffer 5 | 6 | -------------------------------------------------------------------------------- /src/svdfeature/generateDict.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | if __name__=='__main__': 5 | uid_dict ={} 6 | shopid_dict = {} 7 | f_tr = open('../../new/TRAIN_SVMFORMAT.txt','r') 8 | f_te = open('../../new/TEST_SVMFORMAT.txt','r') 9 | 10 | f_tr_new = open('TRAIN_NEW_SVM.txt','w') 11 | f_te_new = open('TEST_NEW_SVM.txt','w') 12 | 13 | u_count = 0 14 | shop_count = 0 15 | 16 | for line in f_tr: 17 | data = line.strip().split(" ") 18 | uid = int(data[4].split(":")[0]) 19 | shopid = int(data[-1].split(":")[0]) 20 | 21 | if uid not in uid_dict.keys(): 22 | uid_dict[uid]=u_count 23 | u_count +=1 24 | if shopid not in shopid_dict.keys(): 25 | shopid_dict[shopid]=shop_count 26 | shop_count +=1 27 | string = data[0]+" "+data[1]+" "+data[2]+" "+data[3]+" " 28 | for i in range(4,len(data)-3): 29 | string += str(uid_dict[uid])+":"+data[i].split(":")[1]+" " 30 | for i in range(len(data)-3,len(data)): 31 | string += str(shopid_dict[shopid])+":"+data[i].split(":")[1]+" " 32 | string = string[:-1]+"\n" 33 | f_tr_new.write(string) 34 | 35 | for line in f_te: 36 | data = line.strip().split(" ") 37 | uid = int(data[4].split(":")[0]) 38 | shopid = int(data[-1].split(":")[0]) 39 | if uid not in uid_dict.keys(): 40 | uid_dict[uid]=u_count 41 | u_count +=1 42 | if shopid not in shopid_dict.keys(): 43 | shopid_dict[shopid]=shop_count 44 | shop_count +=1 45 | string = data[0]+" "+data[1]+" "+data[2]+" "+data[3]+" " 46 | for i in range(4,len(data)-3): 47 | string += str(uid_dict[uid])+":"+data[i].split(":")[1]+" " 48 | for i in range(len(data)-3,len(data)): 49 | string += str(shopid_dict[shopid])+":"+data[i].split(":")[1]+" " 50 | string = string[:-1]+"\n" 51 | f_te_new.write(string) 52 | 53 | 54 | f_key_uid = open('uid_key.txt','w') 55 | f_key_shopid = open('shopid_key.txt','w') 56 | for k,v in uid_dict.items(): 57 | f_key_uid.write(str(k)+":"+str(v)+"\n") 58 | 59 | 60 | for k,v in shopid_dict.items(): 61 | f_key_shopid.write(str(k)+":"+str(v)+"\n") 62 | 63 | f_tr.close() 64 | f_te.close() 65 | -------------------------------------------------------------------------------- /src/svdfeature/generateSVMFormat.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | import pandas as pd 4 | import numpy as np 5 | from time import time 6 | sys.path.append('..') 7 | from config import List_Features,Common_Features 8 | from config import TRAIN,TEST,MYTEST 9 | from config import NUM_NEAREST_SHOPS 10 | from config import NUM_NEAREST_USERS 11 | from config import DESTINATION_NUM_NEAREST_SHOPS 12 | 13 | TRAIN_SVMFORMAT = 'TRAIN_SVMFORMAT.txt' 14 | TEST_SVMFORMAT = 'TEST_SVMFORMAT.txt' 15 | 16 | TRAIN_LRFORMAT = '../new/TRAIN_LRFORMAT.txt' 17 | TEST_LRFORMAT = '../new/TEST_LRFORMAT.txt' 18 | 19 | NUM_USER = NUM_NEAREST_SHOPS* 7 + 3*NUM_NEAREST_USERS+len(Common_Features) 20 | NUM_SHOP = 3 21 | 22 | 23 | 24 | def get_UserFeature(csv_path): 25 | df = pd.read_csv(csv_path,header=0) 26 | ufeature_list = [] 27 | for i in range(len(df)): 28 | cur_gfeature = [] 29 | uid = int(df['USERID'][i]) 30 | for j in range(len(List_Features)): 31 | col_name = List_Features[j] 32 | cur_feature_list = list(eval(df[col_name][i])) 33 | cur_gfeature += cur_feature_list 34 | 35 | for j in range(len(Common_Features)): 36 | col_name = Common_Features[j] 37 | cur_feature = df[col_name][i] 38 | cur_gfeature.append(cur_feature) 39 | print(str(len(cur_gfeature))) 40 | cur_gfeature_str = "" 41 | for j in range(len(cur_gfeature)): 42 | cur_gfeature_str += str(uid)+":"+str(cur_gfeature[j])+" " 43 | #cur_gfeature_str += str(cur_gfeature[j])+" " 44 | ufeature_list.append(cur_gfeature_str[:-1]) 45 | return ufeature_list 46 | 47 | 48 | def get_ShopFeature(csv_path): 49 | df = pd.read_csv(csv_path,header=0) 50 | shopfeature_list = [] 51 | for i in range(len(df)): 52 | shopid = int(df['SHOPID'][i]) 53 | shopclass = df['SHOP_CLASS'][i] 54 | shoplon = df['SHOP_LON'][i] 55 | shoplat = df['SHOP_LAT'][i] 56 | shopfeature_str = str(shopid)+":"+str(shopclass)+" "+str(shopid)+":"+str(shoplon)+" "+str(shopid)+":"+str(shoplat) 57 | #shopfeature_str =str(shopclass)+" "+str(shoplon)+" "+str(shoplat) 58 | shopfeature_list.append(shopfeature_str) 59 | return shopfeature_list 60 | 61 | 62 | 63 | 64 | 65 | 66 | def generate_svmFile(csv_path,IsTrain=True): 67 | df = pd.read_csv(csv_path,header=0) 68 | user_feature_list = get_UserFeature(csv_path) 69 | shop_feature_list = get_ShopFeature(csv_path) 70 | 71 | f_out = open(TRAIN_SVMFORMAT,'w') if IsTrain else open(TEST_SVMFORMAT,'w') 72 | for i in range(len(df)): 73 | target = 0 if df['SHOPID'][i]==0 else 1 74 | svm_str = str(target)+" 0 "+str(NUM_USER)+" "+str(NUM_SHOP)+" "+user_feature_list[i]+" "+shop_feature_list[i]+'\n' 75 | f_out.write(svm_str) 76 | f_out.close() 77 | 78 | def generate_LRFile(csv_path,IsTrain=True): 79 | df = pd.read_csv(csv_path,header=0) 80 | user_feature_list = get_UserFeature(csv_path) 81 | shop_feature_list = get_ShopFeature(csv_path) 82 | 83 | f_out = open(TRAIN_LRFORMAT,'w') if IsTrain else open(TEST_LRFORMAT,'w') 84 | for i in range(len(df)): 85 | target = 0 if df['SHOPID'][i]==0 else 1 86 | lr_str = str(target)+" "+user_feature_list[i]+" "+shop_feature_list[i]+'\n' 87 | f_out.write(lr_str) 88 | f_out.close() 89 | 90 | 91 | 92 | 93 | 94 | def run(): 95 | generate_svmFile(TRAIN,True) 96 | generate_svmFile(MYTEST,False) 97 | 98 | #generate_LRFile(TRAIN,True) 99 | #generate_LRFile(MYTEST,False) 100 | 101 | if __name__=='__main__': 102 | START = time() 103 | run() 104 | END = time() 105 | print('Cost Time:\t'+str((END-START)/3600)+' Hours') 106 | -------------------------------------------------------------------------------- /src/svdfeature/postpreprocess.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | import sys 3 | import pandas as pd 4 | import numpy as np 5 | sys.path.append("..") 6 | from config import * 7 | NUM_CANDIDATE = 8 8 | 9 | def load_shop(): 10 | shop_dict = {} 11 | f = open('shopid_key.txt','r') 12 | for line in f: 13 | data = line.strip().split(":") 14 | shop_dict[int(data[1])] = int(data[0]) 15 | return shop_dict 16 | 17 | 18 | def MatchShopIdForPredictTxt(): 19 | shop_dict = load_shop() 20 | 21 | f_pred = open('pred.txt','r') 22 | f_te = open('TEST_NEW_SVM.txt','r') 23 | f_out = open('true_pred.txt','w') 24 | 25 | pred_list = f_pred.readlines() 26 | test_list = f_te.readlines() 27 | 28 | for cur_predline,cur_testline in zip(pred_list,test_list): 29 | cur_proba = float(cur_predline.strip()) 30 | data = cur_testline.strip().split(" ") 31 | shopid = int(data[-1].split(":")[0]) 32 | true_shopid = shop_dict[shopid] 33 | f_out.write(str(true_shopid)+","+str(cur_proba)+"\n") 34 | 35 | def recommend_shop(): 36 | f = open('true_pred.txt','r') 37 | recommend_list = [] 38 | proba_list = f.readlines() 39 | i = 0 40 | while i < len(proba_list): 41 | dic = {} 42 | for j in range(NUM_CANDIDATE): 43 | cur_shop = proba_list[i+j].strip().split(",")[0] 44 | cur_prob = float(proba_list[i+j].strip().split(",")[1]) 45 | if cur_shop not in dic.keys(): 46 | dic[cur_shop] = cur_prob 47 | else: 48 | dic[cur_shop] += cur_prob 49 | dic_sorted = sorted(dic.iteritems(),key=lambda d:d[1],reverse=True) 50 | if dic_sorted[0][0] == '0': 51 | recommend_list.append('') 52 | else: 53 | recommend_list.append(dic_sorted[0][0]) 54 | i += NUM_CANDIDATE 55 | df_recommend = pd.DataFrame() 56 | df_test = pd.read_csv(TEST,header=0) 57 | df_recommend['USERID'] = df_test['USERID'] 58 | df_recommend['SHOPID'] = recommend_list 59 | df_recommend['ARRIVAL_TIME'] = df_test['ARRIVAL_TIME'] 60 | df_recommend.to_csv('recommond.csv',index=False) 61 | 62 | 63 | 64 | def run(): 65 | MatchShopIdForPredictTxt() 66 | recommend_shop() 67 | 68 | if __name__=='__main__': 69 | run() 70 | 71 | -------------------------------------------------------------------------------- /src/svdfeature/run.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # example running script for basicMF 3 | 4 | # make buffer, transform text format to binary format 5 | ~/tools/svdfeature-1.2.2/tools/make_feature_buffer TRAIN_NEW_SVM.txt TRAIN_NEW_SVM.txt.buffer 6 | ~/tools/svdfeature-1.2.2/tools/make_feature_buffer TEST_NEW_SVM.txt TEST_NEW_SVM.txt.buffer 7 | 8 | ~/tools/svdfeature-1.2.2/svd_feature binaryClassification.conf num_round=500 9 | ~/tools/svdfeature-1.2.2/svd_feature_infer binaryClassification.conf pred=500 10 | 11 | 12 | #../../tools/make_feature_buffer ua.base.example ua.base.buffer 13 | #../../tools/make_feature_buffer ua.test.example ua.test.buffer 14 | # 15 | ## training for 40 rounds 16 | #../../svd_feature binaryClassification.conf num_round=40 17 | ## write out prediction from 0040.model 18 | #../../svd_feature_infer binaryClassification.conf pred=40 19 | -------------------------------------------------------------------------------- /src/type_rf.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | from time import time 4 | import numpy as np 5 | import pandas as pd 6 | 7 | 8 | from sklearn.externals import joblib 9 | from sklearn.ensemble import RandomForestClassifier 10 | from sklearn.cross_validation import KFold 11 | from sklearn.cross_validation import cross_val_score 12 | 13 | from utils import load_data,save_results 14 | from config import TRAIN,TEST,SHOP_PROFILE 15 | from config import Features 16 | 17 | result_csv_path = 'result/type_rf_20161215_7.csv' 18 | 19 | 20 | def getCandidateShopId(df,Idx): 21 | candidate_shopid_list = [] 22 | candidate_neighuser = list(eval(df['NEIGHUSER_SHOPID'][Idx])) 23 | candidate_neighshop = list(eval(df['NEIGHSHOP_ID'][Idx])) 24 | 25 | dic_shopid = {} 26 | for i in range(len(candidate_neighuser)): 27 | if candidate_neighuser[i] != 0: 28 | candidate_shopid_list.append(candidate_neighuser[i]) 29 | dic_shopid[candidate_neighuser[i]] = 1 30 | 31 | for i in range(len(candidate_neighshop)): 32 | cur_shopid = candidate_neighshop[i] 33 | if cur_shopid not in dic_shopid.keys(): 34 | candidate_shopid_list.append(cur_shopid) 35 | return candidate_shopid_list 36 | 37 | 38 | def predict_type(): 39 | tr_x ,shop_y = load_data(TRAIN,True) 40 | df_tr = pd.read_csv(TRAIN,header=0) 41 | tr_y = np.array(df_tr['SHOP_CLASS'].values) 42 | te_x = load_data(TEST,False) 43 | df_te = pd.read_csv(TEST,header=0) 44 | df_shop = pd.read_csv(SHOP_PROFILE,header=0) 45 | df_tr_candidate = pd.read_csv('../new/TRAIN_NEIGHUSER_SHOPID.csv',header=0) 46 | df_te_candidate = pd.read_csv('../new/TEST_NEIGHUSER_SHOPID.csv',header=0) 47 | 48 | rf = RandomForestClassifier( 49 | n_estimators = 215, 50 | max_depth = 11, 51 | min_samples_split =2, 52 | bootstrap =True, 53 | warm_start = True, 54 | max_features = 'sqrt', 55 | criterion='entropy', 56 | class_weight = 'balanced', 57 | n_jobs = -1 58 | ) 59 | 60 | tr_one_x = [] 61 | tr_one_y = [] 62 | for i in range(tr_x.shape[0]): 63 | if df_tr['SHOPID'][i] != 0: 64 | tr_one_x.append(tr_x[i]) 65 | tr_one_y.append(tr_y[i]) 66 | tr_one_x = np.array(tr_one_x) 67 | tr_one_y = np.array(tr_one_y) 68 | 69 | rf.fit(tr_one_x,tr_one_y) 70 | te_pred_shoptype = rf.predict(te_x) 71 | for i in range(te_x.shape[0]): 72 | cur_duration = df_te['DURATION'][i] 73 | if cur_duration <= 15: 74 | te_pred_shoptype[i] = 0 75 | return te_pred_shoptype 76 | 77 | #count = 0 78 | #for i in range(te_pred_shoptype.shape[0]): 79 | # if te_pred_shoptype[i] ==0: 80 | # count += 1 81 | #print(str(count)) 82 | #print('te_pred_shoptype lenght:'+str(te_pred_shoptype.shape[0])) 83 | #te_recommend_shopid_list = [] 84 | #for i in range(te_pred_shoptype.shape[0]): 85 | # cur_pred_shoptype = te_pred_shoptype[i] 86 | # if cur_pred_shoptype == 0: 87 | # print(str(i)+':0') 88 | # te_recommend_shopid_list.append('') 89 | # else: 90 | # candidate_shopid = getCandidateShopId(df_te_candidate,i) 91 | # j = 0 92 | # while j lon1 and lat2 >= lat1: 56 | direction = 1 57 | elif lon2 > lon1 and lat2 < lat1: 58 | direction = 2 59 | elif lon2 <= lon1 and lat2 < lat1: 60 | direction = 3 61 | else: 62 | direction = 4 63 | 64 | 65 | return s ,direction 66 | 67 | 68 | def caltime(date1,date2): 69 | date1 = time.strptime(date1,"%Y%m%d%H%M%S") 70 | date2 = time.strptime(date2,"%Y%m%d%H%M%S") 71 | date1 = datetime.datetime(date1[0],date1[1],date1[2],date1[3],date1[4],date1[5]) 72 | date2 = datetime.datetime(date2[0],date2[1],date2[2],date2[3],date2[4],date2[5]) 73 | return abs((date2 - date1).total_seconds()) 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | def get_list_features(csv_path): 83 | df = pd.read_csv(csv_path,header=0) 84 | feature_neighshop_id = [] 85 | for i in range(len(df)): 86 | cur_id = list(eval(df['NEIGHSHOP_ID'][i])) 87 | feature_neighshop_id.append(cur_id) 88 | feature_neighshop_id = np.array(feature_neighshop_id) 89 | feature_list = np.hstack([feature_neighshop_id]) 90 | for i in range(1,len(List_Features)): 91 | cur_feature_list = [] 92 | for j in range(len(df)): 93 | cur_feature = list(eval(df[List_Features[i]][j])) 94 | cur_feature_list.append(cur_feature) 95 | cur_feature_list = np.array(cur_feature_list) 96 | feature_list = np.hstack([feature_list,cur_feature_list]) 97 | return feature_list 98 | 99 | 100 | def get_common_features(csv_path): 101 | df = pd.read_csv(csv_path,header=0) 102 | feature_common = [] 103 | for i in range(len(Common_Features)): 104 | feature_name = Common_Features[i] 105 | cur_feature = np.array(df[feature_name]).reshape(len(df),1) 106 | feature_common.append(cur_feature) 107 | feature_common = np.hstack(feature_common) 108 | return feature_common 109 | 110 | 111 | def get_tr_y(csv_path): 112 | df = pd.read_csv(csv_path,header=0) 113 | return np.array(df['SHOPID']) 114 | 115 | 116 | def load_data(csv_path,isTrain): 117 | feature_list = get_list_features(csv_path) 118 | feature_common = get_common_features(csv_path) 119 | if isTrain: 120 | tr_y = get_tr_y(csv_path) 121 | #tr_hyk = np.loadtxt('../new/newtrfeature.txt') 122 | tr_x = np.hstack([feature_list,feature_common]) 123 | return tr_x,tr_y 124 | else: 125 | #te_hyk = np.loadtxt('../new/newtefeature.txt') 126 | te_x = np.hstack([feature_list,feature_common]) 127 | return te_x 128 | 129 | 130 | def save_results(result_csv_path,pred_y): 131 | df = pd.read_csv(TEST,header=0) 132 | df_result = pd.DataFrame() 133 | df_result['USERID'] = df['USERID'] 134 | df_result['SHOPID'] = pred_y 135 | df_result['ARRIVAL_TIME'] = df['ARRIVAL_TIME'] 136 | df_result.to_csv(result_csv_path,index=False) 137 | 138 | 139 | def similarity(colname,n): 140 | df_train = pd.read_csv(TRAIN,header=0) 141 | df_shop = pd.read_csv(SHOP_PROFILE,header=0) 142 | x = np.zeros((n,10)) 143 | num_null_ofthisAttr = [0]*n 144 | 145 | for i in range(len(df_train)): 146 | att = int(df_train[colname][i])-1 147 | shopid = (df_train['SHOPID'][i]) 148 | if shopid == 0: 149 | continue 150 | shopclass = int([df_shop[df_shop['ID']==shopid]['CLASSIFICATION'].values][0])-1 151 | x[att][shopclass] += 1 152 | num_count = [0]*n 153 | for i in range(n): 154 | for j in range(10): 155 | if x[i][j]>0: 156 | num_count[i] += 1 157 | 158 | for i in range(n): 159 | y = sum(x[i]) 160 | for j in range(10): 161 | x[i][j] =(x[i][j]+1)/(y+num_count[i]) 162 | for i in range(n): 163 | s = "[" 164 | for j in range(10): 165 | s += str(x[i][j])+',' 166 | print(s[:-1]+']') 167 | 168 | 169 | 170 | def generateMyTestFile(): 171 | df_te = pd.read_csv(TEST,header=0) 172 | uid_list = [] 173 | shopid_list = [] 174 | time_list = [] 175 | for i in range(len(df_te)): 176 | cur_uid = df_te['USERID'][i] 177 | cur_time = df_te['ARRIVAL_TIME'][i] 178 | neighuser_shoplist = list(eval(df_te['NEIGHUSER_SHOPID'][i])) 179 | for j in range(len(neighuser_shoplist)): 180 | uid_list.append(cur_uid) 181 | shopid_list.append(neighuser_shoplist[j]) 182 | time_list.append(cur_time) 183 | df_myte = pd.DataFrame() 184 | df_myte['USERID'] = uid_list 185 | df_myte['SHOPID'] = shopid_list 186 | df_myte['ARRIVAL_TIME'] = time_list 187 | df_myte.to_csv(MYTEST,index=False) 188 | 189 | 190 | 191 | 192 | if __name__=='__main__': 193 | #load_data('../data/TRAIN.csv',True) 194 | #load_data('../data/TEST2.csv',False) 195 | #get_weekday_time('20161016001030') 196 | similarity('INCOME',4) 197 | #generateMyTestFile() 198 | -------------------------------------------------------------------------------- /src/utils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuguanggen/2016CCF-unicom/911fc0afa6447bb6ee0140cdba004f4ce7b43328/src/utils.pyc 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