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Datasets:
asana17
/
ai_can_anomaly_detection_data

Tags:
can-bus
j1939
anomaly-detection
License:
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
logs: list<item: list<item: string>>
train: double
donors: int64
calibration: double
block: double
gap: double
seed: int64
signals: list<item: string>
period: double
max_hold: double
min_speed: double
vs
logs: list<item: string>
signals: list<item: string>
period: double
max_hold: double
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
                  table = pa.concat_tables(self.current_rows)
                File "pyarrow/table.pxi", line 6321, in pyarrow.lib.concat_tables
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              logs: list<item: list<item: string>>
              train: double
              donors: int64
              calibration: double
              block: double
              gap: double
              seed: int64
              signals: list<item: string>
              period: double
              max_hold: double
              min_speed: double
              vs
              logs: list<item: string>
              signals: list<item: string>
              period: double
              max_hold: double
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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pgn
int64
start
float64
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float64
source
float64
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float64
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1,612,438,032.233985
1,610,615,315.583856
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End of preview.

ai_can_anomaly_detection_data

The rows the detectors in asana17/ai_can_anomaly_detection are trained, calibrated and tested on, built from CAN logs by assemble.dataset there. A run reads them at one revision and records that revision, so the models in asana17/ai_can_anomaly_detection_runs each name the data they were fitted on.

python3 -m evaluate.pc.run asana17/ai_can_anomaly_detection_data <revision> out runs_clone

main holds the dataset built from every log. A smaller one built for a quick try goes to a branch of its own.

Source

Built from the University of Turku J1939 truck dataset, a Renault Euro VI truck on the road, normal traffic only. It is CC BY 4.0, and so is this. https://etsin.fairdata.fi/dataset/7586f24f-c91b-41df-92af-283524de8b3e/data

The raw logs are not here. Fetch them from the page above.

Files

file holds
seconds.json MIN_SPEED, and each log's seconds above it, which splits the logs into train and test
grid.json the train logs and the grid settings the grid was built with
grid_raw.npy the train logs on a 0.1 s grid, float32, 17 signals a row. The train and calibration rows are cut from it
grid_t.npy, grid_seg.npy each grid row's time and segment
scale.npy the mean, then the std, every row is z-scored by, fitted to the train rows
built.json the logs and the settings the attack set and the scale were built with
attacked.json where each attack sits, its PGN, time span, rows and how far it moved them
attacked_raw.npy the test logs with one attack each, on the grid
attacked_rows.npy attacked_raw.npy z-scored by scale.npy
attacked_t.npy, attacked_seg.npy, attacked_label.npy, attacked_wheel.npy each test row's time, segment, attack label and wheel speed

The signals and the settings are in grid.json and built.json.

The attacked CAN frames are not stored, only the rows built from them.
Downloads last month
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Total file size:
726 MB

Models trained or fine-tuned on asana17/ai_can_anomaly_detection_data