ommx.adapter

ommx.adapter#

Common interfaces for solving and sampling an OMMX Instance.

Compare results from multiple adapters

Use the same Instance with different adapters to compare their returned Solution objects. This complete example needs the SDK, ommx-highs-adapter, ommx-pyscipopt-adapter, pandas, and matplotlib; follow the installation guide to install compatible versions. No external data or previous example state is needed.

Build one problem and solve it twice

Choose items to maximize their total value without exceeding a weight capacity of 47. Each binary variable is 1 when the corresponding item is selected. The variable name x and its subscript i retain that correspondence for result extraction later. OMMX also assigns each variable an ID within the Instance; names and subscripts are labels, not those IDs. The Instance holds the variables, objective expression, and capacity constraint together.

>>> from ommx import Instance
>>> values = [10, 13, 18, 31, 7, 15]
>>> weights = [11, 25, 20, 35, 10, 33]
>>> instance = Instance.maximize()
>>> x = [instance.new_binary("x", subscripts=[i]) for i in range(len(values))]
>>> instance.objective = sum(value * var for value, var in zip(values, x))
>>> _ = instance.add_constraint(
...     sum(weight * var for weight, var in zip(weights, x)) <= 47,
...     "weight capacity",
... )

Both HiGHS and SCIP can directly accept this linear binary model. Each adapter’s solve prepares an isolated copy, so the same source Instance can be reused. Other models can require different preparation for each backend. See OMMXPySCIPOptAdapter for explicit policy selection and Adapter packages and input contracts for other backends.

>>> from ommx_highs_adapter import OMMXHighsAdapter
>>> from ommx_pyscipopt_adapter import OMMXPySCIPOptAdapter
>>> adapters = {"highs": OMMXHighsAdapter, "scip": OMMXPySCIPOptAdapter}
>>> solutions = {name: adapter.solve(instance) for name, adapter in adapters.items()}

Compare reported values and guarantees

The reported objective is OMMX’s evaluated value; the optimization direction (sense) tells you whether larger or smaller is better. Feasibility includes variable domains and constraints. A feasible candidate, matching objective values, and a proven optimum are separate observations. In this small example, both adapters return an optimal feasible solution with objective 41.

>>> import pandas as pd
>>> comparison = pd.DataFrame([
...     {
...         "adapter": name,
...         "sense": repr(solution.sense),
...         "objective": solution.objective,
...         "feasible": solution.feasible,
...         "optimality": repr(solution.optimality),
...     }
...     for name, solution in solutions.items()
... ]).set_index("adapter")
>>> print(comparison.to_string())
                  sense  objective  feasible          optimality
adapter
highs    Sense.Maximize       41.0      True  Optimality.Optimal
scip     Sense.Maximize       41.0      True  Optimality.Optimal

This demonstrates a comparison procedure, not which solver is generally better. Align the following conditions before drawing conclusions:

Information	Conditions for comparison
Reported objective and sense	Same problem and objective meaning.
Feasibility for the original problem	Same evaluated conditions and numerical tolerances.
Optimality	Check the problem and guarantee represented by each adapter’s result.
Backend bounds, gaps, and termination reasons	Check the solved formulation, solver-facing objective, and backend definitions.
Runtime	Measure separately with defined data, settings, environment, and repetitions.

If you explicitly solve a transformed Instance, evaluate the candidate against the original Instance as needed. Reconstructed values and membership in an adapter’s input class alone do not establish original feasibility or global optimality. See Transform problems and choose a solving method and Evaluate candidates and interpret results for choosing the evaluation target.

The HighsDiagnosticsAnalyzer and SCIPDiagnosticsAnalyzer references explain backend reports. A bound on a sign-changed or penalized objective is not automatically a bound on the original objective. Experiment can organize repeated runs, but is not required for this comparison.

Plot and tabulate the selected items

extract_decision_variables() selects variables named x and returns their values keyed by subscript tuples. Here (i,) corresponds to item i; sort these keys to put the items in model order. Variables without the requested name are omitted. Duplicate subscript tuples under that name are an error rather than silently overwriting a value; use distinct labels or inspect the ID-indexed DataFrame when labels are not unique.

>>> selected = solutions["highs"].extract_decision_variables("x")
>>> [selected[(i,)] for i in range(len(values))]
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0]
>>> from matplotlib import pyplot as plt
>>> fig, ax = plt.subplots()
>>> marks = {"highs": "o", "scip": "+"}
>>> for name, solution in solutions.items():
...     selected = solution.extract_decision_variables("x")
...     keys = sorted(selected)
...     _ = ax.plot(
...         [key[0] for key in keys], [selected[key] for key in keys],
...         marks[name], label=name,
...     )
>>> _ = ax.set(xlabel="Item", ylabel="Selected (1) or not selected (0)")
>>> _ = ax.legend()
>>> len(ax.lines)
2

Display fig in a notebook or call plt.show() interactively. Close the figure when finished to release its resources.

>>> plt.close(fig)

For tabular analysis, decision_variables_df() provides one row per variable. Add a solver column before concatenating the tables so the origin of each row remains explicit; variable IDs can repeat across solutions.

>>> decision_variables = pd.concat([
...     solution.decision_variables_df().assign(solver=name)
...     for name, solution in solutions.items()
... ])
>>> decision_variables.groupby("solver").size().to_dict()
{'highs': 6, 'scip': 6}

Attributes#

`SamplerInput`
`SamplerOutput`
`SolverInput`
`SolverOutput`

Exceptions#

`AdapterNotApplicableError`	Raised when an instance is not applicable to an adapter.
`NoSolutionReturned`	Raised when no solution was returned.
`UnboundedDetected`	Raised when the problem is proven to be unbounded.

Classes#

`DiagnosticReport`	Adapter diagnostic report convertible with `dataclasses.asdict`.
`DiagnosticsSink`	Receiver for adapter-defined diagnostics emitted during a solve.
`SamplerAdapter`	An abstract interface for OMMX Sampler Adapters, defining how samplers should be used with OMMX.
`SolverAdapter`	An abstract interface for OMMX Solver Adapters, defining how solvers should be used with OMMX.

Module Contents#

exception AdapterNotApplicableError(adapter: str, report: InstanceClassMembershipReport)#

Raised when an instance is not applicable to an adapter.

adapter: str#

report: InstanceClassMembershipReport#

exception NoSolutionReturned#

Raised when no solution was returned.

This indicates that the solver did not return any solution (whether feasible or not) (e.g., due to time limits). This does not prove that the mathematical model itself is infeasible.

exception UnboundedDetected#

Raised when the problem is proven to be unbounded.

This corresponds to Optimality.OPTIMALITY_UNBOUNDED and indicates that the mathematical model itself is unbounded. Should not be used when unboundedness cannot be proven (e.g., heuristic solvers).

class DiagnosticReport#: Adapter diagnostic report convertible with dataclasses.asdict.

class DiagnosticsSink#

Receiver for adapter-defined diagnostics emitted during a solve.

Adapters may call record while the backend solver is still running, including from backend callbacks. Sink implementations should keep record append-only, defer validation or serialization until after the solve, and preserve the order in which diagnostics are received.

A conforming sink must not raise from record. If recording fails, the sink should log the failure and return normally. If record does raise, that is a sink contract violation; adapters may let the exception propagate and do not need to recover from it.

record(diagnostic: DiagnosticReport) → None#

Record one adapter-defined dataclass diagnostic report or event.

This method must not raise under normal sink failures. Custom sinks should log failures and return instead.

class SamplerAdapter#

An abstract interface for OMMX Sampler Adapters, defining how samplers should be used with OMMX.

See the Adapter implementation tutorial for the complete implementation contract and reference implementations.

The exact-input, easy-API preparation, strict preparation-free API, and used-variable contracts of SolverAdapter also apply to sampler adapters. A sampler whose backend labels are the OMMX variable IDs may preserve that identity directly; other representations need an explicit correspondence. In either case, each decoded sample must contain a value for every variable in ommx_instance.used_decision_variables before evaluate_samples is called against an unchanged ommx.Instance snapshot mathematically identical to the encoded exact input. Backend formats that omit a zero-interaction variable therefore need an adapter-owned decoding default consistent with that format.

classmethod check_applicability(ommx_instance: Instance) → InstanceClassMembershipReport#: Check INPUT_CLASS membership without mutation.

decode(data: SolverOutput) → Solution#: Decode sampler output and return its best feasible solution.

abstractmethod decode_to_sampleset(data: SamplerOutput) → SampleSet#

classmethod recommended_preparation_policy() → PreparationPolicy#

Return a fresh policy recommended for this Adapter’s INPUT_CLASS.

The easy APIs apply it to an isolated copy. Advanced callers may edit and apply it explicitly before using a preparation-free API. This method itself neither prepares an instance nor guarantees applicability. It does not inspect any particular Instance, and the class and policy are independent values. Application-specific choices such as penalty weights may still need to be supplied by the caller. The default policy is empty.

classmethod require_applicable(ommx_instance: Instance) → InstanceClassMembershipReport#: Return the membership report or raise AdapterNotApplicableError.

classmethod sample(ommx_instance: Instance, *, diagnostics: DiagnosticsSink | None = None) → SampleSet#

Prepare and sample an isolated copy of an OMMX instance.

The input ommx_instance is never modified. The copy is prepared for INPUT_CLASS with recommended_preparation_policy(), then passed to sample_without_preparation().

Run.log_sample owns the reserved diagnostics keyword and uses it the same way as Run.log_solve. None means diagnostics are disabled.

classmethod sample_without_preparation(ommx_instance: Instance, *, diagnostics: DiagnosticsSink | None = None) → SampleSet#

Abstractmethod:

Sample an exact Adapter input without running Instance.prepare.

ommx_instance must belong to INPUT_CLASS. Implementations must reject non-members with AdapterNotApplicableError and must not prepare or otherwise modify the input instance.

classmethod solve(ommx_instance: Instance, *, diagnostics: DiagnosticsSink | None = None) → Solution#

Prepare and solve an isolated copy of an OMMX instance.

The input ommx_instance is never modified. The copy is prepared for INPUT_CLASS with recommended_preparation_policy(), then passed to solve_without_preparation().

Run.log_solve owns the reserved diagnostics keyword. When called with store_diagnostics=True, it passes a sink to the adapter and stores recorded diagnostics with the Solve entry. Adapters may record adapter-defined dataclass diagnostics into the sink during the solve; None means diagnostics are disabled. Adapters do not need to catch exceptions raised by a non-conforming diagnostics sink.

classmethod solve_without_preparation(ommx_instance: Instance, *, diagnostics: DiagnosticsSink | None = None) → Solution#: Return the best feasible result from sample_without_preparation().

INPUT_CLASS: ClassVar[InstanceClass]#: Required condition for an exact Adapter input.

property sampler_input: SamplerInput#

Abstractmethod:

property solver_input: SolverInput#: Expose sampler_input through the SolverAdapter interface.

class SolverAdapter#

An abstract interface for OMMX Solver Adapters, defining how solvers should be used with OMMX.

See the Adapter implementation tutorial for the complete implementation contract and reference implementation.

Subclasses declare INPUT_CLASS as the OMMX-defined structural class of exact inputs they can encode. Membership in that class is the complete applicability condition: check_applicability only checks membership and does not mutate the input. A subclass should call require_applicable before constructing backend input.

Membership describes the exact input, not every problem that could become acceptable after transformation. It does not run preparation or a backend, guarantee that a candidate will be returned, or rule out converter/backend errors such as implementation limits or numerical failures. Those errors are distinct from the membership-based applicability decision.

The easy solve() API prepares an isolated copy with the Adapter’s recommended policy. Use solve_without_preparation() when the caller has prepared an exact input explicitly and wants the Adapter to require INPUT_CLASS membership without applying preparation. Adapter constructors likewise require an already applicable input. The ommx_pyscipopt_adapter.OMMXPySCIPOptAdapter Reference includes a complete custom-policy example from model construction to strict solve.

When encoding OMMX decision variables, use ommx_instance.used_decision_variables rather than the complete decision variable table. Backend-only auxiliary variables may be added separately. Preserve the correspondence between each used OMMX variable ID and its backend representation. Decoding must recover a value for every used OMMX variable, construct a ommx.State, and evaluate it against an unchanged ommx.Instance snapshot that is mathematically identical to the exact input encoded for the backend. The Instance owns population of fixed, dependent, and irrelevant variables and construction of the final ommx.Solution.

classmethod check_applicability(ommx_instance: Instance) → InstanceClassMembershipReport#: Check INPUT_CLASS membership without mutation.

abstractmethod decode(data: SolverOutput) → Solution#

classmethod recommended_preparation_policy() → PreparationPolicy#

Return a fresh policy recommended for this Adapter’s INPUT_CLASS.

classmethod require_applicable(ommx_instance: Instance) → InstanceClassMembershipReport#: Return the membership report or raise AdapterNotApplicableError.

classmethod solve(ommx_instance: Instance, *, diagnostics: DiagnosticsSink | None = None) → Solution#

Prepare and solve an isolated copy of an OMMX instance.

The input ommx_instance is never modified. The copy is prepared for INPUT_CLASS with recommended_preparation_policy(), then passed to solve_without_preparation().

classmethod solve_without_preparation(ommx_instance: Instance, *, diagnostics: DiagnosticsSink | None = None) → Solution#

Abstractmethod:

Solve an exact Adapter input without running Instance.prepare.

ommx_instance must belong to INPUT_CLASS. Implementations must reject non-members with AdapterNotApplicableError and must not prepare or otherwise modify the input instance.

INPUT_CLASS: ClassVar[InstanceClass]#: Required condition for an exact Adapter input.

property solver_input: SolverInput#

Abstractmethod:

SamplerInput#

SamplerOutput#

SolverInput#

SolverOutput#