infrastructure/aios_ech0_integration.py

#!/usr/bin/env python3
"""
ECH0-PRIME AIOS-ECH0 Integration
Complete integration of discovered ECH0 algorithms into AIOS scheduling system.

Copyright (c) 2025 Joshua Hendricks Cole (DBA: Corporation of Light). All Rights Reserved. PATENT PENDING.
"""

import asyncio
from typing import Dict, List, Any, Optional
try:
    from .aios_algorithms import (
        AIOSKernel, create_ai_task, TaskPriority, ResourceType,
        AIFairScheduler, AIWorkStealingScheduler, AIResourceAllocator
    )
    from .ech0_algorithm_integration import (
        ECH0AlgorithmIntegrator, AIOSAlgorithmScheduler
    )
except ImportError:
    # Fallback for direct execution
    import sys
    sys.path.append('/Users/noone/echo_prime')
    from infrastructure.aios_algorithms import (
        AIOSKernel, create_ai_task, TaskPriority, ResourceType,
        AIFairScheduler, AIWorkStealingScheduler, AIResourceAllocator
    )
    from infrastructure.ech0_algorithm_integration import (
        ECH0AlgorithmIntegrator, AIOSAlgorithmScheduler
    )


class AIOS_ECH0_IntegratedSystem:
    """
    Complete integration of ECH0 algorithms with AIOS kernel.
    Provides intelligent algorithm selection and scheduling for AI workloads.
    """

    def __init__(self):
        # Initialize components
        self.aios_kernel = AIOSKernel()
        self.algorithm_integrator = ECH0AlgorithmIntegrator()
        self.algorithm_scheduler = None

        # Integration state
        self.integrated_algorithms = {}
        self.performance_metrics = {}

    async def initialize_integration(self) -> Dict[str, Any]:
        """Initialize the complete AIOS-ECH0 integration."""
        print("🔗 Initializing AIOS-ECH0 Integration...")

        # Discover and integrate algorithms
        num_algorithms = self.algorithm_integrator.discover_and_integrate_algorithms()

        # Create algorithm scheduler
        self.algorithm_scheduler = AIOSAlgorithmScheduler(self.algorithm_integrator)

        # Create integrated algorithm tasks
        await self._create_integrated_tasks()

        integration_stats = {
            "algorithms_integrated": num_algorithms,
            "aios_kernel_initialized": True,
            "algorithm_scheduler_ready": True,
            "integrated_tasks_created": len(self.integrated_algorithms)
        }

        print(f"✅ Integration Complete: {num_algorithms} algorithms ready for AIOS scheduling")
        return integration_stats

    async def _create_integrated_tasks(self):
        """Create AIOS-compatible tasks for all integrated algorithms."""
        for algo_name, algo in self.algorithm_integrator.library.algorithms.items():
            # Create an AIOS task for each algorithm
            task = create_ai_task(
                task_id=f"algo_{algo_name}",
                name=f"Execute {algo_name}",
                priority=self._map_domain_to_priority(algo.domain),
                resource_reqs=self._estimate_resource_requirements(algo),
                duration=self._estimate_execution_time(algo),
                dependencies=[]
            )

            self.integrated_algorithms[algo_name] = {
                "task": task,
                "algorithm": algo,
                "performance_history": []
            }

    def _map_domain_to_priority(self, domain: str) -> TaskPriority:
        """Map algorithm domains to AIOS task priorities."""
        priority_mapping = {
            "machine_learning": TaskPriority.HIGH,
            "optimization": TaskPriority.MEDIUM,
            "computer_science": TaskPriority.MEDIUM,
            "creativity": TaskPriority.LOW
        }
        return priority_mapping.get(domain, TaskPriority.MEDIUM)

    def _estimate_resource_requirements(self, algo) -> Dict[ResourceType, float]:
        """Estimate resource requirements based on algorithm characteristics."""
        base_requirements = {ResourceType.CPU: 1.0, ResourceType.MEMORY: 0.5}

        # Adjust based on complexity
        if "O(n²)" in algo.complexity or "O(n log n)" in algo.complexity:
            base_requirements[ResourceType.CPU] = 2.0
            base_requirements[ResourceType.MEMORY] = 1.0

        # Adjust for domain
        if algo.domain == "machine_learning":
            base_requirements[ResourceType.CPU] = max(base_requirements[ResourceType.CPU], 1.5)

        return base_requirements

    def _estimate_execution_time(self, algo) -> float:
        """Estimate execution time based on algorithm complexity."""
        base_time = 1.0

        # Adjust based on complexity class
        if "O(n²)" in algo.complexity:
            base_time *= 2.0
        elif "O(n log n)" in algo.complexity:
            base_time *= 1.5
        elif "O(log n)" in algo.complexity:
            base_time *= 0.5

        # Adjust for domain
        if algo.domain == "optimization":
            base_time *= 1.2
        elif algo.domain == "creativity":
            base_time *= 0.8  # Creative algorithms can be faster

        return base_time

    async def schedule_algorithm_execution(self, algorithm_name: str,
                                         parameters: Dict[str, Any] = None) -> Optional[str]:
        """Schedule an algorithm for execution through AIOS."""
        if algorithm_name not in self.integrated_algorithms:
            print(f"❌ Algorithm {algorithm_name} not found in integrated system")
            return None

        integrated_algo = self.integrated_algorithms[algorithm_name]
        task = integrated_algo["task"]

        # Schedule the task
        task_id = await self.aios_kernel.submit_task(task)

        print(f"✅ Scheduled algorithm {algorithm_name} with task ID: {task_id}")
        return task_id

    async def execute_algorithm_pipeline(self, algorithm_sequence: List[str]) -> Dict[str, Any]:
        """
        Execute a sequence of algorithms as an integrated pipeline.
        Sets up dependencies between algorithms automatically.
        """
        print(f"🔬 Executing algorithm pipeline: {' → '.join(algorithm_sequence)}")

        # Submit all tasks with dependencies
        submitted_tasks = []
        for i, algo_name in enumerate(algorithm_sequence):
            if algo_name not in self.integrated_algorithms:
                print(f"⚠️  Skipping unknown algorithm: {algo_name}")
                continue

            integrated_algo = self.integrated_algorithms[algo_name]
            task = integrated_algo["task"]

            # Set dependencies (all previous tasks must complete first)
            if i > 0:
                task.dependencies = {submitted_tasks[i-1]}

            task_id = await self.aios_kernel.submit_task(task)
            submitted_tasks.append(task_id)

        # Execute the pipeline
        results = []
        for _ in range(len(submitted_tasks)):
            completed = await self.aios_kernel.execute_task_cycle()
            results.extend(completed)

            # Small delay to prevent tight looping
            await asyncio.sleep(0.1)

        pipeline_results = {
            "pipeline": algorithm_sequence,
            "tasks_executed": len(results),
            "completed_tasks": [task.name for task in results if task.status == "completed"],
            "failed_tasks": [task.name for task in results if task.status == "failed"]
        }

        print(f"🎯 Pipeline execution complete: {pipeline_results['tasks_executed']} tasks processed")
        return pipeline_results

    async def optimize_algorithm_selection(self, task_description: str) -> List[str]:
        """
        Use AIOS algorithm scheduler to optimize algorithm selection for a task.
        """
        task_requirements = self._parse_task_requirements(task_description)

        optimal_algorithms = []
        for algo_name, algo in self.algorithm_integrator.library.algorithms.items():
            # Use algorithm scheduler to score relevance
            score = self._calculate_algorithm_relevance(algo, task_requirements)
            optimal_algorithms.append((score, algo_name))

        # Sort by relevance score
        optimal_algorithms.sort(reverse=True)

        # Return top 3 most relevant algorithms
        return [algo_name for _, algo_name in optimal_algorithms[:3]]

    def _parse_task_requirements(self, description: str) -> Dict[str, Any]:
        """Parse task description to extract requirements."""
        requirements = {
            "type": "general",
            "constraints": {},
            "domain": None
        }

        desc_lower = description.lower()

        # Determine task type
        if any(word in desc_lower for word in ["sort", "search", "data structure"]):
            requirements["type"] = "data_processing"
        elif any(word in desc_lower for word in ["optimize", "train", "gradient"]):
            requirements["type"] = "optimization"
        elif any(word in desc_lower for word in ["learn", "predict", "classify"]):
            requirements["type"] = "machine_learning"
        elif any(word in desc_lower for word in ["create", "design", "innovate"]):
            requirements["type"] = "creative"

        # Determine domain hints
        if "neural" in desc_lower or "deep" in desc_lower:
            requirements["domain"] = "machine_learning"
        elif "algorithm" in desc_lower:
            requirements["domain"] = "computer_science"

        return requirements

    def _calculate_algorithm_relevance(self, algo, requirements: Dict[str, Any]) -> float:
        """Calculate how relevant an algorithm is for given requirements."""
        score = 0.0

        # Task type relevance
        task_type = requirements.get("type", "")
        if task_type in " ".join(algo.use_cases):
            score += 1.0

        # Domain match
        required_domain = requirements.get("domain")
        if required_domain and algo.domain == required_domain:
            score += 0.5

        # Complexity consideration (prefer efficient algorithms)
        if "O(1)" in algo.complexity or "O(log n)" in algo.complexity:
            score += 0.3

        return score

    async def benchmark_algorithm_performance(self, algorithm_name: str,
                                           test_iterations: int = 5) -> Dict[str, Any]:
        """Benchmark algorithm performance across multiple executions."""
        if algorithm_name not in self.integrated_algorithms:
            return {"error": f"Algorithm {algorithm_name} not found"}

        print(f"📊 Benchmarking {algorithm_name} performance...")

        execution_times = []
        success_count = 0

        for i in range(test_iterations):
            start_time = asyncio.get_event_loop().time()

            # Execute algorithm
            task_id = await self.schedule_algorithm_execution(algorithm_name)

            # Wait for completion
            completed_tasks = []
            while len(completed_tasks) < 1:
                completed = await self.aios_kernel.execute_task_cycle()
                completed_tasks.extend(completed)
                await asyncio.sleep(0.05)

            execution_time = asyncio.get_event_loop().time() - start_time
            execution_times.append(execution_time)

            if completed_tasks and completed_tasks[0].status == "completed":
                success_count += 1

        benchmark_results = {
            "algorithm": algorithm_name,
            "iterations": test_iterations,
            "success_rate": success_count / test_iterations,
            "avg_execution_time": sum(execution_times) / len(execution_times),
            "min_execution_time": min(execution_times),
            "max_execution_time": max(execution_times),
            "execution_times": execution_times
        }

        # Store performance metrics
        self.performance_metrics[algorithm_name] = benchmark_results

        print(f"✅ Benchmark complete: {benchmark_results['success_rate']:.1%} success rate, "
              f"{benchmark_results['avg_execution_time']:.3f}s avg time")

        return benchmark_results

    def get_system_status(self) -> Dict[str, Any]:
        """Get comprehensive system status."""
        kernel_status = self.aios_kernel.get_system_status()

        return {
            "aios_kernel": kernel_status,
            "integrated_algorithms": len(self.integrated_algorithms),
            "algorithm_domains": list(set(algo["algorithm"].domain
                                        for algo in self.integrated_algorithms.values())),
            "benchmark_results": len(self.performance_metrics),
            "system_ready": True
        }


# Convenience functions for easy integration
async def create_integrated_aios_system() -> AIOS_ECH0_IntegratedSystem:
    """Create and initialize the complete integrated AIOS-ECH0 system."""
    system = AIOS_ECH0_IntegratedSystem()
    await system.initialize_integration()
    return system

async def run_algorithm_pipeline_demo():
    """Demonstrate the integrated AIOS-ECH0 system."""
    print("🚀 AIOS-ECH0 Integrated System Demo")
    print("=" * 50)

    # Initialize integrated system
    system = await create_integrated_aios_system()

    # Show system status
    status = system.get_system_status()
    print("\n📊 SYSTEM STATUS:")
    print(f"   Integrated Algorithms: {status['integrated_algorithms']}")
    print(f"   Algorithm Domains: {status['algorithm_domains']}")
    print(f"   AIOS Kernel Ready: {status['aios_kernel']['resource_utilization']}")

    # Demonstrate algorithm optimization
    print("\n🎯 ALGORITHM OPTIMIZATION DEMO:")
    task = "Sort a large dataset efficiently"
    optimal_algorithms = await system.optimize_algorithm_selection(task)
    print(f"   Task: '{task}'")
    print(f"   Recommended algorithms: {optimal_algorithms}")

    # Demonstrate pipeline execution
    print("\n🔬 PIPELINE EXECUTION DEMO:")
    pipeline = ["gradient_descent", "quicksort", "binary_search"]
    pipeline_results = await system.execute_algorithm_pipeline(pipeline)
    print(f"   Pipeline: {' → '.join(pipeline)}")
    print(f"   Tasks executed: {pipeline_results['tasks_executed']}")
    print(f"   Completed: {len(pipeline_results['completed_tasks'])}")
    print(f"   Failed: {len(pipeline_results['failed_tasks'])}")

    # Demonstrate performance benchmarking
    print("\n📈 PERFORMANCE BENCHMARKING:")
    if "quicksort" in system.integrated_algorithms:
        benchmark = await system.benchmark_algorithm_performance("quicksort", test_iterations=3)
        print(f"   Algorithm: quicksort")
        print(f"   Success Rate: {benchmark['success_rate']:.1%}")
        print(f"   Avg Time: {benchmark['avg_execution_time']:.3f}s")

    print("\n✅ AIOS-ECH0 Integration Demo Complete!")
    print("🎯 The system now intelligently schedules and executes algorithms")
    print("   from the ech0 collection using advanced AIOS orchestration!")


if __name__ == "__main__":
    asyncio.run(run_algorithm_pipeline_demo())