File size: 1,834 Bytes
2fc6b05 |
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 |
from dataclasses import dataclass
from .agent import Agent
from .interpreter import Interpreter
from .journal import Journal
from omegaconf import OmegaConf
from rich.status import Status
from .utils.config import (
load_task_desc,
prep_agent_workspace,
save_run,
_load_cfg,
prep_cfg,
)
@dataclass
class Solution:
code: str
valid_metric: float
class Experiment:
def __init__(self, data_dir: str, goal: str, eval: str | None = None):
"""Initialize a new experiment run.
Args:
data_dir (str): Path to the directory containing the data files.
goal (str): Description of the goal of the task.
eval (str | None, optional): Optional description of the preferred way for the agent to evaluate its solutions.
"""
_cfg = _load_cfg(use_cli_args=False)
_cfg.data_dir = data_dir
_cfg.goal = goal
_cfg.eval = eval
self.cfg = prep_cfg(_cfg)
self.task_desc = load_task_desc(self.cfg)
with Status("Preparing agent workspace (copying and extracting files) ..."):
prep_agent_workspace(self.cfg)
self.journal = Journal()
self.agent = Agent(
task_desc=self.task_desc,
cfg=self.cfg,
journal=self.journal,
)
self.interpreter = Interpreter(
self.cfg.workspace_dir, **OmegaConf.to_container(self.cfg.exec) # type: ignore
)
def run(self, steps: int) -> Solution:
for _i in range(steps):
self.agent.step(exec_callback=self.interpreter.run)
save_run(self.cfg, self.journal)
self.interpreter.cleanup_session()
best_node = self.journal.get_best_node(only_good=False)
return Solution(code=best_node.code, valid_metric=best_node.metric.value)
|