dagsweep 0.1.1

Declarative, prefix-reusing parameter sweeps over a step DAG.

dagsweep

Declarative, prefix-reusing parameter sweeps over a step DAG. Problem-agnostic: it knows nothing about your algorithms — only ports and variants.

You describe each step as a connector (named inputs/outputs, optional, with a list of variants to sweep). dagsweep wires the steps by port name, topologically orders them, and sweeps every combination — computing each shared prefix exactly once and reusing it for every child. The reuse is keyed on the chosen variant indices (a cheap token), never on a hash of the data, so large intermediate objects cost nothing to cache.

Why

A grid sweep A × B × C is a prefix tree: consecutive configurations share a long prefix. Hand-written nested loops capture that reuse but are rigid — a new step means editing the loop. dagsweep keeps the reuse but makes the structure declarative: add a step by appending one Step.

Example

from dagsweep import Step, Pipeline

pipe = Pipeline([
    Step("rank",  run_rank,  consumes=("rules", "measures"),
         produces="ranked", variants=rankers),
    Step("prune", run_prune, consumes=("ranked",),
         produces="pruned", variants=pruners),
    Step("clf",   run_clf,   consumes=("pruned",),
         produces="model",  variants=classifiers),
])

results = pipe.run(
    seed={"rules": rules, "measures": im},
    sink=lambda ctx: evaluate(ctx["model"]),
)
# [("BordaRank+M1Prune+OrdinalClassifier", <metric>), ...]  one record per leaf

rank runs once per ranker, prune once per (ranker, pruner), clf once per leaf — automatically.

The one rule: steps must be pure

fn(variant, *inputs) -> output must not mutate its inputs after returning them. The executor memoizes and shares outputs across configurations; mutating a shared output in place corrupts siblings. Purity is also what makes per-step parallelism safe (see Step.parallel, reserved).

Concepts

• Port — a named value in the run context. A step produces one port and consumes zero or more. Ports not produced by any step are seeds, supplied to run.
• Variant — one choice on a step's sweep axis. variants=(None,) is a step with no algorithm choice (a fixed transform).
• Optional step — optional=True: when the chosen variant is None the step is skipped and its output is taken from fallback. Put None in variants to sweep "with and without" the step.
• Wiring is implicit — consumes/produces names form the DAG; declaration order does not matter, dependencies decide execution order.

Validation rejects duplicate writers, cycles, unreachable fallbacks, and missing seeds with clear errors.