copilot-swe-agent[bot]
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
"""
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Benchmark Environment Template
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===============================
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Copy this file and implement the TODO sections to add a new benchmark.
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The EnvAdapter is responsible for:
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1. Building train/eval environment payloads
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2. Running rollout and returning scored result rows
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3. Reflecting on results and returning patch candidates
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"""
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from __future__ import annotations
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from skillopt.datasets.base import BatchSpec
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from skillopt.envs._template.loader_template import TemplateBenchmarkDataLoader
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from skillopt.envs.base import EnvAdapter
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class TemplateBenchmarkAdapter(EnvAdapter):
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"""
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Environment adapter for <Your Benchmark Name>.
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Rename this class and implement the abstract methods below.
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"""
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def __init__(
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self,
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split_dir: str = "",
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data_path: str = "",
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split_mode: str = "ratio",
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split_ratio: str = "2:1:7",
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split_seed: int = 42,
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split_output_dir: str = "",
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seed: int = 42,
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limit: int = 0,
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**kwargs,
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) -> None:
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self.dataloader = TemplateBenchmarkDataLoader(
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split_dir=split_dir,
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data_path=data_path,
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split_mode=split_mode,
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split_ratio=split_ratio,
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split_seed=split_seed,
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split_output_dir=split_output_dir,
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seed=seed,
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limit=limit,
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)
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# TODO: initialize runtime options, e.g.
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# self.max_retries = int(kwargs.get("max_retries", 3))
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# self.timeout_s = int(kwargs.get("timeout_s", 120))
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def setup(self, cfg: dict) -> None:
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super().setup(cfg)
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self.dataloader.setup(cfg)
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def get_dataloader(self):
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return self.dataloader
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def build_env_from_batch(self, batch: BatchSpec, **kwargs):
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return list(batch.payload or [])
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def build_train_env(self, batch_size: int, seed: int, **kwargs):
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batch = self.dataloader.build_train_batch(batch_size=batch_size, seed=seed, **kwargs)
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return self.build_env_from_batch(batch, **kwargs)
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def build_eval_env(self, env_num: int, split: str, seed: int, **kwargs):
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batch = self.dataloader.build_eval_batch(env_num=env_num, split=split, seed=seed, **kwargs)
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return self.build_env_from_batch(batch, **kwargs)
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def rollout(self, env_manager, skill_content: str, out_dir: str, **kwargs) -> list[dict]:
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"""
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Run one batch and return list[dict] with at least:
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{"id": str, "hard": int, "soft": float}
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"""
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raise NotImplementedError("Implement rollout() for your benchmark")
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def reflect(self, results: list[dict], skill_content: str, out_dir: str, **kwargs) -> list[dict | None]:
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"""
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Reflect on rollout results and return patch dicts (or None entries).
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"""
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raise NotImplementedError("Implement reflect() for your benchmark")
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def get_task_types(self) -> list[str]:
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return ["your_benchmark"]
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