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Datasets:
TabArena
/
benchmark_results

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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 30 new columns ({'time_train_s_rescaled', 'normalized-error-task', 'elo+', 'time_infer_s_per_1K', 'mrr', 'median_time_train_s_per_1K', 'time_infer_s_rescaled', 'elo-', 'median_time_infer_s_per_1K', 'rank', 'median_normalized-error', 'median_rank', 'median_time_infer_s_rescaled', 'median_time_train_s_rescaled', 'median_loss_rescaled', 'rank>3_count', 'elo', 'median_normalized-error-task', 'winrate', 'rank=2_count', 'median_time_infer_s', 'median_champ_delta', 'time_train_s_per_1K', 'rank=3_count', 'normalized-error', 'median_metric_error', 'champ_delta', 'median_time_train_s', 'rank=1_count', 'loss_rescaled'}) and 8 missing columns ({'problem_type', 'dataset', 'seed', 'metric', 'metric_error', 'Unnamed: 0', 'metric_error_val', 'fold'}).

This happened while the csv dataset builder was generating data using

hf://datasets/TabArena/benchmark_results/tabarena_leaderboard.csv (at revision 8f454116513377309f444e7a09820e4137cd6e88)

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 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, 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 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              method: string
              time_train_s: double
              time_infer_s: double
              time_train_s_per_1K: double
              time_infer_s_per_1K: double
              normalized-error: double
              normalized-error-task: double
              champ_delta: double
              loss_rescaled: double
              time_train_s_rescaled: double
              time_infer_s_rescaled: double
              rank: double
              median_metric_error: double
              median_time_train_s: double
              median_time_infer_s: double
              median_time_train_s_per_1K: double
              median_time_infer_s_per_1K: double
              median_normalized-error: double
              median_normalized-error-task: double
              median_champ_delta: double
              median_loss_rescaled: double
              median_time_train_s_rescaled: double
              median_time_infer_s_rescaled: double
              median_rank: double
              rank=1_count: int64
              rank=2_count: int64
              rank=3_count: int64
              rank>3_count: int64
              elo: double
              elo+: double
              elo-: double
              winrate: double
              mrr: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 4594
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'dataset': Value(dtype='string', id=None), 'fold': Value(dtype='int64', id=None), 'method': Value(dtype='string', id=None), 'metric_error': Value(dtype='float64', id=None), 'time_train_s': Value(dtype='float64', id=None), 'time_infer_s': Value(dtype='float64', id=None), 'metric_error_val': Value(dtype='float64', id=None), 'seed': Value(dtype='int64', id=None), 'problem_type': Value(dtype='string', id=None), 'metric': Value(dtype='string', 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 1433, 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 1050, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1873, 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 30 new columns ({'time_train_s_rescaled', 'normalized-error-task', 'elo+', 'time_infer_s_per_1K', 'mrr', 'median_time_train_s_per_1K', 'time_infer_s_rescaled', 'elo-', 'median_time_infer_s_per_1K', 'rank', 'median_normalized-error', 'median_rank', 'median_time_infer_s_rescaled', 'median_time_train_s_rescaled', 'median_loss_rescaled', 'rank>3_count', 'elo', 'median_normalized-error-task', 'winrate', 'rank=2_count', 'median_time_infer_s', 'median_champ_delta', 'time_train_s_per_1K', 'rank=3_count', 'normalized-error', 'median_metric_error', 'champ_delta', 'median_time_train_s', 'rank=1_count', 'loss_rescaled'}) and 8 missing columns ({'problem_type', 'dataset', 'seed', 'metric', 'metric_error', 'Unnamed: 0', 'metric_error_val', 'fold'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/TabArena/benchmark_results/tabarena_leaderboard.csv (at revision 8f454116513377309f444e7a09820e4137cd6e88)
              
              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.

Unnamed: 0
int64
dataset
string
fold
int64
method
string
metric_error
float64
time_train_s
float64
time_infer_s
float64
metric_error_val
float64
seed
int64
problem_type
string
metric
string
12,240
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roc_auc
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EBM (default)
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roc_auc
12,643
APSFailure
0
XT (default)
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roc_auc
12,844
APSFailure
0
KNN (default)
0.089384
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roc_auc
12,895
APSFailure
0
GBM (default)
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roc_auc
13,096
APSFailure
0
LR (default)
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roc_auc
13,212
APSFailure
0
FASTAI (default)
0.00806
61.790236
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roc_auc
13,413
APSFailure
0
NN_TORCH (default)
0.008323
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roc_auc
13,614
APSFailure
0
RF (default)
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roc_auc
13,815
APSFailure
0
REALMLP (default)
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roc_auc
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APSFailure
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TABDPT (default)
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roc_auc
14,017
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TABICL (default)
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roc_auc
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TABM (default)
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127.12626
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roc_auc
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APSFailure
0
XGB (default)
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roc_auc
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MNCA (default)
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roc_auc
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APSFailure
0
TABPFNV2 (default)
0.00674
34.526603
0.218996
0.013152
0
binary
roc_auc
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APSFailure
1
CAT (default)
0.009555
119.110543
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roc_auc
14,959
APSFailure
1
EBM (default)
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roc_auc
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APSFailure
1
XT (default)
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roc_auc
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APSFailure
1
KNN (default)
0.08325
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roc_auc
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APSFailure
1
GBM (default)
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roc_auc
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APSFailure
1
LR (default)
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roc_auc
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APSFailure
1
FASTAI (default)
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roc_auc
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APSFailure
1
NN_TORCH (default)
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roc_auc
16,131
APSFailure
1
RF (default)
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roc_auc
16,332
APSFailure
1
REALMLP (default)
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roc_auc
16,533
APSFailure
1
TABDPT (default)
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roc_auc
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TABICL (default)
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roc_auc
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TABM (default)
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roc_auc
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XGB (default)
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roc_auc
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APSFailure
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MNCA (default)
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roc_auc
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1
TABPFNV2 (default)
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roc_auc
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2
CAT (default)
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roc_auc
17,476
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2
EBM (default)
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roc_auc
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APSFailure
2
XT (default)
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roc_auc
17,878
APSFailure
2
KNN (default)
0.095655
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roc_auc
17,929
APSFailure
2
GBM (default)
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roc_auc
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APSFailure
2
LR (default)
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roc_auc
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APSFailure
2
FASTAI (default)
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roc_auc
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APSFailure
2
NN_TORCH (default)
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58.029178
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roc_auc
18,648
APSFailure
2
RF (default)
0.013063
31.976869
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roc_auc
18,849
APSFailure
2
REALMLP (default)
0.010389
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roc_auc
19,050
APSFailure
2
TABDPT (default)
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roc_auc
19,051
APSFailure
2
TABICL (default)
0.007881
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roc_auc
19,052
APSFailure
2
TABM (default)
0.013063
31.976869
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roc_auc
19,252
APSFailure
2
XGB (default)
0.007796
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roc_auc
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2
MNCA (default)
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roc_auc
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APSFailure
2
TABPFNV2 (default)
0.013063
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roc_auc
19,791
APSFailure
3
CAT (default)
0.013824
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roc_auc
19,993
APSFailure
3
EBM (default)
0.013026
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roc_auc
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APSFailure
3
XT (default)
0.012316
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roc_auc
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APSFailure
3
KNN (default)
0.095532
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roc_auc
20,446
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3
GBM (default)
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roc_auc
20,647
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3
LR (default)
0.015289
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roc_auc
20,763
APSFailure
3
FASTAI (default)
0.015348
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3
NN_TORCH (default)
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RF (default)
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roc_auc
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3
REALMLP (default)
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3
TABDPT (default)
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TABICL (default)
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TABM (default)
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roc_auc
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XGB (default)
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roc_auc
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APSFailure
3
MNCA (default)
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roc_auc
22,107
APSFailure
3
TABPFNV2 (default)
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roc_auc
22,308
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4
CAT (default)
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APSFailure
4
EBM (default)
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4
XT (default)
0.007539
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roc_auc
22,912
APSFailure
4
KNN (default)
0.074868
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roc_auc
22,963
APSFailure
4
GBM (default)
0.006142
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roc_auc
23,164
APSFailure
4
LR (default)
0.010629
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roc_auc
23,280
APSFailure
4
FASTAI (default)
0.009588
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roc_auc
23,481
APSFailure
4
NN_TORCH (default)
0.008532
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roc_auc
23,682
APSFailure
4
RF (default)
0.007241
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roc_auc
23,883
APSFailure
4
REALMLP (default)
0.007609
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roc_auc
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4
TABDPT (default)
0.008892
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roc_auc
24,085
APSFailure
4
TABICL (default)
0.004979
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0.009521
0
binary
roc_auc
24,086
APSFailure
4
TABM (default)
0.007241
33.32259
0.185444
0.012659
0
binary
roc_auc
24,286
APSFailure
4
XGB (default)
0.006861
25.770984
1.89537
0.010051
0
binary
roc_auc
24,496
APSFailure
4
MNCA (default)
0.007241
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0.185444
0.012659
0
binary
roc_auc
24,624
APSFailure
4
TABPFNV2 (default)
0.007241
33.32259
0.185444
0.012659
0
binary
roc_auc
24,825
APSFailure
5
CAT (default)
0.005955
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0.141827
0.010887
0
binary
roc_auc
25,027
APSFailure
5
EBM (default)
0.007364
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0.01227
0
binary
roc_auc
25,228
APSFailure
5
XT (default)
0.009569
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0.183931
0.011384
0
binary
roc_auc
25,429
APSFailure
5
KNN (default)
0.095546
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2.989676
0.090108
0
binary
roc_auc
25,480
APSFailure
5
GBM (default)
0.006237
20.060068
0.394417
0.012097
0
binary
roc_auc
25,681
APSFailure
5
LR (default)
0.009727
13.875376
5.468547
0.013969
0
binary
roc_auc
25,797
APSFailure
5
FASTAI (default)
0.012223
80.341503
2.994842
0.024556
0
binary
roc_auc
25,998
APSFailure
5
NN_TORCH (default)
0.008033
65.990785
5.65161
0.011342
0
binary
roc_auc
26,199
APSFailure
5
RF (default)
0.009484
32.508189
0.149632
0.013332
0
binary
roc_auc
26,400
APSFailure
5
REALMLP (default)
0.009434
1,461.354524
7.963092
0.012827
0
binary
roc_auc
26,601
APSFailure
5
TABDPT (default)
0.007372
489.674792
213.872278
0.016201
0
binary
roc_auc
26,602
APSFailure
5
TABICL (default)
0.006174
807.685647
139.246708
0.009749
0
binary
roc_auc
26,603
APSFailure
5
TABM (default)
0.009484
32.508189
0.149632
0.013332
0
binary
roc_auc
26,803
APSFailure
5
XGB (default)
0.006646
24.298403
1.869292
0.012161
0
binary
roc_auc
27,013
APSFailure
5
MNCA (default)
0.009484
32.508189
0.149632
0.013332
0
binary
roc_auc
27,141
APSFailure
5
TABPFNV2 (default)
0.009484
32.508189
0.149632
0.013332
0
binary
roc_auc
27,342
APSFailure
6
CAT (default)
0.00683
101.158056
0.133087
0.008478
0
binary
roc_auc
27,544
APSFailure
6
EBM (default)
0.008707
96.326602
1.575792
0.009859
0
binary
roc_auc
27,745
APSFailure
6
XT (default)
0.01064
6.40132
0.175763
0.012684
0
binary
roc_auc
27,946
APSFailure
6
KNN (default)
0.107915
7.103718
2.985205
0.082667
0
binary
roc_auc
End of preview.

Submitting Results to the Leaderboard

First, install the TabArena dependencies:

pip install uv
uv venv --seed --python 3.11 ~/.venvs/tabarena
source ~/.venvs/tabarena

git clone --branch tabarena https://github.com/autogluon/tabrepo.git

# use GIT_LFS_SKIP_SMUDGE=1 in front of the command if installing TabDPT fails due to a broken LFS/pip setup. 
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e tabrepo/[benchmark]

git clone https://huggingface.co/datasets/TabArena/benchmark_results

Next, concatenate your model performance results to 'df_results.csv' and rerun 'get_leaderboard_csv.py' to obtain an updated leaderboard.

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