File size: 2,277 Bytes
ab849c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | """Experiment manager — tracks runs with full provenance."""
from __future__ import annotations
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from optos.constants import ENGINE_VERSION
from optos.engine import OptimizationEngine
from optos.models import ExperimentRun
class ExperimentManager:
def __init__(self, time_limit_sec: float = 15.0) -> None:
self.engine = OptimizationEngine(time_limit_sec)
self._history: list[ExperimentRun] = []
def run(
self,
problem_type: str,
size: str = "medium",
seed: int = 42,
time_limit: float | None = None,
constraints: dict[str, Any] | None = None,
objectives: dict[str, Any] | None = None,
) -> ExperimentRun:
experiment = self.engine.run_experiment(
problem_type, size, seed, time_limit, constraints, objectives,
)
self._history.append(experiment)
return experiment
def history(self) -> list[ExperimentRun]:
return list(self._history)
def export_run(self, run: ExperimentRun) -> dict[str, Any]:
return {
"exported_at": datetime.now(timezone.utc).isoformat(),
"engine_version": ENGINE_VERSION,
"run": run.to_dict(),
}
def save_run(self, run: ExperimentRun, path: Path) -> None:
path.write_text(json.dumps(self.export_run(run), indent=2), encoding="utf-8")
def summary_table(self) -> list[dict[str, Any]]:
rows = []
for run in self._history:
for r in run.results:
rows.append({
"run_id": run.run_id,
"problem_type": run.instance.problem_type,
"size": run.instance.size,
"method": r.method_label,
"category": r.method_category,
"objective": r.metrics.objective_value,
"gap_pct": r.metrics.optimality_gap,
"time_sec": r.metrics.elapsed_time_sec,
"feasible": r.metrics.feasible,
"winner": r.method_id == run.winner,
})
return rows
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