99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
import pickle
|
|
from collections import defaultdict
|
|
from pathlib import Path
|
|
from typing import List, Dict
|
|
|
|
from stable_baselines3.common.callbacks import BaseCallback
|
|
|
|
from environments.helpers import IGNORED_DF_COLUMNS
|
|
from environments.logging.plotting import prepare_plot
|
|
import pandas as pd
|
|
|
|
|
|
class MonitorCallback(BaseCallback):
|
|
|
|
ext = 'png'
|
|
|
|
def __init__(self, filepath=Path('debug_out/monitor.pick'), plotting=True):
|
|
super(MonitorCallback, self).__init__()
|
|
self.filepath = Path(filepath)
|
|
self._monitor_df = pd.DataFrame()
|
|
self._monitor_dicts = defaultdict(dict)
|
|
self.plotting = plotting
|
|
self.started = False
|
|
self.closed = False
|
|
|
|
def __enter__(self):
|
|
self._on_training_start()
|
|
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
|
self._on_training_end()
|
|
|
|
def _on_training_start(self) -> None:
|
|
if self.started:
|
|
pass
|
|
else:
|
|
self.filepath.parent.mkdir(exist_ok=True, parents=True)
|
|
self.started = True
|
|
pass
|
|
|
|
def _on_training_end(self) -> None:
|
|
if self.closed:
|
|
pass
|
|
else:
|
|
# self.out_file.unlink(missing_ok=True)
|
|
with self.filepath.open('wb') as f:
|
|
pickle.dump(self._monitor_df.reset_index(), f, protocol=pickle.HIGHEST_PROTOCOL)
|
|
if self.plotting:
|
|
print('Monitor files were dumped to disk, now plotting....')
|
|
|
|
# %% Load MonitorList from Disk
|
|
with self.filepath.open('rb') as f:
|
|
monitor_list = pickle.load(f)
|
|
df = None
|
|
for m_idx, monitor in enumerate(monitor_list):
|
|
monitor['episode'] = m_idx
|
|
if df is None:
|
|
df = pd.DataFrame(columns=monitor.columns)
|
|
for _, row in monitor.iterrows():
|
|
df.loc[df.shape[0]] = row
|
|
if df is None: # The env exited premature, we catch it.
|
|
self.closed = True
|
|
return
|
|
for column in list(df.columns):
|
|
if column != 'episode':
|
|
df[f'{column}_roll'] = df[column].rolling(window=50).mean()
|
|
# result.tail()
|
|
prepare_plot(filepath=self.filepath, results_df=df.filter(regex=(".+_roll")))
|
|
print('Plotting done.')
|
|
self.closed = True
|
|
|
|
def _on_step(self, alt_infos: List[Dict] = None, alt_dones: List[bool] = None) -> bool:
|
|
infos = alt_infos or self.locals.get('infos', [])
|
|
if alt_dones is not None:
|
|
dones = alt_dones
|
|
elif self.locals.get('dones', None) is not None:
|
|
dones =self.locals.get('dones', None)
|
|
elif self.locals.get('done', None) is not None:
|
|
dones = self.locals.get('done', [None])
|
|
else:
|
|
dones = []
|
|
|
|
for env_idx, (info, done) in enumerate(zip(infos, dones)):
|
|
self._monitor_dicts[env_idx][len(self._monitor_dicts[env_idx])] = {key: val for key, val in info.items()
|
|
if key not in ['terminal_observation', 'episode']
|
|
and not key.startswith('rec_')}
|
|
if done:
|
|
env_monitor_df = pd.DataFrame.from_dict(self._monitor_dicts[env_idx], orient='index')
|
|
self._monitor_dicts[env_idx] = dict()
|
|
columns = [col for col in env_monitor_df.columns if col not in IGNORED_DF_COLUMNS]
|
|
env_monitor_df = env_monitor_df.aggregate(
|
|
{col: 'mean' if col.endswith('ount') else 'sum' for col in columns}
|
|
)
|
|
env_monitor_df['episode'] = len(self._monitor_df)
|
|
self._monitor_df = self._monitor_df.append([env_monitor_df])
|
|
else:
|
|
pass
|
|
return True
|
|
|