Datasets:

---

mahvin92

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guidescope-results

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
job_id: string
mode: string
service: string
version: string
finished: string
runtime_sec: double
query: struct<user_id: string, species_key: string, checkpoint_version: null, spacers: string, pams: string (... 186 chars omitted)
  child 0, user_id: string
  child 1, species_key: string
  child 2, checkpoint_version: null
  child 3, spacers: string
  child 4, pams: string
  child 5, cas_enzyme: string
  child 6, top_k: int64
  child 7, search_multiplier: int64
  child 8, max_pca_distance: null
  child 9, max_hamming: int64
  child 10, require_pam: bool
  child 11, include_nearest_even_if_not_positive: bool
  child 12, max_shards: null
species_key: string
checkpoint: struct<species_name: string, assembly_accession: string, n_fragments: int64, n_checkpoints_queried:  (... 106 chars omitted)
  child 0, species_name: string
  child 1, assembly_accession: string
  child 2, n_fragments: int64
  child 3, n_checkpoints_queried: int64
  child 4, guide_len: int64
  child 5, embedding_mode: string
  child 6, pca_components: int64
  child 7, paths_in_repo: list<item: string>
      child 0, item: string
n_rows: int64
n_positive: int64
rows: list<item: struct<species_key: string, species_name: string, assembly_accession: string, checkpoint_ (... 468 chars omitted)
  child 0, item: struct<species_key: string, species_name: string, assembly_accession: string, checkpoint_path: strin (... 456 chars omitted)
      child 0, species_key: string
      child 1, species_name: string
      child 2, assembly_accession: string
      child 3, checkpoint_path: string
      child 4, shard_key: string
      child 5, cas_enzyme: string
      child 6, query_spacer: string
      child 7, pam_patterns: string
      child 8, rank_within_shard: int64
      child 9, positive_call: bool
      child 10, pca_distance: double
      child 11, hamming_distance: int64
      child 12, exact_spacer_match: bool
      child 13, pam_seq: string
      child 14, pam_pass: bool
      child 15, fragment_id: string
      child 16, fragment_seq: string
      child 17, chrom: string
      child 18, start: int64
      child 19, end: int64
      child 20, strand: string
      child 21, gene_id: string
      child 22, gene_name: string
      child 23, feature_type: string
      child 24, annotation: string
      child 25, domain: null
      child 26, group: null
      child 27, rank: int64
disclaimer: string
entries_queried: int64
n_deduped_candidates: int64
n_raw_candidates: int64
n_screened_candidates: int64
pam_patterns: list<item: string>
  child 0, item: string
to
{'job_id': Value('string'), 'mode': Value('string'), 'service': Value('string'), 'version': Value('string'), 'finished': Value('string'), 'runtime_sec': Value('float64'), 'query': {'user_id': Value('string'), 'species_key': Value('string'), 'checkpoint_version': Value('null'), 'cas_enzyme': Value('string'), 'pam': Value('string'), 'target_type': Value('string'), 'target_value': Value('string'), 'design_region': Value('string'), 'top_n': Value('int64'), 'min_gc': Value('float64'), 'max_gc': Value('float64'), 'homopolymer_n': Value('int64'), 'max_hamming_offtarget': Value('int64'), 'offtarget_top_k': Value('int64'), 'max_candidates_to_screen': Value('int64'), 'max_shards': Value('null')}, 'species_key': Value('string'), 'pam_patterns': List(Value('string')), 'n_raw_candidates': Value('int64'), 'n_deduped_candidates': Value('int64'), 'n_screened_candidates': Value('int64'), 'entries_queried': Value('int64'), 'n_rows': Value('int64'), 'rows': List(Value('null')), 'disclaimer': 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(
                      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
              job_id: string
              mode: string
              service: string
              version: string
              finished: string
              runtime_sec: double
              query: struct<user_id: string, species_key: string, checkpoint_version: null, spacers: string, pams: string (... 186 chars omitted)
                child 0, user_id: string
                child 1, species_key: string
                child 2, checkpoint_version: null
                child 3, spacers: string
                child 4, pams: string
                child 5, cas_enzyme: string
                child 6, top_k: int64
                child 7, search_multiplier: int64
                child 8, max_pca_distance: null
                child 9, max_hamming: int64
                child 10, require_pam: bool
                child 11, include_nearest_even_if_not_positive: bool
                child 12, max_shards: null
              species_key: string
              checkpoint: struct<species_name: string, assembly_accession: string, n_fragments: int64, n_checkpoints_queried:  (... 106 chars omitted)
                child 0, species_name: string
                child 1, assembly_accession: string
                child 2, n_fragments: int64
                child 3, n_checkpoints_queried: int64
                child 4, guide_len: int64
                child 5, embedding_mode: string
                child 6, pca_components: int64
                child 7, paths_in_repo: list<item: string>
                    child 0, item: string
              n_rows: int64
              n_positive: int64
              rows: list<item: struct<species_key: string, species_name: string, assembly_accession: string, checkpoint_ (... 468 chars omitted)
                child 0, item: struct<species_key: string, species_name: string, assembly_accession: string, checkpoint_path: strin (... 456 chars omitted)
                    child 0, species_key: string
                    child 1, species_name: string
                    child 2, assembly_accession: string
                    child 3, checkpoint_path: string
                    child 4, shard_key: string
                    child 5, cas_enzyme: string
                    child 6, query_spacer: string
                    child 7, pam_patterns: string
                    child 8, rank_within_shard: int64
                    child 9, positive_call: bool
                    child 10, pca_distance: double
                    child 11, hamming_distance: int64
                    child 12, exact_spacer_match: bool
                    child 13, pam_seq: string
                    child 14, pam_pass: bool
                    child 15, fragment_id: string
                    child 16, fragment_seq: string
                    child 17, chrom: string
                    child 18, start: int64
                    child 19, end: int64
                    child 20, strand: string
                    child 21, gene_id: string
                    child 22, gene_name: string
                    child 23, feature_type: string
                    child 24, annotation: string
                    child 25, domain: null
                    child 26, group: null
                    child 27, rank: int64
              disclaimer: string
              entries_queried: int64
              n_deduped_candidates: int64
              n_raw_candidates: int64
              n_screened_candidates: int64
              pam_patterns: list<item: string>
                child 0, item: string
              to
              {'job_id': Value('string'), 'mode': Value('string'), 'service': Value('string'), 'version': Value('string'), 'finished': Value('string'), 'runtime_sec': Value('float64'), 'query': {'user_id': Value('string'), 'species_key': Value('string'), 'checkpoint_version': Value('null'), 'cas_enzyme': Value('string'), 'pam': Value('string'), 'target_type': Value('string'), 'target_value': Value('string'), 'design_region': Value('string'), 'top_n': Value('int64'), 'min_gc': Value('float64'), 'max_gc': Value('float64'), 'homopolymer_n': Value('int64'), 'max_hamming_offtarget': Value('int64'), 'offtarget_top_k': Value('int64'), 'max_candidates_to_screen': Value('int64'), 'max_shards': Value('null')}, 'species_key': Value('string'), 'pam_patterns': List(Value('string')), 'n_raw_candidates': Value('int64'), 'n_deduped_candidates': Value('int64'), 'n_screened_candidates': Value('int64'), 'entries_queried': Value('int64'), 'n_rows': Value('int64'), 'rows': List(Value('null')), 'disclaimer': Value('string')}
              because column names don't match