Add files using upload-large-folder tool

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.err

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+[NbConvertApp] Converting notebook RR_pytorch.ipynb to python
+[NbConvertApp] Writing 23203 bytes to RR_pytorch.py
+
+
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).
+  warnings.warn(
+Traceback (most recent call last):
+  File "/weka/proj-fmri/ckadirt/spurious_reconstruction/analysis/1_case_study/feature-decoding/RR_pytorch.py", line 311, in <module>
+    num_cv_channels = features.shape[1]
+                      ^^^^^^^^
+NameError: name 'features' is not defined

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.out

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+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-154-245
+MASTER_PORT=12721
+WORLD_SIZE=1
+PID of this process = 4061359
+Traning with config:
+batch_size: 128
+num_epochs: 20
+weight_decay: 1e-05
+lr: 0.001
+device: cuda
+/weka/proj-medarc/shared/mindeyev2_dataset//wds/subj01/train/{0..39}.tar
+/weka/proj-medarc/shared/mindeyev2_dataset//wds/subj01/new_test/0.tar
+Loaded test dl for subj1!
+
+loading_betas
+betas_ loaded
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+Resized images torch.Size([73000, 3, 256, 256])

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529947.err

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+[NbConvertApp] Converting notebook RR_pytorch.ipynb to python
+[NbConvertApp] Writing 23197 bytes to RR_pytorch.py
+
+
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).
+  warnings.warn(
+
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  0%|          | 0/40 [00:00<?, ?it/s]/weka/proj-fmri/ckadirt/spurious_reconstruction/analysis/1_case_study/feature-decoding/RR_pytorch.py:434: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:261.)
+  best_imagery_predictions = torch.Tensor(current_imagery_predictions)
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spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530209.err

@@ -0,0 +1,274 @@
+[NbConvertApp] Converting notebook RR_pytorch.ipynb to python
+[NbConvertApp] Writing 23197 bytes to RR_pytorch.py
+
+
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).
+  warnings.warn(
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+  best_imagery_predictions = torch.Tensor(current_imagery_predictions)
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spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530209.out

@@ -0,0 +1,1744 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-132-27
+MASTER_PORT=13730
+WORLD_SIZE=1
+PID of this process = 3544602
+Traning with config:
+batch_size: 128
+num_epochs: 40
+weight_decay: 1e-05
+lr: 0.003
+device: cuda
+/weka/proj-medarc/shared/mindeyev2_dataset//wds/subj01/train/{0..39}.tar
+/weka/proj-medarc/shared/mindeyev2_dataset//wds/subj01/new_test/0.tar
+Loaded test dl for subj1!
+
+loading_betas
+betas_ loaded
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+Resized images torch.Size([73000, 3, 256, 256])
+Number of cv channels: 64
+Size of ridge regressions: 3
+Number of ridge regressions: 22
+Starting split 0 with features 0 to 3
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 10.498310089111328
+Epoch 0, Loss: 14.129755973815918
+Epoch 0, Loss: 14.004852294921875
+Epoch 0, Full Train Loss: 14.10479784920102
+Test Loss: 9.05235504925251
+New best loss: 9.05235504925251
+Saved best predictions for split 0
+Epoch 1, Loss: 9.262917518615723
+Epoch 1, Loss: 10.017568588256836
+Epoch 1, Loss: 10.46495246887207
+Epoch 1, Full Train Loss: 10.396275384085518
+Test Loss: 6.936532500743866
+New best loss: 6.936532500743866
+Saved best predictions for split 0
+Epoch 2, Loss: 8.816461563110352
+Epoch 2, Loss: 8.857439041137695
+Epoch 2, Loss: 8.942481994628906
+Epoch 2, Full Train Loss: 9.037779349372501
+Test Loss: 6.138451773881912
+New best loss: 6.138451773881912
+Saved best predictions for split 0
+Epoch 3, Loss: 7.180462837219238
+Epoch 3, Loss: 7.759946346282959
+Epoch 3, Loss: 7.114957332611084
+Epoch 3, Full Train Loss: 7.438501962025961
+Test Loss: 5.170277982532978
+New best loss: 5.170277982532978
+Saved best predictions for split 0
+Epoch 4, Loss: 5.5005998611450195
+Epoch 4, Loss: 6.298573970794678
+Epoch 4, Loss: 6.466527462005615
+Epoch 4, Full Train Loss: 6.469379066285633
+Test Loss: 4.682873241901397
+New best loss: 4.682873241901397
+Saved best predictions for split 0
+Epoch 5, Loss: 5.343594551086426
+Epoch 5, Loss: 6.38245153427124
+Epoch 5, Loss: 5.366363525390625
+Epoch 5, Full Train Loss: 5.490573301769438
+Test Loss: 4.132349397540093
+New best loss: 4.132349397540093
+Saved best predictions for split 0
+Epoch 6, Loss: 4.70658540725708
+Epoch 6, Loss: 4.56108283996582
+Epoch 6, Loss: 4.928694725036621
+Epoch 6, Full Train Loss: 4.6584363358361385
+Test Loss: 3.6488403880596163
+New best loss: 3.6488403880596163
+Saved best predictions for split 0
+Epoch 7, Loss: 3.9427363872528076
+Epoch 7, Loss: 4.070008277893066
+Epoch 7, Loss: 4.88110876083374
+Epoch 7, Full Train Loss: 4.243933971722921
+Test Loss: 3.3931361068487167
+New best loss: 3.3931361068487167
+Saved best predictions for split 0
+Epoch 8, Loss: 3.6113624572753906
+Epoch 8, Loss: 4.01050329208374
+Epoch 8, Loss: 4.137429714202881
+Epoch 8, Full Train Loss: 4.025308351289659
+Test Loss: 3.3471927720308305
+New best loss: 3.3471927720308305
+Saved best predictions for split 0
+Epoch 9, Loss: 3.678206443786621
+Epoch 9, Loss: 4.782622814178467
+Epoch 9, Loss: 4.216233253479004
+Epoch 9, Full Train Loss: 3.931327053478786
+Test Loss: 3.3579490827322007
+Epoch 10, Loss: 3.493082046508789
+Epoch 10, Loss: 3.53546142578125
+Epoch 10, Loss: 3.5408809185028076
+Epoch 10, Full Train Loss: 3.6566419862565542
+Test Loss: 3.1607520560026168
+New best loss: 3.1607520560026168
+Saved best predictions for split 0
+Epoch 11, Loss: 3.2106776237487793
+Epoch 11, Loss: 3.515204906463623
+Epoch 11, Loss: 3.805246353149414
+Epoch 11, Full Train Loss: 3.531828973406837
+Test Loss: 3.1847192597389222
+Epoch 12, Loss: 3.1469855308532715
+Epoch 12, Loss: 3.480693817138672
+Epoch 12, Loss: 3.6590163707733154
+Epoch 12, Full Train Loss: 3.6195510466893515
+Test Loss: 3.203209483385086
+Epoch 13, Loss: 3.1913299560546875
+Epoch 13, Loss: 3.851635694503784
+Epoch 13, Loss: 4.076053142547607
+Epoch 13, Full Train Loss: 3.650191468284244
+Test Loss: 3.2648620322942734
+No improvement for 3 epochs. Stopping training for split 0
+Finished split 0
+Starting split 1 with features 3 to 6
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 26.977924346923828
+Epoch 0, Loss: 33.92470932006836
+Epoch 0, Loss: 36.70016098022461
+Epoch 0, Full Train Loss: 34.765509042285736
+Test Loss: 26.34407890844345
+New best loss: 26.34407890844345
+Saved best predictions for split 1
+Epoch 1, Loss: 26.30923080444336
+Epoch 1, Loss: 28.38741683959961
+Epoch 1, Loss: 29.856674194335938
+Epoch 1, Full Train Loss: 29.132049351646785
+Test Loss: 21.60275700211525
+New best loss: 21.60275700211525
+Saved best predictions for split 1
+Epoch 2, Loss: 24.963001251220703
+Epoch 2, Loss: 25.521099090576172
+Epoch 2, Loss: 31.700925827026367
+Epoch 2, Full Train Loss: 25.51305947984968
+Test Loss: 18.696091297745706
+New best loss: 18.696091297745706
+Saved best predictions for split 1
+Epoch 3, Loss: 18.73508071899414
+Epoch 3, Loss: 20.062793731689453
+Epoch 3, Loss: 21.33806610107422
+Epoch 3, Full Train Loss: 21.683923884800503
+Test Loss: 15.586883907794952
+New best loss: 15.586883907794952
+Saved best predictions for split 1
+Epoch 4, Loss: 15.960676193237305
+Epoch 4, Loss: 18.654212951660156
+Epoch 4, Loss: 18.95102882385254
+Epoch 4, Full Train Loss: 18.92328461238316
+Test Loss: 13.486402921319009
+New best loss: 13.486402921319009
+Saved best predictions for split 1
+Epoch 5, Loss: 17.098392486572266
+Epoch 5, Loss: 16.58783531188965
+Epoch 5, Loss: 17.11594009399414
+Epoch 5, Full Train Loss: 16.37161546434675
+Test Loss: 12.52778054189682
+New best loss: 12.52778054189682
+Saved best predictions for split 1
+Epoch 6, Loss: 19.009803771972656
+Epoch 6, Loss: 13.991510391235352
+Epoch 6, Loss: 14.843910217285156
+Epoch 6, Full Train Loss: 14.20734258379255
+Test Loss: 10.677812150359154
+New best loss: 10.677812150359154
+Saved best predictions for split 1
+Epoch 7, Loss: 11.933002471923828
+Epoch 7, Loss: 12.090652465820312
+Epoch 7, Loss: 14.195722579956055
+Epoch 7, Full Train Loss: 12.622973392123267
+Test Loss: 9.363219209432602
+New best loss: 9.363219209432602
+Saved best predictions for split 1
+Epoch 8, Loss: 10.804899215698242
+Epoch 8, Loss: 10.725508689880371
+Epoch 8, Loss: 10.861801147460938
+Epoch 8, Full Train Loss: 10.546286941709972
+Test Loss: 7.689820135474205
+New best loss: 7.689820135474205
+Saved best predictions for split 1
+Epoch 9, Loss: 7.44830846786499
+Epoch 9, Loss: 9.561738967895508
+Epoch 9, Loss: 9.483293533325195
+Epoch 9, Full Train Loss: 9.083797295888266
+Test Loss: 7.648506831645966
+New best loss: 7.648506831645966
+Saved best predictions for split 1
+Epoch 10, Loss: 8.492274284362793
+Epoch 10, Loss: 7.782277584075928
+Epoch 10, Loss: 8.270673751831055
+Epoch 10, Full Train Loss: 7.847957826796032
+Test Loss: 5.976593420982361
+New best loss: 5.976593420982361
+Saved best predictions for split 1
+Epoch 11, Loss: 6.876170635223389
+Epoch 11, Loss: 6.529590606689453
+Epoch 11, Loss: 6.797163963317871
+Epoch 11, Full Train Loss: 6.772444357190813
+Test Loss: 5.124881214618683
+New best loss: 5.124881214618683
+Saved best predictions for split 1
+Epoch 12, Loss: 4.985393047332764
+Epoch 12, Loss: 5.876883506774902
+Epoch 12, Loss: 7.117623805999756
+Epoch 12, Full Train Loss: 5.931287057059151
+Test Loss: 4.966420472502708
+New best loss: 4.966420472502708
+Saved best predictions for split 1
+Epoch 13, Loss: 5.7258758544921875
+Epoch 13, Loss: 5.226010322570801
+Epoch 13, Loss: 5.8805036544799805
+Epoch 13, Full Train Loss: 5.358129648935227
+Test Loss: 4.21362008869648
+New best loss: 4.21362008869648
+Saved best predictions for split 1
+Epoch 14, Loss: 4.086739540100098
+Epoch 14, Loss: 4.662937164306641
+Epoch 14, Loss: 4.787246227264404
+Epoch 14, Full Train Loss: 4.698066859018235
+Test Loss: 3.8143492525815965
+New best loss: 3.8143492525815965
+Saved best predictions for split 1
+Epoch 15, Loss: 3.971113681793213
+Epoch 15, Loss: 3.8236098289489746
+Epoch 15, Loss: 4.907330513000488
+Epoch 15, Full Train Loss: 4.2284531434377035
+Test Loss: 3.7740435240268706
+New best loss: 3.7740435240268706
+Saved best predictions for split 1
+Epoch 16, Loss: 4.098087310791016
+Epoch 16, Loss: 3.8790178298950195
+Epoch 16, Loss: 4.219099998474121
+Epoch 16, Full Train Loss: 4.053690470967974
+Test Loss: 3.412220309495926
+New best loss: 3.412220309495926
+Saved best predictions for split 1
+Epoch 17, Loss: 3.5228705406188965
+Epoch 17, Loss: 3.658377170562744
+Epoch 17, Loss: 4.266444206237793
+Epoch 17, Full Train Loss: 3.7251893577121553
+Test Loss: 3.4106011506319045
+New best loss: 3.4106011506319045
+Saved best predictions for split 1
+Epoch 18, Loss: 3.310685157775879
+Epoch 18, Loss: 3.2629356384277344
+Epoch 18, Loss: 3.739764928817749
+Epoch 18, Full Train Loss: 3.586356664839245
+Test Loss: 3.2238485507965087
+New best loss: 3.2238485507965087
+Saved best predictions for split 1
+Epoch 19, Loss: 3.1747946739196777
+Epoch 19, Loss: 3.6808948516845703
+Epoch 19, Loss: 4.293059349060059
+Epoch 19, Full Train Loss: 3.6155281532378423
+Test Loss: 3.3324612975120544
+Epoch 20, Loss: 3.1957249641418457
+Epoch 20, Loss: 3.613178014755249
+Epoch 20, Loss: 4.078521728515625
+Epoch 20, Full Train Loss: 3.7028420391536896
+Test Loss: 3.2918283916711806
+Epoch 21, Loss: 3.35098934173584
+Epoch 21, Loss: 3.2758827209472656
+Epoch 21, Loss: 3.675729274749756
+Epoch 21, Full Train Loss: 3.530319551059178
+Test Loss: 3.1706840831041334
+New best loss: 3.1706840831041334
+Saved best predictions for split 1
+Epoch 22, Loss: 3.129256248474121
+Epoch 22, Loss: 3.2911696434020996
+Epoch 22, Loss: 3.8109264373779297
+Epoch 22, Full Train Loss: 3.4321270761035736
+Test Loss: 3.2577552345991134
+Epoch 23, Loss: 3.1120052337646484
+Epoch 23, Loss: 3.3923816680908203
+Epoch 23, Loss: 3.9028029441833496
+Epoch 23, Full Train Loss: 3.5356842778977895
+Test Loss: 3.364257881164551
+Epoch 24, Loss: 3.3747220039367676
+Epoch 24, Loss: 3.47302508354187
+Epoch 24, Loss: 3.952686309814453
+Epoch 24, Full Train Loss: 3.6011893840063185
+Test Loss: 3.374561118841171
+No improvement for 3 epochs. Stopping training for split 1
+Finished split 1
+Starting split 2 with features 6 to 9
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 1.3178472518920898
+Epoch 0, Loss: 1.7600343227386475
+Epoch 0, Loss: 1.7652873992919922
+Epoch 0, Full Train Loss: 3.297049143768492
+Test Loss: 0.9655088590979576
+New best loss: 0.9655088590979576
+Saved best predictions for split 2
+Epoch 1, Loss: 1.3629440069198608
+Epoch 1, Loss: 1.4985984563827515
+Epoch 1, Loss: 1.741715431213379
+Epoch 1, Full Train Loss: 1.5202341868763878
+Test Loss: 1.0574261317253113
+Epoch 2, Loss: 1.5538825988769531
+Epoch 2, Loss: 1.8328189849853516
+Epoch 2, Loss: 2.21115779876709
+Epoch 2, Full Train Loss: 1.9081588915416172
+Test Loss: 1.3436229833364486
+Epoch 3, Loss: 2.315671920776367
+Epoch 3, Loss: 2.809852361679077
+Epoch 3, Loss: 2.7949492931365967
+Epoch 3, Full Train Loss: 2.4691792561894372
+Test Loss: 1.4635016255378723
+No improvement for 3 epochs. Stopping training for split 2
+Finished split 2
+Starting split 3 with features 9 to 12
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 15.750030517578125
+Epoch 0, Loss: 21.422637939453125
+Epoch 0, Loss: 22.34134292602539
+Epoch 0, Full Train Loss: 22.383975510370163
+Test Loss: 14.964318766713143
+New best loss: 14.964318766713143
+Saved best predictions for split 3
+Epoch 1, Loss: 14.393507957458496
+Epoch 1, Loss: 18.970874786376953
+Epoch 1, Loss: 17.955503463745117
+Epoch 1, Full Train Loss: 17.619392808278402
+Test Loss: 12.172026076197625
+New best loss: 12.172026076197625
+Saved best predictions for split 3
+Epoch 2, Loss: 13.509040832519531
+Epoch 2, Loss: 15.441244125366211
+Epoch 2, Loss: 13.0153226852417
+Epoch 2, Full Train Loss: 14.48346831003825
+Test Loss: 9.21891959476471
+New best loss: 9.21891959476471
+Saved best predictions for split 3
+Epoch 3, Loss: 11.763518333435059
+Epoch 3, Loss: 11.851688385009766
+Epoch 3, Loss: 11.830368041992188
+Epoch 3, Full Train Loss: 11.83762921378726
+Test Loss: 8.575169912815094
+New best loss: 8.575169912815094
+Saved best predictions for split 3
+Epoch 4, Loss: 10.356145858764648
+Epoch 4, Loss: 10.846495628356934
+Epoch 4, Loss: 9.913155555725098
+Epoch 4, Full Train Loss: 10.065547404970442
+Test Loss: 7.181643983006477
+New best loss: 7.181643983006477
+Saved best predictions for split 3
+Epoch 5, Loss: 7.601720809936523
+Epoch 5, Loss: 9.287162780761719
+Epoch 5, Loss: 9.041566848754883
+Epoch 5, Full Train Loss: 8.453386733645484
+Test Loss: 5.498692143797874
+New best loss: 5.498692143797874
+Saved best predictions for split 3
+Epoch 6, Loss: 6.882057189941406
+Epoch 6, Loss: 6.831153869628906
+Epoch 6, Loss: 8.143856048583984
+Epoch 6, Full Train Loss: 7.0048246156601675
+Test Loss: 5.454526121020317
+New best loss: 5.454526121020317
+Saved best predictions for split 3
+Epoch 7, Loss: 7.13791561126709
+Epoch 7, Loss: 5.826495170593262
+Epoch 7, Loss: 6.769311428070068
+Epoch 7, Full Train Loss: 6.080981513432094
+Test Loss: 4.029323497056961
+New best loss: 4.029323497056961
+Saved best predictions for split 3
+Epoch 8, Loss: 4.69356107711792
+Epoch 8, Loss: 5.275418281555176
+Epoch 8, Loss: 5.114636421203613
+Epoch 8, Full Train Loss: 5.18692143758138
+Test Loss: 3.3691885294914248
+New best loss: 3.3691885294914248
+Saved best predictions for split 3
+Epoch 9, Loss: 4.111244201660156
+Epoch 9, Loss: 4.7087907791137695
+Epoch 9, Loss: 4.518716812133789
+Epoch 9, Full Train Loss: 4.508303706986563
+Test Loss: 3.084998916506767
+New best loss: 3.084998916506767
+Saved best predictions for split 3
+Epoch 10, Loss: 3.683974027633667
+Epoch 10, Loss: 3.489863872528076
+Epoch 10, Loss: 4.774248123168945
+Epoch 10, Full Train Loss: 3.907414242199489
+Test Loss: 2.889644511938095
+New best loss: 2.889644511938095
+Saved best predictions for split 3
+Epoch 11, Loss: 3.4849295616149902
+Epoch 11, Loss: 3.661607265472412
+Epoch 11, Loss: 3.7425336837768555
+Epoch 11, Full Train Loss: 3.5232860928490046
+Test Loss: 2.4354062123298643
+New best loss: 2.4354062123298643
+Saved best predictions for split 3
+Epoch 12, Loss: 2.8720626831054688
+Epoch 12, Loss: 3.1870334148406982
+Epoch 12, Loss: 2.9994421005249023
+Epoch 12, Full Train Loss: 3.1058576629275367
+Test Loss: 2.2963707596063614
+New best loss: 2.2963707596063614
+Saved best predictions for split 3
+Epoch 13, Loss: 2.9616527557373047
+Epoch 13, Loss: 2.871542453765869
+Epoch 13, Loss: 3.1284937858581543
+Epoch 13, Full Train Loss: 3.001119479678926
+Test Loss: 2.1891396342515947
+New best loss: 2.1891396342515947
+Saved best predictions for split 3
+Epoch 14, Loss: 2.584651231765747
+Epoch 14, Loss: 2.6001009941101074
+Epoch 14, Loss: 3.2110843658447266
+Epoch 14, Full Train Loss: 2.8376849923815044
+Test Loss: 2.243368672132492
+Epoch 15, Loss: 2.688577651977539
+Epoch 15, Loss: 2.982177257537842
+Epoch 15, Loss: 3.0678889751434326
+Epoch 15, Full Train Loss: 2.7788421608152842
+Test Loss: 2.295998074412346
+Epoch 16, Loss: 2.893007278442383
+Epoch 16, Loss: 3.032815933227539
+Epoch 16, Loss: 2.94538950920105
+Epoch 16, Full Train Loss: 2.879037166777111
+Test Loss: 2.255356800675392
+No improvement for 3 epochs. Stopping training for split 3
+Finished split 3
+Starting split 4 with features 12 to 15
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 2.0589852333068848
+Epoch 0, Loss: 3.0052170753479004
+Epoch 0, Loss: 3.1029491424560547
+Epoch 0, Full Train Loss: 4.362370685168675
+Test Loss: 2.021824034512043
+New best loss: 2.021824034512043
+Saved best predictions for split 4
+Epoch 1, Loss: 2.5175652503967285
+Epoch 1, Loss: 2.7208433151245117
+Epoch 1, Loss: 3.3050906658172607
+Epoch 1, Full Train Loss: 2.781808086803981
+Test Loss: 2.1766854541897773
+Epoch 2, Loss: 2.7052505016326904
+Epoch 2, Loss: 3.006204128265381
+Epoch 2, Loss: 3.581285238265991
+Epoch 2, Full Train Loss: 3.0562440054757256
+Test Loss: 2.3443657310009
+Epoch 3, Loss: 3.098252058029175
+Epoch 3, Loss: 3.0297045707702637
+Epoch 3, Loss: 3.878640651702881
+Epoch 3, Full Train Loss: 3.4306172473090037
+Test Loss: 2.496890634059906
+No improvement for 3 epochs. Stopping training for split 4
+Finished split 4
+Starting split 5 with features 15 to 18
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 6.456479072570801
+Epoch 0, Loss: 8.678580284118652
+Epoch 0, Loss: 8.924427032470703
+Epoch 0, Full Train Loss: 10.021456947780791
+Test Loss: 5.869022315979004
+New best loss: 5.869022315979004
+Saved best predictions for split 5
+Epoch 1, Loss: 6.243339538574219
+Epoch 1, Loss: 8.38772201538086
+Epoch 1, Loss: 7.37936544418335
+Epoch 1, Full Train Loss: 7.4147305647532145
+Test Loss: 4.7306556892395015
+New best loss: 4.7306556892395015
+Saved best predictions for split 5
+Epoch 2, Loss: 4.693750381469727
+Epoch 2, Loss: 5.821908950805664
+Epoch 2, Loss: 6.658565044403076
+Epoch 2, Full Train Loss: 6.0589624018896195
+Test Loss: 3.958400779902935
+New best loss: 3.958400779902935
+Saved best predictions for split 5
+Epoch 3, Loss: 5.154273509979248
+Epoch 3, Loss: 5.04305362701416
+Epoch 3, Loss: 4.821010112762451
+Epoch 3, Full Train Loss: 4.994093381790888
+Test Loss: 3.1510616252422334
+New best loss: 3.1510616252422334
+Saved best predictions for split 5
+Epoch 4, Loss: 4.091985702514648
+Epoch 4, Loss: 4.201898574829102
+Epoch 4, Loss: 4.3588151931762695
+Epoch 4, Full Train Loss: 4.271891533760797
+Test Loss: 2.816751226723194
+New best loss: 2.816751226723194
+Saved best predictions for split 5
+Epoch 5, Loss: 3.2839314937591553
+Epoch 5, Loss: 3.576641082763672
+Epoch 5, Loss: 3.885573387145996
+Epoch 5, Full Train Loss: 3.690621968678066
+Test Loss: 2.533659777998924
+New best loss: 2.533659777998924
+Saved best predictions for split 5
+Epoch 6, Loss: 2.9335124492645264
+Epoch 6, Loss: 3.0159518718719482
+Epoch 6, Loss: 3.3177733421325684
+Epoch 6, Full Train Loss: 3.2201490447634744
+Test Loss: 2.456704407155514
+New best loss: 2.456704407155514
+Saved best predictions for split 5
+Epoch 7, Loss: 2.969637632369995
+Epoch 7, Loss: 2.811556816101074
+Epoch 7, Loss: 3.321614980697632
+Epoch 7, Full Train Loss: 2.9715621267046246
+Test Loss: 2.312559148788452
+New best loss: 2.312559148788452
+Saved best predictions for split 5
+Epoch 8, Loss: 2.7947463989257812
+Epoch 8, Loss: 3.0187582969665527
+Epoch 8, Loss: 3.5053558349609375
+Epoch 8, Full Train Loss: 2.9789822919028146
+Test Loss: 2.3412819556593893
+Epoch 9, Loss: 2.989105224609375
+Epoch 9, Loss: 3.2042062282562256
+Epoch 9, Loss: 3.1108052730560303
+Epoch 9, Full Train Loss: 3.0316088994344077
+Test Loss: 2.154877615094185
+New best loss: 2.154877615094185
+Saved best predictions for split 5
+Epoch 10, Loss: 2.6286911964416504
+Epoch 10, Loss: 2.8359909057617188
+Epoch 10, Loss: 3.1516590118408203
+Epoch 10, Full Train Loss: 2.8853686696007137
+Test Loss: 2.255505015492439
+Epoch 11, Loss: 2.7366275787353516
+Epoch 11, Loss: 2.786203384399414
+Epoch 11, Loss: 3.602698802947998
+Epoch 11, Full Train Loss: 2.8854470934186662
+Test Loss: 2.379276875495911
+Epoch 12, Loss: 2.9417645931243896
+Epoch 12, Loss: 2.8408188819885254
+Epoch 12, Loss: 3.4494471549987793
+Epoch 12, Full Train Loss: 2.998360564595177
+Test Loss: 2.3755270317792894
+No improvement for 3 epochs. Stopping training for split 5
+Finished split 5
+Starting split 6 with features 18 to 21
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 5.3657965660095215
+Epoch 0, Loss: 7.964465141296387
+Epoch 0, Loss: 8.408373832702637
+Epoch 0, Full Train Loss: 8.581435725802468
+Test Loss: 5.135387306094169
+New best loss: 5.135387306094169
+Saved best predictions for split 6
+Epoch 1, Loss: 7.743990898132324
+Epoch 1, Loss: 7.651311874389648
+Epoch 1, Loss: 6.661932945251465
+Epoch 1, Full Train Loss: 6.277368332090832
+Test Loss: 3.5868435360193254
+New best loss: 3.5868435360193254
+Saved best predictions for split 6
+Epoch 2, Loss: 5.0106425285339355
+Epoch 2, Loss: 4.649990081787109
+Epoch 2, Loss: 5.1448798179626465
+Epoch 2, Full Train Loss: 5.067499882834298
+Test Loss: 3.0922096975445745
+New best loss: 3.0922096975445745
+Saved best predictions for split 6
+Epoch 3, Loss: 3.752363920211792
+Epoch 3, Loss: 4.344659328460693
+Epoch 3, Loss: 5.1323676109313965
+Epoch 3, Full Train Loss: 4.329890861965361
+Test Loss: 2.86655211019516
+New best loss: 2.86655211019516
+Saved best predictions for split 6
+Epoch 4, Loss: 4.185468673706055
+Epoch 4, Loss: 4.284404754638672
+Epoch 4, Loss: 4.874029159545898
+Epoch 4, Full Train Loss: 3.8878795828138077
+Test Loss: 2.4547571238279344
+New best loss: 2.4547571238279344
+Saved best predictions for split 6
+Epoch 5, Loss: 3.4950575828552246
+Epoch 5, Loss: 3.490293502807617
+Epoch 5, Loss: 3.4155960083007812
+Epoch 5, Full Train Loss: 3.3977671191805885
+Test Loss: 2.2606041987538337
+New best loss: 2.2606041987538337
+Saved best predictions for split 6
+Epoch 6, Loss: 2.977426052093506
+Epoch 6, Loss: 2.6894798278808594
+Epoch 6, Loss: 3.7900867462158203
+Epoch 6, Full Train Loss: 2.9753138371876307
+Test Loss: 2.1881497443914415
+New best loss: 2.1881497443914415
+Saved best predictions for split 6
+Epoch 7, Loss: 2.5437839031219482
+Epoch 7, Loss: 2.9206061363220215
+Epoch 7, Loss: 3.4451675415039062
+Epoch 7, Full Train Loss: 2.901654931477138
+Test Loss: 2.237573241174221
+Epoch 8, Loss: 2.8631157875061035
+Epoch 8, Loss: 2.6358370780944824
+Epoch 8, Loss: 3.291686534881592
+Epoch 8, Full Train Loss: 2.845108885992141
+Test Loss: 2.282462492287159
+Epoch 9, Loss: 2.926443099975586
+Epoch 9, Loss: 2.606757640838623
+Epoch 9, Loss: 4.009398937225342
+Epoch 9, Full Train Loss: 2.9110140176046464
+Test Loss: 2.5728391934037207
+No improvement for 3 epochs. Stopping training for split 6
+Finished split 6
+Starting split 7 with features 21 to 24
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 0.762843132019043
+Epoch 0, Loss: 1.0837228298187256
+Epoch 0, Loss: 0.9688444137573242
+Epoch 0, Full Train Loss: 2.684485161304474
+Test Loss: 0.5264770160466432
+New best loss: 0.5264770160466432
+Saved best predictions for split 7
+Epoch 1, Loss: 0.7427374720573425
+Epoch 1, Loss: 0.9505208730697632
+Epoch 1, Loss: 1.2920067310333252
+Epoch 1, Full Train Loss: 0.9893564235596429
+Test Loss: 0.7095190776288509
+Epoch 2, Loss: 1.2511886358261108
+Epoch 2, Loss: 1.5023880004882812
+Epoch 2, Loss: 1.8248043060302734
+Epoch 2, Full Train Loss: 1.5629911536262149
+Test Loss: 0.9286209919452667
+Epoch 3, Loss: 1.727440595626831
+Epoch 3, Loss: 2.613837242126465
+Epoch 3, Loss: 2.6194562911987305
+Epoch 3, Full Train Loss: 2.2776507383301143
+Test Loss: 1.3106248868107795
+No improvement for 3 epochs. Stopping training for split 7
+Finished split 7
+Starting split 8 with features 24 to 27
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 15.551321029663086
+Epoch 0, Loss: 20.001434326171875
+Epoch 0, Loss: 21.224668502807617
+Epoch 0, Full Train Loss: 21.025236075265067
+Test Loss: 13.807288496732712
+New best loss: 13.807288496732712
+Saved best predictions for split 8
+Epoch 1, Loss: 14.650343894958496
+Epoch 1, Loss: 15.510246276855469
+Epoch 1, Loss: 15.872644424438477
+Epoch 1, Full Train Loss: 16.423139767419723
+Test Loss: 11.871360219240188
+New best loss: 11.871360219240188
+Saved best predictions for split 8
+Epoch 2, Loss: 13.944013595581055
+Epoch 2, Loss: 14.08833122253418
+Epoch 2, Loss: 14.119577407836914
+Epoch 2, Full Train Loss: 13.419040661766415
+Test Loss: 9.8303847001791
+New best loss: 9.8303847001791
+Saved best predictions for split 8
+Epoch 3, Loss: 11.89261531829834
+Epoch 3, Loss: 11.962753295898438
+Epoch 3, Loss: 13.037177085876465
+Epoch 3, Full Train Loss: 11.02422068459647
+Test Loss: 7.112345165371895
+New best loss: 7.112345165371895
+Saved best predictions for split 8
+Epoch 4, Loss: 8.904350280761719
+Epoch 4, Loss: 9.377645492553711
+Epoch 4, Loss: 9.658092498779297
+Epoch 4, Full Train Loss: 9.002745169685001
+Test Loss: 6.574695031642914
+New best loss: 6.574695031642914
+Saved best predictions for split 8
+Epoch 5, Loss: 7.269916534423828
+Epoch 5, Loss: 7.69330358505249
+Epoch 5, Loss: 8.760645866394043
+Epoch 5, Full Train Loss: 7.5931029138110935
+Test Loss: 5.486146688103676
+New best loss: 5.486146688103676
+Saved best predictions for split 8
+Epoch 6, Loss: 6.6487956047058105
+Epoch 6, Loss: 6.016131401062012
+Epoch 6, Loss: 7.428194999694824
+Epoch 6, Full Train Loss: 6.497047369820731
+Test Loss: 4.5161938117742535
+New best loss: 4.5161938117742535
+Saved best predictions for split 8
+Epoch 7, Loss: 5.453005790710449
+Epoch 7, Loss: 5.444803237915039
+Epoch 7, Loss: 5.498658657073975
+Epoch 7, Full Train Loss: 5.64461726461138
+Test Loss: 4.349516659975052
+New best loss: 4.349516659975052
+Saved best predictions for split 8
+Epoch 8, Loss: 5.152617454528809
+Epoch 8, Loss: 5.251161575317383
+Epoch 8, Loss: 5.740882873535156
+Epoch 8, Full Train Loss: 5.006240109034947
+Test Loss: 3.9806869295835496
+New best loss: 3.9806869295835496
+Saved best predictions for split 8
+Epoch 9, Loss: 5.336822032928467
+Epoch 9, Loss: 4.769189834594727
+Epoch 9, Loss: 4.915689468383789
+Epoch 9, Full Train Loss: 4.584184970174517
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+New best loss: 3.4494320073127747
+Saved best predictions for split 8
+Epoch 10, Loss: 3.655801773071289
+Epoch 10, Loss: 3.8969335556030273
+Epoch 10, Loss: 4.74211311340332
+Epoch 10, Full Train Loss: 4.021377069609506
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+Epoch 11, Loss: 3.837796688079834
+Epoch 11, Loss: 3.408114433288574
+Epoch 11, Loss: 4.090115547180176
+Epoch 11, Full Train Loss: 3.6359230359395345
+Test Loss: 3.245317367076874
+New best loss: 3.245317367076874
+Saved best predictions for split 8
+Epoch 12, Loss: 3.596436023712158
+Epoch 12, Loss: 3.5299606323242188
+Epoch 12, Loss: 3.7661609649658203
+Epoch 12, Full Train Loss: 3.5914883806591944
+Test Loss: 3.1912534304857254
+New best loss: 3.1912534304857254
+Saved best predictions for split 8
+Epoch 13, Loss: 3.3165526390075684
+Epoch 13, Loss: 3.450791120529175
+Epoch 13, Loss: 3.871337413787842
+Epoch 13, Full Train Loss: 3.453387513614836
+Test Loss: 3.0858398028612135
+New best loss: 3.0858398028612135
+Saved best predictions for split 8
+Epoch 14, Loss: 3.159667491912842
+Epoch 14, Loss: 3.3443546295166016
+Epoch 14, Loss: 4.040851593017578
+Epoch 14, Full Train Loss: 3.46383045060294
+Test Loss: 3.15559812271595
+Epoch 15, Loss: 3.326017379760742
+Epoch 15, Loss: 3.6357791423797607
+Epoch 15, Loss: 3.9567458629608154
+Epoch 15, Full Train Loss: 3.611546650386992
+Test Loss: 3.199712161540985
+Epoch 16, Loss: 3.3359737396240234
+Epoch 16, Loss: 3.4616518020629883
+Epoch 16, Loss: 4.042063236236572
+Epoch 16, Full Train Loss: 3.687939925420852
+Test Loss: 3.2228151953220365
+No improvement for 3 epochs. Stopping training for split 8
+Finished split 8
+Starting split 9 with features 27 to 30
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 2.4792399406433105
+Epoch 0, Loss: 3.795938014984131
+Epoch 0, Loss: 4.001718521118164
+Epoch 0, Full Train Loss: 4.907455501102266
+Test Loss: 2.511132001876831
+New best loss: 2.511132001876831
+Saved best predictions for split 9
+Epoch 1, Loss: 3.39752459526062
+Epoch 1, Loss: 4.134008407592773
+Epoch 1, Loss: 3.8779468536376953
+Epoch 1, Full Train Loss: 3.464149380865551
+Test Loss: 2.3447285847067834
+New best loss: 2.3447285847067834
+Saved best predictions for split 9
+Epoch 2, Loss: 3.114156484603882
+Epoch 2, Loss: 3.8415184020996094
+Epoch 2, Loss: 3.5421531200408936
+Epoch 2, Full Train Loss: 3.44357336021605
+Test Loss: 2.378020792067051
+Epoch 3, Loss: 3.158607006072998
+Epoch 3, Loss: 3.388441801071167
+Epoch 3, Loss: 3.6502721309661865
+Epoch 3, Full Train Loss: 3.33239727247329
+Test Loss: 2.3467820459604263
+Epoch 4, Loss: 2.9700889587402344
+Epoch 4, Loss: 3.411900281906128
+Epoch 4, Loss: 3.7426915168762207
+Epoch 4, Full Train Loss: 3.3016301836286273
+Test Loss: 2.376381926178932
+No improvement for 3 epochs. Stopping training for split 9
+Finished split 9
+Starting split 10 with features 30 to 33
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 1.6553295850753784
+Epoch 0, Loss: 2.3636507987976074
+Epoch 0, Loss: 2.525390863418579
+Epoch 0, Full Train Loss: 3.5191136842682247
+Test Loss: 1.542780458331108
+New best loss: 1.542780458331108
+Saved best predictions for split 10
+Epoch 1, Loss: 1.9073666334152222
+Epoch 1, Loss: 2.205143451690674
+Epoch 1, Loss: 2.8380141258239746
+Epoch 1, Full Train Loss: 2.3714949006126043
+Test Loss: 1.7874416803121567
+Epoch 2, Loss: 2.5854241847991943
+Epoch 2, Loss: 2.759855270385742
+Epoch 2, Loss: 3.481265068054199
+Epoch 2, Full Train Loss: 2.859102607908703
+Test Loss: 1.9656893581151962
+Epoch 3, Loss: 2.8592708110809326
+Epoch 3, Loss: 2.8951163291931152
+Epoch 3, Loss: 3.3781392574310303
+Epoch 3, Full Train Loss: 3.1722507192975
+Test Loss: 2.091428915977478
+No improvement for 3 epochs. Stopping training for split 10
+Finished split 10
+Starting split 11 with features 33 to 36
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 3.170717716217041
+Epoch 0, Loss: 4.492187023162842
+Epoch 0, Loss: 4.5819411277771
+Epoch 0, Full Train Loss: 5.6961288792746405
+Test Loss: 3.128046622753143
+New best loss: 3.128046622753143
+Saved best predictions for split 11
+Epoch 1, Loss: 4.144195556640625
+Epoch 1, Loss: 4.043963432312012
+Epoch 1, Loss: 4.22022819519043
+Epoch 1, Full Train Loss: 3.9429086231050037
+Test Loss: 2.764812236428261
+New best loss: 2.764812236428261
+Saved best predictions for split 11
+Epoch 2, Loss: 3.40271258354187
+Epoch 2, Loss: 3.4908499717712402
+Epoch 2, Loss: 4.23851203918457
+Epoch 2, Full Train Loss: 3.618559749921163
+Test Loss: 2.80128775703907
+Epoch 3, Loss: 3.4009342193603516
+Epoch 3, Loss: 3.999523639678955
+Epoch 3, Loss: 4.108742713928223
+Epoch 3, Full Train Loss: 3.6201060431344168
+Test Loss: 2.7910416662693023
+Epoch 4, Loss: 3.2649950981140137
+Epoch 4, Loss: 3.6177892684936523
+Epoch 4, Loss: 3.9614553451538086
+Epoch 4, Full Train Loss: 3.564974423817226
+Test Loss: 2.758776741147041
+New best loss: 2.758776741147041
+Saved best predictions for split 11
+Epoch 5, Loss: 3.308558464050293
+Epoch 5, Loss: 3.595571517944336
+Epoch 5, Loss: 3.933688163757324
+Epoch 5, Full Train Loss: 3.5595662798200336
+Test Loss: 2.848511127591133
+Epoch 6, Loss: 3.5145277976989746
+Epoch 6, Loss: 4.04403829574585
+Epoch 6, Loss: 3.9886550903320312
+Epoch 6, Full Train Loss: 3.5754056771596274
+Test Loss: 2.791661252975464
+Epoch 7, Loss: 3.353969097137451
+Epoch 7, Loss: 3.6167373657226562
+Epoch 7, Loss: 3.7999820709228516
+Epoch 7, Full Train Loss: 3.549628988901774
+Test Loss: 2.904082834362984
+No improvement for 3 epochs. Stopping training for split 11
+Finished split 11
+Starting split 12 with features 36 to 39
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 1.6347742080688477
+Epoch 0, Loss: 2.8217904567718506
+Epoch 0, Loss: 2.8275325298309326
+Epoch 0, Full Train Loss: 3.554221547217596
+Test Loss: 1.7273722212314606
+New best loss: 1.7273722212314606
+Saved best predictions for split 12
+Epoch 1, Loss: 2.348355770111084
+Epoch 1, Loss: 2.5551376342773438
+Epoch 1, Loss: 2.9835386276245117
+Epoch 1, Full Train Loss: 2.5204242036456153
+Test Loss: 1.8329220896959304
+Epoch 2, Loss: 2.510819911956787
+Epoch 2, Loss: 3.1232547760009766
+Epoch 2, Loss: 3.1302719116210938
+Epoch 2, Full Train Loss: 2.9820512408301942
+Test Loss: 1.9784420384168624
+Epoch 3, Loss: 2.7748639583587646
+Epoch 3, Loss: 3.7126004695892334
+Epoch 3, Loss: 3.5685582160949707
+Epoch 3, Full Train Loss: 3.304601179985773
+Test Loss: 2.110059624195099
+No improvement for 3 epochs. Stopping training for split 12
+Finished split 12
+Starting split 13 with features 39 to 42
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 15.438520431518555
+Epoch 0, Loss: 21.034332275390625
+Epoch 0, Loss: 23.34676742553711
+Epoch 0, Full Train Loss: 21.247228104727608
+Test Loss: 13.561855938076972
+New best loss: 13.561855938076972
+Saved best predictions for split 13
+Epoch 1, Loss: 15.125814437866211
+Epoch 1, Loss: 18.403564453125
+Epoch 1, Loss: 18.799020767211914
+Epoch 1, Full Train Loss: 16.881104146866573
+Test Loss: 11.978350464224816
+New best loss: 11.978350464224816
+Saved best predictions for split 13
+Epoch 2, Loss: 12.6754789352417
+Epoch 2, Loss: 14.24713134765625
+Epoch 2, Loss: 15.786956787109375
+Epoch 2, Full Train Loss: 14.439976074582054
+Test Loss: 9.49884721827507
+New best loss: 9.49884721827507
+Saved best predictions for split 13
+Epoch 3, Loss: 10.752108573913574
+Epoch 3, Loss: 14.803632736206055
+Epoch 3, Loss: 12.460020065307617
+Epoch 3, Full Train Loss: 12.213921210879372
+Test Loss: 8.390549494981766
+New best loss: 8.390549494981766
+Saved best predictions for split 13
+Epoch 4, Loss: 9.66952133178711
+Epoch 4, Loss: 10.378169059753418
+Epoch 4, Loss: 11.001288414001465
+Epoch 4, Full Train Loss: 10.317401132129488
+Test Loss: 6.513590570569038
+New best loss: 6.513590570569038
+Saved best predictions for split 13
+Epoch 5, Loss: 7.300698280334473
+Epoch 5, Loss: 7.176573753356934
+Epoch 5, Loss: 9.208189010620117
+Epoch 5, Full Train Loss: 8.474167119889032
+Test Loss: 5.298077943444252
+New best loss: 5.298077943444252
+Saved best predictions for split 13
+Epoch 6, Loss: 6.897732734680176
+Epoch 6, Loss: 7.29594612121582
+Epoch 6, Loss: 6.968616485595703
+Epoch 6, Full Train Loss: 7.117208769207909
+Test Loss: 4.628756800889969
+New best loss: 4.628756800889969
+Saved best predictions for split 13
+Epoch 7, Loss: 6.416894435882568
+Epoch 7, Loss: 6.23837423324585
+Epoch 7, Loss: 6.715948104858398
+Epoch 7, Full Train Loss: 6.219929186503093
+Test Loss: 4.030234781980514
+New best loss: 4.030234781980514
+Saved best predictions for split 13
+Epoch 8, Loss: 5.157475471496582
+Epoch 8, Loss: 4.990617752075195
+Epoch 8, Loss: 5.4214301109313965
+Epoch 8, Full Train Loss: 5.187592161269415
+Test Loss: 3.4802089676856993
+New best loss: 3.4802089676856993
+Saved best predictions for split 13
+Epoch 9, Loss: 4.394540786743164
+Epoch 9, Loss: 4.542137145996094
+Epoch 9, Loss: 4.429997444152832
+Epoch 9, Full Train Loss: 4.458068065416246
+Test Loss: 3.0250904738903044
+New best loss: 3.0250904738903044
+Saved best predictions for split 13
+Epoch 10, Loss: 3.5613772869110107
+Epoch 10, Loss: 3.3767967224121094
+Epoch 10, Loss: 3.9909541606903076
+Epoch 10, Full Train Loss: 3.7998002381551834
+Test Loss: 2.6885423263311385
+New best loss: 2.6885423263311385
+Saved best predictions for split 13
+Epoch 11, Loss: 2.92588210105896
+Epoch 11, Loss: 3.2414207458496094
+Epoch 11, Loss: 3.7437446117401123
+Epoch 11, Full Train Loss: 3.449235943385533
+Test Loss: 2.4992929985523222
+New best loss: 2.4992929985523222
+Saved best predictions for split 13
+Epoch 12, Loss: 2.7820546627044678
+Epoch 12, Loss: 3.2320780754089355
+Epoch 12, Loss: 3.526121139526367
+Epoch 12, Full Train Loss: 3.1216244765690395
+Test Loss: 2.4851859533786773
+New best loss: 2.4851859533786773
+Saved best predictions for split 13
+Epoch 13, Loss: 2.9371142387390137
+Epoch 13, Loss: 2.708202362060547
+Epoch 13, Loss: 3.1057686805725098
+Epoch 13, Full Train Loss: 2.95134235336667
+Test Loss: 2.4570380532741547
+New best loss: 2.4570380532741547
+Saved best predictions for split 13
+Epoch 14, Loss: 3.0660314559936523
+Epoch 14, Loss: 2.889157772064209
+Epoch 14, Loss: 3.1525778770446777
+Epoch 14, Full Train Loss: 2.851336407661438
+Test Loss: 2.2482711740732193
+New best loss: 2.2482711740732193
+Saved best predictions for split 13
+Epoch 15, Loss: 2.9149727821350098
+Epoch 15, Loss: 3.112431049346924
+Epoch 15, Loss: 2.970095634460449
+Epoch 15, Full Train Loss: 2.7748603752681187
+Test Loss: 2.1694333003759385
+New best loss: 2.1694333003759385
+Saved best predictions for split 13
+Epoch 16, Loss: 2.512448310852051
+Epoch 16, Loss: 2.712435483932495
+Epoch 16, Loss: 2.9798617362976074
+Epoch 16, Full Train Loss: 2.7462557554244995
+Test Loss: 2.2051617844104765
+Epoch 17, Loss: 2.4455666542053223
+Epoch 17, Loss: 2.681997776031494
+Epoch 17, Loss: 3.032953977584839
+Epoch 17, Full Train Loss: 2.7597748824528288
+Test Loss: 2.2740154329538345
+Epoch 18, Loss: 2.7489733695983887
+Epoch 18, Loss: 3.036323070526123
+Epoch 18, Loss: 3.2255287170410156
+Epoch 18, Full Train Loss: 2.9660174017860776
+Test Loss: 2.323734869122505
+No improvement for 3 epochs. Stopping training for split 13
+Finished split 13
+Starting split 14 with features 42 to 45
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 11.407896041870117
+Epoch 0, Loss: 15.189258575439453
+Epoch 0, Loss: 14.97474193572998
+Epoch 0, Full Train Loss: 16.34671130861555
+Test Loss: 10.47299901008606
+New best loss: 10.47299901008606
+Saved best predictions for split 14
+Epoch 1, Loss: 11.748016357421875
+Epoch 1, Loss: 12.30335807800293
+Epoch 1, Loss: 13.990678787231445
+Epoch 1, Full Train Loss: 12.986490821838379
+Test Loss: 9.386181324779987
+New best loss: 9.386181324779987
+Saved best predictions for split 14
+Epoch 2, Loss: 11.294454574584961
+Epoch 2, Loss: 9.506758689880371
+Epoch 2, Loss: 12.756793022155762
+Epoch 2, Full Train Loss: 11.155948747907367
+Test Loss: 6.60068881225586
+New best loss: 6.60068881225586
+Saved best predictions for split 14
+Epoch 3, Loss: 9.71385383605957
+Epoch 3, Loss: 10.394637107849121
+Epoch 3, Loss: 9.50793743133545
+Epoch 3, Full Train Loss: 9.131884804226104
+Test Loss: 5.644187059223652
+New best loss: 5.644187059223652
+Saved best predictions for split 14
+Epoch 4, Loss: 7.528295516967773
+Epoch 4, Loss: 6.857114315032959
+Epoch 4, Loss: 8.168399810791016
+Epoch 4, Full Train Loss: 7.4718339715685165
+Test Loss: 4.853607506155968
+New best loss: 4.853607506155968
+Saved best predictions for split 14
+Epoch 5, Loss: 6.848165035247803
+Epoch 5, Loss: 6.345325469970703
+Epoch 5, Loss: 6.500482082366943
+Epoch 5, Full Train Loss: 6.217298230670747
+Test Loss: 3.838908193528652
+New best loss: 3.838908193528652
+Saved best predictions for split 14
+Epoch 6, Loss: 4.292004585266113
+Epoch 6, Loss: 5.319397926330566
+Epoch 6, Loss: 5.240181922912598
+Epoch 6, Full Train Loss: 5.178779272806077
+Test Loss: 3.2571636272072793
+New best loss: 3.2571636272072793
+Saved best predictions for split 14
+Epoch 7, Loss: 4.8651862144470215
+Epoch 7, Loss: 4.546087741851807
+Epoch 7, Loss: 4.283312797546387
+Epoch 7, Full Train Loss: 4.392418637729826
+Test Loss: 2.7414145692586898
+New best loss: 2.7414145692586898
+Saved best predictions for split 14
+Epoch 8, Loss: 3.9402570724487305
+Epoch 8, Loss: 3.4671545028686523
+Epoch 8, Loss: 3.7957706451416016
+Epoch 8, Full Train Loss: 3.7836663825171333
+Test Loss: 2.6598091514110567
+New best loss: 2.6598091514110567
+Saved best predictions for split 14
+Epoch 9, Loss: 3.4349353313446045
+Epoch 9, Loss: 3.4549319744110107
+Epoch 9, Loss: 3.6625804901123047
+Epoch 9, Full Train Loss: 3.3254231191816785
+Test Loss: 2.330729043483734
+New best loss: 2.330729043483734
+Saved best predictions for split 14
+Epoch 10, Loss: 2.9865493774414062
+Epoch 10, Loss: 2.967822551727295
+Epoch 10, Loss: 3.208496570587158
+Epoch 10, Full Train Loss: 2.985426245416914
+Test Loss: 2.1040820078849793
+New best loss: 2.1040820078849793
+Saved best predictions for split 14
+Epoch 11, Loss: 2.652763843536377
+Epoch 11, Loss: 2.7911200523376465
+Epoch 11, Loss: 3.0974912643432617
+Epoch 11, Full Train Loss: 2.84702391851516
+Test Loss: 1.8983472172617912
+New best loss: 1.8983472172617912
+Saved best predictions for split 14
+Epoch 12, Loss: 2.521411418914795
+Epoch 12, Loss: 2.4933764934539795
+Epoch 12, Loss: 2.7561683654785156
+Epoch 12, Full Train Loss: 2.58139397416796
+Test Loss: 1.9139075741171836
+Epoch 13, Loss: 2.3984766006469727
+Epoch 13, Loss: 2.4547972679138184
+Epoch 13, Loss: 2.7830886840820312
+Epoch 13, Full Train Loss: 2.621966542516436
+Test Loss: 1.8733215629458428
+New best loss: 1.8733215629458428
+Saved best predictions for split 14
+Epoch 14, Loss: 2.5001707077026367
+Epoch 14, Loss: 2.491084575653076
+Epoch 14, Loss: 2.555842638015747
+Epoch 14, Full Train Loss: 2.513675722621736
+Test Loss: 1.8761100991964341
+Epoch 15, Loss: 2.5208840370178223
+Epoch 15, Loss: 2.4152932167053223
+Epoch 15, Loss: 2.723512649536133
+Epoch 15, Full Train Loss: 2.6104461465563094
+Test Loss: 1.936553641974926
+Epoch 16, Loss: 2.3451828956604004
+Epoch 16, Loss: 2.5328218936920166
+Epoch 16, Loss: 2.8721518516540527
+Epoch 16, Full Train Loss: 2.6162227494376045
+Test Loss: 1.899868665933609
+No improvement for 3 epochs. Stopping training for split 14
+Finished split 14
+Starting split 15 with features 45 to 48
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 20.600736618041992
+Epoch 0, Loss: 27.099559783935547
+Epoch 0, Loss: 28.11395835876465
+Epoch 0, Full Train Loss: 26.80862411317371
+Test Loss: 17.609158729553222
+New best loss: 17.609158729553222
+Saved best predictions for split 15
+Epoch 1, Loss: 20.649093627929688
+Epoch 1, Loss: 22.941511154174805
+Epoch 1, Loss: 27.35445213317871
+Epoch 1, Full Train Loss: 21.53161219642276
+Test Loss: 17.228669089198114
+New best loss: 17.228669089198114
+Saved best predictions for split 15
+Epoch 2, Loss: 18.661243438720703
+Epoch 2, Loss: 18.412677764892578
+Epoch 2, Loss: 18.1877498626709
+Epoch 2, Full Train Loss: 18.230472269512358
+Test Loss: 12.835660787820816
+New best loss: 12.835660787820816
+Saved best predictions for split 15
+Epoch 3, Loss: 14.382689476013184
+Epoch 3, Loss: 13.536739349365234
+Epoch 3, Loss: 17.608341217041016
+Epoch 3, Full Train Loss: 15.400245489392962
+Test Loss: 11.385104596972466
+New best loss: 11.385104596972466
+Saved best predictions for split 15
+Epoch 4, Loss: 12.34451961517334
+Epoch 4, Loss: 13.355033874511719
+Epoch 4, Loss: 12.193145751953125
+Epoch 4, Full Train Loss: 13.23246880485898
+Test Loss: 9.717282317638396
+New best loss: 9.717282317638396
+Saved best predictions for split 15
+Epoch 5, Loss: 9.823997497558594
+Epoch 5, Loss: 12.843421936035156
+Epoch 5, Loss: 11.312907218933105
+Epoch 5, Full Train Loss: 11.150938397362118
+Test Loss: 7.721738573551178
+New best loss: 7.721738573551178
+Saved best predictions for split 15
+Epoch 6, Loss: 9.754352569580078
+Epoch 6, Loss: 11.013964653015137
+Epoch 6, Loss: 10.97449016571045
+Epoch 6, Full Train Loss: 9.817804865610032
+Test Loss: 6.936355108618736
+New best loss: 6.936355108618736
+Saved best predictions for split 15
+Epoch 7, Loss: 7.949197769165039
+Epoch 7, Loss: 8.809255599975586
+Epoch 7, Loss: 8.207082748413086
+Epoch 7, Full Train Loss: 8.534286315100534
+Test Loss: 6.22078505396843
+New best loss: 6.22078505396843
+Saved best predictions for split 15
+Epoch 8, Loss: 6.6041693687438965
+Epoch 8, Loss: 7.951560974121094
+Epoch 8, Loss: 7.483893394470215
+Epoch 8, Full Train Loss: 7.090390107745216
+Test Loss: 5.847580737709999
+New best loss: 5.847580737709999
+Saved best predictions for split 15
+Epoch 9, Loss: 6.265018463134766
+Epoch 9, Loss: 5.774040222167969
+Epoch 9, Loss: 7.370000839233398
+Epoch 9, Full Train Loss: 6.27937220391773
+Test Loss: 5.456841018795967
+New best loss: 5.456841018795967
+Saved best predictions for split 15
+Epoch 10, Loss: 5.939113616943359
+Epoch 10, Loss: 5.381406784057617
+Epoch 10, Loss: 5.8587260246276855
+Epoch 10, Full Train Loss: 5.462632088434129
+Test Loss: 4.682663113713264
+New best loss: 4.682663113713264
+Saved best predictions for split 15
+Epoch 11, Loss: 4.976218223571777
+Epoch 11, Loss: 4.674629211425781
+Epoch 11, Loss: 5.206663131713867
+Epoch 11, Full Train Loss: 4.818514705839611
+Test Loss: 4.137187415122986
+New best loss: 4.137187415122986
+Saved best predictions for split 15
+Epoch 12, Loss: 4.111126899719238
+Epoch 12, Loss: 4.072818756103516
+Epoch 12, Loss: 4.952876091003418
+Epoch 12, Full Train Loss: 4.41715860139756
+Test Loss: 3.856377921819687
+New best loss: 3.856377921819687
+Saved best predictions for split 15
+Epoch 13, Loss: 3.858613967895508
+Epoch 13, Loss: 3.933743953704834
+Epoch 13, Loss: 4.200404644012451
+Epoch 13, Full Train Loss: 4.066423452468145
+Test Loss: 3.4946761330366134
+New best loss: 3.4946761330366134
+Saved best predictions for split 15
+Epoch 14, Loss: 3.4139604568481445
+Epoch 14, Loss: 3.758204936981201
+Epoch 14, Loss: 4.1868133544921875
+Epoch 14, Full Train Loss: 3.8102525256928943
+Test Loss: 3.4934069638252256
+New best loss: 3.4934069638252256
+Saved best predictions for split 15
+Epoch 15, Loss: 3.4165008068084717
+Epoch 15, Loss: 3.494098424911499
+Epoch 15, Loss: 4.089907646179199
+Epoch 15, Full Train Loss: 3.6200477759043377
+Test Loss: 3.46082733130455
+New best loss: 3.46082733130455
+Saved best predictions for split 15
+Epoch 16, Loss: 3.295091152191162
+Epoch 16, Loss: 3.928145170211792
+Epoch 16, Loss: 3.9870028495788574
+Epoch 16, Full Train Loss: 3.6355021079381307
+Test Loss: 3.3327188154459
+New best loss: 3.3327188154459
+Saved best predictions for split 15
+Epoch 17, Loss: 3.326205015182495
+Epoch 17, Loss: 3.592851161956787
+Epoch 17, Loss: 4.233291149139404
+Epoch 17, Full Train Loss: 3.655863220351083
+Test Loss: 3.5197788121700286
+Epoch 18, Loss: 3.4956417083740234
+Epoch 18, Loss: 3.5315303802490234
+Epoch 18, Loss: 4.136011123657227
+Epoch 18, Full Train Loss: 3.6785252968470257
+Test Loss: 3.469285778284073
+Epoch 19, Loss: 3.5658979415893555
+Epoch 19, Loss: 3.6163623332977295
+Epoch 19, Loss: 3.81660532951355
+Epoch 19, Full Train Loss: 3.6086202076503207
+Test Loss: 3.5413660645484923
+No improvement for 3 epochs. Stopping training for split 15
+Finished split 15
+Starting split 16 with features 48 to 51
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 1.9712839126586914
+Epoch 0, Loss: 2.6708984375
+Epoch 0, Loss: 2.762174129486084
+Epoch 0, Full Train Loss: 3.94170305842445
+Test Loss: 1.689654808074236
+New best loss: 1.689654808074236
+Saved best predictions for split 16
+Epoch 1, Loss: 2.10302734375
+Epoch 1, Loss: 2.2835097312927246
+Epoch 1, Loss: 2.961948871612549
+Epoch 1, Full Train Loss: 2.505615554537092
+Test Loss: 1.7694734556674958
+Epoch 2, Loss: 2.5313754081726074
+Epoch 2, Loss: 2.4556329250335693
+Epoch 2, Loss: 3.1662025451660156
+Epoch 2, Full Train Loss: 2.6887895663579306
+Test Loss: 1.8692783344984054
+Epoch 3, Loss: 2.635364532470703
+Epoch 3, Loss: 2.748939275741577
+Epoch 3, Loss: 3.023078441619873
+Epoch 3, Full Train Loss: 2.9700273434321085
+Test Loss: 1.879947629570961
+No improvement for 3 epochs. Stopping training for split 16
+Finished split 16
+Starting split 17 with features 51 to 54
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 7.885293006896973
+Epoch 0, Loss: 12.906851768493652
+Epoch 0, Loss: 10.80902099609375
+Epoch 0, Full Train Loss: 11.865432344164168
+Test Loss: 6.790192364513874
+New best loss: 6.790192364513874
+Saved best predictions for split 17
+Epoch 1, Loss: 9.990468978881836
+Epoch 1, Loss: 10.24746322631836
+Epoch 1, Loss: 7.860285758972168
+Epoch 1, Full Train Loss: 8.657866078331358
+Test Loss: 5.8959261829257015
+New best loss: 5.8959261829257015
+Saved best predictions for split 17
+Epoch 2, Loss: 6.757083892822266
+Epoch 2, Loss: 7.162209510803223
+Epoch 2, Loss: 6.348381996154785
+Epoch 2, Full Train Loss: 6.273441827864874
+Test Loss: 3.6652788877487184
+New best loss: 3.6652788877487184
+Saved best predictions for split 17
+Epoch 3, Loss: 4.348957061767578
+Epoch 3, Loss: 3.9924979209899902
+Epoch 3, Loss: 5.045173645019531
+Epoch 3, Full Train Loss: 4.8205610377447945
+Test Loss: 2.915621439635754
+New best loss: 2.915621439635754
+Saved best predictions for split 17
+Epoch 4, Loss: 3.703808546066284
+Epoch 4, Loss: 3.8839480876922607
+Epoch 4, Loss: 4.046473026275635
+Epoch 4, Full Train Loss: 3.721191524323963
+Test Loss: 2.527314770281315
+New best loss: 2.527314770281315
+Saved best predictions for split 17
+Epoch 5, Loss: 3.118441104888916
+Epoch 5, Loss: 2.9327330589294434
+Epoch 5, Loss: 3.185520887374878
+Epoch 5, Full Train Loss: 3.143801280430385
+Test Loss: 2.2114762897491453
+New best loss: 2.2114762897491453
+Saved best predictions for split 17
+Epoch 6, Loss: 2.619809627532959
+Epoch 6, Loss: 2.8272767066955566
+Epoch 6, Loss: 3.0533080101013184
+Epoch 6, Full Train Loss: 2.834364025933402
+Test Loss: 2.179818817615509
+New best loss: 2.179818817615509
+Saved best predictions for split 17
+Epoch 7, Loss: 2.8295962810516357
+Epoch 7, Loss: 2.9207496643066406
+Epoch 7, Loss: 3.035756826400757
+Epoch 7, Full Train Loss: 2.772565940448216
+Test Loss: 2.14890818464756
+New best loss: 2.14890818464756
+Saved best predictions for split 17
+Epoch 8, Loss: 2.552323341369629
+Epoch 8, Loss: 2.908662796020508
+Epoch 8, Loss: 3.2085299491882324
+Epoch 8, Full Train Loss: 2.826097665514265
+Test Loss: 2.4173327518701555
+Epoch 9, Loss: 3.2473411560058594
+Epoch 9, Loss: 3.0561132431030273
+Epoch 9, Loss: 3.2590339183807373
+Epoch 9, Full Train Loss: 2.9968793471654256
+Test Loss: 2.3440264475345614
+Epoch 10, Loss: 2.9381375312805176
+Epoch 10, Loss: 2.918081283569336
+Epoch 10, Loss: 3.446521520614624
+Epoch 10, Full Train Loss: 3.0140903223128546
+Test Loss: 2.435044761300087
+No improvement for 3 epochs. Stopping training for split 17
+Finished split 17
+Starting split 18 with features 54 to 57
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 22.041128158569336
+Epoch 0, Loss: 27.167247772216797
+Epoch 0, Loss: 28.80615997314453
+Epoch 0, Full Train Loss: 28.878455670674644
+Test Loss: 19.39575527358055
+New best loss: 19.39575527358055
+Saved best predictions for split 18
+Epoch 1, Loss: 21.936031341552734
+Epoch 1, Loss: 20.462860107421875
+Epoch 1, Loss: 22.625606536865234
+Epoch 1, Full Train Loss: 22.870817720322382
+Test Loss: 15.216414442300797
+New best loss: 15.216414442300797
+Saved best predictions for split 18
+Epoch 2, Loss: 17.942096710205078
+Epoch 2, Loss: 20.007719039916992
+Epoch 2, Loss: 20.622966766357422
+Epoch 2, Full Train Loss: 18.84213638305664
+Test Loss: 12.714787320256233
+New best loss: 12.714787320256233
+Saved best predictions for split 18
+Epoch 3, Loss: 13.645350456237793
+Epoch 3, Loss: 14.941645622253418
+Epoch 3, Loss: 17.03213882446289
+Epoch 3, Full Train Loss: 15.816998113904681
+Test Loss: 11.59853811442852
+New best loss: 11.59853811442852
+Saved best predictions for split 18
+Epoch 4, Loss: 12.63665771484375
+Epoch 4, Loss: 13.464526176452637
+Epoch 4, Loss: 16.225303649902344
+Epoch 4, Full Train Loss: 13.35127959478469
+Test Loss: 9.527424606204033
+New best loss: 9.527424606204033
+Saved best predictions for split 18
+Epoch 5, Loss: 10.907313346862793
+Epoch 5, Loss: 9.29513168334961
+Epoch 5, Loss: 12.030200004577637
+Epoch 5, Full Train Loss: 11.352700124468122
+Test Loss: 8.813932047724723
+New best loss: 8.813932047724723
+Saved best predictions for split 18
+Epoch 6, Loss: 9.090322494506836
+Epoch 6, Loss: 9.58853530883789
+Epoch 6, Loss: 10.64987850189209
+Epoch 6, Full Train Loss: 9.87349731808617
+Test Loss: 7.081519022464752
+New best loss: 7.081519022464752
+Saved best predictions for split 18
+Epoch 7, Loss: 7.557902812957764
+Epoch 7, Loss: 7.979937553405762
+Epoch 7, Loss: 9.536582946777344
+Epoch 7, Full Train Loss: 8.605285390218098
+Test Loss: 6.395419254302978
+New best loss: 6.395419254302978
+Saved best predictions for split 18
+Epoch 8, Loss: 7.1147589683532715
+Epoch 8, Loss: 8.62725830078125
+Epoch 8, Loss: 8.070799827575684
+Epoch 8, Full Train Loss: 7.448494595573062
+Test Loss: 5.586477571129799
+New best loss: 5.586477571129799
+Saved best predictions for split 18
+Epoch 9, Loss: 6.3191986083984375
+Epoch 9, Loss: 6.40342903137207
+Epoch 9, Loss: 6.610653877258301
+Epoch 9, Full Train Loss: 6.398397482009161
+Test Loss: 4.583983354330063
+New best loss: 4.583983354330063
+Saved best predictions for split 18
+Epoch 10, Loss: 5.031716346740723
+Epoch 10, Loss: 5.392858028411865
+Epoch 10, Loss: 5.443562984466553
+Epoch 10, Full Train Loss: 5.428752190726144
+Test Loss: 4.204485325098037
+New best loss: 4.204485325098037
+Saved best predictions for split 18
+Epoch 11, Loss: 4.750335216522217
+Epoch 11, Loss: 4.310096740722656
+Epoch 11, Loss: 5.0070109367370605
+Epoch 11, Full Train Loss: 4.819802327383132
+Test Loss: 3.8569662861824034
+New best loss: 3.8569662861824034
+Saved best predictions for split 18
+Epoch 12, Loss: 4.285506248474121
+Epoch 12, Loss: 4.565199851989746
+Epoch 12, Loss: 4.646440505981445
+Epoch 12, Full Train Loss: 4.4752153362546645
+Test Loss: 3.7434883451461793
+New best loss: 3.7434883451461793
+Saved best predictions for split 18
+Epoch 13, Loss: 4.359664440155029
+Epoch 13, Loss: 3.9617438316345215
+Epoch 13, Loss: 3.994868755340576
+Epoch 13, Full Train Loss: 4.024909895942325
+Test Loss: 3.300156569838524
+New best loss: 3.300156569838524
+Saved best predictions for split 18
+Epoch 14, Loss: 3.720844030380249
+Epoch 14, Loss: 3.819319725036621
+Epoch 14, Loss: 3.88813853263855
+Epoch 14, Full Train Loss: 3.7365507091794696
+Test Loss: 3.284670576334
+New best loss: 3.284670576334
+Saved best predictions for split 18
+Epoch 15, Loss: 3.497091770172119
+Epoch 15, Loss: 3.5211806297302246
+Epoch 15, Loss: 3.975804090499878
+Epoch 15, Full Train Loss: 3.5694532621474493
+Test Loss: 3.1834242820739744
+New best loss: 3.1834242820739744
+Saved best predictions for split 18
+Epoch 16, Loss: 3.2120800018310547
+Epoch 16, Loss: 3.5688388347625732
+Epoch 16, Loss: 3.865941047668457
+Epoch 16, Full Train Loss: 3.5021201973869687
+Test Loss: 3.3813653057813644
+Epoch 17, Loss: 3.855689525604248
+Epoch 17, Loss: 3.39228892326355
+Epoch 17, Loss: 4.168292999267578
+Epoch 17, Full Train Loss: 3.5566321747643608
+Test Loss: 3.2590916296243666
+Epoch 18, Loss: 3.4858157634735107
+Epoch 18, Loss: 3.4315202236175537
+Epoch 18, Loss: 3.868466854095459
+Epoch 18, Full Train Loss: 3.5846155643463136
+Test Loss: 3.139840506672859
+New best loss: 3.139840506672859
+Saved best predictions for split 18
+Epoch 19, Loss: 3.234665632247925
+Epoch 19, Loss: 3.3029189109802246
+Epoch 19, Loss: 3.937530517578125
+Epoch 19, Full Train Loss: 3.501489708537147
+Test Loss: 3.152327203154564
+Epoch 20, Loss: 3.239405393600464
+Epoch 20, Loss: 3.34732985496521
+Epoch 20, Loss: 4.1219916343688965
+Epoch 20, Full Train Loss: 3.559396132968721
+Test Loss: 3.2210867500305174
+Epoch 21, Loss: 3.564868927001953
+Epoch 21, Loss: 3.549109935760498
+Epoch 21, Loss: 4.0935258865356445
+Epoch 21, Full Train Loss: 3.6751856451942806
+Test Loss: 3.374684287428856
+No improvement for 3 epochs. Stopping training for split 18
+Finished split 18
+Starting split 19 with features 57 to 60
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 2.148848056793213
+Epoch 0, Loss: 2.960946559906006
+Epoch 0, Loss: 2.730257749557495
+Epoch 0, Full Train Loss: 4.19016311736334
+Test Loss: 1.666335680603981
+New best loss: 1.666335680603981
+Saved best predictions for split 19
+Epoch 1, Loss: 2.555140972137451
+Epoch 1, Loss: 2.439145803451538
+Epoch 1, Loss: 2.4915313720703125
+Epoch 1, Full Train Loss: 2.442668552625747
+Test Loss: 1.602807040810585
+New best loss: 1.602807040810585
+Saved best predictions for split 19
+Epoch 2, Loss: 2.4328646659851074
+Epoch 2, Loss: 2.225893497467041
+Epoch 2, Loss: 2.6911895275115967
+Epoch 2, Full Train Loss: 2.390190627461388
+Test Loss: 1.483979959189892
+New best loss: 1.483979959189892
+Saved best predictions for split 19
+Epoch 3, Loss: 2.2059807777404785
+Epoch 3, Loss: 2.222188949584961
+Epoch 3, Loss: 2.5227468013763428
+Epoch 3, Full Train Loss: 2.4239490304674423
+Test Loss: 1.6967377677559852
+Epoch 4, Loss: 2.801445960998535
+Epoch 4, Loss: 2.833298444747925
+Epoch 4, Loss: 3.1156222820281982
+Epoch 4, Full Train Loss: 2.777419256028675
+Test Loss: 1.7850771641135217
+Epoch 5, Loss: 3.0624194145202637
+Epoch 5, Loss: 3.9812517166137695
+Epoch 5, Loss: 2.8474788665771484
+Epoch 5, Full Train Loss: 3.0696152142116
+Test Loss: 1.7231352966427802
+No improvement for 3 epochs. Stopping training for split 19
+Finished split 19
+Starting split 20 with features 60 to 63
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+3,091,660,800 total
+3,091,660,800 trainable
+Epoch 0, Loss: 4.566344261169434
+Epoch 0, Loss: 5.873503684997559
+Epoch 0, Loss: 7.053010940551758
+Epoch 0, Full Train Loss: 7.276761420567831
+Test Loss: 3.548194052398205
+New best loss: 3.548194052398205
+Saved best predictions for split 20
+Epoch 1, Loss: 4.324273109436035
+Epoch 1, Loss: 4.679067611694336
+Epoch 1, Loss: 5.651883602142334
+Epoch 1, Full Train Loss: 4.511675306728908
+Test Loss: 2.8700913338661196
+New best loss: 2.8700913338661196
+Saved best predictions for split 20
+Epoch 2, Loss: 4.795882225036621
+Epoch 2, Loss: 3.7100415229797363
+Epoch 2, Loss: 3.447141647338867
+Epoch 2, Full Train Loss: 3.468086616198222
+Test Loss: 2.1680324538350106
+New best loss: 2.1680324538350106
+Saved best predictions for split 20
+Epoch 3, Loss: 2.8680408000946045
+Epoch 3, Loss: 3.0300979614257812
+Epoch 3, Loss: 3.2696375846862793
+Epoch 3, Full Train Loss: 3.065514440763564
+Test Loss: 2.1731436389684675
+Epoch 4, Loss: 3.1507277488708496
+Epoch 4, Loss: 2.743762493133545
+Epoch 4, Loss: 3.344527006149292
+Epoch 4, Full Train Loss: 2.8676327909742083
+Test Loss: 2.096704949915409
+New best loss: 2.096704949915409
+Saved best predictions for split 20
+Epoch 5, Loss: 2.8495242595672607
+Epoch 5, Loss: 2.730026960372925
+Epoch 5, Loss: 3.5774569511413574
+Epoch 5, Full Train Loss: 2.850122933160691
+Test Loss: 2.1167582560181617
+Epoch 6, Loss: 3.0221269130706787
+Epoch 6, Loss: 2.785867214202881
+Epoch 6, Loss: 3.1562116146087646
+Epoch 6, Full Train Loss: 3.049552047820318
+Test Loss: 2.1148692564368248
+Epoch 7, Loss: 2.776492118835449
+Epoch 7, Loss: 3.273364543914795
+Epoch 7, Loss: 3.344088315963745
+Epoch 7, Full Train Loss: 3.116995718365624
+Test Loss: 2.0655375396609306
+New best loss: 2.0655375396609306
+Saved best predictions for split 20
+Epoch 8, Loss: 2.8821239471435547
+Epoch 8, Loss: 2.679378032684326
+Epoch 8, Loss: 3.389845609664917
+Epoch 8, Full Train Loss: 2.949054057257516
+Test Loss: 2.0718202531337737
+Epoch 9, Loss: 2.721818685531616
+Epoch 9, Loss: 2.7827811241149902
+Epoch 9, Loss: 3.4789676666259766
+Epoch 9, Full Train Loss: 2.9953861781529016
+Test Loss: 2.2888165258169173
+Epoch 10, Loss: 3.1789710521698
+Epoch 10, Loss: 2.9238154888153076
+Epoch 10, Loss: 3.4575018882751465
+Epoch 10, Full Train Loss: 3.0561986503146943
+Test Loss: 2.1530646167993543
+No improvement for 3 epochs. Stopping training for split 20
+Finished split 20
+Starting split 21 with features 63 to 64
+Creating datasets and dataloaders
+Creating RR model
+param counts:
+1,030,553,600 total
+1,030,553,600 trainable
+Epoch 0, Loss: 0.8904459476470947
+Epoch 0, Loss: 1.0213857889175415
+Epoch 0, Loss: 0.8004237413406372
+Epoch 0, Full Train Loss: 2.476667719227927
+Test Loss: 0.31311856116354464
+New best loss: 0.31311856116354464
+Saved best predictions for split 21
+Epoch 1, Loss: 0.6255568861961365
+Epoch 1, Loss: 0.7087153792381287
+Epoch 1, Loss: 0.9835464954376221
+Epoch 1, Full Train Loss: 0.7551679057734353
+Test Loss: 0.4416113931387663
+Epoch 2, Loss: 1.009950876235962
+Epoch 2, Loss: 1.2008488178253174
+Epoch 2, Loss: 1.8146896362304688
+Epoch 2, Full Train Loss: 1.427700798852103
+Test Loss: 0.8274881052672863
+Epoch 3, Loss: 1.9512654542922974
+Epoch 3, Loss: 3.3924388885498047
+Epoch 3, Loss: 2.386967658996582
+Epoch 3, Full Train Loss: 2.4120475036757334
+Test Loss: 1.1343677823245526
+No improvement for 3 epochs. Stopping training for split 21
+Finished split 21
+Finished all splits

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530260.out

@@ -0,0 +1,5 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-246
+MASTER_PORT=13228
+WORLD_SIZE=1
+PID of this process = 3664742

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530261.err

@@ -0,0 +1,7 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 14580 bytes to RR_sklearn.py
+Traceback (most recent call last):
+  File "/weka/proj-fmri/ckadirt/spurious_reconstruction/analysis/1_case_study/feature-decoding/RR_sklearn.py", line 122, in <module>
+    if current_features == 'all':
+       ^^^^^^^^^^^^^^^^
+NameError: name 'current_features' is not defined. Did you mean: 'current_feature'?

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530261.out

@@ -0,0 +1,5 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-246
+MASTER_PORT=17298
+WORLD_SIZE=1
+PID of this process = 3665950

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530262.err

@@ -0,0 +1,13 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 14581 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+Traceback (most recent call last):
+  File "/weka/proj-fmri/ckadirt/spurious_reconstruction/analysis/1_case_study/feature-decoding/RR_sklearn.py", line 269, in <module>
+    feature_extractor = feature_extractor.to(device)
+                        ^^^^^^^^^^^^^^^^^
+NameError: name 'feature_extractor' is not defined
+Exception ignored in: <function FeatureExtractor.__del__ at 0x7fbc6d459080>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530268.err

@@ -0,0 +1,1544 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 15084 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+
+Exception ignored in: <function FeatureExtractor.__del__ at 0x7f31f1fe5260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+Exception ignored in: <function FeatureExtractor.__del__ at 0x7f6592f49260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+Exception ignored in: <function FeatureExtractor.__del__ at 0x7f9781675260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+Exception ignored in: <function FeatureExtractor.__del__ at 0x7fc546b2d260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+
+
+
  0%|          | 0/8 [00:00<?, ?it/s]
+
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+
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+
 88%|████████▊ | 7/8 [00:07<00:00,  1.82it/s]
+
+
+
+
+
+
+
+Exception ignored in: <function FeatureExtractor.__del__ at 0x7fba235e5260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+
+
+
  0%|          | 0/8 [00:00<?, ?it/s]
+
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+
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+
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+
 88%|████████▊ | 7/8 [00:07<00:00,  1.79it/s]
+
+
+
+
+
+
+
+Exception ignored in: <function FeatureExtractor.__del__ at 0x7f95235dd260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
+
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+
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+
+
+
+
+
+Exception ignored in: <function FeatureExtractor.__del__ at 0x7fbdeeb7d260>
+Traceback (most recent call last):
+  File "/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/torch.py", line 108, in __del__
+TypeError: 'NoneType' object is not callable

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530268.out

@@ -0,0 +1,123 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-139-117
+MASTER_PORT=13189
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[28]
+Calculating for: features[28]
+PID of this process = 3298083
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.4596171623460294, test_score: 0.15508112383216774
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2
+Finished, now scoring
+train_score: 0.45590505666140063, test_score: 0.1481887747840461
+Successfully processed features[28].
+Running RR_sklearn.py with argument: features[30]
+Calculating for: features[30]
+PID of this process = 3302295
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.45958504527115046, test_score: 0.15587002984278647
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2
+Finished, now scoring
+train_score: 0.45962687292991283, test_score: 0.1567194867915378
+Successfully processed features[30].
+Running RR_sklearn.py with argument: features[32]
+Calculating for: features[32]
+PID of this process = 3306002
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.45819538826774553, test_score: 0.15249727089968543
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2
+Finished, now scoring
+train_score: 0.46159722659815194, test_score: 0.15902165914972052
+Successfully processed features[32].
+Running RR_sklearn.py with argument: features[34]
+Calculating for: features[34]
+PID of this process = 3309707
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.47192760353898633, test_score: 0.17910964431325138
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2
+Finished, now scoring
+train_score: 0.47058061967151404, test_score: 0.17653867422417246
+Successfully processed features[34].
+Running RR_sklearn.py with argument: classifier[0]
+Calculating for: classifier[0]
+PID of this process = 3313399
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 4096
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.5227617030555727, test_score: 0.2682422535470108
+Successfully processed classifier[0].
+Running RR_sklearn.py with argument: classifier[3]
+Calculating for: classifier[3]
+PID of this process = 3315036
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 4096
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.4913298512348834, test_score: 0.21615468992479098
+Successfully processed classifier[3].
+Running RR_sklearn.py with argument: classifier[6]
+Calculating for: classifier[6]
+PID of this process = 3316803
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 1000
+Starting ridge regression for split 1
+Finished, now scoring
+train_score: 0.5602790267487533, test_score: 0.3436224048979234
+Successfully processed classifier[6].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530545.out

@@ -0,0 +1,432 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-129-21
+MASTER_PORT=15878
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Calculating for: features[0]
+PID of this process = 1055607
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.32676214947226284, test_score: 0.13707442336274125
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.3288464497745941, test_score: 0.1345664523917284
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.2915409162500128, test_score: 0.0826779509210705
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.27080938859206277, test_score: 0.049042762683754904
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.30395525983977995, test_score: 0.11427283285569294
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.23629862908650953, test_score: -0.00812257122749698
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.3041380289390031, test_score: 0.11331802131928478
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.2920957975077294, test_score: 0.08332204790465313
+Calculating split 9 of 32
+start_feature_index: 16, end_feature_index: 18
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.3006816044261448, test_score: 0.10508913026726484
+Calculating split 10 of 32
+start_feature_index: 18, end_feature_index: 20
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.3126568999323276, test_score: 0.11860761391126841
+Calculating split 11 of 32
+start_feature_index: 20, end_feature_index: 22
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.2573614660415292, test_score: 0.029726137303407715
+Calculating split 12 of 32
+start_feature_index: 22, end_feature_index: 24
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.3038827275603439, test_score: 0.10766490829212247
+Calculating split 13 of 32
+start_feature_index: 24, end_feature_index: 26
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.3597620472050573, test_score: 0.19451248420715525
+Calculating split 14 of 32
+start_feature_index: 26, end_feature_index: 28
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.30302889901361085, test_score: 0.10417056715152957
+Calculating split 15 of 32
+start_feature_index: 28, end_feature_index: 30
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.3515093061237828, test_score: 0.17509350013482114
+Calculating split 16 of 32
+start_feature_index: 30, end_feature_index: 32
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.3285030399650776, test_score: 0.12940261411801474
+Calculating split 17 of 32
+start_feature_index: 32, end_feature_index: 34
+Starting ridge regression for split 17 with alpha 30000
+Finished, now scoring
+train_score: 0.3467714883760104, test_score: 0.16566117250202123
+Calculating split 18 of 32
+start_feature_index: 34, end_feature_index: 36
+Starting ridge regression for split 18 with alpha 30000
+Finished, now scoring
+train_score: 0.23125538570698684, test_score: -0.018684242156174806
+Calculating split 19 of 32
+start_feature_index: 36, end_feature_index: 38
+Starting ridge regression for split 19 with alpha 30000
+Finished, now scoring
+train_score: 0.28853723715266105, test_score: 0.0657895172969321
+Calculating split 20 of 32
+start_feature_index: 38, end_feature_index: 40
+Starting ridge regression for split 20 with alpha 30000
+Finished, now scoring
+train_score: 0.31314902107652287, test_score: 0.1015369741995927
+Calculating split 21 of 32
+start_feature_index: 40, end_feature_index: 42
+Starting ridge regression for split 21 with alpha 30000
+Finished, now scoring
+train_score: 0.25010581109760555, test_score: 0.0218166478818677
+Calculating split 22 of 32
+start_feature_index: 42, end_feature_index: 44
+Starting ridge regression for split 22 with alpha 30000
+Finished, now scoring
+train_score: 0.23208340248690257, test_score: -0.017380701106082752
+Calculating split 23 of 32
+start_feature_index: 44, end_feature_index: 46
+Starting ridge regression for split 23 with alpha 30000
+Finished, now scoring
+train_score: 0.2335787501587585, test_score: -0.014121526476683537
+Calculating split 24 of 32
+start_feature_index: 46, end_feature_index: 48
+Starting ridge regression for split 24 with alpha 30000
+Finished, now scoring
+train_score: 0.2538296788125453, test_score: 0.022295639831851233
+Calculating split 25 of 32
+start_feature_index: 48, end_feature_index: 50
+Starting ridge regression for split 25 with alpha 30000
+Finished, now scoring
+train_score: 0.28168690900543464, test_score: 0.07144159829493696
+Calculating split 26 of 32
+start_feature_index: 50, end_feature_index: 52
+Starting ridge regression for split 26 with alpha 30000
+Finished, now scoring
+train_score: 0.28336305435580134, test_score: 0.05959731796681643
+Calculating split 27 of 32
+start_feature_index: 52, end_feature_index: 54
+Starting ridge regression for split 27 with alpha 30000
+Finished, now scoring
+train_score: 0.3478702276466038, test_score: 0.17134297571913354
+Calculating split 28 of 32
+start_feature_index: 54, end_feature_index: 56
+Starting ridge regression for split 28 with alpha 30000
+Finished, now scoring
+train_score: 0.2607061658263927, test_score: 0.03706366980135305
+Calculating split 29 of 32
+start_feature_index: 56, end_feature_index: 58
+Starting ridge regression for split 29 with alpha 30000
+Finished, now scoring
+train_score: 0.2414052503221088, test_score: 0.0025824695553441045
+Calculating split 30 of 32
+start_feature_index: 58, end_feature_index: 60
+Starting ridge regression for split 30 with alpha 30000
+Finished, now scoring
+train_score: 0.28300227704396363, test_score: 0.05862806837238843
+Calculating split 31 of 32
+start_feature_index: 60, end_feature_index: 62
+Starting ridge regression for split 31 with alpha 30000
+Finished, now scoring
+train_score: 0.32674947925591297, test_score: 0.12625198010034708
+Calculating split 32 of 32
+start_feature_index: 62, end_feature_index: 64
+Starting ridge regression for split 32 with alpha 30000
+Finished, now scoring
+train_score: 0.24171104347366013, test_score: -0.0032921137660655445
+Successfully processed features[0].
+Running RR_sklearn.py with argument: features[2]
+Calculating for: features[2]
+PID of this process = 1357395
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.241139644207065, test_score: -0.004678771054029532
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.31881290884283375, test_score: 0.13859768560272234
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.31808380692521937, test_score: 0.12268214653464668
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.26193811033829034, test_score: 0.03630549115066578
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.27040639911328546, test_score: 0.03987893125503306
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.2693128570762489, test_score: 0.04495764972002918
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.23456184813927597, test_score: -0.014385636637793355
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.2846847052829218, test_score: 0.08447331142472528
+Calculating split 9 of 32
+start_feature_index: 16, end_feature_index: 18
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.2743040174525134, test_score: 0.04968110386360527
+Calculating split 10 of 32
+start_feature_index: 18, end_feature_index: 20
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.28244930695167075, test_score: 0.06913982326395053
+Calculating split 11 of 32
+start_feature_index: 20, end_feature_index: 22
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.23121169117576168, test_score: -0.018681182697622697
+Calculating split 12 of 32
+start_feature_index: 22, end_feature_index: 24
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.32905289730913984, test_score: 0.13292268833625942
+Calculating split 13 of 32
+start_feature_index: 24, end_feature_index: 26
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.3506344668070426, test_score: 0.17519473410964143
+Calculating split 14 of 32
+start_feature_index: 26, end_feature_index: 28
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.28302993524623893, test_score: 0.06987021269845271
+Calculating split 15 of 32
+start_feature_index: 28, end_feature_index: 30
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.23249073064114956, test_score: -0.017446310700823812
+Calculating split 16 of 32
+start_feature_index: 30, end_feature_index: 32
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.2881737261058482, test_score: 0.07778986867548587
+Calculating split 17 of 32
+start_feature_index: 32, end_feature_index: 34
+Starting ridge regression for split 17 with alpha 30000
+Finished, now scoring
+train_score: 0.23449791449105198, test_score: -0.014802523409484653
+Calculating split 18 of 32
+start_feature_index: 34, end_feature_index: 36
+Starting ridge regression for split 18 with alpha 30000
+Finished, now scoring
+train_score: 0.2386322172401931, test_score: -0.007032612831972144
+Calculating split 19 of 32
+start_feature_index: 36, end_feature_index: 38
+Starting ridge regression for split 19 with alpha 30000
+Finished, now scoring
+train_score: 0.24775197408529204, test_score: 0.004965226218360092
+Calculating split 20 of 32
+start_feature_index: 38, end_feature_index: 40
+Starting ridge regression for split 20 with alpha 30000
+Finished, now scoring
+train_score: 0.36002165381866047, test_score: 0.17520317982826134
+Calculating split 21 of 32
+start_feature_index: 40, end_feature_index: 42
+Starting ridge regression for split 21 with alpha 30000
+Finished, now scoring
+train_score: 0.23668769627961136, test_score: -0.011239017145282186
+Calculating split 22 of 32
+start_feature_index: 42, end_feature_index: 44
+Starting ridge regression for split 22 with alpha 30000
+Finished, now scoring
+train_score: 0.2919454788830458, test_score: 0.07119165725481107
+Calculating split 23 of 32
+start_feature_index: 44, end_feature_index: 46
+Starting ridge regression for split 23 with alpha 30000
+Finished, now scoring
+train_score: 0.287492838509235, test_score: 0.07858438469597086
+Calculating split 24 of 32
+start_feature_index: 46, end_feature_index: 48
+Starting ridge regression for split 24 with alpha 30000
+Finished, now scoring
+train_score: 0.23660940974451744, test_score: -0.010440610679705
+Calculating split 25 of 32
+start_feature_index: 48, end_feature_index: 50
+Starting ridge regression for split 25 with alpha 30000
+Finished, now scoring
+train_score: 0.317224625902376, test_score: 0.129042012061141
+Calculating split 26 of 32
+start_feature_index: 50, end_feature_index: 52
+Starting ridge regression for split 26 with alpha 30000
+Finished, now scoring
+train_score: 0.2571260116679147, test_score: 0.01698798601140189
+Calculating split 27 of 32
+start_feature_index: 52, end_feature_index: 54
+Starting ridge regression for split 27 with alpha 30000
+Finished, now scoring
+train_score: 0.2632027404909878, test_score: 0.03657379962262398
+Calculating split 28 of 32
+start_feature_index: 54, end_feature_index: 56
+Starting ridge regression for split 28 with alpha 30000
+Finished, now scoring
+train_score: 0.3090047684069132, test_score: 0.10322126038179665
+Calculating split 29 of 32
+start_feature_index: 56, end_feature_index: 58
+Starting ridge regression for split 29 with alpha 30000
+Finished, now scoring
+train_score: 0.23515059872198674, test_score: -0.012164593671013968
+Calculating split 30 of 32
+start_feature_index: 58, end_feature_index: 60
+Starting ridge regression for split 30 with alpha 30000
+Finished, now scoring
+train_score: 0.23422172275567232, test_score: -0.014263510214209919
+Calculating split 31 of 32
+start_feature_index: 60, end_feature_index: 62
+Starting ridge regression for split 31 with alpha 30000
+Finished, now scoring
+train_score: 0.3462280376044574, test_score: 0.18729289850077976
+Calculating split 32 of 32
+start_feature_index: 62, end_feature_index: 64
+Starting ridge regression for split 32 with alpha 30000
+Finished, now scoring
+train_score: 0.2508748383942621, test_score: 0.011362867878013248
+Successfully processed features[2].
+Running RR_sklearn.py with argument: features[5]
+Calculating for: features[5]
+PID of this process = 1479420
+loading_betas
+betas_ loaded
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 16
+start_feature_index: 0, end_feature_index: 8
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.2749339493589723, test_score: 0.04803570020468433
+Calculating split 2 of 16
+start_feature_index: 8, end_feature_index: 16
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.2533509335539452, test_score: 0.014361670758751062
+Calculating split 3 of 16
+start_feature_index: 16, end_feature_index: 24
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.2509190523765814, test_score: 0.01178625855332259
+Calculating split 4 of 16
+start_feature_index: 24, end_feature_index: 32
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.2692832378177738, test_score: 0.0396415277560192
+Calculating split 5 of 16
+start_feature_index: 32, end_feature_index: 40
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.26964728494456414, test_score: 0.044937013757310795
+Calculating split 6 of 16
+start_feature_index: 40, end_feature_index: 48
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.2801802197327977, test_score: 0.05879942490312568
+Calculating split 7 of 16
+start_feature_index: 48, end_feature_index: 56
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.30574177373509026, test_score: 0.09905831091344587
+Calculating split 8 of 16
+start_feature_index: 56, end_feature_index: 64
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.2825895778886183, test_score: 0.06199436682234161
+Calculating split 9 of 16
+start_feature_index: 64, end_feature_index: 72
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.2730831302104177, test_score: 0.04945605450677316
+Calculating split 10 of 16
+start_feature_index: 72, end_feature_index: 80
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.2722211869250236, test_score: 0.046170024854282045
+Calculating split 11 of 16
+start_feature_index: 80, end_feature_index: 88
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.2742510798161739, test_score: 0.050641612786495274
+Calculating split 12 of 16
+start_feature_index: 88, end_feature_index: 96
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.2803611355762411, test_score: 0.06038968279585554
+Calculating split 13 of 16
+start_feature_index: 96, end_feature_index: 104
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.25689747321815615, test_score: 0.021356085578375632
+Calculating split 14 of 16
+start_feature_index: 104, end_feature_index: 112
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.28366221826493426, test_score: 0.06708515485863258
+Calculating split 15 of 16
+start_feature_index: 112, end_feature_index: 120
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.2872861527243411, test_score: 0.07436323058706157
+Calculating split 16 of 16
+start_feature_index: 120, end_feature_index: 128
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.2728482132118919, test_score: 0.04929669947991068
+Successfully processed features[5].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530891.err

@@ -0,0 +1,84 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 24321 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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  0%|          | 0/8 [00:00<?, ?it/s]slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***
+slurmstepd: error: *** JOB 530891 ON ip-10-0-129-21 CANCELLED AT 2024-10-29T16:23:37 ***
+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530891.out

@@ -0,0 +1,24 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-129-21
+MASTER_PORT=17152
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Configured run_name = subj1_2
+Configured current_features = features[0]
+Configured num_sessions = 2.0
+Configured subj = 1
+PID of this process = 3719810
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([1358, 15724])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.3945603893015767, test_score: 0.07092672661931536
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530964.err

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+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 24321 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
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+
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+
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+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***
+slurmstepd: error: *** JOB 530964 ON ip-10-0-129-21 CANCELLED AT 2024-10-30T00:41:57 ***
+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530964.out

@@ -0,0 +1,19 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-129-21
+MASTER_PORT=11213
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[7]
+Configured run_name = subj1_40
+Configured current_features = features[7]
+Configured num_sessions = 40.0
+Configured subj = 1
+PID of this process = 374370
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 15724])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 16
+start_feature_index: 0, end_feature_index: 8

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530965.err

@@ -0,0 +1,92 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 24321 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
+
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+
 38%|███▊      | 80/211 [00:16<00:14,  9.31it/s]slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***
+slurmstepd: error: *** JOB 530965 ON ip-10-0-136-43 CANCELLED AT 2024-10-30T00:41:51 ***
+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***
+
+
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spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530965.out

@@ -0,0 +1,19 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-43
+MASTER_PORT=14397
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[21]
+Configured run_name = subj1_40
+Configured current_features = features[21]
+Configured num_sessions = 40.0
+Configured subj = 1
+PID of this process = 2976500
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 15724])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 15724]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 15724]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530969.out

@@ -0,0 +1,145 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-43
+MASTER_PORT=15540
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[21]
+Configured run_name = subj2_40
+Configured current_features = features[21]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 2979169
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.3311489967810288, test_score: 0.13984280774509134
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.3218409388370155, test_score: 0.12495426075575147
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.3274188067002176, test_score: 0.1334289011430336
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.3184279974639223, test_score: 0.1205749788845215
+Successfully processed features[21].
+Running RR_sklearn.py with argument: features[23]
+Configured run_name = subj2_40
+Configured current_features = features[23]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 3031012
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.2999453535839129, test_score: 0.09318715005945898
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.30816896880957706, test_score: 0.1061760916014379
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.3025129118883577, test_score: 0.09619254298065984
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.3135183691353918, test_score: 0.11352151352246379
+Successfully processed features[23].
+Running RR_sklearn.py with argument: features[25]
+Configured run_name = subj2_40
+Configured current_features = features[25]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 3061144
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.3079676909117155, test_score: 0.10617542407264369
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.3053207307324829, test_score: 0.10296767980451851
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.3048549115029813, test_score: 0.10218486253677748
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.3079311662524503, test_score: 0.1069014612178009
+Successfully processed features[25].
+Running RR_sklearn.py with argument: features[28]
+Configured run_name = subj2_40
+Configured current_features = features[28]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 3101162
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.34718618957863684, test_score: 0.16561530394839516
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.34265199727424284, test_score: 0.15883669053803992
+Successfully processed features[28].
+Running RR_sklearn.py with argument: features[30]
+Configured run_name = subj2_40
+Configured current_features = features[30]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 3118703
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530973.out

@@ -0,0 +1,69 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-43
+MASTER_PORT=11991
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Configured run_name = subj5_40
+Configured current_features = features[0]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2982245
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.26361815839080527, test_score: 0.1210310376560146
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.24783427170794262, test_score: 0.09568317749703327
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.23161111556226716, test_score: 0.07603095939890953
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.21076765800216604, test_score: 0.04121712728143079
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.254252996853881, test_score: 0.1153473306806458
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.18167384319917068, test_score: -0.005739264056848001
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.25558297550643844, test_score: 0.11381063804776712
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.2273905584877767, test_score: 0.06770885585114189
+Calculating split 9 of 32
+start_feature_index: 16, end_feature_index: 18
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.2503339144391444, test_score: 0.10387148421979359
+Calculating split 10 of 32
+start_feature_index: 18, end_feature_index: 20
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.24233118003724133, test_score: 0.09576051927037997
+Calculating split 11 of 32
+start_feature_index: 20, end_feature_index: 22

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530974.out

@@ -0,0 +1,69 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-43
+MASTER_PORT=13741
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Configured run_name = subj7_40
+Configured current_features = features[0]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 2984611
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.2479261675398688, test_score: 0.0973742978543548
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.23699738372024487, test_score: 0.08159194975224841
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.21990751926491497, test_score: 0.05222633165532662
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.2036890912382389, test_score: 0.03158793161082338
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.23404400601109016, test_score: 0.08301955949364762
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.1788199068191352, test_score: -0.008000647850657456
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.2339640401464736, test_score: 0.08318480912076744
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.21504826079391542, test_score: 0.0509726366329373
+Calculating split 9 of 32
+start_feature_index: 16, end_feature_index: 18
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.23168964422435512, test_score: 0.07522665642833014
+Calculating split 10 of 32
+start_feature_index: 18, end_feature_index: 20
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.22602123222247192, test_score: 0.06468859128400227
+Calculating split 11 of 32
+start_feature_index: 20, end_feature_index: 22

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530975.out

@@ -0,0 +1,207 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-246
+MASTER_PORT=11168
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[7]
+Configured run_name = subj7_40
+Configured current_features = features[7]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 818556
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 16
+start_feature_index: 0, end_feature_index: 8
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.18389858219257688, test_score: -0.0022092904182516534
+Calculating split 2 of 16
+start_feature_index: 8, end_feature_index: 16
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.18248053223463573, test_score: -0.00332191290193475
+Calculating split 3 of 16
+start_feature_index: 16, end_feature_index: 24
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.18550517976575726, test_score: 0.0006154057800239759
+Calculating split 4 of 16
+start_feature_index: 24, end_feature_index: 32
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.19346170774035173, test_score: 0.01207799651491585
+Calculating split 5 of 16
+start_feature_index: 32, end_feature_index: 40
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.19525343840792428, test_score: 0.015376112967608548
+Calculating split 6 of 16
+start_feature_index: 40, end_feature_index: 48
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.1835442339213305, test_score: -0.002212570559999852
+Calculating split 7 of 16
+start_feature_index: 48, end_feature_index: 56
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.1820958989932251, test_score: -0.004953979950402551
+Calculating split 8 of 16
+start_feature_index: 56, end_feature_index: 64
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.1886326878123931, test_score: 0.006708790305753003
+Calculating split 9 of 16
+start_feature_index: 64, end_feature_index: 72
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.2010815206841145, test_score: 0.022934553273452504
+Calculating split 10 of 16
+start_feature_index: 72, end_feature_index: 80
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.18957797158309284, test_score: 0.0066288423730688625
+Calculating split 11 of 16
+start_feature_index: 80, end_feature_index: 88
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.19023412804307668, test_score: 0.008327451832389273
+Calculating split 12 of 16
+start_feature_index: 88, end_feature_index: 96
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.18510472685854573, test_score: -0.00015787675465072794
+Calculating split 13 of 16
+start_feature_index: 96, end_feature_index: 104
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.19507201343338265, test_score: 0.0159513949650208
+Calculating split 14 of 16
+start_feature_index: 104, end_feature_index: 112
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.19365625609811363, test_score: 0.01333516935856269
+Calculating split 15 of 16
+start_feature_index: 112, end_feature_index: 120
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.18601856253633822, test_score: 0.0013562807634599494
+Calculating split 16 of 16
+start_feature_index: 120, end_feature_index: 128
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.19691300136207796, test_score: 0.017432535172906737
+Successfully processed features[7].
+Running RR_sklearn.py with argument: features[10]
+Configured run_name = subj7_40
+Configured current_features = features[10]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 1111192
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 8
+start_feature_index: 0, end_feature_index: 32
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.20524939448684784, test_score: 0.03140243094062013
+Calculating split 2 of 8
+start_feature_index: 32, end_feature_index: 64
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.20073572580867596, test_score: 0.02354107875559519
+Calculating split 3 of 8
+start_feature_index: 64, end_feature_index: 96
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.20884102107872546, test_score: 0.03583336394168263
+Calculating split 4 of 8
+start_feature_index: 96, end_feature_index: 128
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.21082026617366967, test_score: 0.04025191463708657
+Calculating split 5 of 8
+start_feature_index: 128, end_feature_index: 160
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.2061756771685397, test_score: 0.03253178312553266
+Calculating split 6 of 8
+start_feature_index: 160, end_feature_index: 192
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.2096573049075262, test_score: 0.036552058108201925
+Calculating split 7 of 8
+start_feature_index: 192, end_feature_index: 224
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.2087856374012926, test_score: 0.03583864641933732
+Calculating split 8 of 8
+start_feature_index: 224, end_feature_index: 256
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.20933063996598386, test_score: 0.035302286690786576
+Successfully processed features[10].
+Running RR_sklearn.py with argument: features[12]
+Configured run_name = subj7_40
+Configured current_features = features[12]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 1215336
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 8
+start_feature_index: 0, end_feature_index: 32
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.21144492182145513, test_score: 0.04070910979090986
+Calculating split 2 of 8
+start_feature_index: 32, end_feature_index: 64
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.21599015379487763, test_score: 0.04731136926294935
+Calculating split 3 of 8
+start_feature_index: 64, end_feature_index: 96
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.20962274790201568, test_score: 0.03931894395542459
+Calculating split 4 of 8
+start_feature_index: 96, end_feature_index: 128
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.21155313876541795, test_score: 0.039789128415872774
+Calculating split 5 of 8
+start_feature_index: 128, end_feature_index: 160
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.21034231847948232, test_score: 0.039479817033312034
+Calculating split 6 of 8
+start_feature_index: 160, end_feature_index: 192
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.21405815759324653, test_score: 0.04563032354679104
+Calculating split 7 of 8
+start_feature_index: 192, end_feature_index: 224
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.21216040131552102, test_score: 0.0414430878012616
+Calculating split 8 of 8
+start_feature_index: 224, end_feature_index: 256
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.20959377293981174, test_score: 0.03809076121294082
+Successfully processed features[12].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530976.out

@@ -0,0 +1,147 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-246
+MASTER_PORT=12134
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[14]
+Configured run_name = subj7_40
+Configured current_features = features[14]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 818900
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 8
+start_feature_index: 0, end_feature_index: 32
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.22745136911799396, test_score: 0.06539392505421499
+Calculating split 2 of 8
+start_feature_index: 32, end_feature_index: 64
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.22970027229070616, test_score: 0.06955194364924917
+Calculating split 3 of 8
+start_feature_index: 64, end_feature_index: 96
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.23300859796388068, test_score: 0.07430440564412932
+Calculating split 4 of 8
+start_feature_index: 96, end_feature_index: 128
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.23016374862891428, test_score: 0.06938923780620854
+Calculating split 5 of 8
+start_feature_index: 128, end_feature_index: 160
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.23440108214735114, test_score: 0.07737178662229041
+Calculating split 6 of 8
+start_feature_index: 160, end_feature_index: 192
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.22509278073633482, test_score: 0.06226089899689151
+Calculating split 7 of 8
+start_feature_index: 192, end_feature_index: 224
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.23804509117522007, test_score: 0.08305790853252823
+Calculating split 8 of 8
+start_feature_index: 224, end_feature_index: 256
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.2330284818391894, test_score: 0.07199001298269235
+Successfully processed features[14].
+Running RR_sklearn.py with argument: features[16]
+Configured run_name = subj7_40
+Configured current_features = features[16]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 971029
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 8
+start_feature_index: 0, end_feature_index: 32
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.24129368975190957, test_score: 0.05987670614650055
+Calculating split 2 of 8
+start_feature_index: 32, end_feature_index: 64
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.24444653574475336, test_score: 0.06545980034100081
+Calculating split 3 of 8
+start_feature_index: 64, end_feature_index: 96
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.23517725763976516, test_score: 0.05028202424371947
+Calculating split 4 of 8
+start_feature_index: 96, end_feature_index: 128
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.24680757429820058, test_score: 0.07181580584836929
+Calculating split 5 of 8
+start_feature_index: 128, end_feature_index: 160
+Starting ridge regression for split 5 with alpha 25000
+Finished, now scoring
+train_score: 0.23857421744047563, test_score: 0.055732352795224344
+Calculating split 6 of 8
+start_feature_index: 160, end_feature_index: 192
+Starting ridge regression for split 6 with alpha 25000
+Finished, now scoring
+train_score: 0.23650428080995994, test_score: 0.05354146352155302
+Calculating split 7 of 8
+start_feature_index: 192, end_feature_index: 224
+Starting ridge regression for split 7 with alpha 25000
+Finished, now scoring
+train_score: 0.2438680784925008, test_score: 0.06687453889855469
+Calculating split 8 of 8
+start_feature_index: 224, end_feature_index: 256
+Starting ridge regression for split 8 with alpha 25000
+Finished, now scoring
+train_score: 0.23400073354816067, test_score: 0.049348518849611625
+Successfully processed features[16].
+Running RR_sklearn.py with argument: features[19]
+Configured run_name = subj7_40
+Configured current_features = features[19]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 1069006
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.26458581389165936, test_score: 0.09621906292457201
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.26578949653808337, test_score: 0.09778058970893964
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.26869989670151173, test_score: 0.10291225975678608
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.2640110131678027, test_score: 0.09526051553666916
+Successfully processed features[19].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530977.out

@@ -0,0 +1,260 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-142-24
+MASTER_PORT=16800
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[21]
+Configured run_name = subj7_40
+Configured current_features = features[21]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 86422
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.2644259754301861, test_score: 0.09605022710464958
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.25768151154856483, test_score: 0.08525408658825905
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.26101170514525734, test_score: 0.0902307760702417
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.25440379894643916, test_score: 0.08130754745287695
+Successfully processed features[21].
+Running RR_sklearn.py with argument: features[23]
+Configured run_name = subj7_40
+Configured current_features = features[23]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 111102
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.24114095303800118, test_score: 0.060899903157379885
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.2464729585621731, test_score: 0.0696107795530552
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.2420755576181951, test_score: 0.06187806100609831
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.25105696727325866, test_score: 0.076264757055678
+Successfully processed features[23].
+Running RR_sklearn.py with argument: features[25]
+Configured run_name = subj7_40
+Configured current_features = features[25]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 121340
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.24779527407791233, test_score: 0.07173735945379582
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.24547230716857227, test_score: 0.06864745927964158
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.2455149063777864, test_score: 0.06882695183993148
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.24785526208032166, test_score: 0.07212164680527863
+Successfully processed features[25].
+Running RR_sklearn.py with argument: features[28]
+Configured run_name = subj7_40
+Configured current_features = features[28]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 142053
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.27909370522117116, test_score: 0.11883310578588095
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.2763603623617843, test_score: 0.11434490640326563
+Successfully processed features[28].
+Running RR_sklearn.py with argument: features[30]
+Configured run_name = subj7_40
+Configured current_features = features[30]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 146134
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.28250549068952896, test_score: 0.12327333564908359
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.28193524050752194, test_score: 0.12252109245106738
+Successfully processed features[30].
+Running RR_sklearn.py with argument: features[32]
+Configured run_name = subj7_40
+Configured current_features = features[32]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 150613
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.2852391354404222, test_score: 0.1262871654855803
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.28832936562839373, test_score: 0.13078833843368667
+Successfully processed features[32].
+Running RR_sklearn.py with argument: features[34]
+Configured run_name = subj7_40
+Configured current_features = features[34]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 154835
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.3021074909268688, test_score: 0.151360796736779
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.2999789151505738, test_score: 0.14802465943410079
+Successfully processed features[34].
+Running RR_sklearn.py with argument: classifier[0]
+Configured run_name = subj7_40
+Configured current_features = classifier[0]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 158705
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 4096
+Starting ridge regression for split 1 with alpha 20000
+Finished, now scoring
+train_score: 0.3773031332323635, test_score: 0.2321143374738011
+Successfully processed classifier[0].
+Running RR_sklearn.py with argument: classifier[3]
+Configured run_name = subj7_40
+Configured current_features = classifier[3]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 160661
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 4096
+Starting ridge regression for split 1 with alpha 20000
+Finished, now scoring
+train_score: 0.3508145254658991, test_score: 0.18880211639566044
+Successfully processed classifier[3].
+Running RR_sklearn.py with argument: classifier[6]
+Configured run_name = subj7_40
+Configured current_features = classifier[6]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 162295
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 1000
+Starting ridge regression for split 1 with alpha 20000
+Finished, now scoring
+train_score: 0.42782445228528276, test_score: 0.3086555087985498
+Successfully processed classifier[6].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531220.out

@@ -0,0 +1,218 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-129-21
+MASTER_PORT=12969
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Configured run_name = subj7_40
+Configured current_features = features[0]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 1703295
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.2479261675398688, test_score: 0.0973742978543548
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.23699738372024487, test_score: 0.08159194975224841
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.21990751926491497, test_score: 0.05222633165532662
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.2036890912382389, test_score: 0.03158793161082338
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.23404400601109016, test_score: 0.08301955949364762
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.1788199068191352, test_score: -0.008000647850657456
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.2339640401464736, test_score: 0.08318480912076744
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.21504826079391542, test_score: 0.0509726366329373
+Calculating split 9 of 32
+start_feature_index: 16, end_feature_index: 18
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.23168964422435512, test_score: 0.07522665642833014
+Calculating split 10 of 32
+start_feature_index: 18, end_feature_index: 20
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.22602123222247192, test_score: 0.06468859128400227
+Calculating split 11 of 32
+start_feature_index: 20, end_feature_index: 22
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.19694511579759155, test_score: 0.020823764467242312
+Calculating split 12 of 32
+start_feature_index: 22, end_feature_index: 24
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.23623887685642328, test_score: 0.07986178406144076
+Calculating split 13 of 32
+start_feature_index: 24, end_feature_index: 26
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.2716707407389899, test_score: 0.13814444966183284
+Calculating split 14 of 32
+start_feature_index: 26, end_feature_index: 28
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.23475066876393064, test_score: 0.07635455921229282
+Calculating split 15 of 32
+start_feature_index: 28, end_feature_index: 30
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.26181187958286223, test_score: 0.11856974391602561
+Calculating split 16 of 32
+start_feature_index: 30, end_feature_index: 32
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.22944889489743123, test_score: 0.06684022839259908
+Calculating split 17 of 32
+start_feature_index: 32, end_feature_index: 34
+Starting ridge regression for split 17 with alpha 30000
+Finished, now scoring
+train_score: 0.2474668575217741, test_score: 0.09890326684533594
+Calculating split 18 of 32
+start_feature_index: 34, end_feature_index: 36
+Starting ridge regression for split 18 with alpha 30000
+Finished, now scoring
+train_score: 0.1753332087190059, test_score: -0.014941545977507728
+Calculating split 19 of 32
+start_feature_index: 36, end_feature_index: 38
+Starting ridge regression for split 19 with alpha 30000
+Finished, now scoring
+train_score: 0.20479694967104853, test_score: 0.02540557523675628
+Calculating split 20 of 32
+start_feature_index: 38, end_feature_index: 40
+Starting ridge regression for split 20 with alpha 30000
+Finished, now scoring
+train_score: 0.21930028878987617, test_score: 0.047487596679864526
+Calculating split 21 of 32
+start_feature_index: 40, end_feature_index: 42
+Starting ridge regression for split 21 with alpha 30000
+Finished, now scoring
+train_score: 0.18953115404745105, test_score: 0.01238862599519325
+Calculating split 22 of 32
+start_feature_index: 42, end_feature_index: 44
+Starting ridge regression for split 22 with alpha 30000
+Finished, now scoring
+train_score: 0.17574772462610577, test_score: -0.014331825797576489
+Calculating split 23 of 32
+start_feature_index: 44, end_feature_index: 46
+Starting ridge regression for split 23 with alpha 30000
+Finished, now scoring
+train_score: 0.1769993785110619, test_score: -0.011744920468387857
+Calculating split 24 of 32
+start_feature_index: 46, end_feature_index: 48
+Starting ridge regression for split 24 with alpha 30000
+Finished, now scoring
+train_score: 0.1939333622230541, test_score: 0.015962393981996035
+Calculating split 25 of 32
+start_feature_index: 48, end_feature_index: 50
+Starting ridge regression for split 25 with alpha 30000
+Finished, now scoring
+train_score: 0.21515072566017635, test_score: 0.050962035799842355
+Calculating split 26 of 32
+start_feature_index: 50, end_feature_index: 52
+Starting ridge regression for split 26 with alpha 30000
+Finished, now scoring
+train_score: 0.20223234583349928, test_score: 0.022623154249790584
+Calculating split 27 of 32
+start_feature_index: 52, end_feature_index: 54
+Starting ridge regression for split 27 with alpha 30000
+Finished, now scoring
+train_score: 0.2571248057200156, test_score: 0.11544071218448404
+Calculating split 28 of 32
+start_feature_index: 54, end_feature_index: 56
+Starting ridge regression for split 28 with alpha 30000
+Finished, now scoring
+train_score: 0.1993043876348736, test_score: 0.026947043530077357
+Calculating split 29 of 32
+start_feature_index: 56, end_feature_index: 58
+Starting ridge regression for split 29 with alpha 30000
+Finished, now scoring
+train_score: 0.18312172067220434, test_score: 5.2485877988473526e-05
+Calculating split 30 of 32
+start_feature_index: 58, end_feature_index: 60
+Starting ridge regression for split 30 with alpha 30000
+Finished, now scoring
+train_score: 0.20571775577789575, test_score: 0.027201713298898752
+Calculating split 31 of 32
+start_feature_index: 60, end_feature_index: 62
+Starting ridge regression for split 31 with alpha 30000
+Finished, now scoring
+train_score: 0.22834601245155814, test_score: 0.06474376373079498
+Calculating split 32 of 32
+start_feature_index: 62, end_feature_index: 64
+Starting ridge regression for split 32 with alpha 30000
+Finished, now scoring
+train_score: 0.18042424957363692, test_score: -0.007899709383727518
+Successfully processed features[0].
+Running RR_sklearn.py with argument: features[2]
+Configured run_name = subj7_40
+Configured current_features = features[2]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 1978010
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.18067698415255568, test_score: -0.007734245178465855
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.25123020066793256, test_score: 0.1058144159157724
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.24251182397052828, test_score: 0.09013968264669926
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.20317551356910235, test_score: 0.030107361957696692
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.20184842586308077, test_score: 0.022167331878745884
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531223.out

@@ -0,0 +1,29 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-135
+MASTER_PORT=17532
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Configured run_name = subj5_40
+Configured current_features = features[0]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3411997
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.26361815839080527, test_score: 0.1210310376560146
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.24783427170794262, test_score: 0.09568317749703327
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531228.err

@@ -0,0 +1,391 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 24321 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
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+
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+
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+
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+
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+
+
+
  0%|          | 0/8 [00:00<?, ?it/s]
+
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+
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+
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+
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+
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+
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+
 88%|████████▊ | 7/8 [00:09<00:00,  2.20it/s]
+slurmstepd: error: *** JOB 531228 ON ip-10-0-136-135 CANCELLED AT 2024-10-30T15:37:00 DUE TO PREEMPTION ***
+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531235.out

@@ -0,0 +1,43 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-135
+MASTER_PORT=11726
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[32]
+Configured run_name = subj5_40
+Configured current_features = features[32]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3415465
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.3004820266742991, test_score: 0.14246185242098058
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.3042788059591821, test_score: 0.1473596511750585
+Successfully processed features[32].
+Running RR_sklearn.py with argument: features[34]
+Configured run_name = subj5_40
+Configured current_features = features[34]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3421573
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531250.out

@@ -0,0 +1,134 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-150-63
+MASTER_PORT=14766
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[30]
+Configured run_name = subj5_40
+Configured current_features = features[30]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2466317
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.2938212129374484, test_score: 0.13331051329000898
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.292682196571262, test_score: 0.1321197758604717
+Successfully processed features[30].
+Running RR_sklearn.py with argument: features[32]
+Configured run_name = subj5_40
+Configured current_features = features[32]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2474855
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.3004820266742991, test_score: 0.142461847993221
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.3042788059591821, test_score: 0.1473596475868505
+Successfully processed features[32].
+Running RR_sklearn.py with argument: features[34]
+Configured run_name = subj5_40
+Configured current_features = features[34]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2480268
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 2
+start_feature_index: 0, end_feature_index: 256
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.32476481764575865, test_score: 0.1791367410037509
+Calculating split 2 of 2
+start_feature_index: 256, end_feature_index: 512
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.3226681149810569, test_score: 0.17591451281097845
+Successfully processed features[34].
+Running RR_sklearn.py with argument: classifier[0]
+Configured run_name = subj5_40
+Configured current_features = classifier[0]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2485155
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 4096
+Starting ridge regression for split 1 with alpha 20000
+Finished, now scoring
+train_score: 0.42550640698577225, test_score: 0.28852432150595864
+Successfully processed classifier[0].
+Running RR_sklearn.py with argument: classifier[3]
+Configured run_name = subj5_40
+Configured current_features = classifier[3]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2487630
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 4096
+Starting ridge regression for split 1 with alpha 20000
+Finished, now scoring
+train_score: 0.39057975938287925, test_score: 0.240918345519268
+Successfully processed classifier[3].
+Running RR_sklearn.py with argument: classifier[6]
+Configured run_name = subj5_40
+Configured current_features = classifier[6]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2491510
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 1
+start_feature_index: 0, end_feature_index: 1000
+Starting ridge regression for split 1 with alpha 20000
+Finished, now scoring
+train_score: 0.4783268101383453, test_score: 0.3677998997134285
+Successfully processed classifier[6].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531263.out

@@ -0,0 +1,93 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-139-117
+MASTER_PORT=13146
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[16]
+Configured run_name = subj5_40
+Configured current_features = features[16]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3136015
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 8
+start_feature_index: 0, end_feature_index: 32
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.24767024111517483, test_score: 0.06431027449933883
+Calculating split 2 of 8
+start_feature_index: 32, end_feature_index: 64
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.2527069316397772, test_score: 0.07186158859964437
+Calculating split 3 of 8
+start_feature_index: 64, end_feature_index: 96
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.24187062804448745, test_score: 0.054528842202073824
+Calculating split 4 of 8
+start_feature_index: 96, end_feature_index: 128
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.2578700593382285, test_score: 0.08191556890718417
+Calculating split 5 of 8
+start_feature_index: 128, end_feature_index: 160
+Starting ridge regression for split 5 with alpha 25000
+Finished, now scoring
+train_score: 0.2422684841250996, test_score: 0.056808495993559006
+Calculating split 6 of 8
+start_feature_index: 160, end_feature_index: 192
+Starting ridge regression for split 6 with alpha 25000
+Finished, now scoring
+train_score: 0.24130302511023508, test_score: 0.05717256670474872
+Calculating split 7 of 8
+start_feature_index: 192, end_feature_index: 224
+Starting ridge regression for split 7 with alpha 25000
+Finished, now scoring
+train_score: 0.2513153485244927, test_score: 0.0735543255076044
+Calculating split 8 of 8
+start_feature_index: 224, end_feature_index: 256
+Starting ridge regression for split 8 with alpha 25000
+Finished, now scoring
+train_score: 0.23849699393755408, test_score: 0.05139596925857541
+Successfully processed features[16].
+Running RR_sklearn.py with argument: features[19]
+Configured run_name = subj5_40
+Configured current_features = features[19]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3170201
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.27351500029397324, test_score: 0.10197860452983651
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.2744207891247164, test_score: 0.1034188502969365
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.2774930842722253, test_score: 0.10773560981857179
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.2722188198583565, test_score: 0.1002895448775412
+Successfully processed features[19].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531265.out

@@ -0,0 +1,54 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-135
+MASTER_PORT=11214
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[7]
+Configured run_name = subj5_40
+Configured current_features = features[7]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3428556
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 16
+start_feature_index: 0, end_feature_index: 8
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.18516894672888826, test_score: -0.002714393130150117
+Calculating split 2 of 16
+start_feature_index: 8, end_feature_index: 16
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.18416056426702243, test_score: -0.003410252415088087
+Calculating split 3 of 16
+start_feature_index: 16, end_feature_index: 24
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.18705024109030144, test_score: 0.00033861347521627793
+Calculating split 4 of 16
+start_feature_index: 24, end_feature_index: 32
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.1950031597488859, test_score: 0.011407388206031304
+Calculating split 5 of 16
+start_feature_index: 32, end_feature_index: 40
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.19765686214957787, test_score: 0.015502144086601609
+Calculating split 6 of 16
+start_feature_index: 40, end_feature_index: 48
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.1857938654632823, test_score: -0.0012537986835868167
+Calculating split 7 of 16
+start_feature_index: 48, end_feature_index: 56
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.18300273535908446, test_score: -0.005641452831633039
+Calculating split 8 of 16
+start_feature_index: 56, end_feature_index: 64

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531266.out

@@ -0,0 +1,54 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-136-135
+MASTER_PORT=17375
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[0]
+Configured run_name = subj5_40
+Configured current_features = features[0]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 3428552
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.26361815839080527, test_score: 0.1210310376560146
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.24783427170794262, test_score: 0.09568317749703327
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.23161111556226716, test_score: 0.07603095939890953
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.21076765800216604, test_score: 0.04121712728143079
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.254252996853881, test_score: 0.1153473306806458
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.18167384319917068, test_score: -0.005739264056848001
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.25558297550643844, test_score: 0.11381063804776712
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531301.err

@@ -0,0 +1,443 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 24321 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
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+slurmstepd: error: *** JOB 531301 ON ip-10-0-147-212 CANCELLED AT 2024-10-30T16:28:00 DUE TO PREEMPTION ***
+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531302.err

@@ -0,0 +1,483 @@
+[NbConvertApp] Converting notebook RR_sklearn.ipynb to python
+[NbConvertApp] Writing 24321 bytes to RR_sklearn.py
+/admin/home-ckadirt/mindeye/lib/python3.11/site-packages/bdpy/dl/torch/domain/core.py:90: RuntimeWarning: FixedResolutionDomain is an irreversible domain. It does not guarantee the reversibility of `send` and `receive` methods. Please use the combination of `send` and `receive` methods with caution.
+  warnings.warn(
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+
+
+
+
  0%|          | 0/8 [00:00<?, ?it/s]
+
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+
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+slurmstepd: error: *** JOB 531302 ON ip-10-0-147-212 CANCELLED AT 2024-10-30T16:28:00 DUE TO PREEMPTION ***
+slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress ***

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531302.out

@@ -0,0 +1,24 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-147-212
+MASTER_PORT=13718
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[7]
+Configured run_name = subj5_40
+Configured current_features = features[7]
+Configured num_sessions = 40.0
+Configured subj = 5
+PID of this process = 2688788
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 13039])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 13039]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 13039]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 16
+start_feature_index: 0, end_feature_index: 8
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.18516894672888826, test_score: -0.002714393130150117
+Calculating split 2 of 16
+start_feature_index: 8, end_feature_index: 16

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531466.out

@@ -0,0 +1,273 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-165-214
+MASTER_PORT=18060
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[2]
+Configured run_name = subj7_40
+Configured current_features = features[2]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 2433040
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 32
+start_feature_index: 0, end_feature_index: 2
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.18067698415255568, test_score: -0.007734245178465855
+Calculating split 2 of 32
+start_feature_index: 2, end_feature_index: 4
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.25123020066793256, test_score: 0.1058144159157724
+Calculating split 3 of 32
+start_feature_index: 4, end_feature_index: 6
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.24251182397052823, test_score: 0.09013968264669926
+Calculating split 4 of 32
+start_feature_index: 6, end_feature_index: 8
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.2031755135691023, test_score: 0.030107361957696692
+Calculating split 5 of 32
+start_feature_index: 8, end_feature_index: 10
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.20184842586308077, test_score: 0.022167331878745877
+Calculating split 6 of 32
+start_feature_index: 10, end_feature_index: 12
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.20065760199997967, test_score: 0.02481404995723763
+Calculating split 7 of 32
+start_feature_index: 12, end_feature_index: 14
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.177150413735394, test_score: -0.01282194939297352
+Calculating split 8 of 32
+start_feature_index: 14, end_feature_index: 16
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.2186235210347885, test_score: 0.061075780371785626
+Calculating split 9 of 32
+start_feature_index: 16, end_feature_index: 18
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.20681531306171422, test_score: 0.03238087110755142
+Calculating split 10 of 32
+start_feature_index: 18, end_feature_index: 20
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.2138833807210231, test_score: 0.04920925917197072
+Calculating split 11 of 32
+start_feature_index: 20, end_feature_index: 22
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.1757412596112456, test_score: -0.014372224162820255
+Calculating split 12 of 32
+start_feature_index: 22, end_feature_index: 24
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.2444802675446334, test_score: 0.08475527542635751
+Calculating split 13 of 32
+start_feature_index: 24, end_feature_index: 26
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.260248351549748, test_score: 0.11620135896342654
+Calculating split 14 of 32
+start_feature_index: 26, end_feature_index: 28
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.22016635073423294, test_score: 0.05530261075690313
+Calculating split 15 of 32
+start_feature_index: 28, end_feature_index: 30
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.17640444502388886, test_score: -0.01368593598289842
+Calculating split 16 of 32
+start_feature_index: 30, end_feature_index: 32
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.22207990119052437, test_score: 0.05649190897341864
+Calculating split 17 of 32
+start_feature_index: 32, end_feature_index: 34
+Starting ridge regression for split 17 with alpha 30000
+Finished, now scoring
+train_score: 0.17673247429534544, test_score: -0.013641174696809989
+Calculating split 18 of 32
+start_feature_index: 34, end_feature_index: 36
+Starting ridge regression for split 18 with alpha 30000
+Finished, now scoring
+train_score: 0.18057534413921605, test_score: -0.006923302451490284
+Calculating split 19 of 32
+start_feature_index: 36, end_feature_index: 38
+Starting ridge regression for split 19 with alpha 30000
+Finished, now scoring
+train_score: 0.18614399405618476, test_score: 0.0005178015157989073
+Calculating split 20 of 32
+start_feature_index: 38, end_feature_index: 40
+Starting ridge regression for split 20 with alpha 30000
+Finished, now scoring
+train_score: 0.24722042154813678, test_score: 0.08815066624442991
+Calculating split 21 of 32
+start_feature_index: 40, end_feature_index: 42
+Starting ridge regression for split 21 with alpha 30000
+Finished, now scoring
+train_score: 0.17855116727889653, test_score: -0.01059235073953749
+Calculating split 22 of 32
+start_feature_index: 42, end_feature_index: 44
+Starting ridge regression for split 22 with alpha 30000
+Finished, now scoring
+train_score: 0.211061209886058, test_score: 0.03264624257646677
+Calculating split 23 of 32
+start_feature_index: 44, end_feature_index: 46
+Starting ridge regression for split 23 with alpha 30000
+Finished, now scoring
+train_score: 0.219644968361694, test_score: 0.0545428668907646
+Calculating split 24 of 32
+start_feature_index: 46, end_feature_index: 48
+Starting ridge regression for split 24 with alpha 30000
+Finished, now scoring
+train_score: 0.17930763698792537, test_score: -0.008994927031057726
+Calculating split 25 of 32
+start_feature_index: 48, end_feature_index: 50
+Starting ridge regression for split 25 with alpha 30000
+Finished, now scoring
+train_score: 0.2444661342758002, test_score: 0.0919533909069674
+Calculating split 26 of 32
+start_feature_index: 50, end_feature_index: 52
+Starting ridge regression for split 26 with alpha 30000
+Finished, now scoring
+train_score: 0.188257374405059, test_score: 0.0006408115722553173
+Calculating split 27 of 32
+start_feature_index: 52, end_feature_index: 54
+Starting ridge regression for split 27 with alpha 30000
+Finished, now scoring
+train_score: 0.19756312742264304, test_score: 0.022795661047756793
+Calculating split 28 of 32
+start_feature_index: 54, end_feature_index: 56
+Starting ridge regression for split 28 with alpha 30000
+Finished, now scoring
+train_score: 0.2266330777174758, test_score: 0.06439270325608104
+Calculating split 29 of 32
+start_feature_index: 56, end_feature_index: 58
+Starting ridge regression for split 29 with alpha 30000
+Finished, now scoring
+train_score: 0.1783324532138996, test_score: -0.010357112724231263
+Calculating split 30 of 32
+start_feature_index: 58, end_feature_index: 60
+Starting ridge regression for split 30 with alpha 30000
+Finished, now scoring
+train_score: 0.17732996252619204, test_score: -0.012037212616642545
+Calculating split 31 of 32
+start_feature_index: 60, end_feature_index: 62
+Starting ridge regression for split 31 with alpha 30000
+Finished, now scoring
+train_score: 0.2688339382635583, test_score: 0.13449339991405773
+Calculating split 32 of 32
+start_feature_index: 62, end_feature_index: 64
+Starting ridge regression for split 32 with alpha 30000
+Finished, now scoring
+train_score: 0.19035037115583728, test_score: 0.00677547366832466
+Successfully processed features[2].
+Running RR_sklearn.py with argument: features[5]
+Configured run_name = subj7_40
+Configured current_features = features[5]
+Configured num_sessions = 40.0
+Configured subj = 7
+PID of this process = 2736821
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 12682])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 12682]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 12682]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 16
+start_feature_index: 0, end_feature_index: 8
+Starting ridge regression for split 1 with alpha 30000
+Finished, now scoring
+train_score: 0.2075892394114399, test_score: 0.033171909820297246
+Calculating split 2 of 16
+start_feature_index: 8, end_feature_index: 16
+Starting ridge regression for split 2 with alpha 30000
+Finished, now scoring
+train_score: 0.19020832173016267, test_score: 0.006889429768025591
+Calculating split 3 of 16
+start_feature_index: 16, end_feature_index: 24
+Starting ridge regression for split 3 with alpha 30000
+Finished, now scoring
+train_score: 0.18916281862180695, test_score: 0.005432150161233063
+Calculating split 4 of 16
+start_feature_index: 24, end_feature_index: 32
+Starting ridge regression for split 4 with alpha 30000
+Finished, now scoring
+train_score: 0.20064420316445733, test_score: 0.02251641086641173
+Calculating split 5 of 16
+start_feature_index: 32, end_feature_index: 40
+Starting ridge regression for split 5 with alpha 30000
+Finished, now scoring
+train_score: 0.2042806707904147, test_score: 0.030127630034364156
+Calculating split 6 of 16
+start_feature_index: 40, end_feature_index: 48
+Starting ridge regression for split 6 with alpha 30000
+Finished, now scoring
+train_score: 0.21103244182271166, test_score: 0.039659615854848215
+Calculating split 7 of 16
+start_feature_index: 48, end_feature_index: 56
+Starting ridge regression for split 7 with alpha 30000
+Finished, now scoring
+train_score: 0.22849361570661558, test_score: 0.06526088967469577
+Calculating split 8 of 16
+start_feature_index: 56, end_feature_index: 64
+Starting ridge regression for split 8 with alpha 30000
+Finished, now scoring
+train_score: 0.20996509760948867, test_score: 0.03855441034705189
+Calculating split 9 of 16
+start_feature_index: 64, end_feature_index: 72
+Starting ridge regression for split 9 with alpha 30000
+Finished, now scoring
+train_score: 0.20675441557349467, test_score: 0.03331000780798552
+Calculating split 10 of 16
+start_feature_index: 72, end_feature_index: 80
+Starting ridge regression for split 10 with alpha 30000
+Finished, now scoring
+train_score: 0.20481510385466276, test_score: 0.02779439322223019
+Calculating split 11 of 16
+start_feature_index: 80, end_feature_index: 88
+Starting ridge regression for split 11 with alpha 30000
+Finished, now scoring
+train_score: 0.20609437631601715, test_score: 0.03306939599378978
+Calculating split 12 of 16
+start_feature_index: 88, end_feature_index: 96
+Starting ridge regression for split 12 with alpha 30000
+Finished, now scoring
+train_score: 0.20852184716902905, test_score: 0.03551756921746857
+Calculating split 13 of 16
+start_feature_index: 96, end_feature_index: 104
+Starting ridge regression for split 13 with alpha 30000
+Finished, now scoring
+train_score: 0.19366061586289857, test_score: 0.01228222024745152
+Calculating split 14 of 16
+start_feature_index: 104, end_feature_index: 112
+Starting ridge regression for split 14 with alpha 30000
+Finished, now scoring
+train_score: 0.21432993401252812, test_score: 0.0465684031262047
+Calculating split 15 of 16
+start_feature_index: 112, end_feature_index: 120
+Starting ridge regression for split 15 with alpha 30000
+Finished, now scoring
+train_score: 0.21581104995109138, test_score: 0.04980500146708968
+Calculating split 16 of 16
+start_feature_index: 120, end_feature_index: 128
+Starting ridge regression for split 16 with alpha 30000
+Finished, now scoring
+train_score: 0.2051645035091022, test_score: 0.0315438301039321
+Successfully processed features[5].
+All features have been processed.

spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531468.out

@@ -0,0 +1,93 @@
+NUM_GPUS=1
+MASTER_ADDR=ip-10-0-165-214
+MASTER_PORT=12342
+WORLD_SIZE=1
+Running RR_sklearn.py with argument: features[16]
+Configured run_name = subj2_40
+Configured current_features = features[16]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 2435075
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 8
+start_feature_index: 0, end_feature_index: 32
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.296060571337158, test_score: 0.08502578565016444
+Calculating split 2 of 8
+start_feature_index: 32, end_feature_index: 64
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.30030466890835744, test_score: 0.09245269521510306
+Calculating split 3 of 8
+start_feature_index: 64, end_feature_index: 96
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.28871851102708235, test_score: 0.07294735314180754
+Calculating split 4 of 8
+start_feature_index: 96, end_feature_index: 128
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.30272496587947295, test_score: 0.09830486448070007
+Calculating split 5 of 8
+start_feature_index: 128, end_feature_index: 160
+Starting ridge regression for split 5 with alpha 25000
+Finished, now scoring
+train_score: 0.2942673263355155, test_score: 0.0828416866415769
+Calculating split 6 of 8
+start_feature_index: 160, end_feature_index: 192
+Starting ridge regression for split 6 with alpha 25000
+Finished, now scoring
+train_score: 0.28979468235019523, test_score: 0.07750213231775958
+Calculating split 7 of 8
+start_feature_index: 192, end_feature_index: 224
+Starting ridge regression for split 7 with alpha 25000
+Finished, now scoring
+train_score: 0.29884507771577995, test_score: 0.0931295967004737
+Calculating split 8 of 8
+start_feature_index: 224, end_feature_index: 256
+Starting ridge regression for split 8 with alpha 25000
+Finished, now scoring
+train_score: 0.28758817963994254, test_score: 0.07225582457017769
+Successfully processed features[16].
+Running RR_sklearn.py with argument: features[19]
+Configured run_name = subj2_40
+Configured current_features = features[19]
+Configured num_sessions = 40.0
+Configured subj = 2
+PID of this process = 2624917
+loading_betas
+betas_ loaded
+Number of zeros in valid_nsd_ids_full tensor(0)
+Num train examples torch.Size([27000, 14278])
+Loaded all 73k possible NSD images to cpu! torch.Size([73000, 3, 224, 224])
+torch.Size([18, 8, 14278]) torch.Size([18, 3, 425, 425])
+torch.Size([18, 16, 14278]) torch.Size([18, 3, 425, 425])
+Calculating split 1 of 4
+start_feature_index: 0, end_feature_index: 128
+Starting ridge regression for split 1 with alpha 25000
+Finished, now scoring
+train_score: 0.3314577094858509, test_score: 0.13961581587225672
+Calculating split 2 of 4
+start_feature_index: 128, end_feature_index: 256
+Starting ridge regression for split 2 with alpha 25000
+Finished, now scoring
+train_score: 0.3331717864434611, test_score: 0.142430764709132
+Calculating split 3 of 4
+start_feature_index: 256, end_feature_index: 384
+Starting ridge regression for split 3 with alpha 25000
+Finished, now scoring
+train_score: 0.33709111037406714, test_score: 0.1486117054919728
+Calculating split 4 of 4
+start_feature_index: 384, end_feature_index: 512
+Starting ridge regression for split 4 with alpha 25000
+Finished, now scoring
+train_score: 0.32956953208519746, test_score: 0.13689161101094086
+Successfully processed features[19].
+All features have been processed.