Datasets:

---

P2SAMAPA

/

etf-entropy-dataset

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
last_data_update: timestamp[s]
last_training_date: timestamp[s]
best_ma_window: int64
dataset_version: int64
vs
best_ma_window: int64
year_start: int64
split_pct: string
oos_start_date: timestamp[s]
oos_end_date: timestamp[s]
ma3_val_ann_return: int64
ma5_val_ann_return: int64
ma3_val_sharpe: int64
ma5_val_sharpe: int64
ma3_oos_ann_return: int64
ma5_oos_ann_return: int64
ma3_oos_sharpe: int64
ma5_oos_sharpe: int64
split_dates: struct<TLT: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, VNQ: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, GLD: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, SLV: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, VCIT: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, HYG: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, LQD: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>>
last_trained: string
etf_list: list<item: string>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 604, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              last_data_update: timestamp[s]
              last_training_date: timestamp[s]
              best_ma_window: int64
              dataset_version: int64
              vs
              best_ma_window: int64
              year_start: int64
              split_pct: string
              oos_start_date: timestamp[s]
              oos_end_date: timestamp[s]
              ma3_val_ann_return: int64
              ma5_val_ann_return: int64
              ma3_val_sharpe: int64
              ma5_val_sharpe: int64
              ma3_oos_ann_return: int64
              ma5_oos_ann_return: int64
              ma3_oos_sharpe: int64
              ma5_oos_sharpe: int64
              split_dates: struct<TLT: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, VNQ: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, GLD: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, SLV: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, VCIT: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, HYG: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>, LQD: struct<train_start: timestamp[s], train_end: timestamp[s], val_start: timestamp[s], val_end: timestamp[s], oos_start: timestamp[s], oos_end: timestamp[s], n_train: int64, n_val: int64, n_test: int64>>
              last_trained: string
              etf_list: list<item: string>