如何选择特定的数据点进行测试和培训？
如何选择特定的数据点进行测试和培训？
提问于 2022-07-22 17:01:06
我有以下数据集，我想将其分割为25%用于测试，其余用于培训。我想选择在随机生成的训练数据集中包含的最后15个数据点。我该怎么做？.
scaler = preprocessing.MinMaxScaler()
names = data_set.columns
d = scaler.fit_transform(data_set)
scaled_df = pd.DataFrame(d, columns=names)
X, y = scaled_df[[ "Part's Z-Height (mm)","Part's Weight (N)","Part's Volume (cm^3)","Part's Surface Area (cm^2)","Layer Height (mm)","Infill Density (%)","Printing/Scanning Speed (mm/s)","Part's Orientation (Support's height) (mm)","Part's Orientation (Support's volume) (cm^3)"]], scaled_df [["Climate change (kg CO2 eq.)","Climate change, incl biogenic carbon (kg CO2 eq.)","Fine Particulate Matter Formation (kg PM2.5 eq.)","Fossil depletion (kg oil eq.)","Freshwater Consumption (m^3)","Freshwater ecotoxicity (kg 1,4-DB eq.)","Freshwater Eutrophication (kg P eq.)","Human toxicity, cancer (kg 1,4-DB eq.)","Human toxicity, non-cancer (kg 1,4-DB eq.)","Ionizing Radiation (Bq. C-60 eq. to air)","Land use (Annual crop eq. yr)","Marine ecotoxicity (kg 1,4-DB eq.)","Marine Eutrophication (kg N eq.)","Metal depletion (kg Cu eq.)","Photochemical Ozone Formation, Ecosystem (kg NOx eq.)","Photochemical Ozone Formation, Human Health (kg NOx eq.)","Stratospheric Ozone Depletion (kg CFC-11 eq.)","Terrestrial Acidification (kg SO2 eq.)","Terrestrial ecotoxicity (kg 1,4-DB eq.)"]]
scaled_df.head(34)
Part's Z-Height (mm) Part's Weight (N) Part's Volume (cm^3) Part's Surface Area (cm^2) Layer Height (mm) Infill Density (%) Printing/Scanning Speed (mm/s) Part's Orientation (Support's height) (mm) Part's Orientation (Support's volume) (cm^3) Climate change (kg CO2 eq.) Climate change, incl biogenic carbon (kg CO2 eq.) Fine Particulate Matter Formation (kg PM2.5 eq.) Fossil depletion (kg oil eq.) Freshwater Consumption (m^3) Freshwater ecotoxicity (kg 1,4-DB eq.) Freshwater Eutrophication (kg P eq.) Human toxicity, cancer (kg 1,4-DB eq.) Human toxicity, non-cancer (kg 1,4-DB eq.) Ionizing Radiation (Bq. C-60 eq. to air) Land use (Annual crop eq. yr) Marine ecotoxicity (kg 1,4-DB eq.) Marine Eutrophication (kg N eq.) Metal depletion (kg Cu eq.) Photochemical Ozone Formation, Ecosystem (kg NOx eq.) Photochemical Ozone Formation, Human Health (kg NOx eq.) Stratospheric Ozone Depletion (kg CFC-11 eq.) Terrestrial Acidification (kg SO2 eq.) Terrestrial ecotoxicity (kg 1,4-DB eq.)
0 0.419716 0.009814 0.009814 0.000000 0.0 0.00 0.666667 0.380679 0.039133 0.048122 0.097393 0.015227 0.010446 0.160780 0.006969 0.008898 0.161179 0.030612 0.009631 0.007965 0.008285 0.008061 0.110836 0.011551 0.012469 0.008489 0.012848 0.070063
1 0.419716 0.009814 0.009814 0.000000 0.2 0.00 0.666667 0.380679 0.035639 0.019322 0.039110 0.006289 0.004875 0.064476 0.004181 0.005085 0.064558 0.012911 0.004815 0.004707 0.004751 0.004478 0.044499 0.004950 0.004988 0.004669 0.005353 0.028190
2 0.419716 0.018646 0.018646 0.000000 0.4 0.00 0.666667 0.161784 0.030049 0.010755 0.020706 0.004303 0.003482 0.033060 0.004878 0.004661 0.033140 0.007705 0.004357 0.004707 0.004508 0.004478 0.023486 0.003300 0.003325 0.004527 0.003212 0.015331
3 0.419716 0.017664 0.017664 0.000000 0.6 0.00 0.666667 0.161784 0.030049 0.006562 0.011887 0.003310 0.002786 0.018275 0.004878 0.004237 0.018291 0.004581 0.004127 0.004345 0.004142 0.004478 0.013185 0.002475 0.002494 0.004244 0.002141 0.008902
4 0.419716 0.019627 0.019627 0.000000 1.0 0.00 0.666667 0.380679 0.030748 0.002917 0.003067 0.002979 0.003482 0.003080 0.005575 0.005085 0.003013 0.002499 0.004586 0.005069 0.004630 0.004926 0.002884 0.002475 0.002494 0.004810 0.002141 0.002967
5 0.213663 0.008832 0.008832 0.000000 0.0 0.00 0.666667 0.135417 0.029350 0.036274 0.079371 0.007613 0.003482 0.134702 0.000697 0.002119 0.134926 0.021241 0.002752 0.001448 0.002071 0.001343 0.091059 0.004950 0.004988 0.001839 0.005353 0.055391
6 0.213663 0.008832 0.008832 0.000000 0.2 0.00 0.666667 0.135417 0.028651 0.012760 0.030291 0.000993 0.000000 0.052772 0.000000 0.000000 0.052937 0.006664 0.000000 0.000000 0.000000 0.000000 0.035023 0.000000 0.000000 0.000000 0.000000 0.020607
7 0.213663 0.019627 0.019627 0.000000 0.4 0.00 0.666667 0.135417 0.027952 0.009114 0.018021 0.003310 0.002786 0.028953 0.004181 0.003814 0.029051 0.005623 0.003440 0.003621 0.003533 0.003583 0.020190 0.002475 0.002494 0.003679 0.002141 0.013023
8 0.213663 0.019627 0.019627 0.000000 0.6 0.00 0.666667 0.135417 0.027254 0.004739 0.009202 0.001986 0.001393 0.014579 0.003484 0.002966 0.014633 0.003540 0.002752 0.003259 0.002802 0.003135 0.010301 0.001650 0.001663 0.002971 0.001071 0.006759
9 0.213663 0.018646 0.018646 0.000000 1.0 0.00 0.666667 0.135417 0.026555 0.000000 0.000000 0.000000 0.000000 0.000000 0.002787 0.002119 0.000000 0.000000 0.001605 0.002172 0.001706 0.001791 0.000000 0.000000 0.000000 0.001981 0.000000 0.000000
10 0.125603 0.008832 0.008832 0.039373 0.0 0.00 0.000000 0.137778 0.053110 0.063799 0.121166 0.025157 0.019499 0.195072 0.014634 0.017373 0.195180 0.043107 0.018344 0.016293 0.016813 0.016122 0.136794 0.021452 0.021613 0.016836 0.022484 0.090010
11 0.125603 0.008832 0.008832 0.039373 0.0 0.00 0.333333 0.137778 0.053110 0.061065 0.115414 0.024495 0.019499 0.186858 0.014634 0.017373 0.186572 0.042066 0.017886 0.016293 0.016813 0.016122 0.130202 0.020627 0.020781 0.016695 0.022484 0.085064
12 0.125603 0.008832 0.008832 0.039373 0.0 0.00 0.866667 0.137778 0.053110 0.057783 0.108512 0.023502 0.018802 0.174333 0.014634 0.016949 0.174736 0.039983 0.017657 0.015930 0.016813 0.016122 0.122373 0.020627 0.020781 0.016553 0.021413 0.080448
13 0.125603 0.008832 0.008832 0.039373 0.0 0.00 1.000000 0.137778 0.053110 0.057237 0.107745 0.023502 0.018802 0.172485 0.014634 0.016949 0.172800 0.039983 0.017657 0.015930 0.016813 0.016122 0.121137 0.019802 0.019950 0.016412 0.021413 0.079624
14 0.771175 0.000000 0.000000 0.039373 0.0 0.00 0.000000 0.786031 0.073375 0.079657 0.145706 0.035419 0.029248 0.229979 0.022997 0.026271 0.231762 0.056643 0.027287 0.024982 0.026559 0.025078 0.163576 0.031353 0.031588 0.025750 0.032120 0.109792
15 0.771175 0.000000 0.000000 0.039373 0.0 0.00 0.333333 0.786031 0.073375 0.077470 0.140721 0.034757 0.028552 0.221766 0.022997 0.026271 0.223155 0.055602 0.027058 0.024982 0.026559 0.025078 0.157808 0.030528 0.030756 0.025608 0.032120 0.104847
16 0.771175 0.000000 0.000000 0.039373 0.0 0.00 0.866667 0.786031 0.073375 0.074918 0.135736 0.034426 0.028552 0.213552 0.022997 0.025847 0.214547 0.053519 0.026829 0.024620 0.026559 0.024631 0.152040 0.030528 0.030756 0.025467 0.031049 0.101550
17 0.771175 0.000000 0.000000 0.039373 0.0 0.00 1.000000 0.786031 0.073375 0.074553 0.134586 0.034095 0.028552 0.211499 0.022997 0.025847 0.212395 0.053519 0.026829 0.024620 0.025341 0.024631 0.151215 0.030528 0.030756 0.025467 0.031049 0.101550
18 1.000000 0.402355 0.402355 0.239088 0.0 0.25 0.666667 0.695845 0.083857 0.228946 0.263804 0.206885 0.202646 0.303901 0.200697 0.202542 0.309232 0.218034 0.202018 0.202028 0.201998 0.201970 0.273589 0.203795 0.204489 0.202037 0.204497 0.244972
19 1.000000 0.586850 0.586850 0.239088 0.0 0.50 0.666667 0.695845 0.083857 0.303682 0.328988 0.285005 0.280641 0.367556 0.279443 0.280932 0.367334 0.294044 0.282275 0.279508 0.281189 0.280340 0.339102 0.282178 0.282627 0.281268 0.282655 0.315859
20 1.000000 0.770363 0.770363 0.239088 0.0 0.75 0.666667 0.695845 0.083857 0.376595 0.398006 0.361139 0.358635 0.429158 0.357491 0.360169 0.429740 0.370054 0.360238 0.359160 0.359162 0.359606 0.406675 0.359736 0.359933 0.359083 0.359743 0.386746
21 1.000000 0.952895 0.952895 0.239088 0.0 1.00 0.666667 0.695845 0.083857 0.511484 0.597393 0.453823 0.444986 0.706366 0.435540 0.440678 0.707338 0.482507 0.442788 0.438812 0.440789 0.440215 0.620931 0.448020 0.448878 0.441143 0.450749 0.549951
22 0.000000 0.409225 0.409225 0.239088 0.0 0.25 0.666667 0.000000 0.000000 0.177907 0.207439 0.158226 0.155292 0.246407 0.153310 0.154661 0.246826 0.168055 0.156157 0.154598 0.154483 0.154501 0.215904 0.155941 0.156276 0.153933 0.156317 0.190570
23 0.000000 0.590775 0.590775 0.239088 0.0 0.50 0.666667 0.000000 0.000000 0.252643 0.279141 0.235353 0.231894 0.314168 0.230662 0.232203 0.313536 0.244065 0.231828 0.232078 0.232456 0.231975 0.287186 0.232673 0.233583 0.231749 0.233405 0.264754
24 0.000000 0.773307 0.773307 0.239088 0.0 0.75 0.666667 0.000000 0.000000 0.327379 0.348160 0.311486 0.309192 0.377823 0.308711 0.309322 0.378093 0.320075 0.309791 0.308472 0.310429 0.309449 0.357231 0.310231 0.310889 0.309564 0.310493 0.337290
25 0.000000 0.952895 0.952895 0.239088 0.0 1.00 0.666667 0.000000 0.000000 0.456799 0.536043 0.404171 0.393454 0.638604 0.385366 0.389831 0.640628 0.429404 0.392341 0.388125 0.390838 0.390954 0.559126 0.396865 0.398171 0.390209 0.399358 0.492252
26 0.812500 0.682041 0.682041 1.000000 0.0 0.00 0.666667 0.031409 0.000000 0.310973 0.359663 0.278385 0.273677 0.420945 0.269686 0.272458 0.421132 0.295085 0.273102 0.272266 0.272661 0.271832 0.373712 0.275578 0.275977 0.272779 0.277302 0.332344
27 0.812500 0.682041 0.682041 1.000000 0.0 0.00 0.000000 0.031409 0.000000 0.331024 0.401840 0.285005 0.276462 0.490760 0.269686 0.273729 0.492145 0.307580 0.275396 0.272266 0.273879 0.272727 0.419036 0.279703 0.280133 0.274194 0.281585 0.362018
28 0.812500 1.000000 1.000000 1.000000 0.0 0.25 0.666667 0.031409 0.000000 0.445862 0.493865 0.414101 0.409471 0.554415 0.405575 0.406780 0.554551 0.431487 0.408393 0.406227 0.409113 0.408867 0.505562 0.411716 0.412303 0.408602 0.412206 0.467524
29 0.813166 0.663395 0.663395 1.000000 0.0 0.00 0.666667 1.000000 1.000000 0.863288 0.861963 0.860973 0.860724 0.866530 0.860627 0.860169 0.866581 0.864640 0.862417 0.862419 0.862329 0.861173 0.868150 0.859736 0.858687 0.862762 0.860814 0.863172
30 0.813166 0.663395 0.663395 1.000000 0.0 0.00 0.000000 1.000000 1.000000 0.883339 0.904141 0.867594 0.860724 0.936345 0.860627 0.864407 0.939746 0.877135 0.864710 0.862419 0.863548 0.861173 0.913473 0.867987 0.866999 0.864177 0.865096 0.894494
31 0.813166 0.986261 0.986261 1.000000 0.0 0.25 0.666667 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
ABS_FOR_TRAINING=scaled_df[-15:]ABS_FOR_TRAINING
Part's Z-Height (mm) Part's Weight (N) Part's Volume (cm^3) Part's Surface Area (cm^2) Layer Height (mm) Infill Density (%) Printing/Scanning Speed (mm/s) Part's Orientation (Support's height) (mm) Part's Orientation (Support's volume) (cm^3) Climate change (kg CO2 eq.) Climate change, incl biogenic carbon (kg CO2 eq.) Fine Particulate Matter Formation (kg PM2.5 eq.) Fossil depletion (kg oil eq.) Freshwater Consumption (m^3) Freshwater ecotoxicity (kg 1,4-DB eq.) Freshwater Eutrophication (kg P eq.) Human toxicity, cancer (kg 1,4-DB eq.) Human toxicity, non-cancer (kg 1,4-DB eq.) Ionizing Radiation (Bq. C-60 eq. to air) Land use (Annual crop eq. yr) Marine ecotoxicity (kg 1,4-DB eq.) Marine Eutrophication (kg N eq.) Metal depletion (kg Cu eq.) Photochemical Ozone Formation, Ecosystem (kg NOx eq.) Photochemical Ozone Formation, Human Health (kg NOx eq.) Stratospheric Ozone Depletion (kg CFC-11 eq.) Terrestrial Acidification (kg SO2 eq.) Terrestrial ecotoxicity (kg 1,4-DB eq.)
17 0.771175 0.000000 0.000000 0.039373 0.0 0.00 1.000000 0.786031 0.073375 0.074553 0.134586 0.034095 0.028552 0.211499 0.022997 0.025847 0.212395 0.053519 0.026829 0.024620 0.025341 0.024631 0.151215 0.030528 0.030756 0.025467 0.031049 0.101550
18 1.000000 0.402355 0.402355 0.239088 0.0 0.25 0.666667 0.695845 0.083857 0.228946 0.263804 0.206885 0.202646 0.303901 0.200697 0.202542 0.309232 0.218034 0.202018 0.202028 0.201998 0.201970 0.273589 0.203795 0.204489 0.202037 0.204497 0.244972
19 1.000000 0.586850 0.586850 0.239088 0.0 0.50 0.666667 0.695845 0.083857 0.303682 0.328988 0.285005 0.280641 0.367556 0.279443 0.280932 0.367334 0.294044 0.282275 0.279508 0.281189 0.280340 0.339102 0.282178 0.282627 0.281268 0.282655 0.315859
20 1.000000 0.770363 0.770363 0.239088 0.0 0.75 0.666667 0.695845 0.083857 0.376595 0.398006 0.361139 0.358635 0.429158 0.357491 0.360169 0.429740 0.370054 0.360238 0.359160 0.359162 0.359606 0.406675 0.359736 0.359933 0.359083 0.359743 0.386746
21 1.000000 0.952895 0.952895 0.239088 0.0 1.00 0.666667 0.695845 0.083857 0.511484 0.597393 0.453823 0.444986 0.706366 0.435540 0.440678 0.707338 0.482507 0.442788 0.438812 0.440789 0.440215 0.620931 0.448020 0.448878 0.441143 0.450749 0.549951
22 0.000000 0.409225 0.409225 0.239088 0.0 0.25 0.666667 0.000000 0.000000 0.177907 0.207439 0.158226 0.155292 0.246407 0.153310 0.154661 0.246826 0.168055 0.156157 0.154598 0.154483 0.154501 0.215904 0.155941 0.156276 0.153933 0.156317 0.190570
23 0.000000 0.590775 0.590775 0.239088 0.0 0.50 0.666667 0.000000 0.000000 0.252643 0.279141 0.235353 0.231894 0.314168 0.230662 0.232203 0.313536 0.244065 0.231828 0.232078 0.232456 0.231975 0.287186 0.232673 0.233583 0.231749 0.233405 0.264754
24 0.000000 0.773307 0.773307 0.239088 0.0 0.75 0.666667 0.000000 0.000000 0.327379 0.348160 0.311486 0.309192 0.377823 0.308711 0.309322 0.378093 0.320075 0.309791 0.308472 0.310429 0.309449 0.357231 0.310231 0.310889 0.309564 0.310493 0.337290
25 0.000000 0.952895 0.952895 0.239088 0.0 1.00 0.666667 0.000000 0.000000 0.456799 0.536043 0.404171 0.393454 0.638604 0.385366 0.389831 0.640628 0.429404 0.392341 0.388125 0.390838 0.390954 0.559126 0.396865 0.398171 0.390209 0.399358 0.492252
26 0.812500 0.682041 0.682041 1.000000 0.0 0.00 0.666667 0.031409 0.000000 0.310973 0.359663 0.278385 0.273677 0.420945 0.269686 0.272458 0.421132 0.295085 0.273102 0.272266 0.272661 0.271832 0.373712 0.275578 0.275977 0.272779 0.277302 0.332344
27 0.812500 0.682041 0.682041 1.000000 0.0 0.00 0.000000 0.031409 0.000000 0.331024 0.401840 0.285005 0.276462 0.490760 0.269686 0.273729 0.492145 0.307580 0.275396 0.272266 0.273879 0.272727 0.419036 0.279703 0.280133 0.274194 0.281585 0.362018
28 0.812500 1.000000 1.000000 1.000000 0.0 0.25 0.666667 0.031409 0.000000 0.445862 0.493865 0.414101 0.409471 0.554415 0.405575 0.406780 0.554551 0.431487 0.408393 0.406227 0.409113 0.408867 0.505562 0.411716 0.412303 0.408602 0.412206 0.467524
29 0.813166 0.663395 0.663395 1.000000 0.0 0.00 0.666667 1.000000 1.000000 0.863288 0.861963 0.860973 0.860724 0.866530 0.860627 0.860169 0.866581 0.864640 0.862417 0.862419 0.862329 0.861173 0.868150 0.859736 0.858687 0.862762 0.860814 0.863172
30 0.813166 0.663395 0.663395 1.000000 0.0 0.00 0.000000 1.000000 1.000000 0.883339 0.904141 0.867594 0.860724 0.936345 0.860627 0.864407 0.939746 0.877135 0.864710 0.862419 0.863548 0.861173 0.913473 0.867987 0.866999 0.864177 0.865096 0.894494
31 0.813166 0.986261 0.986261 1.000000 0.0 0.25 0.666667 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000回答 1
Stack Overflow用户
回答已采纳
发布于 2022-07-22 19:11:35
样本数据
df = pd.DataFrame({
'feature1': [x for x in range(1, 21)],
'feature2': [x for x in range(21, 41)],
'target': [x for x in range(31,51)]
})如下所示
feature1 feature2 target
0 1 21 31
1 2 22 32
2 3 23 33
3 4 24 34
4 5 25 35
5 6 26 36
6 7 27 37
7 8 28 38
8 9 29 39
9 10 30 40
10 11 31 41
11 12 32 42
12 13 33 43
13 14 34 44
14 15 35 45
15 16 36 46
16 17 37 47
17 18 38 48
18 19 39 49
19 20 40 50提取最后n行(根据需要更改n )
n = -5
# extract last n rows
to_be_added = df[n:]使用train_test_split和拆分剩余行
from sklearn.model_selection import train_test_split
Y = df[:n].loc[:, 'target']
df.drop('target', inplace=True, axis=1)
X_train, X_test, y_train, y_test = train_test_split(df[:n], Y, test_size=0.25)分裂后的火车如下所示
0 31
1 32
3 34
4 35
7 38
8 39
10 41
11 42
12 43
13 44
14 45让我们将x_train和y_train与提取的信息连接起来。
y_train = pd.concat([y_train, to_be_added["target"]])
to_be_added.drop("target", inplace=True, axis=1)
X_train = pd.concat([X_train, to_be_added])打印y_train
0 31
1 32
3 34
4 35
7 38
8 39
10 41
11 42
12 43
13 44
14 45
15 46
16 47
17 48
18 49
19 50最后n行(在我的例子中是5)添加。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/73083756
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