import type { IRow, IViewField } from '../../../interfaces';
import type { IExplainContext, IExplanationStrength } from '../types';
/**
 * Shared base-model / explain-model framework for the model-based
 * explainers (contributing dimension & contributing measure), following the
 * Tableau Explain Data approach: hold out the selected mark, compare
 * cross-validated fit and held-out prediction with vs without the candidate
 * feature, and calibrate against permutations of the candidate.
 *
 * SCORING-FROZEN for the executor agent (see docs/explain-data-worklog.md §1).
 */
export declare const MIN_MARKS = 8;
export declare const PERMUTATIONS = 30;
export declare const PERMUTATION_P_THRESHOLD = 0.05;
export declare const MIN_SCORE = 0.15;
export declare function targetMeasure(ctx: IExplainContext): IViewField | null;
export interface IMarksTable {
    /** one entry per mark, stable order */
    markKeys: string[];
    /** index of the selected mark within markKeys */
    selectedIndex: number;
    /** target measure value per mark (respecting its aggregation) */
    y: number[];
    /** per-mark row counts */
    counts: number[];
    /** raw grouped rows bucketed per mark (for feature construction) */
    rowsByMark: IRow[][];
}
export declare function markKeyOf(row: IRow, ctx: IExplainContext): string;
/**
 * Bucket view-LOD (or finer) grouped rows into marks and derive the target
 * measure per mark from additive sums/counts. Returns null when the marks
 * table is unusable for modeling (too few marks, selected mark missing…).
 */
export declare function buildMarksTable(ctx: IExplainContext, rows: IRow[], target: IViewField): IMarksTable | null;
/**
 * One-hot base features from the view dimensions (drop-first per dimension).
 * When the base would be near-saturated (p > n/2 — e.g. a single view
 * dimension, where dummies memorize each mark), fall back to intercept-only:
 * cross-validated fit then honestly reflects "no generalizable structure".
 */
export declare function baseFeatures(ctx: IExplainContext, table: IMarksTable): number[][];
export interface ICandidateScore {
    score: number;
    fitGain: number;
    predGain: number;
    pValue: number;
    cvBase: number;
    cvExplain: number;
}
/** deterministic string hash for permutation seeding */
export declare function seedFromString(s: string): number;
/**
 * Score a candidate feature block against the base model:
 *   fitGain  — LOO-CV R² improvement on the non-selected marks
 *   predGain — held-out (selected mark) absolute-error improvement, in
 *              units of the training target's standard deviation
 *   pValue   — permutation calibration of the combined score
 */
export declare function scoreCandidate(baseX: number[][], candX: number[][], y: number[], selectedIndex: number, seed: number): ICandidateScore | null;
export declare function modelStrength(score: number): IExplanationStrength;
/** standardized column + its standardized square (second-order term) */
export declare function secondOrderFeatures(values: number[]): number[][];
/** marks scatter evidence: candidate feature vs target, gray context + accent selected */
export declare function scatterSpec(xValues: number[], y: number[], selectedIndex: number, xTitle: string, yTitle: string): Record<string, unknown>;