diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.err new file mode 100644 index 0000000000000000000000000000000000000000..ce4dbd34f3d7622798c12ad14f483c34b6aaeecd --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.err @@ -0,0 +1,11 @@ +[NbConvertApp] Converting notebook RR_pytorch.ipynb to python +[NbConvertApp] Writing 23203 bytes to RR_pytorch.py + 0it [00:00, ?it/s] 1it [00:00, 2.79it/s] 154it [00:00, 438.12it/s] 309it [00:00, 759.35it/s] 464it [00:00, 990.18it/s] 621it [00:00, 1160.42it/s] 763it [00:00, 1229.26it/s] 922it [00:00, 1333.65it/s] 1077it [00:01, 1397.22it/s] 1237it [00:01, 1455.16it/s] 1390it [00:01, 1353.43it/s] 1545it [00:01, 1407.67it/s] 1702it [00:01, 1453.00it/s] 1861it [00:01, 1490.69it/s] 2017it [00:01, 1510.19it/s] 2170it [00:01, 1148.89it/s] 2329it [00:02, 1254.86it/s] 2484it [00:02, 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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 + num_cv_channels = features.shape[1] + ^^^^^^^^ +NameError: name 'features' is not defined diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.out new file mode 100644 index 0000000000000000000000000000000000000000..4201857b79bdddaaf440302ee8b9cb27e9ab4189 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529537.out @@ -0,0 +1,21 @@ +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]) diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529947.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529947.err new file mode 100644 index 0000000000000000000000000000000000000000..c72bd14a41a22b77b4b3c3101f7a364f058af90e --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/529947.err @@ -0,0 +1,550 @@ +[NbConvertApp] Converting notebook RR_pytorch.ipynb to python +[NbConvertApp] Writing 23197 bytes to RR_pytorch.py + 0it 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2962it [00:02, 1460.61it/s] 3000it [00:02, 1457.70it/s] +/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( + 0%| | 0/22 [00:00 + if current_features == 'all': + ^^^^^^^^^^^^^^^^ +NameError: name 'current_features' is not defined. Did you mean: 'current_feature'? diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530261.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530261.out new file mode 100644 index 0000000000000000000000000000000000000000..cfe876c4a39c4d6d6c24640bbeedc55d77e6c4da --- /dev/null +++ b/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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530262.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530262.err new file mode 100644 index 0000000000000000000000000000000000000000..2547f77f4385feab8b2de4f5466609e62e922935 --- /dev/null +++ b/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 + feature_extractor = feature_extractor.to(device) + ^^^^^^^^^^^^^^^^^ +NameError: name 'feature_extractor' is not defined +Exception ignored in: +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530266.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530266.err new file mode 100644 index 0000000000000000000000000000000000000000..4fdc2b7ba41dd3d6d657ed2272b722811acb522b --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530266.err @@ -0,0 +1,12339 @@ +[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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530268.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530268.err new file mode 100644 index 0000000000000000000000000000000000000000..df9fb0bf9772edc7796925207c7f7875193cf998 --- /dev/null +++ b/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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530268.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530268.out new file mode 100644 index 0000000000000000000000000000000000000000..f7abd7814a88d1b7e44c164067f2937c5f2f68ae --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530545.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530545.out new file mode 100644 index 0000000000000000000000000000000000000000..588d9c8b6ca20d8fd83b143ebe476a83f095d407 --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530547.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530547.err new file mode 100644 index 0000000000000000000000000000000000000000..f399da0c618e446b63e7cc88faca71d938dafd60 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530547.err @@ -0,0 +1,6921 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 21906 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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530548.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530548.err new file mode 100644 index 0000000000000000000000000000000000000000..cca32965cbd9951404164a6d688aa660bc048d22 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530548.err @@ -0,0 +1,4744 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 21906 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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/4 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530549.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530549.err new file mode 100644 index 0000000000000000000000000000000000000000..4ced2349db7030c13afcd9655226538e81159263 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530549.err @@ -0,0 +1,3032 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 21906 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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530891.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530891.err new file mode 100644 index 0000000000000000000000000000000000000000..28afeb512d17198e0784d3e26c6b5b83ea4fd292 --- /dev/null +++ b/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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530975.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530975.out new file mode 100644 index 0000000000000000000000000000000000000000..871ebc8099dee30b499430b1bd55760c26053e38 --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530976.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530976.out new file mode 100644 index 0000000000000000000000000000000000000000..6b156919529cc459f6d338ab167333e589e558e1 --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530977.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530977.err new file mode 100644 index 0000000000000000000000000000000000000000..465ce3c92063db6b6ef20fde04995b554e19152f --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530977.err @@ -0,0 +1,4190 @@ +[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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530977.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/530977.out new file mode 100644 index 0000000000000000000000000000000000000000..fe3a70e3b7bacbbd354f70e67e8b52c18c6288de --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531220.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531220.out new file mode 100644 index 0000000000000000000000000000000000000000..a2a8ead89a57e08dce6b312f34d9e0825362fce0 --- /dev/null +++ b/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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531223.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531223.out new file mode 100644 index 0000000000000000000000000000000000000000..3a9b92452c08434663ac7def46e1702c0d07b6c0 --- /dev/null +++ b/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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531226.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531226.err new file mode 100644 index 0000000000000000000000000000000000000000..8c5bfb52e434ffa81e68b7d1b0033243b6e0219f --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531226.err @@ -0,0 +1,7633 @@ +[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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531228.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531228.err new file mode 100644 index 0000000000000000000000000000000000000000..784d5711e1d61d5667e78a0332a8039c533a9786 --- /dev/null +++ b/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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/4 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531263.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531263.out new file mode 100644 index 0000000000000000000000000000000000000000..6a7eea6128d8a0da8da273e9ce5cafa75ede90da --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531265.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531265.out new file mode 100644 index 0000000000000000000000000000000000000000..cea78e3584b0cdde9c89d98dc4c192e3a680f219 --- /dev/null +++ b/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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531266.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531266.out new file mode 100644 index 0000000000000000000000000000000000000000..0d7c27e26010f5dc2b0803f1f3a0e9aedc46ae15 --- /dev/null +++ b/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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531301.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531301.err new file mode 100644 index 0000000000000000000000000000000000000000..bacb5d86d6c3cbee488f8a764d08b8333d573e0f --- /dev/null +++ b/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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/16 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531466.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531466.out new file mode 100644 index 0000000000000000000000000000000000000000..62654f3bbb1b66ee3819edf6449e1de1e11c21ee --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531468.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531468.err new file mode 100644 index 0000000000000000000000000000000000000000..b0285c6b104816222ffcf144729f101c967bb169 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531468.err @@ -0,0 +1,2809 @@ +[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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/4 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531468.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531468.out new file mode 100644 index 0000000000000000000000000000000000000000..04aac01f547f2485b5b2303ff1120499e48779ed --- /dev/null +++ b/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. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531469.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531469.out new file mode 100644 index 0000000000000000000000000000000000000000..bb43576c680349e781656d2ba5cf02cac0d03915 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531469.out @@ -0,0 +1,134 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-165-214 +MASTER_PORT=13009 +WORLD_SIZE=1 +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 = 2436341 +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: 0.3473165299621607, test_score: 0.16590237794454823 +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: 0.34686340193032006, test_score: 0.1656585347292373 +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj2_40 +Configured current_features = features[32] +Configured num_sessions = 40.0 +Configured subj = 2 +PID of this process = 2447776 +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: 0.34662547274280225, test_score: 0.16369507174005665 +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: 0.3506970642438159, test_score: 0.16936743441491306 +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj2_40 +Configured current_features = features[34] +Configured num_sessions = 40.0 +Configured subj = 2 +PID of this process = 2453863 +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: 0.36155905307815084, test_score: 0.18790584833920246 +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: 0.3602025014985332, test_score: 0.18544436750515136 +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj2_40 +Configured current_features = classifier[0] +Configured num_sessions = 40.0 +Configured subj = 2 +PID of this process = 2461411 +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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.43773088338774246, test_score: 0.2631097796789923 +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj2_40 +Configured current_features = classifier[3] +Configured num_sessions = 40.0 +Configured subj = 2 +PID of this process = 2463077 +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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.4009633162532992, test_score: 0.21319186438677656 +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj2_40 +Configured current_features = classifier[6] +Configured num_sessions = 40.0 +Configured subj = 2 +PID of this process = 2466079 +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 1 +start_feature_index: 0, end_feature_index: 1000 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.4767517079119771, test_score: 0.332523957295474 +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531470.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531470.err new file mode 100644 index 0000000000000000000000000000000000000000..8ec23bdeccd9d096ca829ec2e874d9a563911fe9 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531470.err @@ -0,0 +1,5098 @@ +[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( + 0%| | 0/32 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531655.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531655.err new file mode 100644 index 0000000000000000000000000000000000000000..e170d82091167adb96a41ac8c3e5389380d4d609 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531655.err @@ -0,0 +1,1137 @@ +[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( + 0%| | 0/6 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531655.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531655.out new file mode 100644 index 0000000000000000000000000000000000000000..5be8695634816fdf49f037c3f2790ceb803c9963 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531655.out @@ -0,0 +1,49 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-24 +MASTER_PORT=17604 +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 = 3878576 +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 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: 0.27346718334979714, test_score: 0.137342579950388 +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: 0.20874052391012973, test_score: 0.03876557599127246 +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: 0.1862257080818114, test_score: 0.004777392135374554 +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: 0.21431974801090942, test_score: 0.04289519461108372 +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: 0.23678329467750125, test_score: 0.07709038185408712 +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: 0.18224234642562426, test_score: -0.006852157655299852 +Successfully processed features[0]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531656.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531656.err new file mode 100644 index 0000000000000000000000000000000000000000..541a0d13ee711b2bd56fb10d02cf2bda6522b115 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/531656.err @@ -0,0 +1,2761 @@ +[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( + 0%| | 0/15 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532230.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532230.err new file mode 100644 index 0000000000000000000000000000000000000000..0ae98e2bf21b7569c78a23ad8b7511bc163d2ac2 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532230.err @@ -0,0 +1,540 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24513 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( + 0%| | 0/15 [00:00 +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( + 0%| | 0/15 [00:00 +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532236.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532236.err new file mode 100644 index 0000000000000000000000000000000000000000..c44527cdb6c864b0cc4f48cb0303c8bedbbb978a --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532236.err @@ -0,0 +1,5 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24513 bytes to RR_sklearn.py +slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress *** +slurmstepd: error: *** JOB 532236 ON ip-10-0-142-24 CANCELLED AT 2024-11-01T23:08:07 *** +slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress *** diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532237.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532237.err new file mode 100644 index 0000000000000000000000000000000000000000..74ea7808a977967f5b591aa7ea2d5eeef56be18b --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532237.err @@ -0,0 +1,2936 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24513 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( + 0%| | 0/15 [00:00 +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( + 0%| | 0/15 [00:00 +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532237.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532237.out new file mode 100644 index 0000000000000000000000000000000000000000..6e96534b8d2cb5cc64856a9591349c9861651d79 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532237.out @@ -0,0 +1,421 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-24 +MASTER_PORT=13942 +WORLD_SIZE=1 +Running RR_sklearn.py with argument: features[0] +Configured run_name = subj1_20 +Configured current_features = features[0] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1473504 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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 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: 0.2751802508482002, test_score: -0.017989861694658577 +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: 0.32612940218893033, test_score: 0.055859672346824055 +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: 0.34864457223671436, test_score: 0.0892813904941582 +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: 0.29172116785957714, test_score: 0.01719069690512059 +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: 0.27589458479378537, test_score: -0.016804092765744117 +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: 0.2772555884720863, test_score: -0.01383419235127312 +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: 0.2957213442015117, test_score: 0.019956983415772345 +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: 0.32106543600035387, test_score: 0.06528522621611468 +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: 0.32149229523225237, test_score: 0.05033674253368077 +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: 0.38133353111846024, test_score: 0.15827977315972375 +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: 0.3019879814591301, test_score: 0.033458273008472934 +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: 0.2843034812849412, test_score: 0.0006181419442542333 +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: 0.3215014378944905, test_score: 0.04970816534603321 +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: 0.3613546741374546, test_score: 0.11277272124564844 +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: 0.2844764404976202, test_score: -0.004363327205908157 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_20 +Configured current_features = features[2] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1656580 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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 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: 0.2818704960529185, test_score: -0.007418121096889349 +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: 0.28990351172412565, test_score: 0.0030336422965572315 +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: 0.38938423249037757, test_score: 0.15326247702479429 +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: 0.2801336808004492, test_score: -0.01138507452038874 +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: 0.329020289037494, test_score: 0.061088657318948913 +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: 0.3262156375691739, test_score: 0.070875537181275 +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: 0.2800606893004844, test_score: -0.010416052639931454 +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: 0.35295678656041785, test_score: 0.11630011545637582 +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: 0.29743164019303825, test_score: 0.012977614842455581 +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: 0.3028440098193827, test_score: 0.030699998870956202 +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: 0.34577041092198096, test_score: 0.09313725666840802 +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: 0.2785496555284062, test_score: -0.01201795961592273 +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: 0.27764418194167645, test_score: -0.014011579500791004 +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: 0.379305687010113, test_score: 0.16942813293928707 +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: 0.29282454491451454, test_score: 0.009670347835803644 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_20 +Configured current_features = features[5] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1702406 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_20 +Configured current_features = features[7] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1703112 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_20 +Configured current_features = features[10] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1705899 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_20 +Configured current_features = features[12] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1706302 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_20 +Configured current_features = features[14] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1706715 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_20 +Configured current_features = features[16] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1707117 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_20 +Configured current_features = features[19] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1707574 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_20 +Configured current_features = features[21] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1707984 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_20 +Configured current_features = features[23] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1708399 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_20 +Configured current_features = features[25] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1708815 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_20 +Configured current_features = features[28] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1709251 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_20 +Configured current_features = features[30] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1709651 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_20 +Configured current_features = features[32] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1710043 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_20 +Configured current_features = features[34] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1710493 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_20 +Configured current_features = classifier[0] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1710890 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_20 +Configured current_features = classifier[3] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1711259 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_20 +Configured current_features = classifier[6] +Configured num_sessions = 20.0 +Configured subj = 1 +PID of this process = 1711711 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([13509, 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]) +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532238.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532238.out new file mode 100644 index 0000000000000000000000000000000000000000..6f52554b45247629fc1a871f336d8a10e49e4e6a --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532238.out @@ -0,0 +1,421 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-24 +MASTER_PORT=12876 +WORLD_SIZE=1 +Running RR_sklearn.py with argument: features[0] +Configured run_name = subj1_10 +Configured current_features = features[0] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1473812 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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 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: 0.30803351791946487, test_score: -0.017072567503794198 +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: 0.350628741557499, test_score: 0.044146803682999225 +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: 0.3708545290215234, test_score: 0.07415206828700396 +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: 0.3217455052416617, test_score: 0.012052946832462207 +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: 0.3086277993280388, test_score: -0.01604966711386884 +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: 0.3098077390249735, test_score: -0.01349167181559727 +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: 0.3263335754723431, test_score: 0.016890126565333924 +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: 0.34871102344429444, test_score: 0.057144597855504534 +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: 0.346753282591186, test_score: 0.03954779513403772 +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: 0.4028050689434146, test_score: 0.1412251308133371 +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: 0.33174517581014223, test_score: 0.02866087351371819 +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: 0.3156928131922943, test_score: -0.0016871627165130768 +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: 0.3466904173621998, test_score: 0.03885354753758794 +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: 0.3827769603807217, test_score: 0.09634722392989994 +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: 0.31581608988416215, test_score: -0.005697398234672361 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_10 +Configured current_features = features[2] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1604255 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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 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: 0.3137278982750047, test_score: -0.007616662335071763 +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: 0.32060119823240185, test_score: 0.0019356001892446347 +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: 0.40353540701488205, test_score: 0.12742089730117023 +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: 0.31234836438103025, test_score: -0.011342643574621374 +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: 0.3528923229338017, test_score: 0.04893395500146064 +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: 0.35258747969595305, test_score: 0.061748200969686313 +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: 0.31239096809533756, test_score: -0.010260013770390868 +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: 0.3758941067805386, test_score: 0.1018337392393022 +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: 0.3259494135632477, test_score: 0.008465281600023432 +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: 0.3300762107879644, test_score: 0.02352550461047715 +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: 0.370390900420877, test_score: 0.08091781754640978 +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: 0.3109429350242693, test_score: -0.011682739745913948 +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: 0.30987808335911776, test_score: -0.013709155510115388 +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: 0.3991451869069987, test_score: 0.14841410775297575 +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: 0.3236839497222669, test_score: 0.007817111747655084 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_10 +Configured current_features = features[5] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1643210 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_10 +Configured current_features = features[7] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1644757 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_10 +Configured current_features = features[10] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1645258 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_10 +Configured current_features = features[12] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1645787 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_10 +Configured current_features = features[14] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1646321 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_10 +Configured current_features = features[16] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1646757 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_10 +Configured current_features = features[19] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1647135 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_10 +Configured current_features = features[21] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1647541 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_10 +Configured current_features = features[23] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1648507 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_10 +Configured current_features = features[25] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1649404 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_10 +Configured current_features = features[28] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1649894 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_10 +Configured current_features = features[30] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1650307 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_10 +Configured current_features = features[32] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1650678 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_10 +Configured current_features = features[34] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1651131 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_10 +Configured current_features = classifier[0] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1651524 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_10 +Configured current_features = classifier[3] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1651927 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_10 +Configured current_features = classifier[6] +Configured num_sessions = 10.0 +Configured subj = 1 +PID of this process = 1652357 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([6803, 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]) +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532239.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532239.err new file mode 100644 index 0000000000000000000000000000000000000000..3a14a9aaa5e53c659c946b1064698ccb407e1de8 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532239.err @@ -0,0 +1,1101 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24513 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( + 0%| | 0/15 [00:00 +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( + 0%| | 0/15 [00:00 +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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( + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +Exception ignored in: +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532240.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532240.out new file mode 100644 index 0000000000000000000000000000000000000000..ca7adc957bcd123baa04c7ac482711899dd97f8b --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532240.out @@ -0,0 +1,421 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-24 +MASTER_PORT=11735 +WORLD_SIZE=1 +Running RR_sklearn.py with argument: features[0] +Configured run_name = subj1_3 +Configured current_features = features[0] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1474466 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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 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: 0.3374575409698548, test_score: -0.017416929149140673 +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: 0.36951565961265875, test_score: 0.024675319626898267 +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: 0.38304132295220555, test_score: 0.04568657824361852 +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: 0.3470286988409469, test_score: 0.002634221222452654 +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: 0.33784988495481494, test_score: -0.016745329563012407 +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: 0.338868499789565, test_score: -0.014605425851987637 +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: 0.3525163175073622, test_score: 0.009695736672919996 +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: 0.37057927013514314, test_score: 0.041444042020687404 +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: 0.36654475575875767, test_score: 0.02207310883653536 +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: 0.4082587079130417, test_score: 0.10453391418455046 +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: 0.35807194315476004, test_score: 0.018819654384696245 +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: 0.34336434718916115, test_score: -0.006959115956445012 +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: 0.3674043433629027, test_score: 0.020761829371684637 +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: 0.38973559707530414, test_score: 0.06358694408767135 +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: 0.34280146352993635, test_score: -0.009572364164586346 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_3 +Configured current_features = features[2] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1525935 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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 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: 0.3414047460363942, test_score: -0.010305086408810369 +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: 0.34594594728626193, test_score: -0.0037658189268860087 +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: 0.4000237237979711, test_score: 0.07846326251117722 +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: 0.34062543124753175, test_score: -0.013287114114558695 +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: 0.36874261361217336, test_score: 0.024900680592975316 +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: 0.3762189074989283, test_score: 0.04783585948826636 +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: 0.3408778410547861, test_score: -0.012153286022696025 +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: 0.3902994884084831, test_score: 0.07572792327595608 +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: 0.348184743378173, test_score: -0.002264339444079835 +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: 0.3523826476379873, test_score: 0.00883844637584481 +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: 0.3845283304604168, test_score: 0.05598815921019684 +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: 0.3393067152252041, test_score: -0.013458024134681014 +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: 0.3383129214398911, test_score: -0.015194714188968282 +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: 0.4094390106998124, test_score: 0.1131519181588756 +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: 0.34893068940183175, test_score: 0.0017187205112026037 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_3 +Configured current_features = features[5] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1574198 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_3 +Configured current_features = features[7] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1574786 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_3 +Configured current_features = features[10] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1576874 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_3 +Configured current_features = features[12] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1577982 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_3 +Configured current_features = features[14] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1579564 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_3 +Configured current_features = features[16] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1580310 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_3 +Configured current_features = features[19] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1581981 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_3 +Configured current_features = features[21] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1582493 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_3 +Configured current_features = features[23] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1585972 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_3 +Configured current_features = features[25] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1586665 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_3 +Configured current_features = features[28] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1588321 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_3 +Configured current_features = features[30] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1588807 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_3 +Configured current_features = features[32] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1590419 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_3 +Configured current_features = features[34] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1590919 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_3 +Configured current_features = classifier[0] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1592830 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_3 +Configured current_features = classifier[3] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1593419 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_3 +Configured current_features = classifier[6] +Configured num_sessions = 3.0 +Configured subj = 1 +PID of this process = 1594097 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([2049, 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]) +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532241.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532241.out new file mode 100644 index 0000000000000000000000000000000000000000..444cf9f4ef460d9c5d35719387c0d7b8146a6b23 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532241.out @@ -0,0 +1,421 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-247 +MASTER_PORT=17324 +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 = 3727833 +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 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: 0.3396579597428657, test_score: -0.019305641607247313 +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: 0.36781106519018425, test_score: 0.016876963272388433 +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: 0.37889919833035485, test_score: 0.03406396323410748 +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: 0.3478046129831079, test_score: -0.004336501885848153 +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: 0.3400237380238924, test_score: -0.01876836438685391 +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: 0.34075599460210426, test_score: -0.017184383495636734 +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: 0.35392748109039185, test_score: 0.005240272077463716 +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: 0.37040880516179914, test_score: 0.0337979035337342 +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: 0.3653092197570084, test_score: 0.014274539631968954 +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: 0.40376534006530373, test_score: 0.08711850896485836 +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: 0.3592802731640336, test_score: 0.01160666057201495 +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: 0.3443182601146674, test_score: -0.011369048800935132 +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: 0.36559176083958184, test_score: 0.014216362914229667 +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: 0.384730057599465, test_score: 0.05051027244471127 +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: 0.343849737891346, test_score: -0.012534063900364208 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_2 +Configured current_features = features[2] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3747447 +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 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: 0.34289202505117566, test_score: -0.013143921366987476 +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: 0.3474342614172744, test_score: -0.007386776593668096 +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: 0.39302544556729324, test_score: 0.05913888207361967 +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: 0.3424868570772849, test_score: -0.015775075424660924 +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: 0.36545948694906677, test_score: 0.015484734326070294 +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: 0.3783242645183072, test_score: 0.03967873160048185 +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: 0.34296980423848655, test_score: -0.014575223741780923 +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: 0.38874417603851275, test_score: 0.06481740267219882 +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: 0.34820112565088074, test_score: -0.008149653262596252 +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: 0.35229752129988295, test_score: 0.001082487765388625 +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: 0.3821002927039709, test_score: 0.0463174508111098 +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: 0.3414527165037183, test_score: -0.0159125207623703 +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: 0.34024934065581214, test_score: -0.017488646553071 +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: 0.40673417196252737, test_score: 0.09588997266443686 +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: 0.3498839849076439, test_score: -0.002646532455747069 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_2 +Configured current_features = features[5] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3767335 +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]) +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_2 +Configured current_features = features[7] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3767652 +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]) +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_2 +Configured current_features = features[10] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3767983 +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]) +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_2 +Configured current_features = features[12] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3768282 +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]) +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_2 +Configured current_features = features[14] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3768608 +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]) +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_2 +Configured current_features = features[16] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3768956 +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]) +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_2 +Configured current_features = features[19] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3769276 +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]) +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_2 +Configured current_features = features[21] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3769622 +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]) +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_2 +Configured current_features = features[23] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3769910 +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]) +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_2 +Configured current_features = features[25] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3770251 +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]) +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_2 +Configured current_features = features[28] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3770545 +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]) +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_2 +Configured current_features = features[30] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3770884 +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]) +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_2 +Configured current_features = features[32] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3771213 +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]) +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_2 +Configured current_features = features[34] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3771519 +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]) +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_2 +Configured current_features = classifier[0] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3771851 +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]) +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_2 +Configured current_features = classifier[3] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3772158 +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]) +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_2 +Configured current_features = classifier[6] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3772466 +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]) +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532247.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532247.out new file mode 100644 index 0000000000000000000000000000000000000000..6afe72ddc795d3cdddcf8936649b593302b8074a --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532247.out @@ -0,0 +1,1046 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-139-113 +MASTER_PORT=16014 +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 = 3006657 +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 +Starting ridge regression for split 2 with alpha 30000 +Finished, now scoring +train_score: 0.3895184967154032, test_score: 0.061702444180992445 +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: 0.37221870466373824, test_score: 0.029720630358640688 +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: 0.36180702589888936, test_score: 0.018745743870740496 +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: 0.380824585881521, test_score: 0.04200108586790626 +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: 0.3423444209847122, test_score: -0.015609185276788094 +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: 0.3832356144126469, test_score: 0.044793651273692565 +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: 0.3685381992016318, test_score: 0.02140582035783988 +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: 0.38418793915779, test_score: 0.05054267447879119 +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: 0.3778268776256792, test_score: 0.03349185740086535 +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: 0.3560949192140051, test_score: 0.007667925849427305 +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: 0.386063674722308, test_score: 0.05333805697071623 +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: 0.41519948542838714, test_score: 0.1036008315664427 +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: 0.3804635262542525, test_score: 0.049303390241647364 +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: 0.4056296582664241, test_score: 0.08968599083448564 +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: 0.3859501697952678, test_score: 0.051925144125544706 +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: 0.3985314813878602, test_score: 0.07502585569317126 +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: 0.3396579597428657, test_score: -0.019305641607247313 +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: 0.36781106519018425, test_score: 0.016876963272388433 +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: 0.37889919833035485, test_score: 0.03406396323410748 +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: 0.3478046129831079, test_score: -0.004336501885848153 +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: 0.3400237380238924, test_score: -0.01876836438685391 +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: 0.34075599460210426, test_score: -0.017184383495636734 +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: 0.35392748109039185, test_score: 0.005240272077463716 +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: 0.37040880516179914, test_score: 0.0337979035337342 +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: 0.3653092197570084, test_score: 0.014274539631968954 +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: 0.40376534006530373, test_score: 0.08711850896485836 +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: 0.3592802731640336, test_score: 0.01160666057201495 +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: 0.3443182601146674, test_score: -0.011369048800935132 +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: 0.36559176083958184, test_score: 0.014216362914229667 +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: 0.384730057599465, test_score: 0.05051027244471127 +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: 0.343849737891346, test_score: -0.012534063900364208 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_2 +Configured current_features = features[2] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3053674 +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: 0.34423073758259376, test_score: -0.013279742427563563 +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: 0.3877869820313111, test_score: 0.05078609027796771 +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: 0.38966027347662313, test_score: 0.0611391445801062 +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: 0.3574832002361944, test_score: 5.8708278780806546e-05 +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: 0.3577479640429616, test_score: 0.007227462299513333 +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: 0.36278459307892436, test_score: 0.011436231584931449 +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: 0.34126320022146306, test_score: -0.017516573719692972 +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: 0.3692401758096231, test_score: 0.028197956217955806 +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: 0.36357378644850896, test_score: 0.018165634127769083 +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: 0.3698693178301504, test_score: 0.030027393610048053 +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: 0.33983912126098487, test_score: -0.01880926293360511 +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: 0.3878654099451528, test_score: 0.05488352663614354 +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: 0.41150482001418914, test_score: 0.08472019094300252 +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: 0.37559122815401214, test_score: 0.03108660551426536 +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: 0.34022419173217056, test_score: -0.018103714962217497 +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: 0.3754554770531492, test_score: 0.03820989411999344 +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: 0.3407225235861283, test_score: -0.01796632873341623 +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: 0.34289202505117566, test_score: -0.013143921366987476 +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: 0.3474342614172744, test_score: -0.007386776593668096 +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: 0.39302544556729324, test_score: 0.05913888207361967 +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: 0.3424868570772849, test_score: -0.015775075424660924 +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: 0.36545948694906677, test_score: 0.015484734326070294 +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: 0.3783242645183072, test_score: 0.03967873160048185 +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: 0.34296980423848655, test_score: -0.014575223741780923 +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: 0.38874417603851275, test_score: 0.06481740267219882 +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: 0.34820112565088074, test_score: -0.008149653262596252 +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: 0.35229752129988295, test_score: 0.001082487765388625 +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: 0.3821002927039709, test_score: 0.0463174508111098 +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: 0.3414527165037183, test_score: -0.0159125207623703 +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: 0.34024934065581214, test_score: -0.017488646553071 +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: 0.40673417196252737, test_score: 0.09588997266443686 +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: 0.3498839849076439, test_score: -0.002646532455747069 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_2 +Configured current_features = features[5] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3096954 +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 16 +start_feature_index: 0, end_feature_index: 8 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3608639633106675, test_score: 0.01541517207697224 +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: 0.350454077160223, test_score: -0.0038492106706442585 +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: 0.3495938509739723, test_score: -0.004156556490294397 +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: 0.3584544624236594, test_score: 0.010424436740443842 +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: 0.3617164791019898, test_score: 0.015722305817926625 +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: 0.3661276872183323, test_score: 0.02267055004791031 +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: 0.3808100514669528, test_score: 0.04562098302292572 +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: 0.3664990870571261, test_score: 0.022221924706279744 +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: 0.36307616870637277, test_score: 0.01708352965711742 +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: 0.3597548959398523, test_score: 0.013248716317388255 +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: 0.3628060222148428, test_score: 0.018696862183733284 +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: 0.36486057327843946, test_score: 0.019482793953108264 +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: 0.3520620076732195, test_score: 0.00044436614803543983 +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: 0.36826103330809973, test_score: 0.0273474170413971 +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: 0.3694285655318453, test_score: 0.027618701066602715 +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: 0.3608757870027147, test_score: 0.015805091401964084 +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_2 +Configured current_features = features[7] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3118169 +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 16 +start_feature_index: 0, end_feature_index: 8 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3469079867523616, test_score: -0.009602908764929858 +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: 0.3452719873243262, test_score: -0.011683942902259591 +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: 0.3477719272202194, test_score: -0.008174526809124352 +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: 0.35272876999450625, test_score: 0.0002645036481524411 +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: 0.3537876164085477, test_score: 0.00205928865643903 +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: 0.34647648341057696, test_score: -0.010412368481403929 +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: 0.3450081413214223, test_score: -0.012611672774287576 +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: 0.348872825387483, test_score: -0.004506547430488608 +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: 0.3584456441125587, test_score: 0.009225028509362307 +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: 0.35112250456527244, test_score: -0.001818070824934172 +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: 0.3505739504038073, test_score: -0.0016628961566409757 +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: 0.34774632948061834, test_score: -0.00801899441102651 +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: 0.3535850660157689, test_score: 0.0027654452962057076 +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: 0.3530556323534512, test_score: 0.0020605974214105488 +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: 0.3490912266062867, test_score: -0.005978613656286039 +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: 0.3552206638426973, test_score: 0.005433615340164488 +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_2 +Configured current_features = features[10] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3139415 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3608108558299745, test_score: 0.015371328120766078 +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: 0.3580706667098765, test_score: 0.008905810622162466 +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: 0.36414500424683616, test_score: 0.0180331139306676 +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: 0.36497389706024297, test_score: 0.021866400381217257 +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: 0.3618687883030958, test_score: 0.015782638322266358 +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: 0.3642603744504583, test_score: 0.01868726622633689 +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: 0.3635000120736055, test_score: 0.017962583263564397 +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: 0.3642929614369517, test_score: 0.01833498222284876 +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_2 +Configured current_features = features[12] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3150432 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3654522697474053, test_score: 0.02363907126558316 +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: 0.369491234205572, test_score: 0.029742777546519477 +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: 0.36426304826480393, test_score: 0.021161361044498443 +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: 0.36586189956209153, test_score: 0.02132239897593606 +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: 0.3642653061095193, test_score: 0.020247362058856976 +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: 0.3675588107692938, test_score: 0.026545873965575777 +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: 0.3666177754498082, test_score: 0.02440602890083668 +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: 0.36428414734103026, test_score: 0.02038506001534915 +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_2 +Configured current_features = features[14] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3161106 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3765322760535035, test_score: 0.042808792710417924 +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: 0.3780321662866882, test_score: 0.04483244984030135 +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: 0.38132670386137685, test_score: 0.053084856489663705 +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: 0.37845440825820725, test_score: 0.045527894521367615 +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: 0.38204974842656103, test_score: 0.05476882076402717 +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: 0.37489848658698416, test_score: 0.039897265888723575 +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: 0.383174158601242, test_score: 0.055835081168418794 +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: 0.3806334223675195, test_score: 0.04844555702359547 +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_2 +Configured current_features = features[16] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3171881 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4113348456587779, test_score: 0.03758423605487917 +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: 0.41426152992217813, test_score: 0.04254178992834276 +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: 0.4093424452443097, test_score: 0.03278236202402429 +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: 0.4161338385112515, test_score: 0.04795968939582856 +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: 0.41096480052356577, test_score: 0.037275224297263675 +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: 0.4096916860129454, test_score: 0.034286073214387584 +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: 0.41375859571152346, test_score: 0.04175291121087224 +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: 0.4070663091413654, test_score: 0.030597368904885187 +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_2 +Configured current_features = features[19] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3183391 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.42870520939901796, test_score: 0.06810521436435595 +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: 0.4298027252442393, test_score: 0.06972894302766308 +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: 0.4316205912223609, test_score: 0.07399189423775776 +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: 0.4280878028991667, test_score: 0.06703719311561793 +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_2 +Configured current_features = features[21] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3188911 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.42882499363199045, test_score: 0.06886222189110615 +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: 0.42373016225160265, test_score: 0.058691973344733105 +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: 0.4266749380371411, test_score: 0.0642413608654551 +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: 0.421372401511045, test_score: 0.05609719137750314 +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_2 +Configured current_features = features[23] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3194508 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.41282634732916695, test_score: 0.04025784445695356 +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: 0.41726400124881513, test_score: 0.049031372263483565 +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: 0.41382933549392664, test_score: 0.04221400083312303 +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: 0.42020928269185986, test_score: 0.05359950133679653 +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_2 +Configured current_features = features[25] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3200200 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.41902794819485245, test_score: 0.05371323121736775 +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: 0.416814279402379, test_score: 0.050248975845668 +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: 0.41706997860076783, test_score: 0.05044109395187601 +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: 0.41873362794769453, test_score: 0.0539155907731138 +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_2 +Configured current_features = features[28] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3205730 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4395092941657315, test_score: 0.09093566117426424 +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: 0.43687281762093905, test_score: 0.08622818245963527 +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_2 +Configured current_features = features[30] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3208632 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.44040519978133313, test_score: 0.09037712673538507 +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: 0.43959676461774677, test_score: 0.08882050966004752 +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_2 +Configured current_features = features[32] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3211497 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4407880921376152, test_score: 0.0881792634074973 +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: 0.4434372180407085, test_score: 0.09241049185816193 +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_2 +Configured current_features = features[34] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3214405 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.45191854233635953, test_score: 0.11088128458490132 +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: 0.45038736965818965, test_score: 0.10791081600823885 +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_2 +Configured current_features = classifier[0] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3217214 +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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5278478643439949, test_score: 0.15702859221635354 +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_2 +Configured current_features = classifier[3] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3218767 +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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5090607075150884, test_score: 0.11818798531391546 +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_2 +Configured current_features = classifier[6] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 3220292 +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 1 +start_feature_index: 0, end_feature_index: 1000 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5581658385044784, test_score: 0.20805061469370834 +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532248.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532248.out new file mode 100644 index 0000000000000000000000000000000000000000..ae1bc90bb63ef78eab0a91cf60089f7a5b4f29eb --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532248.out @@ -0,0 +1,1046 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-24 +MASTER_PORT=14495 +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 = 1730759 +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 +Starting ridge regression for split 2 with alpha 30000 +Finished, now scoring +train_score: 0.3895184967154032, test_score: 0.061702444180992445 +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: 0.37221870466373824, test_score: 0.029720630358640688 +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: 0.36180702589888936, test_score: 0.018745743870740496 +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: 0.380824585881521, test_score: 0.04200108586790626 +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: 0.3423444209847122, test_score: -0.015609185276788094 +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: 0.3832356144126469, test_score: 0.044793651273692565 +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: 0.3685381992016318, test_score: 0.02140582035783988 +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: 0.38418793915779, test_score: 0.05054267447879119 +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: 0.3778268776256792, test_score: 0.03349185740086535 +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: 0.3560949192140051, test_score: 0.007667925849427305 +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: 0.386063674722308, test_score: 0.05333805697071623 +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: 0.41519948542838714, test_score: 0.1036008315664427 +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: 0.3804635262542525, test_score: 0.049303390241647364 +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: 0.4056296582664241, test_score: 0.08968599083448564 +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: 0.3859501697952678, test_score: 0.051925144125544706 +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: 0.3985314813878602, test_score: 0.07502585569317126 +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: 0.3396579597428657, test_score: -0.019305641607247313 +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: 0.36781106519018425, test_score: 0.016876963272388433 +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: 0.37889919833035485, test_score: 0.03406396323410748 +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: 0.3478046129831079, test_score: -0.004336501885848153 +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: 0.3400237380238924, test_score: -0.01876836438685391 +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: 0.34075599460210426, test_score: -0.017184383495636734 +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: 0.35392748109039185, test_score: 0.005240272077463716 +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: 0.37040880516179914, test_score: 0.0337979035337342 +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: 0.3653092197570084, test_score: 0.014274539631968954 +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: 0.40376534006530373, test_score: 0.08711850896485836 +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: 0.3592802731640336, test_score: 0.01160666057201495 +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: 0.3443182601146674, test_score: -0.011369048800935132 +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: 0.36559176083958184, test_score: 0.014216362914229667 +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: 0.384730057599465, test_score: 0.05051027244471127 +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: 0.343849737891346, test_score: -0.012534063900364208 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_2 +Configured current_features = features[2] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 1847980 +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: 0.34423073758259376, test_score: -0.013279742427563563 +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: 0.3877869820313111, test_score: 0.05078609027796771 +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: 0.38966027347662313, test_score: 0.0611391445801062 +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: 0.3574832002361944, test_score: 5.8708278780806546e-05 +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: 0.3577479640429616, test_score: 0.007227462299513333 +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: 0.36278459307892436, test_score: 0.011436231584931449 +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: 0.34126320022146306, test_score: -0.017516573719692972 +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: 0.3692401758096231, test_score: 0.028197956217955806 +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: 0.36357378644850896, test_score: 0.018165634127769083 +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: 0.3698693178301504, test_score: 0.030027393610048053 +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: 0.33983912126098487, test_score: -0.01880926293360511 +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: 0.3878654099451528, test_score: 0.05488352663614354 +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: 0.41150482001418914, test_score: 0.08472019094300252 +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: 0.37559122815401214, test_score: 0.03108660551426536 +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: 0.34022419173217056, test_score: -0.018103714962217497 +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: 0.3754554770531492, test_score: 0.03820989411999344 +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: 0.3407225235861283, test_score: -0.01796632873341623 +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: 0.34289202505117566, test_score: -0.013143921366987476 +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: 0.3474342614172744, test_score: -0.007386776593668096 +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: 0.39302544556729324, test_score: 0.05913888207361967 +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: 0.3424868570772849, test_score: -0.015775075424660924 +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: 0.36545948694906677, test_score: 0.015484734326070294 +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: 0.3783242645183072, test_score: 0.03967873160048185 +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: 0.34296980423848655, test_score: -0.014575223741780923 +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: 0.38874417603851275, test_score: 0.06481740267219882 +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: 0.34820112565088074, test_score: -0.008149653262596252 +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: 0.35229752129988295, test_score: 0.001082487765388625 +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: 0.3821002927039709, test_score: 0.0463174508111098 +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: 0.3414527165037183, test_score: -0.0159125207623703 +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: 0.34024934065581214, test_score: -0.017488646553071 +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: 0.40673417196252737, test_score: 0.09588997266443686 +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: 0.3498839849076439, test_score: -0.002646532455747069 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_2 +Configured current_features = features[5] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 1948038 +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 16 +start_feature_index: 0, end_feature_index: 8 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3608639633106675, test_score: 0.01541517207697224 +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: 0.350454077160223, test_score: -0.0038492106706442585 +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: 0.3495938509739723, test_score: -0.004156556490294397 +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: 0.3584544624236594, test_score: 0.010424436740443842 +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: 0.3617164791019898, test_score: 0.015722305817926625 +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: 0.3661276872183323, test_score: 0.02267055004791031 +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: 0.3808100514669528, test_score: 0.04562098302292572 +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: 0.3664990870571261, test_score: 0.022221924706279744 +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: 0.36307616870637277, test_score: 0.01708352965711742 +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: 0.3597548959398523, test_score: 0.013248716317388255 +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: 0.3628060222148428, test_score: 0.018696862183733284 +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: 0.36486057327843946, test_score: 0.019482793953108264 +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: 0.3520620076732195, test_score: 0.00044436614803543983 +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: 0.36826103330809973, test_score: 0.0273474170413971 +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: 0.3694285655318453, test_score: 0.027618701066602715 +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: 0.3608757870027147, test_score: 0.015805091401964084 +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_2 +Configured current_features = features[7] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2015566 +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 16 +start_feature_index: 0, end_feature_index: 8 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3469079867523616, test_score: -0.009602907636234443 +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: 0.3452719873243262, test_score: -0.011683942440083825 +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: 0.3477719272202194, test_score: -0.008174525807885545 +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: 0.35272876999450625, test_score: 0.00026450328481018853 +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: 0.3537876164085477, test_score: 0.0020592896651815966 +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: 0.34647648341057696, test_score: -0.010412367676424074 +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: 0.3450081413214223, test_score: -0.01261167263167101 +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: 0.348872825387483, test_score: -0.0045065458080302484 +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: 0.3584456441125587, test_score: 0.00922502888704799 +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: 0.35112250456527244, test_score: -0.0018180717142038964 +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: 0.3505739504038073, test_score: -0.0016628941516289842 +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: 0.34774632948061834, test_score: -0.008018994109496856 +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: 0.3535850660157689, test_score: 0.0027654462893427064 +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: 0.3530556323534512, test_score: 0.0020605980605741983 +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: 0.3490912266062867, test_score: -0.005978612718818217 +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: 0.3552206638426973, test_score: 0.005433616052442311 +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_2 +Configured current_features = features[10] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2073885 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3608108558299745, test_score: 0.015371328120766078 +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: 0.3580706667098765, test_score: 0.008905810622162466 +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: 0.36414500424683616, test_score: 0.0180331139306676 +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: 0.36497389706024297, test_score: 0.021866400381217257 +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: 0.3618687883030958, test_score: 0.015782638322266358 +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: 0.3642603744504583, test_score: 0.01868726622633689 +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: 0.3635000120736055, test_score: 0.017962583263564397 +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: 0.3642929614369517, test_score: 0.01833498222284876 +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_2 +Configured current_features = features[12] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2100676 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3654522697474053, test_score: 0.023639071325023967 +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: 0.369491234205572, test_score: 0.02974277757970916 +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: 0.36426304826480393, test_score: 0.02116136094513941 +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: 0.36586189956209153, test_score: 0.021322399024658552 +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: 0.3642653061095193, test_score: 0.020247361968734515 +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: 0.3675588107692938, test_score: 0.02654587398900172 +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: 0.3666177754498082, test_score: 0.024406028909879623 +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: 0.36428414734103026, test_score: 0.020385060075563396 +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_2 +Configured current_features = features[14] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2128420 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3765322760535035, test_score: 0.042808792710417924 +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: 0.3780321662866882, test_score: 0.04483244984030135 +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: 0.38132670386137685, test_score: 0.053084856489663705 +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: 0.37845440825820725, test_score: 0.045527894521367615 +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: 0.38204974842656103, test_score: 0.05476882076402717 +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: 0.37489848658698416, test_score: 0.039897265888723575 +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: 0.383174158601242, test_score: 0.055835081168418794 +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: 0.3806334223675195, test_score: 0.04844555702359547 +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_2 +Configured current_features = features[16] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2159278 +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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4113348610389665, test_score: 0.03758421078706777 +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: 0.41426153533555643, test_score: 0.04254176498019468 +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: 0.40934244944202686, test_score: 0.032782348458818174 +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: 0.41613385207930703, test_score: 0.047959656462827405 +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: 0.4109648067956285, test_score: 0.037275191144299516 +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: 0.40969170552917705, test_score: 0.034286064587229534 +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: 0.41375861049415313, test_score: 0.04175288627093383 +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: 0.40706631130917964, test_score: 0.03059735318470727 +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_2 +Configured current_features = features[19] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2193289 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.42870520939901796, test_score: 0.06810521398997005 +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: 0.4298027252442393, test_score: 0.06972894292177831 +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: 0.4316205912223609, test_score: 0.07399189393763987 +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: 0.4280878028991667, test_score: 0.06703719293184544 +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_2 +Configured current_features = features[21] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2208678 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.42882499363199045, test_score: 0.06886222287386883 +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: 0.42373016225160265, test_score: 0.058691974314267426 +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: 0.4266749380371411, test_score: 0.06424136142513613 +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: 0.421372401511045, test_score: 0.05609719221610601 +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_2 +Configured current_features = features[23] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2224856 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.41282634732916695, test_score: 0.040257845140012675 +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: 0.41726400124881513, test_score: 0.04903137367596497 +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: 0.41382933549392664, test_score: 0.04221400172496121 +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: 0.42020928269185986, test_score: 0.053599502505653165 +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_2 +Configured current_features = features[25] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2240719 +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 4 +start_feature_index: 0, end_feature_index: 128 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.41902794819485245, test_score: 0.05371323201888986 +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: 0.416814279402379, test_score: 0.050248976669991785 +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: 0.41706997860076783, test_score: 0.05044109523212097 +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: 0.41873362794769453, test_score: 0.0539155920270617 +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_2 +Configured current_features = features[28] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2264967 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4395092941657315, test_score: 0.09093566119670456 +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: 0.43687281762093905, test_score: 0.08622818251278506 +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_2 +Configured current_features = features[30] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2272732 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.44040519978133313, test_score: 0.09037712673538507 +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: 0.43959676461774677, test_score: 0.08882050966004752 +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_2 +Configured current_features = features[32] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2280308 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4407880921376152, test_score: 0.08817926459391405 +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: 0.4434372180407085, test_score: 0.09241049211434527 +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_2 +Configured current_features = features[34] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2287481 +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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.45191854233635953, test_score: 0.11088128458490132 +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: 0.45038736965818965, test_score: 0.10791081600823885 +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_2 +Configured current_features = classifier[0] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2296401 +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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.527847867138568, test_score: 0.1570285853707375 +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_2 +Configured current_features = classifier[3] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2300546 +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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5090607075150884, test_score: 0.11818797657263762 +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_2 +Configured current_features = classifier[6] +Configured num_sessions = 2.0 +Configured subj = 1 +PID of this process = 2304419 +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 1 +start_feature_index: 0, end_feature_index: 1000 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5581658734108098, test_score: 0.20805059969194623 +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532249.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532249.err new file mode 100644 index 0000000000000000000000000000000000000000..1c95f064b6d4f5f293ecf5da88635907a9565cee --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532249.err @@ -0,0 +1,4 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress *** +slurmstepd: error: *** JOB 532249 ON ip-10-0-142-24 CANCELLED AT 2024-11-02T01:32:14 *** +slurmstepd: error: *** REASON: burst_buffer/lua: Stage-out in progress *** diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532249.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532249.out new file mode 100644 index 0000000000000000000000000000000000000000..dd8ef4ebe6596b10577959f84b0d0d46acb3c961 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532249.out @@ -0,0 +1 @@ +NUM_GPUS=1 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532251.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532251.err new file mode 100644 index 0000000000000000000000000000000000000000..23d8ecdf8218c011d6682eebae8ee84ec0d6cbde --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532251.err @@ -0,0 +1,9188 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24512 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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532253.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532253.err new file mode 100644 index 0000000000000000000000000000000000000000..150c209bf906872f1653415f7c752bf23e0eeeeb --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532253.err @@ -0,0 +1,561 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24512 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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532259.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532259.out new file mode 100644 index 0000000000000000000000000000000000000000..8a7e2857121be267d9266a23fff3e4c5b0791c98 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/532259.out @@ -0,0 +1,1046 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-142-24 +MASTER_PORT=17864 +WORLD_SIZE=1 +Running RR_sklearn.py with argument: features[0] +Configured run_name = subj1_1 +Configured current_features = features[0] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 1826199 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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: 0.42782809921279136, test_score: 0.06191303303194205 +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: 0.4243628050661675, test_score: 0.05022974624143686 +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: 0.40551391327789643, test_score: 0.021989555615984854 +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: 0.39998317917876924, test_score: 0.014197293287735236 +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: 0.4174931850767356, test_score: 0.033663333865785974 +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: 0.3821296998298424, test_score: -0.01697366138257298 +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: 0.42099342471007395, test_score: 0.03811363511730947 +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: 0.40700937528198017, test_score: 0.012669183752999533 +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: 0.4188702093777749, test_score: 0.04512830019306679 +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: 0.4089836560796087, test_score: 0.021083118753057305 +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: 0.39411886625536735, test_score: 0.0043607095667776 +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: 0.42033193661042983, test_score: 0.047507461768637065 +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: 0.44828866512844473, test_score: 0.09317434830642891 +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: 0.4138923543420339, test_score: 0.04148832385131858 +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: 0.4375625195511365, test_score: 0.07749014822260786 +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: 0.41739008493465046, test_score: 0.038597226631507914 +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: 0.4305125739650614, test_score: 0.061246967230015145 +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: 0.3794263638482913, test_score: -0.020374631005477935 +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: 0.3995296361063619, test_score: 0.009812927168870897 +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: 0.4113952503750173, test_score: 0.02351338316269174 +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: 0.3869527070747157, test_score: -0.009055791571910127 +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: 0.3796354793438349, test_score: -0.01978570067820693 +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: 0.3807336634060647, test_score: -0.018453762369997167 +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: 0.3930192674255119, test_score: 0.0026176631731831576 +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: 0.40836554244436724, test_score: 0.028589167211787368 +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: 0.39755706376887495, test_score: 0.007498770257076168 +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: 0.43907901435852187, test_score: 0.07557828414718988 +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: 0.398539811620785, test_score: 0.009134979523174703 +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: 0.3840606780847505, test_score: -0.014504191110858631 +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: 0.3989819593961941, test_score: 0.007295360059554708 +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: 0.4160115133242509, test_score: 0.0370430104128293 +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: 0.3827525236564396, test_score: -0.015081505440430966 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_1 +Configured current_features = features[2] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 1937934 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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: 0.382979844421863, test_score: -0.01591196607346466 +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: 0.42387060625404555, test_score: 0.04009498653804023 +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: 0.4250787087306991, test_score: 0.05262427258540428 +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: 0.39830111416991465, test_score: -0.003749564194715901 +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: 0.3933913116506232, test_score: 0.0007057186995001823 +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: 0.3980142179193188, test_score: 0.0046455880849338785 +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: 0.38073921439784825, test_score: -0.019285684335976102 +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: 0.4093869832180552, test_score: 0.022111963823490108 +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: 0.39940904050991655, test_score: 0.010608894080401138 +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: 0.407593132121644, test_score: 0.02435659112513602 +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: 0.37950416260027336, test_score: -0.019975434399151375 +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: 0.41687218401165366, test_score: 0.038683548455799334 +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: 0.4436963030708741, test_score: 0.07441297120585262 +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: 0.41406508238142925, test_score: 0.029102463915644812 +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: 0.38011921422799355, test_score: -0.020023963780625872 +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: 0.41045335765304425, test_score: 0.03302503281508259 +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: 0.3802335549527478, test_score: -0.019628202600568023 +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: 0.3828914399916223, test_score: -0.015438116597610718 +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: 0.38618979583128854, test_score: -0.011152421689028948 +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: 0.42305972586075313, test_score: 0.03875485702019125 +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: 0.3817330418761675, test_score: -0.018045414887066465 +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: 0.39853297851371317, test_score: 0.007812629364693723 +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: 0.412921954582646, test_score: 0.03417837887393223 +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: 0.3823107233350496, test_score: -0.01677965465606554 +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: 0.42033853463085585, test_score: 0.05268167417764213 +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: 0.38555977364101635, test_score: -0.012457253435879763 +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: 0.38980231737698406, test_score: -0.00499126158036287 +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: 0.4160968987804127, test_score: 0.03476359915041577 +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: 0.38095661845112877, test_score: -0.017252156366714063 +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: 0.3805117389451325, test_score: -0.01879755633737257 +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: 0.4369277782025135, test_score: 0.08215080657277152 +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: 0.38837161862271125, test_score: -0.007019913598812396 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_1 +Configured current_features = features[5] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2030574 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3982861150107289, test_score: 0.007087236643515402 +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: 0.3887890964452216, test_score: -0.007712872853083556 +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: 0.3876229598238045, test_score: -0.008599120022745453 +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: 0.39507983847108075, test_score: 0.0030237377354893438 +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: 0.3986307407057602, test_score: 0.009964530308058005 +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: 0.402356243284541, test_score: 0.014889083204823919 +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: 0.41441088403841136, test_score: 0.03456467633998985 +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: 0.40326572951236206, test_score: 0.014783772240187787 +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: 0.39983752692608304, test_score: 0.01081597276614262 +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: 0.3957849342659209, test_score: 0.006215364540767043 +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: 0.39952177418403445, test_score: 0.011607224514582683 +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: 0.40021075921244953, test_score: 0.012770471721064715 +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: 0.38948682619668035, test_score: -0.00473482147328075 +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: 0.40467187165297563, test_score: 0.01820038542173849 +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: 0.4052074467055, test_score: 0.018651526750394103 +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: 0.3988047004621787, test_score: 0.009441680476191004 +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_1 +Configured current_features = features[7] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2079459 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3852366376477134, test_score: -0.013119974999350787 +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: 0.38381628748415364, test_score: -0.01471182346183156 +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: 0.3857088641634553, test_score: -0.011555476827019925 +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: 0.39077016303624823, test_score: -0.005297819897502194 +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: 0.3910880669173605, test_score: -0.003969727149096758 +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: 0.38474024691150976, test_score: -0.013761955219867178 +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: 0.3834519697957437, test_score: -0.015547640874328857 +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: 0.38718197791722514, test_score: -0.008529477935866127 +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: 0.3954471953787452, test_score: 0.00242850739669079 +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: 0.3885394718015554, test_score: -0.006713238608857237 +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: 0.38845213202410145, test_score: -0.006235179511081618 +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: 0.3858394253141201, test_score: -0.011980624765440767 +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: 0.39169772459583785, test_score: -0.0027896737071386795 +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: 0.3910067049254204, test_score: -0.0035545856230571513 +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: 0.3869588402833915, test_score: -0.009676558269228133 +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: 0.3935124143209784, test_score: -0.0006528954289425458 +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_1 +Configured current_features = features[10] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2125801 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.3975257517236396, test_score: 0.007608123555208793 +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: 0.39493814115472575, test_score: 0.0019594941151934143 +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: 0.40012648401844536, test_score: 0.009189887282981245 +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: 0.4013728787458859, test_score: 0.011980474156087486 +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: 0.3981588695940435, test_score: 0.007934679616359764 +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: 0.400637102398345, test_score: 0.0102748841181371 +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: 0.40012239352132234, test_score: 0.008927924834475048 +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: 0.4001622171978989, test_score: 0.009209734310070503 +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_1 +Configured current_features = features[12] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2149156 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.40133415777029985, test_score: 0.014456581777203688 +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: 0.4054490400781399, test_score: 0.01915534300368043 +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: 0.4005400561287231, test_score: 0.01268429186342707 +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: 0.4021641652104507, test_score: 0.012291657761831972 +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: 0.40008362976661216, test_score: 0.011353842867102622 +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: 0.4035000797483658, test_score: 0.01666679419275854 +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: 0.40274115137723115, test_score: 0.01522540847619911 +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: 0.4001722933789016, test_score: 0.010860187504826166 +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_1 +Configured current_features = features[14] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2175377 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.41114762145464967, test_score: 0.030148291847278425 +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: 0.4121004874789035, test_score: 0.03077680207966282 +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: 0.4165163589247229, test_score: 0.03874786171781534 +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: 0.41332223346120367, test_score: 0.032439812400721635 +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: 0.41661764195090967, test_score: 0.04041602455495224 +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: 0.4096064737192762, test_score: 0.027254131047275008 +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: 0.4176329753302277, test_score: 0.04057759888599744 +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: 0.4149725935824293, test_score: 0.0339802039212422 +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_1 +Configured current_features = features[16] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2198814 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4496897683786259, test_score: 0.02569583251057925 +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: 0.45108829402047, test_score: 0.02864062409799189 +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: 0.44738970563219077, test_score: 0.02132824061463769 +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: 0.45375415135443736, test_score: 0.03411820085379473 +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: 0.44861311246226165, test_score: 0.024600158725236278 +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: 0.4480222371733104, test_score: 0.022594022301878958 +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: 0.4518169911517366, test_score: 0.02899295234538741 +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: 0.4460201315253463, test_score: 0.019262663328102673 +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_1 +Configured current_features = features[19] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2223307 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.46349704223804566, test_score: 0.049915534570154234 +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: 0.4645487693661645, test_score: 0.05142747692638989 +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: 0.46621800602393476, test_score: 0.054842491796195374 +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: 0.46318046710123223, test_score: 0.04904812140358001 +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_1 +Configured current_features = features[21] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2237015 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.46396827199776053, test_score: 0.05045400490478183 +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: 0.45951287646675504, test_score: 0.04206228325277681 +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: 0.4624315916201001, test_score: 0.04659253097625112 +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: 0.45710973844513747, test_score: 0.03937360659554988 +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_1 +Configured current_features = features[23] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2253957 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.45053833917661634, test_score: 0.027142272109685242 +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: 0.45414190849693886, test_score: 0.03457810164076504 +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: 0.4509558609634152, test_score: 0.028400390541087197 +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: 0.4567220711357528, test_score: 0.0378011483265113 +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_1 +Configured current_features = features[25] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2270725 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.45687727736302886, test_score: 0.03876637731234202 +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: 0.4545858982790766, test_score: 0.03537281595064962 +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: 0.4548943557181914, test_score: 0.036063974293867526 +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: 0.4564933614913671, test_score: 0.038722146347752735 +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_1 +Configured current_features = features[28] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2284217 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.47399994465630235, test_score: 0.06968052060118078 +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: 0.47181300492086553, test_score: 0.06581440447242022 +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_1 +Configured current_features = features[30] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2289798 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4750487559868934, test_score: 0.06923435894027585 +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: 0.4739643782269174, test_score: 0.06759189358677183 +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_1 +Configured current_features = features[32] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2297807 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4745261959033161, test_score: 0.06789821405061773 +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: 0.476819229232387, test_score: 0.07184189056193688 +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_1 +Configured current_features = features[34] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2304640 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.48669220683969994, test_score: 0.08860392704643 +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: 0.48544137621188127, test_score: 0.08562360807247854 +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_1 +Configured current_features = classifier[0] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2310366 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.557820248091554, test_score: 0.12900885588562622 +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_1 +Configured current_features = classifier[3] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2312703 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5435148304583706, test_score: 0.09571720707850565 +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_1 +Configured current_features = classifier[6] +Configured num_sessions = 1.0 +Configured subj = 1 +PID of this process = 2315580 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([688, 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 1 +start_feature_index: 0, end_feature_index: 1000 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5855086713433993, test_score: 0.1690433902807685 +Successfully processed classifier[6]. +All features have been processed. diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/533761.err b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/533761.err new file mode 100644 index 0000000000000000000000000000000000000000..0538d530332765a5a5e2bcc5d2c4c57c750d7aa6 --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/533761.err @@ -0,0 +1,2254 @@ +[NbConvertApp] Converting notebook RR_sklearn.ipynb to python +[NbConvertApp] Writing 24512 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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/32 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/16 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/8 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/4 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/2 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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( + 0%| | 0/1 [00:00 +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 diff --git a/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/533761.out b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/533761.out new file mode 100644 index 0000000000000000000000000000000000000000..d3202dd84d8758aa5f0047676852279b80a1ebda --- /dev/null +++ b/spurious_reconstruction/analysis/1_case_study/feature-decoding/slurms/533761.out @@ -0,0 +1,1046 @@ +NUM_GPUS=1 +MASTER_ADDR=ip-10-0-150-188 +MASTER_PORT=17792 +WORLD_SIZE=1 +Running RR_sklearn.py with argument: features[0] +Configured run_name = subj1_0.5 +Configured current_features = features[0] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 305233 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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: 0.4396535651996669, test_score: 0.03798351004287852 +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: 0.433710746180522, test_score: 0.02860601259366971 +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: 0.42081123918775726, test_score: 0.01075339088484051 +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: 0.41634385683558095, test_score: 0.0033743496112656093 +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: 0.42921362318531614, test_score: 0.02264081371339464 +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: 0.4015748438465072, test_score: -0.01912390853102812 +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: 0.432648963215011, test_score: 0.024949078399556512 +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: 0.4179603925596212, test_score: 0.005954887649634302 +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: 0.43379301528972347, test_score: 0.026648168688742747 +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: 0.4182334894835947, test_score: 0.012386253756911781 +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: 0.4127226579376699, test_score: -0.0034123547447729085 +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: 0.4354704373191406, test_score: 0.02852590869612069 +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: 0.45615941976879376, test_score: 0.060873052214310754 +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: 0.4286820884042962, test_score: 0.025136913469979132 +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: 0.44598192774537276, test_score: 0.049289084565286786 +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: 0.4258326842276069, test_score: 0.019757692886085268 +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: 0.43699164026511095, test_score: 0.037107589634726046 +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: 0.4002128361105067, test_score: -0.02212064677380009 +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: 0.41309326883776193, test_score: -0.0010324841413823583 +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: 0.421444949408584, test_score: 0.009052179304761038 +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: 0.4039717574637685, test_score: -0.011160501332713227 +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: 0.40028888168444415, test_score: -0.02166231692340612 +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: 0.4008952320545558, test_score: -0.020250434733681044 +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: 0.4118192029078986, test_score: -0.005109466933909056 +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: 0.4245226691343056, test_score: 0.014071166640485853 +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: 0.4117020845722214, test_score: -0.002305935699937356 +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: 0.44743174682559905, test_score: 0.047899313280917157 +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: 0.4161219634751995, test_score: 8.148905803855636e-05 +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: 0.40290314542082317, test_score: -0.016146577245159603 +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: 0.4141869288429943, test_score: -0.0016855523771082063 +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: 0.42477358459328995, test_score: 0.018780663546059614 +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: 0.40204170357483093, test_score: -0.01825458122489611 +Successfully processed features[0]. +Running RR_sklearn.py with argument: features[2] +Configured run_name = subj1_0.5 +Configured current_features = features[2] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 306323 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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: 0.4031709533228998, test_score: -0.018698927658654243 +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: 0.42989512251559175, test_score: 0.024877169123889743 +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: 0.43273810663975587, test_score: 0.028361341914954444 +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: 0.41361683725379383, test_score: -0.008684678525129762 +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: 0.4109490172837422, test_score: -0.005042472079882938 +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: 0.41254806606294797, test_score: -0.0023437889482741704 +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: 0.401696465836825, test_score: -0.02118945791239319 +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: 0.4225360563293593, test_score: 0.009610884451289018 +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: 0.4139674575575527, test_score: 0.0018406582128513982 +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: 0.42455070200398837, test_score: 0.014178899338218039 +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: 0.4000949838501822, test_score: -0.021712273925900763 +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: 0.42777082918079073, test_score: 0.025785184865307776 +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: 0.4462336273502568, test_score: 0.04250838972957196 +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: 0.4299687392089827, test_score: 0.011448948000831967 +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: 0.4009687107786632, test_score: -0.021656716309208804 +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: 0.4281670992971393, test_score: 0.018735848413074456 +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: 0.40109119349114924, test_score: -0.021292544216714254 +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: 0.4024509549454673, test_score: -0.01848784229794754 +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: 0.40625342686022353, test_score: -0.014528539946358039 +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: 0.43333078480553633, test_score: 0.024041305351420947 +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: 0.4025596290144963, test_score: -0.020209321207592706 +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: 0.41460548974858863, test_score: -0.002666181856622024 +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: 0.4314997306635388, test_score: 0.020135213707351014 +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: 0.4027322299360967, test_score: -0.019464395004572536 +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: 0.43607939184006955, test_score: 0.03580410944867407 +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: 0.4055828607505675, test_score: -0.015438053172301323 +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: 0.40482419093458943, test_score: -0.007816553250438998 +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: 0.4230663560233699, test_score: 0.016936663503048132 +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: 0.40095416602285355, test_score: -0.019584123465586113 +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: 0.40096285212834337, test_score: -0.020819827455738793 +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: 0.4507720269996667, test_score: 0.0598318455631196 +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: 0.40748980224445713, test_score: -0.011405253647377085 +Successfully processed features[2]. +Running RR_sklearn.py with argument: features[5] +Configured run_name = subj1_0.5 +Configured current_features = features[5] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 3161206 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.41602803991193044, test_score: 0.0009832828013563724 +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: 0.4074845275556886, test_score: -0.012454903235658298 +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: 0.4067607483375828, test_score: -0.013073819276006706 +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: 0.4122598141295817, test_score: -0.0035422784427958406 +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: 0.4155704575481095, test_score: 0.00038314412280059237 +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: 0.4169121196355341, test_score: 0.003913385997325264 +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: 0.42804059596896316, test_score: 0.020907869578038044 +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: 0.4180090838896077, test_score: 0.0036826407933236195 +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: 0.41703053638962073, test_score: 0.0015078988680378316 +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: 0.413520271696653, test_score: -0.0010310182700141314 +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: 0.41629479938275393, test_score: 0.002300381079662817 +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: 0.41359116634780385, test_score: 0.0013328253075656112 +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: 0.40830278072944615, test_score: -0.009575710899509864 +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: 0.42016452343882305, test_score: 0.008151549920857863 +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: 0.41966160593994933, test_score: 0.008902255578034214 +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: 0.41584166617312424, test_score: 0.0017050706028773248 +Successfully processed features[5]. +Running RR_sklearn.py with argument: features[7] +Configured run_name = subj1_0.5 +Configured current_features = features[7] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 531667 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.4058425653977028, test_score: -0.016253027430950317 +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: 0.40458821659207744, test_score: -0.01683699108050873 +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: 0.40540476837313366, test_score: -0.014942946313783395 +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: 0.40902559888647594, test_score: -0.00958906105916367 +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: 0.4108026669727817, test_score: -0.008567528851047937 +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: 0.4052337226027707, test_score: -0.01684346067167458 +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: 0.4040824557789973, test_score: -0.018082992946281237 +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: 0.4060397597718288, test_score: -0.012587399812220667 +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: 0.41203621581364114, test_score: -0.0049315125209648805 +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: 0.4082138768419706, test_score: -0.011355146630819326 +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: 0.407923649461784, test_score: -0.010164322769794142 +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: 0.40549783982273385, test_score: -0.015484611922532178 +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: 0.4099670354536002, test_score: -0.007212975734802577 +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: 0.40985012654822706, test_score: -0.00855129958995095 +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: 0.4078226044320635, test_score: -0.013820221004106579 +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: 0.4119993012200406, test_score: -0.005793221231384794 +Successfully processed features[7]. +Running RR_sklearn.py with argument: features[10] +Configured run_name = subj1_0.5 +Configured current_features = features[10] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2115782 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.41442097837393227, test_score: -0.0005618745166122713 +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: 0.412908670519621, test_score: -0.004240265134256554 +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: 0.4169441524336711, test_score: 0.001657343323691942 +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: 0.41772981212251215, test_score: 0.003863278591501114 +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: 0.41534250694738445, test_score: 0.00032303596809454187 +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: 0.4168584763728136, test_score: 0.002195033628435784 +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: 0.41630986502433515, test_score: 0.0012372433215393915 +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: 0.416016057689141, test_score: 0.001293798669569337 +Successfully processed features[10]. +Running RR_sklearn.py with argument: features[12] +Configured run_name = subj1_0.5 +Configured current_features = features[12] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2712097 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.41800019637987545, test_score: 0.004781861038490175 +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: 0.42065556671964094, test_score: 0.00859595068221218 +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: 0.4170140360855804, test_score: 0.0032887087867836343 +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: 0.4182042769662959, test_score: 0.0034651681934054707 +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: 0.41697810965934723, test_score: 0.002598009811088183 +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: 0.4192252625886487, test_score: 0.006337764715638213 +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: 0.4184258681540577, test_score: 0.005480272059789577 +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: 0.4166385769172665, test_score: 0.0025161235726912545 +Successfully processed features[12]. +Running RR_sklearn.py with argument: features[14] +Configured run_name = subj1_0.5 +Configured current_features = features[14] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 3306464 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 30000 +Finished, now scoring +train_score: 0.42497062788061885, test_score: 0.017081479746650377 +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: 0.42575263219014653, test_score: 0.017857990808503293 +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: 0.4295153129308834, test_score: 0.023527701736477124 +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: 0.4269136493168219, test_score: 0.019150777182255687 +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: 0.4293081489500866, test_score: 0.02564055378386813 +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: 0.42441804710390935, test_score: 0.015209670640425596 +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: 0.4296952124609594, test_score: 0.025220875361151526 +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: 0.42860342466680734, test_score: 0.0215492052866513 +Successfully processed features[14]. +Running RR_sklearn.py with argument: features[16] +Configured run_name = subj1_0.5 +Configured current_features = features[16] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 3932821 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 8 +start_feature_index: 0, end_feature_index: 32 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.46639810381693253, test_score: 0.013580085518415799 +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: 0.4663054954910914, test_score: 0.01642501975464518 +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: 0.4641684425250574, test_score: 0.009762346293917099 +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: 0.46885772906318096, test_score: 0.020396030538248924 +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: 0.464965752561966, test_score: 0.012307464385421017 +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: 0.4647338452755108, test_score: 0.010367495829055816 +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: 0.467315800917938, test_score: 0.016208490920980954 +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: 0.4628607868781602, test_score: 0.008515995114187986 +Successfully processed features[16]. +Running RR_sklearn.py with argument: features[19] +Configured run_name = subj1_0.5 +Configured current_features = features[19] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 419121 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4757351429581026, test_score: 0.031917240523665895 +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: 0.47682369554342785, test_score: 0.03325637253179156 +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: 0.47835496800338084, test_score: 0.03575267022772327 +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: 0.47554386683811417, test_score: 0.031624568361844546 +Successfully processed features[19]. +Running RR_sklearn.py with argument: features[21] +Configured run_name = subj1_0.5 +Configured current_features = features[21] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 822558 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4761863461981217, test_score: 0.03212626641807288 +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: 0.47308138082957313, test_score: 0.025609494595500812 +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: 0.47540025243917744, test_score: 0.02974738285259765 +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: 0.471041259532683, test_score: 0.02322591031032177 +Successfully processed features[21]. +Running RR_sklearn.py with argument: features[23] +Configured run_name = subj1_0.5 +Configured current_features = features[23] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 1178167 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.46636890268590575, test_score: 0.013792554629245033 +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: 0.46939505005548243, test_score: 0.020038068424286754 +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: 0.4667757851386172, test_score: 0.015047833969722178 +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: 0.47129528823360306, test_score: 0.022509345806403066 +Successfully processed features[23]. +Running RR_sklearn.py with argument: features[25] +Configured run_name = subj1_0.5 +Configured current_features = features[25] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 1537549 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.47145945923843174, test_score: 0.023267883664651764 +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: 0.47006223525165275, test_score: 0.02064997198625534 +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: 0.47012235843442934, test_score: 0.02101500788962486 +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: 0.4712160821486788, test_score: 0.022998441354762887 +Successfully processed features[25]. +Running RR_sklearn.py with argument: features[28] +Configured run_name = subj1_0.5 +Configured current_features = features[28] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 1988720 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.48460692605041206, test_score: 0.04722859680445601 +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: 0.48321212650262313, test_score: 0.04473746483445253 +Successfully processed features[28]. +Running RR_sklearn.py with argument: features[30] +Configured run_name = subj1_0.5 +Configured current_features = features[30] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2167130 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.48607548362499436, test_score: 0.048156089998429574 +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: 0.4851024073929085, test_score: 0.04659932649071431 +Successfully processed features[30]. +Running RR_sklearn.py with argument: features[32] +Configured run_name = subj1_0.5 +Configured current_features = features[32] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2344737 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4859415547938644, test_score: 0.04843604732525773 +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: 0.48800209562973107, test_score: 0.05051999678973101 +Successfully processed features[32]. +Running RR_sklearn.py with argument: features[34] +Configured run_name = subj1_0.5 +Configured current_features = features[34] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2524407 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 2 +start_feature_index: 0, end_feature_index: 256 +Starting ridge regression for split 1 with alpha 25000 +Finished, now scoring +train_score: 0.4955795898378775, test_score: 0.06354845173896163 +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: 0.49484872589514245, test_score: 0.06150695423798792 +Successfully processed features[34]. +Running RR_sklearn.py with argument: classifier[0] +Configured run_name = subj1_0.5 +Configured current_features = classifier[0] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2679049 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5624288457055919, test_score: 0.10245182868004615 +Successfully processed classifier[0]. +Running RR_sklearn.py with argument: classifier[3] +Configured run_name = subj1_0.5 +Configured current_features = classifier[3] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2797738 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 1 +start_feature_index: 0, end_feature_index: 4096 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.551358885446553, test_score: 0.074730783725626 +Successfully processed classifier[3]. +Running RR_sklearn.py with argument: classifier[6] +Configured run_name = subj1_0.5 +Configured current_features = classifier[6] +Configured num_sessions = 0.5 +Configured subj = 1 +PID of this process = 2905337 +loading_betas +betas_ loaded +Number of zeros in valid_nsd_ids_full tensor(0) +Num train examples torch.Size([340, 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 1 +start_feature_index: 0, end_feature_index: 1000 +Starting ridge regression for split 1 with alpha 20000 +Finished, now scoring +train_score: 0.5796497547385874, test_score: 0.13608904569043315 +Successfully processed classifier[6]. +All features have been processed.