switchin _agents_on_benchmark to Study method for flexibility

Author: TLSDC

src/agentlab/experiments/study.py

@@ -1,12 +1,14 @@
-from concurrent.futures import ProcessPoolExecutor
 import gzip
 import logging
 import os
 import pickle
+import random
 import uuid
 from abc import ABC, abstractmethod
+from concurrent.futures import ProcessPoolExecutor
 from dataclasses import dataclass
 from datetime import datetime
+from multiprocessing import Manager, Pool, Queue
 from pathlib import Path
 
 import bgym
@@ -17,10 +19,9 @@
 from agentlab.analyze import inspect_results
 from agentlab.experiments import reproducibility_util as repro
 from agentlab.experiments.exp_utils import RESULTS_DIR, add_dependencies
-from agentlab.experiments.launch_exp import find_incomplete, non_dummy_count, run_experiments
+from agentlab.experiments.launch_exp import (find_incomplete, non_dummy_count,
+                                             run_experiments)
 from agentlab.experiments.multi_server import BaseServer, WebArenaInstanceVars
-from multiprocessing import Pool, Manager, Queue
-import random
 
 logger = logging.getLogger(__name__)
 
@@ -238,7 +239,7 @@ def __post_init__(self):
 
     def make_exp_args_list(self):
         """Generate the exp_args_list from the agent_args and the benchmark."""
-        self.exp_args_list = _agents_on_benchmark(
+        self.exp_args_list = self.agents_on_benchmark(
             self.agent_args,
             self.benchmark,
             logging_level=self.logging_level,
@@ -424,6 +425,82 @@ def load(dir: Path) -> "Study":
     def load_most_recent(root_dir: Path = None, contains=None) -> "Study":
         return Study.load(get_most_recent_study(root_dir, contains=contains))
 
+    def agents_on_benchmark(
+        self,
+        agents: list[AgentArgs] | AgentArgs,
+        benchmark: bgym.Benchmark,
+        demo_mode=False,
+        logging_level: int = logging.INFO,
+        logging_level_stdout: int = logging.INFO,
+        ignore_dependencies=False,
+    ):
+        """Run one or multiple agents on a benchmark.
+
+        Args:
+            agents: list[AgentArgs] | AgentArgs
+                The agent configuration(s) to run.
+            benchmark: bgym.Benchmark
+                The benchmark to run the agents on.
+            demo_mode: bool
+                If True, the experiments will be run in demo mode.
+            logging_level: int
+                The logging level for individual jobs.
+            logging_level_stdout: int
+                The logging level for the stdout.
+            ignore_dependencies: bool
+                If True, the dependencies will be ignored and all experiments can be run in parallel.
+
+        Returns:
+            list[ExpArgs]: The list of experiments to run.
+
+        Raises:
+            ValueError: If multiple agents are run on a benchmark that requires manual reset.
+        """
+
+        if not isinstance(agents, (list, tuple)):
+            agents = [agents]
+
+        if benchmark.name.startswith("visualwebarena") or benchmark.name.startswith("webarena"):
+            if len(agents) > 1:
+                raise ValueError(
+                    f"Only one agent can be run on {benchmark.name} since the instance requires manual reset after each evaluation."
+                )
+
+        for agent in agents:
+            agent.set_benchmark(benchmark, demo_mode)  # the agent can adapt (lightly?) to the benchmark
+
+        env_args_list = benchmark.env_args_list
+        if demo_mode:
+            set_demo_mode(env_args_list)
+
+        exp_args_list = []
+
+        for agent in agents:
+            for env_args in env_args_list:
+                exp_args = ExpArgs(
+                    agent_args=agent,
+                    env_args=env_args,
+                    logging_level=logging_level,
+                    logging_level_stdout=logging_level_stdout,
+                )
+                exp_args_list.append(exp_args)
+
+        for i, exp_args in enumerate(exp_args_list):
+            exp_args.order = i
+
+        # not required with ray, but keeping around if we would need it for visualwebareana on joblib
+        # _flag_sequential_exp(exp_args_list, benchmark)
+
+        if not ignore_dependencies:
+            # populate the depends_on field based on the task dependencies in the benchmark
+            exp_args_list = add_dependencies(exp_args_list, benchmark.dependency_graph_over_tasks())
+        else:
+            logger.warning(
+                f"Ignoring dependencies for benchmark {benchmark.name}. This could lead to different results."
+            )
+
+        return exp_args_list
+
 
 def _make_study_name(agent_names, benchmark_names, suffix=None):
     """Make a study name from the agent and benchmark names."""
@@ -634,82 +711,6 @@ def set_demo_mode(env_args_list: list[EnvArgs]):
         env_args.slow_mo = 1000
 
 
-def _agents_on_benchmark(
-    agents: list[AgentArgs] | AgentArgs,
-    benchmark: bgym.Benchmark,
-    demo_mode=False,
-    logging_level: int = logging.INFO,
-    logging_level_stdout: int = logging.INFO,
-    ignore_dependencies=False,
-):
-    """Run one or multiple agents on a benchmark.
-
-    Args:
-        agents: list[AgentArgs] | AgentArgs
-            The agent configuration(s) to run.
-        benchmark: bgym.Benchmark
-            The benchmark to run the agents on.
-        demo_mode: bool
-            If True, the experiments will be run in demo mode.
-        logging_level: int
-            The logging level for individual jobs.
-        logging_level_stdout: int
-            The logging level for the stdout.
-        ignore_dependencies: bool
-            If True, the dependencies will be ignored and all experiments can be run in parallel.
-
-    Returns:
-        list[ExpArgs]: The list of experiments to run.
-
-    Raises:
-        ValueError: If multiple agents are run on a benchmark that requires manual reset.
-    """
-
-    if not isinstance(agents, (list, tuple)):
-        agents = [agents]
-
-    if benchmark.name.startswith("visualwebarena") or benchmark.name.startswith("webarena"):
-        if len(agents) > 1:
-            raise ValueError(
-                f"Only one agent can be run on {benchmark.name} since the instance requires manual reset after each evaluation."
-            )
-
-    for agent in agents:
-        agent.set_benchmark(benchmark, demo_mode)  # the agent can adapt (lightly?) to the benchmark
-
-    env_args_list = benchmark.env_args_list
-    if demo_mode:
-        set_demo_mode(env_args_list)
-
-    exp_args_list = []
-
-    for agent in agents:
-        for env_args in env_args_list:
-            exp_args = ExpArgs(
-                agent_args=agent,
-                env_args=env_args,
-                logging_level=logging_level,
-                logging_level_stdout=logging_level_stdout,
-            )
-            exp_args_list.append(exp_args)
-
-    for i, exp_args in enumerate(exp_args_list):
-        exp_args.order = i
-
-    # not required with ray, but keeping around if we would need it for visualwebareana on joblib
-    # _flag_sequential_exp(exp_args_list, benchmark)
-
-    if not ignore_dependencies:
-        # populate the depends_on field based on the task dependencies in the benchmark
-        exp_args_list = add_dependencies(exp_args_list, benchmark.dependency_graph_over_tasks())
-    else:
-        logger.warning(
-            f"Ignoring dependencies for benchmark {benchmark.name}. This could lead to different results."
-        )
-
-    return exp_args_list
-
-
 # def _flag_sequential_exp(exp_args_list: list[ExpArgs], benchmark: Benchmark):
 #     if benchmark.name.startswith("visualwebarena"):
 #         sequential_subset = benchmark.subset_from_glob("requires_reset", "True")