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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 |
End of preview.