Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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-simple-dataset/before/generated_employee_usage.csv (at revision c69e19cfca703e1ce6ed6e14b34739773de8f447)
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-simple-dataset/before/generated_employee_usage.csv (at revision c69e19cfca703e1ce6ed6e14b34739773de8f447)
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 |
|---|---|---|---|---|---|---|---|---|
1 | electric | 68,203,678 | electric | 100,884 | 6 | 8 | 35.029167 | 184.992748 |
2 | water_supply | 68,203,678 | water_supply | 3,000 | 24 | 8 | 0.260417 | 184.992748 |
4 | electric | 68,203,677 | electric | 100,884 | 6 | 8 | 35.029167 | 250.062058 |
5 | water_supply | 68,203,677 | water_supply | 3,000 | 24 | 8 | 0.260417 | 250.062058 |
7 | electric | 68,203,674 | electric | 100,884 | 6 | 8 | 35.029167 | 222.85692 |
8 | water_supply | 68,203,674 | water_supply | 3,000 | 24 | 8 | 0.260417 | 222.85692 |
10 | electric | 68,203,672 | electric | 100,884 | 6 | 8 | 35.029167 | 123.846104 |
11 | water_supply | 68,203,672 | water_supply | 3,000 | 24 | 8 | 0.260417 | 123.846104 |
13 | electric | 68,203,671 | electric | 100,884 | 6 | 8 | 35.029167 | 49.229684 |
14 | water_supply | 68,203,671 | water_supply | 3,000 | 24 | 8 | 0.260417 | 49.229684 |
16 | electric | 68,203,669 | electric | 100,884 | 6 | 8 | 35.029167 | 2.5234 |
17 | water_supply | 68,203,669 | water_supply | 3,000 | 24 | 8 | 0.260417 | 2.5234 |
19 | electric | 68,203,668 | electric | 100,884 | 6 | 8 | 35.029167 | 9.9789 |
20 | water_supply | 68,203,668 | water_supply | 3,000 | 24 | 8 | 0.260417 | 9.9789 |
22 | electric | 68,203,666 | electric | 100,884 | 6 | 8 | 35.029167 | 92.323584 |
23 | water_supply | 68,203,666 | water_supply | 3,000 | 24 | 8 | 0.260417 | 92.323584 |
25 | electric | 68,203,665 | electric | 100,884 | 6 | 8 | 35.029167 | 164.571264 |
26 | water_supply | 68,203,665 | water_supply | 3,000 | 24 | 8 | 0.260417 | 164.571264 |
28 | electric | 68,203,663 | electric | 100,884 | 6 | 8 | 35.029167 | 158.48136 |
29 | water_supply | 68,203,663 | water_supply | 3,000 | 24 | 8 | 0.260417 | 158.48136 |
31 | electric | 68,203,662 | electric | 100,884 | 6 | 8 | 35.029167 | 214.22556 |
32 | water_supply | 68,203,662 | water_supply | 3,000 | 24 | 8 | 0.260417 | 214.22556 |
34 | electric | 68,203,659 | electric | 100,884 | 6 | 8 | 35.029167 | 185.904576 |
35 | water_supply | 68,203,659 | water_supply | 3,000 | 24 | 8 | 0.260417 | 185.904576 |
37 | electric | 68,203,657 | electric | 100,884 | 6 | 8 | 35.029167 | 42.368404 |
38 | water_supply | 68,203,657 | water_supply | 3,000 | 24 | 8 | 0.260417 | 42.368404 |
40 | electric | 68,203,656 | electric | 100,884 | 6 | 8 | 35.029167 | 16.841734 |
41 | water_supply | 68,203,656 | water_supply | 3,000 | 24 | 8 | 0.260417 | 16.841734 |
43 | electric | 68,203,654 | electric | 100,884 | 6 | 8 | 35.029167 | 1.9536 |
44 | water_supply | 68,203,654 | water_supply | 3,000 | 24 | 8 | 0.260417 | 1.9536 |
46 | electric | 68,203,653 | electric | 100,884 | 6 | 8 | 35.029167 | 7.7256 |
47 | water_supply | 68,203,653 | water_supply | 3,000 | 24 | 8 | 0.260417 | 7.7256 |
49 | electric | 68,203,651 | electric | 100,884 | 6 | 8 | 35.029167 | 76.93632 |
50 | water_supply | 68,203,651 | water_supply | 3,000 | 24 | 8 | 0.260417 | 76.93632 |
52 | electric | 68,203,650 | electric | 100,884 | 6 | 8 | 35.029167 | 137.14272 |
53 | water_supply | 68,203,650 | water_supply | 3,000 | 24 | 8 | 0.260417 | 137.14272 |
55 | electric | 68,203,648 | electric | 100,884 | 6 | 8 | 35.029167 | 179.58733 |
56 | water_supply | 68,203,648 | water_supply | 3,000 | 24 | 8 | 0.260417 | 179.58733 |
58 | electric | 68,203,646 | electric | 100,884 | 6 | 8 | 35.029167 | 139.290368 |
59 | water_supply | 68,203,646 | water_supply | 3,000 | 24 | 8 | 0.260417 | 139.290368 |
61 | electric | 68,203,645 | electric | 100,884 | 6 | 8 | 35.029167 | 139.500248 |
62 | water_supply | 68,203,645 | water_supply | 3,000 | 24 | 8 | 0.260417 | 139.500248 |
64 | electric | 68,203,644 | electric | 100,884 | 6 | 8 | 35.029167 | 90.528245 |
65 | water_supply | 68,203,644 | water_supply | 3,000 | 24 | 8 | 0.260417 | 90.528245 |
67 | electric | 68,203,643 | electric | 100,884 | 6 | 8 | 35.029167 | 170.30286 |
68 | water_supply | 68,203,643 | water_supply | 3,000 | 24 | 8 | 0.260417 | 170.30286 |
70 | electric | 68,203,641 | electric | 100,884 | 6 | 8 | 35.029167 | 155.06478 |
71 | water_supply | 68,203,641 | water_supply | 3,000 | 24 | 8 | 0.260417 | 155.06478 |
73 | electric | 68,203,640 | electric | 100,884 | 6 | 8 | 35.029167 | 60.08652 |
74 | water_supply | 68,203,640 | water_supply | 3,000 | 24 | 8 | 0.260417 | 60.08652 |
76 | electric | 68,203,639 | electric | 100,884 | 6 | 8 | 35.029167 | 60.08652 |
77 | water_supply | 68,203,639 | water_supply | 3,000 | 24 | 8 | 0.260417 | 60.08652 |
80 | electric | 68,203,638 | electric | 100,884 | 6 | 8 | 35.029167 | 177.278322 |
81 | electric | 68,203,638 | electric | 100,884 | 6 | 8 | 35.029167 | 17.1532 |
82 | water_supply | 68,203,638 | water_supply | 3,000 | 24 | 8 | 0.260417 | 177.278322 |
83 | water_supply | 68,203,638 | water_supply | 3,000 | 24 | 8 | 0.260417 | 17.1532 |
85 | electric | 68,203,636 | electric | 100,884 | 6 | 8 | 35.029167 | 16.381306 |
86 | water_supply | 68,203,636 | water_supply | 3,000 | 24 | 8 | 0.260417 | 16.381306 |
88 | electric | 68,203,635 | electric | 100,884 | 6 | 8 | 35.029167 | 93.828004 |
89 | water_supply | 68,203,635 | water_supply | 3,000 | 24 | 8 | 0.260417 | 93.828004 |
91 | electric | 68,203,634 | electric | 100,884 | 6 | 8 | 35.029167 | 141.48245 |
92 | water_supply | 68,203,634 | water_supply | 3,000 | 24 | 8 | 0.260417 | 141.48245 |
94 | electric | 68,203,632 | electric | 100,884 | 6 | 8 | 35.029167 | 39.62885 |
95 | water_supply | 68,203,632 | water_supply | 3,000 | 24 | 8 | 0.260417 | 39.62885 |
97 | electric | 68,203,631 | electric | 100,884 | 6 | 8 | 35.029167 | 62.7409 |
98 | water_supply | 68,203,631 | water_supply | 3,000 | 24 | 8 | 0.260417 | 62.7409 |
100 | electric | 68,203,630 | electric | 100,884 | 6 | 8 | 35.029167 | 79.9459 |
101 | water_supply | 68,203,630 | water_supply | 3,000 | 24 | 8 | 0.260417 | 79.9459 |
104 | electric | 68,203,628 | electric | 100,884 | 6 | 8 | 35.029167 | 234.429869 |
105 | electric | 68,203,628 | electric | 100,884 | 6 | 8 | 35.029167 | 51.143686 |
106 | water_supply | 68,203,628 | water_supply | 3,000 | 24 | 8 | 0.260417 | 234.429869 |
107 | water_supply | 68,203,628 | water_supply | 3,000 | 24 | 8 | 0.260417 | 51.143686 |
109 | electric | 68,203,626 | electric | 100,884 | 6 | 8 | 35.029167 | 50.568319 |
110 | water_supply | 68,203,626 | water_supply | 3,000 | 24 | 8 | 0.260417 | 50.568319 |
112 | electric | 68,203,625 | electric | 100,884 | 6 | 8 | 35.029167 | 114.689715 |
113 | water_supply | 68,203,625 | water_supply | 3,000 | 24 | 8 | 0.260417 | 114.689715 |
115 | electric | 68,203,624 | electric | 100,884 | 6 | 8 | 35.029167 | 57.153069 |
116 | water_supply | 68,203,624 | water_supply | 3,000 | 24 | 8 | 0.260417 | 57.153069 |
119 | electric | 68,203,622 | electric | 100,884 | 6 | 8 | 35.029167 | 85.106289 |
120 | electric | 68,203,622 | electric | 100,884 | 6 | 8 | 35.029167 | 78.333747 |
121 | water_supply | 68,203,622 | water_supply | 3,000 | 24 | 8 | 0.260417 | 85.106289 |
122 | water_supply | 68,203,622 | water_supply | 3,000 | 24 | 8 | 0.260417 | 78.333747 |
124 | electric | 68,203,621 | electric | 100,884 | 6 | 8 | 35.029167 | 40.09585 |
125 | water_supply | 68,203,621 | water_supply | 3,000 | 24 | 8 | 0.260417 | 40.09585 |
127 | electric | 68,203,620 | electric | 100,884 | 6 | 8 | 35.029167 | 117.650455 |
128 | water_supply | 68,203,620 | water_supply | 3,000 | 24 | 8 | 0.260417 | 117.650455 |
130 | electric | 68,203,619 | electric | 100,884 | 6 | 8 | 35.029167 | 129.637256 |
131 | water_supply | 68,203,619 | water_supply | 3,000 | 24 | 8 | 0.260417 | 129.637256 |
133 | electric | 68,203,618 | electric | 100,884 | 6 | 8 | 35.029167 | 34.526525 |
134 | water_supply | 68,203,618 | water_supply | 3,000 | 24 | 8 | 0.260417 | 34.526525 |
137 | electric | 68,203,617 | electric | 100,884 | 6 | 8 | 35.029167 | 103.290649 |
138 | electric | 68,203,617 | electric | 100,884 | 6 | 8 | 35.029167 | 144.968065 |
139 | water_supply | 68,203,617 | water_supply | 3,000 | 24 | 8 | 0.260417 | 103.290649 |
140 | water_supply | 68,203,617 | water_supply | 3,000 | 24 | 8 | 0.260417 | 144.968065 |
142 | electric | 68,203,615 | electric | 100,884 | 6 | 8 | 35.029167 | 77.287409 |
143 | water_supply | 68,203,615 | water_supply | 3,000 | 24 | 8 | 0.260417 | 77.287409 |
145 | electric | 68,203,614 | electric | 100,884 | 6 | 8 | 35.029167 | 47.889301 |
146 | water_supply | 68,203,614 | water_supply | 3,000 | 24 | 8 | 0.260417 | 47.889301 |
148 | electric | 68,203,613 | electric | 100,884 | 6 | 8 | 35.029167 | 84.004912 |
149 | water_supply | 68,203,613 | water_supply | 3,000 | 24 | 8 | 0.260417 | 84.004912 |
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.
Simple Dataset
It include the data with low variation and 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 smaeprocess_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 underday_amountparameter. 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
- Downloads last month
- 24