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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
match_ids: list<item: string>
child 0, item: string
features: list<item: struct<elo_p1_surface: double, elo_p2_surface: double, elo_diff_surface: double, win_pct_ (... 644 chars omitted)
child 0, item: struct<elo_p1_surface: double, elo_p2_surface: double, elo_diff_surface: double, win_pct_last10_p1: (... 632 chars omitted)
child 0, elo_p1_surface: double
child 1, elo_p2_surface: double
child 2, elo_diff_surface: double
child 3, win_pct_last10_p1: double
child 4, win_pct_last10_p2: double
child 5, win_pct_last25_surface_p1: double
child 6, win_pct_last25_surface_p2: double
child 7, first_serve_win_pct_p1: double
child 8, first_serve_win_pct_p2: double
child 9, second_serve_win_pct_p1: double
child 10, second_serve_win_pct_p2: double
child 11, bp_saved_pct_p1: double
child 12, bp_saved_pct_p2: double
child 13, bp_converted_pct_p1: double
child 14, bp_converted_pct_p2: double
child 15, h2h_p1_wins: int64
child 16, h2h_p2_wins: int64
child 17, h2h_recency_days: int64
child 18, fatigue_matches_7d_p1: int64
child 19, fatigue_matches_7d_p2: int64
child 20, fatigue_sets_14d_p1: int64
child 21, fatigue_sets_14d_p2: int64
child 22, rank_p1: int64
child 23, rank_p2: int64
child 24, rank_diff: int64
child 25, tournament_level: string
child 26, best_of: int64
child 27, surface: string
expected_p1_proba: list<item: dou
...
<item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actua (... 11 chars omitted)
child 0, item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actual: double>
child 0, bin_lower: double
child 1, bin_upper: double
child 2, n: int64
child 3, mean_predicted: double
child 4, mean_actual: double
child 8, market_n: int64
child 9, market_metrics: struct<n: int64, brier: double, log_loss: double, accuracy: double, accuracy_by_rank_gap: struct<>, (... 130 chars omitted)
child 0, n: int64
child 1, brier: double
child 2, log_loss: double
child 3, accuracy: double
child 4, accuracy_by_rank_gap: struct<>
child 5, calibration_bins: list<item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actua (... 11 chars omitted)
child 0, item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actual: double>
child 0, bin_lower: double
child 1, bin_upper: double
child 2, n: int64
child 3, mean_predicted: double
child 4, mean_actual: double
git_commit: string
training_date: timestamp[s]
model_type: string
to
{'tour': Value('string'), 'model_type': Value('string'), 'training_date': Value('timestamp[s]'), 'data_range': List(Value('timestamp[s]')), 'features': List(Value('string')), 'calibration_method': Value('string'), 'git_commit': Value('string'), 'walk_forward': {'n_folds': Value('int64'), 'metrics_pre_calibration_aggregate': {'n_validate_total': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64')}, 'metrics_post_calibration_aggregate': {'n_validate_total': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64')}, 'per_fold': List({'fold_index': Value('int64'), 'validate_year': Value('int64'), 'n_train': Value('int64'), 'n_calibrate': Value('int64'), 'n_validate': Value('int64'), 'calibration_method': Value('string'), 'metrics_pre_calibration': {'n': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64'), 'accuracy_by_rank_gap': {'0_50': Value('float64'), '50_200': Value('float64'), '200_inf': Value('float64')}, 'calibration_bins': List({'bin_lower': Value('float64'), 'bin_upper': Value('float64'), 'n': Value('int64'), 'mean_predicted': Value('float64'), 'mean_actual': Value('float64')})}, 'metrics_post_calibration': {'n': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64'), 'accuracy_by_rank_gap': {'0_50': Value('float64'), '50_200': Value('float64'), '200_inf': Value('float64')}, 'calibration_bins': List({'bin_lower': Value('float64'), 'bin_upper': Value('float64'), 'n': Value('int64'), 'mean_predicted': Value('float64'), 'mean_actual': Value('float64')})}, 'market_n': Value('int64'), 'market_metrics': {'n': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64'), 'accuracy_by_rank_gap': {}, 'calibration_bins': List({'bin_lower': Value('float64'), 'bin_upper': Value('float64'), 'n': Value('int64'), 'mean_predicted': Value('float64'), 'mean_actual': Value('float64')})}})}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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
match_ids: list<item: string>
child 0, item: string
features: list<item: struct<elo_p1_surface: double, elo_p2_surface: double, elo_diff_surface: double, win_pct_ (... 644 chars omitted)
child 0, item: struct<elo_p1_surface: double, elo_p2_surface: double, elo_diff_surface: double, win_pct_last10_p1: (... 632 chars omitted)
child 0, elo_p1_surface: double
child 1, elo_p2_surface: double
child 2, elo_diff_surface: double
child 3, win_pct_last10_p1: double
child 4, win_pct_last10_p2: double
child 5, win_pct_last25_surface_p1: double
child 6, win_pct_last25_surface_p2: double
child 7, first_serve_win_pct_p1: double
child 8, first_serve_win_pct_p2: double
child 9, second_serve_win_pct_p1: double
child 10, second_serve_win_pct_p2: double
child 11, bp_saved_pct_p1: double
child 12, bp_saved_pct_p2: double
child 13, bp_converted_pct_p1: double
child 14, bp_converted_pct_p2: double
child 15, h2h_p1_wins: int64
child 16, h2h_p2_wins: int64
child 17, h2h_recency_days: int64
child 18, fatigue_matches_7d_p1: int64
child 19, fatigue_matches_7d_p2: int64
child 20, fatigue_sets_14d_p1: int64
child 21, fatigue_sets_14d_p2: int64
child 22, rank_p1: int64
child 23, rank_p2: int64
child 24, rank_diff: int64
child 25, tournament_level: string
child 26, best_of: int64
child 27, surface: string
expected_p1_proba: list<item: dou
...
<item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actua (... 11 chars omitted)
child 0, item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actual: double>
child 0, bin_lower: double
child 1, bin_upper: double
child 2, n: int64
child 3, mean_predicted: double
child 4, mean_actual: double
child 8, market_n: int64
child 9, market_metrics: struct<n: int64, brier: double, log_loss: double, accuracy: double, accuracy_by_rank_gap: struct<>, (... 130 chars omitted)
child 0, n: int64
child 1, brier: double
child 2, log_loss: double
child 3, accuracy: double
child 4, accuracy_by_rank_gap: struct<>
child 5, calibration_bins: list<item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actua (... 11 chars omitted)
child 0, item: struct<bin_lower: double, bin_upper: double, n: int64, mean_predicted: double, mean_actual: double>
child 0, bin_lower: double
child 1, bin_upper: double
child 2, n: int64
child 3, mean_predicted: double
child 4, mean_actual: double
git_commit: string
training_date: timestamp[s]
model_type: string
to
{'tour': Value('string'), 'model_type': Value('string'), 'training_date': Value('timestamp[s]'), 'data_range': List(Value('timestamp[s]')), 'features': List(Value('string')), 'calibration_method': Value('string'), 'git_commit': Value('string'), 'walk_forward': {'n_folds': Value('int64'), 'metrics_pre_calibration_aggregate': {'n_validate_total': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64')}, 'metrics_post_calibration_aggregate': {'n_validate_total': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64')}, 'per_fold': List({'fold_index': Value('int64'), 'validate_year': Value('int64'), 'n_train': Value('int64'), 'n_calibrate': Value('int64'), 'n_validate': Value('int64'), 'calibration_method': Value('string'), 'metrics_pre_calibration': {'n': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64'), 'accuracy_by_rank_gap': {'0_50': Value('float64'), '50_200': Value('float64'), '200_inf': Value('float64')}, 'calibration_bins': List({'bin_lower': Value('float64'), 'bin_upper': Value('float64'), 'n': Value('int64'), 'mean_predicted': Value('float64'), 'mean_actual': Value('float64')})}, 'metrics_post_calibration': {'n': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64'), 'accuracy_by_rank_gap': {'0_50': Value('float64'), '50_200': Value('float64'), '200_inf': Value('float64')}, 'calibration_bins': List({'bin_lower': Value('float64'), 'bin_upper': Value('float64'), 'n': Value('int64'), 'mean_predicted': Value('float64'), 'mean_actual': Value('float64')})}, 'market_n': Value('int64'), 'market_metrics': {'n': Value('int64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('float64'), 'accuracy_by_rank_gap': {}, 'calibration_bins': List({'bin_lower': Value('float64'), 'bin_upper': Value('float64'), 'n': Value('int64'), 'mean_predicted': Value('float64'), 'mean_actual': Value('float64')})}})}}
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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