import pickle from pathlib import Path from collections import defaultdict from stable_baselines3.common.callbacks import BaseCallback from environments.logging.plotting import prepare_plot class FactoryMonitor: def __init__(self, env): self._env = env self._monitor = defaultdict(lambda: defaultdict(lambda: 0)) self._last_vals = defaultdict(lambda: 0) def __iter__(self): for key, value in self._monitor.items(): yield key, dict(value) def add(self, key, value, step=None): assert step is None or step >= 1 # Is this good practice? step = step or self._env.steps self._last_vals[key] = self._last_vals[key] + value self._monitor[key][step] = self._last_vals[key] return self._last_vals[key] def set(self, key, value, step=None): assert step is None or step >= 1 # Is this good practice? step = step or self._env.steps self._last_vals[key] = value self._monitor[key][step] = self._last_vals[key] return self._last_vals[key] def remove(self, key, value, step=None): assert step is None or step >= 1 # Is this good practice? step = step or self._env.steps self._last_vals[key] = self._last_vals[key] - value self._monitor[key][step] = self._last_vals[key] return self._last_vals[key] def to_dict(self): return dict(self) def to_pd_dataframe(self): import pandas as pd df = pd.DataFrame.from_dict(self.to_dict()) df.fillna(0) return df def reset(self): raise RuntimeError("DO NOT DO THIS! Always initalize a new Monitor per Env-Run.") class MonitorCallback(BaseCallback): ext = 'png' def __init__(self, env, filepath=Path('debug_out/monitor.pick'), plotting=True): super(MonitorCallback, self).__init__() self.filepath = Path(filepath) self._monitor_list = list() self.env = env self.plotting = plotting self.started = False self.closed = False @property def monitor_as_df_list(self): return [x.to_pd_dataframe() for x in self._monitor_list] 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_as_df_list, f, protocol=pickle.HIGHEST_PROTOCOL) if self.plotting: print('Monitor files were dumped to disk, now plotting....') # %% Imports import pandas as pd # %% 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 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")), tag='monitor') print('Plotting done.') self.closed = True def _on_step(self) -> bool: if self.locals['dones'].item(): self._monitor_list.append(self.env.monitor) else: pass return True