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Remove deprecated monitor format and improve tests

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This commit is contained in:
Antonin Raffin 2020-02-14 13:42:16 +01:00
parent a2b1bf06d3
commit 8b559d71ab
3 changed files with 44 additions and 45 deletions
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1
docs/misc/changelog.rst
View file

@ -12,6 +12,7 @@ Breaking Changes:
- Return type of `evaluation.evaluate_policy()` has been changed
- Refactored the replay buffer to avoid transformation between PyTorch and NumPy
- Created `OffPolicyRLModel` base class
- Remove deprecated JSON format for `Monitor`
New Features:
^^^^^^^^^^^^^

40
tests/test_monitor.py
View file

@ -8,33 +8,47 @@ import gym
from torchy_baselines.common.monitor import Monitor, get_monitor_files, load_results
def test_monitor():
def test_monitor(tmp_path):
"""
test the monitor wrapper
"""
env = gym.make("CartPole-v1")
env.seed(0)
monitor_file = "/tmp/stable_baselines-test-{}.monitor.csv".format(uuid.uuid4())
monitor_file = os.path.join(str(tmp_path), "stable_baselines-test-{}.monitor.csv".format(uuid.uuid4()))
monitor_env = Monitor(env, monitor_file)
monitor_env.reset()
for _ in range(1000):
_, _, done, _ = monitor_env.step(0)
total_steps = 1000
ep_rewards = []
ep_lengths = []
ep_len, ep_reward = 0, 0
for _ in range(total_steps):
_, reward, done, _ = monitor_env.step(0)
ep_len += 1
ep_reward += reward
if done:
ep_rewards.append(ep_reward)
ep_lengths.append(ep_len)
monitor_env.reset()
ep_len, ep_reward = 0, 0
file_handler = open(monitor_file, 'rt')
monitor_env.close()
assert monitor_env.get_total_steps() == total_steps
assert sum(ep_lengths) == sum(monitor_env.get_episode_lengths())
assert sum(monitor_env.get_episode_rewards()) == sum(ep_rewards)
_ = monitor_env.get_episode_times()
first_line = file_handler.readline()
assert first_line.startswith('#')
metadata = json.loads(first_line[1:])
assert metadata['env_id'] == "CartPole-v1"
assert set(metadata.keys()) == {'env_id', 't_start'}, "Incorrect keys in monitor metadata"
with open(monitor_file, 'rt') as file_handler:
first_line = file_handler.readline()
assert first_line.startswith('#')
metadata = json.loads(first_line[1:])
assert metadata['env_id'] == "CartPole-v1"
assert set(metadata.keys()) == {'env_id', 't_start'}, "Incorrect keys in monitor metadata"
last_logline = pandas.read_csv(file_handler, index_col=None)
assert set(last_logline.keys()) == {'l', 't', 'r'}, "Incorrect keys in monitor logline"
file_handler.close()
last_logline = pandas.read_csv(file_handler, index_col=None)
assert set(last_logline.keys()) == {'l', 't', 'r'}, "Incorrect keys in monitor logline"
os.remove(monitor_file)
def test_monitor_load_results(tmp_path):
"""
test load_results on log files produced by the monitor wrapper

48
torchy_baselines/common/monitor.py
View file

@ -14,22 +14,21 @@ import numpy as np
class Monitor(gym.Wrapper):
EXT = "monitor.csv"
file_handler = None
def __init__(self,
env: gym.Env,
filename: Optional[str] = None,
allow_early_resets: bool = True,
reset_keywords=(),
info_keywords=()):
reset_keywords: Tuple[str, ...] = (),
info_keywords: Tuple[str, ...] = ()):
"""
A monitor wrapper for Gym environments, it is used to know the episode reward, length, time and other data.
:param env: (gym.Env) The environment
:param filename: (Optional[str]) the location to save a log file, can be None for no log
:param allow_early_resets: (bool) allows the reset of the environment before it is done
:param reset_keywords: (tuple) extra keywords for the reset call, if extra parameters are needed at reset
:param info_keywords: (tuple) extra information to log, from the information return of environment.step
:param reset_keywords: (Tuple[str, ...]) extra keywords for the reset call, if extra parameters are needed at reset
:param info_keywords: (Tuple[str, ...]) extra information to log, from the information return of environment.step
"""
super(Monitor, self).__init__(env=env)
self.t_start = time.time()
@ -93,12 +92,12 @@ class Monitor(gym.Wrapper):
if done:
self.needs_reset = True
ep_rew = sum(self.rewards)
eplen = len(self.rewards)
ep_info = {"r": round(ep_rew, 6), "l": eplen, "t": round(time.time() - self.t_start, 6)}
ep_len = len(self.rewards)
ep_info = {"r": round(ep_rew, 6), "l": ep_len, "t": round(time.time() - self.t_start, 6)}
for key in self.info_keywords:
ep_info[key] = info[key]
self.episode_rewards.append(ep_rew)
self.episode_lengths.append(eplen)
self.episode_lengths.append(ep_len)
self.episode_times.append(time.time() - self.t_start)
ep_info.update(self.current_reset_info)
if self.logger:
@ -168,41 +167,26 @@ def get_monitor_files(path: str) -> List[str]:
def load_results(path: str) -> pandas.DataFrame:
"""
Load all Monitor logs from a given directory path matching ``*monitor.csv`` and ``*monitor.json``
Load all Monitor logs from a given directory path matching ``*monitor.csv``
:param path: (str) the directory path containing the log file(s)
:return: (pandas.DataFrame) the logged data
"""
# get both csv and (old) json files
monitor_files = (glob(os.path.join(path, "*monitor.json")) + get_monitor_files(path))
if not monitor_files:
monitor_files = get_monitor_files(path)
if len(monitor_files) == 0:
raise LoadMonitorResultsError("no monitor files of the form *%s found in %s" % (Monitor.EXT, path))
data_frames = []
headers = []
data_frames, headers = [], []
for file_name in monitor_files:
with open(file_name, 'rt') as file_handler:
if file_name.endswith('csv'):
first_line = file_handler.readline()
assert first_line[0] == '#'
header = json.loads(first_line[1:])
data_frame = pandas.read_csv(file_handler, index_col=None)
headers.append(header)
elif file_name.endswith('json'): # Deprecated json format
episodes = []
lines = file_handler.readlines()
header = json.loads(lines[0])
headers.append(header)
for line in lines[1:]:
episode = json.loads(line)
episodes.append(episode)
data_frame = pandas.DataFrame(episodes)
else:
assert 0, 'unreachable'
first_line = file_handler.readline()
assert first_line[0] == '#'
header = json.loads(first_line[1:])
data_frame = pandas.read_csv(file_handler, index_col=None)
headers.append(header)
data_frame['t'] += header['t_start']
data_frames.append(data_frame)
data_frame = pandas.concat(data_frames)
data_frame.sort_values('t', inplace=True)
data_frame.reset_index(inplace=True)
data_frame['t'] -= min(header['t_start'] for header in headers)
# data_frame.headers = headers # HACK to preserve backwards compatibility
return data_frame