366f7a08c4
* feat: add cognitive eval harness with CLI integration 20 eval cases across 8 categories (definition, comparison, cause, procedure, recall, correction, verification, unknown). Metrics: intent accuracy, term capture, surface groundedness, versor closure, trace determinism. CLI: `core eval cognition [--json] [--report PATH]`. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add operator calibration replay with deterministic grid search Bounded parameter tuning via eval replay evidence. Grid search over salience_top_k and inhibition_threshold with invariant regression guard (versor closure must not regress). Frozen CalibrationParams, before/after metrics, no pack or identity mutation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
"""Calibration parameter space — bounded, deterministic, immutable."""
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from __future__ import annotations
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from dataclasses import dataclass
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from itertools import product
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@dataclass(frozen=True, slots=True)
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class CalibrationParams:
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salience_top_k: int = 16
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inhibition_threshold: float = 0.3
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teaching_retrieval_limit: int = 8
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def as_dict(self) -> dict[str, int | float]:
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return {
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"salience_top_k": self.salience_top_k,
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"inhibition_threshold": self.inhibition_threshold,
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"teaching_retrieval_limit": self.teaching_retrieval_limit,
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}
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DEFAULT_PARAMS = CalibrationParams()
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PARAM_GRID: dict[str, tuple] = {
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"salience_top_k": (8, 12, 16),
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"inhibition_threshold": (0.2, 0.3, 0.4),
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}
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def grid_candidates(
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grid: dict[str, tuple] | None = None,
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base: CalibrationParams = DEFAULT_PARAMS,
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) -> tuple[CalibrationParams, ...]:
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"""Generate all candidate parameter sets from a grid.
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Each candidate varies exactly one axis from the base; the grid is
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a deterministic Cartesian product over the provided axes.
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"""
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g = grid or PARAM_GRID
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keys = sorted(g.keys())
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values = [g[k] for k in keys]
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candidates = []
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for combo in product(*values):
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overrides = dict(zip(keys, combo))
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candidate = CalibrationParams(
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salience_top_k=overrides.get("salience_top_k", base.salience_top_k),
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inhibition_threshold=overrides.get("inhibition_threshold", base.inhibition_threshold),
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teaching_retrieval_limit=base.teaching_retrieval_limit,
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)
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candidates.append(candidate)
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return tuple(candidates)
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