Datasets:

alirezaaminzadeh
/

scalelab-benchmark-instances

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
name: string
version: string
records: int64
industries: list<item: string>
  child 0, item: string
sizes: list<item: string>
  child 0, item: string
schema: struct<instance_id: string, industry: string, size: string, seed: string, doe: string, best_point: s (... 6 chars omitted)
  child 0, instance_id: string
  child 1, industry: string
  child 2, size: string
  child 3, seed: string
  child 4, doe: string
  child 5, best_point: string
samples: list<item: struct<instance_id: string, industry: string, size: string, best_objective: double>>
  child 0, item: struct<instance_id: string, industry: string, size: string, best_objective: double>
      child 0, instance_id: string
      child 1, industry: string
      child 2, size: string
      child 3, best_objective: double
seed: int64
best_responses: struct<quality_score: double, yield_pct: double, energy_kwh: double, waste_pct: double, cycle_time_m (... 11 chars omitted)
  child 0, quality_score: double
  child 1, yield_pct: double
  child 2, energy_kwh: double
  child 3, waste_pct: double
  child 4, cycle_time_min: double
n_sequential: int64
best_point: struct<temperature_c: double, pressure_bar: double, mixing_speed_rpm: double, reaction_time_min: dou (... 22 chars omitted)
  child 0, temperature_c: double
  child 1, pressure_bar: double
  child 2, mixing_speed_rpm: double
  child 3, reaction_time_min: double
  child 4, ratio_ab: double
instance_id: string
industry: string
size: string
n_initial: int64
label: string
doe: struct<method: string, n_experiments: int64, design_matrix: list<item: struct<temperature_c: double, (... 161 chars omitted)
  child 0, method: string
  child 1, n_experiments: int64
  child 2, design_matrix: list<item: struct<temperature_c: double, pressure_bar: double, mixing_speed_rpm: double, reaction_ti (... 34 chars omitted)
      child 0, item: struct<temperature_c: double, pressure_bar: double, mixing_speed_rpm: double, reaction_time_min: dou (... 22 chars omitted)
          child 0, temperature_c: double
          child 1, pressure_bar: double
          child 2, mixing_speed_rpm: double
          child 3, reaction_time_min: double
          child 4, ratio_ab: double
  child 3, space_filling_metric: double
  child 4, d_efficiency: double
  child 5, level: int64
to
{'instance_id': Value('string'), 'industry': Value('string'), 'size': Value('string'), 'seed': Value('int64'), 'label': Value('string'), 'n_initial': Value('int64'), 'n_sequential': Value('int64'), 'doe': {'method': Value('string'), 'n_experiments': Value('int64'), 'design_matrix': List({'temperature_c': Value('float64'), 'pressure_bar': Value('float64'), 'mixing_speed_rpm': Value('float64'), 'reaction_time_min': Value('float64'), 'ratio_ab': Value('float64')}), 'space_filling_metric': Value('float64'), 'd_efficiency': Value('float64'), 'level': Value('int64')}, 'best_point': {'temperature_c': Value('float64'), 'pressure_bar': Value('float64'), 'mixing_speed_rpm': Value('float64'), 'reaction_time_min': Value('float64'), 'ratio_ab': Value('float64')}, 'best_responses': {'quality_score': Value('float64'), 'yield_pct': Value('float64'), 'energy_kwh': Value('float64'), 'waste_pct': Value('float64'), 'cycle_time_min': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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
              name: string
              version: string
              records: int64
              industries: list<item: string>
                child 0, item: string
              sizes: list<item: string>
                child 0, item: string
              schema: struct<instance_id: string, industry: string, size: string, seed: string, doe: string, best_point: s (... 6 chars omitted)
                child 0, instance_id: string
                child 1, industry: string
                child 2, size: string
                child 3, seed: string
                child 4, doe: string
                child 5, best_point: string
              samples: list<item: struct<instance_id: string, industry: string, size: string, best_objective: double>>
                child 0, item: struct<instance_id: string, industry: string, size: string, best_objective: double>
                    child 0, instance_id: string
                    child 1, industry: string
                    child 2, size: string
                    child 3, best_objective: double
              seed: int64
              best_responses: struct<quality_score: double, yield_pct: double, energy_kwh: double, waste_pct: double, cycle_time_m (... 11 chars omitted)
                child 0, quality_score: double
                child 1, yield_pct: double
                child 2, energy_kwh: double
                child 3, waste_pct: double
                child 4, cycle_time_min: double
              n_sequential: int64
              best_point: struct<temperature_c: double, pressure_bar: double, mixing_speed_rpm: double, reaction_time_min: dou (... 22 chars omitted)
                child 0, temperature_c: double
                child 1, pressure_bar: double
                child 2, mixing_speed_rpm: double
                child 3, reaction_time_min: double
                child 4, ratio_ab: double
              instance_id: string
              industry: string
              size: string
              n_initial: int64
              label: string
              doe: struct<method: string, n_experiments: int64, design_matrix: list<item: struct<temperature_c: double, (... 161 chars omitted)
                child 0, method: string
                child 1, n_experiments: int64
                child 2, design_matrix: list<item: struct<temperature_c: double, pressure_bar: double, mixing_speed_rpm: double, reaction_ti (... 34 chars omitted)
                    child 0, item: struct<temperature_c: double, pressure_bar: double, mixing_speed_rpm: double, reaction_time_min: dou (... 22 chars omitted)
                        child 0, temperature_c: double
                        child 1, pressure_bar: double
                        child 2, mixing_speed_rpm: double
                        child 3, reaction_time_min: double
                        child 4, ratio_ab: double
                child 3, space_filling_metric: double
                child 4, d_efficiency: double
                child 5, level: int64
              to
              {'instance_id': Value('string'), 'industry': Value('string'), 'size': Value('string'), 'seed': Value('int64'), 'label': Value('string'), 'n_initial': Value('int64'), 'n_sequential': Value('int64'), 'doe': {'method': Value('string'), 'n_experiments': Value('int64'), 'design_matrix': List({'temperature_c': Value('float64'), 'pressure_bar': Value('float64'), 'mixing_speed_rpm': Value('float64'), 'reaction_time_min': Value('float64'), 'ratio_ab': Value('float64')}), 'space_filling_metric': Value('float64'), 'd_efficiency': Value('float64'), 'level': Value('int64')}, 'best_point': {'temperature_c': Value('float64'), 'pressure_bar': Value('float64'), 'mixing_speed_rpm': Value('float64'), 'reaction_time_min': Value('float64'), 'ratio_ab': Value('float64')}, 'best_responses': {'quality_score': Value('float64'), 'yield_pct': Value('float64'), 'energy_kwh': Value('float64'), 'waste_pct': Value('float64'), 'cycle_time_min': Value('float64')}}
              because column names don't match

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ScaleLab Benchmark Instances

Synthetic industrial process optimization instances for Design of Experiments, Surrogate Modeling, and Bayesian Optimization benchmarking.

Schema

Field	Type	Description
`instance_id`	string	Unique identifier `{industry}_{size}_s{seed}`
`industry`	string	Process industry (chemical, pharma, materials, etc.)
`size`	string	Instance size (small, medium, large)
`seed`	integer	Random seed for reproducibility
`n_initial`	integer	Initial DoE experiment count
`n_sequential`	integer	Sequential BO experiment budget
`doe`	object	Design matrix and design metrics
`best_point`	object	Best factor settings found
`best_responses`	object	Process responses at best point

Instance Types

Type	Purpose
Small	Algorithm comparison with low experiment budget
Medium	Solver comparison under standard budget
Large	Scalability and quality under extended budget
Random seeds	Robustness evaluation across stochastic noise

Usage

from datasets import load_dataset

ds = load_dataset("alirezaaminzadeh/scalelab-benchmark-instances")
print(ds["train"][0])

Related Resources

Space: experiment-to-scale-industrial-optimization-lab
Benchmark Results: scalelab-benchmark-results
Model: scalelab-surrogate-config

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