jitterbug/benchmarks/plot_train_progress.py at master · RoboticsDesignLab/jitterbug · GitHub

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importos
importsys
importcsv
importglob
importwarnings
importnumpyasnp
frommatplotlibimportpyplotasplt


defma(data, window, *, mode='valid'):
"""Moving average filter

 Args:
 data (numpy array): The data to be smoothed
 window (int): The window used to smooth the data
 mode (str): The mode used to smooth the data
 Returns:
 data_smoothed (numpy array): The data smoothed by the moving average
 """
N=len(data)
data_smoothed=np.zeros(N)
mask=np.ones(window)/window

data_smoothed=np.convolve(data, mask, mode)

returndata_smoothed


defget_reward_from_csv(file):
"""Extract reward vector from a csv file"""
reward= []

print("Reading reward from {}".format(file))

withopen(file, 'rt') asf:
data=csv.reader(f)
forri, rowinenumerate(data):

ifri<2:
# Skip first two rows
continue

iflen(row) ==0:
# Skip empty rows
continue

"""
 XXX ajs 11/Sep/2019 There seems to be bug with
 csv.DictWriter.writerow() as used in
 stable_baselines/bench/monitor.py:98 - we occasionally see reward
 values that are missing the mantissa, e.g.
 'XXX.XXe-5' will simply appear as 'e-5' in the CSV cell.
 To work-around this, we detect these cases and replace with 0
 """
ifrow[0].split("e")[0] =='':
warnings.warn(
"Got malformed reward (no mantissa) "
"at line {} of {}: {}, replacing with 0.0".format(
ri,
file,
row[0]
 )
 )
reward.append(0.0)
continue

reward.append(float(row[0]))

returnnp.array(reward, dtype=float)


defplot_csv_glob(fileglob, window, *, quartiles=[10, 50, 90], **kwargs):
"""Plot performance over many seeds into the current axes

 Args:
 fileglob (str): File path glob matching one or more monitor.csv files
 window (int): Moving average window

 kwargs (dict): Plotting keyword args
 """

files=list(glob.glob(fileglob))

iflen(files) ==0:
warnings.warn("Glob '{}' matched 0 files".format(fileglob))
sys.exit(-1)

print("Loading rewards from {} files".format(len(files)))
rewards= [
get_reward_from_csv(f)
forfinfiles
 ]

reward_len= [
len(r)
forrinrewards
 ]
min_len=min(reward_len)
max_len=max(reward_len)
print("Min training length: {}, max: {}".format(
min_len,
max_len
 ))

# Crop all rewards to the minimum length
rewards=np.array([
r[0:min_len]
forrinrewards
 ], dtype=float)
x=np.arange(0, rewards.shape[1])

# Smooth
q1, q2, q3= (
ma(q, window)
forqinnp.percentile(rewards, q=quartiles, axis=0)
 )

x=np.arange(window//2, window//2+len(q1))

p0=plt.plot(x, q2, **kwargs)
plt.fill_between(
x,
q1,
q3,
color=p0[0].get_color(),
alpha=0.1,
lw=0
 )

returnrewards


defmain(fileglob, window, **kwargs):
"""Plot model training progress"""

# Ensure we are using a GUI frontend so X-Forwarding works
importmatplotlib
matplotlib.use('tkagg')

plt.figure()
r=plot_csv_glob(fileglob, window, **kwargs)
plt.title("{} ({} files)".format(fileglob, len(r)))
plt.xlabel("1e3 timesteps")
plt.ylabel("Reward")
plt.ylim(-50, 1050)
plt.xlim(0, 6e6/1e3)
plt.grid()
plt.tight_layout()
plt.show()
plt.close()


if__name__=='__main__':

fileglob=sys.argv[1]
window=int(sys.argv[2])

main(fileglob, window)