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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 10 new columns ({'pred_sim_gpr_x', 'pred_pwa_x', 'pred_rbf_thin_plate_s1.0_x', 'pred_pwa_y', 'pred_sim_rbf_thin_plate_s1.0_x', 'pred_gpr_y', 'pred_sim_gpr_y', 'pred_gpr_x', 'pred_sim_rbf_thin_plate_s1.0_y', 'pred_rbf_thin_plate_s1.0_y'})

This happened while the csv dataset builder was generating data using

hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release/data/self_all_trials_csv_train.csv (at revision 871225d7a87f7c544d13e49bcf4adfa275f85630), [/tmp/hf-datasets-cache/medium/datasets/63342848855630-config-parquet-and-info-wannabeyourfriend-hf-gaze-a907a4bf/hub/datasets--wannabeyourfriend-hf--gaze-refine-mbr-origin-delta-release/snapshots/871225d7a87f7c544d13e49bcf4adfa275f85630/data/judo_1000_csv_train.csv (origin=hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release@871225d7a87f7c544d13e49bcf4adfa275f85630/data/judo_1000_csv_train.csv), /tmp/hf-datasets-cache/medium/datasets/63342848855630-config-parquet-and-info-wannabeyourfriend-hf-gaze-a907a4bf/hub/datasets--wannabeyourfriend-hf--gaze-refine-mbr-origin-delta-release/snapshots/871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_all_trials_csv_train.csv (origin=hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release@871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_all_trials_csv_train.csv), /tmp/hf-datasets-cache/medium/datasets/63342848855630-config-parquet-and-info-wannabeyourfriend-hf-gaze-a907a4bf/hub/datasets--wannabeyourfriend-hf--gaze-refine-mbr-origin-delta-release/snapshots/871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_s1_csv_train.csv (origin=hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release@871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_s1_csv_train.csv)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dataset: string
              split: string
              sample_id: string
              subject: string
              timestamp: string
              target_index: int64
              origin_gaze_x: double
              origin_gaze_y: double
              target_x: int64
              target_y: int64
              spread: double
              n_samples: int64
              pred_similarity_x: double
              pred_similarity_y: double
              pred_poly_x: double
              pred_poly_y: double
              pred_rbf_thin_plate_s1.0_x: double
              pred_rbf_thin_plate_s1.0_y: double
              pred_rbf_multiquadric_s0.0_x: double
              pred_rbf_multiquadric_s0.0_y: double
              pred_rbf_multiquadric_s1.0_x: double
              pred_rbf_multiquadric_s1.0_y: double
              pred_rbf_multiquadric_s2.0_x: double
              pred_rbf_multiquadric_s2.0_y: double
              pred_tps_x: double
              pred_tps_y: double
              pred_pwa_x: double
              pred_pwa_y: double
              pred_gpr_x: double
              pred_gpr_y: double
              pred_sim_rbf_thin_plate_s1.0_x: double
              pred_sim_rbf_thin_plate_s1.0_y: double
              pred_sim_rbf_multiquadric_s0.0_x: double
              pred_sim_rbf_multiquadric_s0.0_y: double
              pred_sim_rbf_multiquadric_s1.0_x: double
              pred_sim_rbf_multiquadric_s1.0_y: double
              pred_sim_rbf_multiquadric_s2.0_x: double
              pred_sim_rbf_multiquadric_s2.0_y: double
              pred_sim_tps_x: double
              pred_sim_tps_y: double
              pred_sim_pwa_x: double
              pred_sim_pwa_y: double
              pred_sim_gpr_x: double
              pred_sim_gpr_y: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 6241
              to
              {'dataset': Value('string'), 'split': Value('string'), 'sample_id': Value('string'), 'subject': Value('string'), 'timestamp': Value('string'), 'target_index': Value('int64'), 'origin_gaze_x': Value('float64'), 'origin_gaze_y': Value('float64'), 'target_x': Value('float64'), 'target_y': Value('float64'), 'spread': Value('float64'), 'n_samples': Value('float64'), 'pred_similarity_x': Value('float64'), 'pred_similarity_y': Value('float64'), 'pred_poly_x': Value('float64'), 'pred_poly_y': Value('float64'), 'pred_rbf_multiquadric_s0.0_x': Value('float64'), 'pred_rbf_multiquadric_s0.0_y': Value('float64'), 'pred_rbf_multiquadric_s1.0_x': Value('float64'), 'pred_rbf_multiquadric_s1.0_y': Value('float64'), 'pred_rbf_multiquadric_s2.0_x': Value('float64'), 'pred_rbf_multiquadric_s2.0_y': Value('float64'), 'pred_tps_x': Value('float64'), 'pred_tps_y': Value('float64'), 'pred_sim_rbf_multiquadric_s0.0_x': Value('float64'), 'pred_sim_rbf_multiquadric_s0.0_y': Value('float64'), 'pred_sim_rbf_multiquadric_s1.0_x': Value('float64'), 'pred_sim_rbf_multiquadric_s1.0_y': Value('float64'), 'pred_sim_rbf_multiquadric_s2.0_x': Value('float64'), 'pred_sim_rbf_multiquadric_s2.0_y': Value('float64'), 'pred_sim_tps_x': Value('float64'), 'pred_sim_tps_y': Value('float64'), 'pred_sim_pwa_x': Value('float64'), 'pred_sim_pwa_y': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 10 new columns ({'pred_sim_gpr_x', 'pred_pwa_x', 'pred_rbf_thin_plate_s1.0_x', 'pred_pwa_y', 'pred_sim_rbf_thin_plate_s1.0_x', 'pred_gpr_y', 'pred_sim_gpr_y', 'pred_gpr_x', 'pred_sim_rbf_thin_plate_s1.0_y', 'pred_rbf_thin_plate_s1.0_y'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release/data/self_all_trials_csv_train.csv (at revision 871225d7a87f7c544d13e49bcf4adfa275f85630), [/tmp/hf-datasets-cache/medium/datasets/63342848855630-config-parquet-and-info-wannabeyourfriend-hf-gaze-a907a4bf/hub/datasets--wannabeyourfriend-hf--gaze-refine-mbr-origin-delta-release/snapshots/871225d7a87f7c544d13e49bcf4adfa275f85630/data/judo_1000_csv_train.csv (origin=hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release@871225d7a87f7c544d13e49bcf4adfa275f85630/data/judo_1000_csv_train.csv), /tmp/hf-datasets-cache/medium/datasets/63342848855630-config-parquet-and-info-wannabeyourfriend-hf-gaze-a907a4bf/hub/datasets--wannabeyourfriend-hf--gaze-refine-mbr-origin-delta-release/snapshots/871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_all_trials_csv_train.csv (origin=hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release@871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_all_trials_csv_train.csv), /tmp/hf-datasets-cache/medium/datasets/63342848855630-config-parquet-and-info-wannabeyourfriend-hf-gaze-a907a4bf/hub/datasets--wannabeyourfriend-hf--gaze-refine-mbr-origin-delta-release/snapshots/871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_s1_csv_train.csv (origin=hf://datasets/wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release@871225d7a87f7c544d13e49bcf4adfa275f85630/data/self_s1_csv_train.csv)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

dataset
string
split
string
sample_id
string
subject
string
timestamp
string
target_index
int64
origin_gaze_x
float64
origin_gaze_y
float64
target_x
float64
target_y
float64
spread
float64
n_samples
null
pred_similarity_x
float64
pred_similarity_y
float64
pred_poly_x
float64
pred_poly_y
float64
pred_rbf_multiquadric_s0.0_x
float64
pred_rbf_multiquadric_s0.0_y
float64
pred_rbf_multiquadric_s1.0_x
float64
pred_rbf_multiquadric_s1.0_y
float64
pred_rbf_multiquadric_s2.0_x
float64
pred_rbf_multiquadric_s2.0_y
float64
pred_tps_x
float64
pred_tps_y
float64
pred_sim_rbf_multiquadric_s0.0_x
float64
pred_sim_rbf_multiquadric_s0.0_y
float64
pred_sim_rbf_multiquadric_s1.0_x
float64
pred_sim_rbf_multiquadric_s1.0_y
float64
pred_sim_rbf_multiquadric_s2.0_x
float64
pred_sim_rbf_multiquadric_s2.0_y
float64
pred_sim_tps_x
float64
pred_sim_tps_y
float64
pred_sim_pwa_x
float64
pred_sim_pwa_y
float64
judo_1000
train
judo_1000_train_0000000
s_1
t_1
-1
688.514189
632.265541
704
666
58.656421
null
692.032322
631.659418
683.872166
631.355457
703.999966
666.001034
691.635089
630.748529
691.157844
630.363064
704
665.999997
703.999651
665.998182
691.635165
630.748552
691.157995
630.363107
704
665.999999
691.157995
630.363107
judo_1000
train
judo_1000_train_0000001
s_1
t_1
-1
807.8003
483.143243
832
461
96.959121
null
809.893896
483.79364
803.944814
487.019495
831.999802
460.999115
827.340026
461.287907
824.698405
462.092842
831.999999
460.999997
831.999739
460.999145
827.340314
461.288496
824.699026
462.09398
832
461
824.699026
462.09398
judo_1000
train
judo_1000_train_0000002
s_1
t_1
-1
814.043605
623.615116
832
666
164.470297
null
816.316956
622.878452
806.116758
625.055831
831.999451
666.000671
833.09733
659.18713
832.494791
653.619287
831.999999
665.999996
831.999008
665.997837
833.096637
659.187299
832.493299
653.619824
832
665.999998
832.493299
653.619824
judo_1000
train
judo_1000_train_0000003
s_1
t_1
-1
524.111927
638.879817
512
614
89.640164
null
529.252228
638.490858
523.620844
634.633215
511.999527
614.000671
511.157759
613.1592
511.169171
613.351715
512
613.999998
511.999688
613.997709
511.158043
613.159306
511.170147
613.351983
512
613.999998
511.170147
613.351983
judo_1000
train
judo_1000_train_0000004
s_1
t_1
-1
601.487387
630.817417
597
614
29.955645
null
605.855731
630.374746
599.085102
628.419439
597.000057
614.001315
596.74238
609.298243
596.730747
609.938747
597
613.999997
596.999355
613.99849
596.742274
609.298275
596.730704
609.938881
597
613.999998
596.730704
609.938881
judo_1000
train
judo_1000_train_0000005
s_1
t_1
-1
605.145646
481.829129
597
478
50.44718
null
609.222612
482.83998
602.186052
484.355742
596.999626
477.999115
596.778555
471.865589
596.701951
472.717707
597
477.999998
596.99981
477.998275
596.778574
471.865596
596.701958
472.717681
597
477.999999
596.701958
472.717681
judo_1000
train
judo_1000_train_0000006
s_1
t_1
-1
684.899709
559.249128
683
546
46.463344
null
688.328025
559.364624
681.152056
560.704611
683.000603
546.001549
693.036749
556.754901
693.437472
557.122964
683
545.999996
682.999845
545.998726
693.036847
556.754967
693.437675
557.123099
683
545.999999
693.437675
557.123099
judo_1000
train
judo_1000_train_0000007
s_1
t_1
-1
747.204305
601.015563
768
614
85.519018
null
750.09379
600.614968
741.615143
602.113336
768.000061
614.000595
768.922096
609.444751
768.925326
608.661833
768
613.999997
767.99943
613.998025
768.922231
609.444832
768.925629
608.662018
768
613.999999
768.925629
608.662018
judo_1000
train
judo_1000_train_0000008
s_1
t_1
-1
589.706607
606.455255
597
614
68.76049
null
594.148595
606.271491
587.528238
604.901635
597.000061
614.001919
596.332227
612.0059
596.685414
612.622376
597
613.999998
596.999154
613.998099
596.332312
612.00593
596.685616
612.622443
597.000001
613.999999
596.685616
612.622443
judo_1000
train
judo_1000_train_0000009
s_1
t_1
-1
666.987821
616.376603
683
614
49.584447
null
670.689619
615.963081
663.068924
615.70972
683.000175
614.000557
684.00002
616.523543
684.249749
616.632463
683
613.999997
682.999571
613.998146
684.000139
616.523583
684.250048
616.632569
683
613.999999
684.250048
616.632569
judo_1000
train
judo_1000_train_0000010
s_1
t_1
-1
767.874474
489.821321
768
478
33.268915
null
770.37072
490.474763
764.154979
493.392307
768.000137
478
767.838011
477.918662
767.549185
477.580488
768
477.999997
767.999337
477.998231
767.838055
477.918723
767.549269
477.580604
768.000001
478
767.549269
477.580604
judo_1000
train
judo_1000_train_0000011
s_1
t_1
-1
579.248658
408.932215
597
410
85.465183
null
583.454337
410.701731
575.425542
412.488923
596.999817
410.000214
596.307831
410.059568
595.385463
410.503742
597
409.999999
597.000479
410.000437
596.308593
410.06018
595.386557
410.50438
597.000001
409.999997
595.386557
410.50438
judo_1000
train
judo_1000_train_0000012
s_1
t_1
-1
675.354354
595.51952
683
614
101.779042
null
678.938404
595.296008
671.442428
595.731316
683.000313
614.001095
685.13608
613.929456
685.046462
614.102909
682.999999
613.999997
682.999501
613.998772
685.136162
613.929493
685.046614
614.10298
683
613.999999
685.046614
614.10298
judo_1000
train
judo_1000_train_0000013
s_1
t_1
-1
585.08109
543.796474
597
546
77.556332
null
589.460932
544.234578
582.663616
544.481972
597.000557
546.000069
595.279188
547.722446
594.67762
547.991362
597
545.999997
596.999712
545.997831
595.279326
547.722521
594.677885
547.991506
597
545.999998
594.677885
547.991506
judo_1000
train
judo_1000_train_0000014
s_1
t_1
-1
536.831515
753.967576
533
768
121.992034
null
542.04458
752.429164
536.837422
743.130652
532.999176
768.005157
533.004764
768.002031
532.987974
767.993055
533
767.999998
533.000283
768.000346
533.004982
768.001984
532.988333
767.992965
533
767.999997
532.988333
767.992965
judo_1000
train
judo_1000_train_0000015
s_1
t_1
-1
741.917726
299.094649
747
256
215.211631
null
744.341177
301.661363
740.302149
302.032325
746.999878
255.998474
746.450069
258.769331
746.673632
260.107488
746.999999
255.999995
747.000753
255.99553
746.456908
258.78352
746.691714
260.143873
747.000001
256
746.691714
260.143873
judo_1000
train
judo_1000_train_0000016
s_1
t_1
-1
351.906441
603.46
320
597
230.655
null
358.673274
603.713441
353.759419
597.822532
320.001114
597.000671
319.90381
595.927118
321.178899
596.016943
319.999998
596.999995
320.001063
597.003022
319.923289
595.932595
321.226917
596.029882
320.000001
596.999997
321.226917
596.029882
judo_1000
train
judo_1000_train_0000017
s_1
t_1
-1
327.629651
416.697384
320
427
68.963308
null
334.314028
418.822375
320.894288
418.937229
320.00116
427.003906
319.824496
427.135737
319.901003
427.110429
320.000001
427.000003
320.00107
426.999804
319.823704
427.134489
319.910895
427.115199
320
426.999999
319.910895
427.115199
judo_1000
train
judo_1000_train_0000018
s_1
t_1
-1
312.027628
303.809009
320
256
145.504229
null
318.671256
307.066848
298.590884
308.452335
320.00116
255.995544
319.897118
256.353774
319.748203
257.715588
320.000001
256.000001
320.000602
256.000563
319.975625
256.403768
319.911484
257.820657
319.999999
256.000004
319.911484
257.820657
judo_1000
train
judo_1000_train_0000019
s_1
t_1
-1
743.345345
287.645045
747
256
160.876866
null
745.735171
290.321494
741.825913
290.362713
747.0009
256.002075
746.838287
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End of preview.

Gaze Refine MBR Origin-Delta Data

Minimal Hugging Face data release for the corrected multi-baseline residual gaze calibration experiment.

This dataset contains only standardized train/validation/test CSV files, data/manifest.json, and minimal data preparation/validation scripts. Training code is released separately on GitHub:

wannabeyourfriend/gaze-refine-mbr-origin-delta-release

Feature Definition

The corrected experiment uses:

input  = [origin_gaze, baseline_1 - origin_gaze, ..., baseline_K - origin_gaze]
target = target - origin_gaze
final  = origin_gaze + model(input)

The released CSVs provide origin_gaze_x, origin_gaze_y, target_x, target_y, and classical baseline prediction columns such as pred_sim_rbf_multiquadric_s1.0_x/y.

Files

  • data/self_all_trials_csv_{train,val,test}.csv
  • data/self_s1_csv_{train,val,test}.csv
  • data/judo_1000_csv_{train,val,test}.csv
  • data/manifest.json
  • scripts/standardize_datasets.py
  • scripts/validate_release.py

Subject identifiers are anonymized per dataset as s_1, s_2, ... . Timestamp/session identifiers are anonymized as t_1, t_2, ... .

Load

from datasets import load_dataset

ds = load_dataset(
    "wannabeyourfriend-hf/gaze-refine-mbr-origin-delta-release",
    data_files={
        "train": "data/judo_1000_csv_train.csv",
        "validation": "data/judo_1000_csv_val.csv",
        "test": "data/judo_1000_csv_test.csv",
    },
)

Validate

python scripts/validate_release.py --data-dir data
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