Datasets:

mars2titan
/

trace-c-neso-benchmark

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
protocol: string
provenance: string
comparability: string
score_artifact: struct<schema_version: int64, seed: int64, series_sha256: string, trainer_sha256: string, versions:  (... 128 chars omitted)
  child 0, schema_version: int64
  child 1, seed: int64
  child 2, series_sha256: string
  child 3, trainer_sha256: string
  child 4, versions: struct<machine: string, numpy: string, platform: string, python: string, scikit_learn: string, torch (... 9 chars omitted)
      child 0, machine: string
      child 1, numpy: string
      child 2, platform: string
      child 3, python: string
      child 4, scikit_learn: string
      child 5, torch: string
  child 5, n_windows: int64
artifact_hashes: struct<evaluator_sha256: string, baseline_scores_sha256: string, ae_training_history_sha256: string, (... 75 chars omitted)
  child 0, evaluator_sha256: string
  child 1, baseline_scores_sha256: string
  child 2, ae_training_history_sha256: string
  child 3, trace_c_real_report_sha256: string
  child 4, trace_c_holdout_report_sha256: string
baselines: list<item: struct<detector: string, scored_windows: int64, y2019: struct<windows: int64, selection:  (... 875 chars omitted)
  child 0, item: struct<detector: string, scored_windows: int64, y2019: struct<windows: int64, selection: string, rec (... 863 chars omitted)
      child 0, detector: string
      child 1, scored_windows: int64
      child 2, y2019: struct<windows: int64, selection: string, record_alerts: int64, expected_null_alerts: doub
...
 struct<level: double, observed: int64, expected_null: double, windows: int64>
              child 0, level: double
              child 1, observed: int64
              child 2, expected_null: double
              child 3, windows: int64
      child 7, calibration_precovid: list<item: struct<level: double, observed: int64, expected_null: int64, windows: int64>>
          child 0, item: struct<level: double, observed: int64, expected_null: int64, windows: int64>
              child 0, level: double
              child 1, observed: int64
              child 2, expected_null: int64
              child 3, windows: int64
n_windows: int64
cal_end_idx: int64
trainer_sha256: string
seq_len: int64
scores: struct<conv_ae: list<item: double>, iforest: list<item: double>, pca: list<item: double>, spectral_r (... 28 chars omitted)
  child 0, conv_ae: list<item: double>
      child 0, item: double
  child 1, iforest: list<item: double>
      child 0, item: double
  child 2, pca: list<item: double>
      child 0, item: double
  child 3, spectral_residual: list<item: double>
      child 0, item: double
versions: struct<machine: string, numpy: string, platform: string, python: string, scikit_learn: string, torch (... 9 chars omitted)
  child 0, machine: string
  child 1, numpy: string
  child 2, platform: string
  child 3, python: string
  child 4, scikit_learn: string
  child 5, torch: string
seed: int64
series_sha256: string
schema_version: int64
train_end_idx: int64
standardization: string
to
{'cal_end_idx': Value('int64'), 'n_windows': Value('int64'), 'schema_version': Value('int64'), 'scores': {'conv_ae': List(Value('float64')), 'iforest': List(Value('float64')), 'pca': List(Value('float64')), 'spectral_residual': List(Value('float64'))}, 'seed': Value('int64'), 'seq_len': Value('int64'), 'series_sha256': Value('string'), 'standardization': Value('string'), 'train_end_idx': Value('int64'), 'trainer_sha256': Value('string'), 'versions': {'machine': Value('string'), 'numpy': Value('string'), 'platform': Value('string'), 'python': Value('string'), 'scikit_learn': Value('string'), 'torch': Value('string')}}
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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              protocol: string
              provenance: string
              comparability: string
              score_artifact: struct<schema_version: int64, seed: int64, series_sha256: string, trainer_sha256: string, versions:  (... 128 chars omitted)
                child 0, schema_version: int64
                child 1, seed: int64
                child 2, series_sha256: string
                child 3, trainer_sha256: string
                child 4, versions: struct<machine: string, numpy: string, platform: string, python: string, scikit_learn: string, torch (... 9 chars omitted)
                    child 0, machine: string
                    child 1, numpy: string
                    child 2, platform: string
                    child 3, python: string
                    child 4, scikit_learn: string
                    child 5, torch: string
                child 5, n_windows: int64
              artifact_hashes: struct<evaluator_sha256: string, baseline_scores_sha256: string, ae_training_history_sha256: string, (... 75 chars omitted)
                child 0, evaluator_sha256: string
                child 1, baseline_scores_sha256: string
                child 2, ae_training_history_sha256: string
                child 3, trace_c_real_report_sha256: string
                child 4, trace_c_holdout_report_sha256: string
              baselines: list<item: struct<detector: string, scored_windows: int64, y2019: struct<windows: int64, selection:  (... 875 chars omitted)
                child 0, item: struct<detector: string, scored_windows: int64, y2019: struct<windows: int64, selection: string, rec (... 863 chars omitted)
                    child 0, detector: string
                    child 1, scored_windows: int64
                    child 2, y2019: struct<windows: int64, selection: string, record_alerts: int64, expected_null_alerts: doub
              ...
               struct<level: double, observed: int64, expected_null: double, windows: int64>
                            child 0, level: double
                            child 1, observed: int64
                            child 2, expected_null: double
                            child 3, windows: int64
                    child 7, calibration_precovid: list<item: struct<level: double, observed: int64, expected_null: int64, windows: int64>>
                        child 0, item: struct<level: double, observed: int64, expected_null: int64, windows: int64>
                            child 0, level: double
                            child 1, observed: int64
                            child 2, expected_null: int64
                            child 3, windows: int64
              n_windows: int64
              cal_end_idx: int64
              trainer_sha256: string
              seq_len: int64
              scores: struct<conv_ae: list<item: double>, iforest: list<item: double>, pca: list<item: double>, spectral_r (... 28 chars omitted)
                child 0, conv_ae: list<item: double>
                    child 0, item: double
                child 1, iforest: list<item: double>
                    child 0, item: double
                child 2, pca: list<item: double>
                    child 0, item: double
                child 3, spectral_residual: list<item: double>
                    child 0, item: double
              versions: struct<machine: string, numpy: string, platform: string, python: string, scikit_learn: string, torch (... 9 chars omitted)
                child 0, machine: string
                child 1, numpy: string
                child 2, platform: string
                child 3, python: string
                child 4, scikit_learn: string
                child 5, torch: string
              seed: int64
              series_sha256: string
              schema_version: int64
              train_end_idx: int64
              standardization: string
              to
              {'cal_end_idx': Value('int64'), 'n_windows': Value('int64'), 'schema_version': Value('int64'), 'scores': {'conv_ae': List(Value('float64')), 'iforest': List(Value('float64')), 'pca': List(Value('float64')), 'spectral_residual': List(Value('float64'))}, 'seed': Value('int64'), 'seq_len': Value('int64'), 'series_sha256': Value('string'), 'standardization': Value('string'), 'train_end_idx': Value('int64'), 'trainer_sha256': Value('string'), 'versions': {'machine': Value('string'), 'numpy': Value('string'), 'platform': Value('string'), 'python': Value('string'), 'scikit_learn': Value('string'), 'torch': Value('string')}}
              because column names don't match

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TRACE-C NESO evidence

Status: EVAL / EVIDENCE. Frozen TRACE-C reports. Not a trained model.

Machine-readable reports for TRACE-C (rank-calibrated relational anomaly detection) on public GB grid telemetry.

This is not a trained TRACE-C model. TRACE-C is a statistics detector. Code and paper source: github.com/mars-arch/trace-c.

Licence

Reports / code-derived JSON: MIT, Matthew Faucher.
Underlying NESO telemetry (fetched by the GitHub repo, checksums in source-checksums.json): NESO Open Data Licence. Required attribution: Supported by National Energy SO Open Data.

Do not relicense the NESO streams as CC-BY or MIT.

Path	What
`reports/trace-c-real-report.json`	2019 development-year TRACE-C run
`reports/trace-c-holdout-report.json`	frozen 2020 hold-out
`reports/trace-c-ablation-2019.json`	2019 channel ablation (not a hold-out)
`reports/trace-c-v3-preview.json`	peek-informed v3 knobs (not a hold-out)
`reports/baselines-report.json`	post-hoc AE / PCA / IF / SR comparison
`reports/baseline-scores.json`	baseline raw scores
`source-checksums.json`	pinned NESO input hashes and source URLs

Raw half-hourly / frequency CSVs are not mirrored here. Fetch them with bash scripts/fetch-data.sh from the GitHub repo; the checksums above must match before reports are trusted.

Author: Matthew Faucher. ORCID: https://orcid.org/0009-0005-5238-731X

Downloads last month: 18

Total file size:

1.87 MB

TRACE-C NESO evidence

Licence

Contents