Datasets:

---

curveball-steering

/

kpca_models

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curveball-steering 1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
cell: string
components: list<item: int64>
  child 0, item: int64
source_candidate_index: string
source_candidate_index_sha256: string
source_shortlist: string
source_shortlist_sha256: string
candidates: list<item: struct<cell: string, arm: string, parent_candidate: string, config: struct<kernel: string (... 647 chars omitted)
  child 0, item: struct<cell: string, arm: string, parent_candidate: string, config: struct<kernel: string, degree: i (... 635 chars omitted)
      child 0, cell: string
      child 1, arm: string
      child 2, parent_candidate: string
      child 3, config: struct<kernel: string, degree: int64, n_components: int64, alpha: double, gamma: double, coef0: doub (... 29 chars omitted)
          child 0, kernel: string
          child 1, degree: int64
          child 2, n_components: int64
          child 3, alpha: double
          child 4, gamma: double
          child 5, coef0: double
          child 6, quadratic_weight: double
      child 4, activation_normalization: struct<mode: string, scale: double, scale_source: string, normalized_training_mean_norm: double, res (... 38 chars omitted)
          child 0, mode: string
          child 1, scale: double
          child 2, scale_source: string
          child 3, normalized_training_mean_norm: double
          child 4, restore_at: string
          child 5, gamma_policy: string
      child 5, computation_dtype: string
      child 6, activation_path: string
      child 7, activation_sha256: string
      child 8, fit_indices_count: int64
      child 9, validation_indices_count: int64
      child 10, retained_components: int64
      child 11, fixed_validation_nmse: double
      child 12, fixed_validation_inverse_cosine: double
      child 13, pca_same_rank_validation_nmse: double
      child 14, pca_same_rank_validation_inverse_cosine: double
      child 15, kpca_sha256: string
      child 16, implementation: string
arms: list<item: struct<arm: string, candidate: string, parent_arm: string, component_rank: int64, even_sa (... 78 chars omitted)
  child 0, item: struct<arm: string, candidate: string, parent_arm: string, component_rank: int64, even_saturation: d (... 66 chars omitted)
      child 0, arm: string
      child 1, candidate: string
      child 2, parent_arm: string
      child 3, component_rank: int64
      child 4, even_saturation: double
      child 5, decoded_even_weight: double
      child 6, strengths: list<item: double>
          child 0, item: double
design: struct<ranks: list<item: int64>, hypotheses: list<item: string>, q0_eligible: bool, zero_decoded_qua (... 42 chars omitted)
  child 0, ranks: list<item: int64>
      child 0, item: int64
  child 1, hypotheses: list<item: string>
      child 0, item: string
  child 2, q0_eligible: bool
  child 3, zero_decoded_quadratic_eligible: bool
  child 4, linear_tuned: bool
linear: struct<strengths: list<item: double>>
  child 0, strengths: list<item: double>
      child 0, item: double
to
{'arms': List({'arm': Value('string'), 'candidate': Value('string'), 'parent_arm': Value('string'), 'component_rank': Value('int64'), 'even_saturation': Value('float64'), 'decoded_even_weight': Value('float64'), 'strengths': List(Value('float64'))}), 'linear': {'strengths': List(Value('float64'))}, 'design': {'ranks': List(Value('int64')), 'hypotheses': List(Value('string')), 'q0_eligible': Value('bool'), 'zero_decoded_quadratic_eligible': Value('bool'), 'linear_tuned': Value('bool')}}
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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              cell: string
              components: list<item: int64>
                child 0, item: int64
              source_candidate_index: string
              source_candidate_index_sha256: string
              source_shortlist: string
              source_shortlist_sha256: string
              candidates: list<item: struct<cell: string, arm: string, parent_candidate: string, config: struct<kernel: string (... 647 chars omitted)
                child 0, item: struct<cell: string, arm: string, parent_candidate: string, config: struct<kernel: string, degree: i (... 635 chars omitted)
                    child 0, cell: string
                    child 1, arm: string
                    child 2, parent_candidate: string
                    child 3, config: struct<kernel: string, degree: int64, n_components: int64, alpha: double, gamma: double, coef0: doub (... 29 chars omitted)
                        child 0, kernel: string
                        child 1, degree: int64
                        child 2, n_components: int64
                        child 3, alpha: double
                        child 4, gamma: double
                        child 5, coef0: double
                        child 6, quadratic_weight: double
                    child 4, activation_normalization: struct<mode: string, scale: double, scale_source: string, normalized_training_mean_norm: double, res (... 38 chars omitted)
                        child 0, mode: string
                        child 1, scale: double
                        child 2, scale_source: string
                        child 3, normalized_training_mean_norm: double
                        child 4, restore_at: string
                        child 5, gamma_policy: string
                    child 5, computation_dtype: string
                    child 6, activation_path: string
                    child 7, activation_sha256: string
                    child 8, fit_indices_count: int64
                    child 9, validation_indices_count: int64
                    child 10, retained_components: int64
                    child 11, fixed_validation_nmse: double
                    child 12, fixed_validation_inverse_cosine: double
                    child 13, pca_same_rank_validation_nmse: double
                    child 14, pca_same_rank_validation_inverse_cosine: double
                    child 15, kpca_sha256: string
                    child 16, implementation: string
              arms: list<item: struct<arm: string, candidate: string, parent_arm: string, component_rank: int64, even_sa (... 78 chars omitted)
                child 0, item: struct<arm: string, candidate: string, parent_arm: string, component_rank: int64, even_saturation: d (... 66 chars omitted)
                    child 0, arm: string
                    child 1, candidate: string
                    child 2, parent_arm: string
                    child 3, component_rank: int64
                    child 4, even_saturation: double
                    child 5, decoded_even_weight: double
                    child 6, strengths: list<item: double>
                        child 0, item: double
              design: struct<ranks: list<item: int64>, hypotheses: list<item: string>, q0_eligible: bool, zero_decoded_qua (... 42 chars omitted)
                child 0, ranks: list<item: int64>
                    child 0, item: int64
                child 1, hypotheses: list<item: string>
                    child 0, item: string
                child 2, q0_eligible: bool
                child 3, zero_decoded_quadratic_eligible: bool
                child 4, linear_tuned: bool
              linear: struct<strengths: list<item: double>>
                child 0, strengths: list<item: double>
                    child 0, item: double
              to
              {'arms': List({'arm': Value('string'), 'candidate': Value('string'), 'parent_arm': Value('string'), 'component_rank': Value('int64'), 'even_saturation': Value('float64'), 'decoded_even_weight': Value('float64'), 'strengths': List(Value('float64'))}), 'linear': {'strengths': List(Value('float64'))}, 'design': {'ranks': List(Value('int64')), 'hypotheses': List(Value('string')), 'q0_eligible': Value('bool'), 'zero_decoded_quadratic_eligible': Value('bool'), 'linear_tuned': Value('bool')}}
              because column names don't match