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plot.py
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plot.py
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import os
import json
import math
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt; plt.style.use('seaborn-ticks')
from matplotlib.ticker import FuncFormatter
# Avoid Type 3 fonts: http://phyletica.org/matplotlib-fonts/
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# Set font family, bold, and font size
#font = {'family':'normal', 'weight':'normal', 'size': 12}
font = {'size': 15}
matplotlib.rc('font', **font)
# Avoid Type 3 fonts in matplotlib plots: http://phyletica.org/matplotlib-fonts/
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
from utils.helper import make_dir
from utils.plotter import read_file, get_total_combination, symmetric_ema
class Plotter(object):
def __init__(self, cfg):
cfg.setdefault('ci', None)
self.x_label = cfg['x_label']
self.y_label = cfg['y_label']
self.show = cfg['show']
self.imgType = cfg['imgType']
self.ci = cfg['ci']
self.runs = cfg['runs']
make_dir('./figures/')
def get_result(self, exp, config_idx, mode):
'''
Given exp and config index, get the results
'''
total_combination = get_total_combination(exp)
result_list = []
for _ in range(self.runs):
result_file = f'./logs/{exp}/{config_idx}/result_{mode}.feather'
# If result file exist, read and merge
result = read_file(result_file)
if result is not None:
# Add config index as a column
result['Config Index'] = config_idx
result_list.append(result)
config_idx += total_combination
# Do symmetric EMA (exponential moving average)
# Get x's and y's in form of numpy arries
xs, ys = [], []
for result in result_list:
xs.append(result[self.x_label].to_numpy())
ys.append(result[self.y_label].to_numpy())
# Do symetric EMA to get new x's and y's
low = max(x[0] for x in xs)
high = min(x[-1] for x in xs)
n = min(len(x) for x in xs)
for i in range(len(xs)):
new_x, new_y, _ = symmetric_ema(xs[i], ys[i], low, high, n)
result_list[i] = result_list[i][:n]
result_list[i].loc[:, self.x_label] = new_x
result_list[i].loc[:, self.y_label] = new_y
ys = []
for result in result_list:
ys.append(result[self.y_label].to_numpy())
# Compute x_mean, y_mean and y_ci
ys = np.array(ys)
x_mean = result_list[0][self.x_label].to_numpy()
y_mean = np.mean(ys, axis=0)
if self.ci == 'sd':
y_ci = np.std(ys, axis=0, ddof=0)
elif self.ci == 'se':
y_ci = np.std(ys, axis=0, ddof=0)/math.sqrt(len(ys))
return x_mean, y_mean, y_ci
def x_format(x, pos):
#return '$%.1f$x$10^{6}$' % (x/1e6)
return '%.1f' % (x/1e6)
cfg = {
'x_label': 'Step',
'y_label': 'Average Return',
'show': False,
'imgType': 'png',
'ci': 'se',
'x_format': None,
'y_format': None,
'xlim': {'min': None, 'max': None},
'ylim': {'min': None, 'max': None},
'runs': 10,
'loc': 'lower right'
}
def learning_curve(exp, runs=1):
cfg['runs'] = runs
plotter = Plotter(cfg)
label_list = ['PPO', 'RPG']
color_list = ['tab:red', 'tab:blue']
envs = ["HalfCheetah-v2", "Hopper-v2", "Walker2d-v2", "Swimmer-v2", "Ant-v2", "Reacher-v2"]
indexes = {
'PPO': [1, 2, 3, 4, 5, 6],
'RPG': [7, 8, 9, 10, 11, 12]
}
for i in range(len(envs)):
fig, ax = plt.subplots()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Plot
env = envs[i]
for j in range(len(label_list)):
agent = label_list[j]
config_idx = indexes[agent][i]
print(f'[{exp}]: Plot Test results: {config_idx}')
x_mean, y_mean, y_ci = plotter.get_result(exp, config_idx, 'Test')
plt.plot(x_mean, y_mean, linewidth=1.5, color=color_list[j], label=agent)
if cfg['ci'] in ['se', 'sd']:
plt.fill_between(x_mean, y_mean - y_ci, y_mean + y_ci, facecolor=color_list[j], alpha=0.5)
# Set x and y axis
# ax.set_xlabel("Steps (x$10^{6}$)", fontsize=16)
# ax.set_ylabel('Average Return', fontsize=16, rotation='horizontal')
ax.set_xlabel("Step", fontsize=16)
ax.set_ylabel('Average Return', fontsize=16)
plt.yticks(size=11)
plt.xticks(size=11)
# ax.yaxis.set_label_coords(-0.18,0.5)
# ax.xaxis.set_label_coords(0.0,0.5)
# ax.set_xlim(cfg['xlim']['min'], cfg['xlim']['max'])
# ax.set_ylim(cfg['ylim']['min'], cfg['ylim']['max'])
# ax.locator_params(nbins=5, axis='x')
# ax.locator_params(nbins=5, axis='y')
# if not (cfg['x_format'] is None):
# ax.xaxis.set_major_formatter(FuncFormatter(cfg['x_format']))
# if not (cfg['y_format'] is None):
# ax.yaxis.set_major_formatter(FuncFormatter(cfg['y_format']))
# # Set legend
ax.legend(loc=cfg['loc'], frameon=False, fontsize=16)
# Adjust layout automatically
plt.tight_layout()
# Save and show
image_path = f'./figures/{env}.{cfg["imgType"]}'
ax.get_figure().savefig(image_path)
if cfg['show']:
plt.show()
plt.clf() # clear figure
plt.cla() # clear axis
plt.close() # close window
if __name__ == "__main__":
learning_curve('RPG', 30)