Datasets:
PABannier
/
fgfr3mut

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<0: list<item: int64>, 1: list<item: int64>, 2: list<item: int64>, 3: list<item: int64>, 4: list<item: int64>, 5: list<item: int64>, 6: list<item: int64>, 7: list<item: int64>, 8: list<item: int64>, 9: list<item: int64>, 10: list<item: int64>, 11: list<item: int64>, 12: list<item: int64>, 13: list<item: int64>, 14: list<item: int64>, 15: list<item: int64>, 16: list<item: int64>, 17: list<item: int64>>
to
{'0': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '1': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '2': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '3': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '4': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '5': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '6': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '7': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '8': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '9': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '10': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '11': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '12': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '13': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '14': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '15': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '16': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '17': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '18': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<0: list<item: int64>, 1: list<item: int64>, 2: list<item: int64>, 3: list<item: int64>, 4: list<item: int64>, 5: list<item: int64>, 6: list<item: int64>, 7: list<item: int64>, 8: list<item: int64>, 9: list<item: int64>, 10: list<item: int64>, 11: list<item: int64>, 12: list<item: int64>, 13: list<item: int64>, 14: list<item: int64>, 15: list<item: int64>, 16: list<item: int64>, 17: list<item: int64>>
              to
              {'0': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '1': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '2': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '3': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '4': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '5': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '6': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '7': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '8': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '9': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '10': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '11': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '12': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '13': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '14': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '15': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '16': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '17': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '18': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1391, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 990, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2040, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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version
string
tiling_tool_version
string
nb_tiles
int64
total_number_of_tiles
int64
sampling_mode
dict
matter_detector
dict
features_extractor
string
level
int64
tile_size
int64
absolute_tile_size
int64
slide_size
sequence
tile_mpp
float64
level_dimensions
dict
level_mpp_mapping
dict
environment
dict
slide_hash
string
3.0.0
11.6.0
5,561
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{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
16
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{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
97816938bcfac54f2f32ac2ae42d225201760d70ac45654a33a386fc4b071d72
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{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
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{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
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{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
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1d43f33d38e28b01f8201fce812c48a882ba2a096b0a02f82d29bc215cff7d90
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{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
0b6d96a17aea7df2170e706bda6b97ccedc1f8d02d2a026a4a898b09ec935942
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iBOTViTGiant
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{ "0": 59690.1888, "1": 29845.0944, "2": 14922.5472, "3": 7461.2736, "4": 3730.6368, "5": 1865.3184, "6": 932.6592, "7": 466.3296, "8": 233.1648, "9": 116.5824, "10": 58.2912, "11": 29.1456, "12": 14.5728, "13": 7.2864, "14": 3.6432, "15": 1.8216, "16": 0.9108, "17": 0.4554, "18": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
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{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
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[ 90112, 91392 ]
0.9108
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{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
4af8351dcd6dc4cf636e405e24813cd2fc9e7f37bde7458ae433e333f20d9f1b
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11.6.0
6,111
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{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
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End of preview.