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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 583, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2027, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
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 1529, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
_data_files
list | _fingerprint
string | _format_columns
sequence | _format_kwargs
dict | _format_type
null | _indexes
dict | _output_all_columns
bool | _split
null |
|---|---|---|---|---|---|---|---|
[
{
"filename": "dataset.arrow"
}
] | 8b9e5f2e307c548a | [
"feat_Age",
"feat_AnyHealthcare",
"feat_BMI",
"feat_CholCheck",
"feat_DiffWalk",
"feat_Education",
"feat_Fruits",
"feat_GenHlth",
"feat_HeartDiseaseorAttack",
"feat_HighBP",
"feat_HighChol",
"feat_HvyAlcoholConsump",
"feat_Income",
"feat_MentHlth",
"feat_NoDocbcCost",
"feat_PhysActivity",
"feat_PhysHlth",
"feat_Sex",
"feat_Smoker",
"feat_Stroke",
"feat_Veggies",
"target"
] | {} | null | {} | false | null |
AutoTrain Dataset for project: sample-diabetes-predict
Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project sample-diabetes-predict.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"target": 0,
"feat_HighBP": 0.0,
"feat_HighChol": 0.0,
"feat_CholCheck": 1.0,
"feat_BMI": 34.0,
"feat_Smoker": 1.0,
"feat_Stroke": 0.0,
"feat_HeartDiseaseorAttack": 0.0,
"feat_PhysActivity": 1.0,
"feat_Fruits": 1.0,
"feat_Veggies": 1.0,
"feat_HvyAlcoholConsump": 0.0,
"feat_AnyHealthcare": 1.0,
"feat_NoDocbcCost": 0.0,
"feat_GenHlth": 3.0,
"feat_MentHlth": 0.0,
"feat_PhysHlth": 0.0,
"feat_DiffWalk": 0.0,
"feat_Sex": 0.0,
"feat_Age": 6.0,
"feat_Education": 6.0,
"feat_Income": 7.0
},
{
"target": 1,
"feat_HighBP": 0.0,
"feat_HighChol": 0.0,
"feat_CholCheck": 1.0,
"feat_BMI": 46.0,
"feat_Smoker": 1.0,
"feat_Stroke": 0.0,
"feat_HeartDiseaseorAttack": 0.0,
"feat_PhysActivity": 1.0,
"feat_Fruits": 1.0,
"feat_Veggies": 1.0,
"feat_HvyAlcoholConsump": 0.0,
"feat_AnyHealthcare": 1.0,
"feat_NoDocbcCost": 0.0,
"feat_GenHlth": 2.0,
"feat_MentHlth": 1.0,
"feat_PhysHlth": 0.0,
"feat_DiffWalk": 0.0,
"feat_Sex": 1.0,
"feat_Age": 10.0,
"feat_Education": 6.0,
"feat_Income": 5.0
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"target": "ClassLabel(num_classes=2, names=['0.0', '1.0'], id=None)",
"feat_HighBP": "Value(dtype='float64', id=None)",
"feat_HighChol": "Value(dtype='float64', id=None)",
"feat_CholCheck": "Value(dtype='float64', id=None)",
"feat_BMI": "Value(dtype='float64', id=None)",
"feat_Smoker": "Value(dtype='float64', id=None)",
"feat_Stroke": "Value(dtype='float64', id=None)",
"feat_HeartDiseaseorAttack": "Value(dtype='float64', id=None)",
"feat_PhysActivity": "Value(dtype='float64', id=None)",
"feat_Fruits": "Value(dtype='float64', id=None)",
"feat_Veggies": "Value(dtype='float64', id=None)",
"feat_HvyAlcoholConsump": "Value(dtype='float64', id=None)",
"feat_AnyHealthcare": "Value(dtype='float64', id=None)",
"feat_NoDocbcCost": "Value(dtype='float64', id=None)",
"feat_GenHlth": "Value(dtype='float64', id=None)",
"feat_MentHlth": "Value(dtype='float64', id=None)",
"feat_PhysHlth": "Value(dtype='float64', id=None)",
"feat_DiffWalk": "Value(dtype='float64', id=None)",
"feat_Sex": "Value(dtype='float64', id=None)",
"feat_Age": "Value(dtype='float64', id=None)",
"feat_Education": "Value(dtype='float64', id=None)",
"feat_Income": "Value(dtype='float64', id=None)"
}
Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
|---|---|
| train | 56552 |
| valid | 14140 |
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