mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-23 07:16:44 +02:00
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
import pickle
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from pathlib import Path
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from stable_baselines3.common.callbacks import BaseCallback
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from environments.helpers import IGNORED_DF_COLUMNS
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from environments.logging.plotting import prepare_plot
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import pandas as pd
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class MonitorCallback(BaseCallback):
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ext = 'png'
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def __init__(self, env, filepath=Path('debug_out/monitor.pick'), plotting=True):
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super(MonitorCallback, self).__init__()
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self.filepath = Path(filepath)
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self._monitor_df = pd.DataFrame()
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self._monitor_dict = dict()
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self.env = env
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self.plotting = plotting
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self.started = False
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self.closed = False
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def __enter__(self):
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self._on_training_start()
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def __exit__(self, exc_type, exc_val, exc_tb):
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self._on_training_end()
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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.filepath.parent.mkdir(exist_ok=True, parents=True)
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self.started = True
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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.out_file.unlink(missing_ok=True)
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with self.filepath.open('wb') as f:
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pickle.dump(self._monitor_df.reset_index(), f, protocol=pickle.HIGHEST_PROTOCOL)
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if self.plotting:
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print('Monitor files were dumped to disk, now plotting....')
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# %% Load MonitorList from Disk
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with self.filepath.open('rb') as f:
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monitor_list = pickle.load(f)
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df = None
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for m_idx, monitor in enumerate(monitor_list):
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monitor['episode'] = m_idx
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if df is None:
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df = pd.DataFrame(columns=monitor.columns)
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for _, row in monitor.iterrows():
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df.loc[df.shape[0]] = row
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if df is None: # The env exited premature, we catch it.
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self.closed = True
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return
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for column in list(df.columns):
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if column != 'episode':
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df[f'{column}_roll'] = df[column].rolling(window=50).mean()
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# result.tail()
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prepare_plot(filepath=self.filepath, results_df=df.filter(regex=(".+_roll")))
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print('Plotting done.')
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self.closed = True
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def _on_step(self) -> bool:
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for _, info in enumerate(self.locals.get('infos', [])):
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self._monitor_dict[self.num_timesteps] = {key: val for key, val in info.items()
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if key not in ['terminal_observation', 'episode']}
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for env_idx, done in list(enumerate(self.locals.get('dones', []))) + \
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list(enumerate(self.locals.get('done', []))):
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if done:
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env_monitor_df = pd.DataFrame.from_dict(self._monitor_dict, orient='index')
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self._monitor_dict = dict()
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columns = [col for col in env_monitor_df.columns if col not in IGNORED_DF_COLUMNS]
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env_monitor_df = env_monitor_df.aggregate(
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{col: 'mean' if col.endswith('ount') else 'sum' for col in columns}
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)
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env_monitor_df['episode'] = len(self._monitor_df)
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self._monitor_df = self._monitor_df.append([env_monitor_df])
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else:
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pass
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return True
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