OMMX Python SDK 3.0.x

Note

Python SDK 3.0.0 contains breaking API changes. A migration guide is available in the Python SDK v2 to v3 Migration Guide.

Unreleased

Changes merged after the most recent release will be appended here as they land, and promoted to a new version section when the next release is cut.

3.0.0 Alpha 3

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

⚠ *_df accessors are methods + include= filter + sidecar DataFrames (#846)

Every *_df accessor on Instance / ParametricInstance / Solution / SampleSet is now a regular method instead of a #[getter] property. Existing call sites need parentheses:

# Before
df = solution.constraints_df

# After
df = solution.constraints_df()

The wide *_df methods take an include argument that gates the metadata / parameters column families. The default include=("metadata", "parameters") preserves the v2-equivalent wide shape:

solution.decision_variables_df()                       # core + metadata + parameters
solution.decision_variables_df(include=[])             # core only
solution.decision_variables_df(include=["metadata"])   # core + metadata
solution.decision_variables_df(include=["parameters"]) # core + parameters

Six new long-format / id-indexed sidecar accessors read directly from the SoA metadata stores. kind= selects the constraint family ("regular" / "indicator" / "one_hot" / "sos1", default "regular"):

• constraint_metadata_df(kind=...) — id-indexed (name / subscripts / description)

• constraint_parameters_df(kind=...) — long format ({kind}_constraint_id / key / value)

• constraint_provenance_df(kind=...) — long format ({kind}_constraint_id / step / source_kind / source_id)

• constraint_removed_reasons_df(kind=...) — long format ({kind}_constraint_id / reason / key / value)

• variable_metadata_df() — id-indexed

• variable_parameters_df() — long format

Sidecar index names are kind-qualified (regular_constraint_id / indicator_constraint_id / one_hot_constraint_id / sos1_constraint_id / variable_id) so accidental cross-id-space df.join() mistakes surface in df.head() and friends. Long-format *_parameters_df / *_removed_reasons_df rows are sorted by (id, key), and empty long-format DataFrames keep their column schema instead of returning a column-less frame.

⚠ removed_reason column gated by include= (#796, #847)

In v2.5.1 Solution.constraints_df carried a removed_reason column unconditionally. The initial include= gate of that column landed in 3.0.0a2 (#796), and 3.0.0a3 finalizes it into the kind= / include= / removed= dispatch shape documented above (#847): the column is opted in by "removed_reason" in include= (a unit flag that controls both the reason name and removed_reason.{key} parameter columns). Rows whose constraint was not removed before evaluation get NA in those columns.

# Before (2.5.1)
df = solution.constraints_df  # contains a 'removed_reason' column

# After (3.0.0a3 — `*_df` are now methods)
df = solution.constraints_df()  # no removed_reason column
df = solution.constraints_df(include=("metadata", "parameters", "removed_reason"))
# ↳ adds removed_reason / removed_reason.{key} (NA for active rows)

The same kind= / include= shape applies on SampleSet. On Instance and ParametricInstance, removed=True returns active + removed rows in one DataFrame and auto-sets "removed_reason" so removed rows are distinguishable.

⚠ to_bytes / from_bytes removed from non-top-level types (#845)

Bytes serialization is removed from the following component-level types:

• Function, Linear, Quadratic, Polynomial

• Parameter

• NamedFunction, EvaluatedNamedFunction, SampledNamedFunction

• DecisionVariable, EvaluatedDecisionVariable, SampledDecisionVariable

These methods originally existed to ferry values across the Python ↔ Rust boundary back when the Python SDK had its own protobuf-based wrapper layer and had to serialize on every hop. With the v3 transition to direct PyO3 re-exports the boundary disappears, so element-level bytes round-trips no longer serve a purpose, and keeping them aligned with the upcoming metadata-storage redesign would only add maintenance cost. to_bytes / from_bytes remain available on the container types (Instance, ParametricInstance, Solution, SampleSet) and on the cross-evaluate DTOs (State, Samples, Parameters) — use those when you need to persist or exchange data on disk or over the wire.

🆕 Write-through metadata wrappers: AttachedConstraint / AttachedDecisionVariable (#849, #850, #852)

Instance.add_constraint / instance.constraints[id] and the matching accessors on ParametricInstance now return write-through handles bound to the parent host instead of snapshot copies. Reads pull live data from the host and metadata setters write straight to its SoA metadata store, so two handles pointing at the same id observe the same state.

c = instance.add_constraint(x + y == 0)         # AttachedConstraint
c.set_name("budget")                             # writes through to instance
assert instance.constraints[c.constraint_id].name == "budget"

Five write-through types ship: AttachedConstraint, AttachedIndicatorConstraint, AttachedOneHotConstraint, AttachedSos1Constraint, and AttachedDecisionVariable. Constraint and DecisionVariable are unchanged in shape — they remain the snapshot wrappers used for modeling input (operator overloading, Instance.from_components). Each AttachedX exposes .detach() to obtain an equivalent snapshot when you need to break the back-reference to the host.

As part of the same change, instance.decision_variables now returns list[AttachedDecisionVariable] (previously list[DecisionVariable] snapshots), aligning with instance.constraints and the special-constraint accessors.

🆕 OpenTelemetry-based tracing and profiling (#816, #823, #826, #828, #829)

The legacy log + pyo3-log → Python logging bridge is replaced by a tracing + pyo3-tracing-opentelemetry pipeline, so the Rust core’s spans can now be consumed through the Python OTel SDK.

Two entry points ship under ommx.tracing:

• %%ommx_trace — a Jupyter cell magic that renders a per-cell span tree and a Chrome Trace JSON download link

• capture_trace / @traced — a context manager and decorator for the same workflow from regular Python scripts, tests, and CI

See Tracing and Profiling for the full walkthrough, configuring your own TracerProvider, and troubleshooting.

🆕 Tracing spans in solver/sampler adapters (#833)

Every OMMX adapter now emits three OpenTelemetry spans per solve/sample call, so the OTel tracing pipeline above can attribute wall-clock time to the three phases an adapter actually spends time in:

• convert — OMMX Instance → solver-native problem translation

• solve / sample — the call into the underlying solver / sampler itself

• decode — decoding the solver’s response back to Solution / SampleSet (Rust-side evaluate spans nest underneath)

Each adapter uses its own tracer name, so runs from different solvers are easy to distinguish in the tree view:

Adapter	Tracer	Spans
ommx-pyscipopt-adapter	ommx.adapter.pyscipopt	convert / solve / decode
ommx-highs-adapter	ommx.adapter.highs	convert / solve / decode
ommx-python-mip-adapter	ommx.adapter.python_mip	convert / solve / decode
ommx-openjij-adapter	ommx.adapter.openjij	convert / sample / decode

from ommx.tracing import capture_trace
from ommx_pyscipopt_adapter import OMMXPySCIPOptAdapter

with capture_trace() as trace:
    solution = OMMXPySCIPOptAdapter.solve(instance)

print(trace.text_tree())  # shows convert / solve / decode with durations

Spans are emitted through the standard OpenTelemetry API, so they are a no-op when no TracerProvider is installed — there is no runtime cost for users who do not opt in.

🆕 Function.evaluate_bound is now available from Python (#831)

Function.evaluate_bound is now exposed on Function. Given per-variable bounds, it returns a Bound that contains the range of the function value — useful when deriving feasibility bounds or doing simple presolve on the Python side.

from ommx.v1 import Function, Linear, Bound

f = Function(Linear(terms={1: 2}, constant=3))  # 2*x1 + 3
b = f.evaluate_bound({1: Bound(0.0, 2.0)})
# b.lower == 3.0, b.upper == 7.0

The bound is computed monomial-wise and summed, so it is a sound over-approximation of the true range but is not guaranteed to be tight when multiple terms share variables (the classic dependency problem in interval arithmetic). Variable IDs missing from bounds are treated as unbounded.

3.0.0 Alpha 2

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

⚠ Removal of the Constraint.id field (#806)

The id field (along with the .id getter, set_id(), and id= constructor argument) is removed from Constraint and its variants (IndicatorConstraint / OneHotConstraint / Sos1Constraint / EvaluatedConstraint / SampledConstraint / RemovedConstraint). A constraint’s ID now exists only as the key of the dict[int, Constraint] passed to Instance.from_components.

# Before (2.5.1)
c = Constraint(function=x + y, equality=Constraint.EQUAL_TO_ZERO, id=5)
Instance.from_components(..., constraints=[c], ...)

# After (3.0.0a2)
c = Constraint(function=x + y, equality=Constraint.EQUAL_TO_ZERO)
Instance.from_components(..., constraints={5: c}, ...)

Global ID counters (next_constraint_id and friends) and per-constraint to_bytes / from_bytes are also removed. For full details and migration steps, see the Python SDK v2 to v3 Migration Guide.

🆕 First-class special constraint types (#789, #790, #795, #796, #798)

In addition to regular constraints, the following three special constraint types are now first-class citizens — they can be passed to Instance.from_components via indicator_constraints= / one_hot_constraints= / sos1_constraints=, and read back through constraints_df() / constraints_df() with kind= selecting the family.

• IndicatorConstraint — conditional constraint on a binary variable (new)

• OneHotConstraint — replaces the previous ConstraintHints.OneHot metadata

• Sos1Constraint — replaces the previous ConstraintHints.Sos1 metadata

For concrete usage, evaluation-result access, and the Indicator relax / restore workflow, see Special Constraints.

Accordingly, the legacy ConstraintHints / OneHot / Sos1 classes, the Instance.constraint_hints property, and the PySCIPOpt Adapter’s use_sos1 flag are removed.

🆕 Adapter Capability Model (#790, #805, #810, #811, #814)

Alongside the special constraint types, adapters now declare their own supported capabilities via an ADDITIONAL_CAPABILITIES class attribute. When super().__init__(instance) is called, any undeclared special constraint is automatically converted to regular constraints (Big-M for Indicator / SOS1, linear equality for OneHot) before the instance reaches the solver.

Existing OMMX Adapters must be updated for Python SDK 3.0.0 to call super().__init__(instance). Currently the PySCIPOpt Adapter declares support for Indicator and SOS1.

For details and the manual conversion APIs, see Adapter Capability Model and Conversions.

🔄 numpy scalar support (#794)

The Function constructor now accepts numpy.integer and numpy.floating values. In v2.5.1, Function(numpy.int64(3)) raised TypeError.

3.0.0 Alpha 1

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

Complete Rust re-export of ommx.v1 and ommx.artifact types (#770, #771, #774, #775, #782)

Python SDK 3.0.0 is fully based on Rust/PyO3. In 2.0.0, the core implementation was rewritten in Rust while Python wrapper classes remained for compatibility. In 3.0.0, those Python wrappers are removed entirely — all types in ommx.v1 and ommx.artifact are now direct re-exports from Rust, and the protobuf Python runtime dependency is eliminated. The .raw attribute that previously provided access to the underlying PyO3 implementation has also been removed.

Migration to Sphinx and ReadTheDocs hosting (#780, #785)

In v2, the Sphinx-based API Reference and Jupyter Book-based documentation were each hosted on GitHub Pages. In v3, documentation has been fully migrated to Sphinx and is now hosted on ReadTheDocs. GitHub Pages will continue to host the documentation as of v2.5.1, but all future updates will be on ReadTheDocs only.