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updates to the benchmark viewer code + autopep8 (#184)

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* viz docs and syntactic sugar wip

* update viewer yaml to use persistent volume claims

* move plot_util to baselines.common, update links

* use 1Tb hard drive for results viewer

* small updates to benchmark vizualizer code

* autopep8

* autopep8

* any folder can be a benchmark

* massage games image a little bit

* fixed --preload option in app.py

* remove preload from run_viewer.sh

* remove pdb breakpoints

* update bench-viewer.yaml
This commit is contained in:
pzhokhov
2018-11-20 17:23:10 -08:00
committed by Peter Zhokhov
parent 1ddab4bdb5
commit 001597586d
1 changed files with 12 additions and 13 deletions
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25
baselines/results_plotter.py
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@@ -5,7 +5,7 @@ matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode
import matplotlib.pyplot as plt
plt.rcParams['svg.fonttype'] = 'none'
from baselines.bench.monitor import load_results
from baselines.common import plot_util
X_TIMESTEPS = 'timesteps'
X_EPISODES = 'episodes'
@@ -16,7 +16,7 @@ POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME]
EPISODES_WINDOW = 100
COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise',
'darkgreen', 'tan', 'salmon', 'gold', 'lightpurple', 'darkred', 'darkblue']
'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue']
def rolling_window(a, window):
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
@@ -50,7 +50,7 @@ def plot_curves(xy_list, xaxis, yaxis, title):
maxx = max(xy[0][-1] for xy in xy_list)
minx = 0
for (i, (x, y)) in enumerate(xy_list):
color = COLORS[i]
color = COLORS[i % len(COLORS)]
plt.scatter(x, y, s=2)
x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean) #So returns average of last EPISODE_WINDOW episodes
plt.plot(x, y_mean, color=color)
@@ -62,19 +62,18 @@ def plot_curves(xy_list, xaxis, yaxis, title):
fig.canvas.mpl_connect('resize_event', lambda event: plt.tight_layout())
plt.grid(True)
def plot_results(dirs, num_timesteps, xaxis, yaxis, task_name):
tslist = []
for dir in dirs:
ts = load_results(dir)
ts = ts[ts.l.cumsum() <= num_timesteps]
tslist.append(ts)
xy_list = [ts2xy(ts, xaxis, yaxis) for ts in tslist]
plot_curves(xy_list, xaxis, yaxis, task_name)
def split_by_task(taskpath):
return taskpath['dirname'].split('/')[-1].split('-')[0]
def plot_results(dirs, num_timesteps=10e6, xaxis=X_TIMESTEPS, yaxis=Y_REWARD, title='', split_fn=split_by_task):
results = plot_util.load_results(dirs)
plot_util.plot_results(results, xy_fn=lambda r: ts2xy(r['monitor'], xaxis, yaxis), split_fn=split_fn, average_group=True, resample=int(1e6))
# Example usage in jupyter-notebook
# from baselines import results_plotter
# from baselines.results_plotter import plot_results
# %matplotlib inline
# results_plotter.plot_results(["./log"], 10e6, results_plotter.X_TIMESTEPS, "Breakout")
# plot_results("./log")
# Here ./log is a directory containing the monitor.csv files
def main():