机器学习 - 线性回归模型实战 03

1. 针对KNN回归算法，挑选了3500张无漏定位的面单条码样本，计算全局灰度均值、全局Ostu值作为样本的训练特征，以之前（c++）统计样本面单明暗度特征分布统计，定位面单时的算法（C++）确定的最佳分割阈值作为标签（已验证，可准确定位面单区域）；3000张作为训练样本，500张作为测试样本，统计结果如下表1所示：

从表1可以看出，通过大量样本（这里3000张）的特征训练，KNN非线性回归算法的回归拟合，500张的测试样本，可以实现正确98.5%的最佳分割参数预测；

训练的样本特征(每行数据，依次：均值、Otsu值、标签) ：

data_train.csv

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22.744144,50,12.744144
23.480339,63,13.480339
19.528658,67,9.528658
49.695782,66,49.695782
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30.33626,46,46
31.363821,124,21.363821
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59.609455,133,59.609455
73.832703,127,73.832703
130.935928,125,125.935928
146.543289,127,127
104.88369,111,99.88369
115.934685,130,115.934685
173.692032,130,130
141.846512,134,136.846512
163.791199,122,122
149.182327,122,122
63.726112,124,63.726112
103.793015,112,98.793015
136.982635,134,135.491318
122.28727,122,122.143631
65.583389,132,65.583389
57.275429,124,57.275429
104.507813,118,104.507813
90.435196,117,90.435196
58.573547,136,58.573547
49.002979,138,49.002979
140.368088,134,135.368088
100.333023,112,100.333023
51.259686,131,51.259686
109.934807,116,104.934807
70.931717,111,70.931717
60.068039,137,60.068039
65.160812,131,65.160812
62.754402,136,62.754402
146.849564,131,131
127.665703,139,127.665703
106.976662,115,101.976662
128.35553,128,128.177765
111.167809,117,106.167809
91.590828,127,91.590828
50.7071,98,50.7071
107.355919,115,102.355919
120.930283,129,115.930283
77.39006,133,77.39006
79.36084,124,79.36084
106.601563,119,106.601563
106.373711,123,106.373711
78.54039,105,78.54039
62.537418,142,62.537418
103.199875,123,103.199875
113.419067,113,113.209534
83.633446,149,83.633446
101.757614,122,101.757614
74.388985,140,74.388985
57.504387,134,57.504387
67.965195,138,67.965195
114.067108,124,109.067108
56.717915,135,56.717915
114.754044,131,114.754044
117.129807,130,117.129807
71.094986,149,71.094986
73.336105,127,73.336105
77.777161,114,77.777161
117.534004,119,118.266998
105.774048,112,100.774048
117.724686,132,117.724686
55.238289,122,55.238289
48.339939,67,48.339939
49.105286,110,49.105286
59.598137,132,59.598137
144.688858,121,121
64.618317,124,64.618317
131.463776,114,114
66.768341,136,66.768341
156.787445,127,127
61.400139,129,61.400139
109.96608,116,104.96608
104.397743,117,104.397743
109.231613,119,104.231613
147.992233,132,132
137.434357,118,118
62.400227,138,62.400227
58.067726,113,58.067726
58.675804,138,58.675804
103.477242,115,103.477242
122.859467,137,122.859467
92.536644,128,92.536644
50.398357,98,50.398357
58.275414,125,58.275414
54.000214,133,54.000214
120.786003,129,115.786003
90.652092,126,90.652092
78.851822,123,78.851822
69.925148,140,69.925148
73.299965,132,73.299965
78.293945,105,78.293945
62.717873,142,62.717873
107.753166,126,107.753166
101.862671,130,101.862671
107.841667,124,107.841667
113.30732,113,113.153656
58.330257,131,58.330257
83.154037,148,83.154037
82.241425,110,82.241425
75.516068,142,75.516068
80.836494,126,80.836494
57.008514,135,57.008514
114.452271,131,114.452271
152.119812,122,122
71.409073,149,71.409073
93.653542,123,93.653542
105.71154,112,100.71154
117.730537,132,117.730537
52.594269,131,52.594269
75.998634,145,75.998634
141.404007,133,136.404007
162.287292,123,123
67.888489,124,67.888489
145.955521,121,121
64.630112,124,64.630112
103.730888,112,98.730888
96.992737,130,96.992737
77.318268,127,77.318268
65.487831,132,65.487831
86.372314,118,86.372314
104.916077,118,104.916077
87.002945,115,87.002945
98.816147,119,98.816147
66.306915,135,66.306915
81.031059,106,81.031059
102.793709,140,102.793709
51.739429,125,51.739429
109.564842,116,104.564842
105.984528,119,105.984528
74.498825,133,74.498825
64.101868,132,64.101868
147.0923,132,132
62.988422,143,62.988422
53.833847,124,53.833847
82.887665,110,82.887665
140.844177,135,135.844177
91.483086,127,91.483086
60.837391,134,60.837391
63.57423,145,63.57423
58.370529,130,58.370529
53.447002,133,53.447002
120.903633,129,115.903633
62.37188,132,62.37188
97.5728,133,97.5728
70.713707,132,70.713707
79.838112,124,79.838112
70.471191,121,70.471191
77.730835,103,77.730835
62.586517,142,62.586517
64.873764,146,64.873764
115.407806,114,114.703903
82.853355,149,82.853355
68.444496,96,68.444496
74.720985,143,74.720985
59.954037,120,59.954037
114.544144,131,114.544144
71.611542,149,71.611542
99.022057,119,99.022057
117.333961,132,117.333961
53.144367,138,53.144367
60.730873,138,60.730873
91.232109,119,91.232109
72.001549,117,72.001549
67.993202,127,67.993202
64.298294,124,64.298294
103.319229,112,98.319229
65.21817,132,65.21817
99.566826,119,99.566826
98.651054,119,98.651054
63.937149,140,63.937149
77.893265,133,77.893265
66.967644,136,66.967644
61.548462,112,61.548462
105.621368,119,105.621368
59.318287,133,59.318287
55.352005,120,55.352005
83.920654,109,83.920654
61.782124,133,61.782124
62.962563,143,62.962563
54.759571,130,54.759571
97.919716,115,97.919716
81.310837,108,81.310837
140.670044,135,135.670044
92.07531,127,92.07531
93.118103,114,93.118103
53.750957,134,53.750957
97.80941,133,97.80941
79.344322,124,79.344322
74.841377,127,74.841377
77.915459,104,77.915459
73.787186,118,73.787186
112.83535,114,113.417679
83.594528,148,83.594528
75.267555,143,75.267555
55.956909,125,55.956909
59.161629,125,59.161629
71.518059,149,71.518059
114.654579,120,109.654579
84.377747,128,84.377747
98.64444,129,98.64444
48.200211,66,48.200211
114.13723,116,115.068619
51.338676,138,51.338676
83.97834,121,83.97834
70.091324,151,70.091324
67.368294,131,67.368294
60.31308,131,60.31308
99.388641,132,99.388641
124.294609,131,119.294609
69.370026,96,69.370026
57.928707,136,57.928707
60.188568,121,60.188568
79.570969,119,79.570969
65.027763,141,65.027763
79.585983,136,79.585983
62.416603,127,62.416603
55.43985,134,55.43985
95.936226,131,95.936226
60.414837,127,60.414837

测试样本特征（每行数据，同训练样本）：

data_test.csv

34.610958,38,36.305481
3.346996,116,3.346996
19.852621,74,9.852621
41.052353,85,31.052353
6.434115,43,6.434115
31.065107,36,33.532555
5.741952,45,5.741952
8.829904,24,8.829904
11.945321,56,6.945321
38.774372,109,28.774372
43.306019,48,45.653008
25.182449,81,15.182449
50.656876,92,50.656876
25.572765,78,15.572765
57.361225,67,52.361225
21.722696,76,11.722696
37.417675,72,27.417675
36.354385,113,26.354385
25.588921,27,26.29446
36.886761,42,31.886761
12.083743,63,7.083743
33.374779,71,23.374779
20.845728,62,10.845728
30.305107,82,20.305107
35.850388,93,25.850388
38.112534,42,40.056267
37.913109,84,27.913109
20.598684,75,10.598684
38.202301,52,28.202301
44.538506,115,44.538506
31.991585,89,21.991585
60.163536,68,55.163536
32.14386,102,22.14386
33.544342,89,23.544342
24.902182,82,14.902182
46.781448,135,46.781448
17.709394,26,12.709394
36.987617,41,38.993809
19.366886,23,21.183443
29.89473,117,19.89473
13.515056,31,8.515056
30.953436,91,20.953436
24.234415,28,26.117207
26.452538,62,16.452538
28.953983,99,18.953983
35.573261,85,25.573261
57.193542,66,52.193542
25.55652,57,15.55652
30.851215,101,20.851215
51.965885,57,46.965885
5.229326,36,5.229326
5.475486,35,5.475486
2.786513,26,2.786513
9.801806,59,9.801806
12.091529,67,7.091529
4.528471,33,4.528471
4.294135,32,4.294135
3.7015,28,3.7015
9.98307,53,9.98307
4.153631,30,4.153631
4.876001,31,4.876001
10.218566,61,5.218566
4.649951,32,4.649951
4.30841,28,4.30841
11.316783,65,6.316783
12.199903,68,7.199903
5.203396,33,5.203396
2.694812,13,2.694812
11.408694,65,6.408694
4.996669,31,4.996669
5.438232,32,5.438232
5.253961,35,5.253961
10.775092,63,5.775092
4.414789,31,4.414789
5.579135,35,5.579135
4.285006,29,4.285006
5.528887,33,5.528887
4.12157,30,4.12157
3.962454,32,3.962454
5.216369,38,5.216369
9.760649,59,9.760649
7.060116,38,7.060116
7.651458,49,49
8.711101,51,8.711101
9.247845,53,9.247845
10.293608,59,5.293608
4.997649,27,4.997649
3.402946,28,3.402946
1.918291,54,1.918291
10.164237,59,5.164237
4.006736,25,4.006736
4.118125,31,4.118125
6.021222,36,6.021222
3.557638,30,3.557638
10.309443,59,5.309443
11.794813,66,6.794813
7.297502,43,7.297502
8.753753,52,8.753753
6.192482,38,6.192482
10.270559,59,5.270559
9.761035,56,9.761035
9.087008,54,9.087008
5.26259,32,5.26259
9.617766,45,9.617766
7.147732,40,7.147732
10.200027,60,5.200027
8.601176,45,8.601176
6.390631,35,6.390631
7.116443,39,7.116443
6.320671,43,6.320671
5.482214,33,5.482214
12.013244,62,7.013244
10.588711,47,47
8.725661,52,8.725661
10.541021,62,5.541021
10.383012,58,5.383012
9.597002,53,9.597002
9.774297,56,9.774297
7.990617,50,7.990617
7.579581,37,7.579581
4.234293,28,4.234293
27.211752,35,22.211752
7.064547,36,7.064547
6.158052,30,6.158052
18.083481,39,8.083481
7.953556,38,7.953556
8.179549,36,8.179549
5.940541,35,5.940541
1.481915,33,1.481915
9.264028,54,9.264028
10.977699,61,5.977699
5.564646,34,5.564646
6.99717,35,6.99717
5.601728,29,5.601728
6.661428,36,6.661428
5.941431,33,5.941431
7.059155,36,7.059155
10.08208,56,5.08208
9.991819,57,9.991819
28.60166,34,23.60166
5.241675,31,5.241675
6.512276,30,6.512276
6.184775,32,6.184775
6.647437,35,6.647437
6.312063,30,6.312063
7.986105,42,7.986105
6.090766,31,6.090766
6.948818,34,6.948818
25.36018,42,15.36018
7.241805,37,7.241805
41.143703,75,31.143703
17.670971,76,12.670971
8.494156,74,8.494156
25.09428,91,15.09428
12.044358,24,7.044358
7.195629,48,48
37.344101,61,27.344101
17.117414,72,12.117414
28.778164,67,18.778164
16.909248,85,11.909248
33.507877,95,23.507877
11.713397,68,6.713397
14.426616,78,9.426616
4.365325,63,4.365325
37.774452,55,27.774452
15.233495,93,10.233495
17.766384,72,12.766384
14.93892,58,9.93892
12.198055,81,7.198055
17.486244,81,12.486244
8.307367,71,8.307367
8.818887,49,49
9.940237,70,9.940237
10.040579,69,5.040579
14.891448,76,9.891448
6.880139,85,6.880139
14.071385,91,9.071385
9.027193,76,9.027193
13.777058,84,8.777058
15.945816,83,10.945816
10.562931,51,5.562931
32.367722,77,22.367722
15.102614,86,10.102614
8.48933,86,8.48933
7.656585,67,7.656585
12.720745,80,7.720745
9.531564,68,9.531564
9.921285,85,9.921285
20.360197,80,10.360197
21.984753,45,11.984753
9.005699,22,9.005699
13.543499,27,8.543499
4.931559,65,4.931559
5.983317,74,5.983317
21.753807,35,11.753807
27.678898,27,27.339449
16.497736,81,11.497736
22.531609,25,23.765804
9.74607,73,9.74607
7.728232,81,7.728232
10.884642,54,5.884642
9.421863,89,9.421863
17.898438,85,12.898438
13.330063,94,8.330063
10.648492,86,5.648492
17.185091,102,12.185091
7.231105,78,7.231105
26.650333,80,16.650333
29.846445,99,19.846445
19.93413,86,9.93413
12.494806,79,7.494806
13.855819,85,8.855819
24.474703,97,14.474703
13.619632,70,8.619632
10.884563,80,5.884563
26.390434,76,16.390434
24.665455,80,14.665455
12.091378,45,7.091378
48.790485,66,48.790485
58.727077,65,53.727077
3.461775,69,3.461775
10.732383,75,5.732383
19.973185,95,9.973185
16.60914,81,11.60914
11.257355,88,6.257355
8.601539,61,8.601539
26.320282,82,16.320282
10.837441,83,5.837441
14.524035,77,9.524035
28.157633,94,18.157633
14.600282,92,9.600282
7.567971,74,7.567971
9.613257,89,9.613257
14.958475,90,9.958475
10.467405,78,5.467405
40.052055,70,30.052055
14.275487,59,9.275487
16.433928,60,11.433928
34.207832,97,24.207832
41.892746,49,36.892746
11.80199,82,6.80199
7.910464,71,7.910464
20.381308,91,10.381308
28.125576,65,18.125576
12.66227,73,7.66227
21.952679,90,11.952679
17.952971,62,12.952971
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37.497105,92,27.497105
41.519188,96,31.519188
31.643658,83,21.643658
38.255802,101,28.255802
37.812332,107,27.812332
31.81753,83,21.81753
33.149017,89,23.149017
36.973347,101,26.973347
33.656384,81,23.656384
41.051319,87,31.051319
36.375477,98,26.375477
36.562943,99,26.562943
36.430264,94,26.430264
33.223904,79,23.223904
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35.403278,93,25.403278
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32.09473,100,22.09473
33.16827,85,23.16827
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42.514442,104,42.514442
33.606491,98,23.606491
36.088795,97,26.088795
34.366554,90,24.366554
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50.827614,82,50.827614
34.092598,89,24.092598
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47.523186,93,47.523186
37.379986,95,27.379986
40.765888,73,30.765888
31.574436,87,21.574436
27.79917,77,17.79917
23.374029,71,13.374029
32.065231,87,22.065231
28.20451,78,18.20451
29.196819,84,19.196819
24.357103,76,14.357103
25.25563,76,15.25563
33.704288,83,23.704288
28.625233,80,18.625233
22.713417,72,12.713417
93.628845,128,93.628845
119.661911,122,120.830956
101.787872,122,101.787872
57.285397,113,57.285397
68.870117,128,68.870117
60.70155,124,60.70155
63.735428,138,63.735428
64.355179,138,64.355179
61.308041,128,61.308041
60.500786,139,60.500786
114.008118,128,114.008118
58.888134,134,58.888134
97.20256,117,97.20256
99.030006,119,99.030006
93.82383,127,93.82383
60.23233,138,60.23233
73.419373,138,73.419373
70.624283,130,70.624283
123.625648,138,123.625648
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107.746002,120,107.746002
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64.178337,138,64.178337
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54.555874,130,54.555874
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119.041702,133,119.041702
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84.659195,116,84.659195
111.607864,114,112.803932
84.029655,148,84.029655
75.385887,142,75.385887
149.143066,126,126
58.053543,123,58.053543
71.269005,149,71.269005
100.928162,132,100.928162
144.670441,119,119
47.866852,67,47.866852
60.197014,132,60.197014
127.47805,120,122.47805
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101.679253,122,101.679253
134.134186,134,134.067093
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75.041321,93,75.041321
45.930099,130,45.930099
100.523094,120,100.523094
95.58989,131,95.58989
80.481461,111,80.481461
107.88929,120,107.88929
132.809982,123,127.809982
65.339081,103,65.339081
62.947906,131,62.947906
63.128883,143,63.128883
54.407631,130,54.407631
63.150932,133,63.150932
69.525101,143,69.525101
55.441246,129,55.441246
98.606056,112,98.606056
140.183914,135,135.183914
56.523968,125,56.523968
53.234592,132,53.234592
65.979408,96,65.979408
62.519402,104,62.519402
74.656563,127,74.656563
77.875435,104,77.875435
170.987167,129,129
164.232285,142,142
70.229118,133,70.229118
60.162167,131,60.162167
61.55127,132,61.55127
100.352943,132,100.352943
50.619099,124,50.619099
116.33728,130,116.33728
102.232162,122,102.232162
67.978615,127,67.978615
59.896347,121,59.896347
79.02639,118,79.02639
62.444733,127,62.444733
63.423138,135,63.423138
70.027725,129,70.027725
100.317955,120,100.317955
95.081573,129,95.081573
60.672817,130,60.672817
66.656052,139,66.656052
80.588287,111,80.588287
61.506241,134,61.506241
60.734787,131,60.734787
63.20887,132,63.20887
54.791882,131,54.791882
60.529564,126,60.529564
69.333359,145,69.333359
73.760254,144,73.760254
68.874443,132,68.874443
53.691647,133,53.691647
76.995445,143,76.995445
66.874077,99,66.874077
60.246479,134,60.246479
53.921581,125,53.921581
113.351631,130,113.351631
75.039848,127,75.039848
77.565605,103,77.565605
65.072037,145,65.072037
67.566956,124,67.566956
60.274048,131,60.274048
63.001125,120,63.001125
63.092091,141,63.092091
99.861771,132,99.861771
66.166687,135,66.166687
74.794472,142,74.794472
101.808067,122,101.808067
56.347569,125,56.347569
60.330524,122,60.330524
64.193459,139,64.193459
79.926239,119,79.926239
58.825787,130,58.825787
62.32597,127,62.32597
72.176491,125,72.176491
62.043495,133,62.043495
54.970989,129,54.970989
71.015045,121,71.015045
95.243324,130,95.243324
60.903439,132,60.903439
58.694344,120,58.694344
66.881218,141,66.881218
80.736198,112,80.736198
60.387344,137,60.387344
60.11945,131,60.11945
65.386101,132,65.386101
63.377396,132,63.377396
110.935661,121,110.935661
74.357079,147,74.357079
54.05508,123,54.05508
69.3022,131,69.3022
73.567337,124,73.567337
146.13797,127,127
140.169006,133,135.169006
160.342453,123,123
74.721283,110,74.721283
56.900578,127,56.900578
29.553043,83,19.553043
29.828926,83,19.828926
34.848907,91,24.848907
23.91769,73,13.91769
29.175308,84,19.175308
52.926823,122,52.926823
33.314518,123,23.314518
31.35643,100,21.35643
37.166519,97,27.166519
57.088036,121,57.088036
9.667116,59,9.667116
9.928235,58,9.928235
10.462296,54,5.462296
5.685306,38,5.685306
7.851795,48,48
6.617682,40,6.617682
6.89844,36,6.89844
6.366533,38,6.366533
7.848205,43,7.848205
23.359785,39,13.359785
16.432072,74,11.432072
18.626863,89,8.626863
16.041739,56,11.041739
12.498924,78,7.498924
6.578481,75,6.578481
11.915723,60,6.915723
11.940074,82,6.940074
7.465736,81,7.465736
30.35747,53,20.35747
7.71101,73,7.71101
35.511364,92,25.511364
45.867851,72,45.867851
78.638855,74,76.319427
33.504581,85,23.504581
29.098211,87,19.098211
67.197502,74,62.197502
35.616013,91,25.616013
28.203951,75,18.203951
29.834435,83,19.834435
60.240398,76,60.240398
103.5765,112,98.5765
121.243271,121,121.121635
64.774338,131,64.774338
104.356331,118,104.356331
98.874283,119,98.874283

训练及测试代码：

MLR.py

import numpy as np
from numpy import genfromtxt
from sklearn import linear_model
from sklearn.svm  import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
from sklearn.neural_network import MLPRegressor
import xgboost as xgb
import lightgbm as  lgb


# 读取训练数据集
dataTrainPath = r"data_train.csv"
trainData = genfromtxt(dataTrainPath, delimiter=',')
# print("data")
# print(deliveryData)

X = trainData[:, :-1]
Y = trainData[:, -1]

# print("X:")
# print(X)
# print("Y: ")
# print(Y)

f = open('data_out_0929.txt', mode='w')

# regr = SVR()
# regr = DecisionTreeRegressor()
regr = KNeighborsRegressor(n_neighbors=1)
# regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=100)
# regr = MLPRegressor(max_iter=10000000)
# regr = xgb.XGBRegressor(max_depth=3,
#                         learning_rate=0.1,
#                         n_estimators=100,
#                         objective='reg:linear',
#                         n_jobs= -1)

# regr = lgb.LGBMRegressor(num_leaves=31,
#                         learning_rate=0.05,
#                         n_estimators=20)

regr.fit(X, Y)

# test_datas = [[32.831848,92],
#                 [69.91584,76], 
#                 [30.914917,79],
#                 [59.839161,70],
#                  [55.872555,73],
#                  [59.847248,73],
#                  [40.887569,103],
#                  [74.10672,76],
#                  [65.118828,79],
#                  [66.746979,77]]


dataTestPath = r"data_test.csv"
testData = genfromtxt(dataTestPath, delimiter=',')
# print("data")
# print(deliveryData)

X_test = testData[:, :-1]
Y_label = testData[:, -1]


# test_datas = [[33.109421,77],
#                 [37.116528,75], 
#                 [77.644287,79],
#                 [54.451225,73],
#                  [74.37429,82],
#                  [34.216698,96],
#                  [77.074898,74],
#                  [32.381546,97],
#                  [45.806538,102],
#                  [40.518852,71]]

i=0
for data in X_test:    
    X_pred  = data
    X_pred = np.array(X_pred).reshape(1, -1)
    Y_pred = regr.predict(X_pred)
    i= i+1
    data_Str = str(i) + ', '+str(Y_pred[0])+', ' + str(Y_label[i-1])+'\n'
    print(i, Y_pred[0], Y_label[i-1])
    f.write(data_Str)
    
f.close()

 2. 针对以上3000张正确定位面单的条码样本特征训练，挑选漏定位面单的样本作为测试样本，统计结果如下表2所示：

 

 

   从表2中，可以看出：利用漏定位面单的样本计算特征，来进行测试KNN回归的训练拟合模型，仍然能够实现91.92%的正确预测；

   对没有正确预测的样本进行统计分析，发现：条码样本在统计的过程中，受光照影响，样本出现局部光照不均匀、反光、强光等的影响，导致样本整体的像素灰度分布各种情况都有可能出现，以致不满足大多数样本在正常光照下采集时的明暗、灰度分布规律，所以，这也才导致训练的回归模型不能正确预测面单的最佳分割阈值。（这也验证了上一节 中利用漏定位面单的样本进行训练，无论如何都无法正确拟合+测试预测结果都不错的原因).