Datasets:

---

olukotunjosh

/

cdcl-results

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
phase: string
evaluation_method: string
optimal_threshold: double
metrics: struct<accuracy: double, precision: double, recall: double, f1_score: double, auc_roc: double>
  child 0, accuracy: double
  child 1, precision: double
  child 2, recall: double
  child 3, f1_score: double
  child 4, auc_roc: double
confusion_matrix: struct<true_positives: int64, true_negatives: int64, false_positives: int64, false_negatives: int64>
  child 0, true_positives: int64
  child 1, true_negatives: int64
  child 2, false_positives: int64
  child 3, false_negatives: int64
similarity_stats: struct<positive_mean: double, positive_std: double, negative_mean: double, negative_std: double, sep (... 16 chars omitted)
  child 0, positive_mean: double
  child 1, positive_std: double
  child 2, negative_mean: double
  child 3, negative_std: double
  child 4, separation: double
dataset_size: int64
checkpoint: string
dataset_path: string
per_patient: struct<topics: int64, ndcg_gains: struct<0: int64, 1: int64, 2: int64>, per_topic: struct<2021_14: s (... 5112 chars omitted)
  child 0, topics: int64
  child 1, ndcg_gains: struct<0: int64, 1: int64, 2: int64>
      child 0, 0: int64
      child 1, 1: int64
      child 2, 2: int64
  child 2, per_topic: struct<2021_14: struct<eligible_map: double, graded_ndcg_at_10: double, eligible_auc: double, eligib (... 4655 chars omitted)
      child 0, 2021_14: struct<eligible_map: double, graded_ndcg_at_10: double, eligible_auc: double, eligible_vs_not_releva (... 79 
...
eligible_vs_not_r (... 79 chars omitted)
      child 0, eligible_auc: double
      child 1, eligible_map: double
      child 2, eligible_vs_ineligible: double
      child 3, eligible_vs_not_relevant: double
      child 4, graded_ndcg_at_10: double
      child 5, ineligible_vs_not_relevant: double
  child 4, n_topics: struct<eligible_auc: int64, eligible_map: int64, eligible_vs_ineligible: int64, eligible_vs_not_rele (... 73 chars omitted)
      child 0, eligible_auc: int64
      child 1, eligible_map: int64
      child 2, eligible_vs_ineligible: int64
      child 3, eligible_vs_not_relevant: int64
      child 4, graded_ndcg_at_10: int64
      child 5, ineligible_vs_not_relevant: int64
per_grade: struct<counts: struct<not_relevant: int64, ineligible: int64, eligible: int64>, mean_similarity: str (... 188 chars omitted)
  child 0, counts: struct<not_relevant: int64, ineligible: int64, eligible: int64>
      child 0, not_relevant: int64
      child 1, ineligible: int64
      child 2, eligible: int64
  child 1, mean_similarity: struct<not_relevant: double, ineligible: double, eligible: double>
      child 0, not_relevant: double
      child 1, ineligible: double
      child 2, eligible: double
  child 2, pairwise_auc: struct<eligible_vs_not_relevant: double, eligible_vs_ineligible: double, ineligible_vs_not_relevant: (... 8 chars omitted)
      child 0, eligible_vs_not_relevant: double
      child 1, eligible_vs_ineligible: double
      child 2, ineligible_vs_not_relevant: double
to
{'phase': Value('string'), 'evaluation_method': Value('string'), 'optimal_threshold': Value('float64'), 'metrics': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1_score': Value('float64'), 'auc_roc': Value('float64')}, 'confusion_matrix': {'true_positives': Value('int64'), 'true_negatives': Value('int64'), 'false_positives': Value('int64'), 'false_negatives': Value('int64')}, 'similarity_stats': {'positive_mean': Value('float64'), 'positive_std': Value('float64'), 'negative_mean': Value('float64'), 'negative_std': Value('float64'), 'separation': Value('float64')}, 'dataset_size': Value('int64'), 'checkpoint': Value('string'), 'dataset_path': Value('string'), 'per_grade': {'counts': {'not_relevant': Value('int64'), 'ineligible': Value('int64'), 'eligible': Value('int64')}, 'mean_similarity': {'not_relevant': Value('float64'), 'ineligible': Value('float64'), 'eligible': Value('float64')}, 'pairwise_auc': {'eligible_vs_not_relevant': Value('float64'), 'eligible_vs_ineligible': Value('float64'), 'ineligible_vs_not_relevant': 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 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
              phase: string
              evaluation_method: string
              optimal_threshold: double
              metrics: struct<accuracy: double, precision: double, recall: double, f1_score: double, auc_roc: double>
                child 0, accuracy: double
                child 1, precision: double
                child 2, recall: double
                child 3, f1_score: double
                child 4, auc_roc: double
              confusion_matrix: struct<true_positives: int64, true_negatives: int64, false_positives: int64, false_negatives: int64>
                child 0, true_positives: int64
                child 1, true_negatives: int64
                child 2, false_positives: int64
                child 3, false_negatives: int64
              similarity_stats: struct<positive_mean: double, positive_std: double, negative_mean: double, negative_std: double, sep (... 16 chars omitted)
                child 0, positive_mean: double
                child 1, positive_std: double
                child 2, negative_mean: double
                child 3, negative_std: double
                child 4, separation: double
              dataset_size: int64
              checkpoint: string
              dataset_path: string
              per_patient: struct<topics: int64, ndcg_gains: struct<0: int64, 1: int64, 2: int64>, per_topic: struct<2021_14: s (... 5112 chars omitted)
                child 0, topics: int64
                child 1, ndcg_gains: struct<0: int64, 1: int64, 2: int64>
                    child 0, 0: int64
                    child 1, 1: int64
                    child 2, 2: int64
                child 2, per_topic: struct<2021_14: struct<eligible_map: double, graded_ndcg_at_10: double, eligible_auc: double, eligib (... 4655 chars omitted)
                    child 0, 2021_14: struct<eligible_map: double, graded_ndcg_at_10: double, eligible_auc: double, eligible_vs_not_releva (... 79 
              ...
              eligible_vs_not_r (... 79 chars omitted)
                    child 0, eligible_auc: double
                    child 1, eligible_map: double
                    child 2, eligible_vs_ineligible: double
                    child 3, eligible_vs_not_relevant: double
                    child 4, graded_ndcg_at_10: double
                    child 5, ineligible_vs_not_relevant: double
                child 4, n_topics: struct<eligible_auc: int64, eligible_map: int64, eligible_vs_ineligible: int64, eligible_vs_not_rele (... 73 chars omitted)
                    child 0, eligible_auc: int64
                    child 1, eligible_map: int64
                    child 2, eligible_vs_ineligible: int64
                    child 3, eligible_vs_not_relevant: int64
                    child 4, graded_ndcg_at_10: int64
                    child 5, ineligible_vs_not_relevant: int64
              per_grade: struct<counts: struct<not_relevant: int64, ineligible: int64, eligible: int64>, mean_similarity: str (... 188 chars omitted)
                child 0, counts: struct<not_relevant: int64, ineligible: int64, eligible: int64>
                    child 0, not_relevant: int64
                    child 1, ineligible: int64
                    child 2, eligible: int64
                child 1, mean_similarity: struct<not_relevant: double, ineligible: double, eligible: double>
                    child 0, not_relevant: double
                    child 1, ineligible: double
                    child 2, eligible: double
                child 2, pairwise_auc: struct<eligible_vs_not_relevant: double, eligible_vs_ineligible: double, ineligible_vs_not_relevant: (... 8 chars omitted)
                    child 0, eligible_vs_not_relevant: double
                    child 1, eligible_vs_ineligible: double
                    child 2, ineligible_vs_not_relevant: double
              to
              {'phase': Value('string'), 'evaluation_method': Value('string'), 'optimal_threshold': Value('float64'), 'metrics': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1_score': Value('float64'), 'auc_roc': Value('float64')}, 'confusion_matrix': {'true_positives': Value('int64'), 'true_negatives': Value('int64'), 'false_positives': Value('int64'), 'false_negatives': Value('int64')}, 'similarity_stats': {'positive_mean': Value('float64'), 'positive_std': Value('float64'), 'negative_mean': Value('float64'), 'negative_std': Value('float64'), 'separation': Value('float64')}, 'dataset_size': Value('int64'), 'checkpoint': Value('string'), 'dataset_path': Value('string'), 'per_grade': {'counts': {'not_relevant': Value('int64'), 'ineligible': Value('int64'), 'eligible': Value('int64')}, 'mean_similarity': {'not_relevant': Value('float64'), 'ineligible': Value('float64'), 'eligible': Value('float64')}, 'pairwise_auc': {'eligible_vs_not_relevant': Value('float64'), 'eligible_vs_ineligible': Value('float64'), 'ineligible_vs_not_relevant': Value('float64')}}}
              because column names don't match