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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 25.7 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, 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 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2247, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, 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 1796, 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 2103, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1798, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1950, in array_cast
return array.cast(pa_type)
File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
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.ArrowInvalid: Float value 25.7 was truncated converting to int64
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 1433, 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 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1898, 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.
age
int64 | bmi
int64 | children
int64 | sex
string | smoker
string | region
string | prediction
float64 |
|---|---|---|---|---|---|---|
40 | 37 | 2 |
female
|
no
|
southwest
| 7,359.319926 |
40 | 37 | 2 |
female
|
yes
|
southwest
| 43,032.575997 |
40 | 37 | 2 |
male
|
yes
|
southwest
| 42,426.646516 |
34 | 3 | 1 |
male
|
no
|
northwest
| 7,824.32104 |
34 | 3 | 1 |
male
|
no
|
northwest
| 7,824.32104 |
34 | 3 | 1 |
male
|
no
|
northwest
| 7,824.32104 |
34 | 3 | 1 |
male
|
no
|
northwest
| 7,824.32104 |
34 | 3 | 1 |
male
|
yes
|
northwest
| 16,773.531475 |
34 | 3 | 1 |
male
|
yes
|
northwest
| 16,773.531475 |
34 | 3 | 1 |
male
|
yes
|
northwest
| 16,773.531475 |
32 | 12 | 2 |
female
|
yes
|
northeast
| 16,565.936506 |
46 | 10 | 3 |
male
|
no
|
northwest
| 9,519.047247 |
46 | 10 | 0 |
male
|
no
|
northwest
| 8,589.269509 |
46 | 78 | 0 |
male
|
no
|
northwest
| 10,866.626696 |
33 | 117 | 1 |
male
|
no
|
northeast
| 8,168.662122 |
33 | 51 | 1 |
male
|
no
|
northeast
| 8,168.662122 |
33 | 161 | 1 |
male
|
no
|
northeast
| 8,168.662122 |
33 | 161 | 3 |
male
|
no
|
northeast
| 9,770.511679 |
33 | 161 | 3 |
male
|
no
|
southeast
| 7,026.814738 |
33 | 161 | 3 |
male
|
no
|
southwest
| 7,104.121182 |
33 | 161 | 3 |
male
|
no
|
northeast
| 9,770.511679 |
33 | 161 | 3 |
male
|
no
|
northwest
| 7,471.283578 |
33 | 161 | 3 |
male
|
yes
|
northwest
| 47,479.38227 |
33 | 161 | 3 |
male
|
no
|
northwest
| 7,471.283578 |
33 | 161 | 3 |
male
|
yes
|
northwest
| 47,479.38227 |
33 | 161 | 3 |
male
|
no
|
northwest
| 7,471.283578 |
23 | 27 | 2 |
female
|
no
|
southeast
| 7,188.916885 |
23 | 27 | 2 |
female
|
yes
|
southeast
| 19,306.546678 |
41 | 86 | 2 |
male
|
no
|
southeast
| 8,845.522532 |
41 | 86 | 2 |
male
|
no
|
southeast
| 8,845.522532 |
41 | 86 | 2 |
male
|
yes
|
southeast
| 43,995.607406 |
61 | 15 | 2 |
male
|
no
|
northeast
| 16,212.280952 |
61 | 15 | 2 |
male
|
yes
|
northeast
| 25,869.645046 |
75 | 59 | 2 |
male
|
no
|
northeast
| 19,092.241658 |
75 | 59 | 2 |
male
|
yes
|
northeast
| 48,292.276555 |
96 | 59 | 2 |
male
|
yes
|
northeast
| 48,292.276555 |
96 | 59 | 2 |
male
|
no
|
northeast
| 19,092.241658 |
45 | 33 | 1 |
male
|
no
|
southeast
| 8,395.185167 |
45 | 33 | 1 |
male
|
yes
|
southeast
| 42,322.717899 |
19 | 79 | 1 |
male
|
yes
|
southeast
| 39,256.806569 |
35 | 83 | 1 |
male
|
yes
|
southeast
| 44,146.601789 |
35 | 83 | 1 |
male
|
no
|
southeast
| 7,415.229663 |
30 | 70 | 5 |
male
|
no
|
southwest
| 7,180.808139 |
30 | 70 | 5 |
male
|
yes
|
southwest
| 43,751.113128 |
36 | 0 | 1 |
male
|
no
|
southeast
| 7,426.367354 |
23 | 70 | 0 |
female
|
yes
|
southeast
| 39,343.784165 |
23 | 70 | 0 |
female
|
no
|
southeast
| 5,940.411886 |
42 | 57 | 2 |
female
|
yes
|
southeast
| 44,718.186471 |
42 | 57 | 2 |
male
|
yes
|
southeast
| 44,153.095878 |
42 | 57 | 2 |
male
|
no
|
southeast
| 8,886.687401 |
64 | 57 | 2 |
male
|
no
|
southeast
| 19,126.004604 |
88 | 57 | 2 |
male
|
no
|
southeast
| 19,126.004604 |
88 | 57 | 2 |
male
|
no
|
southeast
| 19,126.004604 |
86 | 38 | 3 |
male
|
no
|
southeast
| 19,613.590103 |
86 | 38 | 3 |
male
|
no
|
southeast
| 19,613.590103 |
86 | 38 | 3 |
male
|
yes
|
southeast
| 49,234.122659 |
33 | 38 | 3 |
male
|
no
|
southeast
| 6,623.582118 |
90 | 38 | 3 |
male
|
no
|
southeast
| 19,613.590103 |
79 | 38 | 3 |
male
|
no
|
southeast
| 19,613.590103 |
57 | 39 | 2 |
male
|
no
|
southeast
| 13,673.111576 |
57 | 39 | 2 |
male
|
yes
|
southeast
| 47,735.770042 |
42 | 35 | 2 |
female
|
yes
|
southwest
| 40,312.385665 |
36 | 54 | 2 |
female
|
yes
|
northeast
| 45,374.823615 |
30 | 80 | 2 |
female
|
no
|
southeast
| 6,754.224766 |
30 | 80 | 2 |
female
|
yes
|
southeast
| 43,789.698711 |
17 | 150 | 0 |
male
|
no
|
northwest
| 2,855.636313 |
45 | 25.7 | 3 |
female
|
no
|
southwest
| 9,975.453542 |
19 | 30.59 | 0 |
male
|
no
|
northwest
| 3,093.041322 |
39 | 32.8 | 0 |
female
|
no
|
southwest
| 6,183.392482 |
18 | 30.14 | 0 |
male
|
no
|
southeast
| 4,472.059219 |
45 | 38.285 | 0 |
female
|
no
|
northeast
| 11,888.941538 |
40 | 24.97 | 2 |
male
|
no
|
southeast
| 8,445.92038 |
47 | 19.57 | 1 |
male
|
no
|
northwest
| 9,804.189541 |
21 | 36.85 | 0 |
male
|
no
|
southeast
| 2,893.756152 |
31 | 25.935 | 1 |
male
|
no
|
northwest
| 5,055.123119 |
43 | 38.06 | 2 |
male
|
yes
|
southeast
| 44,383.556197 |
59 | 37.1 | 1 |
male
|
no
|
southwest
| 20,813.086292 |
40 | 41.42 | 1 |
female
|
no
|
northwest
| 7,111.140683 |
58 | 27.17 | 0 |
female
|
no
|
northwest
| 13,073.332296 |
46 | 48.07 | 2 |
female
|
no
|
northeast
| 11,040.859798 |
29 | 29.64 | 1 |
male
|
no
|
northeast
| 5,085.090895 |
42 | 37.18 | 2 |
male
|
no
|
southeast
| 8,691.872044 |
27 | 32.585 | 3 |
male
|
no
|
northeast
| 7,964.43015 |
61 | 21.09 | 0 |
female
|
no
|
northwest
| 15,719.921192 |
60 | 18.335 | 0 |
female
|
no
|
northeast
| 15,143.63757 |
26 | 31.065 | 0 |
male
|
no
|
northwest
| 3,597.931183 |
22 | 52.58 | 1 |
male
|
yes
|
southeast
| 39,256.806569 |
23 | 41.91 | 0 |
male
|
no
|
southeast
| 4,790.834146 |
22 | 39.805 | 0 |
female
|
no
|
northeast
| 3,746.225954 |
18 | 29.165 | 0 |
female
|
no
|
northeast
| 3,559.51485 |
62 | 32.11 | 0 |
male
|
no
|
northeast
| 13,506.264752 |
21 | 26.4 | 1 |
female
|
no
|
southwest
| 2,610.618663 |
62 | 21.4 | 0 |
male
|
no
|
southwest
| 15,314.334706 |
21 | 25.7 | 4 |
male
|
yes
|
southwest
| 18,451.187378 |
25 | 23.9 | 5 |
male
|
no
|
southwest
| 6,253.80115 |
32 | 33.63 | 1 |
male
|
yes
|
northeast
| 42,006.185584 |
52 | 46.75 | 5 |
female
|
no
|
southeast
| 14,379.356648 |
51 | 36.385 | 3 |
female
|
no
|
northwest
| 12,600.835905 |
33 | 28.27 | 1 |
female
|
no
|
southeast
| 5,123.180574 |
60 | 35.1 | 0 |
female
|
no
|
southwest
| 13,306.365696 |
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