Dataset Preview
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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 11 new columns ({'SessionID', 'DstAccount', 'Channel', 'Timestamp', 'Amount', 'Timestamp_dt', 'MCC_Group', 'FraudLabel', 'TxnID', 'DeviceID', 'SrcAccount'}) and 5 missing columns ({'DefaultLabel', 'Utilisation', 'PaymentRatio', 'HardInquiries', 'Week'}).
This happened while the csv dataset builder was generating data using
hf://datasets/lainmn/AgentDS-RetailBanking/RetailBanking/transactions_train.csv (at revision e0760b4bcfc7468a9aa72c0fdbaed6131974d7ad)
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 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, 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
Timestamp_dt: string
TxnID: string
Timestamp: string
SessionID: string
CustomerID: string
SrcAccount: string
DstAccount: string
Channel: string
MCC_Group: string
Amount: double
DeviceID: string
FraudLabel: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1671
to
{'CustomerID': Value('string'), 'Week': Value('int64'), 'Utilisation': Value('float64'), 'PaymentRatio': Value('float64'), 'HardInquiries': Value('int64'), 'DefaultLabel': 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 1455, 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 1054, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, 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 1833, 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 11 new columns ({'SessionID', 'DstAccount', 'Channel', 'Timestamp', 'Amount', 'Timestamp_dt', 'MCC_Group', 'FraudLabel', 'TxnID', 'DeviceID', 'SrcAccount'}) and 5 missing columns ({'DefaultLabel', 'Utilisation', 'PaymentRatio', 'HardInquiries', 'Week'}).
This happened while the csv dataset builder was generating data using
hf://datasets/lainmn/AgentDS-RetailBanking/RetailBanking/transactions_train.csv (at revision e0760b4bcfc7468a9aa72c0fdbaed6131974d7ad)
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.
CustomerID
string | Week
int64 | Utilisation
float64 | PaymentRatio
float64 | HardInquiries
int64 | DefaultLabel
int64 |
|---|---|---|---|---|---|
C000005
| 1
| 0
| 0
| 1
| 0
|
C000005
| 2
| 0
| 0
| 0
| 0
|
C000005
| 3
| 0
| 0
| 1
| 0
|
C000005
| 4
| 0
| 0
| 0
| 0
|
C000005
| 5
| 0
| 0
| 0
| 0
|
C000005
| 6
| 0
| 0
| 0
| 0
|
C000005
| 7
| 0
| 0
| 0
| 0
|
C000005
| 8
| 0
| 0
| 1
| 0
|
C000005
| 9
| 0
| 0
| 0
| 0
|
C000005
| 10
| 0
| 0
| 0
| 0
|
C000005
| 11
| 0
| 0
| 0
| 0
|
C000005
| 12
| 0
| 0
| 0
| 0
|
C000005
| 13
| 0
| 0
| 1
| 0
|
C000005
| 14
| 0
| 0
| 0
| 0
|
C000005
| 15
| 0
| 0
| 1
| 0
|
C000005
| 16
| 0
| 0
| 1
| 0
|
C000005
| 17
| 0
| 0
| 0
| 0
|
C000005
| 18
| 0
| 0
| 1
| 0
|
C000005
| 19
| 0
| 0
| 2
| 0
|
C000005
| 20
| 0
| 0
| 2
| 0
|
C000005
| 21
| 0
| 0
| 0
| 0
|
C000005
| 22
| 0
| 0
| 1
| 0
|
C000005
| 23
| 0
| 0
| 0
| 0
|
C000005
| 24
| 0
| 0
| 0
| 0
|
C000005
| 25
| 0
| 0
| 0
| 0
|
C000005
| 26
| 0
| 0
| 0
| 0
|
C000009
| 1
| 0
| 0
| 0
| 0
|
C000009
| 2
| 0
| 0
| 0
| 0
|
C000009
| 3
| 0
| 0
| 0
| 0
|
C000009
| 4
| 0
| 0
| 1
| 0
|
C000009
| 5
| 0
| 0
| 0
| 0
|
C000009
| 6
| 0
| 0
| 0
| 0
|
C000009
| 7
| 0
| 0
| 0
| 0
|
C000009
| 8
| 0
| 0
| 2
| 0
|
C000009
| 9
| 0
| 0
| 0
| 0
|
C000009
| 10
| 0
| 0
| 2
| 0
|
C000009
| 11
| 0
| 0
| 1
| 0
|
C000009
| 12
| 0
| 0
| 0
| 0
|
C000009
| 13
| 0
| 0
| 1
| 0
|
C000009
| 14
| 0
| 0
| 1
| 0
|
C000009
| 15
| 0
| 0
| 0
| 0
|
C000009
| 16
| 0
| 0
| 0
| 0
|
C000009
| 17
| 0
| 0
| 1
| 0
|
C000009
| 18
| 0
| 0
| 0
| 0
|
C000009
| 19
| 0
| 0
| 0
| 0
|
C000009
| 20
| 0
| 0
| 0
| 0
|
C000009
| 21
| 0
| 0
| 1
| 0
|
C000009
| 22
| 0
| 0
| 0
| 0
|
C000009
| 23
| 0
| 0
| 0
| 0
|
C000009
| 24
| 0
| 0
| 0
| 0
|
C000009
| 25
| 0
| 0
| 0
| 0
|
C000009
| 26
| 0
| 0
| 0
| 0
|
C000009
| 27
| 0
| 0
| 1
| 0
|
C000011
| 1
| 0.023275
| 0.941558
| 1
| 0
|
C000011
| 2
| 0.026551
| 1
| 1
| 0
|
C000011
| 3
| 0.015611
| 0.871966
| 2
| 0
|
C000011
| 4
| 0.021571
| 0.909729
| 0
| 0
|
C000011
| 5
| 0.027269
| 0.574693
| 0
| 0
|
C000011
| 6
| 0.026832
| 0.959601
| 0
| 0
|
C000011
| 7
| 0.012688
| 1
| 0
| 0
|
C000011
| 8
| 0.04531
| 0.760669
| 0
| 0
|
C000011
| 9
| 0.011313
| 0.62208
| 0
| 0
|
C000011
| 10
| 0.014729
| 0.922248
| 1
| 0
|
C000011
| 11
| 0.020646
| 0.910627
| 0
| 0
|
C000011
| 12
| 0.03396
| 0.945025
| 0
| 0
|
C000011
| 13
| 0.01509
| 1
| 2
| 0
|
C000011
| 14
| 0.027414
| 0.872549
| 0
| 0
|
C000011
| 15
| 0.034682
| 0.91443
| 1
| 0
|
C000011
| 16
| 0.014882
| 0.945509
| 0
| 0
|
C000011
| 17
| 0.029495
| 0.916642
| 1
| 0
|
C000011
| 18
| 0.025658
| 0.767464
| 0
| 0
|
C000011
| 19
| 0.021496
| 1
| 1
| 0
|
C000011
| 20
| 0.026733
| 0.522759
| 0
| 0
|
C000011
| 21
| 0.023029
| 0.783418
| 0
| 0
|
C000011
| 22
| 0.015631
| 1
| 0
| 0
|
C000011
| 23
| 0.031762
| 0.961664
| 0
| 0
|
C000011
| 24
| 0.027197
| 1
| 3
| 0
|
C000011
| 25
| 0.045985
| 0.67814
| 0
| 0
|
C000011
| 26
| 0.016686
| 0.710495
| 1
| 0
|
C000011
| 27
| 0.002112
| 1
| 0
| 0
|
C000012
| 1
| 0
| 0
| 1
| 1
|
C000012
| 2
| 0
| 0
| 0
| 0
|
C000012
| 3
| 0
| 0
| 1
| 0
|
C000012
| 4
| 0
| 0
| 0
| 0
|
C000012
| 5
| 0
| 0
| 0
| 0
|
C000012
| 6
| 0
| 0
| 2
| 1
|
C000012
| 7
| 0
| 0
| 1
| 1
|
C000012
| 8
| 0
| 0
| 0
| 0
|
C000012
| 9
| 0
| 0
| 2
| 1
|
C000012
| 10
| 0
| 0
| 0
| 0
|
C000012
| 11
| 0
| 0
| 0
| 0
|
C000012
| 12
| 0
| 0
| 0
| 0
|
C000012
| 13
| 0
| 0
| 1
| 1
|
C000012
| 14
| 0
| 0
| 1
| 1
|
C000012
| 15
| 0
| 0
| 0
| 0
|
C000012
| 16
| 0
| 0
| 0
| 0
|
C000012
| 17
| 0
| 0
| 0
| 0
|
C000012
| 18
| 0
| 0
| 1
| 1
|
C000012
| 19
| 0
| 0
| 0
| 0
|
C000012
| 20
| 0
| 0
| 1
| 0
|
End of preview.
🏦 AgentDS-RetailBanking
This dataset is part of the AgentDS Benchmark — a multi-domain benchmark for evaluating human-AI collaboration in real-world, domain-specific data science.
AgentDS-RetailBanking includes structured transactional data for 2 challenges:
- Credit default prediction
- Transaction-level fraud detection
👉 Files are organized in the RetailBanking/ folder and reused across challenges.
Refer to the included description.md for:
- File usage and challenge mappings
- Task descriptions and data schema notes
- Submission format expectations
📖 More info & challenge details: https://agentds.org/domains
🔐 Get your API key: https://agentds.org/dashboard
🧠 Submit predictions via SDK: pip install agentds-bench (see main AgentDS README for usage)
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