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adding success rate, total distance, and survive time

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This commit is contained in:
Arec
2022-02-22 00:48:35 -08:00
parent e7f4f6a871
commit 68b066ec53
1 changed files with 11 additions and 0 deletions
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11
src/eval_main.py
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@@ -247,6 +247,14 @@ def summary_metrics(metrics:List[Dict[str,list]]) -> Dict[str,float]:
# keys = ['col_all','v_all', 'a_all','j_all', 'v_avg', 'a_avg', 'col', 'brake', 't'] # keys = ['col_all','v_all', 'a_all','j_all', 'v_avg', 'a_avg', 'col', 'brake', 't']
summary_metrics = {} summary_metrics = {}
# mean travel distance
dt = 0.1
travel_ds = []
for iRound in range(len(metrics)):
for iTraj in range(len(metrics[iRound]['v_all'])):
travel_ds.append(dt*sum(metrics[iRound]['v_all'][iTraj]))
summary_metrics['mean travel distance'] = sum(travel_ds)/len(travel_ds)
# average average-velocity # average average-velocity
all_vavgs = sum([d['v_avg'] for d in metrics],[]) # aggregate to single list all_vavgs = sum([d['v_avg'] for d in metrics],[]) # aggregate to single list
summary_metrics['mean average velocity'] = sum(all_vavgs)/len(all_vavgs) summary_metrics['mean average velocity'] = sum(all_vavgs)/len(all_vavgs)
@@ -273,6 +281,7 @@ def summary_metrics(metrics:List[Dict[str,list]]) -> Dict[str,float]:
# collision rate # collision rate
all_collisions = sum([d['col'] for d in metrics],[]) # aggregate to single list all_collisions = sum([d['col'] for d in metrics],[]) # aggregate to single list
summary_metrics['collision rate'] = sum(all_collisions)/len(all_collisions) summary_metrics['collision rate'] = sum(all_collisions)/len(all_collisions)
summary_metrics['success rate'] = 1 - summary_metrics['collision rate']
# hard brake rate # hard brake rate
all_hard_brakes = sum([d['brake'] for d in metrics],[]) # aggregate to single list all_hard_brakes = sum([d['brake'] for d in metrics],[]) # aggregate to single list
@@ -281,6 +290,8 @@ def summary_metrics(metrics:List[Dict[str,list]]) -> Dict[str,float]:
# average number of timesteps # average number of timesteps
all_ts = sum([d['t'] for d in metrics],[]) # aggregate to single list all_ts = sum([d['t'] for d in metrics],[]) # aggregate to single list
summary_metrics['mean episode length'] = sum(all_ts)/len(all_ts) summary_metrics['mean episode length'] = sum(all_ts)/len(all_ts)
summary_metrics['mean episode time'] = summary_metrics['mean episode length'] * dt
for key in summary_metrics.keys(): for key in summary_metrics.keys():
print(f'{key}: {summary_metrics[key]}') print(f'{key}: {summary_metrics[key]}')