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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 320 new columns ({'116', '200', '20', '58', '210', '278', '131', '174', '168', '134', '18', '240', '260', '132', '230', '65', '64', '127', '119', '164', '227', '202', '232', '92', '259', '302', '139', '311', '284', '83', '216', '252', '215', '63', '113', '94', '270', '10', '147', '201', '207', '269', '81', '151', '75', '300', '118', '261', '150', '155', '69', '105', '122', '192', '277', '244', '111', '213', '123', '138', '272', '16', '145', '178', '262', '109', '247', '176', '35', '241', '318', '117', '93', '49', '115', '310', '154', '17', '13', '167', '162', '33', '242', '152', '99', '103', '293', '303', '248', '78', '77', '280', '95', '170', '316', '76', '54', '9', '137', '309', '187', '313', '21', '36', '48', '281', '220', '11', '97', '140', '55', '229', '104', '89', '143', '46', '157', '208', '287', '52', '96', '85', '297', '305', '50', '221', '27', '84', '133', '82', '121', '14', '53', '56', '144', '211', '222', '19', '26', '24', '101', '43', '112', '108', '44', '206', '62', '8', '181', '319', '120', '5', '22', '218', '189', '41', '263', '250', '23', '256', '285', '301', '225', '114', '79', '91', '299', '159', '291', '204', '57', '298', '186', '70', '28', '255', '198', '3', '274', '296', '246', '317', '6', '25', '67', '224', '175', '188', '223', '160', '166', '268', '236', '0', '282', '217', '135', '304', '169', '125', '203', '286', '267', '312', '39', '90', '37', '288', '30', '146', '283', '231', '179', '129', '106', '273', '38', '276', '173', '195', '86', '180', '87', '264', '29', '68', '199', '237', '258', '98', '308', '66', '172', '191', '193', '184', '59', '185', '161', '219', '12', '257', '156', '126', '266', '4', '212', '165', '209', '177', '72', '136', '235', '245', '238', '15', '148', '47', '314', '107', '249', '228', '71', '141', '153', '306', '253', '34', '40', '60', '294', '239', '142', '102', '254', '182', '295', '100', '251', '205', '234', '214', '197', '1', '42', '158', '74', '149', '31', '233', '124', '80', '279', '2', '163', '7', '130', '190', '171', '275', '61', '110', '196', '88', '271', '45', '51', '315', '292', '289', '183', '128', '226', '290', '265', '307', '73', '32', '194', '243'}) and 6 missing columns ({'MULL', 'HUFL', 'LULL', 'MUFL', 'HULL', 'LUFL'}). This happened while the csv dataset builder was generating data using hf://datasets/HachiML/Timeseries-PILE-splitted/train/forecasting/autoformer/electricity.csv (at revision 4da811d2314dd0b322b04ccac8f373a623e24c7c) 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 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast date: string 0: double 1: double 2: double 3: double 4: double 5: double 6: double 7: double 8: double 9: double 10: double 11: double 12: double 13: double 14: double 15: double 16: double 17: double 18: double 19: double 20: double 21: double 22: double 23: double 24: double 25: double 26: double 27: double 28: double 29: double 30: double 31: double 32: double 33: double 34: double 35: double 36: double 37: double 38: double 39: double 40: double 41: double 42: double 43: double 44: double 45: double 46: double 47: double 48: double 49: double 50: double 51: double 52: double 53: double 54: double 55: double 56: double 57: double 58: double 59: double 60: double 61: double 62: double 63: double 64: double 65: double 66: double 67: double 68: double 69: double 70: double 71: double 72: double 73: double 74: double 75: double 76: double 77: double 78: double 79: double 80: double 81: double 82: double 83: double 84: double 85: double 86: double 87: double 88: double 89: double 90: double 91: double 92: double 93: double 94: double 95: double 96: double 97: double 98: double 99: double 100: double 101: double 102: double 103: double 104: double 105: double 106: double 107: double 108: double 109: double 110: double 111: double 112: double 113: double 114: double 115: double 116: double 117: double 118: double 119: double 120: double 121: double 122: double 123: double 124: double 125: double 126: double 127: double 128: double 129: double 130: double 131: double 132: double 1 ... uble 205: double 206: double 207: double 208: double 209: double 210: double 211: double 212: double 213: double 214: double 215: double 216: double 217: double 218: double 219: double 220: double 221: double 222: double 223: double 224: double 225: double 226: double 227: double 228: double 229: double 230: double 231: double 232: double 233: double 234: double 235: double 236: double 237: double 238: double 239: double 240: double 241: double 242: double 243: double 244: double 245: double 246: double 247: double 248: double 249: double 250: double 251: double 252: double 253: double 254: double 255: double 256: double 257: double 258: double 259: double 260: double 261: double 262: double 263: double 264: double 265: double 266: double 267: double 268: double 269: double 270: double 271: double 272: double 273: double 274: double 275: double 276: double 277: double 278: double 279: double 280: double 281: double 282: double 283: double 284: double 285: double 286: double 287: double 288: double 289: double 290: double 291: double 292: double 293: double 294: double 295: double 296: double 297: double 298: double 299: double 300: double 301: double 302: double 303: double 304: double 305: double 306: double 307: double 308: double 309: double 310: double 311: double 312: double 313: double 314: double 315: double 316: double 317: double 318: double 319: double OT: double -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 34496 to {'date': Value(dtype='string', id=None), 'HUFL': Value(dtype='float64', id=None), 'HULL': Value(dtype='float64', id=None), 'MUFL': Value(dtype='float64', id=None), 'MULL': Value(dtype='float64', id=None), 'LUFL': Value(dtype='float64', id=None), 'LULL': Value(dtype='float64', id=None), 'OT': Value(dtype='float64', 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 1312, in compute_config_parquet_and_info_response parquet_operations, partial = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 906, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2013, 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 320 new columns ({'116', '200', '20', '58', '210', '278', '131', '174', '168', '134', '18', '240', '260', '132', '230', '65', '64', '127', '119', '164', '227', '202', '232', '92', '259', '302', '139', '311', '284', '83', '216', '252', '215', '63', '113', '94', '270', '10', '147', '201', '207', '269', '81', '151', '75', '300', '118', '261', '150', '155', '69', '105', '122', '192', '277', '244', '111', '213', '123', '138', '272', '16', '145', '178', '262', '109', '247', '176', '35', '241', '318', '117', '93', '49', '115', '310', '154', '17', '13', '167', '162', '33', '242', '152', '99', '103', '293', '303', '248', '78', '77', '280', '95', '170', '316', '76', '54', '9', '137', '309', '187', '313', '21', '36', '48', '281', '220', '11', '97', '140', '55', '229', '104', '89', '143', '46', '157', '208', '287', '52', '96', '85', '297', '305', '50', '221', '27', '84', '133', '82', '121', '14', '53', '56', '144', '211', '222', '19', '26', '24', '101', '43', '112', '108', '44', '206', '62', '8', '181', '319', '120', '5', '22', '218', '189', '41', '263', '250', '23', '256', '285', '301', '225', '114', '79', '91', '299', '159', '291', '204', '57', '298', '186', '70', '28', '255', '198', '3', '274', '296', '246', '317', '6', '25', '67', '224', '175', '188', '223', '160', '166', '268', '236', '0', '282', '217', '135', '304', '169', '125', '203', '286', '267', '312', '39', '90', '37', '288', '30', '146', '283', '231', '179', '129', '106', '273', '38', '276', '173', '195', '86', '180', '87', '264', '29', '68', '199', '237', '258', '98', '308', '66', '172', '191', '193', '184', '59', '185', '161', '219', '12', '257', '156', '126', '266', '4', '212', '165', '209', '177', '72', '136', '235', '245', '238', '15', '148', '47', '314', '107', '249', '228', '71', '141', '153', '306', '253', '34', '40', '60', '294', '239', '142', '102', '254', '182', '295', '100', '251', '205', '234', '214', '197', '1', '42', '158', '74', '149', '31', '233', '124', '80', '279', '2', '163', '7', '130', '190', '171', '275', '61', '110', '196', '88', '271', '45', '51', '315', '292', '289', '183', '128', '226', '290', '265', '307', '73', '32', '194', '243'}) and 6 missing columns ({'MULL', 'HUFL', 'LULL', 'MUFL', 'HULL', 'LUFL'}). This happened while the csv dataset builder was generating data using hf://datasets/HachiML/Timeseries-PILE-splitted/train/forecasting/autoformer/electricity.csv (at revision 4da811d2314dd0b322b04ccac8f373a623e24c7c) 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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date
string | HUFL
float64 | HULL
float64 | MUFL
float64 | MULL
float64 | LUFL
float64 | LULL
float64 | OT
float64 |
---|---|---|---|---|---|---|---|
2016-07-01 00:00:00 | 5.827 | 2.009 | 1.599 | 0.462 | 4.203 | 1.34 | 30.531 |
2016-07-01 01:00:00 | 5.693 | 2.076 | 1.492 | 0.426 | 4.142 | 1.371 | 27.787001 |
2016-07-01 02:00:00 | 5.157 | 1.741 | 1.279 | 0.355 | 3.777 | 1.218 | 27.787001 |
2016-07-01 03:00:00 | 5.09 | 1.942 | 1.279 | 0.391 | 3.807 | 1.279 | 25.044001 |
2016-07-01 04:00:00 | 5.358 | 1.942 | 1.492 | 0.462 | 3.868 | 1.279 | 21.948 |
2016-07-01 05:00:00 | 5.626 | 2.143 | 1.528 | 0.533 | 4.051 | 1.371 | 21.174 |
2016-07-01 06:00:00 | 7.167 | 2.947 | 2.132 | 0.782 | 5.026 | 1.858 | 22.792 |
2016-07-01 07:00:00 | 7.435 | 3.282 | 2.31 | 1.031 | 5.087 | 2.224 | 23.143999 |
2016-07-01 08:00:00 | 5.559 | 3.014 | 2.452 | 1.173 | 2.955 | 1.432 | 21.667 |
2016-07-01 09:00:00 | 4.555 | 2.545 | 1.919 | 0.817 | 2.68 | 1.371 | 17.445999 |
2016-07-01 10:00:00 | 4.957 | 2.545 | 1.99 | 0.853 | 2.955 | 1.492 | 19.979 |
2016-07-01 11:00:00 | 5.76 | 2.545 | 2.203 | 0.853 | 3.442 | 1.492 | 20.118999 |
2016-07-01 12:00:00 | 4.689 | 2.545 | 1.812 | 0.853 | 2.833 | 1.523 | 19.205 |
2016-07-01 13:00:00 | 4.689 | 2.679 | 1.777 | 1.244 | 3.107 | 1.614 | 18.572001 |
2016-07-01 14:00:00 | 5.09 | 2.947 | 2.452 | 1.35 | 2.559 | 1.432 | 19.556 |
2016-07-01 15:00:00 | 5.09 | 3.148 | 2.487 | 1.35 | 2.589 | 1.523 | 17.305 |
2016-07-01 16:00:00 | 4.22 | 2.411 | 1.706 | 0.782 | 2.619 | 1.492 | 19.486 |
2016-07-01 17:00:00 | 4.756 | 2.344 | 1.635 | 0.711 | 3.076 | 1.492 | 19.134001 |
2016-07-01 18:00:00 | 5.626 | 2.88 | 2.523 | 1.208 | 3.076 | 1.492 | 20.681999 |
2016-07-01 19:00:00 | 5.492 | 3.014 | 2.452 | 1.208 | 3.015 | 1.553 | 18.712 |
2016-07-01 20:00:00 | 5.358 | 3.014 | 2.452 | 1.208 | 2.863 | 1.523 | 17.868 |
2016-07-01 21:00:00 | 5.09 | 2.947 | 2.381 | 1.208 | 2.68 | 1.523 | 18.009001 |
2016-07-01 22:00:00 | 4.823 | 2.947 | 2.203 | 1.173 | 2.619 | 1.523 | 18.009001 |
2016-07-01 23:00:00 | 4.622 | 2.88 | 2.132 | 1.137 | 2.467 | 1.492 | 19.768 |
2016-07-02 00:00:00 | 5.224 | 3.081 | 2.701 | 1.315 | 2.437 | 1.523 | 21.104 |
2016-07-02 01:00:00 | 5.157 | 3.014 | 2.878 | 1.35 | 2.345 | 1.432 | 19.697001 |
2016-07-02 02:00:00 | 5.157 | 3.148 | 2.878 | 1.492 | 2.284 | 1.432 | 20.049 |
2016-07-02 03:00:00 | 5.157 | 3.081 | 2.914 | 1.492 | 2.193 | 1.401 | 20.752001 |
2016-07-02 04:00:00 | 4.555 | 3.081 | 2.452 | 1.492 | 2.193 | 1.401 | 21.385 |
2016-07-02 05:00:00 | 5.425 | 3.282 | 3.092 | 1.706 | 2.437 | 1.462 | 22.23 |
2016-07-02 06:00:00 | 5.492 | 3.282 | 2.523 | 1.492 | 2.985 | 1.462 | 20.26 |
2016-07-02 07:00:00 | 5.626 | 3.215 | 2.487 | 1.492 | 3.076 | 1.523 | 21.104 |
2016-07-02 08:00:00 | 5.559 | 3.282 | 2.594 | 1.67 | 2.924 | 1.523 | 20.612 |
2016-07-02 09:00:00 | 5.224 | 3.215 | 2.559 | 1.564 | 2.68 | 1.462 | 18.361 |
2016-07-02 10:00:00 | 9.913 | 4.957 | 6.645 | 3.305 | 3.046 | 1.553 | 20.962999 |
2016-07-02 11:00:00 | 11.788 | 5.425 | 8.173 | 2.523 | 3.686 | 1.675 | 19.416 |
2016-07-02 12:00:00 | 9.645 | 4.957 | 6.752 | 2.132 | 3.107 | 1.828 | 20.823 |
2016-07-02 13:00:00 | 10.382 | 5.76 | 7.462 | 2.559 | 2.985 | 1.767 | 20.190001 |
2016-07-02 14:00:00 | 8.774 | 4.689 | 6.112 | 2.025 | 2.894 | 1.919 | 21.315001 |
2016-07-02 15:00:00 | 10.449 | 5.157 | 6.965 | 2.452 | 2.772 | 1.736 | 22.018999 |
2016-07-02 16:00:00 | 9.846 | 4.823 | 7.036 | 2.665 | 2.894 | 1.767 | 20.681999 |
2016-07-02 17:00:00 | 9.913 | 4.823 | 6.894 | 2.416 | 3.229 | 1.736 | 25.466 |
2016-07-02 18:00:00 | 10.65 | 4.689 | 6.929 | 2.452 | 3.381 | 1.797 | 25.888 |
2016-07-02 19:00:00 | 10.114 | 4.354 | 6.645 | 1.812 | 3.107 | 1.736 | 27.857 |
2016-07-02 20:00:00 | 9.98 | 4.153 | 6.574 | 1.954 | 3.411 | 1.767 | 27.295 |
2016-07-02 21:00:00 | 9.31 | 4.22 | 6.005 | 2.132 | 3.229 | 1.858 | 22.23 |
2016-07-02 22:00:00 | 9.444 | 4.622 | 6.965 | 2.168 | 2.955 | 1.858 | 21.948 |
2016-07-02 23:00:00 | 9.444 | 4.287 | 6.823 | 2.559 | 2.589 | 1.736 | 27.295 |
2016-07-03 00:00:00 | 10.382 | 5.425 | 7.604 | 2.31 | 2.955 | 1.675 | 29.334999 |
2016-07-03 01:00:00 | 9.779 | 5.224 | 6.716 | 2.843 | 2.65 | 1.675 | 26.028 |
2016-07-03 02:00:00 | 10.382 | 4.689 | 7.32 | 2.203 | 2.985 | 1.858 | 24.34 |
2016-07-03 03:00:00 | 9.779 | 4.153 | 6.823 | 1.99 | 2.528 | 1.675 | 26.450001 |
2016-07-03 04:00:00 | 10.717 | 4.756 | 7.356 | 2.807 | 2.65 | 1.797 | 25.958 |
2016-07-03 05:00:00 | 10.315 | 4.689 | 7.391 | 2.452 | 2.924 | 1.858 | 24.059 |
2016-07-03 06:00:00 | 12.592 | 5.224 | 8.671 | 2.203 | 3.716 | 1.949 | 25.325001 |
2016-07-03 07:00:00 | 11.119 | 4.622 | 7.889 | 2.843 | 3.625 | 1.919 | 23.636999 |
2016-07-03 08:00:00 | 10.65 | 4.421 | 7.036 | 2.025 | 3.594 | 1.919 | 26.379999 |
2016-07-03 09:00:00 | 10.047 | 4.22 | 6.432 | 1.67 | 3.686 | 1.949 | 27.365 |
2016-07-03 10:00:00 | 11.721 | 5.09 | 7.889 | 2.559 | 3.564 | 1.858 | 28.068001 |
2016-07-03 11:00:00 | 12.123 | 5.358 | 8.066 | 2.487 | 4.082 | 1.919 | 29.475 |
2016-07-03 12:00:00 | 9.98 | 5.023 | 6.858 | 2.559 | 3.29 | 1.858 | 26.802 |
2016-07-03 13:00:00 | 9.243 | 4.957 | 6.29 | 2.63 | 3.137 | 1.888 | 29.968 |
2016-07-03 14:00:00 | 10.181 | 5.425 | 7.178 | 3.02 | 3.076 | 1.888 | 30.389999 |
2016-07-03 15:00:00 | 9.645 | 5.425 | 7.107 | 2.665 | 3.015 | 1.828 | 31.164 |
2016-07-03 16:00:00 | 9.779 | 4.89 | 6.503 | 2.985 | 3.076 | 2.01 | 29.757 |
2016-07-03 17:00:00 | 11.119 | 5.157 | 7.32 | 2.914 | 3.807 | 1.98 | 32.289001 |
2016-07-03 18:00:00 | 11.052 | 4.957 | 7.391 | 2.523 | 3.686 | 1.98 | 31.938 |
2016-07-03 19:00:00 | 10.784 | 4.89 | 7.214 | 2.487 | 3.594 | 1.888 | 28.561001 |
2016-07-03 20:00:00 | 11.186 | 4.89 | 7.178 | 2.345 | 3.96 | 1.919 | 21.525999 |
2016-07-03 21:00:00 | 10.449 | 4.89 | 6.61 | 2.31 | 3.807 | 2.041 | 22.23 |
2016-07-03 22:00:00 | 9.578 | 5.76 | 6.787 | 3.127 | 3.259 | 1.888 | 19.416 |
2016-07-03 23:00:00 | 9.31 | 5.76 | 6.61 | 3.056 | 3.168 | 1.888 | 18.572001 |
2016-07-04 00:00:00 | 9.913 | 5.894 | 6.254 | 2.63 | 3.015 | 1.858 | 21.667 |
2016-07-04 01:00:00 | 8.975 | 4.957 | 6.29 | 2.665 | 2.863 | 1.828 | 25.535999 |
2016-07-04 02:00:00 | 8.64 | 4.823 | 6.148 | 2.594 | 2.924 | 1.828 | 27.857 |
2016-07-04 03:00:00 | 9.176 | 5.492 | 5.579 | 2.381 | 2.863 | 1.858 | 27.927999 |
2016-07-04 04:00:00 | 9.109 | 4.823 | 5.65 | 2.523 | 2.772 | 1.797 | 24.621 |
2016-07-04 05:00:00 | 9.846 | 5.559 | 5.97 | 2.949 | 3.107 | 1.888 | 23.848 |
2016-07-04 06:00:00 | 11.588 | 5.425 | 7.391 | 2.807 | 3.807 | 1.98 | 23.073999 |
2016-07-04 07:00:00 | 11.788 | 6.095 | 7.214 | 2.985 | 3.899 | 2.041 | 22.511 |
2016-07-04 08:00:00 | 10.583 | 5.961 | 7.143 | 2.914 | 3.655 | 2.071 | 21.667 |
2016-07-04 09:00:00 | 11.588 | 6.296 | 7.569 | 3.056 | 3.472 | 2.01 | 25.395 |
2016-07-04 10:00:00 | 11.922 | 6.229 | 7.711 | 3.056 | 3.746 | 1.949 | 25.184 |
2016-07-04 11:00:00 | 12.324 | 5.559 | 8.422 | 3.234 | 4.203 | 1.98 | 29.546 |
2016-07-04 12:00:00 | 10.382 | 5.894 | 6.858 | 2.63 | 3.564 | 1.949 | 29.475 |
2016-07-04 13:00:00 | 10.047 | 5.425 | 6.752 | 3.02 | 3.32 | 1.949 | 29.264 |
2016-07-04 14:00:00 | 10.516 | 6.028 | 7.107 | 3.376 | 3.137 | 1.919 | 30.952999 |
2016-07-04 15:00:00 | 10.717 | 6.095 | 6.787 | 3.02 | 3.168 | 2.01 | 31.726 |
2016-07-04 16:00:00 | 9.98 | 5.023 | 6.503 | 2.559 | 3.442 | 2.041 | 33.132999 |
2016-07-04 17:00:00 | 11.32 | 5.09 | 7.356 | 2.452 | 3.868 | 2.041 | 28.983 |
2016-07-04 18:00:00 | 11.387 | 4.957 | 7.356 | 2.452 | 4.295 | 2.193 | 28.983 |
2016-07-04 19:00:00 | 9.377 | 3.885 | 6.894 | 2.239 | 2.467 | 1.188 | 31.726 |
2016-07-04 20:00:00 | 10.114 | 4.086 | 7.143 | 2.239 | 2.955 | 1.462 | 25.184 |
2016-07-04 21:00:00 | 10.382 | 4.823 | 6.894 | 2.31 | 3.503 | 2.01 | 30.531 |
2016-07-04 22:00:00 | 9.645 | 4.89 | 6.61 | 1.919 | 3.259 | 1.919 | 27.646 |
2016-07-04 23:00:00 | 12.726 | 6.497 | 9.346 | 3.482 | 3.168 | 1.98 | 25.466 |
2016-07-05 00:00:00 | 11.989 | 5.626 | 8.777 | 2.949 | 3.198 | 1.98 | 25.958 |
2016-07-05 01:00:00 | 12.525 | 6.296 | 8.955 | 3.163 | 3.137 | 2.01 | 25.958 |
2016-07-05 02:00:00 | 12.324 | 6.296 | 8.813 | 3.376 | 2.985 | 1.919 | 26.028 |
2016-07-05 03:00:00 | 10.717 | 5.425 | 8.066 | 2.878 | 2.833 | 1.858 | 28.913 |
End of preview.