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AiLearning-Theory-Applying
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benjas 4 years ago
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机器学习竞赛实战_优胜解决方案/滴滴——预估到达时间/DCN_12953/dcn_model/test.py
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import joblib
cross_le = joblib.load('/data/didi_2021/model_h5/crossid_le')
print(len(cross_le.classes_.tolist()))

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机器学习竞赛实战_优胜解决方案/滴滴——预估到达时间/DCN_12953/model_for_features/describe_df.csv
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,order_id,link_current_status_sum,link_current_status_mean,date_time_sum,date_time_mean,wk2_total_linkid_cnt_sum,wk2_total_linkid_cnt_mean,wk2_total_linktime_mean_sum,wk2_total_linktime_mean_mean,wk2_total_linktime_std_sum,wk2_total_linktime_std_mean,wk2_total_linktime_q50_sum,wk2_total_linktime_q50_mean,wk2_total_linktime_skew_sum,wk2_total_linktime_skew_mean,m1_total_linkid_cnt_sum,m1_total_linkid_cnt_mean,m1_total_linktime_mean_sum,m1_total_linktime_mean_mean,m1_total_linktime_std_sum,m1_total_linktime_std_mean,m1_total_linktime_q50_sum,m1_total_linktime_q50_mean,m1_total_linktime_skew_sum,m1_total_linktime_skew_mean,win_6_mean_link_ratio_mean_sum,win_6_mean_link_ratio_mean_mean,win_6_std_link_ratio_mean_sum,win_6_std_link_ratio_mean_mean,win_24_mean_link_ratio_mean_sum,win_24_mean_link_ratio_mean_mean,win_24_std_link_ratio_mean_sum,win_24_std_link_ratio_mean_mean,win_72_mean_link_ratio_mean_sum,win_72_mean_link_ratio_mean_mean,win_72_std_link_ratio_mean_sum,win_72_std_link_ratio_mean_mean,win_6_mean_link_time_std_sum,win_6_mean_link_time_std_mean,win_6_std_link_time_std_sum,win_6_std_link_time_std_mean,win_24_mean_link_time_std_sum,win_24_mean_link_time_std_mean,win_24_std_link_time_std_sum,win_24_std_link_time_std_mean,win_72_mean_link_time_std_sum,win_72_mean_link_time_std_mean,win_72_std_link_time_std_sum,win_72_std_link_time_std_mean,win_6_mean_link_c_status_2_mean_sum,win_6_mean_link_c_status_2_mean_mean,win_6_std_link_c_status_2_mean_sum,win_6_std_link_c_status_2_mean_mean,win_24_mean_link_c_status_2_mean_sum,win_24_mean_link_c_status_2_mean_mean,win_24_std_link_c_status_2_mean_sum,win_24_std_link_c_status_2_mean_mean,win_72_mean_link_c_status_2_mean_sum,win_72_mean_link_c_status_2_mean_mean,win_72_std_link_c_status_2_mean_sum,win_72_std_link_c_status_2_mean_mean,win_6_mean_link_c_status_0_mean_sum,win_6_mean_link_c_status_0_mean_mean,win_6_std_link_c_status_0_mean_sum,win_6_std_link_c_status_0_mean_mean,win_24_mean_link_c_status_0_mean_sum,win_24_mean_link_c_status_0_mean_mean,win_24_std_link_c_status_0_mean_sum,win_24_std_link_c_status_0_mean_mean,win_72_mean_link_c_status_0_mean_sum,win_72_mean_link_c_status_0_mean_mean,win_72_std_link_c_status_0_mean_sum,win_72_std_link_c_status_0_mean_mean,win_6_mean_link_ratio_std_sum,win_6_mean_link_ratio_std_mean,win_6_std_link_ratio_std_sum,win_6_std_link_ratio_std_mean,win_24_mean_link_ratio_std_sum,win_24_mean_link_ratio_std_mean,win_24_std_link_ratio_std_sum,win_24_std_link_ratio_std_mean,win_72_mean_link_ratio_std_sum,win_72_mean_link_ratio_std_mean,win_72_std_link_ratio_std_sum,win_72_std_link_ratio_std_mean,win_6_mean_link_c_status_4_mean_sum,win_6_mean_link_c_status_4_mean_mean,win_6_std_link_c_status_4_mean_sum,win_6_std_link_c_status_4_mean_mean,win_24_mean_link_c_status_4_mean_sum,win_24_mean_link_c_status_4_mean_mean,win_24_std_link_c_status_4_mean_sum,win_24_std_link_c_status_4_mean_mean,win_72_mean_link_c_status_4_mean_sum,win_72_mean_link_c_status_4_mean_mean,win_72_std_link_c_status_4_mean_sum,win_72_std_link_c_status_4_mean_mean,win_6_mean_link_c_status_3_mean_sum,win_6_mean_link_c_status_3_mean_mean,win_6_std_link_c_status_3_mean_sum,win_6_std_link_c_status_3_mean_mean,win_24_mean_link_c_status_3_mean_sum,win_24_mean_link_c_status_3_mean_mean,win_24_std_link_c_status_3_mean_sum,win_24_std_link_c_status_3_mean_mean,win_72_mean_link_c_status_3_mean_sum,win_72_mean_link_c_status_3_mean_mean,win_72_std_link_c_status_3_mean_sum,win_72_std_link_c_status_3_mean_mean,win_6_mean_link_time_mean_sum,win_6_mean_link_time_mean_mean,win_6_std_link_time_mean_sum,win_6_std_link_time_mean_mean,win_24_mean_link_time_mean_sum,win_24_mean_link_time_mean_mean,win_24_std_link_time_mean_sum,win_24_std_link_time_mean_mean,win_72_mean_link_time_mean_sum,win_72_mean_link_time_mean_mean,win_72_std_link_time_mean_sum,win_72_std_link_time_mean_mean,win_6_mean_link_c_status_1_mean_sum,win_6_mean_link_c_status_1_mean_mean,win_6_std_link_c_status_1_mean_sum,win_6_std_link_c_status_1_mean_mean,win_24_mean_link_c_status_1_mean_sum,win_24_mean_link_c_status_1_mean_mean,win_24_std_link_c_status_1_mean_sum,win_24_std_link_c_status_1_mean_mean,win_72_mean_link_c_status_1_mean_sum,win_72_mean_link_c_status_1_mean_mean,win_72_std_link_c_status_1_mean_sum,win_72_std_link_c_status_1_mean_mean,win_6_mean_link_time_mean_skew,win_6_mean_link_time_mean_kurt,win_6_std_link_time_mean_skew,win_6_std_link_time_mean_kurt,ata,distance,simple_eta,slice_id,date_time,link_count,link_time_sum,link_ratio_sum,link_current_status_0,link_current_status_1,link_current_status_2,link_current_status_3,link_current_status_4,link_current_status_0_percent,link_current_status_1_percent,link_current_status_2_percent,link_current_status_3_percent,link_current_status_4_percent,weekday,hightemp,lowtemp,driver_id,pr_sum,top_a_sum,dc_sum,pr_mean,top_a_mean,dc_mean
count,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158052.0,2158053.0,2158050.0,2158053.0,2158052.0,2158053.0,2158050.0,2158053.0,2158052.0,2158053.0,2158052.0,2158053.0,2158052.0,2158053.0,2158052.0,2158053.0,2158052.0,2158053.0,2158052.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2155184.0,2158053.0,2134872.0,2158053.0,2156917.0,2158053.0,2154391.0,2158053.0,2156924.0,2158053.0,2154496.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2155184.0,2158053.0,2134872.0,2158053.0,2156917.0,2158053.0,2154391.0,2158053.0,2156924.0,2158053.0,2154496.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2158053.0,2158053.0,2158013.0,2158053.0,2157953.0,2157959.0,2157846.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158053.0,2158041.0,2158041.0,2158041.0
mean,4467661.828779924,89.79694242912477,1.021025083463521,1762768722.0114233,20200827.950593427,624154.5366531777,6499.346030356797,107.18459397707102,1.0457477751229531,595.6869170499464,6.9062763980542075,219.49096299106182,2.500408020714938,553.6435029474947,6.4633324609812535,865959.3644836341,9015.75254131009,93.94376709677661,0.9051677914636824,595.8654238523272,6.907982751186454,220.10615444119156,2.506773281214269,554.0604512002486,6.467212592540564,86.26751497121914,0.9857805874985468,1.629723179224181,0.023088285950048186,86.2615313498214,0.9858256468849965,2.2921654646463776,0.031697170133784404,86.25982621869748,0.9858360552723039,2.6432621611705778,0.03604160623216739,32.34482427864547,0.47375372243773906,28.272010385202297,0.46933956767847557,35.48654766658967,0.47948921217787965,44.80337389038567,0.6112622111731918,35.29664050296828,0.47351498452643714,51.248436166954534,0.6814236392504206,2.1884132478364853,0.02509354505292365,4.772814538391438,0.05525577623637809,2.186381737737402,0.025059645240569674,7.556597415366829,0.08762507453971399,2.1849803960545287,0.025045772120485074,8.914780872226236,0.10319564441022146,2.716482061608397,0.031137421281865082,5.809327599595596,0.06728555413977814,2.7149417001304577,0.031127045930146948,8.925106106240412,0.10366405279263671,2.715227355426387,0.031138485633054862,10.276192924171422,0.1192022983112095,0.9571593590883056,0.015585618096723822,1.0821667471956584,0.019637905293854518,1.0755596533973975,0.015723479661033837,1.9422026272202453,0.027876517711678737,1.074767876113263,0.015616253720558686,2.350282671171,0.032902094523252864,0.3077210024425187,0.0035306000642143153,0.7338806624790072,0.008463081619339036,0.3075325635816437,0.0035297778821450788,1.3670859759231544,0.015708090075820375,0.3068175402582313,0.003524384842123659,1.9657806876813069,0.022415585118994143,1.0691149992787095,0.012251141996505698,2.4505961492000394,0.028280709249226335,1.069187254374715,0.01225896990308865,4.217387437880418,0.04866161994325712,1.0694890817688265,0.012269255753106003,5.415447893201467,0.06227812100125352,599.9173864240867,6.97090541207395,100.1615884459082,1.245840763522761,597.9111310464086,6.935234069860816,137.30607665640738,1.649260435508913,588.8498013828465,6.815035782438041,164.1047804353564,1.9156546651641677,80.98047031226596,0.9279872916047708,11.687974849491923,0.13609502119880584,80.98415836758727,0.9280245610440933,15.4935217431259,0.1810222312837765,80.98568725002217,0.9280221016527614,16.56558713599247,0.19335887261307302,2.33536485789304,9.078896086518606,4.059868542449378,23.06302537211353,842.6362225580187,5284.369708884213,752.9432414310492,110.46978874012825,20200827.950593427,87.26220162340776,599.1405837957179,86.27031101650424,2.718074115881306,80.97677211820098,2.1895407573400654,1.0701697316979704,0.307644900287435,0.03948589002252532,0.9192209479067776,0.025716458780200093,0.011935595165643501,0.0036411081248533426,3.0047084107758244,32.7198660088515,26.59008791721056,40661.525553357584,0.00010176174541111462,2.2065755012762456e-08,0.00031956071163570196,1.1823368755668215e-06,3.343314851055103e-10,3.857914258387011e-06
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1 order_id link_current_status_sum link_current_status_mean date_time_sum date_time_mean wk2_total_linkid_cnt_sum wk2_total_linkid_cnt_mean wk2_total_linktime_mean_sum wk2_total_linktime_mean_mean wk2_total_linktime_std_sum wk2_total_linktime_std_mean wk2_total_linktime_q50_sum wk2_total_linktime_q50_mean wk2_total_linktime_skew_sum wk2_total_linktime_skew_mean m1_total_linkid_cnt_sum m1_total_linkid_cnt_mean m1_total_linktime_mean_sum m1_total_linktime_mean_mean m1_total_linktime_std_sum m1_total_linktime_std_mean m1_total_linktime_q50_sum m1_total_linktime_q50_mean m1_total_linktime_skew_sum m1_total_linktime_skew_mean win_6_mean_link_ratio_mean_sum win_6_mean_link_ratio_mean_mean win_6_std_link_ratio_mean_sum win_6_std_link_ratio_mean_mean win_24_mean_link_ratio_mean_sum win_24_mean_link_ratio_mean_mean win_24_std_link_ratio_mean_sum win_24_std_link_ratio_mean_mean win_72_mean_link_ratio_mean_sum win_72_mean_link_ratio_mean_mean win_72_std_link_ratio_mean_sum win_72_std_link_ratio_mean_mean win_6_mean_link_time_std_sum win_6_mean_link_time_std_mean win_6_std_link_time_std_sum win_6_std_link_time_std_mean win_24_mean_link_time_std_sum win_24_mean_link_time_std_mean win_24_std_link_time_std_sum win_24_std_link_time_std_mean win_72_mean_link_time_std_sum win_72_mean_link_time_std_mean win_72_std_link_time_std_sum win_72_std_link_time_std_mean win_6_mean_link_c_status_2_mean_sum win_6_mean_link_c_status_2_mean_mean win_6_std_link_c_status_2_mean_sum win_6_std_link_c_status_2_mean_mean win_24_mean_link_c_status_2_mean_sum win_24_mean_link_c_status_2_mean_mean win_24_std_link_c_status_2_mean_sum win_24_std_link_c_status_2_mean_mean win_72_mean_link_c_status_2_mean_sum win_72_mean_link_c_status_2_mean_mean win_72_std_link_c_status_2_mean_sum win_72_std_link_c_status_2_mean_mean win_6_mean_link_c_status_0_mean_sum win_6_mean_link_c_status_0_mean_mean win_6_std_link_c_status_0_mean_sum win_6_std_link_c_status_0_mean_mean win_24_mean_link_c_status_0_mean_sum win_24_mean_link_c_status_0_mean_mean win_24_std_link_c_status_0_mean_sum win_24_std_link_c_status_0_mean_mean win_72_mean_link_c_status_0_mean_sum win_72_mean_link_c_status_0_mean_mean win_72_std_link_c_status_0_mean_sum win_72_std_link_c_status_0_mean_mean win_6_mean_link_ratio_std_sum win_6_mean_link_ratio_std_mean win_6_std_link_ratio_std_sum win_6_std_link_ratio_std_mean win_24_mean_link_ratio_std_sum win_24_mean_link_ratio_std_mean win_24_std_link_ratio_std_sum win_24_std_link_ratio_std_mean win_72_mean_link_ratio_std_sum win_72_mean_link_ratio_std_mean win_72_std_link_ratio_std_sum win_72_std_link_ratio_std_mean win_6_mean_link_c_status_4_mean_sum win_6_mean_link_c_status_4_mean_mean win_6_std_link_c_status_4_mean_sum win_6_std_link_c_status_4_mean_mean win_24_mean_link_c_status_4_mean_sum win_24_mean_link_c_status_4_mean_mean win_24_std_link_c_status_4_mean_sum win_24_std_link_c_status_4_mean_mean win_72_mean_link_c_status_4_mean_sum win_72_mean_link_c_status_4_mean_mean win_72_std_link_c_status_4_mean_sum win_72_std_link_c_status_4_mean_mean win_6_mean_link_c_status_3_mean_sum win_6_mean_link_c_status_3_mean_mean win_6_std_link_c_status_3_mean_sum win_6_std_link_c_status_3_mean_mean win_24_mean_link_c_status_3_mean_sum win_24_mean_link_c_status_3_mean_mean win_24_std_link_c_status_3_mean_sum win_24_std_link_c_status_3_mean_mean win_72_mean_link_c_status_3_mean_sum win_72_mean_link_c_status_3_mean_mean win_72_std_link_c_status_3_mean_sum win_72_std_link_c_status_3_mean_mean win_6_mean_link_time_mean_sum win_6_mean_link_time_mean_mean win_6_std_link_time_mean_sum win_6_std_link_time_mean_mean win_24_mean_link_time_mean_sum win_24_mean_link_time_mean_mean win_24_std_link_time_mean_sum win_24_std_link_time_mean_mean win_72_mean_link_time_mean_sum win_72_mean_link_time_mean_mean win_72_std_link_time_mean_sum win_72_std_link_time_mean_mean win_6_mean_link_c_status_1_mean_sum win_6_mean_link_c_status_1_mean_mean win_6_std_link_c_status_1_mean_sum win_6_std_link_c_status_1_mean_mean win_24_mean_link_c_status_1_mean_sum win_24_mean_link_c_status_1_mean_mean win_24_std_link_c_status_1_mean_sum win_24_std_link_c_status_1_mean_mean win_72_mean_link_c_status_1_mean_sum win_72_mean_link_c_status_1_mean_mean win_72_std_link_c_status_1_mean_sum win_72_std_link_c_status_1_mean_mean win_6_mean_link_time_mean_skew win_6_mean_link_time_mean_kurt win_6_std_link_time_mean_skew win_6_std_link_time_mean_kurt ata distance simple_eta slice_id date_time link_count link_time_sum link_ratio_sum link_current_status_0 link_current_status_1 link_current_status_2 link_current_status_3 link_current_status_4 link_current_status_0_percent link_current_status_1_percent link_current_status_2_percent link_current_status_3_percent link_current_status_4_percent weekday hightemp lowtemp driver_id pr_sum top_a_sum dc_sum pr_mean top_a_mean dc_mean
2 count 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158052.0 2158053.0 2158050.0 2158053.0 2158052.0 2158053.0 2158050.0 2158053.0 2158052.0 2158053.0 2158052.0 2158053.0 2158052.0 2158053.0 2158052.0 2158053.0 2158052.0 2158053.0 2158052.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2155184.0 2158053.0 2134872.0 2158053.0 2156917.0 2158053.0 2154391.0 2158053.0 2156924.0 2158053.0 2154496.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2155184.0 2158053.0 2134872.0 2158053.0 2156917.0 2158053.0 2154391.0 2158053.0 2156924.0 2158053.0 2154496.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2158053.0 2158053.0 2158013.0 2158053.0 2157953.0 2157959.0 2157846.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158053.0 2158041.0 2158041.0 2158041.0
3 mean 4467661.828779924 89.79694242912477 1.021025083463521 1762768722.0114233 20200827.950593427 624154.5366531777 6499.346030356797 107.18459397707102 1.0457477751229531 595.6869170499464 6.9062763980542075 219.49096299106182 2.500408020714938 553.6435029474947 6.4633324609812535 865959.3644836341 9015.75254131009 93.94376709677661 0.9051677914636824 595.8654238523272 6.907982751186454 220.10615444119156 2.506773281214269 554.0604512002486 6.467212592540564 86.26751497121914 0.9857805874985468 1.629723179224181 0.023088285950048186 86.2615313498214 0.9858256468849965 2.2921654646463776 0.031697170133784404 86.25982621869748 0.9858360552723039 2.6432621611705778 0.03604160623216739 32.34482427864547 0.47375372243773906 28.272010385202297 0.46933956767847557 35.48654766658967 0.47948921217787965 44.80337389038567 0.6112622111731918 35.29664050296828 0.47351498452643714 51.248436166954534 0.6814236392504206 2.1884132478364853 0.02509354505292365 4.772814538391438 0.05525577623637809 2.186381737737402 0.025059645240569674 7.556597415366829 0.08762507453971399 2.1849803960545287 0.025045772120485074 8.914780872226236 0.10319564441022146 2.716482061608397 0.031137421281865082 5.809327599595596 0.06728555413977814 2.7149417001304577 0.031127045930146948 8.925106106240412 0.10366405279263671 2.715227355426387 0.031138485633054862 10.276192924171422 0.1192022983112095 0.9571593590883056 0.015585618096723822 1.0821667471956584 0.019637905293854518 1.0755596533973975 0.015723479661033837 1.9422026272202453 0.027876517711678737 1.074767876113263 0.015616253720558686 2.350282671171 0.032902094523252864 0.3077210024425187 0.0035306000642143153 0.7338806624790072 0.008463081619339036 0.3075325635816437 0.0035297778821450788 1.3670859759231544 0.015708090075820375 0.3068175402582313 0.003524384842123659 1.9657806876813069 0.022415585118994143 1.0691149992787095 0.012251141996505698 2.4505961492000394 0.028280709249226335 1.069187254374715 0.01225896990308865 4.217387437880418 0.04866161994325712 1.0694890817688265 0.012269255753106003 5.415447893201467 0.06227812100125352 599.9173864240867 6.97090541207395 100.1615884459082 1.245840763522761 597.9111310464086 6.935234069860816 137.30607665640738 1.649260435508913 588.8498013828465 6.815035782438041 164.1047804353564 1.9156546651641677 80.98047031226596 0.9279872916047708 11.687974849491923 0.13609502119880584 80.98415836758727 0.9280245610440933 15.4935217431259 0.1810222312837765 80.98568725002217 0.9280221016527614 16.56558713599247 0.19335887261307302 2.33536485789304 9.078896086518606 4.059868542449378 23.06302537211353 842.6362225580187 5284.369708884213 752.9432414310492 110.46978874012825 20200827.950593427 87.26220162340776 599.1405837957179 86.27031101650424 2.718074115881306 80.97677211820098 2.1895407573400654 1.0701697316979704 0.307644900287435 0.03948589002252532 0.9192209479067776 0.025716458780200093 0.011935595165643501 0.0036411081248533426 3.0047084107758244 32.7198660088515 26.59008791721056 40661.525553357584 0.00010176174541111462 2.2065755012762456e-08 0.00031956071163570196 1.1823368755668215e-06 3.343314851055103e-10 3.857914258387011e-06
4 std 2581237.303292554 56.22209139934151 0.14625504972700396 1054901630.9379686 2.0000954741654255 614629.7589985116 3961.9966697974405 148.725612987228 0.8555934720159407 388.69000240560234 1.5592031037418341 174.80145586966398 1.0101654877402293 349.48867688903704 1.4093281373151436 871458.025808236 5696.244141676745 137.7234202831471 0.7973411613413249 388.92896117766634 1.558271449873249 175.82555249542474 1.0120971386706274 349.9333556207701 1.4099565180802753 52.05461100411868 0.010548153310221232 0.8888492277686458 0.015946012863514315 52.040192401656256 0.01020160642323602 1.1730516582882629 0.018790068605554978 52.035786639133114 0.01013113503731739 1.3282663524398155 0.019660090990265614 22.876310833480222 0.4010438535858314 23.378189120649033 0.4299086554957893 21.64705520790038 0.300803565109309 30.246784913041736 0.3599028383141747 20.442873524700083 0.2794611107443965 33.85473696451345 0.3513061713738496 1.9524313397950857 0.01760596520970008 4.085913142210754 0.0364826797843522 1.5639936578300029 0.01049823579155561 5.050676161845756 0.02973368685134998 1.4881521347861946 0.008893365188365114 5.659572465965043 0.02657607246857608 2.3285704517373973 0.020968564097641883 4.784833427619452 0.04250484893064781 1.872734261093236 0.012147236656869835 5.819429945417835 0.032874660519638485 1.7856592840709156 0.01013879359298155 6.400675198973619 0.028591846014648172 0.6367079111344864 0.017026743290626038 0.8168701201349867 0.02039527985911189 0.6277664194106983 0.01252746189605709 1.1562911159393385 0.0167885288108576 0.6144523343093371 0.012134985331597715 1.3803105223039622 0.017738039160800124 0.5443486629757853 0.006422758922209785 1.2603117780499207 0.014910705764192856 0.354978371177164 0.0038579807121240425 1.4231160954287998 0.014532789266245983 0.31047767869002585 0.003274224837347187 1.6756464566603981 0.014547393992850013 1.2726971788461279 0.013396180349810088 2.816569882444775 0.029570862252902484 0.9197660977852785 0.008147282752955901 3.328667547641834 0.026734637611970674 0.8433565866740418 0.006956191886678332 3.81740731640289 0.024870714495344774 403.23658305367667 1.9320675477872138 78.92258028084426 0.7321167744332737 400.1521481344652 1.8306190051352047 117.23688513082699 0.9472035308645873 392.28458679314593 1.6797448874169334 141.93131140776893 1.0188544628898286 48.5480025817039 0.03401883140845069 8.235677786351873 0.05754907233850395 48.49756542651297 0.02024678050847428 9.569537353339724 0.041187950002267104 48.49434258263024 0.017135690063167088 9.965567727400979 0.03543626885777821 1.3316270272939412 12.903202176440441 1.8747926394961851 22.844461620805273 539.209212443296 4421.50107737492 471.7511945641532 70.75252019456302 2.0000954741654255 52.220712556557764 405.57210046623237 52.21783274310701 4.876658271111734 50.252202999919845 4.347795477801296 3.007631459171947 1.534675171866052 0.08009974606950314 0.1064887060113878 0.05153732956191985 0.03475095978324956 0.019943464660937056 1.9894980687530581 1.2752993259756793 0.9129176751379382 23436.39340102212 6.0652077088920155e-05 3.241527063004052e-05 0.00016999403323419705 3.0072626231287654e-08 4.911418296071371e-07 5.784095168169497e-07
5 min 4.0 0.0 0.0 60602475.0 20200825.0 0.0 1.0 0.0 0.0 0.0 2.17470555291436 0.0 0.3458229496396688 0.0 2.095389473684211 0.0 2.0 0.0 0.0 0.0 2.1397137454977075 0.0 0.0 0.0 2.0856052631578947 1.3359999999997514 0.4453333333332505 0.0 0.0 1.335999999999535 0.4453333333331782 0.0 0.0 1.3359999999981595 0.4453333333327199 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.921189464667503e-16 1.1842378929335003e-16 0.0 0.0 5.282811225508035e-15 1.0565622451016074e-15 0.0 0.0 1.2209688683898407e-14 3.0524221709746018e-15 0.0 0.0 0.0 0.0 0.0 0.0 4.403884664346455e-15 1.4679615547821515e-15 0.0 0.0 3.069043335966454e-14 8.505012636996306e-15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.664001874625056e-08 0.0 0.0 0.0 0.0 1.3061447348531252e-17 4.353815782843749e-18 0.0 0.0 0.0 0.0 0.0 1.7277923356419887e-08 0.0 0.0 0.0 0.0 2.411445230458202e-15 7.986824149649325e-16 0.0 0.0 6.637086666699785 1.897275000010844 0.0 9.282598202400428e-06 6.364615902667396 1.9266249999507856 0.0 1.8778265732536302e-05 6.278358393207272 1.9266249999811784 0.0 0.0 0.5000000000052536 0.05555555555613929 0.0 0.0 0.4999999999998703 0.05555555555554115 0.0 0.0 0.5000000000199729 0.05555555555777477 0.0 0.0 -2.1337138450061683 -5.830538975815136 -1.8952390224981912 -5.8487303528367995 20.0 23.8588 4.0 0.0 20200825.0 3.0 3.8263 1.1001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.0 25.0 0.0 0.0 0.0 0.0 4.912669039568308e-07 8.1860558413547e-129 1.1328659128576882e-06
6 25% 2230851.0 51.0 0.9767441860465116 1030242279.0 20200826.0 205685.0 3474.857142857143 28.3363 0.47232611019736837 339.6437964072864 5.815868689923237 108.20547817336457 1.777201964361006 321.82744999999994 5.487931228813561 277216.0 4659.159314997405 22.762 0.3774791698042168 339.77624420734804 5.817776693333093 108.43692552317074 1.780004878591055 322.0099 5.4912554484657745 50.27611428571368 0.9819328703703776 0.979388859137162 0.012403903130703563 50.278291147342145 0.9820690775187172 1.4381333820002995 0.01870932904183898 50.280706485370274 0.98210970555968 1.6806382799676816 0.02230874852100711 16.709101973327776 0.27330065019641125 12.619085375513382 0.26335494007425786 20.451513166820085 0.3068681602520748 24.414814997477627 0.3938367197009818 20.90553806416662 0.31753550224208715 28.114965955950705 0.46315697269937695 0.8611111111113469 0.013509517044876835 1.9832817933757283 0.031105321937720105 1.1180555555558611 0.018494392865468345 4.083578218715672 0.06792716694009349 1.173866642616915 0.019990287425783824 5.029220056904682 0.0866661496934262 1.1027777777780414 0.01740855762595669 2.468917609115848 0.03928003877000085 1.4234508547027809 0.023897058823549155 4.912895352750648 0.0819732404849539 1.4932573082220677 0.02603677289782749 5.879612063044936 0.10178526423769467 0.4882117784758322 0.00705133094029778 0.4726768690452236 0.008937249467187117 0.6163292081421146 0.00836302235936949 1.0999065231759817 0.016644730887115884 0.6259505925262197 0.008526191491351198 1.349065381931176 0.020749189063212055 4.6259292692714846e-18 7.542275982507856e-20 2.271794648428364e-05 3.0798244857427137e-07 0.08333333333333791 0.0012856606606607606 0.408248670398549 0.006103385039394396 0.11653777333890715 0.0018164036852762481 0.7989411439158044 0.012747767055173342 0.2500000000000033 0.0037165920499253853 0.6094989545187619 0.008980303892072869 0.4525793650795844 0.007108815748523822 1.9153313781097687 0.03048351476135985 0.5091275542784822 0.008239781021901555 2.7856478817563377 0.04630535845257015 337.2363405556818 5.660815226339265 52.96136168335824 0.7702210480120035 336.15935905744567 5.675979704802313 67.96799929420226 1.0270927477588976 331.61806441653164 5.640583239532125 78.3928237540531 1.2339830600323882 47.37619047620034 0.9110566448803502 6.014553635301671 0.09754040508672274 47.40624999999854 0.9176829443535556 8.951570748208589 0.15256983405572813 47.416555939452856 0.9201269859575428 9.737989182900733 0.17051456854060848 1.4685889212871015 2.126323009284821 2.6925383548655297 8.068737159435525 478.0 2605.3329 436.0 53.0 20200826.0 51.0 335.04010000000005 50.1132 0.0 46.0 0.0 0.0 0.0 0.0 0.8842105263157894 0.0 0.0 0.0 1.0 31.0 26.0 20260.0 5.954728164955193e-05 9.086239813771446e-57 0.0001993844006629532 1.1629437413702528e-06 1.3168462605025118e-58 3.4767264222184227e-06
7 50% 4467339.0 76.0 1.0 1515061950.0 20200828.0 433256.0 5767.864156904041 58.05329999999998 0.7957164215686274 494.2322703222725 6.67347392710202 168.78651287466317 2.31247321696397 464.407 6.250633848110464 590755.0 7844.014312001635 48.49329999999999 0.6671349159663865 494.4706774436566 6.676522370589291 169.24212567143655 2.320119195107122 464.68620000000016 6.254179732142859 73.76020833333409 0.9883962779323932 1.4867696109808102 0.01929285119279588 73.75434187514881 0.9883425051824336 2.1158552945347022 0.027700763064297508 73.75386837085969 0.9883385481145052 2.4444900653626003 0.03224175617064338 27.13279513640625 0.3896772348330073 22.542426345425945 0.3799487938948173 30.94151554768452 0.414667523444552 37.84928774276318 0.5353952632390584 31.23884428401885 0.416266193551009 43.59718783480334 0.6147924701915504 1.6722222222223957 0.021666666666674387 3.7309301692769887 0.04841752740281116 1.8128472222232368 0.023814304656720325 6.389866001708138 0.0843178833818178 1.8358346223088944 0.0241726051643738 7.631691634423714 0.10203241694035343 2.117063492063893 0.027276658526667046 4.617662884007031 0.05955081524398804 2.2809576023402616 0.02985585418840136 7.601259256814535 0.09996899620616988 2.30828905116646 0.030360417332279413 8.839805871550805 0.11812998990196735 0.8546704224558319 0.012231256351527569 0.9315375377958406 0.015967809648766637 0.9772547209040668 0.013157311667436423 1.75773729253201 0.025255066754827332 0.9807085813349536 0.013167377676561301 2.130451596259153 0.030561507570116903 0.12135225885225875 0.0014367816091954025 0.2907728532024817 0.0035103362400936517 0.20833333333333456 0.002635458388963082 0.9568523689316 0.012222438887353445 0.2306769722814549 0.002937687176992789 1.552145060781108 0.02013406092866357 0.670634920634921 0.008662280701754389 1.6246951927926214 0.02043738244014944 0.8333333333355979 0.010817519671692971 3.403859455166467 0.04446319249791636 0.8632975933900222 0.011234769725676684 4.543957209511522 0.06020609326712119 493.7307075135082 6.618523599037324 79.67887168625684 1.0650262246483748 492.1270475296155 6.620199074036398 104.1307423983003 1.4113494937854725 485.07732112027253 6.548122094642067 122.4743623818542 1.6651788473658569 69.37142857144767 0.9326538022621322 9.765195482927588 0.12781605355585496 69.38073306524858 0.9297594997595512 13.267676552206972 0.17776051580059546 69.39029087460779 0.9294056585462414 14.269547942587895 0.19262633511139346 1.9902920495165213 4.8701848327743775 3.687167184165265 15.971061036266175 711.0 3998.0657 638.0 108.0 20200828.0 75.0 493.26730000000015 73.7887 0.0 69.0 0.0 0.0 0.0 0.0 0.9553571428571428 0.0 0.0 0.0 3.0 33.0 27.0 40720.0 8.738930245060761e-05 1.694235286004553e-52 0.00028434934412727966 1.181542843062831e-06 2.0660297849084206e-54 3.7222737136752616e-06
8 75% 6702292.0 113.0 1.0517241379310345 2201890252.0 20200830.0 836075.0 8755.672791023842 121.34719999999996 1.3508233840090091 726.9779839729974 7.735142528142925 269.6808714312547 3.0083073198680204 676.3673000000006 7.188994403549806 1151752.0 12208.812500000002 105.0834 1.172454580965909 727.157983600573 7.734032176380113 270.20524531723754 3.020564790610264 676.8649000000001 7.191899318181816 107.9454466666678 0.9926959603174672 2.122802364669244 0.02937599210285965 107.9281410702594 0.9925254853692914 2.9482030921100244 0.0401390437966665 107.9237978946198 0.992487276487212 3.3828684785704284 0.04547914478255236 41.91231846263072 0.5666838023646997 37.15657896674668 0.5591796791052369 45.22371102810958 0.5714149543580508 56.97615683653269 0.737291421500644 45.018300517202455 0.5568752610074897 65.46225668232057 0.8188830643927043 2.9189935064939045 0.032406204906211086 6.36786862407014 0.07093333836130881 2.827876984128165 0.03004906204908607 9.691017330345383 0.10381027420926367 2.794971174388203 0.028983861187507388 11.334631443672912 0.11885620014477424 3.6527777777780552 0.03991317324651103 7.780316871226907 0.0857625904492023 3.5119093631380625 0.03662080170234478 11.418501380687182 0.12158474194211545 3.4681295015619487 0.035023591103002745 13.02561015204931 0.13574932260386796 1.3134547131880725 0.019627548660024093 1.5244120054914725 0.02523329018374914 1.4268346460672607 0.0198531458053726 2.5793708996515687 0.03584161222977891 1.419124025783959 0.019628545228104914 3.1041159421573816 0.042090987299988614 0.3793650793650793 0.004356725146198829 0.9153470348998308 0.010531944125844487 0.4145066738816767 0.0046483650278293384 1.8663900011448225 0.02108061764957301 0.4067881316616704 0.004457311542052089 2.6607921592049735 0.02933698228580314 1.4343434343434482 0.016330631174381202 3.305088829999343 0.03789201197922795 1.4139767237599106 0.0156510114208365 5.58418045097852 0.062020888542653085 1.3921941677154566 0.015007658638304847 7.049747314390178 0.07621419026443862 733.1517577781668 7.854667261909086 121.41262767730957 1.5045892172513544 730.5865643407174 7.815524434993155 164.50988647992375 1.9867848111973208 719.0092481294794 7.672567858933575 197.7154213533789 2.3044918541650605 101.25000000002508 0.950670498084474 15.155434542593962 0.16561187213529918 101.23043311464345 0.9405446023380144 19.45937513233536 0.20623813509317893 101.22696716681679 0.9375770547807696 20.759019725979766 0.21519788472673207 2.790041195854423 10.468515762031824 5.050355672251803 30.15906892256812 1053.0 6314.265 932.0 156.0 20200830.0 109.0 734.1738999999999 108.0498 4.0 102.0 3.0 0.0 0.0 0.04444444444444445 1.0 0.031746031746031744 0.0 0.0 5.0 34.0 27.0 61048.0 0.00012750419506523227 1.6675588380191983e-38 0.0003987688013259063 1.200405034422123e-06 3.2696647555142363e-40 4.095745992639334e-06
9 max 8939076.0 886.0 4.0 17251507966.0 20200831.0 9655801.0 36682.90909090909 3758.3051000000014 52.56945 7286.840813058084 62.63686448863637 3381.0825514783064 30.102298973558373 6563.775749999998 65.1287 11567813.0 44427.0 3762.912499999999 34.30173333333333 7265.973156940879 46.03208501725014 3344.796271598372 26.750360388532 6587.964799999998 42.63668333333335 852.6967666667238 1.0000000000000226 12.397266095578713 0.7071067811866291 852.7670052081507 1.0000000000000546 16.586744465229767 0.7071067811865516 852.7295362648924 0.999994359461974 18.97063621955413 0.7071067811864706 621.2501128994347 92.09507210597572 449.7250809669818 55.42933510377009 398.77833800087006 62.890854936191616 558.9131250501301 42.146949351928185 297.86909315126974 62.89085493619334 564.8715556410666 42.146949351928185 76.900000000001 0.6266666666667025 146.38085593246842 0.7071067811854831 75.0267586580335 0.6250000000003051 105.5159934472998 0.7071067811858912 75.02675865808105 0.6250000000006145 98.57628707455412 0.7071067811869653 82.44444444446353 0.9444444444444748 120.51594431648823 0.7071067811865089 83.4133370286567 0.9444444444447532 102.83997572680616 0.7071067811867108 83.47885744386271 0.9444444444445558 104.20795475175535 0.7071067811871112 7.45498616664199 0.7071067811865834 10.520400683882352 0.5000000000000001 7.523452550093473 0.7071067811866325 15.110032660324507 0.5000000000000001 7.969073249295636 0.7071067811867913 18.31832179490479 0.5 67.73333333333335 0.5480769230769232 76.91332319931284 0.5474634450698099 54.08564997804141 0.5271978021978022 57.98518361298847 0.5195088810097401 53.926670237654626 0.5271978021978027 55.104165813215005 0.5195088810092535 62.9166666666671 0.6666666666666744 76.58503721269176 0.655330085890308 60.0863663812121 0.6666666666668801 60.20179293528384 0.6553300858903782 60.061490187167614 0.6666666666670619 65.81164496039031 0.6553300858898297 10189.878026115346 64.6140030555572 2783.2529650442625 35.14264133965888 9917.094191361286 57.5364663657382 3061.2219767913593 35.14264133921829 9679.55107990508 51.043200988758926 3232.9093910871306 35.142641340007216 765.9777777784097 1.0000000000033051 174.1045305461332 0.7071067811866446 775.2302723589679 1.0000000000014495 150.53067156830278 0.7071067811864671 779.13733759429 1.000000000114923 154.65445308213145 0.7071067811897359 17.599990578162238 382.0302403042582 23.62526066059123 566.2255389010735 10012.0 117909.7931 10601.0 287.0 20200831.0 854.0 10209.1048 852.9918 187.0 848.0 76.0 81.0 55.0 1.0 1.0 1.0 1.0 1.0 6.0 34.0 28.0 80885.0 0.0008704737550512393 0.047619047590635885 0.0024333959808183142 1.775180150693991e-06 0.0007215007210702408 9.818171244766632e-06

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机器学习竞赛实战_优胜解决方案/滴滴——预估到达时间/DCN_12953/model_for_features/features_importance.png
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机器学习竞赛实战_优胜解决方案/滴滴——预估到达时间/DCN_12953/model_for_features/missing_values_table.csv
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,Missing Values,% of Total Values
win_6_std_link_ratio_std_mean,23181,1.1
win_6_std_link_time_std_mean,23181,1.1
win_24_std_link_ratio_std_mean,3662,0.2
win_24_std_link_time_std_mean,3662,0.2
win_72_std_link_ratio_std_mean,3557,0.2
win_72_std_link_time_std_mean,3557,0.2
win_6_mean_link_time_std_mean,2869,0.1
win_6_mean_link_ratio_std_mean,2869,0.1
win_24_mean_link_time_std_mean,1136,0.1
win_24_mean_link_ratio_std_mean,1136,0.1
win_72_mean_link_ratio_std_mean,1129,0.1
win_72_mean_link_time_std_mean,1129,0.1
win_6_std_link_time_mean_kurt,207,0.0
win_6_mean_link_time_mean_kurt,100,0.0
win_6_std_link_time_mean_skew,94,0.0
win_72_std_link_c_status_4_mean_mean,40,0.0
win_24_std_link_c_status_4_mean_mean,40,0.0
win_6_std_link_c_status_4_mean_mean,40,0.0
win_72_std_link_time_mean_mean,40,0.0
win_6_std_link_c_status_3_mean_mean,40,0.0
win_24_std_link_c_status_3_mean_mean,40,0.0
win_72_std_link_c_status_3_mean_mean,40,0.0
win_72_std_link_c_status_0_mean_mean,40,0.0
win_6_std_link_c_status_2_mean_mean,40,0.0
win_24_std_link_c_status_0_mean_mean,40,0.0
win_6_std_link_c_status_0_mean_mean,40,0.0
win_72_std_link_c_status_2_mean_mean,40,0.0
win_24_std_link_c_status_2_mean_mean,40,0.0
win_24_std_link_time_mean_mean,40,0.0
win_72_std_link_ratio_mean_mean,40,0.0
win_24_std_link_ratio_mean_mean,40,0.0
win_6_std_link_ratio_mean_mean,40,0.0
win_6_std_link_c_status_1_mean_mean,40,0.0
win_24_std_link_c_status_1_mean_mean,40,0.0
win_72_std_link_c_status_1_mean_mean,40,0.0
win_6_std_link_time_mean_mean,40,0.0
pr_mean,12,0.0
top_a_mean,12,0.0
dc_mean,12,0.0
wk2_total_linktime_mean_mean,3,0.0
wk2_total_linktime_q50_mean,3,0.0
m1_total_linktime_skew_mean,1,0.0
m1_total_linktime_q50_mean,1,0.0
m1_total_linktime_std_mean,1,0.0
m1_total_linktime_mean_mean,1,0.0
m1_total_linkid_cnt_mean,1,0.0
wk2_total_linktime_skew_mean,1,0.0
wk2_total_linktime_std_mean,1,0.0
wk2_total_linkid_cnt_mean,1,0.0
1 Missing Values % of Total Values
2 win_6_std_link_ratio_std_mean 23181 1.1
3 win_6_std_link_time_std_mean 23181 1.1
4 win_24_std_link_ratio_std_mean 3662 0.2
5 win_24_std_link_time_std_mean 3662 0.2
6 win_72_std_link_ratio_std_mean 3557 0.2
7 win_72_std_link_time_std_mean 3557 0.2
8 win_6_mean_link_time_std_mean 2869 0.1
9 win_6_mean_link_ratio_std_mean 2869 0.1
10 win_24_mean_link_time_std_mean 1136 0.1
11 win_24_mean_link_ratio_std_mean 1136 0.1
12 win_72_mean_link_ratio_std_mean 1129 0.1
13 win_72_mean_link_time_std_mean 1129 0.1
14 win_6_std_link_time_mean_kurt 207 0.0
15 win_6_mean_link_time_mean_kurt 100 0.0
16 win_6_std_link_time_mean_skew 94 0.0
17 win_72_std_link_c_status_4_mean_mean 40 0.0
18 win_24_std_link_c_status_4_mean_mean 40 0.0
19 win_6_std_link_c_status_4_mean_mean 40 0.0
20 win_72_std_link_time_mean_mean 40 0.0
21 win_6_std_link_c_status_3_mean_mean 40 0.0
22 win_24_std_link_c_status_3_mean_mean 40 0.0
23 win_72_std_link_c_status_3_mean_mean 40 0.0
24 win_72_std_link_c_status_0_mean_mean 40 0.0
25 win_6_std_link_c_status_2_mean_mean 40 0.0
26 win_24_std_link_c_status_0_mean_mean 40 0.0
27 win_6_std_link_c_status_0_mean_mean 40 0.0
28 win_72_std_link_c_status_2_mean_mean 40 0.0
29 win_24_std_link_c_status_2_mean_mean 40 0.0
30 win_24_std_link_time_mean_mean 40 0.0
31 win_72_std_link_ratio_mean_mean 40 0.0
32 win_24_std_link_ratio_mean_mean 40 0.0
33 win_6_std_link_ratio_mean_mean 40 0.0
34 win_6_std_link_c_status_1_mean_mean 40 0.0
35 win_24_std_link_c_status_1_mean_mean 40 0.0
36 win_72_std_link_c_status_1_mean_mean 40 0.0
37 win_6_std_link_time_mean_mean 40 0.0
38 pr_mean 12 0.0
39 top_a_mean 12 0.0
40 dc_mean 12 0.0
41 wk2_total_linktime_mean_mean 3 0.0
42 wk2_total_linktime_q50_mean 3 0.0
43 m1_total_linktime_skew_mean 1 0.0
44 m1_total_linktime_q50_mean 1 0.0
45 m1_total_linktime_std_mean 1 0.0
46 m1_total_linktime_mean_mean 1 0.0
47 m1_total_linkid_cnt_mean 1 0.0
48 wk2_total_linktime_skew_mean 1 0.0
49 wk2_total_linktime_std_mean 1 0.0
50 wk2_total_linkid_cnt_mean 1 0.0

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机器学习竞赛实战_优胜解决方案/滴滴——预估到达时间/DCN_12953/model_for_features/model_lgb_for_features.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gc
import os
import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold,KFold
from sklearn.model_selection import train_test_split
def append_all_data(files_list, file_head_path):
"""
concat all the data
:param files_list: the name of data
:param file_head_path: the path of data
:return: DataFrame of data for all
"""
data_all_path = file_head_path + files_list[0]
data_all = pd.read_csv(data_all_path)
data_all = data_all.head(0)
try:
del data_all['Unnamed: 0']
except KeyError as e:
pass
# 循环添加全部数据
for i in files_list:
data_path = file_head_path + i
print("当前文件为：", data_path)
data = pd.read_csv(data_path)
try:
del data['Unnamed: 0']
except KeyError as e:
pass
data_all = data_all.append(data)
return data_all
def file_name(file_dir):
files_list = []
for root, dirs, files in os.walk(file_dir):
# print("success")
for name in files:
files_list.append(name)
return files_list
def del_str_in_list(lst, del_str):
a = []
for i in range(len(lst)):
if del_str not in lst[i]:
a.append(lst[i])
return a
# 自定义lgb评估指标
def lgb_score_mape(preds, train_data):
labels = train_data.get_label()
diff = np.abs(np.array(preds) - np.array(labels))
result = np.mean(diff / labels)
return 'mape',result, False
# 评估指标
def MAPE(true, pred):
diff = np.abs(np.array(pred) - np.array(true))
return np.mean(diff / true)
# Function to calculate missing values by column
def missing_values_table(df):
# Total missing values
mis_val = df.isnull().sum()
# Percentage of missing values
mis_val_percent = 100 * df.isnull().sum() / len(df)
# Make a table with the results
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
# Rename the columns
mis_val_table_ren_columns = mis_val_table.rename(
columns = {0 : 'Missing Values', 1 : '% of Total Values'})
# Sort the table by percentage of missing descending
mis_val_table_ren_columns = mis_val_table_ren_columns[
mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
'% of Total Values', ascending=False).round(1)
# Print some summary information
#print ("Your selected dataframe has " + str(df.shape[1]) + " columns.\n"
# "There are " + str(mis_val_table_ren_columns.shape[0]) +
# " columns that have missing values.")
# Return the dataframe with missing information
return mis_val_table_ren_columns
def model_fit(train_x, train_y):
evals_result = {}
params = {
'boosting_type': 'gbdt',
'objective': 'regression', # 回归目标
#'metric': {'binary_logloss,auc'},
#'max_depth':-1,
'num_leaves': 30,
'learning_rate': 0.07,
#'min_child_samples':21,
#'min_child_weight':0.001,
#'feature_fraction': 0.7,
#'bagging_fraction': 0.6,
#'bagging_freq': 2,
#'min_split_gain':0.5,
'verbose': 0,
#'is_unbalenced':True,
}
n_fold=5
folds = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=42)
# gkf = GroupKFold(n_splits=n_fold)
toof = np.zeros((train_x.shape[0], ))
for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_x,train_y)): # 5折训练
print("fold {}".format(fold_ + 1))
trn_data = lgb.Dataset(train_x.iloc[trn_idx], label=train_y.iloc[trn_idx])
val_data = lgb.Dataset(train_x.iloc[val_idx], label=train_y.iloc[val_idx])
clf = lgb.train(params,
trn_data,
valid_sets=[trn_data, val_data],
valid_names=['train', 'val'],
verbose_eval=10,
feval=lgb_score_mape,
#categorical_feature=[],
evals_result=evals_result,
early_stopping_rounds=20,
num_boost_round = 1000
)
toof[val_idx] = clf.predict(train_x.iloc[val_idx], num_iteration=clf.best_iteration)
#print('拟合情况:')
#lgb.plot_metric(evals_result)
#plt.show()
mape_vale = MAPE(train_y.iloc[val_idx],toof[val_idx])
print("当前MAPE值为：",mape_vale)
print('画特征重要性排序...')
plt.figure(figsize=(10, 30))
ax = lgb.plot_importance(clf, figsize=(10,30))#max_features表示最多展示出前10个重要性特征，可以自行设置
plt.savefig("features_importance.png", dpi=500, bbox_inches='tight')
return clf
if __name__=='__main__':
making_data_dir = '/home/didi2021/didi2021/giscup_2021/order_xt/'
mk_list = file_name(making_data_dir)
mk_list.sort()
mk_data = append_all_data(mk_list, making_data_dir)
print(mk_data.shape)
mk_data['date_time'] = mk_data['date_time'].astype(int)
mk_data = mk_data[mk_data['date_time']!=20200901]
print(mk_data.shape)
describe_df = mk_data.describe()
describe_df.to_csv('describe_df.csv')
print('*-'*40, 'missing_values_table')
ms_table = missing_values_table(mk_data)
ms_table.to_csv('missing_values_table.csv')
train_y = mk_data['ata']
train_x = mk_data.drop(['ata','weather','date_time_dt','order_id','driver_id','date_time'],axis=1)
print(train_y)
print('*-'*40)
print(train_x.head(5))
print('*-'*40, 'model_fit')
model = model_fit(train_x, train_y)
print('................FINISH')
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