def try_ckpt(self, agent, env):
'''Check then run checkpoint log/eval'''
body = agent.body
if self.to_ckpt(env,
'log') and self.env.clock.get('epi') > self.warmup_epi:
body.train_ckpt()
body.log_summary('train')
if self.to_ckpt(env, 'eval'):
avg_return, avg_len, avg_success, avg_p, avg_r, avg_f1, avg_book_rate = analysis.gen_avg_result(
agent, self.eval_env, self.num_eval)
body.eval_ckpt(self.eval_env, avg_return, avg_len, avg_success)
body.log_summary('eval')
if body.eval_reward_ma >= body.best_reward_ma:
body.best_reward_ma = body.eval_reward_ma
agent.save(ckpt='best')
if self.env.clock.get('epi') > self.warmup_epi:
if len(body.train_df
) > 1: # need > 1 row to calculate stability
metrics = analysis.analyze_session(self.spec,
body.train_df, 'train')
if len(body.eval_df
) > 1: # need > 1 row to calculate stability
metrics = analysis.analyze_session(self.spec, body.eval_df,
'eval')
def _retro_analyze_session(session_spec_path):
'''Method to retro analyze a single session given only a path to its spec'''
session_spec = util.read(session_spec_path)
info_prepath = session_spec['meta']['info_prepath']
for df_mode in ('eval', 'train'):
session_df = util.read(f'{info_prepath}_session_df_{df_mode}.csv')
analysis.analyze_session(session_spec, session_df, df_mode)
def run(self):
if util.in_eval_lab_modes():
self.run_eval()
metrics = None
else:
self.run_rl()
metrics = analysis.analyze_session(self.spec, self.agent.body.eval_df, 'eval')
self.close()
return metrics