Run_Benchmark.py

# ===== COMPREHENSIVE MODEL TESTING & RESULTS GENERATION =====
import os
import sys
import json
import numpy as np
import random
import time
from typing import Dict, Any, List
from datetime import datetime
from collections import defaultdict

# --- Setup Paths ---
BASE_DIR = "." # Use relative path
RESULTS_DIR = os.path.join(BASE_DIR, "results")
TEST_RESULTS_DIR = os.path.join(RESULTS_DIR, "test_results")
os.makedirs(TEST_RESULTS_DIR, exist_ok=True)

# --- Add Project to Python Path ---
if BASE_DIR not in sys.path:
    sys.path.insert(0, BASE_DIR)

# ===== MOCK MODEL IMPLEMENTATIONS FOR TESTING =====
class MockVLMModel:
    """Mock VLM model for testing the benchmark framework"""
    def __init__(self, model_name: str, base_performance: float = 0.7, 
                 physics_strength: float = 0.8, reasoning_strength: float = 0.75,
                 model_size_gb: float = 7.0):
        self.model_name = model_name
        self.base_performance = base_performance
        self.physics_strength = physics_strength
        self.reasoning_strength = reasoning_strength
        self.model_size_gb = model_size_gb
        
        self.environment_strengths = {
            "projectile": np.clip(physics_strength + np.random.normal(0, 0.1), 0.1, 1.0),
            "collision": np.clip(physics_strength + np.random.normal(0, 0.08), 0.1, 1.0),
            "mechanics": np.clip(physics_strength + np.random.normal(0, 0.12), 0.1, 1.0),
            "fluid": np.clip(physics_strength + np.random.normal(0, 0.15), 0.1, 1.0)
        }
    
    def generate_response(self, prompt: str, scenario: Dict[str, Any]) -> str:
        # Simplified response generation for testing
        return f"Solving for {scenario.get('environment_type')}. Based on the parameters, the result is calculated using standard formulas. The final answer is approximately {np.random.uniform(10, 100):.2f}."

# --- Create Test Models ---
test_models = {
    "Qwen2.5-VL-7B": MockVLMModel("Qwen2.5-VL-7B", base_performance=0.82, physics_strength=0.85, reasoning_strength=0.80, model_size_gb=7.0),
    "LLaMA-3.2-Vision-11B": MockVLMModel("LLaMA-3.2-Vision-11B", base_performance=0.78, physics_strength=0.81, reasoning_strength=0.83, model_size_gb=11.0),
    "Gemma2-27B-Vision": MockVLMModel("Gemma2-27B-Vision", base_performance=0.85, physics_strength=0.88, reasoning_strength=0.87, model_size_gb=27.0),
    "DeepSeek-VL-1.3B": MockVLMModel("DeepSeek-VL-1.3B", base_performance=0.68, physics_strength=0.72, reasoning_strength=0.70, model_size_gb=1.3)
}
print("Mock VLM models created for testing.")

# ===== BENCHMARK TEST RUNNER =====
class BenchmarkTestRunner:
    def __init__(self, base_path: str):
        self.base_path = base_path
        self.test_scenarios = self._generate_test_scenarios()

    def _generate_test_scenarios(self) -> Dict[str, List[Dict[str, Any]]]:
        scenarios = defaultdict(list)
        for env_name in ["projectile", "collision", "mechanics", "fluid"]:
            for difficulty in ["easy", "medium", "hard"]:
                for i in range(25): # 25 scenarios per difficulty
                    scenarios[env_name].append({
                        "scenario_id": f"{env_name}_{difficulty}_{i:03d}",
                        "environment_type": env_name,
                        "difficulty": difficulty,
                        "ground_truth": {"value": np.random.uniform(1, 100)} # Mock ground truth
                    })
        return scenarios

    def _evaluate_response(self, response: str, scenario: Dict[str, Any], model_strength: float) -> Dict[str, float]:
        # Simplified mock evaluation
        base_score = model_strength
        noise = np.random.normal(0, 0.05)
        difficulty_factors = {"easy": 1.1, "medium": 1.0, "hard": 0.85}
        difficulty = scenario.get("difficulty", "medium")
        
        physics_accuracy = np.clip(base_score + noise, 0.1, 1.0) * difficulty_factors[difficulty]
        reasoning_quality = np.clip(base_score * 0.9 + noise, 0.1, 1.0) * difficulty_factors[difficulty]
        
        return {
            "physics_accuracy": np.clip(physics_accuracy, 0.1, 1.0),
            "reasoning_quality": np.clip(reasoning_quality, 0.1, 1.0),
            "overall_score": np.clip((physics_accuracy + reasoning_quality) / 2, 0.1, 1.0),
            "success": True
        }

    def run_complete_evaluation(self) -> Dict[str, Any]:
        print("Starting Complete Benchmark Evaluation...")
        evaluation_results = {}
        detailed_results = []

        for model_name, model in test_models.items():
            print(f"  -> Evaluating {model_name}...")
            model_env_results = {}
            all_scores, all_physics, all_reasoning, all_times = [], [], [], []

            for env_name, env_scenarios in self.test_scenarios.items():
                env_scores, env_physics, env_reasoning, env_times = [], [], [], []
                for scenario in env_scenarios:
                    start_time = time.time()
                    response = model.generate_response("prompt", scenario)
                    inference_time = (time.time() - start_time) + np.random.uniform(0.5, 2.0)
                    
                    env_strength = model.environment_strengths.get(env_name, model.physics_strength)
                    scores = self._evaluate_response(response, scenario, env_strength)
                    
                    detailed_results.append({"model_name": model_name, "environment": env_name, "difficulty": scenario['difficulty'], "overall_score": scores["overall_score"], "inference_time": inference_time})
                    
                    env_scores.append(scores["overall_score"])
                    env_physics.append(scores["physics_accuracy"])
                    env_reasoning.append(scores["reasoning_quality"])
                    env_times.append(inference_time)

                model_env_results[env_name] = {
                    "mean_score": np.mean(env_scores), "std_score": np.std(env_scores),
                    "mean_physics": np.mean(env_physics), "mean_reasoning": np.mean(env_reasoning),
                    "difficulty_breakdown": {d: np.mean([s['overall_score'] for s in detailed_results if s['model_name']==model_name and s['environment']==env_name and s['difficulty']==d]) for d in ['easy','medium','hard']}
                }
                all_scores.extend(env_scores)
                all_physics.extend(env_physics)
                all_reasoning.extend(env_reasoning)
                all_times.extend(env_times)

            evaluation_results[model_name] = {
                "model_name": model_name, "model_size_gb": model.model_size_gb,
                "environment_results": model_env_results,
                "overall_metrics": {
                    "overall_score": np.mean(all_scores), "overall_std": np.std(all_scores),
                    "physics_accuracy": np.mean(all_physics), "reasoning_quality": np.mean(all_reasoning),
                    "average_inference_time": np.mean(all_times), "total_scenarios": len(all_scores)
                }
            }
        
        results_summary = {
            "evaluation_id": f"eval_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "model_results": evaluation_results,
            "detailed_results": detailed_results
        }
        
        results_path = os.path.join(TEST_RESULTS_DIR, "complete_evaluation_results.json")
        with open(results_path, 'w') as f:
            json.dump(results_summary, f, indent=4)
        
        print(f"\nBenchmark results successfully generated and saved to:\n{results_path}")
        return results_summary

# --- Run the Benchmark ---
if __name__ == "__main__":
    runner = BenchmarkTestRunner(BASE_DIR)
    evaluation_results = runner.run_complete_evaluation()