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theethawats98
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tdce-example-complicated-dataset

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train

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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 4 new columns ({'employee_name', 'type', 'employee_id', 'amount'}) and 4 missing columns ({'name', 'unit_cost', 'hour_amount', 'day_amount'}).

This happened while the csv dataset builder was generating data using

hf://datasets/theethawats98/tdce-example-complicated-dataset/before/generated_employee_usage.csv (at revision 3767168b607f64febfd53f38ec7d8d4e10977f54)

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 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, 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 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              _id: string
              employee_id: string
              process_id: int64
              employee_name: string
              amount: int64
              duration: double
              type: string
              cost: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1288
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), '_id': Value(dtype='string', id=None), 'process_id': Value(dtype='int64', id=None), 'name': Value(dtype='string', id=None), 'cost': Value(dtype='int64', id=None), 'day_amount': Value(dtype='float64', id=None), 'hour_amount': Value(dtype='int64', id=None), 'unit_cost': Value(dtype='float64', id=None), 'duration': 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 1436, 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 1053, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1873, 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 4 new columns ({'employee_name', 'type', 'employee_id', 'amount'}) and 4 missing columns ({'name', 'unit_cost', 'hour_amount', 'day_amount'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/theethawats98/tdce-example-complicated-dataset/before/generated_employee_usage.csv (at revision 3767168b607f64febfd53f38ec7d8d4e10977f54)
              
              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.

Unnamed: 0 int64	_id string	process_id int64	name string	cost int64	day_amount float64	hour_amount int64	unit_cost float64	duration float64
4	electric	68,300,528	electric	100,884	6	8	35.029167	18.83115
19	electric	68,300,518	electric	100,884	6	8	35.029167	3.365964
37	electric	68,300,506	electric	100,884	6	8	35.029167	711.655166
42	electric	68,300,504	electric	100,884	6	8	35.029167	1,393.411341
43	electric	68,300,504	electric	100,884	6	8	35.029167	28.310987
44	electric	68,300,504	electric	100,884	6	8	35.029167	68.265665
56	electric	68,300,496	electric	100,884	6	8	35.029167	183.021855
57	electric	68,300,496	electric	100,884	6	8	35.029167	14.7109
61	electric	68,300,495	electric	100,884	6	8	35.029167	935.325239
73	electric	68,300,485	electric	100,884	6	8	35.029167	17.758224
76	electric	68,300,484	electric	100,884	6	8	35.029167	173.27471
79	electric	68,300,483	electric	100,884	6	8	35.029167	96.998242
103	electric	68,300,472	electric	100,884	6	8	35.029167	401.840621
107	electric	68,300,471	electric	100,884	6	8	35.029167	21.002508
108	electric	68,300,471	electric	100,884	6	8	35.029167	183.382451
112	electric	68,300,470	electric	100,884	6	8	35.029167	107.332404
125	electric	68,300,462	electric	100,884	6	8	35.029167	28.264403
126	electric	68,300,462	electric	100,884	6	8	35.029167	45.005626
131	electric	68,300,460	electric	100,884	6	8	35.029167	249.24384
132	electric	68,300,460	electric	100,884	6	8	35.029167	167.194814
136	electric	68,300,458	electric	100,884	6	8	35.029167	156.00384
142	electric	68,300,454	electric	100,884	6	8	35.029167	1,358.489909
145	electric	68,300,453	electric	100,884	6	8	35.029167	1.767675
148	electric	68,300,452	electric	100,884	6	8	35.029167	209.265525
190	electric	68,300,427	electric	100,884	6	8	35.029167	33.665968
193	electric	68,300,423	electric	100,884	6	8	35.029167	102.822168
259	electric	68,300,387	electric	100,884	6	8	35.029167	199.125675
262	electric	68,300,386	electric	100,884	6	8	35.029167	1,362.414
283	electric	68,300,528	electric	100,884	6	8	35.029167	458.289474
286	electric	68,300,527	electric	100,884	6	8	35.029167	934.910526
313	electric	68,300,518	electric	100,884	6	8	35.029167	55.9188
316	electric	68,300,517	electric	100,884	6	8	35.029167	385.0812
349	electric	68,300,506	electric	100,884	6	8	35.029167	97.210368
352	electric	68,300,505	electric	100,884	6	8	35.029167	5.212512
355	electric	68,300,504	electric	100,884	6	8	35.029167	4.1472
358	electric	68,300,503	electric	100,884	6	8	35.029167	64.302336
373	electric	68,300,498	electric	100,884	6	8	35.029167	12.8772
376	electric	68,300,497	electric	100,884	6	8	35.029167	8.533445
379	electric	68,300,496	electric	100,884	6	8	35.029167	5.5188
382	electric	68,300,495	electric	100,884	6	8	35.029167	25.533648
385	electric	68,300,494	electric	100,884	6	8	35.029167	71.762796
403	electric	68,300,488	electric	100,884	6	8	35.029167	56.137536
406	electric	68,300,487	electric	100,884	6	8	35.029167	10.420488
409	electric	68,300,486	electric	100,884	6	8	35.029167	58.4064
412	electric	68,300,485	electric	100,884	6	8	35.029167	1.976832
415	electric	68,300,484	electric	100,884	6	8	35.029167	0.022464
418	electric	68,300,483	electric	100,884	6	8	35.029167	24.732864
451	electric	68,300,472	electric	100,884	6	8	35.029167	211.524363
454	electric	68,300,471	electric	100,884	6	8	35.029167	139.330178
457	electric	68,300,470	electric	100,884	6	8	35.029167	993.93459
460	electric	68,300,469	electric	100,884	6	8	35.029167	948.410868
478	electric	68,300,463	electric	100,884	6	8	35.029167	296.3115
481	electric	68,300,462	electric	100,884	6	8	35.029167	194.58
484	electric	68,300,461	electric	100,884	6	8	35.029167	487.719
487	electric	68,300,460	electric	100,884	6	8	35.029167	639.3645
490	electric	68,300,459	electric	100,884	6	8	35.029167	68.7375
493	electric	68,300,458	electric	100,884	6	8	35.029167	428.2875
505	electric	68,300,454	electric	100,884	6	8	35.029167	209.31084
508	electric	68,300,453	electric	100,884	6	8	35.029167	619.191
511	electric	68,300,452	electric	100,884	6	8	35.029167	78.248745
514	electric	68,300,451	electric	100,884	6	8	35.029167	210.342825
517	electric	68,300,450	electric	100,884	6	8	35.029167	1,719.1656
586	electric	68,300,427	electric	100,884	6	8	35.029167	348.39964
589	electric	68,300,426	electric	100,884	6	8	35.029167	305.586859
592	electric	68,300,425	electric	100,884	6	8	35.029167	3.685869
595	electric	68,300,424	electric	100,884	6	8	35.029167	1,105.533753
598	electric	68,300,423	electric	100,884	6	8	35.029167	607.884788
706	electric	68,300,387	electric	100,884	6	8	35.029167	1,364.980435
709	electric	68,300,386	electric	100,884	6	8	35.029167	1,458.429096
712	electric	68,300,385	electric	100,884	6	8	35.029167	664.990469
0	65fbd3228382640527b795b8	68,300,530	null	960	null	null	null	103.38318
1	65fbd3228382640527b795b8	68,300,528	null	960	null	null	null	18.83115
2	65fbd3228382640527b795b8	68,300,526	null	960	null	null	null	126.60327
3	65fbd3228382640527b795b8	68,300,523	null	960	null	null	null	34.299555
4	65fbd3228382640527b795b8	68,300,521	null	960	null	null	null	46.32215
5	65fbd3228382640527b795b8	68,300,520	null	960	null	null	null	180.710775
6	65fbd3228382640527b795b8	68,300,518	null	960	null	null	null	3.365964
7	65fbd3228382640527b795b8	68,300,516	null	960	null	null	null	15.35463
8	65fbd3228382640527b795b8	68,300,514	null	960	null	null	null	7.416576
9	65fbd3228382640527b795b8	68,300,511	null	960	null	null	null	396.68181
10	65fbd3228382640527b795b8	68,300,509	null	960	null	null	null	29.01344
11	65fbd3228382640527b795b8	68,300,508	null	960	null	null	null	16.241566
12	65fbd3228382640527b795b8	68,300,506	null	960	null	null	null	711.655166
13	65fbd3228382640527b795b8	68,300,504	null	960	null	null	null	1,393.411341
14	65fbd3228382640527b795b8	68,300,504	null	960	null	null	null	28.310987
15	65fbd3228382640527b795b8	68,300,504	null	960	null	null	null	68.265665
16	65fbd3228382640527b795b8	68,300,500	null	960	null	null	null	74.838635
17	65fbd3228382640527b795b8	68,300,499	null	960	null	null	null	50.288994
18	65fbd3228382640527b795b8	68,300,496	null	960	null	null	null	183.021855
19	65fbd3228382640527b795b8	68,300,496	null	960	null	null	null	14.7109
20	65fbd3228382640527b795b8	68,300,495	null	960	null	null	null	935.325239
21	65fbd3228382640527b795b8	68,300,492	null	960	null	null	null	250.677946
22	65fbd3228382640527b795b8	68,300,492	null	960	null	null	null	1,187.620462
23	65fbd3228382640527b795b8	68,300,489	null	960	null	null	null	1.454835
24	65fbd3228382640527b795b8	68,300,485	null	960	null	null	null	17.758224
25	65fbd3228382640527b795b8	68,300,484	null	960	null	null	null	173.27471
26	65fbd3228382640527b795b8	68,300,483	null	960	null	null	null	96.998242
27	65fbd3228382640527b795b8	68,300,482	null	960	null	null	null	80.817858
28	65fbd3228382640527b795b8	68,300,482	null	960	null	null	null	169.079505
29	65fbd3228382640527b795b8	68,300,482	null	960	null	null	null	407.697723

End of preview.

Example Dataset For Time-Driven Cost Estimation Learning Model

This dataset is the inspired-simulated data (the actual data is removed). This data is related to the Time-Driven Activity-Based Costing (TDABC) Principle.

Complicated Dataset

It include the data with high variation but low dimension.

It includes 4 files that bring from the manufacturing management system, which can be listed as.

Process Data (generated_process_data) it contains the manufacturing process data, process id, product, and its original material.
Material Usage (generated_material_usage) contains the material cost (of the lots, it is the unit cost of 1 kg) and the used amount. 1 material item per 1 record. Link to Process dataset by using the smae process_id
Employee Usage (generated_employee_usage) contains the employee id, (can be represented by a group of employees with an amount), the cost under the effective day under day_amount parameter. It also contains the time duration which is used by
Capital Cost Usage (generated_capital_cost) contains the usage of utility cost under the different types and also contains durations.

These 4 files is pre-processed and remove unused filed. The original data can be founded in the before folder

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Total file size:

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