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C:\Users\Felix\PycharmProjects\pythonProject\venv\Scripts\python.exe "C:\Program Files\JetBrains\PyCharm 2021.2.1\plugins\python\helpers\pydev\pydevconsole.py" --mode=client --port=64936 import sys; print('Python %s on %s' % (sys.version, sys.platform)) sys.path.extend(['C:\\Users\\Felix\\PycharmProjects\\machine-learning2', 'C:/Users/Felix/PycharmProjects/machine-learning2']) PyDev console: starting. Python 3.8.10 (tags/v3.8.10:3d8993a, May 3 2021, 11:48:03) [MSC v.1928 64 bit (AMD64)] on win32 runfile('C:/Users/Felix/PycharmProjects/machine-learning2/main.py', wdir='C:/Users/Felix/PycharmProjects/machine-learning2') C:\Users\Felix\PycharmProjects\machine-learning2 Cross validation fold 1/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 2/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 3/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 4/10: has the lambda value 1e2.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 5/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 6/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 7/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 8/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 9/10: has the lambda value 1e2.0 Offset 0.0 std_hydro 6.493662708593406e+29 std_wind 2.593454102305807e+161 std_solar 3.7559734772364737e+199 std_biomass 0.0 std_nuclear 0.0 std_biofuels 0.0 Cross validation fold 10/10: has the lambda value 1e1.0 Offset 0.0 std_hydro 0.44 std_wind 0.25 std_solar -0.16 std_biomass 0.33 std_nuclear 0.32 std_biofuels -0.22 Linear regression without feature selection: - Training error: 0.2233985382509828 - Test error: 0.23005267968225426 - R^2 train: 0.7765406682799204 - R^2 test: 0.7694048308045904 Regularized linear regression: - Training error: 0.2235752733396504 - Test error: 0.229970193383863 - R^2 train: 0.7763638850963134 - R^2 test: 0.7694875116147427 Weights in last fold: Offset 0.0 std_hydro 0.44 std_wind 0.25 std_solar -0.16 std_biomass 0.33 std_nuclear 0.32 std_biofuels -0.22 Replicate: 1/1 Iter Loss Rel. loss C:\Users\Felix\PycharmProjects\pythonProject\venv\lib\site-packages\torch\nn\modules\loss.py:520: UserWarning: Using a target size (torch.Size([3291])) that is different to the input size (torch.Size([3291, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. return F.mse_loss(input, target, reduction=self.reduction) Final loss: 994 0.98524773 9.679529e-07 Best loss: 0.9852477312088013 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0212284 4.7390648e-05 2000 0.9976872 6.6314087e-06 3000 0.99380046 2.2791007e-06 Final loss: 3507 0.9929806 9.0038907e-07 Best loss: 0.9929805994033813 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9790843 1.4732246e-05 2000 0.9710726 5.094532e-06 3000 0.968206 1.1081138e-06 Final loss: 3051 0.9681487 9.850485e-07 Best loss: 0.9681487083435059 Replicate: 1/1 Iter Loss Rel. loss Final loss: 709 0.9056353 9.872283e-07 Best loss: 0.9056352972984314 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9241547 2.708841e-06 Final loss: 1043 0.9240902 9.675125e-07 Best loss: 0.9240902066230774 Replicate: 1/1 Iter Loss Rel. loss Final loss: 847 0.95595247 9.3526495e-07 Best loss: 0.9559524655342102 Replicate: 1/1 Iter Loss Rel. loss 1000 1.002736 2.7461456e-05 2000 0.9833151 3.9400256e-06 Final loss: 2623 0.98185384 9.712987e-07 Best loss: 0.9818538427352905 Replicate: 1/1 Iter Loss Rel. loss C:\Users\Felix\PycharmProjects\pythonProject\venv\lib\site-packages\torch\nn\modules\loss.py:520: UserWarning: Using a target size (torch.Size([3292])) that is different to the input size (torch.Size([3292, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. return F.mse_loss(input, target, reduction=self.reduction) 1000 0.9824225 3.8160662e-05 Final loss: 1827 0.97609377 9.770305e-07 Best loss: 0.9760937690734863 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0022136 1.4630143e-05 Final loss: 1741 0.99710554 9.564418e-07 Best loss: 0.9971055388450623 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9810098 3.9492843e-06 Final loss: 1865 0.97941804 9.737144e-07 Best loss: 0.9794180393218994 Replicate: 1/1 Iter Loss Rel. loss 1000 0.994765 8.148825e-06 Final loss: 1325 0.99361044 9.598061e-07 Best loss: 0.9936104416847229 Replicate: 1/1 Iter Loss Rel. loss Final loss: 630 1.0226604 9.325417e-07 Best loss: 1.0226603746414185 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9948235 1.3780383e-06 Final loss: 1056 0.9947576 8.987806e-07 Best loss: 0.9947575926780701 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0445946 1.5976794e-06 Final loss: 1064 1.0445077 9.130363e-07 Best loss: 1.0445077419281006 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0431893 3.3139315e-06 Final loss: 1436 1.0422955 9.149742e-07 Best loss: 1.0422954559326172 Replicate: 1/1 Iter Loss Rel. loss Final loss: 923 1.0638434 8.9644163e-07 Best loss: 1.0638433694839478 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9509946 6.894325e-06 2000 0.94780487 1.2577393e-06 Final loss: 2083 0.9477105 9.433987e-07 Best loss: 0.9477105140686035 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0271865 4.4100393e-06 Final loss: 1244 1.0266266 9.2893896e-07 Best loss: 1.0266265869140625 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0817457 2.5235355e-05 Final loss: 1652 1.0767621 9.96397e-07 Best loss: 1.076762080192566 Replicate: 1/1 Iter Loss Rel. loss Final loss: 955 1.0629141 8.9722533e-07 Best loss: 1.0629141330718994 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0561572 2.5960212e-06 Final loss: 1274 1.0556123 9.034315e-07 Best loss: 1.055612325668335 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0456482 1.0260453e-06 Final loss: 1025 1.0456184 9.120664e-07 Best loss: 1.0456184148788452 Replicate: 1/1 Iter Loss Rel. loss Final loss: 970 0.99069345 9.626322e-07 Best loss: 0.9906934499740601 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0086472 3.7819793e-06 2000 1.0063756 1.7768081e-06 3000 1.0047412 1.3051127e-06 Final loss: 3434 1.0041937 9.4969073e-07 Best loss: 1.004193663597107 Replicate: 1/1 Iter Loss Rel. loss Final loss: 796 0.92811173 8.9909895e-07 Best loss: 0.928111732006073 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9500775 1.3802036e-06 Final loss: 1054 0.95001423 9.41111e-07 Best loss: 0.9500142335891724 Replicate: 1/1 Iter Loss Rel. loss Final loss: 471 1.0261054 9.2941076e-07 Best loss: 1.0261054039001465 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0379533 1.6882715e-05 Final loss: 1936 1.0330124 9.231965e-07 Best loss: 1.0330123901367188 Replicate: 1/1 Iter Loss Rel. loss 1000 0.959688 3.0433002e-06 Final loss: 1490 0.9588726 9.945777e-07 Best loss: 0.9588726162910461 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0425172 4.9169207e-06 Final loss: 1708 1.0408254 9.162665e-07 Best loss: 1.040825366973877 Replicate: 1/1 Iter Loss Rel. loss Final loss: 189 1.0630523 8.971087e-07 Best loss: 1.0630522966384888 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0895205 1.3567209e-05 2000 1.0846852 1.5386285e-06 Final loss: 2320 1.0842094 9.895529e-07 Best loss: 1.0842094421386719 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9953064 1.9762251e-06 Final loss: 1243 0.99496275 8.985953e-07 Best loss: 0.994962751865387 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0610036 1.2359059e-06 Final loss: 1031 1.0609652 8.9887345e-07 Best loss: 1.0609651803970337 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0817248 3.3060785e-06 Final loss: 1474 1.0806589 9.928041e-07 Best loss: 1.0806589126586914 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0997015 1.842822e-06 Final loss: 1076 1.0995859 9.757151e-07 Best loss: 1.0995858907699585 Replicate: 1/1 Iter Loss Rel. loss 1000 0.96752995 3.5114704e-06 Final loss: 1495 0.96659315 9.2496913e-07 Best loss: 0.9665931463241577 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0747453 4.5476436e-06 Final loss: 1683 1.0730083 9.998828e-07 Best loss: 1.0730082988739014 Replicate: 1/1 Iter Loss Rel. loss Final loss: 855 1.0574577 9.0185495e-07 Best loss: 1.057457685470581 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0449998 1.9392864e-06 Final loss: 1211 1.04467 9.1289445e-07 Best loss: 1.0446699857711792 Replicate: 1/1 Iter Loss Rel. loss Final loss: 829 1.0358785 9.2064215e-07 Best loss: 1.0358785390853882 Replicate: 1/1 Iter Loss Rel. loss Final loss: 499 1.0208569 9.341892e-07 Best loss: 1.0208568572998047 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0212907 5.9528966e-06 Final loss: 1446 1.0197709 9.35184e-07 Best loss: 1.019770860671997 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0387529 3.7181482e-05 2000 1.0253586 2.5577372e-06 Final loss: 2209 1.0249497 9.304588e-07 Best loss: 1.0249496698379517 Replicate: 1/1 Iter Loss Rel. loss 1000 1.029806 8.681848e-06 Final loss: 1623 1.0275888 9.280691e-07 Best loss: 1.0275888442993164 Replicate: 1/1 Iter Loss Rel. loss 1000 0.97429967 1.6701018e-05 2000 0.969189 1.5989846e-06 Final loss: 2167 0.96897924 9.842041e-07 Best loss: 0.9689792394638062 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0676141 6.476211e-06 Final loss: 1290 1.0666219 8.941064e-07 Best loss: 1.0666218996047974 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9705255 1.7810265e-06 Final loss: 1130 0.97034 9.82824e-07 Best loss: 0.9703400135040283 Replicate: 1/1 Iter Loss Rel. loss C:\Users\Felix\PycharmProjects\pythonProject\venv\lib\site-packages\torch\nn\modules\loss.py:520: UserWarning: Using a target size (torch.Size([3293])) that is different to the input size (torch.Size([3293, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. return F.mse_loss(input, target, reduction=self.reduction) Final loss: 415 1.0525274 9.0607944e-07 Best loss: 1.0525274276733398 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0144153 3.877989e-06 Final loss: 1560 1.0131611 9.412851e-07 Best loss: 1.013161063194275 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0150399 1.4915033e-05 Final loss: 1794 1.0096835 9.445271e-07 Best loss: 1.0096834897994995 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9975023 1.2010388e-05 Final loss: 1925 0.9927323 9.606551e-07 Best loss: 0.9927322864532471 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9673866 1.6019407e-05 2000 0.96263164 1.6098767e-06 Final loss: 2420 0.9620764 9.293117e-07 Best loss: 0.9620764255523682 Replicate: 1/1 Iter Loss Rel. loss 1000 1.063058 4.9340515e-06 2000 1.0598615 1.7996173e-06 Final loss: 2504 1.0591164 9.0044256e-07 Best loss: 1.0591163635253906 Replicate: 1/1 Iter Loss Rel. loss Final loss: 969 0.9920923 9.011952e-07 Best loss: 0.9920923113822937 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0209599 1.2843801e-06 Final loss: 1058 1.0208912 9.3415775e-07 Best loss: 1.0208911895751953 Replicate: 1/1 Iter Loss Rel. loss 1000 1.009589 4.13268e-06 Final loss: 1251 1.0090244 9.4514405e-07 Best loss: 1.0090243816375732 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9344 2.226194e-05 Final loss: 1922 0.92891306 9.624893e-07 Best loss: 0.9289130568504333 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0314684 9.823559e-06 Final loss: 1551 1.0291407 9.266696e-07 Best loss: 1.0291407108306885 Replicate: 1/1 Iter Loss Rel. loss 1000 1.050801 1.4747973e-06 Final loss: 1051 1.0507354 9.076248e-07 Best loss: 1.050735354423523 Replicate: 1/1 Iter Loss Rel. loss Final loss: 771 1.03955 9.173907e-07 Best loss: 1.0395499467849731 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0589944 5.178119e-06 Final loss: 1444 1.0577877 9.015736e-07 Best loss: 1.0577876567840576 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0620677 1.6836368e-06 Final loss: 1278 1.0616021 8.983342e-07 Best loss: 1.0616021156311035 Replicate: 1/1 Iter Loss Rel. loss Final loss: 570 0.9753868 9.1663003e-07 Best loss: 0.9753867983818054 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0271652 2.4371825e-06 Final loss: 1280 1.0266869 9.288844e-07 Best loss: 1.0266869068145752 Replicate: 1/1 Iter Loss Rel. loss 1000 0.92609787 2.2976372e-05 Final loss: 1999 0.9214084 9.703284e-07 Best loss: 0.9214084148406982 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0081984 1.5607426e-05 Final loss: 1828 1.0053587 9.485902e-07 Best loss: 1.0053586959838867 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0467818 4.327486e-06 Final loss: 1413 1.045934 9.117913e-07 Best loss: 1.0459339618682861 Replicate: 1/1 Iter Loss Rel. loss Final loss: 976 0.98493606 8.472269e-07 Best loss: 0.9849360585212708 Replicate: 1/1 Iter Loss Rel. loss Final loss: 790 1.0035611 9.502893e-07 Best loss: 1.0035611391067505 Replicate: 1/1 Iter Loss Rel. loss Final loss: 513 1.004001 9.4987297e-07 Best loss: 1.0040010213851929 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9855558 1.6329088e-06 Final loss: 1398 0.98505986 9.681376e-07 Best loss: 0.9850598573684692 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9984879 5.0740414e-06 Final loss: 1637 0.9968594 8.9688564e-07 Best loss: 0.9968593716621399 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0048 1.0914745e-05 Final loss: 1951 1.0010853 9.5263954e-07 Best loss: 1.0010852813720703 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9955151 3.5325045e-06 Final loss: 1138 0.9952203 8.9836277e-07 Best loss: 0.9952203035354614 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9933245 9.840757e-06 2000 0.98969436 1.2045047e-06 Final loss: 2059 0.9896258 9.0344133e-07 Best loss: 0.9896258115768433 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9809536 6.8660524e-06 Final loss: 1413 0.9797938 9.733409e-07 Best loss: 0.9797937870025635 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0231436 7.689783e-06 Final loss: 1962 1.0199977 9.34976e-07 Best loss: 1.0199977159500122 Replicate: 1/1 Iter Loss Rel. loss 1000 1.012166 2.9207698e-05 2000 1.0023984 2.1406286e-06 Final loss: 2281 1.0019377 9.51829e-07 Best loss: 1.001937747001648 Replicate: 1/1 Iter Loss Rel. loss 1000 0.97911423 2.9220437e-06 Final loss: 1860 0.9756964 9.163392e-07 Best loss: 0.9756963849067688 Replicate: 1/1 Iter Loss Rel. loss Final loss: 966 0.8937203 9.3369727e-07 Best loss: 0.8937203288078308 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9320393 7.2903363e-06 Final loss: 1880 0.9293903 9.619949e-07 Best loss: 0.9293903112411499 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9174606 7.113378e-05 2000 0.90512586 3.6877168e-06 3000 0.90291095 1.5183167e-06 Final loss: 3394 0.9024165 9.907495e-07 Best loss: 0.9024165272712708 Replicate: 1/1 Iter Loss Rel. loss Final loss: 718 0.8903369 9.3724555e-07 Best loss: 0.8903368711471558 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0024183 1.0702839e-05 2000 0.99363106 3.7791476e-06 3000 0.990593 2.406821e-06 4000 0.9888974 1.1452017e-06 Final loss: 4131 0.98875123 9.0424044e-07 Best loss: 0.988751232624054 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9153418 4.890075e-05 Final loss: 1672 0.90187746 9.913417e-07 Best loss: 0.9018774628639221 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9050002 1.2184224e-05 Final loss: 1222 0.9040578 9.889509e-07 Best loss: 0.9040578007698059 Replicate: 1/1 Iter Loss Rel. loss 1000 0.95098484 3.697915e-06 Final loss: 1398 0.9502413 9.408861e-07 Best loss: 0.9502413272857666 Replicate: 1/1 Iter Loss Rel. loss Final loss: 874 0.86648226 9.630483e-07 Best loss: 0.8664822578430176 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9189439 5.124081e-06 Final loss: 1771 0.9168922 9.751079e-07 Best loss: 0.9168921709060669 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9529976 1.250886e-06 Final loss: 1002 0.9529957 9.381667e-07 Best loss: 0.9529957175254822 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0276155 2.1924601e-05 2000 1.0199648 1.8700107e-06 Final loss: 2213 1.019659 9.352866e-07 Best loss: 1.0196590423583984 Replicate: 1/1 Iter Loss Rel. loss Final loss: 514 1.0479833 9.100083e-07 Best loss: 1.0479832887649536 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0320913 1.1550253e-06 Final loss: 1023 1.0320637 9.240451e-07 Best loss: 1.0320637226104736 Replicate: 1/1 Iter Loss Rel. loss Final loss: 540 0.98741287 9.658304e-07 Best loss: 0.9874128699302673 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9617911 4.2141155e-06 Final loss: 1710 0.96022373 9.311047e-07 Best loss: 0.9602237343788147 Replicate: 1/1 Iter Loss Rel. loss 1000 0.98111516 1.0631476e-05 Final loss: 1539 0.9783219 9.748053e-07 Best loss: 0.97832190990448 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0804278 7.7669996e-05 2000 1.0482185 4.5490037e-06 Final loss: 2718 1.0468409 9.1100134e-07 Best loss: 1.0468409061431885 Replicate: 1/1 Iter Loss Rel. loss 1000 0.9759562 3.5422256e-06 Final loss: 1369 0.9752281 9.778978e-07 Best loss: 0.9752280712127686 Replicate: 1/1 Iter Loss Rel. loss 1000 1.0555327 4.743355e-06 Final loss: 1475 1.0542041 9.0463834e-07 Best loss: 1.0542041063308716
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