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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 ({'Total night calls', 'Total day minutes', 'Total intl charge', 'Churn', 'State', 'Voice mail plan', 'Total day calls', 'Number vmail messages', 'Total eve minutes', 'Account length', 'Total intl minutes', 'Area code', 'International plan', 'Total night minutes', 'Total night charge', 'Total eve calls', 'Total intl calls', 'Customer service calls', 'Total eve charge', 'Total day charge'}) and 14 missing columns ({'Age', 'Surname', 'Exited', 'CreditScore', 'RowNumber', 'Gender', 'CustomerId', 'Tenure', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Geography', 'NumOfProducts', 'Balance'}). This happened while the csv dataset builder was generating data using hf://datasets/jskinner215/multi_kaggle_churn/churn-bigml-20.csv (at revision 1342703e30dad09cef7bd3b1c1aaf7591348c2b2) 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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast State: string Account length: int64 Area code: int64 International plan: string Voice mail plan: string Number vmail messages: int64 Total day minutes: double Total day calls: int64 Total day charge: double Total eve minutes: double Total eve calls: int64 Total eve charge: double Total night minutes: double Total night calls: int64 Total night charge: double Total intl minutes: double Total intl calls: int64 Total intl charge: double Customer service calls: int64 Churn: bool -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2860 to {'RowNumber': Value(dtype='int64', id=None), 'CustomerId': Value(dtype='int64', id=None), 'Surname': Value(dtype='string', id=None), 'CreditScore': Value(dtype='int64', id=None), 'Geography': Value(dtype='string', id=None), 'Gender': Value(dtype='string', id=None), 'Age': Value(dtype='int64', id=None), 'Tenure': Value(dtype='int64', id=None), 'Balance': Value(dtype='float64', id=None), 'NumOfProducts': Value(dtype='int64', id=None), 'HasCrCard': Value(dtype='int64', id=None), 'IsActiveMember': Value(dtype='int64', id=None), 'EstimatedSalary': Value(dtype='float64', id=None), 'Exited': Value(dtype='int64', id=None)} 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 1321, 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 935, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 ({'Total night calls', 'Total day minutes', 'Total intl charge', 'Churn', 'State', 'Voice mail plan', 'Total day calls', 'Number vmail messages', 'Total eve minutes', 'Account length', 'Total intl minutes', 'Area code', 'International plan', 'Total night minutes', 'Total night charge', 'Total eve calls', 'Total intl calls', 'Customer service calls', 'Total eve charge', 'Total day charge'}) and 14 missing columns ({'Age', 'Surname', 'Exited', 'CreditScore', 'RowNumber', 'Gender', 'CustomerId', 'Tenure', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Geography', 'NumOfProducts', 'Balance'}). This happened while the csv dataset builder was generating data using hf://datasets/jskinner215/multi_kaggle_churn/churn-bigml-20.csv (at revision 1342703e30dad09cef7bd3b1c1aaf7591348c2b2) 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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RowNumber
int64 | CustomerId
int64 | Surname
string | CreditScore
int64 | Geography
string | Gender
string | Age
int64 | Tenure
int64 | Balance
float64 | NumOfProducts
int64 | HasCrCard
int64 | IsActiveMember
int64 | EstimatedSalary
float64 | Exited
int64 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 15,634,602 | Hargrave | 619 | France | Female | 42 | 2 | 0 | 1 | 1 | 1 | 101,348.88 | 1 |
2 | 15,647,311 | Hill | 608 | Spain | Female | 41 | 1 | 83,807.86 | 1 | 0 | 1 | 112,542.58 | 0 |
3 | 15,619,304 | Onio | 502 | France | Female | 42 | 8 | 159,660.8 | 3 | 1 | 0 | 113,931.57 | 1 |
4 | 15,701,354 | Boni | 699 | France | Female | 39 | 1 | 0 | 2 | 0 | 0 | 93,826.63 | 0 |
5 | 15,737,888 | Mitchell | 850 | Spain | Female | 43 | 2 | 125,510.82 | 1 | 1 | 1 | 79,084.1 | 0 |
6 | 15,574,012 | Chu | 645 | Spain | Male | 44 | 8 | 113,755.78 | 2 | 1 | 0 | 149,756.71 | 1 |
7 | 15,592,531 | Bartlett | 822 | France | Male | 50 | 7 | 0 | 2 | 1 | 1 | 10,062.8 | 0 |
8 | 15,656,148 | Obinna | 376 | Germany | Female | 29 | 4 | 115,046.74 | 4 | 1 | 0 | 119,346.88 | 1 |
9 | 15,792,365 | He | 501 | France | Male | 44 | 4 | 142,051.07 | 2 | 0 | 1 | 74,940.5 | 0 |
10 | 15,592,389 | H? | 684 | France | Male | 27 | 2 | 134,603.88 | 1 | 1 | 1 | 71,725.73 | 0 |
11 | 15,767,821 | Bearce | 528 | France | Male | 31 | 6 | 102,016.72 | 2 | 0 | 0 | 80,181.12 | 0 |
12 | 15,737,173 | Andrews | 497 | Spain | Male | 24 | 3 | 0 | 2 | 1 | 0 | 76,390.01 | 0 |
13 | 15,632,264 | Kay | 476 | France | Female | 34 | 10 | 0 | 2 | 1 | 0 | 26,260.98 | 0 |
14 | 15,691,483 | Chin | 549 | France | Female | 25 | 5 | 0 | 2 | 0 | 0 | 190,857.79 | 0 |
15 | 15,600,882 | Scott | 635 | Spain | Female | 35 | 7 | 0 | 2 | 1 | 1 | 65,951.65 | 0 |
16 | 15,643,966 | Goforth | 616 | Germany | Male | 45 | 3 | 143,129.41 | 2 | 0 | 1 | 64,327.26 | 0 |
17 | 15,737,452 | Romeo | 653 | Germany | Male | 58 | 1 | 132,602.88 | 1 | 1 | 0 | 5,097.67 | 1 |
18 | 15,788,218 | Henderson | 549 | Spain | Female | 24 | 9 | 0 | 2 | 1 | 1 | 14,406.41 | 0 |
19 | 15,661,507 | Muldrow | 587 | Spain | Male | 45 | 6 | 0 | 1 | 0 | 0 | 158,684.81 | 0 |
20 | 15,568,982 | Hao | 726 | France | Female | 24 | 6 | 0 | 2 | 1 | 1 | 54,724.03 | 0 |
21 | 15,577,657 | McDonald | 732 | France | Male | 41 | 8 | 0 | 2 | 1 | 1 | 170,886.17 | 0 |
22 | 15,597,945 | Dellucci | 636 | Spain | Female | 32 | 8 | 0 | 2 | 1 | 0 | 138,555.46 | 0 |
23 | 15,699,309 | Gerasimov | 510 | Spain | Female | 38 | 4 | 0 | 1 | 1 | 0 | 118,913.53 | 1 |
24 | 15,725,737 | Mosman | 669 | France | Male | 46 | 3 | 0 | 2 | 0 | 1 | 8,487.75 | 0 |
25 | 15,625,047 | Yen | 846 | France | Female | 38 | 5 | 0 | 1 | 1 | 1 | 187,616.16 | 0 |
26 | 15,738,191 | Maclean | 577 | France | Male | 25 | 3 | 0 | 2 | 0 | 1 | 124,508.29 | 0 |
27 | 15,736,816 | Young | 756 | Germany | Male | 36 | 2 | 136,815.64 | 1 | 1 | 1 | 170,041.95 | 0 |
28 | 15,700,772 | Nebechi | 571 | France | Male | 44 | 9 | 0 | 2 | 0 | 0 | 38,433.35 | 0 |
29 | 15,728,693 | McWilliams | 574 | Germany | Female | 43 | 3 | 141,349.43 | 1 | 1 | 1 | 100,187.43 | 0 |
30 | 15,656,300 | Lucciano | 411 | France | Male | 29 | 0 | 59,697.17 | 2 | 1 | 1 | 53,483.21 | 0 |
31 | 15,589,475 | Azikiwe | 591 | Spain | Female | 39 | 3 | 0 | 3 | 1 | 0 | 140,469.38 | 1 |
32 | 15,706,552 | Odinakachukwu | 533 | France | Male | 36 | 7 | 85,311.7 | 1 | 0 | 1 | 156,731.91 | 0 |
33 | 15,750,181 | Sanderson | 553 | Germany | Male | 41 | 9 | 110,112.54 | 2 | 0 | 0 | 81,898.81 | 0 |
34 | 15,659,428 | Maggard | 520 | Spain | Female | 42 | 6 | 0 | 2 | 1 | 1 | 34,410.55 | 0 |
35 | 15,732,963 | Clements | 722 | Spain | Female | 29 | 9 | 0 | 2 | 1 | 1 | 142,033.07 | 0 |
36 | 15,794,171 | Lombardo | 475 | France | Female | 45 | 0 | 134,264.04 | 1 | 1 | 0 | 27,822.99 | 1 |
37 | 15,788,448 | Watson | 490 | Spain | Male | 31 | 3 | 145,260.23 | 1 | 0 | 1 | 114,066.77 | 0 |
38 | 15,729,599 | Lorenzo | 804 | Spain | Male | 33 | 7 | 76,548.6 | 1 | 0 | 1 | 98,453.45 | 0 |
39 | 15,717,426 | Armstrong | 850 | France | Male | 36 | 7 | 0 | 1 | 1 | 1 | 40,812.9 | 0 |
40 | 15,585,768 | Cameron | 582 | Germany | Male | 41 | 6 | 70,349.48 | 2 | 0 | 1 | 178,074.04 | 0 |
41 | 15,619,360 | Hsiao | 472 | Spain | Male | 40 | 4 | 0 | 1 | 1 | 0 | 70,154.22 | 0 |
42 | 15,738,148 | Clarke | 465 | France | Female | 51 | 8 | 122,522.32 | 1 | 0 | 0 | 181,297.65 | 1 |
43 | 15,687,946 | Osborne | 556 | France | Female | 61 | 2 | 117,419.35 | 1 | 1 | 1 | 94,153.83 | 0 |
44 | 15,755,196 | Lavine | 834 | France | Female | 49 | 2 | 131,394.56 | 1 | 0 | 0 | 194,365.76 | 1 |
45 | 15,684,171 | Bianchi | 660 | Spain | Female | 61 | 5 | 155,931.11 | 1 | 1 | 1 | 158,338.39 | 0 |
46 | 15,754,849 | Tyler | 776 | Germany | Female | 32 | 4 | 109,421.13 | 2 | 1 | 1 | 126,517.46 | 0 |
47 | 15,602,280 | Martin | 829 | Germany | Female | 27 | 9 | 112,045.67 | 1 | 1 | 1 | 119,708.21 | 1 |
48 | 15,771,573 | Okagbue | 637 | Germany | Female | 39 | 9 | 137,843.8 | 1 | 1 | 1 | 117,622.8 | 1 |
49 | 15,766,205 | Yin | 550 | Germany | Male | 38 | 2 | 103,391.38 | 1 | 0 | 1 | 90,878.13 | 0 |
50 | 15,771,873 | Buccho | 776 | Germany | Female | 37 | 2 | 103,769.22 | 2 | 1 | 0 | 194,099.12 | 0 |
51 | 15,616,550 | Chidiebele | 698 | Germany | Male | 44 | 10 | 116,363.37 | 2 | 1 | 0 | 198,059.16 | 0 |
52 | 15,768,193 | Trevisani | 585 | Germany | Male | 36 | 5 | 146,050.97 | 2 | 0 | 0 | 86,424.57 | 0 |
53 | 15,683,553 | O'Brien | 788 | France | Female | 33 | 5 | 0 | 2 | 0 | 0 | 116,978.19 | 0 |
54 | 15,702,298 | Parkhill | 655 | Germany | Male | 41 | 8 | 125,561.97 | 1 | 0 | 0 | 164,040.94 | 1 |
55 | 15,569,590 | Yoo | 601 | Germany | Male | 42 | 1 | 98,495.72 | 1 | 1 | 0 | 40,014.76 | 1 |
56 | 15,760,861 | Phillipps | 619 | France | Male | 43 | 1 | 125,211.92 | 1 | 1 | 1 | 113,410.49 | 0 |
57 | 15,630,053 | Tsao | 656 | France | Male | 45 | 5 | 127,864.4 | 1 | 1 | 0 | 87,107.57 | 0 |
58 | 15,647,091 | Endrizzi | 725 | Germany | Male | 19 | 0 | 75,888.2 | 1 | 0 | 0 | 45,613.75 | 0 |
59 | 15,623,944 | T'ien | 511 | Spain | Female | 66 | 4 | 0 | 1 | 1 | 0 | 1,643.11 | 1 |
60 | 15,804,771 | Velazquez | 614 | France | Male | 51 | 4 | 40,685.92 | 1 | 1 | 1 | 46,775.28 | 0 |
61 | 15,651,280 | Hunter | 742 | Germany | Male | 35 | 5 | 136,857 | 1 | 0 | 0 | 84,509.57 | 0 |
62 | 15,773,469 | Clark | 687 | Germany | Female | 27 | 9 | 152,328.88 | 2 | 0 | 0 | 126,494.82 | 0 |
63 | 15,702,014 | Jeffrey | 555 | Spain | Male | 33 | 1 | 56,084.69 | 2 | 0 | 0 | 178,798.13 | 0 |
64 | 15,751,208 | Pirozzi | 684 | Spain | Male | 56 | 8 | 78,707.16 | 1 | 1 | 1 | 99,398.36 | 0 |
65 | 15,592,461 | Jackson | 603 | Germany | Male | 26 | 4 | 109,166.37 | 1 | 1 | 1 | 92,840.67 | 0 |
66 | 15,789,484 | Hammond | 751 | Germany | Female | 36 | 6 | 169,831.46 | 2 | 1 | 1 | 27,758.36 | 0 |
67 | 15,696,061 | Brownless | 581 | Germany | Female | 34 | 1 | 101,633.04 | 1 | 1 | 0 | 110,431.51 | 0 |
68 | 15,641,582 | Chibugo | 735 | Germany | Male | 43 | 10 | 123,180.01 | 2 | 1 | 1 | 196,673.28 | 0 |
69 | 15,638,424 | Glauert | 661 | Germany | Female | 35 | 5 | 150,725.53 | 2 | 0 | 1 | 113,656.85 | 0 |
70 | 15,755,648 | Pisano | 675 | France | Female | 21 | 8 | 98,373.26 | 1 | 1 | 0 | 18,203 | 0 |
71 | 15,703,793 | Konovalova | 738 | Germany | Male | 58 | 2 | 133,745.44 | 4 | 1 | 0 | 28,373.86 | 1 |
72 | 15,620,344 | McKee | 813 | France | Male | 29 | 6 | 0 | 1 | 1 | 0 | 33,953.87 | 0 |
73 | 15,812,518 | Palermo | 657 | Spain | Female | 37 | 0 | 163,607.18 | 1 | 0 | 1 | 44,203.55 | 0 |
74 | 15,779,052 | Ballard | 604 | Germany | Female | 25 | 5 | 157,780.84 | 2 | 1 | 1 | 58,426.81 | 0 |
75 | 15,770,811 | Wallace | 519 | France | Male | 36 | 9 | 0 | 2 | 0 | 1 | 145,562.4 | 0 |
76 | 15,780,961 | Cavenagh | 735 | France | Female | 21 | 1 | 178,718.19 | 2 | 1 | 0 | 22,388 | 0 |
77 | 15,614,049 | Hu | 664 | France | Male | 55 | 8 | 0 | 2 | 1 | 1 | 139,161.64 | 0 |
78 | 15,662,085 | Read | 678 | France | Female | 32 | 9 | 0 | 1 | 1 | 1 | 148,210.64 | 0 |
79 | 15,575,185 | Bushell | 757 | Spain | Male | 33 | 5 | 77,253.22 | 1 | 0 | 1 | 194,239.63 | 0 |
80 | 15,803,136 | Postle | 416 | Germany | Female | 41 | 10 | 122,189.66 | 2 | 1 | 0 | 98,301.61 | 0 |
81 | 15,706,021 | Buley | 665 | France | Female | 34 | 1 | 96,645.54 | 2 | 0 | 0 | 171,413.66 | 0 |
82 | 15,663,706 | Leonard | 777 | France | Female | 32 | 2 | 0 | 1 | 1 | 0 | 136,458.19 | 1 |
83 | 15,641,732 | Mills | 543 | France | Female | 36 | 3 | 0 | 2 | 0 | 0 | 26,019.59 | 0 |
84 | 15,701,164 | Onyeorulu | 506 | France | Female | 34 | 4 | 90,307.62 | 1 | 1 | 1 | 159,235.29 | 0 |
85 | 15,738,751 | Beit | 493 | France | Female | 46 | 4 | 0 | 2 | 1 | 0 | 1,907.66 | 0 |
86 | 15,805,254 | Ndukaku | 652 | Spain | Female | 75 | 10 | 0 | 2 | 1 | 1 | 114,675.75 | 0 |
87 | 15,762,418 | Gant | 750 | Spain | Male | 22 | 3 | 121,681.82 | 1 | 1 | 0 | 128,643.35 | 1 |
88 | 15,625,759 | Rowley | 729 | France | Male | 30 | 9 | 0 | 2 | 1 | 0 | 151,869.35 | 0 |
89 | 15,622,897 | Sharpe | 646 | France | Female | 46 | 4 | 0 | 3 | 1 | 0 | 93,251.42 | 1 |
90 | 15,767,954 | Osborne | 635 | Germany | Female | 28 | 3 | 81,623.67 | 2 | 1 | 1 | 156,791.36 | 0 |
91 | 15,757,535 | Heap | 647 | Spain | Female | 44 | 5 | 0 | 3 | 1 | 1 | 174,205.22 | 1 |
92 | 15,731,511 | Ritchie | 808 | France | Male | 45 | 7 | 118,626.55 | 2 | 1 | 0 | 147,132.46 | 0 |
93 | 15,809,248 | Cole | 524 | France | Female | 36 | 10 | 0 | 2 | 1 | 0 | 109,614.57 | 0 |
94 | 15,640,635 | Capon | 769 | France | Male | 29 | 8 | 0 | 2 | 1 | 1 | 172,290.61 | 0 |
95 | 15,676,966 | Capon | 730 | Spain | Male | 42 | 4 | 0 | 2 | 0 | 1 | 85,982.47 | 0 |
96 | 15,699,461 | Fiorentini | 515 | Spain | Male | 35 | 10 | 176,273.95 | 1 | 0 | 1 | 121,277.78 | 0 |
97 | 15,738,721 | Graham | 773 | Spain | Male | 41 | 9 | 102,827.44 | 1 | 0 | 1 | 64,595.25 | 0 |
98 | 15,693,683 | Yuille | 814 | Germany | Male | 29 | 8 | 97,086.4 | 2 | 1 | 1 | 197,276.13 | 0 |
99 | 15,604,348 | Allard | 710 | Spain | Male | 22 | 8 | 0 | 2 | 0 | 0 | 99,645.04 | 0 |
100 | 15,633,059 | Fanucci | 413 | France | Male | 34 | 9 | 0 | 2 | 0 | 0 | 6,534.18 | 0 |
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