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ancs21
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dropoutts-repro-bundle

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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 1 new columns ({'init_sensitivity'})

This happened while the json dataset builder was generating data using

hf://datasets/ancs21/dropoutts-repro-bundle/claim1_sweep_results.json (at revision 985ece07d85f80113b0e95c644ebcb1eaddd0e7e), ['hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_sweep_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim2_ETTh2_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results_OLD_cleanFalse.json'], ['hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_sweep_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim2_ETTh2_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results_OLD_cleanFalse.json']

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/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              model: string
              dataset: string
              noise: double
              input_len: int64
              output_len: int64
              dropout: bool
              num_epochs_cap: int64
              epochs_run: int64
              train_seconds: double
              init_sensitivity: double
              metrics: struct<overall: struct<MAE: double, MSE: double, RMSE: double, MAPE: double, WAPE: double>>
                child 0, overall: struct<MAE: double, MSE: double, RMSE: double, MAPE: double, WAPE: double>
                    child 0, MAE: double
                    child 1, MSE: double
                    child 2, RMSE: double
                    child 3, MAPE: double
                    child 4, WAPE: double
              to
              {'model': Value('string'), 'dataset': Value('string'), 'noise': Value('float64'), 'input_len': Value('int64'), 'output_len': Value('int64'), 'dropout': Value('bool'), 'num_epochs_cap': Value('int64'), 'epochs_run': Value('int64'), 'train_seconds': Value('float64'), 'metrics': {'overall': {'MAE': Value('float64'), 'MSE': Value('float64'), 'RMSE': Value('float64'), 'MAPE': Value('float64'), 'WAPE': 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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 1 new columns ({'init_sensitivity'})
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/ancs21/dropoutts-repro-bundle/claim1_sweep_results.json (at revision 985ece07d85f80113b0e95c644ebcb1eaddd0e7e), ['hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_sweep_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim2_ETTh2_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results_OLD_cleanFalse.json'], ['hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim1_sweep_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim2_ETTh2_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results.json', 'hf://datasets/ancs21/dropoutts-repro-bundle@985ece07d85f80113b0e95c644ebcb1eaddd0e7e/claim5_results_OLD_cleanFalse.json']
              
              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.

model
string
dataset
string
noise
float64
input_len
int64
output_len
int64
dropout
bool
num_epochs_cap
int64
epochs_run
int64
train_seconds
float64
metrics
dict
Informer
SyntheticTS_noise0.1
0.1
96
96
false
100
30
722.9
{ "overall": { "MAE": 1.1178034209, "MSE": 2.0315083395, "RMSE": 1.4253099118, "MAPE": 6.670037969, "WAPE": 1.7321648417 } }
Informer
SyntheticTS_noise0.1
0.1
96
96
true
100
23
657.4
{ "overall": { "MAE": 0.9758591314, "MSE": 1.4428400874, "RMSE": 1.2011827866, "MAPE": 6.4950543872, "WAPE": 1.5258921769 } }
Informer
SyntheticTS_noise0.1
0.1
96
192
false
100
24
626.6
{ "overall": { "MAE": 0.7185614879, "MSE": 0.7922613855, "RMSE": 0.8900906626, "MAPE": 5.7394987361, "WAPE": 1.1770032236 } }
Informer
SyntheticTS_noise0.1
0.1
96
192
true
100
16
577.6
{ "overall": { "MAE": 0.8045535592, "MSE": 1.0809420286, "RMSE": 1.0396836163, "MAPE": 5.0320721409, "WAPE": 1.2315649998 } }
Informer
SyntheticTS_noise0.1
0.1
96
336
false
100
23
987.7
{ "overall": { "MAE": 0.6744753588, "MSE": 0.6897478723, "RMSE": 0.830510608, "MAPE": 1.3024383794, "WAPE": 1.0038132402 } }
Informer
SyntheticTS_noise0.1
0.1
96
336
true
100
27
1,304.1
{ "overall": { "MAE": 0.6721760958, "MSE": 0.6854671295, "RMSE": 0.8279294238, "MAPE": 1.3309529131, "WAPE": 1.0016878993 } }
Informer
SyntheticTS_noise0.1
0.1
96
720
false
100
23
1,664.1
{ "overall": { "MAE": 0.6721777553, "MSE": 0.6874632396, "RMSE": 0.8291340323, "MAPE": 1.3567647102, "WAPE": 1.009762032 } }
Informer
SyntheticTS_noise0.1
0.1
96
720
true
100
17
1,447.7
{ "overall": { "MAE": 0.6439205985, "MSE": 0.6327128164, "RMSE": 0.7954324731, "MAPE": 4.3542103641, "WAPE": 0.9822068456 } }
Informer
SyntheticTS_noise0.3
0.3
96
96
false
100
16
420.1
{ "overall": { "MAE": 1.0920998096, "MSE": 1.9309786229, "RMSE": 1.3895965672, "MAPE": 6.2696753609, "WAPE": 1.6938722893 } }
Informer
SyntheticTS_noise0.3
0.3
96
96
true
100
34
914.2
{ "overall": { "MAE": 1.0176565265, "MSE": 1.5764652264, "RMSE": 1.2555736666, "MAPE": 6.6560612298, "WAPE": 1.6099450187 } }
Informer
SyntheticTS_noise0.3
0.3
96
192
false
100
24
875.8
{ "overall": { "MAE": 0.7018262172, "MSE": 0.7553224454, "RMSE": 0.86909289, "MAPE": 5.1935334367, "WAPE": 1.1587096678 } }
Informer
SyntheticTS_noise0.3
0.3
96
192
true
100
22
785
{ "overall": { "MAE": 0.9999192843, "MSE": 1.538691432, "RMSE": 1.2404400217, "MAPE": 6.6037405853, "WAPE": 1.5226768671 } }
Informer
SyntheticTS_noise0.3
0.3
96
336
false
100
24
791.3
{ "overall": { "MAE": 0.6700868766, "MSE": 0.6782109885, "RMSE": 0.8235356625, "MAPE": 1.2520504272, "WAPE": 1.0075042442 } }
Informer
SyntheticTS_noise0.3
0.3
96
336
true
100
23
1,051.8
{ "overall": { "MAE": 0.7847302241, "MSE": 1.0222629595, "RMSE": 1.0110702026, "MAPE": 4.6145913446, "WAPE": 1.1921646049 } }
Informer
SyntheticTS_noise0.3
0.3
96
720
false
100
30
1,497.2
{ "overall": { "MAE": 0.6685011407, "MSE": 0.6780977736, "RMSE": 0.823466923, "MAPE": 1.5199633472, "WAPE": 1.0128714889 } }
Informer
SyntheticTS_noise0.3
0.3
96
720
true
100
34
1,699
{ "overall": { "MAE": 0.6028410318, "MSE": 0.5522441105, "RMSE": 0.7431312882, "MAPE": 2.9558461512, "WAPE": 0.9225384686 } }
Informer
SyntheticTS_noise0.5
0.5
96
96
false
100
23
382.3
{ "overall": { "MAE": 0.9830647664, "MSE": 1.606239078, "RMSE": 1.2673748785, "MAPE": 5.4190445218, "WAPE": 1.5285433793 } }
Informer
SyntheticTS_noise0.5
0.5
96
96
true
100
35
1,064
{ "overall": { "MAE": 0.9688097182, "MSE": 1.4437784775, "RMSE": 1.2015733297, "MAPE": 6.2020997833, "WAPE": 1.5474754831 } }
Informer
SyntheticTS_noise0.5
0.5
96
192
false
100
27
685.3
{ "overall": { "MAE": 0.9407352056, "MSE": 1.3852157821, "RMSE": 1.1769519045, "MAPE": 5.8399888361, "WAPE": 1.4461033805 } }
Informer
SyntheticTS_noise0.5
0.5
96
192
true
100
22
538.5
{ "overall": { "MAE": 1.4529072056, "MSE": 3.2566574567, "RMSE": 1.8046211339, "MAPE": 10.9041483466, "WAPE": 2.3397894488 } }
Informer
SyntheticTS_noise0.5
0.5
96
336
false
100
23
985.3
{ "overall": { "MAE": 0.6628634162, "MSE": 0.6649145793, "RMSE": 0.8154229468, "MAPE": 1.2733388203, "WAPE": 1.0125970739 } }
Informer
SyntheticTS_noise0.5
0.5
96
336
true
100
27
1,100.7
{ "overall": { "MAE": 0.6505929581, "MSE": 0.638538848, "RMSE": 0.7990862554, "MAPE": 1.2254054121, "WAPE": 0.9963688574 } }
Informer
SyntheticTS_noise0.5
0.5
96
720
false
100
20
1,506.8
{ "overall": { "MAE": 0.7632632011, "MSE": 0.9814956483, "RMSE": 0.9907046264, "MAPE": 4.876091356, "WAPE": 1.1855010952 } }
Informer
SyntheticTS_noise0.5
0.5
96
720
true
100
23
1,403.2
{ "overall": { "MAE": 0.6238147704, "MSE": 0.5916827807, "RMSE": 0.7692091904, "MAPE": 3.6959946961, "WAPE": 0.9757337362 } }
Informer
SyntheticTS_noise0.7
0.7
96
96
false
100
23
431.7
{ "overall": { "MAE": 0.9558730586, "MSE": 1.4090829679, "RMSE": 1.1870480103, "MAPE": 6.392001451, "WAPE": 1.5686899674 } }
Informer
SyntheticTS_noise0.7
0.7
96
96
true
100
22
533.3
{ "overall": { "MAE": 0.7418836065, "MSE": 0.8849984867, "RMSE": 0.9407435769, "MAPE": 3.0497634115, "WAPE": 1.1706581779 } }
Informer
SyntheticTS_noise0.7
0.7
96
192
false
100
27
688.8
{ "overall": { "MAE": 0.9005427969, "MSE": 1.2750427598, "RMSE": 1.129177914, "MAPE": 5.7866134555, "WAPE": 1.4213734589 } }
Informer
SyntheticTS_noise0.7
0.7
96
192
true
100
23
582.6
{ "overall": { "MAE": 1.2379398144, "MSE": 2.4882528964, "RMSE": 1.5774196865, "MAPE": 9.0617618138, "WAPE": 2.0229941595 } }
Informer
SyntheticTS_noise0.7
0.7
96
336
false
100
30
1,013
{ "overall": { "MAE": 0.6387981745, "MSE": 0.6147842812, "RMSE": 0.7840818091, "MAPE": 1.2684373852, "WAPE": 1.0010296318 } }
Informer
SyntheticTS_noise0.7
0.7
96
336
true
100
27
1,312.7
{ "overall": { "MAE": 0.6301497822, "MSE": 0.5960725877, "RMSE": 0.7720573755, "MAPE": 1.2429994737, "WAPE": 0.9896269845 } }
Informer
SyntheticTS_noise0.7
0.7
96
720
false
100
35
1,840.7
{ "overall": { "MAE": 0.7674059203, "MSE": 1.00431867, "RMSE": 1.002157012, "MAPE": 5.3346404536, "WAPE": 1.2202248293 } }
Informer
SyntheticTS_noise0.7
0.7
96
720
true
100
22
1,488.4
{ "overall": { "MAE": 0.5899358079, "MSE": 0.5752483667, "RMSE": 0.7584512915, "MAPE": 4.4247020723, "WAPE": 0.9443748594 } }
Informer
SyntheticTS_noise0.9
0.9
96
96
false
100
27
223.8
{ "overall": { "MAE": 0.8832505346, "MSE": 1.2408229084, "RMSE": 1.1139223142, "MAPE": 5.8673150179, "WAPE": 1.4817886992 } }
Informer
SyntheticTS_noise0.9
0.9
96
96
true
100
34
801.2
{ "overall": { "MAE": 0.8190588518, "MSE": 1.094355873, "RMSE": 1.0461146509, "MAPE": 4.5992359328, "WAPE": 1.350575684 } }
Informer
SyntheticTS_noise0.9
0.9
96
192
false
100
27
685.3
{ "overall": { "MAE": 0.9026454372, "MSE": 1.2760410634, "RMSE": 1.1296198765, "MAPE": 6.4365784031, "WAPE": 1.4692349099 } }
Informer
SyntheticTS_noise0.9
0.9
96
192
true
100
23
824.9
{ "overall": { "MAE": 0.5806299647, "MSE": 0.5209738927, "RMSE": 0.7217852127, "MAPE": 5.5234376097, "WAPE": 1.0344433465 } }
Informer
SyntheticTS_noise0.9
0.9
96
336
false
100
27
721.6
{ "overall": { "MAE": 0.6158056855, "MSE": 0.6078434111, "RMSE": 0.7796431347, "MAPE": 2.9440244387, "WAPE": 0.9924446776 } }
Informer
SyntheticTS_noise0.9
0.9
96
336
true
100
21
1,058.9
{ "overall": { "MAE": 0.7428803979, "MSE": 0.9191980432, "RMSE": 0.9587481609, "MAPE": 4.9855035756, "WAPE": 1.2085815745 } }
Informer
SyntheticTS_noise0.9
0.9
96
720
false
100
23
1,448.4
{ "overall": { "MAE": 0.7190576934, "MSE": 0.8700354445, "RMSE": 0.932756906, "MAPE": 5.162483342, "WAPE": 1.1785983801 } }
Informer
SyntheticTS_noise0.9
0.9
96
720
true
100
30
1,435
{ "overall": { "MAE": 0.5868707831, "MSE": 0.5207760264, "RMSE": 0.7216481305, "MAPE": 4.3842006196, "WAPE": 0.9709206564 } }
Informer
SyntheticTS_noise0.3
0.3
96
96
true
100
11
299.8
{ "overall": { "MAE": 1.1745563111, "MSE": 2.0136991376, "RMSE": 1.4190486685, "MAPE": 8.1649190765, "WAPE": 1.9208284835 } }
Informer
SyntheticTS_noise0.3
0.3
96
192
true
100
16
577.6
{ "overall": { "MAE": 0.8234991163, "MSE": 1.1115090737, "RMSE": 1.0542813074, "MAPE": 5.0372786938, "WAPE": 1.2662684412 } }
Informer
SyntheticTS_noise0.3
0.3
96
336
true
100
27
1,334.1
{ "overall": { "MAE": 0.6675469114, "MSE": 0.6757973142, "RMSE": 0.8220689236, "MAPE": 1.22204961, "WAPE": 1.0038002619 } }
Informer
SyntheticTS_noise0.3
0.3
96
720
true
100
25
2,116.4
{ "overall": { "MAE": 0.6478660937, "MSE": 0.6458252167, "RMSE": 0.8036325152, "MAPE": 4.3111263349, "WAPE": 0.9995762469 } }
Informer
SyntheticTS_noise0.3
0.3
96
96
true
100
11
300
{ "overall": { "MAE": 1.1294132674, "MSE": 1.8901814262, "RMSE": 1.3748386844, "MAPE": 7.8368087192, "WAPE": 1.8299588446 } }
Informer
SyntheticTS_noise0.3
0.3
96
192
true
100
16
578.8
{ "overall": { "MAE": 0.8270398447, "MSE": 1.1291424116, "RMSE": 1.0626111279, "MAPE": 5.0715369563, "WAPE": 1.2733968953 } }
Informer
SyntheticTS_noise0.3
0.3
96
336
true
100
37
1,852.3
{ "overall": { "MAE": 0.6640796696, "MSE": 0.6663128793, "RMSE": 0.8162799057, "MAPE": 1.1728736285, "WAPE": 1.0000960874 } }
Informer
SyntheticTS_noise0.3
0.3
96
720
true
100
17
1,413.8
{ "overall": { "MAE": 0.6399540231, "MSE": 0.6258795208, "RMSE": 0.7911254794, "MAPE": 3.9557083665, "WAPE": 0.9839552286 } }
Informer
SyntheticTS_noise0.3
0.3
96
96
true
100
26
701.7
{ "overall": { "MAE": 0.8096130431, "MSE": 1.059352693, "RMSE": 1.0292486084, "MAPE": 3.3659881038, "WAPE": 1.2270129704 } }
Informer
SyntheticTS_noise0.3
0.3
96
192
true
100
22
797.3
{ "overall": { "MAE": 1.0122539112, "MSE": 1.566268846, "RMSE": 1.2515066317, "MAPE": 6.8244381728, "WAPE": 1.544388681 } }
Informer
SyntheticTS_noise0.3
0.3
96
336
true
100
17
831
{ "overall": { "MAE": 0.7924468919, "MSE": 1.0351324631, "RMSE": 1.0174145937, "MAPE": 4.5986545032, "WAPE": 1.2055231071 } }
Informer
SyntheticTS_noise0.3
0.3
96
720
true
100
16
1,328.4
{ "overall": { "MAE": 0.6172596351, "MSE": 0.5868978055, "RMSE": 0.7660925587, "MAPE": 3.1487299215, "WAPE": 0.9422623695 } }
Informer
ETTh2
null
96
96
false
100
19
195
{ "overall": { "MAE": 2.1664804839, "MSE": 7.9032361885, "RMSE": 2.8112694935, "MAPE": 3.3229451814, "WAPE": 1.6468504426 } }
Informer
ETTh2
null
96
96
true
100
19
195.8
{ "overall": { "MAE": 1.8861950929, "MSE": 6.4671171065, "RMSE": 2.5430527213, "MAPE": 3.0047634742, "WAPE": 1.5613065659 } }
Informer
ETTh2
null
96
192
false
100
32
443.5
{ "overall": { "MAE": 1.4672531901, "MSE": 4.3314044635, "RMSE": 2.0812026305, "MAPE": 2.71308373, "WAPE": 1.2917987822 } }
Informer
ETTh2
null
96
192
true
100
30
469.1
{ "overall": { "MAE": 1.01874779, "MSE": 1.9819658153, "RMSE": 1.4078230718, "MAPE": 2.5404958117, "WAPE": 1.1301259012 } }
Informer
ETTh2
null
96
336
false
100
13
239.2
{ "overall": { "MAE": 0.9326729158, "MSE": 1.8787702109, "RMSE": 1.3706823892, "MAPE": 4.4263397435, "WAPE": 1.6045782321 } }
Informer
ETTh2
null
96
336
true
100
19
243.7
{ "overall": { "MAE": 0.6823454251, "MSE": 0.7670825844, "RMSE": 0.8758325086, "MAPE": 5.2996268287, "WAPE": 1.8409167962 } }
Informer
ETTh2
null
96
720
false
100
52
1,512.2
{ "overall": { "MAE": 0.8798077035, "MSE": 1.3776049212, "RMSE": 1.1737141606, "MAPE": 2.5954477625, "WAPE": 1.1147408021 } }
Informer
ETTh2
null
96
720
true
100
17
570.5
{ "overall": { "MAE": 1.1801368306, "MSE": 2.2130592049, "RMSE": 1.4876354418, "MAPE": 6.6309336539, "WAPE": 2.4583862405 } }
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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

DropoutTS reproduction bundle

Reproduction of DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting (arXiv:2601.21726, OpenReview 7sksHLUvhH) for the Hugging Face "Reproducing ICML 2026" challenge.

Logbook: https://huggingface.co/spaces/ancs21/repro-dropoutts Paper code: https://github.com/CityMind-Lab/DropoutTS

What's here

  • smoke_claim4.py — local check of Claim 4a/4b (param count = 2*num_features+2; eval-mode no-op).
  • modal_repro.py — Modal GPU pipeline: Informer +/- DropoutTS on synthetic sweep (Claim 1), ETTh2 (Claim 2), and the Selective Learning combo (Claim 5). Also times training for Claim 4c.
  • analyze_claim1.py — computes MSE/MAE improvements + the Claim 4c timing.
  • claim1_results.json, claim2_ETTh2_results.json, claim5_results.json — raw run outputs.
  • claim1_table.csv, claim1_plot.html — per-cell synthetic results + figure.

Outcome

Claims 3, 4a, 4b verified. Claim 2 (ETTh2) reproduced (up to +59% MSE). Claim 1 not reproduced under default config (mean -7.5%). Claim 4c contradicted (1.3x slower, not faster). Claim 5 contradicted (combo underperformed Selective Learning alone). See the logbook for details.

Ran on Modal A10G GPUs, single seed, default hyperparameters. ~51 training runs.
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Paper for ancs21/dropoutts-repro-bundle