pypi-fcg-dataset

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
initializer_range: double
layer_norm_epsilon: double
n_embd: int64
n_head: int64
n_layer: int64
n_positions: int64
vocab_size: int64
dropout: double
max_position_embeddings: int64
n_heads: int64
hidden_dim: int64
activation: string
n_layers: int64
tie_weights_: bool
sinusoidal_pos_embds: bool
dim: int64
attention_dropout: double
to
{'activation': Value('string'), 'attention_dropout': Value('float64'), 'dim': Value('int64'), 'dropout': Value('float64'), 'hidden_dim': Value('int64'), 'initializer_range': Value('float64'), 'max_position_embeddings': Value('int64'), 'n_heads': Value('int64'), 'n_layers': Value('int64'), 'sinusoidal_pos_embds': Value('bool'), 'tie_weights_': Value('bool'), 'vocab_size': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              initializer_range: double
              layer_norm_epsilon: double
              n_embd: int64
              n_head: int64
              n_layer: int64
              n_positions: int64
              vocab_size: int64
              dropout: double
              max_position_embeddings: int64
              n_heads: int64
              hidden_dim: int64
              activation: string
              n_layers: int64
              tie_weights_: bool
              sinusoidal_pos_embds: bool
              dim: int64
              attention_dropout: double
              to
              {'activation': Value('string'), 'attention_dropout': Value('float64'), 'dim': Value('int64'), 'dropout': Value('float64'), 'hidden_dim': Value('int64'), 'initializer_range': Value('float64'), 'max_position_embeddings': Value('int64'), 'n_heads': Value('int64'), 'n_layers': Value('int64'), 'sinusoidal_pos_embds': Value('bool'), 'tie_weights_': Value('bool'), 'vocab_size': Value('int64')}
              because column names don't match

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.

YAML Metadata Warning:The task_categories "graph-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

PyPI FCG and TPKG Dataset

This dataset contains the final graph artifacts used in the capstone project: Detecting Python-specific Malicious Behaviors in PyPI Packages based on Threat Pattern Knowledge Graph and AST Analysis.

Dataset Summary

Final metadata: 1,428 PyPI package-version samples
Valid graph samples: 1,416
Valid malicious graph samples: 715
Valid benign graph samples: 701
Split strategy: temporal train/validation/test split

Included Artifacts

Path	Description
`dataset_metadata.csv`	Final package-version metadata, including package, version, label, timestamp, and source
`fcg_malnet/`	Raw Function Call Graph edgelist files in a MalNet-style folder structure
`tpg_malnet_qualified/`	Final TPKG-qualified behavior-level graph edgelist files
`tpg_node_features_qualified/`	NumPy node feature files for the fixed 11 behavior nodes
`split-info-fcg/`	Temporal train/validation/test split files
`file_csv_data/fcg_edges_qualified.csv`	Qualified FCG edge table with resolved API names and confidence values

Notes

The original extracted package source code is not included in this final public dataset artifact because it may contain real malicious code and can be large. The released files are the graph-level artifacts used for model training, baseline evaluation, size bias analysis, and explainability experiments.

Labels

0: benign
1: malicious

Citation

If you use this dataset, please cite the capstone project or refer to the accompanying project report.

Downloads last month: 274

Total file size:

1.54 GB