import { ClassificationMetrics, TrainedClassifier } from './graph-classifier.js';
/**
 * Configuration for the classification evaluation.
 */
export interface ClassificationEvaluationConfig {
    /** Number of training graphs per synthetic class (default: 50) */
    trainPerClass?: number;
    /** Number of test graphs per synthetic class (default: 20) */
    testPerClass?: number;
    /** Minimum node count for generated graphs (default: 30) */
    minNodes?: number;
    /** Maximum node count for generated graphs (default: 100) */
    maxNodes?: number;
    /** Base seed for reproducibility (default: 42) */
    seed?: number;
}
/**
 * Full evaluation result including metrics and the trained classifier.
 */
export interface ClassificationEvaluationResult {
    /** Multi-class classification metrics */
    metrics: ClassificationMetrics;
    /** The trained classifier (for reuse or inspection) */
    classifier: TrainedClassifier;
    /** Number of training examples used */
    trainingSize: number;
    /** Number of test examples evaluated */
    testSize: number;
}
/**
 * Run the full classification evaluation pipeline.
 *
 * 1. Generates training graphs for ER, BA, and WS classes.
 * 2. Loads real-world benchmark graphs for the real-world class.
 * 3. Extracts structural features from all graphs.
 * 4. Trains a nearest-centroid classifier with z-score normalisation.
 * 5. Generates test graphs and classifies them.
 * 6. Computes and returns multi-class metrics.
 *
 * @param config - Optional evaluation configuration
 * @returns Classification metrics including accuracy, per-class F1, and macro F1
 */
export declare const runClassificationEvaluation: (config?: ClassificationEvaluationConfig) => Promise<ClassificationEvaluationResult>;
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