Datasets:

---

MilaAI4Math

/

OpenExpDatasets

like 0

Follow

MILA-AI4Math 4

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
idx: int64
candidate: string
sources: list<item: null>
  child 0, item: null
proof: struct<solved: bool, mcts_iters: int64, tree_size: int64, proof_text: string, tree: list<item: struc (... 282 chars omitted)
  child 0, solved: bool
  child 1, mcts_iters: int64
  child 2, tree_size: int64
  child 3, proof_text: string
  child 4, tree: list<item: struct<id: int64, state_str: string, visits: int64, reward: double, value_estimate: doubl (... 197 chars omitted)
      child 0, item: struct<id: int64, state_str: string, visits: int64, reward: double, value_estimate: double, prior_po (... 185 chars omitted)
          child 0, id: int64
          child 1, state_str: string
          child 2, visits: int64
          child 3, reward: double
          child 4, value_estimate: double
          child 5, prior_policy: double
          child 6, is_dead: bool
          child 7, is_solved: bool
          child 8, is_terminal: bool
          child 9, is_conjunctive: bool
          child 10, solution_action: string
          child 11, parent_id: int64
          child 12, parent_action: string
          child 13, children_ids: list<item: int64>
              child 0, item: int64
hindsight: list<item: struct<goal: string, statement: string, solved: bool, tree_size: int64, tree: list<item:  (... 286 chars omitted)
  child 0, item: struct<goal: string, statement: string, solved: bool, tree_size: int64, tree: list<item: struct<id:  (... 274 chars omitted)
      child 0, goal: string
      child 1, s
...
idate: string, level: string, primary_ancestor: string, all_ancestors: list<item: string> (... 282 chars omitted)
      child 0, candidate: string
      child 1, level: string
      child 2, primary_ancestor: string
      child 3, all_ancestors: list<item: string>
          child 0, item: string
      child 4, first_op: string
      child 5, op_family_coarse: string
      child 6, op_family_fine: string
      child 7, n_unique_ops: int64
      child 8, also_appears_at: list<item: null>
          child 0, item: null
      child 9, cand_id: string
      child 10, iteration: int64
      child 11, minimo_solved: bool
      child 12, minimo_proof_n_actions: int64
      child 13, minimo_logprob: double
      child 14, peano_valid: bool
      child 15, sources: list<item: null>
          child 0, item: null
metadata: struct<parent_dataset: string, source_log_path: string, source_run_dir: string, source_outcomes_file (... 176 chars omitted)
  child 0, parent_dataset: string
  child 1, source_log_path: string
  child 2, source_run_dir: string
  child 3, source_outcomes_files: list<item: string>
      child 0, item: string
  child 4, filter: string
  child 5, n_input: int64
  child 6, n_kept: int64
  child 7, per_iter_kept: struct<Min_it0: int64, Min_it1: int64, Min_it2: int64, Min_it3: int64>
      child 0, Min_it0: int64
      child 1, Min_it1: int64
      child 2, Min_it2: int64
      child 3, Min_it3: int64
  child 8, shape_note: string
seeds: list<item: null>
  child 0, item: null
to
{'seeds': List(Value('null')), 'seed_names': Json(decode=True), 'candidates': List({'candidate': Value('string'), 'level': Value('string'), 'primary_ancestor': Value('string'), 'all_ancestors': List(Value('string')), 'first_op': Value('string'), 'op_family_coarse': Value('string'), 'op_family_fine': Value('string'), 'n_unique_ops': Value('int64'), 'also_appears_at': List(Value('null')), 'cand_id': Value('string'), 'iteration': Value('int64'), 'minimo_solved': Value('bool'), 'minimo_proof_n_actions': Value('int64'), 'minimo_logprob': Value('float64'), 'peano_valid': Value('bool'), 'sources': List(Value('null'))}), 'metadata': {'parent_dataset': Value('string'), 'source_log_path': Value('string'), 'source_run_dir': Value('string'), 'source_outcomes_files': List(Value('string')), 'filter': Value('string'), 'n_input': Value('int64'), 'n_kept': Value('int64'), 'per_iter_kept': {'Min_it0': Value('int64'), 'Min_it1': Value('int64'), 'Min_it2': Value('int64'), 'Min_it3': Value('int64')}, 'shape_note': Value('string')}}
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(
                         ^^^^^^^^^
                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/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              idx: int64
              candidate: string
              sources: list<item: null>
                child 0, item: null
              proof: struct<solved: bool, mcts_iters: int64, tree_size: int64, proof_text: string, tree: list<item: struc (... 282 chars omitted)
                child 0, solved: bool
                child 1, mcts_iters: int64
                child 2, tree_size: int64
                child 3, proof_text: string
                child 4, tree: list<item: struct<id: int64, state_str: string, visits: int64, reward: double, value_estimate: doubl (... 197 chars omitted)
                    child 0, item: struct<id: int64, state_str: string, visits: int64, reward: double, value_estimate: double, prior_po (... 185 chars omitted)
                        child 0, id: int64
                        child 1, state_str: string
                        child 2, visits: int64
                        child 3, reward: double
                        child 4, value_estimate: double
                        child 5, prior_policy: double
                        child 6, is_dead: bool
                        child 7, is_solved: bool
                        child 8, is_terminal: bool
                        child 9, is_conjunctive: bool
                        child 10, solution_action: string
                        child 11, parent_id: int64
                        child 12, parent_action: string
                        child 13, children_ids: list<item: int64>
                            child 0, item: int64
              hindsight: list<item: struct<goal: string, statement: string, solved: bool, tree_size: int64, tree: list<item:  (... 286 chars omitted)
                child 0, item: struct<goal: string, statement: string, solved: bool, tree_size: int64, tree: list<item: struct<id:  (... 274 chars omitted)
                    child 0, goal: string
                    child 1, s
              ...
              idate: string, level: string, primary_ancestor: string, all_ancestors: list<item: string> (... 282 chars omitted)
                    child 0, candidate: string
                    child 1, level: string
                    child 2, primary_ancestor: string
                    child 3, all_ancestors: list<item: string>
                        child 0, item: string
                    child 4, first_op: string
                    child 5, op_family_coarse: string
                    child 6, op_family_fine: string
                    child 7, n_unique_ops: int64
                    child 8, also_appears_at: list<item: null>
                        child 0, item: null
                    child 9, cand_id: string
                    child 10, iteration: int64
                    child 11, minimo_solved: bool
                    child 12, minimo_proof_n_actions: int64
                    child 13, minimo_logprob: double
                    child 14, peano_valid: bool
                    child 15, sources: list<item: null>
                        child 0, item: null
              metadata: struct<parent_dataset: string, source_log_path: string, source_run_dir: string, source_outcomes_file (... 176 chars omitted)
                child 0, parent_dataset: string
                child 1, source_log_path: string
                child 2, source_run_dir: string
                child 3, source_outcomes_files: list<item: string>
                    child 0, item: string
                child 4, filter: string
                child 5, n_input: int64
                child 6, n_kept: int64
                child 7, per_iter_kept: struct<Min_it0: int64, Min_it1: int64, Min_it2: int64, Min_it3: int64>
                    child 0, Min_it0: int64
                    child 1, Min_it1: int64
                    child 2, Min_it2: int64
                    child 3, Min_it3: int64
                child 8, shape_note: string
              seeds: list<item: null>
                child 0, item: null
              to
              {'seeds': List(Value('null')), 'seed_names': Json(decode=True), 'candidates': List({'candidate': Value('string'), 'level': Value('string'), 'primary_ancestor': Value('string'), 'all_ancestors': List(Value('string')), 'first_op': Value('string'), 'op_family_coarse': Value('string'), 'op_family_fine': Value('string'), 'n_unique_ops': Value('int64'), 'also_appears_at': List(Value('null')), 'cand_id': Value('string'), 'iteration': Value('int64'), 'minimo_solved': Value('bool'), 'minimo_proof_n_actions': Value('int64'), 'minimo_logprob': Value('float64'), 'peano_valid': Value('bool'), 'sources': List(Value('null'))}), 'metadata': {'parent_dataset': Value('string'), 'source_log_path': Value('string'), 'source_run_dir': Value('string'), 'source_outcomes_files': List(Value('string')), 'filter': Value('string'), 'n_input': Value('int64'), 'n_kept': Value('int64'), 'per_iter_kept': {'Min_it0': Value('int64'), 'Min_it1': Value('int64'), 'Min_it2': Value('int64'), 'Min_it3': Value('int64')}, 'shape_note': Value('string')}}
              because column names don't match