1238 lines
38 KiB
TypeScript
1238 lines
38 KiB
TypeScript
/**
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* DSPy Benchmark Comparison Framework
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*
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* Comprehensive benchmarking suite for comparing multiple models across
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* quality, performance, cost, learning, and diversity metrics.
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*
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* Features:
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* - Multi-model comparison with statistical significance
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* - Scalability testing (100 to 100K samples)
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* - Cost-effectiveness analysis
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* - Quality convergence tracking
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* - Diversity analysis
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* - Pareto frontier optimization
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* - Use case recommendations
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*/
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import { performance } from 'perf_hooks';
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import * as fs from 'fs/promises';
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import * as path from 'path';
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// ============================================================================
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// Types & Interfaces
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// ============================================================================
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interface ModelConfig {
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name: string;
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provider: 'openrouter' | 'gemini' | 'anthropic' | 'openai';
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model: string;
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costPer1kTokens: number;
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maxTokens: number;
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apiKey?: string;
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}
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interface QualityMetrics {
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accuracy: number;
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coherence: number;
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validity: number;
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consistency: number;
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completeness: number;
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overall: number;
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}
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interface PerformanceMetrics {
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latencyP50: number;
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latencyP95: number;
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latencyP99: number;
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avgLatency: number;
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minLatency: number;
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maxLatency: number;
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throughput: number;
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successRate: number;
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}
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interface CostMetrics {
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totalCost: number;
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costPerSample: number;
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costPerQualityPoint: number;
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tokensUsed: number;
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efficiency: number;
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}
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interface LearningMetrics {
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improvementRate: number;
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convergenceSpeed: number;
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learningCurve: number[];
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plateauGeneration: number;
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finalQuality: number;
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}
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interface DiversityMetrics {
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uniqueValues: number;
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patternVariety: number;
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distributionEntropy: number;
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coverageScore: number;
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noveltyRate: number;
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}
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interface BenchmarkResult {
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modelName: string;
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sampleSize: number;
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quality: QualityMetrics;
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performance: PerformanceMetrics;
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cost: CostMetrics;
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learning: LearningMetrics;
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diversity: DiversityMetrics;
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timestamp: string;
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duration: number;
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}
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interface ComparisonResult {
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models: string[];
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winner: {
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overall: string;
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quality: string;
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performance: string;
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cost: string;
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learning: string;
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diversity: string;
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};
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statisticalSignificance: {
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[key: string]: number; // p-values
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};
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paretoFrontier: string[];
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recommendations: {
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[useCase: string]: string;
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};
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}
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interface ScalabilityResult {
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modelName: string;
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sampleSizes: number[];
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latencies: number[];
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throughputs: number[];
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costs: number[];
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qualities: number[];
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scalingEfficiency: number;
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}
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// ============================================================================
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// Mock Data Generator
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// ============================================================================
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class MockModelSimulator {
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private modelConfig: ModelConfig;
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private baseQuality: number;
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private learningRate: number;
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private generation: number = 0;
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constructor(config: ModelConfig) {
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this.modelConfig = config;
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// Different models have different base qualities
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this.baseQuality = this.getBaseQuality(config.name);
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this.learningRate = this.getLearningRate(config.name);
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}
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private getBaseQuality(modelName: string): number {
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const qualities: { [key: string]: number } = {
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'gpt-4': 0.85,
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'claude-3.5-sonnet': 0.88,
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'gemini-pro': 0.82,
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'gpt-3.5-turbo': 0.75,
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'llama-3-70b': 0.78,
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'mixtral-8x7b': 0.76,
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};
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return qualities[modelName] || 0.70;
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}
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private getLearningRate(modelName: string): number {
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const rates: { [key: string]: number } = {
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'gpt-4': 0.02,
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'claude-3.5-sonnet': 0.025,
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'gemini-pro': 0.018,
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'gpt-3.5-turbo': 0.03,
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'llama-3-70b': 0.022,
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'mixtral-8x7b': 0.028,
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};
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return rates[modelName] || 0.02;
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}
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async generateBatch(count: number, schema: any): Promise<any[]> {
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// Simulate API latency based on model
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const baseLatency = this.getBaseLatency();
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const latency = baseLatency + Math.random() * (baseLatency * 0.3);
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await new Promise(resolve => setTimeout(resolve, latency));
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const data: any[] = [];
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for (let i = 0; i < count; i++) {
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data.push(this.generateSample(schema));
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}
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// Simulate learning improvement
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this.generation++;
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return data;
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}
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private getBaseLatency(): number {
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const latencies: { [key: string]: number } = {
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'gpt-4': 1500,
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'claude-3.5-sonnet': 1200,
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'gemini-pro': 800,
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'gpt-3.5-turbo': 500,
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'llama-3-70b': 600,
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'mixtral-8x7b': 400,
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};
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return latencies[this.modelConfig.model] || 1000;
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}
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private generateSample(schema: any): any {
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const sample: any = {};
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for (const [key, type] of Object.entries(schema)) {
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sample[key] = this.generateField(key, type as string);
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}
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return sample;
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}
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private generateField(key: string, type: string): any {
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if (type.includes('UUID')) {
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return `${Math.random().toString(36).substring(2, 15)}-${Math.random().toString(36).substring(2, 15)}`;
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}
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if (type.includes('email')) {
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return `user${Math.floor(Math.random() * 10000)}@example.com`;
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}
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if (type.includes('name')) {
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const names = ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace', 'Henry', 'Ivy', 'Jack'];
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const lastNames = ['Smith', 'Johnson', 'Williams', 'Brown', 'Jones', 'Garcia', 'Miller', 'Davis', 'Rodriguez'];
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return `${names[Math.floor(Math.random() * names.length)]} ${lastNames[Math.floor(Math.random() * lastNames.length)]}`;
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}
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if (type.includes('number')) {
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const match = type.match(/\((\d+)-(\d+)\)/);
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if (match) {
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const min = parseInt(match[1]);
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const max = parseInt(match[2]);
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return Math.floor(Math.random() * (max - min + 1)) + min;
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}
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return Math.floor(Math.random() * 100);
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}
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return `sample_${key}_${Math.random().toString(36).substring(2, 9)}`;
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}
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getCurrentQuality(): number {
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const learned = Math.min(0.15, this.generation * this.learningRate);
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return Math.min(0.98, this.baseQuality + learned);
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}
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getConfig(): ModelConfig {
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return this.modelConfig;
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}
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}
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// ============================================================================
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// Statistical Utilities
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// ============================================================================
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class StatisticalAnalyzer {
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/**
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* Calculate mean of array
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*/
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static mean(values: number[]): number {
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if (values.length === 0) return 0;
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return values.reduce((sum, val) => sum + val, 0) / values.length;
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}
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/**
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* Calculate standard deviation
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*/
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static stdDev(values: number[]): number {
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const avg = this.mean(values);
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const squareDiffs = values.map(value => Math.pow(value - avg, 2));
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return Math.sqrt(this.mean(squareDiffs));
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}
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/**
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* Calculate percentile
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*/
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static percentile(values: number[], p: number): number {
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if (values.length === 0) return 0;
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const sorted = [...values].sort((a, b) => a - b);
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const index = Math.ceil((p / 100) * sorted.length) - 1;
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return sorted[Math.max(0, index)];
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}
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/**
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* Perform t-test to determine statistical significance
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* Returns p-value
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*/
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static tTest(sample1: number[], sample2: number[]): number {
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const mean1 = this.mean(sample1);
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const mean2 = this.mean(sample2);
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const std1 = this.stdDev(sample1);
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const std2 = this.stdDev(sample2);
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const n1 = sample1.length;
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const n2 = sample2.length;
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const pooledStd = Math.sqrt(
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((n1 - 1) * Math.pow(std1, 2) + (n2 - 1) * Math.pow(std2, 2)) / (n1 + n2 - 2)
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);
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const tStat = Math.abs(mean1 - mean2) / (pooledStd * Math.sqrt(1/n1 + 1/n2));
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// Simplified p-value approximation
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const df = n1 + n2 - 2;
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const pValue = 2 * (1 - this.tDistribution(tStat, df));
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return pValue;
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}
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/**
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* Simplified t-distribution CDF approximation
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*/
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private static tDistribution(t: number, df: number): number {
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// Simplified approximation for demonstration
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const x = df / (df + t * t);
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return 1 - 0.5 * Math.pow(x, df / 2);
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}
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/**
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* Calculate Shannon entropy for diversity measurement
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*/
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static entropy(values: any[]): number {
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const counts = new Map<string, number>();
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for (const val of values) {
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const key = JSON.stringify(val);
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counts.set(key, (counts.get(key) || 0) + 1);
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}
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let entropy = 0;
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const total = values.length;
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const countValues = Array.from(counts.values());
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for (const count of countValues) {
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const p = count / total;
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entropy -= p * Math.log2(p);
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}
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return entropy;
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}
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}
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// ============================================================================
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// Benchmark Suite
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// ============================================================================
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export class BenchmarkSuite {
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private models: MockModelSimulator[] = [];
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private outputDir: string = './training/results/benchmarks';
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private results: BenchmarkResult[] = [];
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constructor(outputDir?: string) {
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if (outputDir) {
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this.outputDir = outputDir;
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}
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}
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/**
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* Add a model configuration to the benchmark suite
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*/
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addModel(config: ModelConfig): void {
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this.models.push(new MockModelSimulator(config));
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}
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/**
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* Add multiple common models for quick testing
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*/
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addCommonModels(): void {
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const commonModels: ModelConfig[] = [
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{ name: 'GPT-4', provider: 'openai', model: 'gpt-4', costPer1kTokens: 0.03, maxTokens: 8192 },
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{ name: 'Claude 3.5 Sonnet', provider: 'anthropic', model: 'claude-3.5-sonnet', costPer1kTokens: 0.015, maxTokens: 200000 },
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{ name: 'Gemini Pro', provider: 'gemini', model: 'gemini-pro', costPer1kTokens: 0.0005, maxTokens: 32768 },
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{ name: 'GPT-3.5 Turbo', provider: 'openai', model: 'gpt-3.5-turbo', costPer1kTokens: 0.0015, maxTokens: 16384 },
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{ name: 'Llama 3 70B', provider: 'openrouter', model: 'llama-3-70b', costPer1kTokens: 0.0008, maxTokens: 8192 },
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{ name: 'Mixtral 8x7B', provider: 'openrouter', model: 'mixtral-8x7b', costPer1kTokens: 0.0005, maxTokens: 32768 },
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];
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for (const config of commonModels) {
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this.addModel(config);
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}
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}
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/**
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* Run comprehensive comparison across all models
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*/
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async runModelComparison(sampleSize: number = 1000): Promise<ComparisonResult> {
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console.log(`\n🔬 Running Model Comparison (${sampleSize} samples)`);
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console.log('='.repeat(70));
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await fs.mkdir(this.outputDir, { recursive: true });
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const schema = {
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id: 'UUID',
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name: 'full name',
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email: 'valid email',
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age: 'number (18-80)',
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occupation: 'job title',
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description: 'text (50-200 words)',
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};
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this.results = [];
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for (const model of this.models) {
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console.log(`\nTesting ${model.getConfig().name}...`);
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const result = await this.benchmarkModel(model, sampleSize, schema);
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this.results.push(result);
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console.log(` Quality: ${result.quality.overall.toFixed(3)}`);
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console.log(` Latency P95: ${result.performance.latencyP95.toFixed(0)}ms`);
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console.log(` Cost/Sample: $${result.cost.costPerSample.toFixed(6)}`);
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console.log(` Diversity: ${result.diversity.coverageScore.toFixed(3)}`);
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}
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return this.compareResults();
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}
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/**
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* Test scalability from 100 to 100K samples
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*/
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async runScalabilityTest(): Promise<ScalabilityResult[]> {
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console.log('\n📊 Running Scalability Test');
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console.log('='.repeat(70));
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const sampleSizes = [100, 500, 1000, 5000, 10000, 50000, 100000];
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const results: ScalabilityResult[] = [];
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const schema = {
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id: 'UUID',
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name: 'full name',
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email: 'valid email',
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};
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for (const model of this.models) {
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console.log(`\nTesting ${model.getConfig().name}...`);
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const latencies: number[] = [];
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const throughputs: number[] = [];
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const costs: number[] = [];
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const qualities: number[] = [];
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for (const size of sampleSizes) {
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console.log(` ${size} samples...`);
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const start = performance.now();
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const data = await model.generateBatch(size, schema);
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const duration = performance.now() - start;
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const latency = duration / size;
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const throughput = (size / duration) * 1000;
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const quality = model.getCurrentQuality();
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const cost = (size * 100 * model.getConfig().costPer1kTokens) / 1000; // Assume 100 tokens per sample
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latencies.push(latency);
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throughputs.push(throughput);
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costs.push(cost);
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qualities.push(quality);
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console.log(` Latency: ${latency.toFixed(2)}ms, Throughput: ${throughput.toFixed(0)}/s`);
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}
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// Calculate scaling efficiency (lower is better, close to 1.0 is linear)
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const scalingEfficiency = latencies[latencies.length - 1] / latencies[0];
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results.push({
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modelName: model.getConfig().name,
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sampleSizes,
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latencies,
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throughputs,
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costs,
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qualities,
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scalingEfficiency,
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});
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}
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await this.saveScalabilityResults(results);
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return results;
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}
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/**
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* Analyze cost-effectiveness across models
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*/
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async runCostAnalysis(): Promise<void> {
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console.log('\n💰 Running Cost Analysis');
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console.log('='.repeat(70));
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if (this.results.length === 0) {
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await this.runModelComparison(1000);
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}
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// Sort by cost per quality point
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const sortedByCost = [...this.results].sort(
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(a, b) => a.cost.costPerQualityPoint - b.cost.costPerQualityPoint
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);
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console.log('\n📈 Cost-Effectiveness Ranking:');
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console.log('-'.repeat(70));
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for (let i = 0; i < sortedByCost.length; i++) {
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const result = sortedByCost[i];
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console.log(`${i + 1}. ${result.modelName}`);
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console.log(` Cost/Sample: $${result.cost.costPerSample.toFixed(6)}`);
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console.log(` Cost/Quality: $${result.cost.costPerQualityPoint.toFixed(6)}`);
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console.log(` Quality: ${result.quality.overall.toFixed(3)}`);
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console.log(` Efficiency: ${result.cost.efficiency.toFixed(3)}`);
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console.log();
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}
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}
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/**
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* Measure quality convergence and learning rates
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*/
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async runQualityConvergence(generations: number = 10): Promise<void> {
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console.log('\n🎯 Running Quality Convergence Test');
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console.log('='.repeat(70));
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const schema = {
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id: 'UUID',
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name: 'full name',
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email: 'valid email',
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age: 'number (18-80)',
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};
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const convergenceData: any[] = [];
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for (const model of this.models) {
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console.log(`\nTesting ${model.getConfig().name}...`);
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const qualities: number[] = [];
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for (let gen = 0; gen < generations; gen++) {
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await model.generateBatch(100, schema);
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const quality = model.getCurrentQuality();
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qualities.push(quality);
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if (gen % 2 === 0) {
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console.log(` Generation ${gen}: Quality ${quality.toFixed(3)}`);
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}
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}
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// Calculate convergence metrics
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const improvementRate = (qualities[qualities.length - 1] - qualities[0]) / generations;
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const plateauGen = this.findPlateauGeneration(qualities);
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convergenceData.push({
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modelName: model.getConfig().name,
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qualities,
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improvementRate,
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plateauGeneration: plateauGen,
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finalQuality: qualities[qualities.length - 1],
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});
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console.log(` Improvement Rate: ${(improvementRate * 100).toFixed(2)}%/gen`);
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console.log(` Plateau at Generation: ${plateauGen}`);
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}
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await this.saveConvergenceData(convergenceData);
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}
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/**
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* Analyze data diversity and variety
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*/
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async runDiversityAnalysis(sampleSize: number = 5000): Promise<void> {
|
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console.log('\n🎨 Running Diversity Analysis');
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console.log('='.repeat(70));
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const schema = {
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id: 'UUID',
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name: 'full name',
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|
email: 'valid email',
|
|
age: 'number (18-80)',
|
|
occupation: 'job title',
|
|
};
|
|
|
|
for (const model of this.models) {
|
|
console.log(`\nAnalyzing ${model.getConfig().name}...`);
|
|
|
|
const data = await model.generateBatch(sampleSize, schema);
|
|
const diversity = this.calculateDiversityMetrics(data);
|
|
|
|
console.log(` Unique Values: ${diversity.uniqueValues}`);
|
|
console.log(` Pattern Variety: ${diversity.patternVariety.toFixed(3)}`);
|
|
console.log(` Entropy: ${diversity.distributionEntropy.toFixed(3)}`);
|
|
console.log(` Coverage: ${diversity.coverageScore.toFixed(3)}`);
|
|
console.log(` Novelty Rate: ${diversity.noveltyRate.toFixed(3)}`);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Benchmark a single model
|
|
*/
|
|
private async benchmarkModel(
|
|
model: MockModelSimulator,
|
|
sampleSize: number,
|
|
schema: any
|
|
): Promise<BenchmarkResult> {
|
|
const startTime = performance.now();
|
|
const latencies: number[] = [];
|
|
const allData: any[] = [];
|
|
|
|
// Run multiple batches to collect performance data
|
|
const batchSize = 100;
|
|
const batches = Math.ceil(sampleSize / batchSize);
|
|
|
|
for (let i = 0; i < batches; i++) {
|
|
const batchStart = performance.now();
|
|
const data = await model.generateBatch(Math.min(batchSize, sampleSize - i * batchSize), schema);
|
|
const batchLatency = performance.now() - batchStart;
|
|
|
|
latencies.push(batchLatency);
|
|
allData.push(...data);
|
|
}
|
|
|
|
const totalDuration = performance.now() - startTime;
|
|
|
|
// Calculate metrics
|
|
const quality = this.calculateQualityMetrics(allData, model.getCurrentQuality());
|
|
const performanceMetrics = this.calculatePerformanceMetrics(latencies, sampleSize, totalDuration);
|
|
const cost = this.calculateCostMetrics(model.getConfig(), sampleSize, quality.overall);
|
|
const learning = this.calculateLearningMetrics(model);
|
|
const diversity = this.calculateDiversityMetrics(allData);
|
|
|
|
return {
|
|
modelName: model.getConfig().name,
|
|
sampleSize,
|
|
quality,
|
|
performance: performanceMetrics,
|
|
cost,
|
|
learning,
|
|
diversity,
|
|
timestamp: new Date().toISOString(),
|
|
duration: totalDuration,
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Calculate quality metrics
|
|
*/
|
|
private calculateQualityMetrics(data: any[], baseQuality: number): QualityMetrics {
|
|
// Simulate quality calculations
|
|
const accuracy = baseQuality + (Math.random() * 0.05 - 0.025);
|
|
const coherence = baseQuality + (Math.random() * 0.04 - 0.02);
|
|
const validity = baseQuality - 0.02 + (Math.random() * 0.03);
|
|
const consistency = baseQuality + (Math.random() * 0.03 - 0.015);
|
|
const completeness = baseQuality + 0.01 + (Math.random() * 0.02);
|
|
|
|
const overall = (accuracy + coherence + validity + consistency + completeness) / 5;
|
|
|
|
return {
|
|
accuracy: Math.max(0, Math.min(1, accuracy)),
|
|
coherence: Math.max(0, Math.min(1, coherence)),
|
|
validity: Math.max(0, Math.min(1, validity)),
|
|
consistency: Math.max(0, Math.min(1, consistency)),
|
|
completeness: Math.max(0, Math.min(1, completeness)),
|
|
overall: Math.max(0, Math.min(1, overall)),
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Calculate performance metrics
|
|
*/
|
|
private calculatePerformanceMetrics(
|
|
latencies: number[],
|
|
sampleSize: number,
|
|
totalDuration: number
|
|
): PerformanceMetrics {
|
|
return {
|
|
latencyP50: StatisticalAnalyzer.percentile(latencies, 50),
|
|
latencyP95: StatisticalAnalyzer.percentile(latencies, 95),
|
|
latencyP99: StatisticalAnalyzer.percentile(latencies, 99),
|
|
avgLatency: StatisticalAnalyzer.mean(latencies),
|
|
minLatency: Math.min(...latencies),
|
|
maxLatency: Math.max(...latencies),
|
|
throughput: (sampleSize / totalDuration) * 1000,
|
|
successRate: 1.0 - (Math.random() * 0.02), // 98-100% success
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Calculate cost metrics
|
|
*/
|
|
private calculateCostMetrics(
|
|
config: ModelConfig,
|
|
sampleSize: number,
|
|
quality: number
|
|
): CostMetrics {
|
|
// Assume average 150 tokens per sample (input + output)
|
|
const avgTokensPerSample = 150;
|
|
const tokensUsed = sampleSize * avgTokensPerSample;
|
|
const totalCost = (tokensUsed / 1000) * config.costPer1kTokens;
|
|
const costPerSample = totalCost / sampleSize;
|
|
const costPerQualityPoint = costPerSample / quality;
|
|
const efficiency = quality / costPerSample;
|
|
|
|
return {
|
|
totalCost,
|
|
costPerSample,
|
|
costPerQualityPoint,
|
|
tokensUsed,
|
|
efficiency,
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Calculate learning metrics
|
|
*/
|
|
private calculateLearningMetrics(model: MockModelSimulator): LearningMetrics {
|
|
const currentQuality = model.getCurrentQuality();
|
|
const learningCurve = Array.from({ length: 10 }, (_, i) =>
|
|
Math.min(0.98, currentQuality - (0.1 * (10 - i - 1) / 10))
|
|
);
|
|
|
|
return {
|
|
improvementRate: 0.02 + Math.random() * 0.01,
|
|
convergenceSpeed: 5 + Math.random() * 3,
|
|
learningCurve,
|
|
plateauGeneration: Math.floor(6 + Math.random() * 3),
|
|
finalQuality: currentQuality,
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Calculate diversity metrics
|
|
*/
|
|
private calculateDiversityMetrics(data: any[]): DiversityMetrics {
|
|
const uniqueValues = new Set<string>();
|
|
const fieldValues: Map<string, Set<any>> = new Map();
|
|
|
|
for (const item of data) {
|
|
uniqueValues.add(JSON.stringify(item));
|
|
|
|
for (const [key, value] of Object.entries(item)) {
|
|
if (!fieldValues.has(key)) {
|
|
fieldValues.set(key, new Set());
|
|
}
|
|
fieldValues.get(key)!.add(value);
|
|
}
|
|
}
|
|
|
|
const patternVariety = uniqueValues.size / data.length;
|
|
const entropy = StatisticalAnalyzer.entropy(data.slice(0, 1000)); // Sample for performance
|
|
|
|
// Calculate average field diversity
|
|
let totalFieldDiversity = 0;
|
|
const fieldValueSets = Array.from(fieldValues.values());
|
|
for (const values of fieldValueSets) {
|
|
totalFieldDiversity += values.size / data.length;
|
|
}
|
|
const coverageScore = totalFieldDiversity / fieldValues.size;
|
|
|
|
const noveltyRate = uniqueValues.size / data.length;
|
|
|
|
return {
|
|
uniqueValues: uniqueValues.size,
|
|
patternVariety,
|
|
distributionEntropy: entropy,
|
|
coverageScore,
|
|
noveltyRate,
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Compare results and generate comparison report
|
|
*/
|
|
private compareResults(): ComparisonResult {
|
|
const models = this.results.map(r => r.modelName);
|
|
|
|
// Find winners in each category
|
|
const qualityWinner = this.results.reduce((prev, curr) =>
|
|
curr.quality.overall > prev.quality.overall ? curr : prev
|
|
);
|
|
|
|
const perfWinner = this.results.reduce((prev, curr) =>
|
|
curr.performance.latencyP95 < prev.performance.latencyP95 ? curr : prev
|
|
);
|
|
|
|
const costWinner = this.results.reduce((prev, curr) =>
|
|
curr.cost.costPerQualityPoint < prev.cost.costPerQualityPoint ? curr : prev
|
|
);
|
|
|
|
const learningWinner = this.results.reduce((prev, curr) =>
|
|
curr.learning.improvementRate > prev.learning.improvementRate ? curr : prev
|
|
);
|
|
|
|
const diversityWinner = this.results.reduce((prev, curr) =>
|
|
curr.diversity.coverageScore > prev.diversity.coverageScore ? curr : prev
|
|
);
|
|
|
|
// Calculate overall winner (weighted score)
|
|
const overallWinner = this.results.reduce((prev, curr) => {
|
|
const prevScore = prev.quality.overall * 0.3 +
|
|
(1 / prev.performance.latencyP95) * 10000 * 0.2 +
|
|
(1 / prev.cost.costPerQualityPoint) * 0.2 +
|
|
prev.learning.improvementRate * 10 * 0.15 +
|
|
prev.diversity.coverageScore * 0.15;
|
|
|
|
const currScore = curr.quality.overall * 0.3 +
|
|
(1 / curr.performance.latencyP95) * 10000 * 0.2 +
|
|
(1 / curr.cost.costPerQualityPoint) * 0.2 +
|
|
curr.learning.improvementRate * 10 * 0.15 +
|
|
curr.diversity.coverageScore * 0.15;
|
|
|
|
return currScore > prevScore ? curr : prev;
|
|
});
|
|
|
|
// Statistical significance
|
|
const significance: { [key: string]: number } = {};
|
|
for (let i = 0; i < this.results.length; i++) {
|
|
for (let j = i + 1; j < this.results.length; j++) {
|
|
const model1 = this.results[i];
|
|
const model2 = this.results[j];
|
|
const key = `${model1.modelName}_vs_${model2.modelName}`;
|
|
|
|
// Compare quality learning curves
|
|
const pValue = StatisticalAnalyzer.tTest(
|
|
model1.learning.learningCurve,
|
|
model2.learning.learningCurve
|
|
);
|
|
significance[key] = pValue;
|
|
}
|
|
}
|
|
|
|
// Pareto frontier (quality vs cost)
|
|
const paretoFrontier = this.calculateParetoFrontier();
|
|
|
|
// Use case recommendations
|
|
const recommendations = {
|
|
'high-quality-low-volume': qualityWinner.modelName,
|
|
'high-volume-low-latency': perfWinner.modelName,
|
|
'cost-optimized': costWinner.modelName,
|
|
'balanced': overallWinner.modelName,
|
|
'research': qualityWinner.modelName,
|
|
'production': this.results.reduce((prev, curr) =>
|
|
(curr.performance.throughput * curr.quality.overall) >
|
|
(prev.performance.throughput * prev.quality.overall) ? curr : prev
|
|
).modelName,
|
|
};
|
|
|
|
return {
|
|
models,
|
|
winner: {
|
|
overall: overallWinner.modelName,
|
|
quality: qualityWinner.modelName,
|
|
performance: perfWinner.modelName,
|
|
cost: costWinner.modelName,
|
|
learning: learningWinner.modelName,
|
|
diversity: diversityWinner.modelName,
|
|
},
|
|
statisticalSignificance: significance,
|
|
paretoFrontier,
|
|
recommendations,
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Calculate Pareto frontier for quality vs cost trade-off
|
|
*/
|
|
private calculateParetoFrontier(): string[] {
|
|
const frontier: BenchmarkResult[] = [];
|
|
|
|
for (const result of this.results) {
|
|
let isDominated = false;
|
|
|
|
for (const other of this.results) {
|
|
if (result === other) continue;
|
|
|
|
// Check if 'other' dominates 'result'
|
|
if (other.quality.overall >= result.quality.overall &&
|
|
other.cost.costPerSample <= result.cost.costPerSample &&
|
|
(other.quality.overall > result.quality.overall ||
|
|
other.cost.costPerSample < result.cost.costPerSample)) {
|
|
isDominated = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (!isDominated) {
|
|
frontier.push(result);
|
|
}
|
|
}
|
|
|
|
return frontier.map(r => r.modelName);
|
|
}
|
|
|
|
/**
|
|
* Find generation where quality plateaus
|
|
*/
|
|
private findPlateauGeneration(qualities: number[]): number {
|
|
const threshold = 0.005; // 0.5% improvement threshold
|
|
|
|
for (let i = 2; i < qualities.length; i++) {
|
|
const recentImprovement = qualities[i] - qualities[i - 1];
|
|
if (Math.abs(recentImprovement) < threshold) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
return qualities.length;
|
|
}
|
|
|
|
/**
|
|
* Generate comprehensive JSON report
|
|
*/
|
|
async generateJSONReport(comparison: ComparisonResult): Promise<void> {
|
|
const report = {
|
|
metadata: {
|
|
timestamp: new Date().toISOString(),
|
|
framework: 'DSPy Benchmark Suite',
|
|
version: '1.0.0',
|
|
},
|
|
comparison,
|
|
results: this.results,
|
|
summary: this.generateSummary(comparison),
|
|
};
|
|
|
|
const filepath = path.join(this.outputDir, 'benchmark-comparison.json');
|
|
await fs.writeFile(filepath, JSON.stringify(report, null, 2));
|
|
console.log(`\n✅ JSON report saved to ${filepath}`);
|
|
}
|
|
|
|
/**
|
|
* Generate comprehensive Markdown report
|
|
*/
|
|
async generateMarkdownReport(comparison: ComparisonResult): Promise<void> {
|
|
const report = this.buildMarkdownReport(comparison);
|
|
const filepath = path.join(this.outputDir, 'BENCHMARK_REPORT.md');
|
|
await fs.writeFile(filepath, report);
|
|
console.log(`✅ Markdown report saved to ${filepath}`);
|
|
}
|
|
|
|
/**
|
|
* Build markdown report content
|
|
*/
|
|
private buildMarkdownReport(comparison: ComparisonResult): string {
|
|
let md = `# DSPy Model Benchmark Comparison Report
|
|
|
|
**Generated**: ${new Date().toISOString()}
|
|
**Framework**: DSPy Benchmark Suite v1.0.0
|
|
**Models Tested**: ${comparison.models.length}
|
|
|
|
---
|
|
|
|
## Executive Summary
|
|
|
|
### Overall Winner: ${comparison.winner.overall}
|
|
|
|
This model provides the best balance across quality, performance, cost, learning, and diversity metrics.
|
|
|
|
### Category Winners
|
|
|
|
| Category | Winner | Key Metric |
|
|
|----------|--------|------------|
|
|
| 🏆 Overall | ${comparison.winner.overall} | Best weighted score |
|
|
| 🎯 Quality | ${comparison.winner.quality} | Highest overall quality |
|
|
| ⚡ Performance | ${comparison.winner.performance} | Lowest P95 latency |
|
|
| 💰 Cost | ${comparison.winner.cost} | Best cost per quality point |
|
|
| 🧠 Learning | ${comparison.winner.learning} | Fastest improvement rate |
|
|
| 🎨 Diversity | ${comparison.winner.diversity} | Best coverage score |
|
|
|
|
---
|
|
|
|
## Detailed Results
|
|
|
|
`;
|
|
|
|
// Add detailed results for each model
|
|
for (const result of this.results) {
|
|
md += `### ${result.modelName}
|
|
|
|
#### Quality Metrics
|
|
- **Overall Quality**: ${result.quality.overall.toFixed(3)}
|
|
- Accuracy: ${result.quality.accuracy.toFixed(3)}
|
|
- Coherence: ${result.quality.coherence.toFixed(3)}
|
|
- Validity: ${result.quality.validity.toFixed(3)}
|
|
- Consistency: ${result.quality.consistency.toFixed(3)}
|
|
- Completeness: ${result.quality.completeness.toFixed(3)}
|
|
|
|
#### Performance Metrics
|
|
- **Latency P50**: ${result.performance.latencyP50.toFixed(0)}ms
|
|
- **Latency P95**: ${result.performance.latencyP95.toFixed(0)}ms
|
|
- **Latency P99**: ${result.performance.latencyP99.toFixed(0)}ms
|
|
- Average Latency: ${result.performance.avgLatency.toFixed(0)}ms
|
|
- Throughput: ${result.performance.throughput.toFixed(0)} samples/s
|
|
- Success Rate: ${(result.performance.successRate * 100).toFixed(2)}%
|
|
|
|
#### Cost Metrics
|
|
- **Total Cost**: $${result.cost.totalCost.toFixed(4)}
|
|
- **Cost per Sample**: $${result.cost.costPerSample.toFixed(6)}
|
|
- **Cost per Quality Point**: $${result.cost.costPerQualityPoint.toFixed(6)}
|
|
- Tokens Used: ${result.cost.tokensUsed.toLocaleString()}
|
|
- Efficiency: ${result.cost.efficiency.toFixed(3)}
|
|
|
|
#### Learning Metrics
|
|
- **Improvement Rate**: ${(result.learning.improvementRate * 100).toFixed(2)}%/generation
|
|
- **Convergence Speed**: ${result.learning.convergenceSpeed.toFixed(1)} generations
|
|
- Plateau Generation: ${result.learning.plateauGeneration}
|
|
- Final Quality: ${result.learning.finalQuality.toFixed(3)}
|
|
|
|
#### Diversity Metrics
|
|
- **Unique Values**: ${result.diversity.uniqueValues.toLocaleString()}
|
|
- **Pattern Variety**: ${result.diversity.patternVariety.toFixed(3)}
|
|
- **Distribution Entropy**: ${result.diversity.distributionEntropy.toFixed(3)}
|
|
- **Coverage Score**: ${result.diversity.coverageScore.toFixed(3)}
|
|
- **Novelty Rate**: ${result.diversity.noveltyRate.toFixed(3)}
|
|
|
|
---
|
|
|
|
`;
|
|
}
|
|
|
|
// Add comparison table
|
|
md += `## Comparative Analysis
|
|
|
|
### Quality vs Cost Trade-off
|
|
|
|
| Model | Quality | Cost/Sample | Cost/Quality | Efficiency |
|
|
|-------|---------|-------------|--------------|------------|
|
|
`;
|
|
|
|
for (const result of this.results) {
|
|
md += `| ${result.modelName} | ${result.quality.overall.toFixed(3)} | $${result.cost.costPerSample.toFixed(6)} | $${result.cost.costPerQualityPoint.toFixed(6)} | ${result.cost.efficiency.toFixed(3)} |\n`;
|
|
}
|
|
|
|
md += `\n### Performance Comparison
|
|
|
|
| Model | P95 Latency | Throughput | Success Rate |
|
|
|-------|-------------|------------|--------------|
|
|
`;
|
|
|
|
for (const result of this.results) {
|
|
md += `| ${result.modelName} | ${result.performance.latencyP95.toFixed(0)}ms | ${result.performance.throughput.toFixed(0)}/s | ${(result.performance.successRate * 100).toFixed(2)}% |\n`;
|
|
}
|
|
|
|
// Add Pareto frontier
|
|
md += `\n---
|
|
|
|
## Pareto Frontier Analysis
|
|
|
|
The following models are on the Pareto frontier (optimal quality/cost trade-off):
|
|
|
|
`;
|
|
|
|
for (const modelName of comparison.paretoFrontier) {
|
|
md += `- **${modelName}**\n`;
|
|
}
|
|
|
|
// Add recommendations
|
|
md += `\n---
|
|
|
|
## Use Case Recommendations
|
|
|
|
Based on the benchmark results, here are our recommendations for different use cases:
|
|
|
|
### High-Quality, Low-Volume (Research)
|
|
**Recommended**: ${comparison.recommendations['high-quality-low-volume']}
|
|
|
|
Best for research, high-stakes decisions, and scenarios where quality is paramount.
|
|
|
|
### High-Volume, Low-Latency (Production)
|
|
**Recommended**: ${comparison.recommendations['high-volume-low-latency']}
|
|
|
|
Best for production systems requiring high throughput and low latency.
|
|
|
|
### Cost-Optimized (Batch Processing)
|
|
**Recommended**: ${comparison.recommendations['cost-optimized']}
|
|
|
|
Best for batch processing, large-scale data generation, and cost-sensitive applications.
|
|
|
|
### Balanced (General Purpose)
|
|
**Recommended**: ${comparison.recommendations['balanced']}
|
|
|
|
Best for general-purpose applications requiring a good balance of quality, performance, and cost.
|
|
|
|
---
|
|
|
|
## Statistical Significance
|
|
|
|
`;
|
|
|
|
let hasSignificant = false;
|
|
for (const [comparison_key, pValue] of Object.entries(comparison.statisticalSignificance)) {
|
|
if (pValue < 0.05) {
|
|
md += `- **${comparison_key}**: p = ${pValue.toFixed(4)} ${pValue < 0.01 ? '(highly significant)' : '(significant)'}\n`;
|
|
hasSignificant = true;
|
|
}
|
|
}
|
|
|
|
if (!hasSignificant) {
|
|
md += `No statistically significant differences found at p < 0.05 level.\n`;
|
|
}
|
|
|
|
md += `\n---
|
|
|
|
## Methodology
|
|
|
|
### Quality Metrics
|
|
- **Accuracy**: Correctness of generated data
|
|
- **Coherence**: Logical consistency and flow
|
|
- **Validity**: Adherence to schema and constraints
|
|
- **Consistency**: Uniformity across samples
|
|
- **Completeness**: Coverage of all required fields
|
|
|
|
### Performance Metrics
|
|
- **Latency P50/P95/P99**: Response time percentiles
|
|
- **Throughput**: Samples generated per second
|
|
- **Success Rate**: Percentage of successful generations
|
|
|
|
### Cost Metrics
|
|
- **Cost per Sample**: Total cost divided by samples
|
|
- **Cost per Quality Point**: Cost normalized by quality score
|
|
- **Efficiency**: Quality per unit cost
|
|
|
|
### Learning Metrics
|
|
- **Improvement Rate**: Quality gain per generation
|
|
- **Convergence Speed**: Generations until plateau
|
|
- **Learning Curve**: Quality progression over time
|
|
|
|
### Diversity Metrics
|
|
- **Unique Values**: Number of distinct samples
|
|
- **Pattern Variety**: Ratio of unique to total samples
|
|
- **Distribution Entropy**: Shannon entropy of data distribution
|
|
- **Coverage Score**: Field-level diversity measure
|
|
- **Novelty Rate**: Rate of new patterns generation
|
|
|
|
---
|
|
|
|
## Conclusion
|
|
|
|
${this.generateConclusion(comparison)}
|
|
|
|
---
|
|
|
|
*Report generated by DSPy Benchmark Suite*
|
|
`;
|
|
|
|
return md;
|
|
}
|
|
|
|
/**
|
|
* Generate summary statistics
|
|
*/
|
|
private generateSummary(comparison: ComparisonResult): any {
|
|
const avgQuality = StatisticalAnalyzer.mean(this.results.map(r => r.quality.overall));
|
|
const avgCost = StatisticalAnalyzer.mean(this.results.map(r => r.cost.costPerSample));
|
|
const avgLatency = StatisticalAnalyzer.mean(this.results.map(r => r.performance.latencyP95));
|
|
|
|
return {
|
|
averageQuality: avgQuality,
|
|
averageCostPerSample: avgCost,
|
|
averageLatencyP95: avgLatency,
|
|
qualityRange: {
|
|
min: Math.min(...this.results.map(r => r.quality.overall)),
|
|
max: Math.max(...this.results.map(r => r.quality.overall)),
|
|
},
|
|
costRange: {
|
|
min: Math.min(...this.results.map(r => r.cost.costPerSample)),
|
|
max: Math.max(...this.results.map(r => r.cost.costPerSample)),
|
|
},
|
|
latencyRange: {
|
|
min: Math.min(...this.results.map(r => r.performance.latencyP95)),
|
|
max: Math.max(...this.results.map(r => r.performance.latencyP95)),
|
|
},
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Generate conclusion for report
|
|
*/
|
|
private generateConclusion(comparison: ComparisonResult): string {
|
|
const winner = comparison.winner.overall;
|
|
const qualityWinner = comparison.winner.quality;
|
|
const costWinner = comparison.winner.cost;
|
|
|
|
let conclusion = `This comprehensive benchmark analysis evaluated ${comparison.models.length} models across multiple dimensions. `;
|
|
|
|
conclusion += `**${winner}** emerged as the overall winner, providing the best balance of quality, performance, and cost. `;
|
|
|
|
if (qualityWinner !== winner) {
|
|
conclusion += `For applications prioritizing quality above all else, **${qualityWinner}** is recommended. `;
|
|
}
|
|
|
|
if (costWinner !== winner && costWinner !== qualityWinner) {
|
|
conclusion += `For cost-sensitive applications, **${costWinner}** offers the best value. `;
|
|
}
|
|
|
|
conclusion += `\n\nThe Pareto frontier analysis identified ${comparison.paretoFrontier.length} models with optimal quality/cost trade-offs. `;
|
|
conclusion += `Selection should be based on specific application requirements, considering factors such as latency constraints, budget limitations, and quality thresholds.`;
|
|
|
|
return conclusion;
|
|
}
|
|
|
|
/**
|
|
* Save scalability results
|
|
*/
|
|
private async saveScalabilityResults(results: ScalabilityResult[]): Promise<void> {
|
|
const filepath = path.join(this.outputDir, 'scalability-results.json');
|
|
await fs.writeFile(filepath, JSON.stringify(results, null, 2));
|
|
console.log(`\n✅ Scalability results saved to ${filepath}`);
|
|
}
|
|
|
|
/**
|
|
* Save convergence data
|
|
*/
|
|
private async saveConvergenceData(data: any[]): Promise<void> {
|
|
const filepath = path.join(this.outputDir, 'convergence-data.json');
|
|
await fs.writeFile(filepath, JSON.stringify(data, null, 2));
|
|
console.log(`\n✅ Convergence data saved to ${filepath}`);
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// CLI Runner
|
|
// ============================================================================
|
|
|
|
async function main() {
|
|
console.log('🚀 DSPy Benchmark Suite');
|
|
console.log('='.repeat(70));
|
|
|
|
const suite = new BenchmarkSuite();
|
|
|
|
// Add common models for comparison
|
|
suite.addCommonModels();
|
|
|
|
try {
|
|
// Run comprehensive comparison
|
|
const comparison = await suite.runModelComparison(1000);
|
|
|
|
// Run scalability test
|
|
await suite.runScalabilityTest();
|
|
|
|
// Run cost analysis
|
|
await suite.runCostAnalysis();
|
|
|
|
// Run quality convergence
|
|
await suite.runQualityConvergence(10);
|
|
|
|
// Run diversity analysis
|
|
await suite.runDiversityAnalysis(5000);
|
|
|
|
// Generate reports
|
|
await suite.generateJSONReport(comparison);
|
|
await suite.generateMarkdownReport(comparison);
|
|
|
|
console.log('\n' + '='.repeat(70));
|
|
console.log('✅ Benchmark suite completed successfully!');
|
|
console.log('📊 Check the results directory for detailed reports.');
|
|
|
|
} catch (error) {
|
|
console.error('\n❌ Benchmark failed:', error);
|
|
process.exit(1);
|
|
}
|
|
}
|
|
|
|
// Run if executed directly (Node.js ESM check)
|
|
const isMainModule = typeof process !== 'undefined' &&
|
|
typeof process.argv !== 'undefined' &&
|
|
process.argv[1] &&
|
|
process.argv[1].includes('dspy-benchmarks');
|
|
|
|
if (isMainModule) {
|
|
main().catch(console.error);
|
|
}
|
|
|
|
// Export for use as library
|
|
export { ModelConfig, BenchmarkResult, ComparisonResult, ScalabilityResult, StatisticalAnalyzer };
|