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import os
import matplotlib.pyplot as plt
import numpy as np
import json
import seaborn as sns; sns.set()
import glob2
import argparse
def smooth_reward_curve(x, y):
halfwidth = int(np.ceil(len(x) / 60)) # Halfwidth of our smoothing convolution
k = halfwidth
xsmoo = x
ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='same') / np.convolve(np.ones_like(y), np.ones(2 * k + 1),
mode='same')
return xsmoo, ysmoo
def load_results(file):
if not os.path.exists(file):
return None
with open(file, 'r') as f:
lines = [line for line in f]
if len(lines) < 2:
return None
keys = [name.strip() for name in lines[0].split(',')]
data = np.genfromtxt(file, delimiter=',', skip_header=1, filling_values=0.)
if data.ndim == 1:
data = data.reshape(1, -1)
assert data.ndim == 2
assert data.shape[-1] == len(keys)
result = {}
for idx, key in enumerate(keys):
result[key] = data[:, idx]
return result
def pad(xs, value=np.nan):
maxlen = np.max([len(x) for x in xs])
padded_xs = []
for x in xs:
if x.shape[0] >= maxlen:
padded_xs.append(x)
padding = np.ones((maxlen - x.shape[0],) + x.shape[1:]) * value
x_padded = np.concatenate([x, padding], axis=0)
assert x_padded.shape[1:] == x.shape[1:]
assert x_padded.shape[0] == maxlen
padded_xs.append(x_padded)
return np.array(padded_xs)
parser = argparse.ArgumentParser()
parser.add_argument('dir', type=str)
parser.add_argument('--smooth', type=int, default=1)
args = parser.parse_args()
# Load all data.
data = {}
paths = [os.path.abspath(os.path.join(path, '..')) for path in glob2.glob(os.path.join(args.dir, '**', 'progress.csv'))]
for curr_path in paths:
if not os.path.isdir(curr_path):
continue
results = load_results(os.path.join(curr_path, 'progress.csv'))
if not results:
print('skipping {}'.format(curr_path))
continue
print('loading {} ({})'.format(curr_path, len(results['epoch'])))
with open(os.path.join(curr_path, 'params.json'), 'r') as f:
params = json.load(f)
success_rate = np.array(results['test/success_rate'])
epoch = np.array(results['epoch']) + 1
env_id = params['env_name']
replay_strategy = params['replay_strategy']
if replay_strategy == 'future':
config = 'her'
else:
config = 'ddpg'
if 'Dense' in env_id:
config += '-dense'
else:
config += '-sparse'
env_id = env_id.replace('Dense', '')
# Process and smooth data.
assert success_rate.shape == epoch.shape
x = epoch
y = success_rate
if args.smooth:
x, y = smooth_reward_curve(epoch, success_rate)
assert x.shape == y.shape
if env_id not in data:
data[env_id] = {}
if config not in data[env_id]:
data[env_id][config] = []
data[env_id][config].append((x, y))
# Plot data.
for env_id in sorted(data.keys()):
print('exporting {}'.format(env_id))
plt.clf()
for config in sorted(data[env_id].keys()):
xs, ys = zip(*data[env_id][config])
xs, ys = pad(xs), pad(ys)
assert xs.shape == ys.shape
plt.plot(xs[0], np.nanmedian(ys, axis=0), label=config)
plt.fill_between(xs[0], np.nanpercentile(ys, 25, axis=0), np.nanpercentile(ys, 75, axis=0), alpha=0.25)
plt.title(env_id)
plt.xlabel('Epoch')
plt.ylabel('Median Success Rate')
plt.legend()
plt.savefig(os.path.join(args.dir, 'fig_{}.png'.format(env_id)))
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