/**
 * @copyright Sister Software
 * @license AGPL-3.0
 * @author Teffen Ellis, et al.
 *
 *   #244 M2 Phase 1 — POST-HOC open-set scoring on the EXISTING (shipped) coarse-placer, NO retrain.
 *   The OA-breadth verdict found the 12-way softmax's own max-prob detector tops out at ~88/88 on
 *   the leave-one-family-out probe (in-map accuracy vs off-map HELDOUT-family caught). This asks:
 *   does a DIFFERENT open-set score, read off the same frozen weights, clear the 90/90 the softmax
 *   can't?
 *
 *   The 11-way routing (argmax over the in-map classes) is FIXED — the model is unchanged. The only
 *   thing each score changes is the REJECT decision (keep the in-map route vs. abstain → OTHER). We
 *   sweep each score's threshold and trace its (in-map accuracy, heldout-caught) Pareto.
 *
 *   Scores compared (all functions of the frozen 12 logits / 11 in-map logits `z`):
 *
 *   - Maxprob : softmax max over the 11 in-map classes (the verdict's baseline detector)
 *   - P_inmap : 1 - P(OTHER) (trust the model's own OTHER head)
 *   - Energy : logsumexp(z) (free-energy; higher = more in-map)
 *   - Maxlogit : max(z)
 *   - Maha : -min_c (z-μ_c)ᵀ Σ⁻¹ (z-μ_c), class-conditional Gaussians (tied Σ) fit on in-map TRAIN
 *       logits (Lee et al. 2018, in the 11-dim in-map-logit space — the linear model's only dense
 *       representation). Higher = closer to the in-map manifold.
 *
 *   In-map accuracy = of the 11-country test rows, fraction NOT rejected AND argmax-in-map == truth.
 *   heldout caught = of the never-trained off-map families (baltic/oceania/middle-east), fraction
 *   rejected. Both move with the threshold; the Pareto is the whole story.
 *
 *   Run: `mailwoman placer eval openset [--model <dir>] [--out-md <path>]`
 */
type ScoreKey = "maxprob" | "p_inmap" | "energy" | "maxlogit" | "maha";
/**
 * Options for {@linkcode evalOpenSet}.
 */
export interface EvalOpenSetOptions {
    /**
     * Model artifact dir. Default `$MAILWOMAN_DATA_ROOT/coarse-placer/model`.
     */
    model?: string;
    /**
     * Dataset dir. Default `<repo>/data/coarse-placer`.
     */
    data?: string;
    /**
     * Mahalanobis fit rows per class. Default 2000.
     */
    fitPerClass?: number;
    /**
     * Also write the markdown report here.
     */
    outMd?: string;
}
/**
 * Result of {@linkcode evalOpenSet}.
 */
export interface EvalOpenSetResult {
    /**
     * Best score by the honest dev→test balanced min.
     */
    winner: ScoreKey;
    honestMin: number;
    clears90: boolean;
    markdown: string;
}
/**
 * Coarse-placer post-hoc open-set score comparison — see the module doc. Emits the markdown report to stdout.
 */
export declare function evalOpenSet(options?: EvalOpenSetOptions, report?: (line: string) => void): Promise<EvalOpenSetResult>;
export {};
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