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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 5 new columns ({'corruption', 'delta_mi_max', 'target', 'delta_mi_total', 'kl'}) and 5 missing columns ({'kl_marginal', 'rate', 'col', 'true_delta_mi', 'predicted_delta_mi'}).

This happened while the csv dataset builder was generating data using

hf://datasets/arifmohamedkhan/dsa-pipeline-quality/data/mi_knn_validation.csv (at revision 9bef941f0d6125c6df74d594de2cdb4dbb3da314), ['hf://datasets/arifmohamedkhan/dsa-pipeline-quality@9bef941f0d6125c6df74d594de2cdb4dbb3da314/data/gaussian_mi_validation.csv', 'hf://datasets/arifmohamedkhan/dsa-pipeline-quality@9bef941f0d6125c6df74d594de2cdb4dbb3da314/data/mi_knn_validation.csv']

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 "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._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
              corruption: string
              delta_mi_total: double
              delta_mi_max: double
              kl: double
              target: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 868
              to
              {'col': Value('int64'), 'rate': Value('float64'), 'true_delta_mi': Value('float64'), 'kl_marginal': Value('float64'), 'predicted_delta_mi': Value('float64')}
              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 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 1683, 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 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 5 new columns ({'corruption', 'delta_mi_max', 'target', 'delta_mi_total', 'kl'}) and 5 missing columns ({'kl_marginal', 'rate', 'col', 'true_delta_mi', 'predicted_delta_mi'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/arifmohamedkhan/dsa-pipeline-quality/data/mi_knn_validation.csv (at revision 9bef941f0d6125c6df74d594de2cdb4dbb3da314), ['hf://datasets/arifmohamedkhan/dsa-pipeline-quality@9bef941f0d6125c6df74d594de2cdb4dbb3da314/data/gaussian_mi_validation.csv', 'hf://datasets/arifmohamedkhan/dsa-pipeline-quality@9bef941f0d6125c6df74d594de2cdb4dbb3da314/data/mi_knn_validation.csv']
              
              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)

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col
int64
rate
float64
true_delta_mi
float64
kl_marginal
float64
predicted_delta_mi
float64
0
0.1
0.518871
0
0.082058
0
0.2
0.650773
0
0.164116
0
0.5
0.782662
0
0.410289
0
1
0.821402
0
0.820579
1
0.1
0.425343
0
0.00165
1
0.2
0.5783
0
0.0033
1
0.5
0.671716
0
0.00825
1
1
0.706766
0
0.016501
2
0.1
0.485495
0
0.000007
2
0.2
0.65622
0
0.000014
2
0.5
0.818693
0
0.000035
2
1
0.872971
0
0.00007
3
0.1
0.535707
0
0
3
0.2
0.707853
0
0
3
0.5
0.950392
0
0
3
1
1.025401
0
0
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

DSA: Dual-Spectrum Auditing for Data Pipeline Quality via LLM Information Proxies

Paper

"DSA: Dual-Spectrum Auditing for Data Pipeline Quality via LLM Information Proxies" Arif Mohamed Khan Rabi Ahamad, Syracuse University

Repository Contents

data/

  • e1_master_table.csv — 79 E1 mutation scenarios with all detection metrics
  • realworld_unified.csv — 72 quarterly real-world periods across 3 pipelines
  • structural_quality_*.csv — Per-period structural indicators for each pipeline
  • documented_events.json — Documented real-world events (COVID, surcharges, etc.)
  • illm_vs_mi_expanded.csv — ILLM vs true MI (kNN Kraskov) on 15 corruptions
  • gaussian_mi_validation.csv — Gaussian graphical model theorem verification
  • actual_baselines.csv — Deequ/AV/SPADE comparison on 17 corruptions
  • cost_utility.json — Cost-sensitive analysis results
  • family_recommendations.csv — Per-family method recommendations

code/

  • reproduce_numbers.py — Reproduces all key paper numbers from the data files

figures/

  • 12 publication figures (PNG format)

Quick Start

pip install pandas scipy scikit-learn
cd code
python reproduce_numbers.py

Key Results

Metric Value
Rule-preserving detection (S_max_adj) 83%
Rule-preserving detection (Auto-Validate) 31%
Construct validity (ILLM vs MI) rho=0.67, p=0.006
Fare-only construct validity rho=0.96, p=0.0005
NYC Taxi longitudinal (Frobenius) rho=-0.51, p=0.002
Chicago longitudinal (MI) rho=-0.62, p=0.014
Meta-classifier F1 0.77
Cost per audit $0.0014

License

CC-BY-4.0

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