引入需要的库

import requests
import numpy as np
import pandas as pd
获取历史双色球的网页
url='https://datachart.500.com/ssq/history/newinc/history.php?start=03001'

参考https://www.jianshu.com/p/79979e7982fa?utm_campaign=hugo获取历史数据

#获取历史所有双色球数据
response = requests.get(url)
response.encoding = 'utf-8'
re_text = response.text#网页数据解析
re=re_text.split('<tbody id="tdata">')[1].split('</tbody>')[0]
result=re.split('<tr class="t_tr1">')[1:]all_numbers=[]
for i in result:each_numbers=[]i=i.replace('<!--<td>2</td>-->','')each=i.split('</td>')[:-1]for j in each:each_numbers.append(j.split('>')[1].replace('&nbsp;',''))all_numbers.append(each_numbers)#定义列名称
col=['期号','红球1','红球2','红球3','红球4','红球5','红球6','蓝球','快乐星期天','奖池奖金(元)','一等奖注数','一等奖奖金(元)','二等奖注数','二等奖奖金(元)','总投注额(元)','开奖日期']#解析完网页数据,生成双色球数据框
df_all=pd.DataFrame(all_numbers,columns=col)
df_all.head()
期号 红球1 红球2 红球3 红球4 红球5 红球6 蓝球 快乐星期天 奖池奖金(元) 一等奖注数 一等奖奖金(元) 二等奖注数 二等奖奖金(元) 总投注额(元) 开奖日期
0 20034 02 08 15 16 26 32 03 878,740,204 11 6,780,706 182 134,531 340,793,680 2020-05-07
1 20033 07 10 12 21 31 32 01 879,873,846 4 10,000,000 97 263,480 320,555,362 2020-05-05
2 20032 03 11 13 14 15 26 13 843,200,936 11 6,588,951 135 161,837 338,904,002 2020-05-03
3 20031 02 05 09 15 16 27 09 850,135,149 14 5,603,722 310 34,081 323,903,392 2020-04-30
4 20030 17 18 21 29 30 32 03 896,891,829 1 10,000,000 74 322,591 327,786,632 2020-04-28
df_all
期号 红球1 红球2 红球3 红球4 红球5 红球6 蓝球 快乐星期天 奖池奖金(元) 一等奖注数 一等奖奖金(元) 二等奖注数 二等奖奖金(元) 总投注额(元) 开奖日期
0 20034 02 08 15 16 26 32 03 878,740,204 11 6,780,706 182 134,531 340,793,680 2020-05-07
1 20033 07 10 12 21 31 32 01 879,873,846 4 10,000,000 97 263,480 320,555,362 2020-05-05
2 20032 03 11 13 14 15 26 13 843,200,936 11 6,588,951 135 161,837 338,904,002 2020-05-03
3 20031 02 05 09 15 16 27 09 850,135,149 14 5,603,722 310 34,081 323,903,392 2020-04-30
4 20030 17 18 21 29 30 32 03 896,891,829 1 10,000,000 74 322,591 327,786,632 2020-04-28
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2539 03005 04 06 15 17 30 31 16 13 14,280,829 0 0 4 260,153 10,661,438 2003-03-09
2540 03004 04 06 07 10 13 25 03 0 11,852,729 0 0 3 281,578 9,517,794 2003-03-06
2541 03003 01 07 10 23 28 32 16 03 9,881,677 0 0 2 332,369 8,917,960 2003-03-02
2542 03002 04 09 19 20 21 26 12 0 8,330,621 0 0 2 264,332 7,398,870 2003-02-27
2543 03001 10 11 12 13 26 28 11 10 2,097,070 0 0 1 898,744 10,307,806 2003-02-23

2544 rows × 16 columns

提取蓝球数据

df_all['蓝球']
0       03
1       01
2       13
3       09
4       03..
2539    16
2540    03
2541    16
2542    12
2543    11
Name: 蓝球, Length: 2544, dtype: object

导入sklearn库

from sklearn.svm import SVR

修改行索引为时间

df_blue = df_all.iloc[::-1]
df_blue = df_blue.set_index('开奖日期')
df_blue
期号 红球1 红球2 红球3 红球4 红球5 红球6 蓝球 快乐星期天 奖池奖金(元) 一等奖注数 一等奖奖金(元) 二等奖注数 二等奖奖金(元) 总投注额(元)
开奖日期
2003-02-23 03001 10 11 12 13 26 28 11 10 2,097,070 0 0 1 898,744 10,307,806
2003-02-27 03002 04 09 19 20 21 26 12 0 8,330,621 0 0 2 264,332 7,398,870
2003-03-02 03003 01 07 10 23 28 32 16 03 9,881,677 0 0 2 332,369 8,917,960
2003-03-06 03004 04 06 07 10 13 25 03 0 11,852,729 0 0 3 281,578 9,517,794
2003-03-09 03005 04 06 15 17 30 31 16 13 14,280,829 0 0 4 260,153 10,661,438
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-04-28 20030 17 18 21 29 30 32 03 896,891,829 1 10,000,000 74 322,591 327,786,632
2020-04-30 20031 02 05 09 15 16 27 09 850,135,149 14 5,603,722 310 34,081 323,903,392
2020-05-03 20032 03 11 13 14 15 26 13 843,200,936 11 6,588,951 135 161,837 338,904,002
2020-05-05 20033 07 10 12 21 31 32 01 879,873,846 4 10,000,000 97 263,480 320,555,362
2020-05-07 20034 02 08 15 16 26 32 03 878,740,204 11 6,780,706 182 134,531 340,793,680

2544 rows × 15 columns

提取20个开奖周期的蓝球数据到数据中

for i in range(1, 21, 1):df_blue.loc[:,'Close Minus ' + str(i)] = df_blue['蓝球'].shift(i)
df_blue
期号 红球1 红球2 红球3 红球4 红球5 红球6 蓝球 快乐星期天 奖池奖金(元) ... Close Minus 11 Close Minus 12 Close Minus 13 Close Minus 14 Close Minus 15 Close Minus 16 Close Minus 17 Close Minus 18 Close Minus 19 Close Minus 20
开奖日期
2003-02-23 03001 10 11 12 13 26 28 11 10 2,097,070 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2003-02-27 03002 04 09 19 20 21 26 12 0 8,330,621 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2003-03-02 03003 01 07 10 23 28 32 16 03 9,881,677 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2003-03-06 03004 04 06 07 10 13 25 03 0 11,852,729 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2003-03-09 03005 04 06 15 17 30 31 16 13 14,280,829 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-04-28 20030 17 18 21 29 30 32 03 896,891,829 ... 03 13 04 07 01 07 14 09 01 09
2020-04-30 20031 02 05 09 15 16 27 09 850,135,149 ... 14 03 13 04 07 01 07 14 09 01
2020-05-03 20032 03 11 13 14 15 26 13 843,200,936 ... 07 14 03 13 04 07 01 07 14 09
2020-05-05 20033 07 10 12 21 31 32 01 879,873,846 ... 02 07 14 03 13 04 07 01 07 14
2020-05-07 20034 02 08 15 16 26 32 03 878,740,204 ... 08 02 07 14 03 13 04 07 01 07

2544 rows × 35 columns

筛选有效数据

sp20 = df_blue[[x for x in df_blue.columns if 'Close Minus' in x or x == '蓝球']].iloc[20:,]
sp20
蓝球 Close Minus 1 Close Minus 2 Close Minus 3 Close Minus 4 Close Minus 5 Close Minus 6 Close Minus 7 Close Minus 8 Close Minus 9 ... Close Minus 11 Close Minus 12 Close Minus 13 Close Minus 14 Close Minus 15 Close Minus 16 Close Minus 17 Close Minus 18 Close Minus 19 Close Minus 20
开奖日期
2003-05-04 01 04 09 13 06 06 13 02 12 12 ... 13 09 08 07 06 16 03 16 12 11
2003-05-08 08 01 04 09 13 06 06 13 02 12 ... 15 13 09 08 07 06 16 03 16 12
2003-05-11 02 08 01 04 09 13 06 06 13 02 ... 12 15 13 09 08 07 06 16 03 16
2003-05-15 14 02 08 01 04 09 13 06 06 13 ... 12 12 15 13 09 08 07 06 16 03
2003-05-18 12 14 02 08 01 04 09 13 06 06 ... 02 12 12 15 13 09 08 07 06 16
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-04-28 03 05 08 06 06 02 08 08 02 07 ... 03 13 04 07 01 07 14 09 01 09
2020-04-30 09 03 05 08 06 06 02 08 08 02 ... 14 03 13 04 07 01 07 14 09 01
2020-05-03 13 09 03 05 08 06 06 02 08 08 ... 07 14 03 13 04 07 01 07 14 09
2020-05-05 01 13 09 03 05 08 06 06 02 08 ... 02 07 14 03 13 04 07 01 07 14
2020-05-07 03 01 13 09 03 05 08 06 06 02 ... 08 02 07 14 03 13 04 07 01 07

2524 rows × 21 columns

提取训练集,留500作为预测集

x_train = sp20[:-500]
x_train
蓝球 Close Minus 1 Close Minus 2 Close Minus 3 Close Minus 4 Close Minus 5 Close Minus 6 Close Minus 7 Close Minus 8 Close Minus 9 ... Close Minus 11 Close Minus 12 Close Minus 13 Close Minus 14 Close Minus 15 Close Minus 16 Close Minus 17 Close Minus 18 Close Minus 19 Close Minus 20
开奖日期
2003-05-04 01 04 09 13 06 06 13 02 12 12 ... 13 09 08 07 06 16 03 16 12 11
2003-05-08 08 01 04 09 13 06 06 13 02 12 ... 15 13 09 08 07 06 16 03 16 12
2003-05-11 02 08 01 04 09 13 06 06 13 02 ... 12 15 13 09 08 07 06 16 03 16
2003-05-15 14 02 08 01 04 09 13 06 06 13 ... 12 12 15 13 09 08 07 06 16 03
2003-05-18 12 14 02 08 01 04 09 13 06 06 ... 02 12 12 15 13 09 08 07 06 16
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2016-12-01 15 10 03 07 14 03 12 13 15 07 ... 01 14 13 16 10 03 10 16 08 09
2016-12-04 12 15 10 03 07 14 03 12 13 15 ... 02 01 14 13 16 10 03 10 16 08
2016-12-06 13 12 15 10 03 07 14 03 12 13 ... 07 02 01 14 13 16 10 03 10 16
2016-12-08 05 13 12 15 10 03 07 14 03 12 ... 15 07 02 01 14 13 16 10 03 10
2016-12-11 06 05 13 12 15 10 03 07 14 03 ... 13 15 07 02 01 14 13 16 10 03

2024 rows × 21 columns

目标值

y_train = sp20['蓝球'].shift(-1)[:-500]
y_train
开奖日期
2003-05-04    08
2003-05-08    02
2003-05-11    14
2003-05-15    12
2003-05-18    16..
2016-12-01    12
2016-12-04    13
2016-12-06    05
2016-12-08    06
2016-12-11    10
Name: 蓝球, Length: 2024, dtype: object

预测集和对比集

x_test = sp20[-500:]
y_test = sp20['蓝球'].shift(-1)[-500:]
x_test
蓝球 Close Minus 1 Close Minus 2 Close Minus 3 Close Minus 4 Close Minus 5 Close Minus 6 Close Minus 7 Close Minus 8 Close Minus 9 ... Close Minus 11 Close Minus 12 Close Minus 13 Close Minus 14 Close Minus 15 Close Minus 16 Close Minus 17 Close Minus 18 Close Minus 19 Close Minus 20
开奖日期
2016-12-13 10 06 05 13 12 15 10 03 07 14 ... 12 13 15 07 02 01 14 13 16 10
2016-12-15 12 10 06 05 13 12 15 10 03 07 ... 03 12 13 15 07 02 01 14 13 16
2016-12-18 14 12 10 06 05 13 12 15 10 03 ... 14 03 12 13 15 07 02 01 14 13
2016-12-20 07 14 12 10 06 05 13 12 15 10 ... 07 14 03 12 13 15 07 02 01 14
2016-12-22 05 07 14 12 10 06 05 13 12 15 ... 03 07 14 03 12 13 15 07 02 01
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-04-28 03 05 08 06 06 02 08 08 02 07 ... 03 13 04 07 01 07 14 09 01 09
2020-04-30 09 03 05 08 06 06 02 08 08 02 ... 14 03 13 04 07 01 07 14 09 01
2020-05-03 13 09 03 05 08 06 06 02 08 08 ... 07 14 03 13 04 07 01 07 14 09
2020-05-05 01 13 09 03 05 08 06 06 02 08 ... 02 07 14 03 13 04 07 01 07 14
2020-05-07 03 01 13 09 03 05 08 06 06 02 ... 08 02 07 14 03 13 04 07 01 07

500 rows × 21 columns

y_test
开奖日期
2016-12-13     12
2016-12-15     14
2016-12-18     07
2016-12-20     05
2016-12-22     07...
2020-04-28     09
2020-04-30     13
2020-05-03     01
2020-05-05     03
2020-05-07    NaN
Name: 蓝球, Length: 500, dtype: object

导入模型,拟合

clf = SVR(kernel='linear')
model = clf.fit(x_train, y_train)
preds = model.predict(x_test)
preds
array([ 8.58676633, 10.04212035,  8.99286766,  7.81664951,  8.85687919,8.17807331,  7.88675707,  9.12631827,  9.86707628,  9.50928296,9.02836386,  7.01681388,  8.8549026 ,  8.36320076,  9.514238  ,9.89583753,  8.12181409,  8.10611289,  8.69316687,  6.95661343,9.28758735,  8.97889805,  8.57062279,  9.47964576,  8.28717634,8.7176217 ,  8.57425781,  8.4514194 , 10.19625642,  7.92558213,7.50841961,  9.56776243,  8.13161002,  8.37967603,  9.07735228,8.67835037,  8.66892358,  9.28955127,  9.73093117,  8.63210465,8.18542601,  8.47619177,  8.71433588,  8.94164347,  7.74923166,8.0290494 ,  8.73319813,  8.89862196,  9.7517356 ,  9.25784447,9.34139293,  7.76386757,  8.71621187, 10.63192191,  8.63566704,8.85784687,  9.20373353,  8.81642518,  8.94184744,  9.32935945,10.1380704 ,  8.3947628 ,  8.64123079, 10.32142012,  9.08417377,8.86278631,  9.20851648,  7.72399751,  8.94238674,  8.25015006,8.92932569,  9.84590386,  8.26172306,  8.78810202,  8.19540522,9.00628716,  8.76530205,  9.29303384,  9.46007778,  9.17797816,7.39612198,  8.96372278, 10.54762032,  8.46384568,  9.77787681,8.87558466,  7.043298  ,  8.99920831,  8.44925127,  9.07705154,9.41660219,  8.38541241,  9.21890421,  8.54078717,  8.62960435,8.41015668,  8.94580875,  9.88939022,  8.63976203,  8.62031344,8.75268473,  8.11734574,  9.61889516,  9.29407703,  9.21098897,8.04105697,  9.59783473,  9.71685777,  9.18833669,  9.82468936,9.59897906,  7.68013441,  8.14455261,  9.3760988 ,  8.95203119,10.37716365, 10.44114214,  7.45247107,  9.38542959,  8.35913058,7.81164495,  9.73742441,  8.17743095,  9.31985947,  9.3599625 ,9.11066203,  8.85810417,  7.41233031,  8.07433288,  8.82599934,7.32655627,  9.38577877,  8.50466695,  8.78407966,  9.11041412,8.06996677,  8.03142014,  9.07850694,  8.85422537,  9.16743652,9.5473424 ,  8.52024396,  8.85845297,  8.01269069,  7.92689016,8.73021864, 10.20886234,  9.79064358,  8.87858553,  8.31592939,8.95579691,  8.00250802,  9.22678545,  9.31060474,  8.17547431,7.88314157,  9.03546515,  7.83289199,  7.82789981,  8.46082521,9.7996846 ,  7.10351517,  8.6908756 ,  8.70377681,  8.28338995,9.13512688,  7.59446757,  9.46477374,  8.37623523,  7.75977077,9.32594739,  8.33945126,  8.77559253,  9.22925321,  8.45836056,9.1597941 ,  9.3142494 ,  8.49366985,  7.98255797,  7.98760949,9.04398843,  8.14048901,  8.99392753,  8.64388447,  8.95796887,7.98834268,  9.76729164,  7.56718879,  8.57143079,  9.19044218,9.05176795,  9.52764316,  7.92642305,  8.376546  ,  9.18667362,8.28305921,  9.87468356,  8.47861223,  8.24944823,  9.30001674,8.06741557,  8.30831829,  9.63323753,  9.08122353,  7.4141506 ,8.55788839,  8.71653865,  7.83489853,  9.98595278, 10.57751475,9.02088601,  9.01491694,  9.1139619 ,  9.123289  ,  8.86248036,10.75222492,  8.56172642,  8.7192524 ,  9.16409649,  8.44308954,9.98260715,  9.23480853,  9.48459594,  8.70702024,  7.23703221,9.27731217,  8.79025182,  8.71373586,  9.52335647,  8.54168366,8.73097331,  9.25416056,  9.03534981,  9.05520839,  9.11663778,9.04749133,  8.47944654,  9.81438931,  9.01441964,  7.47684487,8.85582699,  8.92552577,  8.35441748,  9.1275847 ,  9.64776527,8.86203573,  8.54198461,  9.80352083,  8.16063045,  8.88554805,8.73174713,  8.64411905,  8.31263542,  9.2815712 ,  8.87880302,8.22193847,  9.10355758,  9.97753625,  7.62457492,  8.32021672,9.57287923,  7.93929366,  7.90336997,  9.13307444,  9.214177  ,8.00670382, 10.14327517,  8.89086259,  7.87531793,  9.65669472,9.76025634, 10.24672254,  9.81705915,  7.65693601, 10.02286585,8.32478448,  9.1857039 , 11.06159148,  7.64888748,  8.39394691,8.64290656,  8.36138954,  9.82690075,  8.6416944 ,  9.73837843,8.20274274,  7.95392611, 10.20091045,  8.80705934,  9.53212577,10.63799762,  6.93632862,  9.02839699,  9.05237286,  8.81093175,10.11080219,  9.06786406,  9.11300461,  8.4457192 ,  6.75585618,8.76649629,  8.8132105 ,  7.9615836 ,  9.27740485,  8.4162546 ,7.21073231,  6.94116465,  8.95241416,  8.69584013,  7.86647028,8.59555889,  8.97609799,  8.56667039,  8.72653531, 10.59874911,8.26162894,  7.63103954,  8.67408311,  9.39023857,  9.26900907,9.3962732 ,  9.71496951,  9.25516328,  7.62508116,  7.60458444,9.59621813,  8.25591984,  8.8008075 , 10.23247376,  9.18791843,8.90798302,  7.64298032,  8.94382033, 10.86344729,  8.89845921,8.92924935,  9.73165245,  7.90033258,  9.23674649,  9.53695982,9.29054449,  8.91345427,  8.27243875,  8.75229269,  9.45129901,7.76087736,  7.52857761,  8.99142609,  7.69416645,  8.48868024,7.73687423,  9.57259261,  7.67568725,  7.89184722,  9.20872855,8.29425278,  7.83079287,  9.73131746,  7.96336425,  8.69383879,7.58093308,  9.38637752,  9.08161383,  8.60423298,  8.15600622,8.85871779,  7.75418152,  9.11382437,  9.06194801,  8.35132712,8.28528026,  8.39206   ,  7.98935452,  9.67397555,  8.03195846,9.5347649 ,  8.8821871 ,  8.22867235,  8.48330769, 10.44842799,7.86631999,  8.77922836,  8.41417191,  8.17405712,  9.82266405,10.16471342,  9.19388109,  7.39121275,  9.32162756,  9.08471652,8.78041329,  9.48463257,  8.87974027,  8.17671834,  8.32294427,8.46619554,  7.46938187,  9.37276824,  9.34608469, 10.10050843,9.8652496 ,  8.41954059,  8.54300332,  8.71257141,  9.34343166,9.22338927,  8.64240256,  9.21684098,  9.08615084,  9.3776942 ,10.32916159,  8.569659  , 10.15227339,  9.19148815,  8.12867607,9.37237906,  8.90739615,  8.5545379 ,  9.23484912,  8.51969533,9.3051972 ,  7.71829896,  9.26173291,  8.93609247,  9.83937335,8.48773416,  8.35996003,  9.23122071,  8.19319728,  9.89078363,10.0732114 ,  9.48186304,  8.59211119,  9.26390005, 10.59957576,9.81902182,  9.8013536 , 10.38716622,  8.48734418,  9.84819186,11.18960239,  9.52739786,  9.38003868,  8.4054212 ,  9.33333853,8.33461283,  9.37063959,  9.87256988,  8.86097385,  8.54162744,9.41233592,  8.9490739 ,  8.62019312,  8.72840484,  9.59419651,7.82868303,  7.53581748, 10.06779794,  8.3374766 ,  9.03798276,8.84183194,  8.23103837,  8.43066046,  8.80670356,  8.39586374,8.68708077,  8.51252948,  9.83569563,  8.01033725,  9.60695578,8.47610467,  8.24988492, 10.02792516,  9.81598521,  8.79749459,8.7436275 ,  7.29313677,  9.18171846,  9.15880308,  9.57345822,9.84018262,  9.12219861,  8.27665531,  8.52655564,  8.50303702,10.82241005,  8.24057384,  9.06728118,  8.57496793,  7.9067271 ,9.57301183, 10.28193341,  9.83487469,  9.35375067,  7.85224255,10.05941534,  9.87626911,  8.59939251,  9.54206214,  9.61572063])

四舍五入转换数据类型

preds = np.round(preds)
preds
array([ 9., 10.,  9.,  8.,  9.,  8.,  8.,  9., 10., 10.,  9.,  7.,  9.,8., 10., 10.,  8.,  8.,  9.,  7.,  9.,  9.,  9.,  9.,  8.,  9.,9.,  8., 10.,  8.,  8., 10.,  8.,  8.,  9.,  9.,  9.,  9., 10.,9.,  8.,  8.,  9.,  9.,  8.,  8.,  9.,  9., 10.,  9.,  9.,  8.,9., 11.,  9.,  9.,  9.,  9.,  9.,  9., 10.,  8.,  9., 10.,  9.,9.,  9.,  8.,  9.,  8.,  9., 10.,  8.,  9.,  8.,  9.,  9.,  9.,9.,  9.,  7.,  9., 11.,  8., 10.,  9.,  7.,  9.,  8.,  9.,  9.,8.,  9.,  9.,  9.,  8.,  9., 10.,  9.,  9.,  9.,  8., 10.,  9.,9.,  8., 10., 10.,  9., 10., 10.,  8.,  8.,  9.,  9., 10., 10.,7.,  9.,  8.,  8., 10.,  8.,  9.,  9.,  9.,  9.,  7.,  8.,  9.,7.,  9.,  9.,  9.,  9.,  8.,  8.,  9.,  9.,  9., 10.,  9.,  9.,8.,  8.,  9., 10., 10.,  9.,  8.,  9.,  8.,  9.,  9.,  8.,  8.,9.,  8.,  8.,  8., 10.,  7.,  9.,  9.,  8.,  9.,  8.,  9.,  8.,8.,  9.,  8.,  9.,  9.,  8.,  9.,  9.,  8.,  8.,  8.,  9.,  8.,9.,  9.,  9.,  8., 10.,  8.,  9.,  9.,  9., 10.,  8.,  8.,  9.,8., 10.,  8.,  8.,  9.,  8.,  8., 10.,  9.,  7.,  9.,  9.,  8.,10., 11.,  9.,  9.,  9.,  9.,  9., 11.,  9.,  9.,  9.,  8., 10.,9.,  9.,  9.,  7.,  9.,  9.,  9., 10.,  9.,  9.,  9.,  9.,  9.,9.,  9.,  8., 10.,  9.,  7.,  9.,  9.,  8.,  9., 10.,  9.,  9.,10.,  8.,  9.,  9.,  9.,  8.,  9.,  9.,  8.,  9., 10.,  8.,  8.,10.,  8.,  8.,  9.,  9.,  8., 10.,  9.,  8., 10., 10., 10., 10.,8., 10.,  8.,  9., 11.,  8.,  8.,  9.,  8., 10.,  9., 10.,  8.,8., 10.,  9., 10., 11.,  7.,  9.,  9.,  9., 10.,  9.,  9.,  8.,7.,  9.,  9.,  8.,  9.,  8.,  7.,  7.,  9.,  9.,  8.,  9.,  9.,9.,  9., 11.,  8.,  8.,  9.,  9.,  9.,  9., 10.,  9.,  8.,  8.,10.,  8.,  9., 10.,  9.,  9.,  8.,  9., 11.,  9.,  9., 10.,  8.,9., 10.,  9.,  9.,  8.,  9.,  9.,  8.,  8.,  9.,  8.,  8.,  8.,10.,  8.,  8.,  9.,  8.,  8., 10.,  8.,  9.,  8.,  9.,  9.,  9.,8.,  9.,  8.,  9.,  9.,  8.,  8.,  8.,  8., 10.,  8., 10.,  9.,8.,  8., 10.,  8.,  9.,  8.,  8., 10., 10.,  9.,  7.,  9.,  9.,9.,  9.,  9.,  8.,  8.,  8.,  7.,  9.,  9., 10., 10.,  8.,  9.,9.,  9.,  9.,  9.,  9.,  9.,  9., 10.,  9., 10.,  9.,  8.,  9.,9.,  9.,  9.,  9.,  9.,  8.,  9.,  9., 10.,  8.,  8.,  9.,  8.,10., 10.,  9.,  9.,  9., 11., 10., 10., 10.,  8., 10., 11., 10.,9.,  8.,  9.,  8.,  9., 10.,  9.,  9.,  9.,  9.,  9.,  9., 10.,8.,  8., 10.,  8.,  9.,  9.,  8.,  8.,  9.,  8.,  9.,  9., 10.,8., 10.,  8.,  8., 10., 10.,  9.,  9.,  7.,  9.,  9., 10., 10.,9.,  8.,  9.,  9., 11.,  8.,  9.,  9.,  8., 10., 10., 10.,  9.,8., 10., 10.,  9., 10., 10.])

对比成功率

correct = np.mean(preds == y_test.values)
correct
0.0

结果好像成功率为0,然后发现这里都是类型不对
首先重新取目标集,不需要偏移,然后将字符串数据转为整型

y_test = sp20['蓝球'][-500:]
arr = y_test.values
arr = arr.astype(int)

结果集也重新转换整型,然后再比较

preds = preds.astype(int)
correct = np.mean(preds == arr)
correct
0.056

会发现成功率其实是0.056

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