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asah-dicoding

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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 49 new columns ({'month_aug', 'education_basic.6y', 'month_sep', 'contact_telephone', 'nr.employed', 'education_high.school', 'job_blue-collar', 'default_yes', 'default_unknown', 'previous', 'emp.var.rate', 'job_services', 'day_of_week_wed', 'previously_contacted', 'education_illiterate', 'day_of_week_mon', 'marital_married', 'job_entrepreneur', 'loan_yes', 'housing_unknown', 'month_nov', 'marital_single', 'poutcome_success', 'job_management', 'month_oct', 'campaign', 'age', 'education_professional.course', 'housing_yes', 'month_jul', 'poutcome_nonexistent', 'job_housemaid', 'month_dec', 'cons.conf.idx', 'month_mar', 'job_unemployed', 'job_student', 'day_of_week_thu', 'job_retired', 'education_basic.9y', 'cons.price.idx', 'month_may', 'loan_unknown', 'education_university.degree', 'job_technician', 'euribor3m', 'month_jun', 'day_of_week_tue', 'job_self-employed'}) and 22 missing columns ({'Deforestation', 'Encroachments', 'WetlandLoss', 'Landslides', 'AgriculturalPractices', 'MonsoonIntensity', 'ClimateChange', 'Urbanization', 'IneffectiveDisasterPreparedness', 'TopographyDrainage', 'FloodProbability', 'PoliticalFactors', 'id', 'DrainageSystems', 'RiverManagement', 'DeterioratingInfrastructure', 'Siltation', 'Watersheds', 'DamsQuality', 'PopulationScore', 'CoastalVulnerability', 'InadequatePlanning'}).

This happened while the csv dataset builder was generating data using

hf://datasets/nadyaputriast/asah-dicoding/Capstone/Salinan X_train_preprocessed_all_features.csv (at revision eb579b4171e92523818e228c3a2d84a582df0657), ['hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Machine Learning untuk Pemula/Regression/train.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan X_train_preprocessed_all_features.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan X_train_preprocessed_feature_selection.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan X_train_preprocessed_no_OHE.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan id_train.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan y_train_preprocessed_all_features.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan y_train_preprocessed_feature_selection.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan y_train_preprocessed_no_OHE.csv']

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/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              age: double
              campaign: double
              previous: double
              emp.var.rate: double
              cons.price.idx: double
              cons.conf.idx: double
              euribor3m: double
              nr.employed: double
              previously_contacted: double
              job_blue-collar: bool
              job_entrepreneur: bool
              job_housemaid: bool
              job_management: bool
              job_retired: bool
              job_self-employed: bool
              job_services: bool
              job_student: bool
              job_technician: bool
              job_unemployed: bool
              marital_married: bool
              marital_single: bool
              education_basic.6y: bool
              education_basic.9y: bool
              education_high.school: bool
              education_illiterate: bool
              education_professional.course: bool
              education_university.degree: bool
              default_unknown: bool
              default_yes: bool
              housing_unknown: bool
              housing_yes: bool
              loan_unknown: bool
              loan_yes: bool
              contact_telephone: bool
              month_aug: bool
              month_dec: bool
              month_jul: bool
              month_jun: bool
              month_mar: bool
              month_may: bool
              month_nov: bool
              month_oct: bool
              month_sep: bool
              day_of_week_mon: bool
              day_of_week_thu: bool
              day_of_week_tue: bool
              day_of_week_wed: bool
              poutcome_nonexistent: bool
              poutcome_success: bool
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 6316
              to
              {'id': Value('int64'), 'MonsoonIntensity': Value('int64'), 'TopographyDrainage': Value('int64'), 'RiverManagement': Value('int64'), 'Deforestation': Value('int64'), 'Urbanization': Value('int64'), 'ClimateChange': Value('int64'), 'DamsQuality': Value('int64'), 'Siltation': Value('int64'), 'AgriculturalPractices': Value('int64'), 'Encroachments': Value('int64'), 'IneffectiveDisasterPreparedness': Value('int64'), 'DrainageSystems': Value('int64'), 'CoastalVulnerability': Value('int64'), 'Landslides': Value('int64'), 'Watersheds': Value('int64'), 'DeterioratingInfrastructure': Value('int64'), 'PopulationScore': Value('int64'), 'WetlandLoss': Value('int64'), 'InadequatePlanning': Value('int64'), 'PoliticalFactors': Value('int64'), 'FloodProbability': Value('float64')}
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 49 new columns ({'month_aug', 'education_basic.6y', 'month_sep', 'contact_telephone', 'nr.employed', 'education_high.school', 'job_blue-collar', 'default_yes', 'default_unknown', 'previous', 'emp.var.rate', 'job_services', 'day_of_week_wed', 'previously_contacted', 'education_illiterate', 'day_of_week_mon', 'marital_married', 'job_entrepreneur', 'loan_yes', 'housing_unknown', 'month_nov', 'marital_single', 'poutcome_success', 'job_management', 'month_oct', 'campaign', 'age', 'education_professional.course', 'housing_yes', 'month_jul', 'poutcome_nonexistent', 'job_housemaid', 'month_dec', 'cons.conf.idx', 'month_mar', 'job_unemployed', 'job_student', 'day_of_week_thu', 'job_retired', 'education_basic.9y', 'cons.price.idx', 'month_may', 'loan_unknown', 'education_university.degree', 'job_technician', 'euribor3m', 'month_jun', 'day_of_week_tue', 'job_self-employed'}) and 22 missing columns ({'Deforestation', 'Encroachments', 'WetlandLoss', 'Landslides', 'AgriculturalPractices', 'MonsoonIntensity', 'ClimateChange', 'Urbanization', 'IneffectiveDisasterPreparedness', 'TopographyDrainage', 'FloodProbability', 'PoliticalFactors', 'id', 'DrainageSystems', 'RiverManagement', 'DeterioratingInfrastructure', 'Siltation', 'Watersheds', 'DamsQuality', 'PopulationScore', 'CoastalVulnerability', 'InadequatePlanning'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/nadyaputriast/asah-dicoding/Capstone/Salinan X_train_preprocessed_all_features.csv (at revision eb579b4171e92523818e228c3a2d84a582df0657), ['hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Machine Learning untuk Pemula/Regression/train.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan X_train_preprocessed_all_features.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan X_train_preprocessed_feature_selection.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan X_train_preprocessed_no_OHE.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan id_train.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan y_train_preprocessed_all_features.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan y_train_preprocessed_feature_selection.csv', 'hf://datasets/nadyaputriast/asah-dicoding@eb579b4171e92523818e228c3a2d84a582df0657/Capstone/Salinan y_train_preprocessed_no_OHE.csv']
              
              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)

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id
int64
MonsoonIntensity
int64
TopographyDrainage
int64
RiverManagement
int64
Deforestation
int64
Urbanization
int64
ClimateChange
int64
DamsQuality
int64
Siltation
int64
AgriculturalPractices
int64
Encroachments
int64
IneffectiveDisasterPreparedness
int64
DrainageSystems
int64
CoastalVulnerability
int64
Landslides
int64
Watersheds
int64
DeterioratingInfrastructure
int64
PopulationScore
int64
WetlandLoss
int64
InadequatePlanning
int64
PoliticalFactors
int64
FloodProbability
float64
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6
4
6
3
4
2
2
5
6
0.485
60
7
2
3
4
5
5
5
5
3
6
5
8
6
2
7
10
2
4
2
5
0.5
61
4
5
4
7
5
5
3
3
3
6
10
4
5
4
5
6
5
6
7
7
0.535
62
6
6
2
4
9
5
4
6
4
5
6
5
6
7
5
5
4
7
1
5
0.55
63
3
4
5
2
3
7
7
6
3
4
5
5
3
7
7
8
7
1
6
6
0.495
64
3
4
2
5
7
5
6
6
3
4
7
4
4
3
5
6
6
1
8
8
0.475
65
6
2
7
4
3
6
4
4
3
5
6
5
3
10
2
5
2
8
4
6
0.515
66
4
5
6
4
7
5
5
4
5
1
8
3
3
4
5
5
6
6
5
7
0.49
67
8
6
4
5
6
10
3
4
5
4
4
4
4
5
0
4
7
7
4
8
0.525
68
3
7
3
5
11
1
5
4
6
7
6
3
6
7
3
2
4
7
4
5
0.53
69
3
6
5
3
5
5
4
0
3
5
5
6
5
6
2
2
5
8
5
10
0.49
70
5
4
4
8
6
3
4
8
4
5
4
3
8
6
4
4
5
7
4
6
0.51
71
3
4
1
5
6
5
4
5
6
8
1
7
10
8
9
6
8
5
4
8
0.575
72
3
7
4
5
5
6
4
3
4
6
4
5
3
6
4
9
8
5
4
2
0.465
73
7
3
5
4
3
7
3
5
3
4
7
3
7
6
11
2
6
1
4
3
0.45
74
3
6
5
2
6
7
5
8
5
8
6
5
3
5
5
4
8
6
6
4
0.57
75
4
4
0
3
2
2
4
5
6
1
8
3
7
2
8
3
4
3
9
1
0.415
76
0
5
4
0
4
4
5
2
2
5
6
4
6
4
4
6
3
4
3
4
0.365
77
3
6
6
3
5
5
5
3
5
5
4
5
6
5
7
9
8
4
6
10
0.565
78
2
9
3
4
8
6
5
4
5
4
5
3
5
3
6
0
4
8
3
10
0.485
79
4
10
4
5
4
12
10
3
5
3
6
7
5
4
5
6
4
2
7
4
0.56
80
4
6
3
3
7
2
9
7
6
4
7
2
5
5
3
6
2
4
1
4
0.425
81
2
7
4
3
2
10
5
6
6
6
3
10
5
3
4
0
3
4
6
6
0.47
82
5
6
7
5
3
3
7
6
7
4
8
4
3
5
3
4
4
9
4
9
0.555
83
4
7
4
10
6
7
6
0
6
7
0
7
1
7
9
10
3
2
6
6
0.525
84
3
6
5
3
3
6
2
4
7
4
5
4
6
5
2
7
6
5
8
4
0.47
85
6
4
4
0
4
11
8
6
5
3
5
2
4
5
4
4
5
4
3
4
0.435
86
3
5
5
2
7
6
4
7
5
8
6
6
5
5
5
5
7
7
6
5
0.57
87
6
7
4
9
6
3
3
2
4
5
6
3
7
6
7
3
4
3
8
4
0.505
88
1
8
2
6
1
5
5
4
4
6
6
5
7
2
6
2
10
5
6
5
0.48
89
5
4
7
4
9
6
5
5
4
6
7
5
6
5
4
6
4
10
3
6
0.59
90
6
4
8
3
7
8
4
2
0
6
6
6
5
4
4
1
2
4
4
7
0.495
91
5
2
6
7
4
5
3
5
5
4
10
3
6
4
4
7
6
5
4
2
0.475
92
5
6
3
6
10
4
1
7
5
3
5
4
6
8
4
6
4
4
1
4
0.49
93
2
7
5
4
7
6
6
5
5
6
2
7
3
6
10
6
4
2
4
3
0.53
94
6
8
7
3
4
5
3
5
4
5
5
4
4
2
4
2
4
7
5
4
0.505
95
4
6
5
2
2
6
6
3
3
2
5
6
4
7
6
6
6
4
6
8
0.51
96
6
4
3
5
6
5
3
6
7
2
4
9
6
5
5
4
3
4
8
1
0.49
97
2
7
5
2
6
4
3
8
3
10
6
3
4
8
0
6
2
6
6
4
0.46
98
2
5
6
3
6
4
11
5
8
9
3
3
5
4
3
5
4
4
5
8
0.55
99
4
6
6
8
1
6
5
8
8
5
1
5
2
3
4
4
3
5
7
4
0.52
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