Datasets:

ivopersus
/

LeakProTabular

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 9 new columns ({'Longitude', 'MedInc', 'Population', 'AveOccup', 'Latitude', 'AveRooms', 'HouseAge', 'MedHouseVal', 'AveBedrms'}) and 15 missing columns ({'education', 'age', 'capital-loss', 'race', 'capital-gain', 'income', 'occupation', 'fnlwgt', 'workclass', 'relationship', 'hours-per-week', 'native-country', 'sex', 'education-num', 'marital-status'}).

This happened while the csv dataset builder was generating data using

hf://datasets/ivopersus/LeakProTabular/regression/california_housing.csv (at revision 3f7efd23638118d4afcab18956323f9a0486dace), [/tmp/hf-datasets-cache/medium/datasets/30669423901681-config-parquet-and-info-ivopersus-LeakProTabular-be67b3b3/hub/datasets--ivopersus--LeakProTabular/snapshots/3f7efd23638118d4afcab18956323f9a0486dace/binary/adult/adult.csv (origin=hf://datasets/ivopersus/LeakProTabular@3f7efd23638118d4afcab18956323f9a0486dace/binary/adult/adult.csv), /tmp/hf-datasets-cache/medium/datasets/30669423901681-config-parquet-and-info-ivopersus-LeakProTabular-be67b3b3/hub/datasets--ivopersus--LeakProTabular/snapshots/3f7efd23638118d4afcab18956323f9a0486dace/regression/california_housing.csv (origin=hf://datasets/ivopersus/LeakProTabular@3f7efd23638118d4afcab18956323f9a0486dace/regression/california_housing.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 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              MedInc: double
              HouseAge: double
              AveRooms: double
              AveBedrms: double
              Population: double
              AveOccup: double
              Latitude: double
              Longitude: double
              MedHouseVal: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1326
              to
              {'age': Value('int64'), 'workclass': Value('string'), 'fnlwgt': Value('int64'), 'education': Value('string'), 'education-num': Value('int64'), 'marital-status': Value('string'), 'occupation': Value('string'), 'relationship': Value('string'), 'race': Value('string'), 'sex': Value('string'), 'capital-gain': Value('int64'), 'capital-loss': Value('int64'), 'hours-per-week': Value('int64'), 'native-country': Value('string'), 'income': 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 1348, 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 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, 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 1802, 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 9 new columns ({'Longitude', 'MedInc', 'Population', 'AveOccup', 'Latitude', 'AveRooms', 'HouseAge', 'MedHouseVal', 'AveBedrms'}) and 15 missing columns ({'education', 'age', 'capital-loss', 'race', 'capital-gain', 'income', 'occupation', 'fnlwgt', 'workclass', 'relationship', 'hours-per-week', 'native-country', 'sex', 'education-num', 'marital-status'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/ivopersus/LeakProTabular/regression/california_housing.csv (at revision 3f7efd23638118d4afcab18956323f9a0486dace), [/tmp/hf-datasets-cache/medium/datasets/30669423901681-config-parquet-and-info-ivopersus-LeakProTabular-be67b3b3/hub/datasets--ivopersus--LeakProTabular/snapshots/3f7efd23638118d4afcab18956323f9a0486dace/binary/adult/adult.csv (origin=hf://datasets/ivopersus/LeakProTabular@3f7efd23638118d4afcab18956323f9a0486dace/binary/adult/adult.csv), /tmp/hf-datasets-cache/medium/datasets/30669423901681-config-parquet-and-info-ivopersus-LeakProTabular-be67b3b3/hub/datasets--ivopersus--LeakProTabular/snapshots/3f7efd23638118d4afcab18956323f9a0486dace/regression/california_housing.csv (origin=hf://datasets/ivopersus/LeakProTabular@3f7efd23638118d4afcab18956323f9a0486dace/regression/california_housing.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 int64	workclass string	fnlwgt int64	education string	education-num int64	marital-status string	occupation string	relationship string	race string	sex string	capital-gain int64	capital-loss int64	hours-per-week int64	native-country string	income string
39	State-gov	77,516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2,174	0	40	United-States	<=50K
50	Self-emp-not-inc	83,311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	<=50K
38	Private	215,646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	<=50K
53	Private	234,721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	<=50K
28	Private	338,409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	<=50K
37	Private	284,582	Masters	14	Married-civ-spouse	Exec-managerial	Wife	White	Female	0	0	40	United-States	<=50K
49	Private	160,187	9th	5	Married-spouse-absent	Other-service	Not-in-family	Black	Female	0	0	16	Jamaica	<=50K
52	Self-emp-not-inc	209,642	HS-grad	9	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	45	United-States	>50K
31	Private	45,781	Masters	14	Never-married	Prof-specialty	Not-in-family	White	Female	14,084	0	50	United-States	>50K
42	Private	159,449	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	5,178	0	40	United-States	>50K
37	Private	280,464	Some-college	10	Married-civ-spouse	Exec-managerial	Husband	Black	Male	0	0	80	United-States	>50K
30	State-gov	141,297	Bachelors	13	Married-civ-spouse	Prof-specialty	Husband	Asian-Pac-Islander	Male	0	0	40	India	>50K
23	Private	122,272	Bachelors	13	Never-married	Adm-clerical	Own-child	White	Female	0	0	30	United-States	<=50K
32	Private	205,019	Assoc-acdm	12	Never-married	Sales	Not-in-family	Black	Male	0	0	50	United-States	<=50K
40	Private	121,772	Assoc-voc	11	Married-civ-spouse	Craft-repair	Husband	Asian-Pac-Islander	Male	0	0	40	?	>50K
34	Private	245,487	7th-8th	4	Married-civ-spouse	Transport-moving	Husband	Amer-Indian-Eskimo	Male	0	0	45	Mexico	<=50K
25	Self-emp-not-inc	176,756	HS-grad	9	Never-married	Farming-fishing	Own-child	White	Male	0	0	35	United-States	<=50K
32	Private	186,824	HS-grad	9	Never-married	Machine-op-inspct	Unmarried	White	Male	0	0	40	United-States	<=50K
38	Private	28,887	11th	7	Married-civ-spouse	Sales	Husband	White	Male	0	0	50	United-States	<=50K
43	Self-emp-not-inc	292,175	Masters	14	Divorced	Exec-managerial	Unmarried	White	Female	0	0	45	United-States	>50K
40	Private	193,524	Doctorate	16	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	60	United-States	>50K
54	Private	302,146	HS-grad	9	Separated	Other-service	Unmarried	Black	Female	0	0	20	United-States	<=50K
35	Federal-gov	76,845	9th	5	Married-civ-spouse	Farming-fishing	Husband	Black	Male	0	0	40	United-States	<=50K
43	Private	117,037	11th	7	Married-civ-spouse	Transport-moving	Husband	White	Male	0	2,042	40	United-States	<=50K
59	Private	109,015	HS-grad	9	Divorced	Tech-support	Unmarried	White	Female	0	0	40	United-States	<=50K
56	Local-gov	216,851	Bachelors	13	Married-civ-spouse	Tech-support	Husband	White	Male	0	0	40	United-States	>50K
19	Private	168,294	HS-grad	9	Never-married	Craft-repair	Own-child	White	Male	0	0	40	United-States	<=50K
54	?	180,211	Some-college	10	Married-civ-spouse	?	Husband	Asian-Pac-Islander	Male	0	0	60	South	>50K
39	Private	367,260	HS-grad	9	Divorced	Exec-managerial	Not-in-family	White	Male	0	0	80	United-States	<=50K
49	Private	193,366	HS-grad	9	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
23	Local-gov	190,709	Assoc-acdm	12	Never-married	Protective-serv	Not-in-family	White	Male	0	0	52	United-States	<=50K
20	Private	266,015	Some-college	10	Never-married	Sales	Own-child	Black	Male	0	0	44	United-States	<=50K
45	Private	386,940	Bachelors	13	Divorced	Exec-managerial	Own-child	White	Male	0	1,408	40	United-States	<=50K
30	Federal-gov	59,951	Some-college	10	Married-civ-spouse	Adm-clerical	Own-child	White	Male	0	0	40	United-States	<=50K
22	State-gov	311,512	Some-college	10	Married-civ-spouse	Other-service	Husband	Black	Male	0	0	15	United-States	<=50K
48	Private	242,406	11th	7	Never-married	Machine-op-inspct	Unmarried	White	Male	0	0	40	Puerto-Rico	<=50K
21	Private	197,200	Some-college	10	Never-married	Machine-op-inspct	Own-child	White	Male	0	0	40	United-States	<=50K
19	Private	544,091	HS-grad	9	Married-AF-spouse	Adm-clerical	Wife	White	Female	0	0	25	United-States	<=50K
31	Private	84,154	Some-college	10	Married-civ-spouse	Sales	Husband	White	Male	0	0	38	?	>50K
48	Self-emp-not-inc	265,477	Assoc-acdm	12	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	40	United-States	<=50K
31	Private	507,875	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	43	United-States	<=50K
53	Self-emp-not-inc	88,506	Bachelors	13	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	40	United-States	<=50K
24	Private	172,987	Bachelors	13	Married-civ-spouse	Tech-support	Husband	White	Male	0	0	50	United-States	<=50K
49	Private	94,638	HS-grad	9	Separated	Adm-clerical	Unmarried	White	Female	0	0	40	United-States	<=50K
25	Private	289,980	HS-grad	9	Never-married	Handlers-cleaners	Not-in-family	White	Male	0	0	35	United-States	<=50K
57	Federal-gov	337,895	Bachelors	13	Married-civ-spouse	Prof-specialty	Husband	Black	Male	0	0	40	United-States	>50K
53	Private	144,361	HS-grad	9	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	38	United-States	<=50K
44	Private	128,354	Masters	14	Divorced	Exec-managerial	Unmarried	White	Female	0	0	40	United-States	<=50K
41	State-gov	101,603	Assoc-voc	11	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
29	Private	271,466	Assoc-voc	11	Never-married	Prof-specialty	Not-in-family	White	Male	0	0	43	United-States	<=50K
25	Private	32,275	Some-college	10	Married-civ-spouse	Exec-managerial	Wife	Other	Female	0	0	40	United-States	<=50K
18	Private	226,956	HS-grad	9	Never-married	Other-service	Own-child	White	Female	0	0	30	?	<=50K
47	Private	51,835	Prof-school	15	Married-civ-spouse	Prof-specialty	Wife	White	Female	0	1,902	60	Honduras	>50K
50	Federal-gov	251,585	Bachelors	13	Divorced	Exec-managerial	Not-in-family	White	Male	0	0	55	United-States	>50K
47	Self-emp-inc	109,832	HS-grad	9	Divorced	Exec-managerial	Not-in-family	White	Male	0	0	60	United-States	<=50K
43	Private	237,993	Some-college	10	Married-civ-spouse	Tech-support	Husband	White	Male	0	0	40	United-States	>50K
46	Private	216,666	5th-6th	3	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	40	Mexico	<=50K
35	Private	56,352	Assoc-voc	11	Married-civ-spouse	Other-service	Husband	White	Male	0	0	40	Puerto-Rico	<=50K
41	Private	147,372	HS-grad	9	Married-civ-spouse	Adm-clerical	Husband	White	Male	0	0	48	United-States	<=50K
30	Private	188,146	HS-grad	9	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	5,013	0	40	United-States	<=50K
30	Private	59,496	Bachelors	13	Married-civ-spouse	Sales	Husband	White	Male	2,407	0	40	United-States	<=50K
32	?	293,936	7th-8th	4	Married-spouse-absent	?	Not-in-family	White	Male	0	0	40	?	<=50K
48	Private	149,640	HS-grad	9	Married-civ-spouse	Transport-moving	Husband	White	Male	0	0	40	United-States	<=50K
42	Private	116,632	Doctorate	16	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	45	United-States	>50K
29	Private	105,598	Some-college	10	Divorced	Tech-support	Not-in-family	White	Male	0	0	58	United-States	<=50K
36	Private	155,537	HS-grad	9	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
28	Private	183,175	Some-college	10	Divorced	Adm-clerical	Not-in-family	White	Female	0	0	40	United-States	<=50K
53	Private	169,846	HS-grad	9	Married-civ-spouse	Adm-clerical	Wife	White	Female	0	0	40	United-States	>50K
49	Self-emp-inc	191,681	Some-college	10	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	50	United-States	>50K
25	?	200,681	Some-college	10	Never-married	?	Own-child	White	Male	0	0	40	United-States	<=50K
19	Private	101,509	Some-college	10	Never-married	Prof-specialty	Own-child	White	Male	0	0	32	United-States	<=50K
31	Private	309,974	Bachelors	13	Separated	Sales	Own-child	Black	Female	0	0	40	United-States	<=50K
29	Self-emp-not-inc	162,298	Bachelors	13	Married-civ-spouse	Sales	Husband	White	Male	0	0	70	United-States	>50K
23	Private	211,678	Some-college	10	Never-married	Machine-op-inspct	Not-in-family	White	Male	0	0	40	United-States	<=50K
79	Private	124,744	Some-college	10	Married-civ-spouse	Prof-specialty	Other-relative	White	Male	0	0	20	United-States	<=50K
27	Private	213,921	HS-grad	9	Never-married	Other-service	Own-child	White	Male	0	0	40	Mexico	<=50K
40	Private	32,214	Assoc-acdm	12	Married-civ-spouse	Adm-clerical	Husband	White	Male	0	0	40	United-States	<=50K
67	?	212,759	10th	6	Married-civ-spouse	?	Husband	White	Male	0	0	2	United-States	<=50K
18	Private	309,634	11th	7	Never-married	Other-service	Own-child	White	Female	0	0	22	United-States	<=50K
31	Local-gov	125,927	7th-8th	4	Married-civ-spouse	Farming-fishing	Husband	White	Male	0	0	40	United-States	<=50K
18	Private	446,839	HS-grad	9	Never-married	Sales	Not-in-family	White	Male	0	0	30	United-States	<=50K
52	Private	276,515	Bachelors	13	Married-civ-spouse	Other-service	Husband	White	Male	0	0	40	Cuba	<=50K
46	Private	51,618	HS-grad	9	Married-civ-spouse	Other-service	Wife	White	Female	0	0	40	United-States	<=50K
59	Private	159,937	HS-grad	9	Married-civ-spouse	Sales	Husband	White	Male	0	0	48	United-States	<=50K
44	Private	343,591	HS-grad	9	Divorced	Craft-repair	Not-in-family	White	Female	14,344	0	40	United-States	>50K
53	Private	346,253	HS-grad	9	Divorced	Sales	Own-child	White	Female	0	0	35	United-States	<=50K
49	Local-gov	268,234	HS-grad	9	Married-civ-spouse	Protective-serv	Husband	White	Male	0	0	40	United-States	>50K
33	Private	202,051	Masters	14	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	50	United-States	<=50K
30	Private	54,334	9th	5	Never-married	Sales	Not-in-family	White	Male	0	0	40	United-States	<=50K
43	Federal-gov	410,867	Doctorate	16	Never-married	Prof-specialty	Not-in-family	White	Female	0	0	50	United-States	>50K
57	Private	249,977	Assoc-voc	11	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	0	40	United-States	<=50K
37	Private	286,730	Some-college	10	Divorced	Craft-repair	Unmarried	White	Female	0	0	40	United-States	<=50K
28	Private	212,563	Some-college	10	Divorced	Machine-op-inspct	Unmarried	Black	Female	0	0	25	United-States	<=50K
30	Private	117,747	HS-grad	9	Married-civ-spouse	Sales	Wife	Asian-Pac-Islander	Female	0	1,573	35	?	<=50K
34	Local-gov	226,296	Bachelors	13	Married-civ-spouse	Protective-serv	Husband	White	Male	0	0	40	United-States	>50K
29	Local-gov	115,585	Some-college	10	Never-married	Handlers-cleaners	Not-in-family	White	Male	0	0	50	United-States	<=50K
48	Self-emp-not-inc	191,277	Doctorate	16	Married-civ-spouse	Prof-specialty	Husband	White	Male	0	1,902	60	United-States	>50K
37	Private	202,683	Some-college	10	Married-civ-spouse	Sales	Husband	White	Male	0	0	48	United-States	>50K
48	Private	171,095	Assoc-acdm	12	Divorced	Exec-managerial	Unmarried	White	Female	0	0	40	England	<=50K
32	Federal-gov	249,409	HS-grad	9	Never-married	Other-service	Own-child	Black	Male	0	0	40	United-States	<=50K

End of preview.

LeakProTabular Public Benchmark Datasets

This repository provides public benchmark datasets used with the tabular-GIA research artifact for experiments on gradient inversion attacks in tabular federated learning.

For the experiment setup in tabular-GIA, Adult is downloaded from this Hugging Face repository, while California Housing is downloaded from the official scikit-learn source or here.

This repository does not include MIMIC-IV, the private multiclass benchmark, generated experiment outputs, checkpoints, plots, or attack reconstructions.

Dataset	Task	Hosted path	Source
Adult	Binary classification	`binary/adult/`	UCI Machine Learning Repository
California Housing	Regression	`regression/california_housing/`	scikit-learn California Housing

Dataset Sources

Adult (Census Income): https://archive.ics.uci.edu/dataset/2/adult
California Housing: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html
Original California Housing reference: R. Kelley Pace and Ronald Barry. Sparse spatial autoregressions. Statistics & Probability Letters, 33(3):291-297, 1997.

Processing Notes

The hosted files are provided in CSV-compatible tabular form.

The tabular-GIA codebase applies its own preprocessing during experiments, including feature type handling, train/validation/test splitting, normalization, and model-specific categorical encoding.

Restricted and Private Data

This repository does not redistribute restricted or private datasets:

MIMIC-IV requires credentialed PhysioNet access and must be obtained from PhysioNet.
The private multiclass benchmark used in the paper cannot be shared.

Citation

If you use this repository with the accompanying code, please cite the associated paper after publication.

For the upstream datasets, cite the original dataset sources listed above.

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Total file size:

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LeakProTabular Public Benchmark Datasets

Contents

Dataset Sources

Processing Notes

Restricted and Private Data

Citation