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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 20 new columns ({'overall_risk_score', 'hydration_level', 'alcohol_use', 'oral_hygiene', 'sleep_hours', 'smoking', 'spicy_food_intake', 'water_quality', 'autoimmune_condition', 'lifestyle_score', 'nutritional_score', 'diet_type', 'vitamin_b2_deficiency', 'iron_deficiency', 'stress_level', 'immune_status', 'hygiene_score', 'gut_disorder', 'vitamin_b12_deficiency', 'diabetes'}) and 8 missing columns ({'country', 'age_group', 'climate', 'continent', 'gender', 'income_level', 'region_type', 'age'}).
This happened while the csv dataset builder was generating data using
zip://02_risk_factors.csv::/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip, [/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip (origin=hf://datasets/mahfuzmee-eng/mouth_ulcer_dataset@78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip)]
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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
patient_id: string
vitamin_b2_deficiency: int64
vitamin_b12_deficiency: int64
iron_deficiency: int64
stress_level: string
sleep_hours: double
smoking: int64
alcohol_use: int64
spicy_food_intake: string
oral_hygiene: string
immune_status: string
diet_type: string
diabetes: int64
gut_disorder: int64
autoimmune_condition: int64
hydration_level: string
water_quality: string
nutritional_score: int64
lifestyle_score: double
hygiene_score: int64
overall_risk_score: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2918
to
{'patient_id': Value('string'), 'age': Value('int64'), 'age_group': Value('string'), 'gender': Value('string'), 'country': Value('string'), 'continent': Value('string'), 'region_type': Value('string'), 'income_level': Value('string'), 'climate': Value('string')}
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 1347, 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 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, 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 20 new columns ({'overall_risk_score', 'hydration_level', 'alcohol_use', 'oral_hygiene', 'sleep_hours', 'smoking', 'spicy_food_intake', 'water_quality', 'autoimmune_condition', 'lifestyle_score', 'nutritional_score', 'diet_type', 'vitamin_b2_deficiency', 'iron_deficiency', 'stress_level', 'immune_status', 'hygiene_score', 'gut_disorder', 'vitamin_b12_deficiency', 'diabetes'}) and 8 missing columns ({'country', 'age_group', 'climate', 'continent', 'gender', 'income_level', 'region_type', 'age'}).
This happened while the csv dataset builder was generating data using
zip://02_risk_factors.csv::/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip, [/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip (origin=hf://datasets/mahfuzmee-eng/mouth_ulcer_dataset@78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip)]
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.
patient_id string | age int64 | age_group string | gender string | country string | continent string | region_type string | income_level string | climate string |
|---|---|---|---|---|---|---|---|---|
P000001 | 38 | young_adult | male | Indonesia | Asia | urban | middle | tropical |
P000002 | 27 | young_adult | female | Egypt | Africa | urban | middle | dry |
P000003 | 41 | adult | male | Brazil | South America | urban | middle | tropical |
P000004 | 64 | elderly | female | China | Asia | urban | low | temperate |
P000005 | 26 | young_adult | male | USA | North America | rural | middle | temperate |
P000006 | 26 | young_adult | female | Bangladesh | Asia | urban | high | tropical |
P000007 | 65 | elderly | female | Nigeria | Africa | rural | middle | tropical |
P000008 | 43 | adult | male | Nigeria | Africa | suburban | middle | tropical |
P000009 | 23 | young_adult | male | Japan | Asia | urban | low | temperate |
P000010 | 39 | young_adult | female | India | Asia | suburban | middle | tropical |
P000011 | 23 | young_adult | female | Pakistan | Asia | urban | middle | tropical |
P000012 | 23 | young_adult | female | India | Asia | urban | middle | tropical |
P000013 | 33 | young_adult | female | Bangladesh | Asia | urban | middle | tropical |
P000014 | 11 | child | male | Bangladesh | Asia | rural | low | tropical |
P000015 | 12 | child | male | Indonesia | Asia | urban | low | tropical |
P000016 | 22 | young_adult | male | USA | North America | urban | low | temperate |
P000017 | 18 | teen | male | Egypt | Africa | urban | middle | dry |
P000018 | 35 | young_adult | female | China | Asia | rural | low | temperate |
P000019 | 19 | teen | male | Bangladesh | Asia | rural | high | tropical |
P000020 | 14 | teen | male | India | Asia | urban | middle | tropical |
P000021 | 62 | elderly | male | USA | North America | rural | high | temperate |
P000022 | 26 | young_adult | female | Japan | Asia | urban | middle | temperate |
P000023 | 30 | young_adult | male | UK | Europe | rural | low | temperate |
P000024 | 14 | teen | female | Germany | Europe | urban | low | temperate |
P000025 | 22 | young_adult | female | India | Asia | urban | low | tropical |
P000026 | 31 | young_adult | female | Ethiopia | Africa | rural | low | tropical |
P000027 | 16 | teen | female | Pakistan | Asia | urban | middle | tropical |
P000028 | 36 | young_adult | male | Nigeria | Africa | suburban | middle | tropical |
P000029 | 22 | young_adult | male | Brazil | South America | urban | middle | tropical |
P000030 | 25 | young_adult | female | China | Asia | rural | low | temperate |
P000031 | 22 | young_adult | male | Egypt | Africa | urban | middle | dry |
P000032 | 75 | elderly | female | Egypt | Africa | urban | middle | dry |
P000033 | 29 | young_adult | female | Bangladesh | Asia | suburban | middle | tropical |
P000034 | 17 | teen | male | France | Europe | urban | low | temperate |
P000035 | 45 | adult | male | USA | North America | urban | low | temperate |
P000036 | 16 | teen | female | Australia | Oceania | urban | middle | temperate |
P000037 | 33 | young_adult | female | USA | North America | suburban | low | temperate |
P000038 | 11 | child | female | Bangladesh | Asia | urban | high | tropical |
P000039 | 15 | teen | female | France | Europe | urban | middle | temperate |
P000040 | 33 | young_adult | female | Indonesia | Asia | urban | low | tropical |
P000041 | 43 | adult | male | China | Asia | urban | middle | temperate |
P000042 | 32 | young_adult | male | UK | Europe | urban | low | temperate |
P000043 | 28 | young_adult | female | Bangladesh | Asia | suburban | middle | tropical |
P000044 | 25 | young_adult | female | India | Asia | urban | middle | tropical |
P000045 | 14 | teen | female | Egypt | Africa | urban | low | dry |
P000046 | 20 | young_adult | female | Bangladesh | Asia | rural | low | tropical |
P000047 | 23 | young_adult | male | Canada | North America | urban | middle | temperate |
P000048 | 50 | adult | female | Germany | Europe | suburban | high | temperate |
P000049 | 35 | young_adult | male | Australia | Oceania | rural | low | temperate |
P000050 | 12 | child | male | Philippines | Asia | urban | high | tropical |
P000051 | 35 | young_adult | female | China | Asia | urban | middle | temperate |
P000052 | 24 | young_adult | male | France | Europe | rural | high | temperate |
P000053 | 21 | young_adult | male | UK | Europe | rural | low | temperate |
P000054 | 40 | adult | female | India | Asia | urban | low | tropical |
P000055 | 50 | adult | female | Bangladesh | Asia | urban | low | tropical |
P000056 | 47 | adult | female | Indonesia | Asia | suburban | middle | tropical |
P000057 | 19 | teen | female | Brazil | South America | suburban | low | tropical |
P000058 | 25 | young_adult | male | Egypt | Africa | rural | middle | dry |
P000059 | 35 | young_adult | male | Ethiopia | Africa | urban | middle | tropical |
P000060 | 48 | adult | female | UK | Europe | urban | middle | temperate |
P000061 | 23 | young_adult | female | Nigeria | Africa | urban | middle | tropical |
P000062 | 27 | young_adult | female | Indonesia | Asia | rural | middle | tropical |
P000063 | 17 | teen | female | Mexico | North America | urban | middle | tropical |
P000064 | 16 | teen | male | Argentina | South America | urban | middle | temperate |
P000065 | 44 | adult | female | Bangladesh | Asia | urban | low | tropical |
P000066 | 59 | adult | male | Germany | Europe | suburban | low | temperate |
P000067 | 28 | young_adult | female | Indonesia | Asia | urban | middle | tropical |
P000068 | 49 | adult | male | China | Asia | urban | low | temperate |
P000069 | 35 | young_adult | male | India | Asia | urban | high | tropical |
P000070 | 21 | young_adult | male | USA | North America | urban | low | temperate |
P000071 | 35 | young_adult | female | Bangladesh | Asia | rural | middle | tropical |
P000072 | 64 | elderly | female | Australia | Oceania | suburban | middle | temperate |
P000073 | 29 | young_adult | male | China | Asia | urban | low | temperate |
P000074 | 65 | elderly | male | Mexico | North America | urban | high | tropical |
P000075 | 8 | child | male | Bangladesh | Asia | urban | low | tropical |
P000076 | 45 | adult | female | Pakistan | Asia | urban | middle | tropical |
P000077 | 31 | young_adult | female | India | Asia | rural | high | tropical |
P000078 | 25 | young_adult | male | China | Asia | suburban | low | temperate |
P000079 | 31 | young_adult | male | Bangladesh | Asia | urban | middle | tropical |
P000080 | 11 | child | female | Nigeria | Africa | suburban | middle | tropical |
P000081 | 26 | young_adult | female | Bangladesh | Asia | rural | middle | tropical |
P000082 | 35 | young_adult | male | USA | North America | rural | middle | temperate |
P000083 | 62 | elderly | female | Philippines | Asia | urban | middle | tropical |
P000084 | 23 | young_adult | female | Germany | Europe | urban | low | temperate |
P000085 | 20 | young_adult | male | Canada | North America | urban | high | temperate |
P000086 | 23 | young_adult | male | China | Asia | urban | middle | temperate |
P000087 | 47 | adult | male | China | Asia | suburban | middle | temperate |
P000088 | 35 | young_adult | male | France | Europe | rural | middle | temperate |
P000089 | 22 | young_adult | female | Philippines | Asia | urban | high | tropical |
P000090 | 38 | young_adult | female | Pakistan | Asia | rural | middle | tropical |
P000091 | 31 | young_adult | female | India | Asia | suburban | middle | tropical |
P000092 | 48 | adult | male | USA | North America | urban | middle | temperate |
P000093 | 21 | young_adult | male | Bangladesh | Asia | rural | low | tropical |
P000094 | 25 | young_adult | female | Ethiopia | Africa | urban | low | tropical |
P000095 | 24 | young_adult | female | China | Asia | urban | high | temperate |
P000096 | 14 | teen | male | Nigeria | Africa | urban | middle | tropical |
P000097 | 34 | young_adult | male | India | Asia | suburban | low | tropical |
P000098 | 34 | young_adult | female | UK | Europe | urban | high | temperate |
P000099 | 30 | young_adult | male | UK | Europe | suburban | middle | temperate |
P000100 | 26 | young_adult | male | Germany | Europe | rural | high | temperate |
End of preview.
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