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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 100 new columns ({'1.5119723204174852e7', '1.747541557320995e9', '644083.9796256496', '31285.79404167611', '89033.91875107978', '9999.118749204134', '440.2942465970148', '4.135496481624483e6', '914230.9318943135', '453607.09241822315', '2.5105093021390205e6', '3.5112202237111435e9', '9.67888176463853e6', '5.499283895704416e6', '307552.0375986442', '1.0722751243801019e8', '156102.87857997054', '1.6501619970305355e6', '2.1544519789886147e8', '1.2184640961291596e7', '4.652364283949331', '5.336939070597535e7', '2.7831527833556814e9', '20.34523383094276', '4.328903172452657e8', '18.71718654608385', '2156.491597839866', '1.7073670618737122e8', '10.725618981138922', '4.646921998239603e6', '1.0476245254644096e7', '5.4623497631187e8', '8261.176109422766', '362130.2265175156', '98604.045829953', '3.430495615428214e8', '6.957212254324228e6', '1.3228208333439153e7', '2.4689665109653667e7', '7601.036705058511', '138.20410870949354', '2.6561138724413857e7', '124793.68253340195', '15560.711137754', '28284.18765771204', '8.302302929920467e6', '29113.414944471526', '204746.75387188286', '8.533574181413251e7', '1.2930410968973713e6', '113.89279710684745', '280532.1563857786', '5.866454099689419e6', '6.734202351792376e7', '410190.6065541312', '137164.46082250113', '225995.7516676413', '2.3126812745621093e6', '3.3516299856337853e7', '79.4437386217184', '2.598518193386953e6', '1481.0024059076623', '959.5252280255669', '4.4557897076924715e9', '4.229546364105783e7', '1428.8068249486626', '25305.395712415375', '8.697778548199577e8', '134602.77296895886', '3.194250005158269e8', '1.0989592657382755e9', '1.1660765754055479e6', '1.834470508688416e6', '2.205182127515807e9', '593917.422068759', '425636.4402747737', '3.769039893071624e7', '1959.4453567597564', '511541.50425043004', '728.251934865243', '6.593550737352258e6', '49796.713830108674', '637.5212947020908', '1.3923519414363956e9', '1.0373936681886966e6', '240.82446417693103', '1.353129159246658e8', '11135.113369005081', '1089.2123729186046', '179856.94860532624', '4376.247245905208', '4.655009964120082e7', '2.073497254210325e6', '44937.43160850424', '29061.432896919465', '7.080255454559991e8', '3519.2963887254837', '393.4622944663207', '742500.1549807513', '912911.9059429932'}) and 8 missing columns ({'0.00014510164230918768', '1.7585974958520265e-6', '0.004755984289301053', '0.4430654108438572', '3.1667636635676193e-10', '7.319601638529824e-12', '1.269314663652328e-7', '4.2531540694626194e-13'}).

This happened while the csv dataset builder was generating data using

hf://datasets/CelestineP/signalunmix/Observations.csv (at revision 06e987dbbca275baa9753996a45ec58ff98a5aa6)

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
              1.0373936681886966e6: double
              1.5119723204174852e7: double
              2.5105093021390205e6: double
              1.0476245254644096e7: double
              410190.6065541312: double
              204746.75387188286: double
              9999.118749204134: double
              5.499283895704416e6: double
              1.7073670618737122e8: double
              1.747541557320995e9: double
              8.302302929920467e6: double
              156102.87857997054: double
              728.251934865243: double
              2.205182127515807e9: double
              742500.1549807513: double
              3519.2963887254837: double
              8.697778548199577e8: double
              637.5212947020908: double
              8.533574181413251e7: double
              2.3126812745621093e6: double
              18.71718654608385: double
              1.2184640961291596e7: double
              912911.9059429932: double
              3.3516299856337853e7: double
              240.82446417693103: double
              2.6561138724413857e7: double
              2.7831527833556814e9: double
              1959.4453567597564: double
              2.598518193386953e6: double
              7601.036705058511: double
              4.646921998239603e6: double
              1.6501619970305355e6: double
              1.2930410968973713e6: double
              1.0989592657382755e9: double
              362130.2265175156: double
              4376.247245905208: double
              4.4557897076924715e9: double
              49796.713830108674: double
              225995.7516676413: double
              79.4437386217184: double
              137164.46082250113: double
              644083.9796256496: double
              9.67888176463853e6: double
              4.652364283949331: double
              10.725618981138922: double
              124793.68253340195: double
              138.20410870949354: double
              25305.395712415375: double
              5.4623497631187e8: double
              1.3923519414363956e9: double
              3.769039893071624e7: double
              5.336939070597535e7: double
              1.0722751243801019e8: double
              959.5252280255669: double
              393.4622944663207: double
              593917.422068759: double
              4.655009964120082e7: double
              44937.43160850424: double
              440.2942465970148: double
              5.866454099689419e6: double
              1.353129159246658e8: double
              1.3228208333439153e7: double
              1481.0024059076623: double
              4.328903172452657e8: double
              6.593550737352258e6: double
              1.834470508688416e6: double
              1089.2123729186046: double
              1.1660765754055479e6: double
              280532.1563857786: double
              4.135496481624483e6: double
              3.5112202237111435e9: double
              2.1544519789886147e8: double
              425636.4402747737: double
              511541.50425043004: double
              4.229546364105783e7: double
              15560.711137754: double
              113.89279710684745: double
              29113.414944471526: double
              89033.91875107978: double
              31285.79404167611: double
              6.957212254324228e6: double
              6.734202351792376e7: double
              11135.113369005081: double
              307552.0375986442: double
              3.430495615428214e8: double
              179856.94860532624: double
              2.073497254210325e6: double
              28284.18765771204: double
              134602.77296895886: double
              20.34523383094276: double
              914230.9318943135: double
              98604.045829953: double
              453607.09241822315: double
              2156.491597839866: double
              7.080255454559991e8: double
              1428.8068249486626: double
              3.194250005158269e8: double
              29061.432896919465: double
              2.4689665109653667e7: double
              8261.176109422766: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 14013
              to
              {'0.4430654108438572': Value(dtype='float64', id=None), '0.004755984289301053': Value(dtype='float64', id=None), '0.00014510164230918768': Value(dtype='float64', id=None), '1.7585974958520265e-6': Value(dtype='float64', id=None), '1.269314663652328e-7': Value(dtype='float64', id=None), '3.1667636635676193e-10': Value(dtype='float64', id=None), '7.319601638529824e-12': Value(dtype='float64', id=None), '4.2531540694626194e-13': 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 1321, 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 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 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 100 new columns ({'1.5119723204174852e7', '1.747541557320995e9', '644083.9796256496', '31285.79404167611', '89033.91875107978', '9999.118749204134', '440.2942465970148', '4.135496481624483e6', '914230.9318943135', '453607.09241822315', '2.5105093021390205e6', '3.5112202237111435e9', '9.67888176463853e6', '5.499283895704416e6', '307552.0375986442', '1.0722751243801019e8', '156102.87857997054', '1.6501619970305355e6', '2.1544519789886147e8', '1.2184640961291596e7', '4.652364283949331', '5.336939070597535e7', '2.7831527833556814e9', '20.34523383094276', '4.328903172452657e8', '18.71718654608385', '2156.491597839866', '1.7073670618737122e8', '10.725618981138922', '4.646921998239603e6', '1.0476245254644096e7', '5.4623497631187e8', '8261.176109422766', '362130.2265175156', '98604.045829953', '3.430495615428214e8', '6.957212254324228e6', '1.3228208333439153e7', '2.4689665109653667e7', '7601.036705058511', '138.20410870949354', '2.6561138724413857e7', '124793.68253340195', '15560.711137754', '28284.18765771204', '8.302302929920467e6', '29113.414944471526', '204746.75387188286', '8.533574181413251e7', '1.2930410968973713e6', '113.89279710684745', '280532.1563857786', '5.866454099689419e6', '6.734202351792376e7', '410190.6065541312', '137164.46082250113', '225995.7516676413', '2.3126812745621093e6', '3.3516299856337853e7', '79.4437386217184', '2.598518193386953e6', '1481.0024059076623', '959.5252280255669', '4.4557897076924715e9', '4.229546364105783e7', '1428.8068249486626', '25305.395712415375', '8.697778548199577e8', '134602.77296895886', '3.194250005158269e8', '1.0989592657382755e9', '1.1660765754055479e6', '1.834470508688416e6', '2.205182127515807e9', '593917.422068759', '425636.4402747737', '3.769039893071624e7', '1959.4453567597564', '511541.50425043004', '728.251934865243', '6.593550737352258e6', '49796.713830108674', '637.5212947020908', '1.3923519414363956e9', '1.0373936681886966e6', '240.82446417693103', '1.353129159246658e8', '11135.113369005081', '1089.2123729186046', '179856.94860532624', '4376.247245905208', '4.655009964120082e7', '2.073497254210325e6', '44937.43160850424', '29061.432896919465', '7.080255454559991e8', '3519.2963887254837', '393.4622944663207', '742500.1549807513', '912911.9059429932'}) and 8 missing columns ({'0.00014510164230918768', '1.7585974958520265e-6', '0.004755984289301053', '0.4430654108438572', '3.1667636635676193e-10', '7.319601638529824e-12', '1.269314663652328e-7', '4.2531540694626194e-13'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/CelestineP/signalunmix/Observations.csv (at revision 06e987dbbca275baa9753996a45ec58ff98a5aa6)
              
              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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float64
0.004755984289301053
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0.00014510164230918768
float64
1.7585974958520265e-6
float64
1.269314663652328e-7
float64
3.1667636635676193e-10
float64
7.319601638529824e-12
float64
4.2531540694626194e-13
float64
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End of preview.

Dataset Description

Predict ChemicalConcentrations.csv given Observations.csv. This is a signal unmixing problem because the observations are a weighted sum of the chemical concentrations and pure spectra.

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