@astermind/astermind-elm/dist/core/evaluateEnsembleRetrieval.d.ts

JavaScript Extreme Learning Machine (ELM) library for browser and Node.js.

import { ELMChain } from "./ELMChain";
/** Minimal record shape for this evaluation (kept independent of the store). */
export interface EmbeddingRecord {
    id?: string;
    embedding: number[] | Float32Array;
    metadata: {
        label?: string;
        [k: string]: any;
    };
}
export type EnsembleMetric = "cosine" | "dot";
export type ScoreAgg = "mean" | "sum" | "max" | "weighted";
export interface EnsembleEvalOptions {
    /** Similarity metric across chain embeddings (default: 'cosine') */
    metric?: EnsembleMetric;
    /** How to aggregate scores across chains (default: 'mean') */
    aggregate?: ScoreAgg;
    /** Per-chain weights (only used when aggregate='weighted'). Length must equal chains.length. */
    weights?: number[];
    /** K for Recall@K and Top-K lists (default: 5) */
    k?: number;
    /** If true, drop queries with missing/empty label from metrics (default: true) */
    ignoreUnlabeledQueries?: boolean;
    /** If true, also return per-label stats (slower) */
    reportPerLabel?: boolean;
    /** If true, attach top-K rankings per query to result (slower) */
    returnRankings?: boolean;
    /** Progress callback every N queries (default: 10) */
    logEvery?: number;
}
export interface PerLabelStats {
    count: number;
    hitsAt1: number;
    hitsAtK: number;
    mrrSum: number;
}
export interface QueryRanking {
    queryIndex: number;
    queryId?: string;
    label: string;
    topK: Array<{
        label: string;
        score: number;
    }>;
    correctRank: number;
}
export interface EnsembleEvalResult {
    /** Queries that contributed to the metrics (after filtering) */
    usedQueries: number;
    recallAt1: number;
    recallAtK: number;
    mrr: number;
    /** Optional breakdown by label */
    perLabel?: Record<string, {
        support: number;
        recallAt1: number;
        recallAtK: number;
        mrr: number;
    }>;
    /** Optional top-K rankings per query (debug/inspection) */
    rankings?: QueryRanking[];
}
export declare function evaluateEnsembleRetrieval(queries: EmbeddingRecord[], reference: EmbeddingRecord[], chains: ELMChain[], k: number, options?: EnsembleEvalOptions): EnsembleEvalResult;