Aria AI Operations Research Portfolio
Collection
Enterprise OR, optimization, and decomposition demos by Aria AI • 136 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
name: string
version: string
source_reference: string
records: int64
problem_types: list<item: string>
child 0, item: string
samples: list<item: struct<instance_id: string, problem_type: string, size: string, difficulty: string, recom (... 43 chars omitted)
child 0, item: struct<instance_id: string, problem_type: string, size: string, difficulty: string, recommended_solv (... 31 chars omitted)
child 0, instance_id: string
child 1, problem_type: string
child 2, size: string
child 3, difficulty: string
child 4, recommended_solver: string
child 5, confidence: double
size: string
problem_type: string
feature_vector: list<item: double>
child 0, item: double
label: string
instance_id: string
source_dataset: string
features: struct<n_variables: int64, n_constraints: int64, graph_density: double, graph_diameter: double, cons (... 130 chars omitted)
child 0, n_variables: int64
child 1, n_constraints: int64
child 2, graph_density: double
child 3, graph_diameter: double
child 4, constraint_tightness: double
child 5, symmetry_score: double
child 6, routing_density: double
child 7, capacity_utilization: double
child 8, difficulty_score: double
difficulty: string
seed: int64
prediction: struct<recommended_solver: string, recommended_family: string, confidence: double, predicted_gap_pct (... 223 chars omitted)
child 0, recommended_solver: string
child 1, recommended_family: string
child 2, confidence: double
child 3, predicted_gap_pct: double
child 4, predicted_runtime_sec: double
child 5, rationale: string
child 6, rankings: list<item: struct<solver_id: string, score: double, predicted_gap_pct: double, predicted_runtime_sec (... 52 chars omitted)
child 0, item: struct<solver_id: string, score: double, predicted_gap_pct: double, predicted_runtime_sec: double, p (... 40 chars omitted)
child 0, solver_id: string
child 1, score: double
child 2, predicted_gap_pct: double
child 3, predicted_runtime_sec: double
child 4, predicted_feasible: bool
child 5, family: string
to
{'instance_id': Value('string'), 'problem_type': Value('string'), 'size': Value('string'), 'difficulty': Value('string'), 'seed': Value('int64'), 'label': Value('string'), 'source_dataset': Value('string'), 'features': {'n_variables': Value('int64'), 'n_constraints': Value('int64'), 'graph_density': Value('float64'), 'graph_diameter': Value('float64'), 'constraint_tightness': Value('float64'), 'symmetry_score': Value('float64'), 'routing_density': Value('float64'), 'capacity_utilization': Value('float64'), 'difficulty_score': Value('float64')}, 'feature_vector': List(Value('float64')), 'prediction': {'recommended_solver': Value('string'), 'recommended_family': Value('string'), 'confidence': Value('float64'), 'predicted_gap_pct': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'rationale': Value('string'), 'rankings': List({'solver_id': Value('string'), 'score': Value('float64'), 'predicted_gap_pct': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'predicted_feasible': Value('bool'), 'family': Value('string')})}}
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
source_reference: string
records: int64
problem_types: list<item: string>
child 0, item: string
samples: list<item: struct<instance_id: string, problem_type: string, size: string, difficulty: string, recom (... 43 chars omitted)
child 0, item: struct<instance_id: string, problem_type: string, size: string, difficulty: string, recommended_solv (... 31 chars omitted)
child 0, instance_id: string
child 1, problem_type: string
child 2, size: string
child 3, difficulty: string
child 4, recommended_solver: string
child 5, confidence: double
size: string
problem_type: string
feature_vector: list<item: double>
child 0, item: double
label: string
instance_id: string
source_dataset: string
features: struct<n_variables: int64, n_constraints: int64, graph_density: double, graph_diameter: double, cons (... 130 chars omitted)
child 0, n_variables: int64
child 1, n_constraints: int64
child 2, graph_density: double
child 3, graph_diameter: double
child 4, constraint_tightness: double
child 5, symmetry_score: double
child 6, routing_density: double
child 7, capacity_utilization: double
child 8, difficulty_score: double
difficulty: string
seed: int64
prediction: struct<recommended_solver: string, recommended_family: string, confidence: double, predicted_gap_pct (... 223 chars omitted)
child 0, recommended_solver: string
child 1, recommended_family: string
child 2, confidence: double
child 3, predicted_gap_pct: double
child 4, predicted_runtime_sec: double
child 5, rationale: string
child 6, rankings: list<item: struct<solver_id: string, score: double, predicted_gap_pct: double, predicted_runtime_sec (... 52 chars omitted)
child 0, item: struct<solver_id: string, score: double, predicted_gap_pct: double, predicted_runtime_sec: double, p (... 40 chars omitted)
child 0, solver_id: string
child 1, score: double
child 2, predicted_gap_pct: double
child 3, predicted_runtime_sec: double
child 4, predicted_feasible: bool
child 5, family: string
to
{'instance_id': Value('string'), 'problem_type': Value('string'), 'size': Value('string'), 'difficulty': Value('string'), 'seed': Value('int64'), 'label': Value('string'), 'source_dataset': Value('string'), 'features': {'n_variables': Value('int64'), 'n_constraints': Value('int64'), 'graph_density': Value('float64'), 'graph_diameter': Value('float64'), 'constraint_tightness': Value('float64'), 'symmetry_score': Value('float64'), 'routing_density': Value('float64'), 'capacity_utilization': Value('float64'), 'difficulty_score': Value('float64')}, 'feature_vector': List(Value('float64')), 'prediction': {'recommended_solver': Value('string'), 'recommended_family': Value('string'), 'confidence': Value('float64'), 'predicted_gap_pct': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'rationale': Value('string'), 'rankings': List({'solver_id': Value('string'), 'score': Value('float64'), 'predicted_gap_pct': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'predicted_feasible': Value('bool'), 'family': Value('string')})}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
frontierco-instance-features
Structural feature vectors for FrontierCO-style combinatorial optimization instances.
Features
| Feature | Description |
|---|---|
n_variables |
Decision variable count |
n_constraints |
Constraint count |
graph_density |
Graph edge density |
graph_diameter |
Approximate graph diameter |
constraint_tightness |
Binding constraint ratio |
symmetry_score |
Problem symmetry index |
routing_density |
Routing-specific density |
capacity_utilization |
Capacity utilization factor |
difficulty_score |
Easy/hard difficulty index |
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