Datasets:

---

weihao1115

/

joint_train

like 0

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<pdb_id: string, chain_id: string, pos: int64, true_aa: string, rsa_bin: string, ss_class: string, sampling_strategy: string, start_pos: int64, end_pos: int64, window_size: int64, window_category: string, chain_i: string, pos_i: int64, chain_j: string, pos_j: int64, pair_class: string, distance: double, num_pairs: int64, qc_summary: struct<window_coverage: struct<dssp: struct<has_H: int64, has_E: int64, has_H_and_E: int64, random: int64>, rsa: struct<has_B: int64, has_E: int64, has_B_and_E: int64, random: int64>>, pair_sampling: struct<by_class: struct<short: int64, long: int64, cross_chain: int64>, contact_yn: struct<Contact: int64, NotContact: int64>>, topk_audit: struct<interface: struct<count: int64, avg_actual_k: double>, hotspot: struct<count: int64, avg_actual_k: double>>, warnings: list<item: string>>, num_chains: int64, ordering: string, contact_count: int64, chain_pair: string, B_actual_k: int64, B_score_range: list<item: int64>, C_actual_k: int64, C_score_range: list<item: int64>, selection_method: string, type: string, salt_bridge_count: int64, aa_fill_report: struct<W: int64, H: int64, P: int64, G: int64, V: int64, F: int64, L: int64, S: int64, R: int64, I: int64, E: int64, N: int64, T: int64, Q: int64, D: int64, C: int64, A: int64, M: int64, Y: int64, K: int64>, coverage_report: struct<B: int64, M: int64, E: int64>, total_queries: int64, A_actual_k: int64, A_score_range: list<item: int64>, F_actual_k: int64, F_score_range: list<item: int64>, K_actual_k: int64, K_score_range: list<item: int64>, P_actual_k: int64, P_score_range: list<item: int64>, R_actual_k: int64, R_score_range: list<item: int64>, Y_actual_k: int64, Y_score_range: list<item: int64>, L_actual_k: int64, L_score_range: list<item: int64>, H_actual_k: int64, H_score_range: list<item: int64>, N_actual_k: int64, N_score_range: list<item: int64>, D_actual_k: int64, D_score_range: list<item: int64>, E_actual_k: int64, E_score_range: list<item: int64>, G_actual_k: int64, G_score_range: list<item: int64>, I_actual_k: int64, I_score_range: list<item: int64>, Z_actual_k: int64, Z_score_range: list<item: int64>, M_actual_k: int64, M_score_range: list<item: int64>, J_actual_k: int64, J_score_range: list<item: int64>, U_actual_k: int64, U_score_range: list<item: int64>, 3_actual_k: int64, 3_score_range: list<item: int64>, 7_actual_k: int64, 7_score_range: list<item: int64>, V_actual_k: int64, V_score_range: list<item: int64>, S_actual_k: int64, S_score_range: list<item: int64>, O_actual_k: int64, O_score_range: list<item: int64>, X_actual_k: int64, X_score_range: list<item: int64>>
to
{'pdb_id': Value('string'), 'chain_id': Value('string'), 'pos': Value('int64'), 'true_aa': Value('string'), 'rsa_bin': Value('string'), 'ss_class': Value('string'), 'sampling_strategy': Value('string'), 'start_pos': Value('int64'), 'end_pos': Value('int64'), 'window_size': Value('int64'), 'window_category': Value('string'), 'chain_i': Value('string'), 'pos_i': Value('int64'), 'chain_j': Value('string'), 'pos_j': Value('int64'), 'pair_class': Value('string'), 'distance': Value('float64'), 'num_pairs': Value('int64'), 'qc_summary': {'window_coverage': {'dssp': {'has_H': Value('int64'), 'has_E': Value('int64'), 'has_H_and_E': Value('int64'), 'random': Value('int64')}, 'rsa': {'has_B': Value('int64'), 'has_E': Value('int64'), 'has_B_and_E': Value('int64'), 'random': Value('int64')}}, 'pair_sampling': {'by_class': {'short': Value('int64'), 'long': Value('int64'), 'cross_chain': Value('int64')}, 'contact_yn': {'Contact': Value('int64'), 'NotContact': Value('int64')}}, 'topk_audit': {'interface': {'count': Value('int64'), 'avg_actual_k': Value('float64')}, 'hotspot': {'count': Value('int64'), 'avg_actual_k': Value('float64')}}, 'warnings': List(Value('null'))}, 'num_chains': Value('int64'), 'ordering': Value('string'), 'contact_count': Value('int64'), 'chain_pair': Value('string'), 'B_actual_k': Value('int64'), 'B_score_range': List(Value('int64')), 'H_actual_k': Value('int64'), 'H_score_range': List(Value('int64')), 'selection_method': Value('string'), 'type': Value('string'), 'sal
...
ue('int64'), 'X_score_range': List(Value('int64')), 'J_actual_k': Value('int64'), 'J_score_range': List(Value('int64')), 'C_actual_k': Value('int64'), 'C_score_range': List(Value('int64')), 'A_actual_k': Value('int64'), 'A_score_range': List(Value('int64')), 'M_actual_k': Value('int64'), 'M_score_range': List(Value('int64')), 'L_actual_k': Value('int64'), 'L_score_range': List(Value('int64')), 'K_actual_k': Value('int64'), 'K_score_range': List(Value('int64')), 'N_actual_k': Value('int64'), 'N_score_range': List(Value('int64')), 'W_actual_k': Value('int64'), 'W_score_range': List(Value('int64')), 'D_actual_k': Value('int64'), 'D_score_range': List(Value('int64')), 'I_actual_k': Value('int64'), 'I_score_range': List(Value('int64')), 'F_actual_k': Value('int64'), 'F_score_range': List(Value('int64')), 'G_actual_k': Value('int64'), 'G_score_range': List(Value('int64')), 'S_actual_k': Value('int64'), 'S_score_range': List(Value('int64')), 'T_actual_k': Value('int64'), 'T_score_range': List(Value('int64')), 'Z_actual_k': Value('int64'), 'Z_score_range': List(Value('int64')), 'O_actual_k': Value('int64'), 'O_score_range': List(Value('int64')), 'Q_actual_k': Value('int64'), 'Q_score_range': List(Value('int64')), 'P_actual_k': Value('int64'), 'P_score_range': List(Value('int64')), 'R_actual_k': Value('int64'), 'R_score_range': List(Value('int64')), 'U_actual_k': Value('int64'), 'U_score_range': List(Value('int64')), '1_actual_k': Value('int64'), '1_score_range': List(Value('int64'))}
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
                  cast_array_to_feature(
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2092, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<pdb_id: string, chain_id: string, pos: int64, true_aa: string, rsa_bin: string, ss_class: string, sampling_strategy: string, start_pos: int64, end_pos: int64, window_size: int64, window_category: string, chain_i: string, pos_i: int64, chain_j: string, pos_j: int64, pair_class: string, distance: double, num_pairs: int64, qc_summary: struct<window_coverage: struct<dssp: struct<has_H: int64, has_E: int64, has_H_and_E: int64, random: int64>, rsa: struct<has_B: int64, has_E: int64, has_B_and_E: int64, random: int64>>, pair_sampling: struct<by_class: struct<short: int64, long: int64, cross_chain: int64>, contact_yn: struct<Contact: int64, NotContact: int64>>, topk_audit: struct<interface: struct<count: int64, avg_actual_k: double>, hotspot: struct<count: int64, avg_actual_k: double>>, warnings: list<item: string>>, num_chains: int64, ordering: string, contact_count: int64, chain_pair: string, B_actual_k: int64, B_score_range: list<item: int64>, C_actual_k: int64, C_score_range: list<item: int64>, selection_method: string, type: string, salt_bridge_count: int64, aa_fill_report: struct<W: int64, H: int64, P: int64, G: int64, V: int64, F: int64, L: int64, S: int64, R: int64, I: int64, E: int64, N: int64, T: int64, Q: int64, D: int64, C: int64, A: int64, M: int64, Y: int64, K: int64>, coverage_report: struct<B: int64, M: int64, E: int64>, total_queries: int64, A_actual_k: int64, A_score_range: list<item: int64>, F_actual_k: int64, F_score_range: list<item: int64>, K_actual_k: int64, K_score_range: list<item: int64>, P_actual_k: int64, P_score_range: list<item: int64>, R_actual_k: int64, R_score_range: list<item: int64>, Y_actual_k: int64, Y_score_range: list<item: int64>, L_actual_k: int64, L_score_range: list<item: int64>, H_actual_k: int64, H_score_range: list<item: int64>, N_actual_k: int64, N_score_range: list<item: int64>, D_actual_k: int64, D_score_range: list<item: int64>, E_actual_k: int64, E_score_range: list<item: int64>, G_actual_k: int64, G_score_range: list<item: int64>, I_actual_k: int64, I_score_range: list<item: int64>, Z_actual_k: int64, Z_score_range: list<item: int64>, M_actual_k: int64, M_score_range: list<item: int64>, J_actual_k: int64, J_score_range: list<item: int64>, U_actual_k: int64, U_score_range: list<item: int64>, 3_actual_k: int64, 3_score_range: list<item: int64>, 7_actual_k: int64, 7_score_range: list<item: int64>, V_actual_k: int64, V_score_range: list<item: int64>, S_actual_k: int64, S_score_range: list<item: int64>, O_actual_k: int64, O_score_range: list<item: int64>, X_actual_k: int64, X_score_range: list<item: int64>>
              to
              {'pdb_id': Value('string'), 'chain_id': Value('string'), 'pos': Value('int64'), 'true_aa': Value('string'), 'rsa_bin': Value('string'), 'ss_class': Value('string'), 'sampling_strategy': Value('string'), 'start_pos': Value('int64'), 'end_pos': Value('int64'), 'window_size': Value('int64'), 'window_category': Value('string'), 'chain_i': Value('string'), 'pos_i': Value('int64'), 'chain_j': Value('string'), 'pos_j': Value('int64'), 'pair_class': Value('string'), 'distance': Value('float64'), 'num_pairs': Value('int64'), 'qc_summary': {'window_coverage': {'dssp': {'has_H': Value('int64'), 'has_E': Value('int64'), 'has_H_and_E': Value('int64'), 'random': Value('int64')}, 'rsa': {'has_B': Value('int64'), 'has_E': Value('int64'), 'has_B_and_E': Value('int64'), 'random': Value('int64')}}, 'pair_sampling': {'by_class': {'short': Value('int64'), 'long': Value('int64'), 'cross_chain': Value('int64')}, 'contact_yn': {'Contact': Value('int64'), 'NotContact': Value('int64')}}, 'topk_audit': {'interface': {'count': Value('int64'), 'avg_actual_k': Value('float64')}, 'hotspot': {'count': Value('int64'), 'avg_actual_k': Value('float64')}}, 'warnings': List(Value('null'))}, 'num_chains': Value('int64'), 'ordering': Value('string'), 'contact_count': Value('int64'), 'chain_pair': Value('string'), 'B_actual_k': Value('int64'), 'B_score_range': List(Value('int64')), 'H_actual_k': Value('int64'), 'H_score_range': List(Value('int64')), 'selection_method': Value('string'), 'type': Value('string'), 'sal
              ...
              ue('int64'), 'X_score_range': List(Value('int64')), 'J_actual_k': Value('int64'), 'J_score_range': List(Value('int64')), 'C_actual_k': Value('int64'), 'C_score_range': List(Value('int64')), 'A_actual_k': Value('int64'), 'A_score_range': List(Value('int64')), 'M_actual_k': Value('int64'), 'M_score_range': List(Value('int64')), 'L_actual_k': Value('int64'), 'L_score_range': List(Value('int64')), 'K_actual_k': Value('int64'), 'K_score_range': List(Value('int64')), 'N_actual_k': Value('int64'), 'N_score_range': List(Value('int64')), 'W_actual_k': Value('int64'), 'W_score_range': List(Value('int64')), 'D_actual_k': Value('int64'), 'D_score_range': List(Value('int64')), 'I_actual_k': Value('int64'), 'I_score_range': List(Value('int64')), 'F_actual_k': Value('int64'), 'F_score_range': List(Value('int64')), 'G_actual_k': Value('int64'), 'G_score_range': List(Value('int64')), 'S_actual_k': Value('int64'), 'S_score_range': List(Value('int64')), 'T_actual_k': Value('int64'), 'T_score_range': List(Value('int64')), 'Z_actual_k': Value('int64'), 'Z_score_range': List(Value('int64')), 'O_actual_k': Value('int64'), 'O_score_range': List(Value('int64')), 'Q_actual_k': Value('int64'), 'Q_score_range': List(Value('int64')), 'P_actual_k': Value('int64'), 'P_score_range': List(Value('int64')), 'R_actual_k': Value('int64'), 'R_score_range': List(Value('int64')), 'U_actual_k': Value('int64'), 'U_score_range': List(Value('int64')), '1_actual_k': Value('int64'), '1_score_range': List(Value('int64'))}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset