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66b1c50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """Top-level benchmark orchestration for agent comparison runs."""
from __future__ import annotations
import os
from pathlib import Path
from dotenv import load_dotenv
from dataforge.bench.core import (
AggregateBenchmarkResult,
BenchmarkRunOutput,
SeedBenchmarkResult,
aggregate_seed_results,
estimate_llm_calls,
validate_estimated_calls,
write_run_output,
)
from dataforge.bench.groq_client import GroqBenchClient
from dataforge.bench.methods import (
run_heuristic_episode,
run_llm_react_episode,
run_llm_zeroshot_episode,
run_random_episode,
)
from dataforge.datasets.real_world import load_real_world_dataset
from dataforge.datasets.registry import DATASET_REGISTRY
_SUPPORTED_METHODS = frozenset({"random", "heuristic", "llm_zeroshot", "llm_react"})
def _validate_inputs(methods: list[str], datasets: list[str], seeds: int) -> None:
"""Validate user-selected methods and datasets."""
unknown_methods = sorted(set(methods) - _SUPPORTED_METHODS)
unknown_datasets = sorted(set(datasets) - set(DATASET_REGISTRY))
if unknown_methods:
raise ValueError(f"Unknown benchmark methods: {unknown_methods}")
if unknown_datasets:
raise ValueError(f"Unknown benchmark datasets: {unknown_datasets}")
if seeds <= 0:
raise ValueError("Benchmark seeds must be >= 1.")
def _reproduction_command(methods: list[str], datasets: list[str], seeds: int) -> str:
"""Build the canonical command for reproducing a benchmark run."""
return (
"dataforge bench "
f"--methods {','.join(methods)} "
f"--datasets {','.join(datasets)} "
f"--seeds {seeds}"
)
def _llm_skip_reason() -> str | None:
"""Return a skip reason when LLM methods cannot run."""
provider = os.environ.get("DATAFORGE_LLM_PROVIDER", "").strip().lower()
if provider != "groq":
return "DATAFORGE_LLM_PROVIDER must be set to groq."
if not os.environ.get("GROQ_API_KEY"):
return "GROQ_API_KEY is not set."
return None
def _skipped_result(
*,
method: str,
dataset: str,
seed: int,
reason: str,
reproduction_command: str,
) -> SeedBenchmarkResult:
"""Build a skipped seed result with a clear reason."""
return SeedBenchmarkResult(
method=method,
dataset=dataset,
seed=seed,
status="skipped",
skip_reason=reason,
llm_calls=0,
prompt_tokens=0,
completion_tokens=0,
quota_units=0.0,
runtime_s=0.0,
provider=None,
model=None,
warnings=["provider_unset"],
reproduction_command=reproduction_command,
)
def run_agent_comparison(
*,
methods: list[str],
datasets: list[str],
seeds: int,
output_json: Path,
really_run_big_bench: bool,
cache_root: Path | None = None,
) -> BenchmarkRunOutput:
"""Run the selected benchmark methods across real-world datasets."""
load_dotenv()
_validate_inputs(methods, datasets, seeds)
estimated_calls = estimate_llm_calls(methods=methods, datasets=datasets, seeds=seeds)
validate_estimated_calls(
estimated_calls=estimated_calls,
really_run_big_bench=really_run_big_bench,
)
reproduction_command = _reproduction_command(methods, datasets, seeds)
records: list[SeedBenchmarkResult] = []
loaded_datasets = {
dataset_name: load_real_world_dataset(dataset_name, cache_root=cache_root)
for dataset_name in datasets
}
llm_methods_requested = any(method.startswith("llm_") for method in methods)
skip_reason = _llm_skip_reason() if llm_methods_requested else None
client = (
GroqBenchClient(api_key=os.environ["GROQ_API_KEY"])
if llm_methods_requested and skip_reason is None
else None
)
for dataset_name in datasets:
dataset = loaded_datasets[dataset_name]
for method in methods:
for seed in range(seeds):
if method == "random":
result = run_random_episode(dataset, seed=seed)
elif method == "heuristic":
result = run_heuristic_episode(dataset, seed=seed)
elif method == "llm_zeroshot":
if client is None or skip_reason is not None:
result = _skipped_result(
method=method,
dataset=dataset_name,
seed=seed,
reason=skip_reason or "LLM client unavailable.",
reproduction_command=reproduction_command,
)
else:
result = run_llm_zeroshot_episode(dataset, seed=seed, client=client)
else:
if client is None or skip_reason is not None:
result = _skipped_result(
method=method,
dataset=dataset_name,
seed=seed,
reason=skip_reason or "LLM client unavailable.",
reproduction_command=reproduction_command,
)
else:
result = run_llm_react_episode(dataset, seed=seed, client=client)
if result.reproduction_command != reproduction_command:
result = result.model_copy(
update={"reproduction_command": reproduction_command}
)
if method == "heuristic":
result = result.model_copy(update={"seed": seed})
records.append(result)
aggregates: list[AggregateBenchmarkResult] = aggregate_seed_results(
records, seeds_requested=seeds
)
output = BenchmarkRunOutput(
metadata={
"methods": methods,
"datasets": datasets,
"seeds": seeds,
"reproduction_command": reproduction_command,
},
records=records,
aggregates=aggregates,
)
write_run_output(output, output_json)
return output
|