Dataset Viewer
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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
Random: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Gradient: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Magnitude: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Weight Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Activation Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Edge Imp: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP + Gradient: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP + Weight Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP + Activation Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Gradient + Magnitude: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Magnitude + Weight Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
3 signals: EAP+Gra+Mag: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
4 signals: EAP+Gra+Mag+Wei: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
5 signals: EAP+Gra+Mag+Wei+Act: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
6 signals: EAP+Gra+Mag+Wei+Act+Edg: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Full method (tier rules): struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
vs
baseline_correct: struct<CS1: list<item: int64>, CS2: list<item: int64>, CS3: list<item: int64>, CS4: list<item: int64>, CS5: list<item: int64>>
total: int64
per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>
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 572, 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:
Random: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Gradient: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Magnitude: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Weight Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Activation Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Edge Imp: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP + Gradient: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP + Weight Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
EAP + Activation Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Gradient + Magnitude: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Magnitude + Weight Δ: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
3 signals: EAP+Gra+Mag: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
4 signals: EAP+Gra+Mag+Wei: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
5 signals: EAP+Gra+Mag+Wei+Act: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
6 signals: EAP+Gra+Mag+Wei+Act+Edg: struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
Full method (tier rules): struct<n_signals: int64, skeleton: int64, retention: double, retained: int64, per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>>
vs
baseline_correct: struct<CS1: list<item: int64>, CS2: list<item: int64>, CS3: list<item: int64>, CS4: list<item: int64>, CS5: list<item: int64>>
total: int64
per_cs: struct<CS1: int64, CS2: int64, CS3: int64, CS4: int64, CS5: int64>Need 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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