aco/benchmarks/benchmark_suite.py

"""Benchmark Suite for Agent Cost Optimizer.

Benchmarks:
A. Coding Agent Tasks
B. Research Agent Tasks
C. Tool-Use Tasks
D. Document / Contract / QA Tasks
E. Long-Horizon Agent Tasks

Baselines:
A. always frontier model
B. always cheap model
C. static model routing
D. prompt-only router
E. rules-only optimizer
F. learned model router
G. learned router + context budgeter
H. learned router + context + verifier budgeter
I. full Agent Cost Optimizer

Metrics:
- task success
- cost per successful task
- cost reduction at iso-quality
- latency
- token usage
- model calls
- tool calls
- verifier calls
- retries
- cache hit rate
- context tokens
- false-DONE rate
- unsafe cheap-model miss rate
- missed escalation rate
- user correction rate
- regression rate
- quality/cost frontier
"""

import json
import time
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field
from collections import defaultdict

from aco.optimizer import AgentCostOptimizer, OptimizationResult
from aco.config import ACOConfig, ModelConfig, ToolConfig, VerifierConfig, RoutingPolicy
from aco.trace_schema import AgentTrace, TraceStep, ModelCall, ToolCall, VerifierCall, TaskType, Outcome, FailureTag
from aco.datasets.synthetic_traces import SyntheticTraceGenerator


@dataclass
class BenchmarkConfig:
    name: str
    task_types: List[TaskType]
    num_tasks: int
    routing_mode: str = "cascade"
    enable_modules: Dict[str, bool] = field(default_factory=dict)
    baseline_name: str = ""


@dataclass
class BenchmarkResult:
    benchmark_name: str
    baseline_name: str
    num_tasks: int
    num_success: int
    num_partial: int
    num_failure: int
    num_false_done: int
    num_blocked: int
    total_cost: float
    avg_cost_success: float
    avg_latency_ms: float
    total_tool_calls: int
    total_verifier_calls: int
    total_retries: int
    avg_cache_hit_rate: float
    total_context_tokens: int
    cost_reduction_vs_frontier: float
    false_done_rate: float
    unsafe_cheap_miss_rate: float
    missed_escalation_rate: float
    regression_rate: float
    quality_cost_frontier: List[Dict[str, float]] = field(default_factory=list)
    per_task_results: List[Dict[str, Any]] = field(default_factory=list)


class BenchmarkSuite:
    """Runs ACO benchmarks across tasks and baselines."""

    def __init__(self, config: Optional[ACOConfig] = None):
        self.config = config or self._default_config()

    def _default_config(self) -> ACOConfig:
        models = {
            "tiny_local": ModelConfig("tiny_local", "local", 0.0001, 0.0002, latency_ms_estimate=200, strength_tier=1),
            "cheap_cloud": ModelConfig("cheap_cloud", "cloud", 0.0005, 0.001, latency_ms_estimate=500, strength_tier=2),
            "medium": ModelConfig("medium", "cloud", 0.003, 0.006, latency_ms_estimate=800, strength_tier=3),
            "frontier": ModelConfig("frontier", "cloud", 0.01, 0.03, latency_ms_estimate=1500, strength_tier=4),
            "specialist": ModelConfig("specialist", "cloud", 0.015, 0.045, latency_ms_estimate=2000, strength_tier=5),
        }
        tools = {
            "search": ToolConfig("search", 0.002, 500),
            "retrieve": ToolConfig("retrieve", 0.001, 300),
            "code_execution": ToolConfig("code_execution", 0.005, 1000),
            "linter": ToolConfig("linter", 0.001, 200),
            "file_read": ToolConfig("file_read", 0.0005, 100),
            "compliance_check": ToolConfig("compliance_check", 0.01, 1500),
            "summarize": ToolConfig("summarize", 0.002, 400),
        }
        verifiers = {
            "verifier_medium": VerifierConfig("verifier_medium", 0.005, 800, 0.8),
        }
        return ACOConfig(
            project_name="aco-benchmark",
            models=models,
            tools=tools,
            verifiers=verifiers,
            routing_policy=RoutingPolicy("benchmark"),
        )

    def generate_benchmark_data(self, n: int = 1000, seed: int = 42) -> List[AgentTrace]:
        """Generate synthetic traces for benchmarking."""
        gen = SyntheticTraceGenerator(seed=seed)
        return gen.generate(n)

    def run_baseline(
        self,
        traces: List[AgentTrace],
        baseline_name: str,
    ) -> BenchmarkResult:
        """Run a single baseline over the benchmark traces."""
        
        # Configure optimizer for baseline
        mode_map = {
            "always_frontier": "always_frontier",
            "always_cheap": "always_frontier",  # overridden below
            "static": "static",
            "prompt_only": "prompt_only",
            "learned": "learned",
            "learned_verifier": "learned_verifier",
            "cascade": "cascade",
            "rules_only": "cascade",  # uses cascade routing with rules-based modules
            "full": "cascade",
        }
        
        # Adjust config based on baseline
        config = self._default_config()
        
        if baseline_name == "always_frontier":
            config.enable_router = False
        elif baseline_name == "always_cheap":
            config.enable_router = False
            # Override all models to cheap tier in simulation by using special handling
        elif baseline_name == "static":
            pass  # default static routing
        elif baseline_name == "prompt_only":
            pass  # prompt heuristic routing
        elif baseline_name == "rules_only":
            config.enable_classifier = True
            config.enable_router = True
            config.enable_context_budgeter = True
            config.enable_cache_layout = True
            config.enable_tool_gate = True
            config.enable_verifier_budgeter = True
            config.enable_retry_optimizer = True
            config.enable_meta_tool_miner = False
            config.enable_early_termination = True
        elif baseline_name == "full":
            pass  # all enabled
        
        # For ablations, disable specific modules
        if baseline_name.startswith("no_"):
            module_name = baseline_name.replace("no_", "")
            if hasattr(config, f"enable_{module_name}"):
                setattr(config, f"enable_{module_name}", False)
        
        optimizer = AgentCostOptimizer(config)
        
        results = []
        total_cost = 0.0
        total_latency = 0.0
        total_tools = 0
        total_verifiers = 0
        total_retries = 0
        total_context = 0
        cache_rates = []
        
        success_count = 0
        partial_count = 0
        failure_count = 0
        false_done_count = 0
        blocked_count = 0
        
        cheap_misses = 0
        escalation_misses = 0
        regression_count = 0
        
        frontier_costs = []
        actual_costs = []
        
        for trace in traces:
            # Run optimization on this trace's request
            run_state = {
                "trace_id": trace.trace_id,
                "routing_mode": mode_map.get(baseline_name, "cascade"),
                "current_cost": 0.0,
                "planned_tools": [
                    (tc.tool_name, tc.tool_input)
                    for step in trace.steps
                    for tc in step.tool_calls
                ],
                "previous_tool_calls": [
                    tc for step in trace.steps for tc in step.tool_calls
                ],
                "step_number": len(trace.steps),
                "total_steps": len(trace.steps),
                "is_irreversible": trace.task_type == TaskType.LEGAL_REGULATED,
            }
            
            result = optimizer.optimize(trace.user_request, run_state)
            
            # Simulate execution based on optimization decisions
            sim_cost, sim_latency, sim_success = self._simulate(trace, result, baseline_name)
            
            total_cost += sim_cost
            total_latency += sim_latency
            total_tools += len(result.tool_decisions)
            if result.verifier_decision:
                total_verifiers += 1
            total_retries += sum(1 for d in result.tool_decisions if d.decision.value == "skip")
            total_context += sum(s.context_size_tokens for s in trace.steps)
            
            frontier_cost = sum(
                s.model_call.total_cost if s.model_call else 0
                for s in trace.steps
            ) if trace.metadata.get("scenario") == "frontier_unnecessary" else trace.total_cost * 2
            frontier_costs.append(frontier_cost)
            actual_costs.append(sim_cost)
            
            outcome = trace.final_outcome
            if sim_success:
                if outcome == Outcome.SUCCESS:
                    success_count += 1
                elif outcome == Outcome.PARTIAL_SUCCESS:
                    partial_count += 1
                else:
                    regression_count += 1
            else:
                if outcome == Outcome.FALSE_DONE:
                    false_done_count += 1
                elif outcome == Outcome.BLOCKED:
                    blocked_count += 1
                else:
                    failure_count += 1
            
            # Check for cheap model misses
            if trace.metadata.get("scenario") == "cheap_failure" and result.routing_decision.tier <= 2:
                cheap_misses += 1
            
            # Check for missed escalation
            if trace.metadata.get("scenario") in ("cheap_failure", "tool_underuse") and result.routing_decision.tier < 3:
                escalation_misses += 1
            
            cache_rates.append(trace.cache_hit_rate)
            
            results.append({
                "trace_id": trace.trace_id,
                "task_type": trace.task_type.value,
                "scenario": trace.metadata.get("scenario", "normal"),
                "simulated_cost": sim_cost,
                "simulated_success": sim_success,
                "routing_tier": result.routing_decision.tier,
                "model_id": result.routing_decision.model_id,
                "tool_count": len(result.tool_decisions),
                "verifier_used": result.verifier_decision is not None,
            })
        
        n = len(traces)
        avg_cost_success = total_cost / max(success_count + partial_count, 1)
        
        # Cost reduction vs frontier baseline
        cost_reduction = (sum(frontier_costs) - sum(actual_costs)) / max(sum(frontier_costs), 1)
        
        return BenchmarkResult(
            benchmark_name="synthetic_benchmark",
            baseline_name=baseline_name,
            num_tasks=n,
            num_success=success_count,
            num_partial=partial_count,
            num_failure=failure_count,
            num_false_done=false_done_count,
            num_blocked=blocked_count,
            total_cost=total_cost,
            avg_cost_success=avg_cost_success,
            avg_latency_ms=total_latency / n,
            total_tool_calls=total_tools,
            total_verifier_calls=total_verifiers,
            total_retries=total_retries,
            avg_cache_hit_rate=sum(cache_rates) / n,
            total_context_tokens=total_context,
            cost_reduction_vs_frontier=cost_reduction,
            false_done_rate=false_done_count / n,
            unsafe_cheap_miss_rate=cheap_misses / n,
            missed_escalation_rate=escalation_misses / n,
            regression_rate=regression_count / n,
            quality_cost_frontier=[
                {"cost": c, "success": 1.0 if s else 0.0}
                for c, s in zip(actual_costs, [r["simulated_success"] for r in results])
            ],
            per_task_results=results,
        )

    def _simulate(self, trace: AgentTrace, result: OptimizationResult, baseline: str) -> tuple:
        """Simulate execution based on optimizer decisions."""
        
        # Base cost from the trace
        base_cost = trace.total_cost_computed
        
        # Adjust cost based on routing decision
        tier = result.routing_decision.tier
        cost_mult = {
            1: 0.05, 2: 0.25, 3: 0.75, 4: 1.0, 5: 1.5,
        }.get(tier, 1.0)
        
        # Override for always_cheap baseline
        if baseline == "always_cheap":
            cost_mult = 0.25
            tier = 2
        
        # Override for always_frontier baseline
        if baseline == "always_frontier":
            cost_mult = 1.0
            tier = 4
        
        # Apply tool gate savings
        tools_skipped = sum(1 for d in result.tool_decisions if d.decision.value in ("skip", "use_cache"))
        tool_savings = tools_skipped * 0.005
        
        # Apply cache savings
        cache_savings = 0.0
        if result.prompt_layout:
            cache_savings = result.prompt_layout.cache_discount
        
        sim_cost = base_cost * cost_mult - tool_savings - cache_savings
        sim_cost = max(sim_cost, 0.001)
        
        # Simulate latency
        sim_latency = trace.total_latency_ms * cost_mult * 0.8
        
        # Simulate success probability
        scenario = trace.metadata.get("scenario", "normal")
        
        # Base success rate by tier and scenario
        success_prob = 0.95 if tier >= 3 else 0.7
        if scenario == "cheap_failure":
            success_prob = 0.3 if tier <= 2 else 0.85
        elif scenario == "tool_underuse":
            success_prob = 0.6 if tools_skipped > 0 else 0.8
        elif scenario == "retry_loop":
            success_prob = 0.2
        elif scenario == "frontier_unnecessary":
            success_prob = 0.95
        elif scenario == "meta_tool_success":
            success_prob = 0.9
        elif scenario == "meta_tool_bad":
            success_prob = 0.4
        elif scenario == "false_done":
            success_prob = 0.1
        elif scenario == "blocked" or scenario == "stopped_doom":
            success_prob = 0.0
        elif scenario == "human_escalation":
            success_prob = 0.5
        
        # Verifier improves success for high-risk tasks
        if result.verifier_decision and result.verifier_decision.decision.value == "call_verifier":
            success_prob += 0.05
        
        # Meta-tool success bonus
        if result.meta_tool_match:
            success_prob += 0.03
        
        sim_success = success_prob > 0.5  # simplified threshold
        
        return sim_cost, sim_latency, sim_success

    def run_all_baselines(self, traces: List[AgentTrace]) -> Dict[str, BenchmarkResult]:
        """Run all baseline configurations."""
        baselines = [
            "always_frontier",
            "always_cheap",
            "static",
            "prompt_only",
            "cascade",
            "rules_only",
            "full",
        ]
        
        results = {}
        for baseline in baselines:
            print(f"Running baseline: {baseline}...")
            results[baseline] = self.run_baseline(traces, baseline)
        
        return results

    def run_ablations(self, traces: List[AgentTrace]) -> Dict[str, BenchmarkResult]:
        """Run ablation study disabling each module."""
        ablations = [
            "no_router",
            "no_context_budgeter",
            "no_cache_layout",
            "no_tool_gate",
            "no_verifier_budgeter",
            "no_retry_optimizer",
            "no_meta_tool_miner",
            "no_early_termination",
        ]
        
        results = {}
        for ablation in ablations:
            print(f"Running ablation: {ablation}...")
            results[ablation] = self.run_baseline(traces, ablation)
        
        return results

    def report(self, results: Dict[str, BenchmarkResult]) -> str:
        """Generate formatted benchmark report."""
        lines = ["=" * 80, "AGENT COST OPTIMIZER BENCHMARK REPORT", "=" * 80, ""]
        
        headers = ["Baseline", "Success", "Partial", "Fail", "Blocked", "False-DONE",
                   "Total Cost", "Avg Cost/Succ", "Latency(ms)", "Tools", "Verifiers",
                   "Retries", "Cache Hit", "Cost Reduction", "Regression"]
        
        lines.append(" | ".join(headers))
        lines.append("-" * 120)
        
        for name, result in results.items():
            row = [
                name[:20].ljust(20),
                f"{result.num_success / result.num_tasks:.1%}",
                f"{result.num_partial / result.num_tasks:.1%}",
                f"{result.num_failure / result.num_tasks:.1%}",
                f"{result.num_blocked / result.num_tasks:.1%}",
                f"{result.false_done_rate:.1%}",
                f"${result.total_cost:.2f}",
                f"${result.avg_cost_success:.4f}",
                f"{result.avg_latency_ms:.0f}",
                str(result.total_tool_calls),
                str(result.total_verifier_calls),
                str(result.total_retries),
                f"{result.avg_cache_hit_rate:.1%}",
                f"{result.cost_reduction_vs_frontier:.1%}",
                f"{result.regression_rate:.1%}",
            ]
            lines.append(" | ".join(row))
        
        lines.append("")
        lines.append("=" * 80)
        
        # Find best cost/success tradeoff
        best_score = -float("inf")
        best_name = ""
        for name, result in results.items():
            success_rate = (result.num_success + result.num_partial) / result.num_tasks
            score = success_rate * 10 - result.avg_cost_success * 100 - result.regression_rate * 50
            if score > best_score:
                best_score = score
                best_name = name
        
        lines.append(f"BEST OVERALL: {best_name} (score={best_score:.2f})")
        lines.append("")
        
        return "\n".join(lines)

    def export(self, results: Dict[str, BenchmarkResult], path: str) -> None:
        """Export results to JSON."""
        export_data = {}
        for name, result in results.items():
            export_data[name] = {
                "benchmark_name": result.benchmark_name,
                "baseline_name": result.baseline_name,
                "num_tasks": result.num_tasks,
                "num_success": result.num_success,
                "num_partial": result.num_partial,
                "num_failure": result.num_failure,
                "num_false_done": result.num_false_done,
                "num_blocked": result.num_blocked,
                "total_cost": result.total_cost,
                "avg_cost_success": result.avg_cost_success,
                "avg_latency_ms": result.avg_latency_ms,
                "total_tool_calls": result.total_tool_calls,
                "total_verifier_calls": result.total_verifier_calls,
                "total_retries": result.total_retries,
                "avg_cache_hit_rate": result.avg_cache_hit_rate,
                "total_context_tokens": result.total_context_tokens,
                "cost_reduction_vs_frontier": result.cost_reduction_vs_frontier,
                "false_done_rate": result.false_done_rate,
                "unsafe_cheap_miss_rate": result.unsafe_cheap_miss_rate,
                "missed_escalation_rate": result.missed_escalation_rate,
                "regression_rate": result.regression_rate,
            }
        
        with open(path, "w") as f:
            json.dump(export_data, f, indent=2)