def test_figure_row():
doc @ """
## Test Figure Row
"""
with doc:
import gym
env = gym.make('FetchReach-v1')
img = env.render('rgb_array')
with doc, doc.table() as table:
with table.figure_row() as row:
row.figure(img,
src=f"figures/reach.png?ts={doc.now()}",
title="Before Init",
caption="this is the details")
row.figure(img,
src=f"figures/reach.png?ts={doc.now()}",
title="Before Init",
caption="this is the details")
row.figure(img,
src=f"figures/reach.png?ts={doc.now()}",
title="Before Init",
caption="this is the details")
row.figure(img,
src=f"figures/reach.png?ts={doc.now()}",
title="Before Init",
caption="this is the details")
def test_table():
table = doc.table()
with table.figure_row() as row:
row.figure(src="some_file.png",
title="some title",
caption="some text")
row.figure(src="some_file.png",
title="some title",
caption="some text")
row.figure(src="some_file.png",
title="some title",
caption="some text")
row.figure(src="some_file.png",
title="some title",
caption="some text")
with table.figure_row() as row:
row.figure(src="some_file.png",
title="some title",
caption="some text")
row.figure(src="some_file.png",
title="some title",
caption="some text")
row.figure(src="some_file.png",
title="some title",
caption="some text")
row.figure(src="some_file.png",
title="some title",
caption="some text")
target = """
| **some title** | **some title** | **some title** | **some title** |
|:--------------:|:--------------:|:--------------:|:--------------:|
|  |  |  |  |
| some text | some text | some text | some text |
| **some title** | **some title** | **some title** | **some title** |
|  |  |  |  |
| some text | some text | some text | some text |
"""[1:]
print(doc._md)
assert doc._md == target
doc.children.clear()
loader = ML_Logger(root=os.getcwd(), prefix="data/walker-walk/curl")
with doc @ """Check all the files""":
files = loader.glob(query="**/metrics.pkl", wd=".", recursive=True)
doc.print(files)
with doc @ """Step 1: load the data""":
avg, top, bottom, step = loader.read_metrics(
"train/episode_reward/mean@mean",
"train/episode_reward/mean@84%",
"train/episode_reward/mean@16%",
x_key="step@mean",
path="**/metrics.pkl",
bin_size=40)
with doc @ "Step 2: Plot", doc.table().figure_row() as r:
import matplotlib.pyplot as plt
from matplotlib import ticker
title = "CURL on Walker-walk"
plt.figure()
plt.plot(step, avg.to_list(), color="#23aaff")
plt.fill_between(step, bottom, top, color="#23aaff", alpha=0.15)
plt.gca().xaxis.set_major_formatter(
ticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k" if x else "0"))
plt.title(title)
plt.xlabel("Steps")
plt.ylabel("Return")
x_key="frames@min",
path=path)
plt.plot(step.to_list(),
mean.to_list(),
color=color,
label=f'{label} (pred)',
linestyle='--')
plt.fill_between(step, low, high, alpha=0.1, color=color)
doc @ """Drqv2 (state) -RFF vs Drqv2"""
with doc:
for (b_val, env_name) in tqdm(zip(b_vals, envs), desc="(b_val, env)"):
if loader.every(4, 'figure', start_on=1):
r = doc.table().figure_row()
if env_name == 'dmc:Finger-turn_hard-v1':
menv = 'dmc:Finger-turn_hard2-v1'
else:
menv = env_name
plt.title(f"DDPG {env_name.split(':')[-1][:-3]}", fontsize=18)
plot_line(path=f"mlp/{menv.split(':')[-1][:-3]}/**/metrics.pkl",
color='black',
label='DDPG')
if env_name == 'dmc:Humanoid-run-v1':
tenv = 'dmc:Humanoid-run2-v1'
else:
tenv = env_name
plt.plot(step, avg, color=color, label=label)
plt.fill_between(step, bottom, top, alpha=0.15, color=color)
with doc @ "Step 2: Plot":
title = "CURL"
colors = ['#49b8ff', '#444444', '#ff7575', '#66c56c', '#f4b247']
for domain in [
'walker-walk', 'cartpole-swingup', 'ball_in_cup-catch',
'finger-spin'
]:
name, task = domain.split("-")
doc(name.replace('_', ' ').title(), f"[{task}]")
with loader.Prefix(domain), doc.table().figure_row() as r:
for method in ['curl', 'rad', 'pad']:
with loader.Prefix(method):
group(yKey="episode_reward/mean",
bins=None,
dropna=True,
color=colors[0],
label="Eval")
group(yKey="train/episode_reward/mean",
color=colors[1],
label="Train")
plt.legend(frameon=False)
plt.ylim(0, 1000)
plt.gca().xaxis.set_major_formatter(
ticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k"