Datasets:

---

AdithyaByri

/

aizen-models

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model_version: string
architecture: string
topology_version: string
feature_names: list<item: string>
  child 0, item: string
impute_medians: struct<return_1: double, return_4: double, return_16: double, volatility_16: double, rsi_14: double, (... 188 chars omitted)
  child 0, return_1: double
  child 1, return_4: double
  child 2, return_16: double
  child 3, volatility_16: double
  child 4, rsi_14: double
  child 5, macd_pct: double
  child 6, hl_range: double
  child 7, atr_pct_14: double
  child 8, ma_dist_20: double
  child 9, ma_dist_50: double
  child 10, volume_ratio_20: double
  child 11, vwap_distance: double
  child 12, spy_ret_1: double
  child 13, qqq_ret_past_16: double
split_bounds: struct<train_end: timestamp[s], val_end: timestamp[s], test_end: timestamp[s]>
  child 0, train_end: timestamp[s]
  child 1, val_end: timestamp[s]
  child 2, test_end: timestamp[s]
test_metrics: struct<roc_auc: double, pr_auc: double, log_loss: double, brier: double>
  child 0, roc_auc: double
  child 1, pr_auc: double
  child 2, log_loss: double
  child 3, brier: double
created_at: timestamp[s]
torch_seed: int64
numpy_seed: int64
python_hash_seed: string
created_at_utc: timestamp[s]
frozen_params: struct<tau: double, cost: double>
  child 0, tau: double
  child 1, cost: double
horizon_bars: int64
label_source_note: null
task: string
path: string
dataset: string
features: list<item: string>
  child 0, item: string
to
{'model_version': Value('string'), 'path': Value('string'), 'task': Value('string'), 'dataset': Value('string'), 'horizon_bars': Value('int64'), 'features': List(Value('string')), 'impute_medians': {'return_1': Value('float64'), 'return_4': Value('float64'), 'return_16': Value('float64'), 'volatility_16': Value('float64'), 'rsi_14': Value('float64'), 'macd_pct': Value('float64'), 'hl_range': Value('float64'), 'co_return': Value('float64'), 'atr_pct_14': Value('float64'), 'ma_dist_20': Value('float64'), 'ma_dist_50': Value('float64'), 'volume_ratio_20': Value('float64'), 'volume_change_1': Value('float64'), 'trade_count_ratio_20': Value('float64'), 'vwap_distance': Value('float64'), 'spy_ret_1': Value('float64'), 'spy_ret_past_16': Value('float64'), 'spy_volatility_16': Value('float64'), 'qqq_ret_past_16': Value('float64'), 'qqq_volatility_16': Value('float64')}, 'split_bounds': {'train_end': Value('timestamp[s]'), 'val_start': Value('timestamp[s]'), 'val_end': Value('timestamp[s]'), 'test_start': Value('timestamp[s]'), 'test_end': Value('timestamp[s]')}, 'frozen_params': {'tau': Value('float64'), 'cost': Value('float64')}, 'test_metrics': {'val': {'roc_auc': Value('float64'), 'pr_auc': Value('float64'), 'log_loss': Value('float64'), 'brier': Value('float64'), 'precision@0.5': Value('float64'), 'recall@0.5': Value('float64'), 'base_rate': Value('float64'), 'calibration': List({'bin_mid': Value('float64'), 'mean_pred': Value('float64'), 'observed': Value('float64'), 'n': Value('int64')})}, 'test': {'roc_auc': Value('float64'), 'pr_auc': Value('float64'), 'log_loss': Value('float64'), 'brier': Value('float64'), 'precision@0.5': Value('float64'), 'recall@0.5': Value('float64'), 'base_rate': Value('float64'), 'calibration': List({'bin_mid': Value('float64'), 'mean_pred': Value('float64'), 'observed': Value('float64'), 'n': Value('int64')})}}, 'label_source_note': Value('null'), 'created_at_utc': Value('timestamp[s]')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                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
              model_version: string
              architecture: string
              topology_version: string
              feature_names: list<item: string>
                child 0, item: string
              impute_medians: struct<return_1: double, return_4: double, return_16: double, volatility_16: double, rsi_14: double, (... 188 chars omitted)
                child 0, return_1: double
                child 1, return_4: double
                child 2, return_16: double
                child 3, volatility_16: double
                child 4, rsi_14: double
                child 5, macd_pct: double
                child 6, hl_range: double
                child 7, atr_pct_14: double
                child 8, ma_dist_20: double
                child 9, ma_dist_50: double
                child 10, volume_ratio_20: double
                child 11, vwap_distance: double
                child 12, spy_ret_1: double
                child 13, qqq_ret_past_16: double
              split_bounds: struct<train_end: timestamp[s], val_end: timestamp[s], test_end: timestamp[s]>
                child 0, train_end: timestamp[s]
                child 1, val_end: timestamp[s]
                child 2, test_end: timestamp[s]
              test_metrics: struct<roc_auc: double, pr_auc: double, log_loss: double, brier: double>
                child 0, roc_auc: double
                child 1, pr_auc: double
                child 2, log_loss: double
                child 3, brier: double
              created_at: timestamp[s]
              torch_seed: int64
              numpy_seed: int64
              python_hash_seed: string
              created_at_utc: timestamp[s]
              frozen_params: struct<tau: double, cost: double>
                child 0, tau: double
                child 1, cost: double
              horizon_bars: int64
              label_source_note: null
              task: string
              path: string
              dataset: string
              features: list<item: string>
                child 0, item: string
              to
              {'model_version': Value('string'), 'path': Value('string'), 'task': Value('string'), 'dataset': Value('string'), 'horizon_bars': Value('int64'), 'features': List(Value('string')), 'impute_medians': {'return_1': Value('float64'), 'return_4': Value('float64'), 'return_16': Value('float64'), 'volatility_16': Value('float64'), 'rsi_14': Value('float64'), 'macd_pct': Value('float64'), 'hl_range': Value('float64'), 'co_return': Value('float64'), 'atr_pct_14': Value('float64'), 'ma_dist_20': Value('float64'), 'ma_dist_50': Value('float64'), 'volume_ratio_20': Value('float64'), 'volume_change_1': Value('float64'), 'trade_count_ratio_20': Value('float64'), 'vwap_distance': Value('float64'), 'spy_ret_1': Value('float64'), 'spy_ret_past_16': Value('float64'), 'spy_volatility_16': Value('float64'), 'qqq_ret_past_16': Value('float64'), 'qqq_volatility_16': Value('float64')}, 'split_bounds': {'train_end': Value('timestamp[s]'), 'val_start': Value('timestamp[s]'), 'val_end': Value('timestamp[s]'), 'test_start': Value('timestamp[s]'), 'test_end': Value('timestamp[s]')}, 'frozen_params': {'tau': Value('float64'), 'cost': Value('float64')}, 'test_metrics': {'val': {'roc_auc': Value('float64'), 'pr_auc': Value('float64'), 'log_loss': Value('float64'), 'brier': Value('float64'), 'precision@0.5': Value('float64'), 'recall@0.5': Value('float64'), 'base_rate': Value('float64'), 'calibration': List({'bin_mid': Value('float64'), 'mean_pred': Value('float64'), 'observed': Value('float64'), 'n': Value('int64')})}, 'test': {'roc_auc': Value('float64'), 'pr_auc': Value('float64'), 'log_loss': Value('float64'), 'brier': Value('float64'), 'precision@0.5': Value('float64'), 'recall@0.5': Value('float64'), 'base_rate': Value('float64'), 'calibration': List({'bin_mid': Value('float64'), 'mean_pred': Value('float64'), 'observed': Value('float64'), 'n': Value('int64')})}}, 'label_source_note': Value('null'), 'created_at_utc': Value('timestamp[s]')}
              because column names don't match