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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
run_date: timestamp[s]
universes: struct<FI_COMMODITIES: struct<top_etfs: list<item: struct<ticker: string, tnn_score: double, best_wi (... 7404 chars omitted)
child 0, FI_COMMODITIES: struct<top_etfs: list<item: struct<ticker: string, tnn_score: double, best_window: int64, forecast_s (... 997 chars omitted)
child 0, top_etfs: list<item: struct<ticker: string, tnn_score: double, best_window: int64, forecast_signal: double, ne (... 51 chars omitted)
child 0, item: struct<ticker: string, tnn_score: double, best_window: int64, forecast_signal: double, neighborhood_ (... 39 chars omitted)
child 0, ticker: string
child 1, tnn_score: double
child 2, best_window: int64
child 3, forecast_signal: double
child 4, neighborhood_stability: double
child 5, fit_quality: double
child 1, full_scores: struct<GLD: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stabilit (... 789 chars omitted)
child 0, GLD: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stability: double, f (... 19 chars omitted)
child 0, score: double
child 1, best_window: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 1, SLV: struct<score: double, best_window: int64, forecast_signal: double, neighborho
...
ndow: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 25, XLK: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stability: double, f (... 19 chars omitted)
child 0, score: double
child 1, best_window: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 26, XSD: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stability: double, f (... 19 chars omitted)
child 0, score: double
child 1, best_window: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 2, run_date: timestamp[s]
model_b: struct<train_r2: double, train_corr: double, test_r2: double, test_corr: double>
child 0, train_r2: double
child 1, train_corr: double
child 2, test_r2: double
child 3, test_corr: double
train_days: int64
verdict: string
model_a: struct<train_r2: double, train_corr: double, test_r2: double, test_corr: double>
child 0, train_r2: double
child 1, train_corr: double
child 2, test_r2: double
child 3, test_corr: double
universe: string
test_r2_gain: double
test_days: int64
window: int64
tickers: list<item: string>
child 0, item: string
to
{'run_date': Value('timestamp[s]'), 'universe': Value('string'), 'window': Value('int64'), 'train_days': Value('int64'), 'test_days': Value('int64'), 'tickers': List(Value('string')), 'model_a': {'train_r2': Value('float64'), 'train_corr': Value('float64'), 'test_r2': Value('float64'), 'test_corr': Value('float64')}, 'model_b': {'train_r2': Value('float64'), 'train_corr': Value('float64'), 'test_r2': Value('float64'), 'test_corr': Value('float64')}, 'test_r2_gain': Value('float64'), 'verdict': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 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
run_date: timestamp[s]
universes: struct<FI_COMMODITIES: struct<top_etfs: list<item: struct<ticker: string, tnn_score: double, best_wi (... 7404 chars omitted)
child 0, FI_COMMODITIES: struct<top_etfs: list<item: struct<ticker: string, tnn_score: double, best_window: int64, forecast_s (... 997 chars omitted)
child 0, top_etfs: list<item: struct<ticker: string, tnn_score: double, best_window: int64, forecast_signal: double, ne (... 51 chars omitted)
child 0, item: struct<ticker: string, tnn_score: double, best_window: int64, forecast_signal: double, neighborhood_ (... 39 chars omitted)
child 0, ticker: string
child 1, tnn_score: double
child 2, best_window: int64
child 3, forecast_signal: double
child 4, neighborhood_stability: double
child 5, fit_quality: double
child 1, full_scores: struct<GLD: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stabilit (... 789 chars omitted)
child 0, GLD: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stability: double, f (... 19 chars omitted)
child 0, score: double
child 1, best_window: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 1, SLV: struct<score: double, best_window: int64, forecast_signal: double, neighborho
...
ndow: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 25, XLK: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stability: double, f (... 19 chars omitted)
child 0, score: double
child 1, best_window: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 26, XSD: struct<score: double, best_window: int64, forecast_signal: double, neighborhood_stability: double, f (... 19 chars omitted)
child 0, score: double
child 1, best_window: int64
child 2, forecast_signal: double
child 3, neighborhood_stability: double
child 4, fit_quality: double
child 2, run_date: timestamp[s]
model_b: struct<train_r2: double, train_corr: double, test_r2: double, test_corr: double>
child 0, train_r2: double
child 1, train_corr: double
child 2, test_r2: double
child 3, test_corr: double
train_days: int64
verdict: string
model_a: struct<train_r2: double, train_corr: double, test_r2: double, test_corr: double>
child 0, train_r2: double
child 1, train_corr: double
child 2, test_r2: double
child 3, test_corr: double
universe: string
test_r2_gain: double
test_days: int64
window: int64
tickers: list<item: string>
child 0, item: string
to
{'run_date': Value('timestamp[s]'), 'universe': Value('string'), 'window': Value('int64'), 'train_days': Value('int64'), 'test_days': Value('int64'), 'tickers': List(Value('string')), 'model_a': {'train_r2': Value('float64'), 'train_corr': Value('float64'), 'test_r2': Value('float64'), 'test_corr': Value('float64')}, 'model_b': {'train_r2': Value('float64'), 'train_corr': Value('float64'), 'test_r2': Value('float64'), 'test_corr': Value('float64')}, 'test_r2_gain': Value('float64'), 'verdict': 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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