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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
kappa_B: double
results: list<item: struct<eps: double, K: int64, kappa_B: double, measured_mean: double, measured_max: doubl (... 79 chars omitted)
child 0, item: struct<eps: double, K: int64, kappa_B: double, measured_mean: double, measured_max: double, bound_ti (... 67 chars omitted)
child 0, eps: double
child 1, K: int64
child 2, kappa_B: double
child 3, measured_mean: double
child 4, measured_max: double
child 5, bound_tight: double
child 6, bound_exp: double
child 7, tight_holds: bool
child 8, exp_holds: bool
control_results: list<item: struct<eps: double, K: int64, measured_max: double, bound_tight: double>>
child 0, item: struct<eps: double, K: int64, measured_max: double, bound_tight: double>
child 0, eps: double
child 1, K: int64
child 2, measured_max: double
child 3, bound_tight: double
per_seed: struct<no_straighten: list<item: struct<seed: int64, lambda_straighten: double, wall_clock_s: double (... 327 chars omitted)
child 0, no_straighten: list<item: struct<seed: int64, lambda_straighten: double, wall_clock_s: double, mean_cos_similarity: (... 95 chars omitted)
child 0, item: struct<seed: int64, lambda_straighten: double, wall_clock_s: double, mean_cos_similarity: double, eu (... 83 chars omitted)
child 0, seed: int64
child 1, lambda_straighten: double
child 2, wall_clock_s: double
child 3, mean_cos_similarity: double
child
...
rate: double
no_straighten: struct<wall_clock_s_mean: double, wall_clock_s_std: double, mean_cos_similarity_mean: double, mean_c (... 180 chars omitted)
child 0, wall_clock_s_mean: double
child 1, wall_clock_s_std: double
child 2, mean_cos_similarity_mean: double
child 3, mean_cos_similarity_std: double
child 4, euclid_vs_geodesic_corr_mean: double
child 5, euclid_vs_geodesic_corr_std: double
child 6, plan_success_rate_mean: double
child 7, plan_success_rate_std: double
child 8, n_seeds: int64
args: struct<grid: int64, n_traj_train: int64, n_traj_test: int64, T: int64, d: int64, steps: int64, batch (... 125 chars omitted)
child 0, grid: int64
child 1, n_traj_train: int64
child 2, n_traj_test: int64
child 3, T: int64
child 4, d: int64
child 5, steps: int64
child 6, batch: int64
child 7, lr: double
child 8, lambda_straighten: double
child 9, plan_episodes: int64
child 10, plan_horizon: int64
child 11, n_seeds: int64
child 12, push_repo: string
straighten: struct<wall_clock_s_mean: double, wall_clock_s_std: double, mean_cos_similarity_mean: double, mean_c (... 180 chars omitted)
child 0, wall_clock_s_mean: double
child 1, wall_clock_s_std: double
child 2, mean_cos_similarity_mean: double
child 3, mean_cos_similarity_std: double
child 4, euclid_vs_geodesic_corr_mean: double
child 5, euclid_vs_geodesic_corr_std: double
child 6, plan_success_rate_mean: double
child 7, plan_success_rate_std: double
child 8, n_seeds: int64
to
{'args': {'grid': Value('int64'), 'n_traj_train': Value('int64'), 'n_traj_test': Value('int64'), 'T': Value('int64'), 'd': Value('int64'), 'steps': Value('int64'), 'batch': Value('int64'), 'lr': Value('float64'), 'lambda_straighten': Value('float64'), 'plan_episodes': Value('int64'), 'plan_horizon': Value('int64'), 'n_seeds': Value('int64'), 'push_repo': Value('string')}, 'per_seed': {'no_straighten': List({'seed': Value('int64'), 'lambda_straighten': Value('float64'), 'wall_clock_s': Value('float64'), 'mean_cos_similarity': Value('float64'), 'euclid_vs_geodesic_corr': Value('float64'), 'n_distance_points': Value('int64'), 'plan_success_rate': Value('float64')}), 'straighten': List({'seed': Value('int64'), 'lambda_straighten': Value('float64'), 'wall_clock_s': Value('float64'), 'mean_cos_similarity': Value('float64'), 'euclid_vs_geodesic_corr': Value('float64'), 'n_distance_points': Value('int64'), 'plan_success_rate': Value('float64')})}, 'no_straighten': {'wall_clock_s_mean': Value('float64'), 'wall_clock_s_std': Value('float64'), 'mean_cos_similarity_mean': Value('float64'), 'mean_cos_similarity_std': Value('float64'), 'euclid_vs_geodesic_corr_mean': Value('float64'), 'euclid_vs_geodesic_corr_std': Value('float64'), 'plan_success_rate_mean': Value('float64'), 'plan_success_rate_std': Value('float64'), 'n_seeds': Value('int64')}, 'straighten': {'wall_clock_s_mean': Value('float64'), 'wall_clock_s_std': Value('float64'), 'mean_cos_similarity_mean': Value('float64'), 'mean_cos_similarity_std': Value('float64'), 'euclid_vs_geodesic_corr_mean': Value('float64'), 'euclid_vs_geodesic_corr_std': Value('float64'), 'plan_success_rate_mean': Value('float64'), 'plan_success_rate_std': Value('float64'), 'n_seeds': Value('int64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_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
kappa_B: double
results: list<item: struct<eps: double, K: int64, kappa_B: double, measured_mean: double, measured_max: doubl (... 79 chars omitted)
child 0, item: struct<eps: double, K: int64, kappa_B: double, measured_mean: double, measured_max: double, bound_ti (... 67 chars omitted)
child 0, eps: double
child 1, K: int64
child 2, kappa_B: double
child 3, measured_mean: double
child 4, measured_max: double
child 5, bound_tight: double
child 6, bound_exp: double
child 7, tight_holds: bool
child 8, exp_holds: bool
control_results: list<item: struct<eps: double, K: int64, measured_max: double, bound_tight: double>>
child 0, item: struct<eps: double, K: int64, measured_max: double, bound_tight: double>
child 0, eps: double
child 1, K: int64
child 2, measured_max: double
child 3, bound_tight: double
per_seed: struct<no_straighten: list<item: struct<seed: int64, lambda_straighten: double, wall_clock_s: double (... 327 chars omitted)
child 0, no_straighten: list<item: struct<seed: int64, lambda_straighten: double, wall_clock_s: double, mean_cos_similarity: (... 95 chars omitted)
child 0, item: struct<seed: int64, lambda_straighten: double, wall_clock_s: double, mean_cos_similarity: double, eu (... 83 chars omitted)
child 0, seed: int64
child 1, lambda_straighten: double
child 2, wall_clock_s: double
child 3, mean_cos_similarity: double
child
...
rate: double
no_straighten: struct<wall_clock_s_mean: double, wall_clock_s_std: double, mean_cos_similarity_mean: double, mean_c (... 180 chars omitted)
child 0, wall_clock_s_mean: double
child 1, wall_clock_s_std: double
child 2, mean_cos_similarity_mean: double
child 3, mean_cos_similarity_std: double
child 4, euclid_vs_geodesic_corr_mean: double
child 5, euclid_vs_geodesic_corr_std: double
child 6, plan_success_rate_mean: double
child 7, plan_success_rate_std: double
child 8, n_seeds: int64
args: struct<grid: int64, n_traj_train: int64, n_traj_test: int64, T: int64, d: int64, steps: int64, batch (... 125 chars omitted)
child 0, grid: int64
child 1, n_traj_train: int64
child 2, n_traj_test: int64
child 3, T: int64
child 4, d: int64
child 5, steps: int64
child 6, batch: int64
child 7, lr: double
child 8, lambda_straighten: double
child 9, plan_episodes: int64
child 10, plan_horizon: int64
child 11, n_seeds: int64
child 12, push_repo: string
straighten: struct<wall_clock_s_mean: double, wall_clock_s_std: double, mean_cos_similarity_mean: double, mean_c (... 180 chars omitted)
child 0, wall_clock_s_mean: double
child 1, wall_clock_s_std: double
child 2, mean_cos_similarity_mean: double
child 3, mean_cos_similarity_std: double
child 4, euclid_vs_geodesic_corr_mean: double
child 5, euclid_vs_geodesic_corr_std: double
child 6, plan_success_rate_mean: double
child 7, plan_success_rate_std: double
child 8, n_seeds: int64
to
{'args': {'grid': Value('int64'), 'n_traj_train': Value('int64'), 'n_traj_test': Value('int64'), 'T': Value('int64'), 'd': Value('int64'), 'steps': Value('int64'), 'batch': Value('int64'), 'lr': Value('float64'), 'lambda_straighten': Value('float64'), 'plan_episodes': Value('int64'), 'plan_horizon': Value('int64'), 'n_seeds': Value('int64'), 'push_repo': Value('string')}, 'per_seed': {'no_straighten': List({'seed': Value('int64'), 'lambda_straighten': Value('float64'), 'wall_clock_s': Value('float64'), 'mean_cos_similarity': Value('float64'), 'euclid_vs_geodesic_corr': Value('float64'), 'n_distance_points': Value('int64'), 'plan_success_rate': Value('float64')}), 'straighten': List({'seed': Value('int64'), 'lambda_straighten': Value('float64'), 'wall_clock_s': Value('float64'), 'mean_cos_similarity': Value('float64'), 'euclid_vs_geodesic_corr': Value('float64'), 'n_distance_points': Value('int64'), 'plan_success_rate': Value('float64')})}, 'no_straighten': {'wall_clock_s_mean': Value('float64'), 'wall_clock_s_std': Value('float64'), 'mean_cos_similarity_mean': Value('float64'), 'mean_cos_similarity_std': Value('float64'), 'euclid_vs_geodesic_corr_mean': Value('float64'), 'euclid_vs_geodesic_corr_std': Value('float64'), 'plan_success_rate_mean': Value('float64'), 'plan_success_rate_std': Value('float64'), 'n_seeds': Value('int64')}, 'straighten': {'wall_clock_s_mean': Value('float64'), 'wall_clock_s_std': Value('float64'), 'mean_cos_similarity_mean': Value('float64'), 'mean_cos_similarity_std': Value('float64'), 'euclid_vs_geodesic_corr_mean': Value('float64'), 'euclid_vs_geodesic_corr_std': Value('float64'), 'plan_success_rate_mean': Value('float64'), 'plan_success_rate_std': Value('float64'), 'n_seeds': Value('int64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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