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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 2 new columns ({'train_loss', 'update_step'}) and 2 missing columns ({'time_step', 'mean_nRMSE'}).
This happened while the csv dataset builder was generating data using
hf://datasets/ceyron/apebench-paper-melted-export/train_dataset_size_ablation/train_loss.csv (at revision 2904ecdc0df8ee11257572b0192612f286507ed9)
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
seed: int64
scenario: string
task: string
net: string
train: string
scenario_kwargs: string
update_step: int64
train_loss: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1178
to
{'seed': Value(dtype='int64', id=None), 'scenario': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'net': Value(dtype='string', id=None), 'train': Value(dtype='string', id=None), 'scenario_kwargs': Value(dtype='string', id=None), 'time_step': Value(dtype='int64', id=None), 'mean_nRMSE': Value(dtype='float64', id=None)}
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 1577, 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 1191, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, 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 2 new columns ({'train_loss', 'update_step'}) and 2 missing columns ({'time_step', 'mean_nRMSE'}).
This happened while the csv dataset builder was generating data using
hf://datasets/ceyron/apebench-paper-melted-export/train_dataset_size_ablation/train_loss.csv (at revision 2904ecdc0df8ee11257572b0192612f286507ed9)
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.
seed int64 | scenario string | task string | net string | train string | scenario_kwargs string | time_step int64 | mean_nRMSE float64 |
|---|---|---|---|---|---|---|---|
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 1 | 0.016633 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 2 | 0.029927 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 3 | 0.041323 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 4 | 0.05154 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 5 | 0.060981 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 6 | 0.06986 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 7 | 0.0783 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 8 | 0.086362 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 9 | 0.094087 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 10 | 0.101543 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 11 | 0.108775 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 12 | 0.11582 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 13 | 0.122709 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 14 | 0.129455 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 15 | 0.136075 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 16 | 0.14257 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 17 | 0.148937 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 18 | 0.155154 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 19 | 0.16122 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 20 | 0.167147 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 21 | 0.172953 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 22 | 0.178646 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 23 | 0.184233 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 24 | 0.18971 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 25 | 0.195067 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 26 | 0.200305 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 27 | 0.205435 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 28 | 0.210467 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 29 | 0.215414 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 30 | 0.220289 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 31 | 0.225097 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 32 | 0.229851 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 33 | 0.234545 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 34 | 0.23918 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 35 | 0.243765 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 36 | 0.248281 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 37 | 0.252735 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 38 | 0.257131 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 39 | 0.261487 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 40 | 0.265804 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 41 | 0.270079 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 42 | 0.274312 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 43 | 0.278514 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 44 | 0.282688 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 45 | 0.286835 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 46 | 0.29094 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 47 | 0.294989 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 48 | 0.298968 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 49 | 0.30289 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 50 | 0.30677 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 51 | 0.310618 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 52 | 0.31444 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 53 | 0.318235 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 54 | 0.321993 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 55 | 0.325714 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 56 | 0.329405 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 57 | 0.333071 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 58 | 0.33672 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 59 | 0.340347 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 60 | 0.343957 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 61 | 0.347559 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 62 | 0.351151 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 63 | 0.354719 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 64 | 0.358257 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 65 | 0.361758 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 66 | 0.365218 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 67 | 0.368641 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 68 | 0.372021 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 69 | 0.375367 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 70 | 0.378683 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 71 | 0.381971 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 72 | 0.385235 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 73 | 0.388478 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 74 | 0.391701 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 75 | 0.394899 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 76 | 0.398063 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 77 | 0.401191 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 78 | 0.404284 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 79 | 0.407345 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 80 | 0.410381 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 81 | 0.413398 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 82 | 0.416402 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 83 | 0.419397 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 84 | 0.422388 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 85 | 0.425376 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 86 | 0.428368 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 87 | 0.431373 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 88 | 0.434392 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 89 | 0.437424 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 90 | 0.440473 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 91 | 0.44353 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 92 | 0.446583 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 93 | 0.449624 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 94 | 0.452653 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 95 | 0.455669 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 96 | 0.45867 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 97 | 0.461658 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 98 | 0.464628 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 99 | 0.467582 |
0 | 1d_diff_adv | predict | Conv;34;10;relu | one | {'num_train_samples': 1, 'train_temporal_horizon': 1} | 100 | 0.470517 |
End of preview.