Loading and Plotting A Single Learning Curve
Here is a simple example, showing how to load a single learning curve with 95% confidence range using logger.read_metrics call.
The plotting code is minimal to keep it simple.
Import the CommonMark X module
from cmx import doc
from ml_logger import ML_LoggerInitialize the loader
import os
loader = ML_Logger(root=os.getcwd(), prefix="data/walker-walk/curl")Check all the files
files = loader.glob(query="**/metrics.pkl", wd=".", recursive=True)
doc.print(files)['300/metrics.pkl', '400/metrics.pkl', '100/metrics.pkl', '200/metrics.pkl']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)Step 2: Plot
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")
r.savefig(f"figures/learning_curve.png", title=title, dpi=300, zoom="20%")CURL on Walker-walk
How Come The Figure Looks So Good?
This is because we place the following file: ./matplotlibrc inside this folder. This file contains the following styling options:
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