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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'prediction'}) and 1 missing columns ({'predictions'}).
This happened while the json dataset builder was generating data using
hf://datasets/eparham1981/insurance-charge-mlops-logs-old1/data/data_16e78ae5-e184-4d22-bb24-dd1d732e52fc.json (at revision 770ce558a7d73a12f1662d91c6b88944d1a46f6e)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
age: int64
bmi: double
children: int64
sex: string
smoker: string
region: string
prediction: double
to
{'age': Value(dtype='int64', id=None), 'bmi': Value(dtype='float64', id=None), 'children': Value(dtype='int64', id=None), 'sex': Value(dtype='string', id=None), 'smoker': Value(dtype='string', id=None), 'region': Value(dtype='string', id=None), 'predictions': Value(dtype='float64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, 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 1049, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1872, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'prediction'}) and 1 missing columns ({'predictions'}).
This happened while the json dataset builder was generating data using
hf://datasets/eparham1981/insurance-charge-mlops-logs-old1/data/data_16e78ae5-e184-4d22-bb24-dd1d732e52fc.json (at revision 770ce558a7d73a12f1662d91c6b88944d1a46f6e)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
age int64 | bmi float64 | children int64 | sex string | smoker string | region string | predictions float64 |
|---|---|---|---|---|---|---|
30 | 30 | 3 | male | no | southeast | 6,490.209045 |
47 | 26.6 | 2 | female | no | northeast | 9,963.858573 |
32 | 31.5 | 1 | male | no | southwest | 6,507.30666 |
26 | 30.875 | 2 | male | no | northwest | 5,619.170392 |
53 | 22.88 | 1 | female | yes | southeast | 32,819.71429 |
47 | 29.37 | 1 | female | no | southeast | 9,814.461861 |
47 | 19.57 | 1 | male | no | northwest | 6,779.550131 |
53 | 36.6 | 3 | male | no | southwest | 14,473.526065 |
19 | 24.51 | 1 | female | no | northwest | 1,268.059266 |
19 | 31.92 | 0 | male | yes | northwest | 26,973.173457 |
22 | 31.35 | 1 | male | no | northwest | 4,326.107747 |
30 | 31.57 | 3 | male | no | southeast | 7,019.444352 |
43 | 29.9 | 1 | female | no | southwest | 8,813.283033 |
43 | 27.8 | 0 | male | yes | southwest | 31,312.647055 |
25 | 20.8 | 1 | female | no | southwest | 1,120.178105 |
50 | 32.11 | 2 | male | no | northeast | 12,573.57396 |
25 | 24.3 | 3 | female | no | southwest | 3,150.559604 |
28 | 25.8 | 0 | female | no | southwest | 3,151.289199 |
42 | 34.1 | 0 | male | no | southwest | 9,528.22557 |
27 | 24.75 | 0 | female | yes | southeast | 26,343.430227 |
60 | 28.9 | 0 | male | no | southwest | 12,400.907005 |
20 | 29.6 | 0 | female | no | southwest | 2,376.43525 |
25 | 22.515 | 1 | female | no | northwest | 2,137.41386 |
28 | 27.5 | 2 | female | no | southwest | 4,574.904104 |
59 | 35.2 | 0 | female | no | southeast | 14,438.141126 |
51 | 33.915 | 0 | female | no | northeast | 12,607.035846 |
47 | 38.94 | 2 | male | yes | southeast | 37,098.253531 |
63 | 27.74 | 0 | female | yes | northeast | 37,260.326664 |
41 | 30.59 | 2 | male | no | northwest | 9,377.734602 |
34 | 42.9 | 1 | male | no | southwest | 10,864.113164 |
49 | 25.6 | 2 | male | yes | southwest | 32,963.455242 |
40 | 28.69 | 3 | female | no | northwest | 8,924.153523 |
33 | 35.245 | 0 | male | no | northeast | 8,411.214544 |
59 | 27.83 | 3 | female | no | southeast | 13,229.605368 |
45 | 25.7 | 3 | female | no | southwest | 8,762.003294 |
43 | 35.64 | 1 | female | no | southeast | 10,900.129339 |
31 | 29.1 | 0 | female | no | southwest | 5,034.621738 |
30 | 19.95 | 3 | female | no | northwest | 3,408.20756 |
49 | 41.47 | 4 | female | no | southeast | 15,683.069502 |
29 | 29.64 | 1 | male | no | northeast | 5,919.18675 |
22 | 33.77 | 0 | male | no | southeast | 4,429.405969 |
53 | 33.25 | 0 | female | no | northeast | 12,896.820711 |
41 | 33.06 | 2 | female | no | northwest | 10,228.944897 |
19 | 25.745 | 1 | female | no | northwest | 1,684.368568 |
31 | 25.935 | 1 | male | no | northwest | 4,813.532931 |
18 | 26.125 | 0 | male | no | northeast | 1,482.294883 |
37 | 30.78 | 0 | female | yes | northeast | 31,603.71967 |
36 | 35.2 | 1 | male | yes | southeast | 32,585.515839 |
18 | 29.37 | 1 | male | no | southeast | 2,343.574701 |
23 | 37.1 | 3 | male | no | southwest | 6,932.801166 |
18 | 30.14 | 0 | male | no | southeast | 2,177.857182 |
61 | 36.1 | 3 | male | no | southwest | 16,360.785435 |
19 | 28.9 | 0 | female | no | southwest | 1,883.494758 |
48 | 33.33 | 0 | female | no | southeast | 10,981.045289 |
39 | 42.655 | 0 | male | no | northeast | 12,450.924589 |
49 | 42.68 | 2 | female | no | southeast | 15,240.393923 |
25 | 30.3 | 0 | female | no | southwest | 3,897.278565 |
30 | 27.7 | 0 | female | no | southwest | 4,305.716459 |
58 | 31.825 | 2 | female | no | northeast | 14,551.899921 |
51 | 34.1 | 0 | female | no | southeast | 12,011.533672 |
19 | 31.825 | 1 | female | no | northwest | 3,733.891284 |
34 | 31.92 | 1 | female | yes | northeast | 31,642.356845 |
54 | 32.68 | 0 | female | no | northeast | 12,961.653662 |
26 | 46.53 | 1 | male | no | southeast | 10,183.888539 |
44 | 39.52 | 0 | male | no | northwest | 12,308.340641 |
18 | 26.315 | 0 | female | no | northeast | 1,564.934159 |
24 | 33.345 | 0 | female | no | northwest | 5,105.871708 |
26 | 31.065 | 0 | male | no | northwest | 4,832.66041 |
35 | 34.32 | 3 | male | no | southeast | 9,231.327399 |
25 | 23.9 | 5 | male | no | southwest | 3,847.688459 |
52 | 24.13 | 1 | female | yes | northwest | 33,271.291244 |
43 | 32.6 | 2 | male | no | southwest | 10,130.120015 |
44 | 30.69 | 2 | male | no | southeast | 9,895.184004 |
32 | 28.93 | 1 | male | yes | southeast | 29,444.042715 |
23 | 28.12 | 0 | female | no | northwest | 3,087.587418 |
43 | 38.06 | 2 | male | yes | southeast | 35,773.709262 |
56 | 36.1 | 3 | male | no | southwest | 15,075.906906 |
62 | 38.83 | 0 | male | no | southeast | 16,414.122515 |
32 | 29.8 | 2 | female | no | southwest | 6,378.119797 |
45 | 25.175 | 2 | female | no | northeast | 8,969.550274 |
33 | 27.455 | 2 | male | no | northwest | 6,265.143805 |
41 | 34.2 | 2 | male | no | northwest | 10,594.638715 |
52 | 37.525 | 2 | female | no | northwest | 14,560.795906 |
26 | 29.92 | 1 | female | no | southeast | 4,603.372943 |
19 | 30.59 | 0 | male | no | northwest | 2,873.711507 |
23 | 36.67 | 2 | female | yes | northeast | 30,842.092487 |
56 | 25.65 | 0 | female | no | northwest | 10,735.167107 |
28 | 17.29 | 0 | female | no | northeast | 1,092.430936 |
28 | 33 | 2 | female | no | southeast | 6,580.848198 |
44 | 38.95 | 0 | female | yes | northwest | 35,785.918434 |
29 | 24.6 | 2 | female | no | southwest | 3,854.31141 |
33 | 28.27 | 1 | female | no | southeast | 5,846.000173 |
41 | 40.26 | 0 | male | no | southeast | 11,499.675042 |
26 | 30 | 1 | male | no | southwest | 4,459.813597 |
45 | 38.285 | 0 | female | no | northeast | 12,538.276063 |
20 | 33.33 | 0 | male | no | southeast | 3,767.133835 |
32 | 33.63 | 1 | male | yes | northeast | 31,686.242006 |
37 | 30.8 | 2 | female | no | southeast | 8,152.025936 |
30 | 27.93 | 0 | female | no | northeast | 5,193.047101 |
54 | 47.41 | 0 | female | yes | southeast | 40,920.291512 |
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