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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 1 new columns ({'Engine Condition'}) and 6 missing columns ({'Fuel pressure', 'Coolant temp', 'Engine rpm', 'Coolant pressure', 'lub oil temp', 'Lub oil pressure'}).

This happened while the csv dataset builder was generating data using

hf://datasets/dpanchali/predictive_maintenance/y_train.csv (at revision 7a7534444be7fe28313f395ee6d8f3ffa61760ea)

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
              Engine Condition: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 392
              to
              {'Engine rpm': Value('int64'), 'Lub oil pressure': Value('float64'), 'Fuel pressure': Value('float64'), 'Coolant pressure': Value('float64'), 'lub oil temp': Value('float64'), 'Coolant temp': 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 1 new columns ({'Engine Condition'}) and 6 missing columns ({'Fuel pressure', 'Coolant temp', 'Engine rpm', 'Coolant pressure', 'lub oil temp', 'Lub oil pressure'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/dpanchali/predictive_maintenance/y_train.csv (at revision 7a7534444be7fe28313f395ee6d8f3ffa61760ea)
              
              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)

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Engine rpm
int64
Lub oil pressure
float64
Fuel pressure
float64
Coolant pressure
float64
lub oil temp
float64
Coolant temp
float64
522
3.943601
8.710084
2.226766
77.432066
77.531178
797
2.549862
5.756396
2.790734
78.588426
70.953136
728
4.282491
7.246882
1.899607
74.826532
72.804836
999
6.659097
4.593082
0.818294
76.689208
76.909337
668
4.23022
8.408958
1.742383
81.314954
80.049153
1,569
2.981514
2.969677
2.593787
75.338065
85.904262
810
1.948652
7.204751
1.307335
75.849186
84.839054
957
2.864088
6.208948
2.545857
76.018926
84.539688
1,217
5.073479
8.632456
5.253704
76.092446
91.290996
809
2.994864
6.779157
1.966332
84.925951
82.968599
1,279
0.822259
5.142345
2.368251
76.291095
87.408097
841
2.532712
3.501506
2.581695
76.29418
75.908698
767
2.4207
1.464871
5.099645
77.623795
69.732168
821
4.748496
5.80673
2.791687
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463
4.36716
6.629554
2.919617
75.653156
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735
2.004484
5.423731
2.252468
87.886132
78.752224
810
3.146194
4.763737
1.963656
78.168361
77.71426
437
3.153142
7.875027
2.232743
76.808901
91.384008
1,099
3.115143
6.086617
1.740789
75.328703
79.589553
521
3.934727
6.254172
1.619509
74.318725
80.144108
854
4.577381
6.530662
2.661535
77.245244
84.817691
1,034
5.095522
6.844655
1.976465
75.712027
68.567348
772
4.248029
6.207968
1.384765
75.143969
85.94964
830
3.671951
6.420928
1.71984
77.14451
82.425406
810
3.815752
4.6459
2.524527
83.552361
74.912071
694
3.286427
5.944093
2.939865
76.142882
69.687658
851
4.995421
3.791345
2.432969
76.740755
66.544977
921
5.386984
7.957503
1.569468
77.346763
66.956198
833
3.73558
4.628943
1.875528
76.962339
76.941147
1,282
2.903238
10.190597
2.900709
77.805189
74.608617
1,044
3.158684
6.664862
1.029288
76.826054
75.683261
457
4.034781
4.406394
3.378274
86.866277
72.596444
1,545
2.603245
8.186338
2.176046
76.415368
84.514293
1,097
2.301343
4.341919
1.769488
85.600522
84.044979
498
1.732735
6.478733
3.092444
74.763867
82.697645
607
2.675129
3.393224
1.767393
77.318183
75.633198
660
3.91287
4.416024
1.167141
85.5333
73.036154
544
3.727217
5.910866
1.511878
78.650101
66.609578
913
3.964757
4.636276
2.052323
76.175949
76.145524
855
3.015969
7.734052
5.054032
76.27944
83.034572
617
2.527816
6.74236
1.663972
76.611794
85.013658
860
4.397439
8.091384
1.257467
75.085959
81.319317
764
5.130342
5.196309
3.623317
76.542193
66.756797
471
3.667448
5.577159
2.626215
75.778489
72.451349
1,029
2.528493
8.954362
2.716114
75.952884
76.623336
481
1.9203
2.625457
2.980351
77.392588
70.777824
1,625
2.545094
6.608659
2.481089
77.299091
79.291732
641
0.807347
6.660406
3.556692
75.018889
79.139152
1,125
2.295821
4.818299
1.254211
81.849116
81.4345
888
4.849219
8.229332
3.002829
78.683706
68.709222
1,148
2.401894
2.474664
2.71399
76.148932
72.407399
710
2.620655
11.214721
2.636433
76.669624
72.884364
506
2.363712
8.037096
2.352248
87.762479
84.932908
493
5.042099
4.214
0.553624
78.369336
69.013699
960
2.503391
9.117413
2.455641
77.376284
70.609083
680
2.328922
7.081087
2.879853
76.660067
80.889098
372
2.666574
4.349957
1.703943
79.30518
84.203278
413
2.78473
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2.417565
73.824544
90.717885
892
1.950151
4.987174
2.646948
77.291727
78.28073
656
4.151634
6.293229
3.177781
75.572258
68.317647
579
3.853994
1.531843
1.269678
84.650106
74.984581
611
4.008921
4.657266
0.17574
77.533518
81.074537
988
3.254716
3.459936
2.174546
75.825926
78.00026
528
4.990972
5.323822
1.909121
78.181114
85.017506
724
4.526244
5.502857
3.067767
85.309399
72.66757
986
4.311714
6.543107
0.972666
79.525347
80.094268
880
2.791751
1.704227
2.408042
75.729246
74.300193
695
2.104318
6.08001
1.785612
74.313712
83.694366
852
3.753086
6.725922
2.472245
76.0978
71.545296
769
2.601653
8.028416
2.917873
77.23147
72.592626
1,206
3.895751
5.856937
1.661437
77.021622
74.429779
802
3.122611
4.074255
1.162249
84.883904
91.847113
799
4.826262
4.508942
2.563555
77.462295
82.054137
605
2.219444
8.013818
1.821858
73.796337
88.246889
645
2.200287
4.111758
2.395951
85.434548
82.937727
504
2.893385
6.025191
1.513986
81.417863
82.936301
505
2.803163
5.466616
3.003559
83.478462
71.72423
790
4.612274
4.556135
1.252846
78.65857
85.45535
999
2.007871
3.505646
2.838933
82.46438
74.539453
1,064
3.996542
4.175113
1.681154
76.405217
74.54625
1,183
2.818983
8.621275
1.014461
76.134837
72.367129
817
2.603706
9.145226
1.264798
77.009944
72.566506
1,192
2.423821
8.257579
3.078789
75.546717
91.973457
978
4.366676
6.638085
2.829643
88.038301
78.488782
883
5.013836
7.307238
1.274732
76.566812
79.516546
543
4.296454
5.204659
4.131628
77.009676
85.260218
1,150
2.037448
6.741541
3.672669
74.797736
80.304946
1,068
4.24739
5.281338
1.760173
78.185334
71.933926
560
1.902979
5.064943
1.084354
76.338368
70.085192
936
2.940128
5.477092
1.363027
77.767328
77.453242
586
2.179881
6.722775
2.732388
82.491325
75.874841
517
2.322203
9.620173
2.883032
75.487665
72.324883
651
2.236045
7.660507
2.562027
81.470812
87.506142
959
2.607224
4.572327
2.212804
76.145794
75.12655
923
3.081428
4.081548
2.601776
75.810458
71.960089
1,037
4.311982
1.068646
2.298946
76.431451
70.745497
602
2.708134
5.921391
2.072252
76.257287
83.255222
913
2.135457
5.544887
2.591823
82.12865
76.187767
715
3.162626
6.447004
2.207792
75.913584
81.660966
953
4.366601
4.807785
1.69953
81.682642
68.545026
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