Datasets:

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farama-minari

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D4RL

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Minari 7

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
data_format: string
observation_space: string
action_space: string
env_spec: string
dataset_id: string
algorithm_name: string
author: list<item: string>
author_email: list<item: string>
code_permalink: string
minari_version: string
eval_env_spec: string
ref_max_score: double
ref_min_score: double
num_episodes_average_score: int64
total_episodes: int64
total_steps: int64
dataset_size: double
description: string
requirements: list<item: string>
vs
total_episodes: int64
total_steps: int64
data_format: string
observation_space: string
action_space: string
env_spec: string
dataset_id: string
algorithm_name: string
author: list<item: string>
author_email: list<item: string>
code_permalink: string
minari_version: string
eval_env_spec: string
ref_max_score: double
ref_min_score: double
num_episodes_average_score: int64
dataset_size: double
description: string
requirements: list<item: string>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3357, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2111, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2315, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 504, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              data_format: string
              observation_space: string
              action_space: string
              env_spec: string
              dataset_id: string
              algorithm_name: string
              author: list<item: string>
              author_email: list<item: string>
              code_permalink: string
              minari_version: string
              eval_env_spec: string
              ref_max_score: double
              ref_min_score: double
              num_episodes_average_score: int64
              total_episodes: int64
              total_steps: int64
              dataset_size: double
              description: string
              requirements: list<item: string>
              vs
              total_episodes: int64
              total_steps: int64
              data_format: string
              observation_space: string
              action_space: string
              env_spec: string
              dataset_id: string
              algorithm_name: string
              author: list<item: string>
              author_email: list<item: string>
              code_permalink: string
              minari_version: string
              eval_env_spec: string
              ref_max_score: double
              ref_min_score: double
              num_episodes_average_score: int64
              dataset_size: double
              description: string
              requirements: list<item: string>