Datasets:

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project-telos

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trajectories_maze_120_pilot

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Project Telos 10

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
schema_version: int64
trajectory_id: string
model: string
api_model_id: string
reasoning_setting: string
temperature: double
top_p: double
seed: int64
max_output_tokens: int64
context_mode: string
prompt_sha256: string
sampling_condition: string
grid_id: string
grid_size: int64
difficulty: double
observed_wall_fraction: double
grid_replicate: int64
step_limit: int64
optimal_path_length: int64
step_index: int64
agent_x: int64
agent_y: int64
optimal_actions: list<item: string>
  child 0, item: string
selected_action: string
is_optimal_action: bool
valid_action: bool
api_success: bool
finish_reason: string
truncated_call: bool
latency_seconds: double
prompt_tokens: int64
output_tokens: int64
reasoning_tokens: int64
total_tokens: int64
estimated_cost_usd: double
attempts: int64
error_type: string
error_message: string
goal_reached_after_action: bool
trajectory_terminal: bool
response_text: string
reasoning_content: string
response_id: string
provider_returned_model_id: string
system_fingerprint: string
candidate_index: int64
generation_seed: int64
candidate_pool_size: int64
selection_quantile: double
goal_y: int64
grid_text: string
start_x: int64
start_y: int64
layout: list<item: list<item: string>>
  child 0, item: list<item: string>
      child 0, item: string
candidate_path_length_rank: int64
goal_x: int64
to
{'grid_size': Value('int64'), 'difficulty': Value('float64'), 'candidate_index': Value('int64'), 'generation_seed': Value('int64'), 'layout': List(List(Value('string'))), 'grid_text': Value('string'), 'start_x': Value('int64'), 'start_y': Value('int64'), 'goal_x': Value('int64'), 'goal_y': Value('int64'), 'optimal_path_length': Value('int64'), 'observed_wall_fraction': Value('float64'), 'grid_id': Value('string'), 'grid_replicate': Value('int64'), 'selection_quantile': Value('float64'), 'candidate_pool_size': Value('int64'), 'candidate_path_length_rank': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              schema_version: int64
              trajectory_id: string
              model: string
              api_model_id: string
              reasoning_setting: string
              temperature: double
              top_p: double
              seed: int64
              max_output_tokens: int64
              context_mode: string
              prompt_sha256: string
              sampling_condition: string
              grid_id: string
              grid_size: int64
              difficulty: double
              observed_wall_fraction: double
              grid_replicate: int64
              step_limit: int64
              optimal_path_length: int64
              step_index: int64
              agent_x: int64
              agent_y: int64
              optimal_actions: list<item: string>
                child 0, item: string
              selected_action: string
              is_optimal_action: bool
              valid_action: bool
              api_success: bool
              finish_reason: string
              truncated_call: bool
              latency_seconds: double
              prompt_tokens: int64
              output_tokens: int64
              reasoning_tokens: int64
              total_tokens: int64
              estimated_cost_usd: double
              attempts: int64
              error_type: string
              error_message: string
              goal_reached_after_action: bool
              trajectory_terminal: bool
              response_text: string
              reasoning_content: string
              response_id: string
              provider_returned_model_id: string
              system_fingerprint: string
              candidate_index: int64
              generation_seed: int64
              candidate_pool_size: int64
              selection_quantile: double
              goal_y: int64
              grid_text: string
              start_x: int64
              start_y: int64
              layout: list<item: list<item: string>>
                child 0, item: list<item: string>
                    child 0, item: string
              candidate_path_length_rank: int64
              goal_x: int64
              to
              {'grid_size': Value('int64'), 'difficulty': Value('float64'), 'candidate_index': Value('int64'), 'generation_seed': Value('int64'), 'layout': List(List(Value('string'))), 'grid_text': Value('string'), 'start_x': Value('int64'), 'start_y': Value('int64'), 'goal_x': Value('int64'), 'goal_y': Value('int64'), 'optimal_path_length': Value('int64'), 'observed_wall_fraction': Value('float64'), 'grid_id': Value('string'), 'grid_replicate': Value('int64'), 'selection_quantile': Value('float64'), 'candidate_pool_size': Value('int64'), 'candidate_path_length_rank': Value('int64')}
              because column names don't match
              
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, 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