Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 23 new columns ({'X21', 'X13', 'X9', 'X4', 'X14', 'X3', 'X8', 'X20', 'X16', 'X15', 'X22', 'X1', 'X23', 'X6', 'X17', 'X12', 'X11', 'X19', 'X7', 'X5', 'X10', 'X2', 'X18'}) and 13 missing columns ({'native-country', 'sex', 'fnlwgt', 'age', 'occupation', 'relationship', 'capital-gain', 'workclass', 'education-num', 'capital-loss', 'race', 'marital-status', 'hours-per-week'}).
This happened while the csv dataset builder was generating data using
hf://datasets/kuldeepbishnoi29/dro-vs-naive-datasets/Tabular/Dataset/Bank_Datasets/seed_1/train/train_alpha_0.1.csv (at revision 7dc75450893369f1b1714e8012f455f0a8454e71)
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
X1: double
X2: int64
X3: double
X4: double
X5: double
X6: double
X7: double
X8: double
X9: double
X10: double
X11: double
X12: double
X13: double
X14: double
X15: double
X16: double
X17: double
X18: double
X19: double
X20: double
X21: double
X22: double
X23: double
Y: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2801
to
{'age': Value('float64'), 'workclass': Value('float64'), 'fnlwgt': Value('float64'), 'education-num': Value('float64'), 'marital-status': Value('float64'), 'occupation': Value('float64'), 'relationship': Value('float64'), 'race': Value('float64'), 'sex': Value('int64'), 'capital-gain': Value('float64'), 'capital-loss': Value('float64'), 'hours-per-week': Value('float64'), 'native-country': Value('float64'), 'Y': 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 1339, 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 972, 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 23 new columns ({'X21', 'X13', 'X9', 'X4', 'X14', 'X3', 'X8', 'X20', 'X16', 'X15', 'X22', 'X1', 'X23', 'X6', 'X17', 'X12', 'X11', 'X19', 'X7', 'X5', 'X10', 'X2', 'X18'}) and 13 missing columns ({'native-country', 'sex', 'fnlwgt', 'age', 'occupation', 'relationship', 'capital-gain', 'workclass', 'education-num', 'capital-loss', 'race', 'marital-status', 'hours-per-week'}).
This happened while the csv dataset builder was generating data using
hf://datasets/kuldeepbishnoi29/dro-vs-naive-datasets/Tabular/Dataset/Bank_Datasets/seed_1/train/train_alpha_0.1.csv (at revision 7dc75450893369f1b1714e8012f455f0a8454e71)
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 | workclass float64 | fnlwgt float64 | education-num float64 | marital-status float64 | occupation float64 | relationship float64 | race float64 | sex int64 | capital-gain float64 | capital-loss float64 | hours-per-week float64 | native-country float64 | Y int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.206368 | 2.918927 | 1.854136 | 0.738546 | 2.28281 | -1.239378 | 0.365601 | -0.819999 | 0 | -0.08457 | -0.167484 | -0.153412 | -0.230296 | 1 |
0.641471 | 0.830111 | -0.139091 | -3.187394 | 2.28281 | 0.996323 | -0.884823 | -4.430651 | 1 | -0.170995 | -0.307324 | 0.796275 | -2.202809 | 0 |
-0.342492 | -0.214298 | -0.772773 | -2.7948 | 0.947485 | 0.002678 | 0.365601 | -3.227101 | 1 | -0.147864 | -0.218169 | -0.077081 | -2.038433 | 0 |
-0.115063 | -0.214298 | 2.099193 | -0.439236 | 0.279822 | -0.494145 | 1.616025 | -0.819999 | 1 | -0.147864 | -0.218169 | -0.077081 | -1.874057 | 0 |
0.003146 | 0.830111 | -0.342534 | 1.13114 | -0.387841 | 0.251089 | -0.259611 | -0.819999 | 1 | -0.274983 | -0.227614 | -0.228977 | -3.84657 | 1 |
1.855987 | -0.214298 | 0.753539 | -0.83183 | 2.28281 | 1.244734 | 1.616025 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.948969 | -0.214298 | 0.251531 | -0.439236 | 1.615148 | -0.742556 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.100588 | -0.214298 | 2.603429 | -0.439236 | 0.947485 | -1.487789 | -0.259611 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.750997 | 0.262833 | 0 |
0.036556 | -0.214298 | 1.586567 | -0.439236 | 0.947485 | -0.494145 | 0.990813 | 0.383551 | 1 | -0.147864 | -0.218169 | -2.3957 | 0.262833 | 0 |
1.022081 | 0.830111 | -0.072255 | 1.916328 | -0.387841 | 0.747912 | -0.884823 | -0.819999 | 1 | 13.317986 | -0.218169 | 1.993115 | 0.262833 | 1 |
0.794653 | -0.214298 | -0.617341 | 1.13114 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | 1.579076 | 0.262833 | 1 |
0.514378 | 2.918927 | -1.002873 | -1.617018 | -1.723167 | 0.002678 | -0.259611 | -0.819999 | 0 | -0.093656 | -0.323508 | 1.722471 | -5.490331 | 1 |
-0.948969 | 0.830111 | 0.731489 | 1.13114 | 0.947485 | -1.487789 | -0.259611 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.173701 | -0.214298 | 1.538428 | -0.046642 | -1.723167 | -1.487789 | -0.259611 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.49112 | 0.262833 | 0 |
-0.190873 | -0.214298 | -0.619663 | -0.439236 | -1.723167 | -1.487789 | 1.616025 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.49112 | 0.262833 | 0 |
-0.756321 | -1.258706 | 0.109753 | -2.7948 | 0.279822 | 1.741557 | 1.616025 | -0.819999 | 1 | -0.264249 | -0.317568 | 1.517056 | -2.531561 | 0 |
-0.580214 | 2.918927 | 0.039022 | -0.046642 | -0.387841 | 0.996323 | -0.259611 | -0.819999 | 1 | 0.040691 | -0.013795 | -0.259585 | -0.065919 | 1 |
0.082687 | -1.258706 | 1.007821 | 2.308922 | -1.723167 | -1.487789 | -0.884823 | -2.02355 | 0 | 0.01762 | -0.306565 | -0.203942 | -0.559048 | 1 |
-0.797349 | -0.214298 | -0.066056 | 0.345952 | 0.947485 | 1.244734 | -0.259611 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
0.643033 | -0.214298 | -0.291905 | -0.83183 | -1.723167 | 0.251089 | -0.259611 | 0.383551 | 0 | -0.147864 | -0.218169 | 0.585382 | 0.262833 | 0 |
0.946272 | -0.214298 | 0.464554 | -0.439236 | -0.387841 | -0.245733 | -0.884823 | 0.383551 | 1 | -0.147864 | 4.475346 | 0.088535 | 0.262833 | 1 |
-1.100588 | -0.214298 | 4.399597 | -0.439236 | 0.947485 | -0.990967 | 0.990813 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | -4.832826 | 0 |
0.339795 | -0.214298 | -0.01658 | -0.046642 | -0.387841 | 0.747912 | 2.241237 | 0.383551 | 0 | -0.147864 | 4.43833 | 0.750997 | 0.262833 | 1 |
0.112366 | -0.214298 | 0.180052 | -0.439236 | 0.947485 | -0.990967 | -0.259611 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.72154 | -1.258706 | -0.077687 | 1.13114 | -1.723167 | 0.747912 | -0.259611 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.750997 | 0.262833 | 0 |
-1.024778 | -0.214298 | -0.758942 | -0.046642 | 0.947485 | -0.742556 | -0.259611 | 0.383551 | 0 | 1.012096 | -0.218169 | 0.336958 | 0.262833 | 1 |
0.946272 | -0.214298 | 0.806029 | -0.046642 | 2.28281 | 0.251089 | 1.616025 | 0.383551 | 0 | -0.147864 | -0.218169 | -1.319198 | 0.262833 | 0 |
0.718843 | -0.214298 | 0.175426 | -0.046642 | 0.947485 | 0.251089 | 1.616025 | -2.02355 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.845335 | 3.963336 | 0.685201 | 1.13114 | 2.28281 | -1.239378 | 1.616025 | -2.02355 | 0 | -0.184577 | -0.198639 | 0.008235 | 0.427209 | 0 |
-0.115063 | -0.214298 | -1.013933 | 1.13114 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | 1.875265 | -0.218169 | -0.077081 | 0.262833 | 1 |
-0.72154 | -0.214298 | -0.668077 | -0.439236 | -0.387841 | -1.487789 | 2.241237 | 0.383551 | 0 | -0.147864 | 5.158891 | -0.077081 | 0.262833 | 0 |
0.491414 | 1.874519 | 0.638842 | -0.046642 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | 0.527186 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.024778 | -0.214298 | 0.181199 | -0.439236 | 0.947485 | 0.002678 | 0.990813 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.552749 | 1.874519 | -0.86323 | -0.439236 | -0.387841 | -0.494145 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -1.733237 | 0.262833 | 0 |
-0.569921 | -0.214298 | 0.902734 | 1.523734 | 0.947485 | 0.747912 | -0.259611 | -2.02355 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.569921 | -0.214298 | 1.513667 | -0.439236 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
-1.176398 | -1.258706 | 0.20196 | 1.13114 | 0.947485 | 0.747912 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -2.147276 | 0.262833 | 0 |
-1.328017 | -0.214298 | 1.646802 | 0.345952 | 0.947485 | 0.251089 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
0.567224 | -0.214298 | -0.64798 | 0.738546 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | 1.875265 | -0.218169 | 0.750997 | 0.262833 | 1 |
-0.266682 | -0.214298 | -1.567701 | -0.046642 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
0.415604 | 1.874519 | -0.293137 | -0.046642 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.750997 | 0.262833 | 1 |
-0.72154 | -0.214298 | 0.077176 | -0.439236 | 0.947485 | -1.487789 | -0.259611 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.100588 | -0.214298 | -0.905331 | -0.439236 | -0.387841 | 0.002678 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.176398 | -0.214298 | 2.303646 | 1.13114 | 0.947485 | -0.742556 | -0.259611 | 0.383551 | 1 | -0.147864 | -0.218169 | 1.579076 | 0.262833 | 0 |
1.870827 | -1.258706 | -0.525559 | -0.83183 | 0.279822 | -0.245733 | 1.616025 | -3.227101 | 0 | 1.903638 | -0.089209 | -0.501041 | -2.202809 | 0 |
-0.494111 | -0.214298 | -0.7207 | 1.13114 | -0.387841 | -1.487789 | 2.241237 | 0.383551 | 0 | -0.147864 | -0.218169 | -1.402006 | 0.262833 | 1 |
0.839238 | 1.874519 | 2.544935 | 1.916328 | -1.723167 | 0.4995 | 0.365601 | -4.430651 | 1 | 1.014142 | -0.160854 | -0.530614 | -2.202809 | 0 |
-0.873159 | 2.918927 | 0.026355 | -0.439236 | 0.947485 | 0.996323 | 0.990813 | 0.383551 | 1 | 0.144886 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.100588 | -0.214298 | -0.620877 | -1.224424 | -0.387841 | 1.741557 | -0.884823 | -3.227101 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.427209 | 0 |
-0.494111 | -0.214298 | 1.766638 | -1.224424 | 0.947485 | -0.990967 | 0.365601 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.342492 | -1.258706 | -0.187408 | 1.13114 | -0.387841 | 0.747912 | 2.241237 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
0.339795 | -1.258706 | -0.782045 | 1.13114 | 1.615148 | 0.747912 | 1.616025 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
0.040081 | 0.830111 | -0.315539 | -3.579988 | 2.28281 | -0.990967 | 2.241237 | -4.430651 | 0 | 0.991307 | -0.208941 | 0.739519 | -1.216552 | 0 |
0.27799 | -2.303114 | 0.147226 | -3.579988 | 0.947485 | -0.990967 | 1.616025 | -4.430651 | 0 | -0.098699 | -0.151662 | -0.16481 | -1.70968 | 0 |
0.938557 | -1.258706 | 0.554416 | -0.046642 | 0.947485 | 1.493145 | -0.259611 | -0.819999 | 0 | -0.270651 | -0.332766 | -0.058454 | -4.997202 | 0 |
-0.569921 | -0.214298 | 1.324296 | -0.046642 | -1.723167 | -1.487789 | 1.616025 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.494111 | -0.214298 | -0.422121 | -0.439236 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
1.931797 | -0.214298 | -1.16454 | 1.13114 | -1.723167 | -0.742556 | 1.616025 | 0.383551 | 1 | 3.59945 | -0.218169 | 0.750997 | 0.262833 | 1 |
-0.569921 | -1.258706 | -0.345751 | 1.13114 | -0.387841 | 0.747912 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
0.643033 | 1.874519 | -0.137288 | -0.439236 | 2.28281 | -0.742556 | -0.259611 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.905159 | 0.262833 | 0 |
-0.64573 | -1.258706 | -1.413824 | -0.439236 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.039253 | -0.214298 | 2.237495 | -2.009612 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
1.022081 | 0.830111 | -0.090305 | 1.916328 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.476939 | -0.214298 | 0.575706 | -1.617018 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | 0.549405 | -0.218169 | -0.077081 | 0.262833 | 1 |
0.643033 | -0.214298 | 0.260906 | 1.13114 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.750997 | 0.262833 | 0 |
1.022081 | -0.214298 | -0.531576 | -0.046642 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | 1.165036 | 0.262833 | 0 |
-1.252207 | -0.214298 | 2.148982 | -0.046642 | 0.947485 | -1.487789 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.628558 | -0.214298 | -0.539861 | -0.439236 | -0.387841 | 0.251089 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.552749 | -0.214298 | -0.484489 | -1.617018 | 0.279822 | 0.996323 | -0.259611 | -3.227101 | 1 | -0.147864 | -0.218169 | -0.077081 | -3.024689 | 0 |
1.780178 | -2.303114 | -0.287355 | -0.439236 | 2.28281 | -0.742556 | -0.259611 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.408312 | 0.262833 | 0 |
-0.039253 | -0.214298 | -0.310457 | 1.13114 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
-1.252207 | -0.214298 | 0.74387 | -0.046642 | 0.947485 | 1.244734 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -2.064468 | 0.262833 | 0 |
0.437181 | 1.874519 | -0.350917 | -0.439236 | 0.279822 | 0.4995 | -0.884823 | 0.383551 | 1 | -0.146497 | -0.252879 | -0.734899 | -3.682194 | 1 |
-1.176398 | -0.214298 | 0.387141 | -0.046642 | 0.947485 | 0.251089 | -0.259611 | -3.227101 | 1 | -0.147864 | -0.218169 | -0.077081 | -5.161578 | 0 |
-1.176398 | -0.214298 | -0.232798 | -0.046642 | 0.947485 | 0.002678 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -2.147276 | -4.339698 | 0 |
-1.024778 | -0.214298 | -0.824913 | 1.13114 | -0.387841 | -0.742556 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
0.036556 | -0.214298 | -0.731972 | -0.439236 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.555446 | -0.214298 | -0.049295 | -0.439236 | 0.947485 | 1.244734 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -1.319198 | 0.262833 | 0 |
1.569557 | 3.963336 | -0.303372 | -0.439236 | 0.279822 | -0.494145 | 1.616025 | -0.819999 | 1 | -0.243364 | -0.469715 | -0.174056 | -5.819083 | 1 |
-0.797349 | -0.214298 | -0.055457 | -0.046642 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.750997 | 0.262833 | 0 |
1.807564 | -2.303114 | -0.281797 | -3.187394 | 1.615148 | 0.996323 | 2.241237 | -4.430651 | 1 | -0.124984 | -0.033341 | 4.72338 | -3.189065 | 1 |
-0.115063 | 1.874519 | 0.916414 | -0.439236 | 0.279822 | 0.251089 | 1.616025 | -2.02355 | 0 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.039253 | -0.214298 | -0.096344 | -0.439236 | -0.387841 | -0.245733 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.64573 | -0.214298 | -1.613243 | -0.046642 | 0.947485 | -1.487789 | -0.259611 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.585382 | 0.262833 | 0 |
-0.342492 | -0.214298 | -0.336868 | 0.345952 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.873159 | -0.214298 | 0.27214 | -0.046642 | 0.947485 | -1.487789 | -0.259611 | -2.02355 | 0 | -0.147864 | -0.218169 | -0.077081 | -2.367185 | 0 |
-0.64573 | -0.214298 | -0.470573 | -1.617018 | 1.615148 | 0.251089 | -0.259611 | -2.02355 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.022081 | -0.214298 | 0.030232 | -0.83183 | 1.615148 | 0.002678 | 1.616025 | -2.02355 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-0.115063 | 1.874519 | 0.136047 | -0.439236 | -0.387841 | 1.244734 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | 0.336958 | 0.262833 | 0 |
-1.100588 | -0.214298 | -1.199445 | -0.439236 | 0.947485 | -0.990967 | 0.990813 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
-1.403827 | -0.214298 | -0.544374 | -0.046642 | 0.947485 | 0.747912 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -3.223778 | 0.262833 | 0 |
-0.64573 | -2.303114 | 1.183406 | 1.13114 | -0.387841 | 0.747912 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 1 |
-0.342492 | -0.214298 | -1.298178 | -0.439236 | 0.947485 | 1.741557 | 1.616025 | -2.02355 | 1 | -0.147864 | -0.218169 | -0.905159 | 0.262833 | 0 |
0.718843 | 1.874519 | -0.073175 | 2.308922 | 0.947485 | 0.747912 | 1.616025 | 0.383551 | 0 | -0.147864 | -0.218169 | 1.579076 | 0.262833 | 1 |
1.40113 | -2.303114 | 1.179794 | -0.046642 | -0.387841 | 0.747912 | -0.884823 | -4.430651 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.780178 | -0.214298 | -1.378985 | -0.439236 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.40113 | 1.874519 | 0.964012 | -0.046642 | -0.387841 | -0.990967 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | 0.262833 | 0 |
1.32532 | -0.214298 | 0.432843 | -2.402206 | -0.387841 | -1.487789 | -0.884823 | 0.383551 | 1 | -0.147864 | -0.218169 | -0.077081 | -5.161578 | 0 |
0.567224 | -1.258706 | 1.986126 | 2.308922 | -0.387841 | 0.747912 | 2.241237 | 0.383551 | 0 | -0.147864 | 4.475346 | 0.916613 | 0.262833 | 1 |
-1.100588 | -0.214298 | 0.231547 | -2.402206 | 0.947485 | 0.4995 | 0.990813 | 0.383551 | 0 | -0.147864 | -0.218169 | -0.739543 | -4.832826 | 0 |
End of preview.
DRO vs Naive Datasets
This repository contains preprocessed datasets for comparing Distributionally Robust Optimization (DRO) vs. Naive/Standard ML approaches in fairness-aware machine learning.
Datasets Included
1. Adult Income Dataset
- Task: Binary classification (income >$50K vs ≤$50K)
- Records: 45,222
- Protected Attribute: Gender (sex)
- Features: 12 features (5 numeric, 7 categorical)
2. Bank Credit Default Dataset
- Task: Binary classification (credit default prediction)
- Records: 30,000
- Protected Attribute: Gender/Sex
- Features: 22 features (13 numeric, 9 categorical)
Structure
Tabular/
├── Data/ # Raw processed datasets
│ ├── adult_processed.csv
│ ├── adult_meta.json
│ ├── adult.py
│ ├── bank_processed.csv
│ ├── bank_meta.json
│ └── bank.py
└── Dataset/ # Generated dataset variants
├── Adult_Datasets/
└── Bank_Datasets/
Usage
from datasets import load_dataset
import pandas as pd
# Load Adult dataset
df_adult = pd.read_csv("hf://datasets/kuldeepbishnoi29/dro-vs-naive-datasets/Tabular/Data/adult_processed.csv")
# Load Bank dataset
df_bank = pd.read_csv("hf://datasets/kuldeepbishnoi29/dro-vs-naive-datasets/Tabular/Data/bank_processed.csv")
Citation
If you use these datasets, please cite the original sources:
- Adult Income: UCI Machine Learning Repository
- Bank Default: UCI Machine Learning Repository (ID: 350)
License
MIT License - See repository for details.
- Downloads last month
- 443