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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 9 new columns ({'X6', 'X1', 'X2', 'X3', 'X7', 'X5', 'X4', 'Y1', 'X8'}) and 9 missing columns ({'AveRooms', 'AveOccup', 'Latitude', 'Population', 'Longitude', 'AveBedrms', 'MedInc', 'HouseAge', 'target'}).

This happened while the csv dataset builder was generating data using

hf://datasets/guanwencan/Residual-Bayesian-Attention/energy_efficiency.csv (at revision 1f97b0316a344d57cafdb9d977289ec590ddfc24)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              X1: double
              X2: double
              X3: double
              X4: double
              X5: double
              X6: int64
              X7: double
              X8: int64
              Y1: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1198
              to
              {'MedInc': Value('float64'), 'HouseAge': Value('float64'), 'AveRooms': Value('float64'), 'AveBedrms': Value('float64'), 'Population': Value('float64'), 'AveOccup': Value('float64'), 'Latitude': Value('float64'), 'Longitude': Value('float64'), 'target': 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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 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 9 new columns ({'X6', 'X1', 'X2', 'X3', 'X7', 'X5', 'X4', 'Y1', 'X8'}) and 9 missing columns ({'AveRooms', 'AveOccup', 'Latitude', 'Population', 'Longitude', 'AveBedrms', 'MedInc', 'HouseAge', 'target'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/guanwencan/Residual-Bayesian-Attention/energy_efficiency.csv (at revision 1f97b0316a344d57cafdb9d977289ec590ddfc24)
              
              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.

MedInc
float64
HouseAge
float64
AveRooms
float64
AveBedrms
float64
Population
float64
AveOccup
float64
Latitude
float64
Longitude
float64
target
float64
8.3252
41
6.984127
1.02381
322
2.555556
37.88
-122.23
4.526
8.3014
21
6.238137
0.97188
2,401
2.109842
37.86
-122.22
3.585
7.2574
52
8.288136
1.073446
496
2.80226
37.85
-122.24
3.521
5.6431
52
5.817352
1.073059
558
2.547945
37.85
-122.25
3.413
3.8462
52
6.281853
1.081081
565
2.181467
37.85
-122.25
3.422
4.0368
52
4.761658
1.103627
413
2.139896
37.85
-122.25
2.697
3.6591
52
4.931907
0.951362
1,094
2.128405
37.84
-122.25
2.992
3.12
52
4.797527
1.061824
1,157
1.788253
37.84
-122.25
2.414
2.0804
42
4.294118
1.117647
1,206
2.026891
37.84
-122.26
2.267
3.6912
52
4.970588
0.990196
1,551
2.172269
37.84
-122.25
2.611
3.2031
52
5.477612
1.079602
910
2.263682
37.85
-122.26
2.815
3.2705
52
4.77248
1.024523
1,504
2.049046
37.85
-122.26
2.418
3.075
52
5.32265
1.012821
1,098
2.346154
37.85
-122.26
2.135
2.6736
52
4
1.097701
345
1.982759
37.84
-122.26
1.913
1.9167
52
4.262903
1.009677
1,212
1.954839
37.85
-122.26
1.592
2.125
50
4.242424
1.07197
697
2.640152
37.85
-122.26
1.4
2.775
52
5.939577
1.048338
793
2.39577
37.85
-122.27
1.525
2.1202
52
4.052805
0.966997
648
2.138614
37.85
-122.27
1.555
1.9911
50
5.343675
1.085919
990
2.362768
37.84
-122.26
1.587
2.6033
52
5.465455
1.083636
690
2.509091
37.84
-122.27
1.629
1.3578
40
4.524096
1.108434
409
2.463855
37.85
-122.27
1.475
1.7135
42
4.478142
1.002732
929
2.538251
37.85
-122.27
1.598
1.725
52
5.096234
1.131799
1,015
2.123431
37.84
-122.27
1.139
2.1806
52
5.193846
1.036923
853
2.624615
37.84
-122.27
0.997
2.6
52
5.270142
1.035545
1,006
2.383886
37.84
-122.27
1.326
2.4038
41
4.495798
1.033613
317
2.663866
37.85
-122.28
1.075
2.4597
49
4.728033
1.020921
607
2.539749
37.85
-122.28
0.938
1.808
52
4.780856
1.060453
1,102
2.775819
37.85
-122.28
1.055
1.6424
50
4.401691
1.040169
1,131
2.391121
37.84
-122.28
1.089
1.6875
52
4.703226
1.032258
395
2.548387
37.84
-122.28
1.32
1.9274
49
5.068783
1.18254
863
2.283069
37.84
-122.28
1.223
1.9615
52
4.882086
1.090703
1,168
2.648526
37.84
-122.28
1.152
1.7969
48
5.737313
1.220896
1,026
3.062687
37.84
-122.27
1.104
1.375
49
5.030395
1.112462
754
2.291793
37.83
-122.27
1.049
2.7303
51
4.972015
1.070896
1,258
2.347015
37.83
-122.27
1.097
1.4861
49
4.602273
1.068182
570
2.159091
37.83
-122.27
0.972
1.0972
48
4.807487
1.15508
987
2.639037
37.83
-122.27
1.045
1.4103
52
3.74938
0.967742
901
2.235732
37.83
-122.28
1.039
3.48
52
4.757282
1.067961
689
2.229773
37.83
-122.26
1.914
2.5898
52
3.494253
1.027299
1,377
1.978448
37.83
-122.26
1.76
2.0978
52
4.21519
1.060759
946
2.394937
37.83
-122.26
1.554
1.2852
51
3.759036
1.248996
517
2.076305
37.83
-122.26
1.5
1.025
49
3.772487
1.068783
462
2.444444
37.84
-122.26
1.188
3.9643
52
4.79798
1.020202
467
2.358586
37.84
-122.26
1.888
3.0125
52
4.941781
1.065068
660
2.260274
37.83
-122.26
1.844
2.6768
52
4.335079
1.099476
718
1.879581
37.83
-122.26
1.823
2.026
50
3.700658
1.059211
616
2.026316
37.83
-122.26
1.425
1.7348
43
3.980237
1.233202
558
2.205534
37.82
-122.27
1.375
0.9506
40
3.9
1.21875
423
2.64375
37.82
-122.26
1.875
1.775
40
2.6875
1.065341
700
1.988636
37.82
-122.27
1.125
0.9218
21
2.045662
1.034247
735
1.678082
37.82
-122.27
1.719
1.5045
43
4.589681
1.120393
1,061
2.60688
37.82
-122.27
0.938
1.1108
41
4.473611
1.184722
1,959
2.720833
37.82
-122.27
0.975
1.2475
52
4.075
1.14
1,162
2.905
37.82
-122.27
1.042
1.6098
52
5.021459
1.008584
701
3.008584
37.82
-122.28
0.875
1.4113
52
4.295455
1.104545
576
2.618182
37.82
-122.28
0.831
1.5057
52
4.779923
1.111969
622
2.401544
37.82
-122.28
0.875
0.8172
52
6.102459
1.372951
728
2.983607
37.82
-122.28
0.853
1.2171
52
4.5625
1.121711
1,074
3.532895
37.82
-122.28
0.803
2.5625
2
2.77193
0.754386
94
1.649123
37.82
-122.29
0.6
3.3929
52
5.994652
1.128342
554
2.962567
37.83
-122.29
0.757
6.1183
49
5.869565
1.26087
86
3.73913
37.82
-122.29
0.75
0.9011
50
6.229508
1.557377
377
3.090164
37.81
-122.29
0.861
1.191
52
7.698113
1.490566
521
3.27673
37.81
-122.3
0.761
2.5938
48
6.225564
1.368421
392
2.947368
37.81
-122.3
0.735
1.1667
52
5.40107
1.117647
604
3.229947
37.81
-122.3
0.784
0.8056
48
4.38253
1.066265
788
2.373494
37.81
-122.3
0.844
2.6094
52
6.986395
1.659864
492
3.346939
37.8
-122.29
0.813
1.8516
52
6.97561
1.329268
274
3.341463
37.81
-122.3
0.85
0.9802
46
4.584288
1.05401
1,823
2.983633
37.81
-122.29
1.292
1.7719
26
6.047244
1.19685
392
3.086614
37.81
-122.29
0.825
0.7286
46
3.375451
1.072202
582
2.101083
37.81
-122.29
0.952
1.75
49
5.552632
1.342105
560
3.684211
37.81
-122.29
0.75
0.4999
46
1.714286
0.571429
18
2.571429
37.81
-122.29
0.675
2.483
20
6.278195
1.210526
290
2.180451
37.81
-122.29
1.375
0.9241
17
2.817768
1.052392
762
1.735763
37.81
-122.28
1.775
2.4464
36
5.724951
1.104126
1,236
2.428291
37.81
-122.28
1.021
1.1111
19
5.830918
1.173913
721
3.483092
37.81
-122.28
1.083
0.8026
23
5.369231
1.150769
1,054
3.243077
37.81
-122.29
1.125
2.0114
38
4.412903
1.135484
344
2.219355
37.8
-122.28
1.313
1.5
17
3.197232
1
609
2.107266
37.81
-122.28
1.625
1.1667
52
3.75
1
183
3.267857
37.81
-122.27
1.125
1.5208
52
3.908046
1.114943
200
2.298851
37.81
-122.28
1.125
0.8075
52
2.490323
1.058065
346
2.232258
37.81
-122.28
1.375
1.8088
35
5.609467
1.088757
467
2.763314
37.81
-122.28
1.188
2.4083
52
6.721739
1.243478
377
3.278261
37.81
-122.28
0.982
0.977
40
2.315789
1.186842
582
1.531579
37.81
-122.27
1.188
0.76
10
2.651515
1.054545
546
1.654545
37.81
-122.27
1.625
0.9722
10
2.692308
1.076923
125
3.205128
37.8
-122.27
1.375
1.2434
52
2.929412
0.917647
396
4.658824
37.8
-122.27
5.00001
2.0938
16
2.745856
1.082873
800
2.209945
37.8
-122.27
1.625
0.8668
52
2.443182
0.988636
904
10.272727
37.8
-122.28
1.375
0.75
52
2.823529
0.911765
191
5.617647
37.8
-122.28
1.625
2.6354
27
3.493377
1.149007
718
2.377483
37.79
-122.27
1.875
1.8477
39
3.672377
1.334047
1,327
2.841542
37.8
-122.27
1.792
2.0096
36
2.294016
1.066294
3,469
1.493328
37.8
-122.26
1.3
2.8345
31
3.894915
1.127966
2,048
1.735593
37.82
-122.26
1.838
2.0062
29
3.681319
1.175824
202
2.21978
37.81
-122.26
1.25
1.2185
22
2.9456
1.016
2,024
1.6192
37.82
-122.26
1.7
2.6104
37
3.707143
1.107143
1,838
1.87551
37.82
-122.26
1.931
End of preview.
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

This collection contains six commonly used regression datasets from the UCI Machine Learning Repository.

1. California Housing

  • File: california_housing.csv
  • Samples: 20,640
  • Features: 8 (MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude)
  • Target: Median house value
  • Source: Scikit-learn built-in dataset

2. Household Power Consumption

  • File: household_power_timeseries.csv
  • Samples: 17,520
  • Features: 7 (Global active/reactive power, Voltage, Global intensity, 3 sub-metering channels)
  • Target: Global active power (time series forecasting)
  • Source: UCI ML Repository
  • Note: Time series data with hourly measurements

3. Student Performance

  • File: student_performance.csv
  • Samples: 395
  • Features: 15 (age, parental education, study time, failures, etc.)
  • Target: Final grade (G3)
  • Source: UCI ML Repository

4. Yacht Hydrodynamics

  • File: yacht_hydrodynamics.csv
  • Samples: 308
  • Features: 6 (longitudinal position, prismatic coefficient, displacement-length ratio, etc.)
  • Target: Residuary resistance per unit weight
  • Source: UCI ML Repository

5. Energy Efficiency

  • File: energy_efficiency.csv
  • Samples: 768
  • Features: 8 (relative compactness, surface area, wall area, roof area, etc.)
  • Target: Heating load (Y1)
  • Source: UCI ML Repository

6. Combined Cycle Power Plant

  • File: power_plant.csv
  • Samples: 768
  • Features: 8 (temperature, ambient pressure, relative humidity, exhaust vacuum, etc.)
  • Target: Net hourly electrical energy output
  • Source: UCI ML Repository

Format

All datasets are provided as CSV files with headers. Missing values have been removed. Features are numeric.

License

These datasets are publicly available from their respective sources. Please cite the original sources when using these datasets in publications.

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