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The dataset generation failed
Error code: DatasetGenerationError Exception: ArrowInvalid Message: Float value 30.4 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 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 2245, in cast_table_to_schema arrays = [ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp> cast_array_to_feature( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, 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 1795, 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 2102, in cast_array_to_feature return array_cast( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper return func(array, *args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1949, 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 30.4 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 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 1897, 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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age
int64 | bmi
float64 | children
int64 | sex
string | smoker
string | region
string | prediction
float64 |
---|---|---|---|---|---|---|
40 | 0 | 2 | female | no | southeast | -1,459.497547 |
46 | 2 | 2 | male | no | northwest | 1,025.137071 |
49 | 22 | 1 | male | no | northeast | 8,483.31377 |
39 | 24 | 2 | male | no | northeast | 7,013.020599 |
25 | 29 | 2 | female | no | northeast | 5,119.415169 |
25 | 29 | 2 | female | yes | northeast | 28,770.544025 |
25 | 29 | 2 | female | no | northeast | 5,119.415169 |
25 | 29 | 2 | female | no | southwest | 4,309.615815 |
25 | 29 | 2 | female | no | northwest | 4,748.737843 |
25 | 29 | 2 | female | no | northeast | 5,119.415169 |
20 | 19 | 11 | female | yes | northwest | 27,571.571702 |
23 | 19 | 11 | female | yes | northwest | 28,342.498819 |
26 | 19 | 11 | female | yes | northwest | 29,113.425937 |
31 | 19 | 11 | female | yes | northwest | 30,398.304466 |
39 | 19 | 11 | female | yes | northwest | 32,454.110112 |
39 | 21 | 11 | female | yes | northwest | 33,128.295216 |
39 | 21 | 7 | female | yes | northwest | 31,427.180082 |
39 | 21 | 7 | male | no | northwest | 7,757.459535 |
39 | 21 | 7 | male | no | southeast | 7,470.272564 |
39 | 21 | 7 | male | no | southeast | 7,470.272564 |
39 | 21 | 7 | male | no | southwest | 7,318.337507 |
39 | 21 | 7 | male | no | northwest | 7,757.459535 |
30 | 25 | 2 | male | no | southeast | 4,379.467502 |
27 | 30.4 | 3 | female | no | northwest | 6,159.897611 |
23 | 32.7 | 3 | male | no | southwest | 5,449.593937 |
22 | 33.77 | 0 | male | no | southeast | 4,429.405969 |
45 | 39.805 | 0 | male | no | northeast | 13,032.065051 |
52 | 18.335 | 0 | female | no | northwest | 7,241.432267 |
26 | 29.92 | 1 | female | no | southeast | 4,603.372943 |
61 | 36.1 | 3 | male | no | southwest | 16,360.785435 |
47 | 19.57 | 1 | male | no | northwest | 6,779.550131 |
51 | 36.385 | 3 | female | no | northwest | 14,344.813474 |
35 | 35.86 | 2 | female | no | southeast | 9,343.762837 |
22 | 52.58 | 1 | male | yes | southeast | 34,846.524511 |
26 | 31.065 | 0 | male | no | northwest | 4,832.66041 |
19 | 24.51 | 1 | female | no | northwest | 1,268.059266 |
18 | 26.125 | 0 | male | no | northeast | 1,482.294883 |
42 | 37.18 | 2 | male | no | southeast | 11,568.963255 |
47 | 26.6 | 2 | female | no | northeast | 9,963.858573 |
64 | 37.905 | 0 | male | no | northwest | 16,903.450287 |
30 | 27.7 | 0 | female | no | southwest | 4,305.716459 |
42 | 24.985 | 2 | female | no | northwest | 7,763.898246 |
47 | 25.41 | 1 | male | yes | southeast | 32,112.11252 |
43 | 32.6 | 2 | male | no | southwest | 10,130.120015 |
21 | 36.85 | 0 | male | no | southeast | 5,210.675323 |
43 | 26.885 | 0 | female | yes | northwest | 31,461.921089 |
32 | 28.93 | 1 | male | yes | southeast | 29,444.042715 |
22 | 27.1 | 0 | female | no | southwest | 2,047.655282 |
44 | 38.06 | 1 | male | no | southeast | 11,954.277329 |
31 | 25.935 | 1 | male | no | northwest | 4,813.532931 |
21 | 16.815 | 1 | female | no | northeast | -441.239183 |
62 | 38.095 | 2 | female | no | northeast | 17,693.373045 |
59 | 27.83 | 3 | female | no | southeast | 13,229.605368 |
37 | 30.78 | 0 | female | yes | northeast | 31,603.71967 |
60 | 40.92 | 0 | male | yes | southeast | 40,255.823393 |
30 | 28.405 | 1 | female | no | northwest | 5,407.76752 |
26 | 33.915 | 1 | male | no | northwest | 6,218.652966 |
50 | 31.825 | 0 | male | yes | northeast | 35,278.073871 |
41 | 33.06 | 2 | female | no | northwest | 10,228.944897 |
30 | 19.95 | 3 | female | no | northwest | 3,408.20756 |
33 | 35.75 | 2 | male | no | southeast | 8,774.139553 |
48 | 33.33 | 0 | female | no | southeast | 10,981.045289 |
55 | 27.645 | 0 | male | no | northwest | 11,132.099351 |
64 | 26.885 | 0 | female | yes | northwest | 36,858.410912 |
19 | 22.515 | 0 | female | no | northwest | 170.280841 |
19 | 25.745 | 1 | female | no | northwest | 1,684.368568 |
50 | 32.11 | 2 | male | no | northeast | 12,573.57396 |
23 | 36.67 | 2 | female | yes | northeast | 30,842.092487 |
24 | 35.86 | 0 | male | no | southeast | 5,647.880814 |
30 | 23.655 | 3 | female | yes | northwest | 28,308.264321 |
26 | 30 | 1 | male | no | southwest | 4,459.813597 |
19 | 30.59 | 0 | male | no | northwest | 2,873.711507 |
28 | 33 | 2 | female | no | southeast | 6,580.848198 |
50 | 25.6 | 0 | female | no | southwest | 8,737.336217 |
25 | 20.8 | 1 | female | no | southwest | 1,120.178105 |
54 | 32.68 | 0 | female | no | northeast | 12,961.653662 |
25 | 34.485 | 0 | female | no | northwest | 5,747.132923 |
41 | 34.2 | 2 | male | no | northwest | 10,594.638715 |
20 | 29.6 | 0 | female | no | southwest | 2,376.43525 |
36 | 35.2 | 1 | male | yes | southeast | 32,585.515839 |
30 | 27.93 | 0 | female | no | northeast | 5,193.047101 |
19 | 17.48 | 0 | male | no | northwest | -1,545.571849 |
23 | 28.12 | 0 | female | no | northwest | 3,087.587418 |
62 | 32.11 | 0 | male | no | northeast | 14,806.724863 |
27 | 24.75 | 0 | female | yes | southeast | 26,343.430227 |
51 | 18.05 | 0 | female | no | northwest | 6,888.385184 |
56 | 34.43 | 0 | male | no | southeast | 13,389.061052 |
49 | 25.6 | 2 | male | yes | southwest | 32,963.455242 |
35 | 23.465 | 2 | female | no | northeast | 5,823.364952 |
56 | 35.8 | 1 | female | no | southwest | 14,142.813265 |
19 | 28.6 | 5 | female | no | southwest | 3,908.76091 |
43 | 26.7 | 2 | female | yes | southwest | 31,810.994506 |
53 | 36.6 | 3 | male | no | southwest | 14,473.526065 |
40 | 41.42 | 1 | female | no | northwest | 12,364.784142 |
20 | 33 | 0 | female | no | southeast | 3,674.484984 |
28 | 17.29 | 0 | female | no | northeast | 1,092.430936 |
58 | 33.1 | 0 | female | no | southwest | 13,321.336003 |
42 | 40.37 | 2 | female | yes | southeast | 36,314.009043 |
52 | 26.4 | 3 | male | no | southeast | 10,930.141387 |
37 | 46.53 | 3 | male | no | southeast | 13,861.17887 |
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