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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 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 2116, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1963, 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 1396, 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 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, 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 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 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 |
|---|---|---|---|---|---|---|
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 |
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 |
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 |
18 | 41.14 | 0 | male | no | southeast | 2,538.730157 |
34 | 33.25 | 1 | female | no | northeast | 7,350.27974 |
43 | 35.97 | 3 | male | yes | southeast | 42,372.502835 |
37 | 30.8 | 2 | female | no | southeast | 7,446.633665 |
22 | 32.11 | 0 | male | no | northwest | 2,309.759849 |
57 | 23.18 | 0 | female | no | northwest | 11,940.406599 |
33 | 35.75 | 1 | male | yes | southeast | 44,213.416145 |
42 | 30 | 0 | male | yes | southwest | 36,157.175321 |
52 | 26.4 | 3 | male | no | southeast | 13,949.845152 |
52 | 30.875 | 0 | female | no | northeast | 11,790.61702 |
53 | 36.86 | 3 | female | yes | northwest | 48,985.701856 |
23 | 24.51 | 0 | male | no | northeast | 5,217.211503 |
36 | 29.92 | 0 | female | no | southeast | 5,061.039801 |
49 | 28.69 | 3 | male | no | northwest | 11,213.008458 |
21 | 35.72 | 0 | female | no | northwest | 2,589.487115 |
28 | 33 | 2 | female | no | southeast | 4,898.811795 |
49 | 36.85 | 0 | male | no | southeast | 9,950.166112 |
58 | 32.395 | 1 | female | no | northeast | 12,738.152567 |
32 | 28.93 | 1 | male | yes | southeast | 20,585.856353 |
19 | 22.515 | 0 | female | no | northwest | 2,058.847125 |
39 | 32.5 | 1 | female | no | southwest | 6,735.792071 |
24 | 35.86 | 0 | male | no | southeast | 2,695.861878 |
50 | 28.12 | 3 | female | no | northwest | 12,306.200526 |
63 | 37.7 | 0 | female | yes | southwest | 47,894.153383 |
32 | 31.5 | 1 | male | no | southwest | 4,749.520027 |
58 | 31.825 | 2 | female | no | northeast | 14,072.493162 |
35 | 23.465 | 2 | female | no | northeast | 8,934.909283 |
29 | 24.6 | 2 | female | no | southwest | 5,985.977317 |
29 | 29.59 | 1 | female | no | southeast | 4,570.807311 |
25 | 32.23 | 1 | female | no | southeast | 3,936.806451 |
57 | 23.98 | 1 | female | no | southeast | 13,138.628399 |
30 | 23.655 | 3 | female | yes | northwest | 17,904.699715 |
44 | 31.35 | 1 | male | yes | northeast | 41,392.272505 |
19 | 30.4 | 0 | male | no | southwest | 2,665.605832 |
62 | 26.695 | 0 | male | yes | northeast | 27,886.207606 |
46 | 25.745 | 3 | male | no | northwest | 10,758.245403 |
42 | 24.985 | 2 | female | no | northwest | 7,520.283058 |
24 | 33.345 | 0 | female | no | northwest | 5,089.82822 |
58 | 33.1 | 0 | female | no | southwest | 12,194.36443 |
43 | 32.6 | 2 | male | no | southwest | 9,768.155293 |
47 | 36.63 | 1 | female | yes | southeast | 43,809.662954 |
19 | 27.7 | 0 | male | yes | southwest | 17,320.293207 |
47 | 26.125 | 1 | female | yes | northeast | 27,258.210877 |
34 | 31.92 | 1 | female | yes | northeast | 39,990.430581 |
28 | 27.5 | 2 | female | no | southwest | 4,808.191196 |
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