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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 4 new columns ({'typeTrans', 'Weight', 'Target', 'Source'}) and 9 missing columns ({'step', 'category', 'zipMerchant', 'customer', 'gender', 'age', 'amount', 'zipcodeOri', 'merchant'}).
This happened while the csv dataset builder was generating data using
zip://bsNET140513_032310.csv::hf://datasets/LordNR/AMLGraphX-Banksim@9070a957261ffbcfafa8edce9ec476940fda5041/Banksim.zip, ['hf://datasets/LordNR/AMLGraphX-Banksim@9070a957261ffbcfafa8edce9ec476940fda5041/Banksim.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/python3.14/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
Source: string
Target: string
Weight: double
typeTrans: string
fraud: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 843
to
{'step': Value('int64'), 'customer': Value('string'), 'age': Value('string'), 'gender': Value('string'), 'zipcodeOri': Value('string'), 'merchant': Value('string'), 'zipMerchant': Value('string'), 'category': Value('string'), 'amount': Value('float64'), 'fraud': Value('int64')}
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 4 new columns ({'typeTrans', 'Weight', 'Target', 'Source'}) and 9 missing columns ({'step', 'category', 'zipMerchant', 'customer', 'gender', 'age', 'amount', 'zipcodeOri', 'merchant'}).
This happened while the csv dataset builder was generating data using
zip://bsNET140513_032310.csv::hf://datasets/LordNR/AMLGraphX-Banksim@9070a957261ffbcfafa8edce9ec476940fda5041/Banksim.zip, ['hf://datasets/LordNR/AMLGraphX-Banksim@9070a957261ffbcfafa8edce9ec476940fda5041/Banksim.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.
step int64 | customer string | age string | gender string | zipcodeOri string | merchant string | zipMerchant string | category string | amount float64 | fraud int64 |
|---|---|---|---|---|---|---|---|---|---|
0 | 'C1093826151' | '4' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 4.55 | 0 |
0 | 'C352968107' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 39.68 | 0 |
0 | 'C2054744914' | '4' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 26.89 | 0 |
0 | 'C1760612790' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 17.25 | 0 |
0 | 'C757503768' | '5' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 35.72 | 0 |
0 | 'C1315400589' | '3' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 25.81 | 0 |
0 | 'C765155274' | '1' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 9.1 | 0 |
0 | 'C202531238' | '4' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 21.17 | 0 |
0 | 'C105845174' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 32.4 | 0 |
0 | 'C39858251' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 35.4 | 0 |
0 | 'C98707741' | '4' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 14.95 | 0 |
0 | 'C1551465414' | '1' | 'M' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 1.51 | 0 |
0 | 'C623601481' | '3' | 'M' | '28007' | 'M50039827' | '28007' | 'es_health' | 68.79 | 0 |
0 | 'C1865204568' | '5' | 'M' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 20.32 | 0 |
0 | 'C490238464' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 13.56 | 0 |
0 | 'C194016923' | '3' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 30.19 | 0 |
0 | 'C1207205377' | '4' | 'M' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 17.54 | 0 |
0 | 'C834963773' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 40.69 | 0 |
0 | 'C1897705669' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 21.21 | 0 |
0 | 'C124539163' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 10.09 | 0 |
0 | 'C1687101094' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 19.31 | 0 |
0 | 'C1695454092' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 44.22 | 0 |
0 | 'C986553990' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 44.39 | 0 |
0 | 'C819690995' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 30.72 | 0 |
0 | 'C1622124632' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 29.84 | 0 |
0 | 'C187514477' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 12.1 | 0 |
0 | 'C272748313' | '4' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 24.84 | 0 |
0 | 'C490092965' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 16.42 | 0 |
0 | 'C546957379' | '5' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 2.19 | 0 |
0 | 'C1563705147' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 32.27 | 0 |
0 | 'C1273110804' | '4' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 28.09 | 0 |
0 | 'C1582366224' | '5' | 'M' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 16.13 | 0 |
0 | 'C998987490' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 32.7 | 0 |
0 | 'C1413412440' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 25.53 | 0 |
0 | 'C1166355595' | '4' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 39.22 | 0 |
0 | 'C719598710' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 46.14 | 0 |
0 | 'C1161949399' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 29.55 | 0 |
0 | 'C60351691' | '2' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 36.88 | 0 |
0 | 'C1769927077' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 36.15 | 0 |
0 | 'C995844287' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 2.81 | 0 |
0 | 'C1425441042' | '2' | 'M' | '28007' | 'M1888755466' | '28007' | 'es_otherservices' | 87.67 | 0 |
0 | 'C337109624' | '2' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 11.17 | 0 |
0 | 'C1635613216' | '4' | 'F' | '28007' | 'M1053599405' | '28007' | 'es_health' | 105.59 | 0 |
0 | 'C996804095' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 15.25 | 0 |
0 | 'C1331907286' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 33.36 | 0 |
0 | 'C506520283' | '4' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 32.96 | 0 |
0 | 'C83613815' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 16.96 | 0 |
0 | 'C1697528836' | '3' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 49.91 | 0 |
0 | 'C168310052' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 0.93 | 0 |
0 | 'C2096367617' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 37.71 | 0 |
0 | 'C1870872671' | '2' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 12.53 | 0 |
0 | 'C823286801' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 56.97 | 0 |
0 | 'C1038996959' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 31.32 | 0 |
0 | 'C345742557' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 21.15 | 0 |
0 | 'C638163213' | '1' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 3.12 | 0 |
0 | 'C1941625566' | '3' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 33.41 | 0 |
0 | 'C1180570487' | '6' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 45.92 | 0 |
0 | 'C51444479' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 36.82 | 0 |
0 | 'C153419170' | '3' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 20.05 | 0 |
0 | 'C574062699' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 43.75 | 0 |
0 | 'C1087788850' | '4' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 44.88 | 0 |
0 | 'C2126436338' | '1' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 18.45 | 0 |
0 | 'C1820855004' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 29.66 | 0 |
0 | 'C487569403' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 28.38 | 0 |
0 | 'C923875417' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 62.76 | 0 |
0 | 'C579037870' | '6' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 21.6 | 0 |
0 | 'C662931726' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 30.74 | 0 |
0 | 'C671524709' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 58.33 | 0 |
0 | 'C476477241' | '4' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 44.07 | 0 |
0 | 'C327430270' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 6.52 | 0 |
0 | 'C1879341237' | '4' | 'M' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 2.96 | 0 |
0 | 'C1655037147' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 44.69 | 0 |
0 | 'C864655240' | '2' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 5.01 | 0 |
0 | 'C72353846' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 9.74 | 0 |
0 | 'C1591908890' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 10.1 | 0 |
0 | 'C1156346576' | '4' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 58.64 | 0 |
0 | 'C1771717627' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 29.2 | 0 |
0 | 'C156162339' | '2' | 'F' | '28007' | 'M85975013' | '28007' | 'es_food' | 32.6 | 0 |
0 | 'C588719767' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 52.35 | 0 |
0 | 'C118437987' | '2' | 'M' | '28007' | 'M1053599405' | '28007' | 'es_health' | 159.92 | 0 |
0 | 'C1130259702' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 26.37 | 0 |
0 | 'C1738944491' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 49.07 | 0 |
0 | 'C1950723662' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 35.74 | 0 |
0 | 'C1987405562' | '5' | 'F' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 47.53 | 0 |
0 | 'C1988407051' | '5' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 29.87 | 0 |
0 | 'C1698653726' | '2' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 20.31 | 0 |
0 | 'C1952040134' | '3' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 10.59 | 0 |
0 | 'C1769470125' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 21.92 | 0 |
0 | 'C583110837' | '3' | 'M' | '28007' | 'M480139044' | '28007' | 'es_health' | 44.26 | 1 |
0 | 'C1332295774' | '3' | 'M' | '28007' | 'M480139044' | '28007' | 'es_health' | 324.5 | 1 |
0 | 'C603564532' | '1' | 'M' | '28007' | 'M1823072687' | '28007' | 'es_transportation' | 26.07 | 0 |
0 | 'C2072009750' | '2' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 10.88 | 0 |
0 | 'C545248492' | '4' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 8.05 | 0 |
0 | 'C1055224400' | '3' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 25.59 | 0 |
0 | 'C1619755203' | '5' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 15.26 | 0 |
0 | 'C1697851479' | '3' | 'M' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 20.73 | 0 |
0 | 'C1255236689' | '1' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 68.17 | 0 |
0 | 'C603081336' | '3' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 34.75 | 0 |
0 | 'C274486575' | '2' | 'F' | '28007' | 'M692898500' | '28007' | 'es_health' | 171.07 | 0 |
0 | 'C1569939854' | '6' | 'F' | '28007' | 'M348934600' | '28007' | 'es_transportation' | 50.31 | 0 |
End of preview.
BankSim
BankSim is a synthetic bank payment dataset designed for fraud detection research. Transactions represent payments from customers to merchants and can naturally be modeled as a transaction graph.
- Source node: Customer
- Destination node: Merchant
- Edge: Transaction
- Amount: Transaction amount
- Time: Simulation step
- Label: Fraud / Normal
Source
This dataset was originally developed by Edgar Alonso Lopez-Rojas and Stefan Axelsson and introduced in:
Lopez-Rojas, E. A. and Axelsson, S. (2014).
BankSim: A Bank Payments Simulator for Fraud Detection Research.
Proceedings of the 26th European Modeling and Simulation Symposium (EMSS 2014), pp. 144–152.
The data distributed here originates from the BankSim dataset published on Kaggle.
License
The dataset is distributed under the CC BY-NC-SA 4.0 license. Please refer to the original BankSim dataset and publication when using this data.
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