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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 1 new columns ({'ProdTaken'}) and 13 missing columns ({'NumberOfTrips', 'NumberOfFollowups', 'TypeofContact', 'MaritalStatus', 'NumberOfChildrenVisiting', 'DurationOfPitch', 'Occupation', 'ProductPitched', 'Gender', 'MonthlyIncome', 'NumberOfPersonVisiting', 'Age', 'PreferredPropertyStar'}).
This happened while the csv dataset builder was generating data using
hf://datasets/vamshf/tourism-package-prediction/y_train.csv (at revision ada35c1fbb98191a821005558803f554353b035d), [/tmp/hf-datasets-cache/medium/datasets/48586456502115-config-parquet-and-info-vamshf-tourism-package-pr-06c878e5/hub/datasets--vamshf--tourism-package-prediction/snapshots/ada35c1fbb98191a821005558803f554353b035d/X_train.csv (origin=hf://datasets/vamshf/tourism-package-prediction@ada35c1fbb98191a821005558803f554353b035d/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/48586456502115-config-parquet-and-info-vamshf-tourism-package-pr-06c878e5/hub/datasets--vamshf--tourism-package-prediction/snapshots/ada35c1fbb98191a821005558803f554353b035d/y_train.csv (origin=hf://datasets/vamshf/tourism-package-prediction@ada35c1fbb98191a821005558803f554353b035d/y_train.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/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
ProdTaken: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 377
to
{'Age': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'MonthlyIncome': Value('float64'), 'DurationOfPitch': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'MaritalStatus': Value('string'), 'ProductPitched': 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 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, 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 1889, 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 1 new columns ({'ProdTaken'}) and 13 missing columns ({'NumberOfTrips', 'NumberOfFollowups', 'TypeofContact', 'MaritalStatus', 'NumberOfChildrenVisiting', 'DurationOfPitch', 'Occupation', 'ProductPitched', 'Gender', 'MonthlyIncome', 'NumberOfPersonVisiting', 'Age', 'PreferredPropertyStar'}).
This happened while the csv dataset builder was generating data using
hf://datasets/vamshf/tourism-package-prediction/y_train.csv (at revision ada35c1fbb98191a821005558803f554353b035d), [/tmp/hf-datasets-cache/medium/datasets/48586456502115-config-parquet-and-info-vamshf-tourism-package-pr-06c878e5/hub/datasets--vamshf--tourism-package-prediction/snapshots/ada35c1fbb98191a821005558803f554353b035d/X_train.csv (origin=hf://datasets/vamshf/tourism-package-prediction@ada35c1fbb98191a821005558803f554353b035d/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/48586456502115-config-parquet-and-info-vamshf-tourism-package-pr-06c878e5/hub/datasets--vamshf--tourism-package-prediction/snapshots/ada35c1fbb98191a821005558803f554353b035d/y_train.csv (origin=hf://datasets/vamshf/tourism-package-prediction@ada35c1fbb98191a821005558803f554353b035d/y_train.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)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.
Age
float64 | NumberOfPersonVisiting
int64 | NumberOfFollowups
float64 | PreferredPropertyStar
float64 | NumberOfTrips
float64 | MonthlyIncome
float64 | DurationOfPitch
float64 | NumberOfChildrenVisiting
float64 | TypeofContact
string | Occupation
string | Gender
string | MaritalStatus
string | ProductPitched
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
55
| 4
| 4
| 5
| 8
| 23,118
| 17
| 1
|
Self Enquiry
|
Small Business
|
Female
|
Single
|
Deluxe
|
39
| 3
| 4
| 3
| 7
| 22,622
| 9
| 2
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Basic
|
42
| 3
| 1
| 5
| 1
| 21,272
| 8
| 2
|
Company Invited
|
Small Business
|
Male
|
Divorced
|
Deluxe
|
37
| 3
| 5
| 5
| 2
| 98,678
| 12
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Divorced
|
Basic
|
23
| 3
| 5
| 3
| 8
| 23,453
| 7
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Divorced
|
Deluxe
|
33
| 4
| 4
| 3
| 3
| 23,987
| 31
| 1
|
Company Invited
|
Salaried
|
Male
|
Divorced
|
Deluxe
|
38
| 2
| 5
| 3
| 4
| 20,811
| 24
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Deluxe
|
60
| 4
| 5
| 3
| 5
| 32,404
| 9
| 3
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Super Deluxe
|
53
| 2
| 4
| 4
| 3
| 22,525
| 8
| 0
|
Company Invited
|
Small Business
|
Female
|
Married
|
Standard
|
37
| 4
| 4
| 3
| 8
| 24,025
| 33
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Deluxe
|
60
| 3
| 4
| 5
| 5
| 25,266
| 34
| 0
|
Company Invited
|
Small Business
|
Female
|
Married
|
Standard
|
43
| 3
| 6
| 3
| 6
| 22,950
| 36
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Deluxe
|
35
| 2
| 1
| 4
| 1
| 17,426
| 22
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Basic
|
43
| 4
| 2
| 3
| 4
| 23,909
| 10
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Deluxe
|
52
| 2
| 1
| 3
| 3
| 28,247
| 34
| 0
|
Company Invited
|
Small Business
|
Female
|
Divorced
|
Super Deluxe
|
59
| 3
| 5
| 3
| 2
| 21,058
| 9
| 1
|
Company Invited
|
Salaried
|
Male
|
Married
|
Basic
|
36
| 3
| 3
| 3
| 7
| 20,237
| 33
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Deluxe
|
29
| 3
| 4
| 3
| 3
| 20,822
| 23
| 1
|
Company Invited
|
Small Business
|
Male
|
Single
|
Basic
|
37
| 3
| 5
| 4
| 4
| 27,525
| 16
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Deluxe
|
38
| 2
| 3
| 3
| 1
| 21,553
| 8
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Divorced
|
Deluxe
|
31
| 2
| 5
| 3
| 2
| 16,359
| 6
| 1
|
Company Invited
|
Salaried
|
Female
|
Single
|
Basic
|
46
| 4
| 4
| 5
| 6
| 29,439
| 16
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Standard
|
41
| 3
| 4
| 4
| 3
| 23,339
| 14
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Basic
|
35
| 3
| 3
| 4
| 2
| 20,363
| 13
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Basic
|
29
| 3
| 3
| 3
| 2
| 17,642
| 16
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Basic
|
51
| 3
| 3
| 3
| 1
| 20,441
| 27
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Deluxe
|
39
| 2
| 2
| 3
| 1
| 24,613
| 6
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Standard
|
37
| 3
| 4
| 3
| 5
| 21,334
| 22
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Deluxe
|
33
| 2
| 3
| 3
| 2
| 32,444
| 23
| 1
|
Company Invited
|
Salaried
|
Male
|
Single
|
Super Deluxe
|
51
| 4
| 4
| 3
| 6
| 27,886
| 19
| 3
|
Company Invited
|
Small Business
|
Female
|
Single
|
Standard
|
42
| 3
| 2
| 4
| 5
| 25,548
| 12
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Deluxe
|
33
| 4
| 5
| 4
| 3
| 23,906
| 15
| 1
|
Self Enquiry
|
Large Business
|
Female
|
Divorced
|
Deluxe
|
30
| 4
| 4
| 4
| 2
| 21,969
| 17
| 1
|
Company Invited
|
Salaried
|
Female
|
Married
|
Basic
|
41
| 3
| 6
| 3
| 4
| 26,135
| 7
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Deluxe
|
38
| 3
| 2
| 3
| 2
| 22,178
| 12
| 1
|
Company Invited
|
Large Business
|
Male
|
Single
|
Basic
|
28
| 3
| 6
| 3
| 5
| 23,749
| 9
| 2
|
Company Invited
|
Salaried
|
Male
|
Single
|
Deluxe
|
27
| 4
| 6
| 3
| 3
| 20,983
| 24
| 3
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Basic
|
27
| 2
| 3
| 4
| 2
| 17,478
| 11
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Basic
|
24
| 3
| 2
| 5
| 4
| 21,497
| 11
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Basic
|
34
| 3
| 4
| 3
| 2
| 17,553
| 22
| 2
|
Company Invited
|
Salaried
|
Female
|
Single
|
Basic
|
37
| 3
| 5
| 5
| 2
| 25,772
| 17
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Standard
|
34
| 3
| 4
| 5
| 1
| 20,343
| 7
| 0
|
Company Invited
|
Small Business
|
Male
|
Single
|
Deluxe
|
30
| 2
| 4
| 5
| 6
| 21,696
| 32
| 1
|
Company Invited
|
Small Business
|
Female
|
Single
|
Deluxe
|
27
| 2
| 3
| 4
| 1
| 18,058
| 23
| 0
|
Self Enquiry
|
Large Business
|
Male
|
Married
|
Basic
|
36
| 3
| 5
| 4
| 4
| 28,952
| 9
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Standard
|
40
| 3
| 3
| 3
| 2
| 18,319
| 30
| 1
|
Self Enquiry
|
Large Business
|
Male
|
Married
|
Deluxe
|
38
| 3
| 4
| 3
| 6
| 26,169
| 7
| 2
|
Self Enquiry
|
Large Business
|
Female
|
Single
|
Standard
|
33
| 3
| 5
| 4
| 2
| 28,585
| 9
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Deluxe
|
30
| 2
| 5
| 3
| 2
| 22,661
| 16
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Basic
|
52
| 3
| 3
| 3
| 3
| 32,099
| 6
| 2
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Super Deluxe
|
33
| 3
| 6
| 4
| 8
| 25,413
| 7
| 2
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Deluxe
|
20
| 4
| 5
| 4
| 3
| 20,537
| 17
| 3
|
Company Invited
|
Small Business
|
Female
|
Single
|
Basic
|
38
| 2
| 4
| 3
| 1
| 24,526
| 29
| 0
|
Company Invited
|
Salaried
|
Male
|
Single
|
Standard
|
31
| 2
| 3
| 3
| 4
| 17,356
| 17
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Basic
|
52
| 3
| 4
| 3
| 2
| 21,139
| 11
| 2
|
Self Enquiry
|
Salaried
|
Male
|
Divorced
|
Basic
|
39
| 3
| 4
| 3
| 5
| 22,995
| 10
| 1
|
Self Enquiry
|
Large Business
|
Female
|
Single
|
Deluxe
|
40
| 3
| 5
| 3
| 6
| 24,580
| 11
| 2
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Deluxe
|
26
| 4
| 4
| 3
| 5
| 22,347
| 26
| 3
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Basic
|
47
| 2
| 5
| 3
| 1
| 27,936
| 15
| 1
|
Company Invited
|
Salaried
|
Male
|
Married
|
Super Deluxe
|
28
| 3
| 3
| 4
| 2
| 16,052
| 16
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Basic
|
19
| 4
| 4
| 3
| 3
| 20,582
| 15
| 1
|
Company Invited
|
Small Business
|
Male
|
Single
|
Basic
|
52
| 2
| 4
| 5
| 2
| 31,856
| 9
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Super Deluxe
|
20
| 4
| 6
| 5
| 2
| 21,003
| 7
| 2
|
Company Invited
|
Large Business
|
Female
|
Single
|
Basic
|
43
| 3
| 4
| 4
| 2
| 25,503
| 15
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Deluxe
|
30
| 4
| 4
| 3
| 3
| 22,438
| 8
| 3
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Basic
|
51
| 4
| 4
| 3
| 2
| 25,406
| 7
| 2
|
Company Invited
|
Salaried
|
Male
|
Married
|
Deluxe
|
41
| 4
| 5
| 3
| 2
| 23,554
| 16
| 2
|
Company Invited
|
Salaried
|
Male
|
Married
|
Deluxe
|
33
| 3
| 4
| 3
| 3
| 27,676
| 15
| 2
|
Company Invited
|
Small Business
|
Female
|
Single
|
Standard
|
22
| 3
| 4
| 3
| 3
| 21,288
| 16
| 1
|
Company Invited
|
Small Business
|
Male
|
Single
|
Basic
|
40
| 2
| 1
| 3
| 4
| 17,213
| 16
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Basic
|
53
| 2
| 3
| 5
| 1
| 23,381
| 6
| 1
|
Self Enquiry
|
Small Business
|
Female
|
Single
|
Deluxe
|
29
| 3
| 5
| 5
| 2
| 21,239
| 9
| 1
|
Company Invited
|
Small Business
|
Male
|
Single
|
Basic
|
44
| 4
| 4
| 3
| 5
| 24,357
| 16
| 3
|
Company Invited
|
Small Business
|
Male
|
Married
|
Deluxe
|
23
| 4
| 4
| 3
| 2
| 21,451
| 13
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Basic
|
43
| 3
| 6
| 3
| 6
| 22,950
| 36
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Deluxe
|
33
| 2
| 3
| 3
| 2
| 32,444
| 23
| 0
|
Company Invited
|
Salaried
|
Male
|
Single
|
Super Deluxe
|
37
| 3
| 4
| 3
| 6
| 25,331
| 7
| 2
|
Company Invited
|
Small Business
|
Female
|
Single
|
Deluxe
|
37
| 2
| 1
| 3
| 2
| 28,744
| 16
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Standard
|
40
| 3
| 4
| 3
| 6
| 23,916
| 10
| 2
|
Self Enquiry
|
Small Business
|
Female
|
Married
|
Deluxe
|
36
| 3
| 2
| 3
| 5
| 21,184
| 7
| 2
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Basic
|
50
| 4
| 4
| 5
| 6
| 21,265
| 23
| 2
|
Self Enquiry
|
Small Business
|
Female
|
Married
|
Basic
|
21
| 3
| 4
| 4
| 2
| 17,174
| 6
| 2
|
Company Invited
|
Large Business
|
Female
|
Single
|
Basic
|
28
| 4
| 6
| 4
| 4
| 21,195
| 9
| 2
|
Self Enquiry
|
Small Business
|
Female
|
Single
|
King
|
52
| 3
| 5
| 4
| 7
| 31,168
| 15
| 2
|
Self Enquiry
|
Salaried
|
Male
|
Divorced
|
Standard
|
40
| 3
| 4
| 3
| 2
| 24,094
| 14
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Basic
|
29
| 2
| 3
| 3
| 2
| 18,131
| 12
| 1
|
Self Enquiry
|
Small Business
|
Female
|
Married
|
Basic
|
35
| 3
| 4
| 5
| 3
| 24,884
| 17
| 1
|
Company Invited
|
Small Business
|
Male
|
Divorced
|
Standard
|
38
| 4
| 4
| 3
| 6
| 25,180
| 13
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Deluxe
|
51
| 1
| 4
| 5
| 4
| 22,484
| 6
| 0
|
Company Invited
|
Small Business
|
Female
|
Single
|
Standard
|
22
| 3
| 4
| 3
| 3
| 21,288
| 16
| 1
|
Company Invited
|
Small Business
|
Male
|
Single
|
Basic
|
36
| 2
| 3
| 4
| 5
| 17,143
| 19
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Basic
|
31
| 3
| 3
| 5
| 2
| 21,833
| 17
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Deluxe
|
28
| 3
| 4
| 3
| 3
| 22,783
| 16
| 2
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Deluxe
|
50
| 3
| 5
| 3
| 2
| 32,642
| 7
| 1
|
Self Enquiry
|
Large Business
|
Female
|
Single
|
Super Deluxe
|
28
| 3
| 5
| 3
| 3
| 21,217
| 13
| 2
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Basic
|
40
| 3
| 3
| 5
| 3
| 21,516
| 14
| 0
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Deluxe
|
29
| 2
| 3
| 3
| 2
| 17,340
| 21
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Basic
|
40
| 4
| 4
| 3
| 2
| 32,142
| 17
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Standard
|
29
| 3
| 4
| 3
| 2
| 20,832
| 7
| 1
|
Company Invited
|
Small Business
|
Male
|
Single
|
Basic
|
31
| 4
| 4
| 4
| 2
| 22,257
| 8
| 3
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Basic
|
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