Dataset Preview
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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 3 new columns ({'head', 'timestamp', 'tail'}) and 5 missing columns ({'num_words', 'score', 'dst', 'ts', 'src'}).
This happened while the csv dataset builder was generating data using
hf://datasets/andrewsleader/TGB/thgl-github/thgl-github_edgelist.csv (at revision 6da4cf24684ac76d4caa0058e5ab4bf0214d1c79)
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
timestamp: int64
head: int64
tail: int64
relation_type: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 711
to
{'ts': Value(dtype='int64', id=None), 'src': Value(dtype='int64', id=None), 'dst': Value(dtype='int64', id=None), 'relation_type': Value(dtype='int64', id=None), 'num_words': Value(dtype='int64', id=None), 'score': Value(dtype='int64', 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 1324, 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 938, 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 3 new columns ({'head', 'timestamp', 'tail'}) and 5 missing columns ({'num_words', 'score', 'dst', 'ts', 'src'}).
This happened while the csv dataset builder was generating data using
hf://datasets/andrewsleader/TGB/thgl-github/thgl-github_edgelist.csv (at revision 6da4cf24684ac76d4caa0058e5ab4bf0214d1c79)
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.
ts
int64 | src
int64 | dst
int64 | relation_type
int64 | num_words
int64 | score
int64 |
|---|---|---|---|---|---|
1,388,534,400
| 0
| 1
| 0
| 32
| 1
|
1,388,534,400
| 0
| 2
| 1
| 32
| 1
|
1,388,534,400
| 3
| 4
| 0
| 16
| 2
|
1,388,534,400
| 3
| 5
| 1
| 16
| 2
|
1,388,534,400
| 6
| 7
| 0
| 10
| 3
|
1,388,534,400
| 6
| 8
| 1
| 10
| 3
|
1,388,534,400
| 9
| 10
| 0
| 6
| -1
|
1,388,534,400
| 9
| 11
| 1
| 6
| -1
|
1,388,534,400
| 12
| 13
| 0
| 17
| 0
|
1,388,534,400
| 12
| 14
| 1
| 17
| 0
|
1,388,534,400
| 15
| 16
| 0
| 25
| 5
|
1,388,534,400
| 15
| 17
| 1
| 25
| 5
|
1,388,534,400
| 18
| 19
| 0
| 22
| 5
|
1,388,534,400
| 18
| 20
| 1
| 22
| 5
|
1,388,534,401
| 21
| 22
| 0
| 14
| 1
|
1,388,534,401
| 21
| 23
| 1
| 14
| 1
|
1,388,534,401
| 24
| 25
| 0
| 6
| 2
|
1,388,534,401
| 24
| 26
| 1
| 6
| 2
|
1,388,534,401
| 27
| 28
| 0
| 17
| 0
|
1,388,534,401
| 27
| 2
| 1
| 17
| 0
|
1,388,534,401
| 29
| 30
| 0
| 24
| 1
|
1,388,534,401
| 29
| 31
| 1
| 24
| 1
|
1,388,534,402
| 32
| 33
| 0
| 4
| 1
|
1,388,534,402
| 32
| 34
| 1
| 4
| 1
|
1,388,534,402
| 35
| 36
| 0
| 49
| 12
|
1,388,534,402
| 35
| 37
| 1
| 49
| 12
|
1,388,534,402
| 38
| 39
| 0
| 26
| 1
|
1,388,534,402
| 38
| 40
| 1
| 26
| 1
|
1,388,534,402
| 41
| 42
| 0
| 186
| 2
|
1,388,534,402
| 41
| 43
| 1
| 186
| 2
|
1,388,534,402
| 44
| 45
| 0
| 19
| 1
|
1,388,534,402
| 44
| 46
| 1
| 19
| 1
|
1,388,534,402
| 47
| 48
| 0
| 32
| 3
|
1,388,534,402
| 47
| 49
| 1
| 32
| 3
|
1,388,534,403
| 50
| 51
| 0
| 14
| 8
|
1,388,534,403
| 50
| 52
| 1
| 14
| 8
|
1,388,534,403
| 53
| 54
| 0
| 15
| 62
|
1,388,534,403
| 53
| 55
| 1
| 15
| 62
|
1,388,534,403
| 56
| 57
| 0
| 75
| 3
|
1,388,534,403
| 56
| 58
| 1
| 75
| 3
|
1,388,534,403
| 59
| 59
| 0
| 9
| 1
|
1,388,534,403
| 59
| 60
| 1
| 9
| 1
|
1,388,534,404
| 61
| 62
| 0
| 19
| 1
|
1,388,534,404
| 61
| 63
| 1
| 19
| 1
|
1,388,534,405
| 64
| 65
| 0
| 8
| 14
|
1,388,534,405
| 64
| 66
| 1
| 8
| 14
|
1,388,534,405
| 67
| 68
| 0
| 7
| 2
|
1,388,534,405
| 67
| 69
| 1
| 7
| 2
|
1,388,534,405
| 70
| 71
| 0
| 9
| 1
|
1,388,534,405
| 70
| 2
| 1
| 9
| 1
|
1,388,534,406
| 72
| 73
| 0
| 30
| 1
|
1,388,534,406
| 72
| 74
| 1
| 30
| 1
|
1,388,534,406
| 75
| 76
| 0
| 12
| 1
|
1,388,534,406
| 75
| 77
| 1
| 12
| 1
|
1,388,534,406
| 78
| 79
| 0
| 6
| 2
|
1,388,534,406
| 78
| 80
| 1
| 6
| 2
|
1,388,534,406
| 81
| 82
| 0
| 7
| 1
|
1,388,534,406
| 81
| 83
| 1
| 7
| 1
|
1,388,534,406
| 84
| 85
| 0
| 8
| 1
|
1,388,534,406
| 84
| 63
| 1
| 8
| 1
|
1,388,534,407
| 86
| 87
| 0
| 1
| 1
|
1,388,534,407
| 86
| 88
| 1
| 1
| 1
|
1,388,534,407
| 89
| 90
| 0
| 13
| 11
|
1,388,534,407
| 89
| 60
| 1
| 13
| 11
|
1,388,534,407
| 91
| 92
| 0
| 19
| 2
|
1,388,534,407
| 91
| 93
| 1
| 19
| 2
|
1,388,534,407
| 94
| 95
| 0
| 8
| 174
|
1,388,534,407
| 94
| 96
| 1
| 8
| 174
|
1,388,534,407
| 97
| 98
| 0
| 7
| 1
|
1,388,534,407
| 97
| 99
| 1
| 7
| 1
|
1,388,534,408
| 100
| 101
| 0
| 2
| 1
|
1,388,534,408
| 100
| 102
| 1
| 2
| 1
|
1,388,534,408
| 103
| 104
| 0
| 56
| 1
|
1,388,534,408
| 103
| 93
| 1
| 56
| 1
|
1,388,534,408
| 105
| 59
| 0
| 56
| 1
|
1,388,534,408
| 105
| 60
| 1
| 56
| 1
|
1,388,534,408
| 106
| 107
| 0
| 2
| 1
|
1,388,534,408
| 106
| 2
| 1
| 2
| 1
|
1,388,534,408
| 108
| 109
| 0
| 14
| 1
|
1,388,534,408
| 108
| 110
| 1
| 14
| 1
|
1,388,534,408
| 111
| 112
| 0
| 12
| 3
|
1,388,534,408
| 111
| 55
| 1
| 12
| 3
|
1,388,534,409
| 113
| 114
| 0
| 228
| 7
|
1,388,534,409
| 113
| 20
| 1
| 228
| 7
|
1,388,534,409
| 115
| 116
| 0
| 49
| 2
|
1,388,534,409
| 115
| 117
| 1
| 49
| 2
|
1,388,534,409
| 118
| 119
| 0
| 21
| 2
|
1,388,534,409
| 118
| 120
| 1
| 21
| 2
|
1,388,534,409
| 121
| 122
| 0
| 21
| 3
|
1,388,534,409
| 121
| 55
| 1
| 21
| 3
|
1,388,534,409
| 123
| 124
| 0
| 20
| 2
|
1,388,534,409
| 123
| 37
| 1
| 20
| 2
|
1,388,534,409
| 125
| 126
| 0
| 15
| -11
|
1,388,534,409
| 125
| 127
| 1
| 15
| -11
|
1,388,534,410
| 128
| 129
| 0
| 1
| 12
|
1,388,534,410
| 128
| 130
| 1
| 1
| 12
|
1,388,534,410
| 131
| 132
| 0
| 37
| 0
|
1,388,534,410
| 131
| 2
| 1
| 37
| 0
|
1,388,534,410
| 133
| 134
| 0
| 3
| -1
|
1,388,534,410
| 133
| 2
| 1
| 3
| -1
|
End of preview.
TGB 2.0
Overview of the Temporal Graph Benchmark (TGB) pipeline:
- TGB includes large-scale and realistic datasets from five different domains with both dynamic link prediction and node property prediction tasks.
- TGB automatically downloads datasets and processes them into
numpy,PyTorchandPyG compatible TemporalDataformats. - Novel TG models can be easily evaluated on TGB datasets via reproducible and realistic evaluation protocols.
- TGB provides public and online leaderboards to track recent developments in temporal graph learning domain.
pip install py-tgb
Links and Datasets
The project website can be found here.
The API documentations can be found here.
all dataset download links can be found at info.py
TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets.
if website is unaccessible, please use this link instead.
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