Datasets:

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equitabpfn

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tabzilla_evaluation

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equitabpfn 1

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 1604 new columns ({'f__pymfe.statistical.max.histogram.7', 'f__pymfe.landmarking.linear_discr.histogram.4', 'f__pymfe.statistical.range.histogram.5', 'f__pymfe.statistical.t_mean.count', 'f__pymfe.model-based.var_importance.quantiles.3', 'f__pymfe.info-theory.class_conc.mean', 'f__pymfe.statistical.sparsity.min', 'f__pymfe.statistical.can_cor.quantiles.3', 'f__pymfe.statistical.mean.median', 'f__pymfe.landmarking.naive_bayes.range.relative', 'f__pymfe.landmarking.elite_nn.histogram.6', 'f__pymfe.model-based.nodes_per_level.quantiles.2', 'f__pymfe.statistical.range.histogram.3', 'f__pymfe.relative.best_node.quantiles.1.relative', 'f__pymfe.landmarking.one_nn.skewness.relative', 'f__pymfe.landmarking.worst_node.median.relative', 'f__pymfe.relative.worst_node.histogram.9.relative', 'f__pymfe.landmarking.worst_node.mean', 'f__pymfe.model-based.nodes_per_level.histogram.4', 'f__pymfe.statistical.g_mean.count', 'f__pymfe.relative.elite_nn.histogram.8.relative', 'f__pymfe.statistical.min.histogram.0', 'f__pymfe.statistical.iq_range', 'f__pymfe.relative.linear_discr.quantiles.4', 'f__pymfe.statistical.min.skewness', 'f__pymfe.statistical.iq_range.count', 'f__pymfe.relative.naive_bayes.iq_range', 'f__pymfe.landmarking.random_node.max', 'f__pymfe.statistical.min.max', 'f__pymfe.statistical.gravity', 'f__pymfe.info-theory.mut_inf.histogram.9', 'f__pymfe.model-based.leaves_homo.histogram.8', 'f__pymfe.info-theory.attr_ent.iq_range', 'f__pymfe.info-theory.attr_conc.skewness', 'f__pymfe.relative.elite_nn.ku
...
e.quantiles.4', 'f__pymfe.relative.naive_bayes.min.relative', 'f__pymfe.landmarking.one_nn.quantiles.3', 'f__pymfe.statistical.max.mean', 'f__pymfe.statistical.sparsity.range', 'f__pymfe.info-theory.attr_conc.histogram.9', 'f__pymfe.landmarking.best_node.iq_range.relative', 'f__pymfe.landmarking.linear_discr.histogram.6', 'f__pymfe.landmarking.naive_bayes.histogram.8.relative', 'f__pymfe.relative.random_node.quantiles.0', 'f__pymfe.general.freq_class.histogram.0', 'f__pymfe.landmarking.linear_discr.histogram.6.relative', 'f__pymfe.relative.linear_discr.mean.relative', 'f__pymfe.model-based.nodes_repeated.histogram.5', 'f__pymfe.landmarking.random_node.histogram.5', 'f__pymfe.statistical.eigenvalues.median', 'f__pymfe.model-based.tree_imbalance.histogram.9', 'f__pymfe.landmarking.linear_discr.histogram.7', 'f__pymfe.statistical.sparsity.quantiles.2', 'f__pymfe.landmarking.elite_nn.sd.relative', 'f__pymfe.general.freq_class.histogram.5', 'f__pymfe.landmarking.random_node.histogram.5.relative', 'f__pymfe.model-based.tree_shape.quantiles.3', 'f__pymfe.general.nr_num', 'f__pymfe.statistical.kurtosis.histogram.2', 'f__pymfe.statistical.mean.quantiles.2', 'f__pymfe.model-based.leaves_per_class.sd', 'f__pymfe.model-based.nodes_per_level.count', 'f__pymfe.landmarking.linear_discr.histogram.0.relative', 'f__pymfe.info-theory.attr_conc.quantiles.3', 'f__pymfe.landmarking.random_node.quantiles.1.relative', 'f__pymfe.relative.best_node.mean.relative', 'f__pymfe.info-theory.attr_conc.max'}) and 19 missing columns ({'alg_name', 'AUC__val', 'training_time', 'Log Loss__train', 'F1__train', 'F1__val', 'target_type', 'Log Loss__val', 'eval-time__test', 'F1__test', 'AUC__test', 'Accuracy__train', 'eval-time__train', 'AUC__train', 'Log Loss__test', 'Accuracy__test', 'eval-time__val', 'hparam_source', 'Accuracy__val'}).

This happened while the csv dataset builder was generating data using

zip://metafeatures_clean.csv::/tmp/hf-datasets-cache/medium/datasets/84164885243393-config-parquet-and-info-equitabpfn-tabzilla_evalu-31ef3f32/hub/datasets--equitabpfn--tabzilla_evaluation/snapshots/57cd8d5b1179c260c6853e0b9a97e1c8305a2368/metafeatures_clean.csv.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/python3.12/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
              Unnamed: 0: int64
              dataset_fold_id: string
              dataset_name: string
              f__pymfe.general.attr_to_inst: double
              f__pymfe.general.cat_to_num: double
              f__pymfe.general.freq_class.count: int64
              f__pymfe.general.freq_class.histogram.0: double
              f__pymfe.general.freq_class.histogram.1: double
              f__pymfe.general.freq_class.histogram.2: double
              f__pymfe.general.freq_class.histogram.3: double
              f__pymfe.general.freq_class.histogram.4: double
              f__pymfe.general.freq_class.histogram.5: double
              f__pymfe.general.freq_class.histogram.6: double
              f__pymfe.general.freq_class.histogram.7: double
              f__pymfe.general.freq_class.histogram.8: double
              f__pymfe.general.freq_class.histogram.9: double
              f__pymfe.general.freq_class.iq_range: double
              f__pymfe.general.freq_class.kurtosis: double
              f__pymfe.general.freq_class.max: double
              f__pymfe.general.freq_class.mean: double
              f__pymfe.general.freq_class.median: double
              f__pymfe.general.freq_class.min: double
              f__pymfe.general.freq_class.quantiles.0: double
              f__pymfe.general.freq_class.quantiles.1: double
              f__pymfe.general.freq_class.quantiles.2: double
              f__pymfe.general.freq_class.quantiles.3: double
              f__pymfe.general.freq_class.quantiles.4: double
              f__pymfe.general.freq_class.range: double
              f__pymfe.general.freq_class.sd: double
              f__pymfe.general.freq_class.skewness: double
              f__pymfe.general.inst_to_attr: double
              f__pymfe.general.nr_attr: int64
              f__pymfe.general.nr_bin: int64
              f__pymfe.general.nr_cat: int64
              f__pymfe.general.nr_class: int64
              f__pymfe.general.nr_inst: int64
              f__pymfe.general.nr_num: 
              ...
              .quantiles.1: double
              f__pymfe.statistical.t_mean.quantiles.2: double
              f__pymfe.statistical.t_mean.quantiles.3: double
              f__pymfe.statistical.t_mean.quantiles.4: double
              f__pymfe.statistical.t_mean.range: double
              f__pymfe.statistical.t_mean.sd: double
              f__pymfe.statistical.t_mean.skewness: double
              f__pymfe.statistical.var.count: int64
              f__pymfe.statistical.var.histogram.0: double
              f__pymfe.statistical.var.histogram.1: double
              f__pymfe.statistical.var.histogram.2: double
              f__pymfe.statistical.var.histogram.3: double
              f__pymfe.statistical.var.histogram.4: double
              f__pymfe.statistical.var.histogram.5: double
              f__pymfe.statistical.var.histogram.6: double
              f__pymfe.statistical.var.histogram.7: double
              f__pymfe.statistical.var.histogram.8: double
              f__pymfe.statistical.var.histogram.9: double
              f__pymfe.statistical.var.iq_range: double
              f__pymfe.statistical.var.kurtosis: double
              f__pymfe.statistical.var.max: double
              f__pymfe.statistical.var.mean: double
              f__pymfe.statistical.var.median: double
              f__pymfe.statistical.var.min: double
              f__pymfe.statistical.var.quantiles.0: double
              f__pymfe.statistical.var.quantiles.1: double
              f__pymfe.statistical.var.quantiles.2: double
              f__pymfe.statistical.var.quantiles.3: double
              f__pymfe.statistical.var.quantiles.4: double
              f__pymfe.statistical.var.range: double
              f__pymfe.statistical.var.sd: double
              f__pymfe.statistical.var.skewness: double
              f__pymfe.statistical.w_lambda: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 292435
              to
              {'Unnamed: 0': Value('int64'), 'dataset_fold_id': Value('string'), 'dataset_name': Value('string'), 'target_type': Value('string'), 'alg_name': Value('string'), 'hparam_source': Value('string'), 'Log Loss__train': Value('float64'), 'Log Loss__val': Value('float64'), 'Log Loss__test': Value('float64'), 'AUC__train': Value('float64'), 'AUC__val': Value('float64'), 'AUC__test': Value('float64'), 'Accuracy__train': Value('float64'), 'Accuracy__val': Value('float64'), 'Accuracy__test': Value('float64'), 'F1__train': Value('float64'), 'F1__val': Value('float64'), 'F1__test': Value('float64'), 'training_time': Value('float64'), 'eval-time__train': Value('float64'), 'eval-time__val': Value('float64'), 'eval-time__test': Value('float64')}
              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 1604 new columns ({'f__pymfe.statistical.max.histogram.7', 'f__pymfe.landmarking.linear_discr.histogram.4', 'f__pymfe.statistical.range.histogram.5', 'f__pymfe.statistical.t_mean.count', 'f__pymfe.model-based.var_importance.quantiles.3', 'f__pymfe.info-theory.class_conc.mean', 'f__pymfe.statistical.sparsity.min', 'f__pymfe.statistical.can_cor.quantiles.3', 'f__pymfe.statistical.mean.median', 'f__pymfe.landmarking.naive_bayes.range.relative', 'f__pymfe.landmarking.elite_nn.histogram.6', 'f__pymfe.model-based.nodes_per_level.quantiles.2', 'f__pymfe.statistical.range.histogram.3', 'f__pymfe.relative.best_node.quantiles.1.relative', 'f__pymfe.landmarking.one_nn.skewness.relative', 'f__pymfe.landmarking.worst_node.median.relative', 'f__pymfe.relative.worst_node.histogram.9.relative', 'f__pymfe.landmarking.worst_node.mean', 'f__pymfe.model-based.nodes_per_level.histogram.4', 'f__pymfe.statistical.g_mean.count', 'f__pymfe.relative.elite_nn.histogram.8.relative', 'f__pymfe.statistical.min.histogram.0', 'f__pymfe.statistical.iq_range', 'f__pymfe.relative.linear_discr.quantiles.4', 'f__pymfe.statistical.min.skewness', 'f__pymfe.statistical.iq_range.count', 'f__pymfe.relative.naive_bayes.iq_range', 'f__pymfe.landmarking.random_node.max', 'f__pymfe.statistical.min.max', 'f__pymfe.statistical.gravity', 'f__pymfe.info-theory.mut_inf.histogram.9', 'f__pymfe.model-based.leaves_homo.histogram.8', 'f__pymfe.info-theory.attr_ent.iq_range', 'f__pymfe.info-theory.attr_conc.skewness', 'f__pymfe.relative.elite_nn.ku
              ...
              e.quantiles.4', 'f__pymfe.relative.naive_bayes.min.relative', 'f__pymfe.landmarking.one_nn.quantiles.3', 'f__pymfe.statistical.max.mean', 'f__pymfe.statistical.sparsity.range', 'f__pymfe.info-theory.attr_conc.histogram.9', 'f__pymfe.landmarking.best_node.iq_range.relative', 'f__pymfe.landmarking.linear_discr.histogram.6', 'f__pymfe.landmarking.naive_bayes.histogram.8.relative', 'f__pymfe.relative.random_node.quantiles.0', 'f__pymfe.general.freq_class.histogram.0', 'f__pymfe.landmarking.linear_discr.histogram.6.relative', 'f__pymfe.relative.linear_discr.mean.relative', 'f__pymfe.model-based.nodes_repeated.histogram.5', 'f__pymfe.landmarking.random_node.histogram.5', 'f__pymfe.statistical.eigenvalues.median', 'f__pymfe.model-based.tree_imbalance.histogram.9', 'f__pymfe.landmarking.linear_discr.histogram.7', 'f__pymfe.statistical.sparsity.quantiles.2', 'f__pymfe.landmarking.elite_nn.sd.relative', 'f__pymfe.general.freq_class.histogram.5', 'f__pymfe.landmarking.random_node.histogram.5.relative', 'f__pymfe.model-based.tree_shape.quantiles.3', 'f__pymfe.general.nr_num', 'f__pymfe.statistical.kurtosis.histogram.2', 'f__pymfe.statistical.mean.quantiles.2', 'f__pymfe.model-based.leaves_per_class.sd', 'f__pymfe.model-based.nodes_per_level.count', 'f__pymfe.landmarking.linear_discr.histogram.0.relative', 'f__pymfe.info-theory.attr_conc.quantiles.3', 'f__pymfe.landmarking.random_node.quantiles.1.relative', 'f__pymfe.relative.best_node.mean.relative', 'f__pymfe.info-theory.attr_conc.max'}) and 19 missing columns ({'alg_name', 'AUC__val', 'training_time', 'Log Loss__train', 'F1__train', 'F1__val', 'target_type', 'Log Loss__val', 'eval-time__test', 'F1__test', 'AUC__test', 'Accuracy__train', 'eval-time__train', 'AUC__train', 'Log Loss__test', 'Accuracy__test', 'eval-time__val', 'hparam_source', 'Accuracy__val'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://metafeatures_clean.csv::/tmp/hf-datasets-cache/medium/datasets/84164885243393-config-parquet-and-info-equitabpfn-tabzilla_evalu-31ef3f32/hub/datasets--equitabpfn--tabzilla_evaluation/snapshots/57cd8d5b1179c260c6853e0b9a97e1c8305a2368/metafeatures_clean.csv.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)