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jblitzar
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github-python

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# NOTE: monkey-patching; needs to be imported before any other file imports it
from moma_llm.tasks.patched_scene import MonkeyPatchedInteractiveIndoorScene
from igibson.scenes import igibson_indoor_scene
igibson_indoor_scene.InteractiveIndoorScene._add_object = MonkeyPatchedInteractiveIndoorScene._add_object
igibson_indoor_scene.InteractiveIndoorScene._orig_add_object = MonkeyPatchedInteractiveIndoorScene._orig_add_object
import shutil
from collections import defaultdict
from pathlib import Path
from pprint import pprint
from typing import Any
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from igibson.utils.utils import parse_config
from sklearn.metrics import auc
import wandb
from moma_llm.env.baselines import (GreedyBaseline,
RandomBaseline)
from moma_llm.env.env import OurIGibsonEnv, create_igibson_env
from moma_llm.env.llm_env import JsonLLMEnv, LLMEnv
from moma_llm.llm.llm import LLM
from moma_llm.utils.constants import TEST_SCENES, TRAINING_SCENES
from moma_llm.utils.utils import get_config
def create_env(cfg, agent: str, config_file: str, scene_id: str, control_freq: float, cheap: bool, seed: int) -> LLMEnv:
if agent == "moma_llm":
env_fn = LLMEnv
elif agent == "json_llm":
env_fn = JsonLLMEnv
elif agent == "greedy":
env_fn = GreedyBaseline
elif agent == "random":
env_fn = RandomBaseline
else:
raise ValueError(f"Unknown agent {agent}")
llm_variant = "gpt-3.5-turbo" if cheap else "gpt-4-1106-preview" # "gpt-4"
llm = LLM(debug=True, model=llm_variant, room_classification_model="gpt-3.5-turbo-1106", open_set_rooms=cfg["open_set_room_categories"])
low_level_env = create_igibson_env(config_file=config_file,
control_freq=control_freq,
scene_id=scene_id,
seed=seed)
high_level_env = env_fn(env=low_level_env, llm=llm, seed=seed)
return high_level_env
def calc_area_under_curve(x, y, max_x):
if max(x) > max_x:
idx = (x <= max_x)
x = x[idx]
y = y[idx]
if max(x) < max_x:
x = np.concatenate([x, [max_x]])
y = np.concatenate([y, [y[-1]]])
x = np.concatenate([[0], x])
y = np.concatenate([[0], y])
return auc(x, y) / max_x
def plot_efficiency_curves(episode_infos, max_hl_steps: int):
ll_steps = []
ll_steps_gtDone = []
hl_steps = []
task_success = []
task_success_gtDone = []
for scene_id in sorted(episode_infos.keys()):
for e in episode_infos[scene_id]:
ll_steps.append(e["num_low_level_steps_with_open_cost"])
ll_steps_gtDone.append(e["num_low_level_steps_with_open_cost_gtDone"])
hl_steps.append(e["num_high_level_steps"])
task_success.append(e["task_success"])
task_success_gtDone.append(e["task_success_gtDone"])
task_success = np.array(task_success)
task_success_gtDone = np.array(task_success_gtDone)
ll_steps = np.array(ll_steps)
hl_steps = np.array(hl_steps)
def _plot(steps, task_success):
df = pd.DataFrame({"steps": steps, "task_success": task_success})
df = df.sort_values("steps")
values = [np.logical_and(df["task_success"].values, df["steps"].values <= max_steps).mean() for max_steps in df["steps"]]
df2 = pd.DataFrame({"steps": df["steps"].values, "success": values})
return wandb.Table(dataframe=df2)
def _get_auc(steps, task_success, max_x: int, title: str):
table = _plot(steps=steps, task_success=task_success)
auc = calc_area_under_curve(table.get_dataframe()["steps"].values, table.get_dataframe()["success"].values, max_x=max_x)
wandb_plot = wandb.plot_table("wandb/area-under-curve/v0",
table,
{"x": "steps", "y": "success"},
{"title": title,
"x-axis-title": "Steps",