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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
FI_full: struct<module: string, source: string, next_trading_day: timestamp[s], pick: string, conviction_pct: (... 457 chars omitted)
child 0, module: string
child 1, source: string
child 2, next_trading_day: timestamp[s]
child 3, pick: string
child 4, conviction_pct: double
child 5, pred_return: double
child 6, second_pick: string
child 7, second_conviction: double
child 8, third_pick: string
child 9, third_conviction: double
child 10, lookback_days: int64
child 11, sig_depth: int64
child 12, model_type: string
child 13, regime_id: int64
child 14, regime_name: string
child 15, n_windows_used: null
child 16, macro_pills: struct<VIX: double, T10Y2Y: double, HY_SPREAD: double, IG_SPREAD: double, DXY: double>
child 0, VIX: double
child 1, T10Y2Y: double
child 2, HY_SPREAD: double
child 3, IG_SPREAD: double
child 4, DXY: double
child 17, all_scores: list<item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>>
child 0, item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>
child 0, ticker: string
child 1, pred_return: double
child 2, net_score: double
child 3, conviction_pct: double
FI_consensus: struct<module: string, source: string, next_trading_day: timestamp[s], pick: string, conviction_pct: (... 458 chars omitted)
child 0, module: string
child 1, source: string
child 2, next_trading_da
...
_return: double, net_score: double, conviction_pct: double>
child 0, ticker: string
child 1, pred_return: double
child 2, net_score: double
child 3, conviction_pct: double
EQ_consensus: struct<module: string, source: string, next_trading_day: timestamp[s], pick: string, conviction_pct: (... 458 chars omitted)
child 0, module: string
child 1, source: string
child 2, next_trading_day: timestamp[s]
child 3, pick: string
child 4, conviction_pct: double
child 5, pred_return: double
child 6, second_pick: string
child 7, second_conviction: double
child 8, third_pick: string
child 9, third_conviction: double
child 10, lookback_days: int64
child 11, sig_depth: int64
child 12, model_type: string
child 13, regime_id: int64
child 14, regime_name: string
child 15, n_windows_used: int64
child 16, macro_pills: struct<VIX: double, T10Y2Y: double, HY_SPREAD: double, IG_SPREAD: double, DXY: double>
child 0, VIX: double
child 1, T10Y2Y: double
child 2, HY_SPREAD: double
child 3, IG_SPREAD: double
child 4, DXY: double
child 17, all_scores: list<item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>>
child 0, item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>
child 0, ticker: string
child 1, pred_return: double
child 2, net_score: double
child 3, conviction_pct: double
to
{'EQ_full': {'module': Value('string'), 'source': Value('string'), 'next_trading_day': Value('timestamp[s]'), 'pick': Value('string'), 'conviction_pct': Value('float64'), 'pred_return': Value('float64'), 'second_pick': Value('string'), 'second_conviction': Value('float64'), 'third_pick': Value('string'), 'third_conviction': Value('float64'), 'lookback_days': Value('int64'), 'sig_depth': Value('int64'), 'model_type': Value('string'), 'regime_id': Value('int64'), 'regime_name': Value('string'), 'n_windows_used': Value('null'), 'macro_pills': {'VIX': Value('float64'), 'T10Y2Y': Value('float64'), 'HY_SPREAD': Value('float64'), 'IG_SPREAD': Value('float64'), 'DXY': Value('float64')}, 'all_scores': List({'ticker': Value('string'), 'pred_return': Value('float64'), 'net_score': Value('float64'), 'conviction_pct': Value('float64')})}, 'EQ_consensus': {'module': Value('string'), 'source': Value('string'), 'next_trading_day': Value('timestamp[s]'), 'pick': Value('string'), 'conviction_pct': Value('float64'), 'pred_return': Value('float64'), 'second_pick': Value('string'), 'second_conviction': Value('float64'), 'third_pick': Value('string'), 'third_conviction': Value('float64'), 'lookback_days': Value('int64'), 'sig_depth': Value('int64'), 'model_type': Value('string'), 'regime_id': Value('int64'), 'regime_name': Value('string'), 'n_windows_used': Value('int64'), 'macro_pills': {'VIX': Value('float64'), 'T10Y2Y': Value('float64'), 'HY_SPREAD': Value('float64'), 'IG_SPREAD': Value('float64'), 'DXY': Value('float64')}, 'all_scores': List({'ticker': Value('string'), 'pred_return': Value('float64'), 'net_score': Value('float64'), 'conviction_pct': Value('float64')})}, 'generated_at': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
FI_full: struct<module: string, source: string, next_trading_day: timestamp[s], pick: string, conviction_pct: (... 457 chars omitted)
child 0, module: string
child 1, source: string
child 2, next_trading_day: timestamp[s]
child 3, pick: string
child 4, conviction_pct: double
child 5, pred_return: double
child 6, second_pick: string
child 7, second_conviction: double
child 8, third_pick: string
child 9, third_conviction: double
child 10, lookback_days: int64
child 11, sig_depth: int64
child 12, model_type: string
child 13, regime_id: int64
child 14, regime_name: string
child 15, n_windows_used: null
child 16, macro_pills: struct<VIX: double, T10Y2Y: double, HY_SPREAD: double, IG_SPREAD: double, DXY: double>
child 0, VIX: double
child 1, T10Y2Y: double
child 2, HY_SPREAD: double
child 3, IG_SPREAD: double
child 4, DXY: double
child 17, all_scores: list<item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>>
child 0, item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>
child 0, ticker: string
child 1, pred_return: double
child 2, net_score: double
child 3, conviction_pct: double
FI_consensus: struct<module: string, source: string, next_trading_day: timestamp[s], pick: string, conviction_pct: (... 458 chars omitted)
child 0, module: string
child 1, source: string
child 2, next_trading_da
...
_return: double, net_score: double, conviction_pct: double>
child 0, ticker: string
child 1, pred_return: double
child 2, net_score: double
child 3, conviction_pct: double
EQ_consensus: struct<module: string, source: string, next_trading_day: timestamp[s], pick: string, conviction_pct: (... 458 chars omitted)
child 0, module: string
child 1, source: string
child 2, next_trading_day: timestamp[s]
child 3, pick: string
child 4, conviction_pct: double
child 5, pred_return: double
child 6, second_pick: string
child 7, second_conviction: double
child 8, third_pick: string
child 9, third_conviction: double
child 10, lookback_days: int64
child 11, sig_depth: int64
child 12, model_type: string
child 13, regime_id: int64
child 14, regime_name: string
child 15, n_windows_used: int64
child 16, macro_pills: struct<VIX: double, T10Y2Y: double, HY_SPREAD: double, IG_SPREAD: double, DXY: double>
child 0, VIX: double
child 1, T10Y2Y: double
child 2, HY_SPREAD: double
child 3, IG_SPREAD: double
child 4, DXY: double
child 17, all_scores: list<item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>>
child 0, item: struct<ticker: string, pred_return: double, net_score: double, conviction_pct: double>
child 0, ticker: string
child 1, pred_return: double
child 2, net_score: double
child 3, conviction_pct: double
to
{'EQ_full': {'module': Value('string'), 'source': Value('string'), 'next_trading_day': Value('timestamp[s]'), 'pick': Value('string'), 'conviction_pct': Value('float64'), 'pred_return': Value('float64'), 'second_pick': Value('string'), 'second_conviction': Value('float64'), 'third_pick': Value('string'), 'third_conviction': Value('float64'), 'lookback_days': Value('int64'), 'sig_depth': Value('int64'), 'model_type': Value('string'), 'regime_id': Value('int64'), 'regime_name': Value('string'), 'n_windows_used': Value('null'), 'macro_pills': {'VIX': Value('float64'), 'T10Y2Y': Value('float64'), 'HY_SPREAD': Value('float64'), 'IG_SPREAD': Value('float64'), 'DXY': Value('float64')}, 'all_scores': List({'ticker': Value('string'), 'pred_return': Value('float64'), 'net_score': Value('float64'), 'conviction_pct': Value('float64')})}, 'EQ_consensus': {'module': Value('string'), 'source': Value('string'), 'next_trading_day': Value('timestamp[s]'), 'pick': Value('string'), 'conviction_pct': Value('float64'), 'pred_return': Value('float64'), 'second_pick': Value('string'), 'second_conviction': Value('float64'), 'third_pick': Value('string'), 'third_conviction': Value('float64'), 'lookback_days': Value('int64'), 'sig_depth': Value('int64'), 'model_type': Value('string'), 'regime_id': Value('int64'), 'regime_name': Value('string'), 'n_windows_used': Value('int64'), 'macro_pills': {'VIX': Value('float64'), 'T10Y2Y': Value('float64'), 'HY_SPREAD': Value('float64'), 'IG_SPREAD': Value('float64'), 'DXY': Value('float64')}, 'all_scores': List({'ticker': Value('string'), 'pred_return': Value('float64'), 'net_score': Value('float64'), 'conviction_pct': Value('float64')})}, 'generated_at': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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