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alirezaaminzadeh
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retailopt-benchmark-results

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
benchmark: string
reference_dataset: string
n_cases: int64
backends: list<item: string>
  child 0, item: string
code_execution_rate: double
feasibility_rate: double
objective_match_rate: double
semantic_consistency_rate: double
solve_time_p50_sec: double
per_backend: struct<minizinc_generator: struct<code_execution_rate: double, feasibility_rate: double, objective_m (... 628 chars omitted)
  child 0, minizinc_generator: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
      child 0, code_execution_rate: double
      child 1, feasibility_rate: double
      child 2, objective_match_rate: double
      child 3, semantic_consistency_rate: double
  child 1, pyomo_generator: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
      child 0, code_execution_rate: double
      child 1, feasibility_rate: double
      child 2, objective_match_rate: double
      child 3, semantic_consistency_rate: double
  child 2, qwen_coder: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
      child 0, code_execution_rate: double
      child 1, feasibility_rate: double
      child 2, objective_match_rate: double
      child 3, semantic_consistency_rate: double
  child 3, retailopt_coder: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
      child 0, code_execution_rate: double
      child 1, feasibility_rate: double
      child 2, objective_match_rate: double
      child 3, semantic_consistency_rate: double
  child 4, sirl_reference: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
      child 0, code_execution_rate: double
      child 1, feasibility_rate: double
      child 2, objective_match_rate: double
      child 3, semantic_consistency_rate: double
cases: list<item: struct<case_id: string, family: string, backend: string, code_executed: bool, feasible: b (... 153 chars omitted)
  child 0, item: struct<case_id: string, family: string, backend: string, code_executed: bool, feasible: bool, object (... 141 chars omitted)
      child 0, case_id: string
      child 1, family: string
      child 2, backend: string
      child 3, code_executed: bool
      child 4, feasible: bool
      child 5, objective_match: bool
      child 6, semantic_consistent: bool
      child 7, predicted_objective: double
      child 8, ground_truth_objective: double
      child 9, solve_time_sec: double
      child 10, error: null
to
{'benchmark': Value('string'), 'reference_dataset': Value('string'), 'n_cases': Value('int64'), 'backends': List(Value('string')), 'per_backend': {'minizinc_generator': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'pyomo_generator': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'qwen_coder': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'retailopt_coder': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'sirl_reference': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': 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
              benchmark: string
              reference_dataset: string
              n_cases: int64
              backends: list<item: string>
                child 0, item: string
              code_execution_rate: double
              feasibility_rate: double
              objective_match_rate: double
              semantic_consistency_rate: double
              solve_time_p50_sec: double
              per_backend: struct<minizinc_generator: struct<code_execution_rate: double, feasibility_rate: double, objective_m (... 628 chars omitted)
                child 0, minizinc_generator: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
                    child 0, code_execution_rate: double
                    child 1, feasibility_rate: double
                    child 2, objective_match_rate: double
                    child 3, semantic_consistency_rate: double
                child 1, pyomo_generator: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
                    child 0, code_execution_rate: double
                    child 1, feasibility_rate: double
                    child 2, objective_match_rate: double
                    child 3, semantic_consistency_rate: double
                child 2, qwen_coder: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
                    child 0, code_execution_rate: double
                    child 1, feasibility_rate: double
                    child 2, objective_match_rate: double
                    child 3, semantic_consistency_rate: double
                child 3, retailopt_coder: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
                    child 0, code_execution_rate: double
                    child 1, feasibility_rate: double
                    child 2, objective_match_rate: double
                    child 3, semantic_consistency_rate: double
                child 4, sirl_reference: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, semantic (... 26 chars omitted)
                    child 0, code_execution_rate: double
                    child 1, feasibility_rate: double
                    child 2, objective_match_rate: double
                    child 3, semantic_consistency_rate: double
              cases: list<item: struct<case_id: string, family: string, backend: string, code_executed: bool, feasible: b (... 153 chars omitted)
                child 0, item: struct<case_id: string, family: string, backend: string, code_executed: bool, feasible: bool, object (... 141 chars omitted)
                    child 0, case_id: string
                    child 1, family: string
                    child 2, backend: string
                    child 3, code_executed: bool
                    child 4, feasible: bool
                    child 5, objective_match: bool
                    child 6, semantic_consistent: bool
                    child 7, predicted_objective: double
                    child 8, ground_truth_objective: double
                    child 9, solve_time_sec: double
                    child 10, error: null
              to
              {'benchmark': Value('string'), 'reference_dataset': Value('string'), 'n_cases': Value('int64'), 'backends': List(Value('string')), 'per_backend': {'minizinc_generator': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'pyomo_generator': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'qwen_coder': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'retailopt_coder': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}, 'sirl_reference': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'semantic_consistency_rate': Value('float64')}}}
              because column names don't match

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RetailOpt Benchmark Results

Execution-based comparison of codegen backends on retail supply-chain optimization scenarios.

Compared Backends

Backend	Type
`pyomo_generator`	Deterministic Pyomo + HiGHS
`minizinc_generator`	MiniZinc + Gecode
`qwen_coder`	Qwen2.5-Coder-1.5B base
`sirl_reference`	SIRL-Gurobi reference
`retailopt_coder`	Fine-tuned on RetailOpt-10K

Metrics

Code execution rate
Feasibility rate
Objective match rate (±5% tolerance)
Semantic consistency rate
Solve time P50

Reference

Benchmarked against Jacoblian/RetailOpt-190.

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