def test_print():
for i in range(2):
doc.print(i, "<")
target = """
```
0 <
1 <
```
"""[1:]
print(doc._md)
assert doc._md == target
doc.children.clear()
95% confidence range using `logger.read_metrics` call.
The plotting code is minimal to keep it simple.
"""
with doc @ """Import the CommonMark X module""":
from cmx import doc
from ml_logger import ML_Logger
with doc @ """Initialize the loader""":
import os
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"
from cmx import doc
doc @ """
# This is A Title
"""
with doc:
doc.print("This prints out!")
with doc.skip:
doc.print("This is not executed!")
from cmx import doc
from ml_logger import ML_Logger
doc @ """
# 0. Download Data from Server
You can directly read from the server, which is the standard use-flow. We
download the data here so that you can process the data locally.
"""
PREFIX = "geyang/dmc_gen/01_baselines/train"
with doc @ """Initialize the loader""":
loader = ML_Logger("http://<some-dash-server>:8080", prefix=PREFIX)
with doc @ """Check all the files""":
files = loader.glob(query="**/metrics.pkl", wd=".", recursive=True)
for path in files:
loader.download_file(path, to="data/", relative=True)
doc.print(*files, sep="\n")
doc.flush()
## To Dos
- [x] make the action space Box, and remove discrete option.
- [x] dense reward only, use <dist to goal at t-1> - <dist to goal at t>
- [ ] zero friction [looks good now, did not really tweak]
- [x] test env.step with a servoing policy ( act = [goal - x] ), write the fixtures for gif gen.
- [x] randomize the initial velocity, and add `init_vel=0.1` for obj and distractors.
- [x] set `frame_skip=5` for slower motion.
"""
with doc @ """## Action Space: `Box(2,)`""":
import gym
env = gym.make('many_world.many_world:ManyWorld-v0', n_objs=2)
doc.print("The action space is: ", env.action_space)
doc @ """
## Dense Reward
The reward is computed as `r = old_dist - dist`, dist being the distance to the goal.
"""
with doc:
env.seed(100)
obs = env.reset()
doc.print(obs)
act = env.action_space.sample()
obs, r, done, info = env.step(act)
doc.print('the reward is:', r)
doc @ f"""
## See CMX In Action
- [ ] insert a screencast gif here
"""
from cmx import doc
doc @ __doc__
doc @ """
this works just like a standard jupyter notebook. Only code you include in
a `with doc:` block would be shown. The row context arranges the result
side ways (via html) but it shows as a list on GitHub due to css
restrictions.
"""
with doc:
for i in range(10):
doc.print(i, end=' ')
doc @ """
## Installation
You can install this package from PyPI under the `cmx` package name.
``` python
pip install mdx
```
## Developing Plugins
You can develop plug-ins under the `cmx-<>` prefix
## Discussion
doc @ """
# Comparing Two Learning Curves Side-by-side
Here we compare the training performance versus the performance
on the evaluation domain.
We show the training performance in gray, to accentuate the
evaluation curve.
"""
with doc @ """Initialize the loader""":
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 @ """A Single Time Series""":
def group(xKey="step",
yKey="train/episode_reward/mean",
color=None,
bin=10,
label=None,
dropna=False):
avg, top, bottom, step = loader.read_metrics(f"{yKey}@mean",
f"{yKey}@84%",
f"{yKey}@16%",
x_key=f"{xKey}@mean",
path="**/metrics.pkl",
bin_size=bin,
import pickle
from cmx import doc
from ml_logger import logger
doc @ """
# Debug Logger Overwrite Bug
Reading from metrics file:
"""
logger.configure("http://54.71.92.65:8080", prefix='geyang/project/debug_logs')
logger.remove('debug_logs')
doc.print(logger.root)
logger.log_text("""
charts:
- i
""", dedent=True, filename=".charts.yml")
for i in range(3):
logger.log_key_value(i=i)
logger.flush()
import time
time.sleep(1)
doc @ "```ansi"
doc @ logger.load_text("outputs.log")
doc @ "```"
with doc:
