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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
start_year: int64
evaluated_at: string
n_test_days: int64
ann_return: double
sharpe: double
max_drawdown: double
calmar: double
hit_ratio: double
final_equity: double
benchmark_sharpe: struct<SPY: double, AGG: double>
  child 0, SPY: double
  child 1, AGG: double
benchmark_ann: struct<SPY: double, AGG: double>
  child 0, SPY: double
  child 1, AGG: double
benchmark_equity: struct<SPY: list<item: double>, AGG: list<item: double>>
  child 0, SPY: list<item: double>
      child 0, item: double
  child 1, AGG: list<item: double>
      child 0, item: double
test_dates: list<item: timestamp[s]>
  child 0, item: timestamp[s]
allocation_pct: struct<TLT: double, GLD: double, VNQ: double, SLV: double, HYG: double, CASH: double>
  child 0, TLT: double
  child 1, GLD: double
  child 2, VNQ: double
  child 3, SLV: double
  child 4, HYG: double
  child 5, CASH: double
equity_curve: list<item: double>
  child 0, item: double
allocations: list<item: string>
  child 0, item: string
fee_bps: int64
tsl_pct: double
z_reentry: double
n_features: int64
val_days: int64
n_episodes: int64
lookback: int64
option: string
train_days: int64
history: list<item: struct<episode: int64, train_sharpe: double, train_equity: double, val_sharpe: double, va (... 53 chars omitted)
  child 0, item: struct<episode: int64, train_sharpe: double, train_equity: double, val_sharpe: double, val_equity: d (... 41 chars omitted)
      child 0, episode: int64
      child 1, train_sharpe: double
      child 2, train_equity: double
      child 3, val_sharpe: double
      child 4, val_equity: double
      child 5, avg_loss: double
      child 6, epsilon: double
best_val_sharpe: double
test_days: int64
n_etfs: int64
trained_at: string
test_sharpe: double
state_size: int64
test_equity: double
to
{'option': Value('string'), 'start_year': Value('int64'), 'n_episodes': Value('int64'), 'fee_bps': Value('int64'), 'lookback': Value('int64'), 'state_size': Value('int64'), 'n_features': Value('int64'), 'n_etfs': Value('int64'), 'trained_at': Value('string'), 'best_val_sharpe': Value('float64'), 'test_sharpe': Value('float64'), 'test_equity': Value('float64'), 'train_days': Value('int64'), 'val_days': Value('int64'), 'test_days': Value('int64'), 'history': List({'episode': Value('int64'), 'train_sharpe': Value('float64'), 'train_equity': Value('float64'), 'val_sharpe': Value('float64'), 'val_equity': Value('float64'), 'avg_loss': Value('float64'), 'epsilon': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, 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 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
              start_year: int64
              evaluated_at: string
              n_test_days: int64
              ann_return: double
              sharpe: double
              max_drawdown: double
              calmar: double
              hit_ratio: double
              final_equity: double
              benchmark_sharpe: struct<SPY: double, AGG: double>
                child 0, SPY: double
                child 1, AGG: double
              benchmark_ann: struct<SPY: double, AGG: double>
                child 0, SPY: double
                child 1, AGG: double
              benchmark_equity: struct<SPY: list<item: double>, AGG: list<item: double>>
                child 0, SPY: list<item: double>
                    child 0, item: double
                child 1, AGG: list<item: double>
                    child 0, item: double
              test_dates: list<item: timestamp[s]>
                child 0, item: timestamp[s]
              allocation_pct: struct<TLT: double, GLD: double, VNQ: double, SLV: double, HYG: double, CASH: double>
                child 0, TLT: double
                child 1, GLD: double
                child 2, VNQ: double
                child 3, SLV: double
                child 4, HYG: double
                child 5, CASH: double
              equity_curve: list<item: double>
                child 0, item: double
              allocations: list<item: string>
                child 0, item: string
              fee_bps: int64
              tsl_pct: double
              z_reentry: double
              n_features: int64
              val_days: int64
              n_episodes: int64
              lookback: int64
              option: string
              train_days: int64
              history: list<item: struct<episode: int64, train_sharpe: double, train_equity: double, val_sharpe: double, va (... 53 chars omitted)
                child 0, item: struct<episode: int64, train_sharpe: double, train_equity: double, val_sharpe: double, val_equity: d (... 41 chars omitted)
                    child 0, episode: int64
                    child 1, train_sharpe: double
                    child 2, train_equity: double
                    child 3, val_sharpe: double
                    child 4, val_equity: double
                    child 5, avg_loss: double
                    child 6, epsilon: double
              best_val_sharpe: double
              test_days: int64
              n_etfs: int64
              trained_at: string
              test_sharpe: double
              state_size: int64
              test_equity: double
              to
              {'option': Value('string'), 'start_year': Value('int64'), 'n_episodes': Value('int64'), 'fee_bps': Value('int64'), 'lookback': Value('int64'), 'state_size': Value('int64'), 'n_features': Value('int64'), 'n_etfs': Value('int64'), 'trained_at': Value('string'), 'best_val_sharpe': Value('float64'), 'test_sharpe': Value('float64'), 'test_equity': Value('float64'), 'train_days': Value('int64'), 'val_days': Value('int64'), 'test_days': Value('int64'), 'history': List({'episode': Value('int64'), 'train_sharpe': Value('float64'), 'train_equity': Value('float64'), 'val_sharpe': Value('float64'), 'val_equity': Value('float64'), 'avg_loss': Value('float64'), 'epsilon': Value('float64')})}
              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 1683, 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 1869, 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

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option string	start_year int64	n_episodes int64	fee_bps int64	lookback int64	state_size int64	n_features int64	n_etfs int64	trained_at string	best_val_sharpe float64	test_sharpe float64	test_equity float64	train_days int64	val_days int64	test_days int64	history list
a	2,008	150	10	20	3,048	152	7	2026-06-02T22:17:56.881687	0.2531	1.9825	1.6725	3,674	441	443	[ { "episode": 131, "train_sharpe": -2.725, "train_equity": 0.391, "val_sharpe": 0, "val_equity": 1.0393, "avg_loss": 0, "epsilon": 0.2704 }, { "episode": 132, "train_sharpe": -2.416, "train_equity": 0.5937, "val_sharpe": 0, "val_equity": 1.0384, "avg_loss": 0, ...
b	2,008	150	10	20	8,261	412	20	2026-06-03T00:49:50.398135	0	0	1.0525	3,675	441	443	[ { "episode": 131, "train_sharpe": null, "train_equity": null, "val_sharpe": 0, "val_equity": 1.0588, "avg_loss": 0, "epsilon": 0.2596 }, { "episode": 132, "train_sharpe": null, "train_equity": null, "val_sharpe": 0, "val_equity": 1.0417, "avg_loss": 0, "ep...
null	2,015	220	10	20	3,048	152	null	2026-03-27T05:12:53.388585	2.0916	2.5229	1.4407	2,239	262	263	[ { "episode": 201, "train_sharpe": 10.478, "train_equity": 45883.4125, "val_sharpe": 0.676, "val_equity": 1.0788, "avg_loss": 0.000201, "epsilon": 0.2236 }, { "episode": 202, "train_sharpe": 10.61, "train_equity": 19116.4899, "val_sharpe": 0.652, "val_equity": 1.06...

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