136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
import json
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from collections import defaultdict
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from pathlib import Path
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from typing import Union
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import numpy as np
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import pandas as pd
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import simplejson
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from stable_baselines3.common.callbacks import BaseCallback
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from environments.factory.base.base_factory import REC_TAC
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# noinspection PyAttributeOutsideInit
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from environments.helpers import Constants as c
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class RecorderCallback(BaseCallback):
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def __init__(self, filepath: Union[str, Path], occupation_map: bool = False, trajectory_map: bool = False,
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entities='all'):
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super(RecorderCallback, self).__init__()
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self.trajectory_map = trajectory_map
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self.occupation_map = occupation_map
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self.filepath = Path(filepath)
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self._recorder_dict = defaultdict(list)
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self._recorder_out_list = list()
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self._env_params = None
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self.do_record: bool
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if isinstance(entities, str):
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if entities.lower() == 'all':
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self._entities = None
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else:
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self._entities = [entities]
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else:
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self._entities = entities
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self.started = False
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self.closed = False
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def read_params(self, params):
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self._env_params = params
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def read_info(self, env_idx, info: dict):
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if info_dict := {key.replace(REC_TAC, ''): val for key, val in info.items() if key.startswith(f'{REC_TAC}')}:
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if self._entities:
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info_dict = {k: v for k, v in info_dict.items() if k in self._entities}
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info_dict.update(episode=(self.num_timesteps + env_idx))
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self._recorder_dict[env_idx].append(info_dict)
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else:
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pass
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return
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def read_done(self, env_idx, done):
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if done:
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self._recorder_out_list.append({'steps': self._recorder_dict[env_idx],
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'episode': len(self._recorder_out_list)})
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self._recorder_dict[env_idx] = list()
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else:
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pass
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def start(self, force=False):
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if (hasattr(self.training_env, 'record_episodes') and self.training_env.record_episodes) or force:
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self.do_record = True
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self.filepath.parent.mkdir(exist_ok=True, parents=True)
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self.started = True
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else:
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self.do_record = False
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def stop(self):
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if self.do_record and self.started:
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# self.out_file.unlink(missing_ok=True)
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with self.filepath.open('w') as f:
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out_dict = {'episodes': self._recorder_out_list, 'header': self._env_params}
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try:
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simplejson.dump(out_dict, f, indent=4)
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except TypeError:
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print('Shit')
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if self.occupation_map:
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a = np.zeros((15, 15))
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for episode in out_dict['episodes']:
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df = pd.DataFrame([y for x in episode['steps'] for y in x['Agents']])
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b = list(df[['x', 'y']].to_records(index=False))
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np.add.at(a, tuple(zip(*b)), 1)
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# a = np.rot90(a)
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import seaborn as sns
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from matplotlib import pyplot as plt
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hm = sns.heatmap(data=a)
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hm.set_title('Very Nice Heatmap')
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plt.show()
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if self.trajectory_map:
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print('Recorder files were dumped to disk, now plotting the occupation map...')
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self.closed = True
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self.started = False
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else:
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pass
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def _on_step(self) -> bool:
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if self.do_record and self.started:
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for env_idx, info in enumerate(self.locals.get('infos', [])):
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self.read_info(env_idx, info)
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for env_idx, done in list(
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enumerate(self.locals.get('dones', []))) + list(
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enumerate(self.locals.get('done', []))):
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self.read_done(env_idx, done)
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else:
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pass
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return True
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def __enter__(self):
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self.start(force=True)
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.stop()
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def _on_training_start(self) -> None:
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if self.started:
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pass
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else:
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self.start()
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pass
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def _on_training_end(self) -> None:
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if self.closed:
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pass
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else:
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self.stop()
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